Biology subjects

Tsarfaty, G.

Publications and source records attributed to Tsarfaty, G..

3 recordsLinked to original sources

bioRxiv2020

Predicting Individual Variability in Task-Evoked Brain Activity in Schizophrenia

BACKGROUNDPatients suffering from schizophrenia demonstrate abnormal brain activity, as well as alterations in patterns of functional connectivity assessed by functional magnetic resonance imaging (fMRI). Previous studies in healthy participants suggest a strong association between resting-state functional connectivity and task-evoked brain activity that could be detected at an individual level, and show that brain activation in various tasks could be predicted from task-free fMRI scans. In the current study we aimed to predict brain activity in patients diagnosed with schizophrenia, using a prediction model based on healthy individuals exclusively. This offers novel insights regarding the interrelations between brain connectivity and activity in schizophrenia. METHODSWe generated a prediction model using a group of 80 healthy controls that performed the well-validated N-back task, and used it to predict individual variability in task-evoked brain activation in 20 patients diagnosed with schizophrenia. RESULTSWe demonstrated a successful prediction of individual variability in the task-evoked brain activation based on resting-state functional connectivity. The predictions were highly sensitive, reflected by high correlations between predicted and actual activation maps (Median = 0.589, SD = 0.193) and specific, evaluated by a Kolomogrov-Smirnov test (D = 0.25, p < 0.0001). CONCLUSIONSA Successful prediction of brain activity from resting-state functional connectivity highlights the strong coupling between the two. Moreover, our results support the notion that even though resting-state functional connectivity and task-evoked brain activity are frequently reported to be altered in schizophrenia, the relations between them remains unaffected. This may allow to generate task activity maps for clinical populations without the need the actually perform the task.

neuroscience↗

bioRxiv2019

Short-Term Plasticity Following Motor Sequence Learning Revealed by Diffusion MRI

Current non-invasive methods to detect structural plasticity in humans are mainly used to study long-term changes. Diffusion MRI was recently proposed as a novel approach to reveal gray matter changes following spatial navigation learning and object-location memory tasks. In the present work we used diffusion MRI to investigate the short-term neuroplasticity that accompanies sequential motor learning. Following a 45-minutes training session in which participants learned to accurately play a short sequence on a piano keyboard, changes in diffusion properties were revealed mainly in motor system regions such as the premotor cortex and cerebellum. A second learning session taking place immediately afterwards shifted the attention of participants onto timing of key pressing instead of accuracy. This second session induced a different plasticity pattern, demonstrating the dynamic nature of this phenomenon, formerly thought to require months of training in order to be detectable. These results provide us with an important reminder that the brain is an extremely dynamic structure. Furthermore, diffusion MRI offers a novel measure to follow tissue plasticity particularly over short timescales, allowing new insights into the dynamics of structural brain plasticity.

neuroscience↗

bioRxiv2018

A Framework for Cortical Layer Composition Analysis using Low Resolution T1 MRI Images

The layer composition of the cerebral cortex represents a unique anatomical fingerprint of brain development, function, connectivity and pathology. Historically the cortical layers were investigated solely ex-vivo using histological means, but recent magnetic resonance imaging (MRI) studies suggest that T1 relaxation images can be utilized to separate the layers. Despite technological advancements in the field of high resolution MRI, accurate estimation of whole brain layer composition has remained limited due to partial volume effects, leaving some layers far beyond the image resolution. In this study we offer a simple and accurate method for layer composition analysis, resolving partial volume effects and cortical curvature heterogeneity. We use a low resolution echo planar imaging inversion recovery (EPI IR) MRI scan protocol that provides fast acquisition (~12 minutes) and enables extraction of multiple T1 relaxation time components per voxel, which are assigned to types of brain tissue and utilized to extract the subvoxel composition of each T1 layer. While previous investigation of the layers required the estimation of cortical normals or smoothing of layer widths (similar to VBM), here we developed a sphere-based approach to explore the inner mesoscale architecture of the cortex. Our novel algorithm conducts spatial analysis using volumetric sampling of a system of virtual spheres dispersed throughout the entire cortical space. The methodology offers a robust and powerful framework for quantification and visualization of the layers on the cortical surface, providing a basis for quantitative investigation of their role in cognition, physiology and pathology.

neuroscience↗