Evidence from a rare case study for Hebbian-like changes in structural connectivity induced by long-term deep brain...

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• The existence of Hebbian-like learning in human white matter connections, where structural connectivity (SC) changes correlate with functional connectivity, remains unclear.
• Behavioral changes after deep brain stimulation (DBS) suggest Hebbian-like mechanisms at the structural level with functional implications.

Purpose of the Study:

• To investigate Hebbian-like learning in white matter connections following long-term DBS in a Parkinson's disease (PD) patient.
• To model the effects of DBS-induced structural changes on functional connectivity using computational approaches.
• To identify potential neural mechanisms underlying long-term DBS efficacy and inform future targeting strategies.

Main Methods:

• Acquired structural connectivity (SC) data of a PD patient before and after long-term DBS.
• Utilized a computational model of ongoing brain activity to simulate DBS effects.
• Analyzed changes in SC and functional connectivity, assessing information processing measures like segregation and integration.

Main Results:

• Computational modeling indicated that Hebbian-like changes in structural weights from the subthalamic nucleus (STN) to the putamen and thalamus best explained long-term DBS effects on resting-state functional connectivity.
• Long-term DBS significantly altered SC towards normalized patterns of information processing (segregation and integration).

Conclusions:

• This study provides evidence for Hebbian-like plasticity in human white matter following DBS, causally linking structural changes to functional outcomes.
• The computational modeling approach offers a novel method to investigate neural mechanisms of DBS, potentially aiding in predicting treatment efficacy and identifying new targets.