Application of Functional Magnetic Resonance Imaging in PD and Default Mode Network; Review of the Literature
Bibliographic record
Abstract
Parkinson's disease (PD) has become the second most prominent neurogenerative disorder relating to aging individuals. PD involves the loss of neurons containing dopamine in the midbrain and leads to a number of motor issues as well as non-motor complications such as cognitive and psychological abnormalities. The default mode network (DMN) is a complex brain network primarily active during rest and serves multiple roles relating to memory, self-referential processing, social cognition and consciousness and awareness. Multiple brain regions are involved in the DMN such as the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), the inferior parietal lobule, the precuneus and the lateral temporal cortex. Normal DMN connectivity is vital to preserving consciousness and self-awareness. Neurological pathologies such as PD disrupt DMN connectivity, leading to complex issues. Functional MRI (fMRI) is a neuroimaging modality used to observe brain activity through measuring blood flow differences as it relates to brain activity. DMN connectivity experiments using fMRI find that individuals with PD exhibit impaired DMN connectivity in specific regions including the PCC, mPFC and the precuneus. Individuals with greater PD motor symptoms have also been found to suffer larger alterations in DMN connections anatomically within the frontal lobe and PCC. While fMRI has been utilized as a tool to explore the relationship between PD patients and DMN connectivity, future research should look to develop a better understanding of the specific mechanisms of action that drive this link between DMN abnormality and PD severity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".