A multivariate neuromonitoring approach to neuroplasticity-based computerized cognitive training in recent onset psychosis

Aberrant brain network topology in youth with a familial risk for bipolar disorder: a task-based fMRI connectome study

BACKGROUND

Youth with a family history of bipolar disorder (BD) may be at increased risk for mood disorders and for developing side effects after antidepressant exposure. The neurobiological basis of these risks remains poorly understood. We aimed to identify biomarkers underlying risk by characterizing abnormalities in the brain connectome of symptomatic youth at familial risk for BD.

METHODS

Depressed and/or anxious youth (n = 119, age = 14.9 ± 1.6 years) with a family history of BD but no prior antidepressant exposure and typically developing controls (n = 57, age = 14.8 ± 1.7 years) received functional magnetic resonance imaging (fMRI) during an emotional continuous performance task. A generalized psychophysiological interaction (gPPI) analysis was performed to compare their brain connectome patterns, followed by machine learning of topological metrics.

RESULTS

High-risk youth showed weaker connectivity patterns that were mainly located in the default mode network (DMN) (network weight = 50.1%) relative to controls, and connectivity patterns derived from the visual network (VN) constituted the largest proportion of aberrant stronger pairs (network weight = 54.9%). Global local efficiency (Elocal, p = .022) and clustering coefficient (Cp, p = .029) and nodal metrics of the right superior frontal gyrus (SFG) (Elocal: p < .001; Cp: p = .001) in the high-risk group were significantly higher than those in healthy subjects, and similar patterns were also found in the left insula (degree: p = .004; betweenness: p = .005; age-by-group interaction, p = .038) and right hippocampus (degree: p = .003; betweenness: p = .003). The case-control classifier achieved a cross-validation accuracy of 78.4%.

CONCLUSIONS

Our findings of abnormal connectome organization in the DMN and VN may advance mechanistic understanding of risk for BD. Neuroimaging biomarkers of increased network segregation in the SFG and altered topological centrality in the insula and hippocampus in broader limbic systems may be used to target interventions tailored to mitigate the underlying risk of brain abnormalities in these at-risk youth.

Alterations of Functional Connectivity Dynamics in Affective and Psychotic Disorders

BACKGROUND

Patients with psychosis and patients with depression exhibit widespread neurobiological abnormalities. The analysis of dynamic functional connectivity (dFC) allows for the detection of changes in complex brain activity patterns, providing insights into common and unique processes underlying these disorders.

METHODS

We report the analysis of dFC in a large sample including 127 patients at clinical high risk for psychosis, 142 patients with recent-onset psychosis, 134 patients with recent-onset depression, and 256 healthy control participants. A sliding window-based technique was used to calculate the time-dependent FC in resting-state magnetic resonance imaging data, followed by clustering to reveal recurrent FC states in each diagnostic group.

RESULTS

We identified 5 unique FC states, which could be identified in all groups with high consistency (mean r = 0.889 [SD = 0.116]). Analysis of dynamic parameters of these states showed a characteristic increase in the lifetime and frequency of a weakly connected FC state in patients with recent-onset depression (p < .0005) compared with the other groups and a common increase in the lifetime of an FC state characterized by high sensorimotor and cingulo-opercular connectivities in all patient groups compared with the healthy control group (p < .0002). Canonical correlation analysis revealed a mode that exhibited significant correlations between dFC parameters and clinical variables (r = 0.617, p < .0029), which was associated with positive psychosis symptom severity and several dFC parameters.

CONCLUSIONS

Our findings indicate diagnosis-specific alterations of dFC and underline the potential of dynamic analysis to characterize disorders such as depression and psychosis and clinical risk states.

Anhedonia as a Potential Transdiagnostic Phenotype With Immune-Related Changes in Recent-Onset Mental Health Disorders

BACKGROUND

Chronic low-grade inflammation is observed across mental disorders and is associated with difficult-to-treat-symptoms of anhedonia and functional brain changes, reflecting a potential transdiagnostic dimension. Previous investigations have focused on distinct illness categories in people with enduring illness, but few have explored inflammatory changes. We sought to identify an inflammatory signal and the associated brain function underlying anhedonia among young people with recent-onset psychosis and recent-onset depression.

METHODS

Resting-state functional magnetic resonance imaging, inflammatory markers, and anhedonia symptoms were collected from 108 (mean [SD] age = 26.2 [6.2] years; female = 50) participants with recent-onset psychosis (n = 53) and recent-onset depression (n = 55) from the European Union/Seventh Framework Programme-funded PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Time series were extracted using the Schaefer atlas, defining 100 cortical regions of interest. Using advanced multimodal machine learning, an inflammatory marker model and a functional connectivity model were developed to classify participants into an anhedonic group or a normal hedonic group.

RESULTS

A repeated nested cross-validation model using inflammatory markers classified normal hedonic and anhedonic recent-onset psychosis/recent-onset depression groups with a balanced accuracy of 63.9% and an area under the curve of 0.61. The functional connectivity model produced a balanced accuracy of 55.2% and an area under the curve of 0.57. Anhedonic group assignment was driven by higher levels of interleukin 6, S100B, and interleukin 1 receptor antagonist and lower levels of interferon gamma, in addition to connectivity within the precuneus and posterior cingulate.

CONCLUSIONS

We identified a potential transdiagnostic anhedonic subtype that was accounted for by an inflammatory profile and functional connectivity. Results have implications for anhedonia as an emerging transdiagnostic target across emerging mental disorders.

Structural and Functional Brain Patterns Predict Formal Thought Disorder's Severity and Its Persistence in Recent-Onset Psychosis: Results From the PRONIA Study

BACKGROUND

Formal thought disorder (FThD) is a core feature of psychosis, and its severity and long-term persistence relates to poor clinical outcomes. However, advances in developing early recognition and management tools for FThD are hindered by a lack of insight into the brain-level predictors of FThD states and progression at the individual level.

METHODS

Two hundred thirty-three individuals with recent-onset psychosis were drawn from the multisite European Prognostic Tools for Early Psychosis Management study. Support vector machine classifiers were trained within a cross-validation framework to separate two FThD symptom-based subgroups (high vs. low FThD severity), using cross-sectional whole-brain multiband fractional amplitude of low frequency fluctuations, gray matter volume and white matter volume data. Moreover, we trained machine learning models on these neuroimaging readouts to predict the persistence of high FThD subgroup membership from baseline to 1-year follow-up.

RESULTS

Cross-sectionally, multivariate patterns of gray matter volume within the salience, dorsal attention, visual, and ventral attention networks separated the FThD severity subgroups (balanced accuracy [BAC] = 60.8%). Longitudinally, distributed activations/deactivations within all fractional amplitude of low frequency fluctuation sub-bands (BACslow-5 = 73.2%, BACslow-4 = 72.9%, BACslow-3 = 68.0%), gray matter volume patterns overlapping with the cross-sectional ones (BAC = 62.7%), and smaller frontal white matter volume (BAC = 73.1%) predicted the persistence of high FThD severity from baseline to follow-up, with a combined multimodal balanced accuracy of BAC = 77%.

CONCLUSIONS

We report the first evidence of brain structural and functional patterns predictive of FThD severity and persistence in early psychosis. These findings open up avenues for the development of neuroimaging-based diagnostic, prognostic, and treatment options for the early recognition and management of FThD and associated poor outcomes.

Reduced neural connectivity in the caudate anterior head predicts hallucination severity in schizophrenia

BACKGROUND

Caudate functional abnormalities have been identified as one critical neural substrate underlying sensory gating impairments that lead to auditory phantom hallucinations in both patients with schizophrenia (SZ) and tinnitus, characterized by the perception of internally generated sounds in the absence of external environmental auditory stimuli. In this study, we tested the hypothesis as to whether functional connectivity abnormalities in distinct caudate subdivisions implicated in sensory gating and auditory phantom percepts in tinnitus, which are currently being localized for neuromodulation targeting using deep brain stimulation techniques, would be associated with auditory phantom hallucination severity in SZ.

METHODS

Twenty five SZ and twenty eight demographically-matched healthy control (HC) participants, completed this fMRI resting-state study and clinical assessments.

RESULTS

Between-group seed-to-voxel analyses revealed only one region, the caudate anterior head, which showed reduced functional connectivity with the thalamus that survived whole-brain multiple comparison corrections. Importantly, connectivity between the caudate anterior head with thalamus negatively correlated with hallucination severity.

CONCLUSIONS

In the present study, we deliver the first evidence of caudate subdivision specificity for the neural pathophysiology underlying hallucinations in schizophrenia within a sensory gating framework that has been developed for auditory phantoms in patients with tinnitus. Our findings provide transdiagnostic convergent evidence for the role of the caudate in the gating of auditory phantom hallucinations, observed across patients with SZ and tinnitus by specifying the anterior caudate division is key to mediation of hallucinations, and creating a path towards personalized treatment approaches to arrest auditory phantom hallucinations from reaching perceptual awareness.

Cognitive impairment in psychiatric diseases: Biomarkers of diagnosis, treatment, and prevention

Psychiatric diseases, such as schizophrenia, bipolar disorder, autism spectrum disorder, and major depressive disorder, place a huge health burden on society. Cognitive impairment is one of the core characteristics of psychiatric disorders and a vital determinant of social function and disease recurrence in patients. This review thus aims to explore the underlying molecular mechanisms of cognitive impairment in major psychiatric disorders and identify valuable biomarkers for diagnosis, treatment and prevention of patients.

The impact of visual dysfunctions in recent-onset psychosis and clinical high-risk state for psychosis

Subtle subjective visual dysfunctions (VisDys) are reported by about 50% of patients with schizophrenia and are suggested to predict psychosis states. Deeper insight into VisDys, particularly in early psychosis states, could foster the understanding of basic disease mechanisms mediating susceptibility to psychosis, and thereby inform preventive interventions. We systematically investigated the relationship between VisDys and core clinical measures across three early phase psychiatric conditions. Second, we used a novel multivariate pattern analysis approach to predict VisDys by resting-state functional connectivity within relevant brain systems. VisDys assessed with the Schizophrenia Proneness Instrument (SPI-A), clinical measures, and resting-state fMRI data were examined in recent-onset psychosis (ROP, n = 147), clinical high-risk states of psychosis (CHR, n = 143), recent-onset depression (ROD, n = 151), and healthy controls (HC, n = 280). Our multivariate pattern analysis approach used pairwise functional connectivity within occipital (ON) and frontoparietal (FPN) networks implicated in visual information processing to predict VisDys. VisDys were reported more often in ROP (50.34%), and CHR (55.94%) than in ROD (16.56%), and HC (4.28%). Higher severity of VisDys was associated with less functional remission in both CHR and ROP, and, in CHR specifically, lower quality of life (Qol), higher depressiveness, and more severe impairment of visuospatial constructability. ON functional connectivity predicted presence of VisDys in ROP (balanced accuracy 60.17%, p = 0.0001) and CHR (67.38%, p = 0.029), while in the combined ROP + CHR sample VisDys were predicted by FPN (61.11%, p = 0.006). These large-sample study findings suggest that VisDys are clinically highly relevant not only in ROP but especially in CHR, being closely related to aspects of functional outcome, depressiveness, and Qol. Findings from multivariate pattern analysis support a model of functional integrity within ON and FPN driving the VisDys phenomenon and being implicated in core disease mechanisms of early psychosis states.

The potential of precision psychiatry: what is in reach?

Progress in developing personalised care for mental disorders is supported by numerous proof-of-concept machine learning studies in the area of risk assessment, diagnostics and precision prescribing. Most of these studies primarily use clinical data, but models might benefit from additional neuroimaging, blood and genetic data to improve accuracy. Combined, multimodal models might offer potential for stratification of patients for treatment. Clinical implementation of machine learning is impeded by a lack of wider generalisability, with efforts primarily focused on psychosis and dementia. Studies across all diagnostic groups should work to test the robustness of machine learning models, which is an essential first step to clinical implementation, and then move to prospective clinical validation. Models need to exceed clinicians' heuristics to be useful, and safe, in routine decision-making. Engagement of clinicians, researchers and patients in digitalisation and 'big data' approaches are vital to allow the generation and accessibility of large, longitudinal, prospective data needed for precision psychiatry to be applied into real-world psychiatric care.

Cognitive Performance and Learning Parameters Predict Response to Working Memory Training in Parkinson's Disease

BACKGROUND

Working memory (WM) training (WMT) is a popular intervention approach against cognitive decline in patients with Parkinson's disease (PD). However, heterogeneity in WM responsiveness suggests that WMT may not be equally efficient for all patients.

OBJECTIVE

The present study aims to evaluate a multivariate model to predict post-intervention verbal WM in patients with PD using a supervised machine learning approach. We test the predictive potential of novel learning parameters derived from the WMT and compare their predictiveness to other more commonly used domains including demographic, clinical, and cognitive data.

METHODS

37 patients with PD (age: 64.09±8.56, 48.6% female, 94.7% Hoehn & Yahr stage 2) participated in a 5-week WMT. Four random forest regression models including 1) cognitive variables only, 2) learning parameters only, 3) both cognitive and learning variables, and 4) the entire set of variables (with additional demographic and clinical data, 'all' model), were built to predict immediate and 3-month-follow-up WM.

RESULT

The 'all' model predicted verbal WM with the lowest root mean square error (RMSE) compared to the other models, at both immediate (RMSE = 0.184; 95% -CI=[0.184;0.185]) and 3-month follow-up (RMSE = 0.216; 95% -CI=[0.215;0.217]). Cognitive baseline parameters were among the most important predictors in the 'all' model. The model combining cognitive and learning parameters significantly outperformed the model solely based on cognitive variables.

CONCLUSION

Commonly assessed demographic, clinical, and cognitive variables provide robust prediction of response to WMT. Nonetheless, inclusion of training-inherent learning parameters further boosts precision of prediction models which in turn may augment training benefits following cognitive interventions in patients with PD.

Strategies for Psychiatric Rehabilitation and their Cognitive Outcomes in Schizophrenia: Review of Last Five-year Studies

BACKGROUND

Cognitive deficits are core features of Schizophrenia, showing poor response to antipsychotic treatment, therefore non-pharmacological rehabilitative approaches to such a symptom domain need to be identified. However, since not all patients with Schizophrenia exhibit the same cognitive impairment profile, individualized rehabilitative approaches should be set up.

OBJECTIVES

We explored the last five-year literature addressing the issue of cognitive dysfunction response to rehabilitative methodologies in Schizophrenia to identify possible predictors of response and individualized strategies to treat such a dysfunction.

CONCLUSION

A total of 76 studies were reviewed. Possible predictors of cognitive rehabilitation outcome were identified among patient-specific and approach-specific variables and a general overview of rehabilitative strategies used in the last five years has been depicted. Studies suggest the existence of multifaced and multi-domain variables that could significantly predict pro-cognitive effects of cognitive rehabilitation, which could also be useful for identifying individual-specific rehabilitation trajectories over time.An individualized rehabilitative approach to cognitive impairment in Schizophrenia is possible if taking into account both patient and approach specific predictors of outcomes.