A method for multi-group inter-participant correlation: Abnormal synchrony in patients with schizophrenia during auditory target detection

Abstract

The general linear model (GLM) approach is the most commonly used method in functional magnetic resonance imaging analysis to predict a particular response. Recently, a novel method of analysis, referred to as inter-participant correlation (IPC), was developed that attempts to determine the level of BOLD (Blood Oxygen Level Dependent) synchrony among subjects. The IPC approach enables detection of changes in inter-participant BOLD synchrony in a manner that does not rely on an explicit model of the hemodynamic activity. In this paper, we extend IPC to the case of two groups and derive an approach for thresholding the resulting maps. We demonstrate our approach by comparing 35 patients with paranoid schizophrenia (DSM-IV subtype 295.30) to 35 healthy matched controls during an auditory target detection paradigm. Results showed significantly lower inter-participant BOLD synchrony in patients versus healthy controls in areas including bilateral temporal lobes, medial frontal gyrus, anterior cingulate cortex, dorsolateral prefrontal cortex, thalamus, insula, and cerebellum. The IPC approach is straightforward to use and provides a useful complement to traditional GLM techniques. This approach may also be sensitive to underlying, but unpredictable, changes in inter-participant BOLD synchrony between patients and controls.

Hybrid ICA–Bayesian network approach reveals distinct effective connectivity differences in schizophrenia

“…The sensitivity of a dDBN analysis motivated us to use ICA as a method to remove noise-related artifacts. Our results suggest that the ICA filtering approach seems effective in dealing with patient populations such as schizophrenia, where variability within BOLD activity tends to be greater than healthy controls (Kim et al, 2007). The class-discriminative measures for patients with schizophrenia showed a significantly lower score than controls and filtering seemed to have improved these scores significantly, but not for controls.…”

“…Previous studies have shown the vermis to be implicated in schizophrenia from structural and functional MRI analyses, where it was found to be significantly reduced in patients (Okugawa et al, 2003). Functional studies have also implicated the cerebellum as a dysfunctional area in schizophrenia (Andreasen et al, 1998;Ende et al, 2005;Kim et al, 2007), though the vermis is not often discussed due to the frequent use by researchers of the Talairach atlas, which does not distinguish the vermis as a distinct brain region (Talairach and Tournoux, 1988).…”

“…Our hypothesis was that the ACL scores for healthy controls would be higher than that of patients, based on previous studies that have shown aberrant connectivity and synchrony in schizophrenia patients (Friston and Frith, 1995;Garrity et al, 2007;Kim et al, 2007;Schlosser et al, 2003). We further hypothesized that the classdiscriminative families found from both the original analyses and the ICA filtered approach would represent significant ROIs that characterize schizophrenia during this auditory paradigm and that these regions would be consistent with previous studies that have implicated areas such as the bilateral temporal lobes, bilateral parietal lobes, thalamus, and cerebellum Kim et al, 2007;Laurens et al, 2005).…”

Identifying distributed and overlapping clusters of hemodynamic synchrony in fMRI data sets

“…Our method generally identifies multiple specific cortical clusters across the brain. We attribute the specificity of the results to the ability of our method to find voxel activity patterns with both high coherence and high variance, while other similar methods [17,22,23] focus on coherence only. Another distinguishing feature of voxel sieving is that identified voxel clusters are independent of each other.…”

“…A new class of methods [17,22,23] combines the simplicity of model-based methods with the flexibility of data-driven methods. These methods take advantage of similarities in hemodynamic patterns among subjects.…”