Alzheimer’s disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer’s disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer’s disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer’s disease and cognitively normal subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer’s Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer’s disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

Aim: Human voice contains rich information. Few longitudinal studies have been conducted to investigate the potential of voice to monitor cognitive health. The objective of this study is to identify voice biomarkers that are predictive of future dementia. Methods: Participants were recruited from the Framingham Heart Study. The vocal responses to neuropsychological tests were recorded, which were then diarized to identify participant voice segments. Acoustic features were extracted with the OpenSMILE toolkit (v2.1). The association of each acoustic feature with incident dementia was assessed by Cox proportional hazards models. Results: Our study included 6, 528 voice recordings from 4, 849 participants (mean age 63 ± 15 years old, 54.6% women). The majority of participants (71.2%) had one voice recording, 23.9% had two voice recordings, and the remaining participants (4.9%) had three or more voice recordings. Although all asymptomatic at the time of examination, participants who developed dementia tended to have shorter segments than those who were dementia free (P< 0.001). Additionally, 14 acoustic features were significantly associated with dementia after adjusting for multiple testing (P< 0.05/48 = 1 × 10–3). The most significant acoustic feature was jitterDDP_sma_de (P = 7.9 × 10–7), which represents the differential frame-to-frame Jitter. A voice based linear classifier was also built that was capable of predicting incident dementia with area under curve of 0.812. Conclusions: Multiple acoustic and linguistic features are identified that are associated with incident dementia among asymptomatic participants, which could be used to build better prediction models for passive cognitive health monitoring.

Introduction: Despite the availability of age‐ and education‐adjusted standardized scores for most neuropsychological tests, there is a lack of objective rules in how to interpret multiple concurrent neuropsychological test scores that characterize the heterogeneity of Alzheimer's disease. Methods: Using neuropsychological test scores of 2091 participants from the Framingham Heart Study, we devised an automated algorithm that follows general diagnostic criteria and explores the heterogeneity of Alzheimer's disease. Results: We developed a series of stepwise diagnosis rules that evaluate information from multiple neuropsychological tests to produce an intuitive and objective Alzheimer's disease dementia diagnosis with more than 80% accuracy. Discussion: A data‐driven stepwise diagnosis system is useful for diagnosis of Alzheimer's disease from neuropsychological tests. It demonstrated better performance than the traditional dichotomization of individuals' performance into satisfactory and unsatisfactory outcomes, making it more reflective of dementia as a spectrum disorder. This algorithm can be applied to both within clinic and outside‐of‐clinic settings.

Introduction: Subtle cognitive alterations that precede clinical evidence of cognitive impairment may help predict the progression to Alzheimer's disease (AD). Neuropsychological (NP) testing is an attractive modality for screening early evidence of AD. Methods: Longitudinal NP and demographic data from the Framingham Heart Study (FHS; N = 1696) and the National Alzheimer's Coordinating Center (NACC; N = 689) were analyzed using an unsupervised machine learning framework. Features, including age, logical memory‐immediate and delayed recall, visual reproduction‐immediate and delayed recall, the Boston naming tests, and Trails B, were identified using feature selection, and processed further to predict the risk of development of AD. Results: Our model yielded 83.07 ± 3.52% accuracy in FHS and 87.57 ± 1.19% accuracy in NACC, 80.52 ± 3.93%, 86.74 ± 1.63% sensitivity in FHS and NACC respectively, and 85.63 ± 4.71%, 88.41 ± 1.38% specificity in FHS and NACC, respectively. Discussion: Our results suggest that a subset of NP tests, when analyzed using unsupervised machine learning, may help distinguish between high‐ and low‐risk individuals in the context of subsequent development of AD within 5 years. This approach could be a viable option for early AD screening in clinical practice and clinical trials.

Background: Although some neuropsychological (NP) tests are considered more central for the diagnosis of Alzheimer disease (AD), there is a lack of understanding about the interaction between different cognitive tests. Objective: This study aimed to demonstrate a global view of hierarchical probabilistic dependencies between NP tests and the likelihood of cognitive impairment to assist physicians in recognizing AD precursors. Methods: Our study included 2091 participants from the Framingham Heart Study. These participants had undergone a variety of NP tests, including Wechsler Memory Scale, Wechsler Adult Intelligence Scale, and Boston Naming Test. Heterogeneous cognitive Bayesian networks were developed to understand the relationship between NP tests and the cognitive status. The performance of probabilistic inference was evaluated by the 10-fold cross validation. Results: A total of 4512 NP tests were used to build the Bayesian network for the dementia diagnosis. The network demonstrated conditional dependency between different cognitive functions that precede the development of dementia. The prediction model reached an accuracy of 82.24%, with sensitivity of 63.98% and specificity of 92.74%. This probabilistic diagnostic system can also be applied to participants that exhibit more heterogeneous profiles or with missing responses for some NP tests. Conclusions: We developed a probabilistic dependency network for AD diagnosis from 11 NP tests. Our study revealed important psychological functional segregations and precursor evidence of AD development and heterogeneity.

Because many older adults lack dental insurance and have limited or no access to dental care, it is essential to train future physicians to conduct brief oral health assessments on them. Likewise, interprofessional educational experiences are crucial in teaching medical students the skills necessary to provide comprehensive, team-based care to complex and vulnerable populations. Thus, this workshop was designed to increase fourth-year medical students' knowledge and confidence in performing oral health examinations on older adult patients using an interprofessional and hands-on approach. The curriculum includes an online presession self-study module followed by a 75-minute workshop. The workshop is comprised of a brief introduction (5 minutes), a lecture about the impact of oral health on older adults (30 minutes), a hands-on skill session practicing a focused oral exam led by dental students (30 minutes), and a large-group debrief and wrap-up (10 minutes). A pre-/postsurvey assessed learners' knowledge, attitudes, and confidence in oral health skills. The results were compared to a lecture-only format that was in place prior to the implementation of the workshop. In comparison to medical students who received the lecture-only format, those who participated in the workshop showed a greater increase in confidence and skills over time. The medical students also expressed interest in more frequent opportunities for collaborative learning experiences with dental students. This workshop was successful in introducing an interprofessional experience to medical students in order to ensure more comprehensive and coordinated care for older adult patients in the future.