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Exploratory structural equation modeling of resting-state fMRI: applicability of group models to individual subjects.

ABSTRACT: The extension of group-level connectivity methods to individual subjects remains a hurdle for statistical analyses of neuroimaging data. Previous group analyses of positron emission tomography data in clinically depressed patients, for example, have shown that resting-state connectivity prior to therapy predicts how patients eventually respond to pharmacological and cognitive-behavioral therapy. Such applications would be considerably more informative for clinical decision making if these connectivity methods could be extended into the individual subject domain. To test such an extension, 46 treatment-naïve depressed patients were enrolled in an fMRI study to model baseline resting-state functional connectivity. Resting-state fMRI scans were acquired and submitted to exploratory structural equation modeling (SEM) to derive the optimal group connectivity model. Jackknife and split sample tests confirm that group model was highly reproducible, and path weights were consistent across the best five group models. When this model was applied to data from individual subjects, 85% of patients fit the group model. Histogram analysis of individual subjects' paths indicate that some paths are better representative of group membership. These results suggest that exploratory SEM is a viable technique for neuroimaging connectivity analyses of individual subjects' resting-state fMRI data.

SUBMITTER: James GA

PROVIDER: S-EPMC2653594 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

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Exploratory structural equation modeling of resting-state fMRI: applicability of group models to individual subjects.

James G Andrew GA Kelley Mary E ME Craddock R Cameron RC Holtzheimer Paul E PE Dunlop Boadie W BW Nemeroff Charles B CB Mayberg Helen S HS Hu Xiaoping P XP

NeuroImage 20090107 3

The extension of group-level connectivity methods to individual subjects remains a hurdle for statistical analyses of neuroimaging data. Previous group analyses of positron emission tomography data in clinically depressed patients, for example, have shown that resting-state connectivity prior to therapy predicts how patients eventually respond to pharmacological and cognitive-behavioral therapy. Such applications would be considerably more informative for clinical decision making if these connec ...[more]

PMID: 19162206

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Project description:Purpose:Quality of life (QOL) is an important clinical outcome for patients with schizophrenia, and recent studies have focused on subjective QOL. We evaluated the causal relationship between psychosocial aspect of subjective QOL, symptoms, cognitive functions, and salience network (SN) dysfunction in schizophrenia using structural equation modeling (SEM). Patients and methods:We performed a cross-sectional study of 21 patients with symptomatically stabilized schizophrenia and 21 age-, sex-, and education level-matched healthy controls who underwent resting-state functional magnetic resonance imaging. We evaluated SN dysfunction in schizophrenia using independent component analysis (ICA). We rated participant psychopathology using the Positive and Negative Syndrome Scale (PANSS), the Brief Assessment of Cognition in Schizophrenia (BACS), and the Calgary Depression Scale for Schizophrenia (CDSS). We rated psychosocial aspect of subjective QOL using the Schizophrenia Quality of Life Scale (SQLS) psychosocial subscale. We applied SEM to examine the relationships between SN dysfunction, PANSS positive and negative scores, CDSS total scores, BACS composite scores, and SQLS psychosocial subscale scores. Results:In second-level analysis after group ICA, patient group had significant lower right pallidum functional connectivity (FC) within the SN than the controls did (Montreal Neurological Institute [MNI] [x y z] = [22 -2 -6]) (p = 0.027, family-wise error [FWE] corrected). In SEM, we obtained a good fit for an SEM model in which SN dysfunction causes depressed mood, which in turn determines psychosocial aspect of subjective QOL (chi-squared p = 0.9, root mean square error of approximation (RMSEA) < 0.001, comparative fit index [CFI] = 1.00, and standardized root mean square residual [SRMR]= 0.020). Conclusion:We found a continuous process by which SN dysfunction causes depressed moods that determine psychosocial aspect of subjective QOL in schizophrenia. This is the first report that offers a unified explanation of functional neuroimaging, symptoms, and outcomes. Future studies combining neuroimaging techniques and clinical assessments would elucidate schizophrenia's pathogenesis.

Project description:The human brain at rest exhibits intrinsic dynamics transitioning among the multiple metastable states of the inter-regional functional connectivity. Accordingly, the demand for exploring the state-specific functional connectivity increases for a deeper understanding of mental diseases. Functional connectivity, however, lacks information about the directed causal influences among the brain regions, called effective connectivity. This study presents the dynamic causal modeling (DCM) framework to explore the state-dependent effective connectivity using spectral DCM for the resting-state functional MRI (rsfMRI). We established the sequence of brain states using the hidden Markov model with the multivariate autoregressive coefficients of rsfMRI, summarizing the functional connectivity. We decomposed the state-dependent effective connectivity using a parametric empirical Bayes scheme that models the effective connectivity of consecutive windows with the time course of the discrete states as regressors. We showed the plausibility of the state-dependent effective connectivity analysis in a simulation setting. To test the clinical applicability, we applied the proposed method to characterize the state- and subtype-dependent effective connectivity of the default mode network in children with combined-type attention deficit hyperactivity disorder (ADHD-C) compared with age-matched, typically developed children (TDC). All 88 children were subtyped according to the occupation times (i.e., dwell times) of the three dominant functional connectivity states, independently of clinical diagnosis. The state-dependent effective connectivity differences between ADHD-C and TDC according to the subtypes and those between the subtypes of ADHD-C were expressed mainly in self-inhibition, magnifying the importance of excitation inhibition balance in the subtyping. These findings provide a clear motivation for decomposing the state-dependent dynamic effective connectivity and state-dependent analysis of the directed coupling in exploring mental diseases.

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Exploratory structural equation modeling of resting-state fMRI: applicability of group models to individual subjects.

Publications

Exploratory structural equation modeling of resting-state fMRI: applicability of group models to individual subjects.

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