Biology subjects

Tomassini, V.

Publications and source records attributed to Tomassini, V..

7 recordsLinked to original sources

bioRxiv2026

Thalamic Nuclei Functional Controllability Accounts for Cognitive Impairment in Multiple Sclerosis Over and Above Structural Damage

BackgroundThe thalamus has emerged as a key region involved in cognitive dysfunction in multiple sclerosis (MS). While previous studies have identified associations between thalamic structural damage, altered functional connectivity, and cognitive performance, the specific contributions of individual thalamic nuclei and the added value of integrating structural and functional metrics remain poorly understood. MethodsT1-weighted MRI, diffusion MRI, resting-state fMRI, and neuropsychological data were collected from 102 individuals with MS and 27 healthy controls. Thalamic grey matter volume, white matter microstructural integrity, and functional controllability were calculated for each nucleus and compared between individuals with MS and healthy controls, as well as between MS cognitive subgroups. Partial Spearman correlations were used to examine the relationship between imaging metrics across the three modalities, and also between imaging metrics and cognitive performance in MS. Sparse canonical correlation analysis models were used to examine the covariance between thalamic imaging metrics and cognitive performance in MS. ResultsWidespread atrophy and microstructural damage were observed across all thalamic nuclei in individuals with MS, regardless of cognitive status. In contrast, alterations in functional controllability were more spatially specific, primarily affecting the medial dorsal anterior nuclei, and were most pronounced in cognitively impaired individuals. These functional controllability metrics were independent of grey matter volume, white matter integrity, and lesion load. Combining thalamic functional controllability with structural metrics yielded a stronger association with cognitive performance in MS than either modality alone. ConclusionThis study provides novel evidence that functional controllability in the thalamus, particularly within the medial dorsal anterior nuclei, plays a critical role in cognitive impairment in MS. By applying a network control framework, our findings offer a dynamic systems perspective that extends beyond traditional connectivity analyses, capturing the thalamuss role in supporting flexible cognitive transitions. The integration of structural and functional controllability metrics enhances the ability to characterise individual differences in cognitive performance and may inform future efforts to identify biomarkers of cognitive dysfunction in MS.

neuroscience↗

bioRxiv2023

Thalamic network controllability changes and cognitive impairment in multiple sclerosis

Recent research suggests that individuals with multiple sclerosis (MS) and cognitive impairment exhibit more effortful and less efficient transitions in brain network activity. Previous studies further highlight the increased vulnerability of specific regions, particularly the thalamus, to disease-related damage. This study investigates whether MS affects the controllability of specific brain regions in driving network activity transitions across the brain and examines the relationship between these changes and cognitive impairment in patients. Resting-state functional MRI and neuropsychological data were collected from 102 MS and 27 healthy controls. Functional network controllability analysis was performed to quantify how specific regions influence transitions between brain activity patterns or states. Disease alterations in controllability were assessed in the main dataset and then replicated in an independent dataset of 95 MS and 45 healthy controls. Controllability metrics were then used to distinguish MS from healthy controls and predict cognitive status. MS-specific controllability changes were observed in the subcortical network, particularly the thalamus, which were further confirmed in the replication dataset. Cognitively impaired patients showed significantly greater difficulty in the thalamus steering brain transitions towards difficult-to-reach states, which are typically associated with high-energy-cost cognitive functions. Thalamic network controllability proved more effective than thalamic volume in distinguishing MS from healthy controls (AUC = 88.3%), and in predicting cognitive status in MS (AUC = 80.7%). This study builds on previous research highlighting early thalamic damage in MS, aiming to demonstrate how this damage disrupts activity transitions across the cerebrum and may predict cognitive deficits. Our findings suggest that the thalamus in MS becomes less capable of facilitating broader brain activity transitions essential for high-energy-cost cognitive functions, implying a potential pathological mechanism that links thalamic functional changes to cognitive impairment in MS.

neuroscience↗

bioRxiv2021

Using dual-calibrated functional MRI to map brain oxygen supply and consumption in multiple sclerosis

Evidence suggests that cerebrovascular function and oxygen consumption are altered in multiple sclerosis (MS). Here, we quantified the vascular and oxygen metabolic MRI burden in patients with MS (PwMS) and assessed the relationship between these MRI measures of and metrics of damage and disability. In PwMS and in matched healthy volunteers, we applied a newly developed dual-calibrated fMRI method of acquisition and analysis to map grey matter (GM) cerebral blood flow (CBF), oxygen extraction fraction (OEF), cerebral metabolic rate of oxygen consumption (CMRO2) and effective oxygen diffusivity of the capillary network (DC). We also quantified physical and cognitive function in PwMS and controls. There was no significant difference in GM volume between 22 PwMS and 20 healthy controls (p=0.302). Significant differences in CBF (PwMS vs. controls: 44.91 {+/-} 6.10 vs. 48.90 {+/-} 5.87 ml/100g/min, p=0.010), CMRO2 (117.69 {+/-} 17.31 vs. 136.49 {+/-} 14.48 mol/100g/min p<0.001) and DC (2.70 {+/-} 0.51 vs. 3.18 {+/-} 0.41 mol/100g/mmHg/min, p=0.002) were observed in the PwMS. No significant between-group differences were observed for OEF (PwMS vs. controls: 0.38 {+/-} 0.09 vs. 0.39 {+/-} 0.02, p=0.358). Regional analysis showed widespread reductions in CMRO2 and DC for PwMS compared to healthy volunteers. There was a significant correlation between physiological measures and T2 lesion volume, but no association with current clinical disability. Our findings demonstrate concurrent reductions in oxygen supply and consumption in the absence of an alteration in oxygen extraction that may be indicative of a reduced demand for oxygen (O2), an impaired transfer of O2 from capillaries to mitochondria, and/or a reduced ability to utilise O2 that is available at the mitochondria. With no between-group differences in GM volume, our results suggest that changes in brain physiology may precede MRI-detectable GM loss and thus may be one of the pathological drivers of neurodegeneration and disease progression.

neuroscience↗

bioRxiv2019

A frequency-domain machine learning method for dual-calibrated fMRI mapping of oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen consumption (CMRO2)

Magnetic resonance imaging (MRI) offers the possibility to non-invasively map the brains metabolic oxygen consumption (CMRO2), which is essential for understanding and monitoring neural function in both health and disease. However, in depth study of oxygen metabolism with MRI has so far been hindered by the lack of robust methods. One MRI method of mapping CMRO2 is based on the simultaneous acquisition of cerebral blood flow (CBF) and blood oxygen level dependent (BOLD) weighted images during respiratory modulation of both oxygen and carbon dioxide. Although this dual-calibrated methodology has shown promise in the research setting, current analysis methods are unstable in the presence of noise and/or are computationally demanding. In this paper, we present a machine learning implementation for the multi-parametric assessment of dual-calibrated fMRI data. The proposed method aims to address the issues of stability, accuracy, and computational overhead, removing significant barriers to the investigation of oxygen metabolism with MRI. The method utilizes a time-frequency transformation of the acquired perfusion and BOLD-weighted data, from which appropriate feature vectors are selected for training of machine learning regressors. The implemented machine learning methods are chosen for their robustness to noise and their ability to map complex non-linear relationships (such as those that exist between BOLD signal weighting and blood oxygenation). An extremely randomized trees (ET) regressor is used to estimate resting blood flow and a multi-layer perceptron (MLP) is used to estimate CMRO2 and the oxygen extraction fraction (OEF). Synthetic data with additive noise are used to train the regressors, with data simulated to cover a wide range of physiologically plausible parameters. The performance of the implemented analysis method is compared to published methods both in simulation and with in-vivo data (n=30). The proposed method is demonstrated to significantly reduce computation time, error, and proportional bias in both CMRO2 and OEF estimates. The introduction of the proposed analysis pipeline has the potential to not only increase the detectability of metabolic difference between groups of subjects, but may also allow for single subject examinations within a clinical context.

neuroscience↗

bioRxiv2019

Tractography in the presence of white matter lesions in multiple sclerosis

Accurate anatomical localisation of specific white matter tracts and the quantification of their tractspecific microstructural damage in multiple sclerosis (MS) can contribute to a better understanding of symptomatology, disease progression and intervention effects. Diffusion MRI-based tractography is being used increasingly to segment white matter tracts as regions-of-interest for subsequent quantitative analysis. Since MS lesions can interrupt the tractography algorithms tract reconstruction, clinical studies frequently resort to atlas-based approaches, which are convenient but ignorant to individual variability in tract size and shape. Here, we revisit the problem of individual tractography in MS, comparing tractography algorithms using: (i) The diffusion tensor framework; (ii) constrained spherical deconvoution (CSD); and (iii) damped Richardson-Lucy (dRL) deconvolution. Firstly, using simulated and in vivo data from 29 MS patients and 19 healthy controls, we show that the three tracking algorithms respond differentially to MS pathology. While the tensor-based approach is unable to deal with crossing fibres, CSD produces spurious stream-lines, in particular in tissue with high fibre loss and low diffusion anisotropy. With dRL, streamlines are increasingly interrupted in pathological tissue. Secondly, we demonstrate that despite the effects of lesion on the fibre orientation reconstruction algorithms, fibre tracking algorithms are still able to segment tracts that pass areas with high prevalence of lesions. Combining dRL-based tractography with an automated tract segmentation tool on data from 131 MS patients, the corticospinal tracts and arcuate fasciculi were successfully reconstructed in more than 90% of individuals. Comparing tractspecific microstructural parameters (fractional anisotropy, radial diffusivity and magnetisation transfer ratio) in individually segmented tracts to those from a tract probability map, we showed that there is no systematic disease-related bias in the individually reconstructed tracts, suggesting that lesions and otherwise damaged parts are not systematically omitted during tractography. Thirdly, we demonstrate modest anatomical correspondence between the individual and tract probability-based approach, with a spatial overlap between 35 and 55%. Correlations between tract-averaged microstructural parameters in individually segmented tracts and the probability-map approach ranged between r = .52 (p < .001) for radial diffusivity in the right cortico-spinal tract and r = .97 (p < .001) for magnetization transfer ratio in the arcuate fasciculi. Our results show that MS white matter lesions impact fibre orientation reconstructions but this does not appear to hinder the ability to anatomically localise white matter tracts in MS. Individual tract segmentation in MS is feasible on a large scale and could prove a powerful tool for investigating diagnostic and prognostic markers.

neuroscience↗

bioRxiv2019

Assessment of the effects of aerobic fitness on cerebrovascular function in young adults using multiple inversion time arterial spin labelling MRI

The cross-sectional study investigated the effects of aerobic fitness on cerebrovascular function in the healthy brain. We quantified grey matter (GM) cerebral blood flow (CBF) and cerebrovascular reactivity (CVR), in a sample of young adults within a normal fitness range. Based on existing TCD and fMRI evidence, we predicted a positive relationship between fitness and resting GM CBF, and CVR. Exploratory hypotheses that higher [Formula] peak would be associated with higher GM volume and cognitive performance were also investigated. 20 adults underwent a [Formula] peak test and a battery of cognitive tests. All subjects underwent an MRI scan where multiple inversion time (MTI) pulsed arterial spin labelling (PASL) was used to quantify resting CBF and CVR to 5% CO2. ROI analysis showed a non-significant negative correlation between whole-brain GM CBF and [Formula] peak; r=-0.4, p=0.08, corrected p (p) =0.16 and a significant positive correlation between [Formula] peak and voxelwise whole-brain GM CVR; r=0.62, p=0.003, p =0.006. Voxelwise analysis revealed a significant inverse association between [Formula] peak and resting CBF in the left and right thalamus, brainstem, right lateral occipital cortex, left intracalcarine cortex and cerebellum. The results of this study suggest that aerobic fitness is associated with lower CBF and greater CVR in young adults.

neuroscience↗

bioRxiv2019

Cerebral Metabolic Changes during Visuomotor Adaptation Assessed using Quantitative FMRI

The neural energetics underlying functional brain plasticity have not been thoroughly investigated in the healthy human brain. A better understanding of the blood flow and metabolism changes underlying plasticity will help us to address pathologies in which plasticity is compromised and, with interventions, could be enhanced for patient benefit. Calibrated fMRI was conducted in 20 healthy participants during performance of a serial reaction time task which induces rapid motor adaptation. Regions of interest (ROIs) were defined from areas showing linearly decreasing task-induced BOLD and CBF responses. BOLD, CBF and relative CMRO2 responses were calculated for each block of the task. The flow-metabolism coupling ratio, n, was also calculated for each ROI. Increases from baseline in BOLD, CBF and CMRO2 were observed in multiple brain regions including the motor and sensorimotor cortices, cerebellum and hippocampus during SRT task performance, as well as changes in the response amplitude from early to late task blocks reflecting task adaptation. CMRO2 responses on average decreased faster than BOLD or CBF responses, potentially due to rapid neural adaptation. However, the mean flow-metabolism coupling ratio was not significantly different between ROIs or across blocks. Calibrated fMRI can be used to study energetic changes during learning in the healthy brain and could be used to investigate the vascular and metabolic changes underlying reductions in plasticity in ageing and disease.

neuroscience↗