Dodge, Hiroko Hayama

AbstractBackgroundWe developed the Institute on Methods and Protocols for Advancement of Clinical Trials in ADRD (IMPACT‐AD) to address the shortage of qualified investigators with adequate training and expertise to conduct Alzheimer’s Disease (AD) and AD Related Dementias (ADRD) clinical trials.MethodWe describe the experience of the first two years of funding of the IMPACT‐AD course, specifically the rates of applications to the course, descriptive summaries of the accepted trainees, and the measures of participant satisfaction and learning.ResultOver the first two years of the course, we received 195 eligible applications to the course. Most (n = 121, 62%) applicants were female and nearly half self‐reported as being African American/Black, Asian, or Pacific Islander race and/or Hispanic/Latino ethnicity. Forty‐one applicants (21%) indicated that they were the first in their family to attend collegeOf 69 total trainees accepted to the course (36% acceptance rate), a majority (n = 44, 64%) were female. Twenty‐eight (41%) self‐reported as being African American/Black, Asian, or Pacific Islander race and eleven (16%) were of Hispanic ethnicity. Fourteen trainees (20%) identified as being the first person in their family to attend college. Among trainees, 24 (35%) were MDs, 30 (43%) were PhDs, and 9 (13%) held master’s degrees.Course lecture topics were rated, on average, as “essential” or “valuable” by 98% of trainees. Across lecture topics, on average, 48% of trainees rated their prior knowledge of topics as “very strong” or “strong,” and 11% of trainees rated their prior knowledge as “weak.” Overall, 52% of trainees evaluated their change in knowledge based on the lectures as “very much increased,” 46% as “somewhat” or “slightly” increased, and 2% as “no change.” Results were similar for the two tracks. Based on pre‐ and post‐course assessments, trainees improved their knowledge of covered content (from 62% to 79% correct responses on knowledge assessments).ConclusionThe IMPACT‐AD course is achieving its mission of educating and diversifying the next generation of ADRD trialists.We thank Dr. Laurie Ryan and Dr. Kristina McLinden from the National Institute on Aging for their key contributions in leadership and scientific guidance of IMPACT‐AD.

AbstractIntroductionEarly detection of cognitive decline in older adults is a public health priority. Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA), a multisite study, is validating cognition, emotion, motor, and sensory modules of the National Institutes of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in the aging spectrum from cognitively normal to dementia of the Alzheimer's type (DAT).MethodsParticipants 65 to 85 years old, in demographic groups racially proportional to the general US population, are recruited in one of three groups to validate the NIHTB: cognitively normal, amnestic mild cognitive impairment (aMCI), or mild DAT. Additional special emphasis cohorts include (1) Blacks in the three clinical groups; (2) Spanish‐speakers in the three clinical groups; (3) cognitively normal, population‐proportional, over age 85.DiscussionLongitudinal study will determine whether NIHTB can predict cognitive decline and is associated with Alzheimer's disease biomarkers. Here, we detail the methods for the ARMADA study.

AbstractIntroductionConventional Z‐scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these “adjusted” Z‐scores better represent whether an individual's cognitive performance is abnormal. Extreme negative Z‐scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency.MethodsIn this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance).ResultsCorrected Z‐scores based on nonlinear shape constrained additive models provide improved adjustment for age, sex, and education, as indicated by higher adjusted‐R2.DiscussionNonlinearly corrected Z‐scores with respect to age, sex, and education with age‐varying residual standard deviation allow for improved detection of non‐normative extreme cognitive scores.

Abstract Alzheimer's disease and related dementias (ADRDs) are a global crisis facing the aging population and society as a whole. With the numbers of people with ADRDs predicted to rise dramatically across the world, the scientific community can no longer neglect the need for research focusing on ADRDs among underrepresented ethnoracial diverse groups. The Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART; alz.org/ISTAART ) comprises a number of professional interest areas (PIAs), each focusing on a major scientific area associated with ADRDs. We leverage the expertise of the existing international cadre of ISTAART scientists and experts to synthesize a cross‐PIA white paper that provides both a concise “state‐of‐the‐science” report of ethnoracial factors across PIA foci and updated recommendations to address immediate needs to advance ADRD science across ethnoracial populations.

T1-weighted MRI has been extensively used to extract imaging biomarkers and build classification models for differentiating Alzheimer’s disease (AD) patients from healthy controls, but only recently have brain connectome networks derived from diffusion-weighted MRI been used to model AD progression and various stages of disease such as mild cognitive impairment (MCI). MCI, as a possible prodromal stage of AD, has gained intense interest recently, since it may be used to assess risk factors for AD. Little work has been done to combine information from both white matter and gray matter, and it is unknown how much classification power the diffusion-weighted MRI-derived structural connectome could provide beyond information available from T1-weighted MRI. In this paper, we focused on investigating whether diffusion-weighted MRI-derived structural connectome can improve differentiating healthy controls subjects from those with MCI. Specifically, we proposed a novel feature-ranking method to build classification models using the most highly ranked feature variables to classify MCI with healthy controls. We verified our method on two independent cohorts including the second stage of Alzheimer’s Disease Neuroimaging Initiative (ADNI2) database and the National Alzheimer’s Coordinating Center (NACC) database. Our results indicated that 1) diffusion-weighted MRI-derived structural connectome can complement T1-weighted MRI in the classification task; 2) the feature-rank method is effective because of the identified consistent T1-weighted MRI and network feature variables on ADNI2 and NACC. Furthermore, by comparing the top-ranked feature variables from ADNI2, NACC, and combined dataset, we concluded that cross-validation using independent cohorts is necessary and highly recommended.

Introduction: Racial and ethnic groups are under-represented among research subjects who assent to brain donation in Alzheimer disease research studies. There has been little research on this important topic. Although there are some studies that have investigated the barriers to brain donation among African American study volunteers, there is no known research on the factors that influence whether or not Asians or Latinos are willing to donate their brains for research. Methods: African American, Caucasian, Asian, and Latino research volunteers were surveyed at 15 Alzheimer Disease Centers to identify predictors of willingness to assent to brain donation. Results: Positive predictors included older age, Latino ethnicity, understanding of how the brain is used by researchers, and understanding of what participants need to do to ensure that their brain will be donated. Negative predictors included African/African American race, belief that the body should remain whole at burial, and concern that researchers might not be respectful of the body during autopsy. Discussion: The predictive factors identified in this study may be useful for researchers seeking to increase participation of diverse ethnic groups in brain donation.