Psychiatry Research: Neuroimaging最新文献

Negative symptoms are often found in youth at clinical high risk (CHR) for psychosis. The present study explored the feasibility of using tDCS in conjunction with CBT in the treatment of negative symptoms in 5 youths at CHR. We sought to determine whether the protocol was feasible given the requirement for repeated visits over a three-week period, and to determine if measures of neurobiological change could be included, both acutely and following three weeks of stimulation. The results from this study suggest that the protocol is feasible for these youth, and the inclusion of MRI scanning sessions yielded good quality data.

Recent fMRI resting-state findings show aberrant functional connectivity within somatomotor network (SMN) in schizophrenia. Moreover, functional connectivity aberrations of the motor system are often reported to be related to the severity of psychotic symptoms. Thus, it is important to validate those findings and confirm their relationship with psychopathology. Therefore, we decided to take an entirely data-driven approach in our fMRI resting-state study of 30 chronic schizophrenia outpatients and 30 matched control subjects. We used independent component analysis (ICA), dual regression, and seed-based connectivity analysis. We found reduced functional connectivity within SMN in schizophrenia patients compared to controls and SMN hypoconnectivity with the cerebellum in schizophrenia patients. The latter was strongly correlated with the severity of alogia, one of the main psychotic symptoms, i.e. poverty of speech and reduction in spontaneous speech,. Our results are consistent with the recent knowledge about the role of the cerebellum in cognitive functioning and its abnormalities in psychiatric disorders, e.g. schizophrenia. In conclusion, the presented results, for the first time clearly showed the involvement of the cerebellum hypoconnectivity with SMN in the persistence and severity of alogia symptoms in schizophrenia.

Background

The experience of self-hood in posttraumatic stress disorder (PTSD) is altered cognitively and somatically. Dysfunctional negative cognitions about the self are a central mechanism of PTSD symptomatology and treatment. However, while higher-order brain models of disturbances in self-appraisal (i.e., cognitive processes relating to evaluating the self) have been examined in other psychiatric disorders, it is unclear how normative brain function during self-appraisal is impaired in PTSD.

Methods

This paper presents a PRISMA systematic review of functional neuroimaging studies (n = 5), to establish a neurobiological account of how self-appraisal processes are disturbed in PTSD. The review was prospectively registered with PROSPERO (CRD42023450509).

Results

Self-appraisal in PTSD is linked to disrupted activity in core self-processing regions of the Default Mode Network (DMN); and regions involved in cognitive control and emotion regulation, salience and valuation.

Limitations

Because self-appraisal in PTSD is relatively under-studied, only a small number of studies could be included for review. Cross-study heterogeneity in analytic approaches and trauma-exposure history prohibited a quantitative meta-analysis.

Conclusions

This paper proposes a mechanistic account of how neural dysfunctions may manifest clinically in PTSD and inform targeted selection of appropriate treatment options. We present a research agenda for future work to advance the field.

Empirical findings suggest reduced cortico-striatal structural connectivity in patients with major depressive disorder (MDD). However, the relationship between the abnormal structural covariance and one-year outcome of first-episode drug-naive patients has not been evaluated. This longitudinal study aimed to identify specific changes of ventral striatum-related brain structural covariance and grey matter volume in forty-two first-episode patients with major depression disorder compared with thirty-seven healthy controls at the baseline and the one-year follow-up conditions. At the baseline, patients showed decreased structural covariance between the left ventral striatum and the bilateral superior frontal gyrus (SFG), bilateral middle frontal gyrus (MFG), right supplementary motor area (SMA) and left precentral gyrus and increased grey matter volume at the left fusiform and left parahippocampus. At the one-year follow-up, patients showed decreased structural covariance between the left ventral striatum and the right SFG, right MFG, left precentral gyrus and left postcentral gyrus, and increased structural covariance between the right ventral striatum and the right amygdala, right hippocampus, right parahippocampus, right superior temporal pole, right insula and right olfactory bulb and decreased volume at the left SMA compared with controls. These findings suggest that specific ventral striatum connectivity changes contribute to the early brain development of the MDD.

Obsessive-compulsive disorder (OCD) is characterized by structural alteration within white matter tissues of cortico-striato-thalamo-cortical, temporal and occipital circuits. However, the presence of microstructural changes in the white matter tracts of unaffected first-degree relatives of patients with OCD as a vulnerability marker remains unclear. Therefore, here, diffusion-tensor magnetic resonance imaging (DTI) data were obtained from 29 first-degree relatives of patients with OCD and 59 healthy controls. We investigated the group differences in FA using whole-brain analysis (DTI analysis). For additional regions of interest (ROI) analysis, we focused on the posterior thalamic radiation and sagittal stratum, shown in recent meta-analysis of patients with OCD. In both whole-brain and ROI analyses, using a strict statistical threshold (family-wise error rate [FWE] corrected p<.05 for whole-brain analyses, and p<.0125 (0.05/4) with Bonferroni correction for ROI analyses), we found no significant group differences in FA. Subtle reductions were observed in the anterior corona radiata, forceps minor, cingulum bundle, and corpus callosum only when a lenient statistical was applied (FWE corrected p<.20). These findings suggest that alterations in the white matter microstructure of first-degree relatives, as potential vulnerability markers for OCD, are likely subtle.

Verifying schizophrenia (SZ) can be assisted by deep learning techniques and patterns in brain activity observed in alpha-EEG recordings. The suggested research provides evidence of the reliability of alpha-EEG rhythm in a Gated-Recurrent-Unit-based deep-learning model for investigating SZ. This study suggests Rudiment Densely-Coupled Convolutional Gated Recurrent Unit (RDCGRU) for the various EEG-rhythm-based (gamma, beta, alpha, theta, and delta) diagnoses of SZ. The model includes multiple 1-D-Convolution (Con-1-D) folds with steps greater than 1, which enables the model to programmatically and effectively learn how to reduce the incoming signal. The Con-1-D layers and numerous Gated Recurrent Unit (GRU) layers comprise the Exponential-Linear-Unit activation function. This powerful activation function facilitates in-deep-network training and improves classification performance. The Densely-Coupled Convolutional Gated Recurrent Unit (DCGRU) layers enable RDCGRU to address the training accuracy loss brought on by vanishing or exploding gradients, and this might make it possible to develop intense, deep versions of RDCGRU for more complex problems. The sigmoid activation function is implemented in the digital (binary) classifier's output nodes. The RDCGRU deep learning model attained the most excellent accuracy, 88.88 %, with alpha-EEG rhythm. The research achievements: The RDCGRU deep learning model's GRU cells responded superiorly to the alpha-EEG rhythm in EEG-based verification of SZ.

Background

Current models of major depressive disorder (MDD) primarily focus on the structural and functional changes in key prefrontal areas responsible for emotional regulation. Among these regions some sections such as the dorsal prefrontal area, has received limited attention regarding its structural abnormalities in MDD. This study aims to evaluate volumetric abnormalities in brain regions associated with markers of depression severity and episode frequency.

Methods

The study included 33 MDD patients and 33 healthy subjects. Using an atlas-based method, we measured the volumes of several key brain regions based on MRI data. The regions of interest included prefrontal and posterior sections of the middle frontal gyrus (MFG) and superior frontal gyrus (SFG). Additionally, we evaluated the volumes of the dorsal anterior cingulate cortex (dACC), perigenual (rostral) anterior cingulate cortex (pgACC), subgenual cingulate cortex (sgACC), posterior cingulate cortex (PCC), hippocampus (HPC), and parahippocampus (paraHPC). Hamilton Depression Scale (HAM-D) scores and count of the depressive episodes of patients were also obtained. A regression analysis with sex as the confounding factor has been made.

Results

Analysis of covariances, controlling for sex, showed significant atrophy in the sgACC in the depression group: F(1, 63) = 4.013, p = 0.049 (left) and F(1, 63) = 8.786, p < 0.004 (right). Poisson regression, also controlling for sex, found that each additional depressive episode was associated with a significant reduction in left posterior MFG volume (0.952 times, 95 % CI, 0.906 to 1.000; p = 0.049).

Conclusions

Findings in this study highlight the structural abnormalities in MDD patients in correlation to either current depression severity or chronicity of the disease.

Background

Major Depressive Disorder (MDD), as a chronic mental disorder, causes changes in mood, thoughts, and behavior. The pathophysiology of the disorder and its treatment are still unknown. One of the most notable changes observed in patients with MDD through fMRI is abnormal functional brain connectivity.

Methods

Preprocessed data from 60 MDD patients and 60 normal controls (NCs) were selected, which has been performed using the DPARSF toolbox. The whole-brain functional networks and topologies were extracted using graph theory-based methods. A two-sample, two-tailed t-test was used to compare the topological features of functional brain networks between the MDD and NCs groups using the DPABI-Net/Statistical Analysis toolbox.

Results

The obtained results showed a decrease in both global and local efficiency in MDD patients compared to NCs, and specifically, MDD patients showed significantly higher path length values. Acceptable p-values were obtained with a small sample size and less computational volume compared to the other studies on large datasets. At the node level, MDD patients showed decreased and relatively decreased node degrees in the sensorimotor network (SMN) and the dorsal attention network (DAN), respectively, as well as decreased node efficiency in the SMN, default mode network (DMN), and DAN. Also, MDD patients showed slightly decreased node efficiency in the visual networks (VN) and the ventral attention network (VAN), which were reported after FDR correction with Q < 0.05.

Limitations

All participants were Chinese.

Conclusions

Collectively, increased path length, decreased global and local efficiency, and also decreased nodal degree and efficiency in the SMN, DAN, DAN, VN, and VAN were found in patients compared to NCs.

Significant evidence links obesity and schizophrenia (SZ), but the brain associations are still largely unclear. 48 people with SZ were divided into two subgroups: patients with lower waist circumference (SZ-LWC: n = 24) and patients with higher waist circumference (SZ-HWC: n = 24). Healthy controls (HC) were included for comparison (HC: n = 27). Using tract-based spatial statistics, we compared fractional anisotropy (FA) of the whole-brain white matter skeleton between these three groups (SZ-LWC, SZ-HWC, HC). Using Free Surfer, we compared whole-brain cortical thickness and the selected subcortical volumes between the three groups. FA of widespread white matter and the mean cortical thickness in the right temporal lobe and insular cortex were significantly lower in the SZ-HWC group than in the HC group. The FA of regional white matter was significantly lower in the SZ-LWC group than in the HC group. There were no significant differences in mean subcortical volumes between the groups. Additionally, the cognitive performances were worse in the SZ-HWC group, who had more severe triglycerides elevation. This study provides evidence for microstructural abnormalities of white matter, cortical thickness and neurocognitive deficits in SZ patients with excessive abdominal obesity.

Many psychopathologies tied to internalizing symptomatology emerge during adolescence, therefore identifying neural markers of internalizing behavior in childhood may allow for early intervention. We utilized data from the Adolescent Brain and Cognitive Development (ABCD) Study® to evaluate associations between cortico-amygdalar functional connectivity, polygenic risk for depression (PRS_D), traumatic events experienced, internalizing behavior, and internalizing subscales: withdrawn/depressed behavior, somatic complaints, and anxious/depressed behaviors. Data from 6371 children (ages 9–11) were used to analyze amygdala resting-state fMRI connectivity to Gordon parcellation based whole-brain regions of interest (ROIs). Internalizing behaviors were measured using the parent-reported Child Behavior Checklist. Linear mixed-effects models were used to identify patterns of cortico-amygdalar connectivity associated with internalizing behaviors. Results indicated left amygdala connections to auditory, frontoparietal network (FPN), and dorsal attention network (DAN) ROIs were significantly associated with withdrawn/depressed symptomatology. Connections relevant for withdrawn/depressed behavior were linked to social behaviors. Specifically, amygdala connections to DAN were associated with social anxiety, social impairment, and social problems. Additionally, an amygdala connection to the FPN ROI and the auditory network ROI was associated with social anxiety and social problems, respectively. Therefore, it may be important to account for social behaviors when looking for brain correlates of depression.