Major depressive disorder (MDD) is one of the most prevalent mental disorders. In the brain, the hubs of the brain network play a key role in integrating and transferring information between different functional modules. However, whether the changed pattern in functional network hubs contributes to the onset of MDD remains unclear. Using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory methods, we investigated whether alterations of hubs can be detected in MDD. First, we constructed the whole-brain voxel-wise functional networks and calculated a functional connectivity strength (FCS) map in each subject in 34 MDD patients and 34 gender-, age- and education level-matched healthy controls (HCs). Next, the two-sample t-test was applied to compare the FCS maps between HC and MDD patients and identified significant decrease of FCS in subgenual anterior cingulate cortex (sgACC) in MDD patients. Subsequent functional connectivity analyses of sgACC showed disruptions in functional connectivity with posterior insula, middle and inferior temporal gyrus, lingual gyrus and cerebellum in MDD patients. Furthermore, the changed FCS of sgACC and functional connections to sgACC were significantly correlated with the Hamilton Depression Rating Scale (HDRS) scores in MDD patients. The results of the present study revealed the abnormal hub of sgACC and its corresponding disrupted frontal-limbic-visual cognitive-cerebellum functional networks in MDD. These findings may provide a new insight for the diagnosis and treatment of MDD.

This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.

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Patients with schizophrenia consistently exhibit abnormalities in the N170 event-related potential (ERP) component evoked by images of faces. However, the relationship between these face-specific N170 abnormalities in patients with schizophrenia and the clinical characteristics of this disorder has not been elucidated. Here, ERP recordings were conducted for patients with schizophrenia and healthy controls. The amplitude and latency of the N170 component were recorded while participants passively viewed face and non-face (table) images to explore the correlation between face-specific processing and clinical characteristics in schizophrenia. The results provided evidence for a face-specific N170 latency delay in patients with schizophrenia. The N170 latency in patients with schizophrenia was significantly longer than that in healthy controls when images of faces were presented in both upright and inverted orientations. Importantly, the face-related N170 latencies of the left temporo-occipital electrodes (P7 and PO7) were positively correlated with both negative and general psychiatric symptoms in these patients. The N170 amplitudes were weaker in patients than in controls for inverted images of both faces and non-faces (tables), with a left-hemisphere dominance. The face inversion effect (FIE), meaning the difference in N170 amplitude between upright and inverted faces, was absent in patients with schizophrenia, suggesting an abnormality of holistic face processing. Together, these results revealed a marked symptom-relevant neural delay associated with face-specific processing in patients with schizophrenia, providing additional evidence to support the demyelination hypothesis of schizophrenia.