Measuring saccades can be a reliable, objective, sensitive and rapid way for cognitive impairment assessment
Bibliographic record
Abstract
Abstract Background and ObjectivesAmong the elderly, dementia is a common and disabling disorder with primary manifestations of cognitive impairments. Diagnosis and intervention in its early stages is the key to effective treatment. Practically, the test of cognitive function relies mainly on neuropsychological tests, such as the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Although these tests are widely used at the present, there are noticeable shortcomings, e.g., the biases of subjective judgments from physicians and the cost of the labor of these well-trained physicians. Thus, advanced and objective methods are urgently needed to evaluate cognitive functions. Accumulative evidence indicates that the saccades in certain tasks are highly correlated with the performance in some cognitive functions. However, only a few studies directly compared saccades with the performance in neuropsychological tests depicted by their scores. Thus, the reliability of using saccades as a behavioral biomarker to evaluate cognitive functions has rarely been explored. Methods310 subjects performed three sequential designed oculomotor tasks, pro-saccade (PS), anti-saccade (AS) and memory-guided saccade (MGS) and the saccadic parameters including error rate, saccadic reaction time and spatial error are studied.ResultsIn general, most saccadic parameters correlate well with the MMSE and MoCA scores. Moreover, some subjects with high MMSE and MoCA scores have very high error rates in performing these three tasks due to various errors in saccade control. The primary error types vary among tasks, indicating that different tasks assess certain specific brain functions preferentially. Thus, to improve the accuracy of evaluation through saccadic tasks, we built a weighted model to combine the saccadic parameters of the three saccadic tasks. The receiver operating characteristic (ROC) curve analysis shows that the discrimination between cognitive impairment patients and control subjects is better through the output of our model than the MMSE test. ConclusionMeasuring saccades in multiple tasks could be a reliable, objective and sensitive method to evaluate cognitive function and thus to help diagnosing cognitive impairments.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".