Misc Archives - Foundation of Hope

3 05, 2024

Investigating Psychiatric Symptoms of Dementia | McElligott | $110,000

Investigating Psychiatric Symptoms of Dementia 2024 Award: $110,000 Psychiatric symptoms like agitation, aggression, disinhibition, and impulsivity, amongst others, are common psychiatric symptoms that may precede cognitive decline that is associated with dementia. This project uses a mouse model of dementia (the P301S tauopathy model) to mechanistically examine these psychiatric symptoms antecedent to cognitive decline. We hypothesize that we will observe differences in neurotransmission (both dopaminergic and glutamatergic) in these animals as compared to age matched controls. Furthermore, we expect to observe differences in responses to commonly prescribed psychiatric medications in these mice. By uncovering neural mechanisms impinged upon by tauopathy, we hope to aid in the discovery of biomarkers and development of therapies to alleviate the symptoms associated with dementia. Need/Problem: Behavioral and psychiatric symptoms of dementia (including but not limited to hyperactivity, agitation, mood disturbance, aggression) often precede [...]

3 05, 2024

Epigenetic mechanisms linking psychological stress with dementia risk in minoritized individuals | Zannas | $147,167

Epigenetic mechanisms linking psychological stress with dementia risk in minoritized individuals 2024 Award: $147,167 Dementia has an enormous impact worldwide and disproportionately affects minoritized populations. Such health disparities have been attributed to discrimination and other social determinants of health, but the underlying mechanisms are unknown. This project examines the epigenetic mechanisms through which stress contributes to dementia risk in Black individuals. Need/Problem: Alzheimer’s disease and related dementias (ADRD) have an enormous impact on individuals and societies, with Alzheimer’s disease alone currently afflicting ~6.7 million persons age 65 and older in the US. Importantly, ADRD disproportionately affect minoritized populations, with older Black adults having an estimated two to three times higher risk for cognitive impairment and dementia as compared to older non-Hispanic White adults. However, the mechanisms underlying this health disparity are unknown and no reliable biomarkers exist to guide [...]

15 05, 2023

Uncovering Midlife Dementia Risks from Altered Structural and Functional Coupling Mechanisms | Wu | $47,761

Uncovering Midlife Dementia Risks from Altered Structural and Functional Coupling Mechanisms 2023 Award: $47,761 Dementia is a syndrome of cognitive and functional decline, commonly occurring in later life as a result of neurodegenerative and cerebrovascular processes beginning earlier in the life course. Mounting evidence shows that genetic, demographic, and lifespan environmental exposures closely interact to determine vulnerability to dementia. In this context, we aim to discover the smoking gun of dementia risks and understand the neurobiological mechanism of how these dementia risk factors affect brain structures and functions over time. Need/Problem: Mounting evidence shows that genetic, demographic, and lifespan environmental exposures closely interact to determine vulnerability to dementia. Since pre-symptomatic or early symptomatic interventions may ultimately constitute the best long-term therapeutic strategy, a life-course approach is critical to disentangle the nature and timing of dementia risks that contribute to [...]

15 05, 2023

A Machine Learning Approach to Classify Medical Records by Psychiatric Diagnosis | Nash | $35,784

A Machine Learning Approach to Classify Medical Records by Psychiatric Diagnosis 2023 Award: $35,784 The widespread adoption of electronic health records (EHRs) has been accompanied by the enticing promise of using “big data” to improve patient care and clinical research. Many “big data” models rely on discrete data points, while in the field of mental health, our outcomes and diagnostic criteria are typically found in unstructured clinical narratives. In this study, we will train and evaluate machine learning classification models of psychiatric diagnoses using software capable of mining structured and unstructured data in the EHR, by incorporating unstructured clinical data using natural language processing. Need/Problem: The widespread adoption of electronic health records (EHRs) is accompanied by the enticing promise of using “big data” to improve patient care through large-scale research studies, population health and quality improvement initiatives, and improved [...]