Since combining information from different domains could be useful to increase prediction accuracy over and above what can be achieved at the level of single category of markers, this study aimed to identify distinct and predominant subtypes, i.e., cognitive phenotypes, in people with multiple sclerosis (PwMS) considering both cognitive impairment and mood disorders. A latent class analysis (LCA) was applied on data from 872 PwMS who were tested with Montreal Cognitive Assessment (MoCA), Symbol Digit Modalities Test (SDMT) and Hospital Anxiety and Depression Scale (HADS). Furthermore, the distribution of demographic (i.e., age, gender, years of education) and clinical characteristics (i.e., disease duration, disease course, disability level) was examined amongst the identified phenotypes. Based on model fit and parsimony criteria, LCA identified four cognitive phenotypes: 1) only memory difficulties (n = 247; 28.3%); 2) minor memory and language deficits with mood disorders (n = 185; 21.2%); 3) moderate memory, language and attention impairments (n = 164; 18.8%); 4) severe memory, language, attention, information processing and executive functions difficulties (n = 276; 31.7%). Since less is known about the progressive deterioration of cognition in PwMS, a taxonomy of distinct subtypes that consider information from different clustered domains (i.e., cognition and mood) represents both a challenge and opportunity for an advanced understanding of cognitive impairments and development of tailored cognitive treatments in MS.

Mood disorders, such as depression and anxiety, are frequent in people with Multiple Sclerosis (PwMS). Although anxiety has a well-recognized negative influence on family, work and social life, it has received less attention than depression. Thus, it is still under debate which risk factors can predict anxiety, its evolution over time and the extent of its effect on disability progression. The aim of this retrospective study was to identify potential demographic, clinical and self-reported predictors that contribute to clinically significant anxiety at one-year follow up, measured by the anxiety subscale of the Hospital Anxiety and Depression Scale (HADS). Data was acquired from a cohort of 608 subjects with MS, and included domains potentially meaningful for clinically significant anxiety. Associations between each variable and clinically significant anxiety at one-year follow-up were assessed with univariate and multivariate logistic regression analyses. Lower educational level, relapsing-remitting disease course, presence of clinically significant anxiety at baseline, higher depression and fatigue perception were significant predictors for clinically significant anxiety at one-year follow up. Findings confirm the importance of identifying risk factors for clinically significant anxiety in predicting prognosis and planning early intervention.

Observers with autism spectrum disorders (ASDs) find it difficult to read intentions from movements. However, the computational bases of these difficulties are unknown. Do these difficulties reflect an intention readout deficit, or are they more likely rooted in kinematic (dis-)similarities between typical and ASD kinematics? We combined motion tracking, psychophysics, and computational analyses to uncover single-trial intention readout computations in typically developing (TD) children (n = 35) and children with ASD (n = 35) who observed actions performed by TD children and children with ASD. Average intention discrimination performance was above chance for TD observers but not for ASD observers. However, single-trial analysis showed that both TD and ASD observers read single-trial variations in movement kinematics. TD readers were better able to identify intention-informative kinematic features during observation of TD actions; conversely, ASD readers were better able to identify intention-informative features during observation of ASD actions. Crucially, while TD observers were generally able to extract the intention information encoded in movement kinematics, those with autism were unable to do so. These results extend existing conceptions of mind reading in ASD by suggesting that intention reading difficulties reflect both an interaction failure, rooted in kinematic dissimilarity between TD and ASD kinematics (at the level of feature identification), and an individual readout deficit (at the level of information extraction), accompanied by an overall reduced sensitivity of intention readout to single-trial variations in movement kinematics. Significance A major challenge in studying intention reading is high motor variability. Analyses conducted across trials provide insights into what happens on average; however, they may obscure how individual observers read intention information in individual movements. We combined motion tracking, psychophysics, and computational analyses to examine intention reading in autism spectrum disorders (ASDs) with single-trial resolution. Results revealed that a sizeable fraction of ASD observers can identify intention-informative variations in ASD (but not in typically developing) movement kinematics, but they are nonetheless unable to extract the encoded intention information. This approach not only enhances our basic understanding of mind reading in ASD but also provides potential avenues for the rational design of training procedures to improve the reading of others’ actions.

Everyday-life activities often require performing dual tasks (DT), with consequent possible occurrence of motor-cognitive or motor-motor interference. This could reduce quality of life, in particular in people with neurological diseases. However, there is lack of validated tools to assess the patients' perspective on DT difficulties in this population. Therefore, we developed the Dual-task Impact on Daily-living Activities-Questionnaire (DIDA-Q) and tested its psychometric properties in people with multiple sclerosis (PwMS). Items were generated based on existing scales, DT paradigms used in previous studies and the opinion of a multi-stakeholder group, including both experts and PwMS. Twenty DT constituted the preliminary version of the DIDA-Q which was administered to 230 PwMS. The psychometric properties of the scale were evaluated including internal consistency, validity and reliability. Nineteen items survived after exploratory factor analysis, showing a three-factor solution which identifies the components mostly contributing to DT perceived difficulty (i.e., balance and mobility, cognition and upper-limb ability). The DIDA-Q appropriately fits the graded response model, with first evaluations supporting internal consistency (Cronbach's alpha=0.95), validity (70% of the hypotheses for convergent and discriminant constructs confirmed) and reliability (intraclass correlation coefficients=0.95) of this tool. The DIDA-Q could be used in research and clinical settings to discriminate individuals with low vs. high cognitive-motor or motor-motor interference, and to develop and evaluate the efficacy of personalized DT rehabilitative treatments in PwMS.

Lower urinary tract symptoms (LUTS) are common in individuals with multiple sclerosis (MS), and can have a significant impact on quality of life (QoL). Prevalence of LUTS in MS ranges from 32% to 96.8%, including storage or voiding symptoms or a combination of these. Post-void residual (PVR) is a very well-tolerated, non-invasive test for evaluating voiding dysfunction. The aim of the current study was to describe the distribution of PVR volumes across MS subjects with and without LUTS and to examine relationships between storage symptoms, voiding symptoms, and PVR. A large group of subjects (N = 501) completed a questionnaire on LUTS (current bladder management, number of urinary tract infections in the last year and urological investigations). A bladder ultrasound for PVR was performed and data were collected. We used Chi-Square and the Mann-Whitney non-parametric tests respectively for categorical and continuous variables in order to assess differences between symptomatic and asymptomatic groups. The differences of PVR by LUTS status were explored using the Mann-Whitney non-parametric test for independent samples. Overall mean PVR was 132.4 mL (PVR>100 ml was considered an abnormal residual urine volume). Based on the LUTS questionnaire, 43 subjects (8.6%) were asymptomatic, while 458 subjects (91.4%) reported at least one LUTS. Storage-related symptoms were reported by 87.2% of subjects (437) and 65.1% (326) reported at least one voiding-related symptom. Two-third of subjects (66.5%) reported three or more LUTS. There was a statistically significant association between the presence of LUTS and the number of infections reported (p = 0.0015). The symptomatic group had significantly higher PVR than the asymptomatic group (p = 0.011). PVR significantly correlated with male gender, disability level and a progressive disease course. Results showed a high prevalence of LUTS in subjects with MS and that storage symptoms are predominant. There was an association between the presence of LUTS and a progressive disease course. The relationship between LUTS, higher PVR and the severity of disease course indicates that a comprehensive clinical evaluation should include an assessment of both neurological and micturition disorders and, importantly, PVR should be measured at every clinical assessment, despite the presence or absence of LUTS.

Background: Mobile health (mHealth) apps have proven useful for people with multiple sclerosis (MS). Thus, easy-to-use digital solutions are now strongly required to assess and monitor cognitive impairment, one of the most disturbing symptoms in MS that is experienced by almost 43% to 70% of people with MS. Therefore, we developed DIGICOG-MS (Digital assessment of Cognitive Impairment in Multiple Sclerosis), a smartphone- and tablet-based mHealth app to self-assess cognitive impairment in MS. Objective: This study aimed to test the validity and usability of the novel mHealth app with a sample of people with MS. Methods: DIGICOG-MS includes 4 digital tests assumed to evaluate the most affected cognitive domains in MS (visuospatial memory [VSM], verbal memory [VM], semantic fluency [SF], and information processing speed [IPS]) and inspired by traditional paper-based tests that assess the same cognitive functions (10/36 Spatial Recall Test, Rey Auditory Verbal Learning Test, Word List Generation, Symbol Digit Modalities Test). Participants were asked to complete both digital and traditional assessments in 2 separate sessions. Convergent validity was analyzed using the Pearson correlation coefficient to determine the strength of the associations between digital and traditional tests. To test the app’s reliability, the agreement between 2 repeated measurements was assessed using intraclass correlation coefficients (ICCs). Usability of DIGICOG-MS was evaluated using the System Usability Scale (SUS) and mHealth App Usability Questionnaire (MAUQ) administered at the conclusion of the digital session. Results: The final sample consisted of 92 people with MS (60 women) followed as outpatients at the Italian Multiple Sclerosis Society (AISM) Rehabilitation Service of Genoa (Italy). They had a mean age of 51.38 (SD 11.36) years, education duration of 13.07 (SD 2.74) years, disease duration of 12.91 (SD 9.51) years, and a disability level (Expanded Disability Status Scale) of 3.58 (SD 1.75). Relapsing-remitting MS was most common (68/92, 74%), followed by secondary progressive (15/92, 16%) and primary progressive (9/92, 10%) courses. Pearson correlation analyses indicated significantly strong correlations for VSM, VM, SF, and IPS (all P<.001), with r values ranging from 0.58 to 0.78 for all cognitive domains. Test-retest reliability of the mHealth app was excellent (ICCs>0.90) for VM and IPS and good for VSM and SF (ICCs>0.80). Moreover, the SUS score averaged 84.5 (SD 13.34), and the mean total MAUQ score was 104.02 (SD 17.69), suggesting that DIGICOG-MS was highly usable and well appreciated. Conclusions: The DIGICOG-MS tests were strongly correlated with traditional paper-based evaluations. Furthermore, people with MS positively evaluated DIGICOG-MS, finding it highly usable. Since cognitive impairment poses major limitations for people with MS, these findings open new paths to deploy digital cognitive tests for MS and further support the use of a novel mHealth app for cognitive self-assessment by people with MS in clinical practice.