Biology subjects

John, J. P.

Publications and source records attributed to John, J. P..

14 recordsLinked to original sources

bioRxiv2025

Generating diffusion MRI scalar maps from T1-weighted images using Reversible GANs

Diffusion tensor imaging (DTI) provides valuable insights into brain tissue microstructure, but acquiring high-quality DTI data is time-intensive and not always feasible. To mitigate data scarcity and enhance accessibility, we investigate the generation of synthetic DTI scalar maps--specifically mean diffusivity (MD)--from structural 3D volumetric T1-weighted brain MRI using a reversible generative adversarial network (RevGAN). Unlike conventional pipelines requiring multiple steps, our approach enables a single-step translation from T1 to diffusion-derived measures. We assess the quality and utility of the synthetic maps in two downstream tasks: sex classification and Alzheimers disease classification. Performance comparisons between models trained on real and synthetic DTI maps demonstrate that RevGAN-generated images retain meaningful microstructural features and offer competitive accuracy, underscoring their potential for data augmentation and analysis in neuroimaging workflows. We also examine how well models trained on these data generalize to a new population dataset from India (NIMHANS cohort).

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bioRxiv2025

Deterministic versus Probabilistic Tractography: Impact on White Matter Bundle Shape

In diffusion MRI-based tractography, deterministic and probabilistic algorithms reconstruct white matter using distinct strategies, yet their impact on bundle morphology remains uncertain. Using bundle shape similarity analysis, we compared both methods for the left arcuate fasciculus (AF_L) (The left arcuate fasciculus is a critical white matter tract that connects language comprehension and production areas in the human brain, enabling fluent language processing) across four datasets: Alzheimers Disease Neuroimaging Initiative (ADNI), Human Connectome Project-Aging (HCP-A), National Institute of Mental Health and Neurosciences (NIMHANS), and Pediatric Imaging, Neurocognition, and Genetics (PING). Probabilistic tractography consistently produced higher inter-subject shape similarity, by capturing broader anatomical trajectories and enhancing reproducibility. However, this extensive coverage may obscure subtle pathological variations critical for clinical detection. Bundle shape similarity analysis with atlas corroborated these findings, showing stronger alignment for probabilistic tracking and highlighting its utility in quantitative quality control. These results emphasize the need to balance morphological consistency with sensitivity to neuroanatomical variation when selecting tractography methods for research and clinical applications.

neuroscience↗

bioRxiv2025

Non-duality in brain and experience of advanced meditators - Key role for Intrinsic Neural Timescales

Distinguishing between self (internal) and environment (external) is fundamental to human experience, with ordinary waking consciousness structured around this duality. However, contemplative traditions describe non-dual states where this distinction dissolves. Despite its significance, the neural basis of non-duality remains underexplored. Using psychological questionnaires for non-duality experience and EEG-based intrinsic neural timescales as measured by the autocorrelation window (ACW), we studied non-duality in advanced meditators, novice meditators, and controls. All subjects underwent breath-watching meditation (internal attention) and a visual oddball cognitive task (external attention); this allowed us to conceptualize non-duality as a lack of distinction between internal and external attention. Our key findings include: (a) advanced meditators report greater experience of non-duality during breath-watching (psychological scales), (b) EEG-based ACW is longer during internal attention (breath watch) than external attention (oddball task) in all subjects taken together, (c) advanced meditators show no such distinction with equal duration of their ACW during both internal and external attention (we replicated this finding in another dataset of expert meditators); (d) the advanced meditators internal-external ACW difference correlated with their experience of the degree of non-duality (psychological scales) during internal attention. Together, these findings suggest that the brains intrinsic neural timescales during internal and external attention play a key role in mediating the experience of non-duality in advanced meditators.

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bioRxiv2024

Lifespan Normative Modeling of Brain Microstructure

Normative models of brain metrics based on large populations could be extremely valuable for detecting brain abnormalities in patients with a variety of disorders, including degenerative, psychiatric and neurodevelopmental conditions, but no such models exist for the brains white matter (WM) microstructure. Here we present a large-scale normative model of brain WM microstructure - based on 19 international diffusion MRI datasets covering almost the entire lifespan (totaling N=54,583 individuals; age: 4-91 years). We extracted regional diffusion tensor imaging (DTI) metrics using a standardized analysis and quality control protocol and used hierarchical Bayesian regression (HBR) to model the statistical distribution of derived WM metrics as a function of age and sex. We extracted the average lifespan trajectories and corresponding centile curves for each WM region. We illustrate the utility of the method by applying it to detect and visualize profiles of WM microstructural deviations in a variety of contexts: in mild cognitive impairment, Alzheimers disease, and 22q11.2 deletion syndrome - a neurogenetic condition that markedly increases risk for schizophrenia. The resulting large-scale model provides a common reference to identify disease effects on the brains microstructure in individuals or groups, and to compare disorders, and discover factors affecting WM abnormalities. The derived normative models are a valuable resource publicly available to the community, adaptable and extendable to future datasets as the available data expands.

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bioRxiv2024

Comparison of Explainable AI Models for MRI-based Alzheimer's Disease Classification

Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimers disease or infer dementia severity from 3D T1-weighted brain MRI scans. Here, we examine the value of adding occlusion sensitivity analysis (OSA) and gradient-weighted class activation mapping (Grad-CAM) to these models to make the results more interpretable. Much research in this area focuses on specific datasets such as the Alzheimers Disease Neuroimaging Initiative (ADNI) or National Alzheimers Coordinating Center (NACC), which assess people of North American, predominantly European ancestry, so we examine how well models trained on these data generalize to a new population dataset from India (NIMHANS cohort). We also evaluate the benefit of using a combined dataset to train the CNN models. Our experiments show feature localization consistent with knowledge of AD from other methods. OSA and Grad-CAM resolve features at different scales to help interpret diagnostic inferences made by CNNs.

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bioRxiv2024

The Balanced Mind and its Intrinsic Neural Timescales in Advanced Meditators

A balanced mind, or equanimity, cultivated through meditation and other spiritual practices, is considered one of the highest mental states. Its core features include deidentification and non-duality. Despite its significance, its neural correlates remain unknown. To address this, we acquired 128-channel EEG data (n = 103) from advanced and novice meditators (from the Isha Yoga tradition) and controls during an internal attention (breath-watching) and an external attention task (visual-oddball paradigm). We calculated the auto-correlation window (ACW), a measure of brains intrinsic neural timescales (INTs) and assessed equanimity through self-report questionnaires. Advanced meditators showed higher levels of equanimity and shorter duration of INTs (shorter ACW) during breath-watching, indicating deidentification with mental contents. Furthermore, they demonstrated no significant differences in INTs between tasks, indicating non-dual awareness. Finally, shorter duration of INTs correlated with the participants subjective perceptions of equanimity. In conclusion, we show that the shorter duration of brains INT may serve as a neural marker of equanimity.

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bioRxiv2024

Microstructural Mapping of Neural Pathways in Alzheimer's Disease using Macrostructure-Informed Normative Tractometry

IntroductionDiffusion 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. MethodsHere, 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. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure. ResultsIn two multi-site cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics sensitivity to dementia. DiscussionWe show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brains neural pathways.

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bioRxiv2024

Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts

Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimers 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 Alzheimers 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). We first benchmark our models by predicting "brain age" - the task of predicting a persons chronological age from their MRI scan and proceed to AD classification. We also evaluate the benefit of using a 3D CycleGAN approach to harmonize the imaging datasets before training the CNN models. Our experiments show that classification performance improves after harmonization in most cases, as well as better performance for dMRI as input.

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bioRxiv2024

Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer's Disease

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 DNTs generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNTs capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimers disease, aligning closely with results from the Bundle Analytics (BUAN) tractometry pipeline. By incorporating tract geometry information, DNT may be able to distinguish disease-related abnormalities in anisotropy from tract macrostructure, and shows promise in enhancing fine-scale mapping and detection of white matter alterations in neurodegenerative conditions.

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bioRxiv2023

BundleCleaner: Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography Data

We present BundleCleaner, an unsupervised multi-step frame-work that can filter, denoise and subsample bundles derived from diffusion MRI-based whole-brain tractography. Our approach considers both the global bundle structure and local streamline-wise features. We apply BundleCleaner to bundles generated from single-shell diffusion MRI data in an independent clinical sample of older adults from India using probabilistic tractography and the resulting cleaned bundles can better align with the atlas bundles with reduced overreach. In a downstream tractometry analysis, we show that the cleaned bundles, represented with less than 20% of the original set of points, can robustly localize along-tract microstructural differences between 32 healthy controls and 34 participants with Alzheimers disease ranging in age from 55 to 84 years old. Our approach can help reduce memory burden and improving computational efficiency when working with tractography data, and shows promise for large-scale multi-site tractometry.

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bioRxiv2022

Sample size requirement for achieving multisite harmonization using structural brain MRI features

When data is pooled across multiple sites, the extracted features are confounded by site effects. Harmonization methods attempt to correct these site effects while preserving the biological variability within the features. However, little is known about the sample size requirement for effectively learning the harmonization parameters and their relationship with the increasing number of sites. In this study, we performed experiments to find the minimum sample size required to achieve multisite harmonization (using neuroHarmonize) using volumetric and surface features by leveraging the concept of learning curves. Our first two experiments show that site-effects are effectively removed in a univariate and multivariate manner; however, it is essential to regress the effect of covariates from the harmonized data additionally. Our following two experiments with actual and simulated data showed that the minimum sample size required for achieving harmonization grows with the increasing average Mahalanobis distances between the sites and their reference distribution. We conclude by positing a general framework to understand the site effects using the Mahalanobis distance. Further, we provide insights on the various factors in a cross-validation design to achieve optimal inter-site harmonization.

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bioRxiv2020

Protocol for Magnetic Resonance Imaging Acquisition, Quality Assurance, and Quality Check for the Accelerator Program for Discovery in Brain Disorders using Stem Cells

ObjectiveThe Accelerator Program for Discovery in Brain Disorders using Stem Cells (ADBS) is a longitudinal study focused on collecting and analysing clinical, neuropsychological, neurophysiological, and multimodal neuroimaging data from five cohorts of patients with major psychiatric disorders from genetically high-risk families, their unaffected first-degree relatives, and healthy subjects. Here, we present a complete description of the acquisition of multimodal MRI data along with the quality assurance (QA) and quality check (QC) procedures that we are following in this study. MethodsThe QA procedure consists of monitoring of different quantitative measurements using an agar gel and a geometrical phantom. For the already acquired data from human subjects, we describe QC steps for each imaging modality. To quantify reliability of outcome measurements, we perform test-retest reliability on human volunteers. ResultsWe have presented results from analysis of phantom data and test-retest reliability on a human volunteer. Results show consistency in data acquisition and reliable quantification of different outcome measurements. ConclusionThe acquisition protocol and QA-QC procedures described here can yield consistent and reliable outcome measures. We hope to acquire and eventually release high quality longitudinal neuroimaging dataset that will serve the scientific community and pave the way for interesting discoveries.

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bioRxiv2019

Simultaneous assessment of P50, MMN, ERN and P300 event-related potentials among patients with Schizophrenia - an exploratory study

PurposeP50 suppression (sensory gating or inhibition), MMN (mismatch negativity; bottom-up detection of change), ERN (error related negativity; conflict monitoring) and P300 (attention allocation and memory updating for salient events) are event related potentials (ERPs) widely reported to show abnormal cognitive functioning among patients with schizophrenia. In real-life scenarios the brain processing underlying these ERPs occur simultaneously, and yet prior ERP studies have evaluated them in isolation. The current study uses a novel paradigm that can examine these multiple ERPs simultaneously, and explore if the reported ERP deficits would hold true during a more realistic setting.\n\nMethodData from 21 patients with schizophrenia and 25 age- and gender-matched healthy controls were used, who underwent ERP recordings during the Assessing Neurocognition via Gamified Experimental Logic (ANGEL) paradigm. This is a gamified visual odd-ball paradigm that generates P300, the error responses generate ERN, and paired-tone audio distractors generate P50 and MMN. Peak-peak amplitude, mean amplitude and area-under-curve measures of ERP were measured at electrodes reflecting best morphology.\n\nResultsThough patients showed apparent ERP morphology differences relative to the controls, the standard ERP measures were comparable between groups, except for reduced ERN among patients. Interestingly, significant group differences were seen in N1-P2 complex suppression, despite comparable P50 suppression.\n\nContribution of the researchThe current study is the first to report multiple ERP component measures simultaneously evoked among patients with schizophrenia, and shows greater signs for impaired prediction mechanism. The findings of the study would provide a more ecologically valid evaluation of ERP-based cognitive functioning, and need to be replicated in a larger sample as well as other mental disorders.

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bioRxiv2019

Schizophrenia patients show aberrant brain dynamics associated with gestalt-perception and corollary discharge: Reflections from ERP and fMRI findings

BackgroundSchizophrenia is a disorder of higher mental attributes, and is characterized by psychotic symptoms that are believed to involve a basic inability to make valid predictions about expected sensations and experiences. These have been reported separately while monitoring either externally generated environmental patterns (e.g. gestalt-perception) or self-generated sensory experiences (e.g. corollary-discharge). As the pathophysiology behind predictive dysfunction is better viewed as an aberration in brains functional synchrony, a whole brain assessment using electroencephalographic (EEG) event related potential (ERP) and functional magnetic resonance imaging (fMRI) techniques, would offer a wider perspective to brain network abnormalities in schizophrenia.\n\nMethodWe used our lab-developed game-based task which presents degraded two-tone images to assess gestalt-perception, and simultaneously alters the congruency between participants button-press response and its auditory feedback to assess corollary-discharge. In both patients with schizophrenia and age-matched healthy controls, we explored event-related changes in an EEG-ERP study (n=21 each) and whole brain functional connectivity changes in a fMRI study (patients,n=12; controls,n=16), using the same task.\n\nResultsPatients showed reduced event-related EEG dynamics during both the error-prediction conditions (gestalt-perception and corollary-discharge), which include reduction in average waveforms (around N170 and N1-P2 complex, respectively) and altered theta dynamics (power and phase). Source-level EEG measures were clustered around the cingulo-insular network. fMRI functional connectivity analysis also found the abnormality in these brain regions, forming significantly weak connections with right insular/opercular cortex.\n\nConclusionsThis is the first study to explore thalamo-cortical dysfunction hypothesis in schizophrenia by integrating prediction-error-coding during perception ( gestalt-perception) and action ( corollary-discharge) using two neuroimaging modalities (EEG-ERP and fMRI). Besides adding to the knowledgebase of schizophrenia research, our novel task design and findings on theta-oscillation could benefit in the development of effective neuromodulatory therapeutic tools for patients with schizophrenia such as neurofeedback and transcranial brain stimulation.

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