Neuroplasticity-based Parcellation Using Continuous Microstructural Learning-induced Changes
Poster No:
1993
Submission Type:
Abstract Submission
Authors:
Naama Friedman1, Ido Tavor1
Institutions:
1Tel Aviv University, Tel Aviv, Israel
First Author:
Co-Author:
Introduction:
Brain's connectivity can be represented by different modalities, such as the functional connectome, which reflects similarities of brain function across regions, or the structural connectome, which reflects the number of streamlines connecting each pair of regions (Sporns et al., 2005; de Reus & Van den Heuvel, 2013). Both of these metrics are commonly used to portray the brain as a network and provide distinct and valuable insights into its underlying nature.
Over the course of our lifetime, the brain undergoes constant changes. For instance, when we acquire new skills structural and functional modifications occur, referred to as learning-induced neuroplasticity. Microstructural training-induced changes can be detected after short learning periods using Magnetic Resonance Imaging (MRI) (Sagi et al., 2012; Tavor et al., 2013, 2020; Hofstetter et al., 2017; Brodt et al., 2018; Jacobacci et al., 2020). A complementary, less studied avenue to a network-level perspective on the human brain is studying similarities in structural neuroplasticity patterns across brain area during learning. Such learning-derived modifications in structural connectivity can revel information sourced in the brain's ability to change and adapt.
In this study, we aim to explore how the brain's structural changes during the learning process relate to its connectivity. We'll characterize the continuous microstructural changes by adding the time dimension to structural MRI scans and use this characterization to offer a neuroplasticity-based parcellation of the brain.
Over the course of our lifetime, the brain undergoes constant changes. For instance, when we acquire new skills structural and functional modifications occur, referred to as learning-induced neuroplasticity. Microstructural training-induced changes can be detected after short learning periods using Magnetic Resonance Imaging (MRI) (Sagi et al., 2012; Tavor et al., 2013, 2020; Hofstetter et al., 2017; Brodt et al., 2018; Jacobacci et al., 2020). A complementary, less studied avenue to a network-level perspective on the human brain is studying similarities in structural neuroplasticity patterns across brain area during learning. Such learning-derived modifications in structural connectivity can revel information sourced in the brain's ability to change and adapt.
In this study, we aim to explore how the brain's structural changes during the learning process relate to its connectivity. We'll characterize the continuous microstructural changes by adding the time dimension to structural MRI scans and use this characterization to offer a neuroplasticity-based parcellation of the brain.
Methods:
To follow on continuous microstructural changes during (rather than following) learning, we developed a unique protocol of diffusion tensor imagining (DTI) (Basser et al., 1994) employed while participants perform a learning task within the MRI scanner. Fifty-eight right-handed healthy volunteers were scanned while performing a finger tapping task (Karni et al., 1995) or as a passive control. Using a sliding window method, we calculated continuous measurements of tissue diffusivity indices such as mean diffusivity (MD) and fractional anisotropy (FA). From those 'microstructural time series' (i.e., the change in MD or FA over time), we parcellated the brain into "plasticity networks" based on the correlation of different areas' continuous changes and using an hierarchical clustering algorithm.
Results:
Gray matter motor-related areas displayed a significant decrease in MD, and several tract systems showed an increase in FA following learning (P < 0.05, FDR-corrected) (fig 1a). Different areas demonstrated distinct change patterns, for example, while the parahippocampal gyrus (PHG) displayed a gradual decrease in MD during the learning process, the right cerebellum and the hippocampus displayed a steep decrease in the middle of the learning process (fig 1b). We computed the correlations between the continuous diffusivity indices changes across brain areas, and then used hierarchical clustering to derive a whole-brain plasticity-based parcellation (fig 2).
Conclusions:
In this work we offer a novel, network-level perspective on brain connectivity by examining similarities across regions in short-term training-induced microstructural changes. By adding the time dimension to 3D structural measurements, we were able to detect different in the patterns of change over time during learning, in task-related areas which show similar changes when comparing pre- vs post-task images only. By continuously tracking microstructural changes within brain tissue throughout task execution and clustering brain areas according to their neuroplasticity patterns, we provide a unique approach which may shed new light on brain connectivity and function while learning.
Learning and Memory:
Skill Learning
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Diffusion MRI Modeling and Analysis 2
Segmentation and Parcellation 1
Motor Behavior:
Motor Behavior Other
Keywords:
Learning
Motor
Plasticity
Segmentation
STRUCTURAL MRI
Other - Connectivity; Diffusion MRI
1|2Indicates the priority used for review
·Training-induced structural change
·Neuroplasticity-based parcellation
Provide references using author date format
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