Psychiatry Research: Neuroimaging最新文献

Exploring changes in the intrinsic activity of the brain in people with bipolar disorder (BD) is necessary. However, the findings have not yet led to consistent conclusions. In this regard, this paper aims to extract more obvious differential brain areas and neuroimaging markers, for the purpose of providing assistance for early clinical diagnosis and subsequent treatment. We conducted a meta-analysis of whole-brain resting-state functional magnetic resonance imaging (rs-fMRI) studies using seed-based d-mapping software that examined differences in amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo) between patients with BD and healthy controls (HCs). Seed-based d-Mapping (formerly Signed Differential Mapping) with Permutation of Subject Images, or SDM-PSI, is a statistical technique for meta-analyzing studies of differences in brain activity or structure. A total of 16 articles involving 1112 individuals were included in this study for meta-analysis. This paper confidently analyzes the correlation between the clinical scales HAMD, HAMA, and YMRS, and the area of difference. We found significant changes that increased activation in the anterior connective and left lens nucleus, the nucleus of the shell, and BA 48 in BD patients compared with HC (P < 0.05, uncorrected), as well as a significant correlation between HAMD and the left superior frontal gyrus (after FWE correction P < 0.05). Therefore, basal ganglia and frontal cortex may have important significance in the pathogenesis and pathological basis of BD, making it an important issue to be attached importance to.

Studies have revealed that somatization symptoms are associated with emotional memory in adolescents with depressive disorders. This study investigated somatization symptoms and emotional memory among adolescents with depressive disorders using low-frequency amplitude fluctuations (ALFF). Participants were categorized into the somatization symptoms (FSS) group, non-FSS group and healthy control group (HC). The correctness of negative picture re-recognition was higher in the FFS and HC group than in the non-FSS group. The right superior occipital gyrus and right inferior temporal gyrus were significantly larger in the FSS group than those in the non-FSS and HC groups. Additionally, the ALFF in the superior occipital and inferior temporal gyrus were positively correlated with CSI score. Furthermore, the ALFF values in the temporal region positively correlated with correct negative image re-recognition. The negative image re-recognition rate was positively correlated with the ALFF in the left and right middle occipital gyri. These findings indicated that somatization symptoms in adolescent depression are associated with the superior occipital gyrus and inferior temporal gyrus. Notably, somatization symptoms play a role in memory bias within depressive disorders, with middle occipital and inferior temporal gyri potentially serving as significant brain regions.

Schizophrenia is a persistent neurological disorder profoundly affecting cognitive, emotional, and behavioral functions, prominently characterized by delusions, hallucinations, disordered speech, and abnormal motor activity. These symptoms often present diagnostic challenges due to their overlap with other forms of psychosis. Therefore, the implementation of automated diagnostic methodologies is imperative. This research leverages Functional Magnetic Resonance Imaging (fMRI), a neuroimaging modality capable of delineating functional activations across diverse brain regions. Furthermore, the utilization of evolving machine learning techniques for fMRI data analysis has significantly progressive. Here, our study stands as a novel attempt, focusing on the comprehensive assessment of both classical and atypical symptoms of schizophrenia. We aim to uncover associated changes in brain functional activity. Our study encompasses two distinct fMRI datasets (1.5T and 3T), each comprising 34 schizophrenia patients for the 1.5T dataset and 25 schizophrenia patients for the 3T dataset, along with an equal number of healthy controls. Machine learning algorithms are applied to assess data subsets, enabling an in-depth evaluation of the current functional condition concerning symptom impact. The identified voxels contribute to determining the brain regions most influenced by each symptom, as quantified by symptom intensity. This rigorous approach has yielded various new findings while maintaining an impressive classification accuracy rate of 97 %. By elucidating variations in activation patterns across multiple brain regions in individuals with schizophrenia, this study contributes to the understanding of functional brain changes associated with the disorder. The insights gained may inform differential clinical interventions and provide a means of assessing symptom severity accurately, offering new avenues for the management of schizophrenia.

Background

Bipolar disorder I (BD-I) is a heterogeneous disorder with a high prevalence of comorbid anxiety. The aim of this study was to investigate whether anxiety and mania symptoms define distinct subgroups within BD-I and to explore potential differences in functional network characteristics between these subgroups.

Methods

Subgroups were identified using scores from clinical anxiety and mania scales. After dimension reduction of these scores, data-driven clustering analysis with cross-validation was employed to reveal the existence of subgroups. Resting-state functional magnetic resonance imaging (rs-fMRI) scans were pre-processed using fMRIPrep. After parcellation and network construction, global and regional graph theoretical measures were calculated per subgroup.

Results

Clustering results revealed that, based on anxiety symptomatology, subjects fell into two distinct subgroups, whereas mania symptoms divided subjects into four unique subgroups. These subgroups varied notably on several symptom scales. Network assortativity was significantly associated with anxiety subgroups. Post-hoc pairwise comparisons did not reveal significant global functional network differences between the anxiety subgroups or between mania subgroups. Regional network differences between clinical subgroups were especially apparent for strength and degree in the temporal and frontal lobes.

Limitations

Small sample size of some subgroups is a limitation of this study as is the categorical rather than continuous representation of anxiety and mania symptoms.

Conclusions

BD-I populations may be stratified into robust subgroups based on anxiety and mania symptoms, showing differences in functional network connectivity. Our findings highlight new avenues of research for investigating heterogeneity in psychiatric populations.

Schizophrenia spectrum disorders (SSD) are debilitating, with auditory verbal hallucinations (AVHs) being a core characteristic. While gray matter volume (GMV) reductions are commonly replicated in SSD populations, the neural basis of AVHs remains unclear. Using previously published data, this study comprises two main analyses, one of GMV dissimilarities between SSD and healthy controls (HC), and one of GMV differences specifically associated with AVHs. Structural brain images from 71 adults with (n = 46) and without (n = 25) SSD were employed. Group differences in GMVs of the cortex, anterior cingulate (ACC), superior temporal gyrus (STG), hippocampi, and thalami were assessed. Additionally, volumes of left Heschl's gyrus (HG) in a subgroup experiencing AVHs (AVH+, n = 23) were compared with those of patients who did not (AVH-, n = 23). SSD patients displayed reduced GMVs of the cortex, ACC, STG, hippocampi, and thalami compared to HC. AVH+ had significantly reduced left HG volume when compared to AVH-. Finally, a right-lateralized ventral prefrontal cluster was found to be uniquely associated with AVH severity. This study corroborates previous findings of GMV reductions in SSD cohorts. Chiefly, our secondary analysis suggests that AVHs are associated with language areas and their contralateral homologues.

Puberty is a vulnerable period for the onset of major depressive disorder (MDD) due to considerable neurodevelopmental changes. Prior diffusion tensor imaging (DTI) studies in depressed youth have had heterogeneous participants, making assessment of early pathology challenging due to illness chronicity and medication confounds. This study leveraged whole-brain DTI and graph theory approaches to probe white matter (WM) abnormalities and disturbances in structural network topology related to first-episode, treatment-naïve pediatric MDD. Participants included 36 first-episode, unmedicated adolescents with MDD (mean age 15.8 years) and 29 age- and sex-matched healthy controls (mean age 15.2 years). Compared to controls, the MDD group showed reduced fractional anisotropy in the internal and external capsules, unveiling novel regions of WM disruption in early-onset depression. The right thalamus and superior temporal gyrus were identified as network hubs where betweenness centrality changes mediated links between WM anomalies and depression severity. A diagnostic model incorporating demographics, DTI, and network metrics achieved an AUROC of 0.88 and a F1 score of 0.80 using a neural network algorithm. By examining first-episode, treatment-naïve patients, this work identified novel WM abnormalities and a potential causal pathway linking WM damage to symptom severity via regional structural network alterations in brain hubs.

Understanding the neurophysiological mechanisms of schizophrenia (SZ) is one of the challenges of neuroscience. Many anatomical and functional studies have pointed to problems in brain connectivity in SZ individuals. However, little is known about the relationships between specific brain regions and impairments in brain connectivity in SZ individuals. Herein we propose a new approach using time-varying graphs and the motif synchronization method to build dynamic brain functional networks (BFNs). Dynamic BFNs were constructed from resting-state electroencephalography (rs-EEG) of 14 schizophrenia (SZ) individuals and 14 healthy controls (HCs). BFNs were evaluated based on the percentage of synchronization importance between a pair of regions (considering external and internal interactions) over time. We found differences in the directed interaction between brain regions in SZ individuals compared to the control group. Our method revealed low bilaterally directed interactions between the temporal lobes in SZ individuals compared to HCs, indicating a potential link between altered brain connectivity and the characteristic symptoms of schizophrenia. From a clinical perspective, these results shed light on developing new therapeutic approaches targeting these specific neural interactions that are altered in individuals with SZ. This knowledge allows the application of better interventions focused on restoring or compensating for interrupted connectivity patterns.

Post-traumatic stress disorder (PTSD) has been linked to altered communication within the limbic system, including reduced structural connectivity in the uncinate fasciculus (UNC; i.e., decreased fractional anisotropy; FA) and reduced resting-state functional connectivity (RSFC) between the hippocampus and ventromedial prefrontal cortex (vmPFC). Previous research has demonstrated attenuation of PTSD symptoms and alterations in RSFC following exposure-based psychotherapy. However, the relationship between changes in structural and functional connectivity patterns and PTSD symptoms following treatment remains unclear. To investigate this, we conducted a secondary analysis of data from a randomized clinical trial of intensive exposure therapy, evaluating alterations in UNC FA, hippocampus-vmPFC RSFC, and PTSD symptoms before (pre-treatment), 7 days after (post-treatment), and 30 days after (follow-up) the completion of therapy. Our results showed that post-treatment changes in RSFC were positively correlated with post-treatment and follow-up changes in UNC FA and that post-treatment changes in UNC FA were positively correlated with post-treatment and follow-up changes in PTSD symptoms. These findings suggest that early changes in functional connectivity are associated with sustained changes in anatomical connectivity, which in turn are linked to reduced PTSD symptom severity.

The involvement of the right hemisphere, mainly the activation of the right cerebral regions, in recovery from post-stroke aphasia has been widely recognized. In contrast, the role of the right white matter pathways in the recovery from post-stroke aphasia is rarely understood. In this study, we aimed to provide a primary overview of the correlation between the structural integrity of the right hemispheric neural tracts based on the dual-stream model of language organization and recovery from post-stroke aphasia by systematically reviewing prior longitudinal interventional studies. By searching electronic databases for relevant studies according to a standard protocol, a total of 10 records (seven group studies and three case studies) including 79 participants were finally included. After comprehensively analyzing these studies and reviewing the literature, although no definite correlation was found between the right hemispheric neural tracts and recovery from post-stroke aphasia, our review provideds a new perspective for investigating the linguistic role of the right hemispheric neural tracts. This suggests that the involvement of the right hemispheric neural tracts in recovery from post-stroke aphasia may be mediated by multiple factors; thus, this topic should be comprehensively investigated in the future.

Background

Addiction can alter neural processes during rest and cognitive performance. Subjects with addictive disorders exhibit preoccupation and anticipation for the psychoactive substance when idle and cognitive deficits, during tasks.

Methods

128 channel EEG was recorded in sixty subjects (30, with alcohol, opioid and internet addiction; 30 controls) during rest and while performing working memory task to ascertain underlying differences in cortical activity between the groups while at rest and during performance of the task. Artifactually clean data was then subjected to source analysis using sLORETA software in both the groups.

Results

EEG cortical source analysis in subjects with addictive disorders showed significant activation of areas of Default Mode Network (DMN) and reduced activation in dorsolateral prefrontal cortices (DLPFC), an area known to be involved in executive function, during performance of task. However, control subjects demonstrated significantly reduced activation in areas of DMN; and increased activation of DLPFC during task performance.

Conclusion

Inability to suppress DMN inhibits reallocation of neural resources to areas of executive functioning leading to working memory deficits in subjects with addictive disorder.