Brain Network Models of Motor Recovery After Stroke

Summary

As with other real=world connected systems, studying the network structure of multiple interactions in the brain (holism versus reductionism) has profound implications in the comprehension of emergent complex phenomena like, for example, the capability to functionally reorganize after cerebrovascular "attacks" or stroke. This dynamic skill, which is known in neuroscience as brain plasticity, is not only interesting from a network perspective, but it also plays a crucial role in determining the motor/cognitive recovery of patients who survive a stroke.

Network analysis of functional connectivity (FC) patterns estimated from neuroimaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) has allowed a major breakthrough in the understanding of physiopathology of stroke from a system perspective. Recent evidence from cross=sectional studies1,2 highlights that stroke lesions generally induce i) critical deviation from optimal (i.e. small=world) network topologies supporting both segregated and integrated information processing, ii) altered inter=hemispheric connectivity and modularity, iii) and abnormal region centrality in the ipsilesional hemisphere as well as in the contralesional hemisphere. While these findings provide new descriptors on how stroke lesions affect the functional brain network organization and how this correlates with the resulting behavioral impairment (e.g. hemiplegia, aphasia), they only represent a static picture of the brain plasticity, which is instead intrinsically dynamic, and partially inform on the chances of single patients to recover their motor/cognitive functions. These aspects dramatically limit the investigator's ability to fully understand the brain organizational mechanisms after stroke and to probe the predictive power of possible network=based neuromarkers of recovery. The ATTACK project aims to overcome these technological and methodological barriers by implementing the following three=fold strategy:

1. acquiring a longitudinal dataset of brain and behavioral data in stroke patients and healthy controls,
2. developing new analytic tools to characterize and generate temporally dynamic brain networks,
3. building network=based models of functional recovery after stroke, accounting for individual patients.

Conditions

• Stroke

Interventions

OTHER

Imaging

high= density (64 sensors) EEG and anatomical MRI (T1 and tensor imaging) scan

BEHAVIORAL

Clinical and behavioral testing : Motor recovery progress

NIHSS with motor subitems and Rankin and Barthel score, measure of functional independence and Hemispatial neglect, test of Ashworth

BEHAVIORAL

Clinical and behavioral testing : Motor skills

the ARAT or ACTION RESEARCH ARM TEST, hand grip strength, Fugl Meyer

Sponsors & Collaborators

• Institut National de la Santé Et de la Recherche Médicale, France

  lead OTHER_GOV

Study Design

Allocation

NON_RANDOMIZED

Purpose

OTHER

Masking

NONE

Model

PARALLEL

Eligibility

Min Age

18 Years

Max Age

85 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2019-09-04

Primary Completion

2022-05-31

Completion

2022-05-31

Countries

• France

Study Locations