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.

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.

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.

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.

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.

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.

Learning the structure-activity relationship (SAR) of the Wittig reaction from genetically-encoded substrates.

The Wittig reaction can be used for late stage functionalization of proteins and peptides to ligate glycans, pharmacophores, and many other functionalities. In this manuscript, we modified 160 000 N-terminal glyoxaldehyde peptides displayed on phage with the Wittig reaction by using a biotin labeled ylide under conditions that functionalize only 1% of the library population. Deep-sequencing of the biotinylated and input populations estimated the rate of conversion for each sequence. This "deep conversion" (DC) from deep sequencing correlates with rate constants measured by HPLC. Peptide sequences with fast and slow reactivity highlighted the critical role of primary backbone amides (N-H) in accelerating the rate of the aqueous Wittig reaction. Experimental measurement of reaction rates and density functional theory (DFT) computation of the transition state geometries corroborated this relationship. We also collected deep-sequencing data to build structure-activity relationship (SAR) models that can predict the DC value of the Wittig reaction. By using these data, we trained two classifier models based on gradient boosted trees. These classifiers achieved area under the ROC (receiver operating characteristic) curve (ROC AUC) of 81.2 ± 0.4 and 73.7 ± 0.8 (90-92% accuracy) in determining whether a sequence belonged to the top 5% or the bottom 5% in terms of its reactivity. This model can suggest new peptides never observed experimentally with 'HIGH' or 'LOW' reactivity. Experimental measurement of reaction rates for 11 new sequences corroborated the predictions for 8 of them. We anticipate that phage-displayed peptides and related mRNA or DNA-displayed substrates can be employed in a similar fashion to study the substrate scope and mechanisms of many other chemical reactions.

Pars Triangularis Volume Asymmetry and Schneiderian First Rank Symptoms in Antipsychotic-naïve Schizophrenia.

OBJECTIVE: Schizophrenia is a disorder of language and self, with first-rank symptoms (FRS) as one of the predominant features in a subset of patients. Abnormal language lateralization is hypothesized to underlie the neurobiology of FRS in schizophrenia. The role of Broca's area with its right-hemispheric counterpart, consisting of pars triangularis (PTr) and pars opercularis (POp) of the inferior frontal gyrus in FRS is undetermined. We compared the volumes and asymmetries of PTr & POp in anti-psychotic-naive schizophrenia patients with FRS (FRS[＋]) with those without FRS (FRS[-]) and healthy-controls (HC) using three dimensional, interactive, semi-automated volumetric morphometry. METHODS: Antipsychotic naïve FRS(＋) (n = 27), FRS(-) (n = 24) and HC (n = 51) were carefully assessed with structured and semi-structured clinical tools. T1-weighted images were acquired in a 3T scanner. Volumes of regions of interest were measured independently for both sides using slicer-3D software, and asymmetry indices were calculated. RESULTS: FRS(＋) but not FRS(-) had a significant volume deficit in right PTr after controlling for the potential confounding effects of age, sex, and intracranial volume (p = 0.029). There was a significant leftward asymmetry of PTr in patients with FRS (i.e., leftward asymmetry in patients) (p = 0.026). No significant volume/asymmetry abnormalities were observed in POp. CONCLUSION: Study findings suggest reduced right PTr volume with leftward asymmetry to be associated with FRS in schizophrenia. This is consistent with the loss of Yakovlevian torque in schizophrenia. Role of PTr in the neurobiology of schizophrenia as a disorder of self, speech, and social cognition needs further systematic evaluation in future research.

Extending schizophrenia diagnostic model to predict schizotypy in first-degree relatives.

Recently, we developed a machine-learning algorithm "EMPaSchiz" that learns, from a training set of schizophrenia patients and healthy individuals, a model that predicts if a novel individual has schizophrenia, based on features extracted from his/her resting-state functional magnetic resonance imaging. In this study, we apply this learned model to first-degree relatives of schizophrenia patients, who were found to not have active psychosis or schizophrenia. We observe that the participants that this model classified as schizophrenia patients had significantly higher "schizotypal personality scores" than those who were not. Further, the "EMPaSchiz probability score" for schizophrenia status was significantly correlated with schizotypal personality score. This demonstrates the potential of machine-learned diagnostic models to predict state-independent vulnerability, even when symptoms do not meet the full criteria for clinical diagnosis.

Leukocyte mitochondrial DNA copy number in schizophrenia.

OBJECTIVE: Schizophrenia is a complex neuropsychiatric disorder with significant genetic predisposition. In a subset of schizophrenia patients, mitochondrial dysfunction could be explained by the genomic defects like mitochondrial DNA Copy Number Variations, which are considered as a sensitive index of cellular oxidative stress. Given the high energy demands for neuronal functions, altered Mitochondrial DNA copy number (mtDNAcn) and consequent impaired mitochondrial physiology would significantly influence schizophrenia pathogenesis. In this context, we have made an attempt to study mitochondrial dysfunction in schizophrenia by assessing mtDNAcn in antipsychotic-naïve/free schizophrenia patients. METHOD: mtDNAcn was measured in 90 antipsychotic-naïve / free schizophrenia (SCZ) patients and 147 Healthy Controls (HC). The relative mtDNAcn was determined by quantitative real-time polymerase chain reaction (qPCR) using TaqMan® multiplex assay method. RESULT: A statistically significant difference between groups [t = 5.22, P < 0.001] was observed, with significantly lower mtDNAcn in SCZ compared to HC. The group differences persisted even after controlling for age and sex [F (4, 232) = 22.68, P < 0.001, η2 = 0.09]. CONCLUSION: Lower mtDNAcn in SCZ compared to HC suggests that mtDNAcn may hold potential to serve as an important proxy marker of mitochondrial function in antipsychotic-naïve/free SCZ patients.

Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning.

In the literature, there are substantial machine learning attempts to classify schizophrenia based on alterations in resting-state (RS) brain patterns using functional magnetic resonance imaging (fMRI). Most earlier studies modelled patients undergoing treatment, entailing confounding with drug effects on brain activity, and making them less applicable to real-world diagnosis at the point of first medical contact. Further, most studies with classification accuracies >80% are based on small sample datasets, which may be insufficient to capture the heterogeneity of schizophrenia, limiting generalization to unseen cases. In this study, we used RS fMRI data collected from a cohort of antipsychotic drug treatment-naive patients meeting DSM IV criteria for schizophrenia (N = 81) as well as age- and sex-matched healthy controls (N = 93). We present an ensemble model -- EMPaSchiz (read as 'Emphasis'; standing for 'Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction') that stacks predictions from several 'single-source' models, each based on features of regional activity and functional connectivity, over a range of different a priori parcellation schemes. EMPaSchiz yielded a classification accuracy of 87% (vs. chance accuracy of 53%), which out-performs earlier machine learning models built for diagnosing schizophrenia using RS fMRI measures modelled on large samples (N > 100). To our knowledge, EMPaSchiz is first to be reported that has been trained and validated exclusively on data from drug-naive patients diagnosed with schizophrenia. The method relies on a single modality of MRI acquisition and can be readily scaled-up without needing to rebuild parcellation maps from incoming training images.

Plasma cytokines in minimally treated schizophrenia.

In schizophrenia, plasma cytokines abnormalities offer vital support for immunopathogenetic basis. However, most of the previous studies on plasma cytokines are confounded by examination of antipsychotic-treated schizophrenia patients. In this study, we examined a large sample of antipsychotic-naïve/free schizophrenia patients (N = 75) in comparison with healthy controls (N = 102). Plasma cytokines (Interleukins ([IL] 2, 4, 6, 10, 17), Tumor necrosis factor [TNF] and Interferon gamma [IFN-g]) were assessed using cytometric bead array assay. Schizophrenia patients showed significantly greater levels of IL-6 and lower levels of IL-17 as well as IFN-g in comparison to healthy controls. However, after taking censoring into account and adjusting for potential confounders (sex, age, BMI and smoking), only IL-6 was found to be elevated in patients. Cytokine profile showed differential and pathogenetically relevant correlation with clinical symptoms. Together, these observations offer further support to immunological component in schizophrenia pathogenesis.

Short term effects of brief need based psychoeducation on knowledge, self-stigma, and burden among siblings of persons with schizophrenia: A prospective controlled trial.

Siblings of persons with schizophrenia are important in providing long-term social support to the patients. Interventions addressing their needs are very sparse. Hence, this study aimed at testing the short-term effects of brief need based psychoeducation on knowledge, self-stigma, and burden among siblings of persons with schizophrenia. In this prospective controlled open label trial, 80 siblings of persons with schizophrenia were allocated in equal numbers to the brief need based psychoeducation group and the treatment-as-usual group. The outcomes were measured at baseline, and after the first and third month post-intervention. RM-ANCOVA was conducted to test the effect of the brief psychoeducation on outcome scores. The groups were similar with respect to socio-demographic, clinical, and outcome scores at the baseline. There was a significant group×time interaction effect on knowledge (F=8.71; p<0.01; ηp2=0.14) and self-stigma scores (F=14.47; p<0.001; ηp2=0.21), wherein the brief psychoeducation group showed a significant increase in knowledge and reduction in self-stigma with medium effect size through baseline to the third month follow-up as compared to the treatment as usual group. We also observed a significant main effect of time; irrespective of the group allocation, there was a significant increase in the knowledge through baseline to third month follow-up (F=5.69; p=0.02; ηp2=0.09). No main or interaction effects of group and time were observed on burden. The findings suggest that brief need based psychoeducation may increase knowledge about the illness and reduce self-stigma. Further systematic studies are warranted to test this intervention for long-term effects.

In SilicoModel-driven Assessment of the Effects of Brain-derived Neurotrophic Factor Deficiency on Glutamate and Gamma-Aminobutyric Acid: Implications for Understanding Schizophrenia Pathophysiology.

OBJECTIVE: Deficient brain-derived neurotrophic factor (BDNF) is one of the important mechanisms underlying the neuroplasticity abnormalities in schizophrenia. Aberration in BDNF signaling pathways directly or circuitously influences neurotransmitters like glutamate and gamma-aminobutyric acid (GABA). For the first time, this study attempts to construct and simulate the BDNF-neurotransmitter network in order to assess the effects of BDNF deficiency on glutamate and GABA. METHODS: Using CellDesigner, we modeled BDNF interactions with calcium influx via N-methyl-D-aspartate receptor (NMDAR)- Calmodulin activation; synthesis of GABA via cell cycle regulators protein kinase B, glycogen synthase kinase and ß-catenin; transportation of glutamate and GABA. Steady state stability, perturbation time-course simulation and sensitivity analysis were performed in COPASI after assigning the kinetic functions, optimizing the unknown parameters using random search and genetic algorithm. RESULTS: Study observations suggest that increased glutamate in hippocampus, similar to that seen in schizophrenia, could potentially be contributed by indirect pathway originated from BDNF. Deficient BDNF could suppress Glutamate decarboxylase 67-mediated GABA synthesis. Further, deficient BDNF corresponded to impaired transport via vesicular glutamate transporter, thereby further increasing the intracellular glutamate in GABAergic and glutamatergic cells. BDNF also altered calcium dependent neuroplasticity via NMDAR modulation. Sensitivity analysis showed that Calmodulin, cAMP response element-binding protein (CREB) and CREB regulated transcription coactivator-1 played significant role in this network. CONCLUSION: The study presents in silicoquantitative model of biochemical network constituting the key signaling molecules implicated in schizophrenia pathogenesis. It provides mechanistic insights into putative contribution of deficient BNDF towards alterations in neurotransmitters and neuroplasticity that are consistent with current understanding of the disorder.

Clinical correlates of hippocampus volume and shape in antipsychotic-naïve schizophrenia.

While volume deficit of hippocampus is an established finding in schizophrenia, very few studies have examined large sample of patients without the confounding effect of antipsychotic treatment. Concurrent evaluation of hippocampus shape will offer additional information on the hippocampal aberrations in schizophrenia. In this study, we analyzed the volume and shape of hippocampus in antipsychotic-naïve schizophrenia patients (N=71) in comparison to healthy controls (N=82). Using 3-T MRI data, gray matter (GM) volume (anterior and posterior sub-divisions) and shape of the hippocampus were analyzed. Schizophrenia patients had significant hippocampal GM volume deficits (specifically the anterior sub-division) in comparison to healthy controls. There were significant positive correlations between anterior hippocampus volume and psychopathology scores of positive syndrome. Shape analyses revealed significant inward deformation of bilateral hippocampal surface in patients. In conclusion, our study findings add robust support for volume deficit in hippocampus in antipsychotic-naïve schizophrenia. Hippocampal shape deficits in schizophrenia observed in this study map to anterior CA1 sub-region. The differential relationship of anterior hippocampus (but not posterior hippocampus) with clinical symptoms is in tune with the findings in animal models. Further systematic studies are needed to evaluate the relationship between these hippocampal gray matter deficits with white matter and functional connectivity to facilitate understanding the hippocampal network abnormalities in schizophrenia.

Visual Image-Induced Craving for Ethanol (VICE): Development, Validation, and a Pilot fMRI Study.

BACKGROUND: Craving induction in a controlled environment is helpful in the research of craving mechanism and its role in development of alcohol dependence (AD). We describe a novel tool Visual Image-induced Craving for Ethanol (VICE) and its effects on brain activation with pilot functional magnetic resonance imaging (fMRI). MATERIALS AND METHODS: Alcohol-related visual cues (ARCs) in 5 scenarios were photographed, which included pictures of bars, alcoholic beverage bottles, pouring of alcohol into glasses, glasses filled with alcohol, and scenes of people sipping alcohol, counterbalanced with neutral pictures (involving water, milk etc.,). Craving scores were obtained from 15 hospitalized patients with AD to validate this tool. In the pilot fMRI (3-Tesla) study, 5 patients were examined using VICE in a symptom provocation model. Group level-fixed effect analysis of brain activation differences was done using SPM8. RESULTS: VICE showed a high internal consistency with Cronbach's α coefficient of 0.86, which confirmed its reliability. Concurrent validity of VICE was demonstrated via its convergence with the Penn Alcohol Craving Scale. ARCs had significantly greater mean craving scores than neutral cues in all the 5 scenarios (intentional validity). In the pilot fMRI, patients were found to have greater activation while viewing ARCs compared to the neutral cues in right insular cortex and deficient activation in right orbitofrontal cortex. CONCLUSIONS: The VICE is a reliable and valid measure of alcohol craving with promising clinical and translational research implications. Preliminary fMRI findings indicate it can be used as a symptom provocation tool for fMRI experiments.

Relationship between Interleukin-6 gene polymorphism and hippocampal volume in antipsychotic-naïve schizophrenia: evidence for differential susceptibility?

BACKGROUND: Various lines of evidence including epidemiological, genetic and foetal pathogenetic models suggest a compelling role for Interleukin-6 (IL-6) in the pathogenesis of schizophrenia. IL-6 mediated inflammatory response triggered by maternal infection or stress induces disruption of prenatal hippocampal development which might contribute towards psychopathology during adulthood. There is a substantial lack of knowledge on how genetic predisposition to elevated IL-6 expression effects hippocampal structure in schizophrenia patients. In this first-time study, we evaluated the relationship between functional polymorphism rs1800795 of IL-6 and hippocampal gray matter volume in antipsychotic-naïve schizophrenia patients in comparison with healthy controls. METHODOLOGY: We examined antipsychotic-naïve schizophrenia patients [N = 28] in comparison with healthy controls [N = 37] group matched on age, sex and handedness. Using 3 Tesla - MRI, bilateral hippocampi were manually segmented by blinded raters with good inter-rater reliability using a valid method. Additionally, Voxel-based Morphometry (VBM) analysis was performed using hippocampal mask. The IL-6 level was measured in blood plasma using ELISA technique. SNP rs1800795 was genotyped using PCR and DNA sequencing. Psychotic symptoms were assessed using Scale for Assessment of Positive Symptoms and Scale for Assessment of Negative Symptoms. RESULTS: Schizophrenia patients had significantly deficient left and right hippocampal volumes after controlling for the potential confounding effects of age, sex and total brain volume. Plasma IL-6 levels were significantly higher in patients than controls. There was a significant diagnosis by rs1800795 genotype interaction involving both right and left hippocampal volumes. Interestingly, this effect was significant only in men but not in women. CONCLUSION: Our first time observations suggest a significant relationship between IL-6 rs1800795 and reduced hippocampal volume in antipsychotic-naïve schizophrenia. Moreover, this relationship was antithetical in healthy controls and this effect was observed in men but not in women. Together, these observations support a "differential susceptibility" effect of rs1800795 in schizophrenia pathogenesis mediated through hippocampal volume deficit that is of possible neurodevelopmental origin.

Neurohemodynamic Correlates of Washing Symptoms in Obsessive-compulsive Disorder: A Pilot fMRI Study Using Symptom Provocation Paradigm.

BACKGROUND: Obsessive-compulsive disorder (OCD) is increasingly being viewed as a multidimensional heterogeneous disorder caused due to the dysfunction of several closely related, overlapping frontostriatal circuits. A study investigating the dimensional construct in treatment naïve, co-morbidity free patients with identical handedness is likely to provide the necessary homogeneity and power to elicit neural correlates of the various symptom dimensions, and overcome the limitations of previous studies. MATERIALS AND METHODS: Nine DSM-IV OCD patients with predominant contamination-related obsessive-compulsive symptoms (age=29.8±7.1 years; five males: four females; years-of-education=13.9±1.6, YBOCS total score=28.8±4.7, DYBOCS Contamination dimension score=10.7±1.8) and nine healthy controls matched one to one with the patients for age, sex, and years of education (age=27.8±5.4, five males: four females; years-of-education=14.9±3.0), were examined during symptom provocation task performance in 3TMRI. Paired samples t test of brain activation differences (contamination relevant pictures - neutral pictures), limited to apriori regions of interest was done using SPM8 (uncorrected P<0.005). RESULTS: Patients found significantly more pictures to be anxiety provoking in comparison to healthy controls. Patients were found to have deficient activation in the following areas in comparison with healthy controls: bilateral anterior prefrontal, dorsolateral prefrontal, orbitofrontal, anterior cingulate, insular and parietal cortices, precuneus, and caudate. CONCLUSIONS: Results underscore the importance of frontal, striatal, parietal, and occipital areas in the pathophysiology of OCD. Divergence of findings from previous studies might be attributed to the absence of confounding factors in the current study and may be due to production of intense anxiety in patients.

Relationship between Brain-Derived Neurotrophic Factor and Schneiderian First Rank Symptoms in Antipsychotic-Naïve Schizophrenia.

Neurodevelopmental aberrations influenced by neurotrophic factors are among the important paradigms to understand schizophrenia pathogenesis. Among various neurotrophic factors, Brain-Derived Neurotrophic Factor (BDNF) is strongly implicated by previous research studies. Evaluating co-morbidity free, antipsychotic-naïve schizophrenia patients for BDNF levels and examining the correlates of this factor with symptoms might facilitate elucidation of its pathogenetic role without confounds of potential influencing factors. In this study, 59 co-morbidity free, antipsychotic-naïve schizophrenia patients were compared with 60 healthy controls for serum BDNF levels. In addition, the relationship between Schneiderian First Rank Symptoms (FRS) and BDNF level in patients was examined. As a group, schizophrenia patients (28.8 ± 11.7 ng/mL) had significantly lower serum BDNF than healthy controls (34.9 ± 8.2 ng/mL) after controlling for the potential confounding effects of age and sex (F = 7.8; p = 0.006). Further analyses revealed FRS status to have significant effect on plasma BDNF after controlling for the potential confounding effects of age and sex (F = 4.5; p = 0.01). Follow-up post hoc analyses revealed FRS(+) patients to have significant deficit in plasma BDNF level in comparison with healthy controls (p = 0.002); however, FRS(-) patients did not differ from healthy controls (p = 0.38). Our study observations add further support to the role for BDNF in schizophrenia pathogenesis and suggest a potential novel link between deficient BDNF and FRS.

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.