Repetitive Transcranial Magnetic Stimulation (rTMS) Treatment Reduces Variability in Brain Function in Schizophrenia: Data From a Double-Blind, Randomized, Sham-Controlled Trial.

BACKGROUND/HYPOTHESIS: There is increasing awareness of interindividual variability in brain function, with potentially major implications for repetitive transcranial magnetic stimulation (rTMS) efficacy. We perform a secondary analysis using data from a double-blind randomized controlled 4-week trial of 20 Hz active versus sham rTMS to dorsolateral prefrontal cortex (DLPFC) during a working memory task in participants with schizophrenia. We hypothesized that rTMS would change local functional activity and variability in the active group compared with sham. STUDY DESIGN: 83 participants were randomized in the original trial, and offered neuroimaging pre- and post-treatment. Of those who successfully completed both scans (n = 57), rigorous quality control left n = 42 (active/sham: n = 19/23), who were included in this analysis. Working memory-evoked activity during an N-Back (3-Back vs 1-Back) task was contrasted. Changes in local brain activity were examined from an 8 mm ROI around the rTMS coordinates. Individual variability was examined as the mean correlational distance (MCD) in brain activity pattern from each participant to others within the same group. RESULTS: We observed an increase in task-evoked left DLPFC activity in the active group compared with sham (F1,36 = 5.83, False Discovery Rate (FDR))-corrected P = .04). Although whole-brain activation patterns were similar in both groups, active rTMS reduced the MCD in activation pattern compared with sham (F1,36 = 32.57, P < .0001). Reduction in MCD was associated with improvements in attention performance (F1,16 = 14.82, P = .0014, uncorrected). CONCLUSIONS: Active rTMS to DLPFC reduces individual variability of brain function in people with schizophrenia. Given that individual variability is typically higher in schizophrenia patients compared with controls, such reduction may "normalize" brain function during higher-order cognitive processing.

Neural Circuitry and Therapeutic Targeting of Depressive Symptoms in Schizophrenia Spectrum Disorders.

OBJECTIVE: Conceptual similarities between depressive and negative symptoms complicate biomarker and intervention development. This study employed a data-driven approach to delineate the neural circuitry underlying depressive and negative symptoms in schizophrenia spectrum disorders (SSDs). METHODS: Data from three studies were analyzed (157 participants with SSDs) to assess brain-behavior relationships: two neuroimaging studies and a randomized trial of repetitive transcranial magnetic stimulation (rTMS). Partial least squares correlation (PLSC) was used to investigate associations between resting-state functional connectivity and depressive and negative symptoms. Secondary analyses of rTMS trial data (active, N=37; sham, N=33) were used to assess relationships between PLSC-derived symptom profiles and treatment outcomes. RESULTS: PLSC identified three latent variables (LVs) relating functional brain circuitry with symptom profiles. LV1 related a general depressive symptom factor with positive associations between and within the default mode network (DMN), the frontoparietal network (FPN), and the cingulo-opercular network (CON). LV2 related negative symptoms (no depressive symptoms) via negative associations, especially between the FPN and the CON, but also between the DMN and the FPN and the CON. LV3 related a guilt and early wakening depression factor via negative rather than positive associations with the DMN, FPN, and CON. The secondary visual network had a positive association with general depressive symptoms and negative associations with guilt and negative symptoms. Active (but not sham) rTMS applied bilaterally to the dorsolateral prefrontal cortex (DLPFC) reduced general depressive but not guilt-related or negative symptoms. CONCLUSIONS: The results clearly differentiate the neural circuitry underlying depressive and negative symptoms, and segregated across the two-factor structure of depression in SSDs. These findings support divergent neurobiological pathways of depressive symptoms and negative symptoms in people with SSDs. As treatment options are currently limited, bilateral rTMS to the DLPFC is worth exploring further for general depressive symptoms in people with SSDs.

Multivariate Association between Functional Connectivity Gradients and Cognition in Schizophrenia Spectrum Disorders.

BACKGROUND: Schizophrenia Spectrum Disorders (SSDs), which are characterized by social cognitive deficits, have been associated with dysconnectivity in "unimodal" (e.g., visual, auditory) and "multimodal" (e.g., default-mode and frontoparietal) cortical networks. However, little is known regarding how such dysconnectivity relates to social and non-social cognition, and how such brain-behavioral relationships associate with clinical outcomes of SSDs. METHODS: We analyzed cognitive (non-social and social) measures and resting-state functional magnetic resonance imaging data from the 'Social Processes Initiative in Neurobiology of the Schizophrenia(s) (SPINS)' study (247 stable participants with SSDs and 172 healthy controls, ages 18-55). We extracted gradients from parcellated connectomes and examined the association between the first 3 gradients and the cognitive measures using partial least squares correlation (PLSC). We then correlated the PLSC dimensions with functioning and symptoms in the SSDs group. RESULTS: The SSDs group showed significantly lower differentiation on all three gradients. The first PLSC dimension explained 68.53% (p<.001) of the covariance and showed a significant difference between SSDs and Controls (bootstrap p<.05). PLSC showed that all cognitive measures were associated with gradient scores of unimodal and multimodal networks (Gradient 1), auditory, sensorimotor, and visual networks (Gradient 2), and perceptual networks and striatum (Gradient 3), which were less differentiated in SSDs. Furthermore, the first dimension was positively correlated with negative symptoms and functioning in the SSDs group. CONCLUSIONS: These results suggest a potential role of lower differentiation of brain networks in cognitive and functional impairments in SSDs.

Predictors of attrition during acute pharmacotherapy of psychotic depression in a clinical trial.

Little is known about factors that contribute to attrition in clinical trials of the pharmacotherapy of psychotic depression. The purpose of this study was to identify factors associated with attrition during acute pharmacotherapy in the Study of the Pharmacotherapy of Psychotic Depression II (STOP-PD II) clinical trial. Sociodemographic and clinical variables were assessed at baseline in 269 men and women, aged 18-85 years, who were treated with up to 12 weeks of open-label sertraline plus olanzapine. Univariate analyses examined the association of baseline variables with overall non-completion, as well as reasons for non-completion. Logistic regression was used to model the relationship of the significant univariate predictors with non-completion and its reasons. Seventy-four (27.5 %) participants did not complete the acute treatment phase of STOP-PD II. Male gender, younger age, inpatient status, higher Clinical Global Impression (CGI) severity of illness, and higher severity of psychomotor disturbance were associated with non-completion in univariate analyses. In regression models, higher CGI severity of illness score was the only significant independent predictor of non-completion, explained by withdrawal of consent. Our findings have implications for the retention of persons with psychotic depression in clinical trials.

Task-based functional neural correlates of social cognition across autism and schizophrenia spectrum disorders.

BACKGROUND: Autism and schizophrenia spectrum disorders (SSDs) both feature atypical social cognition. Despite evidence for comparable group-level performance in lower-level emotion processing and higher-level mentalizing, limited research has examined the neural basis of social cognition across these conditions. Our goal was to compare the neural correlates of social cognition in autism, SSDs, and typically developing controls (TDCs). METHODS: Data came from two harmonized studies in individuals diagnosed with autism or SSDs and TDCs (aged 16-35 years), including behavioral social cognitive metrics and two functional magnetic resonance imaging (fMRI) tasks: a social mirroring Imitate/Observe (ImObs) task and the Empathic Accuracy (EA) task. Group-level comparisons, and transdiagnostic analyses incorporating social cognitive performance, were run using FSL's PALM for each task, covarying for age and sex (1000 permutations, thresholded at p < 0.05 FWE-corrected). Exploratory region of interest (ROI)-based analyses were also conducted. RESULTS: ImObs and EA analyses included 164 and 174 participants, respectively (autism N = 56/59, SSD N = 50/56, TDC N = 58/59). EA and both lower- and higher-level social cognition scores differed across groups. While canonical social cognitive networks were activated, no significant whole-brain or ROI-based group-level differences in neural correlates for either task were detected. Transdiagnostically, neural activity during the EA task, but not the ImObs task, was associated with lower- and higher-level social cognitive performance. LIMITATIONS: Despite attempting to match our groups on age, sex, and race, significant group differences remained. Power to detect regional brain differences is also influenced by sample size and multiple comparisons in whole-brain analyses. Our findings may not generalize to autism and SSD individuals with co-occurring intellectual disabilities. CONCLUSIONS: The lack of whole-brain and ROI-based group-level differences identified and the dimensional EA brain-behavior relationship observed across our sample suggest that the EA task may be well-suited to target engagement in novel intervention testing. Our results also emphasize the potential utility of cross-condition approaches to better understand social cognition across autism and SSDs.

A systematic review of structural and functional magnetic resonance imaging studies on the neurobiology of depressive symptoms in schizophrenia spectrum disorders.

Depressive symptoms in Schizophrenia Spectrum Disorders (SSDs) negatively impact suicidality, prognosis, and quality of life. Despite this, efficacious treatments are limited, largely because the neural mechanisms underlying depressive symptoms in SSDs remain poorly understood. We conducted a systematic review to provide an overview of studies that investigated the neural correlates of depressive symptoms in SSDs using neuroimaging techniques. We searched MEDLINE, PsycINFO, EMBASE, Web of Science, and Cochrane Library databases from inception through June 19, 2023. Specifically, we focused on structural and functional magnetic resonance imaging (MRI), encompassing: (1) T1-weighted imaging measuring brain morphology; (2) diffusion-weighted imaging assessing white matter integrity; or (3) T2*-weighted imaging measures of brain function. Our search yielded 33 articles; 14 structural MRI studies, 18 functional (f)MRI studies, and 1 multimodal fMRI/MRI study. Reviewed studies indicate potential commonalities in the neurobiology of depressive symptoms between SSDs and major depressive disorders, particularly in subcortical and frontal brain regions, though confidence in this interpretation is limited. The review underscores a notable knowledge gap in our understanding of the neurobiology of depression in SSDs, marked by inconsistent approaches and few studies examining imaging metrics of depressive symptoms. Inconsistencies across studies' findings emphasize the necessity for more direct and comprehensive research focusing on the neurobiology of depression in SSDs. Future studies should go beyond "total score" depression metrics and adopt more nuanced assessment approaches considering distinct subdomains. This could reveal unique neurobiological profiles and inform investigations of targeted treatments for depression in SSDs.

Transdiagnostic Neurobiology of Social Cognition and Individual Variability as Measured by Fractional Amplitude of Low-Frequency Fluctuation in Schizophrenia and Autism Spectrum Disorders.

Fractional amplitude of low-frequency fluctuation (fALFF) is a validated measure of resting-state spontaneous brain activity. Previous fALFF findings in autism and schizophrenia spectrum disorders (ASDs and SSDs) have been highly heterogeneous. We aimed to use fALFF in a large sample of typically developing control (TDC), ASD and SSD participants to explore group differences and relationships with inter-individual variability of fALFF maps and social cognition. fALFF from 495 participants (185 TDC, 68 ASD, and 242 SSD) was computed using functional magnetic resonance imaging as signal power within two frequency bands (i.e., slow-4 and slow-5), normalized by the power in the remaining frequency spectrum. Permutation analysis of linear models was employed to investigate the relationship of fALFF with diagnostic groups, higher-level social cognition, and lower-level social cognition. Each participant's average distance of fALFF map to all others was defined as a variability score, with higher scores indicating less typical maps. Lower fALFF in the visual and higher fALFF in the frontal regions were found in both SSD and ASD participants compared with TDCs. Limited differences were observed between ASD and SSD participants in the cuneus regions only. Associations between slow-4 fALFF and higher-level social cognitive scores across the whole sample were observed in the lateral occipitotemporal and temporoparietal junction. Individual variability within the ASD and SSD groups was also significantly higher compared with TDC. Similar patterns of fALFF and individual variability in ASD and SSD suggest some common neurobiological deficits across these related heterogeneous conditions.

Genetic influences on brain and cognitive health and their interactions with cardiovascular conditions and depression.

Approximately 40% of dementia cases could be prevented or delayed by modifiable risk factors related to lifestyle and environment. These risk factors, such as depression and vascular disease, do not affect all individuals in the same way, likely due to inter-individual differences in genetics. However, the precise nature of how genetic risk profiles interact with modifiable risk factors to affect brain health is poorly understood. Here we combine multiple data resources, including genotyping and postmortem gene expression, to map the genetic landscape of brain structure and identify 367 loci associated with cortical thickness and 13 loci associated with white matter hyperintensities (P < 5×10-8), with several loci also showing a significant association with cognitive function. We show that among 220 unique genetic loci associated with cortical thickness in our genome-wide association studies (GWAS), 95 also showed evidence of interaction with depression or cardiovascular conditions. Polygenic risk scores based on our GWAS of inferior frontal thickness also interacted with hypertension in predicting executive function in the Canadian Longitudinal Study on Aging. These findings advance our understanding of the genetic underpinning of brain structure and show that genetic risk for brain and cognitive health is in part moderated by treatable mid-life factors.

Neurophysiological and other features of working memory in older adults at risk for dementia.

Theta-gamma coupling (TGC) is a neurophysiological process that supports working memory. Working memory is associated with other clinical and biological features. The extent to which TGC is associated with these other features and whether it contributes to working memory beyond these features is unknown. Two-hundred-and-three older participants at risk for Alzheimer's dementia-98 with mild cognitive impairment (MCI), 39 with major depressive disorder (MDD) in remission, and 66 with MCI and MDD (MCI + MDD)-completed a clinical assessment, N-back-EEG, and brain MRI. Among them, 190 completed genetic testing, and 121 completed [11C] Pittsburgh Compound B ([11C] PIB) PET imaging. Hierarchical linear regressions were used to assess whether TGC is associated with demographic and clinical variables; Alzheimer's disease-related features (APOE ε4 carrier status and ß-amyloid load); and structural features related to working memory. Then, linear regressions were used to assess whether TGC is associated with 2-back performance after accounting for these features. Other than age, TGC was not associated with any non-neurophysiological features. In contrast, TGC (ß = 0.27; p = 0.006), age (ß = - 0.29; p = 0.012), and parietal cortical thickness (ß = 0.24; p = 0.020) were associated with 2-back performance. We also examined two other EEG features that are linked to working memory-theta event-related synchronization and alpha event-related desynchronization-and found them not to be associated with any feature or performance after accounting for TGC. Our findings suggest that TGC is a process that is independent of other clinical, genetic, neurochemical, and structural variables, and supports working memory in older adults at risk for dementia. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-023-09938-y.

Bifactor models of psychopathology using multi-informant and multi-instrument dimensional measures in the ABCD study.

Background: Due to limitations of categorical definitions of mental illness, there is a need for quantitative empirical investigations of the dimensional structure of psychopathology. Using exploratory bifactor methods, this study investigated a comprehensive and representative structure of psychopathology in children to better understand how psychotic-like experiences (PLEs), autism spectrum disorder (ASD) symptoms, impulsivity, and sensitivity to reward and punishment, may be integrated into extant general factor models of psychopathology. Methods: We used seven child-report and three parent-report instruments capturing diverse mental health symptoms in 11,185 children aged 9-10 from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study. We built on previous modeling frameworks by conducting both split sample and full sample factor analytic approaches that harnessed recent methodological advances in bifactor exploratory structural equation modeling (B-ESEM) to examine a wide range of psychopathology measures not previously integrated into a single analysis. Validity of psychopathology dimensions was examined by investigating associations with sex, age, cognition, imaging measures, and medical service usage. Results: All four factor analytic models showed excellent fit and similar structure within informant. PLEs loaded most highly onto a general psychopathology factor, suggesting that they may reflect non-specific risk for mental illness. ASD symptoms loaded separately from attention/hyperactivity symptoms. Symptoms of impulsivity and sensitivity to reward and punishment loaded onto specific factors, distinct from externalizing and internalizing factors. All identified factors were associated with clinically relevant risk factors, providing preliminary evidence for their construct validity. Conclusion: By integrating diverse child-report and parent-report psychopathology measures for children in the ABCD sample, we deliver data on the quantitative structure of psychopathology for an exceptionally large set of measurements and discuss implications for the field.

Elevated senescence-associated secretory phenotype index in late-life bipolar disorder.

BACKGROUND: The senescence-associated secretory phenotype (SASP) is a biomarker index based on the profile of 22 blood proteins associated with cellular senescence. The SASP index has not been assessed in older patients with bipolar disorder (BD). We hypothesized that older adults with BD will have elevated cellular senescence burden as measured by the SASP index. METHODS: We measured the 22 SASP proteins to calculate the SASP index in 38 older patients with BD and 34 non-psychiatric comparison individuals (HC). RESULTS: The SASP index scores were significantly higher in BD than HC after controlling for age, sex, psychopathology, and physical health (F(1,8) = 5.37, p = 0.024, η2 = 0.08). SASP index scores were also associated with higher age, more severe depressive symptoms, and physical illness burden (p < 0.05) in the whole sample. LIMITATION: Cross-sectional study and small sample size. CONCLUSION: This is the first report of increased SASP index scores in older adults with BD. Our results suggest that dysregulation of age-related biological processes may contribute to more severe depressive symptoms and worse physical health in older adults with BD.

SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites.

In neuroimaging studies, combining data collected from multiple study sites or scanners is becoming common to increase the reproducibility of scientific discoveries. At the same time, unwanted variations arise by using different scanners (inter-scanner biases), which need to be corrected before downstream analyses to facilitate replicable research and prevent spurious findings. While statistical harmonization methods such as ComBat have become popular in mitigating inter-scanner biases in neuroimaging, recent methodological advances have shown that harmonizing heterogeneous covariances results in higher data quality. In vertex-level cortical thickness data, heterogeneity in spatial autocorrelation is a critical factor that affects covariance heterogeneity. Our work proposes a new statistical harmonization method called spatial autocorrelation normalization (SAN) that preserves homogeneous covariance vertex-level cortical thickness data across different scanners. We use an explicit Gaussian process to characterize scanner-invariant and scanner-specific variations to reconstruct spatially homogeneous data across scanners. SAN is computationally feasible, and it easily allows the integration of existing harmonization methods. We demonstrate the utility of the proposed method using cortical thickness data from the Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) study. SAN is publicly available as an R package.

Bayesian Optimization of Neurostimulation (BOONStim).

BACKGROUND: Transcranial magnetic stimulation (TMS) treatment response is influenced by individual variability in brain structure and function. Sophisticated, user-friendly approaches, incorporating both established functional magnetic resonance imaging (fMRI) and TMS simulation tools, to identify TMS targets are needed. OBJECTIVE: The current study presents the development and validation of the Bayesian Optimization of Neuro-Stimulation (BOONStim) pipeline. METHODS: BOONStim uses Bayesian optimization for individualized TMS targeting, automating interoperability between surface-based fMRI analytic tools and TMS electric field modeling. Bayesian optimization performance was evaluated in a sample dataset (N=10) using standard circular and functional connectivity-defined targets, and compared to grid optimization. RESULTS: Bayesian optimization converged to similar levels of total electric field stimulation across targets in under 30 iterations, converging within a 5% error of the maxima detected by grid optimization, and requiring less time. CONCLUSIONS: BOONStim is a scalable and configurable user-friendly pipeline for individualized TMS targeting with quick turnaround.

Effect of olanzapine exposure on relapse and brain structure in patients with major depressive disorder with psychotic features.

Some data suggest that antipsychotics may adversely affect brain structure. We examined the relationship among olanzapine exposure, relapse, and changes in brain structure in patients with major depressive disorder with psychotic features. We analyzed data from the Study of the Pharmacotherapy of Psychotic Depression II trial (STOP-PD II), a randomized, placebo-controlled trial in patients with psychotic depression who attained remission on sertraline and olanzapine and were randomized to continue sertraline plus olanzapine or placebo for 36 weeks. Olanzapine steady state concentration (SSC) were calculated based on sparsely-sampled levels. Rates of relapse and changes in brain structure were assessed as outcomes. There were significant associations between dosage and relapse rates (N = 118; HR = 0.94, 95% CI [0.897, 0.977], p = 0.002) or changes in left cortical thickness (N = 44; B = -2.0 × 10-3, 95% CI [-3.1 × 10-3, -9.6 × 10-4], p < 0.001) and between SSC and changes in left cortical thickness (N = 44; B = -8.7 × 10-4, 95% CI [-1.4 × 10-3, -3.6 × 10-4], p = 0.001). Similar results were found for the right cortex. These associations were no longer significant when the analysis was restricted to participants treated with olanzapine. Our findings suggest that, within its therapeutic range, the effect of olanzapine on relapse or cortical thickness does not depend on its dosage or SSC. Further research is needed on the effect of olanzapine and other antipsychotics on mood symptoms and brain structure.

Heterogeneity in functional connectivity: Dimensional predictors of individual variability during rest and task fMRI in psychosis.

BACKGROUND: Individuals with schizophrenia spectrum disorders (SSD) often demonstrate cognitive impairments, associated with poor functional outcomes. While neurobiological heterogeneity has posed challenges when examining social cognition in SSD, it provides a unique opportunity to explore brain-behavior relationships. The aim of this study was to investigate the relationship between individual variability in functional connectivity during resting state and the performance of a social task and social and non-social cognition in a large sample of controls and individuals diagnosed with SSD. METHODS: Neuroimaging and behavioral data were analyzed for 193 individuals with SSD and 155 controls (total n = 348). Individual variability was quantified through mean correlational distance (MCD) of functional connectivity between participants; MCD was defined as a global 'variability score'. Pairwise correlational distance was calculated as 1 - the correlation coefficient between a given pair of participants, and averaging distance from one participant to all other participants provided the mean correlational distance metric. Hierarchical regressions were performed on variability scores derived from resting state and Empathic Accuracy (EA) task functional connectivity data to determine potential predictors (e.g., age, sex, neurocognitive and social cognitive scores) of individual variability. RESULTS: Group comparison between SSD and controls showed greater SSD MCD during rest (p = 0.00038), while no diagnostic differences were observed during task (p = 0.063). Hierarchical regression analyses demonstrated the persistence of a significant diagnostic effect during rest (p = 0.008), contrasting with its non-significance during the task (p = 0.50), after social cognition was added to the model. Notably, social cognition exhibited significance in both resting state and task conditions (both p = 0.01). CONCLUSIONS: Diagnostic differences were more prevalent during unconstrained resting scans, whereas the task pushed participants into a more common pattern which better emphasized transdiagnostic differences in cognitive abilities. Focusing on variability may provide new opportunities for interventions targeting specific cognitive impairments to improve functional outcomes.

Cognitive function based on theta-gamma coupling vs. clinical diagnosis in older adults with mild cognitive impairment with or without major depressive disorder.

Whether individuals with mild cognitive impairment (MCI) and a history of major depressive disorder (MDD) are at a higher risk for cognitive decline than those with MCI alone is still not clear. Previous work suggests that a reduction in prefrontal cortical theta phase-gamma amplitude coupling (TGC) is an early marker of cognitive impairment. This study aimed to determine whether using a TGC cutoff is better at separating individuals with MCI or MCI with remitted MDD (MCI+rMDD) on cognitive performance than their clinical diagnosis. Our hypothesis was that global cognition would differ more between TGC-based groups than diagnostic groups. We analyzed data from 128 MCI (mean age: 71.8, SD: 7.3) and 85 MCI+rMDD (mean age: 70.9, SD: 4.7) participants. Participants completed a comprehensive neuropsychological battery; TGC was measured during the N-back task. An optimal TGC cutoff was determined during the performance of the 2-back. This TGC cutoff was used to classify participants into low vs. high-TGC groups. We then compared Cohen's d of the difference in global cognition between the high and low TGC groups to Cohen's d between the MCI and MCI+rMDD groups. We used bootstrapping to determine 95% confidence intervals for Cohen's d values using the whole sample. As hypothesized, Cohen's d for the difference in global cognition between the TGC groups was larger (0.64 [0.32, 0.88]) than between the diagnostic groups (0.10 [0.004, 0.37]) with a difference between these two Cohen's d's of 0.54 [0.10, 0.80]. Our findings suggest that TGC is a useful marker to identify individuals at high risk for cognitive decline, beyond clinical diagnosis. This could be due to TGC being a sensitive marker of prefrontal cortical dysfunction that would lead to an accelerated cognitive decline.

Heterogeneity of Cognition in Older Adults with Remitted Major Depressive Disorder: A Latent Profile Analysis.

OBJECTIVES: To identify data-driven cognitive profiles in older adults with remitted major depressive disorder (rMDD) with or without mild cognitive impairment (MCI) and examine how the profiles differ regarding demographic, clinical, and neuroimaging measures. DESIGN: Secondary cross-sectional analysis using latent profile analysis. SETTING: Multisite clinical trial in Toronto, Canada. PARTICIPANTS: One hundred seventy-eight participants who met DSM-5 criteria for rMDD without MCI (rMDD-MCI; n = 60) or with MCI (rMDD + MCI; n = 118). MEASUREMENTS: Demographic, clinical, neuroimaging measures, and domain scores from a neuropsychological battery assessing verbal memory, visuospatial memory, processing speed, working memory, language, and executive function. RESULTS: We identified three latent profiles: Profile 1 (poor cognition; n = 75, 42.1%), Profile 2 (intermediate cognition; n = 75, 42.1%), and Profile 3 (normal cognition; n = 28, 15.7%). Compared to participants with Profile 3, those with Profile 1 or 2 were older, had lower education, experienced a greater burden of medical comorbidities, and were more likely to have MCI. The profiles did not differ on the severity of residual symptoms, age of onset of rMDD, number of depressive episodes, psychotropic medication, cerebrovascular risk, ApoE4 carrier status, or family history of depression, dementia, or Alzheimer's disease. The profiles differed in cortical thickness of 15 regions, with the most prominent effects for left precentral and pars opercularis, and right inferior parietal and supramarginal. CONCLUSION: Older patients with rMDD can be grouped cross-sectionally based on data-driven cognitive profiles that differ from the absence or presence of a diagnosis of MCI. Future research should determine the differential risk for dementia of these data-driven subgroups.

Age-Related Alterations in Gray Matter Microstructure in Older People With Remitted Major Depression at Risk for Dementia.

Background: Major depressive disorder (MDD) in late life is a risk factor for mild cognitive impairment (MCI) and Alzheimer's disease. However, studies of gray matter changes have produced varied estimates of which structures are implicated in MDD and dementia. Changes in gray matter volume and cortical thickness are macrostructural measures for the microstructural processes of free water accumulation and dendritic spine loss. Methods: We conducted multishell diffusion imaging to assess gray matter microstructure in 244 older adults with remitted MDD (n = 44), MCI (n = 115), remitted MDD+MCI (n = 61), or without psychiatric disorders or cognitive impairment (healthy control participants; n = 24). We estimated measures related to neurite density, orientation dispersion, and free water (isotropic volume fraction) using a biophysically plausible model (neurite orientation dispersion and density imaging). Results: Results showed that increasing age was correlated with an increase in isotropic volume fraction and a decrease in orientation dispersion index, which is consistent with neuropathology dendritic loss. In addition, this relationship between age and increased isotropic volume fraction was more disrupted in the MCI group than in the remitted MDD or healthy control groups. However, the association between age and orientation dispersion index was similar for all 3 groups. Conclusions: The findings suggest that the neurite orientation dispersion and density imaging measures could be used to identify biological risk factors for Alzheimer's disease, signifying both conventional neurodegeneration observed with MCI and dendritic loss seen in MDD.

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.

An SMS text messaging intervention to improve clinical engagement in early psychosis: A pilot randomized-controlled trial.

Disengagement of youth with psychosis from Early Psychosis Intervention (EPI) services continues to be a significant barrier to recovery, with approximately one-third prematurely discontinuing treatment despite the ongoing need. The current pilot trial sought to evaluate the preliminary efficacy and feasibility of a weekly short message service (SMS) intervention to improve engagement in EPI services. This was a longitudinal single-blinded randomized control trial in which participants were assigned to receive either an active or sham SMS intervention over nine months. Sixty-one participants with early psychosis between the ages of 16 and 29 were enrolled, randomized, and received at least part of the intervention. Primary outcomes consisted of participant clinic attendance rates over the course of the intervention and clinician-rated engagement. Secondary measures included patient-rated therapeutic rapport, attitude toward medication, psychopathology, cognition, functioning, and intervention feedback from participants. Compared to the sham group, participants receiving the active intervention did not show improved appointment attendance rates; however, did exhibit some improvements in aspects of engagement, including improved clinician-rated availability, attitude toward medication, positive symptoms, avolition-apathy and social functioning. Thus, contrary to our hypotheses, digitally augmented care did not result in enhanced engagement in EPI services, as measured by clinic attendance, although with some indication that it may contribute to improved attitude toward medication and, potentially, medication adherence. Weekly SMS text messaging appeared to result in a pattern of engagement whereby individuals who were improving clinically attended appointments less often, possibly due to inadvertent use of the intervention to check in with clinicians. TRIAL REGISTRATION: ClinicalTrials.gov (NCT04379349).