Evaluating Alzheimer's disease with the TMS-EEG perturbation complexity index
Bibliographic record
Abstract
Abstract The Perturbation Complexity Index—State Transitions (PCIST) measures the complexity of the brain’s response to transcranial magnetic stimulation (TMS) using electroencephalography (EEG) and is sensitive to consciousness, such as minimally conscious states. Individuals with early-stage Alzheimer’s disease (AD) show dysfunction of conscious processes, such as attention, working memory, episodic memory, and executive function, with relatively spared unconscious processes, such as procedural memory, operant conditioning, and priming. We sought to test the hypothesis that PCIST would be reduced in AD compared to healthy aging. We assessed 28 participants with AD and 27 healthy controls (HC), measuring cognition with the Montreal Cognitive Assessment (MoCA) and disease severity with the Clinical Dementia Rating scale—Global (CDR-Global) and Sum of Boxes (CDR-SB). Results indicated lower PCIST in the AD group (M = 20.1) compared to controls (M = 28.2) across both the motor cortex (M1) and inferior parietal lobule (IPL) TMS stimulation sites, suggesting that PCIST may reflect the impaired conscious cognitive processes and functional capacity seen in AD. We therefore speculate that cortical dementias involve alterations in cortical complexity that may relate to deterioration of their conscious processes. This research opens the avenue for future studies in individuals with cortical dementia to examine the relationship between conscious processes, global measures of consciousness, and their underlying neuroanatomical correlates, in addition to enhancing our understanding of dementia and suggesting possible therapeutic strategies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".