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.

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.

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.

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.

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.

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.

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.

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.

Familial risk of psychosis in obsessive-compulsive disorder: Impact on clinical characteristics, comorbidity and treatment response.

BACKGROUND: Family studies in obsessive-compulsive disorder (OCD) indicate higher rates of psychosis among their first-degree relatives (FDRs). However, the etiological and clinical relationships between the two disorders remain unclear. We compared the clinical characteristics and pharmacological treatment response in patients diagnosed with OCD with a family history of psychosis (OCD-FHP), with a family history of OCD (OCD-FHO) and those with sporadic OCD (OCD-S). METHODS: A total of 226 patients who met DSM-IV criteria for OCD (OCD-FHP = 59, OCD-FHO = 112, OCD-S = 55) were included for analysis. All patients were evaluated using the Mini International Neuropsychiatric Interview (MINI 6.0.0), Yale-Brown Obsessive-Compulsive Scale (YBOCS), and the Family Interview for Genetic Studies (FIGS). Treatment response was characterized over naturalistic follow-up. RESULTS: The three groups did not differ across any demographic or clinical variables other than treatment response. Patients in the OCD-FHP group were found to have received a greater number of trials with serotonin reuptake inhibitors (SRI) [F (2,223) = 7.99, p < 0.001], were more likely to have failed ≥2 trials of SRIs (χ2 = 8.45, p = 0.014), and less likely to have attained remission (χ2 = 6.57, p = 0.037) CONCLUSIONS: We observed that having a relative with psychosis may predispose to treatment resistance in OCD. Further research on the influence of genetic liability to psychosis on treatment response in OCD may offer novel translational leads.

Neurocognition and its association with adverse childhood experiences and familial risk of mental illness.

Environmental factors such as adverse childhood experiences (ACEs) may affect neurocognition, an endophenotype for several mental illnesses. This study examines the effect of ACEs on neurocognitive performance in first-degree relatives (FDRs) of patients with severe mental illness to determine whether familial risk has a moderating effect on the relationship between ACEs and neurocognition. Unaffected FDRs from multiplex families with severe mental illnesses (schizophrenia, bipolar disorder, obsessive-compulsive disorder, or alcohol use disorder) (n = 324) and healthy controls (with no familial risk) (n = 188) underwent neurocognitive tests for processing speed, new learning, working memory and Theory of Mind. ACEs were measured using the WHO ACE-International Questionnaire (ACE-IQ). Regression models were done to predict each neurocognitive domain by the effect of familial risk, ACE-IQ Score and their interaction (familial risk*ACE-IQ score). The main effect of familial risk predicted poor performance in all domains of neurocognition (p < 0.01), and the interaction had a negative association with global neurocognition (ß = -0.093, p = 0.009), processing speed (ß = -0.109, p = 0.003) and working memory (ß = -0.092, p = 0.01). Among the ACEs sub-domains, only maltreatment (specifically the main effect of physical neglect and the interaction effect of sexual abuse with familial risk) predicted poorer neurocognition. In FDRs of schizophrenia and bipolar disorder, only the main effects of familial risk were significantly associated with poorer neurocognition. We conclude that there is a relationship between ACEs (especially maltreatment) and neurocognitive functioning, which is moderated by the familial risk of mental illnesses. Genetic/familial vulnerability may have a stronger association with neurocognition in schizophrenia and bipolar disorder.

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.

Cross-diagnostic evaluation of minor physical anomalies in psychiatric disorders.

BACKGROUND: Minor physical anomalies (MPA) are markers of impaired neurodevelopment during the prenatal stage. Assessing MPA across psychiatric disorders may help understand their shared nature. In addition, MPA in family members would indicate a shared liability and endophenotype potential. We examined familial aggregation of MPA and their role as transdiagnostic and disorder-specific markers of 5 major psychiatric/neuropsychiatric conditions (schizophrenia, bipolar disorder, substance dependence, obsessive-compulsive disorder, and Alzheimer's dementia). METHODS: Modified Waldrop's MPA scale was applied on 1321 individuals from 439 transdiagnostic multiplex families and 125 healthy population controls (HC). Stage of fetal development (morphogenetic/phenogenetic)- and anatomical location (craniofacial/peripheral)-based sub-scores were calculated. Familiality and endophenotypic potential of MPA were analyzed with serial negative binomial mixed-effect regression. Cross-diagnostic differences and the effect of family history density (FHD) of each diagnosis on MPA were assessed. Mixed-effects Cox models estimated the influence of MPA on age-at-onset of illness (AAO). RESULTS: MPA were found to be heritable in families with psychiatric disorders, with a familiality of 0.52. MPA were higher in psychotic disorders after controlling for effects of sex and intrafamilial correlation. Morphogenetic variant MPA was noted to be lower in dementia in comparison to HC. FHD of schizophrenia and bipolar disorder predicted higher, and that of dementia and substance dependence predicted lower MPA. MPA brought forward the AAO [HR:1.07 (1.03-1.11)], and this was more apparent in psychotic disorders. CONCLUSION: MPA are transmissible in families, are specifically related to the risk of developing psychoses, and predict an earlier age at onset. Neurodevelopmentally informed classification of MPA has the potential to enhance the etiopathogenic and translational understanding of psychiatric disorders.

Protocol for magnetic resonance imaging acquisition, quality assurance, and quality check for the Accelerator program for Discovery in Brain disorders using Stem cells.

OBJECTIVE: The Accelerator program for Discovery in Brain disorders using Stem cells (ADBS) is a longitudinal study on five cohorts of patients with major psychiatric disorders from genetically high-risk families, their unaffected first-degree relatives, and healthy subjects. We describe the ADBS protocols for acquisition, quality assurance (QA), and quality check (QC) for multimodal magnetic resonance brain imaging studies. METHODS: We describe the acquisition and QC protocols for structural, functional, and diffusion images. For QA, we acquire proton density and functional images on phantoms, along with repeated scans on human volunteer. We describe the analysis of phantom data and test-retest reliability of volumetric and diffusion measures. RESULTS: Analysis of acquired phantom data shows linearity of proton density signal with increasing proton fraction, and an overall stability of various spatial and temporal QA measures. Examination of dice coefficient and statistical analyses of coefficient of variation in test-retest data on the human volunteer showed consistency of volumetric and diffusivity measures at whole-brain, regional, and voxel-level. CONCLUSION: The described acquisition and QA-QC procedures can yield consistent and reliable quantitative measures. It is expected that this longitudinal neuroimaging dataset will, upon its release, serve the scientific community well and pave the way for interesting discoveries.

Psychiatric symptoms and syndromes transcending diagnostic boundaries in Indian multiplex families: The cohort of ADBS study.

Syndromes of schizophrenia, bipolar disorder, obsessive-compulsive disorder, substance use disorders and Alzheimer's dementia are highly heritable. About 10-20% of subjects have another affected first degree relative (FDR), and thus represent a 'greater' genetic susceptibility. We screened 3583 families to identify 481 families with multiple affected members, assessed 1406 individuals in person, and collected information systematically about other relatives. Within the selected families, a third of all FDRs were affected with serious mental illness. Although similar diagnoses aggregated within families, 62% of the families also had members with other syndromes. Moreover, 15% of affected individuals met criteria for co-occurrence of two or more syndromes, across their lifetime. Using dimensional assessments, we detected a range of symptom clusters in both affected and unaffected individuals, and across diagnostic categories. Our findings suggest that in multiplex families, there is considerable heterogeneity of clinical syndromes, as well as sub-threshold symptoms. These families would help provide an opportunity for further research using both genetic analyses and biomarkers.

Estimating the familial risk of psychiatric illnesses: A review of family history scores.

A history of psychiatric illnesses in family members of those diagnosed to have an illness has been of significant interest both in research and in clinical practice. Almost all of the major psychiatric illnesses have a familial component to them, perhaps influenced by genetics and a shared environment or their combination. Systematic attempts have been made to quantify these familial risks, as obtained from family history (FH) of psychiatric illnesses. The methods range from a simple dichotomous or count scores to those quantifying as weighted risks such as the Family history density (FHD) measures. This article reviews the available literature on such FH methods and discusses their advantages and limitations. Validation studies have shown that FHD measures may be preferred over dichotomous measures as indicators of familial risk. However, the FHD method has certain limitations, like mostly relying on categorical diagnosis and ignoring other familial risk factors. By critically analysing various existing density measures based on 'ideal characteristics', we suggest a modified version of FHD that would benefit psychiatric research.

Methodological considerations in designing and implementing the harmonized diagnostic assessment of dementia for longitudinal aging study in India (LASI-DAD).

The Harmonized Diagnostic Assessment of Dementia for Longitudinal Aging Study in India (LASI-DAD) is a population-representative, prospective cohort study of late-life cognition and dementia. It is part of an ongoing international research collaboration that aims to measure and understand cognitive impairment and dementia risk by collecting a set of cognitive and neuropsychological assessments and informant reports, referred to as the Harmonized Cognitive Assessment Protocol (HCAP). LASI-DAD provides nationally representative data drawn from a subsample of the ongoing Longitudinal Aging Study in India (LASI). One of LASI-DAD's distinctive features is its rich geriatric assessment, including the collection of venous blood samples and brain imaging data for a subsample of respondents. In this paper, we discuss the methodological considerations of developing and implementing the HCAP protocol in India. The lessons we learned from translating and applying the HCAP protocol in an environment where illiteracy and innumeracy are high will provide important insights to researchers interested in measuring and collecting data on late-life cognition and dementia in developing countries. We further developed an innovative blood management system that enables us to follow the collection, transportation, assay, and storage of samples. Such innovation can benefit other population surveys collecting biomarker data.