Atomic dynamic functional interaction patterns for characterization of ADHD

Modeling abnormal temporal dynamics of functional interactions in psychiatric disorders has been of great interest in the neuroimaging field, and thus a variety of methods have been proposed so far. However, the temporal dynamics and disease‐related abnormalities of functional interactions within specific data‐driven discovered subnetworks have been rarely explored yet. In this work, we propose a novel computational framework composed of an effective Bayesian connectivity change point model for modeling functional brain interactions and their dynamics simultaneously and an effective variant of nonnegative matrix factorization for assessing the functional interaction abnormalities within subnetworks. This framework has been applied on the resting state fmagnetic resonance imaging (fMRI) datasets of 23 children with attention‐deficit/hyperactivity disorder (ADHD) and 45 normal control (NC) children, and has revealed two atomic functional interaction patterns (AFIPs) discovered for ADHD and another two AFIPs derived for NC. Together, these four AFIPs could be grouped into two pairs, one common pair representing the common AFIPs in ADHD and NC, and the other abnormal pair representing the abnormal AFIPs in ADHD. Interestingly, by comparing the abnormal AFIP pair, two data‐driven abnormal functional subnetworks are derived. Strikingly, by evaluating the approximation based on the four AFIPs, all of the ADHD children were successfully differentiated from NCs without any false positive. Hum Brain Mapp 35:5262–5278, 2014. © 2014 Wiley Periodicals, Inc.

[1]  Jun S. Liu,et al.  Monte Carlo strategies in scientific computing , 2001 .

[2]  Tao Li,et al.  The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering , 2006, Sixth International Conference on Data Mining (ICDM'06).

[3]  Jorma Laaksonen,et al.  Projective Non-Negative Matrix Factorization with Applications to Facial Image Processing , 2007, Int. J. Pattern Recognit. Artif. Intell..

[4]  Samuele Cortese,et al.  The neurobiology and genetics of Attention-Deficit/Hyperactivity Disorder (ADHD): what every clinician should know. , 2012, European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society.

[5]  Martin A. Lindquist,et al.  Modeling state-related fMRI activity using change-point theory , 2007, NeuroImage.

[6]  C. Gilbert,et al.  Brain States: Top-Down Influences in Sensory Processing , 2007, Neuron.

[7]  Dajiang Zhu,et al.  Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients , 2014, Human brain mapping.

[8]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[9]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[10]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[11]  Russell A. Poldrack,et al.  The future of fMRI in cognitive neuroscience , 2012, NeuroImage.

[12]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[13]  F Xavier Castellanos,et al.  Brain development and ADHD. , 2006, Clinical psychology review.

[14]  Hyunsoo Kim,et al.  Sparse Non-negative Matrix Factorizations via Alternating Non-negativity-constrained Least Squares , 2006 .

[15]  Stephen M. Smith,et al.  Temporally-independent functional modes of spontaneous brain activity , 2012, Proceedings of the National Academy of Sciences.

[16]  Junwei Han,et al.  Inferring functional interaction and transition patterns via dynamic bayesian variable partition models , 2013, Human brain mapping.

[17]  Chris H. Q. Ding,et al.  On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering , 2005, SDM.

[18]  D. Shen,et al.  DICCCOL: dense individualized and common connectivity-based cortical landmarks. , 2013, Cerebral cortex.

[19]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[20]  Xin Zhang,et al.  Characterization of Task-Free/Task-Performance Brain States , 2012, MICCAI.

[21]  Michalis Vazirgiannis,et al.  c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. On Clustering Validation Techniques , 2022 .

[22]  Jin Li,et al.  A New Hierarchical ID-Based Cryptosystem and CCA-Secure PKE , 2006, EUC Workshops.

[23]  Francisco Tirado,et al.  bioNMF: a versatile tool for non-negative matrix factorization in biology , 2006, BMC Bioinformatics.

[24]  Degang Zhang,et al.  Optimization of functional brain ROIs via maximization of consistency of structural connectivity profiles , 2011, NeuroImage.

[25]  F. Xavier Castellanos,et al.  Large-scale brain systems in ADHD: beyond the prefrontal–striatal model , 2012, Trends in Cognitive Sciences.

[26]  E. Bullmore,et al.  Behavioral / Systems / Cognitive Functional Connectivity and Brain Networks in Schizophrenia , 2010 .

[27]  Kaiming Li,et al.  Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis , 2012, Neuroinformatics.

[28]  Pablo Tamayo,et al.  Metagenes and molecular pattern discovery using matrix factorization , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Waqas Majeed,et al.  Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans , 2011, NeuroImage.

[30]  Erkki Oja,et al.  Linear and Nonlinear Projective Nonnegative Matrix Factorization , 2010, IEEE Transactions on Neural Networks.

[31]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[32]  Xin Zhang,et al.  Characterization of task-free and task-performance brain states via functional connectome patterns , 2013, Medical Image Anal..

[33]  Martin A. Lindquist,et al.  Change point estimation in multi-subject fMRI studies , 2010, NeuroImage.

[34]  Yingdong Zhao,et al.  Non-negative matrix factorization of gene expression profiles: a plug-in for BRB-ArrayTools , 2009, Bioinform..

[35]  Scott T. Grafton,et al.  Dynamic reconfiguration of human brain networks during learning , 2010, Proceedings of the National Academy of Sciences.

[36]  Klaas E. Stephan,et al.  The anatomical basis of functional localization in the cortex , 2002, Nature Reviews Neuroscience.

[37]  Lucie N. Hutchins,et al.  Position-dependent motif characterization using non-negative matrix factorization , 2008, Bioinform..

[38]  Xiang Yu,et al.  Meta-Analysis of Functional Roles of DICCCOLs , 2012, Neuroinformatics.

[39]  Karl J. Friston Functional and effective connectivity in neuroimaging: A synthesis , 1994 .

[40]  Robert T. Schultz,et al.  Connectivity Subnetwork Learning for Pathology and Developmental Variations , 2013, MICCAI.

[41]  Catie Chang,et al.  Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.

[42]  N. Ryan,et al.  Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. , 1997, Journal of the American Academy of Child and Adolescent Psychiatry.

[43]  Jinglei Lv,et al.  Exploring functional brain dynamics via a Bayesian connectivity change point model , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[44]  Tianming Liu A few thoughts on brain ROIs , 2011, Brain Imaging and Behavior.

[45]  G. Celeux,et al.  An entropy criterion for assessing the number of clusters in a mixture model , 1996 .

[46]  Jan Derrfuss,et al.  Lost in localization: The need for a universal coordinate database , 2009, NeuroImage.

[47]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.