A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia.

Friedreich's ataxia (FA) is caused by a variant of the Frataxin (FXN) gene, leading to its downregulation and progressively impaired cardiac and neurological function. Current gold-standard clinical scales use simplistic behavioral assessments, which require 18- to 24-month-long trials to determine if therapies are beneficial. Here we captured full-body movement kinematics from patients with wearable sensors, enabling us to define digital behavioral features based on the data from nine FA patients (six females and three males) and nine age- and sex-matched controls, who performed the 8-m walk (8-MW) test and 9-hole peg test (9 HPT). We used machine learning to combine these features to longitudinally predict the clinical scores of the FA patients, and compared these with two standard clinical assessments, Spinocerebellar Ataxia Functional Index (SCAFI) and Scale for the Assessment and Rating of Ataxia (SARA). The digital behavioral features enabled longitudinal predictions of personal SARA and SCAFI scores 9 months into the future and were 1.7 and 4 times more precise than longitudinal predictions using only SARA and SCAFI scores, respectively. Unlike the two clinical scales, the digital behavioral features accurately predicted FXN gene expression levels for each FA patient in a cross-sectional manner. Our work demonstrates how data-derived wearable biomarkers can track personal disease trajectories and indicates the potential of such biomarkers for substantially reducing the duration or size of clinical trials testing disease-modifying therapies and for enabling behavioral transcriptomics

The Muslim headscarf and face perception: "they all look the same, don't they?"

YesThe headscarf conceals hair and other external features of a head (such as the ears). It therefore may have implications for the way in which such faces are perceived. Images of faces with hair (H) or alternatively, covered by a headscarf (HS) were used in three experiments. In Experiment 1 participants saw both H and HS faces in a yes/no recognition task in which the external features either remained the same between learning and test (Same) or switched (Switch). Performance was similar for H and HS faces in both the Same and Switch condition, but in the Switch condition it dropped substantially compared to the Same condition. This implies that the mere presence of the headscarf does not reduce performance, rather, the change between the type of external feature (hair or headscarf) causes the drop in performance. In Experiment 2, which used eye-tracking methodology, it was found that almost all fixations were to internal regions, and that there was no difference in the proportion of fixations to external features between the Same and Switch conditions, implying that the headscarf influenced processing by virtue of extrafoveal viewing. In Experiment 3, similarity ratings of the internal features of pairs of HS faces were higher than pairs of H faces, confirming that the internal and external features of a face are perceived as a whole rather than as separate components.The Educational Charity of the Federation of Ophthalmic and Dispensing Opticians

CT scan quiz: A 13-year-old boy who fell from height

link_to_OA_fulltex

Data-derived wearable digital biomarkers predict Frataxin gene expression levels and longitudinal disease progression in Friedreich’s Ataxia

Friedreich’s ataxia (FA) is caused by repression of the Frataxin gene which impacts patients’ motor behaviour. With current gold-standard clinical scales, it requires 18-24 month-long clinical trials to determine if disease-modifying therapies are beneficial. Our approach captures the full-body movement kinematics from human subjects using a wearable motion-capture-suit. We extracted digital behavioural features from the movement data (eight-meter walk (8MW) and nine-hole peg test (9HPT)) that can distinguish FA patients and controls and then used machine learning to combine these features to longitudinally predict two different ‘gold-standard’ clinical scales (SCAFI34 and SARA). These predictions outperformed predictions from the clinical scales (leave-one-subject-out cross-validated R2 using suit features of 8MW and 9HPT tasks: 0.80 and 0.85 vs R2 of 0.47 using SARA). Unlike the clinical scales, our wearable-derived digital features can accurately cross-sectionally predict for each patient their personal FXN gene expression levels (R2 of suit features of 8MW and 9HPT: 0.59 and 0.53 vs R2 of SARA and SCAFI: 0.01 and 0.01), demonstrating the sensitivity of our approach and the importance of FXN levels in FA. Our work demonstrates that data-derived wearable biomarkers have the potential to substantially reduce clinical trial durations