Biology subjects

Finoia, P.

Publications and source records attributed to Finoia, P..

8 recordsLinked to original sources

bioRxiv2024

Comparing the effect of multi gradient echo and multi band fMRI during a semantic task

The Blood Oxygenation Level Dependent (BOLD) signal, as measured using functional magnetic resonance imaging (fMRI), is known to vary in sensitivity across the brain due to magnetic susceptibility artefacts. In particular, the ventral anterior temporal lobes (vATL) have been implicated with semantic cognition using convergent methods (i.e., neuropsychology, PET, MEG, brain stimulation) but less so with fMRI using conventional gradient-echo protocols. There are methods to alleviate signal loss but multi-echo fMRI has gained popularity. Here, additional volumes are collected that span across a range of T2* values, however, this results sub-optimum parameters (i.e., repetition times, resolution, acceleration). "Multi-band" imaging has been used with multi-echo to speed up data acquisition; however, it is unclear how these modifications contribute to fMRI sensitivity across the brain and for univariate/multivariate analyses. In the current study, we used a factorial design where we manipulated the echo and/or band to assess how well the semantic network can be detected. When comparing the precision with which activations were detected (i.e, average T-statistics), we found that multi-band protocols were beneficial, with no evidence of signal leakage artefacts. When comparing the magnitude of activations, multi-echo protocols increased activations in regions prone to susceptibility artefacts (specifically the anterior temporal lobes, ATLs). Both multi-banding and independent component analysis (ICA)-denoising of multi-echo data tended to improve multi-voxel decoding of conditions. However, multi-echo protocols reduced activation magnitude in more central regions, such as the medial temporal lobes, possibly due to higher in-plane acceleration required to collect multiple-echoes. Nonetheless, the multi-echo multi-band protocol is a promising default option for fMRI on most regions, particularly those that suffer from susceptibility artefacts, as well as offering the potential to apply advanced post-processing methods to take advantage of the increased temporal (or spatial) resolution of multi-band protocols and more principled ICA-denoising based on TE-dependence of BOLD signals.

neuroscience↗

bioRxiv2022

Reduced emergent character of neural dynamics in patients with a disrupted connectome

High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems -- including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be due to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.

neuroscience↗

bioRxiv2021

Network dynamics scale with levels of awareness

Small world topologies are thought to provide a valuable insight into human brain organisation and consciousness. However, functional magnetic resonance imaging studies in consciousness have not yielded consistent results. Given the importance of dynamics for both consciousness and cognition, here we investigate how the diversity of brain dynamics pertaining to small world topology (quantified by sample entropy; dSW-E) scales with decreasing levels of awareness (i.e., sedation and disorders of consciousness). Paying particular attention to result reproducibility, we show that dSW-E is a consistent predictor of levels of awareness even when controlling for the underlying functional connectivity dynamics. We find that dSW-E of subcortical and cortical areas are predictive, with the former showing higher and more robust effect sizes across analyses. Consequently, we propose that the dynamic reorganisation of the functional information architecture, in particular of the subcortex, is a characteristic that emerges with awareness and has explanatory power beyond that of the complexity of dynamic functional connectivity.

neuroscience↗

bioRxiv2021

Resting-state based prediction of task-related activation in patients with disorders of consciousness

BackgroundAssessment of the level of awareness of people with disorders of consciousness (DOC) is clinically challenging, motivating several studies to combine brain imaging with machine learning to improve this process. While this work has shown promise, it has limited clinical utility, as misdiagnosis of DOC patients is relatively high. As machine learning algorithms rely on accurately labelled data, any error in diagnosis will be learned by the algorithm, resulting in an equally limited diagnostic tool. The goal of the present study is to overcome this problem by stratifying patients, not by diagnosis, but by their capacity to perform volitional tasks during functional magnetic resonance imaging (fMRI) scanning. MethodsA total of 71 patients were assessed for inclusion. They were excluded for the final analysis if they had large focal brain damage, excessive head motion during scanning, or suboptimal MRI preprocessing. Patients underwent both resting-state and task-based fMRI scanning. Univariate fMRI analysis was performed to determine if an individual patient had brain activity consistent with having retained volitional capacity (VC). Differences in resting brain network connectivity between patients with VC and patients without volitional capacity (non-VC) were measured. Connectivity data was then entered as input to a deep learning framework. We used a deep graph convolutional neural network (DGCNN) on connectivity data to identify a specific brain network that most significantly differentiates patients. FindingsWe included 30 patients in our final analysis. Univariate analysis revealed that 13 patients displayed signs of VC, while 17 did not. We found that resting-state connectivity between frontoparietal control and salience network was significantly different between VC and non-VC patients (T(28) = 3.347, p = 0.0023, Bonferroni corrected p = 0.042). Furthermore, we found that using frontoparietal control network connectivity as input to the DGCNN resulted in the best classification performance (test accuracy = 0.85; ROC AUC = 0.92). InterpretationWe found that the DGCNN performed best at discriminating between patients with VC when using only the frontoparietal control network as input to the model. The use of this deep learning method is a significant advance since its inherent flexibility permits the inclusion of both whole-brain and network-specific properties as input, allowing us to classify patients as either having or not having VC. This inclusion of multi-scale inputs (e.g. whole-brain and network-level) facilitates model interpretability and increases our understanding of the neurobiology of DOC. The results propose that the integrity of frontoparietal control network, a brain network well known to play a key role in executive functions and cognitive control, is essential for volitional capacity preservation in patients with DOC. The study also lays groundwork for development of a biomarker to aid in the diagnosis of DOC patients. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSDisorders of consciousness (DOC) are a group of severe brain disorders characterised by damage to the neural systems underlying wakefulness and awareness. DOC are often caused by traumatic brain injury, hypoxia, or neurodegenerative diseases. The motor and cognitive impairments in DOC patients make providing an accurate diagnosis very challenging. Diagnosis is primarily made at the bedside by assessing a patients response to motor commands.

neuroscience↗

bioRxiv2021

Paths to Oblivion: Common Neural Mechanisms of Anaesthesia and Disorders of Consciousness

The human brain generates a rich repertoire of spatiotemporal dynamics during normal wakefulness, supporting a wide variety of conscious experiences and cognitive functions. However, neural dynamics are reconfigured, in comparable ways, when consciousness is lost either due to anaesthesia or disorders of consciousness (DOC). Here, leveraging a neurobiologically realistic whole-brain computational model informed by functional MRI, diffusion MRI, and PET, we sought to identify the neurobiological mechanisms that explain the common reconfiguration of neural dynamics observed both for transient pharmacological intervention and chronic neuroanatomical injury. Our results show that, by incorporating local inhibitory action through a PET-based GABA receptor density map, our model can reproduce the brain dynamics of subjects undergoing propofol anaesthesia, and that this effect depends specifically on the spatial distribution of GABA receptors across cortical regions. Additionally, using a structural connectome obtained from DOC patients, we demonstrate how the dynamics that characterise loss of consciousness can emerge from changes in neuroanatomical connectivity. Crucially, we find that each of these two interventions generalises across datasets: a model with increased GABA-mediated inhibition can reproduce the dynamics of DOC patients brains, and a model with a DOC connectome is also compatible with brain dynamics observed during propofol anaesthesia. These results demonstrate how increased inhibition and connectome randomisation represent different neurobiological paths towards the characteristic dynamics of the unconscious brain. Overall, the present findings begin to disentangle the neurobiological mechanisms by which highly dissimilar perturbations of the brains neurodynamics can lead to unconsciousness.

neuroscience↗

bioRxiv2020

A Synergistic Workspace for Human Consciousness Revealed by Integrated Information Decomposition

A central goal of neuroscience is to understand how the brain orchestrates information from multiple input streams into a unified conscious experience. Here, we address two fundamental questions: how is the human information-processing architecture functionally organised, and how does its organisation support consciousness? We combine network science and a rigorous information-theoretic notion of synergy to delineate a "synergistic global workspace", comprising gateway regions that gather synergistic information from specialised modules across the brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the brains default mode network, whereas broadcasters coincide with the executive control network. Demonstrating the empirical relevance of our proposed architecture for neural information processing, we show that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to a diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory. Taken together, this work provides a new perspective on the role of prominent resting-state networks within the human information-processing architecture, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.

neuroscience↗

bioRxiv2020

Connectome Harmonic Decomposition of Human Brain Dynamics Reveals a Landscape of Consciousness

AbstractA central question in neuroscience is how consciousness arises from the dynamic interplay of brain structure and function. Departing from the predominant location- centric view in neuroimaging, here we provide an alternative perspective on the neural signatures of human consciousness: one that is intrinsically centered on how the distributed network architecture of the human structural connectome shapes functional activation across scales. We decompose cortical dynamics of resting-state functional MRI into fundamental distributed patterns of structure- function association: the harmonic modes of the human structural connectome. We contrast wakefulness with a wide spectrum of states of consciousness, spanning chronic disorders of consciousness but also pharmacological perturbations of consciousness induced with the anaesthetic propofol and the psychoactive drugs ketamine and LSD. Decomposing this wide spectrum of states of consciousness in terms of "connectome harmonics" reveals a generalisable structure-function signature of loss of consciousness, whether due to anaesthesia or brain injury. A mirror-reverse of this harmonic signature characterises the altered state induced by LSD or ketamine, reflecting psychedelic-induced decoupling of brain function from structure. The topology and neuroanatomy of the human connectome are crucial for shaping the repertoire of connectome harmonics into a fine-tuned indicator of consciousness, correlating with physiological and subjective scores across datasets and capable of discriminating between behaviourally indistinguishable sub-categories of brain-injured patients, tracking the presence of covert consciousness. Overall, connectome harmonic decomposition identifies meaningful relationships between neurobiology, brain function, and conscious experience.

neuroscience↗

bioRxiv2019

Fractal Dimension of Cortical Functional Connectivity Networks Predicts Severity in Disorders of Consciousness

Recent evidence suggests that the quantity and quality of conscious experience may be a function of the complexity of activity in the brain, and that consciousness emerges in a critical zone on the axes of order/randomness and integration/differentiation. We propose fractal shapes as a measure of proximity to this critical point, as fractal dimension encodes information about complexity beyond simple entropy or randomness, and fractal structures are known to emerge in systems nearing a critical point. To validate this, we tested the several measures of fractal dimension on the brain activity from healthy volunteers and patients with disorders of consciousness of varying severity. We used a Compact Box Burning algorithm to compute the fractal dimension of cortical functional connectivity networks as well as computing the fractal dimension of the associated adjacency matrices using a 2D box-counting algorithm. To test whether brain activity is fractal in time as well as space, we used the Higuchi temporal fractal dimension on BOLD time-series. We found significant decreases in the fractal dimension between healthy volunteers (n=15), patients in a minimally conscious state (n=10), and patients in a vegetative state (n=8), regardless of the mechanism of injury. We also found significant decreases in adjacency matrix fractal dimension and Higuchi temporal fractal dimension, which correlated with decreasing level of consciousness. These results suggest that cortical functional connectivity networks display fractal character and that this is predictive of level of consciousness in a clinically relevant population, with more fractal (i.e. more complex) networks being associated with higher levels of consciousness. This supports the hypothesis that level of consciousness and system complexity are positively associated, and is consistent with previous EEG, MEG, and fMRI studies.

neuroscience↗