P1–194: A noninvasive device‐based approach to aid in the diagnosis of mTBI and Alzheimer's disease: Preliminary findings from Clinical Pilot Studies
Bibliographic record
Abstract
Non-invasive biomarkers are emerging as essential tools for the development of effective drugs and as aids to primary care physicians and neurologists. Static imaging (MRI/PET/SPECT) and fluid based (blood and CSF) biomarkers have the inherent limitation that they don't typically capture brain physiology and dynamic processes. Cerora is developing its MindReader™ platform as a non-invasive, accurate, accessible and affordable mobile solution to aid in the diagnosis of mTBI and Alzheimer's. The present study extends an activated EEG medical device for the physiological assessment of brain health looking at multivariate classifiers from both mild Traumatic Brain Injury (Concussion) as well as Alzheimer's disease. Recent advances in wireless electroencephalography (EEG) hardware have enabled the development of a novel activated EEG system (MindReader TM) to physiologically focus the assessment of brain health to various sensory circuits and cognitive tasks. The MindReader assesses brain function while actively stimulating the subject with various sensory and cognitive stimuli. At AAIC Vancouver, Cerora reported diagnostic signatures which discriminated AD from controls in a small pilot study which replicated and extended the published EEG diagnostic literature. This year, pilot diagnostic data were collected in concussed and healthy controls using a similar but different physiologically focused data acquisition paradigm. This presentation will provide an overview of the device along with the “cloud” based neuro diagnostics as a service IT infrastructure. Univariate and multivariate statistical models were built looking for meaningful predictors of concussion (mTBI). More advanced techniques like tree based methods, boosting, and neural nets were also assessed using the pilot concussion data. Comparison between Alzheimer's and concussion features will be highlighted. A physiologically focused battery of activated EEG tasks is able to probe elements of brain circuits not presently assessed with standard resting state (Eyes Open or Eyes Closed) quantitative EEG. The MindReader offers a novel alternative to rapidly, portably and non-invasively assess brain health and function. Multi-variate predictive models offer an opportunity to enhance classifier performance, although further work is required to develop the necessary activated EEG signatures to map the brain for its physiologic defects.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".