Longitudinal study of disease severity and external factors in cognitive failure after COVID-19 among Indonesian population.

The COVID-19 infection is assumed to induce cognitive failure. Identifying the relationship between COVID-19, the effect of vaccination and medication, and accommodating non-COVID-19 factors to cognitive failure is essential. This study was conducted in Indonesia from September 2021 to January 2023. Demographic information, clinical data, comorbidities, vaccination, and medication during COVID-19 were obtained, as well as a 6-month cognitive assessment with Cognitive Failures Questionnaire/CFQ, Fatigue Severity Score, and Generalized Anxiety Disorder (GAD-7). A Structural Equation Model explains the relationship between potential predictors and cognitive failure. The average score of CFQ after 6 months was 45.6 ± 23.1 out of 100. The severity of the disease, which was associated with vaccination status, age, previous infection, and unit of treatment (p < 0.05), was not related to cognitive failure (p = 0.519), although there is a significant direct impact of worst vaccination status to cognitive failure(p < 0.001). However, age, fatigue, and current anxiety were associated with higher cognitive failure (p < 0.001), although comorbidities and recent headaches were not significant in other models (p > 0.05). This study concludes that cognitive failure after COVID-19 is a multifactorial event and does not solely depend on COVID-19 severity. It is crucial to re-address the factors related to the long-term efficacy of vaccination and medication and focus on non-health factors affecting cognitive failure.Trial Registration: NCT05060562.

COVID-19 transmission risk in Surabaya and Sidoarjo: an inhomogeneous marked Poisson point process approach.

Understanding the spatio-temporal dynamics of COVID-19 transmission is necessary to plan better strategies for controlling the spread of the disease. However, only a few studies explore the COVID-19 transmission risk over a fine spatial resolution while considering relevant spatial and temporal factors. To this aim, we consider an inhomogeneous marked Poisson point process model to assess COVID-19 transmission risk using data of home addresses of confirmed cases, in relation to locations of sources of crowd (enterprise, market, and place of worship) and population density in Surabaya and Sidoarjo, Indonesia. Our marked model is able to analyze how the spatial covariates are varying with time, helping authorities to evaluate the information of covariates depending on the period in which restrictions are taking place. Our results show that enterprise, place of worship, and population densities have significant impact to the transmission risk in Surabaya and Sidoarjo. We finally provide predicted risk maps which provide additional information based on the demographic-based risk analysis to help conduct more efficient testing, tracing, and vaccination programs.

Ipsilateral hand impairment predicts long-term outcome in patients with subacute stroke.

BACKGROUND: Ipsilateral hand (ILH) impairment is documented following motor stroke, but its impact on long-term outcome remains unknown. We assessed ILH impairment in subacute stroke and tested whether ILH impairment predicted long-term outcome. METHODS: We performed a longitudinal study in 209 consecutive patients with unilateral stroke and sensorimotor deficit at admission. ILH impairment was evaluated using the Purdue Pegboard Test (PPT) and handgrip strength and defined as mild (z-score < -1) or moderate (z-score < -1.65). We used logistic regression (LR) to predict outcome assessed 9 (range, 7-12) months post-stroke with the modified Rankin scale (mRS) categorized into good (mRS ≤ 1) and poor outcome (mRS ≥ 2). For internal validation, LR-bootstrapping and cross-validation with LASSO and Random Forest were performed. RESULTS: ILH impairment assessed at 89.04 ± 45.82 days post-stroke was moderate in 10.53% (95% CI 6.7, 14.83) for PPT and 17.22% (95% CI 11.96, 22.49) for grip, and mild in 21.05% (95% CI 15.78, 26.79) for PPT and 35.89 (95% CI 29.67, 42.58) for grip. Good outcome was predicted by ILH-PPT (B = 1.03 [95% CI 0.39, 3.31]), ILH-grip (B = 1.16 [95% CI 0.54, 3.53]), low NIHSS-discharge (B = -1.57 [95% CI -4.0, -1.19]), and no depression (B = -0.62 [95% CI -1.63, -0.43]), accounting for stroke delay (B = -0.011 [95% CI -0.06, 0.01]). Model efficiency was 91.6% (AUC = 0.977; 95% CI 0.959, 0.996). LASSO and Random Forest methods provided similar results, confirming the LR model robustness. CONCLUSIONS: ILH impairment is frequent after motor stroke and predicts long-term outcome. We propose to integrate ILH impairment into rehabilitation programs to improve recovery and serve research interventions such as neuromodulation.

White matter tract disruption is associated with ipsilateral hand impairment in subacute stroke: a diffusion MRI study.

PURPOSE: The ipsilateral hand (ILH) is impaired after unilateral stroke, but the underlying mechanisms remain unresolved. Based on the degeneracy theory of network connectivity that many connectivity patterns are functionally equivalent, we hypothesized that ILH impairment would result from the summation of microstructural white matter (WM) disruption in the motor network, with a task-related profile. We aimed to determine the WM disruption patterns associated with ILH impairment. METHODS: This was a cross-sectional analysis of patients in the ISIS-HERMES Study with ILH and diffusion-MRI data collected 1 month post-stroke. Patients performed three tasks, the Purdue Pegboard Test (PPT), handgrip strength, and movement time. Fractional anisotropy (FA) derived from diffusion MRI was measured in 33 WM regions. We used linear regression models controlling for age, sex, and education to determine WM regions associated with ILH impairment. RESULTS: PPT was impaired in 42%, grip in 59%, and movement time in 24% of 29 included patients (mean age, 51.9 ± 10.5 years; 21 men). PPT was predicted by ipsilesional corticospinal tract (i-CST) (B = 17.95; p = 0.002) and superior longitudinal Fasciculus (i-SLF) (B = 20.52; p = 0.008); handgrip by i-CST (B = 109.58; p = 0.016) and contralesional anterior corona radiata (B = 42.69; p = 0.039); and movement time by the corpus callosum (B = - 1810.03; p = 0.003) i-SLF (B = - 917.45; p = 0.015), contralesional pons-CST (B = 1744.31; p = 0.016), and i-corticoreticulospinal pathway (B = - 380.54; p = 0.037). CONCLUSION: ILH impairment was associated with WM disruption to a combination of ipsilateral and contralesional tracts with a pattern influenced by task-related processes, supporting the degeneracy theory. We propose to integrate ILH assessment in rehabilitation programs and treatment interventions such as neuromodulation.

Parietal operculum and motor cortex activities predict motor recovery in moderate to severe stroke.

While motor recovery following mild stroke has been extensively studied with neuroimaging, mechanisms of recovery after moderate to severe strokes of the types that are often the focus for novel restorative therapies remain obscure. We used fMRI to: 1) characterize reorganization occurring after moderate to severe subacute stroke, 2) identify brain regions associated with motor recovery and 3) to test whether brain activity associated with passive movement measured in the subacute period could predict motor outcome six months later. Because many patients with large strokes involving sensorimotor regions cannot engage in voluntary movement, we used passive flexion-extension of the paretic wrist to compare 21 patients with subacute ischemic stroke to 24 healthy controls one month after stroke. Clinical motor outcome was assessed with Fugl-Meyer motor scores (motor-FMS) six months later. Multiple regression, with predictors including baseline (one-month) motor-FMS and sensorimotor network regional activity (ROI) measures, was used to determine optimal variable selection for motor outcome prediction. Sensorimotor network ROIs were derived from a meta-analysis of arm voluntary movement tasks. Bootstrapping with 1000 replications was used for internal model validation. During passive movement, both control and patient groups exhibited activity increases in multiple bilateral sensorimotor network regions, including the primary motor (MI), premotor and supplementary motor areas (SMA), cerebellar cortex, putamen, thalamus, insula, Brodmann area (BA) 44 and parietal operculum (OP1-OP4). Compared to controls, patients showed: 1) lower task-related activity in ipsilesional MI, SMA and contralesional cerebellum (lobules V-VI) and 2) higher activity in contralesional MI, superior temporal gyrus and OP1-OP4. Using multiple regression, we found that the combination of baseline motor-FMS, activity in ipsilesional MI (BA4a), putamen and ipsilesional OP1 predicted motor outcome measured 6 months later (adjusted-R2 = 0.85; bootstrap p < 0.001). Baseline motor-FMS alone predicted only 54% of the variance. When baseline motor-FMS was removed, the combination of increased activity in ipsilesional MI-BA4a, ipsilesional thalamus, contralesional mid-cingulum, contralesional OP4 and decreased activity in ipsilesional OP1, predicted better motor outcome (djusted-R2 = 0.96; bootstrap p < 0.001). In subacute stroke, fMRI brain activity related to passive movement measured in a sensorimotor network defined by activity during voluntary movement predicted motor recovery better than baseline motor-FMS alone. Furthermore, fMRI sensorimotor network activity measures considered alone allowed excellent clinical recovery prediction and may provide reliable biomarkers for assessing new therapies in clinical trial contexts. Our findings suggest that neural reorganization related to motor recovery from moderate to severe stroke results from balanced changes in ipsilesional MI (BA4a) and a set of phylogenetically more archaic sensorimotor regions in the ventral sensorimotor trend, in which OP1 and OP4 processes may complement the ipsilesional dorsal motor cortex in achieving compensatory sensorimotor recovery.