Personalized Theta Transcranial Alternating Current Stimulation and Gamma Transcranial Alternating Current Stimulation Bring Differential Neuromodulatory Effects on the Resting Electroencephalogram: Characterizing the Temporal, Spatial, and Spectral Dimensions of Transcranial Alternating Current Stimulation.

OBJECTIVES: The neuromodulatory effects of transcranial alternating current stimulation (tACS) on electroencephalogram (EEG) dynamics are quite heterogenous. The primary objective of the study is to comprehensively characterize the effects of two tACS protocols on resting-state EEG. MATERIALS AND METHODS: A total of 36 healthy participants were recruited and were randomized into three groups. Two groups received either personalized theta (4-8 Hz) or gamma (40 Hz) stimulation bilaterally in the frontal regions for 20 minutes (4 minutes ON, 1 minute OFF, four cycles). The third group performed relaxed breath watching for 20 minutes. Artifact-free, 1-minute EEG segments from the baseline, during tACS, and after stimulation resting EEG were characterized to see the effects of tACS. Threshold-free cluster enhanced permutation tests (for spectral measures) and two-way mixed analysis of variance (for aperiodic slope) were used for statistical inferences. RESULTS: Current modeling simulation using ROAST with preset parameters (800 µA, AF3 AF4 locations) showed that induced electric fields can activate frontal cortical regions. During the stimulation period, personalized theta tACS entrained theta band power in the centro-parietal areas. There was a compensatory power decrease in the beta and gamma bands after theta tACS. No entrainment effects were observed for gamma tACS during stimulation, but a significant entrainment was observed in the theta and beta bands in the parieto-occipital regions after stimulation. The delta band power decreased in the central regions. No spectral modulations were seen after breath watching. The spectral slope, which measures aperiodic activity, was not affected by either breath watching or tACS. CONCLUSIONS: Characterizing the effects of multiple tACS protocols is critical to effectively target specific neural oscillatory patterns and to personalize the protocols. The study can be extended to target specific oscillatory patterns associated with cognitive deficits in neuro-psychiatric conditions.

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.

Resting state functional connectivity and structural abnormalities of the brain in acute retarded catatonia: an exploratory MRI study.

In this first cross-sectional MRI study in acute catatonia, we compared the resting state whole-brain, within-network and seed (left precentral gyrus)-to-voxel connectivity, as well as cortical surface complexity between a sample of patients in acute retarded catatonic state (n = 15) diagnosed as per DSM-5 criteria and a demographically matched healthy control sample (n = 15). The patients had comorbid Axis-I psychiatric disorders including schizophrenia spectrum disorders and psychotic mood disorders, but did not have diagnosable neurological disorders. Acute retarded catatonia was characterized by reduced resting state functional connectivity, most robustly within the sensorimotor network; diffuse region of interest (ROI)-ROI hyperconnectivity; and seed-to-voxel hyperconnectivity in the frontoparietal and cerebellar regions. The seed (left precentral gyrus)-to-voxel connectivity was positively correlated to the catatonia motor ratings. The ROI-ROI as well as seed-to-voxel functional hyperconnectivity were noted to be higher in lorazepam responders (n = 9) in comparison to the non-responders (n = 6). The overall Hedges' g effect sizes for these analyses ranged between 0.82 and 3.53, indicating robustness of these results, while the average Dice coefficients from jackknife reliability analyses ranged between 0.6 and 1, indicating fair (inter-regional ROI-ROI connectivity) to perfect (within-sensorimotor network connectivity) reliability of the results. The catatonia sample showed reduced vertex-wise cortical complexity in the right insular cortex and contiguous areas. Thus, we have identified neuroimaging markers of the acute retarded catatonic state that may show an association with treatment response to benzodiazepines. We discuss how these novel findings have important translational implications for understanding the pathophysiology of catatonia as well as for the mechanistic understanding and prediction of treatment response to benzodiazepines.

Systematic evaluation of the impact of defacing on quality and volumetric assessments on T1-weighted MR-images.

BACKGROUND AND PURPOSE: Facial features can be potentially reconstructed from structural magnetic resonance images, thereby compromising the confidentiality of study participants. Defacing methods can be applied to MRI images to ensure privacy of study participants. These methods remove facial features, thereby rendering the image unidentifiable. It is commonly assumed that defacing would not have any impact on quantitative assessments of the brain. In this study, we have assessed the impact of different defacing methods on quality and volumetric estimates. MATERIALS AND METHODS: We performed SPM-, Freesurfer-, pydeface, and FSL-based defacing on 30 T1-weighted images. We statistically compared the change in quality measurements (from MRIQC) and volumes (from SPM, CAT, and Freesurfer) between non-defaced and defaced images. We also calculated the Dice coefficient of each tissue class between non-defaced and defaced images. RESULTS: Almost all quality measurements and tissue volumes changed after defacing, irrespective of the method used. All tissue volumes decreased post-defacing for CAT, but no such consistent trend was seen for SPM and Freesurfer. Dice coefficients indicated that segmentations are relatively robust; however, partial volumes might be affected leading to changed volumetric estimates. CONCLUSION: In this study, we demonstrated that volumes and quality measurements get affected differently by defacing methods. It is likely that this will have a significant impact on the reproducibility of experiments. We provide suggestions on ways to minimize the impact of defacing on outcome measurements. Our results warrant the need for robust handling of defaced images at different steps of image processing.

Discovery biology of neuropsychiatric syndromes (DBNS): a center for integrating clinical medicine and basic science.

BACKGROUND: There is emerging evidence that there are shared genetic, environmental and developmental risk factors in psychiatry, that cut across traditional diagnostic boundaries. With this background, the Discovery biology of neuropsychiatric syndromes (DBNS) proposes to recruit patients from five different syndromes (schizophrenia, bipolar disorder, obsessive compulsive disorder, Alzheimer's dementia and substance use disorders), identify those with multiple affected relatives, and invite these families to participate in this study. The families will be assessed: 1) To compare neuro-endophenotype measures between patients, first degree relatives (FDR) and healthy controls., 2) To identify cellular phenotypes which differentiate the groups., 3) To examine the longitudinal course of neuro-endophenotype measures., 4) To identify measures which correlate with outcome, and 5) To create a unified digital database and biorepository. METHODS: The identification of the index participants will occur at well-established specialty clinics. The selected individuals will have a strong family history (with at least another affected FDR) of mental illness. We will also recruit healthy controls without family history of such illness. All recruited individuals (N = 4500) will undergo brief clinical assessments and a blood sample will be drawn for isolation of DNA and peripheral blood mononuclear cells (PBMCs). From among this set, a subset of 1500 individuals (300 families and 300 controls) will be assessed on several additional assessments [detailed clinical assessments, endophenotype measures (neuroimaging- structural and functional, neuropsychology, psychophysics-electroencephalography, functional near infrared spectroscopy, eye movement tracking)], with the intention of conducting repeated measurements every alternate year. PBMCs from this set will be used to generate lymphoblastoid cell lines, and a subset of these would be converted to induced pluripotent stem cell lines and also undergo whole exome sequencing. DISCUSSION: We hope to identify unique and overlapping brain endophenotypes for major psychiatric syndromes. In a proportion of subjects, we expect these neuro-endophenotypes to progress over time and to predict treatment outcome. Similarly, cellular assays could differentiate cell lines derived from such groups. The repository of biomaterials as well as digital datasets of clinical parameters, will serve as a valuable resource for the broader scientific community who wish to address research questions in the area.

A Multimodal Structural and Functional Neuroimaging Study of Amnestic Mild Cognitive Impairment.

Examination of brain structural and functional abnormalities in amnestic mild cognitive impairment (aMCI) has the potential to enhance our understanding of the initial pathophysiological changes in dementia. We examined gray matter volumes and white matter microstructural integrity, as well as resting state functional connectivity (rsFC) in patients with aMCI (N = 48) in comparison to elderly cognitively healthy comparison subjects (N = 48). Brain volumetric comparisons were carried out using voxel-based morphometric analysis of T1-weighted images using the FMRIB Software Library. White matter microstructural integrity was examined using whole-brain tract-based spatial statistics analysis of fractional anisotropy maps generated from diffusion tensor imaging data. Finally, rsFC differences between the samples were examined by Multivariate Exploratory Linear Optimised Decomposition into Independent Components of the resting state functional magnetic resonance imaging time series, followed by between-group comparisons of selected networks using dual regression analysis. Patients with aMCI showed significant gray matter volumetric reductions in bilateral parahippocampal gyri as well as multiple other brain regions including frontal, temporal, and parietal cortices. Additionally, reduced rsFC in the anterior subdivision of the default mode network (DMN) and increased rsFC in the executive network were noted in the absence of demonstrable impairment of white matter microstructural integrity. We conclude that the demonstrable neuroimaging findings in aMCI include significant gray matter volumetric reductions in the fronto-temporo-parietal structures as well as resting state functional connectivity disturbances in DMN and executive network. These findings differentiate aMCI from healthy aging and could constitute the earliest demonstrable neuroimaging findings of incipient dementia.

Resting-State Functional Connectivity Changes Associated with Visuospatial Cognitive Deficits in Patients with Mild Alzheimer Disease.

BACKGROUND/AIMS: Alzheimer disease (AD) is a neurodegenerative disorder characterized by progressive disconnection of various brain networks leading to neuropsychological impairment. Pathology in the visual association areas has been documented in presymptomatic AD and therefore we aimed at examining the relationship between brain connectivity and visuospatial (VS) cognitive deficits in early AD. METHODS: Tests for VS working memory, episodic memory and construction were used to classify patients with AD (n = 48) as having severe VS deficits (n = 12, female = 4) or mild deficits (n = 11, female = 4). Resting-state functional magnetic resonance imaging and structural images were acquired as per the standard protocols. Between-group differences in resting-state functional connectivity (rsFC) were examined by dual regression analysis correcting for age, gender, and total brain volume. RESULTS: Patients with AD having severe VS deficits exhibited significantly reduced rsFC in bilateral lingual gyri of the visual network compared to patients with mild VS deficits. CONCLUSION: Reduced rsFC in the visual network in patients with more severe VS deficits may be a functional neuroimaging biomarker reflecting hypoconnectivity of the brain with progressive VS deficits during early AD.

Just a minute meditation: Rapid voluntary conscious state shifts in long term meditators.

Meditation induces a modified state of consciousness that remains under voluntary control. Can meditators rapidly and reversibly bring about mental state changes on demand? To check, we carried out 128 channel EEG recordings on Brahma Kumaris Rajayoga meditators (36 long term: median 14240h meditation; 25 short term: 1095h) and controls (25) while they tried to switch every minute between rest and meditation states in different conditions (eyes open and closed; before and after an engaging task). Long term meditators robustly shifted states with enhanced theta power (4-8Hz) during meditation. Short term meditators had limited ability to shift between states and showed increased lower alpha power (8-10Hz) during eyes closed meditation only when pre and post task data were combined. Controls could not shift states. Thus trained beginners can reliably meditate but it takes long term practice to exercise more refined control over meditative states.

A systematic examination of brain volumetric abnormalities in recent-onset schizophrenia using voxel-based, surface-based and region-of-interest-based morphometric analyses.

BACKGROUND: Brain morphometric abnormalities in schizophrenia have been extensively reported in the literature. Whole-brain volumetric reductions are almost universally reported by most studies irrespective of the characteristics of the samples studied (e.g., chronic/recent-onset; medicated/neuroleptic-naïve etc.). However, the same cannot be said of the reported regional morphometric abnormalities in schizophrenia. While certain regional morphometric abnormalities are more frequently reported than others, there are no such abnormalities that are universally reported across studies. Variability of socio-demographic and clinical characteristics across study samples as well as technical and methodological issues related to acquisition and analyses of brain structural images may contribute to inconsistency of brain morphometric findings in schizophrenia. The objective of the present study therefore was to systematically examine brain morphometry in patients with recent-onset schizophrenia to find out if there are significant whole-brain or regional volumetric differences detectable at the appropriate significance threshold, after attempting to control for various confounding factors that could impact brain volumes. METHODS: Structural magnetic resonance images of 90 subjects (schizophrenia = 45; healthy subjects = 45) were acquired using a 3 Tesla magnet. Morphometric analyses were carried out following standard analyses pipelines of three most commonly used strategies, viz., whole-brain voxel-based morphometry, whole-brain surface-based morphometry, and between-group comparisons of regional volumes generated by automated segmentation and parcellation. RESULTS: In our sample of patients having recent-onset schizophrenia with limited neuroleptic exposure, there were no significant whole brain or regional brain morphometric abnormalities noted at the appropriate statistical significance thresholds with or without including age, gender and intracranial volume or total brain volume in the statistical analyses. CONCLUSIONS: In the background of the conflicting findings in the literature, our findings indicate that brain morphometric abnormalities may not be directly related to the schizophrenia phenotype. Analysis of the reasons for the inconsistent results across studies as well as consideration of alternate sources of variability of brain morphology in schizophrenia such as epistatic and epigenetic mechanisms could perhaps advance our understanding of structural brain alterations in schizophrenia.

Dose response of transcranial near infrared light stimulation on brain functional connectivity and cognition in older adults-A randomized comparison.

Photobiomodulation, also called low-level light therapy, has been reported in animal studies to have an effect on brain activity and cognition. However, studies in humans regarding its effect on cognition and brain functional connectivity, and the required dose threshold for achieving the same have been very limited. We compared the effects of different doses of photobiomodulation (PBM) on cognition and resting state brain functional connectivity in 25 cognitively normal adults aged 55-70 years. They were randomized to a single session of the sham group, "low-dose" and "high-dose" groups receiving NIR light with transcranial fluence of 26 and 52 J/cm2 respectively, and intranasal fluence of 9 and 18 J/cm2 respectively. There was a significant increase in resting state functional connectivity of the left superior frontal gyrus (SFG) with the left planum temporale (PT), p = 0.0016, and with the left inferior frontal gyrus, pars triangularis, p = 0.0235 in the "high-dose" group only compared to the "sham" group. There was also a significant improvement in visual search and processing speed (p = 0.012) in the "high-dose" group. Replication of these findings in an adequately powered randomized sham-controlled study in healthy older adults can pave the way for clinical application of NIRL as a therapeutic modality in patients with Alzheimer's disease.

Practice and proficiency of Isha Yoga for better mental health outcomes: insights from a COVID-19 survey.

Introduction: The COVID-19 pandemic has brought about unparalleled suffering on a global scale, affecting both physical and mental well-being. In such challenging times, it becomes crucial to identify interventions that can alleviate negative mental health outcomes, such as stress, while promoting positive mental health outcomes, like well-being. We report the effectiveness of a mind-body practise, Isha Yoga, in promoting well-being. Methods: We conducted an online survey, during the COVID-19 pandemic, with Yoga practitioners (n = 1,352) from the Isha Yoga tradition in Karnataka, India. We evaluated stress and well-being attributes using conventional psychometric questionnaires. Subsequently, we requested the Isha Yoga practitioners to share another survey with their friends and family members, assessing similar outcomes. From the respondents of this shared survey (n = 221), we identified individuals who currently did not engage in any form of Yoga or meditation, constituting the non-Yoga control group (n = 110). To enhance the reliability and validity of our study and minimize the limitations commonly associated with online surveys, we adhered to the CHERRIES guidelines for reporting survey studies. Results: Isha Yoga practitioners had significantly lower levels of stress (p < 0.001, gHedges = 0.94) and mental distress (p < 0.001, gHedges = 0.75) while reporting significantly higher levels of well-being (p < 0.001, gHedges = 0.78) and affective balance (p < 0.001, gHedges = 0.80) compared to the control group. Furthermore, expertise-related improvements were observed in these outcomes, and a dose-response relationship was found between regularity of Isha Yoga practice and outcome changes. A minimum 3-4 days of weekly practice showed significant differences with the control group. In addition, we investigated the effect of Isha Yoga on stress and well-being among the healthcare workers (HCWs) in our sample and observed better mental health outcomes. Discussion: These findings collectively underscore the benefits of Mind and Body practices like Isha Yoga on various aspects of mental health and well-being, emphasizing its potential as an effective and holistic approach for promoting a healthy lifestyle among diverse populations, including healthcare workers, even in difficult circumstances such as the COVID-19 pandemic.

Efficacy of solution-focused interventions with caregivers of persons living with different conditions: A systematic review.

PURPOSE: In this review, we aim to determine the efficacy of Solution-Focused Interventions (SFI) among caregivers of persons living with different conditions. METHOD: Articles published between 1 January 2000 and 31 December 2022 were used. Databases used included EBSCOhost, PubMed, ProQuest, ERIC, and Google Scholar. We used Zotero to remove the duplicate studies. Further, we used the Risk of Bias for RCTs and the JBI Critical Appraisal Checklist for Quasi-Experimental and non-randomized experimental studies. RESULTS: Total number of studies from five databases was 2,693. After evaluating the eligibility and duplication removal, 10 relevant articles were found suitable for this review, including 3 RCTs, 3 single group pre-post, 2 quasi-experimental, and 1 each from case study, and multiple baseline designs. Stress, quality of life, and coping variables were studied. Studies compared with alternative treatments: Though 9 out of 10 studies were superior to alternative treatment, and none were equivalent to solution-focused intervention. SFI has shown efficacy on all the variables selected in the study. In these studies, the efficacy is compared with the alternative treatment, and SFI has demonstrated better outcomes than the alternative treatments. CONCLUSION: Based on the studies reviewed, robust evidence supports SFI as a treatment approach for caregivers. SFI can also benefit caregivers in shorter sessions, making it more affordable than other treatments.

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 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). We first benchmark our models by predicting 'brain age' - the task of predicting a person's 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.

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 DNT's generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNT's capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimer's 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.

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 Alzheimer's 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 Alzheimer's Disease Neuroimaging Initiative (ADNI) or National Alzheimer's 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.

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

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. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure. Results: In 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. Discussion: We show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brain's neural pathways.

Shared deficits of education, marital and occupational functioning in unaffected siblings of multiple affected families with major psychiatric illness.

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. For comparison, we used the unaffected siblings from the same families (N = 229) and a matched random subset of healthy control (HC) data (N = 229) from India's National Mental Health Survey, 2016 (NMHS). We compared the three groups' educational attainment, functional marital status, and occupational status. RESULTS: The highest educational attainment was significantly different between the groups. The affected and unaffected siblings had poorer educational attainment compared to HC. Similarly, the affected and unaffected siblings more often remained single, in contrast to HC. Moreover, employment rates were significantly higher in the unaffected siblings, especially female siblings. Overall, females had spent fewer years at school, were primarily married, and were majority homemakers across the three groups compared to males. DISCUSSION: Affected and unaffected siblings had lower education and marriage rates than HC. The unaffected siblings were more likely to be employed than HC. Whether the poor educational attainment and lower marriage rates in unaffected siblings is a biological marker of shared endophenotype or the effect of the social burden of having an affected family member requires further systematic evaluation.

Patterns of Impaired Neurocognitive Performance on the Global Neuropsychological Assessment, and Their Brain Structural Correlates in Recent-onset and Chronic Schizophrenia.

Objective: Schizophrenia is associated with impairment in multiple cognitive domains. There is a paucity of research on the effect of prolonged illness duration (≥ 15 years) on cognitive performance along multiple domains. In this pilot study, we used the Global Neuropsychological Assessment (GNA), a brief cognitive battery, to explore the patterns of cognitive impairment in recent-onset (≤ 2 years) compared to chronic schizophrenia (≥ 15 years), and correlate cognitive performance with brain morphometry in patients and healthy adults. Methods: We assessed cognitive performance in patients with recent-onset (n = 17, illness duration ≤ 2 years) and chronic schizophrenia (n = 14, duration ≥ 15 years), and healthy adults (n = 16) using the GNA and examined correlations between cognitive scores and gray matter volumes computed from T1-weighted magnetic resonance imaging images. Results: We observed cognitive deficits affecting multiple domains in the schizophrenia samples. Selectively greater impairment of perceptual comparison speed was found in adults with chronic schizophrenia (p = 0.009, η2partial = 0.25). In the full sample (n = 47), perceptual comparison speed correlated significantly with gray matter volumes in the anterior and medial temporal lobes (TFCE, FWE p < 0.01). Conclusion: Along with generalized deficit across multiple cognitive domains, selectively greater impairment of perceptual comparison speed appears to characterize chronic schizophrenia. This pattern might indicate an accelerated or premature cognitive aging. Anterior-medial temporal gray matter volumes especially of the left hemisphere might underlie the impairment noted in this domain in schizophrenia.

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

We present BundleCleaner, an unsupervised multi-step framework 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 Alzheimer's 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.

Deep phenotyping and genomic data from a nationally representative study on dementia in India.

The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is a nationally representative in-depth study of cognitive aging and dementia. We present a publicly available dataset of harmonized cognitive measures of 4,096 adults 60 years of age and older in India, collected across 18 states and union territories. Blood samples were obtained to carry out whole blood and serum-based assays. Results are included in a venous blood specimen datafile that can be linked to the Harmonized LASI-DAD dataset. A global screening array of 960 LASI-DAD respondents is also publicly available for download, in addition to neuroimaging data on 137 LASI-DAD participants. Altogether, these datasets provide comprehensive information on older adults in India that allow researchers to further understand risk factors associated with cognitive impairment and dementia.