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Group ICA for identifying biomarkers in schizophrenia: 'Adaptive' networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression.

ABSTRACT: Brain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio-temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG-ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject-specific networks using these two methods separately from resting-state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs). We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross-validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG-ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45 ± 8.9%, sensitivity 0.74 ± 0.11, specificity 0.79 ± 0.11) than STR (accuracy 67.45 ± 8.13%, sensitivity 0.65 ± 0.11, specificity 0.71 ± 0.11). Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG-ICA are more sensitive to group differences, and GIG-ICA is promising for identifying image-derived biomarkers of brain disease.

SUBMITTER: Salman MS

PROVIDER: S-EPMC6438914 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Principal component analysis (PCA) is widely used for data reduction in group independent component analysis (ICA) of fMRI data. Commonly, group-level PCA of temporally concatenated datasets is computed prior to ICA of the group principal components. This work focuses on reducing very high dimensional temporally concatenated datasets into its group PCA space. Existing randomized PCA methods can determine the PCA subspace with minimal memory requirements and, thus, are ideal for solving large PCA problems. Since the number of dataloads is not typically optimized, we extend one of these methods to compute PCA of very large datasets with a minimal number of dataloads. This method is coined multi power iteration (MPOWIT). The key idea behind MPOWIT is to estimate a subspace larger than the desired one, while checking for convergence of only the smaller subset of interest. The number of iterations is reduced considerably (as well as the number of dataloads), accelerating convergence without loss of accuracy. More importantly, in the proposed implementation of MPOWIT, the memory required for successful recovery of the group principal components becomes independent of the number of subjects analyzed. Highly efficient subsampled eigenvalue decomposition techniques are also introduced, furnishing excellent PCA subspace approximations that can be used for intelligent initialization of randomized methods such as MPOWIT. Together, these developments enable efficient estimation of accurate principal components, as we illustrate by solving a 1600-subject group-level PCA of fMRI with standard acquisition parameters, on a regular desktop computer with only 4 GB RAM, in just a few hours. MPOWIT is also highly scalable and could realistically solve group-level PCA of fMRI on thousands of subjects, or more, using standard hardware, limited only by time, not memory. Also, the MPOWIT algorithm is highly parallelizable, which would enable fast, distributed implementations ideal for big data analysis. Implications to other methods such as expectation maximization PCA (EM PCA) are also presented. Based on our results, general recommendations for efficient application of PCA methods are given according to problem size and available computational resources. MPOWIT and all other methods discussed here are implemented and readily available in the open source GIFT software.

Project description:Although individuals at clinical high risk (CHR) for psychosis exhibit a psychosis-risk syndrome involving attenuated forms of the positive symptoms typical of schizophrenia (SZ), it remains unclear whether their resting-state brain intrinsic functional networks (INs) show attenuated or qualitatively distinct patterns of functional dysconnectivity relative to SZ patients. Based on resting-state functional magnetic imaging data from 70 healthy controls (HCs), 53 CHR individuals (among which 41 subjects were antipsychotic medication-naive), and 58 early illness SZ (ESZ) patients (among which 53 patients took antipsychotic medication) within five years of illness onset, we estimated subject-specific INs using a novel group information guided independent component analysis (GIG-ICA) and investigated group differences in INs. We found that when compared to HCs, both CHR and ESZ groups showed significant differences, primarily in default mode, salience, auditory-related, visuospatial, sensory-motor, and parietal INs. Our findings suggest that widespread INs were diversely impacted. More than 25% of voxels in the identified significant discriminative regions (obtained using all 19 possible changing patterns excepting the no-difference pattern) from six of the 15 interrogated INs exhibited monotonically decreasing Z-scores (in INs) from the HC to CHR to ESZ, and the related regions included the left lingual gyrus of two vision-related networks, the right postcentral cortex of the visuospatial network, the left thalamus region of the salience network, the left calcarine region of the fronto-occipital network and fronto-parieto-occipital network. Compared to HCs and CHR individuals, ESZ patients showed both increasing and decreasing connectivity, mainly hypo-connectivity involving 15% of the altered voxels from four INs. The left supplementary motor area from the sensory-motor network and the right inferior occipital gyrus in the vision-related network showed a common abnormality in CHR and ESZ groups. Some brain regions also showed a CHR-unique alteration (primarily the CHR-increasing connectivity). In summary, CHR individuals generally showed intermediate connectivity between HCs and ESZ patients across multiple INs, suggesting that some dysconnectivity patterns evident in ESZ predate psychosis in attenuated form during the psychosis risk stage. Hence, these connectivity measures may serve as possible biomarkers to predict schizophrenia progression.

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Group ICA for identifying biomarkers in schizophrenia: 'Adaptive' networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression.

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