Resting-state brain oscillations predict cognitive function in psychiatric disorders: A transdiagnostic machine learning approach

Examining the Most Important Risk Factors for Predicting Youth Persistent and Distressing Psychotic-Like Experiences

BACKGROUND

Persistence and distress distinguish more clinically significant psychotic-like experiences (PLEs) from those that are less likely to be associated with impairment and/or need for care. Identifying risk factors that identify clinically relevant PLEs early in development is important for improving our understanding of the etiopathogenesis of these experiences. Machine learning analyses were used to examine the most important baseline factors distinguishing persistent distressing PLEs.

METHODS

Using Adolescent Brain Cognitive Development (ABCD) Study data on PLEs from 3 time points (ages 9-13 years), we created the following groups: individuals with persistent distressing PLEs (n = 305), individuals with transient distressing PLEs (n = 374), and individuals with low-level PLEs demographically matched to either the persistent distressing PLEs group (n = 305) or the transient distressing PLEs group (n = 374). Random forest classification models were trained to distinguish persistent distressing PLEs from low-level PLEs, transient distressing PLEs from low-level PLEs, and persistent distressing PLEs from transient distressing PLEs. Models were trained using identified baseline predictors as input features (i.e., cognitive, neural [cortical thickness, resting-state functional connectivity], developmental milestone delays, internalizing symptoms, adverse childhood experiences).

RESULTS

The model distinguishing persistent distressing PLEs from low-level PLEs showed the highest accuracy (test sample accuracy = 69.33%; 95% CI, 61.29%-76.59%). The most important predictors included internalizing symptoms, adverse childhood experiences, and cognitive functioning. Models for distinguishing persistent PLEs from transient distressing PLEs generally performed poorly.

CONCLUSIONS

Model performance metrics indicated that while most important factors overlapped across models (e.g., internalizing symptoms), adverse childhood experiences were especially important for predicting persistent distressing PLEs. Machine learning analyses proved useful for distinguishing the most clinically relevant group from the least clinically relevant group but showed limited ability to distinguish among clinically relevant groups that differed in PLE persistence.

Trying Harder: How Cognitive Effort Sculpts Neural Representations during Working Memory

While the exertion of mental effort improves performance on cognitive tasks, the neural mechanisms by which motivational factors impact cognition remain unknown. Here, we used fMRI to test how changes in cognitive effort, induced by changes in task difficulty, impact neural representations of working memory (WM). Participants (both sexes) were precued whether WM difficulty would be hard or easy. We hypothesized that hard trials demanded more effort as a later decision required finer mnemonic precision. Behaviorally, pupil size was larger and response times were slower on hard compared with easy trials suggesting our manipulation of effort succeeded. Neurally, we observed robust persistent activity during delay periods in the prefrontal cortex (PFC), especially during hard trials. Yet, details of the memoranda could not be decoded from patterns in prefrontal activity. In the patterns of activity in the visual cortex, however, we found strong decoding of memorized targets, where accuracy was higher on hard trials. To potentially link these across-region effects, we hypothesized that effort, carried by persistent activity in the PFC, impacts the quality of WM representations encoded in the visual cortex. Indeed, we found that the amplitude of delay period activity in the frontal cortex predicted decoded accuracy in the visual cortex on a trial-wise basis. These results indicate that effort-related feedback signals sculpt population activity in the visual cortex, improving mnemonic fidelity.

Association between gait speed deterioration and EEG abnormalities

Physical and cognitive decline at an older age is preceded by changes that accumulate over time until they become clinically evident difficulties. These changes, frequently overlooked by patients and health professionals, may respond better than fully established conditions to strategies designed to prevent disabilities and dependence in later life. The objective of this study was twofold; to provide further support for the need to screen for early functional changes in older adults and to look for an early association between decline in mobility and cognition. A cross-sectional cohort study was conducted on 95 active functionally independent community-dwelling older adults in Havana, Cuba. We measured their gait speed at the usual pace and the cognitive status using the MMSE. A value of 0.8 m/s was used as the cut-off point to decide whether they presented a decline in gait speed. A quantitative analysis of their EEG at rest was also performed to look for an associated subclinical decline in brain function. Results show that 70% of the sample had a gait speed deterioration (i.e., lower than 0.8 m/s), of which 80% also had an abnormal EEG frequency composition for their age. While there was no statistically significant difference in the MMSE score between participants with a gait speed above and below the selected cut-off, individuals with MMSE scores below 25 also had a gait speed<0.8 m/s and an abnormal EEG frequency composition. Our results provide further evidence of early decline in older adults-even if still independent and active-and point to the need for clinical pathways that incorporate screening and early intervention targeted at early deterioration to prolong the years of functional life in older age.

Trying harder: how cognitive effort sculpts neural representations during working memory

The neural mechanisms by which motivational factors influence cognition remain unknown. Using fMRI, we tested how cognitive effort impacts working memory (WM). Participants were precued whether WM difficulty would be hard or easy. Hard trials demanded more effort as a later decision required finer mnemonic precision. Behaviorally, pupil size was larger and response times were slower on hard trials suggesting our manipulation of effort succeeded. Neurally, we observed robust persistent activity in prefrontal cortex, especially during hard trials. We found strong decoding of location in visual cortex, where accuracy was higher on hard trials. Connecting these across-region effects, we found that the amplitude of delay period activity in frontal cortex predicted decoded accuracy in visual cortex on a trial-wise basis. We conclude that the gain of persistent activity in frontal cortex may be the source of effort-related feedback signals that improve the quality of WM representations stored in visual cortex.

Pre-treatment frontal beta events are associated with executive dysfunction improvement after repetitive transcranial magnetic stimulation for depression: A preliminary report

Repetitive transcranial magnetic stimulation (rTMS) is an established clinical treatment for major depressive disorder (MDD) that has also been found to improve aspects of executive functioning. The objective of this study was to examine whether oscillatory burst-like events within the beta band (15-29 Hz) prior to treatment could predict subsequent change in self-reported executive dysfunction (EDF) across a clinical course of rTMS for MDD. Twenty-eight adults (64% female) with MDD completed the self-report Frontal Systems Behavior Scale (FrSBe) and provided eyes-closed resting-state electroencephalography (EEG) before and after a clinical course of rTMS therapy for primary MDD. The rate, power, duration, and frequency span of transient EEG measured oscillatory beta events were calculated. Events within delta/theta and alpha bands were examined to assess for beta specificity. After controlling for improvement in primary depressive symptoms, a lower rate of beta events at F3, Fz, F4, and Cz prior to rTMS treatment was associated with a larger improvement in EDF after rTMS treatment. In addition, a decrease in beta event rate at Fz pre-to-post treatment was associated with a larger improvement in EDF after treatment. Results were largely specific to the beta band. In this study, the rate of frontrocentral beta events prior to treatment significantly predicted the likelihood of subsequent improvement in EDF symptoms following a clinical course of rTMS for MDD. These preliminary findings suggest the potential utility of EEG measured beta events and rTMS for targeting EDF across an array of neuropsychiatric disorders.

Evolution of brain network dynamics in early Parkinson's disease with mild cognitive impairment

How mild cognitive impairment (MCI) is instantiated in dynamically interacting and spatially distributed functional brain networks remains an unexplored mystery in early Parkinson's disease (PD). We applied a machine-learning technology based on personalized sliding-window algorithm to track continuously time-varying and overlapping subnetworks under the functional brain networks calculated form resting state electroencephalogram data within a sample of 33 early PD patients (13 early PD patients with MCI and 20 early PD patients without MCI). We decoded a set of subnetworks that captured surprisingly dynamically varying and integrated interactions among certain brain lobes. We observed that the master expressed subnetworks were particularly transient, and flexibly switching between high and low expression during integration into a dynamic brain network. This transience was particularly salient in a subnetwork predominantly linking temporal-parietal-occipital lobes, which decreases in both expression and flexibility in early PD patients with MCI and expresses their degree of cognitive impairment. Moreover, MCI induced a regularly interrupted, slow evolution of subnetworks in functional brain network dynamics in early PD at the individual level, and the dynamic expression characteristics of subnetworks also reflected the degree of cognitive impairment in patients with early PD. Collectively, these results provide novel and deeper insights regarding MCI-induced abnormal dynamical interaction and large-scale changes in functional brain network of early PD.

Resting-state EEG delta and alpha power predict response to cognitive behavioral therapy in depression: a Canadian biomarker integration network for depression study

Cognitive behavioral therapy (CBT) is often recommended as a first-line treatment in depression. However, access to CBT remains limited, and up to 50% of patients do not benefit from this therapy. Identifying biomarkers that can predict which patients will respond to CBT may assist in designing optimal treatment allocation strategies. In a Canadian Biomarker Integration Network for Depression (CAN-BIND) study, forty-one adults with depression were recruited to undergo a 16-week course of CBT with thirty having resting-state electroencephalography (EEG) recorded at baseline and week 2 of therapy. Successful clinical response to CBT was defined as a 50% or greater reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline to post-treatment completion. EEG relative power spectral measures were analyzed at baseline, week 2, and as early changes from baseline to week 2. At baseline, lower relative delta (0.5-4 Hz) power was observed in responders. This difference was predictive of successful clinical response to CBT. Furthermore, responders exhibited an early increase in relative delta power and a decrease in relative alpha (8-12 Hz) power compared to non-responders. These changes were also found to be good predictors of response to the therapy. These findings showed the potential utility of resting-state EEG in predicting CBT outcomes. They also further reinforce the promise of an EEG-based clinical decision-making tool to support treatment decisions for each patient.

Adolescent Δ-9-tetrahydrocannabinol exposure induces differential acute and long-term neuronal and molecular disturbances in dorsal vs. ventral hippocampal subregions

Chronic exposure to Δ-9-tetrahydrocannabinol (THC) during adolescence is associated with long-lasting cognitive impairments and enhanced susceptibility to anxiety and mood disorders. Previous evidence has revealed functional and anatomical dissociations between the posterior vs. anterior portions of the hippocampal formation, which are classified as the dorsal and ventral regions in rodents, respectively. Notably, the dorsal hippocampus is critical for cognitive and contextual processing, whereas the ventral region is critical for affective and emotional processing. While adolescent THC exposure can induce significant morphological disturbances and glutamatergic signaling abnormalities in the hippocampus, it is not currently understood how the dorsal vs. ventral hippocampal regions are affected by THC during neurodevelopment. In the present study, we used an integrative combination of behavioral, molecular, and neural assays in a neurodevelopmental rodent model of adolescent THC exposure. We report that adolescent THC exposure induces long-lasting memory deficits and anxiety like-behaviors concomitant with a wide range of differential molecular and neuronal abnormalities in dorsal vs. ventral hippocampal regions. In addition, using matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS), we show for the first time that adolescent THC exposure induces significant and enduring dysregulation of GABA and glutamate levels in dorsal vs. ventral hippocampus. Finally, adolescent THC exposure induced dissociable dysregulations of hippocampal glutamatergic signaling, characterized by differential glutamatergic receptor expression markers, profound alterations in pyramidal neuronal activity and associated oscillatory patterns in dorsal vs. ventral hippocampal subregions.

Executive Functioning Constructs in Anxiety, Obsessive-Compulsive, Post-Traumatic Stress, and Related Disorders

PURPOSE OF REVIEW

We synthesize theories proposing complex relations between cognitive functioning and anxiety-related concepts. We evaluate vulnerability theories suggesting that deficits in various cognitive functioning domains predict future anxiety-associated concepts. We examine scar theories asserting the opposite direction of effects (i.e., anxiety predicting cognitive dysfunction). Furthermore, we examine more novel frameworks on this topic.

RECENT FINDINGS

Reliable evidence exists for the scar and vulnerability theories. This includes mounting data on diverse anxiety symptoms predicting cognitive dysfunction (and conversely) unfolding at between- and within-person levels (dynamic mutualism theory). It also includes data on the stronger effects or central influence of anxiety (versus non-anxiety) symptoms on executive functioning (EF; i.e., higher-order cognitive control governing myriad thinking and action repertoires) versus non-EF domains and vice versa (network theory). In addition, it reviews emerging evidence that enhanced cognitive control can correlate with higher anxiety among children (overgeneralized control theory). The generally inverse relations between anxiety symptoms and cognitive dysfunction are bidirectional and complex within and between persons. Plausible mediators and moderators merit more attention, including immune, metabolism, and neural markers and the social determinants of health.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Alpha power during task performance predicts individual language comprehension

Alpha power attenuation during cognitive task performing has been suggested to reflect a process of release of inhibition, increase of excitability, and thereby benefit the improvement of performance. Here, we hypothesized that changes in individual alpha power during the execution of a complex language comprehension task may correlate with the individual performance in that task. We tested this using magnetoencephalography (MEG) recorded during comprehension of German sentences of different syntactic complexity. Results showed that neither the frequency nor the power of the spontaneous oscillatory activity at rest were associated with the individual performance. However, during the execution of a sentences processing task, the individual alpha power attenuation did correlate with individual language comprehension performance. Source reconstruction localized these effects in left temporal-parietal brain regions known to be associated with language processing and their right-hemisphere homologues. Our results support the notion that in-task attenuation of individual alpha power is related to the essential mechanisms of the underlying cognitive processes, rather than merely to general phenomena like attention or vigilance.

Predictive Role of Executive Function in the Efficacy of Intermittent Theta Burst Transcranial Magnetic Stimulation Modalities for Treating Methamphetamine Use Disorder-A Randomized Clinical Trial

Background: Repetitive transcranial magnetic stimulation (rTMS) has therapeutic effects on craving in methamphetamine (METH) use disorder (MUD). The chronic abuse of METH causes impairments in executive function, and improving executive function reduces relapse and improves treatment outcomes for drug use disorder. The purpose of this study was to determine whether executive function helped predict patients' responses to rTMS treatment. Methods: This study employed intermittent theta burst stimulation (iTBS) rTMS modalities and observed their therapeutic effects on executive function and craving in MUD patients. MUD patients from an isolated Drug Rehabilitation Institute in China were chosen and randomly allocated to the iTBS group and sham-stimulation group. All participants underwent the Behavior Rating Inventory of Executive Function - Adult Version Scale (BRIEF-A) and Visual Analog Scales (VAS) measurements. Sixty-five healthy adults matched to the general condition of MUD patients were also recruited as healthy controls. Findings: Patients with MUD had significantly worse executive function. iTBS groups had better treatment effects on the MUD group than the sham-stimulation group. Further Spearman rank correlation and stepwise multivariate regression analysis revealed that reduction rates of the total score of the BRIEF-A and subscale scores of the inhibition factor and working memory factor in the iTBS group positively correlated with improvements in craving. ROC curve analysis showed that working memory (AUC = 87.4%; 95% CI = 0.220, 0.631) and GEC (AUC = 0.761%; 95% CI = 0.209, 0.659) had predictive power to iTBS therapeutic efficacy. The cutoff values are 13.393 and 59.804, respectively. Conclusions: The iTBS rTMS had a better therapeutic effect on the executive function of patients with MUD, and the improved executive function had the potential to become a predictor for the efficacy of iTBS modality for MUD treatment. Clinical Trial Registration: ClinicalTrials.gov, identifier: ChiCTR2100046954.