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Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding
2014
Medical Image Analysis
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We propose a unified framework of non-negative matrix factorization and graph embedding for learning sub-network patterns of connectivity by their projective non-negative decomposition into a reconstructive ...
Such a network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. ...
Acknowledgments This research was supported by the following grants from National Institutes of Health: MH092862 and MH098010 (PI: Ragini Verma), and grants from the Pennsylvania Department of Health: ...
doi:10.1016/j.media.2014.06.006
pmid:25037933
pmcid:PMC4205764
fatcat:vghm2bpjcndb5bobwjy623rxqy
A Network Neuroscience Approach to Typical and Atypical Brain Development
2018
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
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Human brain networks based on neuroimaging data have already proven useful in characterizing both normal and abnormal brain structure and function. ...
We begin with an overview of recent large-scale efforts to map healthy brain development, and we describe the key role played by longitudinal data including repeated measurements over a long period of ...
via a unified framework of nonnegative matrix factorization and graph embedding Ghanbari et al [111] 2014 24 ASD, 59 controls
6-18 No DWI
The Emergence of Network Inefficiencies in Infants With ...
doi:10.1016/j.bpsc.2018.03.003
pmid:29703679
pmcid:PMC6986924
fatcat:jlpx2gvecrceflaux3g2z6l4rm
Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data
2021
Basic and Clinical Neuroscience
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Finally, we discuss the state-of-the-art of individualized prediction of intelligence from neuroimaging data and point out future strategies. ...
Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence. ...
Acknowledgments The authors thank the Cognitive Sciences and Technologies Council, CSTC, Iran, and the São Paulo Research Foundation, FAPESP, Brazil for supporting the project. ...
doi:10.32598/bcn.12.1.574.1
pmid:33995924
pmcid:PMC8114859
fatcat:vkdjjavpyzgs7ejma2ajog77ua
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
[article]
2021
arXiv
pre-print
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We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis. ...
them as a matrix. ...
In the framework which is illustrated in Fig. 16 , each pathology is illustrated with semantic vectors via a word embedding, and the graph representation is learned from the co-occurrence matrix of training ...
arXiv:2105.13137v1
fatcat:gm7d2ziagba7bj3g34u4t3k43y
Introduction to JINS Special Issue on Human Brain Connectivity in the Modern Era: Relevance to Understanding Health and Disease
2016
Journal of the International Neuropsychological Society
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JINS Vo l u m e 2 2 , N u m b e r 2 F e b r u a r y 2 0 1 6 I S S N 1 3 5 5 -6 1 7 7 ...
Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function. ...
Martinos Center for Biomedical Imaging at the Massachusetts General Hospital, which receives funding from P41EB015896, a Biotechnology Resource Grant supported by the National Institute of Biomedical Imaging ...
doi:10.1017/s1355617716000047
fatcat:f2preenihbes5ftkrxbo7tgt64
Graph Neural Networks for Natural Language Processing: A Survey
[article]
2021
arXiv
pre-print
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We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder ...
As a result, thereis a surge of interests in developing new deep learning techniques on graphs for a large numberof NLP tasks. ...
., 2018) on the sub-graph only from the knowledge base and then
take average of the linked words’ feature in the connected documents. ...
arXiv:2106.06090v1
fatcat:zvkhinpcvzbmje4kjpwjs355qu
Subject-adaptive Integration of Multiple SICE Brain Networks with Different Sparsity
2017
Pattern Recognition
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Both covariance descriptor and SICE matrix belong to the set of symmetric positive-definite (SPD) ma- ...
In many contexts of computer vision, the data are represented by or converted to covariance-based representations, including covariance descriptor and sparse inverse covariance estimation (SICE), due to ...
This chapter proposes a learning based framework that integrates a set of SICE networks with the aim of attaining more discriminative power. ...
doi:10.1016/j.patcog.2016.09.024
fatcat:mkwvr4jtxbgyjb2fxjcszp3d4i
Poster Session I
2013
Neuropsychopharmacology
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Results: Thirty-five subjects with a mean age of 50±9 years and 70% Caucasian. ...
KYNA-based animal model of SZ; and (3) the relative roles of a7nAChRs vs NMDA receptors in performance in the DNMTP task.To this end, we determined, in a separate group of intact animals, the effects ...
Slifstein, A. Abi-Dargham, and S. Kapur, Arch Gen Psychiatry 69, 776 (2012). 2 ...
doi:10.1038/npp.2013.279
fatcat:54ipecxjarcvljrvn5fgtgif5u
25th Annual Computational Neuroscience Meeting: CNS-2016
2016
BMC Neuroscience
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These results outline a framework for categorizing neuronal types based on their functional properties. ...
I will discuss theoretical results that point to functional advantages of splitting neural populations into subtypes, both in feedforward and recurrent networks. ...
Acknowledgements: The work of JB, RG, and SMC was supported in part by R01MH1006674 from the National Institutes of Health. ...
doi:10.1186/s12868-016-0283-6
pmid:27534393
pmcid:PMC5001212
fatcat:bt45etzj2bbolfcxlxo7hlv6ju
Modeling & Analysis
2003
NeuroImage
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The computation of the functional interactions through correlation coefficients (functional connectivity) or through linear regression (effective connectivity) are based on an average over many subjects ...
Abstract Constrained Principal Component Analysis (CPCA) is introduced as a correlation-based method of identifying (a) connectivity between neuronal structures and (b) functional interactions between ...
Acknowledgement This study was conducted by the authors on behalf of the NEST-DD consortium with support from the European Commission (Framework V). ...
doi:10.1016/s1053-8119(05)70006-9
fatcat:zff2suxcofbxvetfrwfwcxi3zm
Final Program, Fortieth Annual Meeting International Neuropsychological Society February 15–18, 2012 Montréal, Québec, Canada
2012
Journal of the International Neuropsychological Society
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In this symposium a panel of neuropsychologists will provide direction and insights towards developing a program of sustained externally funded research. ...
Securing external funding from NIH or other federal agencies is critical to support research costs and academic promotion. ...
Results: An F-test on the thalamic connection density maps reveals that children with RD show a different connectivity-based sub-thalamic pattern from controls. ...
doi:10.1017/s1355617712000537
fatcat:4p2fkgxxzna3vcf3uxxgac6rsi
30th Annual Computational Neuroscience Meeting: CNS*2021–Meeting Abstracts
2021
Journal of Computational Neuroscience
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within the constraints of biological networks. ...
Recently, the framework of predictive coding (Sajikumar et al., 2014 ...
Acknowledgments This project was funded by the Helmholtz Association Initiative and Networking Fund (project number SO-092, Advanced Computing Architectures), and the European Union's Horizon 2020 Framework ...
doi:10.1007/s10827-021-00801-9
pmid:34931275
pmcid:PMC8687879
fatcat:evpmmfpaivgpxdqpive5xdgmwu
A sequential distance-based approach for imputing missing data: Forward Imputation
2016
Advances in Data Analysis and Classification
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The aim is to identify the structure of the graph from data, assuming that the variables satisfy a given ordering. ...
Optimal embedding parameters used in the DVV analysis are obtained via a differential entropy based method using wavelet-based surrogates. ...
doi:10.1007/s11634-016-0243-0
fatcat:yvrqlgllsbesbnvnzzci2egpl4
ACNP 58th Annual Meeting: Poster Session III
2019
Neuropsychopharmacology
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This study obtains the approval from Japanese association of Neuro-Psychiatric Clinics Study Ethical Review Board, and the data investigated were retrieved from databases and de-identified before data ...
Methods: Participants were 4,964 youths (ages 5-17 years) from seven international sites, presenting with a wide range of symptom severity (healthy, non-selected, high-risk, or clinicallyanxious youth) ...
Graph-based network analysis is a technique used to interrogate topological properties of functional brain networks. ...
doi:10.1038/s41386-019-0547-9
pmid:31801974
pmcid:PMC6957926
fatcat:dd7d43ysfvc5bbbstfl73szya4
2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception
2015
Alzheimer's & Dementia
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of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker ...
fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. ...
served as a consultant for Astra Zeneca, Araclon, Medivation/Pfizer, Ipsen, TauRx Therapeutics LTD, Bayer Healthcare, Biogen Idec, Exonhit Therapeutics, SA, Servier, Synarc, Pfizer, and Janssen; has received ...
doi:10.1016/j.jalz.2014.11.001
pmid:26073027
pmcid:PMC5469297
fatcat:2k7ag6astffy5gphqxf5lodkdq
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