Brain functional connectivity data enhance prediction of clinical outcome in youth at risk for psychosis

Systematically altered connectome gradient in benign childhood epilepsy with centrotemporal spikes: Potential effect on cognitive function

OBJECTIVE

Benign childhood epilepsy with centrotemporal spikes (BECTS) affects brain network hierarchy and cognitive function; however, itremainsunclearhowhierarchical changeaffectscognition in patients with BECTS. A major aim of this study was to examine changes in the macro-network function hierarchy in BECTS and its potential contribution to cognitive function.

METHODS

Overall, the study included 50 children with BECTS and 69 healthy controls. Connectome gradient analysis was used to determine the brain network hierarchy of each group. By comparing gradient scores at each voxel level and network between groups, we assessed changes in whole-brain voxel-level and network hierarchy. Functional connectivity was used to detect the functional reorganization of epilepsy caused by these abnormal brain regions based on these aberrant gradients. Lastly, we explored the relationships between the change gradient and functional connectivity values and clinical variables and further predicted the cognitive function associated with BECTS gradient changes.

RESULTS

In children with BECTS, the gradient was extended at different network and voxel levels. The gradient scores frontoparietal network was increased in the principal gradient of patients with BECTS. The left precentral gyrus (PCG) and right angular gyrus gradient scores were significantly increased in the principal gradient of children with BECTS. Moreover, in regions of the brain with abnormal principal gradients, functional connectivity was disrupted. The left PCG gradient score of children with BECTS was correlated with the verbal intelligence quotient (VIQ), and the disruption of functional connectivity in brain regions with abnormal principal gradients was closely related to cognitive function. VIQ was significantly predicted by the principal gradient map of patients.

SIGNIFICANCE

The results indicate connectome gradient disruption in children with BECTS and its relationship to cognitive function, thereby increasing our understanding of the functional connectome hierarchy and providing potential biomarkers for cognitive function of children with BECTS.

Untargeted metabolomic, and proteomic analysis identifies metabolic biomarkers and pathway alterations in individuals with 22q11.2 deletion syndrome

INTRODUCTION

The chromosome 22q11.2 deletion syndrome (22q11.2DS) is characterized by a well-defined microdeletion and is associated with a wide range of brain-related phenotypes including schizophrenia spectrum disorders (SCZ), autism spectrum disorders (ASD), anxiety disorders and attention deficit disorders (ADHD). The typically deleted region in 22q11.2DS contains multiple genes which haploinsufficiency has the potential of altering the protein and the metabolic profiles.

OBJECTIVES

Alteration in metabolic processes and downstream protein pathways during the early brain development may help to explain the increased prevalence of the observed neurodevelopmental phenotypes in 22q11.2DS. However, relatively little is known about the correlation of dysregulated protein/metabolite expression and neurobehavioral impairments in individuals who developed them over time.

METHODS

In this study, we performed untargeted metabolic and proteomic analysis in plasma samples derived from 30 subjects including 16 participants with 22q11.2DS and 14 healthy controls (TD) enrolled in a longitudinal study, aiming to identify a metabolic and protein signature informing about the underlying mechanisms involved in disease development and progression. The metabolic and proteomic profiles were also compared between the participants with 22q11.2DS with and without various comorbidities, such as medical involvement, psychiatric conditions, and autism spectrum disorder (ASD) to detect potential changes among multiple specimens, collected overtime, with the aim to understand the basic underlying mechanisms involved in disease development and progression.

RESULTS

We observed a large number of statistically significant differences in metabolites between the two groups. Among them, the levels of taurine and arachidonic acid were significantly lower in 22q11.2DS compared to the TD group. In addition, we identified 16 proteins that showed significant changes in expression levels (adjusted P < 0.05) in 22q11.2DS as compared to TD, including those involved in 70 pathways such as gene expression, the PI3K-Akt signaling pathway and the complement system. Within participants with 22q11.2DS, no significant changes in those with and without medical or psychiatric conditions were observed.

CONCLUSION

To our knowledge, this is the first report on plasma metabolic and proteomic profiling and on the identification of unique biomarkers in 22q11.2DS. These findings may suggest the potential role of the identified metabolites and proteins as biomarkers for the onset of comorbid conditions in 22q11.2DS. Ultimately, the altered protein pathways in 22q11.2DS may provide insights of the biological mechanisms underlying the neurodevelopmental phenotype and may provide missing molecular outcome measures in future clinical trials to assess early-diagnosis treatment and the efficacy of response to targeted treatment.

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.

A systematic review exploring the association between the human gut microbiota and brain connectivity in health and disease

A body of pre-clinical evidence shows how the gut microbiota influence brain functioning, including brain connectivity. Linking measures of brain connectivity to the gut microbiota can provide important mechanistic insights into the bi-directional gut-brain communication. In this systematic review, we therefore synthesized the available literature assessing this association, evaluating the degree of consistency in microbiota-connectivity associations. Following the PRISMA guidelines, a PubMed search was conducted, including studies published up to September 1, 2022. We identified 16 studies that met the inclusion criteria. Several bacterial genera, including Prevotella, Bacteroides, Ruminococcus, Blautia, and Collinsella were most frequently reported in association with brain connectivity. Additionally, connectivity of the salience (specifically the insula and anterior cingulate cortex), default mode, and frontoparietal networks were most frequently associated with the gut microbiota, both in terms of microbial diversity and composition. There was no discernible pattern in the association between microbiota and brain connectivity. Altogether, based on our synthesis, there is evidence for an association between the gut microbiota and brain connectivity. However, many findings were poorly replicated across studies, and the specificity of the association is yet unclear. The current studies show substantial inter-study heterogeneity in methodology and reporting, limiting the robustness and reproducibility of the findings and emphasizing the need to harmonize methodological approaches. To enhance comparability and replicability, future research should focus on further standardizing processing pipelines and employing data-driven multivariate analysis strategies.

Brain-Based Predictions of Psychiatric Illness-Linked Behaviors Across the Sexes

BACKGROUND

Individual differences in functional brain connectivity can be used to predict both the presence of psychiatric illness and variability in associated behaviors. However, despite evidence for sex differences in functional network connectivity and in the prevalence, presentation, and trajectory of psychiatric illnesses, the extent to which disorder-relevant aspects of network connectivity are shared or unique across the sexes remains to be determined.

METHODS

In this work, we used predictive modeling approaches to evaluate whether shared or unique functional connectivity correlates underlie the expression of psychiatric illness-linked behaviors in males and females in data from the Adolescent Brain Cognitive Development Study (N = 5260; 2571 females).

RESULTS

We demonstrate that functional connectivity profiles predict individual differences in externalizing behaviors in males and females but predict internalizing behaviors only in females. Furthermore, models trained to predict externalizing behaviors in males generalize to predict internalizing behaviors in females, and models trained to predict internalizing behaviors in females generalize to predict externalizing behaviors in males. Finally, the neurobiological correlates of many behaviors are largely shared within and across sexes: functional connections within and between heteromodal association networks, including default, limbic, control, and dorsal attention networks, are associated with internalizing and externalizing behaviors.

CONCLUSIONS

Taken together, these findings suggest that shared neurobiological patterns may manifest as distinct behaviors across the sexes. Based on these results, we recommend that both clinicians and researchers carefully consider how sex may influence the presentation of psychiatric illnesses, especially those along the internalizing-externalizing spectrum.

Effectiveness of enhancing cognitive reserve in children, adolescents and young adults at genetic risk for psychosis: Study protocol for a randomized controlled trial

BACKGROUND

Offspring of patients diagnosed with bipolar disorder and schizophrenia (Off-BDSZ) have a high genetic risk of developing a mental illness. The aim of this project is to develop and investigate the efficacy of an intervention aimed at this population, based on the concept of cognitive reserve.

METHODS

This is a multicenter randomized trial with an experimental test-retest design study with control group. Two groups will be included: a community comparison group (CC) and a Off-BDSZ group. A total of 108 Off-BDSZ and 65 CC aged between 6 and 25 years will be recruited. Off-BDSZ participants will be randomized to receive either Cognitive Reserve EnhAncement ThErapy (CREATE) (n=54), or a supportive approach (n=54). The CC group will be assessed at baseline. The duration of the intervention will be 3 months, with 12 weekly group sessions. The primary outcome will be the improvement in CR measured according to change in the Cognitive Reserve Assessment Scale in Health (CRASH) and Cognitive Reserve scale for Adolescents (CORE-A). All participants will be blindly evaluated using clinical, cognitive and neuroimaging measures at baseline, at three months (after the psychological intervention), and at twelve-month follow-up after treatment completion.

DISCUSSION

The results will provide insight into whether the CREATE-Offspring version may enhance cognitive reserve (CR) in child, adolescent and young adult Off-BDSZ as well as advance knowledge about changes in clinical manifestations, neuropsychological performance and brain structure and function associated with improving CR. This novel and cost-effective intervention represents an advance in the framework of preventive interventions in mental health.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT03722082. Registered on 26 October 2018.

A Review of Potential Neuroimaging Biomarkers of Schizophrenia-Risk

The risk for developing schizophrenia is increased among first-degree relatives of those with psychotic disorders, but the risk is even higher in those meeting established criteria for clinical high risk (CHR), a clinical construct most often comprising of attenuated psychotic experiences. Conversion to psychosis among CHR youth has been reported to be about 15-35% over three years. Accurately identifying individuals whose psychotic symptoms will worsen would facilitate earlier intervention, but this has been difficult to do using behavior measures alone. Brain-based risk markers have the potential to improve the accuracy of predicting outcomes in CHR youth. This narrative review provides an overview of neuroimaging studies used to investigate psychosis risk, including studies involving structural, functional, and diffusion imaging, functional connectivity, positron emission tomography, arterial spin labeling, magnetic resonance spectroscopy, and multi-modality approaches. We present findings separately in those observed in the CHR state and those associated with psychosis progression or resilience. Finally, we discuss future research directions that could improve clinical care for those at high risk for developing psychotic disorders.

Large-Scale Thalamocortical Triple Network Dysconnectivities in Patients With First-Episode Psychosis and Individuals at Risk for Psychosis

BACKGROUND AND HYPOTHESIS

Aberrant thalamocortical connectivity and large-scale network interactions among the default mode network (DMN), salience network (SN), and executive control network (ECN) (ie, triple networks) have been regarded as critical in schizophrenia pathophysiology. Despite the importance of network properties and the role of the thalamus as an integrative hub, large-scale thalamocortical triple network functional connectivities (FCs) in different stages of the psychotic disorder have not yet been reported.

STUDY DESIGN

Thirty-nine first-episode psychosis (FEP) patients, 75 individuals at clinical high risk (CHR) for psychosis, 46 unaffected relatives (URs) of schizophrenia patients with high genetic loading, and 110 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Modular community detection was used to identify cortical and thalamic resting-state networks, and thalamocortical network interactions were compared across the groups.

STUDY RESULTS

Thalamic triple networks included higher-order thalamic nuclei. Thalamic SN-cortical ECN FC was greater in the FEP group than in the CHR, UR, and HC groups. Thalamic DMN-cortical DMN and thalamic SN-cortical DMN FCs were greater in FEP and CHR participants. Thalamic ECN-cortical DMN and thalamic ECN-cortical SN FCs were greater in FEP patients and URs.

CONCLUSIONS

These results highlight critical modulatory functions of thalamic triple networks and the shared and distinct patterns of thalamocortical triple network dysconnectivities across different stages of psychotic disorders. The current study findings suggest that large-scale thalamocortical triple network dysconnectivities may be used as an integrative biomarker for extending our understanding of the psychosis pathophysiology and for targeting network-based neuromodulation therapeutics.

Functional network alterations in young brain tumor patients with radiotherapy-induced memory impairments and vascular injury

Background

Cognitive impairment and cerebral microbleeds (CMBs) are long-term side-effects of cranial radiation therapy (RT). Previously we showed that memory function is disrupted in young patients and that the rate of cognitive decline correlates with CMB development. However, vascular injury alone cannot explain RT-induced cognitive decline. Here we use resting-state functional MRI (rsfMRI) to further investigate the complex mechanisms underlying memory impairment after RT.

Methods

Nineteen young patients previously treated with or without focal or whole-brain RT for a brain tumor underwent cognitive testing followed by 7T rsfMRI and susceptibility-weighted imaging for CMB detection. Global brain modularity and efficiency, and rsfMRI signal variability within the dorsal attention, salience, and frontoparietal networks were computed. We evaluated whether MR metrics could distinguish age- and sex-matched controls (N = 19) from patients and differentiate patients based on RT exposure and aggressiveness. We also related MR metrics with memory performance, CMB burden, and risk factors for cognitive decline after RT.

Results

Compared to controls, patients exhibited widespread hyperconnectivity, similar modularity, and significantly increased efficiency (p < 0.001) and network variability (p < 0.001). The most abnormal values were detected in patients treated with high dose whole-brain RT, having supratentorial tumors, and who did not undergo RT but had hydrocephalus. MR metrics and memory performance were correlated (R = 0.34–0.53), though MR metrics were more strongly related to risk factors for cognitive worsening and CMB burden with evidence of functional recovery.

Conclusions

MR metrics describing brain connectivity and variability represent promising candidate imaging biomarkers for monitoring of long-term cognitive side-effects after RT.

Alterations of Cerebral Perfusion and Functional Connectivity in Children With Idiopathic Generalized Epilepsy

Background

Studies have demonstrated that adults with idiopathic generalized epilepsy (IGE) have functional abnormalities; however, the neuropathological pathogenesis differs between adults and children. This study aimed to explore alterations in the cerebral blood flow (CBF) and functional connectivity (FC) to comprehensively elucidate the neuropathological mechanisms of IGE in children.

Methods

We obtained arterial spin labeling (ASL) and resting state functional magnetic resonance imaging data of 28 children with IGE and 35 matched controls. We used ASL to determine differential CBF regions in children with IGE. A seed-based whole-brain FC analysis was performed for regions with significant CBF changes. The mean CBF and FC of brain areas with significant group differences was extracted, then its correlation with clinical variables in IGE group was analyzed by using Pearson correlation analysis.

Results

Compared to controls, children with IGE had CBF abnormalities that were mainly observed in the right middle temporal gyrus, right middle occipital gyrus (MOG), right superior frontal gyrus (SFG), left inferior frontal gyrus (IFG), and triangular part of the left IFG (IFGtriang). We observed that the FC between the left IFGtriang and calcarine fissure (CAL) and that between the right MOG and bilateral CAL were decreased in children with IGE. The CBF in the right SFG was correlated with the age at IGE onset. FC in the left IFGtriang and left CAL was correlated with the IGE duration.

Conclusion

This study found that CBF and FC were altered simultaneously in the left IFGtriang and right MOG of children with IGE. The combination of CBF and FC may provide additional information and insight regarding the pathophysiology of IGE from neuronal and vascular integration perspectives.

Altered Resting State Functional Connectivity and Its Correlation with Cognitive Functions at Ultra High Risk for Psychosis

The aim of this study is to identify robust resting state-functional connectivity (rs-FC) alterations and their correlations with the neuropsychological characteristics of Ultra-High Risk (UHR) for psychosis subjects compared to healthy controls (HCs). Twenty individuals with UHR and sixteen HCs underwent resting-state functional magnetic resonance imaging (rs-fMRI) and a cognitive battery evaluating attention, episodic memory and executive functions. Compared to HCs, UHR individuals showed working memory and set-shifting impairments. In functional connectivity (FC) analyses, the Default Mode Network (DMN) of the UHR subjects displayed increased FC with the visual areas and decreased FC with the Dorsal Attention Network (DAN). Additionally, the salience network (SN) of the UHR subjects displayed increased connectivity with wide posterior cortical areas in the temporal, parietal and occipital lobes, corresponding to posterior nodes of the SN itself, the Somato-Motor Network (SMN) and the DAN. The SN connectivity with the left SMN and DAN was positively correlated with the Trail Making Test - B scores of the UHR subjects. These findings show that the SN and DMN, which mostly show abnormal connectivity patterns in psychosis, are also affected in UHR subjects, while the SN plays a more central role with its hyperconnectivity to the DAN and SMN.

N400 event-related brain potential and functional outcome in persons at clinical high risk for psychosis: A longitudinal study

BACKGROUND

The N400 event-related brain potential (ERP) semantic priming effect is thought to reflect activation by meaningful stimuli of related concepts in semantic memory and has been found to be deficient in schizophrenia. We tested the hypothesis that, among individuals at clinical high risk (CHR) for psychosis, N400 semantic priming deficits predict worse symptomatic and functional outcomes after one year.

METHODS

We measured N400 semantic priming at baseline in CHR patients (n = 47) and healthy control participants (n = 25) who viewed prime words each followed by a related or unrelated target word, at stimulus-onset asynchronies (SOAs) of 300 or 750 ms. We measured patients' psychosis-like symptoms with the Scale of Prodromal Symptoms (SOPS) Positive subscale, and academic/occupational and social functioning with the Global Functioning (GF):Role and Social scales, respectively, at baseline and one-year follow-up (n = 29).

RESULTS

CHR patients exhibited less N400 semantic priming than controls across SOAs; planned contrasts indicated this difference was significant at the 750-ms but not the 300-ms SOA. In patients, reduced N400 semantic priming at the 750-ms SOA was associated with lower GF:Social scores at follow-up, and greater GF:Social decrements from baseline to follow-up. Patients' N400 semantic priming was not associated with SOPS Positive or GF:Role scores at follow-up, or change in these from baseline to follow-up.

CONCLUSIONS

In CHR patients, reduced N400 semantic priming at baseline predicted worse social functioning after one year, and greater decline in social functioning over this period. Thus, the N400 may be a useful prognostic biomarker of real-world functional outcome in CHR patients.

Infant ultrasonic vocalizations predict adolescent social behavior in rats: Effects of early life adversity

Early life adversity (ELA) increases risk for psychopathologies that often manifest during adolescence and involve disrupted social functioning. ELA affects development of the prefrontal cortex (PFC), which plays a role in social behavior. PFC oxytocin and vasopressin are important regulators of, first, mother-infant attachment, and, later, social behavior, and are implicated in psychiatric disorders. Here, we tested whether infant social communication is predictive of PFC development and adolescent social behavior. We used the limited bedding (LB) ELA model in rats during postnatal days (P)2-14, and measured isolation-induced ultrasonic vocalizations (USVs) at P10 to characterize differences in an early social response. Rats were tested for dyadic social interaction in adolescence (P34). Adolescent oxytocin receptor (Oxtr) and arginine-vasopressin receptor 1a mRNA were measured in the PFC. Relationships between infant USVs, adolescent behavior, and gene expression were assessed. LB-reared rats exhibited fewer USVs at P10. While social behaviors were not robustly affected by rearing, fewer total and complex-type infant USVs predicted fewer interactions in adolescence. LB increased Oxtr in both sexes but Oxtr was not directly predicted by USVs. Findings support the use of USVs as indicators of differential early life experience in rodents, toward further characterization of early factors associated with vulnerability.

Inferring the Individual Psychopathologic Deficits with Structural Connectivity in a Longitudinal Cohort of Schizophrenia

The prediction of schizophrenia-related psychopathologic deficits is exceedingly important in the fields of psychiatry and clinical practice. However, objective association of the brain structure alterations to the illness clinical symptoms is challenging. Although, schizophrenia has been characterized as a brain dysconnectivity syndrome, evidence accounting for neuroanatomical network alterations remain scarce. Moreover, the absence of generalized connectome biomarkers for the assessment of illness progression further perplexes the prediction of long-term symptom severity. In this paper, a combination of individualized prediction models with quantitative graph theoretical analysis was adopted, providing a comprehensive appreciation of the extent to which the brain network properties are affected over time in schizophrenia. Specifically, Connectome-based Prediction Models were employed on Structural Connectivity (SC) features, efficiently capturing individual network-related differences, while identifying the anatomical connectivity disturbances contributing to the prediction of psychopathological deficits. Our results demonstrated distinctions among widespread cortical circuits responsible for different domains of symptoms, indicating the complex neural mechanisms underlying schizophrenia. Furthermore, the generated models were able to significantly predict changes of symptoms using SC features at follow-up, while the preserved SC features suggested an association with improved positive and overall symptoms. Moreover, cross-sectional significant deficits were observed in network efficiency and a progressive aberration of global integration in patients compared to healthy controls, representing a group-consensus pathological map, while supporting the dysconnectivity hypothesis.

How Machine Learning is Powering Neuroimaging to Improve Brain Health

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

Big contributions of the little brain for precision psychiatry

Our previous work using 3T functional Magnetic Resonance Imaging (fMRI) parcellated the human dentate nuclei (DN), the primary output of the cerebellum, to three distinct functional zones each contributing uniquely to default-mode, salience-motor, and visual brain networks. In this perspective piece, we highlight the possibility to target specific functional territories within the cerebellum using non-invasive brain stimulation, potentially leading to the refinement of cerebellar-based therapeutics for precision psychiatry. Significant knowledge gap exists in our functional understanding of cerebellar systems. Intervening early, gauging severity of illness, developing intervention strategies and assessing treatment response, are all dependent on our understanding of the cerebello-cerebral networks underlying the pathology of psychotic disorders. A promising yet under-examined avenue for biomarker discovery is disruptions in cerebellar output circuitry. This is primarily because most 3T MRI studies in the past had to exclude cerebellum from the field of view due to limitations in spatiotemporal resolutions. Using recent technological advances in 7T MRI (e.g., parallel transmit head coils) to identify functional territories of the DN, with a focus on dentato-cerebello-thalamo-cortical (CTC) circuitry can lead to better characterization of brain-behavioral correlations and assessments of co-morbidities. Such an improved mechanistic understanding of psychiatric illnesses can reveal aspects of CTC circuitry that can aid in neuroprognosis, identification of subtypes, and generate testable hypothesis for future studies.

Validation of the youth mood recurrences risk calculator in an adult sample with bipolar disorder

BACKGROUND

The ability to predict an individual's risk of mood episode recurrence can facilitate personalized medicine in bipolar disorder (BD). We sought to externally validate, in an adult sample, a risk calculator of mood episode recurrence developed in youth/young adults with BD from the Course and Outcome of Bipolar Youth (COBY) study.

METHODS

Adult participants from the National Institute of Mental Health Collaborative Depression Study (CDS; N=258; mean(SD) age=35.5(12.0) years; mean follow-up=24.9 years) were utilized as a sample to validate the youth COBY risk calculator for onset of depressive, manic, or any mood episodes.

RESULTS

In this older validation sample, the risk calculator predicted recurrence of any episode over 1, 2, 3, or 5-year follow-up intervals, with Area Under the Curves (AUCs) approximating 0.77. The AUC for prediction of depressive episodes was about 0.81 for each of the time windows, which was higher than for manic or hypomanic episodes (AUC=0.72). While the risk calculator was well-calibrated across the range of risk scores, it systematically underestimated risk in the CDS sample by about 20%. The length of current remission was a highly significant predictor of recurrence risk in the CDS sample.

LIMITATIONS

Predominantly self-reported White samples may limit generalizability; the risk calculator does not assess more proximal risk (e.g., 1 month).

CONCLUSIONS

Risk of mood episode recurrence can be predicted with good accuracy in youth and adults with BD in remission. The risk calculators may help identify higher risk BD subgroups for treatment and research.

Predictive waves in the autism-schizophrenia continuum: A novel biobehavioral model

The brain is a predictive machine. Converging data suggests a diametric predictive strategy from autism spectrum disorders (ASD) to schizophrenic spectrum disorders (SSD). Whereas perceptual inference in ASD is rigidly shaped by incoming sensory information, the SSD population is prone to overestimate the precision of their priors' models. Growing evidence considers brain oscillations pivotal biomarkers to understand how top-down predictions integrate bottom-up input. Starting from the conceptualization of ASD and SSD as oscillopathies, we introduce an integrated perspective that ascribes the maladjustments of the predictive mechanism to dysregulation of neural synchronization. According to this proposal, disturbances in the oscillatory profile do not allow the appropriate trade-off between descending predictive signal, overweighted in SSD, and ascending prediction errors, overweighted in ASD. These opposing imbalances both result in an ill-adapted reaction to external challenges. This approach offers a neuro-computational model capable of linking predictive coding theories with electrophysiological findings, aiming to increase knowledge on the neuronal foundations of the two spectra features and stimulate hypothesis-driven rehabilitation/research perspectives.

Global fractional anisotropy predicts transition to psychosis after 12 months in individuals at ultra-high risk for psychosis

OBJECTIVE

Psychosis spectrum disorders are associated with cerebral changes, but the prognostic value and clinical utility of these findings are unclear. Here, we applied a multivariate statistical model to examine the predictive accuracy of global white matter fractional anisotropy (FA) for transition to psychosis in individuals at ultra-high risk for psychosis (UHR).

METHODS

110 UHR individuals underwent 3 Tesla diffusion-weighted imaging and clinical assessments at baseline, and after 6 and 12 months. Using logistic regression, we examined the reliability of global FA at baseline as a predictor for psychosis transition after 12 months. We tested the predictive accuracy, sensitivity and specificity of global FA in a multivariate prediction model accounting for potential confounders to FA (head motion in scanner, age, gender, antipsychotic medication, parental socioeconomic status and activity level). In secondary analyses, we tested FA as a predictor of clinical symptoms and functional level using multivariate linear regression.

RESULTS

Ten UHR individuals had transitioned to psychosis after 12 months (9%). The model reliably predicted transition at 12 months (χ²  = 17.595, p = 0.040), accounted for 15-33% of the variance in transition outcome with a sensitivity of 0.70, a specificity of 0.88 and AUC of 0.87. Global FA predicted level of UHR symptoms (R²  = 0.055, F = 6.084, p = 0.016) and functional level (R²  = 0.040, F = 4.57, p = 0.036) at 6 months, but not at 12 months.

CONCLUSION

Global FA provided prognostic information on clinical outcome and symptom course of UHR individuals. Our findings suggest that the application of prediction models including neuroimaging data can inform clinical management on risk for psychosis transition.

Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

Cognitive gains following cognitive training interventions are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single-subject level. We employed whole-brain multivariate pattern analysis with support vector machine (SVM) modeling to identify gray matter (GM) patterns that predicted higher vs. lower functioning after 40 h of ABCT at the single-subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an out-of-sample cross-validation analysis to unseen SCZ patients from an independent validation sample who underwent 50 h of ABCT. The whole-brain GM volume-based pattern classification predicted higher vs. lower functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%). In particular, greater baseline GM volumes in regions within superior temporal gyrus, thalamus, anterior cingulate, and cerebellum predicted improved functioning at the single-subject level following ABCT in SCZ participants. The present findings provide a structural MRI fingerprint associated with preserved GM volumes at a single baseline timepoint, which predicted improved functioning following an ABCT intervention, and serve as a model for how to facilitate precision clinical therapies for SCZ based on imaging data, operating at the single-subject level.

Neuroimaging Markers of Resiliency in Youth at Clinical High Risk for Psychosis: A Qualitative Review

Psychotic disorders are highly debilitating and constitute a major public health burden. Identifying markers of psychosis risk and resilience is a necessary step toward understanding etiology and informing prevention and treatment efforts in individuals at clinical high risk (CHR) for psychosis. In this context, it is important to consider that neural risk markers have been particularly useful in identifying mechanistic determinants along with predicting clinical outcomes. Notably, despite a growing body of supportive literature and the promise of recent findings identifying potential neural markers, the current work on CHR resilience markers has received little attention. The present review provides a brief overview of brain-based risk markers with a focus on predicting symptom course. Next, the review turns to protective markers, examining research from nonpsychiatric and schizophrenia fields to build an understanding of framing, priorities, and potential, applying these ideas to contextualizing a small but informative body of resiliency-relevant CHR research. Four domains (neurocognition, emotion regulation, allostatic load, and sensory and sensorimotor function) were identified and are discussed in terms of behavioral and neural markers. Taken together, the literature suggests significant predictive value for brain-based markers for individuals at CHR for psychosis, and the limited but compelling resiliency work highlights the critical importance of expanding this promising area of inquiry.