Basal ganglia as an fMRI motor neurofeedback target in Parkinson’s disease
Bibliographic record
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor impairments. While pharmacological treatments offer symptom alleviation, their long-term effectiveness is insufficient. Deep Brain Stimulation (DBS) is a neurosurgical treatment that targets brain pathways to alleviate motor symptoms in PD. It is a highly invasive procedure and carries associated risks. This prompts investigation of non-invasive alternatives, such as real-time functional Magnetic Resonance Imaging (rt-fMRI) neurofeedback (NF). This work investigates the feasibility of using the basal ganglia, more specifically the putamen, a key structure in the motor network, as a potential NF target region. Two rt-fMRI studies were conducted: (i) Twelve healthy individuals participated in a single-blind, crossover study involving one MRI session targeting the putamen and the supplementary motor area (SMA) in separate runs. (ii) Twelve PD patients followed the same protocol but with three MRI sessions. We investigated whether participants could learn to voluntarily control brain activity through NF training. The PD patients successfully recruited the putamen during NF-reinforced motor imagery, which was also found at trend level in the healthy participants. We found no learning effect and no difference in putamen activation when it was directly targeted versus when the target signals came from the SMA. Overall, widespread cortical and subcortical areas involved in motor control were activated during neurofeedback. This study demonstrates for the first time that PD patients can modulate putamen activity through NF training, supporting its potential as a non-invasive neuromodulation target. This opens opportunities for integrating invasive and non-invasive neuromodulation for PD treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".