A SPARSE REDUCED RANK FRAMEWORK FOR GROUP ANALYSIS OF FUNCTIONAL NEUROIMAGING DATA.

In spatial-temporal neuroimaging studies, there is an evolving literature on the analysis of functional imaging data in order to learn the intrinsic functional connectivity patterns among different brain regions. However, there are only few efficient approaches for integrating functional connectivity pattern across subjects, while accounting for spatial-temporal functional variation across multiple groups of subjects. The objective of this paper is to develop a new sparse reduced rank (SRR) modeling framework for carrying out functional connectivity analysis across multiple groups of subjects in the frequency domain. Our new framework not only can extract both frequency and spatial factors across subjects, but also imposes sparse constraints on the frequency factors. It thus leads to the identification of important frequencies with high power spectra. In addition, we propose two novel adaptive criteria for automatic selection of sparsity level and model rank. Using simulated data, we demonstrate that SRR outperforms several existing methods. Finally, we apply SRR to detect group differences between controls and two subtypes of attention deficit hyperactivity disorder (ADHD) patients, through analyzing the ADHD-200 data.

[1]  Peter Craven,et al.  Smoothing noisy data with spline functions , 1978 .

[2]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[3]  Vinod Menon,et al.  Parietal attentional system aberrations during target detection in adolescents with attention deficit hyperactivity disorder: event-related fMRI evidence. , 2006, The American journal of psychiatry.

[4]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[5]  Karl J. Friston.  Modalities, Modes, and Models in Functional Neuroimaging , 2009, Science.

[6]  Jianhua Z. Huang,et al.  Biclustering via Sparse Singular Value Decomposition , 2010, Biometrics.

[7]  Mariya V Cherkasova,et al.  Neuroimaging in Attention-Deficit Hyperactivity Disorder: Beyond the Frontostriatal Circuitry , 2009, Canadian journal of psychiatry. Revue canadienne de psychiatrie.

[8]  Stewart H Mostofsky,et al.  Variability in post-error behavioral adjustment is associated with functional abnormalities in the temporal cortex in children with ADHD. , 2011, Journal of child psychology and psychiatry, and allied disciplines.

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

[10]  Michael Elad,et al.  Stable recovery of sparse overcomplete representations in the presence of noise , 2006, IEEE Transactions on Information Theory.

[11]  R. Tannock,et al.  Temporal information processing in ADHD: Findings to date and new methods , 2006, Journal of Neuroscience Methods.

[12]  Daniel S. Margulies,et al.  Integration of a neuroimaging processing pipeline into a pan-canadian computing grid , 2012, HPC 2012.

[13]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[14]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[15]  G. Wahba.  Smoothing noisy data with spline functions , 1975 .

[16]  Sungho Tak,et al.  A Data-Driven Sparse GLM for fMRI Analysis Using Sparse Dictionary Learning With MDL Criterion , 2011, IEEE Transactions on Medical Imaging.

[17]  Aapo Hyvärinen,et al.  Independent component analysis of fMRI group studies by self-organizing clustering , 2005, NeuroImage.

[18]  Perry F. Renshaw,et al.  Functional deficits in basal ganglia of children with attention-deficit/hyperactivity disorder shown with functional magnetic resonance imaging relaxometry , 2000, Nature Medicine.

[19]  G. Bush,et al.  Functional Neuroimaging of Attention-Deficit/Hyperactivity Disorder: A Review and Suggested Future Directions , 2005, Biological Psychiatry.

[20]  Ying Guo,et al.  A unified framework for group independent component analysis for multi-subject fMRI data , 2008, NeuroImage.

[21]  Moo K. Chung,et al.  Sparse Brain Network Recovery Under Compressed Sensing , 2011, IEEE Transactions on Medical Imaging.

[22]  J. Pekar,et al.  A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.

[23]  Vince D. Calhoun,et al.  A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data , 2009, NeuroImage.

[24]  K. Davis,et al.  Two systems of resting state connectivity between the insula and cingulate cortex , 2009, Human brain mapping.

[25]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.

[26]  Tianzi Jiang,et al.  Enhanced resting-state brain activities in ADHD patients: A fMRI study , 2008, Brain and Development.

[27]  Gaël Varoquaux,et al.  Multi-subject Dictionary Learning to Segment an Atlas of Brain Spontaneous Activity , 2011, IPMI.

[28]  J. Pekar,et al.  fMRI Activation in a Visual-Perception Task: Network of Areas Detected Using the General Linear Model and Independent Components Analysis , 2001, NeuroImage.

[29]  Ziyad Mahfoud,et al.  What Is an Intracluster Correlation Coefficient? Crucial Concepts for Primary Care Researchers , 2004, The Annals of Family Medicine.

[30]  Andrzej Cichocki,et al.  Nonnegative Matrix and Tensor Factorization T , 2007 .

[31]  H. Akaike.  Maximum likelihood identification of Gaussian autoregressive moving average models , 1973 .

[32]  Haipeng Shen,et al.  A supervised singular value decomposition for independent component analysis of fMRI , 2008 .

[33]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[34]  Geert Molenberghs,et al.  The Effective Sample Size and an Alternative Small-Sample Degrees-of-Freedom Method , 2009 .

[35]  Stephen M. Smith,et al.  The future of FMRI connectivity , 2012, NeuroImage.

[36]  Markus Svensén,et al.  ICA of fMRI Group Study Data , 2002, NeuroImage.

[37]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[38]  T. Adali,et al.  Latency (in)sensitive ICA Group independent component analysis of fMRI data in the temporal frequency domain , 2003, NeuroImage.

[39]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[40]  H. Critchley,et al.  Conjoint activity of anterior insular and anterior cingulate cortex: awareness and response , 2010, Brain Structure and Function.