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1. Joint High-Order Multi-Task Feature Learning to Predict the Progression of Alzheimer's Disease

   https://doi.org/10.1007/978-3-030-00928-1_63  

   Brand, Lodewijk; Wang, Hua; Huang, Heng; Risacher, Shannon; Saykin, Andrew; Shen, Li (September 2018, The Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018))

   Alzheimer’s disease (AD) is a degenerative brain disease that affects millions of people around the world. As populations in the United States and worldwide age, the prevalence of Alzheimer’s disease will only increase. In turn, the social and financial costs of AD will create a difficult environment for many families and caregivers across the globe.By combining genetic information, brain scans, and clinical data, gathered over time through the Alzheimer’s Disease Neuroimaging Initiative(ADNI), we propose a newJoint High-Order Multi-Modal Multi-Task Feature Learning method to predict the cognitive performance and diagnosis of patients with and without AD. 

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2. Cognitive measures during walking with and without lower-limb prosthesis: protocol for a scoping review

   https://doi.org/10.1136/bmjopen-2020-039975  

   Yuan, Jing; Cline, Emily; Liu, Ming; Huang, He; Feng, Jing (February 2021, BMJ Open)

   null (Ed.)

   Introduction Tuning of lower-limb (LL) robotic prosthesis control is necessary to provide personalised assistance to each human wearer during walking. Prostheses wearers’ adaptation processes are subjective and the efficiency largely depends on one’s mental processes. Therefore, beyond physical motor performance, prosthesis personalisation should consider the wearer’s preference and cognitive performance during walking. As a first step, it is necessary to examine the current measures of cognitive performance when a wearer walks with an LL prosthesis, identify the gaps and methodological considerations, and explore additional measures in a walking setting. In this protocol, we outlined a scoping review that will systematically summarise and evaluate the measures of cognitive performance during walking with and without LL prosthesis. Methods and analysis The review process will be guided and documented by CADIMA, an open-access online data management portal for evidence synthesis. Keyword searches will be conducted in seven databases (Web of Science, MEDLINE, BIOSIS, SciELO Citation Index, ProQuest, CINAHL and PsycINFO) up to 2020 supplemented with grey literature searches. Retrieved records will be screened by at least two independent reviewers on the title-and-abstract level and then the full-text level. Selected studies will be evaluated for reporting bias. Data on sample characteristics, type of cognitive function, characteristics of cognitive measures, task prioritisation, experimental design and walking setting will be extracted. Ethics and dissemination This scoping review will evaluate the measures used in previously published studies thus does not require ethical approval. The results will contribute to the advancement of prosthesis tuning processes by reviewing the application status of cognitive measures during walking with and without prosthesis and laying the foundation for developing needed measures for cognitive assessment during walking. The results will be disseminated through conferences and journals. 

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3. Aberrant features of in vivo striatal dynamics in Parkinson's disease

   https://doi.org/10.1002/jnr.24519  

   Lee, Kwang; Masmanidis, Sotiris C. (September 2019, Journal of Neuroscience Research)

   Abstract The striatum plays an important role in learning, selecting, and executing actions. As a major input hub of the basal ganglia, it receives and processes a diverse array of signals related to sensory, motor, and cognitive information. Aberrant neural activity in this area is implicated in a wide variety of neurological and psychiatric disorders. It is therefore important to understand the hallmarks of disrupted striatal signal processing. This review surveys literature examining howin vivostriatal microcircuit dynamics are impacted in animal models of one of the most widely studied movement disorders, Parkinson's disease. The review identifies four major features of aberrant striatal dynamics: altered relative levels of direct and indirect pathway activity, impaired information processing by projection neurons, altered information processing by interneurons, and increased synchrony. 

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4. Unsupervised Machine Learning to Identify Separable Clinical Alzheimer’s Disease Sub-Populations

   https://doi.org/10.3390/brainsci11080977  

   Prakash, Jayant; Wang, Velda; Quinn, Robert E.; Mitchell, Cassie S. (August 2021, Brain Sciences)

   Heterogeneity among Alzheimer’s disease (AD) patients confounds clinical trial patient selection and therapeutic efficacy evaluation. This work defines separable AD clinical sub-populations using unsupervised machine learning. Clustering (t-SNE followed by k-means) of patient features and association rule mining (ARM) was performed on the ADNIMERGE dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Patient sociodemographics, brain imaging, biomarkers, cognitive tests, and medication usage were included for analysis. Four AD clinical sub-populations were identified using between-cluster mean fold changes [cognitive performance, brain volume]: cluster-1 represented least severe disease [+17.3, +13.3]; cluster-0 [−4.6, +3.8] and cluster-3 [+10.8, −4.9] represented mid-severity sub-populations; cluster-2 represented most severe disease [−18.4, −8.4]. ARM assessed frequently occurring pharmacologic substances within the 4 sub-populations. No drug class was associated with the least severe AD (cluster-1), likely due to lesser antecedent disease. Anti-hyperlipidemia drugs associated with cluster-0 (mid-severity, higher volume). Interestingly, antioxidants vitamin C and E associated with cluster-3 (mid-severity, higher cognition). Anti-depressants like Zoloft associated with most severe disease (cluster-2). Vitamin D is protective for AD, but ARM identified significant underutilization across all AD sub-populations. Identification and feature characterization of four distinct AD sub-population “clusters” using standard clinical features enhances future clinical trial selection criteria and cross-study comparative analysis. 

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5. Haptic Technology Interaction Framework in Engineering Learning: A Taxonomical Conceptualization

   https://doi.org/10.1002/cae.70009  

   Mutis, Ivan; Oberemok, Marina (March 2025, Computer Applications in Engineering Education)

   Iskander, Magdy F (Ed.)

   ABSTRACT Innovative technology helps students foster creative thinking and problem‐solving abilities by augmenting human sensing and enriching input and output information. New technology can incorporate haptic sensing features—a sensing modality for user operations. Learning with haptic sensing features promises new ways to master cognitive and motor skills and higher‐order cognitive reasoning tasks (e.g., decision‐making and problem‐solving). This study conceptualizes haptic technology within the human‐technology interaction (HTI) framework. It aims to investigate the components of haptic systems to define their impact on learning and facilitate understanding of haptic technology, including application development to ease entry barriers for educators. The research builds a haptic HTI framework based on a systematic literature review on haptic applications in engineering learning over the last two decades. The review utilizes the SALSA methodology to analyze relevant studies comprehensively. The framework outcome is a haptic HTI taxonomy to build visual representations of the explicit connection between the taxonomy components and practical educational applications (by means of heatmaps). The approach led to a robust conceptualization of HTI into a taxonomy—a structured framework encompassing categories for interaction modalities, immersive technologies, and learning methodologies in engineering education. The model assists in understanding how haptic feedback can be utilized in learning with technology experiences. Applying haptic technology in engineering education includes mastering fundamental science concepts and creating customized haptic prototypes for engineering processes. A growing trend focuses on wearable haptics, such as gloves and vests, which involve kinesthetic movement, fine motor skills, and spatial awareness—all fostering spatial and temporal cognitive abilities (the ability to effectively manage and comprehend significant amounts ofspatial(how design components or resources are related to one another in the 3D space) andtemporal(the logic in a process, such as the order, sequences, and hierarchies of the resources information). The haptic human‐technology interaction (H‐HTI) framework guides future research in developing cognitive reasoning through H‐HTI, unlocking new frontiers in engineering education. 

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