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Brain connectivity in dystonia

Grant number: 21/14108-4
Support Opportunities:Regular Research Grants
Duration: July 01, 2022 - June 30, 2024
Field of knowledge:Health Sciences - Medicine
Principal Investigator:Patrícia Maria de Carvalho Aguiar
Grantee:Patrícia Maria de Carvalho Aguiar
Host Institution: Instituto Israelita de Ensino e Pesquisa Albert Einstein (IIEPAE). Sociedade Beneficente Israelita Brasileira Albert Einstein (SBIBAE). São Paulo , SP, Brazil
Associated researchers: Christiane Thielemann ; Francisco Aparecido Rodrigues ; Henrique Ballalai Ferraz ; João Ricardo Sato ; Joselisa Péres Queiroz de Paiva ; Sonia Maria Cesar de Azevedo Silva Moura Magalhaes Gomes ; Vanderci Borges


Dystonia is a neurological syndrome characterized by sustained, involuntary muscle contractions, leading to repetitive twisting movements and/or postural changes. Dystonias lead to functional incapacity, have no curative treatment and, in most cases, pharmacological treatment is unsatisfactory. Understanding the pathophysiology of dystonias, both from a molecular and electrophysiological point of view, is necessary so that we can plan new therapeutic strategies that are well-founded from a neurophysiological point of view, such as transcranial magnetic stimulation or deep brain stimulation. Possibly, people with dystonia have a disorder of sensorimotor integration, causing cortical hyperexcitability and a decrease in inhibitory mechanisms, leading to co-contraction of antagonist muscles and, consequently, to involuntary movements and abnormal postures. More recently, dystonia has been considered a network disorder. Tools for the study of brain connectivity, such as functional and structural magnetic resonance imaging (MRI) (DTI-diffusion tensor imaging), electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) can help in the study of the intricate circuitry of movement control, including cortical and subcortical structures. These three tools have different temporal-spatial resolution powers. The fNIRS technology, still little explored in the study of dystonias, as well as the EEG, bring the advantage of allowing the patient to be examined while performing tasks that could not be performed in an MRI equipment. On the other hand, MRI has greater analytical power for subcortical structures. The combined analysis of hemodynamic and electrophysiological responses through the integration of data obtained by these three tools will bring greater power to identify connectivity changes in patients with dystonia. In addition, new approaches using machine learning/deep learning algorithms can aid in the development of diagnostic tools and improve the understanding of pathophysiology, which may guide the development of new neuromodulation strategies. This study aims to investigate changes of brain connectivity in patients with idiopathic dystonia, using MRI, EEG and fNIRS methodologies and apply deep learning algorithms to EEG and MR data to analyze complex networks to see if they can distinguish between dystonia patients from healthy controls, in addition to identifying which areas contribute most to dystonia. Patients with idiopathic dystonia and healthy controls, matched for sex, age, laterality and education, will undergo a study of brain connectivity through the methods of EEG, functional MRI, DTI and fNIRS during the resting state and performing motor tasks. Data will be analyzed, and we will verify the congruence of measurements obtained by these techniques to identify and validate possible findings of cortical hyperexcitability and decrease of inhibitory mechanisms that lead to dystonia. Machine learning algorithms will be used to generate models (classification) to predict whether or not a person has dystonia and which brain areas most contribute to the phenotype. The identification of these areas may help in the development of neuromodulation strategies. (AU)

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