Estimating presymptomatic episodic memory impairment using simple hand movement tests: A cross-sectional study of a large sample of older adults.

INTRODUCTION: Finding low-cost methods to detect early-stage Alzheimer's disease (AD) is a research priority for neuroprotective drug development. Presymptomatic Alzheimer's is associated with gait impairment but hand motor tests, which are more accessible, have hardly been investigated. This study evaluated how home-based Tasmanian (TAS) Test keyboard tapping tests predict episodic memory performance. METHODS: 1169 community participants (65.8 ± 7.4 years old; 73% female) without cognitive symptoms completed online single-key and alternate-key tapping tests and episodic memory, working memory, and executive function cognitive tests. RESULTS: All single-key (R2 adj  = 8.8%, ΔAIC = 5.2) and alternate-key (R2 adj  = 9.1%, ΔAIC = 8.8) motor features predicted episodic memory performance relative to demographic and mood confounders only (R2 adj  = 8.1%). No tapping features improved estimation of working memory. DISCUSSION: Brief self-administered online hand movement tests predict asymptomatic episodic memory impairment. This provides a potential low-cost home-based method for stratification of enriched cohorts. HIGHLIGHTS: We devised two brief online keyboard tapping tests to assess hand motor function. 1169 cognitively asymptomatic adults completed motor- and cognitive tests online. Impaired hand motor function predicted reduced episodic memory performance. This brief self-administered test may aid stratification of community cohorts.

Deep Learning-Based Identification of Intraocular Pressure-Associated Genes Influencing Trabecular Meshwork Cell Morphology.

Purpose: Genome-wide association studies have recently uncovered many loci associated with variation in intraocular pressure (IOP). Artificial intelligence (AI) can be used to interrogate the effect of specific genetic knockouts on the morphology of trabecular meshwork cells (TMCs) and thus, IOP regulation. Design: Experimental study. Subjects: Primary TMCs collected from human donors. Methods: Sixty-two genes at 55 loci associated with IOP variation were knocked out in primary TMC lines. All cells underwent high-throughput microscopy imaging after being stained with a 5-channel fluorescent cell staining protocol. A convolutional neural network was trained to distinguish between gene knockout and normal control cell images. The area under the receiver operator curve (AUC) metric was used to quantify morphological variation in gene knockouts to identify potential pathological perturbations. Main Outcome Measures: Degree of morphological variation as measured by deep learning algorithm accuracy of differentiation from normal controls. Results: Cells where LTBP2 or BCAS3 had been perturbed demonstrated the greatest morphological variation from normal TMCs (AUC 0.851, standard deviation [SD] 0.030; and AUC 0.845, SD 0.020, respectively). Of 7 multigene loci, 5 had statistically significant differences in AUC (P < 0.05) between genes, allowing for pathological gene prioritization. The mitochondrial channel most frequently showed the greatest degree of morphological variation (33.9% of cell lines). Conclusions: We demonstrate a robust method for functionally interrogating genome-wide association signals using high-throughput microscopy and AI. Genetic variations inducing marked morphological variation can be readily identified, allowing for the gene-based dissection of loci associated with complex traits. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Real-time automated detection of older adults' hand gestures in home and clinical settings.

There is an urgent need, accelerated by the COVID-19 pandemic, for methods that allow clinicians and neuroscientists to remotely evaluate hand movements. This would help detect and monitor degenerative brain disorders that are particularly prevalent in older adults. With the wide accessibility of computer cameras, a vision-based real-time hand gesture detection method would facilitate online assessments in home and clinical settings. However, motion blur is one of the most challenging problems in the fast-moving hands data collection. The objective of this study was to develop a computer vision-based method that accurately detects older adults' hand gestures using video data collected in real-life settings. We invited adults over 50 years old to complete validated hand movement tests (fast finger tapping and hand opening-closing) at home or in clinic. Data were collected without researcher supervision via a website programme using standard laptop and desktop cameras. We processed and labelled images, split the data into training, validation and testing, respectively, and then analysed how well different network structures detected hand gestures. We recruited 1,900 adults (age range 50-90 years) as part of the TAS Test project and developed UTAS7k-a new dataset of 7071 hand gesture images, split 4:1 into clear: motion-blurred images. Our new network, RGRNet, achieved 0.782 mean average precision (mAP) on clear images, outperforming the state-of-the-art network structure (YOLOV5-P6, mAP 0.776), and mAP 0.771 on blurred images. A new robust real-time automated network that detects static gestures from a single camera, RGRNet, and a new database comprising the largest range of individual hands, UTAS7k, both show strong potential for medical and research applications. Supplementary Information: The online version contains supplementary material available at 10.1007/s00521-022-08090-8.

Differences in clinical manifestations of late-onset, compared to earlier-onset essential tremor: A scoping review.

Across the world, Essential Tremor (ET) is the most common tremor diagnosis but up to half of these diagnoses are inaccurate. The misdiagnosis rate is particularly high in late-onset ET, when tremor begins after the age of 60 years. Currently, ET is reported to affect 5.5% of those over 65 years old and 21.7% aged over 95 but there is emerging evidence that late-onset ET has associations with dementia, mortality and more rapid progression. With ageing populations, and a range of new surgical treatments for ET, there is urgent need to clarify whether the clinical manifestations of late-onset ET are the same as for earlier-onset ET. This scoping review used MEDLINE, EMBASE and CINAHL as the information sources of published peer-reviewed research articles between 2011 and 2021. Analysis was done by narrative synthesis. 14 relevant papers were retrieved from studies conducted in Denmark, India, Italy, Germany, Spain and the US and, together, they comprised 7684 participants in total. Compared to older adults with earlier-onset ET, there is evidence that late-onset ET is associated with higher risk of cognitive impairment and dementia, higher mortality rate, faster rate of progression, lack of family history, altered cortical electrical activity, prolonged pupillary responses, and less propensity to demonstrate characteristic alcohol sensitivity. There is evidence that late-onset ET has different clinical manifestations to earlier-onset ET; in particular there is higher risk of dementia and mortality. The prognosis is important for clinicians to consider when selecting candidates for deep brain stimulation surgery and also for advanced care planning.

Recurrent intussusception in infants.

AIM: Clinical features to identify infants at increased risk of recurrence after a primary episode of intussusception (IS) are poorly defined. METHODS: Prospective study of the clinical presentation, treatment and outcome in infants <2 years presenting with acute IS to the National Hospital of Pediatrics, Hanoi, over a 14-month period (1 November 2002 to 31 December 2003). A retrospective review of medical records was performed to verify complete patient ascertainment. RESULTS: Five hundred ninety-eight children were recruited, including 513 (86%) with a primary episode only and 53 (9%) with ≥1 recurrent episodes. Another 32 (5%) infants presented with recurrent IS, but the primary episode of IS occurred outside the study period. Estimated recurrence risk at 6 months following a primary episode was 14%. A pathological lead point was rare in primary (n= 1) and recurrent IS (n= 1). Most infants were successfully treated with enema reduction. CONCLUSIONS: This study describes the natural history of recurrent IS in infants and may assist in interpreting data from post-marketing surveillance following introduction of rotavirus vaccines.

Hand tremor detection in videos with cluttered background using neural network based approaches.

With the increasing prevalence of neurodegenerative diseases, including Parkinson's disease, hand tremor detection has become a popular research topic because it helps with the diagnosis and tracking of disease progression. Conventional hand tremor detection algorithms involved wearable sensors. A non-invasive hand tremor detection algorithm using videos as input is desirable but the existing video-based algorithms are sensitive to environmental conditions. An algorithm, with the capability of detecting hand tremor from videos with a cluttered background, would allow the videos recorded in a non-research environment to be used. Clinicians and researchers could use videos collected from patients and participants in their own home environment or standard clinical settings. Neural network based machine learning architectures provide high accuracy classification results in related fields including hand gesture recognition and body movement detection systems. We thus investigated the accuracy of advanced neural network architectures to automatically detect hand tremor in videos with a cluttered background. We examined configurations with different sets of features and neural network based classification models. We compared the performance of different combinations of features and classification models and then selected the combination which provided the highest accuracy of hand tremor detection. We used cross validation to test the accuracy of the trained model predictions. The highest classification accuracy for automatically detecting tremor (vs non tremor) was 80.6% and this was obtained using Convolutional Neural Network-Long Short-Term Memory and features based on measures of frequency and amplitude change.

Sequence Classification Restricted Boltzmann Machines With Gated Units.

For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features. However, learning and inference in RTRBMs can be difficult because of the exponential nature of its gradient computations when maximizing log likelihoods. In this article, first, we address this intractability by optimizing a conditional rather than a joint probability distribution when performing sequence classification. This results in the "sequence classification restricted Boltzmann machine" (SCRBM). Second, we introduce gated SCRBMs (gSCRBMs), which use an information processing gate, as an integration of SCRBMs with long short-term memory (LSTM) models. In the experiments reported in this article, we evaluate the proposed models on optical character recognition, chunking, and multiresident activity recognition in smart homes. The experimental results show that gSCRBMs achieve the performance comparable to that of the state of the art in all three tasks. gSCRBMs require far fewer parameters in comparison with other recurrent networks with memory gates, in particular, LSTMs and gated recurrent units (GRUs).

Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks.

Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whether it can improve learning performance when it is available, and to the extraction of knowledge from trained deep networks, and whether it can offer a better understanding of the representations learned by such networks. To this end, we use a simple symbolic language-a set of logical rules that we call confidence rules-and show that it is suitable for the representation of quantitative reasoning in deep networks. We show by knowledge extraction that confidence rules can offer a low-cost representation for layerwise networks (or restricted Boltzmann machines). We also show that layerwise extraction can produce an improvement in the accuracy of deep belief networks. Furthermore, the proposed symbolic characterization of deep networks provides a novel method for the insertion of prior knowledge and training of deep networks. With the use of this method, a deep neural-symbolic system is proposed and evaluated, with the experimental results indicating that modularity through the use of confidence rules and knowledge insertion can be beneficial to network performance.