Three-Dimensional Face Recognition in Mild Cognitive Impairment: A Psychophysical and Structural MR Study
Bibliographic record
Abstract
OBJECTIVES: Mild cognitive impairment (MCI) has been associated with a high risk of conversion to Alzheimer's dementia. In addition to memory complaints, impairments in the visuospatial domain have been reported in this condition. We have previously shown that deficits in perceiving structure-from-motion (SFM) objects are reflected in functional reorganization of brain activity within the visual ventral stream. Here we aimed to identify structural correlates of psychophysical complex face and object recognition performance in amnestic MCI patients (n=30 vs. n=25 controls). This study was, therefore, motivated by evidence from recent studies showing that a combination of visual information across dorsal and ventral visual streams may be needed for the perception of three-dimensional (3D) SFM objects. METHODS: In our experimental paradigm, participants had to discriminate 3D SFM shapes (faces and objects) from 3D SFM meaningless (scrambled) shapes. RESULTS: Morphometric analysis established neuroanatomical evidence for impairment in MCI as demonstrated by smaller hippocampal volumes. We found association between cortical thickness and face recognition performance, comprising the occipital lobe and visual ventral stream fusiform regions (overlapping the known location of face fusiform area) in the right hemisphere, in MCI. CONCLUSIONS: We conclude that impairment of 3D visual integration exists at the MCI stage involving also the visual ventral stream and contributing to face recognition deficits. The specificity of such observed structure-function correlation for faces suggests a special role of this processing pathway in health and disease. (JINS, 2016, 22, 744-754).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".