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1. Phenotyping Cognitive Impairment using Graphomotor and Latency Features in Digital Clock Drawing Test*

   https://doi.org/10.1109/EMBC44109.2020.9176469  

   Davoudi, Anis; Dion, Catherine; Amini, Shawna; Libon, David J.; Tighe, Patrick J.; Price, Catherine C.; Rashidi, Parisa (July 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))

   null (Ed.)

   The Clock Drawing Test, where the participant is asked to draw a clock from memory and copy a model clock, is widely used for screening of cognitive impairment. The digital version of the clock test, the digital clock drawing test (dCDT), employs accelerometer and pressure sensors of a digital pen to capture time and pressure information from a participant's performance in a granular digital format. While visual features of the clock drawing test have previously been studied, little is known about the relationship between demographic and cognitive impairment characteristics with dCDT latency and graphomotor features. Here, we examine dCDT feature clusters with respect to sociodemographic and cognitive impairment outcomes. Our results show that the clusters are not significantly different in terms of age and gender, but did significantly differ in terms of education, Mini-Mental State Exam scores, and cognitive impairment diagnoses.This study shows that features extracted from digital clock drawings can provide important information regarding cognitive reserve and cognitive impairments. 

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2. Reliability of energy landscape analysis of resting‐state functional MRI data

   https://doi.org/10.1111/ejn.16390  

   Khanra, Pitambar; Nakuci, Johan; Muldoon, Sarah; Watanabe, Takamitsu; Masuda, Naoki (June 2024, European Journal of Neuroscience)

   Abstract Energy landscape analysis is a data‐driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test–retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e. within‐participant reliability) than across different sets of sessions from different participants (i.e. between‐participant reliability). We show that the energy landscape analysis has significantly higher within‐participant than between‐participant test–retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test–retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual‐level energy landscape analysis for given data sets with a statistically controlled reliability. 

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3. Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models

   https://doi.org/10.1002/alz.13886  

   Amini, Samad; Hao, Boran; Yang, Jingmei; Karjadi, Cody; Kolachalama, Vijaya_B; Au, Rhoda; Paschalidis, Ioannis_C (June 2024, Alzheimer's & Dementia)

   Abstract INTRODUCTIONIdentification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. METHODSWe applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews ofn = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. RESULTSOur best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI‐to‐AD progression within 6 years. DISCUSSIONThe proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy‐to‐administer screening tool for MCI‐to‐AD progression prediction, facilitating development of remote assessment. HighlightsVoice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment.The study leveraged AI methods for speech recognition and processed the resulting text using language models.The developed AI‐powered pipeline can lead to fully automated assessment that could enable remote and cost‐effective screening and prognosis for Alzehimer's disease. 

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4. Multimodal Fusion of Smart Home and Text-based Behavior Markers for Clinical Assessment Prediction

   https://doi.org/10.1145/3531231  

   Sprint, Gina; Cook, Diane J.; Schmitter-Edgecombe, Maureen; Holder, Lawrence B. (October 2022, ACM Transactions on Computing for Healthcare)

   New modes of technology are offering unprecedented opportunities to unobtrusively collect data about people's behavior. While there are many use cases for such information, we explore its utility for predicting multiple clinical assessment scores. Because clinical assessments are typically used as screening tools for impairment and disease, such as mild cognitive impairment (MCI), automatically mapping behavioral data to assessment scores can help detect changes in health and behavior across time. In this article, we aim to extract behavior markers from two modalities, a smart home environment and a custom digital memory notebook app, for mapping to 10 clinical assessments that are relevant for monitoring MCI onset and changes in cognitive health. Smart-home-based behavior markers reflect hourly, daily, and weekly activity patterns, while app-based behavior markers reflect app usage and writing content/style derived from free-form journal entries. We describe machine learning techniques for fusing these multimodal behavior markers and utilizing joint prediction. We evaluate our approach using three regression algorithms and data from 14 participants with MCI living in a smart-home environment. We observed moderate to large correlations between predicted and ground-truth assessment scores, ranging from r = 0.601 to r = 0.871 for each clinical assessment. 

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5. An Analysis of Low-Scoring Blind and Low Vision Individuals; Selected Answers on a Tactile Spatial Ability Instrument

   Kane, D.; Shaheen, N. L.; and Goodridge, W. H. (June 2023, 2023 ASEE Annual Conference & Exposition)

   Spatial ability has been shown through numerous studies to be a strong predictor of student success in STEM fields. Beyond the classroom, professionals demonstrating higher levels of spatial ability are also more likely to be successful in their STEM careers than their peers with lower spatial ability. Research has also shown that spatial ability is a malleable skill that can be strengthened through targeted intervention and leads to better retention in rigorous STEM fields. For this reason, spatial ability has been a significant focus of engineering education research. Despite the focus on spatial ability in engineering education research, members of the blind and low vision (BLV) population have largely been omitted from research in this area, likely due to the lack of a nonvisually accessible instrument for measuring spatial ability in a tactile format. This work utilizes the Tactile Mental Cutting Test (TMCT), a fully accessible adaptation of the commonly used multiple-choice Mental Cutting Test (MCT) spatial ability instrument which requires participants to identify cross sectional outlines from a three-dimensional object with a cut through it. This paper explores data collected from BLV participants who completed a TMCT test at National Federation of the Blind (NFB) sponsored summer programs for BLV youth, blindness training centers, and state and national NFB conventions. Raw scores from each TMCT participant were analyzed and ranked into high, medium, and low performing groups to help identify main characteristics of each group. In this study we examined patterns in the selected answer choices of the low scoring group to determine frequency of participant selection of distractors for each item of the TMCT. Analysis of the low-performer scores indicate that the majority of low scoring participants select incorrect answer choices that represent a side view or top view of the TMCT object as opposed to the true cross-sectional shape. Furthermore, the results suggest that certain answer choices may be overly difficult to distinguish between due to the tactile format of the exam. Results from this study can inform academia of the inherent differences between tactile and traditional spatial ability instruments and aid in the design of new tactile instruments. 

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