Multi-modal machine learning detection and… (R44AG060861) – Project Grant to ADM Diagnostics, Inc.

Abstract: The goal or our sbir phase II work is to develop a diagnostic tool using brain imaging and other biomarkers to identify chronic traumatic encephalopathy (CTE) and preceding stages in living individuals, and to differentiate these from alzheimer's disease (ad) and other dementias. CTE is a devastating neurodegenerative disorder found in individuals who have experienced repetitive head impact (RHI), causing symptoms of cognitive impairment that lead to dementia, and mood and behavioral disturbances that may lead to violence or suicide. While CTE has been most publicized in retired NFL players and "punch drunk" boxers, exposure to repetitive head impact occurs in soccer, hockey, military combat, domestic violence, repeated falls in elderly, and other persons, with over 300,000,000 individuals at potential risk. Currently, although a clinical diagnosis of traumatic encephalopathy syndrome (TES) has been developed to suggest probable CTE, CTE can only be diagnosed at autopsy and can be misdiagnosed during life as ad or other dementias. There are no treatments and no means to detect earlier, progressive stages that could support the development of interventional treatments. Neuroimaging biomarkers and their combination with fluid biomarkers have the potential to address the need for a CTE diagnostic by detecting changes in brain connectivity, volume, function, and chemistries that comprise CTE's progressive, cascade-like deterioration. In our phase i sbir work, we applied machine learning methods to the volumetric (T1) and diffusion tensor (DTI) magnetic resonance imaging (MRI) scans of fighters in the cleveland clinic professional fighters brain health study (pfbhs). We demonstrated a progressive pattern of effects and differentiation of persons with TES and likely CTE, patterns of atrophy differentiating the effects of traumatic brain injury (TBI) from those in patients with ad related cognitive impairment, and preliminary relationships to TAU. Our phase II aims expand this work to include different populations with RHI, within-subject longitudinal data analyses, and inclusion of functional imaging and fluid biomarkers toward achieving a broadly applicable commercially available tool that can (a) detect and differentiate CTE from ad and (b) detect and stage earlier progressive effects of TBI. We will use a uniquely comprehensive data set of multi-modality MRI, TAU pet, clinical endpoints, and fluid biomarkers from (a) 719 boxers, mixed martial artists, martial artists, and controls in the pfbhs set, of whom 165 have at least 3 imaging visits; (b) 240 former professional and college football players and controls (diagnose- CTE); (c) 219 collegiate contact sports athletes and controls (care); (d) 600 vietnam veterans with TBI and/or post traumatic stress disorder and controls (adni-DOD); and (e) individuals from our reference set of over 30,000 MRI and pet scans from individuals representing a spectrum of cognitively normal and cognitively impaired states associated with ad and other dementias. Building on our success from phase i, we will develop expanded canonical variate and deep learning classifiers using imaging and fluid biomarkers that can be applied in the clinic to evaluate persons with a history of RHI. Input regarding clinical utility and interpretability from our expert advisors will be used to guide report design. These aims provide the foundation for commercial products and services supporting CTE differential diagnosis and treatment development.

Action	Obligated	Date
Amendment	$0	Jan 7, 2026
Amendment	$913,471	Jul 25, 2023
Amendment	$1,044,935	Sep 23, 2022
Original award	$1,044,935	Sep 23, 2022

Multi-modal machine learning detection and tracking of traumatic brain injury neurodegeneration and its differentiation from alzheimer's disease

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