Biology subjects

Adhikari, M. H.

Publications and source records attributed to Adhikari, M. H..

11 recordsLinked to original sources

bioRxiv2025

Long-term dietary interventions fail to mitigate functional connectivity loss and cognitive decline in the TgF344-AD rat model of Alzheimers Disease

IntroductionAlzheimers disease (AD) is a complex neurodegenerative disorder, characterized by altered brain functional connectivity and network activity, detectable using resting-state functional MRI (rsfMRI). Caloric restriction (CR) and its mimetic resveratrol (Rsv) have shown potential in reducing AD-related pathology and preserving brain function, though research on long-term efficacy is limited. Therefore, we aimed to assess the effects of short- term CR and Rsv administrations on resting-state (rs) functional connectivity (FC), after which we assessed the effects of long-term CR and Rsv interventions on rs-FC, spatial memory, amyloid burden, and neuroinflammation in TgF344-AD rats (Tg) and wild-type (WT) littermates. Materials and MethodsWe used rsfMRI to investigate rs-FC changes after CR (40%) or daily Rsv supplementation (10 mg/kg, oral) in male and female, WT and TgF344-AD (Tg) rats at the age of 4 months and 11 months, after, respectively, 1 month (short-term) and 8 months (long- term) of either Rsv supplementation, control (Ctrl) or CR diet. Additionally, spatial memory was assessed utilizing the Morris Water Maze (MWM), followed by histological validation of amyloid plaque load (X34), astrogliosis (GFAP) and microgliosis (IBA-1) at 11 months of age. ResultsBoth long-term CR or Rsv supplementation exert limited effects in the TgF344-AD model on the known AD-related pathological hallmarks. Short-term CR led to a reduction in rs-FC in female TgF344-AD rats compared to Rsv Tg supplemented rats for connections primarily within and between the hippocampal network, while between other connections, CR reduced rs-FC compared to Ctrl in females, irrespective of genotype. Rsv increased rs-FC compared to CR for only a few connections in females, again irrespective of genotype. Long- term CR decreased rs-FC in male Tg CR rats, compared to Tg Ctrl and WT CR rats, primarily for connections within the lateral cortical network (LCN). For other connections, CR reduced rs- FC when compared to Ctrl, irrespective of genotype. Overall, Rsv supplementation showed negligible effects on rs-FC. Moreover, long-term CR yielded modest cognitive improvements in male Tg rats, as evidenced by enhanced performance in the MWM but not in females. Histological validation after long-term dietary intervention revealed region-specific increase or decrease of amyloid burden after CR or Rsv supplementation, respectively. Additionally, CR reduced IBA-1 levels in males, irrespective of genotype, GFAP levels were unaffected by long- term dietary intervention. ConclusionAltogether, our findings indicate that long-term CR and Rsv exert distinct and limited effects on AD-related pathology in the TgF344-AD model, with CR demonstrating a modest but greater effect on functional and cognitive measures compared to Rsv. This study underscores the difficulty of altering key disease processes with these long-term dietary approaches, highlighting the need for more comprehensive long-term studies to elucidate the potential modulatory role of dietary interventions on AD pathophysiology.

neuroscience↗

bioRxiv2025

Longitudinal Investigation of Structural and Resting-State Effective Connectivity Alterations in a Non-Human Primate Model of Huntington's Disease

Huntingtons disease (HD) is a genetic neurodegenerative disorder caused by expanded CAG repeats in the huntingtin gene which produce a mutant huntingtin (mHTT) protein that contributes to progressive striatal, cortical, and white-matter atrophy, resulting in motor dysfunction and cognitive decline. Recently, a non-human primate (NHP) model of HD was developed via stereotaxic delivery of an adeno-associated viral vector expressing 85 CAG repeats (85Q) into the striatum. This model recapitulates several neuropathological changes and symptoms observed in people with HD (PwHD) including chorea and mild cognitive impairment. A previous longitudinal, multimodal MRI investigation in this model revealed volumetric and resting-state functional connectivity (rs-FC) changes compared to controls, in key regions involved in HD, over the course of 30 months. We aimed to study longitudinal changes in structural connectivity (SC), obtained from diffusion MRI scans from the same animals, comparing the 85Q animals to the control (Buffer) group. Additionally, going beyond the correlative rs-FC analyses, we investigated changes in causal, inter-regional functional interactions by estimating effective connectivity (EC) from rs functional MRI scans, constrained to strong structural connections. We found that the SC between basal ganglia regions and the cortex was reduced in the 85Q primates compared to the Buffer group at 14-months post virus injection, aligning with the pathological process observed in PwHD at later stages of the disease. EC from the caudate and putamen to the motor cortex was significantly reduced in the 85Q animals as early as 3-months post-injection providing novel insights into early alterations in causal functional interactions.

neuroscience↗

bioRxiv2025

Resting-state functional dynamics alterations relate to plasma amyloid markers and explain memory impairments in the TgF344-AD model of Alzheimer's disease

Resting-state (RS) fMRI studies of Alzheimers diseases (AD) impact on brain function commonly use functional connectivity (FC), ignoring short-timescale network dynamics, captured by co-activation patterns (CAPs), shown to accurately classify transgenic rodents from the wild-type (WT). We acquired high temporal resolution RS-fMRI data in the TgF344-AD rat model at pre-plaque and plaque stages and delineated brain functional alterations using FC and CAPs. We also assessed plaque-stage blood amyloid levels and memory performance in the same animals and investigated the statistical relationship between pathological, RS-functional, and behavioral phenotypes. TgF344-AD (TG) rats had elevated blood amyloid levels, committed more working and reference memory errors and showed reduced hippocampal FC with the lateral cortical and default-mode-like network (DMLN) compared to WT at the plaque stage. They showed DMLN and hippocampal hyper- and hypo-activation at pre- and plaque stages respectively in multiple CAPs. While blood amyloid levels were explained better by plaque-stage, than pre-plaque stage, FC values and CAP activations, it was the pre-plaque stage, more than the plaque stage, CAP activations that accurately explained memory impairments. Our findings not only identify early signatures of AD in brain functional dynamics in this translational rat model but demonstrate their relevance for prognosis of memory deficits.

neuroscience↗

bioRxiv2024

Short-term caloric restriction or resveratrol supplementation alters large-scale brain network connectivity in male and female rats

IntroductionDietary interventions such as caloric restriction (CR) exert positive effects on brain health. Unfortunately, poor compliance hinders the success of this approach. A proposed alternative is resveratrol (Rsv), a CR-mimetic known to promote brain health. Direct comparison between the effects of Rsv and CR on brain health is lacking, with limited knowledge on their sex-specific effects. Therefore, we aimed to compare and unravel the sex-specific impact of these dietary interventions on spontaneous brain activity. MethodsHere, we used resting-state fMRI to investigate functional connectivity (FC) changes in five prominent resting-state brain networks (RSNs) in healthy four month old male and female F344 rats supplemented to either 40% CR or daily Rsv supplementation (10 mg/kg, oral) for the duration of one month. ResultsOur results demonstrated a decreased body weight (BW) in CR rats, as well as an increase in body weight in male Rsv supplemented rats, compared to female Rsv supplemented rats, whereas this difference between sexes was not observed in the control or CR groups. Furthermore, we found that both CR or Rsv supplementation induce a female-specific decrease of FC between the subcortical network and hippocampal network, and between the subcortical network and lateral cortical network. Moreover, Rsv supplementation lowered FC within the hippocampal network and between the hippocampal and the default mode like network, the lateral cortical network and the sensory network - an effect not observed for the CR rats. Finally, we observed an overall lower FC in male rats compared to females, irrespective of dietary intervention, within the subcortical network and between the subcortical, the sensory and default mode like network. DiscussionOur findings reveal that both CR and Rsv induce a similar female-specific decrease of FC in RSNs associated with memory and emotion, all the while CR and Rsv induce dissimilar changes in body weight and other within- and between RSN FC measures. Altogether, this study provides insight into the effects and comparability of short-term CR and Rsv supplementation on brain connectivity within and between RSNs in both male and female F344 rats, providing a FC reference for future research of dietary effects.

neuroscience↗

bioRxiv2024

Early altered directionality of resting brain network state transitions in the TgF344-AD rat model of Alzheimer's disease

Alzheimers disease (AD) is a progressive neurodegenerative disease resulting in memory loss and cognitive decline. Synaptic dysfunction is an early hallmark of the disease whose effects on whole-brain functional architecture can be identified using resting-state functional MRI (rsfMRI). Insights into mechanisms of early, whole-brain network alterations can help our understanding of the functional impact of ADs pathophysiology. Here, we obtained rsfMRI data in the TgF344-AD rat model at the pre- and early-plaque stages. This model recapitulates the major pathological and behavioural hallmarks of AD. We used co-activation pattern (CAP) analysis to investigate if and how the dynamic organization of intrinsic brain functional networks states, undetectable by earlier methods, is altered at these early stages. We identified and characterized six intrinsic brain states as CAPs, their spatial and temporal features, and the transitions between the different states. At the pre-plaque stage, the TgF344-AD rats showed reduced co-activation of hub regions in the CAPs corresponding to the default mode-like and lateral cortical network. Default mode-like network activity segregated into two distinct brain states, with one state characterised by high co-activation of the basal forebrain. This basal forebrain co-activation was reduced in TgF344-AD animals mainly at the pre-plaque stage. Brain state transition probabilities were altered at the pre-plaque stage between states involving the default mode-like network, lateral cortical network, and basal forebrain regions. Additionally, while the directionality preference in the network-state transitions observed in the wild-type animals at the pre-plaque stage had diminished at the early-plaque stage, TgF344-AD animals continued to show directionality preference at both stages. Our study enhances the understanding of intrinsic brain state dynamics and how they are impacted at the early stages of AD, providing a nuanced characterization of the early, functional impact of the diseases neurodegenerative process.

neuroscience↗

bioRxiv2023

Evolution of aberrant brain-wide spatiotemporal dynamics of resting-state networks in a Huntington's disease mouse model

Distinct resting-state networks (RSNs) are differentially altered in the course of Huntingtons disease (HD). However, these RSN changes are depicted using traditional functional connectivity analyses which ignore the dynamic brain states that constitute these RSNs and their time-dependent relationship. Dynamic states are represented by recurring spatiotemporal patterns of propagating cortical and subcortical brain activity observed in low-frequency BOLD fluctuations, called quasi-periodic patterns (QPPs). In this study, we used resting-state fMRI to investigate QPPs in the zQ175DN mouse model of HD at 3, 6 and 10 months of age. We identified age- and genotype-specific short (3s) QPPs, representative of the lateral cortical network (LCN) and the default mode-like network (DMLN), and a long (10s) QPP, the homolog of the human primary QPP, exhibiting the propagation of activity between the LCN and the DMLN. Hyperactivity was present in the caudate putamen and the somatosensory cortex in zQ175DN mice at 3 and 6 months of age. Moreover, DMLN-wide reduction in activation was observed at all ages, where at 6 and 10 months of age the reduced activity gradually advanced into a breakdown of the LCN-to-DMLN propagation. We then investigated the relationship in the timing of peak activity of six brain regions involved in the long QPPs and found that the retrosplenial cortex, a transmodal region which orchestrates multisensory integration, has a premature peak of BOLD activation in zQ175DN mice at 6 months of age, as compared to age-matched controls. Irrespective of either LCN or DMLN activation, this resulted in an asynchrony of the retrosplenial cortex in the peak timing relationships relative to other regions during the long (10s) QPPs. Finally, the normative, age-dependent, wild-type QPPs were significantly decreased in occurrence in the zQ175DN group at each age, indicating the presence of phenotypically-driven LCN and DMLN states as captured with QPPs. As BOLD-dependent variations result from neurovascular coupling, we assessed mutant huntingtin (mHTT) deposition in astrocytes and pericytes, known components of the neurogliovascular unit. These analyses showed increased cell-type dependent deposition starting at 6 months in the caudate putamen, somatosensory and motor cortex, regions that are prominently involved in HD pathology as seen in humans. Our findings provide meaningful insights into the development and progression of altered functional brain dynamics in this HD model, opening potential new avenues for its application in clinical HD research. SUMMARYHuntingtons disease (HD) is marked by irreversible loss of neuronal function for which currently no availability for disease-modifying treatment exists. Advances in the understanding of disease progression can aid biomarker development, which in turn can accelerate therapeutic discovery. We characterized the progression of altered dynamics of whole-brain network states in the zQ175DN mouse model of HD using a dynamic functional connectivity (FC) approach to resting-state fMRI and identified quasi-periodic patterns (QPPs) of brain activity constituting the most prominent resting-state networks. The occurrence of the normative QPPs, as observed in healthy controls, was reduced in the HD model as the phenotype progressed. This uncovered progressive cessation of synchronous brain activity with phenotypic progression, which is not observed with the conventional static FC approaches. This work opens new avenues in assessing the dynamics of whole brain states, through QPPs, in clinical HD research.

neuroscience↗

bioRxiv2022

Resting-State fMRI reveals Longitudinal Alterations in Brain Network Connectivity in a Mouse Model of Huntington's Disease

Huntingtons disease is an autosomal, dominantly inherited neurodegenerative disease caused by an expansion of the CAG repeats in exon 1 of the huntingtin gene. Neuronal degeneration and dysfunction that precedes regional atrophy result in the impairment of striatal and cortical circuits that affect the brains large-scale network functionality. However, the evolution of these disease-driven, large-scale connectivity alterations is still poorly understood. Here we used resting-state fMRI to investigate functional connectivity changes in a mouse model of Huntingtons disease in several relevant brain networks and how they are affected at different ages that follow a disease-like phenotypic progression. Towards this, we used the heterozygous (HET) form of the zQ175DN Huntingtons disease mouse model that recapitulates aspects of human disease pathology. Seed- and Region-based analyses were performed at different ages, on 3-, 6-, 10-, and 12-month-old HET and age-matched wild-type mice. Our results demonstrate decreased connectivity starting at 6 months of age, most prominently in regions such as the retrosplenial and cingulate cortices, pertaining to the default mode-like network and auditory and visual cortices, part of the associative cortical network. At 12 months, we observe a shift towards decreased connectivity in regions such as the somatosensory cortices, pertaining to the lateral cortical network, and the caudate putamen, a constituent of the subcortical network. Moreover, we assessed the impact of distinct Huntingtons Disease-like pathology of the zQ175DN HET mice on age-dependent connectivity between different brain regions and networks where we demonstrate that connectivity strength follows a nonlinear, inverted U-shape pattern, a well-known phenomenon of development and normal aging. Conversely, the neuropathologically driven alteration of connectivity, especially in the default mode and associative cortical networks, showed diminished age-dependent evolution of functional connectivity. These findings reveal that in this Huntingtons disease model, altered connectivity starts with cortical network aberrations which precede striatal connectivity changes, which appear only at a later age. Taken together, these results suggest that the age-dependent cortical network dysfunction seen in rodents could represent a relevant pathological process in Huntingtons disease progression.

neuroscience↗

bioRxiv2022

Identification of pre-synaptic density networks using SV2A PET imaging and ICA in healthy and diseased mice

BackgroundSynaptic vesicle glycoprotein 2A (SV2A) is a vesicle glycoprotein involved in neurotransmitter release. SV2A is located on the pre-synaptic terminals of neurons and visualized using the radioligand [11C]UCB-J and positron emission tomography (PET) imaging. Thus, SV2A PET imaging can provide a proxy for pre-synaptic density in health and disease. This study aims to apply independent component analysis (ICA) to SV2A PET data acquired in mice to identify pre-synaptic density networks (pSDNs), explore how ageing affects these pSDNs, and determine the impact of a neurological disorder on these networks. MethodsWe used [11C]UCB-J PET imaging data (n=135) available at different ages (3, 7, 10, and 16 months) in wild-type (WT) C57BL/6J mice and in diseased mice (mouse model of Huntingtons disease, HD). First, ICA was performed on a healthy dataset after it was split into two equal-sized samples (n=36 each) and the analysis was repeated 50 times in different partitions. We tested different model orders (8, 12, and 16) and identified the pSDNs. Next, we investigated the effect of age on the loading weights of the identified pSDNs. Additionally, the identified pSDNs were compared to those of diseased mice to assess the impact of disease on each pSDNs. ResultsModel order 12 resulted in the preferred choice to provide six reliable and reproducible independent components (ICs) as supported by the cluster-quality index (IQ) and regression coefficients ({beta}) values. Temporal analysis showed age-related statistically significant changes on the loading weights in four ICs. ICA in an HD model revealed a statistically significant disease-related effect on the loading weights in several pSDNs in line with the progression of the disease. ConclusionThis study validated the use of ICA on SV2A PET data acquired with [11C]UCB-J for the identification of cerebral pre-synaptic density networks in mice in a rigorous and reproducible manner. Furthermore, we showed that different pSDNs change with age and are affected in a disease condition. These findings highlight the potential value of ICA in understanding pre-synaptic density networks in the mouse brain.

neuroscience↗

bioRxiv2022

Longitudinal investigation of changes in resting-state co-activation patterns and their predictive ability in the zQ175 DN mouse model of Huntington's disease

Huntingtons disease (HD) is a neurodegenerative disorder caused by expanded ([≥]40) glutamine-encoding CAG repeats in the huntingtin gene, which leads to dysfunction and death of predominantly striatal and cortical neurons. While the genetic profile and behavioural signs of the disease are better known, changes in the functional architecture of the brain, especially before the behavioural symptoms become apparent, are not fully and consistently characterized. In this study, we sought markers at pre, early and late manifest states of phenotypic progression in the heterozygous (HET) zQ175 delta-neo (DN) mouse model, using resting-state functional magnetic resonance imaging (RS-fMRI). This mouse model shows molecular, cellular and circuitry alterations that resemble those seen in HD in humans. Specifically, we investigated, longitudinally, changes in co-activation patterns (CAPs) that are the transient states of brain activity constituting the resting-state networks (RSNs). Most robust changes in the temporal properties of CAPs occurred at the late manifest state; the durations of two anti-correlated CAPs, characterized by simultaneous co-activation of default-mode like network (DMLN) and co-deactivation of lateral-cortical network (LCN) and vice-versa, were reduced in the zQ175 DN HET animals compared to the wild-type mice. Changes in the spatial properties, measured in terms of activation levels of different brain regions, during CAPs were found at all three states and became progressively more pronounced at the manifest states. We then assessed the cross-validated predictive power of CAP metrics to distinguish HET animals from controls. Spatial properties of CAPs performed significantly better than the chance level at all three states with 80% classification accuracy at the early and late manifest states.

neuroscience↗

bioRxiv2020

Resting-state Co-activation Patterns as Accurate Predictors of Alzheimer's Disease in Aged Mice.

Alzheimers disease (AD), a neurodegenerative disorder marked by accumulation of extracellular amyloid-beta (A{beta}) plaques leads to progressive loss of memory and cognitive function. Resting state fMRI (RS-fMRI) studies have provided links between these two observations in terms of disruption of default mode and task positive resting state networks (RSNs). Important insights underlying these disruptions were recently obtained by investigating dynamic fluctuations in RS-fMRI signals in old TG2576 mice (mouse model of amyloidosis) using a set of quasi-periodic patterns (QPP). QPPs represent repeating spatiotemporal patterns of neural activity of predefined temporal length. In this article, we used an alternative methodology of co-activation patterns (CAPs) that represent instantaneous and transient brain configurations that are likely contributors to the emergence of commonly observed resting state networks (RSNs) and QPPs. We followed a recently published approach for obtaining CAPs that divided all time frames, instead of those corresponding to supra-threshold activations of a seed region as done traditionally, to extract CAPs from RS-fMRI recordings in 10 TG2576 female mice and 8 wild type littermates at 18 months of age. Subsequently we matched the CAPs from the two groups using the Hungarian method and compared the temporal (duration, occurrence rate) and the spatial (lateralization of significantly activated voxels) properties of matched CAPs. We found robust differences in the spatial components of matched CAPs. Finally, we used supervised learning to train a classifier using either the temporal or the spatial component of CAPs to distinguish the transgenic mice from the WT. We found that while duration and occurrence rates of all CAPs performed the classification with significantly higher accuracy than the chance-level, blood oxygen level dependent (BOLD) signals of significantly activated voxels from individual CAPs turned out to be a significantly better predictive feature demonstrating a near perfect classification accuracy. Our results demonstrate resting-state co-activation patterns are a promising candidate for a diagnostic, and potentially, prognostic biomarker of Alzheimers disease.

neuroscience↗

bioRxiv2019

MOU-EC: model-based whole-brain effective connectivity to extract biomarkers for brain dynamics from fMRI data and study distributed cognition

Neuroimaging techniques are increasingly used to study brain cognition in humans. Beyond their individual activation, the functional associations between brain areas have become a standard proxy to describe how information is distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. In particular, much effort has been devoted to the assessment of directional interactions between brain areas from their observed activity. This paper summarizes our recent approach to analyze fMRI data based on our whole-brain effective connectivity referred to as MOU-EC, while discussing the pros and cons of its underlying assumptions with respect to other established approaches. Once tuned, the model provides a connectivity measure that reflects the dynamical state of BOLD activity obtained using fMRI, which can be used to explore the brain cognition. We focus on two important applications. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools presents some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. To illustrate our framework, we use a dataset where subjects were recorded in two conditions, watching a movie and a black screen (referred to as rest). Our framework provides a comprehensive set of tools that open exciting perspectives for the study of distributed cognition, as well as neuropathologies.

neuroscience↗