Atypical brain aging and its association with working memory performance in major depressive disorder.

BACKGROUND: Patients with major depressive disorder (MDD) can present with altered brain structure and deficits in cognitive function similar to aging. Yet, the interaction between age-related brain changes and brain development in MDD remains understudied. In a cohort of adolescents and adults with and without MDD, we assessed brain aging differences and associations through a newly developed tool quantifying normative neurodevelopmental trajectories. METHODS: 304 MDD participants and 236 non-depressed controls were recruited and scanned from three studies under the Canadian Biomarker Integration Network for Depression. Volumetric data were used to generate brain centile scores, which were examined for: a) differences in MDD relative to controls; b) differences in individuals with versus without severe childhood maltreatment; and c) correlations with depressive symptom severity, neurocognitive assessment domains, or escitalopram treatment response. RESULTS: Brain centiles were significantly lower in the MDD group compared to controls. It was also significantly correlated with working memory in controls, but not the MDD group. No significant associations were observed in depression severity or antidepressant treatment response with brain centiles. Likewise, childhood maltreatment history did not significantly affect brain centiles. CONCLUSIONS: Consistent with prior work on machine learning models that predict "brain age", brain centile scores differed in people diagnosed with MDD, and MDD was associated with differential relationships between centile scores and working memory. The results support the notion of atypical development and aging in MDD, with implications on neurocognitive deficits associated with aging-related cognitive function.

Spontaneous expression of the CIC::DUX4 fusion oncoprotein from a conditional allele potently drives sarcoma formation in genetically engineered mice.

CIC::DUX4 sarcoma (CDS) is a rare but highly aggressive undifferentiated small round cell sarcoma driven by a fusion between the tumor suppressor Capicua (CIC) and DUX4. Currently, there are no effective treatments and efforts to identify and translate better therapies are limited by the scarcity of patient tumor samples and cell lines. To address this limitation, we generated three genetically engineered mouse models of CDS (Ch7CDS, Ai9CDS, and TOPCDS). Remarkably, chimeric mice from all three conditional models developed spontaneous soft tissue tumors and disseminated disease in the absence of Cre-recombinase. The penetrance of spontaneous (Cre-independent) tumor formation was complete irrespective of bi-allelic Cic function and the distance between adjacent loxP sites. Characterization of soft tissue and presumed metastatic tumors showed that they consistently expressed the CIC::DUX4 fusion protein and many downstream markers of the disease credentialing the models as CDS. In addition, tumor-derived cell lines were generated and ChIP-seq was preformed to map fusion-gene specific binding using an N-terminal HA epitope tag. These datasets, along with paired H3K27ac ChIP-sequencing maps, validate CIC::DUX4 as a neomorphic transcriptional activator. Moreover, they are consistent with a model where ETS family transcription factors are cooperative and redundant drivers of the core regulatory circuitry in CDS.

The BRD4-NUT Fusion Alone Drives Malignant Transformation of NUT Carcinoma.

NUT carcinoma (NC) is an aggressive squamous carcinoma defined by the BRD4-NUT fusion oncoprotein. Routinely effective systemic treatments are unavailable for most NC patients. The lack of an adequate animal model precludes identifying and leveraging cell-extrinsic factors therapeutically in NC. Here, we created a genetically engineered mouse model (GEMM) of NC that forms a Brd4::NUTM1 fusion gene upon tamoxifen induction of Sox2-driven Cre. The model displayed complete disease penetrance, with tumors arising from the squamous epithelium weeks after induction and all mice succumbing to the disease shortly thereafter. Closely resembling human NC (hNC), GEMM tumors (mNC) were poorly differentiated squamous carcinomas with high expression of MYC that metastasized to solid organs and regional lymph nodes. Two GEMM-derived cell lines were developed whose transcriptomic and epigenetic landscapes harbored key features of primary GEMM tumors. Importantly, GEMM tumor and cell line transcriptomes co-classified with those of human NC. BRD4-NUT also blocked differentiation and maintained the growth of mNC as in hNC. Mechanistically, GEMM primary tumors and cell lines formed large histone H3K27ac-enriched domains, termed megadomains, that were invariably associated with the expression of key NC-defining proto-oncogenes, Myc and Trp63. Small-molecule BET bromodomain inhibition (BETi) of mNC induced differentiation and growth arrest and prolonged survival of NC GEMMs, as it does in hNC models. Overall, tumor formation in the NC GEMM is definitive evidence that BRD4-NUT alone can potently drive the malignant transformation of squamous progenitor cells into NC. SIGNIFICANCE: The development of an immunocompetent model of NUT carcinoma that closely mimics the human disease provides a valuable global resource for mechanistic and preclinical studies to improve treatment of this incurable disease.

Open Science and Software Assistance: Commentary on "Artificial Intelligence Can Generate Fraudulent but Authentic-Looking Scientific Medical Articles: Pandora's Box Has Been Opened".

Májovský and colleagues have investigated the important issue of ChatGPT being used for the complete generation of scientific works, including fake data and tables. The issues behind why ChatGPT poses a significant concern to research reach far beyond the model itself. Once again, the lack of reproducibility and visibility of scientific works creates an environment where fraudulent or inaccurate work can thrive. What are some of the ways in which we can handle this new situation?

Predictors of perceived symptom change with acute cannabis use for mental health conditions in a naturalistic sample: A machine learning approach.

BACKGROUND: Despite limited clinical evidence of its efficacy, cannabis use has been commonly reported for the management of various mental health concerns in naturalistic field studies. The aim of the current study was to use machine learning methods to investigate predictors of perceived symptom change across various mental health symptoms with acute cannabis use in a large naturalistic sample. METHODS: Data from 68,819 unique observations of cannabis use from 1307 individuals using cannabis to manage mental health symptoms were analyzed. Data were extracted from Strainprint®, a mobile app that allows users to monitor their cannabis use for therapeutic purposes. Machine learning models were employed to predict self-perceived symptom change after cannabis use, and SHapley Additive exPlanations (SHAP) value plots were used to assess feature importance of individual predictors in the model. Interaction effects of symptom severity pre-scores of anxiety, depression, insomnia, and gender were also examined. RESULTS: The factors that were most strongly associated with perceived symptom change following acute cannabis use were pre-symptom severity, age, gender, and the ratio of CBD to THC. Further examination on the impact of baseline severity for the most commonly reported symptoms revealed distinct responses, with cannabis being reported to more likely benefit individuals with lower pre-symptom severity for depression, and higher pre-symptom severity for insomnia. Responses to cannabis use also differed between genders. CONCLUSIONS: Findings from this study highlight the importance of several factors in predicting perceived symptom change with acute cannabis use for mental health symptom management. Mental health profiles and baseline symptom severity may play a large role in perceived responses to cannabis. Distinct response patterns were also noted across commonly reported mental health symptoms, emphasizing the need for placebo-controlled cannabis trials for specific user profiles.

Gray matter volume drives the brain age gap in schizophrenia: a SHAP study.

Neuroimaging-based brain age is a biomarker that is generated by machine learning (ML) predictions. The brain age gap (BAG) is typically defined as the difference between the predicted brain age and chronological age. Studies have consistently reported a positive BAG in individuals with schizophrenia (SCZ). However, there is little understanding of which specific factors drive the ML-based brain age predictions, leading to limited biological interpretations of the BAG. We gathered data from three publicly available databases - COBRE, MCIC, and UCLA - and an additional dataset (TOPSY) of early-stage schizophrenia (82.5% untreated first-episode sample) and calculated brain age with pre-trained gradient-boosted trees. Then, we applied SHapley Additive Explanations (SHAP) to identify which brain features influence brain age predictions. We investigated the interaction between the SHAP score for each feature and group as a function of the BAG. These analyses identified total gray matter volume (group × SHAP interaction term ß = 1.71 [0.53; 3.23]; pcorr < 0.03) as the feature that influences the BAG observed in SCZ among the brain features that are most predictive of brain age. Other brain features also presented differences in SHAP values between SCZ and HC, but they were not significantly associated with the BAG. We compared the findings with a non-psychotic depression dataset (CAN-BIND), where the interaction was not significant. This study has important implications for the understanding of brain age prediction models and the BAG in SCZ and, potentially, in other psychiatric disorders.

Predictors of illicit substance abuse/dependence during young adulthood: A machine learning approach.

Prior studies have found an especially high prevalence of illicit substance use among adolescents and young adults in Brazil. The current study aimed to employ machine learning techniques to identify predictors of illicit substance abuse/dependence among a large community sample of young adults followed for 5 years. This prospective, population-based cohort study included a sample of young adults between the ages of 18-24 years from Pelotas, Brazil at baseline (T1). The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) was used to assess illicit substance abuse/dependence. A clinical interview was conducted to collect data on sociodemographic characteristics and psychopathology. Elastic net was used to generate a regularized linear model for the machine learning component of this study, which followed standard machine learning protocols. A total of 1560 young adults were assessed at T1, while 1244 were reassessed at the 5-year follow-up period (T2). The strongest predictors of illicit substance abuse/dependence at baseline (AUC of 0.83) were alcohol abuse/dependence, tobacco abuse/dependence, being in a current major depressive episode, history of a lifetime manic episode, current suicide risk, and male sex. The strongest predictors for illicit substance abuse/dependence at the 5-year follow-up (AUC: 0.79) were tobacco abuse/dependence at T1, history of a lifetime manic episode at T1, male sex, alcohol abuse/dependence at T1, and current suicide risk at T1. Our findings indicate that machine learning techniques hold the potential to predict illicit substance abuse/dependence among young adults using sociodemographic/clinical characteristics, with relatively high accuracy.

Lifestyle predictors of depression and anxiety during COVID-19: a machine learning approach.

INTRODUCTION: Recent research has suggested an increase in the global prevalence of psychiatric symptoms during the COVID-19 pandemic. This study aimed to assess whether lifestyle behaviors can predict the presence of depression and anxiety in the Brazilian general population, using a model developed in Spain. METHODS: A web survey was conducted during April-May 2020, which included the Short Multidimensional Inventory Lifestyle Evaluation (SMILE) scale, assessing lifestyle behaviors during the COVID-19 pandemic. Depression and anxiety were examined using the PHQ-2 and the GAD-7, respectively. Elastic net, random forest, and gradient tree boosting were used to develop predictive models. Each technique used a subset of the Spanish sample to train the models, which were then tested internally (vs. the remainder of the Spanish sample) and externally (vs. the full Brazilian sample), evaluating their effectiveness. RESULTS: The study sample included 22,562 individuals (19,069 from Brazil, and 3,493 from Spain). The models developed performed similarly and were equally effective in predicting depression and anxiety in both tests, with internal test AUC-ROC values of 0.85 (depression) and 0.86 (anxiety), and external test AUC-ROC values of 0.85 (depression) and 0.84 (anxiety). Meaning of life was the strongest predictor of depression, while sleep quality was the strongest predictor of anxiety during the COVID-19 epidemic. CONCLUSIONS: Specific lifestyle behaviors during the early COVID-19 epidemic successfully predicted the presence of depression and anxiety in a large Brazilian sample using machine learning models developed on a Spanish sample. Targeted interventions focused on promoting healthier lifestyles are encouraged.

Brain age in mood and psychotic disorders: a systematic review and meta-analysis.

OBJECTIVE: To evaluate whether accelerated brain aging occurs in individuals with mood or psychotic disorders. METHODS: A systematic review following PRISMA guidelines was conducted. A meta-analysis was then performed to assess neuroimaging-derived brain age gap in three independent groups: (1) schizophrenia and first-episode psychosis, (2) major depressive disorder, and (3) bipolar disorder. RESULTS: A total of 18 papers were included. The random-effects model meta-analysis showed a significantly increased neuroimaging-derived brain age gap relative to age-matched controls for the three major psychiatric disorders, with schizophrenia (3.08; 95%CI [2.32; 3.85]; p < 0.01) presenting the largest effect, followed by bipolar disorder (1.93; [0.53; 3.34]; p < 0.01) and major depressive disorder (1.12; [0.41; 1.83]; p < 0.01). The brain age gap was larger in older compared to younger individuals. CONCLUSION: Individuals with mood and psychotic disorders may undergo a process of accelerated brain aging reflected in patterns captured by neuroimaging data. The brain age gap tends to be more pronounced in older individuals, indicating a possible cumulative biological effect of illness burden.

Prediction of suicide attempts in a prospective cohort study with a nationally representative sample of the US population.

BACKGROUND: There is still little knowledge of objective suicide risk stratification. METHODS: This study aims to develop models using machine-learning approaches to predict suicide attempt (1) among survey participants in a nationally representative sample and (2) among participants with lifetime major depressive episodes. We used a cohort called the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) that was conducted in two waves and included a nationally representative sample of the adult population in the United States. Wave 1 involved 43 093 respondents and wave 2 involved 34 653 completed face-to-face reinterviews with wave 1 participants. Predictor variables included clinical, stressful life events, and sociodemographic variables from wave 1; outcome included suicide attempt between wave 1 and wave 2. RESULTS: The model built with elastic net regularization distinguished individuals who had attempted suicide from those who had not with an area under the ROC curve (AUC) of 0.89, balanced accuracy 81.86%, specificity 89.22%, and sensitivity 74.51% for the general population. For participants with lifetime major depressive episodes, AUC was 0.89, balanced accuracy 81.64%, specificity 85.86%, and sensitivity 77.42%. The most important predictor variables were a diagnosis of borderline personality disorder, post-traumatic stress disorder, and being of Asian descent for the model in all participants; and previous suicide attempt, borderline personality disorder, and overnight stay in hospital because of depressive symptoms for the model in participants with lifetime major depressive episodes. Random forest and artificial neural networks had similar performance. CONCLUSIONS: Risk for suicide attempt can be estimated with high accuracy.

Lifestyle predictors of depression and anxiety during COVID-19: a machine learning approach

Abstract Introduction Recent research has suggested an increase in the global prevalence of psychiatric symptoms during the COVID-19 pandemic. This study aimed to assess whether lifestyle behaviors can predict the presence of depression and anxiety in the Brazilian general population, using a model developed in Spain. Methods A web survey was conducted during April-May 2020, which included the Short Multidimensional Inventory Lifestyle Evaluation (SMILE) scale, assessing lifestyle behaviors during the COVID-19 pandemic. Depression and anxiety were examined using the PHQ-2 and the GAD-7, respectively. Elastic net, random forest, and gradient tree boosting were used to develop predictive models. Each technique used a subset of the Spanish sample to train the models, which were then tested internally (vs. the remainder of the Spanish sample) and externally (vs. the full Brazilian sample), evaluating their effectiveness. Results The study sample included 22,562 individuals (19,069 from Brazil, and 3,493 from Spain). The models developed performed similarly and were equally effective in predicting depression and anxiety in both tests, with internal test AUC-ROC values of 0.85 (depression) and 0.86 (anxiety), and external test AUC-ROC values of 0.85 (depression) and 0.84 (anxiety). Meaning of life was the strongest predictor of depression, while sleep quality was the strongest predictor of anxiety during the COVID-19 epidemic. Conclusions Specific lifestyle behaviors during the early COVID-19 epidemic successfully predicted the presence of depression and anxiety in a large Brazilian sample using machine learning models developed on a Spanish sample. Targeted interventions focused on promoting healthier lifestyles are encouraged.

5-year incidence of suicide-risk in youth: A gradient tree boosting and SHAP study.

BACKGROUND: Machine learning methods for suicidal behavior so far have failed to be implemented as a prediction tool. In order to use the capabilities of machine learning to model complex phenomenon, we assessed the predictors of suicide risk using state-of-the-art model explanation methods. METHODS: Prospective cohort study including a community sample of 1,560 young adults aged between 18 and 24. The first wave took place between 2007 and 2009, and the second wave took place between 2012 and 2014. Sociodemographic and clinical characteristics were assessed at baseline. Incidence of suicide risk at five-years of follow-up was the main outcome. The outcome was assessed using the Mini Neuropsychiatric Interview (MINI) at both waves. RESULTS: The risk factors for the incidence of suicide risk at follow-up were: female sex, lower socioeconomic status, older age, not studying, presence of common mental disorder symptoms, and poor quality of life. The interaction between overall health and socioeconomic status in relation to suicide risk was also captured and shows a shift from protection to risk by socioeconomic status as overall health increases. LIMITATIONS: Proximal factors associated with the incidence of suicide risk were not assessed. CONCLUSIONS: Our findings indicate that factors related to poor quality of life, not studying, and common mental disorder symptoms of young adults are already in place prior to suicide risk. Most factors present critical non-linear patterns that were identified. These findings are clinically relevant because they can help clinicians to early detect suicide risk.

Accelerated brain aging in major depressive disorder and antidepressant treatment response: A CAN-BIND report.

OBJECTIVES: Previous studies suggest that major depressive disorder (MDD) may be associated with volumetric indications of accelerated brain aging. This study investigated neuroanatomical signs of accelerated aging in MDD and evaluated whether a brain age gap is associated with antidepressant response. METHODS: Individuals in a major depressive episode received escitalopram treatment (10-20 mg/d) for 8 weeks. Depression severity was assessed at baseline and at weeks 8 and 16 using the Montgomery-Asberg Depression Rating Scale (MADRS). Response to treatment was characterized by a significant reduction in the MADRS (≥50%). Nonresponders received adjunctive aripiprazole treatment (2-10 mg/d) for a further 8 weeks. The brain-predicted age difference (brain-PAD) at baseline was determined using machine learning methods trained on 3377 healthy individuals from seven publicly available datasets. The model used features from all brain regions extracted from structural magnetic resonance imaging data. RESULTS: Brain-PAD was significantly higher in older MDD participants compared to younger MDD participants [t(147.35) = -2.35, p < 0.03]. BMI was significantly associated with brain-PAD in the MDD group [r(155) = 0.19, p < 0.03]. Response to treatment was not significantly associated with brain-PAD. CONCLUSION: We found an elevated brain age gap in older individuals with MDD. Brain-PAD was not associated with overall treatment response to escitalopram monotherapy or escitalopram plus adjunctive aripiprazole.

Predicting Brain Age at Slice Level: Convolutional Neural Networks and Consequences for Interpretability.

Problem: Chronological aging in later life is associated with brain degeneration processes and increased risk for disease such as stroke and dementia. With a worldwide tendency of aging populations and increased longevity, mental health, and psychiatric research have paid increasing attention to understanding brain-related changes of aging. Recent findings suggest there is a brain age gap (a difference between chronological age and brain age predicted by brain imaging indices); the magnitude of the gap may indicate early onset of brain aging processes and disease. Artificial intelligence has allowed for a narrowing of the gap in chronological and predicted brain age. However, the factors that drive model predictions of brain age are still unknown, and there is not much about these factors that can be gleaned from the black-box nature of machine learning models. The goal of the present study was to test a brain age regression approach that is more amenable to interpretation by researchers and clinicians. Methods: Using convolutional neural networks we trained multiple regressor models to predict brain age based on single slices of magnetic resonance imaging, which included gray matter- or white matter-segmented inputs. We evaluated the trained models in all brain image slices to generate a final prediction of brain age. Unlike whole-brain approaches to classification, the slice-level predictions allows for the identification of which brain slices and associated regions have the largest difference between chronological and neuroimaging-derived brain age. We also evaluated how model predictions were influenced by slice index and plane, participant age and sex, and MRI data collection site. Results: The results show, first, that the specific slice used for prediction affects prediction error (i.e., difference between chronological age and neuroimaging-derived brain age); second, the MRI site-stratified separation of training and test sets removed site effects and also minimized sex effects; third, the choice of MRI slice plane influences the overall error of the model. Conclusion: Compared to whole brain-based predictive models of neuroimaging-derived brain age, slice-based approach improves the interpretability and therefore the reliability of the prediction of brain age using MRI data.

Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force.

OBJECTIVES: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. METHOD: A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. RESULTS: The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. CONCLUSION: Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.