Characteristics of Multimodal Brain Connectomics in Patients With Schizophrenia and the Unaffected First-Degree Relatives

A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis

BACKGROUND

Psychotic disorders are characterized by structural and functional abnormalities in brain networks. Neuroimaging techniques map and characterize such abnormalities using unique features (e.g., structural integrity, coactivation). However, it is unclear if a specific method, or a combination of modalities, is particularly effective in identifying differences in brain networks of someone with a psychotic disorder.

METHODS

A systematic meta-analysis evaluated machine learning classification of schizophrenia spectrum disorders in comparison to healthy control participants using various neuroimaging modalities (i.e., T1-weighted imaging (T1), diffusion tensor imaging (DTI), resting state functional connectivity (rs-FC), or some combination (multimodal)). Criteria for manuscript inclusion included whole-brain analyses and cross-validation to provide a complete picture regarding the predictive ability of large-scale brain systems in psychosis. For this meta-analysis, we searched Ovid MEDLINE, PubMed, PsychInfo, Google Scholar, and Web of Science published between inception and March 13th 2023. Prediction results were averaged for studies using the same dataset, but parallel analyses were run that included studies with pooled sample across many datasets. We assessed bias through funnel plot asymmetry. A bivariate regression model determined whether differences in imaging modality, demographics, and preprocessing methods moderated classification. Separate models were run for studies with internal prediction (via cross-validation) and external prediction.

RESULTS

93 studies were identified for quantitative review (30 T1, 9 DTI, 40 rs-FC, and 14 multimodal). As a whole, all modalities reliably differentiated those with schizophrenia spectrum disorders from controls (OR = 2.64 (95%CI = 2.33 to 2.95)). However, classification was relatively similar across modalities: no differences were seen across modalities in the classification of independent internal data, and a small advantage was seen for rs-FC studies relative to T1 studies in classification in external datasets. We found large amounts of heterogeneity across results resulting in significant signs of bias in funnel plots and Egger's tests. Results remained similar, however, when studies were restricted to those with less heterogeneity, with continued small advantages for rs-FC relative to structural measures. Notably, in all cases, no significant differences were seen between multimodal and unimodal approaches, with rs-FC and unimodal studies reporting largely overlapping classification performance. Differences in demographics and analysis or denoising were not associated with changes in classification scores.

CONCLUSIONS

The results of this study suggest that neuroimaging approaches have promise in the classification of psychosis. Interestingly, at present most modalities perform similarly in the classification of psychosis, with slight advantages for rs-FC relative to structural modalities in some specific cases. Notably, results differed substantially across studies, with suggestions of biased effect sizes, particularly highlighting the need for more studies using external prediction and large sample sizes. Adopting more rigorous and systematized standards will add significant value toward understanding and treating this critical population.

Temporal-spatial dynamic functional connectivity analysis in schizophrenia classification

With the development of resting-state functional magnetic resonance imaging (rs-fMRI) technology, the functional connectivity network (FCN) which reflects the statistical similarity of temporal activity between brain regions has shown promising results for the identification of neuropsychiatric disorders. Alteration in FCN is believed to have the potential to locate biomarkers for classifying or predicting schizophrenia (SZ) from healthy control. However, the traditional FCN analysis with stationary assumption, i.e., static functional connectivity network (SFCN) at the time only measures the simple functional connectivity among brain regions, ignoring the dynamic changes of functional connectivity and the high-order dynamic interactions. In this article, the dynamic functional connectivity network (DFCN) is constructed to delineate the characteristic of connectivity variation across time. A high-order functional connectivity network (HFCN) designed based on DFCN, could characterize more complex spatial interactions across multiple brain regions with the potential to reflect complex functional segregation and integration. Specifically, the temporal variability and the high-order network topology features, which characterize the brain FCNs from region and connectivity aspects, are extracted from DFCN and HFCN, respectively. Experiment results on SZ identification prove that our method is more effective (i.e., obtaining a significantly higher classification accuracy, 81.82%) than other competing methods. Post hoc inspection of the informative features in the individualized classification task further could serve as the potential biomarkers for identifying associated aberrant connectivity in SZ.

Age-Related Changes in Topological Properties of Individual Brain Metabolic Networks in Rats

Normal aging causes profound changes of structural degeneration and glucose hypometabolism in the human brain, even in the absence of disease. In recent years, with the extensive exploration of the topological characteristics of the human brain, related studies in rats have begun to investigate. However, age-related alterations of topological properties in individual brain metabolic network of rats remain unknown. In this study, a total of 48 healthy female Sprague–Dawley (SD) rats were used, including 24 young rats and 24 aged rats. We used Jensen-Shannon Divergence Similarity Estimation (JSSE) method for constructing individual metabolic networks to explore age-related topological properties and rich-club organization changes. Compared with the young rats, the aged rats showed significantly decreased clustering coefficient (Cp) and local efficiency (Eloc) across the whole-brain metabolic network. In terms of changes in local network measures, degree (D) and nodal efficiency (Enod) of left posterior dorsal hippocampus, and Enod of left olfactory tubercle were higher in the aged rats than in the young rats. About the rich-club analysis, the existence of rich-club organization in individual brain metabolic networks of rats was demonstrated. In addition, our findings further confirmed that rich-club connections were susceptible to aging. Relative to the young rats, the overall strength of rich-club connections was significantly reduced in the aged rats, while the overall strength of feeder and local connections was significantly increased. These findings demonstrated the age-related reorganization principle of the brain structure and improved our understanding of brain alternations during aging.

Not Only in Sensorimotor Network: Local and Distant Cerebral Inherent Activity of Chronic Ankle Instability—A Resting-State fMRI Study

Background

Increasing evidence has proved that chronic ankle instability (CAI) is highly related to the central nervous system (CNS). However, it is still unclear about the inherent cerebral activity among the CAI patients.

Purpose

To investigate the differences of intrinsic functional cerebral activity between the CAI patients and healthy controls (HCs) and further explore its correlation with clinical measurement in CAI patients.

Materials and Methods

A total of 25 CAI patients and 39 HCs were enrolled in this study. Resting-state functional magnetic resonance imaging (rs-fMRI) was used to detect spontaneous cerebral activity. The metrics of amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) of the two groups were compared by two-sample t-test. The brain regions that demonstrated altered functional metrics were selected as the regions of interest (ROIs). The functional connectivity (FC) was analyzed based on the ROIs. The Spearman correlation was calculated between rs-fMRI metrics and clinical scale scores.

Results

Compared with HCs, CAI patients showed higher ALFF and ReHo values in the right postcentral gyrus, the right precentral gyrus, and the right middle frontal gyrus, while lower fALFF values in the orbital-frontal cortex (OFC, p < 0.01 after correction). Increasing FC between the right precentral gyrus and the right postcentral gyrus while decreasing FC between the right precentral gyrus and the anterior cingulum cortex (ACC), the right middle frontal gyrus and the left middle temporal gyrus, and the OFC and left inferior parietal lobule (IPL) was observed. In addition, in the CAI group, the ReHo value negatively correlated with the Cumberland Ankle Instability Tool score in the right middle frontal gyrus (r = −0.52, p = 0.007).

Conclusion

The CAI patients exhibited enhanced and more coherent regional inherent neuronal activity within the sensorimotor network while lower regional inherent activity in pain/emotion modulation related region. In addition, the information exchanges were stronger within the sensorimotor network while weaker between distant interhemispheric regions. Besides, the increased inherent activity in the right middle frontal gyrus was related to clinical severity. These findings may provide insights into the pathophysiological alteration in CNS among CAI patients.

Mindfulness-Based Cognitive Therapy Regulates Brain Connectivity in Patients With Late-Life Depression

Late-life depression (LLD) is an important public health problem among the aging population. Recent studies found that mindfulness-based cognitive therapy (MBCT) can effectively alleviate depressive symptoms in major depressive disorder. The present study explored the clinical effect and potential neuroimaging mechanism of MBCT in the treatment of LLD. We enrolled 60 participants with LLD in an 8-week, randomized, controlled trial (ChiCTR1800017725). Patients were randomized to the treatment-as-usual (TAU) group or a MBCT+TAU group. The Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA) were used to evaluate symptoms. Magnetic resonance imaging (MRI) was used to measure changes in resting-state functional connectivity and structural connectivity. We also measured the relationship between changes in brain connectivity and improvements in clinical symptoms. HAMD total scores in the MBCT+TAU group were significantly lower than in the TAU group after 8 weeks of treatment (p < 0.001) and at the end of the 3-month follow-up (p < 0.001). The increase in functional connections between the amygdala and middle frontal gyrus (MFG) correlated with decreases in HAMA and HAMD scores in the MBCT+TAU group. Diffusion tensor imaging analyses showed that fractional anisotropy of the MFG-amygdala significantly increased in the MBCT+TAU group after 8-week treatment compared with the TAU group. Our study suggested that MBCT improves depression and anxiety symptoms that are associated with LLD. MBCT strengthened functional and structural connections between the amygdala and MFG, and this increase in communication correlated with improvements in clinical symptoms. Randomized Controlled Trial; Follow-Up Study; fMRI; Brain Connectivity.

Evaluating the Performance of Machine Learning Models for Automatic Diagnosis of Patients with Schizophrenia Based on a Single Site Dataset of 440 Participants

Background

Methods

Results

Conclusions