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Connectivity subnetwork learning for pathology and developmental variations

Yasser Ghanbari, Alex R Smith, Robert T Schultz, Ragini Verma
2013
In this paper, we present a unified framework for learning subnetwork patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis  ...  Network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging.  ...  This paper presents a framework for learning sparse subnetwork patterns of non-negative connectivity matrices by their projective non-negative decomposition into sets of i) discriminative or pathology-specific  ... 
pmid:24505653 pmcid:PMC4054863 fatcat:tlo73i7ehrfy5dcqdwodacujdq

Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding

Yasser Ghanbari, Alex R. Smith, Robert T. Schultz, Ragini Verma
2014 Medical Image Analysis  
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  ...  The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism that shows localized sparse sub-networks which mostly capture the changes related to pathology and developmental  ...  SAP # 4100042728 and SAP # 4100047863 (PI: Robert T.  ... 
doi:10.1016/j.media.2014.06.006 pmid:25037933 pmcid:PMC4205764 fatcat:vghm2bpjcndb5bobwjy623rxqy

Multivariate dynamical modelling of structural change during development

Gabriel Ziegler, Gerard R. Ridgway, Sarah-Jayne Blakemore, John Ashburner, Will Penny
2017 NeuroImage  
, due to competition for space, or structural connectivity, and suchlike.  ...  connectivity.  ...  -3343, N01-MH9-0002, and N01-NS-9-2314, -2315, -2316, -2317, -2319 and -2320).  ... 
doi:10.1016/j.neuroimage.2016.12.017 pmid:27979788 pmcid:PMC5315058 fatcat:wxnpz2ung5dqddlcg7plrtxq5e

Disinhibition and Detachment in Adolescence: A Developmental Cognitive Neuroscience Perspective on the Alternative Model for Personality Disorders

Timothy A. Allen, Michael N. Hallquist
2020 Psychopathology  
personality pathology in adolescence and early adulthood.  ...  In contrast, dimensional models of personality pathology, such as the Alternative Model for Personality Disorders (AMPD) in DSM-5, may provide a stronger foundation for neurobiological investigations of  ...  Statement of Ethics This article was prepared in compliance with internationally accepted standards for research practice and reporting.  ... 
doi:10.1159/000509984 pmid:32777787 pmcid:PMC7530016 fatcat:7t5zboqbujcd7dkrxtoh65oryy

Rich-club neurocircuitry: function, evolution, and vulnerability

Alessandra Griffa, Martijn P Van den Heuvel
2018 Dialogues in Clinical Neuroscience  
An interesting property of neural networks is that elements rich in connections are central to the network organization and tend to interconnect strongly with each other, forming so-called rich clubs.  ...  its relevance to human cognition and behavior, and vulnerability to brain disorders.  ...  This evidence has possible implications for the understanding of pathological mechanisms linking structural connectivity alterations, brain dynamics, and symptomatological/cognitive consequences. 5, 37  ... 
pmid:30250389 pmcid:PMC6136122 fatcat:mvlpxiyhsjgd5a7p6xz6kyg6fa

A joint network optimization framework to predict clinical severity from resting state functional MRI data

N.S. D'Souza, M.B. Nebel, N. Wymbs, S.H. Mostofsky, A. Venkataraman
2019 NeuroImage  
Our method outperforms standard semi-supervised frameworks, which employ conventional graph theoretic and statistical representation learning techniques to relate the rs-fMRI correlations to behavior.  ...  These subnetworks are modeled as rank-one outer-products which correspond to the elemental patterns of co-activation across the brain; the subnetworks are combined via patient-specific non-negative coefficients  ...  For the ANN, we use the weight matrix learned at the input layer to inform us of the subnetwork connectivity.  ... 
doi:10.1016/j.neuroimage.2019.116314 pmid:31678501 pmcid:PMC7985860 fatcat:b5zeugz6znahpngwuzzsvlcedq

Schizophrenia at a Genetics Crossroads: Where to Now?

A. Corvin
2013 Schizophrenia Bulletin  
Identified modules or local subnetworks can by directly tested to identify enrichment of common risk variation and rare functional mutations.  ...  Because we are learning from cancer research and other fields in medicine, clinical or even pathological diagnostics may bear little relation to the underlying molecular mechanisms of disease.  ... 
doi:10.1093/schbul/sbt041 pmid:23519022 pmcid:PMC3627765 fatcat:rp6juzsdnvdwpl4yvexzh56kwq

Machine Learning on Human Connectome Data from MRI [article]

Colin J Brown, Ghassan Hamarneh
2016 arXiv   pre-print
Connectome data has unique properties, which present both special challenges and opportunities when used for machine learning.  ...  Recently, researchers have been exploring the application of machine learning models to connectome data in order to predict clinical outcomes and analyze the importance of subnetworks in the brain.  ...  Also, the connectivity of the brain is altered by learning and expierences [50] , as well as injury and pathology, so the exact structure of each person's brain is inherently unique, at least on fine  ... 
arXiv:1611.08699v1 fatcat:opmtmr3eejbjjm4swfmg54g4q4

A Joint Network Optimization Framework to Predict Clinical Severity from Resting State Functional MRI Data [article]

Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs, Stewart H. Mostofsky, Archana Venkataraman
2020 arXiv   pre-print
Our method outperforms standard semi-supervised frameworks, which employ conventional graph theoretic and statistical representation learning techniques to relate the rs-fMRI correlations to behavior.  ...  These subnetworks are modeled as rank-one outer-products which correspond to the elemental patterns of co-activation across the brain; the subnetworks are combined via patient-specific non-negative coefficients  ...  MH106564), the National Institute of Neurological Disorders and Stroke (R01NS048527-08), and the Autism Speaks foundation.  ... 
arXiv:2009.03238v1 fatcat:qd7gwymghja6hcrknwehwaqmvu

Solving the paradox of the equipotential and modular brain: A neurocomputational model of stroke vs. slow-growing glioma

James L. Keidel, Stephen R. Welbourne, Matthew A. Lambon Ralph
2010 Neuropsychologia  
of connectivity; experiencedependent plasticity; and the time course of damage.  ...  A clear paradox arises in low-grade glioma where an even greater amount of cortex may be affected and resected without impairment.  ...  On each trial one of the 100 input patterns for subnetwork 1 was presented to input layer 1 and at the same time one of the input patterns for subnetwork 2 was applied to input layer 2.  ... 
doi:10.1016/j.neuropsychologia.2010.02.019 pmid:20188115 fatcat:ymiu67kvsrdi5g7pn2mjilpk44

Intersectional analysis of chronic mild stress-induced lncRNA-mRNA interaction networks in rat hippocampus reveals potential anti-depression/anxiety drug targets

Wei Liao, Yanchen Liu, Haojun Huang, Hong Xie, Weibo Gong, Dan Liu, Fenfang Tian, Rongzhong Huang, Faping Yi, Jian Zhou
2021 Neurobiology of Stress  
Further intersectional analysis of phenotype-associated and drug-associated lncRNA-mRNA networks and subnetworks assisted in identifying 16 hub lncRNAs as potential targets of anti-depression/anxiety drugs  ...  , revealing several important lncRNAs that represent potentially new therapeutic drug targets for depression and anxiety disorders.  ...  BP analysis revealed significant enrichment for developmental process, and positive regulation of biological and cellular processes.  ... 
doi:10.1016/j.ynstr.2021.100347 pmid:34113696 pmcid:PMC8170419 fatcat:4hkfi6g25zas7nfnhhthu74zmy

Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectomics data for Multidimensional Clinical Characterizations [article]

Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti, Nicholas Wymbs, Joshua Robinson, Stewart H. Mostofsky, Archana Venkataraman
2020 arXiv   pre-print
We use the DTI tractography to regularize this matrix factorization and learn anatomically informed functional connectivity profiles.  ...  Our hybrid model outperforms several state-of-the-art approaches at clinical outcome prediction and learns interpretable multimodal neural signatures of brain organization.  ...  and R01 MH106564), the National Institute of Neurological Disorders and Stroke (R01NS048527-08), and the Autism Speaks foundation.  ... 
arXiv:2008.12410v1 fatcat:54f6z2o2krevtkiq53eboldnce

The Human Connectome: A Structural Description of the Human Brain

Olaf Sporns, Giulio Tononi, Rolf Kötter
2005 PLoS Computational Biology  
T he connection matrix of the human brain (the human "connectome") represents an indispensable foundation for basic and applied neurobiological research.  ...  While some databases or collations of largescale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated  ...  Some individual variations may be due to genetic differences, others may be the result of developmental and experiential history, gender differences, pathologies, or responses to injury.  ... 
doi:10.1371/journal.pcbi.0010042 pmid:16201007 pmcid:PMC1239902 fatcat:zt54gj2bs5aghik4lyxlnzixry

Integrated Analysis of Brain Transcriptome Reveals Convergent Molecular Pathways in Autism Spectrum Disorder

Xiaodan Li, Yuncong Zhang, Luxi Wang, Yunqing Lin, Zhaomin Gao, Xiaolei Zhan, Yan Huang, Caihong Sun, Dong Wang, Shuang Liang, Lijie Wu
2019 Frontiers in Psychiatry  
We further determined transcriptional and post-transcriptional regulation subnetwork for each ASD-correlated module, including 47 pivot transcription factors, 130 pivot miRNAs, and 7 pivot lncRNAs.  ...  Co-expression network analysis revealed that a total of seven (four for CC set, three for PFC set) core dysfunctional modules strongly enriched for known ASD-risk genes.  ...  ACKNOWLEDGMENTS This study was supported by Province Key Laboratory of Children development and genetic research in Harbin Medical University, Heilongjiang, China.  ... 
doi:10.3389/fpsyt.2019.00706 pmid:31649562 pmcid:PMC6795181 fatcat:6nrawfsqvrgdhl2j3vb7i4uqdq

The Zebrafish Equivalent of Alzheimer's Disease-Associated PRESENILIN Isoform PS2V Regulates Inflammatory and Other Responses to Hypoxic Stress

Esmaeil Ebrahimie, Seyyed Hani Moussavi Nik, Morgan Newman, Mark Van Der Hoek, Michael Lardelli, Francesco Amenta
2016 Journal of Alzheimer's Disease  
In contrast, genetic variation at the PSEN1 and PSEN2 loci does not appear to contribute to risk for the sporadic, late onset form of the disease (sAD), leading to doubts that these genes play a role in  ...  Our results imply an important role for PS2V in sAD as a component of a pathological mechanism that includes hypoxia / oxidative stress and support investigation of PS2V's role in other diseases, including  ...  AD [5] ) no genetic variation at the PSEN1 or PSEN2 loci has yet been discovered to contribute to the risk for developing sAD [6] .  ... 
doi:10.3233/jad-150678 pmid:27031468 fatcat:laekgwv5njesfmnuxjcda7mrga
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