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نمایش مقاله

Title:

Pre-training directed effective connectivity predicts individual responsiveness to motor learning

Subject:

Clinical Applications of Brain Mappin

Presenter Author:
Name:	Mohammad Rezaei

Author:
Name 1:	Mohammad Rezaei - Presenter
Affiliation 1:	Department of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran

Name 2:	Alireza Talesh Jafadideh
Affiliation 2:	Department of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran

Name 3:	Shahzad Tahmasebi Boroujeni
Affiliation 3:	Faculty of Physical Education & Sport Sciences, University of Tehran, Tehran, Iran

Name 4:	Fariba Bahrami - Corresponding
Affiliation 4:	School of Electrical and Computer Engineering, College of Engineering, University of Tehran Tehran, Iran

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Abstract

Original Research

introduction :

Prior work has shown substantial individual differences in the rate of motor-skill acquisition under a single standardized practice protocol. This matters in clinical settings, because an identical training dose can lead to substantial improvement for one person but little change for another. Here, we tested whether baseline (pre-training) directed effective connectivity within a motor–parietal–insula–cerebellar network predicts an individual’s subsequent learning rate.

Methods:

Thirteen participants were enrolled in a 7-session motor learning protocol; nine had complete matched behavioral and baseline DCM data for the final analysis. In each session, participants viewed target shapes on a monitor and traced them on a tablet using foot movements, while the tablet recorded their kinematic trajectories. We processed the raw trajectory files and extracted behavioral features across sessions. These behavioral measures improved systematically over training, and indicated successful learning at the group level. We summarized individual learning with SpeedGain (%), which reflects improvement in drawing speed across training (higher values indicate greater improvement), and z-scored SpeedGain for group analysis. Before training, each participant underwent an fMRI scan (278 volumes; TR = 2 s). In our prior work, we estimated baseline directed effective connectivity using spectral Dynamic Causal Modeling (DCM) in SPM12. Time series were extracted from 10 bilateral regions spanning a motor–parietal–insula–cerebellar network: primary motor cortex (M1), postcentral gyrus, angular gyrus, insula, and cerebellar Crus I. For each participant, a spectral DCM (cross-spectral density) was fit with a fully connected A-matrix (MAR order = 8; maximum iterations = 256). Here, we used these pre-training connectivity estimates to test whether baseline network organization predicts individual SpeedGain. At the group level, we employed parametric empirical Bayes (PEB) to model the relationship between baseline effective connectivity (A-connections) and SpeedGain, followed by Bayesian model reduction and averaging (BMR/BMA) to identify the most reliable connectivity–behavior relationships.

Results:

The strongest and most consistent effects were centered on the right angular gyrus. Higher SpeedGain was associated with (1) lower baseline influence from left postcentral gyrus → right angular gyrus (posterior probability ≈ 0.99), (2) lower baseline influence from right M1 → right angular gyrus (posterior probability ≈ 0.99), and (3) higher baseline influence from right angular gyrus → left insula (posterior probability ≈ 0.98). Overall, the pattern suggests that baseline communication between sensorimotor and cerebellar regions and parietal–insula regions relates to how much a person later improves on the foot-drawing task.

Conclusion:

These results suggest a plausible baseline network marker for “readiness to learn,” with a prominent role for parietal–insula integration in motor improvement. Clinically, this type of baseline marker could help stratify individuals (or patients) by their expected training response and support more personalized rehabilitation planning. Because this is a small sample, the findings should be treated as pilot and exploratory and should be validated in a larger cohort.

Keywords:

Motor Skills; Brain Mapping; Magnetic Resonance Imaging; Cerebellum; Insular Cortex; Motor Cortex