Community Series in Advanced PET, SPECT, MR and Optical Imaging in Clinical and Pre-Clinical Research of Neuropsychiatric Disorders - Volume I

Psychotic disorder refers to a spectrum of disorders that have multiple etiologies, due to the complex interaction of biological and genetic vulnerability with familial and cultural factors. A clinical high risk (CHR) for schizophrenia is defined as the presence of brief, attenuated, or intermittent psychotic symptoms in non-schizophrenic individuals. The transition to schizophrenia appears significantly more frequent in this at-risk population than in the general population. Moreover, the ability to attribute mental states to others, known as mentalizing or theory of mind, and its neural correlates found in individuals with CHR are similar to those described in patients with schizophrenia. We have therefore explored neurofunctional correlates of mentalizing in individuals with CHR vs. healthy controls, in order to identify the differences in brain activation. A neural coordinate-based activation likelihood estimation meta-analysis of existing neuroimaging data revealed that three regions displayed decreased activation in individuals with CHR, compared with healthy controls: the right temporoparietal junction, the right middle temporal gyrus, and the left precuneus. These results, combined with those in the literature, further support the hypothesis that abnormal activation of posterior brain regions involved in mentalizing correlates with psychotic symptoms in help-seeking individuals.

Recent studies suggest the brain functional connectivity impairment is the early event occurred in case of Alzheimer’s disease (AD) as well as mild cognitive impairment (MCI). We model the brain as a graph based network to study these impairment. In this paper, we present a new diagnosis approach using graph theory based features from functional magnetic resonance (fMR) images to discriminate AD, MCI, and healthy control (HC) subjects using different classification techniques. These techniques include linear support vector machine (LSVM), and regularized extreme learning machine (RELM). We used pairwise Pearson’s correlation-based functional connectivity to construct the brain network. We compare the classification performance of brain network using Alzheimer’s disease neuroimaging initiative (ADNI) datasets. Node2vec graph embedding approach is employed to convert graph features to feature vectors. Experimental results show that the SVM with LASSO feature selection method generates better classification accuracy compared to other classification technique.

Major Depressive Disorder (MDD) with childhood trauma is one of the functional subtypes of depression. Frequency-dependent changes in the amplitude of low-frequency fluctuations (ALFF) have been reported in MDD patients. However, there are few studies on ALFF about MDD with childhood trauma. Resting-state functional magnetic resonance imaging was used to measure the ALFF in 69 MDD patients with childhood trauma (28.7 ± 9.6 years) and 30 healthy subjects (28.12 ± 4.41 years). Two frequency bands (slow-5: 0.010–0.027 Hz; slow-4: 0.027–0.073 Hz) were analyzed. Compared with controls, the MDD with childhood trauma had decreased ALFF in left S1 (Primary somatosensory cortex), and increased ALFF in left insula. More importantly, significant group × frequency interactions were found in right dorsal anterior cingulate cortex (dACC). Our finding may provide insights into the pathophysiology of MDD with childhood trauma.

Background: In recent years, neuroimaging has been used increasingly to explore the biological underpinnings of violence carried out by schizophrenia patients (SPs). Studies have focused mostly on patients with a history of carrying out severe physical assaults, or comorbid with substance abuse/personality disorder (SA/PD). As a result, participants were unrepresentative and the interpretation of brain-structure changes was confusing. Here, we concentrated on SPs on a general psychiatric ward with a history of relatively lower violence, and individuals comorbid with SA or PD were excluded. We expected to identify the characteristics of brain morphometry in this population, and to explore whether the morphometric changes were universal.

Methods: Forty-eight violent schizophrenia patients (VSPs), twenty-seven non-VSPs (nVSPs) and 28 nonviolent healthy controls (HCs) were investigated. Voxel-based morphometry was used to evaluate the gray matter volume (GMV) of all study participants. Whole-brain analyses were used to reveal group effects and differences between any two groups. Correlation analyses were undertaken between significant brain regions and behavioral measurements in the VSP group.

Results: Patients showed a significantly smaller GMV in widespread frontal, temporal, and limbic regions compared with HCs. No region was found in which the two patient groups had significantly larger volumes compared with that in HCs. A significant decrease in the GMV of the right fusiform gyrus was found in the VSP group compared with that in the nVSP group (p = 0.004), where the GMV of this region had a negative correlation with the Physical Aggression [subscale of the Modified Overt Aggression Scale (MOAS)] or Hostility score. The VSP group showed a trend of GMV decrease in the left middle temporal cortex compared with that in the nVSP group (p = 0.077). Negative correlation was also found between the GMV of left inferior temporal gyrus/left Superior frontal gyrus, medial and the Hostility score.

Conclusions: Our results provide initial evidence demonstrating the generalizability of GMV abnormalities in SPs engaged in varying levels of violence, even when SA or PD have not been implicated. GMV reduction was correlated with only the Physical Aggression subscale score of the MOAS, suggesting that this change in brain morphology may be dependent upon different types of violent actions.