Biology subjects

Vohryzek, J.

Publications and source records attributed to Vohryzek, J..

39 records · Page 3Linked to original sources

bioRxiv2021

Distinct profiles of spatio-temporal brain dynamics along symptoms dimensions in autism

Autism Spectrum Disorders are accompanied by atypical brain activity and impairments in brain connectivity. In particular, dynamic functional connectivity approaches highlighted aberrant brain fluctuations at rest in individuals with autism compared to a group composed of typically developed individuals, matched in age and gender. However, the characterization of these variations remains unclear. Here, we quantified the spatio-temporal network dynamics using two novel dynamic group-based measures, namely system diversity and spatio-temporal diversity. Using the public database ABIDE 1, we explored the differences between individuals with autism and typically developed individuals. Our results show evidence that individuals with autism have atypical connectivity patterns over time characterized by a lower integration of heterogeneous cognitive processes and unstable functional activity, except for the default mode network presenting its own specific dynamic pattern. Within the autism group, we find this pattern of results to be stronger in more severely affected patients with a predominance of symptoms in the social affect domain. However, patients with prominently restricted and repetitive behaviours demonstrate a more conservative profile of brain dynamics characterized by a lower spatio-temporal diversity of the default mode network.

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

Stable Biomarker Identification For Predicting Schizophrenia in the Human Connectome

Schizophrenia, as a psychiatric disorder, has recognized brain alterations both at the structural and at the functional magnetic resonance imaging level. The developing field of connec-tomics has attracted much attention as it allows researchers to take advantage of powerful tools of network analysis in order to study structural and functional connectivity abnormalities in schizophrenia. Many methods have been proposed to identify biomarkers in schizophrenia, focusing mainly on improving the classification performance or performing statistical comparisons between groups. However, the stability of biomarkers selection has been for long overlooked in the connectomics field. In this study, we follow a machine learning approach where the identification of biomarkers is addressed as a feature selection problem for a classification task. We perform a recursive feature elimination and support vector machines (RFE-SVM) approach to identify the most meaningful biomarkers from the structural, functional, and multi-modal connectomes of healthy controls and patients. Furthermore, the stability of the retrieved biomarkers is assessed across different subsamplings of the dataset, allowing us to identify the affected core of the pathology. Considering our technique altogether, it demonstrates a principled way to achieve both accurate and stable biomarkers while highlighting the importance of multi-modal approaches to brain pathology as they tend to reveal complementary information.

neuroscience↗