A tablet-based quantitative assessment of manual dexterity for detection of early psychosis

BackgroundWe performed a pilot study on whether tablet-based measures of manual dexterity can provide behavioral markers for detection of first-episode psychosis (FEP), and whether cortical excitability/inhibition was altered in FEP.MethodsBehavioral and neurophysiological testing was undertaken in persons diagnosed with FEP (N = 20), schizophrenia (SCZ, N = 20), autism spectrum disorder (ASD, N = 20), and in healthy control subjects (N = 20). Five tablet tasks assessed different motor and cognitive functions: Finger Recognition for effector (finger) selection and mental rotation, Rhythm Tapping for temporal control, Sequence Tapping for control/memorization of motor sequences, Multi Finger Tapping for finger individuation, and Line Tracking for visuomotor control. Discrimination of FEP (from other groups) based on tablet-based measures was compared to discrimination through clinical neurological soft signs (NSS). Cortical excitability/inhibition, and cerebellar brain inhibition were assessed with transcranial magnetic stimulation.ResultsCompared to controls, FEP patients showed slower reaction times and higher errors in Finger Recognition, and more variability in Rhythm Tapping. Variability in Rhythm Tapping showed highest specificity for the identification of FEP patients compared to all other groups (FEP vs. ASD/SCZ/Controls; 75% sensitivity, 90% specificity, AUC = 0.83) compared to clinical NSS (95% sensitivity, 22% specificity, AUC = 0.49). Random Forest analysis confirmed FEP discrimination vs. other groups based on dexterity variables (100% sensitivity, 85% specificity, balanced accuracy = 92%). The FEP group had reduced short-latency intra-cortical inhibition (but similar excitability) compared to controls, SCZ, and ASD. Cerebellar inhibition showed a non-significant tendency to be weaker in FEP.ConclusionFEP patients show a distinctive pattern of dexterity impairments and weaker cortical inhibition. Easy-to-use tablet-based measures of manual dexterity capture neurological deficits in FEP and are promising markers for detection of FEP in clinical practice

Common vs. Distinct Visuomotor Control Deficits in Autism Spectrum Disorder and Schizophrenia

International audienceAutism spectrum disorder (ASD) and schizophrenia (SCZ) are neurodevelopmental disorders with partly overlapping clinical phenotypes including sensorimotor impairments. However, direct comparative studies on sensorimotor control across these two disorders are lacking. We set out to compare visuomotor upper limb impairment, quantitatively, in ASD and SCZ. Patients with ASD (N = 24) were compared to previously published data from healthy control participants (N = 24) and patients with SCZ (N = 24). All participants performed a visuomotor grip force-tracking task in single and dual-task conditions. The dual-task (high cognitive load) presented either visual distractors or required mental addition during grip force-tracking. Motor inhibition was measured by duration of force release and from principal component analysis (PCA) of the participant's force-trajectory. Common impairments in patients with ASD and SCZ included increased force-tracking error in single-task condition compared to controls, a further increase in error in dual-task conditions, and prolonged duration of force release. These three sensorimotor impairments were found in both patient groups. In contrast, distinct impairments in patients with ASD included greater error under high cognitive load and delayed onset of force release compared to SCZ. The PCA inhibition component was higher in ASD than SCZ and controls, correlated to duration of force release, and explained group differences in tracking error. In conclusion, sensorimotor impairments related to motor inhibition are common to ASD and SCZ, but more severe in ASD, consistent with enhanced neurodevelopmental load in ASD. Furthermore, impaired motor anticipation may represent a further specific impairment in ASD. LAY SUMMARY: Autism spectrum disorder (ASD) and schizophrenia (SCZ) are neurodevelopmental disorders with partly overlapping and partly distinct clinical symptoms. Sensorimotor impairments rank among these symptoms, but it is less clear whether they are shared or distinct. In this study, we showed using a grip force task that sensorimotor impairments related to motor inhibition are common to ASD and SCZ, but more severe in ASD. Impaired motor anticipation may represent a further specific impairment in ASD

Model data: functional consequences of gain changes.

<p><b>A</b>. Single trial runs with identical seed for a simulated average control subject (left) and an average schizophrenia patient (right) at the low force level. <b>C–F</b>. Performance measures as a function of gains. Twenty runs with pseudo-randomized initial seeds were computed for each condition. Performance measures (mean ± SD) were calculated similar to the empirical data. Black: low force condition (F_L), gray: high force condition (F_H). <b>C, E</b>. Influence of SDN-gain on relative error (C) and on release duration (E). Increasing SDN-gains provides higher relative error (and higher CV, not shown), but has no effect on release duration. In C, stippled vertical lines indicate the average SDN_gain for controls (0.016) and patients (0.028). <b>D, F</b>. Impact of inhibition-gain on relative error (D), and on release duration (F). Increasing inhibitory gain has little effect on relative error (and CV, not shown), but decreases the release duration. In F, stippled vertical lines indicate the average I_Gain for controls (0.2) and patients (0.12). Note that, for a given gain, error and CV are always higher for the low force compared to the high force condition (c.f. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111853#pone-0111853-g001" target="_blank">Fig. 1C, D</a>). <b>B</b>. Relation between I_Gain and SDN_Gain after fitting the gains to each subject's performance. There is a significant negative correlation (regression line stippled), across the whole population [controls, medicated patients, and non-medicated patients (NMP)], with patients tending to have lower I_Gains and higher SDN_Gains. Note: this resembles the correlation found empirically between mean error and release duration (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111853#s3" target="_blank">Results</a>).</p

Block-scheme of the computational model of sensorimotor integration for grip force tracking.

<p>Three input signals are integrated to form a motor command: (i) visual information on the ramp-hold-and-release target force trajectory for two different force levels (F_L, F_H for low and high force, respectively), (ii) inhibition modulated as a function of target force (stronger modulation for high force F_H, weaker modulation for low force F_L condition), and (iii) tactile/proprioceptive feedback (a function of force). Each input signal has a gain (gray triangle): the sum of these gains needs to be  = 1. Grip force (the model output) is regulated by a negative feedback-controller as a function of the error between the motor command and the actual grip force. Signal-dependent noise (SDN), with an adjustable gain (black-and-white triangle) is added to the grip force. The goal is to simulate empirically observed behavioral differences between patients and control subjects. Main assumption: a change in gains is sufficient to explain the behavioral difference.</p

Clinical and demographic details.

<p>Clinical and demographic details.</p

Comparison between model and empirical performance in grip force tracking.

<p>Comparison between model and empirical performance in grip force tracking.</p

Separate PCA of force tracking traces for control subjects and patients.

<p><b>A</b>. Average force trace for each control subject (left) and each patient (medicated and non-medicated patients pooled, right). Note higher variations of baseline force in patients. <b>B–D</b>. PC loading as a function of time for PC1, PC2 and PC3, respectively. <b>B</b>. PC1 loading as a function of time for controls (left) and patients (right). Strong resemblance to force trace present in controls, less so in patients. <b>C</b>. PC2 loading as a function of time for controls (left) and patients (right). Resemblance to the inverse force profile in both groups. <b>D</b>. PC3 loading as a function of time for controls (left) and patients (right). Strongest loading during force transitions (ramp and release) for both groups.</p

PCA of force tracking traces across subjects.

<p><b>A</b>. Average force trace over all conditions and subjects (N = 38). <b>B–D</b>. PC loading as a function of time for PC1, PC2 and PC3, respectively. <b>B</b>. Loading profile similar to force profile for PC1, positive and increasing scores during ramp, more stable and strongest positive scores during hold. <b>C</b>. Inverse loading profile compared to force for PC2. <b>D</b>. Strongest loading during force transitions (ramp and release) for PC3. <b>E</b>. Average factor score (±SD) for PC1, PC2 and PC3 for control subjects vs medicated patients, and non-medicated patients (NMP). Significant difference between controls and both groups of patients only found for PC2 (more negative scores for controls: asterisk). <b>F</b>. Positive correlation between PC2 factor score and release duration for control subjects and patients. Correlation remained significant with exclusion of outlier subject (p = 0.003). No correlation was found between PC2 factor score and relative error or CV (p>0.5). <b>G</b>. Positive rank correlation between PC2 factor scores and PANSS scores in patients.</p

A novel tablet-based application for assessment of manual dexterity and its components: a reliability and validity study in healthy subjects

International audienceBackground: We developed five tablet-based tasks (applications) to measure multiple components of manual dexterity.Aim: to test reliability and validity of tablet-based dexterity measures in healthy participants.Methods: Tasks included: (1) Finger recognition to assess mental rotation capacity. The subject taps with the finger indicated on a virtual hand in three orientations (reaction time, correct trials). (2) Rhythm tapping to evaluate timing of finger movements performed with, and subsequently without, an auditory cue (inter-stimulus interval). (3) Multi-finger tapping to assess independent finger movements (reaction time, correct trials, unwanted finger movements). (4) Sequence tapping to assess production and memorization of visually cued finger sequences (successful taps). (5) Line-tracking to assess movement speed and accuracy while tracking an unpredictably moving line on the screen with the fingertip (duration, error). To study inter-rater reliability, 34 healthy subjects (mean age 35 years) performed the tablet tasks twice with two raters. Relative reliability (Intra-class correlation, ICC) and absolute reliability (Standard error of measurement, SEM) were established. Task validity was evaluated in 54 healthy subjects (mean age 49 years, range: 20-78 years) by correlating tablet measures with age, clinical dexterity assessments (time taken to pick-up objects in Box and Block Test, BBT and Moberg Pick Up Test, MPUT) and with measures obtained using a finger force-sensor device.Results: Most timing measures showed excellent reliability. Poor to excellent reliability was found for correct trials across tasks, and reliability was poor for unwanted movements. Inter-session learning occurred in some measures. Age correlated with slower and more variable reaction times in finger recognition, less correct trials in multi-finger tapping, and slower line-tracking. Reaction times correlated with those obtained using a finger force-sensor device. No significant correlations between tablet measures and BBT or MPUT were found. Inter-task correlation among tablet-derived measures was weak.Conclusions: Most tablet-based dexterity measures showed good-to-excellent reliability (ICC ≥ 0.60) except for unwanted movements during multi-finger tapping. Age-related decline in performance and association with finger force-sensor measures support validity of tablet measures. Tablet-based components of dexterity complement conventional clinical dexterity assessments. Future work is required to establish measurement properties in patients with neurological and psychiatric disorders

Theory of mind and schizophrenia in young and middle-aged patients: Influence of executive functions

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