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

Effects on Multimodal Connectivity Patterns in Female Schizophrenia During 8 Weeks of Antipsychotic Treatment

BACKGROUND AND HYPOTHESIS

Respective abnormal structural connectivity (SC) and functional connectivity (FC) have been reported in individuals with schizophrenia. However, transmodal associations between SC and FC following antipsychotic treatment, especially in female schizophrenia, remain unclear. We hypothesized that increased SC-FC coupling may be found in female schizophrenia, and could be normalized after antipsychotic treatment.

STUDY DESIGN

Sixty-four female drug-naïve patients with first-diagnosed schizophrenia treated with antipsychotic drugs for 8 weeks, and 55 female healthy controls (HCs) were enrolled. Magnetic resonance imaging (MRI) data were collected from HCs at baseline and from patients at baseline and after treatment. SC and FC were analyzed by network-based statistics, calculating nonzero SC-FC coupling of the whole brain and altered connectivity following treatment. Finally, an Elastic-net logistic regression analysis was employed to establish a predictive model for evaluating the clinical efficacy treatment.

STUDY RESULTS

At baseline, female schizophrenia patients exhibited abnormal SC in cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, and limbic-cerebellar connectivity compared to HCs, while FC showed no abnormalities. Following treatment, cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, temporal-cerebellar, and limbic-cerebellar connectivity were altered in both SC and FC. Additionally, SC-FC coupling of altered connectivity was higher in patients at baseline than in HC, trending toward normalization after treatment. Furthermore, identified FC or/and SC predicted changes in psychopathological symptoms and cognitive impairment among female schizophrenia following treatment.

CONCLUSIONS

SC-FC coupling may be a potential predictive biomarker of treatment response. Cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, temporal-cerebellar, and limbic-cerebellar could represent major targets for antipsychotic drugs in female schizophrenia.

Connectome-based predictive modeling of early and chronic psychosis symptoms

Previous research indicates that early (EP) and chronic (CP) psychosis share brain correlates and symptoms. However, notable clinical differences, such as treatment responses and symptom severity, exist, suggesting the need for further investigation. For example, the brain networks underlying EP and CP symptoms may be distinct, driven by factors like symptom severity and disease-related burden. Differences, if any, in these brain networks are largely unknown because EP and CP have predominantly been studied, characterized, and compared to control populations independently. This study's objective was to directly compare the neural correlates of CP (n = 123) and EP (n = 107) symptoms using connectome-based predictive modeling (CPM) and resting-state functional magnetic resonance imaging. We predicted both samples' positive and negative symptoms from the Positive and Negative Syndrome Scale (PANSS). Prediction effect sizes were higher in CP, and prediction of general psychopathology and total symptoms was only possible in CP. Virtual lesioning analyses revealed the frontoparietal network as a critical component of EP and CP symptom networks. Predictive models were broadly similar between EP and CP. We also generalized the EP positive score model to CP positive symptoms and identified group differences between CP and matched HCs more robustly than EP. Overall, broadly similar networks were found in CP and EP, but larger effects were observed in CP. Our findings provide a foundation for longitudinal studies to track connectivity changes in symptom networks throughout the psychosis lifespan. Similar stage-comparative approaches can enhance understanding of the etiology of early and chronic psychosis symptoms for therapeutic applications.

Prediction of antipsychotic drug efficacy for schizophrenia treatment based on neural features of the resting-state functional connectome

Neuroimaging studies have identified a large number of biomarkers associated with schizophrenia (SZ), but there is still a lack of biomarkers that can predict the efficacy of antipsychotic medication in SZ patients. The aim of this study was to identify neuroimaging biomarkers of antipsychotic drug response among features of the resting-state connectome. Resting-state functional magnetic resonance scans were acquired from a discovery cohort of 105 patients with SZ at baseline and after 8 weeks of antipsychotic medication treatment. Baseline clinical status and post-treatment outcome were assessed using the Positive and Negative Symptom Scale (PANSS), and clinical improvement was rated by the total score reduction. Based on acquired imaging data, a resting-state functional connectivity matrix was constructed for each patient, and a connectome-based predictive model was subsequently established and trained to predict individual PANSS total score reduction. Model performance was assessed by calculating Pearson correlation coefficients between predicted and true score reduction with leave-one-out cross-validation. Finally, the generalizability of the model was tested using an independent validation cohort of 52 SZ patients. The model incorporating resting-state connectome characteristics predicted individual treatment outcomes in both the discovery cohort (prediction vs. truth r = 0.59, mean squared error (MSE) = 0.021) and validation cohort (r = 0.41, MSE = 0.036). The model identified four positive features and eight negative features, which were respectively correlated positively and negatively with PANSS total score reduction. Among these positive features, the specific connections within the parietal lobe played a crucial role in the model's predictive performance. As for the negative features, they included the frontoparietal control network and the cerebello-thalamo-cortical connections. This study discovered and validated a set of functional features based on resting-state connectome, where higher connectivity of positive features and lower connectivity of negative features at baseline were associated with a higher reduction rate of PANSS total score in patients and a better therapeutic effect. These functional features can be used to predict the PANSS total score reduction rate of SZ patients through a model. Clinical doctors can potentially infer the individual treatment response of antipsychotic medication treatment for patients based on the predicted results.

Abnormalities in cognitive-related functional connectivity can be used to identify patients with schizophrenia and individuals in clinical high-risk

BACKGROUND

Clinical high-risk (CHR) refers to prodromal phase before schizophrenia onset, characterized by attenuated psychotic symptoms and functional decline. They exhibit similar but milder cognitive impairments, brain abnormalities and eye movement change compared with first-episode schizophrenia (FSZ). These alterations may increase vulnerability to transitioning to the disease. This study explores cognitive-related functional connectivity (FC) and eye movement abnormalities to examine differences in the progression of schizophrenia.

METHODS

Thirty drug-naive FSZ, 28 CHR, and 30 healthy controls (HCs) were recruited to undergo resting-state functional magnetic resonance imaging (rs-fMRI). Connectome-based predictive modeling (CPM) was employed to extract cognitive-related brain regions, which were then selected as seeds to form FC networks. Support vector machine (SVM) was used to distinguish FSZ from CHR. Smooth pursuit eye-tracking tasks were conducted to assess eye movement features.

RESULTS

FSZ displayed decreased cognitive-related FC between right posterior cingulate cortex and right superior frontal gyrus compared with HCs and between right amygdala and left inferior parietal gyrus (IPG) compared with CHR. SVM analysis indicated a combination of BACS-SC and CFT-A scores, and FC between right amygdala and left IPG could serve as a potential biomarker for distinguishing FSZ from CHR with high sensitivity. FSZ also exhibited a wide range of eye movement abnormalities compared with HCs, which were associated with alterations in cognitive-related FC.

CONCLUSIONS

FSZ and CHR exhibited different patterns of cognitive-related FC and eye movement alteration. Our findings illustrate potential neuroimaging and cognitive markers for early identification of psychosis that could help in the intervention of schizophrenia in high-risk groups.

Whole-brain functional connectivity predicts regional tau PET in preclinical Alzheimer's disease

Preclinical Alzheimer's disease (AD), characterized by the abnormal accumulation of amyloid prior to cognitive symptoms, presents a critical opportunity for early intervention. Past work has described functional connectivity changes in preclinical disease, yet the interplay between AD pathology and the functional connectome during this window remains unexplored. We applied connectome-based predictive modeling to investigate the ability of resting-state whole-brain functional connectivity to predict tau (18F-flortaucipir) and amyloid (18F-florbetapir) PET binding in a preclinical AD cohort (A4, n =342, age 65-85). Separate predictive models were developed for each of 14 regions, and model performance was assessed using a Spearman's correlation between predicted and observed PET binding standard uptake value ratios. We assessed the validity of significant models by applying them to an external dataset, and visualized the underlying connectivity that was positively and negatively correlated to posterior cingulate tau binding, the most successful model. We found that whole brain functional connectivity predicts regional tau PET, outperforming amyloid PET models. The best performing tau models were for regions affected in Braak stage IV-V regions (posterior cingulate, precuneus, lateral occipital cortex, middle temporal, inferior temporal, and Bank STS), while models for regions of earlier tau pathology (entorhinal, parahippocampal, fusiform, and amygdala) performed poorly. Importantly, tau models generalized to a symptomatic AD cohort (ADNI; amyloid positive, n = 211, age 55-90), in tau-elevated but not tau-negative individuals. For the posterior cingulate A4 tau model, the most successful model, the predictive edges positively correlated with posterior cingulate tau predominantly came from nodes within temporal, limbic, and cerebellar regions. The most predictive edges negatively associated to tau were from nodes of heteromodal association areas, particularly within the prefrontal and parietal cortices. These findings reveal that whole-brain functional connectivity predicts tau PET in preclinical AD and generalizes to a clinical dataset specifically in individuals with abnormal tau PET, highlighting the relevance of the functional connectome for the early detection and monitoring of AD pathology.

Biomarker discovery using machine learning in the psychosis spectrum

The past decade witnessed substantial discoveries related to the psychosis spectrum. Many of these discoveries resulted from pursuits of objective and quantifiable biomarkers in tandem with the application of analytical tools such as machine learning. These approaches provided exciting new insights that significantly helped improve precision in diagnosis, prognosis, and treatment. This article provides an overview of how machine learning has been employed in recent biomarker discovery research in the psychosis spectrum, which includes schizophrenia, schizoaffective disorders, bipolar disorder with psychosis, first episode psychosis, and clinical high risk for psychosis. It highlights both human and animal model studies and explores a varying range of the most impactful biomarkers including cognition, neuroimaging, electrophysiology, and digital markers. We specifically highlight new applications and opportunities for machine learning to impact noninvasive symptom monitoring, prediction of future diagnosis and treatment outcomes, integration of new methods with traditional clinical research and practice, and personalized medicine approaches.

Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction

Functional magnetic resonance imaging-based functional connectivity enables the non-invasive mapping of individual differences in brain functional organization to individual differences in a vast array of behavioral phenotypes. This flexibility has renewed the search for neuroimaging-based biomarkers that exhibit reproducibility, prediction, and precision. Functional connectivity-based measures that share these three characteristics are key to achieving this goal. Here, we review the functional connectome fingerprinting approach and discuss its value, not only as a simple and intuitive conceptualization of the "functional connectome" that provides new insights into how the connectome is altered in association with psychiatric symptoms, but also as a straightforward and interpretable method for indexing the reproducibility of functional connectivity-based measures. We discuss how these advantages provide new avenues for strengthening reproducibility, precision, and behavioral prediction for functional connectomics and we consider new directions toward discovering better biomarkers for neuropsychiatric conditions.

Neuroimaging Biomarkers for Drug Discovery and Development in Schizophrenia

Schizophrenia is a chronic mental illness that affects up to 1% of the population. While efficacious therapies are available for positive symptoms, effective treatment of cognitive and negative symptoms remains an unmet need after decades of research. New developments in the field of neuroimaging are accelerating our knowledge gain regarding the underlying pathophysiology of symptoms in schizophrenia and psychosis spectrum disorders, inspiring new targets for drug development. However, no validated and qualified biomarkers are currently available to support the development of new therapeutics. This review summarizes the current use of neuroimaging technology in clinical drug development for psychotic disorders. As exemplified by drug development programs that target NMDA receptor hypofunction, neuroimaging results play a critical role in target discovery and establishing target engagement and dose selection. Furthermore, pharmacological neuroimaging may provide response biomarkers that allow for early decision making in proof-of-concept studies that leverage pharmacological challenge models in healthy volunteers. That said, while response and predictive biomarkers are starting to be evaluated in patient populations, they continue to play a limited role. Novel approaches to neuroimaging data acquisition and analysis may aid the establishment of biomarkers that are predictive at the individual level in the future. Nevertheless, various gaps in knowledge need to be addressed and biomarkers need to be validated to establish them as "fit for purpose" in drug development.

Incremental predictive value of genetic risk and functional brain connectivity in determining antipsychotic response in schizophrenia

We aimed to assess the incremental value of schizophrenia polygenic risk score (PgRS) and resting-state functional brain connectivity (rsFC) when added to clinical data in predicting the six-week response to oral risperidone (Risperdal) in schizophrenia. Fifty-seven, 54, and 43 individuals in a group of never-before-treated first-episode schizophrenia had good quality whole-genome sequencing (10x), rsFC, and both genomic and rsFC data, respectively, at baseline. Symptom severity ratings were obtained at baseline and six-weeks of oral risperidone (Risperdal) treatment. The primary outcome was the percentage change in the Positive and Negative Syndrome Scale Total scores after risperidone (Risperdal) treatment. Clinical, PgRS, and rsFC determinants of treatment response were first evaluated independently. Subsequently, three blocks of hierarchical multiple regression analyses with leave-one-out cross-validation (n = 43), were implemented to study clinical, clinical + PgRS and clinical + PgRS + rsFC determinants of treatment response. While the combined clinical variables did not show a statistically significant prediction of treatment response, adding PgRS (9 % R2 change) and rsFC (26 % R2 change) in hierarchical steps, significantly improved the overall proportion of variance explained in treatment response. This proof-of-concept investigation underscores the incremental benefits offered by genetic and neuroimaging metrics over clinical measures in determining prospectively-ascertained short-term treatment response in first-episode schizophrenia.

Higher-Order Intrinsic Brain Network Trajectories After Antipsychotic Treatment in Medication-Naïve Patients With First-Episode Psychosis

BACKGROUND

Intrinsic brain network connectivity is already altered in first-episode psychosis (FEP), but the longitudinal trajectories of network connectivity, especially in response to antipsychotic treatment, remain poorly understood. The goal of this study was to investigate how antipsychotic medications affect higher-order intrinsic brain network connectivity in FEP.

METHODS

Data from 87 antipsychotic medication-naïve patients with FEP and 87 healthy control participants were used. Medication-naïve patients received antipsychotic treatment for 16 weeks. Resting-state functional connectivity (FC) of the default mode, salience, dorsal attention, and executive control networks were assessed prior to treatment and at 6 and 16 weeks after treatment. We evaluated baseline and FC changes using linear mixed models to test group × time interactions within each network. Associations between FC changes after 16 weeks and response to treatment were also evaluated.

RESULTS

Prior to treatment, significant group differences in all networks were found. However, significant trajectory changes in FC were found only in the default mode and executive control networks. Changes in FC in these networks were associated with treatment response. Several sensitivity analyses showed a consistent normalization of executive control network FC in response to antipsychotic treatment.

CONCLUSIONS

Here, we found that alterations in intrinsic brain network FC were not only alleviated with antipsychotic treatment, but the extent of this normalization was also associated with the degree of reduction in symptom severity. Taken together, our data suggest modulation of intrinsic brain network connectivity (mainly frontoparietal connectivity) as a mechanism underlying antipsychotic treatment response in FEP.

Baseline symptom-related white matter tracts predict individualized treatment response to 12-week antipsychotic monotherapies in first-episode schizophrenia

There is significant heterogeneity in individual responses to antipsychotic drugs, but there is no reliable predictor of antipsychotics response in first-episode psychosis. This study aimed to investigate whether psychotic symptom-related alterations in fractional anisotropy (FA) and mean diffusivity (MD) of white matter (WM) at the early stage of the disorder may aid in the individualized prediction of drug response. Sixty-eight first-episode patients underwent baseline structural MRI scans and were subsequently randomized to receive a single atypical antipsychotic throughout the first 12 weeks. Clinical symptoms were evaluated using the eight "core symptoms" selected from the Positive and Negative Syndrome Scale (PANSS-8). Follow-up assessments were conducted at the 4th, 8th, and 12th weeks by trained psychiatrists. LASSO regression model and cross-validation were conducted to examine the performance of baseline symptom-related alterations FA and MD of WM in the prediction of individualized treatment outcome. Fifty patients completed both clinical follow-up assessments by the 8th and 12th weeks. 30 patients were classified as responders, and 20 patients were classified as nonresponders. At baseline, the altered diffusion properties of fiber tracts in the anterior thalamic radiation, corticospinal tract, callosum forceps minor, longitudinal fasciculi (ILF), inferior frontal-occipital fasciculi (IFOF) and superior longitudinal fasciculus (SLF) were related to the severity of symptoms. These abnormal fiber tracts, especially the ILF, IFOF, and SLF, significantly predicted the response to antipsychotic treatment at the individual level (AUC = 0.828, P < 0.001). These findings demonstrate that early microstructural WM changes contribute to the pathophysiology of psychosis and may serve as meaningful individualized predictors of response to antipsychotics.