Publications by authors named "Venkatasubramanian Ganesan"

Introduction: Diffusion-weighted magnetic resonance imaging (dMRI) is sensitive to the microstructural properties of brain tissues and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest without considering the underlying fiber geometry.

Methods: We propose a novel macrostructure-informed normative tractometry (MINT) framework to investigate how white matter (WM) microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia.

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Wilson disease is a rare genetic disorder of copper metabolism causing hepatic dysfunction and neuro-psychiatric manifestations. While psychosis in Wilson disease is uncommon, it can occur, especially with certain medications. We describe a 40-year-old woman diagnosed with Wilson disease who developed psychotic symptoms following the initiation and dose escalation of amantadine, a drug commonly used to treat parkinsonism associated with the disorder.

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  • Deep learning models using convolutional neural networks (CNNs) have been applied to classify Alzheimer's disease and assess dementia severity through 3D brain MRI scans, with a focus on improving interpretability using occlusion sensitivity analysis (OSA) and gradient-weighted class activation mapping (Grad-CAM).
  • The study investigates the effectiveness of these models, primarily trained on North American datasets, when applied to a different population in India (NIMHANS cohort), and assesses the advantages of utilizing a combined dataset for training.
  • Results demonstrate that feature localization aligns with established knowledge of Alzheimer's disease, indicating that OSA and Grad-CAM enhance interpretability by resolving diagnostic features at various scales.
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We aimed to assess the incremental value of schizophrenia polygenic risk score (PgRS) and resting-state functional brain connectivity (rsFC) when added to clinical data in predicting the six-week response to oral risperidone (Risperdal) in schizophrenia. Fifty-seven, 54, and 43 individuals in a group of never-before-treated first-episode schizophrenia had good quality whole-genome sequencing (10x), rsFC, and both genomic and rsFC data, respectively, at baseline. Symptom severity ratings were obtained at baseline and six-weeks of oral risperidone (Risperdal) treatment.

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Background: Major psychiatric illnesses often cluster in families, and their impact on affected and unaffected members within families may reflect the consequence of both genetic and social liability.

Methods: Data was derived from 202 families with multiple affected individuals. Affected individuals (N = 259) had a diagnosis of schizophrenia, bipolar disorder, obsessive-compulsive disorder or substance use disorder.

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  • Transcranial Direct Current Stimulation (tDCS) is a noninvasive brain stimulation method that shows promise in helping individuals with schizophrenia manage auditory verbal hallucinations (AVH).
  • Traditional in-clinic tDCS requires frequent visits due to its short-lived effects, but home-based tDCS could provide a more accessible solution if safety and standardization measures are implemented.
  • A case study demonstrated successful administration of tDCS at home with proper caregiver training and a standard operating procedure (SOP), leading to sustained improvements in AVH over ten days without adverse effects.
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  • Dysregulated prediction error-signalling may lead to auditory hallucinations in schizophrenia, with metrics like rMMN, RP, DN, and MMN helping to assess this relationship.* -
  • In a study involving 23 schizophrenia patients with auditory hallucinations and 23 healthy controls, significant differences were found in deviance detection and prediction error signaling, particularly with DN and MMN amplitudes being lower in the patients.* -
  • The results suggest that auditory hallucinations interfere with sensory adaptation and detection abilities, indicating that schizophrenia patients may not process auditory information effectively despite unimpaired short-term memory responses.*
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  • The research investigates the effects of a six-month yoga intervention combined with standard treatment for individuals with opioid use disorder (OUD), focusing on both clinical outcomes like abstinence and pain reduction, as well as underlying mechanisms such as brain activation and hormone levels.
  • A randomized controlled trial will be conducted with 164 OUD patients in India, who will be divided into two groups: one receiving yoga plus standard treatment and the other receiving only standard treatment ("waitlist" group).
  • The yoga group will participate in initial in-person sessions followed by remote sessions over six months, with assessments taking place at various intervals to evaluate effectiveness and outcomes on stress, anxiety, and overall functioning.
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Background: Yoga has shown promise as an add-on therapy for patients with schizophrenia. However, most studies have been short-term, with methodological limitations.

Methods: We conducted a six-month parallel-group randomized-controlled trial (with rater blinding) to evaluate the effectiveness of a yoga-based intervention in improving symptoms and quality of life in patients with schizophrenia.

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The investigational potential of TMS in psychiatry is largely underutilized. In the current article, we present the results of five studies with similar TMS protocols that looked at the investigative applications of TMS via measuring cortical reactivity as potential biomarkers in mood disorders. The first two studies, evaluate potential of TMS parameters and Motor neuron system (MNS) as state or trait markers of BD.

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Background: Mismatch negativity (MMN) amplitude is attenuated in schizophrenia patients (SZ). However, variability in illness course among SZ samples and types of deviant stimuli used in MMN paradigms have contributed to inconsistent findings across studies. Though MMN is suggested to be impaired in schizotypy, the potential link between the two is yet to be systematically examined in unaffected first-degree relatives of schizophrenia patients (FDR).

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Introduction: Diffusion MRI is sensitive to the microstructural properties of brain tissues, and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest, without considering the underlying fiber geometry.

Methods: Here, we propose a novel Macrostructure-Informed Normative Tractometry (MINT) framework, to investigate how white matter microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia.

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Background: Neurological soft signs (NSSs), minor physical anomalies (MPAs), and oculomotor abnormalities were plausible biomarkers in bipolar disorder (BD). However, specific impairments in these markers in patients after the first episode mania (FEM), in comparison with first-degree relatives (high risk [HR]) of BD and healthy subjects (health control [HC]) are sparse.

Aim Of The Study: This study aimed at examining NSSs, MPAs, and oculomotor abnormalities in remitted adult subjects following FEM and HR subjects in comparison with matched healthy controls.

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Electroconvulsive therapy (ECT) is one of the most effective treatments in psychiatry. However, it has many cognitive and non-cognitive adverse effects (AEs). There are lacunae in the literature on systematic assessment of non-cognitive AEs.

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Background: Environmental factors considerably influence the development of the human cortex during the perinatal period, early childhood, and adolescence. Urban upbringing in the first 15 years of life is a known risk factor for schizophrenia (SCZ). Though the risk of urban birth and upbringing is well-examined from an epidemiological perspective, the biological mechanisms underlying urban upbringing remain unknown.

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This study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro-and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNT's generalizability using transfer learning on data from an independent cohort acquired in India.

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Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMRI) as an input to these models. Much research in this area focuses on specific datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), which assesses people of North American, largely European ancestry, so we examine how models trained on ADNI, generalize to a new population dataset from India (the NIMHANS cohort).

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Background: Identifying stable and consistent resting-state functional connectivity patterns across illness trajectories has the potential to be considered fundamental to the pathophysiology of schizophrenia. We aimed to identify consistent resting-state functional connectivity patterns across heterogeneous schizophrenia groups defined based on treatment response.

Methods: In phase 1, we used a cross-sectional case-control design to characterize and compare stable independent component networks from resting-state functional magnetic resonance imaging scans of antipsychotic-naïve participants with first-episode schizophrenia (n = 54) and healthy participants (n = 43); we also examined associations with symptoms, cognition, and disability.

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White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group.

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