Resting-state anticorrelated networks in Schizophrenia

Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features

BACKGROUND AND HYPOTHESIS

Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (MRI) as a tool to enhance SZ diagnosis and provide objective references and biomarkers. Using deep learning with graph convolution, we represent MRI data as graphs, aligning with brain structure, and improving feature extraction, and classification. Integration of multiple modalities is expected to enhance classification.

STUDY DESIGN

Our study enrolled 683 SZ patients and 606 healthy controls from 7 hospitals, collecting structural MRI and functional MRI data. Both data types were represented as graphs, processed by 2 graph attention networks, and fused for classification. Grad-CAM with graph convolution ensured interpretability, and partial least squares analyzed gene expression in brain regions.

STUDY RESULTS

Our method excelled in the classification task, achieving 83.32% accuracy, 83.41% sensitivity, and 83.20% specificity in 10-fold cross-validation, surpassing traditional methods. And our multimodal approach outperformed unimodal methods. Grad-CAM identified potential brain biomarkers consistent with gene analysis and prior research.

CONCLUSIONS

Our study demonstrates the effectiveness of deep learning with graph attention networks, surpassing previous SZ diagnostic methods. Multimodal MRI's superiority over unimodal MRI confirms our initial hypothesis. Identifying potential brain biomarkers alongside gene biomarkers holds promise for advancing objective SZ diagnosis and research in SZ.

Higher-order functional brain networks and anterior cingulate glutamate + glutamine (Glx) in antipsychotic-naïve first episode psychosis patients

Human connectome studies have provided abundant data consistent with the hypothesis that functional dysconnectivity is predominant in psychosis spectrum disorders. Converging lines of evidence also suggest an interaction between dorsal anterior cingulate cortex (dACC) cortical glutamate with higher-order functional brain networks (FC) such as the default mode (DMN), dorsal attention (DAN), and executive control networks (ECN) in healthy controls (HC) and this mechanism may be impaired in psychosis. Data from 70 antipsychotic-medication naïve first-episode psychosis (FEP) and 52 HC were analyzed. 3T Proton magnetic resonance spectroscopy (1H-MRS) data were acquired from a voxel in the dACC and assessed correlations (positive FC) and anticorrelations (negative FC) of the DMN, DAN, and ECN. We then performed regressions to assess associations between glutamate + glutamine (Glx) with positive and negative FC of these same networks and compared them between groups. We found alterations in positive and negative FC in all networks (HC > FEP). A relationship between dACC Glx and positive and negative FC was found in both groups, but when comparing these relationships between groups, we found contrasting associations between these variables in FEP patients compared to HC. We demonstrated that both positive and negative FC in three higher-order resting state networks are already altered in antipsychotic-naïve FEP, underscoring the importance of also considering anticorrelations for optimal characterization of large-scale functional brain networks as these represent biological processes as well. Our data also adds to the growing body of evidence supporting the role of dACC cortical Glx as a mechanism underlying alterations in functional brain network connectivity. Overall, the implications for these findings are imperative as this particular mechanism may differ in untreated or chronic psychotic patients; therefore, understanding this mechanism prior to treatment could better inform clinicians.Clinical trial registration: Trajectories of Treatment Response as Window into the Heterogeneity of Psychosis: A Longitudinal Multimodal Imaging Study, NCT03442101 . Glutamate, Brain Connectivity and Duration of Untreated Psychosis (DUP), NCT02034253 .

Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.

Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis

INTRODUCTION

High-inflammation subgroups of patients with psychosis demonstrate cognitive deficits and neuroanatomical alterations. Systemic inflammation assessed using IL-6 and C-reactive protein may alter functional connectivity within and between resting-state networks, but the cognitive and clinical implications of these alterations remain unknown. We aim to determine the relationships of elevated peripheral inflammation subgroups with resting-state functional networks and cognition in psychosis spectrum disorders.

METHODS

Serum and resting-state fMRI were collected from psychosis probands (schizophrenia, schizoaffective, psychotic bipolar disorder) and healthy controls (HC) from the B-SNIP1 (Chicago site) study who were stratified into inflammatory subgroups based on factor and cluster analyses of 13 cytokines (HC Low n = 32, Proband Low n = 65, Proband High n = 29). Nine resting-state networks derived from independent component analysis were used to assess functional and multilayer connectivity. Inter-network connectivity was measured using Fisher z-transformation of correlation coefficients. Network organization was assessed by investigating networks of positive and negative connections separately, as well as investigating multilayer networks using both positive and negative connections. Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia. Linear regressions, Spearman correlations, permutations tests and multiple comparison corrections were used for analyses in R.

RESULTS

Anterior default mode network (DMNa) connectivity was significantly reduced in the Proband High compared to Proband Low (Cohen's d = -0.74, p = 0.002) and HC Low (d = -0.85, p = 0.0008) groups. Inter-network connectivity between the DMNa and the right-frontoparietal networks was lower in Proband High compared to Proband Low (d = -0.66, p = 0.004) group. Compared to Proband Low, the Proband High group had lower negative (d = 0.54, p = 0.021) and positive network (d = 0.49, p = 0.042) clustering coefficient, and lower multiplex network participation coefficient (d = -0.57, p = 0.014). Network findings in high inflammation subgroups correlate with worse verbal fluency, verbal memory, symbol coding, and overall cognition.

CONCLUSION

These results expand on our understanding of the potential effects of peripheral inflammatory signatures and/or subgroups on network dysfunction in psychosis and how they relate to worse cognitive performance. Additionally, the novel multiplex approach taken in this study demonstrated how inflammation may disrupt the brain's ability to maintain healthy co-activation patterns between the resting-state networks while inhibiting certain connections between them.

Aberrant resting-state functional connectivity of the globus pallidus interna in first-episode schizophrenia

BACKGROUND

The striatal-pallidal pathway plays an important role in cognitive control and modulation of behaviors. Globus pallidus interna (GPi), as a primary output structure, is crucial in modulating excitation and inhibition. Studies of GPi in psychiatric illnesses are lacking given the technical challenges of examining this small and functionally diverse subcortical structure.

METHODS

71 medication-naïve first episode schizophrenia (FES) participants and 73 healthy controls (HC) were recruited at the Shanghai Mental Health Center. Clinical symptoms and imaging data were collected at baseline and, in a subset of patients, 8 weeks after initiating treatment. Resting-state functional connectivity of sub-regions of the GP were assessed using a novel mask that combines two atlases to create 8 ROIs in the GP.

RESULTS

Baseline imaging data from 63 FES patients and 55 HC met quality standards and were analyzed. FES patients exhibited less negative connectivity and increased positive connectivity between the right anterior GPi and several cortical and subcortical areas at baseline compared to HC (PFWE < 0.05). Positive functional connectivity between the right anterior GPi and several brain areas, including the right dorsal anterior cingulate gyrus, was associated with severity of positive symptoms (PFWE < 0.05) and predicted treatment response after 8 weeks (n = 28, adjusted R2 = 0.486, p < 0.001).

CONCLUSIONS

Our results implicate striatal-pallidal-thalamic pathways in antipsychotic efficacy. If replicated, these findings may reflect failure of neurodevelopmental processes in adolescence and early adulthood that decrease functional connectivity as an index of failure of the limbic/associative GPi to appropriately inhibit irrelevant signals in psychosis.

Homogeneous-Multiset-CCA-Based Brain Covariation and Contravariance Connectivity Network Modeling

Brain connectivity networks based on functional magnetic resonance imaging (fMRI) have expanded our understanding of brain functions in both healthy and diseased states. However, most current studies construct connectivity networks using averaged regional time courses with the strong assumption that the activities of voxels contained in each brain region are similar, ignoring their possible variations. Additionally, pairwise correlation analysis is often adopted with more attention to positive relationships, while joint interactions at the network level as well as anti-correlations are less investigated. In this paper, to provide a new strategy for regional activity representation and brain connectivity modeling, a novel homogeneous multiset canonical correlation analysis (HMCCA) model is proposed, which enforces sign constraints on the weights of voxels to guarantee homogeneity within each brain region. It is capable of obtaining regional representative signals and constructing covariation and contravariance networks simultaneously, at both group and subject levels. Validations on two sessions of fMRI data verified its reproducibility and reliability when dealing with brain connectivity networks. Further experiments on subjects with and without Parkinson's disease (PD) revealed significant alterations in brain connectivity patterns, which were further associated with clinical scores and demonstrated superior prediction ability, indicating its potential in clinical practice.

Disrupted resting-state functional connectivity of frontal network in opium use disorder

Opioid use disorder (OUD) as a chronic relapsing disorder is initially driven by dysfunction of brain reward networks and associated with several psychiatric disorders. Resting-state EEG was recorded in 24 healthy participants as well as 31 patients with OUD. Healthy participants do not meet OUD criteria. After pre-processing of the raw EEG, functional connectivity in the frontal network using eLORETA and all networks using graph analysis method were calculated. Patients with OUD had higher electrical neuronal activity compared to healthy participants in higher frequency bands. The statistical analysis revealed that patients with OUD had significantly decreased phase synchronization in β1 and β2 frequency bands compared with the healthy group in the frontal network. Regarding global network topology, we found a significant decrease in the characteristic path length and an increase in global efficiency, clustering coefficient, and transitivity in patients compared with the healthy group. These changes indicated that local specialization and global integration of the brain were disrupted in OUD and it suggests a tendency toward random network configuration of functional brain networks in patients with OUD. Disturbances in EEG-based brain network indices might reflect an altered cortical functional network in OUD. These findings might provide useful biomarkers to understand cortical brain pathology in opium use disorder.

Amygdala-based functional connectivity mediates the relationship between thought control ability and trait anxiety

Thought control ability (TCA) refers to the ability to exclude unwanted thoughts. There has been consistent evidence on the protective effect of TCA on anxiety, that higher TCA is associated with lower anxiety. However, the underlying neural mechanism remains unclear. In this study, with a large sample (N = 495), we investigated how seed-based resting-state functional connectivity (RSFC) mediates the relationship between TCA and anxiety. Our behaviour results replicated previous findings that TCA is negatively associated with trait anxiety after controlling for gender, age, and depression. More importantly, the RSFC results revealed that TCA is negatively associated with the left amygdala - left frontal pole (LA-LFP), left amygdala - left inferior temporal gyrus (LA-LITG), and left hippocampus - left inferior frontal gyrus (LH-LIFG) connectivity. In addition, a mediation analysis demonstrated that the LA-LFP and LA-LITG connectivity in particular mediated the influence of TCA on trait anxiety. Overall, our study extends previous research by revealing the neural bases underlying the protective effect of TCA on anxiety and pinpointing specific mediating RSFC pathways. Future studies could explore whether targeted TCA training (behavioural or neural) can help alleviate anxiety.

Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment

PURPOSE OF REVIEW

This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psychosis, by summarizing the studies published in the last three years focusing on illness detection and treatment.

RECENT FINDINGS

Multiple ML tools that include mostly supervised approaches such as support vector machine, gradient boosting, and random forest showed promising results by applying these algorithms to various sources of data: socio-demographic information, EEG, language, digital content, blood biomarkers, neuroimaging, and electronic health records. However, the overall performance, in the binary classification case, varied from 0.49, which is to be considered very low (i.e., noise), to over 0.90. These results are fully justified by different factors, some of which may be attributable to the preprocessing of the data, the wide variety of the data, and the a-priori setting of hyperparameters. One of the main limitations of the field is the lack of stratification of results based on biological sex, given that psychosis presents differently in men and women; hence, the necessity to tailor identification tools and data analytic strategies. Timely identification and appropriate treatment are key factors in reducing the consequences of psychotic disorders. In recent years, the emergence of new analytical tools based on artificial intelligence such as supervised ML approaches showed promises as a potential breakthrough in this field. However, ML applications in everyday practice are still in its infancy.

A neural mechanism underlying predictive visual motion processing in patients with schizophrenia

Psychotic symptoms may be traced back to sensory sensitivity. Thereby, visual motion (VM) processing particularly has been suggested to be impaired in schizophrenia (SCZ). In healthy brains, VM underlies predictive processing within hierarchically structured systems. However, less is known about predictive VM processing in SCZ. Therefore, we performed fMRI during a VM paradigm with three conditions of varying predictability, i.e., Predictable-, Random-, and Arbitrary motion. The study sample comprised 17 SCZ patients and 23 healthy controls. We calculated general linear model (GLM) analysis to assess group differences in VM processing across motion conditions. Here, we identified significantly lower activity in right temporoparietal junction (TPJ) for SCZ patients. Therefore, right TPJ was set as seed for connectivity analyses. For patients, across conditions we identified increased connections to higher regions, namely medial prefrontal cortex, or paracingulate gyrus. Healthy subjects activated sensory regions as area V5, or superior parietal lobule. Reduced TPJ activity may reflect both a failure in the bottom-up flow of visual information and a decrease of signal processing as consequence of increased top-down input from frontal areas. In sum, these altered neural patterns provide a framework for future studies focusing on predictive VM processing to identify potential biomarkers of psychosis.

Decreased resting-state neural signal in the left angular gyrus as a potential neuroimaging biomarker of schizophrenia: An amplitude of low-frequency fluctuation and support vector machine analysis

Objective

Schizophrenia (SCH) is primarily diagnosed based on specific clinical symptoms, with the lack of any objective SCH-related biomarkers often resulting in patient misdiagnosis and the underdiagnosis of this condition. This study was developed to assess the utility of amplitude of low-frequency fluctuation (ALFF) values analyzed via support vector machine (SVM) methods as a means of diagnosing SCH.

Methods

In total, 131 SCH patients and 128 age- and gender-matched healthy control (HC) individuals underwent resting-state functional magnetic resonance imaging (rs-fMRI), with the resultant data then being analyzed using ALFF values and SVM methods.

Results

Relative to HC individuals, patients with SCH exhibited ALFF reductions in the left angular gyrus (AG), fusiform gyrus, anterior cingulate cortex (ACC), right cerebellum, bilateral middle temporal gyrus (MTG), and precuneus (PCu) regions. No SCH patient brain regions exhibited significant increases in ALFF relative to HC individuals. SVM results indicated that reductions in ALFF values in the bilateral PCu can be used to effectively differentiate between SCH patients and HCs with respective accuracy, sensitivity, and specificity values of 73.36, 91.60, and 54.69%.

Conclusion

These data indicate that SCH patients may exhibit characteristic reductions in regional brain activity, with decreased ALFF values of the bilateral PCu potentially offering value as a candidate biomarker capable of distinguishing between SCH patients and HCs.

Frontal Cortical Functional Connectivity Is Impacted by Anaesthesia in Macaques

A critical aspect of neuroscience is to establish whether and how brain networks evolved across primates. To date, most comparative studies have used resting-state functional magnetic resonance imaging (rs-fMRI) in anaesthetized nonhuman primates and in awake humans. However, anaesthesia strongly affects rs-fMRI signals. The present study investigated the impact of the awareness state (anaesthesia vs. awake) within the same group of macaque monkeys on the rs-fMRI functional connectivity organization of a well-characterized network in the human brain, the cingulo-frontal lateral network. Results in awake macaques show that rostral seeds in the cingulate sulcus exhibited stronger correlation strength with rostral compared to caudal lateral frontal cortical areas, while more caudal seeds displayed stronger correlation strength with caudal compared to anterior lateral frontal cortical areas. Critically, this inverse rostro-caudal functional gradient was abolished under anaesthesia. This study demonstrated a similar functional connectivity (FC) organization of the cingulo-frontal cortical network in awake macaque to that previously uncovered in the human brain pointing toward a preserved FC organization from macaque to human. However, it can only be observed in awake state suggesting that this network is sensitive to anaesthesia and warranting significant caution when comparing FC patterns across species under different states.

Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review

Non-invasive measurements of brain function and structure as neuroimaging in patients with mental illnesses are useful and powerful tools for studying discriminatory biomarkers. To date, functional MRI (fMRI), structural MRI (sMRI) represent the most used techniques to provide multiple perspectives on brain function, structure, and their connectivity. Recently, there has been rising attention in using machine-learning (ML) techniques, pattern recognition methods, applied to neuroimaging data to characterize disease-related alterations in brain structure and function and to identify phenotypes, for example, for translation into clinical and early diagnosis. Our aim was to provide a systematic review according to the PRISMA statement of Support Vector Machine (SVM) techniques in making diagnostic discrimination between SCZ patients from healthy controls using neuroimaging data from functional MRI as input. We included studies using SVM as ML techniques with patients diagnosed with Schizophrenia. From an initial sample of 660 papers, at the end of the screening process, 22 articles were selected, and included in our review. This technique can be a valid, inexpensive, and non-invasive support to recognize and detect patients at an early stage, compared to any currently available assessment or clinical diagnostic methods in order to save crucial time. The higher accuracy of SVM models and the new integrated methods of ML techniques could play a decisive role to detect patients with SCZ or other major psychiatric disorders in the early stages of the disease or to potentially determine their neuroimaging risk factors in the near future.

Examination of retinal vascular trajectory in schizophrenia and bipolar disorder

AIM

Evidence suggests microvascular dysfunction (wider retinal venules and narrower arterioles) in schizophrenia (SCZ) and bipolar disorder (BD). The vascular development is synchronous with neuronal development in the retina and brain. The retinal vessel trajectory is related to retinal nerve fiber layer thinning and cerebrovascular abnormalities in SCZ and BD and has not yet been examined. Hence, in this study we examined the retinal vascular trajectory in SCZ and BD in comparison with healthy volunteers (HV).

METHODS

Retinal images were acquired from 100 HV, SCZ patients, and BD patients, respectively, with a non-mydriatic fundus camera. Images were quantified to obtain the retinal arterial and venous trajectories using a validated, semiautomated algorithm. Analysis of covariance and regression analyses were conducted to examine group differences. A supervised machine-learning ensemble of bagged-trees method was used for automated classification of trajectory values.

RESULTS

There was a significant difference among groups in both the retinal venous trajectory (HV: 0.17 ± 0.08; SCZ: 0.25 ± 0.17; BD: 0.27 ± 0.20; P < 0.001) and the arterial trajectory (HV: 0.34 ± 0.15; SCZ: 0.29 ± 0.10; BD: 0.29 ± 0.11; P = 0.003) even after adjusting for age and sex (P < 0.001). On post-hoc analysis, the SCZ and BD groups differed from the HV on retinal venous and arterial trajectories, but there was no difference between SCZ and BD patients. The machine learning showed an accuracy of 86% and 73% for classifying HV versus SCZ and BD, respectively.

CONCLUSION

Smaller trajectories of retinal arteries indicate wider and flatter curves in SCZ and BD. Considering the relation between retinal/cerebral vasculatures and retinal nerve fiber layer thinness, the retinal vascular trajectory is a potential marker for SCZ and BD. As a relatively affordable investigation, retinal fundus photography should be further explored in SCZ and BD as a potential screening measure.

Evaluation of the brain functional activities in rats various location-endometriosis pain model

Background

Endometriosis (EM) is a common gynecological disease in women of reproductive age. These patients in approximately 80% suffer the various degree pain. This study will investigate synergistically the mechanism of the higher-position central sensitization and offer a pre-clinical experiment evidence for treatment of various location-EM patients with pain.

Methods

Twenty Sprague-Dawley rats were induced three types EM including abdominal EM (n=5), gastrocnemius EM (n=5) and ovary EM group (n=5) and one sham control group (n=5). All groups were measured the pain sensitization by hotplate test, then scanned by the functional magnetic resonance imaging (fMRI). The resting-state fMRI (rs-fMRI) date was analyzed using regional homogeneity (ReHo) approach to find out the abnormal functional activity brain regions. Nissl staining method observed the state of neurons in aberrant ReHo signal brain regions.

Results

Rats with EM pain sensitization were increased in abdominal EM and gastrocnemius EM than ovary EM group and sham control. The ReHo value is decreased in gastrocnemius EM in right thalamus and left olfactory tubercle compared with other three groups. The number of neurons was decreased; cavitation around nucleus, and pyknotic homogenous nuclei. Nissl bodies were stained deeply, and the shape was irregular in gastrocnemius EM by Nissl staining in right thalamus. In left olfactory tubercle, there was no significant difference in 4 groups.

Conclusions

The thalamus may be the potential key brain region for the central sensitization mechanism of various location-EM pain. The oxidative activation may be weakened in thalamus in gastrocnemius EM group with more severe pain. This finding could lend support for future research on the imageology and pathology of various location-EM pain.

Alterations in basal ganglia-cerebello-thalamo-cortical connectivity and whole brain functional network topology in Tourette's syndrome

Tourette Syndrome (TS) is a neuropsychiatric disorder characterized by the presence of motor and vocal tics. Major pathophysiological theories posit a dysfunction of the cortico-striato-thalamo-cortical circuits as being a representative hallmark of the disease. Recent evidence suggests a more widespread dysfunction of brain networks in TS including the cerebellum and going even beyond classic motor pathways. In order to characterize brain network dysfunction in TS, in this study we investigated functional and effective-like connectivity as well as topological changes of basal ganglia-thalamo-cortical and cortico-cerebellar brain networks. We collected resting-state fMRI data from 28 TS patients (age: 32 ± 11 years) and 28 age-matched, healthy controls (age: 31 ± 9 years). Region of interest based (ROI-ROI) bivariate correlation and ROI-ROI bivariate regression were employed as measures of functional and effective-like connectivity, respectively. Graph theoretical measures of centrality (degree, cost, betweenness centrality), functional segregation (clustering coefficient, local efficiency) and functional integration (average path length, global efficiency) were used to assess topological brain network changes. In this study, TS patients exhibited increased basal ganglia-cortical and thalamo-cortical connectivity, reduced cortico-cerebellar connectivity, and an increase in parallel communication through the basal ganglia, thalamus and cerebellum (increased global efficiency). Additionally, we observed a reduction in serial information transfer (reduction in average path length) within the default mode and the salience network. In summary, our findings show that TS is characterized by increased connectivity and functional integration of multiple basal ganglia-thalamo-cortical circuits, suggesting a predominance of excitatory neurotransmission and a lack of brain maturation. Moreover, topological changes of cortico-cerebellar and brain networks involved in interoception may be underestimated neural correlates of tics and the crucial premonitory urge feeling.