Dataset Information

Multivariate Pattern Classification of Primary Insomnia Using Three Types of Functional Connectivity Features.

ABSTRACT: Objective: To explore whether or not functional connectivity (FC) could be used as a potential biomarker for classification of primary insomnia (PI) at the individual level by using multivariate pattern analysis (MVPA). Methods: Thirty-eight drug-naive patients with PI, and 44 healthy controls (HC) underwent resting-state functional MR imaging. Voxel-wise functional connectivity strength (FCS), large-scale functional connectivity (large-scale FC) and regional homogeneity (ReHo) were calculated for each participant. We used support vector machine (SVM) with the three types of metrics as features separately to classify patients from healthy controls. Then we evaluated its classification performances. Finally, FC metrics with significant high classification performance were compared between the two groups and were correlated with clinical characteristics, i.e., Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS) in the patients' group. Results: The best classifier could reach up to an accuracy of 81.5%, with a sensitivity of 84.9%, specificity of 79.1%, and area under the receiver operating characteristic curve (AUC) of 83.0% (all P < 0.001). Right anterior insular cortex (BA48), left precuneus (BA7), and left middle frontal gyrus (BA8) showed high classification weights. In addition, the right anterior insular cortex (BA48) and left middle frontal gyrus (BA8) were the overlapping regions between MVPA and group comparison. Correlation analysis showed that FCS in left middle frontal gyrus and head of right caudate nucleus were correlated with PSQI and SDS, respectively. Conclusion: The current study suggests abnormal FCS in right anterior insular cortex (BA48) and left middle frontal gyrus (BA8) might serve as a potential neuromarkers for PI.

SUBMITTER: Li C

PROVIDER: S-EPMC6783513 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Multivariate Pattern Classification of Primary Insomnia Using Three Types of Functional Connectivity Features.

Li Chao C Mai Yuanqi Y Dong Mengshi M Yin Yi Y Hua Kelei K Fu Shishun S Wu Yunfan Y Jiang Guihua G

Frontiers in neurology 20191002

<b>Objective:</b> To explore whether or not functional connectivity (FC) could be used as a potential biomarker for classification of primary insomnia (PI) at the individual level by using multivariate pattern analysis (MVPA). <b>Methods:</b> Thirty-eight drug-naive patients with PI, and 44 healthy controls (HC) underwent resting-state functional MR imaging. Voxel-wise functional connectivity strength (FCS), large-scale functional connectivity (large-scale FC) and regional homogeneity (ReHo) wer ...[more]

PMID: 31632335

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Project description:Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (p < 0.0001) when using 19 effective connections and 89.36% when using 6,650 functional connections. The functional connections with high discriminative power were mainly located within or across the whole brain resting-state networks while the discriminative effective connections located in several specific regions, such as posterior cingulate cortex (PCC), ventromedial prefrontal cortex (vmPFC), dorsal cingulate cortex (dACC), and inferior parietal lobes (IPL). To further compare the discriminative power of functional connections and effective connections, a classification analysis only using the functional connections from those four networks was conducted and the highest accuracy achieved 78.33% (p < 0.0001). Our study demonstrated that the effective connectivity measures might play a more important role than functional connectivity in exploring the alterations between patients and health controls and afford a better mechanistic interpretability. Moreover, our results showed a diagnostic potential of the effective connectivity for the diagnosis of MDD patients with high accuracies allowing for earlier prevention or intervention.

Project description:Emotions can be perceived from both facial and bodily expressions. Our previous study has found the successful decoding of facial expressions based on the functional connectivity (FC) patterns. However, the role of the FC patterns in the recognition of bodily expressions remained unclear, and no neuroimaging studies have adequately addressed the question of whether emotions perceiving from facial and bodily expressions are processed rely upon common or different neural networks. To address this, the present study collected functional magnetic resonance imaging (fMRI) data from a block design experiment with facial and bodily expression videos as stimuli (three emotions: anger, fear, and joy), and conducted multivariate pattern classification analysis based on the estimated FC patterns. We found that in addition to the facial expressions, bodily expressions could also be successfully decoded based on the large-scale FC patterns. The emotion classification accuracies for the facial expressions were higher than that for the bodily expressions. Further contributive FC analysis showed that emotion-discriminative networks were widely distributed in both hemispheres, containing regions that ranged from primary visual areas to higher-level cognitive areas. Moreover, for a particular emotion, discriminative FCs for facial and bodily expressions were distinct. Together, our findings highlight the key role of the FC patterns in the emotion processing, indicating how large-scale FC patterns reconfigure in processing of facial and bodily expressions, and suggest the distributed neural representation for the emotion recognition. Furthermore, our results also suggest that the human brain employs separate network representations for facial and bodily expressions of the same emotions. This study provides new evidence for the network representations for emotion perception and may further our understanding of the potential mechanisms underlying body language emotion recognition.

Dataset Information

Multivariate Pattern Classification of Primary Insomnia Using Three Types of Functional Connectivity Features.

Publications

Multivariate Pattern Classification of Primary Insomnia Using Three Types of Functional Connectivity Features.

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