Biology subjects

Parekh, P.

Publications and source records attributed to Parekh, P..

18 recordsLinked to original sources

bioRxiv2026

Molecular MRI of Neuroinflammation in Alzheimer's Disease using a Folate-targeted Nanoprobe

Background: Neuroinflammation, driven by activated microglia, plays a central role in the pathogenesis of Alzheimer's disease (AD). Non-invasive imaging of neuroinflammation holds promise for advancing our understanding of disease mechanisms and evaluating anti-inflammatory therapeutic strategies. The folate receptor (FR), specifically its {beta} isoform, has been identified as a marker of pro-inflammatory myeloid cells in peripheral inflammatory diseases; however, its association with AD neuroinflammation remains unknown. In this work, we first determined FR expression in microglia in mouse models of AD and subsequently evaluated molecular MRI (mMRI) using a folate-targeted nanoprobe for in vivo imaging of the folate receptor expression. Methods and results: Microglial activation and FR expression in APP/PS1 and P301S transgenic mouse models of AD was measured using flow cytometry. A FR-targeted nanoprobe for mMRI was prepared by functionalizing high T1-relaxivity liposomal gadolinium nanoparticle with folic acid. Following intravenous injection, localization of the nanoprobe in the brains of aged transgenic and wild-type mice was measured using T1-weighted MRI. Post-mortem microscopy measured nanoprobe localization relative to microglia and amyloid pathology. In both mouse models, FR{beta} was significantly (p < 0.05) upregulated in pro-inflammatory microglia expressing TNF and IL-1{beta}. The folate-targeted nanoprobe demonstrated MR signal enhancement in the cortical and hippocampal regions of transgenic mice but not in wild type mice; non-targeted nanoprobe control did not demonstrate signal enhancement in brain of transgenic mice. Microscopy confirmed colocalization of FR-targeted nanoprobe with IBA1-positive microglia/macrophages and amyloid plaques. Conclusions: Pro-inflammatory microglia exhibit elevated levels of FR, and its measurement by non-invasive mMRI is a promising marker of neuroinflammation in AD.

neuroscience↗

bioRxiv2026

A novel multicellular model of the adult mouse sinoatrial node retains spontaneous electrical activity and enables live investigation of the S100B-associated cell population

Approximately half of the adult sinoatrial node (SAN) consists of non-myocyte populations, indicating that cardiac pacemaking depends on interactions within a multicellular tissue rather than on pacemaker cardiomyocytes alone. Among these, an S100B-associated cell population has been implicated in pacemaker function, yet its identity and physiological roles remain poorly understood. These cells are rare and dispersed throughout the small, structurally complex SAN, making them difficult to observe repeatedly while preserving the native multicellular environment. Here, we established a dissociated multicellular culture of adult mouse SAN tissue on soft collagen-gelatin hydrogels that retains spontaneous electrical activity and permits longitudinal live imaging of S100B-associated cells. Using an S100B-EGFP+ reporter, we identified at least six reproducible morphological and behavioral phenotypes, including migration, proliferation, phagocytic behavior, and spontaneous self-organization into three-dimensional clusters. Cultures remained spontaneously electrically active for more than 10 days in vitro, with peak activity around day 10. This multicellular culture model bridges the gap between intact SAN preparations and isolated-cell cultures, allowing repeated observation of rare S100B-associated cells within a spontaneously active multicellular environment. HighlightsO_LIThe platform enables longitudinal live imaging of rare S100B-associated cells within an diverse multicellular SAN culture. C_LIO_LILive imaging reveals at least six reproducible morphological and behavioral phenotypes of S100B-associated cells. C_LIO_LIDissociated multicellular SAN cultures remain spontaneously electrically active for more than 10 days in vitro. C_LI

cell biology↗

bioRxiv2026

Stress-induced motivational impairment is marked by diminished frontocortical cellular communication and neuropeptide signaling

BackgroundRepeated stress is a risk factor for developing motivational deficits which are common across a variety of disease states including depression and are particularly resistant to treatment with conventional pharmacotherapies. Amotivation is multifaceted and can be caused by impairments in value learning, reward anticipation, and cost-benefit decision-making. Importantly, not all individuals who experience chronic stress develop motivational symptoms, suggesting there may be neurobiological signatures of resilience. MethodsWe developed a novel head-restrained effortful reinforcement task in which anticipatory and consummatory behavior can be tracked. Chronic non-discriminatory social defeat stress combined with behavioral analysis and spatially resolved RNA sequencing were used to determine the transcriptional signatures of stress in the anterior cingulate cortex of mice with varying levels of motivational impairment as well as unstressed controls. ResultsWhile stress led to a general impairment in effortful reward seeking, animals differed in the extent of behavioral deficit, with increased susceptibility marked by a unique set of differentially expressed genes within the anterior cingulate cortex (ACC). By leveraging the spatial component of our data, we were further able to identify altered interactions from inhibitory neurons and astrocytes to excitatory pyramidal cells, which correlated with intact or impaired motivated responding following stress exposure. ConclusionsChronic psychosocial stress results in divergent effects on motivated behavior and distinct ACC transcriptional signatures that are concentrated in excitatory pyramidal neurons. Cell interaction analysis implicates enhanced inhibitory neuropeptide signaling and reduced astrocytic contact signaling as upstream markers of motivational resilience and point toward ACC hyperexcitability as a targetable feature of stress susceptibility.

neuroscience↗

bioRxiv2026

A 3D Brain Geometry Toolkit for Multisite Neuroimaging Analysis

Compared to traditional gross volumetrics, surface-based models provide greater spatial precision for understanding brain alterations related to developmental, neurological, and psychiatric disorders. Large-scale brain initiatives are combining data from around the world to discover and improve illness-related brain markers. Here, we present a toolkit for 3D brain geometry analysis aimed at addressing key challenges facing large-scale neuroimaging studies. Our framework incorporates scalable methods for multisite data integration, site-specific confound correction, accelerated statistical modeling, interpretable machine learning, and interactive results visualization. The toolkit was tested on data from 21 independently collected study samples participating in the ENIGMA Bipolar Disorder Working Group (N = 3,373). Compared to traditional volume features, we show how subcortical shape measures can be combined across study sites to capture spatially complex differences between diagnostic groups and associations with common treatments. Statistical modeling was accelerated using the Fast and Efficient Mixed-Effects Algorithm (FEMA) and achieved a 16-fold reduction in computation time compared to traditional approaches. Machine learning models showed shape features may provide greater predictive performance over traditional volumes for both diagnostic and treatment prediction tasks, with interpretable weight maps providing insights into the local features driving model performance.

neuroscience↗

bioRxiv2026

Nonlinear associations between body mass index and brain microstructure across adolescence in the ABCD Study

Importance: Adolescent body mass index (BMI) is linked to brain microstructural alterations, but it is not known if this association is uniform across the BMI range. Objective: To characterize the shape of the association between adolescent BMI and brain microstructure across the BMI range. Design: Baseline, and 2-, 4-, and 6-year follow-up brain imaging and BMI from the Adolescent Brain Cognitive DevelopmentSM Study, analyzed between June 2025 and June 2026. Setting: Multicenter, population-based study including 21 sites distributed across the US. Participants: 10,479 adolescents (5,078 [48.5%] female; aged 9-18 years) from this population-based cohort study were included after quality control of complete data. Exposures: Centers for Disease Control BMI extended percentiles and percent from median BMI. Main Outcome and Measure: The primary outcome was the restricted normalized isotropic (RNI) signal fraction derived from diffusion imaging which indexes water confined within spherical cellular structures and is sensitive to microstructural properties including cellularity. Results: A total of 10,479 participants (5,078 [48.5%] female; aged 9-18 years) contributed 22,049 imaging observations across four timepoints. Higher BMI percentile was associated with greater RNI in bilateral reward- and appetite-related subcortical gray matter and white matter tracts, with effects varying spatially within structures, and with generally greater magnitude in males. Across these structures, the association increased steeply above the 80th percentile. This acceleration reflected compression of the percentile scale at high BMI; when BMI was reparameterized as percent from median, the association with RNI was approximately linear. Conclusions and Relevance: Our results suggest that BMI is associated with brain microstructure in a graded manner within white matter tracts and subcortical gray matter related to reward, executive function, and appetitive control. This indicates that the effect of BMI on microstructure is not confined to weight extremes and should be accounted for when modeling brain-body-behavior relationships during adolescence.

neuroscience↗

bioRxiv2026

A new therapeutic approach for Parkinson's disease: dual targeting of alpha-Synuclein aggregation and microglial function by the novel immunomodulator 3-Monothiopomalidomide

Background-Synuclein (-Syn) plays a central role in Parkinsons disease (PD). Under pathological conditions, -Syn aggregates into toxic oligomers and fibrils that act as damage-associated molecular patterns (DAMPs), stimulating microglial reactivity. This -Syn-microglia axis creates a self-perpetuating cycle of neuroinflammation and neurodegeneration, accelerating dopaminergic neuron loss in the substantia nigra pars compacta (SNpc) and contributing to motor deficits. Moreover, -Syn pathology spreads through the brain, disrupting synaptic plasticity in cognitive regions like the cortex and hippocampus, leading to early cognitive decline. Thus, targeting -Syn aggregation and its inflammatory consequences presents a promising dual-hit therapeutic strategy for PD. MethodsThis study investigates the therapeutic potential of 3-monothiopomalidomide (3MP), a novel thalidomide derivative designed to reduce neuroinflammation with a potentially better safety profile than Pomalidomide (POM). The neuroprotective and anti-inflammatory effects of 3MP were evaluated in rat primary mesencephalic mixed neuron-microglia cultures exposed to human -Syn oligomers (H-SynOs). Anti-aggregation activity was assessed via Thioflavin T (ThT) assays and Thioflavin S (ThS) staining in SH-SY5Y cells. Finally, the anti-aggregation, anti-inflammatory, and neuroprotective effects of 3MP were evaluated in vivo in a rat model of PD induced by intracerebral infusion of H-SynOs. ResultsIn primary cell cultures, 3MP dose-dependently reduced -Syn-induced neuronal death and microglial inflammatory responses. It also significantly inhibited -Syn aggregation in vitro in the ThT assay and in SH-SY5Y cells exposed to -Syn protofibrils, outperforming POM. When chronically administered in vivo, 3MP preserved dopaminergic neurons within the SNpc and yielded functional benefits on motor and cognitive readouts. Notably, 3MP markedly attenuated -Syn aggregates induced by the H-SynOs infusion in the SNpc more efficiently than POM, as shown by reduced intraneuronal staining for pSer129--Syn+ and reduced pSer129-Syn in both cytoplasmic and phagolysosomal compartments of microglia. In addition, mesencephalic and cortical inflammatory microgliosis that followed to intranigral H-SynOs-infusion, were significantly dampened by 3MP. ConclusionsOverall, 3MP emerges as a dual-action drug candidate capable of modulating neuroinflammation and -Syn aggregation and thereby disrupting the -Syn-driven inflammatory cycle. Its neuroprotective effects and favourable safety profile support its potential as a disease-modifying therapy for PD, with promising implications for clinical translation.

neuroscience↗

bioRxiv2025

Predicting Human Pharmacokinetic Parameters of Drugs using a Multi-Tissue Chip Platform Integrating Liver, Kidney, and Skeletal Muscle Microphysiological Systems

Accurate prediction of human pharmacokinetic (PK) parameters remains a critical challenge in drug discovery. While simple in vitro assays provide insight into discrete metabolic processes, they cannot recapitulate the complex multi-organ interactions that involve absorption, distribution, metabolism, and excretion (ADME) processes. This limitation has perpetuated a heavy reliance on animal experimentation to predict integrated outcomes like hepatic and renal clearance in humans. In this study, we developed a multi-tissue chip (MTC) to evaluate the PK parameters of intravenously (IV) administered drugs. The MTC platform provides continuous oxygenation through media recirculation and interconnects three MPSs (liver, kidney, skeletal muscle) to study drug metabolism, excretion and distribution within a single in vitro system. The functionality of each MPS was characterized for more than 15 days with single- and multi-tissue chips. A diverse set of drugs from all extended clearance classification system (ECCS) classes with various clearance mechanisms was evaluated on the MTCs and on-chip PK parameters were evaluated, such as hepatic metabolism, uptake, and disposition, tubular secretion and reabsorption, and muscle distribution. These PK parameters were successfully correlated to clinical parameters for IV drugs: hepatic clearance (CLH), renal clearance (CLR), volume of distribution (VD) and then these parameters were extrapolated to human drug exposures using physiologically-based pharmacokinetic (PBPK) modeling. This study demonstrated that the MTC platform could deliver accurate prediction for clinical pharmacokinetics of intravenously administered small molecules without the need for animal studies.

bioengineering↗

bioRxiv2025

Age associations with cortical and subcortical brain structure in adolescents age 9-17

IntroductionAdolescence is a pivotal period in brain structural development and maturation. However, investigation of cortical and subcortical brain changes during this time have been limited by small sample size and have generally examined the brain at the level of predetermined regions of interest. The recently developed Fast Efficient Mixed-Effects Algorithm (FEMA) allows for increased computational speed using mixed-effects models applied at the voxel or vertex level, as well as across multiple regions of interest. MethodsWe extended the existing FEMA framework to represent predictors using natural spline basis functions, enabling us to model nonlinear trajectories of brain structure as a function of age. We then applied this model to the The Adolescent Brain Cognitive DevelopmentSM Study (22,651 observations from 10,521 unique subjects aged 9.00-17.77) to study the age-related trajectories of tabulated cortical and subcortical volumes, vertexwise cortical thickness and surface area, and voxelwise volume assessed using the Jacobian. Models are reported separately in males and females. ResultsGlobal volume variables, including total subcortical gray matter volume, peaked near 13 years in females and 15 years in males. Vertexwise cortical surface area followed an inverted U-shaped curve, whereas vertexwise cortical thickness followed a monotonic decrease during the age range studied. Voxelwise imaging analysis revealed regional differences in age trajectories at the subregional level. DiscussionThe results of this work replicate and extend prior findings related to adolescent brain development, and illustrate distinct spatiotemporal patterns of structural changes in subcortical regions. The updated FEMA framework is publicly available for use in similar large datasets.

neuroscience↗

bioRxiv2025

FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets

While linear mixed-effects (LME) models are common for analyzing longitudinal data, most users rely on random intercepts or simple stationary covariance, due to unavailability of computationally tractable solutions. Here, we extend the Fast and Efficient Mixed-Effects Algorithm (FEMA) and present FEMA-Long, a computationally tractable approach to flexibly modeling longitudinal covariance suitable for high-dimensional data. FEMA-Long can: i) model unstructured covariance, ii) model covariates as smooth functions using splines, iii) discover time-dependent effects of covariates with spline interactions, and iv) use these flexible longitudinal modeling strategies to perform longitudinal genome-wide association studies and discover time-dependent genetic effects, in a computationally scalable manner, suitable for high-dimensional data. Through extensive simulations, we show that estimates from FEMA-Long are accurate, while being up to several thousand times faster and with minimal carbon footprint. To show the utility of FEMA-Long for discovering novel biological signal, using data from the Norwegian Mother, Father and Child Cohort Study (MoBa), we performed a longitudinal genome-wide association study with non-linear SNP-by-time interaction on length, weight, and BMI of 68,273 infants with up to six measurements in the first year of life. We found dynamic patterns of random effects including time-varying heritability and genetic correlations, as well as several genetic variants showing time-dependent effects, highlighting the applicability of FEMA-Long to enable novel discoveries. FEMA-Long is available at: https://github.com/cmig-research-group/cmig_tools. Author summaryMost large-scale datasets have complexities such as repeated measures, related individuals, or other dependencies across samples, preventing the use of standard regression approaches for analysis. In such circumstances, linear mixed-effects modeling is often employed. However, for high-dimensional datasets, fitting these models is quite challenging. Further, most standard uses of linear mixed-effects modeling focus on simpler covariance models, which may not hold. Here, we introduce FEMA-Long, a novel computationally efficient analytical framework for fitting linear mixed-effects models with time-varying random effects, as well as allowing the effect of the covariates to change smoothly over time by using splines. This is particularly relevant when, for example, studying the effect of genetic variants on phenotypes, where the effects could be non-linear over time. The FEMA-Long framework allows time-varying heritability as well as discovery of genetic variants that show time-dependent effects. By performing a genome-wide association study on data from the Norwegian Mother, Father and Child Cohort Study (MoBa) using FEMA-Long, we show the discovery of genetic variants with time-dependent effects on infant length, weight, and BMI during the first year of life. Our results highlight the potential of using FEMA-Long to make novel discoveries that can lead to biological insights on the genetics of complex traits as well as improve the potential of using genetics for personalized prediction.

genetics↗

bioRxiv2024

Utilization of a Human Liver Tissue Chip for Drug-Metabolizing Enzyme Induction Studies of Perpetrator and Victim Drugs

Polypharmacy-related drug-drug interactions (DDIs) are a significant and growing healthcare concern. Increasing number of therapeutic drugs on the market underscores the necessity to accurately assess the new drug combinations during pre-clinical evaluation for DDIs. In vitro primary human hepatocyte (PHH)-only models are commonly used only for perpetrator DDI studies due to their rapid loss of metabolic function. But co-culturing non-human cells with human PHHs can stabilize metabolic activity and be utilized for both perpetrator and victim studies, though this raises concerns about human specificity for accurate clinical assessment. In this study, we evaluated Liver Tissue Chip (LTC) with PHH-only liver microphysiological system (MPS) for DDI induction studies. Liver MPS from three individual donors maintained their functional and metabolic activity for up to 4 weeks demonstrating suitability for long-term pharmacokinetics (PK) studies. The responses to rifampicin induction of three PHH donors were assessed using CYP activity and mRNA changes. Additionally, victim PK studies were conducted with midazolam (high clearance) and alprazolam (low clearance) following rifampicin-mediated induction which resulted in a 2-fold and a 2.6-fold increase in midazolam and alprazolam intrinsic clearance values respectively compared to the untreated liver MPS. We also investigated the induction effects of different dosing regimens of the perpetrator drug (rifampicin) on CYP activity levels, showing minimal variation in the intrinsic clearance of the victim drug (midazolam). This study demonstrates the utility of the LTC for in vitro liver-specific DDI induction studies, providing a translational experimental system to predict clinical clearance values of both perpetrator and victim drugs. SIGNIFICANCE STATEMENTThis study demonstrates the utility of the Liver Tissue Chip (LTC) with primary human hepatocyte (PHH)-only liver microphysiological system (MPS) for drug-drug interaction (DDI) induction studies. This unique in vitro system with continuous recirculation maintains long-term functionality and metabolic activity for up to 4 weeks, enabling the study of perpetrator and victim drug pharmacokinetics, quantification of drug-induced CYP mRNA and activity levels, investigation of patient variability, and ultimately clinical predictions.

bioengineering↗

bioRxiv2024

Pathway-based similarity measurement to quantify transcriptomics similarity between human tissues and preclinical models.

Accurate clinical translation of preclinical research remains challenging, primarily due to species-specific differences and disease and patient heterogeneity. An important recent advancement has been development of microphysiological systems that consist of multiple human cell types that recapitulate key characteristics of their respective human systems, allowing essential physiologic processes to be accurately assessed during drug development. However, an unmet need remains regarding a quantitative method to evaluate the similarity between diverse sample types for various contexts of use (CoU)-specific pathways. To address this gap, this study describes the development of pathway-based similarity measurement (PBSM), which leverages RNA-seq data and pathway-based information to assess the human relevance of preclinical models for specific CoU. PBSM offers a quantitative method to compare the transcriptomic similarity of preclinical models to human tissues, shown here as proof of concept for liver and cardiac tissues, enabling improved model selection and validation. Thus, PBSM can successfully support CoU selection for preclinical models, assess the impact of different gene sets on similarity calculations, and differentiate among various in vitro and in vivo models. PBSM has potential to reduce the translational gap in drug development by allowing quantitative evaluation of the similarity of preclinical models to human tissues, facilitating model selection, and improving understanding of context-specific applications. PBSM can serve as a foundation for enhancing the physiological relevance of in vitro models and supporting the development of more effective therapeutic interventions.

bioinformatics↗

bioRxiv2023

A Novel Milli-fluidic Liver Tissue Chip with continuous Recirculation for Predictive pharmacokinetics Applications

A crucial step in lead selection during drug development is accurate estimation and optimization of hepatic clearance using in vitro methods. However, current methods are limited by factors such as lack of physiological relevance, short culture/incubation times that are not consistent with drug exposure patterns in patients, use of drug absorbing materials, and evaporation during long-term incubation. To address these technological needs, we developed a novel milli-fluidic human liver tissue chip (LTC) that was designed with continuous media recirculation and optimized for hepatic cultures using human primary hepatocytes. Here, we characterized the LTC using a series of physiologically relevant metrics and test compounds to demonstrate that we could accurately predict the PK of both low and high clearance compounds. The non-biological characterization indicated that the cyclic olefin copolymer (COC)-based LTC exhibited negligible evaporation and minimal non-specific binding of drugs of varying ionic states and lipophilicity. Biologically, the LTC exhibited functional and polarized hepatic culture with sustained metabolic CYP activity for at least 15 days. This long-term culture was then used for drug clearance studies for low and high-clearance compounds for at least 12 days and clearance was estimated for a range of compounds with high in vitro in vivo correlation (IVIVC). We also demonstrated that LTC can be induced by rifampicin, and the culture age had insignificant effect on depletion kinetic and predicted clearance value. Thus, we used advances in bioengineering to develop a novel purpose-built platform with high reproducibility and minimal variability to address unmet needs for PK applications.

pharmacology and toxicology↗

bioRxiv2023

Partitioning variance in cortical morphometry into genetic, environmental, and subject-specific components using a large adolescent sample

The relative contributions of genetic variation and experience in shaping the morphology of the adolescent brain are not fully understood. Using longitudinal data from 11,665 subjects in the ABCD Study(R), we fit vertex-wise variance components including family effects, genetic effects, and subject-level effects using a computationally efficient framework. Variance in cortical thickness and surface area is largely attributable to genetic influence, whereas sulcal depth is primarily explained by subject-level effects. Our results identify areas with heterogeneous distributions of heritability estimates that have not been seen in previous work using data from cortical regions. We discuss the biological importance of subject-specific variance and its implications for environmental influences on cortical development and maturation.

neuroscience↗

bioRxiv2023

Reports of the death of brain-behavior associations have been greatly exaggerated

Magnetic resonance imaging (MRI) is a popular and useful non-invasive method to map patterns of brain structure and function to complex human traits. Recently published observations in multiple large scale studies cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional MRI, which seems to account for little behavioral variability. We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM (ABCD(R)) Study to inform the replication sample size required with both univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and [~]100 subjects for structural MRI. Even with 100 random re-samplings of 50 subjects in the discovery sample, prediction can be adequately powered with 98 subjects in the replication sample for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many investigators research programs and grants.

neuroscience↗

bioRxiv2022

Heritability estimation of cognitive phenotypes in the ABCD Study using mixed models

Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development Study (ABCD Study(R)), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study(R) sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study(R) sample.

genetics↗

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.

neuroscience↗

bioRxiv2021

Lymphotoxin-alpha expression in the meninges causes lymphoid tissue formation and neurodegeneration

Lymphotoxin alpha (LT) plays an important role in lymphoid organ development and cellular cytotoxicity in the immune system. LT expression is increased in the cerebrospinal fluid of naive and progressive multiple sclerosis (MS) patients and post-mortem meningeal tissue. Here we show that persistently increased levels of LT in the cerebral meninges can give rise to lymphoid-like structures and underlying MS-like cortical pathology. Stereotaxic injections of recombinant LT into the rat meninges leads to acute meningeal inflammation and subpial demyelination that resolves after 28 days. Injection of an LT lentiviral vector induces lymphoid-like immune cell aggregates, maintained over 3 months, including T-cell rich zones containing podoplanin+ fibroblastic reticular stromal cells and B-cell rich zones with a network of follicular dendritic cells, together with expression of lymphoid chemokines and their receptors. Extensive microglial activation, subpial demyelination and marked neuronal loss occurs in the underlying cortical parenchyma. These results show that chronic LT overexpression is sufficient to induce formation of meningeal lymphoid-like structures and subsequent neurodegeneration. SummaryIncreased release of lymphotoxin-alpha contributes to the pro-inflammatory milieu of the cerebrospinal fluid of MS patients. A persistent elevated expression of this cytokine in the meninges of rats gives rise to chronic inflammation with lymphoid tissue induction and accompanying neurodegenerative and demyelinating pathology in the underlying brain tissue.

neuroscience↗

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.

neuroscience↗