The cognitive role of connectivity gradients

What is the optimal methodological framework for a cognitively-informed connectome model? What is the role of hierarchy and the relationships between functional networks in domain-specific adaptability? Recent methodological developments in approaching resting-state fMRI connectivity data have introduced connectivity gradients as a data-driven solution for the high-dimensional connectivity matrix. Connectivity gradients represent connections between voxels along a continuum thereby overcoming the need in a-priori parcellation into networks with distinct boundaries. Along the continuum, voxels are arranged in a hierarchical manner, ranging from unimodal areas such as sensorimotor at the one end, to multimodal areas and transmodal areas (default-mode network) at the other end. As a newly established method, the cognitive and clinical value of connectivity gradients is yet to be fully understood. We study both methodological aspects of how stable this measure is over time, and its functional utility to understand plasticity within individual functional domains.

Individual variability of connectivity gradients based on resting-state fMRI This project is focused on the individual uniqueness carried in connectivity gradients by looking at the stability of this measure in repeated scanning and the contribution of specific networks to the individual signature of connectivity gradients.

Motor skill learning as a gate for individual differences in plasticity

We are all unique in the degree to which we can learn and adapt. In this research line we devise novel motoric task and test its ability to characterize consolidation in its ecological setting. We also develop and utilize methodologies for the user-free assessment of motor performance in well-established motor skill learning tasks. All this with the purpose of characterizing fine grained individual differences in performance and learning (in healthy population), as well as in deficits and recovery (in patients after stroke).

A validation study of a novel ecological bimanual motor learning task Individual differences in performance and learning are best expressed in ecological, complex tasks. In this project we develop and validate a new motor skill learning paradigm using a bimanual ecological tool, that involves multi-sensory learning. This task will be used to test underlying neural mechanisms mediating individual differences and to characterize MSL in pathological populations such as patients after stroke.

Characterizing fine motoric movements and their deficits after stroke This project is aimed at utilizing and developing user-independent rating of motor performance in a well-established motor task – finger to thumb opposition (FOS) (Karni et al., 1995). We are using deep-learning based algorithms (Mathis et al.,2018) and developing a pipeline for the postprocessing of the data. This in the purpose of providing a detailed characterization of motoric deficits after stroke with data collected at bed-side.

Reorganization in response to stroke

The brain constantly balances stability and flexibility to create an effective homeostatic balance. Pathological states such as stroke serve as a unique neurocognitive perturbation model through which we can study the relationship between alterations in brain connections and behavioral alterations, symptomology and ideally recovery. Although stroke causes localized structural damage, it also causes network-wide dysfunction through connections between the lesion site and other parts of the affected network. fMRI connectivity data can be used as a powerful tool to test both basic questions pertaining to what features of brain organization restrict or afford the spread of a local damage, as well as to devise neurophysiologically informed markers associated with symptomatology, recovery, or intervention. We study the links between connectivity status within affected networks and along the connectome and its predictive clinical value.

Using resting-state fMRI to predict the benefit of prismatic adaptation in neglect patients This project is a collaboration with Dr. Marine Lunven and Prof. Paolo Bartolomeo (CNRS, Paris, France). One of the common complications following stroke in the right hemisphere is Visuospatial (or hemispatial) neglect syndrome. A promising rehabilitation method for neglect is Prism Adaptation (PA) which is a well-known technique for changing visual-motor perception and has been shown to have a positive effect on neglect symptomology. Nevertheless, PA is not an effective treatment in all neglect patients and there is a need in finding neural markers associated with its effectivity. The current project aims to test, using measures based on functional connectivity magnetic resonance imaging during resting conditions, what alterations in connections are associated with the magnitude of responsiveness to PA.

The impact of non-invasive brain stimulation of connectome configuration

Understanding how noninvasive brain stimulation using transcranial direct current stimulation (tDCS) impacts macro-scale network reorganization is crucial for elucidating the neural mechanism underlying its action. Despite wide usage for both basic research questions and clinical interventions, the mechanism of action is not fully resolved. The aim of this research line is to investigate the impact of a well-established tDCS protocols on connectome configuration to disentangle factors impacting responsiveness and creating a basis for their individualization.

The impact of anodal M1 transcranial direct current stimulation (tDCS) on whole-brain connectivity This project is a collaboration with Dr. Bernhard Sehm (MPI, Leipzig, Germany) and his group. The aim of this project is to characterize within-network and between-networks changes as a result of anodal stimulation of primary motor cortex in both healthy subjects, elderly and in patients after stroke with motoric deficits.