Brain Representation of Animal and Non-Animal Images in Patients with Mild Cognitive Impairment and Alzheimer’s Disease
Bibliographic record
Abstract
Background: In early Alzheimer's disease (AD), high-level visual functions and processing speed are impacted. Few functional magnetic resonance imaging (fMRI) studies have investigated high-level visual deficits in AD, yet none have explored brain activity patterns during rapid animal/non-animal categorization tasks. Objective: To address this, we utilized the previously known Integrated Cognitive Assessment (ICA) to collect fMRI data and compare healthy controls (HC) to individuals with mild cognitive impairment (MCI) and mild AD. Methods: The ICA encompasses a rapid visual categorization task that involves distinguishing between animals and non-animals within natural scenes. To comprehensively explore variations in brain activity levels and patterns, we conducted both univariate and multivariate analyses of fMRI data. Results: The ICA task elicited activation across a range of brain regions, encompassing the temporal, parietal, occipital, and frontal lobes. Univariate analysis, which compared responses to animal versus non-animal stimuli, showed no significant differences in the regions of interest (ROIs) across all groups, with the exception of the left anterior supramarginal gyrus in the HC group. In contrast, multivariate analysis revealed that in both HC and MCI groups, several regions could differentiate between animals and non-animals based on distinct patterns of activity. Notably, such differentiation was absent within the mild AD group. Conclusions: Our study highlights the ICA task's potential as a valuable cognitive assessment tool designed for MCI and AD. Additionally, our use of fMRI pattern analysis provides valuable insights into the complex changes in brain function associated with AD. This approach holds promise for enhancing our understanding of the disease's progression.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".