The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism

PubMed Central

Di Martino, Adriana; Yan, Chao-Gan; Li, Qingyang; Denio, Erin; Castellanos, Francisco X.; Alaerts, Kaat; Anderson, Jeffrey S.; Assaf, Michal; Bookheimer, Susan Y.; Dapretto, Mirella; Deen, Ben; Delmonte, Sonja; Dinstein, Ilan; Ertl-Wagner, Birgit; Fair, Damien A.; Gallagher, Louise; Kennedy, Daniel P.; Keown, Christopher L.; Keysers, Christian; Lainhart, Janet E.; Lord, Catherine; Luna, Beatriz; Menon, Vinod; Minshew, Nancy; Monk, Christopher S.; Mueller, Sophia; Müller, Ralph-Axel; Nebel, Mary Beth; Nigg, Joel T.; O’Hearn, Kirsten; Pelphrey, Kevin A.; Peltier, Scott J.; Rudie, Jeffrey D.; Sunaert, Stefan; Thioux, Marc; Tyszka, J. Michael; Uddin, Lucina Q.; Verhoeven, Judith S.; Wenderoth, Nicole; Wiggins, Jillian L.; Mostofsky, Stewart H.; Milham, Michael P.

2014-01-01

Autism spectrum disorders (ASD) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, life-long nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. While the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE) – a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) datasets with corresponding structural MRI and phenotypic information from 539 individuals with ASD and 573 age-matched typical controls (TC; 7–64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 males with ASD and 403 male age-matched TC. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo and hyperconnectivity in the ASD literature; both were detected, though hypoconnectivity dominated, particularly for cortico-cortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASD (mid and posterior insula, posterior cingulate cortex), and highlighted less commonly explored regions such as thalamus. The survey of the ABIDE R-fMRI datasets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international datasets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies. PMID:23774715

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism.

PubMed

Di Martino, A; Yan, C-G; Li, Q; Denio, E; Castellanos, F X; Alaerts, K; Anderson, J S; Assaf, M; Bookheimer, S Y; Dapretto, M; Deen, B; Delmonte, S; Dinstein, I; Ertl-Wagner, B; Fair, D A; Gallagher, L; Kennedy, D P; Keown, C L; Keysers, C; Lainhart, J E; Lord, C; Luna, B; Menon, V; Minshew, N J; Monk, C S; Mueller, S; Müller, R-A; Nebel, M B; Nigg, J T; O'Hearn, K; Pelphrey, K A; Peltier, S J; Rudie, J D; Sunaert, S; Thioux, M; Tyszka, J M; Uddin, L Q; Verhoeven, J S; Wenderoth, N; Wiggins, J L; Mostofsky, S H; Milham, M P

2014-06-01

Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.

Large-scale extraction of brain connectivity from the neuroscientific literature

PubMed Central

Richardet, Renaud; Chappelier, Jean-Cédric; Telefont, Martin; Hill, Sean

2015-01-01

Motivation: In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles. One challenge for modern neuroinformatics is finding methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and the integration of such data into computational models. A key example of this is metascale brain connectivity, where results are not reported in a normalized repository. Instead, these experimental results are published in natural language, scattered among individual scientific publications. This lack of normalization and centralization hinders the large-scale integration of brain connectivity results. In this article, we present text-mining models to extract and aggregate brain connectivity results from 13.2 million PubMed abstracts and 630 216 full-text publications related to neuroscience. The brain regions are identified with three different named entity recognizers (NERs) and then normalized against two atlases: the Allen Brain Atlas (ABA) and the atlas from the Brain Architecture Management System (BAMS). We then use three different extractors to assess inter-region connectivity. Results: NERs and connectivity extractors are evaluated against a manually annotated corpus. The complete in litero extraction models are also evaluated against in vivo connectivity data from ABA with an estimated precision of 78%. The resulting database contains over 4 million brain region mentions and over 100 000 (ABA) and 122 000 (BAMS) potential brain region connections. This database drastically accelerates connectivity literature review, by providing a centralized repository of connectivity data to neuroscientists. Availability and implementation: The resulting models are publicly available at github.com/BlueBrain/bluima. Contact: renaud.richardet@epfl.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25609795

When Neuroscience 'Touches' Architecture: From Hapticity to a Supramodal Functioning of the Human Brain.

PubMed

Papale, Paolo; Chiesi, Leonardo; Rampinini, Alessandra C; Pietrini, Pietro; Ricciardi, Emiliano

2016-01-01

In the last decades, the rapid growth of functional brain imaging methodologies allowed cognitive neuroscience to address open questions in philosophy and social sciences. At the same time, novel insights from cognitive neuroscience research have begun to influence various disciplines, leading to a turn to cognition and emotion in the fields of planning and architectural design. Since 2003, the Academy of Neuroscience for Architecture has been supporting 'neuro-architecture' as a way to connect neuroscience and the study of behavioral responses to the built environment. Among the many topics related to multisensory perceptual integration and embodiment, the concept of hapticity was recently introduced, suggesting a pivotal role of tactile perception and haptic imagery in architectural appraisal. Arguments have thus risen in favor of the existence of shared cognitive foundations between hapticity and the supramodal functional architecture of the human brain. Precisely, supramodality refers to the functional feature of defined brain regions to process and represent specific information content in a more abstract way, independently of the sensory modality conveying such information to the brain. Here, we highlight some commonalities and differences between the concepts of hapticity and supramodality according to the distinctive perspectives of architecture and cognitive neuroscience. This comparison and connection between these two different approaches may lead to novel observations in regard to people-environment relationships, and even provide empirical foundations for a renewed evidence-based design theory.

Interaction and the Architecture of the Brain. Perspectives

ERIC Educational Resources Information Center

Friedman, Dorian

2006-01-01

Recent advances in developmental science can teach us a great deal about the value of specific kinds of human interactions in the earliest years of life for the developing brain architecture. Animal experiments indicate that enriched environments with opportunity for frequent interaction and new experiences can help the animals' brains develop…

Ugliness as the fourth wall-breaker. Comment on "Move me, astonish me... delight my eyes and brain: The Vienna Integrated Model of top-down and bottom-up processes in Art Perception (VIMAP) and corresponding affective, evaluative, and neurophysiological correlates" by Matthew Pelowski et al.

NASA Astrophysics Data System (ADS)

Ishizu, Tomohiro; Sakamoto, Yasuhiro

2017-07-01

In this extensive and valuable theoretical article, Pelowski et al. propose a psychological architecture in art appreciation by introducing the concepts of early/bottom-up and relatively late/top-down stages. The former is dictated as automatic processing on perceptual features of visual images, while the latter comprises cognitive and evaluative processes where modulations from acquired knowledge and memories come into play with recurrent loops to form final experiences, as well as brain areas/networks which possibly have a role in each processing component [9].

In pursuit of resilience: stress, epigenetics, and brain plasticity.

PubMed

McEwen, Bruce S

2016-06-01

The brain is the central organ for adaptation to experiences, including stressors, which are capable of changing brain architecture as well as altering systemic function through neuroendocrine, autonomic, immune, and metabolic systems. Because the brain is the master regulator of these systems, as well as of behavior, alterations in brain function by chronic stress can have direct and indirect effects on cumulative allostatic overload, which refers to the cost of adaptation. There is much new knowledge on the neural control of systemic physiology and the feedback actions of physiologic mediators on brain regions regulating higher cognitive function, emotional regulation, and self-regulation. The healthy brain has a considerable capacity for resilience, based upon its ability to respond to interventions designed to open "windows of plasticity" and redirect its function toward better health. As a result, plasticity-facilitating treatments should be given within the framework of a positive behavioral intervention; negative experiences during this window may even make matters worse. Indeed, there are no magic bullets and drugs cannot substitute for targeted interventions that help an individual become resilient, of which mindfulness-based stress reduction and meditation are emerging as useful tools. © 2016 New York Academy of Sciences.

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

PubMed

Guo, Xinyu; Dominick, Kelli C; Minai, Ali A; Li, Hailong; Erickson, Craig A; Lu, Long J

2017-01-01

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t -test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t -test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided.

Early Exposure to Toxic Substances Damages Brain Architecture. Working Paper #4

ERIC Educational Resources Information Center

National Scientific Council on the Developing Child, 2006

2006-01-01

New science shows that exposure to toxins prenatally or early in life can have a devastating and lifelong effect on the developing architecture of the brain. Exposures to many chemicals have much more severe consequences for embryos, fetuses, and young children, whose brains are still developing, than for adults. Substances that can have a truly…

Walking through Architectural Spaces: The Impact of Interior Forms on Human Brain Dynamics

PubMed Central

Banaei, Maryam; Hatami, Javad; Yazdanfar, Abbas; Gramann, Klaus

2017-01-01

Neuroarchitecture uses neuroscientific tools to better understand architectural design and its impact on human perception and subjective experience. The form or shape of the built environment is fundamental to architectural design, but not many studies have shown the impact of different forms on the inhabitants’ emotions. This study investigated the neurophysiological correlates of different interior forms on the perceivers’ affective state and the accompanying brain activity. To understand the impact of naturalistic three-dimensional (3D) architectural forms, it is essential to perceive forms from different perspectives. We computed clusters of form features extracted from pictures of residential interiors and constructed exemplary 3D room models based on and representing different formal clusters. To investigate human brain activity during 3D perception of architectural spaces, we used a mobile brain/body imaging (MoBI) approach recording the electroencephalogram (EEG) of participants while they naturally walk through different interior forms in virtual reality (VR). The results revealed a strong impact of curvature geometries on activity in the anterior cingulate cortex (ACC). Theta band activity in ACC correlated with specific feature types (rs (14) = 0.525, p = 0.037) and geometry (rs (14) = −0.579, p = 0.019), providing evidence for a role of this structure in processing architectural features beyond their emotional impact. The posterior cingulate cortex and the occipital lobe were involved in the perception of different room perspectives during the stroll through the rooms. This study sheds new light on the use of mobile EEG and VR in architectural studies and provides the opportunity to study human brain dynamics in participants that actively explore and realistically experience architectural spaces. PMID:29033807

Walking through Architectural Spaces: The Impact of Interior Forms on Human Brain Dynamics.

PubMed

Banaei, Maryam; Hatami, Javad; Yazdanfar, Abbas; Gramann, Klaus

2017-01-01

Neuroarchitecture uses neuroscientific tools to better understand architectural design and its impact on human perception and subjective experience. The form or shape of the built environment is fundamental to architectural design, but not many studies have shown the impact of different forms on the inhabitants' emotions. This study investigated the neurophysiological correlates of different interior forms on the perceivers' affective state and the accompanying brain activity. To understand the impact of naturalistic three-dimensional (3D) architectural forms, it is essential to perceive forms from different perspectives. We computed clusters of form features extracted from pictures of residential interiors and constructed exemplary 3D room models based on and representing different formal clusters. To investigate human brain activity during 3D perception of architectural spaces, we used a mobile brain/body imaging (MoBI) approach recording the electroencephalogram (EEG) of participants while they naturally walk through different interior forms in virtual reality (VR). The results revealed a strong impact of curvature geometries on activity in the anterior cingulate cortex (ACC). Theta band activity in ACC correlated with specific feature types ( r s (14) = 0.525, p = 0.037) and geometry ( r s (14) = -0.579, p = 0.019), providing evidence for a role of this structure in processing architectural features beyond their emotional impact. The posterior cingulate cortex and the occipital lobe were involved in the perception of different room perspectives during the stroll through the rooms. This study sheds new light on the use of mobile EEG and VR in architectural studies and provides the opportunity to study human brain dynamics in participants that actively explore and realistically experience architectural spaces.

Neural codes of seeing architectural styles

PubMed Central

Choo, Heeyoung; Nasar, Jack L.; Nikrahei, Bardia; Walther, Dirk B.

2017-01-01

Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people’s visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture. PMID:28071765

Neural codes of seeing architectural styles.

PubMed

Choo, Heeyoung; Nasar, Jack L; Nikrahei, Bardia; Walther, Dirk B

2017-01-10

Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people's visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture.

A Collaborative Knowledge Plane for Autonomic Networks

NASA Astrophysics Data System (ADS)

Mbaye, Maïssa; Krief, Francine

Autonomic networking aims to give network components self-managing capabilities. Several autonomic architectures have been proposed. Each of these architectures includes sort of a knowledge plane which is very important to mimic an autonomic behavior. Knowledge plane has a central role for self-functions by providing suitable knowledge to equipment and needs to learn new strategies for more accuracy.However, defining knowledge plane's architecture is still a challenge for researchers. Specially, defining the way cognitive supports interact each other in knowledge plane and implementing them. Decision making process depends on these interactions between reasoning and learning parts of knowledge plane. In this paper we propose a knowledge plane's architecture based on machine learning (inductive logic programming) paradigm and situated view to deal with distributed environment. This architecture is focused on two self-functions that include all other self-functions: self-adaptation and self-organization. Study cases are given and implemented.

Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning.

PubMed

Hearne, Luke J; Cocchi, Luca; Zalesky, Andrew; Mattingley, Jason B

2017-08-30

Our capacity for higher cognitive reasoning has a measurable limit. This limit is thought to arise from the brain's capacity to flexibly reconfigure interactions between spatially distributed networks. Recent work, however, has suggested that reconfigurations of task-related networks are modest when compared with intrinsic "resting-state" network architecture. Here we combined resting-state and task-driven functional magnetic resonance imaging to examine how flexible, task-specific reconfigurations associated with increasing reasoning demands are integrated within a stable intrinsic brain topology. Human participants (21 males and 28 females) underwent an initial resting-state scan, followed by a cognitive reasoning task involving different levels of complexity, followed by a second resting-state scan. The reasoning task required participants to deduce the identity of a missing element in a 4 × 4 matrix, and item difficulty was scaled parametrically as determined by relational complexity theory. Analyses revealed that external task engagement was characterized by a significant change in functional brain modules. Specifically, resting-state and null-task demand conditions were associated with more segregated brain-network topology, whereas increases in reasoning complexity resulted in merging of resting-state modules. Further increments in task complexity did not change the established modular architecture, but affected selective patterns of connectivity between frontoparietal, subcortical, cingulo-opercular, and default-mode networks. Larger increases in network efficiency within the newly established task modules were associated with higher reasoning accuracy. Our results shed light on the network architectures that underlie external task engagement, and highlight selective changes in brain connectivity supporting increases in task complexity. SIGNIFICANCE STATEMENT Humans have clear limits in their ability to solve complex reasoning problems. It is thought that such limitations arise from flexible, moment-to-moment reconfigurations of functional brain networks. It is less clear how such task-driven adaptive changes in connectivity relate to stable, intrinsic networks of the brain and behavioral performance. We found that increased reasoning demands rely on selective patterns of connectivity within cortical networks that emerged in addition to a more general, task-induced modular architecture. This task-driven architecture reverted to a more segregated resting-state architecture both immediately before and after the task. These findings reveal how flexibility in human brain networks is integral to achieving successful reasoning performance across different levels of cognitive demand. Copyright © 2017 the authors 0270-6474/17/378399-13$15.00/0.

When Neuroscience ‘Touches’ Architecture: From Hapticity to a Supramodal Functioning of the Human Brain

PubMed Central

Papale, Paolo; Chiesi, Leonardo; Rampinini, Alessandra C.; Pietrini, Pietro; Ricciardi, Emiliano

2016-01-01

In the last decades, the rapid growth of functional brain imaging methodologies allowed cognitive neuroscience to address open questions in philosophy and social sciences. At the same time, novel insights from cognitive neuroscience research have begun to influence various disciplines, leading to a turn to cognition and emotion in the fields of planning and architectural design. Since 2003, the Academy of Neuroscience for Architecture has been supporting ‘neuro-architecture’ as a way to connect neuroscience and the study of behavioral responses to the built environment. Among the many topics related to multisensory perceptual integration and embodiment, the concept of hapticity was recently introduced, suggesting a pivotal role of tactile perception and haptic imagery in architectural appraisal. Arguments have thus risen in favor of the existence of shared cognitive foundations between hapticity and the supramodal functional architecture of the human brain. Precisely, supramodality refers to the functional feature of defined brain regions to process and represent specific information content in a more abstract way, independently of the sensory modality conveying such information to the brain. Here, we highlight some commonalities and differences between the concepts of hapticity and supramodality according to the distinctive perspectives of architecture and cognitive neuroscience. This comparison and connection between these two different approaches may lead to novel observations in regard to people–environment relationships, and even provide empirical foundations for a renewed evidence-based design theory. PMID:27375542

Large-scale brain networks in affective and social neuroscience: Towards an integrative functional architecture of the brain

PubMed Central

Barrett, Lisa Feldman; Satpute, Ajay

2013-01-01

Understanding how a human brain creates a human mind ultimately depends on mapping psychological categories and concepts to physical measurements of neural response. Although it has long been assumed that emotional, social, and cognitive phenomena are realized in the operations of separate brain regions or brain networks, we demonstrate that it is possible to understand the body of neuroimaging evidence using a framework that relies on domain general, distributed structure-function mappings. We review current research in affective and social neuroscience and argue that the emerging science of large-scale intrinsic brain networks provides a coherent framework for a domain-general functional architecture of the human brain. PMID:23352202

Successful Architectural Knowledge Sharing: Beware of Emotions

NASA Astrophysics Data System (ADS)

Poort, Eltjo R.; Pramono, Agung; Perdeck, Michiel; Clerc, Viktor; van Vliet, Hans

This chapter presents the analysis and key findings of a survey on architectural knowledge sharing. The responses of 97 architects working in the Dutch IT Industry were analyzed by correlating practices and challenges with project size and success. Impact mechanisms between project size, project success, and architectural knowledge sharing practices and challenges were deduced based on reasoning, experience and literature. We find that architects run into numerous and diverse challenges sharing architectural knowledge, but that the only challenges that have a significant impact are the emotional challenges related to interpersonal relationships. Thus, architects should be careful when dealing with emotions in knowledge sharing.

An architecture for a brain-image database

NASA Technical Reports Server (NTRS)

Herskovits, E. H.

2000-01-01

The widespread availability of methods for noninvasive assessment of brain structure has enabled researchers to investigate neuroimaging correlates of normal aging, cerebrovascular disease, and other processes; we designate such studies as image-based clinical trials (IBCTs). We propose an architecture for a brain-image database, which integrates image processing and statistical operators, and thus supports the implementation and analysis of IBCTs. The implementation of this architecture is described and results from the analysis of image and clinical data from two IBCTs are presented. We expect that systems such as this will play a central role in the management and analysis of complex research data sets.

Effects of prenatal methamphetamine exposure: a review of cognitive and neuroimaging studies.

PubMed

Kwiatkowski, Maja A; Roos, Annerine; Stein, Dan J; Thomas, Kevin G F; Donald, Kirsty

2014-06-01

Prenatal methamphetamine exposure (PME) is a significant problem in several parts of the world and poses important health risks for the developing fetus. Research on the short- and long-term outcomes of PME is scarce, however. Here, we summarize present knowledge on the cognitive and behavioral outcomes of PME, based on a review of the neuroimaging, neuropsychology, and neuroscience literature published in the past 15 years. Several studies have reported that the behavioral and cognitive sequelae of PME include broad deficits in the domains of attention, memory, and visual-motor integration. Knowledge regarding brain-behavior relationships is poor, however, in large part because imaging studies are rare. Hence, the effects of PME on developing neurocircuitry and brain architecture remain speculative, and are largely deductive. Some studies have implicated the dopamine-rich fronto-striatal pathways; however, cognitive deficits (e.g., impaired visual-motor integration) that should be associated with damage to those pathways are not manifested consistently across studies. We conclude by discussing challenges endemic to research on prenatal drug exposure, and argue that they may account for some of the inconsistencies in the extant research on PME. Studies confirming predicted brain-behavior relationships in PME, and exploring possible mechanisms underlying those relationships, are needed if neuroscience is to address the urgency of this growing public health problem.

A Laboratory Manual for Stepwise Cerebral White Matter Fiber Dissection.

PubMed

Koutsarnakis, Christos; Liakos, Faidon; Kalyvas, Aristotelis V; Sakas, Damianos E; Stranjalis, George

2015-08-01

White matter fiber dissection is an important method in acquiring a thorough neuroanatomic knowledge for surgical practice. Previous studies have definitely improved our understanding of intrinsic brain anatomy and emphasized on the significance of this technique in modern neurosurgery. However, current literature lacks a complete and concentrated laboratory guide about the entire dissection procedure. Hence, our primary objective is to introduce a detailed laboratory manual for cerebral white matter dissection by highlighting consecutive dissection steps, and to stress important technical comments facilitating this complex procedure. Twenty adult, formalin-fixed cerebral hemispheres were included in the study. Ten specimens were dissected in the lateromedial and 10 in the mediolateral direction, respectively, using the fiber dissection technique and the microscope. Eleven and 8 consecutive and distinctive dissection steps are recommended for the lateromedial and mediolateral dissection procedures, respectively. Photographs highlighting various anatomic landmarks accompany every step. Technical recommendations, facilitating the dissection process, are also indicated. The fiber dissection technique, although complex and time consuming, offers a three-dimensional knowledge of intrinsic brain anatomy and architecture, thus improving both the quality of microneurosurgery and the patient's standard of care. The present anatomic study provides a thorough dissection manual to those who study brain anatomy using this technique. Copyright © 2015 Elsevier Inc. All rights reserved.

Altered Whole-Brain and Network-Based Functional Connectivity in Parkinson's Disease.

PubMed

de Schipper, Laura J; Hafkemeijer, Anne; van der Grond, Jeroen; Marinus, Johan; Henselmans, Johanna M L; van Hilten, Jacobus J

2018-01-01

Background: Functional imaging methods, such as resting-state functional magnetic resonance imaging, reflect changes in neural connectivity and may help to assess the widespread consequences of disease-specific network changes in Parkinson's disease. In this study we used a relatively new graph analysis approach in functional imaging: eigenvector centrality mapping. This model-free method, applied to all voxels in the brain, identifies prominent regions in the brain network hierarchy and detects localized differences between patient populations. In other neurological disorders, eigenvector centrality mapping has been linked to changes in functional connectivity in certain nodes of brain networks. Objectives: Examining changes in functional brain connectivity architecture on a whole brain and network level in patients with Parkinson's disease. Methods: Whole brain resting-state functional architecture was studied with a recently introduced graph analysis approach (eigenvector centrality mapping). Functional connectivity was further investigated in relation to eight known resting-state networks. Cross-sectional analyses included group comparison of functional connectivity measures of Parkinson's disease patients ( n = 107) with control subjects ( n = 58) and correlations with clinical data, including motor and cognitive impairment and a composite measure of predominantly non-dopaminergic symptoms. Results: Eigenvector centrality mapping revealed that frontoparietal regions were more prominent in the whole-brain network function in patients compared to control subjects, while frontal and occipital brain areas were less prominent in patients. Using standard resting-state networks, we found predominantly increased functional connectivity, namely within sensorimotor system and visual networks in patients. Regional group differences in functional connectivity of both techniques between patients and control subjects partly overlapped for highly connected posterior brain regions, in particular in the posterior cingulate cortex and precuneus. Clinico-functional imaging relations were not found. Conclusions: Changes on the level of functional brain connectivity architecture might provide a different perspective of pathological consequences of Parkinson's disease. The involvement of specific, highly connected (hub) brain regions may influence whole brain functional network architecture in Parkinson's disease.

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method

PubMed Central

Guo, Xinyu; Dominick, Kelli C.; Minai, Ali A.; Li, Hailong; Erickson, Craig A.; Lu, Long J.

2017-01-01

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t-test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided. PMID:28871217

All-memristive neuromorphic computing with level-tuned neurons

NASA Astrophysics Data System (ADS)

Pantazi, Angeliki; Woźniak, Stanisław; Tuma, Tomas; Eleftheriou, Evangelos

2016-09-01

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

All-memristive neuromorphic computing with level-tuned neurons.

PubMed

Pantazi, Angeliki; Woźniak, Stanisław; Tuma, Tomas; Eleftheriou, Evangelos

2016-09-02

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

Knowledge Production in an Architectural Practice and a University Architectural Department

ERIC Educational Resources Information Center

Winberg, Chris

2006-01-01

Processes of knowledge production by professional architects and architects-in-training were studied and compared. Both professionals and students were involved in the production of knowledge about the architectural heritage of historical buildings in Cape Town. In a study of the artefacts produced, observations of the processes by means of which…

A Knowledge Conversion Model Based on the Cognitive Load Theory for Architectural Design Education

ERIC Educational Resources Information Center

Wu, Yun-Wu; Liao, Shin; Wen, Ming-Hui; Weng, Kuo-Hua

2017-01-01

The education of architectural design requires balanced curricular arrangements of respectively theoretical knowledge and practical skills to really help students build their knowledge structures, particularly helping them in solving the problems of cognitive load. The purpose of this study is to establish an architectural design knowledge…

Remodeling Functional Connectivity in Multiple Sclerosis: A Challenging Therapeutic Approach.

PubMed

Stampanoni Bassi, Mario; Gilio, Luana; Buttari, Fabio; Maffei, Pierpaolo; Marfia, Girolama A; Restivo, Domenico A; Centonze, Diego; Iezzi, Ennio

2017-01-01

Neurons in the central nervous system are organized in functional units interconnected to form complex networks. Acute and chronic brain damage disrupts brain connectivity producing neurological signs and/or symptoms. In several neurological diseases, particularly in Multiple Sclerosis (MS), structural imaging studies cannot always demonstrate a clear association between lesion site and clinical disability, originating the "clinico-radiological paradox." The discrepancy between structural damage and disability can be explained by a complex network perspective. Both brain networks architecture and synaptic plasticity may play important roles in modulating brain networks efficiency after brain damage. In particular, long-term potentiation (LTP) may occur in surviving neurons to compensate network disconnection. In MS, inflammatory cytokines dramatically interfere with synaptic transmission and plasticity. Importantly, in addition to acute and chronic structural damage, inflammation could contribute to reduce brain networks efficiency in MS leading to worse clinical recovery after a relapse and worse disease progression. These evidence suggest that removing inflammation should represent the main therapeutic target in MS; moreover, as synaptic plasticity is particularly altered by inflammation, specific strategies aimed at promoting LTP mechanisms could be effective for enhancing clinical recovery. Modulation of plasticity with different non-invasive brain stimulation (NIBS) techniques has been used to promote recovery of MS symptoms. Better knowledge of features inducing brain disconnection in MS is crucial to design specific strategies to promote recovery and use NIBS with an increasingly tailored approach.

[Estrogens and feminine brain maturation during adolescence: emergency contraceptive pill].

PubMed

López Moratalla, Natalia; Errasti Alcalá, Tania; Santiago, Esteban

2011-01-01

In the period between puberty and maturity takes place the process of brain maturation. Hormone levels induce changes in neurons and direct the architecture and structural functionality thus affecting patterns of development of different brain areas. The onset of puberty brings with it the invasion of the female brain by high levels of hormones, cyclic surges of estrogen and progesterone in addition to steroids produced in situ. Control centers of emotions (amygdala), memory and learning (hippocampus) and sexual activity (hypothalamus) are modified according to the cyclical concentrations of both hormones. Sex hormones stimulate multimodal actions, both short and longer terms, because neurons in various brain areas have different types of receptors, membrane, cytoplasmic and nuclear. The composition of emergency contraceptive pill (postcoital pill) with high hormonal content raises the urgency of a thorough knowledge about the possible effect that the lack of control of the menstrual cycle in a time of consolidation of brain maturation, can bring in structuring and development of brain circuitry. Changes in the availability of sex steroids during puberty and adolescence underlie psychiatric disorders whose prevalence is typically feminine, such as depression, anxiety disorders. It is a fundamental ethical duty to present scientific data about the influence of estrogen in young female brain maturation, both for full information to potential users, and also to induce the appropriate public health measures.

Hubs of Anticorrelation in High-Resolution Resting-State Functional Connectivity Network Architecture.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Cabanban, Romeo; Crosson, Bruce A

2015-06-01

A major focus of brain research recently has been to map the resting-state functional connectivity (rsFC) network architecture of the normal brain and pathology through functional magnetic resonance imaging. However, the phenomenon of anticorrelations in resting-state signals between different brain regions has not been adequately examined. The preponderance of studies on resting-state fMRI (rsFMRI) have either ignored anticorrelations in rsFC networks or adopted methods in data analysis, which have rendered anticorrelations in rsFC networks uninterpretable. The few studies that have examined anticorrelations in rsFC networks using conventional methods have found anticorrelations to be weak in strength and not very reproducible across subjects. Anticorrelations in rsFC network architecture could reflect mechanisms that subserve a number of important brain processes. In this preliminary study, we examined the properties of anticorrelated rsFC networks by systematically focusing on negative cross-correlation coefficients (CCs) among rsFMRI voxel time series across the brain with graph theory-based network analysis. A number of methods were implemented to enhance the neuronal specificity of resting-state functional connections that yield negative CCs, although at the cost of decreased sensitivity. Hubs of anticorrelation were seen in a number of cortical and subcortical brain regions. Examination of the anticorrelation maps of these hubs indicated that negative CCs in rsFC network architecture highlight a number of regulatory interactions between brain networks and regions, including reciprocal modulations, suppression, inhibition, and neurofeedback.

Child Development Is Economic Development. A Conversation with Economist Art Rolnick. Perspectives

ERIC Educational Resources Information Center

National Scientific Council on the Developing Child, 2006

2006-01-01

The public dollars spent to stimulate economic development would be more wisely invested in child development programs, according to two different streams of research. Brain research shows the impact of experiences and environments on the developing brain architecture, with weaker architecture leading to increased vulnerability to later problems…

Wains: a pattern-seeking artificial life species.

PubMed

de Buitléir, Amy; Russell, Michael; Daly, Mark

2012-01-01

We describe the initial phase of a research project to develop an artificial life framework designed to extract knowledge from large data sets with minimal preparation or ramp-up time. In this phase, we evolved an artificial life population with a new brain architecture. The agents have sufficient intelligence to discover patterns in data and to make survival decisions based on those patterns. The species uses diploid reproduction, Hebbian learning, and Kohonen self-organizing maps, in combination with novel techniques such as using pattern-rich data as the environment and framing the data analysis as a survival problem for artificial life. The first generation of agents mastered the pattern discovery task well enough to thrive. Evolution further adapted the agents to their environment by making them a little more pessimistic, and also by making their brains more efficient.

Small-world human brain networks: Perspectives and challenges.

PubMed

Liao, Xuhong; Vasilakos, Athanasios V; He, Yong

2017-06-01

Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field. Copyright © 2017 Elsevier Ltd. All rights reserved.

Image Understanding Architecture

DTIC Science & Technology

1991-09-01

architecture to support real-time, knowledge -based image understanding , and develop the software support environment that will be needed to utilize...NUMBER OF PAGES Image Understanding Architecture, Knowledge -Based Vision, AI Real-Time Computer Vision, Software Simulator, Parallel Processor IL PRICE... information . In addition to sensory and knowledge -based processing it is useful to introduce a level of symbolic processing. Thus, vision researchers

Foundational model of structural connectivity in the nervous system with a schema for wiring diagrams, connectome, and basic plan architecture

PubMed Central

Swanson, Larry W.; Bota, Mihail

2010-01-01

The nervous system is a biological computer integrating the body's reflex and voluntary environmental interactions (behavior) with a relatively constant internal state (homeostasis)—promoting survival of the individual and species. The wiring diagram of the nervous system's structural connectivity provides an obligatory foundational model for understanding functional localization at molecular, cellular, systems, and behavioral organization levels. This paper provides a high-level, downwardly extendible, conceptual framework—like a compass and map—for describing and exploring in neuroinformatics systems (such as our Brain Architecture Knowledge Management System) the structural architecture of the nervous system's basic wiring diagram. For this, the Foundational Model of Connectivity's universe of discourse is the structural architecture of nervous system connectivity in all animals at all resolutions, and the model includes two key elements—a set of basic principles and an internally consistent set of concepts (defined vocabulary of standard terms)—arranged in an explicitly defined schema (set of relationships between concepts) allowing automatic inferences. In addition, rules and procedures for creating and modifying the foundational model are considered. Controlled vocabularies with broad community support typically are managed by standing committees of experts that create and refine boundary conditions, and a set of rules that are available on the Web. PMID:21078980

Presenting an Approach for Conducting Knowledge Architecture within Large-Scale Organizations

PubMed Central

Varaee, Touraj; Habibi, Jafar; Mohaghar, Ali

2015-01-01

Knowledge architecture (KA) establishes the basic groundwork for the successful implementation of a short-term or long-term knowledge management (KM) program. An example of KA is the design of a prototype before a new vehicle is manufactured. Due to a transformation to large-scale organizations, the traditional architecture of organizations is undergoing fundamental changes. This paper explores the main strengths and weaknesses in the field of KA within large-scale organizations and provides a suitable methodology and supervising framework to overcome specific limitations. This objective was achieved by applying and updating the concepts from the Zachman information architectural framework and the information architectural methodology of enterprise architecture planning (EAP). The proposed solution may be beneficial for architects in knowledge-related areas to successfully accomplish KM within large-scale organizations. The research method is descriptive; its validity is confirmed by performing a case study and polling the opinions of KA experts. PMID:25993414

Presenting an Approach for Conducting Knowledge Architecture within Large-Scale Organizations.

PubMed

Varaee, Touraj; Habibi, Jafar; Mohaghar, Ali

2015-01-01

Knowledge architecture (KA) establishes the basic groundwork for the successful implementation of a short-term or long-term knowledge management (KM) program. An example of KA is the design of a prototype before a new vehicle is manufactured. Due to a transformation to large-scale organizations, the traditional architecture of organizations is undergoing fundamental changes. This paper explores the main strengths and weaknesses in the field of KA within large-scale organizations and provides a suitable methodology and supervising framework to overcome specific limitations. This objective was achieved by applying and updating the concepts from the Zachman information architectural framework and the information architectural methodology of enterprise architecture planning (EAP). The proposed solution may be beneficial for architects in knowledge-related areas to successfully accomplish KM within large-scale organizations. The research method is descriptive; its validity is confirmed by performing a case study and polling the opinions of KA experts.

A knowledge-base generating hierarchical fuzzy-neural controller.

PubMed

Kandadai, R M; Tien, J M

1997-01-01

We present an innovative fuzzy-neural architecture that is able to automatically generate a knowledge base, in an extractable form, for use in hierarchical knowledge-based controllers. The knowledge base is in the form of a linguistic rule base appropriate for a fuzzy inference system. First, we modify Berenji and Khedkar's (1992) GARIC architecture to enable it to automatically generate a knowledge base; a pseudosupervised learning scheme using reinforcement learning and error backpropagation is employed. Next, we further extend this architecture to a hierarchical controller that is able to generate its own knowledge base. Example applications are provided to underscore its viability.

Neural stem cells and neuro/gliogenesis in the central nervous system: understanding the structural and functional plasticity of the developing, mature, and diseased brain.

PubMed

Yamaguchi, Masahiro; Seki, Tatsunori; Imayoshi, Itaru; Tamamaki, Nobuaki; Hayashi, Yoshitaka; Tatebayashi, Yoshitaka; Hitoshi, Seiji

2016-05-01

Neurons and glia in the central nervous system (CNS) originate from neural stem cells (NSCs). Knowledge of the mechanisms of neuro/gliogenesis from NSCs is fundamental to our understanding of how complex brain architecture and function develop. NSCs are present not only in the developing brain but also in the mature brain in adults. Adult neurogenesis likely provides remarkable plasticity to the mature brain. In addition, recent progress in basic research in mental disorders suggests an etiological link with impaired neuro/gliogenesis in particular brain regions. Here, we review the recent progress and discuss future directions in stem cell and neuro/gliogenesis biology by introducing several topics presented at a joint meeting of the Japanese Association of Anatomists and the Physiological Society of Japan in 2015. Collectively, these topics indicated that neuro/gliogenesis from NSCs is a common event occurring in many brain regions at various ages in animals. Given that significant structural and functional changes in cells and neural networks are accompanied by neuro/gliogenesis from NSCs and the integration of newly generated cells into the network, stem cell and neuro/gliogenesis biology provides a good platform from which to develop an integrated understanding of the structural and functional plasticity that underlies the development of the CNS, its remodeling in adulthood, and the recovery from diseases that affect it.

How the Timing and Quality of Early Experiences Influence the Development of Brain Architecture

ERIC Educational Resources Information Center

Fox, Sharon E.; Levitt, Pat; Nelson, Charles A., III.

2010-01-01

Early life events can exert a powerful influence on both the pattern of brain architecture and behavioral development. In this study a conceptual framework is provided for considering how the structure of early experience gets "under the skin." The study begins with a description of the genetic framework that lays the foundation for brain…

DICCCOL: Dense Individualized and Common Connectivity-Based Cortical Landmarks

PubMed Central

Zhu, Dajiang; Guo, Lei; Jiang, Xi; Zhang, Tuo; Zhang, Degang; Chen, Hanbo; Deng, Fan; Faraco, Carlos; Jin, Changfeng; Wee, Chong-Yaw; Yuan, Yixuan; Lv, Peili; Yin, Yan; Hu, Xiaolei; Duan, Lian; Hu, Xintao; Han, Junwei; Wang, Lihong; Shen, Dinggang; Miller, L Stephen

2013-01-01

Is there a common structural and functional cortical architecture that can be quantitatively encoded and precisely reproduced across individuals and populations? This question is still largely unanswered due to the vast complexity, variability, and nonlinearity of the cerebral cortex. Here, we hypothesize that the common cortical architecture can be effectively represented by group-wise consistent structural fiber connections and take a novel data-driven approach to explore the cortical architecture. We report a dense and consistent map of 358 cortical landmarks, named Dense Individualized and Common Connectivity–based Cortical Landmarks (DICCCOLs). Each DICCCOL is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. Our results have shown that these 358 landmarks are remarkably reproducible over more than one hundred human brains and possess accurate intrinsically established structural and functional cross-subject correspondences validated by large-scale functional magnetic resonance imaging data. In particular, these 358 cortical landmarks can be accurately and efficiently predicted in a new single brain with DTI data. Thus, this set of 358 DICCCOL landmarks comprehensively encodes the common structural and functional cortical architectures, providing opportunities for many applications in brain science including mapping human brain connectomes, as demonstrated in this work. PMID:22490548

Learning pattern recognition and decision making in the insect brain

NASA Astrophysics Data System (ADS)

Huerta, R.

2013-01-01

We revise the current model of learning pattern recognition in the Mushroom Bodies of the insects using current experimental knowledge about the location of learning, olfactory coding and connectivity. We show that it is possible to have an efficient pattern recognition device based on the architecture of the Mushroom Bodies, sparse code, mutual inhibition and Hebbian leaning only in the connections from the Kenyon cells to the output neurons. We also show that despite the conventional wisdom that believes that artificial neural networks are the bioinspired model of the brain, the Mushroom Bodies actually resemble very closely Support Vector Machines (SVMs). The derived SVM learning rules are situated in the Mushroom Bodies, are nearly identical to standard Hebbian rules, and require inhibition in the output. A very particular prediction of the model is that random elimination of the Kenyon cells in the Mushroom Bodies do not impair the ability to recognize odorants previously learned.

Neurogenetics: Advancing the “Next-Generation” of Brain Research

PubMed Central

Zoghbi, Huda Y.; Warren, Stephen T.

2010-01-01

There can be little doubt that genetics has transformed our understanding of mechanisms mediating brain disorders. The last two decades have brought tremendous progress in terms of accurate molecular diagnoses and knowledge of the genes and pathways that are involved in a large number of neurological and psychiatric disorders. Likewise, new methods and analytical approaches, including genome array studies and “next-generation” sequencing technologies, are bringing us deeper insights into the subtle complexities of the genetic architecture that determines our risks for these disorders. As we now seek to translate these discoveries back to clinical applications, a major challenge for the field will be in bridging the gap between genes and biology. In this Overview of Neuron’s special review issue on neurogenetics, we reflect on progress made over the last two decades and highlight the challenges as well as the exciting opportunities for the future. PMID:20955921

Ultrastructural findings in transplanted experimental brain tumors and their significance for the cytogenesis of such tumors.

PubMed

Mennel, H D

1988-01-01

Tumors induced by transplacental action in the spinal cord of rats were transplanted into the brains of the same rat strain. They were followed up by electron microscopy during the first ten passages. Three architectural features were detected: First pure tumor parts, second myelin breakdown and phagocytosis, and third the resulting accumulation of resting macrophages. Architecture two and three were interpreted as result of considerable phagocytotic activity of tumor cells localized within the white substance of the brain and spinal cord. Only architecture one was considered to represent proper tumor. Since this was low differentiated and partial astrocytic differentiation only occurred around vessels to remarkable extent, the thesis is put forward that these transplacentally induced tumors correspond to human primitive neuroectodermal tumors.

Supercomputer algorithms for efficient linear octree encoding of three-dimensional brain images.

PubMed

Berger, S B; Reis, D J

1995-02-01

We designed and implemented algorithms for three-dimensional (3-D) reconstruction of brain images from serial sections using two important supercomputer architectures, vector and parallel. These architectures were represented by the Cray YMP and Connection Machine CM-2, respectively. The programs operated on linear octree representations of the brain data sets, and achieved 500-800 times acceleration when compared with a conventional laboratory workstation. As the need for higher resolution data sets increases, supercomputer algorithms may offer a means of performing 3-D reconstruction well above current experimental limits.

A "Knowledge Trading Game" for Collaborative Design Learning in an Architectural Design Studio

ERIC Educational Resources Information Center

Wang, Wan-Ling; Shih, Shen-Guan; Chien, Sheng-Fen

2010-01-01

Knowledge-sharing and resource exchange are the key to the success of collaborative design learning. In an architectural design studio, design knowledge entails learning efforts that need to accumulate and recombine dispersed and complementary pieces of knowledge. In this research, firstly, "Knowledge Trading Game" is proposed to be a way for…

Development of a High Angular Resolution Diffusion Imaging Human Brain Template

PubMed Central

Varentsova, Anna; Zhang, Shengwei; Arfanakis, Konstantinos

2014-01-01

Brain diffusion templates contain rich information about the microstructure of the brain, and are used as references in spatial normalization or in the development of brain atlases. The accuracy of diffusion templates constructed based on the diffusion tensor (DT) model is limited in regions with complex neuronal micro-architecture. High angular resolution diffusion imaging (HARDI) overcomes limitations of the DT model and is capable of resolving intravoxel heterogeneity. However, when HARDI is combined with multiple-shot sequences to minimize image artifacts, the scan time becomes inappropriate for human brain imaging. In this work, an artifact-free HARDI template of the human brain was developed from low angular resolution multiple-shot diffusion data. The resulting HARDI template was produced in ICBM-152 space based on Turboprop diffusion data, was shown to resolve complex neuronal micro-architecture in regions with intravoxel heterogeneity, and contained fiber orientation information consistent with known human brain anatomy. PMID:24440528

Generalisation, decision making, and embodiment effects in mental rotation: A neurorobotic architecture tested with a humanoid robot.

PubMed

Seepanomwan, Kristsana; Caligiore, Daniele; Cangelosi, Angelo; Baldassarre, Gianluca

2015-12-01

Mental rotation, a classic experimental paradigm of cognitive psychology, tests the capacity of humans to mentally rotate a seen object to decide if it matches a target object. In recent years, mental rotation has been investigated with brain imaging techniques to identify the brain areas involved. Mental rotation has also been investigated through the development of neural-network models, used to identify the specific mechanisms that underlie its process, and with neurorobotics models to investigate its embodied nature. Current models, however, have limited capacities to relate to neuro-scientific evidence, to generalise mental rotation to new objects, to suitably represent decision making mechanisms, and to allow the study of the effects of overt gestures on mental rotation. The work presented in this study overcomes these limitations by proposing a novel neurorobotic model that has a macro-architecture constrained by knowledge held on brain, encompasses a rather general mental rotation mechanism, and incorporates a biologically plausible decision making mechanism. The model was tested using the humanoid robot iCub in tasks requiring the robot to mentally rotate 2D geometrical images appearing on a computer screen. The results show that the robot gained an enhanced capacity to generalise mental rotation to new objects and to express the possible effects of overt movements of the wrist on mental rotation. The model also represents a further step in the identification of the embodied neural mechanisms that may underlie mental rotation in humans and might also give hints to enhance robots' planning capabilities. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

The neuron classification problem

PubMed Central

Bota, Mihail; Swanson, Larry W.

2007-01-01

A systematic account of neuron cell types is a basic prerequisite for determining the vertebrate nervous system global wiring diagram. With comprehensive lineage and phylogenetic information unavailable, a general ontology based on structure-function taxonomy is proposed and implemented in a knowledge management system, and a prototype analysis of select regions (including retina, cerebellum, and hypothalamus) presented. The supporting Brain Architecture Knowledge Management System (BAMS) Neuron ontology is online and its user interface allows queries about terms and their definitions, classification criteria based on the original literature and “Petilla Convention” guidelines, hierarchies, and relations—with annotations documenting each ontology entry. Combined with three BAMS modules for neural regions, connections between regions and neuron types, and molecules, the Neuron ontology provides a general framework for physical descriptions and computational modeling of neural systems. The knowledge management system interacts with other web resources, is accessible in both XML and RDF/OWL, is extendible to the whole body, and awaits large-scale data population requiring community participation for timely implementation. PMID:17582506

Tuning In: Parents of Young Children Speak up about What They Think, Know, and Need

ERIC Educational Resources Information Center

Lerner, Claire; Nightingale, Marisa O.

2016-01-01

So much more is known now than a generation ago about how and when brain architecture is built and how deeply it is influenced by early experiences. We know for certain now that the way adult caregivers, parents in particular, interact with children during the first 5 years can actually shape their brain architecture for life--for better and for…

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.

PubMed

Akkus, Zeynettin; Galimzianova, Alfiia; Hoogi, Assaf; Rubin, Daniel L; Erickson, Bradley J

2017-08-01

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

Fluid and flexible minds: Intelligence reflects synchrony in the brain’s intrinsic network architecture

PubMed Central

Ferguson, Michael A.; Anderson, Jeffrey S.; Spreng, R. Nathan

2017-01-01

Human intelligence has been conceptualized as a complex system of dissociable cognitive processes, yet studies investigating the neural basis of intelligence have typically emphasized the contributions of discrete brain regions or, more recently, of specific networks of functionally connected regions. Here we take a broader, systems perspective in order to investigate whether intelligence is an emergent property of synchrony within the brain’s intrinsic network architecture. Using a large sample of resting-state fMRI and cognitive data (n = 830), we report that the synchrony of functional interactions within and across distributed brain networks reliably predicts fluid and flexible intellectual functioning. By adopting a whole-brain, systems-level approach, we were able to reliably predict individual differences in human intelligence by characterizing features of the brain’s intrinsic network architecture. These findings hold promise for the eventual development of neural markers to predict changes in intellectual function that are associated with neurodevelopment, normal aging, and brain disease.

Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm.

PubMed

Savareh, Behrouz Alizadeh; Emami, Hassan; Hajiabadi, Mohamadreza; Azimi, Seyed Majid; Ghafoori, Mahyar

2018-05-29

Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.

The development of hub architecture in the human functional brain network.

PubMed

Hwang, Kai; Hallquist, Michael N; Luna, Beatriz

2013-10-01

Functional hubs are brain regions that play a crucial role in facilitating communication among parallel, distributed brain networks. The developmental emergence and stability of hubs, however, is not well understood. The current study used measures of network topology drawn from graph theory to investigate the development of functional hubs in 99 participants, 10-20 years of age. We found that hub architecture was evident in late childhood and was stable from adolescence to early adulthood. Connectivity between hub and non-hub ("spoke") regions, however, changed with development. From childhood to adolescence, the strength of connections between frontal hubs and cortical and subcortical spoke regions increased. From adolescence to adulthood, hub-spoke connections with frontal hubs were stable, whereas connectivity between cerebellar hubs and cortical spoke regions increased. Our findings suggest that a developmentally stable functional hub architecture provides the foundation of information flow in the brain, whereas connections between hubs and spokes continue to develop, possibly supporting mature cognitive function.

A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data From Healthy Versus Addiction Treated Versus Addiction Not Treated Subjects.

PubMed

Doborjeh, Maryam Gholami; Wang, Grace Y; Kasabov, Nikola K; Kydd, Robert; Russell, Bruce

2016-09-01

This paper introduces a method utilizing spiking neural networks (SNN) for learning, classification, and comparative analysis of brain data. As a case study, the method was applied to electroencephalography (EEG) data collected during a GO/NOGO cognitive task performed by untreated opiate addicts, those undergoing methadone maintenance treatment (MMT) for opiate dependence and a healthy control group. the method is based on an SNN architecture called NeuCube, trained on spatiotemporal EEG data. NeuCube was used to classify EEG data across subject groups and across GO versus NOGO trials, but also facilitated a deeper comparative analysis of the dynamic brain processes. This analysis results in a better understanding of human brain functioning across subject groups when performing a cognitive task. In terms of the EEG data classification, a NeuCube model obtained better results (the maximum obtained accuracy: 90.91%) when compared with traditional statistical and artificial intelligence methods (the maximum obtained accuracy: 50.55%). more importantly, new information about the effects of MMT on cognitive brain functions is revealed through the analysis of the SNN model connectivity and its dynamics. this paper presented a new method for EEG data modeling and revealed new knowledge on brain functions associated with mental activity which is different from the brain activity observed in a resting state of the same subjects.

Pattern Search in Multi-structure Data: A Framework for the Next-Generation Evidence-based Medicine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sukumar, Sreenivas R; Ainsworth, Keela C

With the advent of personalized and evidence-based medicine, the need for a framework to analyze/interpret quantitative measurements (blood work, toxicology, etc.) with qualitative descriptions (specialist reports after reading images, bio-medical knowledge-bases) to predict diagnostic risks is fast emerging. Addressing this need, we pose and address the following questions (i) How can we jointly analyze both qualitative and quantitative data ? (ii) Is the fusion of multi-structure data expected to provide better insights than either of them individually ? We present experiments on two bio-medical data sets - mammography and traumatic brain studies to demonstrate architectures and tools for evidence-pattern search.

Design and Architecture of Collaborative Online Communities: A Quantitative Analysis

ERIC Educational Resources Information Center

Aviv, Reuven; Erlich, Zippy; Ravid, Gilad

2004-01-01

This paper considers four aspects of online communities. Design, mechanisms, architecture, and the constructed knowledge. We hypothesize that different designs of communities drive different mechanisms, which give rise to different architectures, which in turn result in different levels of collaborative knowledge construction. To test this chain…

Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of Established Firms.

ERIC Educational Resources Information Center

Henderson, Rebecca M.; Clark, Kim B.

1990-01-01

Using an empirical study of the semiconductor photolithographic alignment equipment industry, this paper shows that architectural innovations destroy the usefulness of established firms' architectural knowledge. Because this knowledge is embedded in the firms' structure and information-processing procedures, the destruction is hard to detect.…

Neural basis of bilingual language control.

PubMed

Calabria, Marco; Costa, Albert; Green, David W; Abutalebi, Jubin

2018-06-19

Acquiring and speaking a second language increases demand on the processes of language control for bilingual as compared to monolingual speakers. Language control for bilingual speakers involves the ability to keep the two languages separated to avoid interference and to select one language or the other in a given conversational context. This ability is what we refer with the term "bilingual language control" (BLC). It is now well established that the architecture of this complex system of language control encompasses brain networks involving cortical and subcortical structures, each responsible for different cognitive processes such as goal maintenance, conflict monitoring, interference suppression, and selective response inhibition. Furthermore, advances have been made in determining the overlap between the BLC and the nonlinguistic executive control networks, under the hypothesis that the BLC processes are just an instantiation of a more domain-general control system. Here, we review the current knowledge about the neural basis of these control systems. Results from brain imaging studies of healthy adults and on the performance of bilingual individuals with brain damage are discussed. © 2018 New York Academy of Sciences.

An Object-Oriented Architecture for Intelligent Tutoring Systems. Technical Report No. LSP-3.

ERIC Educational Resources Information Center

Bonar, Jeffrey; And Others

This technical report describes a generic architecture for building intelligent tutoring systems which is developed around objects that represent the knowledge elements to be taught by the tutor. Each of these knowledge elements, called "bites," inherits both a knowledge organization describing the kind of knowledge represented and…

An architecture for rule based system explanation

NASA Technical Reports Server (NTRS)

Fennel, T. R.; Johannes, James D.

1990-01-01

A system architecture is presented which incorporate both graphics and text into explanations provided by rule based expert systems. This architecture facilitates explanation of the knowledge base content, the control strategies employed by the system, and the conclusions made by the system. The suggested approach combines hypermedia and inference engine capabilities. Advantages include: closer integration of user interface, explanation system, and knowledge base; the ability to embed links to deeper knowledge underlying the compiled knowledge used in the knowledge base; and allowing for more direct control of explanation depth and duration by the user. User models are suggested to control the type, amount, and order of information presented.

Large scale digital atlases in neuroscience

NASA Astrophysics Data System (ADS)

Hawrylycz, M.; Feng, D.; Lau, C.; Kuan, C.; Miller, J.; Dang, C.; Ng, L.

2014-03-01

Imaging in neuroscience has revolutionized our current understanding of brain structure, architecture and increasingly its function. Many characteristics of morphology, cell type, and neuronal circuitry have been elucidated through methods of neuroimaging. Combining this data in a meaningful, standardized, and accessible manner is the scope and goal of the digital brain atlas. Digital brain atlases are used today in neuroscience to characterize the spatial organization of neuronal structures, for planning and guidance during neurosurgery, and as a reference for interpreting other data modalities such as gene expression and connectivity data. The field of digital atlases is extensive and in addition to atlases of the human includes high quality brain atlases of the mouse, rat, rhesus macaque, and other model organisms. Using techniques based on histology, structural and functional magnetic resonance imaging as well as gene expression data, modern digital atlases use probabilistic and multimodal techniques, as well as sophisticated visualization software to form an integrated product. Toward this goal, brain atlases form a common coordinate framework for summarizing, accessing, and organizing this knowledge and will undoubtedly remain a key technology in neuroscience in the future. Since the development of its flagship project of a genome wide image-based atlas of the mouse brain, the Allen Institute for Brain Science has used imaging as a primary data modality for many of its large scale atlas projects. We present an overview of Allen Institute digital atlases in neuroscience, with a focus on the challenges and opportunities for image processing and computation.

The Neonatal Connectome During Preterm Brain Development

PubMed Central

van den Heuvel, Martijn P.; Kersbergen, Karina J.; de Reus, Marcel A.; Keunen, Kristin; Kahn, René S.; Groenendaal, Floris; de Vries, Linda S.; Benders, Manon J.N.L.

2015-01-01

The human connectome is the result of an elaborate developmental trajectory. Acquiring diffusion-weighted imaging and resting-state fMRI, we studied connectome formation during the preterm phase of macroscopic connectome genesis. In total, 27 neonates were scanned at week 30 and/or week 40 gestational age (GA). Examining the architecture of the neonatal anatomical brain network revealed a clear presence of a small-world modular organization before term birth. Analysis of neonatal functional connectivity (FC) showed the early formation of resting-state networks, suggesting that functional networks are present in the preterm brain, albeit being in an immature state. Moreover, structural and FC patterns of the neonatal brain network showed strong overlap with connectome architecture of the adult brain (85 and 81%, respectively). Analysis of brain development between week 30 and week 40 GA revealed clear developmental effects in neonatal connectome architecture, including a significant increase in white matter microstructure (P < 0.01), small-world topology (P < 0.01) and interhemispheric FC (P < 0.01). Computational analysis further showed that developmental changes involved an increase in integration capacity of the connectivity network as a whole. Taken together, we conclude that hallmark organizational structures of the human connectome are present before term birth and subject to early development. PMID:24833018

How Early Events Affect Growing Brains. An Interview with Neuroscientist Pat Levitt

ERIC Educational Resources Information Center

National Scientific Council on the Developing Child, 2006

2006-01-01

Recent advances in neuroscience show clearly how experience can change brain neurochemicals, and how this in turn affects the way the brain functions. As a result, early negative events actually get built into the growing brain's neurochemistry, altering the brain's architecture. Research is continuing to investigate how children with genetic…

A Novel Architecture for E-Learning Knowledge Assessment Systems

ERIC Educational Resources Information Center

Gierlowski, Krzysztof; Nowicki, Krzysztof

2009-01-01

In this article we propose a novel e-learning system, dedicated strictly to knowledge assessment tasks. In its functioning it utilizes web-based technologies, but its design differs radically from currently popular e-learning solutions which rely mostly on thin-client architecture. Our research proved that such architecture, while well suited for…

Leonardo da Vinci: the search for the soul.

PubMed

Del Maestro, R F

1998-11-01

The human race has always contemplated the question of the anatomical location of the soul. During the Renaissance the controversy crystallized into those individuals who supported the heart ("cardiocentric soul") and others who supported the brain ("cephalocentric soul") as the abode for this elusive entity. Leonardo da Vinci (1452-1519) joined a long list of other explorers in the "search for the soul." The method he used to resolve this anatomical problem involved the accumulation of information from ancient and contemporary sources, careful notetaking, discussions with acknowledged experts, and his own personal search for the truth. Leonardo used a myriad of innovative methods acquired from his knowledge of painting, sculpture, and architecture to define more clearly the site of the "senso comune"--the soul. In this review the author examines the sources of this ancient question, the knowledge base tapped by Leonardo for his personal search for the soul, and the views of key individuals who followed him.

DataHub knowledge based assistance for science visualization and analysis using large distributed databases

NASA Technical Reports Server (NTRS)

Handley, Thomas H., Jr.; Collins, Donald J.; Doyle, Richard J.; Jacobson, Allan S.

1991-01-01

Viewgraphs on DataHub knowledge based assistance for science visualization and analysis using large distributed databases. Topics covered include: DataHub functional architecture; data representation; logical access methods; preliminary software architecture; LinkWinds; data knowledge issues; expert systems; and data management.

Metacognition: Strategies for 'Fourthought.'

ERIC Educational Resources Information Center

Hanson, J. Robert

The brain's architecture serves as the basic model for theorizing about how the brain works. Current brain research confirms earlier suspicions that thoughtfulness has a great deal more complexity than the simplistic left/right distinctions of earlier research. This paper draws on the work of Carl Jung to propose the brain-psyche model as an…

Software Architecture Evaluation in Global Software Development Projects

NASA Astrophysics Data System (ADS)

Salger, Frank

Due to ever increasing system complexity, comprehensive methods for software architecture evaluation become more and more important. This is further stressed in global software development (GSD), where the software architecture acts as a central knowledge and coordination mechanism. However, existing methods for architecture evaluation do not take characteristics of GSD into account. In this paper we discuss what aspects are specific for architecture evaluations in GSD. Our experiences from GSD projects at Capgemini sd&m indicate, that architecture evaluations differ in how rigorously one has to assess modularization, architecturally relevant processes, knowledge transfer and process alignment. From our project experiences, we derive nine good practices, the compliance to which should be checked in architecture evaluations in GSD. As an example, we discuss how far the standard architecture evaluation method used at Capgemini sd&m already considers the GSD-specific good practices, and outline what extensions are necessary to achieve a comprehensive architecture evaluation framework for GSD.

Study on establishment of Body of Knowledge of Taiwan's Traditional Wooden Structure Technology

NASA Astrophysics Data System (ADS)

Huang, M. T.; Chiou, S. C.; Hsu, T. W.; Su, P. C.

2015-08-01

The timber technology of the Taiwan traditional architecture is brought by the immigrants in the Southern Fujian of China in the early, which has been inherited for a hundred years. In the past, these traditional timber technologies were taught by mentoring, however, due to the change of the social form, the construction of the traditional architecture was faded away, and what is gradually replaced is the repair work of the traditional architecture, therefore, the construction method of the timber technology, use form of the tool and other factors are very different from previous one, and the core technology is faced with the dilemma of endangered loss. There are many relevant studies on architectural style, construction method of technology, schools of craftsman, technical capacity of craftsman and other timber technologies, or the technology preservation is carried out by dictating the historical record, studying the skills and other ways, but for the timber craftsman repairing the traditional architecture on the front line, there is still space for discussing whether to maintain the original construction method and maintain the due repair quality for the core technology. This paper classified the timber technology knowledge with the document analysis method and expert interview method, carried out the architecture analysis of knowledge hierarchy, and finally, built the preliminary framework of the timber technology knowledge system of the Taiwan traditional architecture, and built the standard formulation available for craftsman training and skills identification by virtue of the knowledge system, so that the craftsman did not affect the technical capacity due to the change of the knowledge instruction system, thus, affecting the repair quality of the traditional architecture; and in addition, the building of the database system can also be derived by means of the knowledge structure, so as to integrate the consistency of the contents of core technical capacity. It can be used as the interpretation data; the knowledge is standardized and the authority file is established, which is regarded as a technical specification, so that the technology is standardized, thus, avoid loss or distort.

Development of a high angular resolution diffusion imaging human brain template.

PubMed

Varentsova, Anna; Zhang, Shengwei; Arfanakis, Konstantinos

2014-05-01

Brain diffusion templates contain rich information about the microstructure of the brain, and are used as references in spatial normalization or in the development of brain atlases. The accuracy of diffusion templates constructed based on the diffusion tensor (DT) model is limited in regions with complex neuronal micro-architecture. High angular resolution diffusion imaging (HARDI) overcomes limitations of the DT model and is capable of resolving intravoxel heterogeneity. However, when HARDI is combined with multiple-shot sequences to minimize image artifacts, the scan time becomes inappropriate for human brain imaging. In this work, an artifact-free HARDI template of the human brain was developed from low angular resolution multiple-shot diffusion data. The resulting HARDI template was produced in ICBM-152 space based on Turboprop diffusion data, was shown to resolve complex neuronal micro-architecture in regions with intravoxel heterogeneity, and contained fiber orientation information consistent with known human brain anatomy. Copyright © 2014 Elsevier Inc. All rights reserved.

A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing

DOE PAGES

van de Burgt, Yoeri; Lubberman, Ewout; Fuller, Elliot J.; ...

2017-02-20

The brain is capable of massively parallel information processing while consuming only ~1- 100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low energymore » (<10 pJ for 10 3 μm 2 devices) and voltage, displays >500 distinct, non-volatile conductance states within a ~1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODEs are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems. Mechanical flexibility makes ENODes compatible with 3D architectures, opening a path towards extreme interconnectivity comparable to the human brain.« less

A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing

NASA Astrophysics Data System (ADS)

van de Burgt, Yoeri; Lubberman, Ewout; Fuller, Elliot J.; Keene, Scott T.; Faria, Grégorio C.; Agarwal, Sapan; Marinella, Matthew J.; Alec Talin, A.; Salleo, Alberto

2017-04-01

The brain is capable of massively parallel information processing while consuming only ~1-100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (<10 pJ for 103 μm2 devices), displays >500 distinct, non-volatile conductance states within a ~1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.

A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing.

PubMed

van de Burgt, Yoeri; Lubberman, Ewout; Fuller, Elliot J; Keene, Scott T; Faria, Grégorio C; Agarwal, Sapan; Marinella, Matthew J; Alec Talin, A; Salleo, Alberto

2017-04-01

The brain is capable of massively parallel information processing while consuming only ∼1-100 fJ per synaptic event. Inspired by the efficiency of the brain, CMOS-based neural architectures and memristors are being developed for pattern recognition and machine learning. However, the volatility, design complexity and high supply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors complicate the path to achieve the interconnectivity, information density, and energy efficiency of the brain using either approach. Here we describe an electrochemical neuromorphic organic device (ENODe) operating with a fundamentally different mechanism from existing memristors. ENODe switches at low voltage and energy (<10 pJ for 10 3  μm 2 devices), displays >500 distinct, non-volatile conductance states within a ∼1 V range, and achieves high classification accuracy when implemented in neural network simulations. Plastic ENODes are also fabricated on flexible substrates enabling the integration of neuromorphic functionality in stretchable electronic systems. Mechanical flexibility makes ENODes compatible with three-dimensional architectures, opening a path towards extreme interconnectivity comparable to the human brain.

NASA Enterprise Architecture and Its Use in Transition of Research Results to Operations

NASA Astrophysics Data System (ADS)

Frisbie, T. E.; Hall, C. M.

2006-12-01

Enterprise architecture describes the design of the components of an enterprise, their relationships and how they support the objectives of that enterprise. NASA Stennis Space Center leads several projects involving enterprise architecture tools used to gather information on research assets within NASA's Earth Science Division. In the near future, enterprise architecture tools will link and display the relevant requirements, parameters, observatories, models, decision systems, and benefit/impact information relationships and map to the Federal Enterprise Architecture Reference Models. Components configured within the enterprise architecture serving the NASA Applied Sciences Program include the Earth Science Components Knowledge Base, the Systems Components database, and the Earth Science Architecture Tool. The Earth Science Components Knowledge Base systematically catalogues NASA missions, sensors, models, data products, model products, and network partners appropriate for consideration in NASA Earth Science applications projects. The Systems Components database is a centralized information warehouse of NASA's Earth Science research assets and a critical first link in the implementation of enterprise architecture. The Earth Science Architecture Tool is used to analyze potential NASA candidate systems that may be beneficial to decision-making capabilities of other Federal agencies. Use of the current configuration of NASA enterprise architecture (the Earth Science Components Knowledge Base, the Systems Components database, and the Earth Science Architecture Tool) has far exceeded its original intent and has tremendous potential for the transition of research results to operational entities.

Invisible Brain: Knowledge in Research Works and Neuron Activity.

PubMed

Segev, Aviv; Curtis, Dorothy; Jung, Sukhwan; Chae, Suhyun

2016-01-01

If the market has an invisible hand, does knowledge creation and representation have an "invisible brain"? While knowledge is viewed as a product of neuron activity in the brain, can we identify knowledge that is outside the brain but reflects the activity of neurons in the brain? This work suggests that the patterns of neuron activity in the brain can be seen in the representation of knowledge-related activity. Here we show that the neuron activity mechanism seems to represent much of the knowledge learned in the past decades based on published articles, in what can be viewed as an "invisible brain" or collective hidden neural networks. Similar results appear when analyzing knowledge activity in patents. Our work also tries to characterize knowledge increase as neuron network activity growth. The results propose that knowledge-related activity can be seen outside of the neuron activity mechanism. Consequently, knowledge might exist as an independent mechanism.

Modeling and Improving Information Flows in the Development of Large Business Applications

NASA Astrophysics Data System (ADS)

Schneider, Kurt; Lübke, Daniel

Designing a good architecture for an application is a wicked problem. Therefore, experience and knowledge are considered crucial for informing work in software architecture. However, many organizations do not pay sufficient attention to experience exploitation and architectural learning. Many users of information systems are not aware of the options and the needs to report problems and requirements. They often do not have time to describe a problem encountered in sufficient detail for developers to remove it. And there may be a lengthy process for providing feedback. Hence, the knowledge about problems and potential solutions is not shared effectively. Architectural knowledge needs to include evaluative feedback as well as decisions and their reasons (rationale).

Circadian Clock Dysfunction and Psychiatric Disease: Could Fruit Flies have a Say?

PubMed Central

Zordan, Mauro Agostino; Sandrelli, Federica

2015-01-01

There is evidence of a link between the circadian system and psychiatric diseases. Studies in humans and mammals suggest that environmental and/or genetic disruption of the circadian system leads to an increased liability to psychiatric disease. Disruption of clock genes and/or the clock network might be related to the etiology of these pathologies; also, some genes, known for their circadian clock functions, might be associated to mental illnesses through clock-independent pleiotropy. Here, we examine the features which we believe make Drosophila melanogaster a model apt to study the role of the circadian clock in psychiatric disease. Despite differences in the organization of the clock system, the molecular architecture of the Drosophila and mammalian circadian oscillators are comparable and many components are evolutionarily related. In addition, Drosophila has a rather complex nervous system, which shares much at the cell and neurobiological level with humans, i.e., a tripartite brain, the main neurotransmitter systems, and behavioral traits: circadian behavior, learning and memory, motivation, addiction, social behavior. There is evidence that the Drosophila brain shares some homologies with the vertebrate cerebellum, basal ganglia, and hypothalamus-pituitary-adrenal axis, the dysfunctions of which have been tied to mental illness. We discuss Drosophila in comparison to mammals with reference to the: organization of the brain and neurotransmitter systems; architecture of the circadian clock; clock-controlled behaviors. We sum up current knowledge on behavioral endophenotypes, which are amenable to modeling in flies, such as defects involving sleep, cognition, or social interactions, and discuss the relationship of the circadian system to these traits. Finally, we consider if Drosophila could be a valuable asset to understand the relationship between circadian clock malfunction and psychiatric disease. PMID:25941512

Brain-machine interfaces: electrophysiological challenges and limitations.

PubMed

Lega, Bradley C; Serruya, Mijail D; Zaghloul, Kareem A

2011-01-01

Brain-machine interfaces (BMI) seek to directly communicate with the human nervous system in order to diagnose and treat intrinsic neurological disorders. While the first generation of these devices has realized significant clinical successes, they often rely on gross electrical stimulation using empirically derived parameters through open-loop mechanisms of action that are not yet fully understood. Their limitations reflect the inherent challenge in developing the next generation of these devices. This review identifies lessons learned from the first generation of BMI devices (chiefly deep brain stimulation), identifying key problems for which the solutions will aid the development of the next generation of technologies. Our analysis examines four hypotheses for the mechanism by which brain stimulation alters surrounding neurophysiologic activity. We then focus on motor prosthetics, describing various approaches to overcoming the problems of decoding neural signals. We next turn to visual prosthetics, an area for which the challenges of signal coding to match neural architecture has been partially overcome. Finally, we close with a review of cortical stimulation, examining basic principles that will be incorporated into the design of future devices. Throughout the review, we relate the issues of each specific topic to the common thread of BMI research: translating new knowledge of network neuroscience into improved devices for neuromodulation.

Accelerating Cogent Confabulation: An Exploration in the Architecture Design Space

DTIC Science & Technology

2008-06-01

DATES COVERED (From - To) 1-8 June 2008 4. TITLE AND SUBTITLE ACCELERATING COGENT CONFABULATION: AN EXPLORATION IN THE ARCHITECTURE DESIGN SPACE 5a...spiking neural networks is proposed in reference [8]. Reference [9] investigates the architecture design of a Brain-state-in-a-box model. The...Richard Linderman2, Thomas Renz2, Qing Wu1 Accelerating Cogent Confabulation: an Exploration in the Architecture Design Space POSTPRINT complexity

An architecture for intelligent task interruption

NASA Technical Reports Server (NTRS)

Sharma, D. D.; Narayan, Srini

1990-01-01

In the design of real time systems the capability for task interruption is often considered essential. The problem of task interruption in knowledge-based domains is examined. It is proposed that task interruption can be often avoided by using appropriate functional architectures and knowledge engineering principles. Situations for which task interruption is indispensable, a preliminary architecture based on priority hierarchies is described.

FRIEND: a brain-monitoring agent for adaptive and assistive systems.

PubMed

Morris, Alexis; Ulieru, Mihaela

2012-01-01

This paper presents an architectural design for adaptive-systems agents (FRIEND) that use brain state information to make more effective decisions on behalf of a user; measuring brain context versus situational demands. These systems could be useful for alerting users to cognitive workload levels or fatigue, and could attempt to compensate for higher cognitive activity by filtering noise information. In some cases such systems could also share control of devices, such as pulling over in an automated vehicle. These aim to assist people in everyday systems to perform tasks better and be more aware of internal states. Achieving a functioning system of this sort is a challenge, involving a unification of brain- computer-interfaces, human-computer-interaction, soft-computin deliberative multi-agent systems disciplines. Until recently, these were not able to be combined into a usable platform due largely to technological limitations (e.g., size, cost, and processing speed), insufficient research on extracting behavioral states from EEG signals, and lack of low-cost wireless sensing headsets. We aim to surpass these limitations and develop control architectures for making sense of brain state in applications by realizing an agent architecture for adaptive (human-aware) technology. In this paper we present an early, high-level design towards implementing a multi-purpose brain-monitoring agent system to improve user quality of life through the assistive applications of psycho-physiological monitoring, noise-filtering, and shared system control.

Cortical Folding of the Primate Brain: An Interdisciplinary Examination of the Genetic Architecture, Modularity, and Evolvability of a Significant Neurological Trait in Pedigreed Baboons (Genus Papio)

PubMed Central

Atkinson, Elizabeth G.; Rogers, Jeffrey; Mahaney, Michael C.; Cox, Laura A.; Cheverud, James M.

2015-01-01

Folding of the primate brain cortex allows for improved neural processing power by increasing cortical surface area for the allocation of neurons. The arrangement of folds (sulci) and ridges (gyri) across the cerebral cortex is thought to reflect the underlying neural network. Gyrification, an adaptive trait with a unique evolutionary history, is affected by genetic factors different from those affecting brain volume. Using a large pedigreed population of ∼1000 Papio baboons, we address critical questions about the genetic architecture of primate brain folding, the interplay between genetics, brain anatomy, development, patterns of cortical–cortical connectivity, and gyrification’s potential for future evolution. Through Mantel testing and cluster analyses, we find that the baboon cortex is quite evolvable, with high integration between the genotype and phenotype. We further find significantly similar partitioning of variation between cortical development, anatomy, and connectivity, supporting the predictions of tension-based models for sulcal development. We identify a significant, moderate degree of genetic control over variation in sulcal length, with gyrus-shape features being more susceptible to environmental effects. Finally, through QTL mapping, we identify novel chromosomal regions affecting variation in brain folding. The most significant QTL contain compelling candidate genes, including gene clusters associated with Williams and Down syndromes. The QTL distribution suggests a complex genetic architecture for gyrification with both polygeny and pleiotropy. Our results provide a solid preliminary characterization of the genetic basis of primate brain folding, a unique and biomedically relevant phenotype with significant implications in primate brain evolution. PMID:25873632

Establishment of a Digital Knowledge Conversion Architecture Design Learning with High User Acceptance

ERIC Educational Resources Information Center

Wu, Yun-Wu; Weng, Apollo; Weng, Kuo-Hua

2017-01-01

The purpose of this study is to design a knowledge conversion and management digital learning system for architecture design learning, helping students to share, extract, use and create their design knowledge through web-based interactive activities based on socialization, internalization, combination and externalization process in addition to…

An Object-Oriented Software Architecture for the Explorer-2 Knowledge Management Environment

PubMed Central

Tarabar, David B.; Greenes, Robert A.; Slosser, Eric T.

1989-01-01

Explorer-2 is a workstation based environment to facilitate knowledge management. It provides consistent access to a broad range of knowledge on the basis of purpose, not type. We have developed a software architecture based on Object-Oriented programming for Explorer-2. We have defined three classes of program objects: Knowledge ViewFrames, Knowledge Resources, and Knowledge Bases. This results in knowledge management at three levels: the screen level, the disk level and the meta-knowledge level. We have applied this design to several knowledge bases, and believe that there is a broad applicability of this design.

Influential aspects of leader’s Bourdieu capitals on Malaysian landscape architecture subordinates’ creativity

NASA Astrophysics Data System (ADS)

Zahari, R.; Ariffin, M. H.; Othman, N.

2018-02-01

Free Trade Agreements as implemented by Malaysian government calls out local businesses such as landscape architecture consultant firm to explore internationally and strengthen their performance to compete locally. Performance of landscape architecture firm as a design firm depends entirely on creativity of the subordinates in the firm. Past research has neglected studying the influence of a leader’s capitals on subordinates’ creativity, especially in Malaysian landscape architecture firms. The aim of this research is to investigate the influence of subordinates’ perceptions of the leader’s Bourdieu capitals towards promoting subordinate’s creative behaviours in Malaysian Landscape Architecture firms. The sample chosen for this research are subordinates in registered landscape architecture firm. Data was collected using qualitative semi-structured interviews with 13 respondents and analysed using Qualitative Category Coding. Aspects of the leader’s social capital (i.e. knowledge acquisition, problem solving, motivation boosting), human capital (guidance, demotivating leadership, experiential knowledge, knowledge acquisition), and emotional capital (chemistry with leader, respect, knowledge acquisition, trust, understanding, self-inflicted demotivation) that influence subordinates’ creativity were uncovered from the data. The main finding is that the leader’s capitals promote the subordinate landscape architects or assistant landscape architect to be more creative based on three main things, first is knowledge acquisition, motivation, and ability for the leader to influence through positive relationship. The finding contributes to a new way of understanding the leader’s characteristics that influence subordinates’ creativity.

How the Timing and Quality of Early Experiences Influence the Development of Brain Architecture

PubMed Central

Fox, Sharon E.; Levitt, Pat; Nelson, Charles A.

2009-01-01

Early life events can exert a powerful influence on both the pattern of brain architecture and behavioral development. In this paper a conceptual framework is provided for considering how the structure of early experience gets “under the skin.” The paper begins with a description of the genetic framework that lays the foundation for brain development, and then to the ways experience interacts with and modifies the structures and functions of the developing brain. Much of the attention is focused on early experience and sensitive periods, although it is made clear that later experience also plays an important role in maintaining and elaborating this early wiring diagram, which is critical to establishing a solid footing for development beyond the early years. PMID:20331653

Dynamical system with plastic self-organized velocity field as an alternative conceptual model of a cognitive system.

PubMed

Janson, Natalia B; Marsden, Christopher J

2017-12-05

It is well known that architecturally the brain is a neural network, i.e. a collection of many relatively simple units coupled flexibly. However, it has been unclear how the possession of this architecture enables higher-level cognitive functions, which are unique to the brain. Here, we consider the brain from the viewpoint of dynamical systems theory and hypothesize that the unique feature of the brain, the self-organized plasticity of its architecture, could represent the means of enabling the self-organized plasticity of its velocity vector field. We propose that, conceptually, the principle of cognition could amount to the existence of appropriate rules governing self-organization of the velocity field of a dynamical system with an appropriate account of stimuli. To support this hypothesis, we propose a simple non-neuromorphic mathematical model with a plastic self-organized velocity field, which has no prototype in physical world. This system is shown to be capable of basic cognition, which is illustrated numerically and with musical data. Our conceptual model could provide an additional insight into the working principles of the brain. Moreover, hardware implementations of plastic velocity fields self-organizing according to various rules could pave the way to creating artificial intelligence of a novel type.

Human brain lesion-deficit inference remapped.

PubMed

Mah, Yee-Haur; Husain, Masud; Rees, Geraint; Nachev, Parashkev

2014-09-01

Our knowledge of the anatomical organization of the human brain in health and disease draws heavily on the study of patients with focal brain lesions. Historically the first method of mapping brain function, it is still potentially the most powerful, establishing the necessity of any putative neural substrate for a given function or deficit. Great inferential power, however, carries a crucial vulnerability: without stronger alternatives any consistent error cannot be easily detected. A hitherto unexamined source of such error is the structure of the high-dimensional distribution of patterns of focal damage, especially in ischaemic injury-the commonest aetiology in lesion-deficit studies-where the anatomy is naturally shaped by the architecture of the vascular tree. This distribution is so complex that analysis of lesion data sets of conventional size cannot illuminate its structure, leaving us in the dark about the presence or absence of such error. To examine this crucial question we assembled the largest known set of focal brain lesions (n = 581), derived from unselected patients with acute ischaemic injury (mean age = 62.3 years, standard deviation = 17.8, male:female ratio = 0.547), visualized with diffusion-weighted magnetic resonance imaging, and processed with validated automated lesion segmentation routines. High-dimensional analysis of this data revealed a hidden bias within the multivariate patterns of damage that will consistently distort lesion-deficit maps, displacing inferred critical regions from their true locations, in a manner opaque to replication. Quantifying the size of this mislocalization demonstrates that past lesion-deficit relationships estimated with conventional inferential methodology are likely to be significantly displaced, by a magnitude dependent on the unknown underlying lesion-deficit relationship itself. Past studies therefore cannot be retrospectively corrected, except by new knowledge that would render them redundant. Positively, we show that novel machine learning techniques employing high-dimensional inference can nonetheless accurately converge on the true locus. We conclude that current inferences about human brain function and deficits based on lesion mapping must be re-evaluated with methodology that adequately captures the high-dimensional structure of lesion data. © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain.

Reading, Complexity and the Brain

ERIC Educational Resources Information Center

Goswami, Usha

2008-01-01

Brain imaging offers a new technology for understanding the acquisition of reading by children. It can contribute novel evidence concerning the key mechanisms supporting reading, and the brain systems that are involved. The extensive neural architecture that develops to support efficient reading testifies to the complex developmental processes…

A system architecture for sharing de-identified, research-ready brain scans and health information across clinical imaging centers.

PubMed

Chervenak, Ann L; van Erp, Theo G M; Kesselman, Carl; D'Arcy, Mike; Sobell, Janet; Keator, David; Dahm, Lisa; Murry, Jim; Law, Meng; Hasso, Anton; Ames, Joseph; Macciardi, Fabio; Potkin, Steven G

2012-01-01

Progress in our understanding of brain disorders increasingly relies on the costly collection of large standardized brain magnetic resonance imaging (MRI) data sets. Moreover, the clinical interpretation of brain scans benefits from compare and contrast analyses of scans from patients with similar, and sometimes rare, demographic, diagnostic, and treatment status. A solution to both needs is to acquire standardized, research-ready clinical brain scans and to build the information technology infrastructure to share such scans, along with other pertinent information, across hospitals. This paper describes the design, deployment, and operation of a federated imaging system that captures and shares standardized, de-identified clinical brain images in a federation across multiple institutions. In addition to describing innovative aspects of the system architecture and our initial testing of the deployed infrastructure, we also describe the Standardized Imaging Protocol (SIP) developed for the project and our interactions with the Institutional Review Board (IRB) regarding handling patient data in the federated environment.

A System Architecture for Sharing De-Identified, Research-Ready Brain Scans and Health Information Across Clinical Imaging Centers

PubMed Central

Chervenak, Ann L.; van Erp, Theo G.M.; Kesselman, Carl; D’Arcy, Mike; Sobell, Janet; Keator, David; Dahm, Lisa; Murry, Jim; Law, Meng; Hasso, Anton; Ames, Joseph; Macciardi, Fabio; Potkin, Steven G.

2015-01-01

Progress in our understanding of brain disorders increasingly relies on the costly collection of large standardized brain magnetic resonance imaging (MRI) data sets. Moreover, the clinical interpretation of brain scans benefits from compare and contrast analyses of scans from patients with similar, and sometimes rare, demographic, diagnostic, and treatment status. A solution to both needs is to acquire standardized, research-ready clinical brain scans and to build the information technology infrastructure to share such scans, along with other pertinent information, across hospitals. This paper describes the design, deployment, and operation of a federated imaging system that captures and shares standardized, de-identified clinical brain images in a federation across multiple institutions. In addition to describing innovative aspects of the system architecture and our initial testing of the deployed infrastructure, we also describe the Standardized Imaging Protocol (SIP) developed for the project and our interactions with the Institutional Review Board (IRB) regarding handling patient data in the federated environment. PMID:22941984

Architecture and Initial Development of a Knowledge-as-a-Service Activator for Computable Knowledge Objects for Health.

PubMed

Flynn, Allen J; Boisvert, Peter; Gittlen, Nate; Gross, Colin; Iott, Brad; Lagoze, Carl; Meng, George; Friedman, Charles P

2018-01-01

The Knowledge Grid (KGrid) is a research and development program toward infrastructure capable of greatly decreasing latency between the publication of new biomedical knowledge and its widespread uptake into practice. KGrid comprises digital knowledge objects, an online Library to store them, and an Activator that uses them to provide Knowledge-as-a-Service (KaaS). KGrid's Activator enables computable biomedical knowledge, held in knowledge objects, to be rapidly deployed at Internet-scale in cloud computing environments for improved health. Here we present the Activator, its system architecture and primary functions.

Optimization of knowledge sharing through multi-forum using cloud computing architecture

NASA Astrophysics Data System (ADS)

Madapusi Vasudevan, Sriram; Sankaran, Srivatsan; Muthuswamy, Shanmugasundaram; Ram, N. Sankar

2011-12-01

Knowledge sharing is done through various knowledge sharing forums which requires multiple logins through multiple browser instances. Here a single Multi-Forum knowledge sharing concept is introduced which requires only one login session which makes user to connect multiple forums and display the data in a single browser window. Also few optimization techniques are introduced here to speed up the access time using cloud computing architecture.

Software synthesis using generic architectures

NASA Technical Reports Server (NTRS)

Bhansali, Sanjay

1993-01-01

A framework for synthesizing software systems based on abstracting software system designs and the design process is described. The result of such an abstraction process is a generic architecture and the process knowledge for customizing the architecture. The customization process knowledge is used to assist a designer in customizing the architecture as opposed to completely automating the design of systems. Our approach using an implemented example of a generic tracking architecture which was customized in two different domains is illustrated. How the designs produced using KASE compare to the original designs of the two systems, and current work and plans for extending KASE to other application areas are described.

Mapping Research in Landscape Architecture: Balancing Supply of Academic Knowledge and Demand of Professional Practice

ERIC Educational Resources Information Center

Chen, Zheng; Miller, Patrick A.; Clements, Terry L.; Kim, Mintai

2017-01-01

With increasing academic research in the past few decades, the knowledge scope of landscape architecture has expanded from traditional focus on aesthetics to a broad range of ecological, cultural and psychological issues. In order to understand how academic research and knowledge expansion may have redefined the practice, two surveys were…

Integrative medicine: Breaking down silos of knowledge and practice an epigenetic approach.

PubMed

McEwen, Bruce S

2017-04-01

The future of medicine is discussed in the context of epigenetic influences during the entire life course and the lived experiences of each person, avoiding as much as possible the "medicalization" of the individual and taking a more humanistic view. The reciprocal communication between brain and body via the neuroendocrine, autonomic, metabolic and immune systems and the plasticity of brain architecture provide the basis for devising better "top down" interventions that engage the whole person in working towards his or her welfare. The life course perspective emphasizes the importance of intervening early in life to prevent adverse early life experiences, including the effects of poverty, that can have lifelong consequences, referred to as "biological embedding". In the spirit of integrative, humanistic medicine, treatments that "open windows of plasticity" allow targeted behavioral interventions to redirect brain and body functions and behavior in healthier directions. Policies of government and the private sector, particularly at the local, community level, can create a supporting environment for such interventions. See "Common Ground for Health: Personalized, Precision and Social Medicine McEwen & Getz - https://www.youtube.com/watch?v=IRy_uUWyrEw. Copyright © 2017. Published by Elsevier Inc.

Unexpected Events Induce Motor Slowing via a Brain Mechanism for Action-Stopping with Global Suppressive Effects

PubMed Central

Aron, Adam R.

2013-01-01

When an unexpected event occurs in everyday life (e.g., a car honking), one experiences a slowing down of ongoing action (e.g., of walking into the street). Motor slowing following unexpected events is a ubiquitous phenomenon, both in laboratory experiments as well as such everyday situations, yet the underlying mechanism is unknown. We hypothesized that unexpected events recruit the same inhibition network in the brain as does complete cancellation of an action (i.e., action-stopping). Using electroencephalography and independent component analysis in humans, we show that a brain signature of successful outright action-stopping also exhibits activity following unexpected events, and more so in blocks with greater motor slowing. Further, using transcranial magnetic stimulation to measure corticospinal excitability, we show that an unexpected event has a global motor suppressive effect, just like outright action-stopping. Thus, unexpected events recruit a common mechanism with outright action-stopping, moreover with global suppressive effects. These findings imply that we can now leverage the considerable extant knowledge of the neural architecture and functional properties of the stopping system to better understand the processing of unexpected events, including perhaps how they induce distraction via global suppression. PMID:24259571

Interferon-α acutely impairs whole-brain functional connectivity network architecture - A preliminary study.

PubMed

Dipasquale, Ottavia; Cooper, Ella A; Tibble, Jeremy; Voon, Valerie; Baglio, Francesca; Baselli, Giuseppe; Cercignani, Mara; Harrison, Neil A

2016-11-01

Interferon-alpha (IFN-α) is a key mediator of antiviral immune responses used to treat Hepatitis C infection. Though clinically effective, IFN-α rapidly impairs mood, motivation and cognition, effects that can appear indistinguishable from major depression and provide powerful empirical support for the inflammation theory of depression. Though inflammation has been shown to modulate activity within discrete brain regions, how it affects distributed information processing and the architecture of whole brain functional connectivity networks have not previously been investigated. Here we use a graph theoretic analysis of resting state functional magnetic resonance imaging (rfMRI) to investigate acute effects of systemic interferon-alpha (IFN-α) on whole brain functional connectivity architecture and its relationship to IFN-α-induced mood change. Twenty-two patients with Hepatitis-C infection, initiating IFN-α-based therapy were scanned at baseline and 4h after their first IFN-α dose. The whole brain network was parcellated into 110 cortical and sub-cortical nodes based on the Oxford-Harvard Atlas and effects assessed on higher-level graph metrics, including node degree, betweenness centrality, global and local efficiency. IFN-α was associated with a significant reduction in global network connectivity (node degree) (p=0.033) and efficiency (p=0.013), indicating a global reduction of information transfer among the nodes forming the whole brain network. Effects were similar for highly connected (hub) and non-hub nodes, with no effect on betweenness centrality (p>0.1). At a local level, we identified regions with reduced efficiency of information exchange and a sub-network with decreased functional connectivity after IFN-α. Changes in local and particularly global functional connectivity correlated with associated changes in mood measured on the Profile of Mood States (POMS) questionnaire. IFN-α rapidly induced a profound shift in whole brain network structure, impairing global functional connectivity and the efficiency of parallel information exchange. Correlations with multiple indices of mood change support a role for global changes in brain functional connectivity architecture in coordinated behavioral responses to IFN-α. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Knowledge of Traumatic Brain Injury among Educators

ERIC Educational Resources Information Center

Ernst, William J.; Gallo, Adrienne B.; Sellers, Amanda L.; Mulrine, Jessica; MacNamara, Luciana; Abrahamson, Allison; Kneavel, Meredith

2016-01-01

The purpose of this study is to determine knowledge of traumatic brain injury among educators. Few studies have examined knowledge of traumatic brain injury in this population and fewer still have included a substantial proportion of general education teachers. Examining knowledge of traumatic brain injury in educators is important as the vast…

The Present of Architectural Psychology Researches in China- Based on the Bibliometric Analysis and Knowledge Mapping

NASA Astrophysics Data System (ADS)

Zhu, LeiYe; Wang, Qi; Xu, JunHua; Wu, Qing; Jin, MeiDong; Liao, RongJun; Wang, HaiBin

2018-03-01

Architectural Psychology is an interdisciplinary subject of psychology and architecture that focuses on architectural design by using Gestalt psychology, cognitive psychology and other related psychology principles. Researchers from China have achieved fruitful achievements in the field of architectural psychology during past thirty-three years. To reveal the current situation of the field in China, 129 related papers from the China National Knowledge Infrastructure (CNKI) were analyzed by CiteSpace II software. The results show that: (1) the studies of the field in China have been started since 1984 and the annual number of the papers increased dramatically from 2008 and reached a historical peak in 2016. Shanxi Architecture tops the list of contributing publishing journals; Wuhan University, Southwest Jiaotong University and Chongqing University are the best performer among the contributing organizations. (2) “Environmental Psychology”, “Architectural Design” and “Architectural Psychology” are the most frequency keywords. The frontiers of the field in China are “architectural creation” and “environmental psychology” while the popular research topics were“residential environment”, “spatial environment”, “environmental psychology”, “architectural theory” and “architectural psychology”.

Molecular basis of angiosperm tree architecture

USDA-ARS?s Scientific Manuscript database

The shoot architecture of trees greatly impacts orchard and forest management methods. Amassing greater knowledge of the molecular genetics behind tree form can benefit these industries as well as contribute to basic knowledge of plant developmental biology. This review covers basic components of ...

Architectural Considerations for Classrooms for Exceptional Children.

ERIC Educational Resources Information Center

Texas Education Agency, Austin.

Definitions are provided of the following exceptionalities: blind, partially sighted, physically handicapped, minimally brain injured, deaf, educable mentally retarded (primary, junior, and senior high levels), trainable mentally retarded, speech handicapped, and emotionally disturbed. Architectural guidelines specify classroom location, size,…

Abdominal fat distribution and its relationship to brain changes: the differential effects of age on cerebellar structure and function: a cross-sectional, exploratory study

PubMed Central

Raschpichler, Matthias; Straatman, Kees; Schroeter, Matthias Leopold; Arelin, Katrin; Schlögl, Haiko; Fritzsch, Dominik; Mende, Meinhard; Pampel, André; Böttcher, Yvonne; Stumvoll, Michael; Villringer, Arno; Mueller, Karsten

2013-01-01

Objectives To investigate whether the metabolically important visceral adipose tissue (VAT) relates differently to structural and functional brain changes in comparison with body weight measured as body mass index (BMI). Moreover, we aimed to investigate whether these effects change with age. Design Cross-sectional, exploratory. Setting University Clinic, Integrative Research and Treatment Centre. Participants We included 100 (mean BMI=26.0 kg/m², 42 women) out of 202 volunteers randomly invited by the city's registration office, subdivided into two age groups: young-to-mid-age (n=51, 20–45 years of age, mean BMI=24.9, 24 women) versus old (n=49, 65–70 years of age, mean BMI=27.0, 18 women). Main outcome measures VAT, BMI, subcutaneous abdominal adipose tissue, brain structure (grey matter density), functional brain architecture (eigenvector centrality, EC). Results We discovered a loss of cerebellar structure with increasing VAT in the younger participants, most significantly in regions involved in motor processing. This negative correlation disappeared in the elderly. Investigating functional brain architecture showed again inverse VAT–cerebellum correlations, whereas now regions involved in cognitive and emotional processing were significant. Although we detected similar results for EC using BMI, significant age interaction for both brain structure and functional architecture was only found using VAT. Conclusions Visceral adiposity is associated with cerebellar changes of both structure and function, whereas the regions involved contribute to motor, cognitive and emotional processes. Furthermore, these associations seem to be age dependent, with younger adults’ brains being adversely affected. PMID:23355665

Altered functional connectivity architecture of the brain in medication overuse headache using resting state fMRI.

PubMed

Chen, Zhiye; Chen, Xiaoyan; Liu, Mengqi; Dong, Zhao; Ma, Lin; Yu, Shengyuan

2017-12-01

Functional connectivity density (FCD) could identify the abnormal intrinsic and spontaneous activity over the whole brain, and a seed-based resting-state functional connectivity (RSFC) could further reveal the altered functional network with the identified brain regions. This may be an effective assessment strategy for headache research. This study is to investigate the RSFC architecture changes of the brain in the patients with medication overuse headache (MOH) using FCD and RSFC methods. 3D structure images and resting-state functional MRI data were obtained from 37 MOH patients, 18 episodic migraine (EM) patients and 32 normal controls (NCs). FCD was calculated to detect the brain regions with abnormal functional activity over the whole brain, and the seed-based RSFC was performed to explore the functional network changes in MOH and EM. The decreased FCD located in right parahippocampal gyrus, and the increased FCD located in left inferior parietal gyrus and right supramarginal gyrus in MOH compared with NC, and in right caudate and left insula in MOH compared with EM. RSFC revealed that decreased functional connectivity of the brain regions with decreased FCD anchored in the right dorsal-lateral prefrontal cortex, right frontopolar cortex in MOH, and in left temporopolar cortex and bilateral visual cortices in EM compared with NC, and in frontal-temporal-parietal pattern in MOH compared with EM. These results provided evidence that MOH and EM suffered from altered intrinsic functional connectivity architecture, and the current study presented a new perspective for understanding the neuromechanism of MOH and EM pathogenesis.

Modelling verbal aggression, physical aggression and inappropriate sexual behaviour after acquired brain injury

PubMed Central

James, Andrew I. W.; Böhnke, Jan R.; Young, Andrew W.; Lewis, Gary J.

2015-01-01

Understanding the underpinnings of behavioural disturbances following brain injury is of considerable importance, but little at present is known about the relationships between different types of behavioural disturbances. Here, we take a novel approach to this issue by using confirmatory factor analysis to elucidate the architecture of verbal aggression, physical aggression and inappropriate sexual behaviour using systematic records made across an eight-week observation period for a large sample (n = 301) of individuals with a range of brain injuries. This approach offers a powerful test of the architecture of these behavioural disturbances by testing the fit between observed behaviours and different theoretical models. We chose models that reflected alternative theoretical perspectives based on generalized disinhibition (Model 1), a difference between aggression and inappropriate sexual behaviour (Model 2), or on the idea that verbal aggression, physical aggression and inappropriate sexual behaviour reflect broadly distinct but correlated clinical phenomena (Model 3). Model 3 provided the best fit to the data indicating that these behaviours can be viewed as distinct, but with substantial overlap. These data are important both for developing models concerning the architecture of behaviour as well as for clinical management in individuals with brain injury. PMID:26136449

Holism at the National Intrepid Center of Excellence (NICoE).

PubMed

Foote, Frederick O; Schwartz, Lora

2012-01-01

Traumatic brain injury and posttraumatic stress disorder are the signature injuries of the Iraq and Afghanistan wars. Holistic medicine (comprising multispecialty care integration, patient/family-centered care, wellness interventions, and the construction of architectural "healing environments") has much to offer these patients. In this work we describe the architecture and holistic medicine programming of the National Intrepid Center of Excellence (NICoE), a new clinical research center for traumatic brain injury and posttraumatic stress disorder in the Military Health System. Architecture and clinical process are united in a "design/care continuum" for optimal healing. A groundbreaking institution, the NICoE foreshadows many trends in national healthcare for the 21st century. Copyright © 2012 Elsevier Inc. All rights reserved.

A Biologically Plausible Action Selection System for Cognitive Architectures: Implications of Basal Ganglia Anatomy for Learning and Decision-Making Models

ERIC Educational Resources Information Center

Stocco, Andrea

2018-01-01

Several attempts have been made previously to provide a biological grounding for cognitive architectures by relating their components to the computations of specific brain circuits. Often, the architecture's action selection system is identified with the basal ganglia. However, this identification overlooks one of the most important features of…

Disrupted resting-state functional architecture of the brain after 45-day simulated microgravity

PubMed Central

Zhou, Yuan; Wang, Yun; Rao, Li-Lin; Liang, Zhu-Yuan; Chen, Xiao-Ping; Zheng, Dang; Tan, Cheng; Tian, Zhi-Qiang; Wang, Chun-Hui; Bai, Yan-Qiang; Chen, Shan-Guang; Li, Shu

2014-01-01

Long-term spaceflight induces both physiological and psychological changes in astronauts. To understand the neural mechanisms underlying these physiological and psychological changes, it is critical to investigate the effects of microgravity on the functional architecture of the brain. In this study, we used resting-state functional MRI (rs-fMRI) to study whether the functional architecture of the brain is altered after 45 days of −6° head-down tilt (HDT) bed rest, which is a reliable model for the simulation of microgravity. Sixteen healthy male volunteers underwent rs-fMRI scans before and after 45 days of −6° HDT bed rest. Specifically, we used a commonly employed graph-based measure of network organization, i.e., degree centrality (DC), to perform a full-brain exploration of the regions that were influenced by simulated microgravity. We subsequently examined the functional connectivities of these regions using a seed-based resting-state functional connectivity (RSFC) analysis. We found decreased DC in two regions, the left anterior insula (aINS) and the anterior part of the middle cingulate cortex (MCC; also called the dorsal anterior cingulate cortex in many studies), in the male volunteers after 45 days of −6° HDT bed rest. Furthermore, seed-based RSFC analyses revealed that a functional network anchored in the aINS and MCC was particularly influenced by simulated microgravity. These results provide evidence that simulated microgravity alters the resting-state functional architecture of the brains of males and suggest that the processing of salience information, which is primarily subserved by the aINS–MCC functional network, is particularly influenced by spaceflight. The current findings provide a new perspective for understanding the relationships between microgravity, cognitive function, autonomic neural function, and central neural activity. PMID:24926242

A connectionist modeling study of the neural mechanisms underlying pain's ability to reorient attention.

PubMed

Dowman, Robert; Ritz, Benjamin; Fowler, Kathleen

2016-08-01

Connectionist modeling was used to investigate the brain mechanisms responsible for pain's ability to shift attention away from another stimulus modality and toward itself. Different connectionist model architectures were used to simulate the different possible brain mechanisms underlying this attentional bias, where nodes in the model simulated the brain areas thought to mediate the attentional bias, and the connections between the nodes simulated the interactions between the brain areas. Mathematical optimization techniques were used to find the model parameters, such as connection strengths, that produced the best quantitative fits of reaction time and event-related potential data obtained in our previous work. Of the several architectures tested, two produced excellent quantitative fits of the experimental data. One involved an unexpected pain stimulus activating somatic threat detectors in the dorsal posterior insula. This threat detector activity was monitored by the medial prefrontal cortex, which in turn evoked a phasic response in the locus coeruleus. The locus coeruleus phasic response resulted in a facilitation of the cortical areas involved in decision and response processes time-locked to the painful stimulus. The second architecture involved the presence of pain causing an increase in general arousal. The increase in arousal was mediated by locus coeruleus tonic activity, which facilitated responses in the cortical areas mediating the sensory, decision, and response processes involved in the task. These two neural network architectures generated competing predictions that can be tested in future studies.

The Association of Type 2 Diabetes Mellitus with Cerebral Gray Matter Volume Is Independent of Retinal Vascular Architecture and Retinopathy.

PubMed

Moran, C; Tapp, R J; Hughes, A D; Magnussen, C G; Blizzard, L; Phan, T G; Beare, R; Witt, N; Venn, A; Münch, G; Amaratunge, B C; Srikanth, V

2016-01-01

It is uncertain whether small vessel disease underlies the relationship between Type 2 Diabetes Mellitus (T2DM) and brain atrophy. We aimed to study whether retinal vascular architecture, as a proxy for cerebral small vessel disease, may modify or mediate the associations of T2DM with brain volumes. In this cross-sectional study using Magnetic Resonance Imaging (MRI) scans and retinal photographs in 451 people with and without T2DM, we measured brain volumes, geometric measures of retinal vascular architecture, clinical retinopathy, and MRI cerebrovascular lesions. There were 270 people with (mean age 67.3 years) and 181 without T2DM (mean age 72.9 years). T2DM was associated with lower gray matter volume (p = 0.008). T2DM was associated with greater arteriolar diameter (p = 0.03) and optimality ratio (p = 0.04), but these associations were attenuated by adjustments for age and sex. Only optimality ratio was associated with lower gray matter volume (p = 0.03). The inclusion of retinal measures in regression models did not attenuate the association of T2DM with gray matter volume. The association of T2DM with lower gray matter volume was independent of retinal vascular architecture and clinical retinopathy. Retinal vascular measures or retinopathy may not be sufficiently sensitive to confirm a microvascular basis for T2DM-related brain atrophy.

Design, Analysis and User Acceptance of Architectural Design Education in Learning System Based on Knowledge Management Theory

ERIC Educational Resources Information Center

Wu, Yun-Wu; Lin, Yu-An; Wen, Ming-Hui; Perng, Yeng-Hong; Hsu, I-Ting

2016-01-01

The major purpose of this study is to develop an architectural design knowledge management learning system with corresponding learning activities to help the students have meaningful learning and improve their design capability in their learning process. Firstly, the system can help the students to obtain and share useful knowledge. Secondly,…

An architecture for automated fault diagnosis. [Space Station Module/Power Management And Distribution

NASA Technical Reports Server (NTRS)

Ashworth, Barry R.

1989-01-01

A description is given of the SSM/PMAD power system automation testbed, which was developed using a systems engineering approach. The architecture includes a knowledge-based system and has been successfully used in power system management and fault diagnosis. Architectural issues which effect overall system activities and performance are examined. The knowledge-based system is discussed along with its associated automation implications, and interfaces throughout the system are presented.

Developmental amnesia: a new pattern of dissociation with intact episodic memory.

PubMed

Temple, Christine M; Richardson, Paul

2004-01-01

A case of developmental amnesia is reported for a child, CL, of normal intelligence, who has intact episodic memory but impaired semantic memory for both semantic knowledge of facts and semantic knowledge of words, including general world knowledge, knowledge of word meanings and superordinate knowledge of words. In contrast to the deficits in semantic memory, there are no impairments in episodic memory for verbal or visual material, assessed by recall or recognition. Lexical decision was also intact, indicating impairment in semantic knowledge of vocabulary rather than absence of lexical representations. The case forms a double dissociation to the cases of Vargha-Khadem et al. [Science 277 (1997) 376; Episodic memory: new directions in research (2002) 153]; Gadian et al. [Brain 123 (2000) 499] for whom semantic memory was intact but episodic memory was impaired. This double dissociation suggests that semantic memory and episodic memory have the capacity to develop separately and supports models of modularity within memory development and a functional architecture for the developmental disorders within which there is residual normality rather than pervasive abnormality. Knowledge of arithmetical facts is also spared for CL, consistent with adult studies arguing for numeracy knowledge distinct from other semantics. Reading was characterised by difficulty with irregular words and homophones but intact reading of nonwords. CL has surface dyslexia with poor lexico-semantic reading skills but good phonological reading skills. The case was identified following screening from a population of normal schoolchildren suggesting that developmental amnesias may be more pervasive than has been recognised previously.

A Core Knowledge Architecture of Visual Working Memory

ERIC Educational Resources Information Center

Wood, Justin N.

2011-01-01

Visual working memory (VWM) is widely thought to contain specialized buffers for retaining spatial and object information: a "spatial-object architecture." However, studies of adults, infants, and nonhuman animals show that visual cognition builds on core knowledge systems that retain more specialized representations: (1) spatiotemporal…

Architecture and Initial Development of a Digital Library Platform for Computable Knowledge Objects for Health.

PubMed

Flynn, Allen J; Bahulekar, Namita; Boisvert, Peter; Lagoze, Carl; Meng, George; Rampton, James; Friedman, Charles P

2017-01-01

Throughout the world, biomedical knowledge is routinely generated and shared through primary and secondary scientific publications. However, there is too much latency between publication of knowledge and its routine use in practice. To address this latency, what is actionable in scientific publications can be encoded to make it computable. We have created a purpose-built digital library platform to hold, manage, and share actionable, computable knowledge for health called the Knowledge Grid Library. Here we present it with its system architecture.

Large-scale topology and the default mode network in the mouse connectome

PubMed Central

Stafford, James M.; Jarrett, Benjamin R.; Miranda-Dominguez, Oscar; Mills, Brian D.; Cain, Nicholas; Mihalas, Stefan; Lahvis, Garet P.; Lattal, K. Matthew; Mitchell, Suzanne H.; David, Stephen V.; Fryer, John D.; Nigg, Joel T.; Fair, Damien A.

2014-01-01

Noninvasive functional imaging holds great promise for serving as a translational bridge between human and animal models of various neurological and psychiatric disorders. However, despite a depth of knowledge of the cellular and molecular underpinnings of atypical processes in mouse models, little is known about the large-scale functional architecture measured by functional brain imaging, limiting translation to human conditions. Here, we provide a robust processing pipeline to generate high-resolution, whole-brain resting-state functional connectivity MRI (rs-fcMRI) images in the mouse. Using a mesoscale structural connectome (i.e., an anterograde tracer mapping of axonal projections across the mouse CNS), we show that rs-fcMRI in the mouse has strong structural underpinnings, validating our procedures. We next directly show that large-scale network properties previously identified in primates are present in rodents, although they differ in several ways. Last, we examine the existence of the so-called default mode network (DMN)—a distributed functional brain system identified in primates as being highly important for social cognition and overall brain function and atypically functionally connected across a multitude of disorders. We show the presence of a potential DMN in the mouse brain both structurally and functionally. Together, these studies confirm the presence of basic network properties and functional networks of high translational importance in structural and functional systems in the mouse brain. This work clears the way for an important bridge measurement between human and rodent models, enabling us to make stronger conclusions about how regionally specific cellular and molecular manipulations in mice relate back to humans. PMID:25512496

Increasingly diverse brain dynamics in the developmental arc: using Pareto-optimization to infer a mechanism

NASA Astrophysics Data System (ADS)

Tang, Evelyn; Giusti, Chad; Baum, Graham; Gu, Shi; Pollock, Eli; Kahn, Ari; Roalf, David; Moore, Tyler; Ruparel, Kosha; Gur, Ruben; Gur, Raquel; Satterthwaite, Theodore; Bassett, Danielle

Motivated by a recent demonstration that the network architecture of white matter supports emerging control of diverse neural dynamics as children mature into adults, we seek to investigate structural mechanisms that support these changes. Beginning from a network representation of diffusion imaging data, we simulate network evolution with a set of simple growth rules built on principles of network control. Notably, the optimal evolutionary trajectory displays a striking correspondence to the progression of child to adult brain, suggesting that network control is a driver of development. More generally, and in comparison to the complete set of available models, we demonstrate that all brain networks from child to adult are structured in a manner highly optimized for the control of diverse neural dynamics. Within this near-optimality, we observe differences in the predicted control mechanisms of the child and adult brains, suggesting that the white matter architecture in children has a greater potential to increasingly support brain state transitions, potentially underlying cognitive switching.

The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture.

PubMed

Fan, Lingzhong; Li, Hai; Zhuo, Junjie; Zhang, Yu; Wang, Jiaojian; Chen, Liangfu; Yang, Zhengyi; Chu, Congying; Xie, Sangma; Laird, Angela R; Fox, Peter T; Eickhoff, Simon B; Yu, Chunshui; Jiang, Tianzi

2016-08-01

The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states. © The Author 2016. Published by Oxford University Press.

Development of the Adolescent Brain: Implications for Executive Function and Social Cognition

ERIC Educational Resources Information Center

Blakemore, Sarah-Jayne; Choudhury, Suparna

2006-01-01

Adolescence is a time of considerable development at the level of behaviour, cognition and the brain. This article reviews histological and brain imaging studies that have demonstrated specific changes in neural architecture during puberty and adolescence, outlining trajectories of grey and white matter development. The implications of brain…

AKM in Open Source Communities

NASA Astrophysics Data System (ADS)

Stamelos, Ioannis; Kakarontzas, George

Previous chapters in this book have dealt with Architecture Knowledge Management in traditional Closed Source Software (CSS) projects. This chapterwill attempt to examine the ways that knowledge is shared among participants in Free Libre Open Source Software (FLOSS 1) projects and how architectural knowledge is managed w.r.t. CSS. FLOSS projects are organized and developed in a fundamentally different way than CSS projects. FLOSS projects simply do not develop code as CSS projects do. As a consequence, their knowledge management mechanisms are also based on different concepts and tools.

Invisible Brain: Knowledge in Research Works and Neuron Activity

PubMed Central

Segev, Aviv; Curtis, Dorothy; Jung, Sukhwan; Chae, Suhyun

2016-01-01

If the market has an invisible hand, does knowledge creation and representation have an “invisible brain”? While knowledge is viewed as a product of neuron activity in the brain, can we identify knowledge that is outside the brain but reflects the activity of neurons in the brain? This work suggests that the patterns of neuron activity in the brain can be seen in the representation of knowledge-related activity. Here we show that the neuron activity mechanism seems to represent much of the knowledge learned in the past decades based on published articles, in what can be viewed as an “invisible brain” or collective hidden neural networks. Similar results appear when analyzing knowledge activity in patents. Our work also tries to characterize knowledge increase as neuron network activity growth. The results propose that knowledge-related activity can be seen outside of the neuron activity mechanism. Consequently, knowledge might exist as an independent mechanism. PMID:27439199

Beyond localized and distributed accounts of brain functions. Comment on “Understanding brain networks and brain organization” by Pessoa

NASA Astrophysics Data System (ADS)

Cauda, Franco; Costa, Tommaso; Tamietto, Marco

2014-09-01

Recent evidence in cognitive neuroscience lends support to the idea that network models of brain architecture provide a privileged access to the understanding of the relation between brain organization and cognitive processes [1]. The core perspective holds that cognitive processes depend on the interactions among distributed neuronal populations and brain structures, and that the impact of a given region on behavior largely depends on its pattern of anatomical and functional connectivity [2,3].

Artificial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface.

PubMed

Merolla, Paul A; Arthur, John V; Alvarez-Icaza, Rodrigo; Cassidy, Andrew S; Sawada, Jun; Akopyan, Filipp; Jackson, Bryan L; Imam, Nabil; Guo, Chen; Nakamura, Yutaka; Brezzo, Bernard; Vo, Ivan; Esser, Steven K; Appuswamy, Rathinakumar; Taba, Brian; Amir, Arnon; Flickner, Myron D; Risk, William P; Manohar, Rajit; Modha, Dharmendra S

2014-08-08

Inspired by the brain's structure, we have developed an efficient, scalable, and flexible non-von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic cores interconnected via an intrachip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. Chips can be tiled in two dimensions via an interchip communication interface, seamlessly scaling the architecture to a cortexlike sheet of arbitrary size. The architecture is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification. With 400-pixel-by-240-pixel video input at 30 frames per second, the chip consumes 63 milliwatts. Copyright © 2014, American Association for the Advancement of Science.

Super-resolution Imaging of Chemical Synapses in the Brain

PubMed Central

Dani, Adish; Huang, Bo; Bergan, Joseph; Dulac, Catherine; Zhuang, Xiaowei

2010-01-01

Determination of the molecular architecture of synapses requires nanoscopic image resolution and specific molecular recognition, a task that has so far defied many conventional imaging approaches. Here we present a super-resolution fluorescence imaging method to visualize the molecular architecture of synapses in the brain. Using multicolor, three-dimensional stochastic optical reconstruction microscopy, the distributions of synaptic proteins can be measured with nanometer precision. Furthermore, the wide-field, volumetric imaging method enables high-throughput, quantitative analysis of a large number of synapses from different brain regions. To demonstrate the capabilities of this approach, we have determined the organization of ten protein components of the presynaptic active zone and the postsynaptic density. Variations in synapse morphology, neurotransmitter receptor composition, and receptor distribution were observed both among synapses and across different brain regions. Combination with optogenetics further allowed molecular events associated with synaptic plasticity to be resolved at the single-synapse level. PMID:21144999

Mapping Cortical Laminar Structure in the 3D BigBrain.

PubMed

Wagstyl, Konrad; Lepage, Claude; Bludau, Sebastian; Zilles, Karl; Fletcher, Paul C; Amunts, Katrin; Evans, Alan C

2018-07-01

Histological sections offer high spatial resolution to examine laminar architecture of the human cerebral cortex; however, they are restricted by being 2D, hence only regions with sufficiently optimal cutting planes can be analyzed. Conversely, noninvasive neuroimaging approaches are whole brain but have relatively low resolution. Consequently, correct 3D cross-cortical patterns of laminar architecture have never been mapped in histological sections. We developed an automated technique to identify and analyze laminar structure within the high-resolution 3D histological BigBrain. We extracted white matter and pial surfaces, from which we derived histologically verified surfaces at the layer I/II boundary and within layer IV. Layer IV depth was strongly predicted by cortical curvature but varied between areas. This fully automated 3D laminar analysis is an important requirement for bridging high-resolution 2D cytoarchitecture and in vivo 3D neuroimaging. It lays the foundation for in-depth, whole-brain analyses of cortical layering.

Development of the brain's functional network architecture.

PubMed

Vogel, Alecia C; Power, Jonathan D; Petersen, Steven E; Schlaggar, Bradley L

2010-12-01

A full understanding of the development of the brain's functional network architecture requires not only an understanding of developmental changes in neural processing in individual brain regions but also an understanding of changes in inter-regional interactions. Resting state functional connectivity MRI (rs-fcMRI) is increasingly being used to study functional interactions between brain regions in both adults and children. We briefly review methods used to study functional interactions and networks with rs-fcMRI and how these methods have been used to define developmental changes in network functional connectivity. The developmental rs-fcMRI studies to date have found two general properties. First, regional interactions change from being predominately anatomically local in children to interactions spanning longer cortical distances in young adults. Second, this developmental change in functional connectivity occurs, in general, via mechanisms of segregation of local regions and integration of distant regions into disparate subnetworks.

Development of the Brain's Functional Network Architecture

PubMed Central

Power, Jonathan D.; Petersen, Steven E.; Schlaggar, Bradley L.

2013-01-01

A full understanding of the development of the brain's functional network architecture requires not only an understanding of developmental changes in neural processing in individual brain regions but also an understanding of changes in inter-regional interactions. Resting state functional connectivity MRI (rs-fcMRI) is increasingly being used to study functional interactions between brain regions in both adults and children. We briefly review methods used to study functional interactions and networks with rs-fcMRI and how these methods have been used to define developmental changes in network functional connectivity. The developmental rs-fcMRI studies to date have found two general properties. First, regional interactions change from being predominately anatomically local in children to interactions spanning longer cortical distances in young adults. Second, this developmental change in functional connectivity occurs, in general, via mechanisms of segregation of local regions and integration of distant regions into disparate subnetworks. PMID:20976563

Fluid intelligence and brain functional organization in aging yoga and meditation practitioners

PubMed Central

Gard, Tim; Taquet, Maxime; Dixit, Rohan; Hölzel, Britta K.; de Montjoye, Yves-Alexandre; Brach, Narayan; Salat, David H.; Dickerson, Bradford C.; Gray, Jeremy R.; Lazar, Sara W.

2014-01-01

Numerous studies have documented the normal age-related decline of neural structure, function, and cognitive performance. Preliminary evidence suggests that meditation may reduce decline in specific cognitive domains and in brain structure. Here we extended this research by investigating the relation between age and fluid intelligence and resting state brain functional network architecture using graph theory, in middle-aged yoga and meditation practitioners, and matched controls. Fluid intelligence declined slower in yoga practitioners and meditators combined than in controls. Resting state functional networks of yoga practitioners and meditators combined were more integrated and more resilient to damage than those of controls. Furthermore, mindfulness was positively correlated with fluid intelligence, resilience, and global network efficiency. These findings reveal the possibility to increase resilience and to slow the decline of fluid intelligence and brain functional architecture and suggest that mindfulness plays a mechanistic role in this preservation. PMID:24795629

A task-based support architecture for developing point-of-care clinical decision support systems for the emergency department.

PubMed

Wilk, S; Michalowski, W; O'Sullivan, D; Farion, K; Sayyad-Shirabad, J; Kuziemsky, C; Kukawka, B

2013-01-01

The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter. The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate. The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE--a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED. The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs.

A Priori Knowledge and Heuristic Reasoning in Architectural Design.

ERIC Educational Resources Information Center

Rowe, Peter G.

1982-01-01

It is proposed that the various classes of a priori knowledge incorporated in heuristic reasoning processes exert a strong influence over architectural design activity. Some design problems require exercise of some provisional set of rules, inference, or plausible strategy which requires heuristic reasoning. A case study illustrates this concept.…

Information Architecture and the Comic Arts: Knowledge Structure and Access

ERIC Educational Resources Information Center

Farmer, Lesley S. J.

2015-01-01

This article explains information architecture, focusing on comic arts' features for representing and structuring knowledge. Then it details information design theory and information behaviors relative to this format, also noting visual literacy. Next , applications of comic arts in education are listed. With this background, several research…

Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets.

PubMed

Sengupta, Abhronil; Shim, Yong; Roy, Kaushik

2016-12-01

Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Device-level simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by  ∼  100× in comparison to a corresponding digital/analog CMOS neuron implementation.

Influences of Brain Size, Sex, and Sex Chromosome Complement on the Architecture of Human Cortical Folding.

PubMed

Fish, Ari M; Cachia, Arnaud; Fischer, Clara; Mankiw, Catherine; Reardon, P K; Clasen, Liv S; Blumenthal, Jonathan D; Greenstein, Deanna; Giedd, Jay N; Mangin, Jean-François; Raznahan, Armin

2017-12-01

Gyrification is a fundamental property of the human cortex that is increasingly studied by basic and clinical neuroscience. However, it remains unclear if and how the global architecture of cortical folding varies with 3 interwoven sources of anatomical variation: brain size, sex, and sex chromosome dosage (SCD). Here, for 375 individuals spanning 7 karyotype groups (XX, XY, XXX, XYY, XXY, XXYY, XXXXY), we use structural neuroimaging to measure a global sulcation index (SI, total sulcal/cortical hull area) and both determinants of sulcal area: total sulcal length and mean sulcal depth. We detail large and patterned effects of sex and SCD across all folding metrics, but show that these effects are in fact largely consistent with the normative scaling of cortical folding in health: larger human brains have disproportionately high SI due to a relative expansion of sulcal area versus hull area, which arises because disproportionate sulcal lengthening overcomes a lack of proportionate sulcal deepening. Accounting for these normative allometries reveals 1) brain size-independent sulcal lengthening in males versus females, and 2) insensitivity of overall folding architecture to SCD. Our methodology and findings provide a novel context for future studies of human cortical folding in health and disease. Published by Oxford University Press 2016.

The Study on Collaborative Manufacturing Platform Based on Agent

NASA Astrophysics Data System (ADS)

Zhang, Xiao-yan; Qu, Zheng-geng

To fulfill the trends of knowledge-intensive in collaborative manufacturing development, we have described multi agent architecture supporting knowledge-based platform of collaborative manufacturing development platform. In virtue of wrapper service and communication capacity agents provided, the proposed architecture facilitates organization and collaboration of multi-disciplinary individuals and tools. By effectively supporting the formal representation, capture, retrieval and reuse of manufacturing knowledge, the generalized knowledge repository based on ontology library enable engineers to meaningfully exchange information and pass knowledge across boundaries. Intelligent agent technology increases traditional KBE systems efficiency and interoperability and provides comprehensive design environments for engineers.

Distinct neural substrates for visual short-term memory of actions.

PubMed

Cai, Ying; Urgolites, Zhisen; Wood, Justin; Chen, Chuansheng; Li, Siyao; Chen, Antao; Xue, Gui

2018-06-26

Fundamental theories of human cognition have long posited that the short-term maintenance of actions is supported by one of the "core knowledge" systems of human visual cognition, yet its neural substrates are still not well understood. In particular, it is unclear whether the visual short-term memory (VSTM) of actions has distinct neural substrates or, as proposed by the spatio-object architecture of VSTM, shares them with VSTM of objects and spatial locations. In two experiments, we tested these two competing hypotheses by directly contrasting the neural substrates for VSTM of actions with those for objects and locations. Our results showed that the bilateral middle temporal cortex (MT) was specifically involved in VSTM of actions because its activation and its functional connectivity with the frontal-parietal network (FPN) were only modulated by the memory load of actions, but not by that of objects/agents or locations. Moreover, the brain regions involved in the maintenance of spatial location information (i.e., superior parietal lobule, SPL) was also recruited during the maintenance of actions, consistent with the temporal-spatial nature of actions. Meanwhile, the frontoparietal network (FPN) was commonly involved in all types of VSTM and showed flexible functional connectivity with the domain-specific regions, depending on the current working memory tasks. Together, our results provide clear evidence for a distinct neural system for maintaining actions in VSTM, which supports the core knowledge system theory and the domain-specific and domain-general architectures of VSTM. © 2018 Wiley Periodicals, Inc.

Vision's Brain.

ERIC Educational Resources Information Center

Miller, Julie Ann

1978-01-01

The functional architecture of the primary visual cortex has been explored by monitoring the responses of individual brain cells to visual stimuli. A combination of anatomical and physiological techniques reveals groups of functionally related cells, juxtaposed and superimposed, in a sometimes complex, but presumably efficient, structure. (BB)

Architectural Large Constructed Environment. Modeling and Interaction Using Dynamic Simulations

NASA Astrophysics Data System (ADS)

Fiamma, P.

2011-09-01

How to use for the architectural design, the simulation coming from a large size data model? The topic is related to the phase coming usually after the acquisition of the data, during the construction of the model and especially after, when designers must have an interaction with the simulation, in order to develop and verify their idea. In the case of study, the concept of interaction includes the concept of real time "flows". The work develops contents and results that can be part of the large debate about the current connection between "architecture" and "movement". The focus of the work, is to realize a collaborative and participative virtual environment on which different specialist actors, client and final users can share knowledge, targets and constraints to better gain the aimed result. The goal is to have used a dynamic micro simulation digital resource that allows all the actors to explore the model in powerful and realistic way and to have a new type of interaction in a complex architectural scenario. On the one hand, the work represents a base of knowledge that can be implemented more and more; on the other hand the work represents a dealt to understand the large constructed architecture simulation as a way of life, a way of being in time and space. The architectural design before, and the architectural fact after, both happen in a sort of "Spatial Analysis System". The way is open to offer to this "system", knowledge and theories, that can support architectural design work for every application and scale. We think that the presented work represents a dealt to understand the large constructed architecture simulation as a way of life, a way of being in time and space. Architecture like a spatial configuration, that can be reconfigurable too through designing.

The past, present, and future of cognitive architectures.

PubMed

Taatgen, Niels; Anderson, John R

2010-10-01

Cognitive architectures are theories of cognition that try to capture the essential representations and mechanisms that underlie cognition. Research in cognitive architectures has gradually moved from a focus on the functional capabilities of architectures to the ability to model the details of human behavior, and, more recently, brain activity. Although there are many different architectures, they share many identical or similar mechanisms, permitting possible future convergence. In judging the quality of a particular cognitive model, it is pertinent to not just judge its fit to the experimental data but also its simplicity and ability to make predictions. Copyright © 2009 Cognitive Science Society, Inc.

Quantitative Architectural Analysis: A New Approach to Cortical Mapping

ERIC Educational Resources Information Center

Schleicher, Axel; Morosan, Patricia; Amunts, Katrin; Zilles, Karl

2009-01-01

Results from functional imaging studies are often still interpreted using the classical architectonic brain maps of Brodmann and his successors. One obvious weakness in traditional, architectural mapping is the subjective nature of localizing borders between cortical areas by means of a purely visual, microscopical examination of histological…

Neurons on the couch.

PubMed

Marić, Nadja P; Jašović-Gašić, Miroslava

2010-12-01

A hundred years after psychoanalysis was introduced, neuroscience has taken a giant step forward. It seems nowadays that effects of psychotherapy could be monitored and measured by state-of-the art brain imaging techniques. Today, the psychotherapy is considered as a strategic and purposeful environmental influence intended to enhance learning. Since gene expression is regulated by environmental influences throughout life and these processes create brain architecture and influence the strength of synaptic connections, psychotherapy (as a kind of learning) should be explored in the context of aforementioned paradigm. In other words, when placing a client on the couch, therapist actually placed client's neuronal network; while listening and talking, expressing and analyzing, experiencing transference and counter transference, therapist tends to stabilize synaptic connections and influence dendritic growth by regulating gene-transcriptional activity. Therefore, we strongly believe that, in the near future, an increasing knowledge on cellular and molecular interactions and mechanisms of action of different psycho- and pharmaco-therapeutic procedures will enable us to tailor a sophisticated therapeutic approach toward a person, by combining major therapeutic strategies in psychiatry on the basis of rational goals and evidence-based therapeutic expectations.

Structural architecture supports functional organization in the human aging brain at a regionwise and network level.

PubMed

Zimmermann, Joelle; Ritter, Petra; Shen, Kelly; Rothmeier, Simon; Schirner, Michael; McIntosh, Anthony R

2016-07-01

Functional interactions in the brain are constrained by the underlying anatomical architecture, and structural and functional networks share network features such as modularity. Accordingly, age-related changes of structural connectivity (SC) may be paralleled by changes in functional connectivity (FC). We provide a detailed qualitative and quantitative characterization of the SC-FC coupling in human aging as inferred from resting-state blood oxygen-level dependent functional magnetic resonance imaging and diffusion-weighted imaging in a sample of 47 adults with an age range of 18-82. We revealed that SC and FC decrease with age across most parts of the brain and there is a distinct age-dependency of regionwise SC-FC coupling and network-level SC-FC relations. A specific pattern of SC-FC coupling predicts age more reliably than does regionwise SC or FC alone (r = 0.73, 95% CI = [0.7093, 0.8522]). Hence, our data propose that regionwise SC-FC coupling can be used to characterize brain changes in aging. Hum Brain Mapp 37:2645-2661, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Architectural Physics: Lighting.

ERIC Educational Resources Information Center

Hopkinson, R. G.

The author coordinates the many diverse branches of knowledge which have dealt with the field of lighting--physiology, psychology, engineering, physics, and architectural design. Part I, "The Elements of Architectural Physics", discusses the physiological aspects of lighting, visual performance, lighting design, calculations and measurements of…

Abnormal functional global and local brain connectivity in female patients with anorexia nervosa

PubMed Central

Geisler, Daniel; Borchardt, Viola; Lord, Anton R.; Boehm, Ilka; Ritschel, Franziska; Zwipp, Johannes; Clas, Sabine; King, Joseph A.; Wolff-Stephan, Silvia; Roessner, Veit; Walter, Martin; Ehrlich, Stefan

2016-01-01

Background Previous resting-state functional connectivity studies in patients with anorexia nervosa used independent component analysis or seed-based connectivity analysis to probe specific brain networks. Instead, modelling the entire brain as a complex network allows determination of graph-theoretical metrics, which describe global and local properties of how brain networks are organized and how they interact. Methods To determine differences in network properties between female patients with acute anorexia nervosa and pairwise matched healthy controls, we used resting-state fMRI and computed well-established global and local graph metrics across a range of network densities. Results Our analyses included 35 patients and 35 controls. We found that the global functional network structure in patients with anorexia nervosa is characterized by increases in both characteristic path length (longer average routes between nodes) and assortativity (more nodes with a similar connectedness link together). Accordingly, we found locally decreased connectivity strength and increased path length in the posterior insula and thalamus. Limitations The present results may be limited to the methods applied during preprocessing and network construction. Conclusion We demonstrated anorexia nervosa–related changes in the network configuration for, to our knowledge, the first time using resting-state fMRI and graph-theoretical measures. Our findings revealed an altered global brain network architecture accompanied by local degradations indicating wide-scale disturbance in information flow across brain networks in patients with acute anorexia nervosa. Reduced local network efficiency in the thalamus and posterior insula may reflect a mechanism that helps explain the impaired integration of visuospatial and homeostatic signals in patients with this disorder, which is thought to be linked to abnormal representations of body size and hunger. PMID:26252451

Abnormal functional global and local brain connectivity in female patients with anorexia nervosa.

PubMed

Geisler, Daniel; Borchardt, Viola; Lord, Anton R; Boehm, Ilka; Ritschel, Franziska; Zwipp, Johannes; Clas, Sabine; King, Joseph A; Wolff-Stephan, Silvia; Roessner, Veit; Walter, Martin; Ehrlich, Stefan

2016-01-01

Previous resting-state functional connectivity studies in patients with anorexia nervosa used independent component analysis or seed-based connectivity analysis to probe specific brain networks. Instead, modelling the entire brain as a complex network allows determination of graph-theoretical metrics, which describe global and local properties of how brain networks are organized and how they interact. To determine differences in network properties between female patients with acute anorexia nervosa and pairwise matched healthy controls, we used resting-state fMRI and computed well-established global and local graph metrics across a range of network densities. Our analyses included 35 patients and 35 controls. We found that the global functional network structure in patients with anorexia nervosa is characterized by increases in both characteristic path length (longer average routes between nodes) and assortativity (more nodes with a similar connectedness link together). Accordingly, we found locally decreased connectivity strength and increased path length in the posterior insula and thalamus. The present results may be limited to the methods applied during preprocessing and network construction. We demonstrated anorexia nervosa-related changes in the network configuration for, to our knowledge, the first time using resting-state fMRI and graph-theoretical measures. Our findings revealed an altered global brain network architecture accompanied by local degradations indicating wide-scale disturbance in information flow across brain networks in patients with acute anorexia nervosa. Reduced local network efficiency in the thalamus and posterior insula may reflect a mechanism that helps explain the impaired integration of visuospatial and homeostatic signals in patients with this disorder, which is thought to be linked to abnormal representations of body size and hunger.

Evolutionary Local Search of Fuzzy Rules through a novel Neuro-Fuzzy encoding method.

PubMed

Carrascal, A; Manrique, D; Ríos, J; Rossi, C

2003-01-01

This paper proposes a new approach for constructing fuzzy knowledge bases using evolutionary methods. We have designed a genetic algorithm that automatically builds neuro-fuzzy architectures based on a new indirect encoding method. The neuro-fuzzy architecture represents the fuzzy knowledge base that solves a given problem; the search for this architecture takes advantage of a local search procedure that improves the chromosomes at each generation. Experiments conducted both on artificially generated and real world problems confirm the effectiveness of the proposed approach.

Distributed XQuery-Based Integration and Visualization of Multimodality Brain Mapping Data

PubMed Central

Detwiler, Landon T.; Suciu, Dan; Franklin, Joshua D.; Moore, Eider B.; Poliakov, Andrew V.; Lee, Eunjung S.; Corina, David P.; Ojemann, George A.; Brinkley, James F.

2008-01-01

This paper addresses the need for relatively small groups of collaborating investigators to integrate distributed and heterogeneous data about the brain. Although various national efforts facilitate large-scale data sharing, these approaches are generally too “heavyweight” for individual or small groups of investigators, with the result that most data sharing among collaborators continues to be ad hoc. Our approach to this problem is to create a “lightweight” distributed query architecture, in which data sources are accessible via web services that accept arbitrary query languages but return XML results. A Distributed XQuery Processor (DXQP) accepts distributed XQueries in which subqueries are shipped to the remote data sources to be executed, with the resulting XML integrated by DXQP. A web-based application called DXBrain accesses DXQP, allowing a user to create, save and execute distributed XQueries, and to view the results in various formats including a 3-D brain visualization. Example results are presented using distributed brain mapping data sources obtained in studies of language organization in the brain, but any other XML source could be included. The advantage of this approach is that it is very easy to add and query a new source, the tradeoff being that the user needs to understand XQuery and the schemata of the underlying sources. For small numbers of known sources this burden is not onerous for a knowledgeable user, leading to the conclusion that the system helps to fill the gap between ad hoc local methods and large scale but complex national data sharing efforts. PMID:19198662

Virtual Neurorobotics (VNR) to Accelerate Development of Plausible Neuromorphic Brain Architectures.

PubMed

Goodman, Philip H; Buntha, Sermsak; Zou, Quan; Dascalu, Sergiu-Mihai

2007-01-01

Traditional research in artificial intelligence and machine learning has viewed the brain as a specially adapted information-processing system. More recently the field of social robotics has been advanced to capture the important dynamics of human cognition and interaction. An overarching societal goal of this research is to incorporate the resultant knowledge about intelligence into technology for prosthetic, assistive, security, and decision support applications. However, despite many decades of investment in learning and classification systems, this paradigm has yet to yield truly "intelligent" systems. For this reason, many investigators are now attempting to incorporate more realistic neuromorphic properties into machine learning systems, encouraged by over two decades of neuroscience research that has provided parameters that characterize the brain's interdependent genomic, proteomic, metabolomic, anatomic, and electrophysiological networks. Given the complexity of neural systems, developing tenable models to capture the essence of natural intelligence for real-time application requires that we discriminate features underlying information processing and intrinsic motivation from those reflecting biological constraints (such as maintaining structural integrity and transporting metabolic products). We propose herein a conceptual framework and an iterative method of virtual neurorobotics (VNR) intended to rapidly forward-engineer and test progressively more complex putative neuromorphic brain prototypes for their ability to support intrinsically intelligent, intentional interaction with humans. The VNR system is based on the viewpoint that a truly intelligent system must be driven by emotion rather than programmed tasking, incorporating intrinsic motivation and intentionality. We report pilot results of a closed-loop, real-time interactive VNR system with a spiking neural brain, and provide a video demonstration as online supplemental material.

Local Patterns to Global Architectures: Influences of Network Topology on Human Learning.

PubMed

Karuza, Elisabeth A; Thompson-Schill, Sharon L; Bassett, Danielle S

2016-08-01

A core question in cognitive science concerns how humans acquire and represent knowledge about their environments. To this end, quantitative theories of learning processes have been formalized in an attempt to explain and predict changes in brain and behavior. We connect here statistical learning approaches in cognitive science, which are rooted in the sensitivity of learners to local distributional regularities, and network science approaches to characterizing global patterns and their emergent properties. We focus on innovative work that describes how learning is influenced by the topological properties underlying sensory input. The confluence of these theoretical approaches and this recent empirical evidence motivate the importance of scaling-up quantitative approaches to learning at both the behavioral and neural levels. Copyright © 2016 Elsevier Ltd. All rights reserved.

Brain Network Architecture and Global Intelligence in Children with Focal Epilepsy.

PubMed

Paldino, M J; Golriz, F; Chapieski, M L; Zhang, W; Chu, Z D

2017-02-01

The biologic basis for intelligence rests to a large degree on the capacity for efficient integration of information across the cerebral network. We aimed to measure the relationship between network architecture and intelligence in the pediatric, epileptic brain. Patients were retrospectively identified with the following: 1) focal epilepsy; 2) brain MR imaging at 3T, including resting-state functional MR imaging; and 3) full-scale intelligence quotient measured by a pediatric neuropsychologist. The cerebral cortex was parcellated into approximately 700 gray matter network "nodes." The strength of a connection between 2 nodes was defined by the correlation between their blood oxygen level-dependent time-series. We calculated the following topologic properties: clustering coefficient, transitivity, modularity, path length, and global efficiency. A machine learning algorithm was used to measure the independent contribution of each metric to the intelligence quotient after adjusting for all other metrics. Thirty patients met the criteria (4-18 years of age); 20 patients required anesthesia during MR imaging. After we accounted for age and sex, clustering coefficient and path length were independently associated with full-scale intelligence quotient. Neither motion parameters nor general anesthesia was an important variable with regard to accurate intelligence quotient prediction by the machine learning algorithm. A longer history of epilepsy was associated with shorter path lengths ( P = .008), consistent with reorganization of the network on the basis of seizures. Considering only patients receiving anesthesia during machine learning did not alter the patterns of network architecture contributing to global intelligence. These findings support the physiologic relevance of imaging-based metrics of network architecture in the pathologic, developing brain. © 2017 by American Journal of Neuroradiology.

Integration of language and sensor information

NASA Astrophysics Data System (ADS)

Perlovsky, Leonid I.; Weijers, Bertus

2003-04-01

The talk describes the development of basic technologies of intelligent systems fusing data from multiple domains and leading to automated computational techniques for understanding data contents. Understanding involves inferring appropriate decisions and recommending proper actions, which in turn requires fusion of data and knowledge about objects, situations, and actions. Data might include sensory data, verbal reports, intelligence intercepts, or public records, whereas knowledge ought to encompass the whole range of objects, situations, people and their behavior, and knowledge of languages. In the past, a fundamental difficulty in combining knowledge with data was the combinatorial complexity of computations, too many combinations of data and knowledge pieces had to be evaluated. Recent progress in understanding of natural intelligent systems, including the human mind, leads to the development of neurophysiologically motivated architectures for solving these challenging problems, in particular the role of emotional neural signals in overcoming combinatorial complexity of old logic-based approaches. Whereas past approaches based on logic tended to identify logic with language and thinking, recent studies in cognitive linguistics have led to appreciation of more complicated nature of linguistic models. Little is known about the details of the brain mechanisms integrating language and thinking. Understanding and fusion of linguistic information with sensory data represent a novel challenging aspect of the development of integrated fusion systems. The presentation will describe a non-combinatorial approach to this problem and outline techniques that can be used for fusing diverse and uncertain knowledge with sensory and linguistic data.

"Re-Casting Terra Nullius Design-Blindness": Better Teaching of Indigenous Knowledge and Protocols in Australian Architecture Education

ERIC Educational Resources Information Center

Tucker, Richard; Choy, Darryl Low; Heyes, Scott; Revell, Grant; Jones, David

2018-01-01

This paper reviews the current status and focus of Australian Architecture programs with respect to Indigenous Knowledge and the extent to which these tertiary programs currently address reconciliation and respect to Indigenous Australians in relation to their professional institutions and accreditation policies. The paper draws upon the findings…

Reinforcement of the Brain's Rich-Club Architecture Following Early Neurodevelopmental Disruption Caused by Very Preterm Birth

PubMed Central

Karolis, Vyacheslav R.; Froudist-Walsh, Sean; Brittain, Philip J.; Kroll, Jasmin; Ball, Gareth; Edwards, A. David; Dell'Acqua, Flavio; Williams, Steven C.; Murray, Robin M.; Nosarti, Chiara

2016-01-01

The second half of pregnancy is a crucial period for the development of structural brain connectivity, and an abrupt interruption of the typical processes of development during this phase caused by the very preterm birth (<33 weeks of gestation) is likely to result in long-lasting consequences. We used structural and diffusion imaging data to reconstruct the brain structural connectome in very preterm-born adults. We assessed its rich-club organization and modularity as 2 characteristics reflecting the capacity to support global and local information exchange, respectively. Our results suggest that the establishment of global connectivity patterns is prioritized over peripheral connectivity following early neurodevelopmental disruption. The very preterm brain exhibited a stronger rich-club architecture than the control brain, despite possessing a relative paucity of white matter resources. Using a simulated lesion approach, we also investigated whether putative structural reorganization takes place in the very preterm brain in order to compensate for its anatomical constraints. We found that connections between the basal ganglia and (pre-) motor regions, as well as connections between subcortical regions, assumed an altered role in the structural connectivity of the very preterm brain, and that such alterations had functional implications for information flow, rule learning, and verbal IQ. PMID:26742566

Reconfiguration of brain network architecture to support executive control in aging.

PubMed

Gallen, Courtney L; Turner, Gary R; Adnan, Areeba; D'Esposito, Mark

2016-08-01

Aging is accompanied by declines in executive control abilities and changes in underlying brain network architecture. Here, we examined brain networks in young and older adults during a task-free resting state and an N-back task and investigated age-related changes in the modular network organization of the brain. Compared with young adults, older adults showed larger changes in network organization between resting state and task. Although young adults exhibited increased connectivity between lateral frontal regions and other network modules during the most difficult task condition, older adults also exhibited this pattern of increased connectivity during less-demanding task conditions. Moreover, the increase in between-module connectivity in older adults was related to faster task performance and greater fractional anisotropy of the superior longitudinal fasciculus. These results demonstrate that older adults who exhibit more pronounced network changes between a resting state and task have better executive control performance and greater structural connectivity of a core frontal-posterior white matter pathway. Copyright © 2016 Elsevier Inc. All rights reserved.

Sculpting the Intrinsic Modular Organization of Spontaneous Brain Activity by Art.

PubMed

Lin, Chia-Shu; Liu, Yong; Huang, Wei-Yuan; Lu, Chia-Feng; Teng, Shin; Ju, Tzong-Ching; He, Yong; Wu, Yu-Te; Jiang, Tianzi; Hsieh, Jen-Chuen

2013-01-01

Artistic training is a complex learning that requires the meticulous orchestration of sophisticated polysensory, motor, cognitive, and emotional elements of mental capacity to harvest an aesthetic creation. In this study, we investigated the architecture of the resting-state functional connectivity networks from professional painters, dancers and pianists. Using a graph-based network analysis, we focused on the art-related changes of modular organization and functional hubs in the resting-state functional connectivity network. We report that the brain architecture of artists consists of a hierarchical modular organization where art-unique and artistic form-specific brain states collectively mirror the mind states of virtuosos. We show that even in the resting state, this type of extraordinary and long-lasting training can macroscopically imprint a neural network system of spontaneous activity in which the related brain regions become functionally and topologically modularized in both domain-general and domain-specific manners. The attuned modularity reflects a resilient plasticity nurtured by long-term experience.

Sculpting the Intrinsic Modular Organization of Spontaneous Brain Activity by Art

PubMed Central

Lin, Chia-Shu; Liu, Yong; Huang, Wei-Yuan; Lu, Chia-Feng; Teng, Shin; Ju, Tzong-Ching; He, Yong; Wu, Yu-Te; Jiang, Tianzi; Hsieh, Jen-Chuen

2013-01-01

Artistic training is a complex learning that requires the meticulous orchestration of sophisticated polysensory, motor, cognitive, and emotional elements of mental capacity to harvest an aesthetic creation. In this study, we investigated the architecture of the resting-state functional connectivity networks from professional painters, dancers and pianists. Using a graph-based network analysis, we focused on the art-related changes of modular organization and functional hubs in the resting-state functional connectivity network. We report that the brain architecture of artists consists of a hierarchical modular organization where art-unique and artistic form-specific brain states collectively mirror the mind states of virtuosos. We show that even in the resting state, this type of extraordinary and long-lasting training can macroscopically imprint a neural network system of spontaneous activity in which the related brain regions become functionally and topologically modularized in both domain-general and domain-specific manners. The attuned modularity reflects a resilient plasticity nurtured by long-term experience. PMID:23840527

Beyond functional architecture in cognitive neuropsychology: a reply to Coltheart (2010).

PubMed

Plaut, David C; Patterson, Karalyn

2010-01-01

We (Patterson & Plaut, 2009) argued that cognitive neuropsychology has had a limited impact on cognitive science due to a nearly exclusive reliance on (a) single-case studies, (b) dissociations in cognitive performance, and (c) shallow, box-and-arrow theorizing, and we advocated adopting a case-series methodology, considering associations as well as dissociations, and employing explicit computational modeling in studying "how the brain does its cognitive business." In reply, Coltheart (2010) claims that our concern is misplaced because cognitive neuropsychology is concerned only with studying the mind, in terms of its "functional architecture," without regard to how this is implemented in the brain. In this response, we do not dispute his characterization of cognitive neuropsychology as it has typically been practiced over the last 40 years, but we suggest that our understanding of brain structure and function has advanced to the point where studying the mind without regard to the brain is unwise and perpetuates the field's isolation. Copyright © 2009 Cognitive Science Society, Inc.

Public School Teachers' Knowledge, Perception, and Implementation of Brain-Based Learning Practices

ERIC Educational Resources Information Center

Wachob, David A.

2012-01-01

The purpose of this study was to determine K-12 teachers' knowledge, beliefs, and practices of brain-based learning strategies in western Pennsylvania schools. The following five research questions were explored: (a) What is the extent of knowledge K-12 public school teachers have about the indicators of brain-based learning and Brain Gym?; (b) To…

Rich-club organization of the newborn human brain

PubMed Central

Ball, Gareth; Aljabar, Paul; Zebari, Sally; Tusor, Nora; Arichi, Tomoki; Merchant, Nazakat; Robinson, Emma C.; Ogundipe, Enitan; Rueckert, Daniel; Edwards, A. David; Counsell, Serena J.

2014-01-01

Combining diffusion magnetic resonance imaging and network analysis in the adult human brain has identified a set of highly connected cortical hubs that form a “rich club”—a high-cost, high-capacity backbone thought to enable efficient network communication. Rich-club architecture appears to be a persistent feature of the mature mammalian brain, but it is not known when this structure emerges during human development. In this longitudinal study we chart the emergence of structural organization in mid to late gestation. We demonstrate that a rich club of interconnected cortical hubs is already present by 30 wk gestation. Subsequently, until the time of normal birth, the principal development is a proliferation of connections between core hubs and the rest of the brain. We also consider the impact of environmental factors on early network development, and compare term-born neonates to preterm infants at term-equivalent age. Though rich-club organization remains intact following premature birth, we reveal significant disruptions in both in cortical–subcortical connectivity and short-distance corticocortical connections. Rich club organization is present well before the normal time of birth and may provide the fundamental structural architecture for the subsequent emergence of complex neurological functions. Premature exposure to the extrauterine environment is associated with altered network architecture and reduced network capacity, which may in part account for the high prevalence of cognitive problems in preterm infants. PMID:24799693

Implementation of a metadata architecture and knowledge collection to support semantic interoperability in an enterprise data warehouse.

PubMed

Dhaval, Rakesh; Borlawsky, Tara; Ostrander, Michael; Santangelo, Jennifer; Kamal, Jyoti; Payne, Philip R O

2008-11-06

In order to enhance interoperability between enterprise systems, and improve data validity and reliability throughout The Ohio State University Medical Center (OSUMC), we have initiated the development of an ontology-anchored metadata architecture and knowledge collection for our enterprise data warehouse. The metadata and corresponding semantic relationships stored in the OSUMC knowledge collection are intended to promote consistency and interoperability across the heterogeneous clinical, research, business and education information managed within the data warehouse.

Modulation of the brain's functional network architecture in the transition from wake to sleep

PubMed Central

Larson-Prior, Linda J.; Power, Jonathan D.; Vincent, Justin L.; Nolan, Tracy S.; Coalson, Rebecca S.; Zempel, John; Snyder, Abraham Z.; Schlaggar, Bradley L.; Raichle, Marcus E.; Petersen, Steven E.

2013-01-01

The transition from quiet wakeful rest to sleep represents a period over which attention to the external environment fades. Neuroimaging methodologies have provided much information on the shift in neural activity patterns in sleep, but the dynamic restructuring of human brain networks in the transitional period from wake to sleep remains poorly understood. Analysis of electrophysiological measures and functional network connectivity of these early transitional states shows subtle shifts in network architecture that are consistent with reduced external attentiveness and increased internal and self-referential processing. Further, descent to sleep is accompanied by the loss of connectivity in anterior and posterior portions of the default-mode network and more locally organized global network architecture. These data clarify the complex and dynamic nature of the transitional period between wake and sleep and suggest the need for more studies investigating the dynamics of these processes. PMID:21854969

Functional connectomics from a "big data" perspective.

PubMed

Xia, Mingrui; He, Yong

2017-10-15

In the last decade, explosive growth regarding functional connectome studies has been observed. Accumulating knowledge has significantly contributed to our understanding of the brain's functional network architectures in health and disease. With the development of innovative neuroimaging techniques, the establishment of large brain datasets and the increasing accumulation of published findings, functional connectomic research has begun to move into the era of "big data", which generates unprecedented opportunities for discovery in brain science and simultaneously encounters various challenging issues, such as data acquisition, management and analyses. Big data on the functional connectome exhibits several critical features: high spatial and/or temporal precision, large sample sizes, long-term recording of brain activity, multidimensional biological variables (e.g., imaging, genetic, demographic, cognitive and clinic) and/or vast quantities of existing findings. We review studies regarding functional connectomics from a big data perspective, with a focus on recent methodological advances in state-of-the-art image acquisition (e.g., multiband imaging), analysis approaches and statistical strategies (e.g., graph theoretical analysis, dynamic network analysis, independent component analysis, multivariate pattern analysis and machine learning), as well as reliability and reproducibility validations. We highlight the novel findings in the application of functional connectomic big data to the exploration of the biological mechanisms of cognitive functions, normal development and aging and of neurological and psychiatric disorders. We advocate the urgent need to expand efforts directed at the methodological challenges and discuss the direction of applications in this field. Copyright © 2017 Elsevier Inc. All rights reserved.

The Need for Software Architecture Evaluation in the Acquisition of Software-Intensive Sysetms

DTIC Science & Technology

2014-01-01

Function and Performance Specification GIG Global Information Grid ISO International Standard Organisation MDA Model Driven Architecture...architecture and design, which is a key part of knowledge-based economy UNCLASSIFIED DSTO-TR-2936 UNCLASSIFIED 24  Allow Australian SMEs to

Using ArchE in the Classroom: One Experience

DTIC Science & Technology

2007-09-01

The Architecture Expert (ArchE) tool serves as a software architecture design assistant. It embodies knowledge of quality attributes and the relation...between the achievement of quality attribute requirements and architecture design . This technical note describes the use of a pre-alpha release of

The AI Bus architecture for distributed knowledge-based systems

NASA Technical Reports Server (NTRS)

Schultz, Roger D.; Stobie, Iain

1991-01-01

The AI Bus architecture is layered, distributed object oriented framework developed to support the requirements of advanced technology programs for an order of magnitude improvement in software costs. The consequent need for highly autonomous computer systems, adaptable to new technology advances over a long lifespan, led to the design of an open architecture and toolbox for building large scale, robust, production quality systems. The AI Bus accommodates a mix of knowledge based and conventional components, running on heterogeneous, distributed real world and testbed environment. The concepts and design is described of the AI Bus architecture and its current implementation status as a Unix C++ library or reusable objects. Each high level semiautonomous agent process consists of a number of knowledge sources together with interagent communication mechanisms based on shared blackboards and message passing acquaintances. Standard interfaces and protocols are followed for combining and validating subsystems. Dynamic probes or demons provide an event driven means for providing active objects with shared access to resources, and each other, while not violating their security.

Artificial neuron operations and spike-timing-dependent plasticity using memristive devices for brain-inspired computing

NASA Astrophysics Data System (ADS)

Marukame, Takao; Nishi, Yoshifumi; Yasuda, Shin-ichi; Tanamoto, Tetsufumi

2018-04-01

The use of memristive devices for creating artificial neurons is promising for brain-inspired computing from the viewpoints of computation architecture and learning protocol. We present an energy-efficient multiplier accumulator based on a memristive array architecture incorporating both analog and digital circuitries. The analog circuitry is used to full advantage for neural networks, as demonstrated by the spike-timing-dependent plasticity (STDP) in fabricated AlO x /TiO x -based metal-oxide memristive devices. STDP protocols for controlling periodic analog resistance with long-range stability were experimentally verified using a variety of voltage amplitudes and spike timings.

Mind and Language Architecture

PubMed Central

Logan, Robert K

2010-01-01

A distinction is made between the brain and the mind. The architecture of the mind and language is then described within a neo-dualistic framework. A model for the origin of language based on emergence theory is presented. The complexity of hominid existence due to tool making, the control of fire and the social cooperation that fire required gave rise to a new level of order in mental activity and triggered the simultaneous emergence of language and conceptual thought. The mind is shown to have emerged as a bifurcation of the brain with the emergence of language. The role of language in the evolution of human culture is also described. PMID:20922045

EXPECT: Explicit Representations for Flexible Acquisition

NASA Technical Reports Server (NTRS)

Swartout, BIll; Gil, Yolanda

1995-01-01

To create more powerful knowledge acquisition systems, we not only need better acquisition tools, but we need to change the architecture of the knowledge based systems we create so that their structure will provide better support for acquisition. Current acquisition tools permit users to modify factual knowledge but they provide limited support for modifying problem solving knowledge. In this paper, the authors argue that this limitation (and others) stem from the use of incomplete models of problem-solving knowledge and inflexible specification of the interdependencies between problem-solving and factual knowledge. We describe the EXPECT architecture which addresses these problems by providing an explicit representation for problem-solving knowledge and intent. Using this more explicit representation, EXPECT can automatically derive the interdependencies between problem-solving and factual knowledge. By deriving these interdependencies from the structure of the knowledge-based system itself EXPECT supports more flexible and powerful knowledge acquisition.

Molecular basis of angiosperm tree architecture.

PubMed

Hollender, Courtney A; Dardick, Chris

2015-04-01

The architecture of trees greatly impacts the productivity of orchards and forestry plantations. Amassing greater knowledge on the molecular genetics that underlie tree form can benefit these industries, as well as contribute to basic knowledge of plant developmental biology. This review describes the fundamental components of branch architecture, a prominent aspect of tree structure, as well as genetic and hormonal influences inferred from studies in model plant systems and from trees with non-standard architectures. The bulk of the molecular and genetic data described here is from studies of fruit trees and poplar, as these species have been the primary subjects of investigation in this field of science. No claim to original US Government works. New Phytologist © 2014 New Phytologist Trust.

Developing a New Framework for Integration and Teaching of Computer Aided Architectural Design (CAAD) in Nigerian Schools of Architecture

ERIC Educational Resources Information Center

Uwakonye, Obioha; Alagbe, Oluwole; Oluwatayo, Adedapo; Alagbe, Taiye; Alalade, Gbenga

2015-01-01

As a result of globalization of digital technology, intellectual discourse on what constitutes the basic body of architectural knowledge to be imparted to future professionals has been on the increase. This digital revolution has brought to the fore the need to review the already overloaded architectural education curriculum of Nigerian schools of…

Integrating planning, execution, and learning

NASA Technical Reports Server (NTRS)

Kuokka, Daniel R.

1989-01-01

To achieve the goal of building an autonomous agent, the usually disjoint capabilities of planning, execution, and learning must be used together. An architecture, called MAX, within which cognitive capabilities can be purposefully and intelligently integrated is described. The architecture supports the codification of capabilities as explicit knowledge that can be reasoned about. In addition, specific problem solving, learning, and integration knowledge is developed.

The architecture of personality.

PubMed

Cervone, David

2004-01-01

This article presents a theoretical framework for analyzing psychological systems that contribute to the variability, consistency, and cross-situational coherence of personality functioning. In the proposed knowledge-and-appraisal personality architecture (KAPA), personality structures and processes are delineated by combining 2 principles: distinctions (a) between knowledge structures and appraisal processes and (b) among intentional cognitions with varying directions of fit, with the latter distinction differentiating among beliefs, evaluative standards, and aims. Basic principles of knowledge activation and use illuminate relations between knowledge and appraisal, yielding a synthetic account of personality structures and processes. Novel empirical data illustrate the heuristic value of the knowledge/appraisal distinction by showing how self-referent and situational knowledge combine to foster cross-situational coherence in appraisals of self-efficacy.

A systematic review and meta-analysis of sleep architecture and chronic traumatic brain injury.

PubMed

Mantua, Janna; Grillakis, Antigone; Mahfouz, Sanaa H; Taylor, Maura R; Brager, Allison J; Yarnell, Angela M; Balkin, Thomas J; Capaldi, Vincent F; Simonelli, Guido

2018-02-02

Sleep quality appears to be altered by traumatic brain injury (TBI). However, whether persistent post-injury changes in sleep architecture are present is unknown and relatively unexplored. We conducted a systematic review and meta-analysis to assess the extent to which chronic TBI (>6 months since injury) is characterized by changes to sleep architecture. We also explored the relationship between sleep architecture and TBI severity. In the fourteen included studies, sleep was assessed with at least one night of polysomnography in both chronic TBI participants and controls. Statistical analyses, performed using Comprehensive Meta-Analysis software, revealed that chronic TBI is characterized by relatively increased slow wave sleep (SWS). A meta-regression showed moderate-severe TBI is associated with elevated SWS, reduced stage 2, and reduced sleep efficiency. In contrast, mild TBI was not associated with any significant alteration of sleep architecture. The present findings are consistent with the hypothesis that increased SWS after moderate-severe TBI reflects post-injury cortical reorganization and restructuring. Suggestions for future research are discussed, including adoption of common data elements in future studies to facilitate cross-study comparability, reliability, and replicability, thereby increasing the likelihood that meaningful sleep (and other) biomarkers of TBI will be identified. Copyright © 2018 Elsevier Ltd. All rights reserved.

Buildings, Beauty, and the Brain: A Neuroscience of Architectural Experience.

PubMed

Coburn, Alex; Vartanian, Oshin; Chatterjee, Anjan

2017-09-01

A burgeoning interest in the intersection of neuroscience and architecture promises to offer biologically inspired insights into the design of spaces. The goal of such interdisciplinary approaches to architecture is to motivate construction of environments that would contribute to peoples' flourishing in behavior, health, and well-being. We suggest that this nascent field of neuroarchitecture is at a pivotal point in which neuroscience and architecture are poised to extend to a neuroscience of architecture. In such a research program, architectural experiences themselves are the target of neuroscientific inquiry. Here, we draw lessons from recent developments in neuroaesthetics to suggest how neuroarchitecture might mature into an experimental science. We review the extant literature and offer an initial framework from which to contextualize such research. Finally, we outline theoretical and technical challenges that lie ahead.

A digital protection system incorporating knowledge based learning

NASA Astrophysics Data System (ADS)

Watson, Karan; Russell, B. Don; McCall, Kurt

A digital system architecture used to diagnoses the operating state and health of electric distribution lines and to generate actions for line protection is presented. The architecture is described functionally and to a limited extent at the hardware level. This architecture incorporates multiple analysis and fault-detection techniques utilizing a variety of parameters. In addition, a knowledge-based decision maker, a long-term memory retention and recall scheme, and a learning environment are described. Preliminary laboratory implementations of the system elements have been completed. Enhanced protection for electric distribution feeders is provided by this system. Advantages of the system are enumerated.

Harnessing the Risk-Related Data Supply Chain: An Information Architecture Approach to Enriching Human System Research and Operations Knowledge

NASA Technical Reports Server (NTRS)

Buquo, Lynn E.; Johnson-Throop, Kathy A.

2011-01-01

An Information Architecture facilitates the understanding and, hence, harnessing of the human system risk-related data supply chain which enhances the ability to securely collect, integrate, and share data assets that improve human system research and operations. By mapping the risk-related data flow from raw data to useable information and knowledge (think of it as a data supply chain), the Human Research Program (HRP) and Space Life Science Directorate (SLSD) are building an information architecture plan to leverage their existing, and often shared, IT infrastructure.

Neurobiology as Information Physics

PubMed Central

Street, Sterling

2016-01-01

This article reviews thermodynamic relationships in the brain in an attempt to consolidate current research in systems neuroscience. The present synthesis supports proposals that thermodynamic information in the brain can be quantified to an appreciable degree of objectivity, that many qualitative properties of information in systems of the brain can be inferred by observing changes in thermodynamic quantities, and that many features of the brain’s anatomy and architecture illustrate relatively simple information-energy relationships. The brain may provide a unique window into the relationship between energy and information. PMID:27895560

Imaging laminar structures in the gray matter with diffusion MRI.

PubMed

Assaf, Yaniv

2018-01-05

The cortical layers define the architecture of the gray matter and its neuroanatomical regions and are essential for brain function. Abnormalities in cortical layer development, growth patterns, organization, or size can affect brain physiology and cognition. Unfortunately, while large population studies are underway that will greatly increase our knowledge about these processes, current non-invasive techniques for characterizing the cortical layers remain inadequate. For decades, high-resolution T1 and T2 Weighted Magnetic Resonance Imaging (MRI) have been the method-of-choice for gray matter and layer characterization. In the past few years, however, diffusion MRI has shown increasing promise for its unique insights into the fine structure of the cortex. Several different methods, including surface analysis, connectivity exploration, and sub-voxel component modeling, are now capable of exploring the diffusion characteristics of the cortex. In this review, we will discuss current advances in the application of diffusion imaging for cortical characterization and its unique features, with a particular emphasis on its spatial resolution, arguably its greatest limitation. In addition, we will explore the relationship between the diffusion MRI signal and the cellular components of the cortex, as visualized by histology. While the obstacles facing the widespread application of cortical diffusion imaging remain daunting, the information it can reveal may prove invaluable. Within the next few years, we predict a surge in the application of this technique and a concomitant expansion of our knowledge of cortical layers. Copyright © 2018 Elsevier Inc. All rights reserved.

NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data.

PubMed

Kasabov, Nikola K

2014-04-01

The brain functions as a spatio-temporal information processing machine. Spatio- and spectro-temporal brain data (STBD) are the most commonly collected data for measuring brain response to external stimuli. An enormous amount of such data has been already collected, including brain structural and functional data under different conditions, molecular and genetic data, in an attempt to make a progress in medicine, health, cognitive science, engineering, education, neuro-economics, Brain-Computer Interfaces (BCI), and games. Yet, there is no unifying computational framework to deal with all these types of data in order to better understand this data and the processes that generated it. Standard machine learning techniques only partially succeeded and they were not designed in the first instance to deal with such complex data. Therefore, there is a need for a new paradigm to deal with STBD. This paper reviews some methods of spiking neural networks (SNN) and argues that SNN are suitable for the creation of a unifying computational framework for learning and understanding of various STBD, such as EEG, fMRI, genetic, DTI, MEG, and NIRS, in their integration and interaction. One of the reasons is that SNN use the same computational principle that generates STBD, namely spiking information processing. This paper introduces a new SNN architecture, called NeuCube, for the creation of concrete models to map, learn and understand STBD. A NeuCube model is based on a 3D evolving SNN that is an approximate map of structural and functional areas of interest of the brain related to the modeling STBD. Gene information is included optionally in the form of gene regulatory networks (GRN) if this is relevant to the problem and the data. A NeuCube model learns from STBD and creates connections between clusters of neurons that manifest chains (trajectories) of neuronal activity. Once learning is applied, a NeuCube model can reproduce these trajectories, even if only part of the input STBD or the stimuli data is presented, thus acting as an associative memory. The NeuCube framework can be used not only to discover functional pathways from data, but also as a predictive system of brain activities, to predict and possibly, prevent certain events. Analysis of the internal structure of a model after training can reveal important spatio-temporal relationships 'hidden' in the data. NeuCube will allow the integration in one model of various brain data, information and knowledge, related to a single subject (personalized modeling) or to a population of subjects. The use of NeuCube for classification of STBD is illustrated in a case study problem of EEG data. NeuCube models result in a better accuracy of STBD classification than standard machine learning techniques. They are robust to noise (so typical in brain data) and facilitate a better interpretation of the results and understanding of the STBD and the brain conditions under which data was collected. Future directions for the use of SNN for STBD are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case.

PubMed

Russ, Thomas A; Ramakrishnan, Cartic; Hovy, Eduard H; Bota, Mihail; Burns, Gully A P C

2011-08-22

We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871) that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain. The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED) based on experimental variables and their interdependencies. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database) as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system) to execute queries using spatial reasoning over the BAMS neuroanatomical ontology. We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger, more specialized bioinformatics system: the Brain Architecture Management System (BAMS).

Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case

PubMed Central

2011-01-01

Background We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871) that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain. Results The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED) based on experimental variables and their interdependencies. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database) as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system) to execute queries using spatial reasoning over the BAMS neuroanatomical ontology. Conclusions We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger, more specialized bioinformatics system: the Brain Architecture Management System (BAMS). PMID:21859449

A conceptual cognitive architecture for robots to learn behaviors from demonstrations in robotic aid area.

PubMed

Tan, Huan; Liang, Chen

2011-01-01

This paper proposes a conceptual hybrid cognitive architecture for cognitive robots to learn behaviors from demonstrations in robotic aid situations. Unlike the current cognitive architectures, this architecture puts concentration on the requirements of the safety, the interaction, and the non-centralized processing in robotic aid situations. Imitation learning technologies for cognitive robots have been integrated into this architecture for rapidly transferring the knowledge and skills between human teachers and robots.

Lexical is as lexical does: computational approaches to lexical representation

PubMed Central

Woollams, Anna M.

2015-01-01

In much of neuroimaging and neuropsychology, regions of the brain have been associated with ‘lexical representation’, with little consideration as to what this cognitive construct actually denotes. Within current computational models of word recognition, there are a number of different approaches to the representation of lexical knowledge. Structural lexical representations, found in original theories of word recognition, have been instantiated in modern localist models. However, such a representational scheme lacks neural plausibility in terms of economy and flexibility. Connectionist models have therefore adopted distributed representations of form and meaning. Semantic representations in connectionist models necessarily encode lexical knowledge. Yet when equipped with recurrent connections, connectionist models can also develop attractors for familiar forms that function as lexical representations. Current behavioural, neuropsychological and neuroimaging evidence shows a clear role for semantic information, but also suggests some modality- and task-specific lexical representations. A variety of connectionist architectures could implement these distributed functional representations, and further experimental and simulation work is required to discriminate between these alternatives. Future conceptualisations of lexical representations will therefore emerge from a synergy between modelling and neuroscience. PMID:25893204

Changes in functional and structural brain connectome along the Alzheimer's disease continuum.

PubMed

Filippi, Massimo; Basaia, Silvia; Canu, Elisa; Imperiale, Francesca; Magnani, Giuseppe; Falautano, Monica; Comi, Giancarlo; Falini, Andrea; Agosta, Federica

2018-05-09

The aim of this study was two-fold: (i) to investigate structural and functional brain network architecture in patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI), stratified in converters (c-aMCI) and non-converters (nc-aMCI) to AD; and to assess the relationship between healthy brain network functional connectivity and the topography of brain atrophy in patients along the AD continuum. Ninety-four AD patients, 47 aMCI patients (25 c-aMCI within 36 months) and 53 age- and sex-matched healthy controls were studied. Graph analysis and connectomics assessed global and local, structural and functional topological network properties and regional connectivity. Healthy topological features of brain regions were assessed based on their connectivity with the point of maximal atrophy (epicenter) in AD and aMCI patients. Brain network graph analysis properties were severely altered in AD patients. Structural brain network was already altered in c-aMCI patients relative to healthy controls in particular in the temporal and parietal brain regions, while functional connectivity did not change. Structural connectivity alterations distinguished c-aMCI from nc-aMCI cases. In both AD and c-aMCI, the point of maximal atrophy was located in left hippocampus (disease-epicenter). Brain regions most strongly connected with the disease-epicenter in the healthy functional connectome were also the most atrophic in both AD and c-aMCI patients. Progressive degeneration in the AD continuum is associated with an early breakdown of anatomical brain connections and follows the strongest connections with the disease-epicenter. These findings support the hypothesis that the topography of brain connectional architecture can modulate the spread of AD through the brain.

Developing a New Thesaurus for Art and Architecture.

ERIC Educational Resources Information Center

Petersen, Toni

1990-01-01

This description of the development of the Art and Architecture Thesaurus from 1979 to the present explains the processes and policies that were used to construct a language designed to represent knowledge in art and architecture, as well as to be a surrogate for the image and object being described. (EAM)

The Architecture of "Educare": Motion and Emotion in Postwar Educational Spaces

ERIC Educational Resources Information Center

Kozlovsky, Roy

2010-01-01

This essay explores the interplay between educational and architectural methodologies for analysing the school environment. It historicises the affinity between architectural and educational practices and modes of knowledge pertaining to the child's body during the period of postwar reconstruction in England to argue that educational spaces were…

Frances: A Tool for Understanding Computer Architecture and Assembly Language

ERIC Educational Resources Information Center

Sondag, Tyler; Pokorny, Kian L.; Rajan, Hridesh

2012-01-01

Students in all areas of computing require knowledge of the computing device including software implementation at the machine level. Several courses in computer science curricula address these low-level details such as computer architecture and assembly languages. For such courses, there are advantages to studying real architectures instead of…

An Integrated Self-Aware Cognitive Architecture

DTIC Science & Technology

2008-03-01

human-like cognitive growth. Our approach is inspired by studies of the human brain -mind: in particular, by theoretical models of representations of...agency in the higher associative human brain areas. This feature (a theory of mind including representations of one’s self) allows the system to...self-aware cognition that we believe is necessary for human-like cognitive growth. Our approach is inspired by studies of the human brain -mind: in

A Brain-Machine-Brain Interface for Rewiring of Cortical Circuitry after Traumatic Brain Injury

DTIC Science & Technology

2013-09-01

implemented to significantly decrease the IIR system response time, especially when artifacts were highly reproducible in consecutive stimulation...cycles. The proposed system architecture was hardware- implemented on a field- programmable gate array (FPGA) and tested using two sets of prerecorded...its FPGA implementation and testing with prerecorded neural datasets are reported in a manuscript currently in press with the IEEE Transactions on

Brain architecture of the Pacific White Shrimp Penaeus vannamei Boone, 1931 (Malacostraca, Dendrobranchiata): correspondence of brain structure and sensory input?

PubMed

Meth, Rebecca; Wittfoth, Christin; Harzsch, Steffen

2017-08-01

Penaeus vannamei (Dendrobranchiata, Decapoda) is best known as the "Pacific White Shrimp" and is currently the most important crustacean in commercial aquaculture worldwide. Although the neuroanatomy of crustaceans has been well examined in representatives of reptant decapods ("ground-dwelling decapods"), there are only a few studies focusing on shrimps and prawns. In order to obtain insights into the architecture of the brain of P. vannamei, we use neuroanatomical methods including X-ray micro-computed tomography, 3D reconstruction and immunohistochemical staining combined with confocal laser-scanning microscopy and serial sectioning. The brain of P. vannamei exhibits all the prominent neuropils and tracts that characterize the ground pattern of decapod crustaceans. However, the size proportion of some neuropils is salient. The large lateral protocerebrum that comprises the visual neuropils as well as the hemiellipsoid body and medulla terminalis is remarkable. This observation corresponds with the large size of the compound eyes of these animals. In contrast, the remaining median part of the brain is relatively small. It is dominated by the paired antenna 2 neuropils, while the deutocerebral chemosensory lobes play a minor role. Our findings suggest that visual input from the compound eyes and mechanosensory input from the second pair of antennae are major sensory modalities, which this brain processes.

Knowledge-Based Systems Research

DTIC Science & Technology

1990-08-24

P. S., Laird, J. E., Newell, A. and McCarl, R. 1991. A Preliminary Analysis of the SOAR Architecture as a Basis for General Intelligence . Artifcial ...on reverse of neceSSjr’y gnd identify by block nhmber) FIELD I GRO’= SUB-C.OROUC Artificial Intelligence , Blackboard Systems, U°nstraint Satisfaction...knowledge acquisition; symbolic simulation; logic-based systems with self-awareness; SOAR, an architecture for general intelligence and learning

Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

PubMed Central

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

2016-01-01

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain. PMID:27282108

Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.

PubMed

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

2016-06-10

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain.

Random synaptic feedback weights support error backpropagation for deep learning

NASA Astrophysics Data System (ADS)

Lillicrap, Timothy P.; Cownden, Daniel; Tweed, Douglas B.; Akerman, Colin J.

2016-11-01

The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning.

A functional architecture of the human brain: Emerging insights from the science of emotion

PubMed Central

Lindquist, Kristen A.; Barrett, Lisa Feldman

2012-01-01

The ‘faculty psychology’ approach to the mind, which attempts to explain mental function in terms of categories that reflect modular ‘faculties’, such as emotions, cognitions, and perceptions, has dominated research into the mind and its physical correlates. In this paper, we argue that brain organization does not respect the commonsense categories belonging to the faculty psychology approach. We review recent research from the science of emotion demonstrating that the human brain contains broadly distributed functional networks that can each be re-described as basic psychological operations that interact to produce a range of mental states, including, but not limited to, anger, sadness, fear, disgust, and so on. When compared to the faculty psychology approach, this ‘constructionist’ approach provides an alternative functional architecture to guide the design and interpretation of experiments in cognitive neuroscience. PMID:23036719

Random synaptic feedback weights support error backpropagation for deep learning

PubMed Central

Lillicrap, Timothy P.; Cownden, Daniel; Tweed, Douglas B.; Akerman, Colin J.

2016-01-01

The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning. PMID:27824044

Alterations of Brain Functional Architecture Associated with Psychopathic Traits in Male Adolescents with Conduct Disorder.

PubMed

Pu, Weidan; Luo, Qiang; Jiang, Yali; Gao, Yidian; Ming, Qingsen; Yao, Shuqiao

2017-09-12

Psychopathic traits of conduct disorder (CD) have a core callous-unemotional (CU) component and an impulsive-antisocial component. Previous task-driven fMRI studies have suggested that psychopathic traits are associated with dysfunction of several brain areas involved in different cognitive functions (e.g., empathy, reward, and response inhibition etc.), but the relationship between psychopathic traits and intrinsic brain functional architecture has not yet been explored in CD. Using a holistic brain-wide functional connectivity analysis, this study delineated the alterations in brain functional networks in patients with conduct disorder. Compared with matched healthy controls, we found decreased anti-synchronization between the fronto-parietal network (FPN) and default mode network (DMN), and increased intra-network synchronization within the frontothalamic-basal ganglia, right frontoparietal, and temporal/limbic/visual networks in CD patients. Correlation analysis showed that the weakened FPN-DMN interaction was associated with CU traits, while the heightened intra-network functional connectivity was related to impulsivity traits in CD patients. Our findings suggest that decoupling of cognitive control (FPN) with social understanding of others (DMN) is associated with the CU traits, and hyper-functions of the reward and motor inhibition systems elevate impulsiveness in CD.

Knowledge-based processing for aircraft flight control

NASA Technical Reports Server (NTRS)

Painter, John H.; Glass, Emily; Economides, Gregory; Russell, Paul

1994-01-01

This Contractor Report documents research in Intelligent Control using knowledge-based processing in a manner dual to methods found in the classic stochastic decision, estimation, and control discipline. Such knowledge-based control has also been called Declarative, and Hybid. Software architectures were sought, employing the parallelism inherent in modern object-oriented modeling and programming. The viewpoint adopted was that Intelligent Control employs a class of domain-specific software architectures having features common over a broad variety of implementations, such as management of aircraft flight, power distribution, etc. As much attention was paid to software engineering issues as to artificial intelligence and control issues. This research considered that particular processing methods from the stochastic and knowledge-based worlds are duals, that is, similar in a broad context. They provide architectural design concepts which serve as bridges between the disparate disciplines of decision, estimation, control, and artificial intelligence. This research was applied to the control of a subsonic transport aircraft in the airport terminal area.

Glial architecture of the ghost shark (Callorhinchus milii, Holocephali, Chondrichthyes) as revealed by different immunohistochemical markers.

PubMed

Ari, Csilla; Kálmán, Mihály

2008-09-15

This article presents the first study on the glial architecture of a representative species of Holocephali, Callorhinchus milii (ghost shark). Holocephali are a small subclass of Chondrichthyes, with only a few extant genera, and those are considered to have a brain organization more similar to squalomorph sharks than to galeomorph sharks, skates, and rays. Three different astroglial markers--glial fibrillary acidic protein, S-100 protein, and glutamine synthetase (GS)--were investigated by immunohistochemical methods, applying both diaminobenzidine (DAB) and fluorescent techniques. They revealed similar glial structures, although most of them were detected by immunohistochemical reaction against GS and visualized by DAB. The predominant elements were radial ependymoglia spanning the area between the ventricular and meningeal surfaces, as in squalomorph sharks. Other similar features were the light appearance of myelinated neural tracts devoid of immunoreactivity, and the glial architecture of the reticular formation of the brain stem, cerebellum, and tectum, the latter with recognizable layers. The immunoreactivity of the vascular walls was similar; however, it is believed that different cell types form the blood-brain barrier in chimeras and in elasmobranchs. Some glial structures, however, resembled those of skates, rays, and galeomorph sharks. In C. milii astrocyte-like elements were observed in the telencephalon, using GS and S-100, although typical astrocyte-rich regions were not found. In some areas, especially the telencephalon, not only endfeet but also cell bodies were observed to be attached to the meningeal surface, with processes extending into the brain substance.

Development of emergent processing loops as a system of systems concept

NASA Astrophysics Data System (ADS)

Gainey, James C., Jr.; Blasch, Erik P.

1999-03-01

This paper describes an engineering approach toward implementing the current neuroscientific understanding of how the primate brain fuses, or integrates, 'information' in the decision-making process. We describe a System of Systems (SoS) design for improving the overall performance, capabilities, operational robustness, and user confidence in Identification (ID) systems and show how it could be applied to biometrics security. We use the Physio-associative temporal sensor integration algorithm (PATSIA) which is motivated by observed functions and interactions of the thalamus, hippocampus, and cortical structures in the brain. PATSIA utilizes signal theory mathematics to model how the human efficiently perceives and uses information from the environment. The hybrid architecture implements a possible SoS-level description of the Joint Directors of US Laboratories for Fusion Working Group's functional description involving 5 levels of fusion and their associated definitions. This SoS architecture propose dynamic sensor and knowledge-source integration by implementing multiple Emergent Processing Loops for predicting, feature extracting, matching, and Searching both static and dynamic database like MSTAR's PEMS loops. Biologically, this effort demonstrates these objectives by modeling similar processes from the eyes, ears, and somatosensory channels, through the thalamus, and to the cortices as appropriate while using the hippocampus for short-term memory search and storage as necessary. The particular approach demonstrated incorporates commercially available speaker verification and face recognition software and hardware to collect data and extract features to the PATSIA. The PATSIA maximizes the confidence levels for target identification or verification in dynamic situations using a belief filter. The proof of concept described here is easily adaptable and scaleable to other military and nonmilitary sensor fusion applications.

Cognitive Consilience: Primate Non-Primary Neuroanatomical Circuits Underlying Cognition

PubMed Central

Solari, Soren Van Hout; Stoner, Rich

2011-01-01

Interactions between the cerebral cortex, thalamus, and basal ganglia form the basis of cognitive information processing in the mammalian brain. Understanding the principles of neuroanatomical organization in these structures is critical to understanding the functions they perform and ultimately how the human brain works. We have manually distilled and synthesized hundreds of primate neuroanatomy facts into a single interactive visualization. The resulting picture represents the fundamental neuroanatomical blueprint upon which cognitive functions must be implemented. Within this framework we hypothesize and detail 7 functional circuits corresponding to psychological perspectives on the brain: consolidated long-term declarative memory, short-term declarative memory, working memory/information processing, behavioral memory selection, behavioral memory output, cognitive control, and cortical information flow regulation. Each circuit is described in terms of distinguishable neuronal groups including the cerebral isocortex (9 pyramidal neuronal groups), parahippocampal gyrus and hippocampus, thalamus (4 neuronal groups), basal ganglia (7 neuronal groups), metencephalon, basal forebrain, and other subcortical nuclei. We focus on neuroanatomy related to primate non-primary cortical systems to elucidate the basis underlying the distinct homotypical cognitive architecture. To display the breadth of this review, we introduce a novel method of integrating and presenting data in multiple independent visualizations: an interactive website (http://www.frontiersin.org/files/cognitiveconsilience/index.html) and standalone iPhone and iPad applications. With these tools we present a unique, annotated view of neuroanatomical consilience (integration of knowledge). PMID:22194717

Genetic evidence for role of integration of fast and slow neurotransmission in schizophrenia.

PubMed

Devor, A; Andreassen, O A; Wang, Y; Mäki-Marttunen, T; Smeland, O B; Fan, C-C; Schork, A J; Holland, D; Thompson, W K; Witoelar, A; Chen, C-H; Desikan, R S; McEvoy, L K; Djurovic, S; Greengard, P; Svenningsson, P; Einevoll, G T; Dale, A M

2017-06-01

The most recent genome-wide association studies (GWAS) of schizophrenia (SCZ) identified hundreds of risk variants potentially implicated in the disease. Further, novel statistical methodology designed for polygenic architecture revealed more potential risk variants. This can provide a link between individual genetic factors and the mechanistic underpinnings of SCZ. Intriguingly, a large number of genes coding for ionotropic and metabotropic receptors for various neurotransmitters-glutamate, γ-aminobutyric acid (GABA), dopamine, serotonin, acetylcholine and opioids-and numerous ion channels were associated with SCZ. Here, we review these findings from the standpoint of classical neurobiological knowledge of neuronal synaptic transmission and regulation of electrical excitability. We show that a substantial proportion of the identified genes are involved in intracellular cascades known to integrate 'slow' (G-protein-coupled receptors) and 'fast' (ionotropic receptors) neurotransmission converging on the protein DARPP-32. Inspection of the Human Brain Transcriptome Project database confirms that that these genes are indeed expressed in the brain, with the expression profile following specific developmental trajectories, underscoring their relevance to brain organization and function. These findings extend the existing pathophysiology hypothesis by suggesting a unifying role of dysregulation in neuronal excitability and synaptic integration in SCZ. This emergent model supports the concept of SCZ as an 'associative' disorder-a breakdown in the communication across different slow and fast neurotransmitter systems through intracellular signaling pathways-and may unify a number of currently competing hypotheses of SCZ pathophysiology.

Police Officers' Knowledge and Attitudes Toward Brain Death and Organ Donation in Korea.

PubMed

Kim, H S; Yoo, Y S; Cho, O-H; Lee, C E; Choi, Y-H; Kim, H J; Park, J Y; Park, H S; Kwon, Y J

2018-05-01

Administrative processing by the police may affect the process involved in organ donation in the event of an accidental brain injury. The purpose of this study was to evaluate the knowledge and attitude of police toward brain-dead donors and organ donation. This was a descriptive research study using a 41-item questionnaire. As of July 19, 2017, 11 police stations in Seoul had collected questionnaires completed by 115 police officers. Data were analyzed using SAS (version 9.4) software. There were statistically significant differences in the scores on knowledge about brain death/donation according to religion (P = .022). Attitude was significantly positively correlated with the knowledge about brain-death organ donation (P = .029). It is necessary to understand and cooperate with the police when processing brain death organs from accidents. Education about organ donation can enhance the information and knowledge of the police and can also help to establish a positive attitude about organ donation. Copyright © 2018 Elsevier Inc. All rights reserved.

Characterizing attention with predictive network models

PubMed Central

Rosenberg, M. D.; Finn, E. S.; Scheinost, D.; Constable, R. T.; Chun, M. M.

2017-01-01

Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals’ attentional abilities. Some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that (1) attention is a network property of brain computation, (2) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task, and (3) this architecture supports a general attentional ability common to several lab-based tasks and impaired in attention deficit hyperactivity disorder. Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. PMID:28238605

Disrupted Brain Functional Network Architecture in Chronic Tinnitus Patients

PubMed Central

Chen, Yu-Chen; Feng, Yuan; Xu, Jin-Jing; Mao, Cun-Nan; Xia, Wenqing; Ren, Jun; Yin, Xindao

2016-01-01

Purpose: Resting-state functional magnetic resonance imaging (fMRI) studies have demonstrated the disruptions of multiple brain networks in tinnitus patients. Nonetheless, several studies found no differences in network processing between tinnitus patients and healthy controls (HCs). Its neural bases are poorly understood. To identify aberrant brain network architecture involved in chronic tinnitus, we compared the resting-state fMRI (rs-fMRI) patterns of tinnitus patients and HCs. Materials and Methods: Chronic tinnitus patients (n = 24) with normal hearing thresholds and age-, sex-, education- and hearing threshold-matched HCs (n = 22) participated in the current study and underwent the rs-fMRI scanning. We used degree centrality (DC) to investigate functional connectivity (FC) strength of the whole-brain network and Granger causality to analyze effective connectivity in order to explore directional aspects involved in tinnitus. Results: Compared to HCs, we found significantly increased network centrality in bilateral superior frontal gyrus (SFG). Unidirectionally, the left SFG revealed increased effective connectivity to the left middle orbitofrontal cortex (OFC), left posterior lobe of cerebellum (PLC), left postcentral gyrus, and right middle occipital gyrus (MOG) while the right SFG exhibited enhanced effective connectivity to the right supplementary motor area (SMA). In addition, the effective connectivity from the bilateral SFG to the OFC and SMA showed positive correlations with tinnitus distress. Conclusions: Rs-fMRI provides a new and novel method for identifying aberrant brain network architecture. Chronic tinnitus patients have disrupted FC strength and causal connectivity mostly in non-auditory regions, especially the prefrontal cortex (PFC). The current findings will provide a new perspective for understanding the neuropathophysiological mechanisms in chronic tinnitus. PMID:27458377

Complex function in the dynamic brain. Comment on “Understanding brain networks and brain organization” by Luiz Pessoa

NASA Astrophysics Data System (ADS)

Anderson, Michael L.

2014-09-01

There is much to commend in this excellent overview of the progress we've made toward-and the challenges that remain for-developing an empirical framework for neuroscience that is adequate to the dynamic complexity of the brain [17]. Here I will limit myself first to highlighting the concept of dynamic affiliation, which I take to be the central feature of the functional architecture of the brain, and second to clarifying Pessoa's brief discussion of the ontology of cognition, to be sure readers appreciate this crucial issue.

Reptiles: a new model for brain evo-devo research.

PubMed

Nomura, Tadashi; Kawaguchi, Masahumi; Ono, Katsuhiko; Murakami, Yasunori

2013-03-01

Vertebrate brains exhibit vast amounts of anatomical diversity. In particular, the elaborate and complex nervous system of amniotes is correlated with the size of their behavioral repertoire. However, the evolutionary mechanisms underlying species-specific brain morphogenesis remain elusive. In this review we introduce reptiles as a new model organism for understanding brain evolution. These animal groups inherited ancestral traits of brain architectures. We will describe several unique aspects of the reptilian nervous system with a special focus on the telencephalon, and discuss the genetic mechanisms underlying reptile-specific brain morphology. The establishment of experimental evo-devo approaches to studying reptiles will help to shed light on the origin of the amniote brains. Copyright © 2013 Wiley Periodicals, Inc.

Stress and the Architecture of the Brain. Perspectives

ERIC Educational Resources Information Center

Friedman, Dorian

2006-01-01

When faced with threats to physical or psychological well-being, our bodies and brains respond in a variety of self-protective ways, including the production of stress hormones adrenaline and cortisol. Our ability to turn this response on and off is critical to healthy functioning in society, and scientists now believe that significant…

Design and Training of Limited-Interconnect Architectures

DTIC Science & Technology

1991-07-16

and signal processing. Neuromorphic (brain like) models, allow an alternative for achieving real-time operation tor such tasks, while having a...compact and robust architecture. Neuromorphic models consist of interconnections of simple computational nodes. In this approach, each node computes a...operational performance. I1. Research Objectives The research objectives were: 1. Development of on- chip local training rules specifically designed for

Impulsivity and the Modular Organization of Resting-State Neural Networks

PubMed Central

Davis, F. Caroline; Knodt, Annchen R.; Sporns, Olaf; Lahey, Benjamin B.; Zald, David H.; Brigidi, Bart D.; Hariri, Ahmad R.

2013-01-01

Impulsivity is a complex trait associated with a range of maladaptive behaviors, including many forms of psychopathology. Previous research has implicated multiple neural circuits and neurotransmitter systems in impulsive behavior, but the relationship between impulsivity and organization of whole-brain networks has not yet been explored. Using graph theory analyses, we characterized the relationship between impulsivity and the functional segregation (“modularity”) of the whole-brain network architecture derived from resting-state functional magnetic resonance imaging (fMRI) data. These analyses revealed remarkable differences in network organization across the impulsivity spectrum. Specifically, in highly impulsive individuals, regulatory structures including medial and lateral regions of the prefrontal cortex were isolated from subcortical structures associated with appetitive drive, whereas these brain areas clustered together within the same module in less impulsive individuals. Further exploration of the modular organization of whole-brain networks revealed novel shifts in the functional connectivity between visual, sensorimotor, cortical, and subcortical structures across the impulsivity spectrum. The current findings highlight the utility of graph theory analyses of resting-state fMRI data in furthering our understanding of the neurobiological architecture of complex behaviors. PMID:22645253

Optical imaging of architecture and function in the living brain sheds new light on cortical mechanisms underlying visual perception.

PubMed

Grinvald, A

1992-01-01

Long standing questions related to brain mechanisms underlying perception can finally be resolved by direct visualization of the architecture and function of mammalian cortex. This advance has been accomplished with the aid of two optical imaging techniques with which one can literally see how the brain functions. The upbringing of this technology required a multi-disciplinary approach integrating brain research with organic chemistry, spectroscopy, biophysics, computer sciences, optics and image processing. Beyond the technological ramifications, recent research shed new light on cortical mechanisms underlying sensory perception. Clinical applications of this technology for precise mapping of the cortical surface of patients during neurosurgery have begun. Below is a brief summary of our own research and a description of the technical specifications of the two optical imaging techniques. Like every technique, optical imaging also suffers from severe limitations. Here we mostly emphasize some of its advantages relative to all alternative imaging techniques currently in use. The limitations are critically discussed in our recent reviews. For a series of other reviews, see Cohen (1989).

From Hippocampus to Whole-Brain: The Role of Integrative Processing in Episodic Memory Retrieval

PubMed Central

Geib, Benjamin R.; Stanley, Matthew L.; Dennis, Nancy A.; Woldorff, Marty G.; Cabeza, Roberto

2017-01-01

Multivariate functional connectivity analyses of neuroimaging data have revealed the importance of complex, distributed interactions between disparate yet interdependent brain regions. Recent work has shown that topological properties of functional brain networks are associated with individual and group differences in cognitive performance, including in episodic memory. After constructing functional whole-brain networks derived from an event-related fMRI study of memory retrieval, we examined differences in functional brain network architecture between forgotten and remembered words. This study yielded three main findings. First, graph theory analyses showed that successfully remembering compared to forgetting was associated with significant changes in the connectivity profile of the left hippocampus and a corresponding increase in efficient communication with the rest of the brain. Second, bivariate functional connectivity analyses indicated stronger interactions between the left hippocampus and a retrieval assembly for remembered versus forgotten items. This assembly included the left precuneus, left caudate, bilateral supramarginal gyrus, and the bilateral dorsolateral superior frontal gyrus. Integrative properties of the retrieval assembly were greater for remembered than forgotten items. Third, whole-brain modularity analyses revealed that successful memory retrieval was marginally significantly associated with a less segregated modular architecture in the network. The magnitude of the decreases in modularity between remembered and forgotten conditions was related to memory performance. These findings indicate that increases in integrative properties at the nodal, retrieval assembly, and whole-brain topological levels facilitate memory retrieval, while also underscoring the potential of multivariate brain connectivity approaches for providing valuable new insights into the neural bases of memory processes. PMID:28112460

Personality Is Reflected in the Brain's Intrinsic Functional Architecture

PubMed Central

Adelstein, Jonathan S.; Shehzad, Zarrar; Mennes, Maarten; DeYoung, Colin G.; Zuo, Xi-Nian; Kelly, Clare; Margulies, Daniel S.; Bloomfield, Aaron; Gray, Jeremy R.; Castellanos, F. Xavier; Milham, Michael P.

2011-01-01

Personality describes persistent human behavioral responses to broad classes of environmental stimuli. Investigating how personality traits are reflected in the brain's functional architecture is challenging, in part due to the difficulty of designing appropriate task probes. Resting-state functional connectivity (RSFC) can detect intrinsic activation patterns without relying on any specific task. Here we use RSFC to investigate the neural correlates of the five-factor personality domains. Based on seed regions placed within two cognitive and affective ‘hubs’ in the brain—the anterior cingulate and precuneus—each domain of personality predicted RSFC with a unique pattern of brain regions. These patterns corresponded with functional subdivisions responsible for cognitive and affective processing such as motivation, empathy and future-oriented thinking. Neuroticism and Extraversion, the two most widely studied of the five constructs, predicted connectivity between seed regions and the dorsomedial prefrontal cortex and lateral paralimbic regions, respectively. These areas are associated with emotional regulation, self-evaluation and reward, consistent with the trait qualities. Personality traits were mostly associated with functional connections that were inconsistently present across participants. This suggests that although a fundamental, core functional architecture is preserved across individuals, variable connections outside of that core encompass the inter-individual differences in personality that motivate diverse responses. PMID:22140453

The brain connectome as a personalized biomarker of seizure outcomes after temporal lobectomy.

PubMed

Bonilha, Leonardo; Jensen, Jens H; Baker, Nathaniel; Breedlove, Jesse; Nesland, Travis; Lin, Jack J; Drane, Daniel L; Saindane, Amit M; Binder, Jeffrey R; Kuzniecky, Ruben I

2015-05-05

We examined whether individual neuronal architecture obtained from the brain connectome can be used to estimate the surgical success of anterior temporal lobectomy (ATL) in patients with temporal lobe epilepsy (TLE). We retrospectively studied 35 consecutive patients with TLE who underwent ATL. The structural brain connectome was reconstructed from all patients using presurgical diffusion MRI. Network links in patients were standardized as Z scores based on connectomes reconstructed from healthy controls. The topography of abnormalities in linkwise elements of the connectome was assessed on subnetworks linking ipsilateral temporal with extratemporal regions. Predictive models were constructed based on the individual prevalence of linkwise Z scores >2 and based on presurgical clinical data. Patients were more likely to achieve postsurgical seizure freedom if they exhibited fewer abnormalities within a subnetwork composed of the ipsilateral hippocampus, amygdala, thalamus, superior frontal region, lateral temporal gyri, insula, orbitofrontal cortex, cingulate, and lateral occipital gyrus. Seizure-free surgical outcome was predicted by neural architecture alone with 90% specificity (83% accuracy), and by neural architecture combined with clinical data with 94% specificity (88% accuracy). Individual variations in connectome topography, combined with presurgical clinical data, may be used as biomarkers to better estimate surgical outcomes in patients with TLE. © 2015 American Academy of Neurology.

Multimodal investigation of epileptic networks: The case of insular cortex epilepsy.

PubMed

Zerouali, Y; Ghaziri, J; Nguyen, D K

2016-01-01

The insula is a deep cortical structure sharing extensive synaptic connections with a variety of brain regions, including several frontal, temporal, and parietal structures. The identification of the insular connectivity network is obviously valuable for understanding a number of cognitive processes, but also for understanding epilepsy since insular seizures involve a number of remote brain regions. Ultimately, knowledge of the structure and causal relationships within the epileptic networks associated with insular cortex epilepsy can offer deeper insights into this relatively neglected type of epilepsy enabling the refining of the clinical approach in managing patients affected by it. In the present chapter, we first review the multimodal noninvasive tests performed during the presurgical evaluation of epileptic patients with drug refractory focal epilepsy, with particular emphasis on their value for the detection of insular cortex epilepsy. Second, we review the emerging multimodal investigation techniques in the field of epilepsy, that aim to (1) enhance the detection of insular cortex epilepsy and (2) unveil the architecture and causal relationships within epileptic networks. We summarize the results of these approaches with emphasis on the specific case of insular cortex epilepsy. © 2016 Elsevier B.V. All rights reserved.

SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture.

PubMed

Liu, Junxiu; Harkin, Jim; Maguire, Liam P; McDaid, Liam J; Wade, John J

2018-04-01

Recent research has shown that a glial cell of astrocyte underpins a self-repair mechanism in the human brain, where spiking neurons provide direct and indirect feedbacks to presynaptic terminals. These feedbacks modulate the synaptic transmission probability of release (PR). When synaptic faults occur, the neuron becomes silent or near silent due to the low PR of synapses; whereby the PRs of remaining healthy synapses are then increased by the indirect feedback from the astrocyte cell. In this paper, a novel hardware architecture of Self-rePAiring spiking Neural NEtwoRk (SPANNER) is proposed, which mimics this self-repairing capability in the human brain. This paper demonstrates that the hardware can self-detect and self-repair synaptic faults without the conventional components for the fault detection and fault repairing. Experimental results show that SPANNER can maintain the system performance with fault densities of up to 40%, and more importantly SPANNER has only a 20% performance degradation when the self-repairing architecture is significantly damaged at a fault density of 80%.

Brain tumor segmentation with Deep Neural Networks.

PubMed

Havaei, Mohammad; Davy, Axel; Warde-Farley, David; Biard, Antoine; Courville, Aaron; Bengio, Yoshua; Pal, Chris; Jodoin, Pierre-Marc; Larochelle, Hugo

2017-01-01

In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster. Copyright © 2016 Elsevier B.V. All rights reserved.

On Some Aspects of Study on Dimensions and Proportions of Church Architecture

NASA Astrophysics Data System (ADS)

Kolobaeva, T. V.

2017-11-01

Architecture forms and arranges the environment required for a comfortable life and human activity. The modern principles of architectural space arrangement and form making are represented by a reliable system of buildings which are used in design. Architects apply these principles and knowledge of space arrangement in regard to the study of special and regulatory literature when performing a particular creative task. This system of accumulated knowledge is perceived in the form of an existing stereotype with no regard for understanding of the form making and experience inherent to the architects and thinkers of previous ages. We make an attempt to restore this connection as the form-making specific regularities known by ancient architects should be taken into account. The paper gives an insight into some aspects of traditional dimensions and proportions of church architecture.

Knowledge Framework Implementation with Multiple Architectures - 13090

DOE Office of Scientific and Technical Information (OSTI.GOV)

Upadhyay, H.; Lagos, L.; Quintero, W.

2013-07-01

Multiple kinds of knowledge management systems are operational in public and private enterprises, large and small organizations with a variety of business models that make the design, implementation and operation of integrated knowledge systems very difficult. In recent days, there has been a sweeping advancement in the information technology area, leading to the development of sophisticated frameworks and architectures. These platforms need to be used for the development of integrated knowledge management systems which provides a common platform for sharing knowledge across the enterprise, thereby reducing the operational inefficiencies and delivering cost savings. This paper discusses the knowledge framework andmore » architecture that can be used for the system development and its application to real life need of nuclear industry. A case study of deactivation and decommissioning (D and D) is discussed with the Knowledge Management Information Tool platform and framework. D and D work is a high priority activity across the Department of Energy (DOE) complex. Subject matter specialists (SMS) associated with DOE sites, the Energy Facility Contractors Group (EFCOG) and the D and D community have gained extensive knowledge and experience over the years in the cleanup of the legacy waste from the Manhattan Project. To prevent the D and D knowledge and expertise from being lost over time from the evolving and aging workforce, DOE and the Applied Research Center (ARC) at Florida International University (FIU) proposed to capture and maintain this valuable information in a universally available and easily usable system. (authors)« less

Outline of a novel architecture for cortical computation.

PubMed

Majumdar, Kaushik

2008-03-01

In this paper a novel architecture for cortical computation has been proposed. This architecture is composed of computing paths consisting of neurons and synapses. These paths have been decomposed into lateral, longitudinal and vertical components. Cortical computation has then been decomposed into lateral computation (LaC), longitudinal computation (LoC) and vertical computation (VeC). It has been shown that various loop structures in the cortical circuit play important roles in cortical computation as well as in memory storage and retrieval, keeping in conformity with the molecular basis of short and long term memory. A new learning scheme for the brain has also been proposed and how it is implemented within the proposed architecture has been explained. A few mathematical results about the architecture have been proposed, some of which are without proof.

From hippocampus to whole-brain: The role of integrative processing in episodic memory retrieval.

PubMed

Geib, Benjamin R; Stanley, Matthew L; Dennis, Nancy A; Woldorff, Marty G; Cabeza, Roberto

2017-04-01

Multivariate functional connectivity analyses of neuroimaging data have revealed the importance of complex, distributed interactions between disparate yet interdependent brain regions. Recent work has shown that topological properties of functional brain networks are associated with individual and group differences in cognitive performance, including in episodic memory. After constructing functional whole-brain networks derived from an event-related fMRI study of memory retrieval, we examined differences in functional brain network architecture between forgotten and remembered words. This study yielded three main findings. First, graph theory analyses showed that successfully remembering compared to forgetting was associated with significant changes in the connectivity profile of the left hippocampus and a corresponding increase in efficient communication with the rest of the brain. Second, bivariate functional connectivity analyses indicated stronger interactions between the left hippocampus and a retrieval assembly for remembered versus forgotten items. This assembly included the left precuneus, left caudate, bilateral supramarginal gyrus, and the bilateral dorsolateral superior frontal gyrus. Integrative properties of the retrieval assembly were greater for remembered than forgotten items. Third, whole-brain modularity analyses revealed that successful memory retrieval was marginally significantly associated with a less segregated modular architecture in the network. The magnitude of the decreases in modularity between remembered and forgotten conditions was related to memory performance. These findings indicate that increases in integrative properties at the nodal, retrieval assembly, and whole-brain topological levels facilitate memory retrieval, while also underscoring the potential of multivariate brain connectivity approaches for providing valuable new insights into the neural bases of memory processes. Hum Brain Mapp 38:2242-2259, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Evidence for sex-specific selection in brain: a case study of the nine-spined stickleback.

PubMed

Herczeg, G; Välimäki, K; Gonda, A; Merilä, J

2014-08-01

Theory predicts that the sex making greater investments into reproductive behaviours demands higher cognitive ability, and as a consequence, larger brains or brain parts. Further, the resulting sexual dimorphism can differ between populations adapted to different environments, or among individuals developing under different environmental conditions. In the nine-spine stickleback (Pungitius pungitius), males perform nest building, courtship, territory defence and parental care, whereas females perform mate choice and produce eggs. Also, predation-adapted marine and competition-adapted pond populations have diverged in a series of ecologically relevant traits, including the level of phenotypic plasticity. Here, we studied sexual dimorphism in brain size and architecture in nine-spined stickleback from marine and pond populations reared in a factorial experiment with predation and food treatments in a common garden experiment. Males had relatively larger brains, larger telencephala, cerebella and hypothalami (6-16% divergence) than females, irrespective of habitat. Females tended to have larger bulbi olfactorii than males (13%) in the high food treatment, whereas no such difference was found in the low food treatment. The strong sexual dimorphism in brain architecture implies that the different reproductive allocation strategies (behaviour vs. egg production) select for different investments into the costly brains between males and females. The lack of habitat dependence in brain sexual dimorphism suggests that the sex-specific selection forces on brains differ only negligibly between habitats. Although significance of the observed sex-specific brain plasticity in the size of bulbus olfactorius remains unclear, it demonstrates the potential for sex-specific neural plasticity. © 2014 The Authors. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

Segregated Systems of Human Brain Networks.

PubMed

Wig, Gagan S

2017-12-01

The organization of the brain network enables its function. Evaluation of this organization has revealed that large-scale brain networks consist of multiple segregated subnetworks of interacting brain areas. Descriptions of resting-state network architecture have provided clues for understanding the functional significance of these segregated subnetworks, many of which correspond to distinct brain systems. The present report synthesizes accumulating evidence to reveal how maintaining segregated brain systems renders the human brain network functionally specialized, adaptable to task demands, and largely resilient following focal brain damage. The organizational properties that support system segregation are harmonious with the properties that promote integration across the network, but confer unique and important features to the brain network that are central to its function and behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

A study on knowledge and attitude toward brain death and organ retrieval among health care professionals in Korea.

PubMed

Jeon, K O; Kim, B N; Kim, H S; Byeon, N-I; Hong, J J; Bae, S H; Son, S Y

2012-05-01

The practice of retrieving vital organs from brain-dead donors is legally and medically accepted in Korea, but health care professionals' beliefs and opinions regarding these matters have not been sufficiently explored. The purpose of this study was to evaluate the knowledge and attitudes of health care professionals to the concepts of brain death and organ retrieval. Data were collected using a 41-item questionnaire during a week in June 2011. Sixty-one doctors and 109 nurses from five hospitals with more than 2000 beds in Seoul, Korea, participated in the survey. The data was analyzed using SPSS version 17.0 (SPSS Inc. Chicago, Illinois, USA). There were statistically significant differences in the scores on knowledge according to marital status (P = .001) education level (P = .019), whether the participants were informed about organ donation from a brain-dead donor (P = .002), and the participant's experience managing potential brain-dead patients (P = .037). There were statistically significant differences in the scores on the attitude according to gender (P < .001), age (P < .001), marital status (P < .001), education level (P = .003), job position (P < .001), and the participant's experience referring brain-dead patients to the hospital-based organ procurement organization (P = .001). Significantly, attitude's positively correlated with knowledge about brain-dead organ donation (P < .001). Compared with previous studies, the knowledge and attitudes of health care professionals' regarding brain death and organ retrieval were not improved. There are passive attitudes to brain death and organ retrieval. More research must be performed to promote knowledge and understanding toward brain death and organ retrieval among health care professionals. Copyright © 2012 Elsevier Inc. All rights reserved.

Students' Knowledge Sources and Knowledge Sharing in the Design Studio--An Exploratory Study

ERIC Educational Resources Information Center

Chiu, Sheng-Hsiao

2010-01-01

Architectural design is a knowledge-intensive activity; however, students frequently lack sufficient knowledge when they practice design. Collaborative learning can supplement the students' insufficient expertise. Successful collaborative learning relies on knowledge sharing between students. This implies that the peers are a considerable design…

Battery-Less Electroencephalogram System Architecture Optimization

DTIC Science & Technology

2016-12-01

disorders, especially in real-world situations, such as when a Soldier is in theater. There are several methods to study the electrical activity in the brain...to measure the electrical activity in the brain that can still be used to study brain activity. Currently, most EEGs are recorded in highly controlled...base to build a larger system as its power consumption would allow it to operate from a AA battery for more than 72 h. While this might be acceptable

Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations.

PubMed

Lohse, Christian; Bassett, Danielle S; Lim, Kelvin O; Carlson, Jean M

2014-10-01

Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.

Utilizing Expert Knowledge in Estimating Future STS Costs

NASA Technical Reports Server (NTRS)

Fortner, David B.; Ruiz-Torres, Alex J.

2004-01-01

A method of estimating the costs of future space transportation systems (STSs) involves classical activity-based cost (ABC) modeling combined with systematic utilization of the knowledge and opinions of experts to extend the process-flow knowledge of existing systems to systems that involve new materials and/or new architectures. The expert knowledge is particularly helpful in filling gaps that arise in computational models of processes because of inconsistencies in historical cost data. Heretofore, the costs of planned STSs have been estimated following a "top-down" approach that tends to force the architectures of new systems to incorporate process flows like those of the space shuttles. In this ABC-based method, one makes assumptions about the processes, but otherwise follows a "bottoms up" approach that does not force the new system architecture to incorporate a space-shuttle-like process flow. Prototype software has been developed to implement this method. Through further development of software, it should be possible to extend the method beyond the space program to almost any setting in which there is a need to estimate the costs of a new system and to extend the applicable knowledge base in order to make the estimate.

The blind brain: how (lack of) vision shapes the morphological and functional architecture of the human brain.

PubMed

Ricciardi, Emiliano; Handjaras, Giacomo; Pietrini, Pietro

2014-11-01

Since the early days, how we represent the world around us has been a matter of philosophical speculation. Over the last few decades, modern neuroscience, and specifically the development of methodologies for the structural and the functional exploration of the brain have made it possible to investigate old questions with an innovative approach. In this brief review, we discuss the main findings from a series of brain anatomical and functional studies conducted in sighted and congenitally blind individuals by our's and others' laboratories. Historically, research on the 'blind brain' has focused mainly on the cross-modal plastic changes that follow sensory deprivation. More recently, a novel line of research has been developed to determine to what extent visual experience is truly required to achieve a representation of the surrounding environment. Overall, the results of these studies indicate that most of the brain fine morphological and functional architecture is programmed to develop and function independently from any visual experience. Distinct cortical areas are able to process information in a supramodal fashion, that is, independently from the sensory modality that carries that information to the brain. These observations strongly support the hypothesis of a modality-independent, i.e. more abstract, cortical organization, and may contribute to explain how congenitally blind individuals may interact efficiently with an external world that they have never seen. © 2014 by the Society for Experimental Biology and Medicine.

Recognizing resilience: Learning from the effects of stress on the brain

PubMed Central

McEwen, Bruce S.; Gray, Jason D.; Nasca, Carla

2014-01-01

As the central organ of stress and adaptation to stressors, the brain plays a pivotal role in behavioral and physiological responses that may lead to successful adaptation or to pathophysiology and mental and physical disease. In this context, resilience can be defined as “achieving a positive outcome in the face of adversity”. Underlying this deceptively simple statement are several questions; first, to what extent is this ability limited to those environments that have shaped the individual or can it be more flexible; second, when in the life course does the brain develop capacity for flexibility for adapting positively to new challenges; and third, can such flexibility be instated in individuals where early life experiences have limited that capacity? Brain architecture continues to show plasticity throughout adult life and studies of gene expression and epigenetic regulation reveal a dynamic and ever-changing brain. The goal is to recognize those biological changes that underlie flexible adaptability, and to recognize gene pathways, epigenetic factors and structural changes that indicate lack of resilience leading to negative outcomes, particularly when the individual is challenged by new circumstances. Early life experiences determine individual differences in such capabilities via epigenetic pathways and laying down of brain architecture that determine the later capacity for flexible adaptation or the lack thereof. Reactivation of such plasticity in individuals lacking such resilience is a new challenge for research and practical application. Finally, sex differences in the plasticity of the brain are often overlooked and must be more fully investigated. PMID:25506601

Rich Experiences, Physical Activity Create Healthy Brains: An Interview with Developmental Psychologist William Greenough. Perspectives

ERIC Educational Resources Information Center

Ray, Marcy, Ed.

2006-01-01

In this interview, Council member William Greenough discusses the need for rich, complex experiences combined with physical activity in early childhood to help build a strong foundation for learning. He explains how rich, complex experiences are necessary for the development of sound brain architecture, particularly during early childhood, but…

Understand the cogs to understand cognition.

PubMed

Marblestone, Adam H; Wayne, Greg; Kording, Konrad P

2017-01-01

Lake et al. suggest that current AI systems lack the inductive biases that enable human learning. However, Lake et al.'s proposed biases may not directly map onto mechanisms in the developing brain. A convergence of fields may soon create a correspondence between biological neural circuits and optimization in structured architectures, allowing us to systematically dissect how brains learn.

Young Children Develop in an Environment of Relationships. Working Paper #1

ERIC Educational Resources Information Center

National Scientific Council on the Developing Child, 2006

2006-01-01

New research shows the critical impact of a child's "environment of relationships" on developing brain architecture during the first months and years of life. We have long known that interactions with parents, caregivers, and other adults are important in a child's life, but new evidence shows that these relationships actually shape brain circuits…

Understanding emotion with brain networks.

PubMed

Pessoa, Luiz

2018-02-01

Emotional processing appears to be interlocked with perception, cognition, motivation, and action. These interactions are supported by the brain's large-scale non-modular anatomical and functional architectures. An important component of this organization involves characterizing the brain in terms of networks. Two aspects of brain networks are discussed: brain networks should be considered as inherently overlapping (not disjoint) and dynamic (not static). Recent work on multivariate pattern analysis shows that affective dimensions can be detected in the activity of distributed neural systems that span cortical and subcortical regions. More broadly, the paper considers how we should think of causation in complex systems like the brain, so as to inform the relationship between emotion and other mental aspects, such as cognition.

An Exploratory Study of Special Educational Needs Co-Ordinators' Knowledge and Experience of Working with Children Who Have Sustained a Brain Injury

ERIC Educational Resources Information Center

Howe, Julia; Ball, Heather

2017-01-01

This research aimed to measure Special Educational Needs Co-ordinators' knowledge of the educational implications of acquired brain injury in children and young people and whether experience of working with pupils with a brain injury or additional training impacts upon this knowledge. Data was collected within one local authority in England using…

The adult brain tissue response to hollow fiber membranes of varying surface architecture with or without cotransplanted cells

NASA Astrophysics Data System (ADS)

Zhang, Ning

A variety of biomaterials have been chronically implanted into the central nervous system (CNS) for repair or therapeutic purposes. Regardless of the application, chronic implantation of materials into the CNS induces injury and elicits a wound healing response, eventually leading to the formation of a dense extracellular matrix (ECM)-rich scar tissue that is associated with the segregation of implanted materials from the surrounding normal tissue. Often this reaction results in impaired performance of indwelling CNS devices. In order to enhance the performance of biomaterial-based implantable devices in the CNS, this thesis investigated whether adult brain tissue response to implanted biomaterials could be manipulated by changing biomaterial surface properties or further by utilizing the biology of co-transplanted cells. Specifically, the adult rat brain tissue response to chronically implanted poly(acrylonitrile-vinylchloride) (PAN-PVC) hollow fiber membranes (HFMs) of varying surface architecture were examined temporally at 2, 4, and 12 weeks postimplantation. Significant differences were discovered in the brain tissue response to the PAN-PVC HFMs of varying surface architecture at 4 and 12 weeks. To extend this work, whether the soluble factors derived from a co-transplanted cellular component further affect the brain tissue response to an implanted HFM in a significant way was critically exploited. The cells used were astrocytes, whose ability to influence scar formation process following CNS injury by physical contact with the host tissue had been documented in the literature. Data indicated for the first time that astrocyte-derived soluble factors ameliorate the adult brain tissue reactivity toward HFM implants in an age-dependent manner. While immature astrocytes secreted soluble factors that suppressed the brain tissue reactivity around the implants, mature astrocytes secreted factors that enhanced the gliotic response. These findings prove the feasibility of ameliorating the CNS tissue reactivity toward biomaterials implants by varying biomaterial surface properties or incorporating scar-reductive factors derived from functional cells into implant constructs, therefore, provide guidance in the design of more integrative biomaterial-based implantable devices for CNS repair.

Progressive gender differences of structural brain networks in healthy adults: a longitudinal, diffusion tensor imaging study.

PubMed

Sun, Yu; Lee, Renick; Chen, Yu; Collinson, Simon; Thakor, Nitish; Bezerianos, Anastasios; Sim, Kang

2015-01-01

Sexual dimorphism in the brain maturation during childhood and adolescence has been repeatedly documented, which may underlie the differences in behaviors and cognitive performance. However, our understanding of how gender modulates the development of structural connectome in healthy adults is still not entirely clear. Here we utilized graph theoretical analysis of longitudinal diffusion tensor imaging data over a five-year period to investigate the progressive gender differences of brain network topology. The brain networks of both genders showed prominent economical "small-world" architecture (high local clustering and short paths between nodes). Additional analysis revealed a more economical "small-world" architecture in females as well as a greater global efficiency in males regardless of scan time point. At the regional level, both increased and decreased efficiency were found across the cerebral cortex for both males and females, indicating a compensation mechanism of cortical network reorganization over time. Furthermore, we found that weighted clustering coefficient exhibited significant gender-time interactions, implying different development trends between males and females. Moreover, several specific brain regions (e.g., insula, superior temporal gyrus, cuneus, putamen, and parahippocampal gyrus) exhibited different development trajectories between males and females. Our findings further prove the presence of sexual dimorphism in brain structures that may underlie gender differences in behavioral and cognitive functioning. The sex-specific progress trajectories in brain connectome revealed in this work provide an important foundation to delineate the gender related pathophysiological mechanisms in various neuropsychiatric disorders, which may potentially guide the development of sex-specific treatments for these devastating brain disorders.

CUDA-based acceleration and BPN-assisted automation of bilateral filtering for brain MR image restoration.

PubMed

Chang, Herng-Hua; Chang, Yu-Ning

2017-04-01

Bilateral filters have been substantially exploited in numerous magnetic resonance (MR) image restoration applications for decades. Due to the deficiency of theoretical basis on the filter parameter setting, empirical manipulation with fixed values and noise variance-related adjustments has generally been employed. The outcome of these strategies is usually sensitive to the variation of the brain structures and not all the three parameter values are optimal. This article is in an attempt to investigate the optimal setting of the bilateral filter, from which an accelerated and automated restoration framework is developed. To reduce the computational burden of the bilateral filter, parallel computing with the graphics processing unit (GPU) architecture is first introduced. The NVIDIA Tesla K40c GPU with the compute unified device architecture (CUDA) functionality is specifically utilized to emphasize thread usages and memory resources. To correlate the filter parameters with image characteristics for automation, optimal image texture features are subsequently acquired based on the sequential forward floating selection (SFFS) scheme. Subsequently, the selected features are introduced into the back propagation network (BPN) model for filter parameter estimation. Finally, the k-fold cross validation method is adopted to evaluate the accuracy of the proposed filter parameter prediction framework. A wide variety of T1-weighted brain MR images with various scenarios of noise levels and anatomic structures were utilized to train and validate this new parameter decision system with CUDA-based bilateral filtering. For a common brain MR image volume of 256 × 256 × 256 pixels, the speed-up gain reached 284. Six optimal texture features were acquired and associated with the BPN to establish a "high accuracy" parameter prediction system, which achieved a mean absolute percentage error (MAPE) of 5.6%. Automatic restoration results on 2460 brain MR images received an average relative error in terms of peak signal-to-noise ratio (PSNR) less than 0.1%. In comparison with many state-of-the-art filters, the proposed automation framework with CUDA-based bilateral filtering provided more favorable results both quantitatively and qualitatively. Possessing unique characteristics and demonstrating exceptional performances, the proposed CUDA-based bilateral filter adequately removed random noise in multifarious brain MR images for further study in neurosciences and radiological sciences. It requires no prior knowledge of the noise variance and automatically restores MR images while preserving fine details. The strategy of exploiting the CUDA to accelerate the computation and incorporating texture features into the BPN to completely automate the bilateral filtering process is achievable and validated, from which the best performance is reached. © 2017 American Association of Physicists in Medicine.

Connecting Learning: Brain-Based Strategies for Linking Prior Knowledge in the Library Media Center

ERIC Educational Resources Information Center

Vanderbilt, Kathi L.

2005-01-01

The brain is a complex organ and learning is a complex process. While there is not complete agreement among researchers about brain-based learning and its direct connection to neuroscience, knowledge about the brain as well as the examination of cognitive psychology, anthropology, professional experience, and educational research can provide…

Knowledge Management in Role Based Agents

NASA Astrophysics Data System (ADS)

Kır, Hüseyin; Ekinci, Erdem Eser; Dikenelli, Oguz

In multi-agent system literature, the role concept is getting increasingly researched to provide an abstraction to scope beliefs, norms, goals of agents and to shape relationships of the agents in the organization. In this research, we propose a knowledgebase architecture to increase applicability of roles in MAS domain by drawing inspiration from the self concept in the role theory of sociology. The proposed knowledgebase architecture has granulated structure that is dynamically organized according to the agent's identification in a social environment. Thanks to this dynamic structure, agents are enabled to work on consistent knowledge in spite of inevitable conflicts between roles and the agent. The knowledgebase architecture is also implemented and incorporated into the SEAGENT multi-agent system development framework.

Age-associated changes in rich-club organisation in autistic and neurotypical human brains

PubMed Central

Watanabe, Takamitsu; Rees, Geraint

2015-01-01

Macroscopic structural networks in the human brain have a rich-club architecture comprising both highly inter-connected central regions and sparsely connected peripheral regions. Recent studies show that disruption of this functionally efficient organisation is associated with several psychiatric disorders. However, despite increasing attention to this network property, whether age-associated changes in rich-club organisation occur during human adolescence remains unclear. Here, analysing a publicly shared diffusion tensor imaging dataset, we found that, during adolescence, brains of typically developing (TD) individuals showed increases in rich-club organisation and inferred network functionality, whereas individuals with autism spectrum disorders (ASD) did not. These differences between TD and ASD groups were statistically significant for both structural and functional properties. Moreover, this typical age-related changes in rich-club organisation were characterised by progressive involvement of the right anterior insula. In contrast, in ASD individuals, did not show typical increases in grey matter volume, and this relative anatomical immaturity was correlated with the severity of ASD social symptoms. These results provide evidence that rich-club architecture is one of the bases of functionally efficient brain networks underpinning complex cognitive functions in adult human brains. Furthermore, our findings suggest that immature rich-club organisation might be associated with some neurodevelopmental disorders. PMID:26537477

Integrin suppresses neurogenesis and regulates brain tissue assembly in planarian regeneration.

PubMed

Bonar, Nicolle A; Petersen, Christian P

2017-03-01

Animals capable of adult regeneration require specific signaling to control injury-induced cell proliferation, specification and patterning, but comparatively little is known about how the regeneration blastema assembles differentiating cells into well-structured functional tissues. Using the planarian Schmidtea mediterranea as a model, we identify β1-integrin as a crucial regulator of blastema architecture. β1-integrin(RNAi) animals formed small head blastemas with severe tissue disorganization, including ectopic neural spheroids containing differentiated neurons normally found in distinct organs. By mimicking aspects of normal brain architecture but without normal cell-type regionalization, these spheroids bore a resemblance to mammalian tissue organoids synthesized in vitro We identified one of four planarian integrin-alpha subunits inhibition of which phenocopied these effects, suggesting that a specific receptor controls brain organization through regeneration. Neoblast stem cells and progenitor cells were mislocalized in β1-integrin(RNAi) animals without significantly altered body-wide patterning. Furthermore, tissue disorganization phenotypes were most pronounced in animals undergoing brain regeneration and not homeostatic maintenance or regeneration-induced remodeling of the brain. These results suggest that integrin signaling ensures proper progenitor recruitment after injury, enabling the generation of large-scale tissue organization within the regeneration blastema. © 2017. Published by The Company of Biologists Ltd.

Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

NASA Astrophysics Data System (ADS)

Alizadeh Savareh, Behrouz; Emami, Hassan; Hajiabadi, Mohamadreza; Ghafoori, Mahyar; Majid Azimi, Seyed

2018-03-01

Manual analysis of brain tumors magnetic resonance images is usually accompanied by some problem. Several techniques have been proposed for the brain tumor segmentation. This study will be focused on searching popular databases for related studies, theoretical and practical aspects of Convolutional Neural Network surveyed in brain tumor segmentation. Based on our findings, details about related studies including the datasets used, evaluation parameters, preferred architectures and complementary steps analyzed. Deep learning as a revolutionary idea in image processing, achieved brilliant results in brain tumor segmentation too. This can be continuing until the next revolutionary idea emerging.

A Lovely Building for Difficult Knowledge: The Architecture of the Canadian Museum for Human Rights

ERIC Educational Resources Information Center

Wodtke, Larissa

2015-01-01

One only needs to look at the Canadian Museum for Human Rights (CMHR) logo, with its abstract outline of the CMHR building, to see the way in which the museum's architecture has come to stand for the CMHR's immaterial meanings and content. The CMHR's architecture becomes a material intersection of discourses of cosmopolitanism, human rights, and…

Teaching History of Architecture--Moving from a Knowledge Transfer to a Multi-Participative Methodology Based on IT Tools

ERIC Educational Resources Information Center

Cimadomo, Guido

2014-01-01

The changes that the European Higher Education Area (EHEA) framework obliged the School of Architecture of Malaga, University of Malaga. to make to its "History of Architecture" course are discussed in this paper. It was taken up as an opportunity to modify the whole course, introducing creative teaching and "imaginative…

An Architecture for Performance Optimization in a Collaborative Knowledge-Based Approach for Wireless Sensor Networks

PubMed Central

Gadeo-Martos, Manuel Angel; Fernandez-Prieto, Jose Angel; Canada-Bago, Joaquin; Velasco, Juan Ramon

2011-01-01

Over the past few years, Intelligent Spaces (ISs) have received the attention of many Wireless Sensor Network researchers. Recently, several studies have been devoted to identify their common capacities and to set up ISs over these networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks for the purpose of implementing ISs. This work presents a distributed architecture proposal for collaborative Fuzzy Rule-Based Systems embedded in Wireless Sensor Networks, which has been designed to optimize the implementation of ISs. This architecture includes the following: (a) an optimized design for the inference engine; (b) a visual interface; (c) a module to reduce the redundancy and complexity of the knowledge bases; (d) a module to evaluate the accuracy of the new knowledge base; (e) a module to adapt the format of the rules to the structure used by the inference engine; and (f) a communications protocol. As a real-world application of this architecture and the proposed methodologies, we show an application to the problem of modeling two plagues of the olive tree: prays (olive moth, Prays oleae Bern.) and repilo (caused by the fungus Spilocaea oleagina). The results show that the architecture presented in this paper significantly decreases the consumption of resources (memory, CPU and battery) without a substantial decrease in the accuracy of the inferred values. PMID:22163687

An architecture for performance optimization in a collaborative knowledge-based approach for wireless sensor networks.

PubMed

Gadeo-Martos, Manuel Angel; Fernandez-Prieto, Jose Angel; Canada-Bago, Joaquin; Velasco, Juan Ramon

2011-01-01

Over the past few years, Intelligent Spaces (ISs) have received the attention of many Wireless Sensor Network researchers. Recently, several studies have been devoted to identify their common capacities and to set up ISs over these networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks for the purpose of implementing ISs. This work presents a distributed architecture proposal for collaborative Fuzzy Rule-Based Systems embedded in Wireless Sensor Networks, which has been designed to optimize the implementation of ISs. This architecture includes the following: (a) an optimized design for the inference engine; (b) a visual interface; (c) a module to reduce the redundancy and complexity of the knowledge bases; (d) a module to evaluate the accuracy of the new knowledge base; (e) a module to adapt the format of the rules to the structure used by the inference engine; and (f) a communications protocol. As a real-world application of this architecture and the proposed methodologies, we show an application to the problem of modeling two plagues of the olive tree: prays (olive moth, Prays oleae Bern.) and repilo (caused by the fungus Spilocaea oleagina). The results show that the architecture presented in this paper significantly decreases the consumption of resources (memory, CPU and battery) without a substantial decrease in the accuracy of the inferred values.

Enzyme-linked DNA dendrimer nanosensors for acetylcholine

PubMed Central

Walsh, Ryan; Morales, Jennifer M.; Skipwith, Christopher G.; Ruckh, Timothy T.; Clark, Heather A.

2015-01-01

It is currently difficult to measure small dynamics of molecules in the brain with high spatial and temporal resolution while connecting them to the bigger picture of brain function. A step towards understanding the underlying neural networks of the brain is the ability to sense discrete changes of acetylcholine within a synapse. Here we show an efficient method for generating acetylcholine-detecting nanosensors based on DNA dendrimer scaffolds that incorporate butyrylcholinesterase and fluorescein in a nanoscale arrangement. These nanosensors are selective for acetylcholine and reversibly respond to levels of acetylcholine in the neurophysiological range. This DNA dendrimer architecture has the potential to overcome current obstacles to sensing in the synaptic environment, including the nanoscale size constraints of the synapse and the ability to quantify the spatio-temporal fluctuations of neurotransmitter release. By combining the control of nanosensor architecture with the strategic placement of fluorescent reporters and enzymes, this novel nanosensor platform can facilitate the development of new selective imaging tools for neuroscience. PMID:26442999

Enzyme-linked DNA dendrimer nanosensors for acetylcholine.

PubMed

Walsh, Ryan; Morales, Jennifer M; Skipwith, Christopher G; Ruckh, Timothy T; Clark, Heather A

2015-10-07

It is currently difficult to measure small dynamics of molecules in the brain with high spatial and temporal resolution while connecting them to the bigger picture of brain function. A step towards understanding the underlying neural networks of the brain is the ability to sense discrete changes of acetylcholine within a synapse. Here we show an efficient method for generating acetylcholine-detecting nanosensors based on DNA dendrimer scaffolds that incorporate butyrylcholinesterase and fluorescein in a nanoscale arrangement. These nanosensors are selective for acetylcholine and reversibly respond to levels of acetylcholine in the neurophysiological range. This DNA dendrimer architecture has the potential to overcome current obstacles to sensing in the synaptic environment, including the nanoscale size constraints of the synapse and the ability to quantify the spatio-temporal fluctuations of neurotransmitter release. By combining the control of nanosensor architecture with the strategic placement of fluorescent reporters and enzymes, this novel nanosensor platform can facilitate the development of new selective imaging tools for neuroscience.

Enzyme-linked DNA dendrimer nanosensors for acetylcholine

NASA Astrophysics Data System (ADS)

Walsh, Ryan; Morales, Jennifer M.; Skipwith, Christopher G.; Ruckh, Timothy T.; Clark, Heather A.

2015-10-01

It is currently difficult to measure small dynamics of molecules in the brain with high spatial and temporal resolution while connecting them to the bigger picture of brain function. A step towards understanding the underlying neural networks of the brain is the ability to sense discrete changes of acetylcholine within a synapse. Here we show an efficient method for generating acetylcholine-detecting nanosensors based on DNA dendrimer scaffolds that incorporate butyrylcholinesterase and fluorescein in a nanoscale arrangement. These nanosensors are selective for acetylcholine and reversibly respond to levels of acetylcholine in the neurophysiological range. This DNA dendrimer architecture has the potential to overcome current obstacles to sensing in the synaptic environment, including the nanoscale size constraints of the synapse and the ability to quantify the spatio-temporal fluctuations of neurotransmitter release. By combining the control of nanosensor architecture with the strategic placement of fluorescent reporters and enzymes, this novel nanosensor platform can facilitate the development of new selective imaging tools for neuroscience.

Cortical network architecture for context processing in primate brain

PubMed Central

Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka

2015-01-01

Context is information linked to a situation that can guide behavior. In the brain, context is encoded by sensory processing and can later be retrieved from memory. How context is communicated within the cortical network in sensory and mnemonic forms is unknown due to the lack of methods for high-resolution, brain-wide neuronal recording and analysis. Here, we report the comprehensive architecture of a cortical network for context processing. Using hemisphere-wide, high-density electrocorticography, we measured large-scale neuronal activity from monkeys observing videos of agents interacting in situations with different contexts. We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity. These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows. This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition. DOI: http://dx.doi.org/10.7554/eLife.06121.001 PMID:26416139

Caring for Patients with traumatic brain injury: a survey of nurses' perceptions.

PubMed

Oyesanya, Tolu O; Brown, Roger L; Turkstra, Lyn S

2017-06-01

The purpose of this study was to determine nurses' perceptions about caring for patients with traumatic brain injury. Annually, it is estimated that over 10 million people sustain a traumatic brain injury around the world. Patients with traumatic brain injury and their families are often concerned with expectations about recovery and seek information from nurses. Nurses' perceptions of care might influence information provided to patients and families, particularly if inaccurate knowledge and perceptions are held. Thus, nurses must be knowledgeable about care of these patients. A cross-sectional survey, the Perceptions of Brain Injury Survey (PBIS), was completed electronically by 513 nurses between October and December 2014. Data were analysed with structural equation modelling, factor analysis, and pairwise comparisons. Using latent class analysis, authors were able to divide nurses into three homogeneous sub-groups based on perceived knowledge: low, moderate and high. Findings showed that nurses who care for patients with traumatic brain injury the most have the highest perceived confidence but the lowest perceived knowledge. Nurses also had significant variations in training. As there is limited literature on nurses' perceptions of caring for patients with traumatic brain injury, these findings have implications for training and educating nurses, including direction for development of nursing educational interventions. As the incidence of traumatic brain injury is growing, it is imperative that nurses be knowledgeable about care of patients with these injuries. The traumatic brain injury PBIS can be used to determine inaccurate perceptions about caring for patients with traumatic brain injury before educating and training nurses. © 2016 John Wiley & Sons Ltd.

Engineering Knowledge for Assistive Living

NASA Astrophysics Data System (ADS)

Chen, Liming; Nugent, Chris

This paper introduces a knowledge based approach to assistive living in smart homes. It proposes a system architecture that makes use of knowledge in the lifecycle of assistive living. The paper describes ontology based knowledge engineering practices and discusses mechanisms for exploiting knowledge for activity recognition and assistance. It presents system implementation and experiments, and discusses initial results.

Conservation Process Model (cpm): a Twofold Scientific Research Scope in the Information Modelling for Cultural Heritage

NASA Astrophysics Data System (ADS)

Fiorani, D.; Acierno, M.

2017-05-01

The aim of the present research is to develop an instrument able to adequately support the conservation process by means of a twofold approach, based on both BIM environment and ontology formalisation. Although BIM has been successfully experimented within AEC (Architecture Engineering Construction) field, it has showed many drawbacks for architectural heritage. To cope with unicity and more generally complexity of ancient buildings, applications so far developed have shown to poorly adapt BIM to conservation design with unsatisfactory results (Dore, Murphy 2013; Carrara 2014). In order to combine achievements reached within AEC through BIM environment (design control and management) with an appropriate, semantically enriched and flexible The presented model has at its core a knowledge base developed through information ontologies and oriented around the formalization and computability of all the knowledge necessary for the full comprehension of the object of architectural heritage an its conservation. Such a knowledge representation is worked out upon conceptual categories defined above all within architectural criticism and conservation scope. The present paper aims at further extending the scope of conceptual modelling within cultural heritage conservation already formalized by the model. A special focus is directed on decay analysis and surfaces conservation project.

Perceptions, Knowledge, Incentives, and Barriers of Brain Donation among African American Elders Enrolled in an Alzheimer's Research Program

ERIC Educational Resources Information Center

Lambe, Susan; Cantwell, Nicole; Islam, Fareesa; Horvath, Kathy; Jefferson, Angela L.

2011-01-01

Purpose: To learn about African American older adults' knowledge and perceptions of brain donation, factors that relate to participating or not participating in a brain donation research program, and methods to increase African American brain donation commitment rates in the context of an Alzheimer's disease (AD) research program. Design and…

Brain Science and Teaching: A Forty-Year Personal History

ERIC Educational Resources Information Center

Kirby, Linda Faye

2016-01-01

This paper is the sharing of a record, the personal history of an educator pursuing an interest in knowledge of the brain. Over the years, this fascination sparked the idea to create a course for teachers based on brain science, with a twist. Certain course assignments would require teachers to interpret knowledge of the brain in the context of…

Participative Knowledge Production of Learning Objects for E-Books.

ERIC Educational Resources Information Center

Dodero, Juan Manuel; Aedo, Ignacio; Diaz, Paloma

2002-01-01

Defines a learning object as any digital resource that can be reused to support learning and thus considers electronic books as learning objects. Highlights include knowledge management; participative knowledge production, i.e. authoring electronic books by a distributed group of authors; participative knowledge production architecture; and…

The Digital Anatomist Distributed Framework and Its Applications to Knowledge-based Medical Imaging

PubMed Central

Brinkley, James F.; Rosse, Cornelius

1997-01-01

Abstract The domain of medical imaging is anatomy. Therefore, anatomic knowledge should be a rational basis for organizing and analyzing images. The goals of the Digital Anatomist Program at the University of Washington include the development of an anatomically based software framework for organizing, analyzing, visualizing and utilizing biomedical information. The framework is based on representations for both spatial and symbolic anatomic knowledge, and is being implemented in a distributed architecture in which multiple client programs on the Internet are used to update and access an expanding set of anatomical information resources. The development of this framework is driven by several practical applications, including symbolic anatomic reasoning, knowledge based image segmentation, anatomy information retrieval, and functional brain mapping. Since each of these areas involves many difficult image processing issues, our research strategy is an evolutionary one, in which applications are developed somewhat independently, and partial solutions are integrated in a piecemeal fashion, using the network as the substrate. This approach assumes that networks of interacting components can synergistically work together to solve problems larger than either could solve on its own. Each of the individual projects is described, along with evaluations that show that the individual components are solving the problems they were designed for, and are beginning to interact with each other in a synergistic manner. We argue that this synergy will increase, not only within our own group, but also among groups as the Internet matures, and that an anatomic knowledge base will be a useful means for fostering these interactions. PMID:9147337

Test Your Knowledge: Exercise Your Brain and Test Your Knowledge of Drugs and How They Affect the Brain and Body

MedlinePlus

... inhalants on the rump. Leave this field blank Marijuana Question 1 Tetrahydrocannabinol is: Question 1: Options * A high tech gasoline A chemical in marijuana A brain neurotransmitter A dinosaur in Jurassic Park ...

Mapping Network Centric Operational Architectures to C2 and Software Architectures

DTIC Science & Technology

2007-06-01

Instead, the termite mound structure emerges as a result of the termites following very simple rules, and exchanging very simple pheromone signals...Each worker need make only fairly simple decisions.” For example, in far northern Australia, “magnetic termites ” build large termite mounds which are...oriented north-south and contain a complex ventilation system which controls temperature, humidity, and oxygen levels. But termite brains are too

Information processing architecture of functionally defined clusters in the macaque cortex.

PubMed

Shen, Kelly; Bezgin, Gleb; Hutchison, R Matthew; Gati, Joseph S; Menon, Ravi S; Everling, Stefan; McIntosh, Anthony R

2012-11-28

Computational and empirical neuroimaging studies have suggested that the anatomical connections between brain regions primarily constrain their functional interactions. Given that the large-scale organization of functional networks is determined by the temporal relationships between brain regions, the structural limitations may extend to the global characteristics of functional networks. Here, we explored the extent to which the functional network community structure is determined by the underlying anatomical architecture. We directly compared macaque (Macaca fascicularis) functional connectivity (FC) assessed using spontaneous blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) to directed anatomical connectivity derived from macaque axonal tract tracing studies. Consistent with previous reports, FC increased with increasing strength of anatomical connection, and FC was also present between regions that had no direct anatomical connection. We observed moderate similarity between the FC of each region and its anatomical connectivity. Notably, anatomical connectivity patterns, as described by structural motifs, were different within and across functional modules: partitioning of the functional network was supported by dense bidirectional anatomical connections within clusters and unidirectional connections between clusters. Together, our data directly demonstrate that the FC patterns observed in resting-state BOLD-fMRI are dictated by the underlying neuroanatomical architecture. Importantly, we show how this architecture contributes to the global organizational principles of both functional specialization and integration.

Knowledge Innovation System: The Common Language.

ERIC Educational Resources Information Center

Rogers, Debra M. Amidon

1993-01-01

The Knowledge Innovation System is a management technique in which a networked enterprise uses knowledge flow as a collaborative advantage. Enterprise Management System-Architecture, which can be applied to collaborative activities, has five domains: economic, sociological, psychological, managerial, and technological. (SK)

The Architecture of Personality

ERIC Educational Resources Information Center

Cervone, Daniel

2004-01-01

This article presents a theoretical framework for analyzing psychological systems that contribute to the variability, consistency, and cross-situational coherence of personality functioning. In the proposed knowledge-and-appraisal personality architecture (KAPA), personality structures and processes are delineated by combining 2 principles:…

The Center for Advanced Systems and Engineering (CASE)

DTIC Science & Technology

2012-01-01

targets from multiple sensors. Qinru Qiu, State University of New York at Binghamton – A Neuromorphic Approach for Intelligent Text Recognition...Rogers, SUNYIT, Basic Research, Development and Emulation of Derived Models of Neuromorphic Brain Processes to Investigate the Computational Architecture...Issues They Present Work pertaining to the basic research, development and emulation of derived models of Neuromorphic brain processes to

Toxic Stress: Implications for Policy & Practice. An Interview with Developmental Psychologist Megan R. Gunnar

ERIC Educational Resources Information Center

Gunnar, Megan R.

2006-01-01

A growing body of science shows the critical effects of an extreme and sustained stressful environment for children on their developing brain architecture and the expression of genes in later life. Toxic stress can shift the brain into surviving in a way that's more rigid and less adaptive. For example, as a result of biologically altered brain…

Fast associative memory + slow neural circuitry = the computational model of the brain.

NASA Astrophysics Data System (ADS)

Berkovich, Simon; Berkovich, Efraim; Lapir, Gennady

1997-08-01

We propose a computational model of the brain based on a fast associative memory and relatively slow neural processors. In this model, processing time is expensive but memory access is not, and therefore most algorithmic tasks would be accomplished by using large look-up tables as opposed to calculating. The essential feature of an associative memory in this context (characteristic for a holographic type memory) is that it works without an explicit mechanism for resolution of multiple responses. As a result, the slow neuronal processing elements, overwhelmed by the flow of information, operate as a set of templates for ranking of the retrieved information. This structure addresses the primary controversy in the brain architecture: distributed organization of memory vs. localization of processing centers. This computational model offers an intriguing explanation of many of the paradoxical features in the brain architecture, such as integration of sensors (through DMA mechanism), subliminal perception, universality of software, interrupts, fault-tolerance, certain bizarre possibilities for rapid arithmetics etc. In conventional computer science the presented type of a computational model did not attract attention as it goes against the technological grain by using a working memory faster than processing elements.

An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation.

PubMed

Hoseini, Farnaz; Shahbahrami, Asadollah; Bayat, Peyman

2018-02-27

Image segmentation is one of the most common steps in digital image processing, classifying a digital image into different segments. The main goal of this paper is to segment brain tumors in magnetic resonance images (MRI) using deep learning. Tumors having different shapes, sizes, brightness and textures can appear anywhere in the brain. These complexities are the reasons to choose a high-capacity Deep Convolutional Neural Network (DCNN) containing more than one layer. The proposed DCNN contains two parts: architecture and learning algorithms. The architecture and the learning algorithms are used to design a network model and to optimize parameters for the network training phase, respectively. The architecture contains five convolutional layers, all using 3 × 3 kernels, and one fully connected layer. Due to the advantage of using small kernels with fold, it allows making the effect of larger kernels with smaller number of parameters and fewer computations. Using the Dice Similarity Coefficient metric, we report accuracy results on the BRATS 2016, brain tumor segmentation challenge dataset, for the complete, core, and enhancing regions as 0.90, 0.85, and 0.84 respectively. The learning algorithm includes the task-level parallelism. All the pixels of an MR image are classified using a patch-based approach for segmentation. We attain a good performance and the experimental results show that the proposed DCNN increases the segmentation accuracy compared to previous techniques.

The why, what, where, when and how of goal-directed choice: neuronal and computational principles

PubMed Central

Verschure, Paul F. M. J.; Pennartz, Cyriel M. A.; Pezzulo, Giovanni

2014-01-01

The central problems that goal-directed animals must solve are: ‘What do I need and Why, Where and When can this be obtained, and How do I get it?' or the H4W problem. Here, we elucidate the principles underlying the neuronal solutions to H4W using a combination of neurobiological and neurorobotic approaches. First, we analyse H4W from a system-level perspective by mapping its objectives onto the Distributed Adaptive Control embodied cognitive architecture which sees the generation of adaptive action in the real world as the primary task of the brain rather than optimally solving abstract problems. We next map this functional decomposition to the architecture of the rodent brain to test its consistency. Following this approach, we propose that the mammalian brain solves the H4W problem on the basis of multiple kinds of outcome predictions, integrating central representations of needs and drives (e.g. hypothalamus), valence (e.g. amygdala), world, self and task state spaces (e.g. neocortex, hippocampus and prefrontal cortex, respectively) combined with multi-modal selection (e.g. basal ganglia). In our analysis, goal-directed behaviour results from a well-structured architecture in which goals are bootstrapped on the basis of predefined needs, valence and multiple learning, memory and planning mechanisms rather than being generated by a singular computation. PMID:25267825

Genetic control of inflorescence architecture in legumes

PubMed Central

Benlloch, Reyes; Berbel, Ana; Ali, Latifeh; Gohari, Gholamreza; Millán, Teresa; Madueño, Francisco

2015-01-01

The architecture of the inflorescence, the shoot system that bears the flowers, is a main component of the huge diversity of forms found in flowering plants. Inflorescence architecture has also a strong impact on the production of fruits and seeds, and on crop management, two highly relevant agronomical traits. Elucidating the genetic networks that control inflorescence development, and how they vary between different species, is essential to understanding the evolution of plant form and to being able to breed key architectural traits in crop species. Inflorescence architecture depends on the identity and activity of the meristems in the inflorescence apex, which determines when flowers are formed, how many are produced and their relative position in the inflorescence axis. Arabidopsis thaliana, where the genetic control of inflorescence development is best known, has a simple inflorescence, where the primary inflorescence meristem directly produces the flowers, which are thus borne in the main inflorescence axis. In contrast, legumes represent a more complex inflorescence type, the compound inflorescence, where flowers are not directly borne in the main inflorescence axis but, instead, they are formed by secondary or higher order inflorescence meristems. Studies in model legumes such as pea (Pisum sativum) or Medicago truncatula have led to a rather good knowledge of the genetic control of the development of the legume compound inflorescence. In addition, the increasing availability of genetic and genomic tools for legumes is allowing to rapidly extending this knowledge to other grain legume crops. This review aims to describe the current knowledge of the genetic network controlling inflorescence development in legumes. It also discusses how the combination of this knowledge with the use of emerging genomic tools and resources may allow rapid advances in the breeding of grain legume crops. PMID:26257753

Brain networks, structural realism, and local approaches to the scientific realism debate.

PubMed

Yan, Karen; Hricko, Jonathon

2017-08-01

We examine recent work in cognitive neuroscience that investigates brain networks. Brain networks are characterized by the ways in which brain regions are functionally and anatomically connected to one another. Cognitive neuroscientists use various noninvasive techniques (e.g., fMRI) to investigate these networks. They represent them formally as graphs. And they use various graph theoretic techniques to analyze them further. We distinguish between knowledge of the graph theoretic structure of such networks (structural knowledge) and knowledge of what instantiates that structure (nonstructural knowledge). And we argue that this work provides structural knowledge of brain networks. We explore the significance of this conclusion for the scientific realism debate. We argue that our conclusion should not be understood as an instance of a global structural realist claim regarding the structure of the unobservable part of the world, but instead, as a local structural realist attitude towards brain networks in particular. And we argue that various local approaches to the realism debate, i.e., approaches that restrict realist commitments to particular theories and/or entities, are problematic insofar as they don't allow for the possibility of such a local structural realist attitude. Copyright © 2017 Elsevier Ltd. All rights reserved.

A knowledge base architecture for distributed knowledge agents

NASA Technical Reports Server (NTRS)

Riedesel, Joel; Walls, Bryan

1990-01-01

A tuple space based object oriented model for knowledge base representation and interpretation is presented. An architecture for managing distributed knowledge agents is then implemented within the model. The general model is based upon a database implementation of a tuple space. Objects are then defined as an additional layer upon the database. The tuple space may or may not be distributed depending upon the database implementation. A language for representing knowledge and inference strategy is defined whose implementation takes advantage of the tuple space. The general model may then be instantiated in many different forms, each of which may be a distinct knowledge agent. Knowledge agents may communicate using tuple space mechanisms as in the LINDA model as well as using more well known message passing mechanisms. An implementation of the model is presented describing strategies used to keep inference tractable without giving up expressivity. An example applied to a power management and distribution network for Space Station Freedom is given.

The neurobiology of cognitive disorders in temporal lobe epilepsy

PubMed Central

Bell, Brian; Lin, Jack J.; Seidenberg, Michael; Hermann, Bruce

2013-01-01

Cognitive impairment and especially memory disruption is a major complicating feature of the epilepsies. In this review we begin with a focus on the problem of memory impairment in temporal lobe epilepsy. We start with a brief overview of the early development of knowledge regarding the anatomic substrates of memory disorder in temporal lobe epilepsy, followed by discussion of the refinement of that knowledge over time as informed by the outcomes of epilepsy surgery (anterior temporal lobectomy) and the clinical efforts to predict those patients at greatest risk of adverse cognitive outcomes following epilepsy surgery. These efforts also yielded new theoretical insights regarding the function of the human hippocampus and a few examples of these insights are touched on briefly. Finally, the vastly changing view of temporal lobe epilepsy is examined including findings demonstrating that anatomic abnormalities extend far outside the temporal lobe, cognitive impairments extend beyond memory function, with linkage of these distributed cognitive and anatomic abnormalities pointing to a new understanding of the anatomic architecture of cognitive impairment in epilepsy. Challenges remain in understanding the origin of these cognitive and anatomic abnormalities, their progression over time, and most importantly, how to intervene to protect cognitive and brain health in epilepsy. PMID:21304484

Computer Architects.

ERIC Educational Resources Information Center

Betts, Janelle Lyon

2001-01-01

Describes a high school art assignment in which students utilize Appleworks or Claris Works to design their own house, after learning about architectural styles and how to use the computer program. States that the project develops student computer skills and increases student knowledge about architecture. (CMK)

Linking Brain Principles to High-Quality Early Childhood Education

ERIC Educational Resources Information Center

Rushton, Stephen; Juola-Rushton, Anne

2011-01-01

Many educators are already knowledgeable about and skilled in best practices. And much of what is happening in developmentally appropriate programs exemplifies "brain compatible" practices. Being educated in the connections between best practices and brain compatibility is an important part of the knowledge base of early childhood educators. Just…

Cortical circuits for mathematical knowledge: evidence for a major subdivision within the brain's semantic networks.

PubMed

Amalric, Marie; Dehaene, Stanislas

2017-02-19

Is mathematical language similar to natural language? Are language areas used by mathematicians when they do mathematics? And does the brain comprise a generic semantic system that stores mathematical knowledge alongside knowledge of history, geography or famous people? Here, we refute those views by reviewing three functional MRI studies of the representation and manipulation of high-level mathematical knowledge in professional mathematicians. The results reveal that brain activity during professional mathematical reflection spares perisylvian language-related brain regions as well as temporal lobe areas classically involved in general semantic knowledge. Instead, mathematical reflection recycles bilateral intraparietal and ventral temporal regions involved in elementary number sense. Even simple fact retrieval, such as remembering that 'the sine function is periodical' or that 'London buses are red', activates dissociated areas for math versus non-math knowledge. Together with other fMRI and recent intracranial studies, our results indicated a major separation between two brain networks for mathematical and non-mathematical semantics, which goes a long way to explain a variety of facts in neuroimaging, neuropsychology and developmental disorders.This article is part of a discussion meeting issue 'The origins of numerical abilities'. © 2017 The Author(s).

The Virtual Brain: a simulator of primate brain network dynamics.

PubMed

Sanz Leon, Paula; Knock, Stuart A; Woodman, M Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R; Jirsa, Viktor

2013-01-01

We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.

BRAPH: A graph theory software for the analysis of brain connectivity

PubMed Central

Mijalkov, Mite; Kakaei, Ehsan; Pereira, Joana B.; Westman, Eric; Volpe, Giovanni

2017-01-01

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment. PMID:28763447

BRAPH: A graph theory software for the analysis of brain connectivity.

PubMed

Mijalkov, Mite; Kakaei, Ehsan; Pereira, Joana B; Westman, Eric; Volpe, Giovanni

2017-01-01

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH-BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer's disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson's patients with mild cognitive impairment.

The Virtual Brain: a simulator of primate brain network dynamics

PubMed Central

Sanz Leon, Paula; Knock, Stuart A.; Woodman, M. Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R.; Jirsa, Viktor

2013-01-01

We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications. PMID:23781198

Effects of cannabis on the adolescent brain.

PubMed

Jacobus, Joanna; Tapert, Susan F

2014-01-01

This article reviews neuroimaging, neurocognitive, and preclinical findings on the effects of cannabis on the adolescent brain. Marijuana is the second most widely used intoxicant in adolescence, and teens who engage in heavy marijuana use often show disadvantages in neurocognitive performance, macrostructural and microstructural brain development, and alterations in brain functioning. It remains unclear whether such disadvantages reflect pre-existing differences that lead to increased substances use and further changes in brain architecture and behavioral outcomes. Future work should focus on prospective investigations to help disentangle dose-dependent effects from pre-existing effects, and to better understand the interactive relationships with other commonly abused substances (e.g., alcohol) to better understand the role of regular cannabis use on neurodevelopmental trajectories.

Neuromorphic Data Microscope

DOE Office of Scientific and Technical Information (OSTI.GOV)

Naegle, John H.; Suppona, Roger A.; Aimone, James Bradley

In 2016, Lewis Rhodes Labs, (LRL), shipped the first commercially viable Neuromorphic Processing Unit, (NPU), branded as a Neuromorphic Data Microscope (NDM). This product leverages architectural mechanisms derived from the sensory cortex of the human brain to efficiently implement pattern matching. LRL and Sandia National Labs have optimized this product for streaming analytics, and demonstrated a 1,000x power per operation reduction in an FPGA format. When reduced to an ASIC, the efficiency will improve to 1,000,000x. Additionally, the neuromorphic nature of the device gives it powerful computational attributes that are counterintuitive to those schooled in traditional von Neumann architectures. Themore » Neuromorphic Data Microscope is the first of a broad class of brain-inspired, time domain processors that will profoundly alter the functionality and economics of data processing.« less

Characterizing Attention with Predictive Network Models.

PubMed

Rosenberg, M D; Finn, E S; Scheinost, D; Constable, R T; Chun, M M

2017-04-01

Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals' attentional abilities. While being some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that: (i) attention is a network property of brain computation; (ii) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task; and (iii) this architecture supports a general attentional ability that is common to several laboratory-based tasks and is impaired in attention deficit hyperactivity disorder (ADHD). Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction. Copyright © 2017 Elsevier Ltd. All rights reserved.

From Architectural Photogrammetry Toward Digital Architectural Heritage Education

NASA Astrophysics Data System (ADS)

Baik, A.; Alitany, A.

2018-05-01

This paper considers the potential of using the documentation approach proposed for the heritage buildings in Historic Jeddah, Saudi Arabia (as a case study) by using the close-range photogrammetry / the Architectural Photogrammetry techniques as a new academic experiment in digital architectural heritage education. Moreover, different than most of engineering educational techniques related to architecture education, this paper will be focusing on the 3-D data acquisition technology as a tool to document and to learn the principals of the digital architectural heritage documentation. The objective of this research is to integrate the 3-D modelling and visualisation knowledge for the purposes of identifying, designing and evaluating an effective engineering educational experiment. Furthermore, the students will learn and understand the characteristics of the historical building while learning more advanced 3-D modelling and visualisation techniques. It can be argued that many of these technologies alone are difficult to improve the education; therefore, it is important to integrate them in an educational framework. This should be in line with the educational ethos of the academic discipline. Recently, a number of these technologies and methods have been effectively used in education sectors and other purposes; such as in the virtual museum. However, these methods are not directly coincided with the traditional education and teaching architecture. This research will be introduced the proposed approach as a new academic experiment in the architecture education sector. The new teaching approach will be based on the Architectural Photogrammetry to provide semantically rich models. The academic experiment will require students to have suitable knowledge in both Photogrammetry applications to engage with the process.

Three-dimensional high-resolution diffusion tensor imaging and tractography of the developing rabbit brain.

PubMed

D'Arceuil, Helen; Liu, Christina; Levitt, Pat; Thompson, Barbara; Kosofsky, Barry; de Crespigny, Alex

2008-01-01

Diffusion tensor imaging (DTI) is sensitive to structural ordering in brain tissue particularly in the white matter tracts. Diffusion anisotropy changes with disease and also with neural development. We used high-resolution DTI of fixed rabbit brains to study developmental changes in regional diffusion anisotropy and white matter fiber tract development. Imaging was performed on a 4.7-tesla Bruker Biospec Avance scanner using custom-built solenoid coils and DTI was performed at various postnatal ages. Trace apparent diffusion coefficient, fractional diffusion anisotropy maps and fiber tracts were generated and compared across the ages. The brain was highly anisotropic at birth and white matter anisotropy increased with age. Regional DTI tractography of the internal capsule showed refinement in regional tract architecture with maturation. Interestingly, brains with congenital deficiencies of the callosal commissure showed selectively strikingly different fiber architecture compared to age-matched brains. There was also some evidence of subcortical to cortical fiber connectivity. DTI tractography of the anterior and posterior limbs of the internal capsule showed reproducibly coherent fiber tracts corresponding to known corticospinal and corticobulbar tract anatomy. There was some minor interanimal tract variability, but there was remarkable similarity between the tracts in all animals. Therefore, ex vivo DTI tractography is a potentially powerful tool for neuroscience investigations and may also reveal effects (such as fiber tract pruning during development) which may be important targets for in vivo human studies. Copyright 2007 S. Karger AG, Basel.

Sleep and its disorders in translational medicine.

PubMed

Paterson, Louise M; Nutt, David J; Wilson, Sue J

2011-09-01

The study of sleep is a useful approach to studying the brain in psychiatric disorders and in investigating the effects of psychotropic drugs. Sleep physiology lends itself well to pharmacological and physiological manipulation, as it has the advantage of a functional output, the electroencephalograph, which is common to all mammals, and can be measured in freely moving (or naturally sleeping) animals under controlled laboratory conditions or in a naturalistic home environment. The complexity of sleep architecture varies between species but all share features which are comparable. In addition, sleep architecture is sensitive to changes in brain neurotransmitters such as serotonin, so cross-species sleep measurement can be combined with pharmacological manipulation to investigate the receptor mechanisms controlling sleep-wake regulation and sleep architecture in response to known and novel agents. Translational approaches such as these have improved our understanding of sleep circuitry and facilitated the development of new treatments for sleep disorders, particularly insomnia. This review provides examples of how research findings within the sleep field have been translated between animal models, healthy volunteers and patient populations with particular focus on the serotonergic system.

A Collaborative Reasoning Maintenance System for a Reliable Application of Legislations

NASA Astrophysics Data System (ADS)

Tamisier, Thomas; Didry, Yoann; Parisot, Olivier; Feltz, Fernand

Decision support systems are nowadays used to disentangle all kinds of intricate situations and perform sophisticated analysis. Moreover, they are applied in areas where the knowledge can be heterogeneous, partially un-formalized, implicit, or diffuse. The representation and management of this knowledge become the key point to ensure the proper functioning of the system and keep an intuitive view upon its expected behavior. This paper presents a generic architecture for implementing knowledge-base systems used in collaborative business, where the knowledge is organized into different databases, according to the usage, persistence and quality of the information. This approach is illustrated with Cadral, a customizable automated tool built on this architecture and used for processing family benefits applications at the National Family Benefits Fund of the Grand-Duchy of Luxembourg.

Linking Neural and Symbolic Representation and Processing of Conceptual Structures

PubMed Central

van der Velde, Frank; Forth, Jamie; Nazareth, Deniece S.; Wiggins, Geraint A.

2017-01-01

We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking), which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures. PMID:28848460

Building the Ferretome

PubMed Central

Sukhinin, Dmitrii I.; Engel, Andreas K.; Manger, Paul; Hilgetag, Claus C.

2016-01-01

Databases of structural connections of the mammalian brain, such as CoCoMac (cocomac.g-node.org) or BAMS (https://bams1.org), are valuable resources for the analysis of brain connectivity and the modeling of brain dynamics in species such as the non-human primate or the rodent, and have also contributed to the computational modeling of the human brain. Another animal model that is widely used in electrophysiological or developmental studies is the ferret; however, no systematic compilation of brain connectivity is currently available for this species. Thus, we have started developing a database of anatomical connections and architectonic features of the ferret brain, the Ferret(connect)ome, www.Ferretome.org. The Ferretome database has adapted essential features of the CoCoMac methodology and legacy, such as the CoCoMac data model. This data model was simplified and extended in order to accommodate new data modalities that were not represented previously, such as the cytoarchitecture of brain areas. The Ferretome uses a semantic parcellation of brain regions as well as a logical brain map transformation algorithm (objective relational transformation, ORT). The ORT algorithm was also adopted for the transformation of architecture data. The database is being developed in MySQL and has been populated with literature reports on tract-tracing observations in the ferret brain using a custom-designed web interface that allows efficient and validated simultaneous input and proofreading by multiple curators. The database is equipped with a non-specialist web interface. This interface can be extended to produce connectivity matrices in several formats, including a graphical representation superimposed on established ferret brain maps. An important feature of the Ferretome database is the possibility to trace back entries in connectivity matrices to the original studies archived in the system. Currently, the Ferretome contains 50 reports on connections comprising 20 injection reports with more than 150 labeled source and target areas, the majority reflecting connectivity of subcortical nuclei and 15 descriptions of regional brain architecture. We hope that the Ferretome database will become a useful resource for neuroinformatics and neural modeling, and will support studies of the ferret brain as well as facilitate advances in comparative studies of mesoscopic brain connectivity. PMID:27242503

Building the Ferretome.

PubMed

Sukhinin, Dmitrii I; Engel, Andreas K; Manger, Paul; Hilgetag, Claus C

2016-01-01

Databases of structural connections of the mammalian brain, such as CoCoMac (cocomac.g-node.org) or BAMS (https://bams1.org), are valuable resources for the analysis of brain connectivity and the modeling of brain dynamics in species such as the non-human primate or the rodent, and have also contributed to the computational modeling of the human brain. Another animal model that is widely used in electrophysiological or developmental studies is the ferret; however, no systematic compilation of brain connectivity is currently available for this species. Thus, we have started developing a database of anatomical connections and architectonic features of the ferret brain, the Ferret(connect)ome, www.Ferretome.org. The Ferretome database has adapted essential features of the CoCoMac methodology and legacy, such as the CoCoMac data model. This data model was simplified and extended in order to accommodate new data modalities that were not represented previously, such as the cytoarchitecture of brain areas. The Ferretome uses a semantic parcellation of brain regions as well as a logical brain map transformation algorithm (objective relational transformation, ORT). The ORT algorithm was also adopted for the transformation of architecture data. The database is being developed in MySQL and has been populated with literature reports on tract-tracing observations in the ferret brain using a custom-designed web interface that allows efficient and validated simultaneous input and proofreading by multiple curators. The database is equipped with a non-specialist web interface. This interface can be extended to produce connectivity matrices in several formats, including a graphical representation superimposed on established ferret brain maps. An important feature of the Ferretome database is the possibility to trace back entries in connectivity matrices to the original studies archived in the system. Currently, the Ferretome contains 50 reports on connections comprising 20 injection reports with more than 150 labeled source and target areas, the majority reflecting connectivity of subcortical nuclei and 15 descriptions of regional brain architecture. We hope that the Ferretome database will become a useful resource for neuroinformatics and neural modeling, and will support studies of the ferret brain as well as facilitate advances in comparative studies of mesoscopic brain connectivity.

Abnormal hubs of white matter networks in the frontal-parieto circuit contribute to depression discrimination via pattern classification.

PubMed

Qin, Jiaolong; Wei, Maobin; Liu, Haiyan; Chen, Jianhuai; Yan, Rui; Hua, Lingling; Zhao, Ke; Yao, Zhijian; Lu, Qing

2014-12-01

Previous studies had explored the diagnostic and prognostic value of the structural neuroimaging data of MDD and treated the whole brain voxels, the fractional anisotropy and the structural connectivity as classification features. To our best knowledge, no study examined the potential diagnostic value of the hubs of anatomical brain networks in MDD. The purpose of the current study was to provide an exploratory examination of the potential diagnostic and prognostic values of hubs of white matter brain networks in MDD discrimination and the corresponding impaired hub pattern via a multi-pattern analysis. We constructed white matter brain networks from 29 depressions and 30 healthy controls based on diffusion tensor imaging data, calculated nodal measures and identified hubs. Using these measures as features, two types of feature architectures were established, one only included hubs (HUB) and the other contained both hubs and non hubs. The support vector machine classifiers with Gaussian radial basis kernel were used after the feature selection. Moreover, the relative contribution of the features was estimated by means of the consensus features. Our results presented that the hubs (including the bilateral dorsolateral part of superior frontal gyrus, the left middle frontal gyrus, the bilateral middle temporal gyrus, and the bilateral inferior temporal gyrus) played an important role in distinguishing the depressions from healthy controls with the best accuracy of 83.05%. Moreover, most of the HUB consensus features located in the frontal-parieto circuit. These findings provided evidence that the hubs could be served as valuable potential diagnostic measure for MDD, and the hub-concentrated lesion distribution of MDD was primarily anchored within the frontal-parieto circuit. Copyright © 2014 Elsevier Inc. All rights reserved.

Connecting the Brain to Itself through an Emulation

PubMed Central

Serruya, Mijail D.

2017-01-01

Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids. Arrays of modules can be constructed as early stage whole brain emulators, following canonical intra- and inter-regional circuits. By using machine learning algorithms and classic tasks known to activate quasi-orthogonal functional connectivity patterns, bedside testing can rapidly identify ensemble tuning properties and in turn cycle through a sequence of external module architectures to explore which can causatively alter perception and behavior. Whole brain emulation both (1) serves to augment human neural function, compensating for disease and injury as an auxiliary parallel system, and (2) has its independent operation bootstrapped by a human-in-the-loop to identify optimal micro- and macro-architectures, update synaptic weights, and entrain behaviors. In this manner, closed-loop brain-computer interface pilot clinical trials can advance strong artificial intelligence development and forge new therapies to restore independence in children and adults with neurological conditions. PMID:28713235

Hardware-based Artificial Neural Networks for Size, Weight, and Power Constrained Platforms (Preprint)

DTIC Science & Technology

2012-11-01

few sensors/complex computations, and many sensors/simple computation. II. CHALLENGES WITH NANO-ENABLED NEUROMORPHIC CHIPS A wide variety of...scenarios. Neuromorphic processors, which are based on the highly parallelized computing architecture of the mammalian brain, show great promise in...in the brain. This fundamentally different approach, frequently referred to as neuromorphic computing, is thought to be better able to solve fuzzy

Evidence of common and separate eye and hand accumulators underlying flexible eye-hand coordination

PubMed Central

Jana, Sumitash; Gopal, Atul

2016-01-01

Eye and hand movements are initiated by anatomically separate regions in the brain, and yet these movements can be flexibly coupled and decoupled, depending on the need. The computational architecture that enables this flexible coupling of independent effectors is not understood. Here, we studied the computational architecture that enables flexible eye-hand coordination using a drift diffusion framework, which predicts that the variability of the reaction time (RT) distribution scales with its mean. We show that a common stochastic accumulator to threshold, followed by a noisy effector-dependent delay, explains eye-hand RT distributions and their correlation in a visual search task that required decision-making, while an interactive eye and hand accumulator model did not. In contrast, in an eye-hand dual task, an interactive model better predicted the observed correlations and RT distributions than a common accumulator model. Notably, these two models could only be distinguished on the basis of the variability and not the means of the predicted RT distributions. Additionally, signatures of separate initiation signals were also observed in a small fraction of trials in the visual search task, implying that these distinct computational architectures were not a manifestation of the task design per se. Taken together, our results suggest two unique computational architectures for eye-hand coordination, with task context biasing the brain toward instantiating one of the two architectures. NEW & NOTEWORTHY Previous studies on eye-hand coordination have considered mainly the means of eye and hand reaction time (RT) distributions. Here, we leverage the approximately linear relationship between the mean and standard deviation of RT distributions, as predicted by the drift-diffusion model, to propose the existence of two distinct computational architectures underlying coordinated eye-hand movements. These architectures, for the first time, provide a computational basis for the flexible coupling between eye and hand movements. PMID:27784809

Applying Service-Oriented Architecture on The Development of Groundwater Modeling Support System

NASA Astrophysics Data System (ADS)

Li, C. Y.; WANG, Y.; Chang, L. C.; Tsai, J. P.; Hsiao, C. T.

2016-12-01

Groundwater simulation has become an essential step on the groundwater resources management and assessment. There are many stand-alone pre- and post-processing software packages to alleviate the model simulation loading, but the stand-alone software do not consider centralized management of data and simulation results neither do they provide network sharing functions. Hence, it is difficult to share and reuse the data and knowledge (simulation cases) systematically within or across companies. Therefore, this study develops a centralized and network based groundwater modeling support system to assist model construction. The system is based on service-oriented architecture and allows remote user to develop their modeling cases on internet. The data and cases (knowledge) are thus easy to manage centralized. MODFLOW is the modeling engine of the system, which is the most popular groundwater model in the world. The system provides a data warehouse to restore groundwater observations, MODFLOW Support Service, MODFLOW Input File & Shapefile Convert Service, MODFLOW Service, and Expert System Service to assist researchers to build models. Since the system architecture is service-oriented, it is scalable and flexible. The system can be easily extended to include the scenarios analysis and knowledge management to facilitate the reuse of groundwater modeling knowledge.

A Brief Note on Knowledge.

ERIC Educational Resources Information Center

Harvey, Neil

1999-01-01

Points out that learning in Western cultures is predominately concerned with left-brain, or rational, knowledge. Discusses the importance of balancing our learning systems with right-brain, or intuitive, knowledge; how outdoor and experiential learning can help foster this balance; and the role of the instructor as facilitator rather than teacher.…

Beginning to manage drug discovery and development knowledge.

PubMed

Sumner-Smith, M

2001-05-01

Knowledge management approaches and technologies are beginning to be implemented by the pharmaceutical industry in support of new drug discovery and development processes aimed at greater efficiencies and effectiveness. This trend coincides with moves to reduce paper, coordinate larger teams with more diverse skills that are distributed around the globe, and to comply with regulatory requirements for electronic submissions and the associated maintenance of electronic records. Concurrently, the available technologies have implemented web-based architectures with a greater range of collaborative tools and personalization through portal approaches. However, successful application of knowledge management methods depends on effective cultural change management, as well as proper architectural design to match the organizational and work processes within a company.

Examining the volume efficiency of the cortical architecture in a multi-processor network model.

PubMed

Ruppin, E; Schwartz, E L; Yeshurun, Y

1993-01-01

The convoluted form of the sheet-like mammalian cortex naturally raises the question whether there is a simple geometrical reason for the prevalence of cortical architecture in the brains of higher vertebrates. Addressing this question, we present a formal analysis of the volume occupied by a massively connected network or processors (neurons) and then consider the pertaining cortical data. Three gross macroscopic features of cortical organization are examined: the segregation of white and gray matter, the circumferential organization of the gray matter around the white matter, and the folded cortical structure. Our results testify to the efficiency of cortical architecture.

Sensitivity analysis by approximation formulas - Illustrative examples. [reliability analysis of six-component architectures

NASA Technical Reports Server (NTRS)

White, A. L.

1983-01-01

This paper examines the reliability of three architectures for six components. For each architecture, the probabilities of the failure states are given by algebraic formulas involving the component fault rate, the system recovery rate, and the operating time. The dominant failure modes are identified, and the change in reliability is considered with respect to changes in fault rate, recovery rate, and operating time. The major conclusions concern the influence of system architecture on failure modes and parameter requirements. Without this knowledge, a system designer may pick an inappropriate structure.

Ground support system methodology and architecture

NASA Technical Reports Server (NTRS)

Schoen, P. D.

1991-01-01

A synergistic approach to systems test and support is explored. A building block architecture provides transportability of data, procedures, and knowledge. The synergistic approach also lowers cost and risk for life cycle of a program. The determination of design errors at the earliest phase reduces cost of vehicle ownership. Distributed scaleable architecture is based on industry standards maximizing transparency and maintainability. Autonomous control structure provides for distributed and segmented systems. Control of interfaces maximizes compatibility and reuse, reducing long term program cost. Intelligent data management architecture also reduces analysis time and cost (automation).

A Multiscale Parallel Computing Architecture for Automated Segmentation of the Brain Connectome

PubMed Central

Knobe, Kathleen; Newton, Ryan R.; Schlimbach, Frank; Blower, Melanie; Reid, R. Clay

2015-01-01

Several groups in neurobiology have embarked into deciphering the brain circuitry using large-scale imaging of a mouse brain and manual tracing of the connections between neurons. Creating a graph of the brain circuitry, also called a connectome, could have a huge impact on the understanding of neurodegenerative diseases such as Alzheimer’s disease. Although considerably smaller than a human brain, a mouse brain already exhibits one billion connections and manually tracing the connectome of a mouse brain can only be achieved partially. This paper proposes to scale up the tracing by using automated image segmentation and a parallel computing approach designed for domain experts. We explain the design decisions behind our parallel approach and we present our results for the segmentation of the vasculature and the cell nuclei, which have been obtained without any manual intervention. PMID:21926011

Segregation of the Brain into Gray and White Matter: A Design Minimizing Conduction Delays

PubMed Central

Wen, Quan; Chklovskii, Dmitri B

2005-01-01

A ubiquitous feature of the vertebrate anatomy is the segregation of the brain into white and gray matter. Assuming that evolution maximized brain functionality, what is the reason for such segregation? To answer this question, we posit that brain functionality requires high interconnectivity and short conduction delays. Based on this assumption we searched for the optimal brain architecture by comparing different candidate designs. We found that the optimal design depends on the number of neurons, interneuronal connectivity, and axon diameter. In particular, the requirement to connect neurons with many fast axons drives the segregation of the brain into white and gray matter. These results provide a possible explanation for the structure of various regions of the vertebrate brain, such as the mammalian neocortex and neostriatum, the avian telencephalon, and the spinal cord. PMID:16389299

Learning Outcomes in Affective Domain within Contemporary Architectural Curricula

ERIC Educational Resources Information Center

Savic, Marko; Kashef, Mohamad

2013-01-01

Contemporary architectural education has shifted from the traditional focus on providing students with specific knowledge and skill sets or "inputs" to outcome based, student-centred educational approach. Within the outcome based model, students' performance is assessed against measureable objectives that relate acquired knowledge…

Correspondence of the brain's functional architecture during activation and rest.

PubMed

Smith, Stephen M; Fox, Peter T; Miller, Karla L; Glahn, David C; Fox, P Mickle; Mackay, Clare E; Filippini, Nicola; Watkins, Kate E; Toro, Roberto; Laird, Angela R; Beckmann, Christian F

2009-08-04

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is "at rest." In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically "active" even when at "rest."

Imaging White Matter in Human Brainstem

PubMed Central

Ford, Anastasia A.; Colon-Perez, Luis; Triplett, William T.; Gullett, Joseph M.; Mareci, Thomas H.; FitzGerald, David B.

2013-01-01

The human brainstem is critical for the control of many life-sustaining functions, such as consciousness, respiration, sleep, and transfer of sensory and motor information between the brain and the spinal cord. Most of our knowledge about structure and organization of white and gray matter within the brainstem is derived from ex vivo dissection and histology studies. However, these methods cannot be applied to study structural architecture in live human participants. Tractography from diffusion-weighted magnetic resonance imaging (MRI) may provide valuable insights about white matter organization within the brainstem in vivo. However, this method presents technical challenges in vivo due to susceptibility artifacts, functionally dense anatomy, as well as pulsatile and respiratory motion. To investigate the limits of MR tractography, we present results from high angular resolution diffusion imaging of an intact excised human brainstem performed at 11.1 T using isotropic resolution of 0.333, 1, and 2 mm, with the latter reflecting resolution currently used clinically. At the highest resolution, the dense fiber architecture of the brainstem is evident, but the definition of structures degrades as resolution decreases. In particular, the inferred corticopontine/corticospinal tracts (CPT/CST), superior (SCP) and middle cerebellar peduncle (MCP), and medial lemniscus (ML) pathways are clearly discernable and follow known anatomical trajectories at the highest spatial resolution. At lower resolutions, the CST/CPT, SCP, and MCP pathways are artificially enlarged due to inclusion of collinear and crossing fibers not inherent to these three pathways. The inferred ML pathways appear smaller at lower resolutions, indicating insufficient spatial information to successfully resolve smaller fiber pathways. Our results suggest that white matter tractography maps derived from the excised brainstem can be used to guide the study of the brainstem architecture using diffusion MRI in vivo. PMID:23898254

Imaging white matter in human brainstem.

PubMed

Ford, Anastasia A; Colon-Perez, Luis; Triplett, William T; Gullett, Joseph M; Mareci, Thomas H; Fitzgerald, David B

2013-01-01

The human brainstem is critical for the control of many life-sustaining functions, such as consciousness, respiration, sleep, and transfer of sensory and motor information between the brain and the spinal cord. Most of our knowledge about structure and organization of white and gray matter within the brainstem is derived from ex vivo dissection and histology studies. However, these methods cannot be applied to study structural architecture in live human participants. Tractography from diffusion-weighted magnetic resonance imaging (MRI) may provide valuable insights about white matter organization within the brainstem in vivo. However, this method presents technical challenges in vivo due to susceptibility artifacts, functionally dense anatomy, as well as pulsatile and respiratory motion. To investigate the limits of MR tractography, we present results from high angular resolution diffusion imaging of an intact excised human brainstem performed at 11.1 T using isotropic resolution of 0.333, 1, and 2 mm, with the latter reflecting resolution currently used clinically. At the highest resolution, the dense fiber architecture of the brainstem is evident, but the definition of structures degrades as resolution decreases. In particular, the inferred corticopontine/corticospinal tracts (CPT/CST), superior (SCP) and middle cerebellar peduncle (MCP), and medial lemniscus (ML) pathways are clearly discernable and follow known anatomical trajectories at the highest spatial resolution. At lower resolutions, the CST/CPT, SCP, and MCP pathways are artificially enlarged due to inclusion of collinear and crossing fibers not inherent to these three pathways. The inferred ML pathways appear smaller at lower resolutions, indicating insufficient spatial information to successfully resolve smaller fiber pathways. Our results suggest that white matter tractography maps derived from the excised brainstem can be used to guide the study of the brainstem architecture using diffusion MRI in vivo.

Teaching About "Brain and Learning" in High School Biology Classes: Effects on Teachers' Knowledge and Students' Theory of Intelligence.

PubMed

Dekker, Sanne; Jolles, Jelle

2015-01-01

This study evaluated a new teaching module about "Brain and Learning" using a controlled design. The module was implemented in high school biology classes and comprised three lessons: (1) brain processes underlying learning; (2) neuropsychological development during adolescence; and (3) lifestyle factors that influence learning performance. Participants were 32 biology teachers who were interested in "Brain and Learning" and 1241 students in grades 8-9. Teachers' knowledge and students' beliefs about learning potential were examined using online questionnaires. Results indicated that before intervention, biology teachers were significantly less familiar with how the brain functions and develops than with its structure and with basic neuroscientific concepts (46 vs. 75% correct answers). After intervention, teachers' knowledge of "Brain and Learning" had significantly increased (64%), and more students believed that intelligence is malleable (incremental theory). This emphasizes the potential value of a short teaching module, both for improving biology teachers' insights into "Brain and Learning," and for changing students' beliefs about intelligence.

What Does the Brain Tell Us about the Mind?

ERIC Educational Resources Information Center

Ruz, Maria; Acero, Juan J.; Tudela, Pio

2006-01-01

The present paper explores the relevance that brain data have in constructing theories about the human mind. In the Cognitive Science era it was assumed that knowledge of the mind and the brain correspond to different levels of analysis. This independence among levels led to the epistemic argument that knowledge of the biological basis of…

Executable Architecture Research at Old Dominion University

NASA Technical Reports Server (NTRS)

Tolk, Andreas; Shuman, Edwin A.; Garcia, Johnny J.

2011-01-01

Executable Architectures allow the evaluation of system architectures not only regarding their static, but also their dynamic behavior. However, the systems engineering community do not agree on a common formal specification of executable architectures. To close this gap and identify necessary elements of an executable architecture, a modeling language, and a modeling formalism is topic of ongoing PhD research. In addition, systems are generally defined and applied in an operational context to provide capabilities and enable missions. To maximize the benefits of executable architectures, a second PhD effort introduces the idea of creating an executable context in addition to the executable architecture. The results move the validation of architectures from the current information domain into the knowledge domain and improve the reliability of such validation efforts. The paper presents research and results of both doctoral research efforts and puts them into a common context of state-of-the-art of systems engineering methods supporting more agility.

Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders.

PubMed

Zhang, Jie; Cheng, Wei; Liu, Zhaowen; Zhang, Kai; Lei, Xu; Yao, Ye; Becker, Benjamin; Liu, Yicen; Kendrick, Keith M; Lu, Guangming; Feng, Jianfeng

2016-08-01

SEE MATTAR ET AL DOI101093/AWW151 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demonstrate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architecture is modulated by local blood oxygen level-dependent activity and α-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dynamic organization of the resting brain and how it changes in brain disorders. The nodal variability measure may also be potentially useful as a predictor for learning and neural rehabilitation. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The role of neuroimaging in the discovery of processing stages. A review.

PubMed

Mulder, G; Wijers, A A; Lange, J J; Buijink, B M; Mulder, L J; Willemsen, A T; Paans, A M

1995-11-01

In this contribution we show how neuroimaging methods can augment behavioural methods to discover processing stages. Event Related Brain Potentials (ERPs), Brain Electrical Source Analysis (BESA) and regional changes in cerebral blood flow (rCBF) do not necessarily require behavioural responses. With the aid of rCBF we are able to discover several cortical and subcortical brain systems (processors) active in selective attention and memory search tasks. BESA describes cortical activity with high temporal resolution in terms of a limited number of neural generators within these brain systems. The combination of behavioural methods and neuroimaging provides a picture of the functional architecture of the brain. The review is organized around three processors: the Visual, Cognitive and Manual Motor Processors.

Effects of Cannabis on the Adolescent Brain

PubMed Central

Jacobus, Joanna; Tapert, Susan F.

2014-01-01

This article reviews neuroimaging, neurocognitive, and preclinical findings on the effects of cannabis on the adolescent brain. Marijuana is the second most widely used intoxicant in adolescence, and teens who engage in heavy marijuana use often show disadvantages in neurocognitive performance, macrostructural and microstructural brain development, and alterations in brain functioning. It remains unclear whether such disadvantages reflect pre-existing differences that lead to increased substances use and further changes in brain architecture and behavioral outcomes. Future work should focus on prospective investigations to help disentangle dose-dependent effects from pre-existing effects, and to better understand the interactive relationships with other commonly abused substances (e.g., alcohol) to better understand the role of regular cannabis use on neurodevelopmental trajectories. PMID:23829363

Real Time Data Acquisition and Online Signal Processing for Magnetoencephalography

NASA Astrophysics Data System (ADS)

Rongen, H.; Hadamschek, V.; Schiek, M.

2006-06-01

To establish improved therapies for patients suffering from severe neurological and psychiatric diseases, a demand controlled and desynchronizing brain-pacemaker has been developed with techniques from statistical physics and nonlinear dynamics. To optimize the novel therapeutic approach, brain activity is investigated with a Magnetoencephalography (MEG) system prior to surgery. For this, a real time data acquisition system for a 148 channel MEG and online signal processing for artifact rejection, filtering, cross trial phase resetting analysis and three-dimensional (3-D) reconstruction of the cerebral current sources was developed. The developed PCI bus hardware is based on a FPGA and DSP design, using the benefits from both architectures. The reconstruction and visualization of the 3-D volume data is done by the PC which hosts the real time DAQ and pre-processing board. The framework of the MEG-online system is introduced and the architecture of the real time DAQ board and online reconstruction is described. In addition we show first results with the MEG-Online system for the investigation of dynamic brain activities in relation to external visual stimulation, based on test data sets.

Neuroscience and Education: Issues and Challenges for Curriculum

ERIC Educational Resources Information Center

Clement, Neville D.; Lovat, Terence

2012-01-01

The burgeoning knowledge of the human brain generated by the proliferation of new brain imaging technology from in recent decades has posed questions about the potential for this new knowledge of neural processing to be translated into "usable knowledge" that teachers can employ in their practical curriculum work. The application of the findings…

The Impact of Non-Conscious Knowledge on Educational Technology Research and Design

ERIC Educational Resources Information Center

Clark, Richard E.

2011-01-01

There are at least three powerful insights for educational technology researchers and designers from recent neuroscience studies of the brain and from cognitive science research findings: First, our brains learn and process two very different types of knowledge; non-conscious, automated, procedural, or implicit knowledge, and conscious,…

Contrasting Effects of Vocabulary Knowledge on Temporal and Parietal Brain Structure across Lifespan

ERIC Educational Resources Information Center

Richardson, Fiona M.; Thomas, Michael S. C.; Filippi, Roberto; Harth, Helen; Price, Cathy J.

2010-01-01

Using behavioral, structural, and functional imaging techniques, we demonstrate contrasting effects of vocabulary knowledge on temporal and parietal brain structure in 47 healthy volunteers who ranged in age from 7 to 73 years. In the left posterior supramarginal gyrus, vocabulary knowledge was positively correlated with gray matter density in…

Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap.

PubMed

Gandal, Michael J; Haney, Jillian R; Parikshak, Neelroop N; Leppa, Virpi; Ramaswami, Gokul; Hartl, Chris; Schork, Andrew J; Appadurai, Vivek; Buil, Alfonso; Werge, Thomas M; Liu, Chunyu; White, Kevin P; Horvath, Steve; Geschwind, Daniel H

2018-02-09

The predisposition to neuropsychiatric disease involves a complex, polygenic, and pleiotropic genetic architecture. However, little is known about how genetic variants impart brain dysfunction or pathology. We used transcriptomic profiling as a quantitative readout of molecular brain-based phenotypes across five major psychiatric disorders-autism, schizophrenia, bipolar disorder, depression, and alcoholism-compared with matched controls. We identified patterns of shared and distinct gene-expression perturbations across these conditions. The degree of sharing of transcriptional dysregulation is related to polygenic (single-nucleotide polymorphism-based) overlap across disorders, suggesting a substantial causal genetic component. This comprehensive systems-level view of the neurobiological architecture of major neuropsychiatric illness demonstrates pathways of molecular convergence and specificity. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

A convergent functional architecture of the insula emerges across imaging modalities.

PubMed

Kelly, Clare; Toro, Roberto; Di Martino, Adriana; Cox, Christine L; Bellec, Pierre; Castellanos, F Xavier; Milham, Michael P

2012-07-16

Empirical evidence increasingly supports the hypothesis that patterns of intrinsic functional connectivity (iFC) are sculpted by a history of evoked coactivation within distinct neuronal networks. This, together with evidence of strong correspondence among the networks defined by iFC and those delineated using a variety of other neuroimaging techniques, suggests a fundamental brain architecture detectable across multiple functional and structural imaging modalities. Here, we leverage this insight to examine the functional organization of the human insula. We parcellated the insula on the basis of three distinct neuroimaging modalities - task-evoked coactivation, intrinsic (i.e., task-independent) functional connectivity, and gray matter structural covariance. Clustering of these three different covariance-based measures revealed a convergent elemental organization of the insula that likely reflects a fundamental brain architecture governing both brain structure and function at multiple spatial scales. While not constrained to be hierarchical, our parcellation revealed a pseudo-hierarchical, multiscale organization that was consistent with previous clustering and meta-analytic studies of the insula. Finally, meta-analytic examination of the cognitive and behavioral domains associated with each of the insular clusters obtained elucidated the broad functional dissociations likely underlying the topography observed. To facilitate future investigations of insula function across healthy and pathological states, the insular parcels have been made freely available for download via http://fcon_1000.projects.nitrc.org, along with the analytic scripts used to perform the parcellations. Copyright © 2012 Elsevier Inc. All rights reserved.

A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks.

PubMed

Gallos, Lazaros K; Makse, Hernán A; Sigman, Mariano

2012-02-21

The human brain is organized in functional modules. Such an organization presents a basic conundrum: Modules ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer. It is commonly accepted that small-world architecture of short paths and large local clustering may solve this problem. However, there is intrinsic tension between shortcuts generating small worlds and the persistence of modularity, a global property unrelated to local clustering. Here, we present a possible solution to this puzzle. We first show that a modified percolation theory can define a set of hierarchically organized modules made of strong links in functional brain networks. These modules are "large-world" self-similar structures and, therefore, are far from being small-world. However, incorporating weaker ties to the network converts it into a small world preserving an underlying backbone of well-defined modules. Remarkably, weak ties are precisely organized as predicted by theory maximizing information transfer with minimal wiring cost. This trade-off architecture is reminiscent of the "strength of weak ties" crucial concept of social networks. Such a design suggests a natural solution to the paradox of efficient information flow in the highly modular structure of the brain.

A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks

PubMed Central

Gallos, Lazaros K.; Makse, Hernán A.; Sigman, Mariano

2012-01-01

The human brain is organized in functional modules. Such an organization presents a basic conundrum: Modules ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer. It is commonly accepted that small-world architecture of short paths and large local clustering may solve this problem. However, there is intrinsic tension between shortcuts generating small worlds and the persistence of modularity, a global property unrelated to local clustering. Here, we present a possible solution to this puzzle. We first show that a modified percolation theory can define a set of hierarchically organized modules made of strong links in functional brain networks. These modules are “large-world” self-similar structures and, therefore, are far from being small-world. However, incorporating weaker ties to the network converts it into a small world preserving an underlying backbone of well-defined modules. Remarkably, weak ties are precisely organized as predicted by theory maximizing information transfer with minimal wiring cost. This trade-off architecture is reminiscent of the “strength of weak ties” crucial concept of social networks. Such a design suggests a natural solution to the paradox of efficient information flow in the highly modular structure of the brain. PMID:22308319

Joint representation of consistent structural and functional profiles for identification of common cortical landmarks.

PubMed

Zhang, Shu; Zhao, Yu; Jiang, Xi; Shen, Dinggang; Liu, Tianming

2018-06-01

In the brain mapping field, there have been significant interests in representation of structural/functional profiles to establish structural/functional landmark correspondences across individuals and populations. For example, from the structural perspective, our previous studies have identified hundreds of consistent DICCCOL (dense individualized and common connectivity-based cortical landmarks) landmarks across individuals and populations, each of which possess consistent DTI-derived fiber connection patterns. From the functional perspective, a large collection of well-characterized HAFNI (holistic atlases of functional networks and interactions) networks based on sparse representation of whole-brain fMRI signals have been identified in our prior studies. However, due to the remarkable variability of structural and functional architectures in the human brain, it is challenging for earlier studies to jointly represent the connectome-scale structural and functional profiles for establishing a common cortical architecture which can comprehensively encode both structural and functional characteristics across individuals. To address this challenge, we propose an effective computational framework to jointly represent the structural and functional profiles for identification of consistent and common cortical landmarks with both structural and functional correspondences across different brains based on DTI and fMRI data. Experimental results demonstrate that 55 structurally and functionally common cortical landmarks can be successfully identified.

The polygenic risk for bipolar disorder influences brain regional function relating to visual and default state processing of emotional information.

PubMed

Dima, Danai; de Jong, Simone; Breen, Gerome; Frangou, Sophia

2016-01-01

Genome-wise association studies have identified a number of common single-nucleotide polymorphisms (SNPs), each of small effect, associated with risk to bipolar disorder (BD). Several risk-conferring SNPs have been individually shown to influence regional brain activation thus linking genetic risk for BD to altered brain function. The current study examined whether the polygenic risk score method, which models the cumulative load of all known risk-conferring SNPs, may be useful in the identification of brain regions whose function may be related to the polygenic architecture of BD. We calculated the individual polygenic risk score for BD (PGR-BD) in forty-one patients with the disorder, twenty-five unaffected first-degree relatives and forty-six unrelated healthy controls using the most recent Psychiatric Genomics Consortium data. Functional magnetic resonance imaging was used to define task-related brain activation patterns in response to facial affect and working memory processing. We found significant effects of the PGR-BD score on task-related activation irrespective of diagnostic group. There was a negative association between the PGR-BD score and activation in the visual association cortex during facial affect processing. In contrast, the PGR-BD score was associated with failure to deactivate the ventromedial prefrontal region of the default mode network during working memory processing. These results are consistent with the threshold-liability model of BD, and demonstrate the usefulness of the PGR-BD score in identifying brain functional alternations associated with vulnerability to BD. Additionally, our findings suggest that the polygenic architecture of BD is not regionally confined but impacts on the task-dependent recruitment of multiple brain regions.

Resolving Structural Variability in Network Models and the Brain

PubMed Central

Klimm, Florian; Bassett, Danielle S.; Carlson, Jean M.; Mucha, Peter J.

2014-01-01

Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling—in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity) do not in general simultaneously display a second (e.g., hierarchy). This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data. PMID:24675546

Concept of operations for knowledge discovery from Big Data across enterprise data warehouses

NASA Astrophysics Data System (ADS)

Sukumar, Sreenivas R.; Olama, Mohammed M.; McNair, Allen W.; Nutaro, James J.

2013-05-01

The success of data-driven business in government, science, and private industry is driving the need for seamless integration of intra and inter-enterprise data sources to extract knowledge nuggets in the form of correlations, trends, patterns and behaviors previously not discovered due to physical and logical separation of datasets. Today, as volume, velocity, variety and complexity of enterprise data keeps increasing, the next generation analysts are facing several challenges in the knowledge extraction process. Towards addressing these challenges, data-driven organizations that rely on the success of their analysts have to make investment decisions for sustainable data/information systems and knowledge discovery. Options that organizations are considering are newer storage/analysis architectures, better analysis machines, redesigned analysis algorithms, collaborative knowledge management tools, and query builders amongst many others. In this paper, we present a concept of operations for enabling knowledge discovery that data-driven organizations can leverage towards making their investment decisions. We base our recommendations on the experience gained from integrating multi-agency enterprise data warehouses at the Oak Ridge National Laboratory to design the foundation of future knowledge nurturing data-system architectures.

Subcortical surgical anatomy of the lateral frontal region: human white matter dissection and correlations with functional insights provided by intraoperative direct brain stimulation: laboratory investigation.

PubMed

De Benedictis, Alessandro; Sarubbo, Silvio; Duffau, Hugues

2012-12-01

Recent neuroimaging and surgical results support the crucial role of white matter in mediating motor and higher-level processing within the frontal lobe, while suggesting the limited compensatory capacity after damage to subcortical structures. Consequently, an accurate knowledge of the anatomofunctional organization of the pathways running within this region is mandatory for planning safe and effective surgical approaches to different diseases. The aim of this dissection study was to improve the neurosurgeon's awareness of the subcortical anatomofunctional architecture for a lateral approach to the frontal region, to optimize both resection and postoperative outcome. Ten human hemispheres (5 left, 5 right) were dissected according to the Klingler technique. Proceeding lateromedially, the main association and projection tracts as well as the deeper basal structures were identified. The authors describe the anatomy and the relationships among the exposed structures in both a systematic and topographical surgical perspective. Structural results were also correlated to the functional responses obtained during resections of infiltrative frontal tumors guided by direct cortico-subcortical electrostimulation with patients in the awake condition. The eloquent boundaries crucial for a safe frontal lobectomy or an extensive lesionectomy are as follows: 1) the motor cortex; 2) the pyramidal tract and premotor fibers in the posterior and posteromedial part of the surgical field; 3) the inferior frontooccipital fascicle and the superior longitudinal fascicle posterolaterally; and 4) underneath the inferior frontal gyrus, the head of the caudate nucleus, and the tip of the frontal horn of the lateral ventricle in the depth. Optimization of results following brain surgery, especially within the frontal lobe, requires a perfect knowledge of functional anatomy, not only at the cortical level but also with regard to subcortical white matter connectivity.

Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging.

PubMed

Mohseni Salehi, Seyed Sadegh; Erdogmus, Deniz; Gholipour, Ali

2017-11-01

Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and the robustness of brain extraction, therefore, are crucial for the accuracy of the entire brain analysis process. The state-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry, and therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent, and registration-free brain extraction tool, in this paper, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2-D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2-D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3-D image information without the need for computationally expensive 3-D convolutions and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark data sets, namely, LPBA40 and OASIS, in which we obtained the Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI) data sets. In this application, our voxelwise auto-context CNN performed much better than the other methods (Dice coefficient: 95.97%), where the other methods performed poorly due to the non-standard orientation and geometry of the fetal brain in MRI. Through training, our method can provide accurate brain extraction in challenging applications. This, in turn, may reduce the problems associated with image registration in segmentation tasks.

Stereotactically Standard Areas: Applied Mathematics in the Service of Brain Targeting in Deep Brain Stimulation.

PubMed

Mavridis, Ioannis N

2017-12-11

The concept of stereotactically standard areas (SSAs) within human brain nuclei belongs to the knowledge of the modern field of stereotactic brain microanatomy. These are areas resisting the individual variability of the nuclear location in stereotactic space. This paper summarizes the current knowledge regarding SSAs. A mathematical formula of SSAs was recently invented, allowing for their robust, reproducible, and accurate application to laboratory studies and clinical practice. Thus, SSAs open new doors for the application of stereotactic microanatomy to highly accurate brain targeting, which is mainly useful for minimally invasive neurosurgical procedures, such as deep brain stimulation.

Knowledge Management System Model for Learning Organisations

ERIC Educational Resources Information Center

Amin, Yousif; Monamad, Roshayu

2017-01-01

Based on the literature of knowledge management (KM), this paper reports on the progress of developing a new knowledge management system (KMS) model with components architecture that are distributed over the widely-recognised socio-technical system (STS) aspects to guide developers for selecting the most applicable components to support their KM…

Module Architecture for in Situ Space Laboratories

NASA Technical Reports Server (NTRS)

Sherwood, Brent

2010-01-01

The paper analyzes internal outfitting architectures for space exploration laboratory modules. ISS laboratory architecture is examined as a baseline for comparison; applicable insights are derived. Laboratory functional programs are defined for seven planet-surface knowledge domains. Necessary and value-added departures from the ISS architecture standard are defined, and three sectional interior architecture options are assessed for practicality and potential performance. Contemporary guidelines for terrestrial analytical laboratory design are found to be applicable to the in-space functional program. Densepacked racks of system equipment, and high module volume packing ratios, should not be assumed as the default solution for exploration laboratories whose primary activities include un-scriptable investigations and experimentation on the system equipment itself.

Real-Time Cognitive Computing Architecture for Data Fusion in a Dynamic Environment

NASA Technical Reports Server (NTRS)

Duong, Tuan A.; Duong, Vu A.

2012-01-01

A novel cognitive computing architecture is conceptualized for processing multiple channels of multi-modal sensory data streams simultaneously, and fusing the information in real time to generate intelligent reaction sequences. This unique architecture is capable of assimilating parallel data streams that could be analog, digital, synchronous/asynchronous, and could be programmed to act as a knowledge synthesizer and/or an "intelligent perception" processor. In this architecture, the bio-inspired models of visual pathway and olfactory receptor processing are combined as processing components, to achieve the composite function of "searching for a source of food while avoiding the predator." The architecture is particularly suited for scene analysis from visual data and odorant.

Ervin Zube and landscape architecture

Treesearch

Paul H. Gobster

2002-01-01

As he grew in his knowledge about the landscape through his involvemment in it as a person, student, practitioner, teacher, program director, and researcher, Ervin Zube's ideas about what landscape architecture is and should be continually evolved. He was a prolific writer whose publications span a broad range of audiences, and his contributions to ...

Proposing an Optimal Learning Architecture for the Digital Enterprise.

ERIC Educational Resources Information Center

O'Driscoll, Tony

2003-01-01

Discusses the strategic role of learning in information age organizations; analyzes parallels between the application of technology to business and the application of technology to learning; and proposes a learning architecture that aligns with the knowledge-based view of the firm and optimizes the application of technology to achieve proficiency…

Cooperating knowledge-based systems

NASA Technical Reports Server (NTRS)

Feigenbaum, Edward A.; Buchanan, Bruce G.

1988-01-01

This final report covers work performed under Contract NCC2-220 between NASA Ames Research Center and the Knowledge Systems Laboratory, Stanford University. The period of research was from March 1, 1987 to February 29, 1988. Topics covered were as follows: (1) concurrent architectures for knowledge-based systems; (2) methods for the solution of geometric constraint satisfaction problems, and (3) reasoning under uncertainty. The research in concurrent architectures was co-funded by DARPA, as part of that agency's Strategic Computing Program. The research has been in progress since 1985, under DARPA and NASA sponsorship. The research in geometric constraint satisfaction has been done in the context of a particular application, that of determining the 3-D structure of complex protein molecules, using the constraints inferred from NMR measurements.

Knowledge Resources - A Knowledge Management Approach for Digital Ecosystems

NASA Astrophysics Data System (ADS)

Kurz, Thomas; Eder, Raimund; Heistracher, Thomas

The paper at hand presents an innovative approach for the conception and implementation of knowledge management in Digital Ecosystems. Based on a reflection of Digital Ecosystem research of the past years, an architecture is outlined which utilizes Knowledge Resources as the central and simplest entities of knowledge transfer. After the discussion of the related conception, the result of a first prototypical implementation is described that helps the transformation of implicit knowledge to explicit knowledge for wide use.

The Perceptions of Teachers Regarding Their Knowledge, Beliefs, and Practices of Brain-Based Learning Strategies

ERIC Educational Resources Information Center

Ridley, Janice Rebecca Becky

2012-01-01

The purpose of this dissertation was to assess K-12 teachers' perceptions of knowledge, beliefs, and practices toward brain-based learning strategies, how their knowledge relates to their beliefs and practices, and how their beliefs relate to their classroom practices. This research also investigated relationships between teachers' gender, years…

Sex differences in brain organization: implications for human communication.

PubMed

Hanske-Petitpierre, V; Chen, A C

1985-12-01

This article reviews current knowledge in two major research domains: sex differences in neuropsychophysiology, and in human communication. An attempt was made to integrate knowledge from several areas of brain research with human communication and to clarify how such a cooperative effort may be beneficial to both fields of study. By combining findings from the area of brain research, a communication paradigm was developed which contends that brain-related sex differences may reside largely in the area of communication of emotion.

An architecture and protocol for communications satellite constellations regarded as multi-agent systems

NASA Technical Reports Server (NTRS)

Lindley, Craig A.

1995-01-01

This paper presents an architecture for satellites regarded as intercommunicating agents. The architecture is based upon a postmodern paradigm of artificial intelligence in which represented knowledge is regarded as text, inference procedures are regarded as social discourse and decision making conventions and the semantics of representations are grounded in the situated behaviour and activity of agents. A particular protocol is described for agent participation in distributed search and retrieval operations conducted as joint activities.

The sixth generation robot in space

NASA Technical Reports Server (NTRS)

Butcher, A.; Das, A.; Reddy, Y. V.; Singh, H.

1990-01-01

The knowledge based simulator developed in the artificial intelligence laboratory has become a working test bed for experimenting with intelligent reasoning architectures. With this simulator, recently, small experiments have been done with an aim to simulate robot behavior to avoid colliding paths. An automatic extension of such experiments to intelligently planning robots in space demands advanced reasoning architectures. One such architecture for general purpose problem solving is explored. The robot, seen as a knowledge base machine, goes via predesigned abstraction mechanism for problem understanding and response generation. The three phases in one such abstraction scheme are: abstraction for representation, abstraction for evaluation, and abstraction for resolution. Such abstractions require multimodality. This multimodality requires the use of intensional variables to deal with beliefs in the system. Abstraction mechanisms help in synthesizing possible propagating lattices for such beliefs. The machine controller enters into a sixth generation paradigm.

Implementation of a frame-based representation in CLIPS

NASA Technical Reports Server (NTRS)

Assal, Hisham; Myers, Leonard

1990-01-01

Knowledge representation is one of the major concerns in expert systems. The representation of domain-specific knowledge should agree with the nature of the domain entities and their use in the real world. For example, architectural applications deal with objects and entities such as spaces, walls, and windows. A natural way of representing these architectural entities is provided by frames. This research explores the potential of using the expert system shell CLIPS, developed by NASA, to implement a frame-based representation that can accommodate architectural knowledge. These frames are similar but quite different from the 'template' construct in version 4.3 of CLIPS. Templates support only the grouping of related information and the assignment of default values to template fields. In addition to these features frames provide other capabilities including definition of classes, inheritance between classes and subclasses, relation of objects of different classes with 'has-a', association of methods (demons) of different types (standard and user-defined) to fields (slots), and creation of new fields at run-time. This frame-based representation is implemented completely in CLIPS. No change to the source code is necessary.

Extended artificial neural networks: incorporation of a priori chemical knowledge enables use of ion selective electrodes for in-situ measurement of ions at environmentally relevant levels.

PubMed

Mueller, Amy V; Hemond, Harold F

2013-12-15

A novel artificial neural network (ANN) architecture is proposed which explicitly incorporates a priori system knowledge, i.e., relationships between output signals, while preserving the unconstrained non-linear function estimator characteristics of the traditional ANN. A method is provided for architecture layout, disabling training on a subset of neurons, and encoding system knowledge into the neuron structure. The novel architecture is applied to raw readings from a chemical sensor multi-probe (electric tongue), comprised of off-the-shelf ion selective electrodes (ISEs), to estimate individual ion concentrations in solutions at environmentally relevant concentrations and containing environmentally representative ion mixtures. Conductivity measurements and the concept of charge balance are incorporated into the ANN structure, resulting in (1) removal of estimation bias typically seen with use of ISEs in mixtures of unknown composition and (2) improvement of signal estimation by an order of magnitude or more for both major and minor constituents relative to use of ISEs as stand-alone sensors and error reduction by 30-50% relative to use of standard ANN models. This method is suggested as an alternative to parameterization of traditional models (e.g., Nikolsky-Eisenman), for which parameters are strongly dependent on both analyte concentration and temperature, and to standard ANN models which have no mechanism for incorporation of system knowledge. Network architecture and weighting are presented for the base case where the dot product can be used to relate ion concentrations to both conductivity and charge balance as well as for an extension to log-normalized data where the model can no longer be represented in this manner. While parameterization in this case study is analyte-dependent, the architecture is generalizable, allowing application of this method to other environmental problems for which mathematical constraints can be explicitly stated. © 2013 Elsevier B.V. All rights reserved.

Brain-wide Maps Reveal Stereotyped Cell-Type-Based Cortical Architecture and Subcortical Sexual Dimorphism.

PubMed

Kim, Yongsoo; Yang, Guangyu Robert; Pradhan, Kith; Venkataraju, Kannan Umadevi; Bota, Mihail; García Del Molino, Luis Carlos; Fitzgerald, Greg; Ram, Keerthi; He, Miao; Levine, Jesse Maurica; Mitra, Partha; Huang, Z Josh; Wang, Xiao-Jing; Osten, Pavel

2017-10-05

The stereotyped features of neuronal circuits are those most likely to explain the remarkable capacity of the brain to process information and govern behaviors, yet it has not been possible to comprehensively quantify neuronal distributions across animals or genders due to the size and complexity of the mammalian brain. Here we apply our quantitative brain-wide (qBrain) mapping platform to document the stereotyped distributions of mainly inhibitory cell types. We discover an unexpected cortical organizing principle: sensory-motor areas are dominated by output-modulating parvalbumin-positive interneurons, whereas association, including frontal, areas are dominated by input-modulating somatostatin-positive interneurons. Furthermore, we identify local cell type distributions with more cells in the female brain in 10 out of 11 sexually dimorphic subcortical areas, in contrast to the overall larger brains in males. The qBrain resource can be further mined to link stereotyped aspects of neuronal distributions to known and unknown functions of diverse brain regions. Copyright © 2017 Elsevier Inc. All rights reserved.

Extracting the inclination angle of nerve fibers within the human brain with 3D-PLI independent of system properties

NASA Astrophysics Data System (ADS)

Reckfort, Julia; Wiese, Hendrik; Dohmen, Melanie; Grässel, David; Pietrzyk, Uwe; Zilles, Karl; Amunts, Katrin; Axer, Markus

2013-09-01

The neuroimaging technique 3D-polarized light imaging (3D-PLI) has opened up new avenues to study the complex nerve fiber architecture of the human brain at sub-millimeter spatial resolution. This polarimetry technique is applicable to histological sections of postmortem brains utilizing the birefringence of nerve fibers caused by the regular arrangement of lipids and proteins in the myelin sheaths surrounding axons. 3D-PLI provides a three-dimensional description of the anatomical wiring scheme defined by the in-section direction angle and the out-of-section inclination angle. To date, 3D-PLI is the only available method that allows bridging the microscopic and the macroscopic description of the fiber architecture of the human brain. Here we introduce a new approach to retrieve the inclination angle of the fibers independently of the properties of the used polarimeters. This is relevant because the image resolution and the signal transmission inuence the measured birefringent signal (retardation) significantly. The image resolution was determined using the USAF- 1951 testchart applying the Rayleigh criterion. The signal transmission was measured by elliptical polarizers applying the Michelson contrast and histological slices of the optic tract of a postmortem brain. Based on these results, a modified retardation-inclination transfer function was proposed to extract the fiber inclination. The comparison of the actual and the inclination angles calculated with the theoretically proposed and the modified transfer function revealed a significant improvement in the extraction of the fiber inclinations.

Temporal Dynamics Assessment of Spatial Overlap Pattern of Functional Brain Networks Reveals Novel Functional Architecture of Cerebral Cortex.

PubMed

Jiang, Xi; Li, Xiang; Lv, Jinglei; Zhao, Shijie; Zhang, Shu; Zhang, Wei; Zhang, Tuo; Han, Junwei; Guo, Lei; Liu, Tianming

2018-06-01

Various studies in the brain mapping field have demonstrated that there exist multiple concurrent functional networks that are spatially overlapped and interacting with each other during specific task performance to jointly realize the total brain function. Assessing such spatial overlap patterns of functional networks (SOPFNs) based on functional magnetic resonance imaging (fMRI) has thus received increasing interest for brain function studies. However, there are still two crucial issues to be addressed. First, the SOPFNs are assessed over the entire fMRI scan assuming the temporal stationarity, while possibly time-dependent dynamics of the SOPFNs is not sufficiently explored. Second, the SOPFNs are assessed within individual subjects, while group-wise consistency of the SOPFNs is largely unknown. To address the two issues, we propose a novel computational framework of group-wise sparse representation of whole-brain fMRI temporal segments to assess the temporal dynamic spatial patterns of SOPFNs that are consistent across different subjects. Experimental results based on the recently publicly released Human Connectome Project grayordinate task fMRI data demonstrate that meaningful SOPFNs exhibiting dynamic spatial patterns across different time periods are effectively and robustly identified based on the reconstructed concurrent functional networks via the proposed framework. Specifically, those SOPFNs locate significantly more on gyral regions than on sulcal regions across different time periods. These results reveal novel functional architecture of cortical gyri and sulci. Moreover, these results help better understand functional dynamics mechanisms of cerebral cortex in the future.

A Knowledge Management Technology Architecture for Educational Research Organisations: Scaffolding Research Projects and Workflow Processing

ERIC Educational Resources Information Center

Muthukumar; Hedberg, John G.

2005-01-01

There is growing recognition that the economic climate of the world is shifting towards a knowledge-based economy where knowledge will be cherished as the most prized asset. In this regard, technology can be leveraged as a useful tool in effectually managing the knowledge capital of an organisation. Although several research studies have advanced…

A knowledge Management Technology Architecture for Educational Research Organisations: Scaffolding Research Projects and Workflow Processing

ERIC Educational Resources Information Center

Muthukumar; Hedberg, John G.

2005-01-01

There is growing recognition that the economic climate of the world is shifting towards a knowledge-based economy where knowledge will be cherished as the most prized asset. In this regard, technology can be leveraged as a useful tool in effectually managing the knowledge capital of an organisation. Although several research studies have advanced…

A hardware experimental platform for neural circuits in the auditory cortex

NASA Astrophysics Data System (ADS)

Rodellar-Biarge, Victoria; García-Dominguez, Pablo; Ruiz-Rizaldos, Yago; Gómez-Vilda, Pedro

2011-05-01

Speech processing in the human brain is a very complex process far from being fully understood although much progress has been done recently. Neuromorphic Speech Processing is a new research orientation in bio-inspired systems approach to find solutions to automatic treatment of specific problems (recognition, synthesis, segmentation, diarization, etc) which can not be adequately solved using classical algorithms. In this paper a neuromorphic speech processing architecture is presented. The systematic bottom-up synthesis of layered structures reproduce the dynamic feature detection of speech related to plausible neural circuits which work as interpretation centres located in the Auditory Cortex. The elementary model is based on Hebbian neuron-like units. For the computation of the architecture a flexible framework is proposed in the environment of Matlab®/Simulink®/HDL, which allows building models in different description styles, complexity and implementation levels. It provides a flexible platform for experimenting on the influence of the number of neurons and interconnections, in the precision of the results and in performance evaluation. The experimentation with different architecture configurations may help both in better understanding how neural circuits may work in the brain as well as in how speech processing can benefit from this understanding.

Prognostic value of continuous electroencephalography monitoring in children with severe brain damage.

PubMed

Lan, Yan-huai; Zhu, Xiao-mei; Zhou, Yuan-feng; Qiu, Peng-ling; Lu, Guo-ping; Sun, Dao-kai; Wang, Yi

2015-06-01

The purpose of this study is to determine whether there is a relationship between continuous electroencephalography (EEG) monitoring patterns and prognosis for children with severe brain damage. Patients and The different patterns of EEG were analyzed for 103 children (Glasgow Coma Scale [GCS] score < 8) who were monitored with continuous video-EEG (CVEEG) within 72 hours after the onset of coma. The clinical outcomes were scored and evaluated at hospital discharge by the modified Pediatric Cerebral and Overall Performance Category Scale (PCOPCS). EEG parameters of the different prognosis groups were compared and risk factors for prognosis were identified. Of the 103 children, 36 were in the good prognosis group (PCOPCS scores 1 and 2) and 67 were in the poor prognosis group (PCOPCS scores 3-6). The poor prognosis group had the lower proportion of events in reactive EEG patterns and sleep architecture, and a higher proportion of low-voltage events. Multivariate analyses showed that the lower GCS score and no sleep architecture were significantly associated with poor prognosis. Comatose children with higher GCS score and sleep architecture have better clinical outcomes in terms of morbidity and mortality. Georg Thieme Verlag KG Stuttgart · New York.

Convolutional neural networks for event-related potential detection: impact of the architecture.

PubMed

Cecotti, H

2017-07-01

The detection of brain responses at the single-trial level in the electroencephalogram (EEG) such as event-related potentials (ERPs) is a difficult problem that requires different processing steps to extract relevant discriminant features. While most of the signal and classification techniques for the detection of brain responses are based on linear algebra, different pattern recognition techniques such as convolutional neural network (CNN), as a type of deep learning technique, have shown some interests as they are able to process the signal after limited pre-processing. In this study, we propose to investigate the performance of CNNs in relation of their architecture and in relation to how they are evaluated: a single system for each subject, or a system for all the subjects. More particularly, we want to address the change of performance that can be observed between specifying a neural network to a subject, or by considering a neural network for a group of subjects, taking advantage of a larger number of trials from different subjects. The results support the conclusion that a convolutional neural network trained on different subjects can lead to an AUC above 0.9 by using an appropriate architecture using spatial filtering and shift invariant layers.

Tai Chi Chuan optimizes the functional organization of the intrinsic human brain architecture in older adults

PubMed Central

Wei, Gao-Xia; Dong, Hao-Ming; Yang, Zhi; Luo, Jing; Zuo, Xi-Nian

2014-01-01

Whether Tai Chi Chuan (TCC) can influence the intrinsic functional architecture of the human brain remains unclear. To examine TCC-associated changes in functional connectomes, resting-state functional magnetic resonance images were acquired from 40 older individuals including 22 experienced TCC practitioners (experts) and 18 demographically matched TCC-naïve healthy controls, and their local functional homogeneities across the cortical mantle were compared. Compared to the controls, the TCC experts had significantly greater and more experience-dependent functional homogeneity in the right post-central gyrus (PosCG) and less functional homogeneity in the left anterior cingulate cortex (ACC) and the right dorsal lateral prefrontal cortex. Increased functional homogeneity in the PosCG was correlated with TCC experience. Intriguingly, decreases in functional homogeneity (improved functional specialization) in the left ACC and increases in functional homogeneity (improved functional integration) in the right PosCG both predicted performance gains on attention network behavior tests. These findings provide evidence for the functional plasticity of the brain’s intrinsic architecture toward optimizing locally functional organization, with great implications for understanding the effects of TCC on cognition, behavior and health in aging population. PMID:24860494

Graphical explanation in an expert system for Space Station Freedom rack integration

NASA Technical Reports Server (NTRS)

Craig, F. G.; Cutts, D. E.; Fennel, T. R.; Purves, B.

1990-01-01

The rationale and methodology used to incorporate graphics into explanations provided by an expert system for Space Station Freedom rack integration is examined. The rack integration task is typical of a class of constraint satisfaction problems for large programs where expertise from several areas is required. Graphically oriented approaches are used to explain the conclusions made by the system, the knowledge base content, and even at more abstract levels the control strategies employed by the system. The implemented architecture combines hypermedia and inference engine capabilities. The advantages of this architecture include: closer integration of user interface, explanation system, and knowledge base; the ability to embed links to deeper knowledge underlying the compiled knowledge used in the knowledge base; and allowing for more direct control of explanation depth and duration by the user. The graphical techniques employed range from simple statis presentation of schematics to dynamic creation of a series of pictures presented motion picture style. User models control the type, amount, and order of information presented.

Strategies for memory-based decision making: Modeling behavioral and neural signatures within a cognitive architecture.

PubMed

Fechner, Hanna B; Pachur, Thorsten; Schooler, Lael J; Mehlhorn, Katja; Battal, Ceren; Volz, Kirsten G; Borst, Jelmer P

2016-12-01

How do people use memories to make inferences about real-world objects? We tested three strategies based on predicted patterns of response times and blood-oxygen-level-dependent (BOLD) responses: one strategy that relies solely on recognition memory, a second that retrieves additional knowledge, and a third, lexicographic (i.e., sequential) strategy, that considers knowledge conditionally on the evidence obtained from recognition memory. We implemented the strategies as computational models within the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, which allowed us to derive behavioral and neural predictions that we then compared to the results of a functional magnetic resonance imaging (fMRI) study in which participants inferred which of two cities is larger. Overall, versions of the lexicographic strategy, according to which knowledge about many but not all alternatives is searched, provided the best account of the joint patterns of response times and BOLD responses. These results provide insights into the interplay between recognition and additional knowledge in memory, hinting at an adaptive use of these two sources of information in decision making. The results highlight the usefulness of implementing models of decision making within a cognitive architecture to derive predictions on the behavioral and neural level. Copyright © 2016 Elsevier B.V. All rights reserved.

Teaching About “Brain and Learning” in High School Biology Classes: Effects on Teachers' Knowledge and Students' Theory of Intelligence

PubMed Central

Dekker, Sanne; Jolles, Jelle

2015-01-01

This study evaluated a new teaching module about “Brain and Learning” using a controlled design. The module was implemented in high school biology classes and comprised three lessons: (1) brain processes underlying learning; (2) neuropsychological development during adolescence; and (3) lifestyle factors that influence learning performance. Participants were 32 biology teachers who were interested in “Brain and Learning” and 1241 students in grades 8–9. Teachers' knowledge and students' beliefs about learning potential were examined using online questionnaires. Results indicated that before intervention, biology teachers were significantly less familiar with how the brain functions and develops than with its structure and with basic neuroscientific concepts (46 vs. 75% correct answers). After intervention, teachers' knowledge of “Brain and Learning” had significantly increased (64%), and more students believed that intelligence is malleable (incremental theory). This emphasizes the potential value of a short teaching module, both for improving biology teachers' insights into “Brain and Learning,” and for changing students' beliefs about intelligence. PMID:26648900

"Do octopuses have a brain?" Knowledge, perceptions and attitudes towards neuroscience at school.

PubMed

Sperduti, Alessandra; Crivellaro, Federica; Rossi, Paola Francesca; Bondioli, Luca

2012-01-01

The present study contributes to the question of school literacy about the brain, with an original survey conducted on Italian students from the 3(rd) to 10(th) grades (n=508). The main goal was to test student's knowledge, attitudes, and interests about neuroscience, to assess needs, prospects, and difficulties in teaching about the brain from elementary to high school. A written questionnaire, maintaining anonymity, asked 12 close-ended multiple choice questions on topics related to human and animal brains, plus one facultative open-ended question about interests and curiosities on brain topics. The results show that respondents have a fragmentary level of basic knowledge about the brain, with aspects related to brain functions and consciousness the most challenging. As expected, degrees of performance improve with school level; elementary school students answered correctly an average number of 5.3 questions, middle school 6.5, and high school 7.4. Overall, students show great interest in the brain, as shown by the large number of questions gathered through the open-ended question (n=384). Other topics are addressed, mostly related to brain structure/functions and the role of the brain in the everyday life. The survey indicates the need of more thorough school programs on this subject, reinforced by interdisciplinary teaching where comparative anatomy and evolutionary aspects of brain development are covered.

A unified approach to the design of clinical reporting systems.

PubMed

Gouveia-Oliveira, A; Salgado, N C; Azevedo, A P; Lopes, L; Raposo, V D; Almeida, I; de Melo, F G

1994-12-01

Computer-based Clinical Reporting Systems (CRS) for diagnostic departments that use structured data entry have a number of functional and structural affinities suggesting that a common software architecture for CRS may be defined. Such an architecture should allow easy expandability and reusability of a CRS. We report the development methodology and the architecture of SISCOPE, a CRS originally designed for gastrointestinal endoscopy that is expandable and reusable. Its main components are a patient database, a knowledge base, a reports base, and screen and reporting engines. The knowledge base contains the description of the controlled vocabulary and all the information necessary to control the menu system, and is easily accessed and modified with a conventional text editor. The structure of the controlled vocabulary is formally presented as an entity-relationship diagram. The screen engine drives a dynamic user interface and the reporting engine automatically creates a medical report; both engines operate by following a set of rules and the information contained in the knowledge base. Clinical experience has shown this architecture to be highly flexible and to allow frequent modifications of both the vocabulary and the menu system. This structure provided increased collaboration among development teams, insulating the domain expert from the details of the database, and enabling him to modify the system as necessary and to test the changes immediately. The system has also been reused in several different domains.

New Zealand Teachers' Understanding of Childhood Mild Traumatic Brain Injury: Investigating and Enhancing Teacher Knowledge and Practice

ERIC Educational Resources Information Center

Case, Rosalind Jane Leamy; Starkey, Nicola J.; Jones, Kelly; Barker-Collo, Suzanne; Feigin, Valery

2017-01-01

This two-phase study investigated New Zealand primary school teachers' knowledge and perceptions of childhood mild Traumatic Brain Injury (mTBI), and evaluated the effectiveness of a professional development workshop for enhancing teacher knowledge regarding mTBI. In phase one, 19 teachers from schools in the Waikato and Bay of Plenty engaged in…

Adding Learning to Knowledge-Based Systems: Taking the "Artificial" Out of AI

Treesearch

Daniel L. Schmoldt

1997-01-01

Both, knowledge-based systems (KBS) development and maintenance require time-consuming analysis of domain knowledge. Where example cases exist, KBS can be built, and later updated, by incorporating learning capabilities into their architecture. This applies to both supervised and unsupervised learning scenarios. In this paper, the important issues for learning systems-...

Knowledge management model for teleconsulting in telemedicine.

PubMed

Pico, Lilia Edith Aparicio; Cuenca, Orlando Rodriguez; Alvarez, Daniel José Salas; Salgado, Piere Augusto Peña

2008-01-01

The present article shows a study about requirements for teleconsulting in a telemedicine solution in order to create a knowledge management system. Several concepts have been found related to the term teleconsulting in telemedicine which will serve to clear up their corresponding applications, potentialities, and scope. Afterwards, different theories about the art state in knowledge management have been considered by exploring methodologies and architectures to establish the trends of knowledge management and the possibilities of using them in teleconsulting. Furthermore, local and international experiences have been examined to assess knowledge management systems focused on telemedicine. The objective of this study is to obtain a model for developing teleconsulting systems in Colombia because we have many health-information management systems but they don't offer telemedicine services for remote areas. In Colombia there are many people in rural areas with different necessities and they don't have medicine services, teleconsulting will be a good solution to this problem. Lastly, a model of a knowledge system is proposed for teleconsulting in telemedicine. The model has philosophical principles and architecture that shows the fundamental layers for its development.

On the challenges and mechanisms of embodied decisions

PubMed Central

Cisek, Paul; Pastor-Bernier, Alexandre

2014-01-01

Neurophysiological studies of decision-making have focused primarily on elucidating the mechanisms of classic economic decisions, for which the relevant variables are the values of expected outcomes and action is simply the means of reporting the selected choice. By contrast, here we focus on the particular challenges of embodied decision-making faced by animals interacting with their environment in real time. In such scenarios, the choices themselves as well as their relative costs and benefits are defined by the momentary geometry of the immediate environment and change continuously during ongoing activity. To deal with the demands of embodied activity, animals require an architecture in which the sensorimotor specification of potential actions, their valuation, selection and even execution can all take place in parallel. Here, we review behavioural and neurophysiological data supporting a proposed brain architecture for dealing with such scenarios, which we argue set the evolutionary foundation for the organization of the mammalian brain. PMID:25267821

Neural Systems Approaches to Understanding Major Depressive Disorder: An Intrinsic Functional Organization Perspective

PubMed Central

Hamilton, J. Paul; Chen, Michael C.; Gotlib, Ian H.

2012-01-01

Recent research detailing the intrinsic functional organization of the brain provides a unique and useful framework to gain a better understanding of the neural bases of Major Depressive Disorder (MDD). In this review, we first present a brief history of neuroimaging research that has increased our understanding of the functional macro-architecture of the brain. From this macro-architectural perspective, we examine the extant body of functional neuroimaging research assessing MDD with a specific emphasis on the contributions of default-mode, executive, and salience networks in this debilitating disorder. Next, we describe recent investigations conducted in our laboratory in which we explicitly adopt a neural-systems perspective in examining the relations among these networks in MDD. Finally, we offer directions for future research that we believe will facilitate the development of more detailed and integrative models of neural dysfunction in depression. PMID:23477309

Using architecture and technology to promote improved quality of life for military service members with traumatic brain injury.

PubMed

Pasquina, Paul F; Pasquina, Lavinia Fici; Anderson-Barnes, Victoria C; Giuggio, Jeffrey S; Cooper, Rory A

2010-02-01

Today, injured service members are surviving wounds that would have been fatal in previous wars. A recent RAND report estimates that approximately 320,000 service members may have experienced a traumatic brain injury (TBI) during deployment, and it is not uncommon for a soldier to sustain multiple associated injuries such as limb loss, paralysis, sensory loss, and psychological damage. As a result, many military service members and their families face significant challenges returning to a high quality of independent life. The architectural concepts of universal design (UD) and evidence-based design (EBD) are gaining interest as an integral part of the rehabilitation process of veterans with TBI. This article examines the possibilities presented by UD and EBD in accordance with the Americans with Disabilities Act of 1990, in terms of high-end building and interior design quality, and possible technological options for individuals with disabilities.

Norrin/Frizzled4 signaling in retinal vascular development and blood brain barrier plasticity.

PubMed

Wang, Yanshu; Rattner, Amir; Zhou, Yulian; Williams, John; Smallwood, Philip M; Nathans, Jeremy

2012-12-07

Norrin/Frizzled4 (Fz4) signaling activates the canonical Wnt pathway to control retinal vascular development. Using genetically engineered mice, we show that precocious Norrin production leads to premature retinal vascular invasion and delayed Norrin production leads to characteristic defects in intraretinal vascular architecture. In genetic mosaics, wild-type endothelial cells (ECs) instruct neighboring Fz4(-/-) ECs to produce an architecturally normal mosaic vasculature, a cell nonautonomous effect. However, over the ensuing weeks, Fz4(-/-) ECs are selectively eliminated from the mosaic vasculature, implying the existence of a quality control program that targets defective ECs. In the adult retina and cerebellum, gain or loss of Norrin/Fz4 signaling results in a cell-autonomous gain or loss, respectively, of blood retina barrier and blood brain barrier function, indicating an ongoing requirement for Frizzled signaling in barrier maintenance and substantial plasticity in mature CNS vascular structure. Copyright © 2012 Elsevier Inc. All rights reserved.

From data towards knowledge: revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data.

PubMed

Lu, Songjian; Jin, Bo; Cowart, L Ashley; Lu, Xinghua

2013-01-01

Genetic and pharmacological perturbation experiments, such as deleting a gene and monitoring gene expression responses, are powerful tools for studying cellular signal transduction pathways. However, it remains a challenge to automatically derive knowledge of a cellular signaling system at a conceptual level from systematic perturbation-response data. In this study, we explored a framework that unifies knowledge mining and data mining towards the goal. The framework consists of the following automated processes: 1) applying an ontology-driven knowledge mining approach to identify functional modules among the genes responding to a perturbation in order to reveal potential signals affected by the perturbation; 2) applying a graph-based data mining approach to search for perturbations that affect a common signal; and 3) revealing the architecture of a signaling system by organizing signaling units into a hierarchy based on their relationships. Applying this framework to a compendium of yeast perturbation-response data, we have successfully recovered many well-known signal transduction pathways; in addition, our analysis has led to many new hypotheses regarding the yeast signal transduction system; finally, our analysis automatically organized perturbed genes as a graph reflecting the architecture of the yeast signaling system. Importantly, this framework transformed molecular findings from a gene level to a conceptual level, which can be readily translated into computable knowledge in the form of rules regarding the yeast signaling system, such as "if genes involved in the MAPK signaling are perturbed, genes involved in pheromone responses will be differentially expressed."

A Socio-Cognitive Approach to Knowledge Construction in Design Studio through Blended Learning

ERIC Educational Resources Information Center

Kocaturk, Tuba

2017-01-01

This paper results from an educational research project that was undertaken by the School of Architecture, at the University of Liverpool funded by the Higher Education Academy in UK. The research explored technology driven shifts in architectural design studio education, identified their cognitive effects on design learning and developed an…

Polynomial Calculus: Rethinking the Role of Calculus in High Schools

ERIC Educational Resources Information Center

Grant, Melva R.; Crombie, William; Enderson, Mary; Cobb, Nell

2016-01-01

Access to advanced study in mathematics, in general, and to calculus, in particular, depends in part on the conceptual architecture of these knowledge domains. In this paper, we outline an alternative conceptual architecture for elementary calculus. Our general strategy is to separate basic concepts from the particular advanced techniques used in…

Stable functional networks exhibit consistent timing in the human brain.

PubMed

Chapeton, Julio I; Inati, Sara K; Zaghloul, Kareem A

2017-03-01

Despite many advances in the study of large-scale human functional networks, the question of timing, stability, and direction of communication between cortical regions has not been fully addressed. At the cellular level, neuronal communication occurs through axons and dendrites, and the time required for such communication is well defined and preserved. At larger spatial scales, however, the relationship between timing, direction, and communication between brain regions is less clear. Here, we use a measure of effective connectivity to identify connections between brain regions that exhibit communication with consistent timing. We hypothesized that if two brain regions are communicating, then knowledge of the activity in one region should allow an external observer to better predict activity in the other region, and that such communication involves a consistent time delay. We examine this question using intracranial electroencephalography captured from nine human participants with medically refractory epilepsy. We use a coupling measure based on time-lagged mutual information to identify effective connections between brain regions that exhibit a statistically significant increase in average mutual information at a consistent time delay. These identified connections result in sparse, directed functional networks that are stable over minutes, hours, and days. Notably, the time delays associated with these connections are also highly preserved over multiple time scales. We characterize the anatomic locations of these connections, and find that the propagation of activity exhibits a preferred posterior to anterior temporal lobe direction, consistent across participants. Moreover, networks constructed from connections that reliably exhibit consistent timing between anatomic regions demonstrate features of a small-world architecture, with many reliable connections between anatomically neighbouring regions and few long range connections. Together, our results demonstrate that cortical regions exhibit functional relationships with well-defined and consistent timing, and the stability of these relationships over multiple time scales suggests that these stable pathways may be reliably and repeatedly used for large-scale cortical communication. Published by Oxford University Press on behalf of the Guarantors of Brain 2017. This work is written by US Government employees and is in the public domain in the United States.

A Quick Tour of the Brain.

ERIC Educational Resources Information Center

Hart, Leslie

1983-01-01

Using a question-and-answer format, the author discusses brain research, its relationship to existing learning theory, left- and right-brain differences and their relationship to logical thinking, brain growth spurts, learning styles, and the effects of future brain knowledge on learning, especially on schools' development of brain-compatible…

Triangulation of the neurocomputational architecture underpinning reading aloud

PubMed Central

Hoffman, Paul; Lambon Ralph, Matthew A.; Woollams, Anna M.

2015-01-01

The goal of cognitive neuroscience is to integrate cognitive models with knowledge about underlying neural machinery. This significant challenge was explored in relation to word reading, where sophisticated computational-cognitive models exist but have made limited contact with neural data. Using distortion-corrected functional MRI and dynamic causal modeling, we investigated the interactions between brain regions dedicated to orthographic, semantic, and phonological processing while participants read words aloud. We found that the lateral anterior temporal lobe exhibited increased activation when participants read words with irregular spellings. This area is implicated in semantic processing but has not previously been considered part of the reading network. We also found meaningful individual differences in the activation of this region: Activity was predicted by an independent measure of the degree to which participants use semantic knowledge to read. These characteristics are predicted by the connectionist Triangle Model of reading and indicate a key role for semantic knowledge in reading aloud. Premotor regions associated with phonological processing displayed the reverse characteristics. Changes in the functional connectivity of the reading network during irregular word reading also were consistent with semantic recruitment. These data support the view that reading aloud is underpinned by the joint operation of two neural pathways. They reveal that (i) the ATL is an important element of the ventral semantic pathway and (ii) the division of labor between the two routes varies according to both the properties of the words being read and individual differences in the degree to which participants rely on each route. PMID:26124121

Modern and Contemporary Cultural Heritage Documentation and Knowledge by Surveying and its Representation

NASA Astrophysics Data System (ADS)

Balletti, C.; Costa, M.; Guerra, F.; Martinello, F.; Vernier, P.

2018-05-01

Conservation of modern and contemporary cultural heritage, which goes from design objects, to architecture, to cities and territories, is certainly a current topic and in the development phase as it is underway - in the same modernity - a process of systematic replacement of architectural elements, outcome of solutions then experimental, which today are reproduced with contemporary materials, analogous in the appearance, but intimately different especially in the technological content.The paper describes the particular case of La Tour de Meudon, better known as The Tower, (1966) by André Bloc, a contemporary architect of Le Corbusier, founder of L'Architecture d'aujourd'hui, who created his habitable sculptures. All his works mark the evolution of geometric abstraction to the free form, and they are still admirable testimonies of a journey that led him from architecture to architecture. His Architecture and his sculpture intertwine, opening the plastic unity of form in physical space-time. The survey is a fundamental moment for the knowledge of these hybrid architectures, where the structural component is hidden by its evident plasticity, as if it were a large sculpture with abstract and overlapping geometric shapes.Survey isn't only an analysis of geometries: it is instrumental to the other structural and material analyses since it provides a metric and topological basis on which to spatially locate the phenomena being studied. The integrated survey of the building (laser scanning, photogrammetry, topography) has allowed to document his project, contributing to the to definition of the actual construction characteristics and ascertain both the material consistency and the state of conservation.

Correspondence of the brain's functional architecture during activation and rest

PubMed Central

Smith, Stephen M.; Fox, Peter T.; Miller, Karla L.; Glahn, David C.; Fox, P. Mickle; Mackay, Clare E.; Filippini, Nicola; Watkins, Kate E.; Toro, Roberto; Laird, Angela R.; Beckmann, Christian F.

2009-01-01

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is “at rest.” In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically “active” even when at “rest.” PMID:19620724

Image and Morphology in Modern Theory of Architecture

NASA Astrophysics Data System (ADS)

Yankovskaya, Y. S.; Merenkov, A. V.

2017-11-01

This paper is devoted to some important and fundamental problems of the modern Russian architectural theory. These problems are: methodological and technological retardation; substitution of the modern professional architectural theoretical knowledge by the humanitarian concepts; preference of the traditional historical or historical-theoretical research. One of the most probable ways is the formation of useful modern subject (and multi-subject)-oriented concepts in architecture. To get over the criticism and distrust of the architectural theory is possible through the recognition of an important role of the subject (architect, consumer, contractor, ruler, etc.) and direction of the practical tasks of the forming human environment in the today’s rapidly changing world and post-industrial society. In this article we consider the evolution of two basic concepts for the theory of architecture such as the image and morphology.

How is the brain working?: Research on brain oscillations and connectivities in a new "Take-Off" state.

PubMed

Başar, Erol; Düzgün, Aysel

2016-05-01

The present report is a trial to survey analysis and applications of brain oscillations in cognitive impairment for opening the way to a new take off in research on brain oscillation. Although the number of papers related to brain oscillations rapidly increases, it is important to indicate the common principles governing the functioning of brain oscillations in the brain and body. Research scientists need a global view on the types of analysis, applications and existing oscillations. Further, scientists dealing with brain oscillations must have some knowledge from theoretical physics, system theory, and also general philosophy. The neuroscientists working on brain oscillations can mentally integrate several papers in the present report, and try to discover new avenues to augment knowledge on brain functions. A new take off in the search of brain oscillations indicates the strong need to survey this brunch of neuroscience in a broad panoply of science. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection

PubMed Central

Pardiñas, Antonio F.; Holmans, Peter; Pocklington, Andrew J.; Escott-Price, Valentina; Ripke, Stephan; Carrera, Noa; Legge, Sophie E.; Bishop, Sophie; Cameron, Darren; Hamshere, Marian L.; Han, Jun; Hubbard, Leon; Lynham, Amy; Mantripragada, Kiran; Rees, Elliott; MacCabe, James H.; McCarroll, Steven A.; Baune, Bernhard T.; Breen, Gerome; Byrne, Enda M.; Dannlowski, Udo; Eley, Thalia C.; Hayward, Caroline; Martin, Nicholas G.; McIntosh, Andrew M.; Plomin, Robert; Porteous, David J.; Wray, Naomi R.; Caballero, Armando; Geschwind, Daniel H.; Huckins, Laura M.; Ruderfer, Douglas M.; Santiago, Enrique; Sklar, Pamela; Stahl, Eli A.; Won, Hyejung; Agerbo, Esben; Als, Thomas D.; Andreassen, Ole A.; Bækvad-Hansen, Marie; Mortensen, Preben Bo; Pedersen, Carsten Bøcker; Børglum, Anders D.; Bybjerg-Grauholm, Jonas; Djurovic, Srdjan; Durmishi, Naser; Pedersen, Marianne Giørtz; Golimbet, Vera; Grove, Jakob; Hougaard, David M.; Mattheisen, Manuel; Molden, Espen; Mors, Ole; Nordentoft, Merete; Pejovic-Milovancevic, Milica; Sigurdsson, Engilbert; Silagadze, Teimuraz; Hansen, Christine Søholm; Stefansson, Kari; Stefansson, Hreinn; Steinberg, Stacy; Tosato, Sarah; Werge, Thomas; Collier, David A.; Rujescu, Dan; Kirov, George; Owen, Michael J.; O’Donovan, Michael C.; Walters, James T. R.

2018-01-01

Schizophrenia is a debilitating psychiatric condition often associated with poor quality of life and decreased life expectancy. Lack of progress in improving treatment outcomes has been attributed to limited knowledge of the underlying biology, although large-scale genomic studies have begun to provide insights. We report a new genome-wide association study of schizophrenia (11,260 cases and 24,542 controls), and through meta-analysis with existing data we identify 50 novel associated loci and 145 loci in total. Through integrating genomic fine-mapping with brain expression and chromosome conformation data, we identify candidate causal genes within 33 loci. We also show for the first time that the common variant association signal is highly enriched among genes that are under strong selective pressures. These findings provide new insights into the biology and genetic architecture of schizophrenia, highlight the importance of mutation-intolerant genes and suggest a mechanism by which common risk variants persist in the population. PMID:29483656

Epigenetic Research of Neurodegenerative Disorders Using Patient iPSC-Based Models

PubMed Central

2016-01-01

Epigenetic mechanisms play a role in human disease but their involvement in pathologies from the central nervous system has been hampered by the complexity of the brain together with its unique cellular architecture and diversity. Until recently, disease targeted neural types were only available as postmortem materials after many years of disease evolution. Current in vitro systems of induced pluripotent stem cells (iPSCs) generated by cell reprogramming of somatic cells from patients have provided valuable disease models recapitulating key pathological molecular events. Yet whether cell reprogramming on itself implies a truly epigenetic reprogramming, the epigenetic mechanisms governing this process are only partially understood. Moreover, elucidating epigenetic regulation using patient-specific iPSC-derived neural models is expected to have a great impact to unravel the pathophysiology of neurodegenerative diseases and to hopefully expand future therapeutic possibilities. Here we will critically review current knowledge of epigenetic involvement in neurodegenerative disorders focusing on the potential of iPSCs as a promising tool for epigenetic research of these diseases. PMID:26697081

Loss of Neuronal Integrity During Progressive HIV-1 Infection of Humanized Mice

PubMed Central

Dash, Prasanta K.; Gorantla, Santhi; Gendelman, Howard E; Knibbe, Jaclyn; Casale, George P; Makarov, Edward; Epstein, Adrian A; Gelbard, Harris A; Boska, Michael D; Poluektova, Larisa Y

2011-01-01

Neuronal damage induced by ongoing HIV-1 infection was investigated in humanized NOD/scid-IL-2Rgcnull mice transplanted at birth with human CD34-positive hematopoietic stem cells. Mice infected at 5 months of age and followed for up to 15 weeks maintained significant plasma viral loads and showed reduced numbers of CD4+ T cells. Prospective serial proton magnetic resonance spectroscopy tests showed selective reductions in cortical N-acetyl aspartate in infected animals. Diffusion tensor imaging revealed structural changes in cortical gray matter. Postmortem immunofluorescence brain tissue examinations for neuronal and glial markers, captured by multispectral imaging microscopy and quantified by morphometric and fluorescence emission, showed regional reduction of neuronal soma and synaptic architectures. This was evidenced by loss of microtubule-associated protein 2, synaptophysin and neurofilament antigens. This study is the first, to our knowledge, demonstrating lost neuronal integrity following HIV-1 infection in humanized mice. As such, the model permits studies of the relationships between ongoing viral replication and virus-associated neurodegeneration. PMID:21368026

Building the Knowledge Base to Support the Automatic Animation Generation of Chinese Traditional Architecture

NASA Astrophysics Data System (ADS)

Wei, Gongjin; Bai, Weijing; Yin, Meifang; Zhang, Songmao

We present a practice of applying the Semantic Web technologies in the domain of Chinese traditional architecture. A knowledge base consisting of one ontology and four rule bases is built to support the automatic generation of animations that demonstrate the construction of various Chinese timber structures based on the user's input. Different Semantic Web formalisms are used, e.g., OWL DL, SWRL and Jess, to capture the domain knowledge, including the wooden components needed for a given building, construction sequence, and the 3D size and position of every piece of wood. Our experience in exploiting the current Semantic Web technologies in real-world application systems indicates their prominent advantages (such as the reasoning facilities and modeling tools) as well as the limitations (such as low efficiency).

Meta-Design and the Triple Learning Organization in Architectural Design Process

NASA Astrophysics Data System (ADS)

Barelkowski, Robert

2017-10-01

The paper delves into the improvement of Meta-Design methodology being the result of implementation of triple learning organization. Grown from the concept of reflective practice, it offers an opportunity to segregate and hierarchize both criteria and knowledge management and at least twofold application. It induces constant feedback loops recharging the basic level of “design” with second level of “learning from design” and third level of “learning from learning”. While learning from design reflects the absorption of knowledge, structuralization of skills, management of information, learning from learning gives deeper understanding and provides axiological perspective which is necessary when combining cultural, social, and abstract conceptual problems. The second level involves multidisciplinary applications imported from many engineering disciplines, technical sciences, but also psychological background, or social environment. The third level confronts these applications with their respective sciences (wide extra-architectural knowledge) and axiological issues. This distinction may be represented in difference between e.g. purposeful, systemic use of participatory design which again generates experience-by-doing versus use of disciplinary knowledge starting from its theoretical framework, then narrowed down to be relevant to particular design task. The paper discusses the application in two cases: awarded competition proposal of Digital Arts Museum in Madrid and BAIRI university building. Both cases summarize the effects of implementation and expose the impact of triple-loop knowledge circles onto design, teaching the architect or helping them to learn how to manage information flows and how to accommodate paradigm shifts in the architectural design process.

The Relationship between Concussion Knowledge and the High School Athlete's Intention to Report Traumatic Brain Injury Symptoms: A Systematic Review of the Literature

ERIC Educational Resources Information Center

Taylor, Mary Ellen; Sanner, Jennifer E.

2017-01-01

Sports-related concussion or traumatic brain injury (TBI) is a frequent occurrence among high school athletes. Long-term and short-term effects of TBI on the athlete's developing brain can be minimized if the athlete reports and is effectively treated for TBI symptoms. Knowledge of concussion symptoms and a school culture of support are critical…

Special Education Teachers' Knowledge and Use of Brain-Based Teaching, Common Core State Standards, Formative Feedback Practices and Instructional Efficacy for the Diverse Learning Needs of Students in High and Low Proficiency Groups

ERIC Educational Resources Information Center

Walker-Thompson, Malasia

2014-01-01

This study examined special education teachers' knowledge and use of: brain-based teaching strategies, Common Core State Standards, formative feedback, and instructional efficacy for diverse students. The study identified the differences amongst special education teachers' responses on the dimensions of brain-based teaching strategies, Common Core…

Split My Brain: A Case Study of Seizure Disorder and Brain Function

ERIC Educational Resources Information Center

Omarzu, Julia

2004-01-01

This case involves a couple deciding whether or not their son should undergo brain surgery to treat a severe seizure disorder. In examining this dilemma, students apply knowledge of brain anatomy and function. They also learn about brain scanning techniques and discuss the plasticity of the brain.

Bringing the Brain into Assessment.

ERIC Educational Resources Information Center

Caine, Geoffrey; Caine, Renate Nummela

1999-01-01

Brain research explains why testing for surface knowledge (memorization) reveals relatively little about real, usable knowledge. Assessment must contribute to real-world experience, relate to real-world performance, can never be fully translated into representative symbols or numbers, and can induce both helplessness (interference with meaningful…

Extensions to the Parallel Real-Time Artificial Intelligence System (PRAIS) for fault-tolerant heterogeneous cycle-stealing reasoning

NASA Technical Reports Server (NTRS)

Goldstein, David

1991-01-01

Extensions to an architecture for real-time, distributed (parallel) knowledge-based systems called the Parallel Real-time Artificial Intelligence System (PRAIS) are discussed. PRAIS strives for transparently parallelizing production (rule-based) systems, even under real-time constraints. PRAIS accomplished these goals (presented at the first annual C Language Integrated Production System (CLIPS) conference) by incorporating a dynamic task scheduler, operating system extensions for fact handling, and message-passing among multiple copies of CLIPS executing on a virtual blackboard. This distributed knowledge-based system tool uses the portability of CLIPS and common message-passing protocols to operate over a heterogeneous network of processors. Results using the original PRAIS architecture over a network of Sun 3's, Sun 4's and VAX's are presented. Mechanisms using the producer-consumer model to extend the architecture for fault-tolerance and distributed truth maintenance initiation are also discussed.

Prediction of brain maturity in infants using machine-learning algorithms.

PubMed

Smyser, Christopher D; Dosenbach, Nico U F; Smyser, Tara A; Snyder, Abraham Z; Rogers, Cynthia E; Inder, Terrie E; Schlaggar, Bradley L; Neil, Jeffrey J

2016-08-01

Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23-29weeks of gestation and without moderate-severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p<0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants. Copyright © 2016 Elsevier Inc. All rights reserved.

Prediction of brain maturity in infants using machine-learning algorithms

PubMed Central

Smyser, Christopher D.; Dosenbach, Nico U.F.; Smyser, Tara A.; Snyder, Abraham Z.; Rogers, Cynthia E.; Inder, Terrie E.; Schlaggar, Bradley L.; Neil, Jeffrey J.

2016-01-01

Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23–29 weeks of gestation and without moderate–severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p < 0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants. PMID:27179605

Functional disorganization of small-world brain networks in mild Alzheimer's Disease and amnestic Mild Cognitive Impairment: an EEG study using Relative Wavelet Entropy (RWE).

PubMed

Frantzidis, Christos A; Vivas, Ana B; Tsolaki, Anthoula; Klados, Manousos A; Tsolaki, Magda; Bamidis, Panagiotis D

2014-01-01

Previous neuroscientific findings have linked Alzheimer's Disease (AD) with less efficient information processing and brain network disorganization. However, pathological alterations of the brain networks during the preclinical phase of amnestic Mild Cognitive Impairment (aMCI) remain largely unknown. The present study aimed at comparing patterns of the detection of functional disorganization in MCI relative to Mild Dementia (MD). Participants consisted of 23 cognitively healthy adults, 17 aMCI and 24 mild AD patients who underwent electroencephalographic (EEG) data acquisition during a resting-state condition. Synchronization analysis through the Orthogonal Discrete Wavelet Transform (ODWT), and directional brain network analysis were applied on the EEG data. This computational model was performed for networks that have the same number of edges (N = 500, 600, 700, 800 edges) across all participants and groups (fixed density values). All groups exhibited a small-world (SW) brain architecture. However, we found a significant reduction in the SW brain architecture in both aMCI and MD patients relative to the group of Healthy controls. This functional disorganization was also correlated with the participant's generic cognitive status. The deterioration of the network's organization was caused mainly by deficient local information processing as quantified by the mean cluster coefficient value. Functional hubs were identified through the normalized betweenness centrality metric. Analysis of the local characteristics showed relative hub preservation even with statistically significant reduced strength. Compensatory phenomena were also evident through the formation of additional hubs on left frontal and parietal regions. Our results indicate a declined functional network organization even during the prodromal phase. Degeneration is evident even in the preclinical phase and coexists with transient network reorganization due to compensation.

Topological relationships between brain and social networks.

PubMed

Sakata, Shuzo; Yamamori, Tetsuo

2007-01-01

Brains are complex networks. Previously, we revealed that specific connected structures are either significantly abundant or rare in cortical networks. However, it remains unknown whether systems from other disciplines have similar architectures to brains. By applying network-theoretical methods, here we show topological similarities between brain and social networks. We found that the statistical relevance of specific tied structures differs between social "friendship" and "disliking" networks, suggesting relation-type-specific topology of social networks. Surprisingly, overrepresented connected structures in brain networks are more similar to those in the friendship networks than to those in other networks. We found that balanced and imbalanced reciprocal connections between nodes are significantly abundant and rare, respectively, whereas these results are unpredictable by simply counting mutual connections. We interpret these results as evidence of positive selection of balanced mutuality between nodes. These results also imply the existence of underlying common principles behind the organization of brain and social networks.

Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof-of-concept and roadmap for future studies

PubMed Central

Anttila, Verneri; Hibar, Derrek P; van Hulzen, Kimm J E; Arias-Vasquez, Alejandro; Smoller, Jordan W; Nichols, Thomas E; Neale, Michael C; McIntosh, Andrew M; Lee, Phil; McMahon, Francis J; Meyer-Lindenberg, Andreas; Mattheisen, Manuel; Andreassen, Ole A; Gruber, Oliver; Sachdev, Perminder S; Roiz-Santiañez, Roberto; Saykin, Andrew J; Ehrlich, Stefan; Mather, Karen A; Turner, Jessica A; Schwarz, Emanuel; Thalamuthu, Anbupalam; Shugart, Yin Yao; Ho, Yvonne YW; Martin, Nicholas G; Wright, Margaret J

2016-01-01

Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between schizophrenia cases and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant genetic architecture or for single genetic markers. The current study provides proof-of-concept (albeit based on a limited set of structural brain measures), and defines a roadmap for future studies investigating the genetic covariance between structural/functional brain phenotypes and risk for psychiatric disorders. PMID:26854805

Organization and hierarchy of the human functional brain network lead to a chain-like core.

PubMed

Mastrandrea, Rossana; Gabrielli, Andrea; Piras, Fabrizio; Spalletta, Gianfranco; Caldarelli, Guido; Gili, Tommaso

2017-07-07

The brain is a paradigmatic example of a complex system: its functionality emerges as a global property of local mesoscopic and microscopic interactions. Complex network theory allows to elicit the functional architecture of the brain in terms of links (correlations) between nodes (grey matter regions) and to extract information out of the noise. Here we present the analysis of functional magnetic resonance imaging data from forty healthy humans at rest for the investigation of the basal scaffold of the functional brain network organization. We show how brain regions tend to coordinate by forming a highly hierarchical chain-like structure of homogeneously clustered anatomical areas. A maximum spanning tree approach revealed the centrality of the occipital cortex and the peculiar aggregation of cerebellar regions to form a closed core. We also report the hierarchy of network segregation and the level of clusters integration as a function of the connectivity strength between brain regions.

Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept.

PubMed

Franke, Barbara; Stein, Jason L; Ripke, Stephan; Anttila, Verneri; Hibar, Derrek P; van Hulzen, Kimm J E; Arias-Vasquez, Alejandro; Smoller, Jordan W; Nichols, Thomas E; Neale, Michael C; McIntosh, Andrew M; Lee, Phil; McMahon, Francis J; Meyer-Lindenberg, Andreas; Mattheisen, Manuel; Andreassen, Ole A; Gruber, Oliver; Sachdev, Perminder S; Roiz-Santiañez, Roberto; Saykin, Andrew J; Ehrlich, Stefan; Mather, Karen A; Turner, Jessica A; Schwarz, Emanuel; Thalamuthu, Anbupalam; Shugart, Yin Yao; Ho, Yvonne Yw; Martin, Nicholas G; Wright, Margaret J; O'Donovan, Michael C; Thompson, Paul M; Neale, Benjamin M; Medland, Sarah E; Sullivan, Patrick F

2016-03-01

Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between people with schizophrenia and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant genetic architecture or for single genetic markers. These results provide a proof of concept (albeit based on a limited set of structural brain measures) and define a roadmap for future studies investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders.

XML-based data model and architecture for a knowledge-based grid-enabled problem-solving environment for high-throughput biological imaging.

PubMed

Ahmed, Wamiq M; Lenz, Dominik; Liu, Jia; Paul Robinson, J; Ghafoor, Arif

2008-03-01

High-throughput biological imaging uses automated imaging devices to collect a large number of microscopic images for analysis of biological systems and validation of scientific hypotheses. Efficient manipulation of these datasets for knowledge discovery requires high-performance computational resources, efficient storage, and automated tools for extracting and sharing such knowledge among different research sites. Newly emerging grid technologies provide powerful means for exploiting the full potential of these imaging techniques. Efficient utilization of grid resources requires the development of knowledge-based tools and services that combine domain knowledge with analysis algorithms. In this paper, we first investigate how grid infrastructure can facilitate high-throughput biological imaging research, and present an architecture for providing knowledge-based grid services for this field. We identify two levels of knowledge-based services. The first level provides tools for extracting spatiotemporal knowledge from image sets and the second level provides high-level knowledge management and reasoning services. We then present cellular imaging markup language, an extensible markup language-based language for modeling of biological images and representation of spatiotemporal knowledge. This scheme can be used for spatiotemporal event composition, matching, and automated knowledge extraction and representation for large biological imaging datasets. We demonstrate the expressive power of this formalism by means of different examples and extensive experimental results.

Vision, Instruction and Action

DTIC Science & Technology

1990-04-01

been developing a new theory of activity over the past five years (3, 4, 5, 6, 7, 36, 37, 38]. This theory proposes that activity arises from the...decide what to do before it is too late to do it. We also demand that the theory not contradict established facts about how the brain works. Sonja is the...architecture. I present some basic facts about the brain and argue that they imply that the human cognitive machinery makes traditional programming

Cognitive Robotics, Embodied Cognition and Human-Robot Interaction

DTIC Science & Technology

2010-11-03

architecture is a specification of the structure of the brain at a level of abstraction that explains how it achieves the function of the mind (Anderson...predictions about brain regions (fMRI) Wednesday, November 3, 2010 Embodied Cognitive Modeling • We use an MDS robot (Trafton et al., 2010...passed memory and/or reality control questions (e.g., “Where did Maxi put the chocolate ?” or “Where is the chocolate now?”). Our reasoning was that age

Human white matter and knowledge representation

PubMed Central

2018-01-01

Understanding how knowledge is represented in the human brain is a fundamental challenge in neuroscience. To date, most of the work on this topic has focused on knowledge representation in cortical areas and debated whether knowledge is represented in a distributed or localized fashion. Fang and colleagues provide evidence that brain connections and the white matter supporting such connections might play a significant role. The work opens new avenues of investigation, breaking through disciplinary boundaries across network neuroscience, computational neuroscience, cognitive science, and classical lesion studies. PMID:29698391

Human white matter and knowledge representation.

PubMed

Pestilli, Franco

2018-04-01

Understanding how knowledge is represented in the human brain is a fundamental challenge in neuroscience. To date, most of the work on this topic has focused on knowledge representation in cortical areas and debated whether knowledge is represented in a distributed or localized fashion. Fang and colleagues provide evidence that brain connections and the white matter supporting such connections might play a significant role. The work opens new avenues of investigation, breaking through disciplinary boundaries across network neuroscience, computational neuroscience, cognitive science, and classical lesion studies.

A Study on Nursing Students' Knowledge, Attitude, and Educational Needs for Brain-Death Organ Transplantation and Donation and Intent to Donate Organs.

PubMed

Ju, M K; Sim, M K; Son, S Y

2018-05-01

The purpose of this study was to identify the knowledge, attitude, educational needs, and will of nursing students on organ donation from brain-dead donors. Data were collected by using a 40-item questionnaire to measure knowledge, attitude, educational needs, and will for organ donation of 215 nursing college students in one university in Dangjin city from May 11 to May 31, 2017. The data were analyzed using SPSS 22 program (Data Solution Inc, Seoul). In the general characteristics, 85.1% of the subjects did not receive education on donation, and 99.5% of the subjects responded that education is needed. The desired methods of education were special lecture in school (55.3%), "webtoons" on the Internet (19.5%), formal curriculum (15.8%). Points to improve to increase brain-death organ transplantation and donation included "active publicity through pan-national campaign activities" (56.3%), "respecting prior consent from brain-dead donors" (21.9%), and "encouragement and increased support for organ donors" (12.1%). There was a significant difference in knowledge according to will for organ donation (t = 3.29, P = .001) and consent to brain-death organ donation in family members (t = 3.29, P = .001). There was a statistically significant positive correlation between attitude and knowledge of the subjects regarding brain-death organ donation. The knowledge, attitude, educational need, and will for organ donation of nursing students revealed in this study will be used as basic data to provide systematic transplant education including contents about organ transplantation in the regular nursing curriculum in the future. It will contribute to the activation of organ donation. Copyright © 2018 Elsevier Inc. All rights reserved.

Cerebral vessels segmentation for light-sheet microscopy image using convolutional neural networks

NASA Astrophysics Data System (ADS)

Hu, Chaoen; Hui, Hui; Wang, Shuo; Dong, Di; Liu, Xia; Yang, Xin; Tian, Jie

2017-03-01

Cerebral vessel segmentation is an important step in image analysis for brain function and brain disease studies. To extract all the cerebrovascular patterns, including arteries and capillaries, some filter-based methods are used to segment vessels. However, the design of accurate and robust vessel segmentation algorithms is still challenging, due to the variety and complexity of images, especially in cerebral blood vessel segmentation. In this work, we addressed a problem of automatic and robust segmentation of cerebral micro-vessels structures in cerebrovascular images acquired by light-sheet microscope for mouse. To segment micro-vessels in large-scale image data, we proposed a convolutional neural networks (CNNs) architecture trained by 1.58 million pixels with manual label. Three convolutional layers and one fully connected layer were used in the CNNs model. We extracted a patch of size 32x32 pixels in each acquired brain vessel image as training data set to feed into CNNs for classification. This network was trained to output the probability that the center pixel of input patch belongs to vessel structures. To build the CNNs architecture, a series of mouse brain vascular images acquired from a commercial light sheet fluorescence microscopy (LSFM) system were used for training the model. The experimental results demonstrated that our approach is a promising method for effectively segmenting micro-vessels structures in cerebrovascular images with vessel-dense, nonuniform gray-level and long-scale contrast regions.

What We Know About the Brain Structure-Function Relationship.

PubMed

Batista-García-Ramó, Karla; Fernández-Verdecia, Caridad Ivette

2018-04-18

How the human brain works is still a question, as is its implication with brain architecture: the non-trivial structure–function relationship. The main hypothesis is that the anatomic architecture conditions, but does not determine, the neural network dynamic. The functional connectivity cannot be explained only considering the anatomical substrate. This involves complex and controversial aspects of the neuroscience field and that the methods and methodologies to obtain structural and functional connectivity are not always rigorously applied. The goal of the present article is to discuss about the progress made to elucidate the structure–function relationship of the Central Nervous System, particularly at the brain level, based on results from human and animal studies. The current novel systems and neuroimaging techniques with high resolutive physio-structural capacity have brought about the development of an integral framework of different structural and morphometric tools such as image processing, computational modeling and graph theory. Different laboratories have contributed with in vivo, in vitro and computational/mathematical models to study the intrinsic neural activity patterns based on anatomical connections. We conclude that multi-modal techniques of neuroimaging are required such as an improvement on methodologies for obtaining structural and functional connectivity. Even though simulations of the intrinsic neural activity based on anatomical connectivity can reproduce much of the observed patterns of empirical functional connectivity, future models should be multifactorial to elucidate multi-scale relationships and to infer disorder mechanisms.

The graphical brain: Belief propagation and active inference

PubMed Central

Friston, Karl J.; Parr, Thomas; de Vries, Bert

2018-01-01

This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference—and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models. Crucially, these models can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to elucidate the requisite message passing in terms of its form and scheduling. To accommodate mixed generative models (of discrete and continuous states), one also has to consider link nodes or factors that enable discrete and continuous representations to talk to each other. When mapping the implicit computational architecture onto neuronal connectivity, several interesting features emerge. For example, Bayesian model averaging and comparison, which link discrete and continuous states, may be implemented in thalamocortical loops. These and other considerations speak to a computational connectome that is inherently state dependent and self-organizing in ways that yield to a principled (variational) account. We conclude with simulations of reading that illustrate the implicit neuronal message passing, with a special focus on how discrete (semantic) representations inform, and are informed by, continuous (visual) sampling of the sensorium. Author Summary This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference—and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models that can entertain both discrete and continuous states. This leads to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to characterize the requisite message passing, and link this formal characterization to canonical microcircuits and extrinsic connectivity in the brain. PMID:29417960

Role of Graph Architecture in Controlling Dynamical Networks with Applications to Neural Systems.

PubMed

Kim, Jason Z; Soffer, Jonathan M; Kahn, Ari E; Vettel, Jean M; Pasqualetti, Fabio; Bassett, Danielle S

2018-01-01

Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviors such as synchronization. While descriptions of these behaviors are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behavior. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain's control performance by removing single edges in the network. Generally, our results ground the expectation of a control system's behavior in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

Role of graph architecture in controlling dynamical networks with applications to neural systems

NASA Astrophysics Data System (ADS)

Kim, Jason Z.; Soffer, Jonathan M.; Kahn, Ari E.; Vettel, Jean M.; Pasqualetti, Fabio; Bassett, Danielle S.

2018-01-01

Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviours such as synchronization. Although descriptions of these behaviours are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behaviour. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain's control performance by removing single edges in the network. Generally, our results ground the expectation of a control system's behaviour in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

Sleep in disorders of consciousness

PubMed Central

Cologan, Victor; Schabus, Manvel; Ledoux, Didier; Moonen, Gustave; Maquet, Pierre; Laureys, Steven

2010-01-01

SUMMARY From a behavioral as well as neurobiological point of view, sleep and consciousness are intimately connected. A better understanding of sleep cycles and sleep architecture of patients suffering from disorders of consciousness (DOC) might therefore improve the clinical care for these patients as well as our understanding of the neural correlations of consciousness. Defining sleep in severely brain-injured patients is however problematic as both their electrophysiological and sleep patterns differ in many ways from healthy individuals. This paper discusses the concepts involved in the study of sleep of patients suffering from DOC and critically assesses the applicability of standard sleep criteria in these patients. The available literature on comatose and vegetative states as well as that on locked-in and related states following traumatic or non-traumatic severe brain injury will be reviewed. A wide spectrum of sleep disturbances ranging from almost normal patterns to severe loss and architecture disorganization are reported in cases of DOC and some patterns correlate with diagnosis and prognosis. At the present time the interactions of sleep and consciousness in brain-injured patients are a little studied subject but, the authors suggest, a potentially very interesting field of research. PMID:19524464

Into the Fourth Dimension: Dysregulation of Genome Architecture in Aging and Alzheimer's Disease.

PubMed

Winick-Ng, Warren; Rylett, R Jane

2018-01-01

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by synapse dysfunction and cognitive impairment. Understanding the development and progression of AD is challenging, as the disease is highly complex and multifactorial. Both environmental and genetic factors play a role in AD pathogenesis, highlighted by observations of complex DNA modifications at the single gene level, and by new evidence that also implicates changes in genome architecture in AD patients. The four-dimensional structure of chromatin in space and time is essential for context-dependent regulation of gene expression in post-mitotic neurons. Dysregulation of epigenetic processes have been observed in the aging brain and in patients with AD, though there is not yet agreement on the impact of these changes on transcription. New evidence shows that proteins involved in genome organization have altered expression and localization in the AD brain, suggesting that the genomic landscape may play a critical role in the development of AD. This review discusses the role of the chromatin organizers and epigenetic modifiers in post-mitotic cells, the aging brain, and in the development and progression of AD. How these new insights can be used to help determine disease risk and inform treatment strategies will also be discussed.

Quantum neural network-based EEG filtering for a brain-computer interface.

PubMed

Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

2014-02-01

A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

Culture in the mind's mirror: how anthropology and neuroscience can inform a model of the neural substrate for cultural imitative learning.

PubMed

Losin, Elizabeth A Reynolds; Dapretto, Mirella; Iacoboni, Marco

2009-01-01

Cultural neuroscience, the study of how cultural experience shapes the brain, is an emerging subdiscipline in the neurosciences. Yet, a foundational question to the study of culture and the brain remains neglected by neuroscientific inquiry: "How does cultural information get into the brain in the first place?" Fortunately, the tools needed to explore the neural architecture of cultural learning - anthropological theories and cognitive neuroscience methodologies - already exist; they are merely separated by disciplinary boundaries. Here we review anthropological theories of cultural learning derived from fieldwork and modeling; since cultural learning theory suggests that sophisticated imitation abilities are at the core of human cultural learning, we focus our review on cultural imitative learning. Accordingly we proceed to discuss the neural underpinnings of imitation and other mechanisms important for cultural learning: learning biases, mental state attribution, and reinforcement learning. Using cultural neuroscience theory and cognitive neuroscience research as our guides, we then propose a preliminary model of the neural architecture of cultural learning. Finally, we discuss future studies needed to test this model and fully explore and explain the neural underpinnings of cultural imitative learning.

Dialogic e-Learning2learn: Creating Global Digital Networks and Educational Knowledge Building Architectures across Diversity

ERIC Educational Resources Information Center

Sorensen, Elsebeth Korsgaard

2007-01-01

Purpose: The purpose of this paper is to address the challenge and potential of online higher and continuing education, of fostering and promoting, in a global perspective across time and space, democratic values working for a better world. Design/methodology/approach: The paper presents a generalized dialogic learning architecture of networked…

Tutorial on architectural acoustics

NASA Astrophysics Data System (ADS)

Shaw, Neil; Talaske, Rick; Bistafa, Sylvio

2002-11-01

This tutorial is intended to provide an overview of current knowledge and practice in architectural acoustics. Topics covered will include basic concepts and history, acoustics of small rooms (small rooms for speech such as classrooms and meeting rooms, music studios, small critical listening spaces such as home theatres) and the acoustics of large rooms (larger assembly halls, auditoria, and performance halls).

Expanding the Responsibility of Architectural Education: Civic Professionalism in Two Schools of Architecture

ERIC Educational Resources Information Center

Rinehart, Michelle A.

2010-01-01

There has been a renewed interest in the purposes of professional education and the teaching of civic professionalism, whereby future professionals are exposed to their responsibility to use their specialized skills and knowledge to serve the public good. Recent studies on civic purposes in professional education, however, have largely ignored the…

A Model Based Framework for Semantic Interpretation of Architectural Construction Drawings

ERIC Educational Resources Information Center

Babalola, Olubi Oluyomi

2011-01-01

The study addresses the automated translation of architectural drawings from 2D Computer Aided Drafting (CAD) data into a Building Information Model (BIM), with emphasis on the nature, possible role, and limitations of a drafting language Knowledge Representation (KR) on the problem and process. The central idea is that CAD to BIM translation is a…

A Critical Mapping of Practice-Based Research as Evidenced by Swedish Architectural Theses

ERIC Educational Resources Information Center

Buchler, Daniela; Biggs, Michael A. R.; Stahl, Lars-Henrik

2011-01-01

This article presents an investigation that was funded by the Swedish Institute into the role of creative practice in architectural research as evidenced in Swedish doctoral theses. The sample was mapped and analysed in terms of clusters of interest, approaches, cultures of knowledge and uses of creative practice. This allowed the identification…

Automatic acquisition of domain and procedural knowledge

NASA Technical Reports Server (NTRS)

Ferber, H. J.; Ali, M.

1988-01-01

The design concept and performance of AKAS, an automated knowledge-acquisition system for the development of expert systems, are discussed. AKAS was developed using the FLES knowledge base for the electrical system of the B-737 aircraft and employs a 'learn by being told' strategy. The system comprises four basic modules, a system administration module, a natural-language concept-comprehension module, a knowledge-classification/extraction module, and a knowledge-incorporation module; details of the module architectures are explored.

Testing Proposed Neuronal Models of Effective Connectivity Within the Cortico-basal Ganglia-thalamo-cortical Loop During Loss of Consciousness.

PubMed

Crone, Julia Sophia; Lutkenhoff, Evan Scott; Bio, Branden Joseph; Laureys, Steven; Monti, Martin Max

2017-04-01

In recent years, a number of brain regions and connectivity patterns have been proposed to be crucial for loss and recovery of consciousness but have not been compared in detail. In a 3 T resting-state functional magnetic resonance imaging paradigm, we test the plausibility of these different neuronal models derived from theoretical and empirical knowledge. Specifically, we assess the fit of each model to the dynamic change in effective connectivity between specific cortical and subcortical regions at different consecutive levels of propofol-induced sedation by employing spectral dynamic causal modeling. Surprisingly, our findings indicate that proposed models of impaired consciousness do not fit the observed patterns of effective connectivity. Rather, the data show that loss of consciousness, at least in the context of propofol-induced sedation, is marked by a breakdown of corticopetal projections from the globus pallidus. Effective connectivity between the globus pallidus and the ventral posterior cingulate cortex, present during wakefulness, fades in the transition from lightly sedated to full loss of consciousness and returns gradually as consciousness recovers, thereby, demonstrating the dynamic shift in brain architecture of the posterior cingulate "hub" during changing states of consciousness. These findings highlight the functional role of a previously underappreciated direct pallido-cortical connectivity in supporting consciousness. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The genome in three dimensions: a new frontier in human brain research.

PubMed

Mitchell, Amanda C; Bharadwaj, Rahul; Whittle, Catheryne; Krueger, Winfried; Mirnics, Karoly; Hurd, Yasmin; Rasmussen, Theodore; Akbarian, Schahram

2014-06-15

Less than 1.5% of the human genome encodes protein. However, vast portions of the human genome are subject to transcriptional and epigenetic regulation, and many noncoding regulatory DNA elements are thought to regulate the spatial organization of interphase chromosomes. For example, chromosomal "loopings" are pivotal for the orderly process of gene expression, by enabling distal regulatory enhancer or silencer elements to directly interact with proximal promoter and transcription start sites, potentially bypassing hundreds of kilobases of interspersed sequence on the linear genome. To date, however, epigenetic studies in the human brain are mostly limited to the exploration of DNA methylation and posttranslational modifications of the nucleosome core histones. In contrast, very little is known about the regulation of supranucleosomal structures. Here, we show that chromosome conformation capture, a widely used approach to study higher-order chromatin, is applicable to tissue collected postmortem, thereby informing about genome organization in the human brain. We introduce chromosome conformation capture protocols for brain and compare higher-order chromatin structures at the chromosome 6p22.2-22.1 schizophrenia and bipolar disorder susceptibility locus, and additional neurodevelopmental risk genes, (DPP10, MCPH1) in adult prefrontal cortex and various cell culture systems, including neurons derived from reprogrammed skin cells. We predict that the exploration of three-dimensional genome architectures and function will open up new frontiers in human brain research and psychiatric genetics and provide novel insights into the epigenetic risk architectures of regulatory noncoding DNA. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain

PubMed Central

Krienen, Fenna M.; Yeo, B. T. Thomas; Ge, Tian; Buckner, Randy L.; Sherwood, Chet C.

2016-01-01

The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute’s human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections. PMID:26739559

Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain.

PubMed

Krienen, Fenna M; Yeo, B T Thomas; Ge, Tian; Buckner, Randy L; Sherwood, Chet C

2016-01-26

The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute's human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections.

Towards Methodologies for Building Knowledge-Based Instructional Systems.

ERIC Educational Resources Information Center

Duchastel, Philippe

1992-01-01

Examines the processes involved in building instructional systems that are based on artificial intelligence and hypermedia technologies. Traditional instructional systems design methodology is discussed; design issues including system architecture and learning strategies are addressed; and a new methodology for building knowledge-based…

Modelling Teaching Strategies.

ERIC Educational Resources Information Center

Major, Nigel

1995-01-01

Describes a modelling language for representing teaching strategies, based in the context of the COCA intelligent tutoring system. Examines work on meta-reasoning in knowledge-based systems and describes COCA's architecture, giving details of the language used for representing teaching knowledge. Discusses implications for future work. (AEF)

The Human Brain Project and neuromorphic computing

PubMed Central

Calimera, Andrea; Macii, Enrico; Poncino, Massimo

Summary Understanding how the brain manages billions of processing units connected via kilometers of fibers and trillions of synapses, while consuming a few tens of Watts could provide the key to a completely new category of hardware (neuromorphic computing systems). In order to achieve this, a paradigm shift for computing as a whole is needed, which will see it moving away from current “bit precise” computing models and towards new techniques that exploit the stochastic behavior of simple, reliable, very fast, low-power computing devices embedded in intensely recursive architectures. In this paper we summarize how these objectives will be pursued in the Human Brain Project. PMID:24139655

Microclimate and architectural tectonic: vernacular floating house resilience in Seberang Ulu 1, Palembang

NASA Astrophysics Data System (ADS)

Puspitasari, P.; Kadri, T.; Indartoyo, I.; Kusumawati, L.

2018-01-01

This paper aims to describe the results of preliminary research on floating houses on the Musi River, Seberang Ulu 1, Palembang, focused on studying the influence of microclimates to the tectonics of Rumah Rakit (Floating House). The increase of water surface due to global warming will increase the need of using floating house typology in the future. The description of the inhabitants’ experiences on applying technics to create vernacular floating houses is considered as significant knowledge to develop advance technology on the basis of local characteristic. Vernacular floating houses resilience consists of natural experiences of inhabitants in adapting their daily activities to the characteristic of local climate. By using qualitative approach, the Rumah Rakit inhabitants’ verbal information in this article becomes the main aspect in exploring local knowledge. At the end, the conceptual model of vernacular Rumah Rakit in Seberang Ulu 1, Palembang is formulated, in terms of building architectural tectonic that is closely related to the local climate characteristic. The knowledge can be utilized in the context of rehabilitation or preservation of such architectural objects that are their existences tend to be extinct at this time.

Combining metric episodes with semantic event concepts within the Symbolic and Sub-Symbolic Robotics Intelligence Control System (SS-RICS)

NASA Astrophysics Data System (ADS)

Kelley, Troy D.; McGhee, S.

2013-05-01

This paper describes the ongoing development of a robotic control architecture that inspired by computational cognitive architectures from the discipline of cognitive psychology. The Symbolic and Sub-Symbolic Robotics Intelligence Control System (SS-RICS) combines symbolic and sub-symbolic representations of knowledge into a unified control architecture. The new architecture leverages previous work in cognitive architectures, specifically the development of the Adaptive Character of Thought-Rational (ACT-R) and Soar. This paper details current work on learning from episodes or events. The use of episodic memory as a learning mechanism has, until recently, been largely ignored by computational cognitive architectures. This paper details work on metric level episodic memory streams and methods for translating episodes into abstract schemas. The presentation will include research on learning through novelty and self generated feedback mechanisms for autonomous systems.

Integrity Constraint Monitoring in Software Development: Proposed Architectures

NASA Technical Reports Server (NTRS)

Fernandez, Francisco G.

1997-01-01

In the development of complex software systems, designers are required to obtain from many sources and manage vast amounts of knowledge of the system being built and communicate this information to personnel with a variety of backgrounds. Knowledge concerning the properties of the system, including the structure of, relationships between and limitations of the data objects in the system, becomes increasingly more vital as the complexity of the system and the number of knowledge sources increases. Ensuring that violations of these properties do not occur becomes steadily more challenging. One approach toward managing the enforcement or system properties, called context monitoring, uses a centralized repository of integrity constraints and a constraint satisfiability mechanism for dynamic verification of property enforcement during program execution. The focus of this paper is to describe possible software architectures that define a mechanism for dynamically checking the satisfiability of a set of constraints on a program. The next section describes the context monitoring approach in general. Section 3 gives an overview of the work currently being done toward the addition of an integrity constraint satisfiability mechanism to a high-level program language, SequenceL, and demonstrates how this model is being examined to develop a general software architecture. Section 4 describes possible architectures for a general constraint satisfiability mechanism, as well as an alternative approach that, uses embedded database queries in lieu of an external monitor. The paper concludes with a brief summary outlining the, current state of the research and future work.

The anterior-ventrolateral temporal lobe contributes to boosting visual working memory capacity for items carrying semantic information.

PubMed

Chiou, Rocco; Lambon Ralph, Matthew A

2018-04-01

Working memory (WM) is a buffer that temporarily maintains information, be it visual or auditory, in an active state, caching its contents for online rehearsal or manipulation. How the brain enables long-term semantic knowledge to affect the WM buffer is a theoretically significant issue awaiting further investigation. In the present study, we capitalise on the knowledge about famous individuals as a 'test-case' to study how it impinges upon WM capacity for human faces and its neural substrate. Using continuous theta-burst transcranial stimulation combined with a psychophysical task probing WM storage for varying contents, we provide compelling evidence that (1) faces (regardless of familiarity) continued to accrue in the WM buffer with longer encoding time, whereas for meaningless stimuli (colour shades) there was little increment; (2) the rate of WM accrual was significantly more efficient for famous faces, compared to unknown faces; (3) the right anterior-ventrolateral temporal lobe (ATL) causally mediated this superior WM storage for famous faces. Specifically, disrupting the ATL (a region tuned to semantic knowledge including person identity) selectively hinders WM accrual for celebrity faces while leaving the accrual for unfamiliar faces intact. Further, this 'semantically-accelerated' storage is impervious to disruption of the right middle frontal gyrus and vertex, supporting the specific and causative contribution of the right ATL. Our finding advances the understanding of the neural architecture of WM, demonstrating that it depends on interaction with long-term semantic knowledge underpinned by the ATL, which causally expands the WM buffer when visual content carries semantic information. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.

PubMed

Dolz, Jose; Desrosiers, Christian; Ben Ayed, Ismail

2018-04-15

This study investigates a 3D and fully convolutional neural network (CNN) for subcortical brain structure segmentation in MRI. 3D CNN architectures have been generally avoided due to their computational and memory requirements during inference. We address the problem via small kernels, allowing deeper architectures. We further model both local and global context by embedding intermediate-layer outputs in the final prediction, which encourages consistency between features extracted at different scales and embeds fine-grained information directly in the segmentation process. Our model is efficiently trained end-to-end on a graphics processing unit (GPU), in a single stage, exploiting the dense inference capabilities of fully CNNs. We performed comprehensive experiments over two publicly available datasets. First, we demonstrate a state-of-the-art performance on the ISBR dataset. Then, we report a large-scale multi-site evaluation over 1112 unregistered subject datasets acquired from 17 different sites (ABIDE dataset), with ages ranging from 7 to 64 years, showing that our method is robust to various acquisition protocols, demographics and clinical factors. Our method yielded segmentations that are highly consistent with a standard atlas-based approach, while running in a fraction of the time needed by atlas-based methods and avoiding registration/normalization steps. This makes it convenient for massive multi-site neuroanatomical imaging studies. To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data. Copyright © 2017 Elsevier Inc. All rights reserved.

Relating brain signal variability to knowledge representation.

PubMed

Heisz, Jennifer J; Shedden, Judith M; McIntosh, Anthony R

2012-11-15

We assessed the hypothesis that brain signal variability is a reflection of functional network reconfiguration during memory processing. In the present experiments, we use multiscale entropy to capture the variability of human electroencephalogram (EEG) while manipulating the knowledge representation associated with faces stored in memory. Across two experiments, we observed increased variability as a function of greater knowledge representation. In Experiment 1, individuals with greater familiarity for a group of famous faces displayed more brain signal variability. In Experiment 2, brain signal variability increased with learning after multiple experimental exposures to previously unfamiliar faces. The results demonstrate that variability increases with face familiarity; cognitive processes during the perception of familiar stimuli may engage a broader network of regions, which manifests as higher complexity/variability in spatial and temporal domains. In addition, effects of repetition suppression on brain signal variability were observed, and the pattern of results is consistent with a selectivity model of neural adaptation. Crown Copyright © 2012. Published by Elsevier Inc. All rights reserved.

GAMES II Project: a general architecture for medical knowledge-based systems.

PubMed

Bruno, F; Kindler, H; Leaning, M; Moustakis, V; Scherrer, J R; Schreiber, G; Stefanelli, M

1994-10-01

GAMES II aims at developing a comprehensive and commercially viable methodology to avoid problems ordinarily occurring in KBS development. GAMES II methodology proposes to design a KBS starting from an epistemological model of medical reasoning (the Select and Test Model). The design is viewed as a process of adding symbol level information to the epistemological model. The architectural framework provided by GAMES II integrates the use of different formalisms and techniques providing a large set of tools. The user can select the most suitable one for representing a piece of knowledge after a careful analysis of its epistemological characteristics. Special attention is devoted to the tools dealing with knowledge acquisition (both manual and automatic). A panel of practicing physicians are assessing the medical value of such a framework and its related tools by using it in a practical application.

The kinematic architecture of the Active Headframe: A new head support for awake brain surgery.

PubMed

Malosio, Matteo; Negri, Simone Pio; Pedrocchi, Nicola; Vicentini, Federico; Cardinale, Francesco; Tosatti, Lorenzo Molinari

2012-01-01

This paper presents the novel hybrid kinematic structure of the Active Headframe, a robotic head support to be employed in brain surgery operations for an active and dynamic control of the patient's head position and orientation, particularly addressing awake surgery requirements. The topology has been conceived in order to satisfy all the installation, functional and dynamic requirements. A kinetostatic optimization has been performed to obtain the actual geometric dimensions of the prototype currently being developed.

Association mapping of brassinosteroid candidate genes and plant architecture in a diverse panel of Sorghum bicolor.

PubMed

Mantilla Perez, Maria B; Zhao, Jing; Yin, Yanhai; Hu, Jieyun; Salas Fernandez, Maria G

2014-12-01

This first association analysis between plant architecture and BR candidate genes in sorghum suggests that natural allelic variation has significant and pleiotropic effects on plant architecture phenotypes. Sorghum bicolor (L) Moench is a self-pollinated species traditionally used as a staple crop for human consumption and as a forage crop for livestock feed. Recently, sorghum has received attention as a bioenergy crop due to its water use efficiency and biomass yield potential. Breeding for superior bioenergy-type lines requires knowledge of the genetic mechanisms controlling plant architecture. Brassinosteroids (BRs) are a group of hormones that determine plant growth, development, and architecture. Biochemical and genetic information on BRs are available from model species but the application of that knowledge to crop species has been very limited. A candidate gene association mapping approach and a diverse sorghum collection of 315 accessions were used to assess marker-trait associations between BR biosynthesis and signaling genes and six plant architecture traits. A total of 263 single nucleotide polymorphisms (SNPs) from 26 BR genes were tested, 73 SNPs were significantly associated with the phenotypes of interest and 18 of those were associated with more than one trait. An analysis of the phenotypic variation explained by each BR pathway revealed that the signaling pathway had a larger effect for most phenotypes (R (2) = 0.05-0.23). This study constitutes the first association analysis between plant architecture and BR genes in sorghum and the first LD mapping for leaf angle, stem circumference, panicle exsertion and panicle length. Markers on or close to BKI1 associated with all phenotypes and thus, they are the most important outcomes of this study and will be further validated for their future application in breeding programs.

Dynamics of a neural system with a multiscale architecture

PubMed Central

Breakspear, Michael; Stam, Cornelis J

2005-01-01

The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales—neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are ‘slaved’ to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested. PMID:16087448

Thermal Control System Automation Project (TCSAP)

NASA Technical Reports Server (NTRS)

Boyer, Roger L.

1991-01-01

Information is given in viewgraph form on the Space Station Freedom (SSF) Thermal Control System Automation Project (TCSAP). Topics covered include the assembly of the External Thermal Control System (ETCS); the ETCS functional schematic; the baseline Fault Detection, Isolation, and Recovery (FDIR), including the development of a knowledge based system (KBS) for application of rule based reasoning to the SSF ETCS; TCSAP software architecture; the High Fidelity Simulator architecture; the TCSAP Runtime Object Database (RODB) data flow; KBS functional architecture and logic flow; TCSAP growth and evolution; and TCSAP relationships.

4Bs or Not 4Bs: Bricks, Bytes, Brains, and Bandwidth

ERIC Educational Resources Information Center

Treat, Tod

2011-01-01

The effective integration of planning to include bricks, bytes, brains, and bandwidth (the 4Bs) represents an opportunity for community colleges to extend their capacity as knowledge-intensive organizations, coupling knowledge, technology, and learning. Integration is important to ensure that the interplay among organizations, agents within them,…

Influence of Wiring Cost on the Large-Scale Architecture of Human Cortical Connectivity

PubMed Central

Samu, David; Seth, Anil K.; Nowotny, Thomas

2014-01-01

In the past two decades some fundamental properties of cortical connectivity have been discovered: small-world structure, pronounced hierarchical and modular organisation, and strong core and rich-club structures. A common assumption when interpreting results of this kind is that the observed structural properties are present to enable the brain's function. However, the brain is also embedded into the limited space of the skull and its wiring has associated developmental and metabolic costs. These basic physical and economic aspects place separate, often conflicting, constraints on the brain's connectivity, which must be characterized in order to understand the true relationship between brain structure and function. To address this challenge, here we ask which, and to what extent, aspects of the structural organisation of the brain are conserved if we preserve specific spatial and topological properties of the brain but otherwise randomise its connectivity. We perform a comparative analysis of a connectivity map of the cortical connectome both on high- and low-resolutions utilising three different types of surrogate networks: spatially unconstrained (‘random’), connection length preserving (‘spatial’), and connection length optimised (‘reduced’) surrogates. We find that unconstrained randomisation markedly diminishes all investigated architectural properties of cortical connectivity. By contrast, spatial and reduced surrogates largely preserve most properties and, interestingly, often more so in the reduced surrogates. Specifically, our results suggest that the cortical network is less tightly integrated than its spatial constraints would allow, but more strongly segregated than its spatial constraints would necessitate. We additionally find that hierarchical organisation and rich-club structure of the cortical connectivity are largely preserved in spatial and reduced surrogates and hence may be partially attributable to cortical wiring constraints. In contrast, the high modularity and strong s-core of the high-resolution cortical network are significantly stronger than in the surrogates, underlining their potential functional relevance in the brain. PMID:24699277

The Brain.

ERIC Educational Resources Information Center

Hubel, David H.

1979-01-01

This article on the brain is part of an entire issue about neurobiology and the question of how the human brain works. The brain as an intricate tissue composed of cells is discussed based on the current knowledge and understanding of its composition and structure. (SA)

An Intelligent Propulsion Control Architecture to Enable More Autonomous Vehicle Operation

NASA Technical Reports Server (NTRS)

Litt, Jonathan S.; Sowers, T. Shane; Simon, Donald L.; Owen, A. Karl; Rinehart, Aidan W.; Chicatelli, Amy K.; Acheson, Michael J.; Hueschen, Richard M.; Spiers, Christopher W.

2018-01-01

This paper describes an intelligent propulsion control architecture that coordinates with the flight control to reduce the amount of pilot intervention required to operate the vehicle. Objectives of the architecture include the ability to: automatically recognize the aircraft operating state and flight phase; configure engine control to optimize performance with knowledge of engine condition and capability; enhance aircraft performance by coordinating propulsion control with flight control; and recognize off-nominal propulsion situations and to respond to them autonomously. The hierarchical intelligent propulsion system control can be decomposed into a propulsion system level and an individual engine level. The architecture is designed to be flexible to accommodate evolving requirements, adapt to technology improvements, and maintain safety.

Big data processing in the cloud - Challenges and platforms

NASA Astrophysics Data System (ADS)

Zhelev, Svetoslav; Rozeva, Anna

2017-12-01

Choosing the appropriate architecture and technologies for a big data project is a difficult task, which requires extensive knowledge in both the problem domain and in the big data landscape. The paper analyzes the main big data architectures and the most widely implemented technologies used for processing and persisting big data. Clouds provide for dynamic resource scaling, which makes them a natural fit for big data applications. Basic cloud computing service models are presented. Two architectures for processing big data are discussed, Lambda and Kappa architectures. Technologies for big data persistence are presented and analyzed. Stream processing as the most important and difficult to manage is outlined. The paper highlights main advantages of cloud and potential problems.

Machine Learning for the Knowledge Plane

DTIC Science & Technology

2006-06-01

this idea is to combine techniques from machine learning with new architectural concepts in networking to make the internet self-aware and self...work on the machine learning portion of the Knowledge Plane. This consisted of three components: (a) we wrote a document formulating the various

Perceptual telerobotics

NASA Technical Reports Server (NTRS)

Ligomenides, Panos A.

1989-01-01

A sensory world modeling system, congruent with a human expert's perception, is proposed. The Experiential Knowledge Base (EKB) system can provide a highly intelligible communication interface for telemonitoring and telecontrol of a real time robotic system operating in space. Paradigmatic acquisition of empirical perceptual knowledge, and real time experiential pattern recognition and knowledge integration are reviewed. The cellular architecture and operation of the EKB system are also examined.

Concept of Operations for Collaboration and Discovery from Big Data Across Enterprise Data Warehouses

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olama, Mohammed M; Nutaro, James J; Sukumar, Sreenivas R

2013-01-01

The success of data-driven business in government, science, and private industry is driving the need for seamless integration of intra and inter-enterprise data sources to extract knowledge nuggets in the form of correlations, trends, patterns and behaviors previously not discovered due to physical and logical separation of datasets. Today, as volume, velocity, variety and complexity of enterprise data keeps increasing, the next generation analysts are facing several challenges in the knowledge extraction process. Towards addressing these challenges, data-driven organizations that rely on the success of their analysts have to make investment decisions for sustainable data/information systems and knowledge discovery. Optionsmore » that organizations are considering are newer storage/analysis architectures, better analysis machines, redesigned analysis algorithms, collaborative knowledge management tools, and query builders amongst many others. In this paper, we present a concept of operations for enabling knowledge discovery that data-driven organizations can leverage towards making their investment decisions. We base our recommendations on the experience gained from integrating multi-agency enterprise data warehouses at the Oak Ridge National Laboratory to design the foundation of future knowledge nurturing data-system architectures.« less

Fetal brain extracellular matrix boosts neuronal network formation in 3D bioengineered model of cortical brain tissue.

PubMed

Sood, Disha; Chwalek, Karolina; Stuntz, Emily; Pouli, Dimitra; Du, Chuang; Tang-Schomer, Min; Georgakoudi, Irene; Black, Lauren D; Kaplan, David L

2016-01-01

The extracellular matrix (ECM) constituting up to 20% of the organ volume is a significant component of the brain due to its instructive role in the compartmentalization of functional microdomains in every brain structure. The composition, quantity and structure of ECM changes dramatically during the development of an organism greatly contributing to the remarkably sophisticated architecture and function of the brain. Since fetal brain is highly plastic, we hypothesize that the fetal brain ECM may contain cues promoting neural growth and differentiation, highly desired in regenerative medicine. Thus, we studied the effect of brain-derived fetal and adult ECM complemented with matricellular proteins on cortical neurons using in vitro 3D bioengineered model of cortical brain tissue. The tested parameters included neuronal network density, cell viability, calcium signaling and electrophysiology. Both, adult and fetal brain ECM as well as matricellular proteins significantly improved neural network formation as compared to single component, collagen I matrix. Additionally, the brain ECM improved cell viability and lowered glutamate release. The fetal brain ECM induced superior neural network formation, calcium signaling and spontaneous spiking activity over adult brain ECM. This study highlights the difference in the neuroinductive properties of fetal and adult brain ECM and suggests that delineating the basis for this divergence may have implications for regenerative medicine.

Semantic interoperability--HL7 Version 3 compared to advanced architecture standards.

PubMed

Blobel, B G M E; Engel, K; Pharow, P

2006-01-01

To meet the challenge for high quality and efficient care, highly specialized and distributed healthcare establishments have to communicate and co-operate in a semantically interoperable way. Information and communication technology must be open, flexible, scalable, knowledge-based and service-oriented as well as secure and safe. For enabling semantic interoperability, a unified process for defining and implementing the architecture, i.e. structure and functions of the cooperating systems' components, as well as the approach for knowledge representation, i.e. the used information and its interpretation, algorithms, etc. have to be defined in a harmonized way. Deploying the Generic Component Model, systems and their components, underlying concepts and applied constraints must be formally modeled, strictly separating platform-independent from platform-specific models. As HL7 Version 3 claims to represent the most successful standard for semantic interoperability, HL7 has been analyzed regarding the requirements for model-driven, service-oriented design of semantic interoperable information systems, thereby moving from a communication to an architecture paradigm. The approach is compared with advanced architectural approaches for information systems such as OMG's CORBA 3 or EHR systems such as GEHR/openEHR and CEN EN 13606 Electronic Health Record Communication. HL7 Version 3 is maturing towards an architectural approach for semantic interoperability. Despite current differences, there is a close collaboration between the teams involved guaranteeing a convergence between competing approaches.

A scalable architecture for incremental specification and maintenance of procedural and declarative clinical decision-support knowledge.

PubMed

Hatsek, Avner; Shahar, Yuval; Taieb-Maimon, Meirav; Shalom, Erez; Klimov, Denis; Lunenfeld, Eitan

2010-01-01

Clinical guidelines have been shown to improve the quality of medical care and to reduce its costs. However, most guidelines exist in a free-text representation and, without automation, are not sufficiently accessible to clinicians at the point of care. A prerequisite for automated guideline application is a machine-comprehensible representation of the guidelines. In this study, we designed and implemented a scalable architecture to support medical experts and knowledge engineers in specifying and maintaining the procedural and declarative aspects of clinical guideline knowledge, resulting in a machine comprehensible representation. The new framework significantly extends our previous work on the Digital electronic Guidelines Library (DeGeL) The current study designed and implemented a graphical framework for specification of declarative and procedural clinical knowledge, Gesher. We performed three different experiments to evaluate the functionality and usability of the major aspects of the new framework: Specification of procedural clinical knowledge, specification of declarative clinical knowledge, and exploration of a given clinical guideline. The subjects included clinicians and knowledge engineers (overall, 27 participants). The evaluations indicated high levels of completeness and correctness of the guideline specification process by both the clinicians and the knowledge engineers, although the best results, in the case of declarative-knowledge specification, were achieved by teams including a clinician and a knowledge engineer. The usability scores were high as well, although the clinicians' assessment was significantly lower than the assessment of the knowledge engineers.

Brain Computation Is Organized via Power-of-Two-Based Permutation Logic.

PubMed

Xie, Kun; Fox, Grace E; Liu, Jun; Lyu, Cheng; Lee, Jason C; Kuang, Hui; Jacobs, Stephanie; Li, Meng; Liu, Tianming; Song, Sen; Tsien, Joe Z

2016-01-01

There is considerable scientific interest in understanding how cell assemblies-the long-presumed computational motif-are organized so that the brain can generate intelligent cognition and flexible behavior. The Theory of Connectivity proposes that the origin of intelligence is rooted in a power-of-two-based permutation logic ( N = 2 i -1), producing specific-to-general cell-assembly architecture capable of generating specific perceptions and memories, as well as generalized knowledge and flexible actions. We show that this power-of-two-based permutation logic is widely used in cortical and subcortical circuits across animal species and is conserved for the processing of a variety of cognitive modalities including appetitive, emotional and social information. However, modulatory neurons, such as dopaminergic (DA) neurons, use a simpler logic despite their distinct subtypes. Interestingly, this specific-to-general permutation logic remained largely intact although NMDA receptors-the synaptic switch for learning and memory-were deleted throughout adulthood, suggesting that the logic is developmentally pre-configured. Moreover, this computational logic is implemented in the cortex via combining a random-connectivity strategy in superficial layers 2/3 with nonrandom organizations in deep layers 5/6. This randomness of layers 2/3 cliques-which preferentially encode specific and low-combinatorial features and project inter-cortically-is ideal for maximizing cross-modality novel pattern-extraction, pattern-discrimination and pattern-categorization using sparse code, consequently explaining why it requires hippocampal offline-consolidation. In contrast, the nonrandomness in layers 5/6-which consists of few specific cliques but a higher portion of more general cliques projecting mostly to subcortical systems-is ideal for feedback-control of motivation, emotion, consciousness and behaviors. These observations suggest that the brain's basic computational algorithm is indeed organized by the power-of-two-based permutation logic. This simple mathematical logic can account for brain computation across the entire evolutionary spectrum, ranging from the simplest neural networks to the most complex.

Temporal Requirements of the Fragile X Mental Retardation Protein in Modulating Circadian Clock Circuit Synaptic Architecture

PubMed Central

Gatto, Cheryl L.; Broadie, Kendal

2009-01-01

Loss of fragile X mental retardation 1 (FMR1) gene function is the most common cause of inherited mental retardation and autism spectrum disorders, characterized by attention disorder, hyperactivity and disruption of circadian activity cycles. Pursuit of effective intervention strategies requires determining when the FMR1 product (FMRP) is required in the regulation of neuronal circuitry controlling these behaviors. In the well-characterized Drosophila disease model, loss of the highly conserved dFMRP causes circadian arrhythmicity and conspicuous abnormalities in the circadian clock circuitry. Here, a novel Sholl Analysis was used to quantify over-elaborated synaptic architecture in dfmr1-null small ventrolateral neurons (sLNvs), a key subset of clock neurons. The transgenic Gene-Switch system was employed to drive conditional neuronal dFMRP expression in the dfmr1-null mutant background in order to dissect temporal requirements within the clock circuit. Introduction of dFMRP during early brain development, including the stages of neurogenesis, neuronal fate specification and early pathfinding, provided no rescue of dfmr1 mutant phenotypes. Similarly, restoring normal dFMRP expression in the adult failed to restore circadian circuit architecture. In sharp contrast, supplying dFMRP during a transient window of very late brain development, wherein synaptogenesis and substantial subsequent synaptic reorganization (e.g. use-dependent pruning) occur, provided strong morphological rescue to reestablish normal sLNvs synaptic arbors. We conclude that dFMRP plays a developmentally restricted role in sculpting synaptic architecture in these neurons that cannot be compensated for by later reintroduction of the protein at maturity. PMID:19738924

Energy-efficient STDP-based learning circuits with memristor synapses

NASA Astrophysics Data System (ADS)

Wu, Xinyu; Saxena, Vishal; Campbell, Kristy A.

2014-05-01

It is now accepted that the traditional von Neumann architecture, with processor and memory separation, is ill suited to process parallel data streams which a mammalian brain can efficiently handle. Moreover, researchers now envision computing architectures which enable cognitive processing of massive amounts of data by identifying spatio-temporal relationships in real-time and solving complex pattern recognition problems. Memristor cross-point arrays, integrated with standard CMOS technology, are expected to result in massively parallel and low-power Neuromorphic computing architectures. Recently, significant progress has been made in spiking neural networks (SNN) which emulate data processing in the cortical brain. These architectures comprise of a dense network of neurons and the synapses formed between the axons and dendrites. Further, unsupervised or supervised competitive learning schemes are being investigated for global training of the network. In contrast to a software implementation, hardware realization of these networks requires massive circuit overhead for addressing and individually updating network weights. Instead, we employ bio-inspired learning rules such as the spike-timing-dependent plasticity (STDP) to efficiently update the network weights locally. To realize SNNs on a chip, we propose to use densely integrating mixed-signal integrate-andfire neurons (IFNs) and cross-point arrays of memristors in back-end-of-the-line (BEOL) of CMOS chips. Novel IFN circuits have been designed to drive memristive synapses in parallel while maintaining overall power efficiency (<1 pJ/spike/synapse), even at spike rate greater than 10 MHz. We present circuit design details and simulation results of the IFN with memristor synapses, its response to incoming spike trains and STDP learning characterization.

The architecture of the chess player's brain.

PubMed

Hänggi, Jürgen; Brütsch, Karin; Siegel, Adrian M; Jäncke, Lutz

2014-09-01

The game of chess can be seen as a typical example for an expertise task requiring domain-specific training and experience. Despite intensive behavioural studies the neural underpinnings of chess performance and expertise are not entirely understood. A few functional neuroimaging studies have shown that expert chess players recruit different psychological functions and activate different brain areas while they are engaged in chess-related activities. Based on this functional literature, we predicted to find morphological differences in a network comprised by parietal and frontal areas and especially the occipito-temporal junction (OTJ), fusiform gyrus, and caudate nucleus. Twenty expert chess players and 20 control subjects were investigated using voxel-based and surface-based morphometry as well as diffusion tensor imaging. Grey matter volume and cortical thickness were reduced in chess players compared with those of control men in the OTJ and precunei. The volumes of both caudate nuclei were not different between groups, but correlated inversely with the years of chess playing experience. Mean diffusivity was increased in chess players compared with that of controls in the left superior longitudinal fasciculus and the Elo score (a chess tournament ranking) was inversely related to mean diffusivity within the right superior longitudinal fasciculus. To the best of our knowledge we showed for the first time that there are specific differences in grey and white matter morphology between chess players and control subjects in brain regions associated with cognitive functions important for playing chess. Whether these anatomical alterations are the cause or consequence of the intensive and long-term chess training and practice remains to be shown in future studies. Copyright © 2014 Elsevier Ltd. All rights reserved.

Volumetric multimodality neural network for brain tumor segmentation

NASA Astrophysics Data System (ADS)

Silvana Castillo, Laura; Alexandra Daza, Laura; Carlos Rivera, Luis; Arbeláez, Pablo

2017-11-01

Brain lesion segmentation is one of the hardest tasks to be solved in computer vision with an emphasis on the medical field. We present a convolutional neural network that produces a semantic segmentation of brain tumors, capable of processing volumetric data along with information from multiple MRI modalities at the same time. This results in the ability to learn from small training datasets and highly imbalanced data. Our method is based on DeepMedic, the state of the art in brain lesion segmentation. We develop a new architecture with more convolutional layers, organized in three parallel pathways with different input resolution, and additional fully connected layers. We tested our method over the 2015 BraTS Challenge dataset, reaching an average dice coefficient of 84%, while the standard DeepMedic implementation reached 74%.

Towards integration of clinical decision support in commercial hospital information systems using distributed, reusable software and knowledge components.

PubMed

Müller, M L; Ganslandt, T; Eich, H P; Lang, K; Ohmann, C; Prokosch, H U

2001-12-01

Clinicians' acceptance of clinical decision support depends on its workflow-oriented, context-sensitive accessibility and availability at the point of care, integrated into the Electronic Patient Record (EPR). Commercially available Hospital Information Systems (HIS) often focus on administrative tasks and mostly do not provide additional knowledge based functionality. Their traditionally monolithic and closed software architecture encumbers integration of and interaction with external software modules. Our aim was to develop methods and interfaces to integrate knowledge sources into two different commercial hospital information systems to provide the best decision support possible within the context of available patient data. An existing, proven standalone scoring system for acute abdominal pain was supplemented by a communication interface. In both HIS we defined data entry forms and developed individual and reusable mechanisms for data exchange with external software modules. We designed an additional knowledge support frontend which controls data exchange between HIS and the knowledge modules. Finally, we added guidelines and algorithms to the knowledge library. Despite some major drawbacks which resulted mainly from the HIS' closed software architectures we showed exemplary, how external knowledge support can be integrated almost seamlessly into different commercial HIS. This paper describes the prototypical design and current implementation and discusses our experiences.

Flexible cognitive resources: competitive content maps for attention and memory

PubMed Central

Franconeri, Steven L.; Alvarez, George A.; Cavanagh, Patrick

2013-01-01

The brain has finite processing resources so that, as tasks become harder, performance degrades. Where do the limits on these resources come from? We focus on a variety of capacity-limited buffers related to attention, recognition, and memory that we claim have a two-dimensional ‘map’ architecture, where individual items compete for cortical real estate. This competitive format leads to capacity limits that are flexible, set by the nature of the content and their locations within an anatomically delimited space. We contrast this format with the standard ‘slot’ architecture and its fixed capacity. Using visual spatial attention and visual short-term memory as case studies, we suggest that competitive maps are a concrete and plausible architecture that limits cognitive capacity across many domains. PMID:23428935

Knowledge Management for the Analysis of Complex Experimentation.

ERIC Educational Resources Information Center

Maule, R.; Schacher, G.; Gallup, S.

2002-01-01

Describes a knowledge management system that was developed to help provide structure for dynamic and static data and to aid in the analysis of complex experimentation. Topics include quantitative and qualitative data; mining operations using artificial intelligence techniques; information architecture of the system; and transforming data into…

Individual T1-weighted/T2-weighted ratio brain networks: Small-worldness, hubs and modular organization

NASA Astrophysics Data System (ADS)

Wu, Huijun; Wang, Hao; Lü, Linyuan

Applying network science to investigate the complex systems has become a hot topic. In neuroscience, understanding the architectures of complex brain networks was a vital issue. An enormous amount of evidence had supported the brain was cost/efficiency trade-off with small-worldness, hubness and modular organization through the functional MRI and structural MRI investigations. However, the T1-weighted/T2-weighted (T1w/T2w) ratio brain networks were mostly unexplored. Here, we utilized a KL divergence-based method to construct large-scale individual T1w/T2w ratio brain networks and investigated the underlying topological attributes of these networks. Our results supported that the T1w/T2w ratio brain networks were comprised of small-worldness, an exponentially truncated power-law degree distribution, frontal-parietal hubs and modular organization. Besides, there were significant positive correlations between the network metrics and fluid intelligence. Thus, the T1w/T2w ratio brain networks open a new avenue to understand the human brain and are a necessary supplement for future MRI studies.

Brain stem death and organ donation.

PubMed

Davies, C

1996-01-01

Our understanding of the concept and definition of death has changed over time. The British contribution to the body of knowledge on the diagnosis of brain steam death was the publication by the medical royal colleges (1976) of diagnostic criteria. Most literature and research which explores the knowledge and attitudes of nurses towards the concept of brain stem death is from the USA. Several issues which arise from the literature are discussed in relation to organ donation. Further UK-based research is required.

Transcriptional Landscape of the Prenatal Human Brain

PubMed Central

Miller, Jeremy A.; Ding, Song-Lin; Sunkin, Susan M.; Smith, Kimberly A; Ng, Lydia; Szafer, Aaron; Ebbert, Amanda; Riley, Zackery L.; Aiona, Kaylynn; Arnold, James M.; Bennet, Crissa; Bertagnolli, Darren; Brouner, Krissy; Butler, Stephanie; Caldejon, Shiella; Carey, Anita; Cuhaciyan, Christine; Dalley, Rachel A.; Dee, Nick; Dolbeare, Tim A.; Facer, Benjamin A. C.; Feng, David; Fliss, Tim P.; Gee, Garrett; Goldy, Jeff; Gourley, Lindsey; Gregor, Benjamin W.; Gu, Guangyu; Howard, Robert E.; Jochim, Jayson M.; Kuan, Chihchau L.; Lau, Christopher; Lee, Chang-Kyu; Lee, Felix; Lemon, Tracy A.; Lesnar, Phil; McMurray, Bergen; Mastan, Naveed; Mosqueda, Nerick F.; Naluai-Cecchini, Theresa; Ngo, Nhan-Kiet; Nyhus, Julie; Oldre, Aaron; Olson, Eric; Parente, Jody; Parker, Patrick D.; Parry, Sheana E.; Player, Allison Stevens; Pletikos, Mihovil; Reding, Melissa; Royall, Joshua J.; Roll, Kate; Sandman, David; Sarreal, Melaine; Shapouri, Sheila; Shapovalova, Nadiya V.; Shen, Elaine H.; Sjoquist, Nathan; Slaughterbeck, Clifford R.; Smith, Michael; Sodt, Andy J.; Williams, Derric; Zöllei, Lilla; Fischl, Bruce; Gerstein, Mark B.; Geschwind, Daniel H.; Glass, Ian A.; Hawrylycz, Michael J.; Hevner, Robert F.; Huang, Hao; Jones, Allan R.; Knowles, James A.; Levitt, Pat; Phillips, John W.; Sestan, Nenad; Wohnoutka, Paul; Dang, Chinh; Bernard, Amy; Hohmann, John G.; Lein, Ed S.

2014-01-01

Summary The anatomical and functional architecture of the human brain is largely determined by prenatal transcriptional processes. We describe an anatomically comprehensive atlas of mid-gestational human brain, including de novo reference atlases, in situ hybridization, ultra-high resolution magnetic resonance imaging (MRI) and microarray analysis on highly discrete laser microdissected brain regions. In developing cerebral cortex, transcriptional differences are found between different proliferative and postmitotic layers, wherein laminar signatures reflect cellular composition and developmental processes. Cytoarchitectural differences between human and mouse have molecular correlates, including species differences in gene expression in subplate, although surprisingly we find minimal differences between the inner and human-expanded outer subventricular zones. Both germinal and postmitotic cortical layers exhibit fronto-temporal gradients, with particular enrichment in frontal lobe. Finally, many neurodevelopmental disorder and human evolution-related genes show patterned expression, potentially underlying unique features of human cortical formation. These data provide a rich, freely-accessible resource for understanding human brain development. PMID:24695229

Law, evolution and the brain: applications and open questions.

PubMed Central

Jones, Owen D

2004-01-01

This paper discusses several issues at the intersection of law and brain science. It focuses principally on ways in which an improved understanding of how evolutionary processes affect brain function and human behaviour may improve law's ability to regulate behaviour. It explores sample uses of such 'evolutionary analysis in law' and also raises questions about how that analysis might be improved in the future. Among the discussed uses are: (i) clarifying cost-benefit analyses; (ii) providing theoretical foundation and potential predictive power; (iii) assessing comparative effectiveness of legal strategies; and (iv) revealing deep patterns in legal architecture. Throughout, the paper emphasizes the extent to which effective law requires: (i) building effective behavioural models; (ii) integrating life-science perspectives with social-science perspectives; (iii) considering the effects of brain biology on behaviours that law seeks to regulate; and (iv) examining the effects of evolutionary processes on brain design. PMID:15590611

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

PubMed

Kriegeskorte, Nikolaus

2015-11-24

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

Stimulation-Based Control of Dynamic Brain Networks

PubMed Central

Pasqualetti, Fabio; Gu, Shi; Cieslak, Matthew

2016-01-01

The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbations. To study the system wide impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of targeted stimulation. Validating predictions from network control theory, we uncover the relationship between regional controllability and the focal versus global impact of stimulation, and we relate these findings to differences in the underlying network architecture. Finally, by mapping brain regions to cognitive systems, we observe that the default mode system imparts large global change despite being highly constrained by structural connectivity. This work forms an important step towards the development of personalized stimulation protocols for medical treatment or performance enhancement. PMID:27611328

Law, evolution and the brain: applications and open questions.

PubMed

Jones, Owen D

2004-11-29

This paper discusses several issues at the intersection of law and brain science. It focuses principally on ways in which an improved understanding of how evolutionary processes affect brain function and human behaviour may improve law's ability to regulate behaviour. It explores sample uses of such 'evolutionary analysis in law' and also raises questions about how that analysis might be improved in the future. Among the discussed uses are: (i) clarifying cost-benefit analyses; (ii) providing theoretical foundation and potential predictive power; (iii) assessing comparative effectiveness of legal strategies; and (iv) revealing deep patterns in legal architecture. Throughout, the paper emphasizes the extent to which effective law requires: (i) building effective behavioural models; (ii) integrating life-science perspectives with social-science perspectives; (iii) considering the effects of brain biology on behaviours that law seeks to regulate; and (iv) examining the effects of evolutionary processes on brain design.

SAMS--a systems architecture for developing intelligent health information systems.

PubMed

Yılmaz, Özgün; Erdur, Rıza Cenk; Türksever, Mustafa

2013-12-01

In this paper, SAMS, a novel health information system architecture for developing intelligent health information systems is proposed and also some strategies for developing such systems are discussed. The systems fulfilling this architecture will be able to store electronic health records of the patients using OWL ontologies, share patient records among different hospitals and provide physicians expertise to assist them in making decisions. The system is intelligent because it is rule-based, makes use of rule-based reasoning and has the ability to learn and evolve itself. The learning capability is provided by extracting rules from previously given decisions by the physicians and then adding the extracted rules to the system. The proposed system is novel and original in all of these aspects. As a case study, a system is implemented conforming to SAMS architecture for use by dentists in the dental domain. The use of the developed system is described with a scenario. For evaluation, the developed dental information system will be used and tried by a group of dentists. The development of this system proves the applicability of SAMS architecture. By getting decision support from a system derived from this architecture, the cognitive gap between experienced and inexperienced physicians can be compensated. Thus, patient satisfaction can be achieved, inexperienced physicians are supported in decision making and the personnel can improve their knowledge. A physician can diagnose a case, which he/she has never diagnosed before, using this system. With the help of this system, it will be possible to store general domain knowledge in this system and the personnel's need to medical guideline documents will be reduced.

The elements of a comprehensive education for future architectural acousticians

NASA Astrophysics Data System (ADS)

Wang, Lily M.

2005-04-01

Curricula for students who seek to become consultants of architectural acoustics or researchers in the field are few in the United States and in the world. This paper will present the author's opinions on the principal skills a student should obtain from a focused course of study in architectural acoustics. These include: (a) a solid command of math and wave theory, (b) fluency with digital signal processing techniques and sound measurement equipment, (c) expertise in using architectural acoustic software with an understanding of its limitations, (d) knowledge of building mechanical systems, (e) an understanding of human psychoacoustics, and (f) an appreciation for the artistic aspects of the discipline. Additionally, writing and presentation skills should be emphasized and participation in professional societies encouraged. Armed with such abilities, future architectural acousticians will advance the field significantly.

Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features.

PubMed

Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri; Khanna, Shashank; Frisoni, Giovanni B; Hofmann-Apitius, Martin

2017-01-01

Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.

Matrix metalloproteinases in the brain and blood–brain barrier: Versatile breakers and makers

PubMed Central

Rempe, Ralf G; Hartz, Anika MS

2016-01-01

Matrix metalloproteinases are versatile endopeptidases with many different functions in the body in health and disease. In the brain, matrix metalloproteinases are critical for tissue formation, neuronal network remodeling, and blood–brain barrier integrity. Many reviews have been published on matrix metalloproteinases before, most of which focus on the two best studied matrix metalloproteinases, the gelatinases MMP-2 and MMP-9, and their role in one or two diseases. In this review, we provide a broad overview of the role various matrix metalloproteinases play in brain disorders. We summarize and review current knowledge and understanding of matrix metalloproteinases in the brain and at the blood–brain barrier in neuroinflammation, multiple sclerosis, cerebral aneurysms, stroke, epilepsy, Alzheimer’s disease, Parkinson’s disease, and brain cancer. We discuss the detrimental effects matrix metalloproteinases can have in these conditions, contributing to blood–brain barrier leakage, neuroinflammation, neurotoxicity, demyelination, tumor angiogenesis, and cancer metastasis. We also discuss the beneficial role matrix metalloproteinases can play in neuroprotection and anti-inflammation. Finally, we address matrix metalloproteinases as potential therapeutic targets. Together, in this comprehensive review, we summarize current understanding and knowledge of matrix metalloproteinases in the brain and at the blood–brain barrier in brain disorders. PMID:27323783

Character, School Leadership, and the Brain: Learning How To Integrate Knowledge with Behavioral Change.

ERIC Educational Resources Information Center

Calabrese, Raymond L.; Roberts, Brian

2002-01-01

Asserts that character is at the core of leadership, and that character development requires behavioral change as well as knowledge acquisition. Discusses how incorporating behavioral change into administrator preparation programs requires faculty to consider recent findings in neuroscience on how the brain learns, and the incorporation of these…

How Does Neuroscience Inform the Study of Cognitive Development?

ERIC Educational Resources Information Center

Nelson, Charles A.; Moulson, Margaret C.; Richmond, Jenny

2006-01-01

The fields of developmental psychology and developmental neuroscience have existed independently of one another for many years. This is unfortunate, as knowledge of how the brain develops can inform the study of behavioral development. In this paper, we provide two examples of how knowledge about brain development has improved our understanding of…

A multiscale approach for the reconstruction of the fiber architecture of the human brain based on 3D-PLI

PubMed Central

Reckfort, Julia; Wiese, Hendrik; Pietrzyk, Uwe; Zilles, Karl; Amunts, Katrin; Axer, Markus

2015-01-01

Structural connectivity of the brain can be conceptionalized as a multiscale organization. The present study is built on 3D-Polarized Light Imaging (3D-PLI), a neuroimaging technique targeting the reconstruction of nerve fiber orientations and therefore contributing to the analysis of brain connectivity. Spatial orientations of the fibers are derived from birefringence measurements of unstained histological sections that are interpreted by means of a voxel-based analysis. This implies that a single fiber orientation vector is obtained for each voxel, which reflects the net effect of all comprised fibers. We have utilized two polarimetric setups providing an object space resolution of 1.3 μm/px (microscopic setup) and 64 μm/px (macroscopic setup) to carry out 3D-PLI and retrieve fiber orientations of the same tissue samples, but at complementary voxel sizes (i.e., scales). The present study identifies the main sources which cause a discrepancy of the measured fiber orientations observed when measuring the same sample with the two polarimetric systems. As such sources the differing optical resolutions and diverging retardances of the implemented waveplates were identified. A methodology was implemented that enables the compensation of measured different systems' responses to the same birefringent sample. This opens up new ways to conduct multiscale analysis in brains by means of 3D-PLI and to provide a reliable basis for the transition between different scales of the nerve fiber architecture. PMID:26388744

Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation.

PubMed

Kamnitsas, Konstantinos; Ledig, Christian; Newcombe, Virginia F J; Simpson, Joanna P; Kane, Andrew D; Menon, David K; Rueckert, Daniel; Glocker, Ben

2017-02-01

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Regulating Cortical Oscillations in an Inhibition-Stabilized Network.

PubMed

Jadi, Monika P; Sejnowski, Terrence J

2014-04-21

Understanding the anatomical and functional architecture of the brain is essential for designing neurally inspired intelligent systems. Theoretical and empirical studies suggest a role for narrowband oscillations in shaping the functional architecture of the brain through their role in coding and communication of information. Such oscillations are ubiquitous signals in the electrical activity recorded from the brain. In the cortex, oscillations detected in the gamma range (30-80 Hz) are modulated by behavioral states and sensory features in complex ways. How is this regulation achieved? Although several underlying principles for the genesis of these oscillations have been proposed, a unifying account for their regulation has remained elusive. In a network of excitatory and inhibitory neurons operating in an inhibition-stabilized regime, we show that strongly superlinear responses of inhibitory neurons facilitate bidirectional regulation of oscillation frequency and power. In such a network, the balance of drives to the excitatory and inhibitory populations determines how the power and frequency of oscillations are modulated. The model accounts for the puzzling increase in their frequency with the salience of visual stimuli, and a decrease with their size. Oscillations in our model grow stronger as the mean firing level is reduced, accounting for the size dependence of visually evoked gamma rhythms, and suggesting a role for oscillations in improving the signal-to-noise ratio (SNR) of signals in the brain. Empirically testing such predictions is still challenging, and implementing the proposed coding and communication strategies in neuromorphic systems could assist in our understanding of the biological system.

Research and application of knowledge resources network for product innovation.

PubMed

Li, Chuan; Li, Wen-qiang; Li, Yan; Na, Hui-zhen; Shi, Qian

2015-01-01

In order to enhance the capabilities of knowledge service in product innovation design service platform, a method of acquiring knowledge resources supporting for product innovation from the Internet and providing knowledge active push is proposed. Through knowledge modeling for product innovation based on ontology, the integrated architecture of knowledge resources network is put forward. The technology for the acquisition of network knowledge resources based on focused crawler and web services is studied. Knowledge active push is provided for users by user behavior analysis and knowledge evaluation in order to improve users' enthusiasm for participation in platform. Finally, an application example is illustrated to prove the effectiveness of the method.

The Functional Architecture of the Brain Underlies Strategic Deception in Impression Management

PubMed Central

Luo, Qiang; Ma, Yina; Bhatt, Meghana A.; Montague, P. Read; Feng, Jianfeng

2017-01-01

Impression management, as one of the most essential skills of social function, impacts one's survival and success in human societies. However, the neural architecture underpinning this social skill remains poorly understood. By employing a two-person bargaining game, we exposed three strategies involving distinct cognitive processes for social impression management with different levels of strategic deception. We utilized a novel adaptation of Granger causality accounting for signal-dependent noise (SDN), which captured the directional connectivity underlying the impression management during the bargaining game. We found that the sophisticated strategists engaged stronger directional connectivity from both dorsal anterior cingulate cortex and retrosplenial cortex to rostral prefrontal cortex, and the strengths of these directional influences were associated with higher level of deception during the game. Using the directional connectivity as a neural signature, we identified the strategic deception with 80% accuracy by a machine-learning classifier. These results suggest that different social strategies are supported by distinct patterns of directional connectivity among key brain regions for social cognition. PMID:29163095

Early Exposure to Haloperidol or Olanzapine Induces Long-Term Alterations of Dendritic Form

PubMed Central

Frost, Douglas O.; Page, Stephanie Cerceo; Carroll, Cathy; Kolb, Bryan

2009-01-01

Exposure of the developing brain to a wide variety of drugs of abuse (eg., stimulants, opioids, ethanol, etc.) can induce life-long changes in behavior and neural circuitry. However, the long-term effects of exposure to therapeutic, psychotropic drugs have only recently begun to be appreciated. Antipsychotic drugs are little studied in this regard. Here we quantitatively analyzed dendritic architecture in adult mice treated with paradigmatic typical- (haloperidol) or atypical (olanzapine) antipsychotic drugs at developmental stages corresponding to fetal or fetal plus early childhood stages in humans. In layer 3 pyramidal cells of the medial and orbital prefrontal cortices and the parietal cortex and in spiny neurons of the core of the nucleus accumbens, both drugs induced significant changes (predominantly reductions) in the amount and complexity of dendritic arbor and the density of dendritic spines. The drug-induced plasticity of dendritic architecture suggests changes in patterns of neuronal connectivity in multiple brain regions that are likely to be functionally significant. PMID:19862684

Lateral specialization in unilateral spatial neglect: a cognitive robotics model.

PubMed

Conti, Daniela; Di Nuovo, Santo; Cangelosi, Angelo; Di Nuovo, Alessandro

2016-08-01

In this paper, we present the experimental results of an embodied cognitive robotic approach for modelling the human cognitive deficit known as unilateral spatial neglect (USN). To this end, we introduce an artificial neural network architecture designed and trained to control the spatial attentional focus of the iCub robotic platform. Like the human brain, the architecture is divided into two hemispheres and it incorporates bio-inspired plasticity mechanisms, which allow the development of the phenomenon of the specialization of the right hemisphere for spatial attention. In this study, we validate the model by replicating a previous experiment with human patients affected by the USN and numerical results show that the robot mimics the behaviours previously exhibited by humans. We also simulated recovery after the damage to compare the performance of each of the two hemispheres as additional validation of the model. Finally, we highlight some possible advantages of modelling cognitive dysfunctions of the human brain by means of robotic platforms, which can supplement traditional approaches for studying spatial impairments in humans.

The Functional Architecture of the Brain Underlies Strategic Deception in Impression Management.

PubMed

Luo, Qiang; Ma, Yina; Bhatt, Meghana A; Montague, P Read; Feng, Jianfeng

2017-01-01

Impression management, as one of the most essential skills of social function, impacts one's survival and success in human societies. However, the neural architecture underpinning this social skill remains poorly understood. By employing a two-person bargaining game, we exposed three strategies involving distinct cognitive processes for social impression management with different levels of strategic deception. We utilized a novel adaptation of Granger causality accounting for signal-dependent noise (SDN), which captured the directional connectivity underlying the impression management during the bargaining game. We found that the sophisticated strategists engaged stronger directional connectivity from both dorsal anterior cingulate cortex and retrosplenial cortex to rostral prefrontal cortex, and the strengths of these directional influences were associated with higher level of deception during the game. Using the directional connectivity as a neural signature, we identified the strategic deception with 80% accuracy by a machine-learning classifier. These results suggest that different social strategies are supported by distinct patterns of directional connectivity among key brain regions for social cognition.

The Relationship Between Concussion Knowledge and the High School Athlete's Intention to Report Traumatic Brain Injury Symptoms.

PubMed

Taylor, Mary Ellen; Sanner, Jennifer E

2017-02-01

Sports-related concussion or traumatic brain injury (TBI) is a frequent occurrence among high school athletes. Long-term and short-term effects of TBI on the athlete's developing brain can be minimized if the athlete reports and is effectively treated for TBI symptoms. Knowledge of concussion symptoms and a school culture of support are critical in order to promote the student's intention to report TBI symptoms. The purpose of this systematic review is to examine the relationship between the high school athlete's concussion knowledge and an intention to report TBI symptoms. One hundred eleven articles were retrieved and four articles met established criteria and were included in this systematic review. A link appears to exist between high school athlete concussion knowledge and an intention to report TBI symptoms. School nurses can provide a supportive environment and concussion knowledge to the high school athlete in order to ultimately facilitate TBI symptom reporting.

Hybrid knowledge systems

NASA Technical Reports Server (NTRS)

Subrahmanian, V. S.

1994-01-01

An architecture called hybrid knowledge system (HKS) is described that can be used to interoperate between a specification of the control laws describing a physical system, a collection of databases, knowledge bases and/or other data structures reflecting information about the world in which the physical system controlled resides, observations (e.g. sensor information) from the external world, and actions that must be taken in response to external observations.

Relation between brain architecture and mathematical ability in children: a DBM study.

PubMed

Han, Zhaoying; Davis, Nicole; Fuchs, Lynn; Anderson, Adam W; Gore, John C; Dawant, Benoit M

2013-12-01

Population-based studies indicate that between 5 and 9 percent of US children exhibit significant deficits in mathematical reasoning, yet little is understood about the brain morphological features related to mathematical performances. In this work, deformation-based morphometry (DBM) analyses have been performed on magnetic resonance images of the brains of 79 third graders to investigate whether there is a correlation between brain morphological features and mathematical proficiency. Group comparison was also performed between Math Difficulties (MD-worst math performers) and Normal Controls (NC), where each subgroup consists of 20 age and gender matched subjects. DBM analysis is based on the analysis of the deformation fields generated by non-rigid registration algorithms, which warp the individual volumes to a common space. To evaluate the effect of registration algorithms on DBM results, five nonrigid registration algorithms have been used: (1) the Adaptive Bases Algorithm (ABA); (2) the Image Registration Toolkit (IRTK); (3) the FSL Nonlinear Image Registration Tool; (4) the Automatic Registration Tool (ART); and (5) the normalization algorithm available in SPM8. The deformation field magnitude (DFM) was used to measure the displacement at each voxel, and the Jacobian determinant (JAC) was used to quantify local volumetric changes. Results show there are no statistically significant volumetric differences between the NC and the MD groups using JAC. However, DBM analysis using DFM found statistically significant anatomical variations between the two groups around the left occipital-temporal cortex, left orbital-frontal cortex, and right insular cortex. Regions of agreement between at least two algorithms based on voxel-wise analysis were used to define Regions of Interest (ROIs) to perform an ROI-based correlation analysis on all 79 volumes. Correlations between average DFM values and standard mathematical scores over these regions were found to be significant. We also found that the choice of registration algorithm has an impact on DBM-based results, so we recommend using more than one algorithm when conducting DBM studies. To the best of our knowledge, this is the first study that uses DBM to investigate brain anatomical features related to mathematical performance in a relatively large population of children. © 2013.

Computers for symbolic processing

NASA Technical Reports Server (NTRS)

Wah, Benjamin W.; Lowrie, Matthew B.; Li, Guo-Jie

1989-01-01

A detailed survey on the motivations, design, applications, current status, and limitations of computers designed for symbolic processing is provided. Symbolic processing computations are performed at the word, relation, or meaning levels, and the knowledge used in symbolic applications may be fuzzy, uncertain, indeterminate, and ill represented. Various techniques for knowledge representation and processing are discussed from both the designers' and users' points of view. The design and choice of a suitable language for symbolic processing and the mapping of applications into a software architecture are then considered. The process of refining the application requirements into hardware and software architectures is treated, and state-of-the-art sequential and parallel computers designed for symbolic processing are discussed.

Text-based discovery in biomedicine: the architecture of the DAD-system.

PubMed

Weeber, M; Klein, H; Aronson, A R; Mork, J G; de Jong-van den Berg, L T; Vos, R

2000-01-01

Current scientific research takes place in highly specialized contexts with poor communication between disciplines as a likely consequence. Knowledge from one discipline may be useful for the other without researchers knowing it. As scientific publications are a condensation of this knowledge, literature-based discovery tools may help the individual scientist to explore new useful domains. We report on the development of the DAD-system, a concept-based Natural Language Processing system for PubMed citations that provides the biomedical researcher such a tool. We describe the general architecture and illustrate its operation by a simulation of a well-known text-based discovery: The favorable effects of fish oil on patients suffering from Raynaud's disease [1].

A reinforcement learning-based architecture for fuzzy logic control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1992-01-01

This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

Portable inference engine: An extended CLIPS for real-time production systems

NASA Technical Reports Server (NTRS)

Le, Thach; Homeier, Peter

1988-01-01

The present C-Language Integrated Production System (CLIPS) architecture has not been optimized to deal with the constraints of real-time production systems. Matching in CLIPS is based on the Rete Net algorithm, whose assumption of working memory stability might fail to be satisfied in a system subject to real-time dataflow. Further, the CLIPS forward-chaining control mechanism with a predefined conflict resultion strategy may not effectively focus the system's attention on situation-dependent current priorties, or appropriately address different kinds of knowledge which might appear in a given application. Portable Inference Engine (PIE) is a production system architecture based on CLIPS which attempts to create a more general tool while addressing the problems of real-time expert systems. Features of the PIE design include a modular knowledge base, a modified Rete Net algorithm, a bi-directional control strategy, and multiple user-defined conflict resolution strategies. Problems associated with real-time applications are analyzed and an explanation is given for how the PIE architecture addresses these problems.

Executive control systems in the engineering design environment. M.S. Thesis

NASA Technical Reports Server (NTRS)

Hurst, P. W.

1985-01-01

An executive control system (ECS) is a software structure for unifying various applications codes into a comprehensive system. It provides a library of applications, a uniform access method through a cental user interface, and a data management facility. A survey of twenty-four executive control systems designed to unify various CAD/CAE applications for use in diverse engineering design environments within government and industry was conducted. The goals of this research were to establish system requirements to survey state-of-the-art architectural design approaches, and to provide an overview of the historical evolution of these systems. Foundations for design are presented and include environmental settings, system requirements, major architectural components, and a system classification scheme based on knowledge of the supported engineering domain(s). An overview of the design approaches used in developing the major architectural components of an ECS is presented with examples taken from the surveyed systems. Attention is drawn to four major areas of ECS development: interdisciplinary usage; standardization; knowledge utilization; and computer science technology transfer.

Application of a Multimedia Service and Resource Management Architecture for Fault Diagnosis

PubMed Central

Castro, Alfonso; Sedano, Andrés A.; García, Fco. Javier; Villoslada, Eduardo

2017-01-01

Nowadays, the complexity of global video products has substantially increased. They are composed of several associated services whose functionalities need to adapt across heterogeneous networks with different technologies and administrative domains. Each of these domains has different operational procedures; therefore, the comprehensive management of multi-domain services presents serious challenges. This paper discusses an approach to service management linking fault diagnosis system and Business Processes for Telefónica’s global video service. The main contribution of this paper is the proposal of an extended service management architecture based on Multi Agent Systems able to integrate the fault diagnosis with other different service management functionalities. This architecture includes a distributed set of agents able to coordinate their actions under the umbrella of a Shared Knowledge Plane, inferring and sharing their knowledge with semantic techniques and three types of automatic reasoning: heterogeneous, ontology-based and Bayesian reasoning. This proposal has been deployed and validated in a real scenario in the video service offered by Telefónica Latam. PMID:29283398

Application of a Multimedia Service and Resource Management Architecture for Fault Diagnosis.

PubMed

Castro, Alfonso; Sedano, Andrés A; García, Fco Javier; Villoslada, Eduardo; Villagrá, Víctor A

2017-12-28

Nowadays, the complexity of global video products has substantially increased. They are composed of several associated services whose functionalities need to adapt across heterogeneous networks with different technologies and administrative domains. Each of these domains has different operational procedures; therefore, the comprehensive management of multi-domain services presents serious challenges. This paper discusses an approach to service management linking fault diagnosis system and Business Processes for Telefónica's global video service. The main contribution of this paper is the proposal of an extended service management architecture based on Multi Agent Systems able to integrate the fault diagnosis with other different service management functionalities. This architecture includes a distributed set of agents able to coordinate their actions under the umbrella of a Shared Knowledge Plane, inferring and sharing their knowledge with semantic techniques and three types of automatic reasoning: heterogeneous, ontology-based and Bayesian reasoning. This proposal has been deployed and validated in a real scenario in the video service offered by Telefónica Latam.

RT-Syn: A real-time software system generator

NASA Technical Reports Server (NTRS)

Setliff, Dorothy E.

1992-01-01

This paper presents research into providing highly reusable and maintainable components by using automatic software synthesis techniques. This proposal uses domain knowledge combined with automatic software synthesis techniques to engineer large-scale mission-critical real-time software. The hypothesis centers on a software synthesis architecture that specifically incorporates application-specific (in this case real-time) knowledge. This architecture synthesizes complex system software to meet a behavioral specification and external interaction design constraints. Some examples of these external constraints are communication protocols, precisions, timing, and space limitations. The incorporation of application-specific knowledge facilitates the generation of mathematical software metrics which are used to narrow the design space, thereby making software synthesis tractable. Success has the potential to dramatically reduce mission-critical system life-cycle costs not only by reducing development time, but more importantly facilitating maintenance, modifications, and extensions of complex mission-critical software systems, which are currently dominating life cycle costs.

A multimedia Anatomy Browser incorporating a knowledge base and 3D images.

PubMed Central

Eno, K.; Sundsten, J. W.; Brinkley, J. F.

1991-01-01

We describe a multimedia program for teaching anatomy. The program, called the Anatomy Browser, displays cross-sectional and topographical images, with outlines around structures and regions of interest. The user may point to these structures and retrieve text descriptions, view symbolic relationships between structures, or view spatial relationships by accessing 3-D graphics animations from videodiscs produced specifically for this program. The software also helps students exercise what they have learned by asking them to identify structures by name and location. The program is implemented in a client-server architecture, with the user interface residing on a Macintosh, while images, data, and a growing symbolic knowledge base of anatomy are stored on a fileserver. This architecture allows us to develop practical tutorial modules that are in current use, while at the same time developing the knowledge base that will lead to more intelligent tutorial systems. PMID:1807699

Nanoparticle-assisted-multiphoton microscopy for in vivo brain imaging of mice

NASA Astrophysics Data System (ADS)

Qian, Jun

2015-03-01

Neuro/brain study has attracted much attention during past few years, and many optical methods have been utilized in order to obtain accurate and complete neural information inside the brain. Relying on simultaneous absorption of two or more near-infrared photons by a fluorophore, multiphoton microscopy can achieve deep tissue penetration and efficient light detection noninvasively, which makes it very suitable for thick-tissue and in vivo bioimaging. Nanoparticles possess many unique optical and chemical properties, such as anti-photobleaching, large multiphoton absorption cross-section, and high stability in biological environment, which facilitates their applications in long-term multiphoton microscopy as contrast agents. In this paper, we will introduce several typical nanoparticles (e.g. organic dye doped polymer nanoparticles and gold nanorods) with high multiphoton fluorescence efficiency. We further applied them in two- and three-photon in vivo functional brain imaging of mice, such as brain-microglia imaging, 3D architecture reconstruction of brain blood vessel, and blood velocity measurement.

A Technical Infrastructure to Integrate Dynamics AX ERP and CRM into University Curriculum

ERIC Educational Resources Information Center

Wimmer, Hayden; Hall, Kenneth

2016-01-01

Enterprise Resource Planning and Customer Relationship Management are becoming important topics at the university level, and are increasingly receiving course-level attention in the curriculum. In fact, the Information Systems Body of Knowledge specifically identifies Enterprise Architecture as an Information Systems-specific knowledge area. The…