Identifying the neurostimulation target for treatment of cognitive impairment in aging and early cerebral small vessel disease
Bibliographic record
Abstract
OBJECTIVE: To develop individualized approaches to the use of neuromodulation as a non-pharmacological treatment of cognitive impairment (CI) based on the assessment of compensatory brain reserves in functional MRI (fMRI). MATERIAL AND METHODS: Twenty-one adults over 45 years of age, representing a continuum from healthy norm to mild cognitive impairment due to aging and early cerebral small vessel disease, were studied. All participants underwent fMRI while performing two executive tasks - a modified Stroop task and selective counting. To assess the ability to compensate for CI in real life, functional activation and connectivity were analyzed using the BRIEF-MoCA score as a covariate, which is the difference in ratings between the Behavior Rating Inventory of Executive Function (BRIEF) and the Montreal Cognitive Assessment Scale (MoCA). RESULTS: Both fMRI tasks were associated with activation of areas of the frontoparietal control network, as well as supplementary motor area (SMA) and the pre-SMA, the lateral premotor cortex, and the cerebellum. An increase in pre- SMA connectivity was observed during the tasks. The BRIEF-MoCA score correlated firstly with connectivity of the left dorsolateral prefrontal cortex (DLPFC) and secondly with involvement of the occipital cortex during the counting task. CONCLUSIONS: The developed technique allows identification of the functionally relevant target within the left DLPFC in patients with CI in aging and early cerebral microangiopathy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".