Decoding six basic emotions from brain functional connectivity patterns

Although distinctive neural and physiological states are suggested to underlie the six basic emotions,basic emotions are often indistinguishable from functional magnetic resonance imaging(f MRI)voxelwise activation(VA)patterns.Here,we hypothesize that functional connectivity(FC)patterns across brain regions may contain emotion-representation information beyond VA patterns.We collected whole-brain f MRI data while human participants viewed pictures of faces expressing one of the six basic emotions(i.e.,anger,disgust,fear,happiness,sadness,and surprise)or showing neutral expressions.We obtained FC patterns for each emotion across brain regions over the whole brain and applied multivariate pattern decoding to decode emotions in the FC pattern representation space.Our results showed that the whole-brain FC patterns successfully classified not only the six basic emotions from neutral expressions but also each basic emotion from other emotions.An emotion-representation network for each basic emotion that spanned beyond the classical brain regions for emotion processing was identified.Finally,we demonstrated that within the same brain regions,FC-based decoding consistently performed better than VA-based decoding.Taken together,our findings revealed that FC patterns contained emotional information and advocated for paying further attention to the contribution of FCs to emotion processing.

A Systematic Characterization of Structural Brain Changes in Schizophrenia

A systematic characterization of the similarities and differences among different methods for detecting structural brain abnormalities in schizophrenia,such as voxel-based morphometry(VBM),tensor-based morphometry(TBM),and projection-based thickness(PBT),is important for understanding the brain pathology in schizophrenia and for developing effective biomarkers for a diagnosis of schizophrenia.However,such studies are still lacking.Here,we performed VBM,TBM,and PBT analyses on T1-weighted brain MR images acquired from 116 patients with schizophrenia and 116 healthy controls.We found that,although all methods detected wide-spread structural changes,different methods captured different information-only 10.35%of the grey matter changes in cortex were detected by all three methods,and VBM only detected 11.36%of the white matter changes detected by TBM.Further,pattern classification between patients and controls revealed that combining different measures improved the classification accuracy(81.9%),indicating that fusion of different structural measures serves as a better neuroimaging marker for the objective diagnosis of schizophrenia.

Antidepressant Effects of Electroconvulsive Therapy Unrelated to the Brain＇s Functional Network Connectivity alterations at an Individual Level

Background： Electroconvulsive therapy （ECT） can alleviate the symptoms of treatment-resistant depression （TRD）. Functional network connectivity （FNC） is a newly developed method to investigate the brain＇s functional connectivity patterns. The first aim of this study was to investigate FNC alterations between TRD patients and healthy controls. The second aim was to explore the relationship between the ECT treatment response and pre-ECT treatment FNC alterations in individual TRD patients. Methods： This study included 82 TRD patients and 41 controls. Patients were screened at baseline and after 2 weeks of treatment with a combination of ECT and antidepressants. Group information guided-independent component analysis （G1G-ICA） was used to compute subject-specific functional networks （FNs）. Grassmann maniibld and step-wise forward component selection using support vector machines were adopted to perform the FNC measure and extract the functional networks＇ connectivity patterns （FCP）. Pearson＇s correlation analysis was used to calculate the correlations between the FCP and ECT response. Results： A total of 82 TRD patients in the ECT group were successfully treated. On an average, 8.50 ~ 2.00 ECT sessions were conducted. After ECT treatment, only 42 TRD patients had an improved response to ECT （the Hamilton scores reduction rate was more than 50%）, response rate 51%. 8 FNs （anterior and posterior default mode network, bilateral frontoparietal network, audio network, visual network, dorsal attention network, and sensorimotor network） were obtained using GIG-ICA. We did not found that FCPs were significantly different between TRD patients and healthy controls. Moreover, the baseline FCP was unrelated to the ECT treatment response. Conclusions： The FNC was not significantly different between the TRD patients and healthy controls, and the baseline FCP was unrelated to the ECT treatment response. These findings will necessitate that we modify the experimental scheme to explore the mechanisms underlying ECT＇s effects on depression and explore the specific predictors of the effects of ECT based on the pre-ECT treatment magnetic resonance imaging.