Classification of schizophrenia by intersubject correlation in functional connectome

Heterogeneous Brain Abnormalities in Schizophrenia Converge on a Common Network Associated With Symptom Remission

BACKGROUND AND HYPOTHESIS

There is a huge heterogeneity of magnetic resonance imaging findings in schizophrenia studies. Here, we hypothesized that brain regions identified by structural and functional imaging studies of schizophrenia could be reconciled in a common network.

STUDY DESIGN

We systematically reviewed the case-control studies that estimated the brain morphology or resting-state local function for schizophrenia patients in the literature. Using the healthy human connectome (n = 652) and a validated technique "coordinate network mapping" to identify a common brain network affected in schizophrenia. Then, the specificity of this schizophrenia network was examined by independent data collected from 13 meta-analyses. The clinical relevance of this schizophrenia network was tested on independent data of medication, neuromodulation, and brain lesions.

STUDY RESULTS

We identified 83 morphological and 60 functional studies comprising 7389 patients with schizophrenia and 7408 control subjects. The "coordinate network mapping" showed that the atrophy and dysfunction coordinates were functionally connected to a common network although they were spatially distant from each other. Taking all 143 studies together, we identified the schizophrenia network with hub regions in the bilateral anterior cingulate cortex, insula, temporal lobe, and subcortical structures. Based on independent data from 13 meta-analyses, we showed that these hub regions were specifically connected with regions of cortical thickness changes in schizophrenia. More importantly, this schizophrenia network was remarkably aligned with regions involving psychotic symptom remission.

CONCLUSIONS

Neuroimaging abnormalities in cross-sectional schizophrenia studies converged into a common brain network that provided testable targets for developing precise therapies.

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.

Depression circuit adaptation in post-stroke depression

BACKGROUND

Lesion locations of post-stroke depression (PSD) mapped to a depression circuit which centered by the left dorsolateral prefrontal cortex (DLPFC). However, it remains unknown whether the compensatory adaptations that may occur in this depression circuit due to the lesions in PSD.

METHODS

Rs-fMRI data were collected from 82 non-depressed stroke patients (Stroke), 39 PSD patients and 74 healthy controls (HC). We tested the existence of depression circuit, examined PSD-related alterations of DLPFC-seeded connectivity and their associations with depression severity, and analyzed the connectivity between each repetitive transcranial magnetic stimulation (rTMS) target and DLPFC to find the best treatment target for PSD.

RESULTS

We found that: 1) the left DLPFC showed significantly stronger connectivity to lesions of PSD than Stroke group; 2) in comparison to both Stroke and HC groups, PSD exhibited increased connectivity with DLPFC in bilateral lingual gyrus, contralesional superior frontal gyrus, precuneus, and middle frontal gyrus (MFG); 3) the connectivity between DLPFC and the contralesional lingual gyrus positively correlated with depression severity; 4) the rTMS target in center of MFG showed largest between-group difference in connectivity with DLPFC, and also reported the highest predicted clinical efficacy.

LIMITATIONS

Longitudinal studies are required to explore the alterations of depression circuit in PSD as the disease progress.

CONCLUSION

PSD underwent specific alterations in depression circuit, which may help to establish objective imaging markers for early diagnosis and interventions of the disease.

Intra and inter-individual variability in functional connectomes of patients with First Episode of Psychosis

Patients with Schizophrenia may show different clinical presentations, not only regarding inter-individual comparisons but also in one specific subject over time. In fMRI studies, functional connectomes have been shown to carry valuable individual level information, which can be associated with cognitive and behavioral variables. Moreover, functional connectomes have been used to identify subjects within a group, as if they were fingerprints. For the particular case of Schizophrenia, it has been shown that there is reduced connectome stability as well as higher inter-individual variability. Here, we studied inter and intra-individual heterogeneity by exploring functional connectomes' variability and related it with clinical variables (PANSS Total scores and antipsychotic's doses). Our sample consisted of 30 patients with First Episode of Psychosis and 32 Healthy Controls, with a test-retest approach of two resting-state fMRI scanning sessions. In our patients' group, we found increased deviation from healthy functional connectomes and increased intragroup inter-subject variability, which was positively correlated to symptoms' levels in six subnetworks (visual, somatomotor, dorsal attention, ventral attention, frontoparietal and DMN). Moreover, changes in symptom severity were positively related to changes in deviation from healthy functional connectomes. Regarding intra-subject variability, we were unable to replicate previous findings of reduced connectome stability (i.e., increased intra-subject variability), but we found a trend suggesting that result. Our findings highlight the relevance of variability characterization in Schizophrenia, and they can be related to evidence of Schizophrenia patients having a noisy functional connectome.

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

Searching for Imaging Biomarkers of Psychotic Dysconnectivity

BACKGROUND

Progress in precision psychiatry is predicated on identifying reliable individual-level diagnostic biomarkers. For psychosis, measures of structural and functional connectivity could be promising biomarkers given consistent reports of dysconnectivity across psychotic disorders using magnetic resonance imaging.

METHODS

We leveraged data from four independent cohorts of patients with psychosis and control subjects with observations from approximately 800 individuals. We used group-level analyses and two supervised machine learning algorithms (support vector machines and ridge regression) to test within-, between-, and across-sample classification performance of white matter and resting-state connectivity metrics.

RESULTS

Although we replicated group-level differences in brain connectivity, individual-level classification was suboptimal. Classification performance within samples was variable across folds (highest area under the curve [AUC] range = 0.30) and across datasets (average support vector machine AUC range = 0.50; average ridge regression AUC range = 0.18). Classification performance between samples was similarly variable or resulted in AUC values of approximately 0.65, indicating a lack of model generalizability. Furthermore, collapsing across samples (resting-state functional magnetic resonance imaging, N = 888; diffusion tensor imaging, N = 860) did not improve model performance (maximal AUC = 0.67). Ridge regression models generally outperformed support vector machine models, although classification performance was still suboptimal in terms of clinical relevance. Adjusting for demographic covariates did not greatly affect results.

CONCLUSIONS

Connectivity measures were not suitable as diagnostic biomarkers for psychosis as assessed in this study. Our results do not negate that other approaches may be more successful, although it is clear that a systematic approach to individual-level classification with large independent validation samples is necessary to properly vet neuroimaging features as diagnostic biomarkers.

Dynamic brain connectome and high risk of mental problem in clinical nurses

With the growing population and rapid change in the social environment, nurses in China are suffering from high rates of stress; however, the neural mechanism underlying this occupation related stress is largely unknown. In this study, mental status was determined for 81 nurses and 61 controls using the Symptom Checklist 90 (SCL-90) scale. A subgroup (n = 57) was further scanned by resting-state functional MRI with two sessions. Based on the SCL-90 scale, "somatic complaints" and "diet/sleeping" exhibited the most prominent difference between nurses and controls. This mental health change in nurses was further supported by the spatial independent component analysis on functional MRI data. First, dynamic functional connectome analysis identified two discrete connectivity configurations (States I and II). Controls had more time in the State I than II, while the nurses had more time in the State II than I. Second, nurses showed a similar static network topology as controls, but altered dynamic properties. Third, the symptom-imaging correlation analysis suggested the functional alterations in nurses as potential imaging biomarkers indicating a high risk for "diet/sleeping" problems. In summary, this study emphasized the high risk of mental deficits in nurses and explored the underlying neural mechanism using dynamic brain connectome, which provided valuable information for future psychological intervention.

Brain functional specialization and cooperation in Parkinson's disease

Cerebral specialization and inter-hemispheric cooperation are two of the most prominent functional architectures of the human brain. Their dysfunctions may be related to pathophysiological changes in patients with Parkinson's disease (PD), who are characterized by unbalanced onset and progression of motor symptoms. This study aimed to characterize the two intrinsic architectures of hemispheric functions in PD using resting-state functional magnetic resonance imaging. Seventy idiopathic PD patients and 70 age-, sex-, and education-matched healthy subjects were recruited. All participants underwent magnetic resonance image scanning and clinical evaluations. The cerebral specialization (Autonomy index, AI) and inter-hemispheric cooperation (Connectivity between Functionally Homotopic voxels, CFH) were calculated and compared between groups. Compared with healthy controls, PD patients showed stronger AI in the left angular gyrus. Specifically, this difference in specialization resulted from increased functional connectivity (FC) of the ipsilateral areas (e.g., the left prefrontal area), and decreased FC in the contralateral area (e.g., the right supramarginal gyrus). Imaging-cognitive correlation analysis indicated that these connectivity were positively related to the score of Montreal Cognitive Assessment in PD patients. CFH between the bilateral sensorimotor regions was significantly decreased in PD patients compared with controls. No significant correlation between CFH and cognitive scores was found in PD patients. This study illustrated a strong leftward specialization but weak inter-hemispheric coordination in PD patients. It provided new insights to further clarify the pathological mechanism of PD.

Frequency-specific alterations of the resting-state BOLD signals in nocturnal enuresis: an fMRI Study

Resting state functional magnetic resonance imaging studies of nocturnal enuresis have focused primarily on regional metrics in the blood oxygen level dependent (BOLD) signal ranging from 0.01 to 0.08 Hz. However, it remains unclear how local metrics show in sub-frequency band. 129 children with nocturnal enuresis (NE) and 37 healthy controls were included in this study. The patients were diagnosed by the pediatricians in Shanghai Children's Medical Center affiliated to Shanghai Jiao Tong University School of Medicine, according to the criteria from International Children's Continence Society (ICCS). Questionnaires were used to evaluate the symptoms of enuresis and completed by the participants. In this study, fALFF, ReHo and PerAF were calculated within five different frequency bands: typical band (0.01-0.08 Hz), slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz), and slow-2 (0.198-0.25 Hz). In the typical band, ReHo increased in the left insula and the right thalamus, while fALFF decreased in the right insula in children with NE. Besides, PerAF was increased in the right middle temporal gyrus in these children. The results regarding ReHo, fALFF and PerAF in the typical band was similar to those in slow-5 band, respectively. A correlation was found between the PerAF value of the right middle temporal gyrus and scores of the urinary intention-related wakefulness. Results in other bands were either negative or in white matter. NE children might have abnormal intrinsic neural oscillations mainly on slow-5 bands.

Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity

At present, lots of studies have tried to apply machine learning to different electroencephalography (EEG) measures for diagnosing schizophrenia (SZ) patients. However, most EEG measures previously used are either a univariate measure or a single type of brain connectivity, which may not fully capture the abnormal brain changes of SZ patients. In this paper, event-related potentials were collected from 45 SZ patients and 30 healthy controls (HCs) during a learning task, and then a combination of partial directed coherence (PDC) effective and phase lag index (PLI) functional connectivity were used as features to train a support vector machine classifier with leave-one-out cross-validation for classification of SZ from HCs. Our results indicated that an excellent classification performance (accuracy = 95.16%, specificity = 94.44%, and sensitivity = 96.15%) was obtained when the combination of functional and effective connectivity features was used, and the corresponding optimal feature number was 15, which included 12 PDC and three PLI connectivity features. The selected effective connectivity features were mainly located between the frontal/temporal/central and visual/parietal lobes, and the selected functional connectivity features were mainly located between the frontal/temporal and visual cortexes of the right hemisphere. In addition, most of the selected effective connectivity abnormally enhanced in SZ patients compared with HCs, whereas all the selected functional connectivity features decreased in SZ patients. The above results showed that our proposed method has great potential to become a tool for the auxiliary diagnosis of SZ.

Using Minimal-Redundant and Maximal-Relevant Whole-Brain Functional Connectivity to Classify Bipolar Disorder

Background

A number of mental illness is often re-diagnosed to be bipolar disorder (BD). Furthermore, the prefronto-limbic-striatal regions seem to be associated with the main dysconnectivity of BD. Functional connectivity is potentially an appropriate objective neurobiological marker that can assist with BD diagnosis.

Methods

Health controls (HC; n = 173) and patients with BD who had been diagnosed by experienced physicians (n = 192) were separated into 10-folds, namely, a ninefold training set and a onefold testing set. The classification involved feature selection of the training set using minimum redundancy/maximum relevance. Support vector machine was used for training. The classification was repeated 10 times until each fold had been used as the testing set.

Results

The mean accuracy of the 10 testing sets was 76.25%, and the area under the curve was 0.840. The selected functional within-network/between-network connectivity was mainly in the subcortical/cerebellar regions and the frontoparietal network. Furthermore, similarity within the BD patients, calculated by the cosine distance between two functional connectivity matrices, was smaller than between groups before feature selection and greater than between groups after the feature selection.

Limitations

The major limitations were that all the BD patients were receiving medication and that no independent dataset was included.

Conclusion

Our approach effectively separates a relatively large group of BD patients from HCs. This was done by selecting functional connectivity, which was more similar within BD patients, and also seems to be related to the neuropathological factors associated with BD.

Classification of schizophrenia by intersubject correlation in functional connectome

Functional connectomes have been suggested as fingerprinting for individual identification. Accordingly, we hypothesized that subjects in the same phenotypic group have similar functional connectome features, which could help to discriminate schizophrenia (SCH) patients from healthy controls (HCs) and from depression patients. To this end, we included resting-state functional magnetic resonance imaging data of SCH, depression patients, and HCs from three centers. We first investigated the characteristics of connectome similarity between individuals, and found higher similarity between subjects belonging to the same group (i.e., SCH-SCH) than different groups (i.e., HC-SCH). These findings suggest that the average connectome within group (termed as group-specific functional connectome [GFC]) may help in individual classification. Consistently, significant accuracy (75-77%) and area under curve (81-86%) were found in discriminating SCH from HC or depression patients by GFC-based leave-one-out cross-validation. Cross-center classification further suggests a good generalizability of the GFC classification. We additionally included normal aging data (255 young and 242 old subjects with different scanning sequences) to show factors could be improved for better classification performance, and the findings emphasized the importance of increasing sample size but not temporal resolution during scanning. In conclusion, our findings suggest that the average functional connectome across subjects contained group-specific biological features and may be helpful in clinical diagnosis for schizophrenia.