Biology subjects

Pustina, D.

Publications and source records attributed to Pustina, D..

9 recordsLinked to original sources

bioRxiv2025

Disruption of Striatal Intrinsic Ignition in Huntington's Disease: an In-Silico Perturbational Approach to Emulate Disease Progression and Recovery

Huntingtons disease (HD) is a neurodegenerative disorder caused by a single gene that severely affects motor, cognitive, and behavioral functions and is ultimately fatal. While disruptions in the structural integrity of the striatum are well documented, less is known about how the underlying functional impairments relate to the disease. We developed a large-scale modeling framework based on intrinsic ignition to uncover functional anomalies during HD progression. We first measured how much a spontaneous event in one brain region influences the rest of the brain, then systematically perturbed the activity of the striatum to predict the empirical changes in functional connectivity (FC) by re-optimizing the model parameters and conducting in-silico experiments. Results showed that disruption of the activity dynamics in the caudate, and to some extent the putamen, are the key factors that explain a substantial amount of the variance in FC alterations during HD progression. We then assessed the ability to recover whole-brain functional activity by driving the caudate and putamen activity towards a healthy-like regime and found that, by tuning the local bifurcation parameter of caudate and putamen, it is possible to recover whole-brain healthy functional activity, albeit moderately. Surprisingly, although the functional disruptions in the caudate better explained the alterations in FC, the recovery of the dynamics was more effective in the putamen. Overall, our results suggest that the disruptions of the dynamics in the striatum can explain the gross functional connectivity changes in HD. Our study demonstrates that computational modeling is an important tool to reveal the underlying patterns behind the observed data and provides an opportunity to test interventional mechanisms in silico.

neuroscience↗

bioRxiv2025

Large Scale Functional and Effective Connectivity Alterations cross the Huntington's Disease Integrated Staging System

Huntingtons disease (HD) is a progressive neurodegenerative disease with severe motor, cognitive and behavioral symptoms. There is a recent impetus to develop treatments that slow progression before clinical signs emerge. Such early interventions require biomarkers sensitive to the very earliest HD progression. Here we applied cutting-edge fMRI analysis on data collected in the Track-On HD study to evaluate whether functional and effective connectivity obtained from model-free and model-based approaches can produce useful biomarkers of HD progression. We analyzed data from 231 participants with up to three annual visits each and created five groups comprising normative controls and four HD groups according to the Huntingtons Disease Integrated Staging System (HD-ISS). We found significant differences across the HD-ISS stages but not in their longitudinal change. Specifically, we found attenuated functional and effective connectivity in the caudate nucleus in HD-ISS-2, and this effect extended to other corticostriatal connections in HD-ISS-3. Overall, most of the alterations were only evident in advanced HD stages, and we did not observe widespread alterations in cortical connectivity and graph topography. Although HD-ISS groups did not differ in the amount of in-scanner motion, we did find measures of functional and effective connectivity to be sensitive to motion. We conclude that fMRI can indeed capture attenuation of cortico-striatal functional and effective connectivity across HD progression. This is the first study to investigate fMRI alterations across the recently created HD-ISS stages within a novel framework of signal flow across the whole brain.

neuroscience↗

bioRxiv2025

Mapping brain volume changes in the zQ175DN mouse model of Huntington's disease: a longitudinal MRI study

Huntingtons disease (HD) is a progressive neurodegenerative disease affecting motor and cognitive abilities, as well as exhibiting psychiatric manifestations. Studies in people with HD (PwHD) consistently report atrophy of the caudate and putamen as an early pathological event and is therefore considered an early biomarker. Investigating whether similar phenotypic features are apparent in rodent HD models is important since it could have translational potential in evaluating the efficacy of novel therapeutic strategies. We used high-resolution anatomical images to longitudinally investigate brain morphology in the zQ175DN heterozygous mouse model (HET) and wildtype littermates (WT) at 3, 6, and 10 months of age (M), which reflect different stages of phenotypic progression. We investigated volumetric alterations using semi-automatic segmentations of HD on relevant regions-of-interest (striatum, cerebellum, corpus callosum, cerebral cortex, ventricles, and total brain volume) and whole brain voxel-wise Tensor Based Morphometry (TBM) analysis. The striatum showed the earliest progressive lower absolute volume in HET mice compared to WT, starting from 3M, followed by lower absolute volume of cortex and corpus callosum concomitantly at 6 and 10M. TBM highlighted lower relative local volume in the rostral-medial striatum at all ages, and in cerebral cortex in HET mice at 6 and 10M. A bigger relative local volume in the cerebellum was observed at all ages in HET mice, and in the globus pallidus, substantia nigra, at 6 and 10M. Overall, this study revealed key structural abnormalities that resemble the natural history of PwHD. Hence, analysis of brain structure through MRI in the zQ175DN heterozygous mouse model holds potential for testing disease-modifying treatments expected to slow down or prevent structural degeneration. HighlightsO_LIThe striatum of zQ175DN heterozygous mice shows volume decrease at 3 months compared to WT mice C_LIO_LIWidespread volume reductions are observed from 6 months in zQ175DN heterozygous mice C_LIO_LITensor Based Morphometry highlights vulnerability of the dorsomedial striatum in the zQ175DN heterozygous mouse model C_LI

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

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↗

bioRxiv2021

Simulated attack reveals how lesions affect network properties in post-stroke aphasia

Aphasia is one of the most prevalent cognitive syndromes caused by stroke. The rarity of premorbid imaging and heterogeneity of lesion size and extent obfuscates the links between the local effects of the lesion, global anatomical network organization, and aphasia symptoms. We applied a simulated attack approach to examine the effects of 39 stroke lesions on network topology by simulating their effects in a control sample of 36 healthy brain networks. We focused on measures of global network organization thought to support overall brain function and resilience in the whole brain and within the left hemisphere. After removing lesion volume from the network topology measures and behavioral scores (the Western Aphasia Battery Aphasia Quotient; WAB-AQ), four behavioral factor scores obtained from a neuropsychological battery, and a factor sum), we compared the behavioral variance accounted for by simulated post-stroke connectomes to that observed in the randomly permuted data. Overall, global measures of network topology in the whole brain and left hemisphere accounted for 10% variance or more of the WAB-AQ and the lexical factor score beyond lesion volume and null permutations. Streamline networks provided more reliable point estimates than FA networks. Edge weights and network efficiency were weighted most highly in predicting the WAB-AQ for FA networks. Overall, our results suggest that global network measures can provide modest statistical value predicting overall aphasia severity, but less value in predicting specific behaviors. Variability in estimates could be induced by premorbid ability, deafferentation and diaschisis, and neuroplasticity following stroke.

neuroscience↗

bioRxiv2021

Glass Half Full: Preserved Anatomical Bypasses Predict Variance in Language Functions After Stroke

The severity of post-stroke aphasia is related to damage to white matter connections. However, neural signaling can route not only through direct connections, but also along multi-step network paths. When brain networks are damaged by stroke, paths can bypass around the damage to restore communication. The shortest network paths between regions could be the most efficient routes for mediating bypasses. We examined how shortest-path bypasses after left hemisphere strokes were related to language performance. Regions within and outside of the canonical language network could be important in aphasia recovery. Therefore, we innovated methods to measure the influence of bypasses in the whole brain. Distinguishing bypasses from all residual shortest paths is difficult without pre-stroke imaging. We identified bypasses by finding shortest paths in subjects with stroke that were longer than those observed in the average network of the most reliably observed connections in age-matched controls. We tested whether features of those bypasses predicted scores in four orthogonal dimensions of language performance derived from a factor analysis of a battery of language tasks. The features were the length of each bypass in steps, and how many bypasses overlapped on each individual direct connection. We related these bypass features to language factors using grid-search cross-validated Support Vector Regression, a technique that extracts robust relationships in high-dimensional data analysis. We discovered that the length of bypasses reliably predicted variance in lexical production (R2 = .576) and auditory comprehension scores (R2 = .164). Bypass overlaps reliably predicted variance in Lexical Production scores (R2 = .247). The predictive elongation features revealed that bypass efficiency along the dorsal stream and ventral stream were most related to Lexical Production and Auditory Comprehension, respectively. Among the predictive bypass overlaps, increased bypass routing through the right hemisphere putamen was negatively related to lexical production ability.

neuroscience↗

bioRxiv2017

Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations

Lesion to symptom mapping (LSM) is a crucial tool for understanding the causality of brain-behavior relationships. The analyses are typically performed by applying statistical methods on individual brain voxels (VLSM), a method called the mass-univariate approach. Several authors have shown that VLSM suffers from limitations that may decrease the accuracy and reliability of the findings, and have proposed the use of multivariate methods to overcome these limitations. In this study, we propose a multivariate optimization technique known as sparse canonical correlation analysis for neuroimaging (SCCAN) for lesion to symptom mapping. To validate the method and compare it with mass-univariate results, we used data from 131 patients with chronic stroke lesions in the territory of the middle cerebral artery, and created synthetic behavioral scores based on the lesion load of 93 brain regions (putative functional units). LSM analyses were performed with univariate VLSM or SCCAN, and the accuracy of the two methods was compared in terms of both overlap and and displacement from the simulated functional areas. Overall, SCCAN produced more accurate results - higher dice overlap and smaller average displacement - compared to VLSM. This advantage persisted at different sample sizes (N=20-131) and different multiple comparison corrections (false discovery rate, FDR; Bonferroni; permutation-based family wise error rate, FWER). These findings were replicated with a fully automated SCCAN routine that relied on cross-validated predictive accuracy to find the optimal sparseness value. Simulations of one, two, and three brain regions showed a systematic advantage of SCCAN over VLSM; under no circumstance could VLSM exceed the accuracy obtained with SCCAN. When considering functional units composed of multiple brain areas VLSM identified fewer areas than SCCAN. The investigation of real scores of aphasia severity (aphasia quotient and picture naming) showed that SCCAN could accurately identify known language-critical areas, while VLSM either produced diffuse maps (FDR correction) or few scattered voxels (FWER correction). Overall, this study shows that a multivariate method, such as, SCCAN, outperforms VLSM in a number of scenarios, including functional dependency on single or multiple areas, different sample sizes, different multi-area combinations, and different thresholding mechanisms (FWER, Bonferroni, FDR). These results support previous claims that multivariate methods are in general more accurate than mass-univariate approaches, and should be preferred over traditional VLSM approaches. All the methods described in this study are available in the newly developed LESYMAP package for R.

neuroscience↗