Influence of correlation between HLA-G polymorphism and Interleukin-6 (IL6) gene expression on the risk of schizophrenia.

Converging evidence suggests important implications of immuno-inflammatory pathway in the risk and progression of schizophrenia. Prenatal infection resulting in maternal immune activation and developmental neuroinflammation reportedly increases the risk of schizophrenia in the offspring by generating pro-inflammatory cytokines including IL-6. However, it is not known how prenatal infection can induce immuno-inflammatory responses despite the presence of immuno-inhibitory Human Leukocyte Antigen-G (HLA-G) molecules. To address this, the present study was aimed at examining the correlation between 14 bp Insertion/Deletion (INDEL) polymorphism of HLA-G and IL-6 gene expression in schizophrenia patients. The 14 bp INDEL polymorphism was studied by PCR amplification/direct sequencing and IL-6 gene expression was quantified by using real-time RT-PCR in 56 schizophrenia patients and 99 healthy controls. We observed significantly low IL6 gene expression in the peripheral mononuclear cells (PBMCs) of schizophrenia patients (t = 3.8, p = .004) compared to the controls. In addition, schizophrenia patients carrying Del/Del genotype of HLA-G 14 bp INDEL exhibited significantly lower IL6 gene expression (t = 3.1; p = .004) than the Del/Ins as well as Ins/Ins carriers. Our findings suggest that presence of "high-expressor" HLA-G 14 bp Del/Del genotype in schizophrenia patients could attenuate IL-6 mediated inflammation in schizophrenia. Based on these findings it can be assumed that HLA-G and cytokine interactions might play an important role in the immunological underpinnings of schizophrenia.

CHRFAM7A gene expression in schizophrenia: clinical correlates and the effect of antipsychotic treatment.

Earlier studies have implicated CHRNA7, coding α-7 nicotinic acetylcholine receptor (α7 nAChR), and its partially duplicated chimeric gene CHRFAM7A in schizophrenia. However, the relationship between the alterations in peripheral gene expression of CHRFAM7A and severity of clinical symptoms has not been examined. Furthermore, potential influence of the antipsychotic medication on CHRFAM7A expression in drug-naive or drug-free schizophrenia is an unexplored area. CHRFAM7A gene expression in lymphocytes was analyzed in 90 antipsychotic-naïve or free schizophrenia patients using TaqMan-based quantitative RT-PCR. Psychotic symptoms were assessed using Scale for Assessment of Positive Symptoms and Scale for Assessment of Negative Symptoms (SANS). The relationship between psychopathology and CHRFAM7A expression was examined. In addition, measurement of CHRFAM7A gene expression was repeated during follow-up after short-term antipsychotic treatment in 38 patients. There was significant inverse correlation between CHRFAM7A expression and total negative psychopathology score-SANS, and this relationship persisted after accounting for possible confounders such as age, sex and smoking. On exploration of the factor structure of psychopathology using principal component analysis, all the negative symptoms-affective flattening, alogia, apathy, anhedonia and inattention were found to be inversely associated with CHRFAM7A expression. Furthermore, analysis of repeated measures revealed a significant increase in CHRFAM7A expression in patients after short-term administration of antipsychotic medication. Our study observations support the role for CHRFAM7A gene in schizophrenia pathogenesis and suggest a potential novel link between deficient CHRFAM7A expression and negative psychopathology. Furthermore, up-regulation of CHRFAM7A gene expression by antipsychotics suggests that it could be a potential state marker for clinical severity.

Gene polymorphisms and response to transcranial direct current stimulation for auditory verbal hallucinations in schizophrenia.

OBJECTIVE: Recent observations demonstrate a significant ameliorative effect of add-on transcranial direct current stimulation (tDCS) on auditory verbal hallucinations (AVHs) in schizophrenia. Of the many SNPs, NRG1 rs35753505 and catechol-o-methyl transferase (COMT) rs4680 polymorphisms have shown to have a strong association with neuroplasticity effect in schizophrenia. METHODS: Schizophrenia patients (n=32) with treatment resistant auditory hallucinations were administered with an add-on tDCS. The COMT (rs4680) and NRG1 (rs35753505) genotypes were determined. The COMT genotypes were categorised into Val group (GG; n=15) and Met group (GG/AG; n=17) and NRG1 genotypes were categorised into AA group (n=12) and AG/GG group (n=20). RESULTS: The reduction in auditory hallucination sub-scale score was significantly affected by COMT-GG genotype [Time×COMT interaction: F(1,28)=10.55, p=0.003, ɳ2=0.27]. Further, COMT-GG effect was epistatically influenced by the co-occurrence of NRG1-AA genotype [Time×COMT×NRG1 interaction: F(1,28)=8.09, p=0.008, ɳ2=0.22]. Irrespective of genotype, females showed better tDCS response than males [Time×Sex interaction: F(1,21)=4.67, p=0.04, ɳ2=0.18]. CONCLUSION: COMT-GG and NRG1-AA genotypes aid the tDCS-induced improvement in AVHs in schizophrenia patients. Our preliminary observations need replication and further systematic research to understand the neuroplastic gene determinants that modulate the effect of tDCS.

Transcranial direct current stimulation and neuroplasticity genes: implications for psychiatric disorders.

BACKGROUND AND AIM: Transcranial direct current stimulation (tDCS) is a non-invasive and well-tolerated brain stimulation technique with promising efficacy as an add-on treatment for schizophrenia and for several other psychiatric disorders. tDCS modulates neuroplasticity; psychiatric disorders are established to be associated with neuroplasticity abnormalities. This review presents the summary of research on potential genetic basis of neuroplasticity-modulation mechanism underlying tDCS and its implications for treating various psychiatric disorders. METHOD: A systematic review highlighting the genes involved in neuroplasticity and their role in psychiatric disorders was carried out. The focus was on the established genetic findings of tDCS response relationship with BDNF and COMT gene polymorphisms. RESULT: Synthesis of these preliminary observations suggests the potential influence of neuroplastic genes on tDCS treatment response. These include several animal models, pharmacological studies, mentally ill and healthy human subject trials. CONCLUSION: Taking into account the rapidly unfolding understanding of tDCS and the role of synaptic plasticity disturbances in neuropsychiatric disorders, in-depth evaluation of the mechanism of action pertinent to neuroplasticity modulation with tDCS needs further systematic research. Genes such as NRG1, DISC1, as well as those linked with the glutamatergic receptor in the context of their direct role in the modulation of neuronal signalling related to neuroplasticity aberrations, are leading candidates for future research in this area. Such research studies might potentially unravel observations that might have potential translational implications in psychiatry.

Clinical correlates of superior temporal gyrus volume abnormalities in antipsychotic-naïve schizophrenia.

In this study, the authors report superior temporal gyrus (STG) and Heschl's gyrus (HG) volume deficits in a large sample of medication-naïve patients with schizophrenia (N=55) in comparison with healthy control subjects (N=45) with structural MRI using voxel-based morphometry. Patients had significantly smaller volumes of left HG [X=-41, Y=-22, Z=11; Brodmann's area (BA)-41), right HG (X=47, Y=-18, Z=11; BA-41), and left STG (X=-50, Y=-34, Z=11; BA-42] compared with healthy control subjects. In addition, Positive and Negative Syndrome Scale positive score had a significant negative correlation with left HG. Findings observed in a large sample of antipsychotic-naive patients with schizophrenia emphasize the role of HG and STG in the pathophysiology of schizophrenia.

Pituitary volume in medication-naïve adults with obsessive compulsive disorder.

Pituitary volume is considered to reflect hypothalamic-pituitary-adrenal axis dysregulation, and this has been studied in various psychiatric disorders. This study demonstrates that pituitary volume as assessed through the region of interest manual tracing method in 50 medication-naïve adult patients with obsessive-compulsive disorder was not significantly different compared with 40 healthy control subjects (687.80 ± 126.60 versus 694.73 ± 131.59, F=0.55, p=0.46). The authors also compared the patients with obsessive-compulsive disorder without any comorbid axis I conditions (N=35) with healthy control subjects and found no difference in the pituitary volumes (681.62 ± 130.85 versus 694.72 ± 131.59, F=0.90, p=0.35). This emphasizes the need to examine hypothalamo-pituitary axis structures after taking into consideration various potential confounders such as medications and depression.

Neural basis of tDCS effects on auditory verbal hallucinations in schizophrenia: a case report evidence for cortical neuroplasticity modulation.

Transcranial direct current stimulation (tDCS) has been reported to ameliorate auditory hallucinations that are nonresponsive/minimally responsive to antipsychotic treatment in schizophrenia. The neurobiological basis of the tDCS effects in ameliorating auditory hallucinations is yet to be explored. In this case report, for the first time, using the novel method for noninvasive assessment of cortical plasticity, we demonstrate potential neuroplasticity effect of tDCS in improving treatment-resistant auditory hallucinations in a schizophrenic patient.

Diagnostic and Prognostic Models Based on Electrocardiograms for Rapid Clinical Applications.

Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECG) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, non-cardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases (CVD) in the last five years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, a majority of these studies are single-center, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for <15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration (FDA) have been developed through commercial collaborations, with about half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multi-center large datasets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in CVD management.

Development and validation of machine learning algorithms based on electrocardiograms for cardiovascular diagnoses at the population level.

Artificial intelligence-enabled electrocardiogram (ECG) algorithms are gaining prominence for the early detection of cardiovascular (CV) conditions, including those not traditionally associated with conventional ECG measures or expert interpretation. This study develops and validates such models for simultaneous prediction of 15 different common CV diagnoses at the population level. We conducted a retrospective study that included 1,605,268 ECGs of 244,077 adult patients presenting to 84 emergency departments or hospitals, who underwent at least one 12-lead ECG from February 2007 to April 2020 in Alberta, Canada, and considered 15 CV diagnoses, as identified by International Classification of Diseases, 10th revision (ICD-10) codes: atrial fibrillation (AF), supraventricular tachycardia (SVT), ventricular tachycardia (VT), cardiac arrest (CA), atrioventricular block (AVB), unstable angina (UA), ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), pulmonary embolism (PE), hypertrophic cardiomyopathy (HCM), aortic stenosis (AS), mitral valve prolapse (MVP), mitral valve stenosis (MS), pulmonary hypertension (PHTN), and heart failure (HF). We employed ResNet-based deep learning (DL) using ECG tracings and extreme gradient boosting (XGB) using ECG measurements. When evaluated on the first ECGs per episode of 97,631 holdout patients, the DL models had an area under the receiver operating characteristic curve (AUROC) of <80% for 3 CV conditions (PTE, SVT, UA), 80-90% for 8 CV conditions (CA, NSTEMI, VT, MVP, PHTN, AS, AF, HF) and an AUROC > 90% for 4 diagnoses (AVB, HCM, MS, STEMI). DL models outperformed XGB models with about 5% higher AUROC on average. Overall, ECG-based prediction models demonstrated good-to-excellent prediction performance in diagnosing common CV conditions.

Clinical correlates of nucleus accumbens volume in drug-naive, adult patients with obsessive-compulsive disorder.

BACKGROUND: Reward-processing deficits have been demonstrated in obsessive-compulsive disorder (OCD) and this has been linked to ventral striatal abnormalities. However, volumetric abnormalities of the nucleus accumbens (NAcc), a key structure in the reward pathway, have not been examined in OCD. We report on the volumetric abnormalities of NAcc and its correlation with illness severity in drug-naïve, adult patients with OCD. METHOD: In this cross-sectional study of case-control design, the magnetic resonance imaging (MRI) 1.5-T (1-mm) volume of NAcc was measured using 3D Slicer software in drug-naïve OCD patients (n = 44) and age, sex and handedness-matched healthy controls (HCs) (n = 36) using a valid and reliable method. OCD symptoms were assessed using the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) Symptom checklist and severity and the Clinical Global Impression-Severity (CGI-S) scale. RESULTS: There was no significant difference in NAcc volumes on either side between OCD patients and HCs (F = 3.45, p = 0.07). However, there was significant negative correlation between the right NAcc volume and Y-BOCS compulsion score (r = -0.48, p = 0.001). CONCLUSIONS: Study observations suggest involvement of the NAcc in the pathogenesis of OCD, indicating potential reward-processing deficits. Correlation between the right NAcc volume deficit and severity of compulsions offers further support for this region as a candidate for deep brain stimulation treatment in OCD.

Revisiting Geschwind's hypothesis on brain lateralisation: a functional MRI study of digit ratio (2D:4D) and sex interaction effects on spatial working memory.

The Geschwind-Behan-Galaburda (GBG) hypothesis links cerebral lateralisation with prenatal testosterone exposure. Digit ratio measures in adults have been established as potential markers of foetal sex hormonal milieu. The aim of the study was to evaluate the sex-dependent interaction of digit ratio measures and cerebral lateralization as well as their neurohemodynamic correlates using functional MRI (fMRI). Digit ratio measures-ratio of index finger (2D) length to ring finger (4D) length (2D:4D) and difference between 2D:4D of two hands, i.e., right minus left (DR-L)-were calculated using high resolution digital images in 70 right-handed participants (42 men) based on reliable and valid method. fMRI was acquired during the performance of a spatial working memory task in a subset of 25 individuals (14 men), and analysed using Statistical Parametric Mapping 8 (SPM8) and the Laterality Index toolbox for SPM8. Men had significantly less bilateral 2D:4D than women. There was a significant negative correlation between right 2D:4D and 2-Back task accuracy (2BACC) in women. A significant sex-by-right 2D:4D interaction was observed in left parahippocampal gyrus activation. Additionally, sex-by-DR-L interaction was observed in left IPL activation. DR-L showed a significant positive correlation with the whole brain Laterality Index (LI), and LI, in turn, demonstrated a significant negative correlation with 2BACC. Our study observations suggest several novel sex-differential relationships between 2D:4D measures and fMRI activation during spatial working memory task performance. Given the pre-existing background data supporting digit ratio measures as putative indicator of prenatal sex hormonal milieu, our study findings add support to the Geschwind-Behan-Galaburda (GBG) hypothesis.

Rapid improvement of auditory verbal hallucinations in schizophrenia after add-on treatment with transcranial direct-current stimulation.

Treatment of nonresponsive auditory hallucinations in schizophrenia have been reported to improve with transcranial direct-current stimulation. This case description illustrates the use of add-on transcranial direct-current stimulation for rapid amelioration of auditory hallucinations in schizophrenia during the acute phase. Because transcranial direct-current stimulation is safe, largely well tolerated, and relatively inexpensive, this add-on treatment option is worth exploring through further rigorous studies.

Resting-state functional connectivity correlates of antipsychotic treatment in unmedicated schizophrenia.

BACKGROUND: Antipsychotics may modulate the resting state functional connectivity(rsFC) to improve clinical symptoms in schizophrenia(Sz). Existing literature has potential confounders like past medication effects and evaluating preselected regions/networks. We aimed to evaluate connectivity pattern changes with antipsychotics in unmedicated Sz using Multivariate pattern analysis(MVPA), a data-driven technique for whole-brain connectome analysis. METHODS: Forty-seven unmedicated patients with Sz(DSM-IV-TR) underwent clinical evaluation and neuroimaging at baseline and after 3-months of antipsychotic treatment. Resting-state functional MRI was analysed using group-MVPA to derive 5-components. The brain region with significant connectivity pattern changes with antipsychotics was identified, and post-hoc seed-to-voxel analysis was performed to identify connectivity changes and their association with symptom changes. RESULTS: Connectome-MVPA analysis revealed the connectivity pattern of a cluster localised to left anterior cingulate and paracingulate gyri (ACC/PCG) (peak coordinates:x = -04,y = +30,z = +26;k = 12;cluster-pFWE=0.002) to differ significantly after antipsychotics. Specifically, its connections with clusters of precuneus/posterior cingulate cortex(PCC) and left inferior temporal gyrus(ITG) correlated with improvement in positive and negative symptoms scores, respectively. CONCLUSION: ACC/PCG, a hub of the default mode network, seems to mediate the antipsychotic effects in unmedicated Sz. Evaluating causality models with data from randomised controlled design using the MVPA approach would further enhance our understanding of therapeutic connectomics in Sz.

Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms.

The feasibility and value of linking electrocardiogram (ECG) data to longitudinal population-level administrative health data to facilitate the development of a learning healthcare system has not been fully explored. We developed ECG-based machine learning models to predict risk of mortality among patients presenting to an emergency department or hospital for any reason. Using the 12-lead ECG traces and measurements from 1,605,268 ECGs from 748,773 healthcare episodes of 244,077 patients (2007-2020) in Alberta, Canada, we developed and validated ResNet-based Deep Learning (DL) and gradient boosting-based XGBoost (XGB) models to predict 30-day, 1-year, and 5-year mortality. The models for 30-day, 1-year, and 5-year mortality were trained on 146,173, 141,072, and 111,020 patients and evaluated on 97,144, 89,379, and 55,650 patients, respectively. In the evaluation cohort, 7.6%, 17.3%, and 32.9% patients died by 30-days, 1-year, and 5-years, respectively. ResNet models based on ECG traces alone had good-to-excellent performance with area under receiver operating characteristic curve (AUROC) of 0.843 (95% CI: 0.838-0.848), 0.812 (0.808-0.816), and 0.798 (0.792-0.803) for 30-day, 1-year and 5-year prediction, respectively; and were superior to XGB models based on ECG measurements with AUROC of 0.782 (0.776-0.789), 0.784 (0.780-0.788), and 0.746 (0.740-0.751). This study demonstrates the validity of ECG-based DL mortality prediction models at the population-level that can be leveraged for prognostication at point of care.

Development and validation of echocardiography-based machine-learning models to predict mortality.

BACKGROUND: Echocardiography (echo) based machine learning (ML) models may be useful in identifying patients at high-risk of all-cause mortality. METHODS: We developed ML models (ResNet deep learning using echo videos and CatBoost gradient boosting using echo measurements) to predict 1-year, 3-year, and 5-year mortality. Models were trained on the Mackay dataset, Taiwan (6083 echos, 3626 patients) and validated in the Alberta HEART dataset, Canada (997 echos, 595 patients). We examined the performance of the models overall, and in subgroups (healthy controls, at risk of heart failure (HF), HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF)). We compared the models' performance to the MAGGIC risk score, and examined the correlation between the models' predicted probability of death and baseline quality of life as measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ). FINDINGS: Mortality rates at 1-, 3- and 5-years were 14.9%, 28.6%, and 42.5% in the Mackay cohort, and 3.0%, 10.3%, and 18.7%, in the Alberta HEART cohort. The ResNet and CatBoost models achieved area under the receiver-operating curve (AUROC) between 85% and 92% in internal validation. In external validation, the AUROCs for the ResNet (82%, 82%, and 78%) were significantly better than CatBoost (78%, 73%, and 75%), for 1-, 3- and 5-year mortality prediction respectively, with better or comparable performance to the MAGGIC score. ResNet models predicted higher probability of death in the HFpEF and HFrEF (30%-50%) subgroups than in controls and at risk patients (5%-20%). The predicted probabilities of death correlated with KCCQ scores (all p < 0.05). INTERPRETATION: Echo-based ML models to predict mortality had good internal and external validity, were generalizable, correlated with patients' quality of life, and are comparable to an established HF risk score. These models can be leveraged for automated risk stratification at point-of-care. FUNDING: Funding for Alberta HEART was provided by an Alberta Innovates - Health Solutions Interdisciplinary Team Grant no. AHFMRITG 200801018. P.K. holds a Canadian Institutes of Health Research (CIHR) Sex and Gender Science Chair and a Heart & Stroke Foundation Chair in Cardiovascular Research. A.V. and V.S. received funding from the Mitacs Globalink Research Internship.

Multi-Source Domain Adaptation Techniques for Mitigating Batch Effects: A Comparative Study.

The past decade has seen an increasing number of applications of deep learning (DL) techniques to biomedical fields, especially in neuroimaging-based analysis. Such DL-based methods are generally data-intensive and require a large number of training instances, which might be infeasible to acquire from a single acquisition site, especially for data, such as fMRI scans, due to the time and costs that they demand. We can attempt to address this issue by combining fMRI data from various sites, thereby creating a bigger heterogeneous dataset. Unfortunately, the inherent differences in the combined data, known as batch effects, often hamper learning a model. To mitigate this issue, techniques such as multi-source domain adaptation [Multi-source Domain Adversarial Networks (MSDA)] aim at learning an effective classification function that uses (learned) domain-invariant latent features. This article analyzes and compares the performance of various popular MSDA methods [MDAN, Domain AggRegation Networks (DARN), Multi-Domain Matching Networks (MDMN), and Moment Matching for MSDA (M3SDA)] at predicting different labels (illness, age, and sex) of images from two public rs-fMRI datasets: ABIDE 1and ADHD-200. It also evaluates the impact of various conditions such as class imbalance, the number of sites along with a comparison of the degree of adaptation of each of the methods, thereby presenting the effectiveness of MSDA models in neuroimaging-based applications.

Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets.

Many researchers try to understand a biological condition by identifying biomarkers. This is typically done using univariate hypothesis testing over a labeled dataset, declaring a feature to be a biomarker if there is a significant statistical difference between its values for the subjects with different outcomes. However, such sets of proposed biomarkers are often not reproducible - subsequent studies often fail to identify the same sets. Indeed, there is often only a very small overlap between the biomarkers proposed in pairs of related studies that explore the same phenotypes over the same distribution of subjects. This paper first defines the Reproducibility Score for a labeled dataset as a measure (taking values between 0 and 1) of the reproducibility of the results produced by a specified fixed biomarker discovery process for a given distribution of subjects. We then provide ways to reliably estimate this score by defining algorithms that produce an over-bound and an under-bound for this score for a given dataset and biomarker discovery process, for the case of univariate hypothesis testing on dichotomous groups. We confirm that these approximations are meaningful by providing empirical results on a large number of datasets and show that these predictions match known reproducibility results. To encourage others to apply this technique to analyze their biomarker sets, we have also created a publicly available website, https://biomarker.shinyapps.io/BiomarkerReprod/, that produces these Reproducibility Score approximations for any given dataset (with continuous or discrete features and binary class labels).

Prediction of Obsessive-Compulsive Disorder: Importance of Neurobiology-Aided Feature Design and Cross-Diagnosis Transfer Learning.

BACKGROUND: Machine learning applications using neuroimaging provide a multidimensional, data-driven approach that captures the level of complexity necessary for objectively aiding diagnosis and prognosis in psychiatry. However, models learned from small training samples often have limited generalizability, which continues to be a problem with automated diagnosis of mental illnesses such as obsessive-compulsive disorder (OCD). Earlier studies have shown that features incorporating prior neurobiological knowledge of brain function and combining brain parcellations from various sources can potentially improve the overall prediction. However, it is unknown whether such knowledge-driven methods can provide a performance that is comparable to state-of-the-art approaches based on neural networks. METHODS: In this study, we apply a transparent and explainable multiparcellation ensemble learning framework EMPaSchiz (Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction) to the task of predicting OCD, based on a resting-state functional magnetic resonance imaging dataset of 350 subjects. Furthermore, we apply transfer learning using the features found effective for schizophrenia to OCD to leverage the commonality in brain alterations across these psychiatric diagnoses. RESULTS: We show that our knowledge-based approach leads to a prediction performance of 80.3% accuracy for OCD diagnosis that is better than domain-agnostic and automated feature design using neural networks. Furthermore, we show that a selection of reduced feature sets can be transferred from schizophrenia to the OCD prediction model without significant loss in prediction performance. CONCLUSIONS: This study presents a machine learning framework for OCD prediction with neurobiology-aided feature design using resting-state functional magnetic resonance imaging that is generalizable and reasonably interpretable.

Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study.

Transcranial direct current stimulation (tDCS) is a promising adjuvant treatment for persistent auditory verbal hallucinations (AVH) in Schizophrenia (SZ). Nonetheless, there is considerable inter-patient variability in the treatment response of AVH to tDCS in SZ. Machine-learned models have the potential to predict clinical response to tDCS in SZ. This study aims to examine the feasibility of identifying SZ patients with persistent AVH (SZ-AVH) who will respond to tDCS based on resting-state functional connectivity (rs-FC). Thirty-four SZ-AVH patients underwent resting-state functional MRI at baseline followed by add-on, twice-daily, 20-min sessions with tDCS (conventional/high-definition) for 5 days. A machine learning model was developed to identify tDCS treatment responders based on the rs-FC pattern, using the left superior temporal gyrus (LSTG) as the seed region. Functional connectivity between LSTG and brain regions involved in auditory and sensorimotor processing emerged as the important predictors of the tDCS treatment response. L1-regularized logistic regression model had an overall accuracy of 72.5% in classifying responders vs. non-responders. This model outperformed the state-of-the-art convolutional neural networks (CNN) model-both without (59.41%) and with pre-training (68.82%). It also outperformed the L1-logistic regression model trained with baseline demographic features and clinical scores of SZ patients. This study reports the first evidence that rs-fMRI-derived brain connectivity pattern can predict the clinical response of persistent AVH to add-on tDCS in SZ patients with 72.5% accuracy.

Factors associated with SARS-CoV-2 test positivity in long-term care homes: A population-based cohort analysis using machine learning.

BACKGROUND: SARS-Cov-2 infection rates are high among residents of long-term care (LTC) homes. We used machine learning to identify resident and community characteristics predictive of SARS-Cov-2 infection. METHODS: We linked 26 population-based health and administrative databases to identify the population of all LTC residents tested for SARS-Cov-2 infection in Ontario, Canada. Using ensemble-based algorithms, we examined 484 factors, including individual-level demographics, healthcare use, comorbidities, functional status, and laboratory results; and community-level characteristics to identify factors predictive of infection. Analyses were performed separately for January to April (early wave 1) and May to August (late wave 1). FINDINGS: Among 80,784 LTC residents, 64,757 (80.2%) were tested for SARS-Cov-2 (median age 86 (78-91) years, 30.6% male), of whom 10.2% of 33,519 and 5.2% of 31,238 tested positive in early and late wave 1, respectively. In the late phase (when restriction of visitors, closure of communal spaces, and universal masking in LTC were routine), regional-level characteristics comprised 33 of the top 50 factors associated with testing positive, while laboratory values and comorbidities were also predictive. The c-index of the final model was 0.934, and sensitivity was 0.887. In the highest versus lowest risk quartiles, the odds ratio for infection was 114.3 (95% CI 38.6-557.3). LTC-related geographic variations existed in the distribution of observed infection rates and the proportion of residents at highest risk. INTERPRETATION: Machine learning informed evaluation of predicted and observed risks of SARS-CoV-2 infection at the resident and LTC levels, and may inform initiatives to improve care quality in this setting. FUNDING: Funded by a Canadian Institutes of Health Research, COVID-19 Rapid Research Funding Opportunity grant (# VR4 172736) and a Peter Munk Cardiac Centre Innovation Grant. Dr. D. Lee is the Ted Rogers Chair in Heart Function Outcomes, University Health Network, University of Toronto. Dr. Austin is supported by a Mid-Career investigator award from the Heart and Stroke Foundation. Dr. McAlister is supported by an Alberta Health Services Chair in Cardiovascular Outcomes Research. Dr. Kaul is the CIHR Sex and Gender Science Chair and the Heart & Stroke Chair in Cardiovascular Research. Dr. Rochon holds the RTO/ERO Chair in Geriatric Medicine from the University of Toronto. Dr. B. Wang holds a CIFAR AI chair at the Vector Institute.