Combination of Resting State fMRI, DTI, and sMRI Data to Discriminate Schizophrenia by N-way MCCA + jICA

Multimodal brain imaging data have shown increasing utility in answering both scientifically interesting and clinically relevant questions. Each brain imaging technique provides a different view of brain function or structure, while multimodal fusion capitalizes on the strength of each and may uncover hidden relationships that can merge findings from separate neuroimaging studies. However, most current approaches have focused on pair-wise fusion and there is still relatively little work on N-way data fusion and examination of the relationships among multiple data types. We recently developed an approach called “mCCA + jICA” as a novel multi-way fusion method which is able to investigate the disease risk factors that are either shared or distinct across multiple modalities as well as the full correspondence across modalities. In this paper, we applied this model to combine resting state fMRI (amplitude of low-frequency fluctuation, ALFF), gray matter (GM) density, and DTI (fractional anisotropy, FA) data, in order to elucidate the abnormalities underlying schizophrenia patients (SZs, n = 35) relative to healthy controls (HCs, n = 28). Both modality-common and modality-unique abnormal regions were identified in SZs, which were then used for successful classification for seven modality-combinations, showing the potential for a broad applicability of the mCCA + jICA model and its results. In addition, a pair of GM-DTI components showed significant correlation with the positive symptom subscale of Positive and Negative Syndrome Scale (PANSS), suggesting that GM density changes in default model network along with white-matter disruption in anterior thalamic radiation are associated with increased positive PANSS. Findings suggest the DTI anisotropy changes in frontal lobe may relate to the corresponding functional/structural changes in prefrontal cortex and superior temporal gyrus that are thought to play a role in the clinical expression of SZ.

[1]  S. Kay,et al.  The positive and negative syndrome scale (PANSS) for schizophrenia. , 1987, Schizophrenia bulletin.

[2]  Shlomo Geva,et al.  Adaptive nearest neighbor pattern classification , 1991, IEEE Trans. Neural Networks.

[3]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[4]  Karl J. Friston,et al.  Spatial registration and normalization of images , 1995 .

[5]  Streichenwein Sm,et al.  Am J Psychiatry , 1996 .

[6]  M. First,et al.  Structured clinical interview for DSM-IV axis I disorders : SCID-I: clinical version : administration booklet , 1996 .

[7]  M. First,et al.  Structured clinical interview for DSM-IV axis I disorders : SCID-I : clinical version : scoresheet , 1997 .

[8]  Andrew McCallum,et al.  A comparison of event models for naive bayes text classification , 1998, AAAI 1998.

[9]  Si Wu,et al.  Improving support vector machine classifiers by modifying kernel functions , 1999, Neural Networks.

[10]  N. Andreasen,et al.  Anatomic and Functional Variability: The Effects of Filter Size in Group fMRI Data Analysis , 2001, NeuroImage.

[11]  Jean-Francois Mangin,et al.  What is the best similarity measure for motion correction in fMRI time series? , 2002, IEEE Transactions on Medical Imaging.

[12]  T. Klingberg,et al.  Combined analysis of DTI and fMRI data reveals a joint maturation of white and grey matter in a fronto-parietal network. , 2003, Brain research. Cognitive brain research.

[13]  Guinevere F. Eden,et al.  Multivariate analysis of neuronal interactions in the generalized partial least squares framework: simulations and empirical studies , 2003, NeuroImage.

[14]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[15]  Leanne M Williams,et al.  Dysregulation of arousal and amygdala-prefrontal systems in paranoid schizophrenia. , 2004, The American journal of psychiatry.

[16]  J. Kwon,et al.  Gray matter abnormalities in paranoid schizophrenia and their clinical correlations , 2004, Psychiatry Research: Neuroimaging.

[17]  R. Kikinis,et al.  Middle and inferior temporal gyrus gray matter volume abnormalities in chronic schizophrenia: an MRI study. , 2004, The American journal of psychiatry.

[18]  M. Keshavan,et al.  Optimized voxel-based morphometry of gray matter volume in first-episode, antipsychotic-naïve schizophrenia , 2005, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

[19]  Jung-Seok Choi,et al.  Decreased caudal anterior cingulate gyrus volume and positive symptoms in schizophrenia , 2005, Psychiatry Research: Neuroimaging.

[20]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[21]  Manzar Ashtari,et al.  White matter abnormalities in first-episode schizophrenia or schizoaffective disorder: a diffusion tensor imaging study. , 2005, The American journal of psychiatry.

[22]  J. Pekar,et al.  Method for multimodal analysis of independent source differences in schizophrenia: Combining gray matter structural and auditory oddball functional data , 2006, Human brain mapping.

[23]  R. Kikinis,et al.  Middle and inferior temporal gyrus gray matter volume abnormalities in first-episode schizophrenia: an MRI study. , 2006, The American journal of psychiatry.

[24]  Yong He,et al.  Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. , 2007, Brain & development.

[25]  Timothy Edward John Behrens,et al.  Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. , 2007, Brain : a journal of neurology.

[26]  V. Calhoun,et al.  Aberrant "default mode" functional connectivity in schizophrenia. , 2007, The American journal of psychiatry.

[27]  Yuan Zhou,et al.  Functional disintegration in paranoid schizophrenia using resting-state fMRI , 2007, Schizophrenia Research.

[28]  Y. Zang,et al.  Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI , 2007, Brain and Development.

[29]  J. Suckling,et al.  Cerebral grey, white matter and csf in never-medicated, first-episode schizophrenia , 2007, Schizophrenia Research.

[30]  Tülay Adali,et al.  Estimating the number of independent components for functional magnetic resonance imaging data , 2007, Human brain mapping.

[31]  P. Matthews,et al.  White matter abnormalities and brain activation in schizophrenia: A combined DTI and fMRI study , 2007, Schizophrenia Research.

[32]  Yuan Zhou,et al.  Functional dysconnectivity of the dorsolateral prefrontal cortex in first-episode schizophrenia using resting-state fMRI , 2007, Neuroscience Letters.

[33]  A. Caprihan,et al.  Application of principal component analysis to distinguish patients with schizophrenia from healthy controls based on fractional anisotropy measurements , 2008, NeuroImage.

[34]  Patrick R Hof,et al.  Diffusion tensor imaging findings in first-episode and chronic schizophrenia patients. , 2008, The American journal of psychiatry.

[35]  Peter A. Calabresi,et al.  Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification , 2008, NeuroImage.

[36]  V. Calhoun,et al.  Temporal lobe and “default” hemodynamic brain modes discriminate between schizophrenia and bipolar disorder , 2008, Human brain mapping.

[37]  Martha E Shenton,et al.  Combining ERP and Structural MRI Information in First Episode Schizophrenia and Bipolar Disorder , 2008, Clinical EEG and neuroscience.

[38]  Chaozhe Zhu,et al.  An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF , 2008, Journal of Neuroscience Methods.

[39]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[40]  G. Gratton,et al.  Combining structural and functional neuroimaging data for studying brain connectivity: a review. , 2008, Psychophysiology.

[41]  Chunshui Yu,et al.  Altered resting-state functional connectivity and anatomical connectivity of hippocampus in schizophrenia , 2008, Schizophrenia Research.

[42]  Vince D. Calhoun,et al.  Fusion of fMRI, sMRI, and EEG data using canonical correlation analysis , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[43]  Tülay Adali,et al.  A method for accurate group difference detection by constraining the mixing coefficients in an ICA framework , 2009, Human brain mapping.

[44]  Vince D. Calhoun,et al.  Feature-Based Fusion of Medical Imaging Data , 2009, IEEE Transactions on Information Technology in Biomedicine.

[45]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[46]  J. Gabrieli,et al.  Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia , 2009, Proceedings of the National Academy of Sciences.

[47]  Richard J. Caselli,et al.  Linking functional and structural brain images with multivariate network analyses: A novel application of the partial least square method , 2009, NeuroImage.

[48]  Vince D. Calhoun,et al.  An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques , 2009, NeuroImage.

[49]  James McKirdy,et al.  White matter abnormalities in bipolar disorder and schizophrenia detected using diffusion tensor magnetic resonance imaging. , 2009, Bipolar disorders.

[50]  Liberty S. Hamilton,et al.  Alterations in functional activation in euthymic bipolar disorder and schizophrenia during a working memory task , 2009, Human brain mapping.

[51]  Michele T. Diaz,et al.  Voxel-based morphometric multisite collaborative study on schizophrenia. , 2009, Schizophrenia bulletin.

[52]  Lai Xu,et al.  Joint source based morphometry identifies linked gray and white matter group differences , 2009, NeuroImage.

[53]  Vince D. Calhoun,et al.  Joint Blind Source Separation by Multiset Canonical Correlation Analysis , 2009, IEEE Transactions on Signal Processing.

[54]  Vince D. Calhoun,et al.  Identification of Imaging Biomarkers in Schizophrenia: A Coefficient-constrained Independent Component Analysis of the Mind Multi-site Schizophrenia Study , 2010, Neuroinformatics.

[55]  Xi-Nian Zuo,et al.  Amplitude of low-frequency oscillations in schizophrenia: A resting state fMRI study , 2010, Schizophrenia Research.

[56]  Maximilian Reiser,et al.  White matter microstructure underlying default mode network connectivity in the human brain , 2010, NeuroImage.

[57]  R. Baldessarini,et al.  International consensus study of antipsychotic dosing. , 2010, The American journal of psychiatry.

[58]  Vince D. Calhoun,et al.  A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia , 2010, NeuroImage.

[59]  Rex E. Jung,et al.  Does function follow form?: Methods to fuse structural and functional brain images show decreased linkage in schizophrenia , 2010, NeuroImage.

[60]  Carl-Fredrik Westin,et al.  Joint Generative Model for fMRI/DWI and Its Application to Population Studies , 2010, MICCAI.

[61]  Vince D. Calhoun,et al.  Human Neuroscience , 2022 .

[62]  Thomas F. Münte,et al.  Microstructural Brain Differences Predict Functional Hemodynamic Responses in a Reward Processing Task , 2010, The Journal of Neuroscience.

[63]  John A Sweeney,et al.  White matter microstructure in untreated first episode bipolar disorder with psychosis: comparison with schizophrenia , 2011, Bipolar disorders.

[64]  Vince D. Calhoun,et al.  Human Neuroscience , 2022 .

[65]  Wei Deng,et al.  Assessment of white matter abnormalities in paranoid schizophrenia and bipolar mania patients , 2011, Psychiatry Research: Neuroimaging.

[66]  Erik B. Erhardt,et al.  On Network Derivation, Classification, and Visualization: A Response to Habeck and Moeller , 2011, Brain Connect..

[67]  Christopher J. Bell,et al.  Altered functional and anatomical connectivity in schizophrenia. , 2011, Schizophrenia bulletin.

[68]  Vince D. Calhoun,et al.  Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model , 2011, NeuroImage.

[69]  Mark W. Woolrich,et al.  Linked independent component analysis for multimodal data fusion , 2011, NeuroImage.

[70]  V. Calhoun,et al.  Brain connectivity networks in schizophrenia underlying resting state functional magnetic resonance imaging. , 2012, Current topics in medicinal chemistry.

[71]  Mark W. Woolrich,et al.  Benefits of multi-modal fusion analysis on a large-scale dataset: Life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure , 2012, NeuroImage.

[72]  M. Kaufman,et al.  Prefrontal and limbic resting state brain network functional connectivity differs between nicotine-dependent smokers and non-smoking controls. , 2012, Drug and alcohol dependence.

[73]  Wei Sun,et al.  Functional and Anatomical Connectivity Abnormalities in Cognitive Division of Anterior Cingulate Cortex in Schizophrenia , 2012, PloS one.

[74]  J. Cadet,et al.  Altered spatial learning, cortical plasticity and hippocampal anatomy in a neurodevelopmental model of schizophrenia‐related endophenotypes , 2012, The European journal of neuroscience.

[75]  Vince D. Calhoun,et al.  A review of multivariate methods for multimodal fusion of brain imaging data , 2012, Journal of Neuroscience Methods.

[76]  V. Calhoun,et al.  Differences in Resting-State Functional Magnetic Resonance Imaging Functional Network Connectivity Between Schizophrenia and Psychotic Bipolar Probands and Their Unaffected First-Degree Relatives , 2012, Biological Psychiatry.

[77]  Rex E. Jung,et al.  Neuroinformatics Original Research Article Correspondence between Structure and Function in the Human Brain at Rest , 2022 .

[78]  G. A. Miller,et al.  Temporal and frontal cortical thickness associations with M100 auditory activity and attention in healthy controls and individuals with schizophrenia , 2012, Schizophrenia Research.

[79]  D. Kong,et al.  Automatic Classification of Early Parkinson's Disease with Multi-Modal MR Imaging , 2012, PloS one.

[80]  Stephan Heckers,et al.  Thalamocortical dysconnectivity in schizophrenia. , 2012, The American journal of psychiatry.

[81]  Jessica A. Turner,et al.  Reliability of the amplitude of low-frequency fluctuations in resting state fMRI in chronic schizophrenia , 2012, Psychiatry Research: Neuroimaging.

[82]  Vince D. Calhoun,et al.  Decomposing the brain: components and modes, networks and nodes , 2012, Trends in Cognitive Sciences.

[83]  V. Calhoun,et al.  A Selective Review of Multimodal Fusion Methods in Schizophrenia , 2012, Front. Hum. Neurosci..

[84]  Hao He,et al.  Three-way (N-way) fusion of brain imaging data based on mCCA+jICA and its application to discriminating schizophrenia , 2013, NeuroImage.

[85]  V. Calhoun,et al.  Disrupted correlation between low frequency power and connectivity strength of resting state brain networks in schizophrenia , 2013, Schizophrenia Research.

[86]  Klaudius Kalcher,et al.  RESCALE: Voxel-specific task-fMRI scaling using resting state fluctuation amplitude , 2013, NeuroImage.

[87]  F. Dickerson,et al.  Cigarette smoking among persons with schizophrenia or bipolar disorder in routine clinical settings, 1999-2011. , 2012, Psychiatric services.

[88]  Vince D Calhoun,et al.  Extracting Intrinsic Functional Networks with Feature-Based Group Independent Component Analysis , 2012, Psychometrika.

[89]  H. Hwu,et al.  Frequency‐specific alternations in the amplitude of low‐frequency fluctuations in schizophrenia , 2014, Human brain mapping.