Resting-state functional connectivity in treatment response and resistance in schizophrenia: A systematic review

Predicting Clinical Improvement in Early Psychosis Using Circuit-Based Resting-State Functional Magnetic Resonance Imaging

BACKGROUND AND HYPOTHESIS

Identifying biomarkers that predict treatment response in early psychosis (EP) is a priority for psychiatry research. Previous work suggests that resting-state connectivity biomarkers may have promise as predictive measures, although prior results vary considerably in direction and magnitude. Here, we evaluated the relationship between intrinsic functional connectivity of the attention, default mode, and salience resting-state networks and 12-month clinical improvement in EP.

STUDY DESIGN

Fifty-eight individuals with EP (less than 2 years from illness onset, 35 males, average age 20 years) had baseline and follow-up clinical data and were included in the final sample. Of these, 30 EPs showed greater than 20% improvement in Brief Psychiatric Rating Scale (BPRS) total score at follow-up and were classified as "Improvers."

STUDY RESULTS

The overall logistic regression predicting Improver status was significant (χ2 = 23.66, Nagelkerke's R2 = 0.45, P < .001, with 85% concordance). Significant individual predictors of Improver status included higher default mode within-network connectivity, higher attention-default mode between-network connectivity, and higher attention-salience between-network connectivity. Including baseline BPRS as a predictor increased model significance and concordance to 92%, and the model was not significantly influenced by the dose of antipsychotic medication (chlorpromazine equivalents). Linear regression models predicting percent change in BPRS were also significant.

CONCLUSIONS

Overall, these results suggest that resting-state functional magnetic resonance imaging connectivity may serve as a useful biomarker of clinical outcomes in recent-onset psychosis.

Elevated intrinsic cortical curvature in treatment-resistant schizophrenia: Evidence of structural deformation in functional connectivity areas and comparison with alternate indices of structure

BACKGROUND

Research suggests structural and connectivity abnormalities in patients with treatment-resistant schizophrenia (TRS) compared to first-line responders and healthy-controls. However, measures of these abnormalities are often influenced by external factors like nicotine and antipsychotics, limiting their clinical utility. Intrinsic-cortical-curvature (ICC) presents a millimetre-scale measure of brain gyrification, highly sensitive to schizophrenia differences, and associated with TRS-like traits in early stages of the disorder. Despite this evidence, ICC in TRS remains unexplored. This study investigates ICC as a marker for treatment resistance in TRS, alongside structural indices for comparison.

METHODS

We assessed ICC in anterior cingulate, dorsolateral prefrontal, temporal, and parietal cortices of 38 first-line responders, 30 clozapine-resistant TRS, 37 clozapine-responsive TRS, and 52 healthy-controls. For comparative purposes, Fold and Curvature indices were also analyzed.

RESULTS

Adjusting for age, sex, nicotine-use, and chlorpromazine equivalence, principal findings indicate ICC elevations in the left hemisphere dorsolateral prefrontal (p < 0.001, η2partial = 0.142) and temporal cortices (LH p = 0.007, η2partial = 0.060; RH p = 0.011, η2partial = 0.076) of both TRS groups, and left anterior cingulate cortex of clozapine-resistant TRS (p = 0.026, η2partial = 0.065), compared to healthy-controls. Elevations that correlated with reduced cognition (p = 0.001) and negative symptomology (p < 0.034) in clozapine-resistant TRS. Fold and Curvature indices only detected group differences in the right parietal cortex, showing interactions with age, sex, and nicotine use. ICC showed interactions with age.

CONCLUSION

ICC elevations were found among patients with TRS, and correlated with symptom severity. ICCs relative independence from sex, nicotine-use, and antipsychotics, may support ICC's potential as a viable marker for TRS, though age interactions should be considered.

Aberrant connectivity in the hippocampus, bilateral insula and temporal poles precedes treatment resistance in first-episode psychosis: a prospective resting-state functional magnetic resonance imaging study with connectivity concordance mapping

Functional connectivity resting-state functional magnetic resonance imaging has been proposed to predict antipsychotic treatment response in schizophrenia. However, only a few prospective studies have examined baseline resting-state functional magnetic resonance imaging data in drug-naïve first-episode schizophrenia patients with regard to subsequent treatment response. Data-driven approaches to conceptualize and measure functional connectivity patterns vary broadly, and model-free, voxel-wise, whole-brain analysis techniques are scarce. Here, we apply such a method, called connectivity concordance mapping to resting-state functional magnetic resonance imaging data acquired from an Asian sample (n = 60) with first-episode psychosis, prior to pharmaceutical treatment. Using a longitudinal design, 12 months after the resting-state functional magnetic resonance imaging, we measured and classified patients into two groups based on psychometric testing: treatment responsive and treatment resistant. Next, we compared the two groups' connectivity concordance maps that were derived from the resting-state functional magnetic resonance imaging data at baseline. We have identified consistently higher functional connectivity in the treatment-resistant group in a network including the left hippocampus, bilateral insula and temporal poles. These data-driven novel findings can help researchers to consider new regions of interest and facilitate biomarker development in order to identify treatment-resistant schizophrenia patients early, in advance of treatment and at the time of their first psychotic episode.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

TSP-GNN: a novel neuropsychiatric disorder classification framework based on task-specific prior knowledge and graph neural network

Neuropsychiatric disorder (ND) is often accompanied by abnormal functional connectivity (FC) patterns in specific task contexts. The distinctive task-specific FC patterns can provide valuable features for ND classification models using deep learning. However, most previous studies rely solely on the whole-brain FC matrix without considering the prior knowledge of task-specific FC patterns. Insight by the decoding studies on brain-behavior relationship, we develop TSP-GNN, which extracts task-specific prior (TSP) connectome patterns and employs graph neural network (GNN) for disease classification. TSP-GNN was validated using publicly available datasets. Our results demonstrate that different ND types show distinct task-specific connectivity patterns. Compared with the whole-brain node characteristics, utilizing task-specific nodes enhances the accuracy of ND classification. TSP-GNN comprises the first attempt to incorporate prior task-specific connectome patterns and the power of deep learning. This study elucidates the association between brain dysfunction and specific cognitive processes, offering valuable insights into the cognitive mechanism of neuropsychiatric disease.

A Functional Connectome-Based Neural Signature for Individualized Prediction of Antipsychotic Response in First-Episode Psychosis

OBJECTIVE

Identification of robust biomarkers that predict individualized response to antipsychotic treatment at the early stage of psychotic disorders remains a challenge in precision psychiatry. The aim of this study was to investigate whether any functional connectome-based neural traits could serve as such a biomarker.

METHODS

In a discovery sample, 49 patients with first-episode psychosis received multi-paradigm fMRI scans at baseline and were clinically followed up for 12 weeks under antipsychotic monotherapies. Treatment response was evaluated at the individual level based on the psychosis score of the Brief Psychiatric Rating Scale. Cross-paradigm connectivity and connectome-based predictive modeling were employed to train a predictive model that uses baseline connectomic measures to predict individualized change rates of psychosis scores, with model performance evaluated as the Pearson correlations between the predicted change rates and the observed change rates, based on cross-validation. The model generalizability was further examined in an independent validation sample of 24 patients in a similar design.

RESULTS

The results revealed a paradigm-independent connectomic trait that significantly predicted individualized treatment outcome in both the discovery sample (predicted-versus-observed r=0.41) and the validation sample (predicted-versus-observed r=0.47, mean squared error=0.019). Features that positively predicted psychosis change rates primarily involved connections related to the cerebellar-cortical circuitry, and features that negatively predicted psychosis change rates were chiefly connections within the cortical cognitive systems.

CONCLUSIONS

This study discovers and validates a connectome-based functional signature as a promising early predictor for individualized response to antipsychotic treatment in first-episode psychosis, thus highlighting the potential clinical value of this biomarker in precision psychiatry.

Altered functional connectivity in anterior cingulate cortex subregions in treatment-resistant schizophrenia patients

BACKGROUND

The anterior cingulate cortex (ACC) plays a key role in motor control, attention, and cognitive control. It is well established that schizophrenia is associated with impaired functional connectivity (FC) of the ACC pathway. So far, however, there has been little discussion about the ACC subregions function in patients with treatment-resistant schizophrenia (TRS).

AIM

This study aims to characterize resting-state functional connectivity (rs-FC) profiles of ACC subregions in patients with TRS. The association between these FC and clinical symptoms, neurocognitive function, and grey matter volume (GMV) was studied as well.

METHODS

A total of 81 patients with schizophrenia (40 patients with TRS = 40, 41 patients with non-treatment-resistant schizophrenia (NTRS)) and 39 age- and gender-matched healthy controls (HC) were enrolled, and underwent structural magnetic resonance imaging (MRI), resting-state functional MRI (rs-fMRI), clinical evaluation. The ACC subregions, including subgenual ACC (sgACC), pregenual ACC (pgACC), and dorsal ACC (dACC), were selected as seed regions from the automated anatomical labelling atlas 3 (AAL3). The GMV of the ACC subregions were calculated and seed-based FC maps for all ACC subregions were generated and compared between the TRS and NTRS, HC group. Additionally, correlations between altered FC and clinical symptoms, GMV, and neurocognitive functions in the TRS patients were explored.

RESULT

Compared with HC, increased FC was observed in TRS and NTRS groups between bilateral sgACC and left cuneus, right cuneus, and left lingual gyrus, while decreased FC was found between bilateral dACC and thalamic. Additionally, compared with NTRS, the TRS group showed increased FC between bilateral dACC and right cuneus and decreased FC between bilateral dACC and thalamic. The TRS group showed decreased GMV in all ACC subregions than the HC group, and there is no significant difference between the TRS group and the NTRS group.

CONCLUSION

The findings in this study suggest that disrupted FC of subregional ACC has the potential as a marker for TRS. The dysconnectivity of bilateral dACC- right cuneus and bilateral dACC-thalamus, are likely to be the unique FC profiles of TRS. These findings further our understanding of the neurobiological impairments in TRS.

fMRI connectivity as a biomarker of antipsychotic treatment response: A systematic review

BACKGROUND

Antipsychotic drugs are the first-choice therapy for psychotic episodes, but antipsychotic treatment response (AP-R) is unpredictable and only becomes clear after weeks of therapy. A biomarker for AP-R is currently unavailable. We reviewed the evidence for the hypothesis that functional magnetic resonance imaging functional connectivity (fMRI-FC) is a predictor of AP-R or could serve as a biomarker for AP-R in psychosis.

METHOD

A systematic review of longitudinal fMRI studies examining the predictive performance and relationship between FC and AP-R was performed following PRISMA guidelines. Technical and clinical aspects were critically assessed for the retrieved studies. We addressed three questions: Q1) is baseline fMRI-FC related to subsequent AP-R; Q2) is AP-R related to a change in fMRI-FC; and Q3) can baseline fMRI-FC predict subsequent AP-R?

RESULTS

In total, 28 articles were included. Most studies were of good quality. fMRI-FC analysis pipelines included seed-based-, independent component- / canonical correlation analysis, network-based statistics, and graph-theoretical approaches. We found high heterogeneity in methodological approaches and results. For Q1 (N = 17) and Q2 (N = 18), the most consistent evidence was found for FC between the striatum and ventral attention network as a potential biomarker of AP-R. For Q3 (N = 9) accuracy's varied form 50 till 93%, and prediction models were based on FC between various brain regions.

CONCLUSION

The current fMRI-FC literature on AP-R is hampered by heterogeneity of methodological approaches. Methodological uniformity and further improvement of the reliability and validity of fMRI connectivity analysis is needed before fMRI-FC analysis can have a place in clinical applications of antipsychotic treatment.

Resting-state functional MRI in treatment-resistant schizophrenia

BACKGROUND

Abnormalities in brain regions involved in the pathophysiology of schizophrenia (SCZ) may present insight into individual clinical symptoms. Specifically, functional connectivity irregularities may provide potential biomarkers for treatment response or treatment resistance, as such changes can occur before any structural changes are visible. We reviewed resting-state functional magnetic resonance imaging (rs-fMRI) findings from the last decade to provide an overview of the current knowledge on brain functional connectivity abnormalities and their associations to symptoms in treatment-resistant schizophrenia (TRS) and ultra-treatment-resistant schizophrenia (UTRS) and to look for support for the dysconnection hypothesis.

METHODS

PubMed database was searched for articles published in the last 10 years applying rs-fMRI in TRS patients, i.e., who had not responded to at least two adequate treatment trials with different antipsychotic drugs.

RESULTS

Eighteen articles were selected for this review involving 648 participants (TRS and control cohorts). The studies showed frontal hypoconnectivity before the initiation of treatment with CLZ or riluzole, an increase in frontal connectivity after riluzole treatment, fronto-temporal hypoconnectivity that may be specific for non-responders, widespread abnormal connectivity during mixed treatments, and ECT-induced effects on the limbic system.

CONCLUSION

Probably due to the heterogeneity in the patient cohorts concerning antipsychotic treatment and other clinical variables (e.g., treatment response, lifetime antipsychotic drug exposure, duration of illness, treatment adherence), widespread abnormalities in connectivity were noted. However, irregularities in frontal brain regions, especially in the prefrontal cortex, were noted which are consistent with previous SCZ literature and the dysconnectivity hypothesis. There were major limitations, as most studies did not differentiate between TRS and UTRS (i.e., CLZ-resistant schizophrenia) and investigated heterogeneous cohorts treated with mixed treatments (with or without CLZ). This is critical as in different subtypes of the disorder an interplay between dopaminergic and glutamatergic pathways involving frontal, striatal, and hippocampal brain regions in separate ways is likely. Better definitions of TRS and UTRS are necessary in future longitudinal studies to correctly differentiate brain regions underlying the pathophysiology of SCZ, which could serve as potential functional biomarkers for treatment resistance.

Neurofunctional correlates of glutamate and GABA imbalance in psychosis: A systematic review

Glutamatergic and GABAergic dysfunction are implicated in the pathophysiology of schizophrenia. Previous work has shown relationships between glutamate, GABA, and brain activity in healthy volunteers. We conducted a systematic review to evaluate whether these relationships are disrupted in psychosis. Primary outcomes were the relationship between metabolite levels and fMRI BOLD response in psychosis relative to healthy volunteers. 17 case-control studies met inclusion criteria (594 patients and 538 healthy volunteers). Replicated findings included that in psychosis, positive associations between ACC glutamate levels and brain activity are reduced during resting state conditions and increased during cognitive control tasks, and negative relationships between GABA and local activation in the ACC are reduced. There was evidence that antipsychotic medication may alter the relationship between glutamate levels and brain activity. Emerging literature is providing insights into disrupted relationships between neurometabolites and brain activity in psychosis. Future studies determining a link to clinical variables may develop this approach for biomarker applications, including development or targeting novel therapeutics.

Graph-Theory-Based Degree Centrality Combined with Machine Learning Algorithms Can Predict Response to Treatment with Antipsychotic Medications in Patients with First-Episode Schizophrenia

Objectives

Schizophrenia (SCZ) is associated with disrupted functional brain connectivity, and antipsychotic medications are the primary and most commonly used treatment for schizophrenia. However, not all patients respond to antipsychotic medications.

Methods

The study is aimed at investigating whether the graph-theory-based degree centrality (DC), derived from resting-state functional MRI (rs-fMRI), can predict the treatment outcomes. rs-fMRI data from 38 SCZ patients were collected and compared with findings from 38 age- and gender-matched healthy controls (HCs). The patients were treated with antipsychotic medications for 16 weeks before undergoing a second rs-fMRI scan. DC data were processed using DPABI and SPM12 software.

Results

SCZ patients at baseline showed increased DC in the frontal and temporal gyrus, anterior cingulate cortex, and precuneus and reduced DC in bilateral subcortical gray matter structures. However, those abnormalities showed a clear renormalization after antipsychotic medication treatments. Support vector machine analysis using leave-one-out cross-validation achieved a correct classification rate of 84.2% (sensitivity 78.9%, specificity 89.5%, and area under the receiver operating characteristic curve (AUC) 0.925) for differentiating effective subjects from ineffective subjects. Brain areas that contributed most to the classification model were mainly located within the bilateral putamen, left inferior frontal gyrus, left middle occipital cortex, bilateral middle frontal gyrus, left cerebellum, left medial frontal gyrus, left inferior temporal gyrus, and left angular. Furthermore, the DC change within the bilateral putamen is negatively correlated with the symptom improvements after treatment.

Conclusions

Our study confirmed that graph-theory-based measures, combined with machine-learning algorithms, can provide crucial insights into pathophysiological mechanisms and the effectiveness of antipsychotic medications.

Resting-state functional connectivity predictors of treatment response in schizophrenia - A systematic review and meta-analysis

We aimed to systematically synthesize and quantify the utility of pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI) in predicting antipsychotic response in schizophrenia. We searched the PubMed/MEDLINE database for studies that examined the magnitude of association between baseline rs-fMRI assessment and subsequent response to antipsychotic treatment in persons with schizophrenia. We also performed meta-analyses for quantifying the magnitude and accuracy of predicting response defined continuously and categorically. Data from 22 datasets examining 1280 individuals identified striatal and default mode network functional segregation and integration metrics as consistent determinants of treatment response. The pooled correlation coefficient for predicting improvement in total symptoms measured continuously was ~0.47 (12 datasets; 95% CI: 0.35 to 0.59). The pooled odds ratio of predicting categorically defined treatment response was 12.66 (nine datasets; 95% CI: 7.91-20.29), with 81% sensitivity and 76% specificity. rs-fMRI holds promise as a predictive biomarker of antipsychotic treatment response in schizophrenia. Future efforts need to focus on refining feature characterization to improve prediction accuracy, validate prediction models, and evaluate their implementation in clinical practice.

Bayes estimate of primary threshold in clusterwise functional magnetic resonance imaging inferences

Clusterwise statistical inference is the most widely used technique for functional magnetic resonance imaging (fMRI) data analyses. Clusterwise statistical inference consists of two steps: (i) primary thresholding that excludes less significant voxels by a prespecified cut-off (eg, p < . 001 ); and (ii) clusterwise thresholding that controls the familywise error rate caused by clusters consisting of false positive suprathreshold voxels. The selection of the primary threshold is critical because it determines both statistical power and false discovery rate (FDR). However, in most existing statistical packages, the primary threshold is selected based on prior knowledge (eg, p < . 001 ) without taking into account the information in the data. In this article, we propose a data-driven approach to algorithmically select the optimal primary threshold based on an empirical Bayes framework. We evaluate the proposed model using extensive simulation studies and real fMRI data. In the simulation, we show that our method can effectively increase statistical power by 20% to over 100% while effectively controlling the FDR. We then investigate the brain response to the dose-effect of chlorpromazine in patients with schizophrenia by analyzing fMRI scans and generate consistent results.

A review of resting-state fMRI and its use to examine psychiatric disorders

Resting-state fMRI (rs-fMRI) has emerged as an alternative method to study brain function in human and animal models. In humans, it has been widely used to study psychiatric disorders including schizophrenia, bipolar disorder, autism spectrum disorders, and attention deficit hyperactivity disorders. In this review, rs-fMRI and its advantages over task based fMRI, its currently used analysis methods, and its application in psychiatric disorders using different analysis methods are discussed. Finally, several limitations and challenges of rs-fMRI applications are also discussed.

Functional connectivity changes of nucleus Accumbens Shell portion in left mesial temporal lobe epilepsy patients

Growing evidence has supported that the nucleus accumbens (NAc), especially its shell portion, has been involved in epileptogenesis. However, relevant studies on vivo human brain are quite limited. In this study, we investigated left mesial temporal lobe epilepsy (MTLE) related function connectivity (FC) changes of NAc subregions using resting-state functional magnetic resonance imaging. We calculated functional connectivity from two NAc subregions to both whole brain and 16 related targets. Two-sample t-test (Alphasim multiple comparisons corrected) was performed to identify the effect of the disease on each seed's whole brain network. Repeated-measures ANOVA and Post hoc pairwise t test (Bonferroni corrections) were performed to visualize the seed to target FC group differences in each subdivision. In whole brain FC networks, neither the left or right core show different FC changes. The left shell showed decreased FC with a cluster located around the right inferior frontal gyrus. The right shell portion showed increased FC with a cluster located around the left inferior temporal gyrus. The seed to targets results showed that the left shell of LTLE group exhibited lower FC with left posterior-parahippocampal gyrus and right caudate, putamen, thalamus, paracingulate gyrus but higher FC with right subcallosal cortex. The right core of LTLE group exhibited higher FC with right frontal pole and the right shell exhibited lower FC with left thalamus and left anterior-parahippocampal gyrus. This is the first study to investigate the functional connectivity changes of NAc subdivisions of epilepsy in vivo human brain. Our results showed that the left MTLE related FC changes on NAc are mainly on shell portion rather than core. The decrease FC between the left shell and right frontal area and the decrease FC between the right shell and left temporal area suggested they serve vital roles for MTLE.

Common and distinct functional stability abnormalities across three major psychiatric disorders

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Functional stability is a recently developed dynamic functional connectivity approach.

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Schizophrenia individuals had a distributed pattern of higher and lower stability.

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Individuals with bipolar disorder only manifested local higher stability.

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Individuals with attention deficit/hyperactivity disorder exhibited no stability differences.

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Psychiatric disorders show common and distinct functional stability abnormalities.

Delineating the neuropathological characteristics across psychiatric disorders is critical for understanding their pathophysiology. The purpose of this study was to investigate common and distinct brain functional abnormalities across psychiatric disorders by using functional stability, a recently developed dynamic functional connectivity approach. Resting-state functional magnetic resonance imaging data were collected from a transdisease sample of healthy controls (n = 115) and individuals with schizophrenia (SZ) (n = 47), bipolar disorder (BD) (n = 44), and attention deficit/hyperactivity disorder (ADHD) (n = 40). Functional stability of each voxel was calculated by measuring the concordance of dynamic functional connectivity over time. Differences in functional stability among the four groups were assessed voxel-wisely. Compared to healthy controls, individuals with SZ demonstrated a distributed pattern of higher functional stability in the bilateral inferior temporal gyrus yet lower stability in the bilateral calcarine sulcus and left insula; individuals with BD only manifested local higher stability in the left inferior temporal gyrus; no differences were found between ADHD and healthy individuals. Notably, individuals with SZ and BD had common higher functional stability in the left inferior temporal gyrus, whereas higher functional stability in the right inferior temporal gyrus and lower stability in the bilateral calcarine sulcus and left insula were unique abnormalities in individuals with SZ. Additionally, direct comparisons between disorders revealed that individuals with SZ showed lower functional stability in the right calcarine sulcus compared to those with BD and higher stability in the left inferior temporal gyrus compared to those with ADHD. However, no significant associations between functional stability and clinical symptoms were observed. Our findings suggest that the functional stability approach has the potential to be extended to the domain of psychiatry and encourage further investigations of shared and unique neuropathology of psychiatric disorders.