Multiple imputation integrated to machine learning: predicting post-stroke recovery of ambulation after intensive inpatient rehabilitation.

Good data quality is vital for personalising plans in rehabilitation. Machine learning (ML) improves prognostics but integrating it with Multiple Imputation (MImp) for dealing missingness is an unexplored field. This work aims to provide post-stroke ambulation prognosis, integrating MImp with ML, and identify the prognostic influential factors. Stroke survivors in intensive rehabilitation were enrolled. Data on demographics, events, clinical, physiotherapy, and psycho-social assessment were collected. An independent ambulation at discharge, using the Functional Ambulation Category scale, was the outcome. After handling missingness using MImp, ML models were optimised, cross-validated, and tested. Interpretability techniques analysed predictor contributions. Pre-MImp, the dataset included 54.1% women, 79.2% ischaemic patients, median age 80.0 (interquartile range: 15.0). Post-MImp, 368 non-ambulatory patients on 10 imputed datasets were used for training, 80 for testing. The random forest (the validation best-performing algorithm) obtained 75.5% aggregated balanced accuracy on the test set. The main predictors included modified Barthel index, Fugl-Meyer assessment/motricity index, short physical performance battery, age, Charlson comorbidity index/cumulative illness rating scale, and trunk control test. This is among the first studies applying ML, together with MImp, to predict ambulation recovery in post-stroke rehabilitation. This pipeline reliably exploits the potential of incomplete datasets for healthcare prognosis, identifying relevant predictors.

Stroke-related epilepsy in the rehabilitation setting: Insights from the inpatient post-stroke rehabilitation study - RIPS.

In stroke survivors, persistent seizure activity could be associated with poor functional outcomes. At the same time, antiepileptic over-treatment could hamper post-stroke recovery. We systematically investigated the occurrence of seizures, the prevalence of epileptic discharges, and delta slow waves on electroencephalogram (EEG) and anti-seizure medication (ASM) management in relation to clinical manifestations and EEG abnormalities. This was a multi-centre prospective study involving two intensive rehabilitation units (IRUs). Clinical and EEG data were acquired at admission to the IRU, discharge (T1), and six-month follow-up (T2). A total of 163 patients underwent EEG recording upon admission to the IRU, while 149 were available for analysis at discharge from the IRU. Eighteen patients were treated with ASMs upon IRU admission despite only five of these patients having early seizures. Among the 145 patients not treated upon admission to the IRU, eight had late seizures, of which six were during the IRU stay, while two were after discharge from the IRU. During IRU stay, ASMs were generally discontinued in patients with no early seizures reported and were started in patients with late seizures. Among the 18 patients treated with ASMs at admission to the IRU, only six maintained the therapy also at T2. Our results suggest that post-acute inpatient rehabilitation is a proper setting to observe patients treated with ASMs after stroke and provide personalized post-stroke epilepsy management.

Spontaneous eye blinking as a diagnostic and prognostic marker in disorders of consciousness: Protocol of an international multicentre longitudinal study.

BACKGROUND: Diagnostic and prognostic decision-making in patients with Disorders of Consciousness (DoC) is challenging. It has been suggested that spontaneous eye blink rate is an index of patients' level of consciousness easy to detect in clinical practice. Further blinking features (i.e., amplitude, duration, variability in intervals between blinks) may change as a function of cognitive load, but have not been investigated in patients with DoC. OBJECTIVE: This multicentre, longitudinal study aims at exploring the diagnostic and prognostic value of spontaneous eye blinking features in DoC. METHODS: Eight European medical institutions will enrol consecutively admitted adult patients with DoC. Within two weeks from study entry demographic, anamnestic and clinical data will be collected. Moreover, patients will undergo two 20-minute EEG-EOG recordings at rest, to collect blinking features and EEG activity. A clinical follow-up will be performed after 6 months. A group of healthy individuals will be enrolled for reference. RESULTS: Possible differences in blink features between patients and the reference group, differences across diagnostic sub-groups, and correlations between blinking features and clinical outcome will be investigated. CONCLUSION: The results of this study might help clinicians to reduce misdiagnosis rate in DoC and provide useful information for prognostication and care pathway plan.

An open computational toolbox to analyze multi- and single-unit sympathetic nerve activity in microneurography.

Microelectrode recordings from human peripheral and cranial nerves provide a means to study both afferent and efferent axonal signals at different levels of detail, from multi- to single-unit activity. Their analysis can lead to advancements both in diagnostic and in the understanding of the genesis of neural disorders. However, most of the existing computational toolboxes for the analysis of microneurographic recordings are limited in scope or not open-source. Additionally, conventional burst-based metrics are not suited to analyze pathological conditions and are highly sensitive to distance of the microelectrode tip from the active axons. To address these challenges, we developed an open-source toolbox that offers advanced analysis capabilities for studying neuronal reflexes and physiological responses to peripheral nerve activity. Our toolbox leverages the observation of temporal sequences of action potentials within inherently cyclic signals, introducing innovative methods and indices to enhance analysis accuracy. Importantly, we have designed our computational toolbox to be accessible to novices in biomedical signal processing. This may include researchers and professionals in healthcare domains, such as clinical medicine, life sciences, and related fields. By prioritizing user-friendliness, our software application serves as a valuable resource for the scientific community, allowing to extract advanced metrics of neural activity in short time and evaluate their impact on other physiological variables in a consistent and standardized manner, with the final aim to widen the use of microneurography among researchers and clinicians.

Low-density EEG-based Functional Connectivity Discriminates Minimally Conscious State plus from minus.

OBJECTIVE: Within the continuum of consciousness, patients in a Minimally Conscious State (MCS) may exhibit high-level behavioral responses (MCS+) or may not (MCS-). The evaluation of residual consciousness and related classification is crucial to propose tailored rehabilitation and pharmacological treatments, considering the inherent differences among groups in diagnosis and prognosis. Currently, differential diagnosis relies on behavioral assessments posing a relevant risk of misdiagnosis. In this context, EEG offers a non-invasive approach to model the brain as a complex network. The search for discriminating features could reveal whether behavioral responses in post-comatose patients have a defined physiological background. Additionally, it is essential to determine whether the standard behavioral assessment for quantifying responsiveness holds physiological significance. METHODS: In this prospective observational study, we investigated whether low-density EEG-based graph metrics could discriminate MCS+/- patients by enrolling 57 MCS patients (MCS-: 30; males: 28). At admission to intensive rehabilitation, 30 min resting-state closed-eyes EEG recordings were performed together with consciousness diagnosis following international guidelines. After EEG preprocessing, graphs' metrics were estimated using different connectivity measures, at multiple connection densities and frequency bands (α,θ,δ). Metrics were also provided to cross-validated Machine Learning (ML) models with outcome MCS+/-. RESULTS: A lower level of brain activity integration was found in the MCS- group in the α band. Instead, in the δ band MCS- group presented an higher level of clustering (weighted clustering coefficient) respect to MCS+. The best-performing solution in discriminating MCS+/- through the use of ML was an Elastic-Net regularized logistic regression with a cross-validation accuracy of 79% (sensitivity and specificity of 74% and 85% respectively). CONCLUSION: Despite tackling the MCS+/- differential diagnosis is highly challenging, a daily-routine low-density EEG might allow to differentiate across these differently responsive brain networks. SIGNIFICANCE: Graph-theoretical features are shown to discriminate between these two neurophysiologically similar conditions, and may thus support the clinical diagnosis.

A neurophysiological profiling of the heartbeat-evoked potential in severe acquired brain injuries: A focus on unconsciousness.

Unconsciousness in severe acquired brain injury (sABI) patients occurs with different cognitive and neural profiles. Perturbational approaches, which enable the estimation of proxies for brain reorganization, have added a new avenue for investigating the non-behavioural diagnosis of consciousness. In this prospective observational study, we conducted a comparative analysis of the topological patterns of heartbeat-evoked potentials (HEP) between patients experiencing a prolonged disorder of consciousness (pDoC) and patients emerging from a minimally consciousness state (eMCS). A total of 219 sABI patients were enrolled, each undergoing a synchronous EEG-ECG resting-state recording, together with a standardized consciousness diagnosis. A number of graph metrics were computed before/after the HEP (Before/After) using the R-peak on the ECG signal. The peak value of the global field power of the HEP was found to be significantly higher in eMCS patients with no difference in latency. Power spectrum was not able to discriminate consciousness neither Before nor After. Node assortativity and global efficiency were found to vary with different trends at unconsciousness. Lastly, the Perturbational Complexity Index of the HEP was found to be significantly higher in eMCS patients compared with pDoC. Given that cortical elaboration of peripheral inputs may serve as a non-behavioural determinant of consciousness, we have devised a low-cost and translatable technique capable of estimating causal proxies of brain functionality with an endogenous, non-invasive stimulus. Thus, we present an effective means to enhance consciousness assessment by incorporating the interaction between the autonomic nervous system (ANS) and central nervous system (CNS) into the loop.

Quantitative EEG and prognosis for recovery in post-stroke patients: The effect of lesion laterality.

OBJECTIVE: There is emerging confidence that quantitative EEG (qEEG) has the potential to inform clinical decision-making and guide individualized rehabilitation after stroke, but consensus on the best EEG biomarkers is needed for translation to clinical practice. This study investigates the spatial qEEG spectral and symmetry distribution in patients with a left/right hemispheric stroke, to evaluate their side-specific prognostic power in post-acute rehabilitation outcome. METHODS: Resting-state 19-channel EEG recordings were collected with clinical information on admission to intensive inpatient rehabilitation (within 30 days post stroke), and six months post stroke. After preprocessing, spectral (Delta-to-Alpha Ratio, DAR) and symmetry (pairwise and hemispheric Brain Symmetry Index) features were extracted. Patients were divided into Affected Right and Left (AR/AL) groups, according to the location of their lesion. Within each group, DAR was compared between homologous electrode pairs and the pairwise difference between pairs was compared across pairs in the scalp. Then, the prognostic power of qEEG admission metrics was evaluated by performing correlations between admission metrics and discharge mBI values. RESULTS: Fifty-two patients with hemorrhagic or ischemic stroke (20 females, 38.5 %, median age 76 years [IQR = 22]) were included in the study. DAR was significantly higher in the affected hemisphere for both AR and AL groups, and, a higher frontal (to posterior) asymmetry was found independent of the side of the lesion. DAR was found to be a prognostic marker of 6-months modified Barthel Index (mBI) only for the AL group, while hemispheric asymmetry did not correlate with follow-up outcomes in either group. DISCUSSION: While the presence of EEG abnormalities in the affected hemisphere of a stroke is well recognized, we have shown that the extent of DAR abnormalities seen correlates with disability at 6 months post stroke, but only for left hemispheric lesions. Routine prognostic evaluation, in addition to motor and functional scales, can add information concerning neuro-prognostication and reveal neurophysiological abnormalities to be assessed during rehabilitation.

MoCA Domain-Specific Pattern of Cognitive Impairment in Stroke Patients Attending Intensive Inpatient Rehabilitation: A Prospective Study.

A domain-specific perspective to cognitive functioning in stroke patients may predict their cognitive recovery over time and target stroke rehabilitation intervention. However, data about domain-specific cognitive impairment after stroke are still scarce. This study prospectively investigated the domain-specific pattern of cognitive impairments, using the classification proposed by the Montreal Cognitive Assessment (MoCA), in a cohort of 49 stroke patients at admission (T0), discharge (T1), and six-month follow-up (T2) from subacute intensive rehabilitation. The predictive value of T0 cognitive domains cognitive impairment at T1 and T2 was also investigated. Patients' cognitive functioning at T0, T1, and T2 was assessed through the MoCA domains for executive functioning, attention, language, visuospatial, orientation, and memory. Different evolutionary trends of cognitive domain impairments emerged across time-points. Patients' impairments in all domains decreased from T0 to T1. Attention and executive impairments decreased from T0 to T2 (42.9% and 26.5% to 10.2% and 18.4%, respectively). Conversely, altered visuospatial, language, and orientation increased between T1 and T2 (16.3%, 36.7%, and 40.8%, respectively). Additionally, patients' global cognitive functioning at T1 was predicted by the language and executive domains in a subacute phase (p = 0.031 and p = 0.001, respectively), while in the long term, only attention (p = 0.043) and executive (p = 0.019) domains intervened. Overall, these results confirm the importance of a domain-specific approach to target cognitive recovery across time in stroke patients.

Impact of age on the predictive value of NT-proBNP in patients with diabetes mellitus stabilised after an acute coronary syndrome.

AIMS: To assess the impact of age on the prognostic value of NT-proBNP concentration in patients with type-2 diabetes mellitus (T2DM) stabilised after an Acute Coronary Syndrome (ACS). METHODS: The AleCardio study compared aleglitazar with placebo in 7226 patients with T2DM and recent ACS. Patients with heart failure were excluded. Median follow-up was 104 weeks. Baseline NT-proBNP plasma concentration was measured centrally. Multivariable Cox regression was used to determine the mortality predictive information provided by NT-proBNP across age groups. RESULTS: Median age was 61y (IQR 54, 67). NT-proBNP concentration increased by quartile (Q) of age (median 264, 318, 391, and 588 pg/ml). Compared to Q1, patients in Q4 of NT-proBNP had higher (p < 0.001) adjusted HR for all-cause (aHR 6.9; 95 % CI 4.0-12) and cardiovascular (11; 5.4-23) death. Within each age Q, baseline NT-proBNP in patients who died was 3 times higher than in survivors (all p < 0.001). When age and NT-proBNP levels were modeled as continuous variables, their interaction term was nonsignificant. The relative prognostic information provided by NT-proBNP (percent of total X2) increased from 38 % in age Q1 to 75 % in age Q4 for mortality, and from 50 % to 88 % for CV death. CONCLUSIONS: Among patients with T2DM stabilised after an ACS, NT-proBNP level predicts death irrespective of age.

Which information derived from the Coma Recovery Scale-Revised provides the most reliable prediction of clinical diagnosis and recovery of consciousness? A comparative study using machine learning techniques.

BACKGROUND: The Coma Recovery Scale-Revised (CRS-R) is the most recommended clinical tool to examine the neurobehavioral condition of individuals with disorders of consciousness (DOCs). Different studies have investigated the prognostic value of the information provided by the conventional administration of the scale, while other measures derived from the scale have been proposed to improve the prognosis of DOCs. However, the heterogeneity of the data used in the different studies prevents a reliable comparison of the identified predictors and measures. AIM: This study investigates which information derived from the CRS-R provides the most reliable prediction of both the clinical diagnosis and recovery of consciousness at the discharge of a long-term neurorehabilitation program. DESIGN: Retrospective observational multisite study. SETTING: The enrollment was performed in three neurorehabilitation facilities of the same hospital network. POPULATION: A total of 171 individuals with DOCs admitted to an inpatient neurorehabilitation program for a minimum of 3 months were enrolled. METHODS: Machine learning classifiers were trained to predict the clinical diagnosis and recovery of consciousness at discharge using clinical confounders and different metrics extracted from the CRS-R scale. RESULTS: Results showed that the neurobehavioral state at discharge was predicted with acceptable and comparable predictive value with all the indices and measures derived from the CRS-R, but for the clinical diagnosis and the Consciousness Domain Index, and the recovery of consciousness was predicted with higher accuracy and similarly by all the investigated measures, with the exception of initial clinical diagnosis. CONCLUSIONS: Interestingly, the total score in the CRS-R and, especially, the total score in its subscales provided the best overall results, in contrast to the clinical diagnosis, which could indicate that a comprehensive measure of the clinical diagnosis rather than the condition of the individuals could provide a more reliable prediction of the neurobehavioral progress of individuals with prolonged DOC. CLINICAL REHABILITATION IMPACT: The results of this work have important implications in clinical practice, offering a more accurate prognosis of patients and thus giving the possibility to personalize and optimize the rehabilitation plan of patients with DoC using low-cost and easily collectable information.

Trunk Control Test as a Main Predictor of the Modified Barthel Index Score at Discharge From Intensive Post-acute Stroke Rehabilitation: Results From a Multicenter Italian Study.

OBJECTIVES: To verify whether trunk control test (TCT) upon admission to intensive inpatient post-stroke rehabilitation, combined with other confounding variables, is independently associated with discharge mBI. DESIGN: Multicentric retrospective observational cohort study. SETTING: Two Italian inpatient rehabilitation units. PARTICIPANTS: A total of 220 post-stroke adult patients, within 30 days from the acute event, were consecutively enrolled. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: The outcome measure considered was the modified Barthel Index (mBI), one of the most widely recommended tools for assessing stroke rehabilitation functional outcomes. RESULTS: All variables collected at admission and significantly associated with mBI at discharge in the univariate analysis (TCT, mBI at admission, pre-stroke modified Rankin Scale [mRS], sex, age, communication ability, time from the event, Cumulative Illness Rating Scale, bladder catheter, and pressure ulcers) entered the multivariate analysis. TCT, mBI at admission, premorbid disability (mRS), communication ability and pressure ulcers (P<.001) independently predicted discharge mBI (adjusted R2=68.5%). Concerning the role of TCT, the model with all covariates and without TCT presented an R2 of 65.1%. On the other side, the model with the TCT only presented an R2 of 53.1%. Finally, with the inclusion of both TCT and all covariates, the model showed an R2 increase up to 68.5%. CONCLUSIONS: TCT, with other features suggesting functional/clinical complexity, collected upon admission to post-acute intensive inpatient stroke rehabilitation, independently predicted discharge mBI.

Predicting the functional outcome of intensive inpatient rehabilitation after stroke: results from the RIPS Study.

BACKGROUND: The complexity of stroke sequelae, the heterogeneity of outcome measures and rehabilitation pathways, and the lack of extensively validated prediction models represent a challenge in predicting stroke rehabilitation outcomes. AIM: To prospectively investigate a multidimensional set of variables collected at admission to inpatient post-stroke rehabilitation as potential predictors of the functional level at discharge. DESIGN: Multicentric prospective observational study. SETTING: Patients were enrolled in four Intensive Rehabilitation Units (IRUs). POPULATION: Patients were consecutively recruited in the period December 2019-December 2020 with the following inclusion criteria: aged 18+, with ischemic/haemorrhagic stroke, and undergoing inpatient rehabilitation within 30 days from stroke. METHODS: This is a multicentric prospective observational study. The rehabilitation pathway was reproducible and evidence-based. The functional outcome was disability in activities of daily living, measured by the modified Barthel Index (mBI) at discharge. Potential multidimensional predictors, assessed at admission, included demographics, event description, clinical assessment, functional and cognitive profile, and psycho-social domains. The variables statistically associated with the outcome in the univariate analysis were fed into a multivariable model using multiple linear regression. RESULTS: A total of 220 patients were included (median [IQR] age: 80 [15], 112 women, 175 ischemic). Median mBI was 26 (43) at admission and 62.5 (52) at discharge. In the multivariable analysis younger age, along with better functioning, fewer comorbidities, higher cognitive abilities, reduced stroke severity, and higher motor functions at admission, remained independently associated with higher discharge mBI. The final model allowed a reliable prediction of discharge functional outcome (adjusted R2=77.2%). CONCLUSIONS: The model presented in this study, based on easily collectable, reliable admission variables, could help clinicians and researchers to predict the discharge scores of the global functional outcome for persons enrolled in an evidence-based inpatient stroke rehabilitation program. CLINICAL REHABILITATION IMPACT: A reliable outcome prediction derived from standardized assessment measures and validated treatment protocols could guide clinicians in the management of patients in the subacute phase of stroke and help improve the planning of the rehabilitation individualized project.

Brain microstate spatio-temporal dynamics as a candidate endotype of consciousness.

Consciousness can be defined as a phenomenological experience continuously evolving. Current research showed how conscious mental activity can be subdivided into a series of atomic brain states converging to a discrete spatiotemporal pattern of global neuronal firing. Using the high temporal resolution of EEG recordings in patients with a severe Acquired Brain Injury (sABI) admitted to an Intensive Rehabilitation Unit (IRU), we detected a novel endotype of consciousness from the spatiotemporal brain dynamics identified via microstate analysis. Also, we investigated whether microstate features were associated with common neurophysiological alterations. Finally, the prognostic information comprised in such descriptors was analysed in a sub-cohort of patients with prolonged Disorder of Consciousness (pDoC). Occurrence of frontally-oriented microstates (C microstate), likelihood of maintaining such brain state or transitioning to the C topography and complexity were found to be indicators of consciousness presence and levels. Features of left-right asymmetric microstates and transitions toward them were found to be negatively correlated with antero-posterior brain reorganization and EEG symmetry. Substantial differences in microstates' sequence complexity and presence of C topography were found between groups of patients with alpha dominant background, cortical reactivity and antero-posterior gradient. Also, transitioning from left-right to antero-posterior microstates was found to be an independent predictor of consciousness recovery, stronger than consciousness levels at IRU's admission. In conclusions, global brain dynamics measured with scale-free estimators can be considered an indicator of consciousness presence and a candidate marker of short-term recovery in patients with a pDoC.

The Exercise aNd hEArt transplant (ENEA) trial - a registry-based randomized controlled trial evaluating the safety and efficacy of cardiac telerehabilitation after heart transplant.

BACKGROUND: Heart transplant (HTx) is gold-standard therapy for patients with end-stage heart failure. Cardiac rehabilitation (CR) is a multidisciplinary intervention shown to improve cardiovascular prognosis and quality of life. The aim in this randomized controlled trial is to explore the safety and efficacy of cardiac telerehabilitation after HTx. In addition, biomarkers of rehabilitation outcomes will be identified, as data that will enable treatment to be tailored to patient phenotype. METHODS: Patients after HTx will be recruited at IRCCS S. Maria Nascente - Fondazione Don Gnocchi, Milan, Italy (n = 40). Consenting participants will be randomly allocated to either of two groups (1:1): an intervention group who will receive on-site CR followed by 12 weeks of telerehabilitation, or a control group who will receive on-site CR followed by standard homecare and exercise programme. Recruitment began on 20th May 2023 and is expected to continue until 20th May 2025. Socio-demographic characteristics, lifestyle, health status, cardiovascular events, cognitive function, anxiety and depression symptoms, and quality of life will be assessed, as well as exercise capacity and muscular endurance. Participants will be evaluated before the intervention, post-CR and after 6 months. In addition, analysis of circulating extracellular vesicles using Surface Plasmon Resonance imaging (SPRi), based on a rehabilomic approach, will be applied to both groups pre- and post-CR. CONCLUSION: This study will explore the safety and efficacy of cardiac telerehabilitation after HTx. In addition, a rehabilomic approach will be used to investigate biomolecular phenotypization in HTx patients. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Identifier: NCT05824364.

Application of a Custom Device to Measure Isometric Knee Strength: Possible Injury Correlation in Professional Soccer (Football) Players.

Injury in sports is an occurrence that prevents athletes from participating in training and competitions and has an incidence of 8.1 injuries/1000 h of practice. This translates into a cost and also into danger, especially if the event is repeated, for the health of the athlete; the injury certainly has a multifactorial causality. On the other hand, having instruments that can represent an alarm could be helpful for those involved in sports science. We used a specifically designed instrument, presented in a previous work, which shows excellent reliability and repeatability in measuring the strength of the knee flexors and extensors to test 107 players belonging to three different teams playing in the Italian Serie A. We took three measurements, beginning of the season, mid-season, and close to the end of the season. This retrospective study on 107 professional soccer players demonstrates that isometric force-related parameters of the knee extensors and flexors are associated with the risk of injury to lower limbs. Logistic regression evidenced a significant correlation between the parameter indicating the imbalance of the force between the flexors of the two limbs (p≤0.05, OR = 1.089) and the occurrence of injuries. Survival analyses (p≤0.001) evidenced a correlation between the population survival time and the injury incidence. We demonstrated that the analysis of the strength imbalance is correlated with injury occurrence, but it is well known that sports injuries are a multifactorial event; so, they cannot be predicted by only one parameter. However, the method proposed in this paper could represent a useful tool for sport scientists.

Estimation of Spatiotemporal Gait Parameters in Walking on a Photoelectric System: Validation on Healthy Children by Standard Gait Analysis.

The use of stereophotogrammetry systems is challenging when targeting children's gait analysis due to the time required and the need to keep physical markers in place. For this reason, marker-less photoelectric systems appear to be a solution for accurate and fast gait analysis in youth. The aim of this study is to validate a photoelectric system and its configurations (LED filter setting) on healthy children, comparing the kinematic gait parameters with those obtained from a three-dimensional stereophotogrammetry system. Twenty-seven healthy children were enrolled. Three LED filter settings for the OptoGait were compared to the BTS P6000. The analysis included the non-parametric 80% limits of agreement and the intraclass correlation coefficient (ICC). Additionally, normalised limits of agreement and bias (NLoAs and Nbias) were compared to the clinical experience of physical therapists (i.e., assuming an error lower than 5% is acceptable). ICCs showed excellent consistency for most of the parameters and filter settings; NLoAs varied between 1.39% and 12.62%. An inverse association between the number of LEDs for filter setting and the bias values was also observed. Observations confirm the validity of the OptoGait system for the evaluation of spatiotemporal gait parameters in children.

Artificial intelligence for the recognition of benign lesions of vocal folds from audio recordings.

Objective: The diagnosis of benign lesions of the vocal fold (BLVF) is still challenging. The analysis of the acoustic signals through the implementation of machine learning models can be a viable solution aimed at offering support for clinical diagnosis. Materials and methods: In this study, a support vector machine was trained and cross-validated (10-fold cross-validation) using 138 features extracted from the acoustic signals of 418 patients with polyps, nodules, oedema, and cysts. The model's performance was presented as accuracy and average F1-score. The results were also analysed in male (M) and female (F) subgroups. Results: The validation accuracy was 55%, 80%, and 54% on the overall cohort, and in M and F, respectively. Better performances were observed in the detection of cysts and nodules (58% and 62%, respectively) vs polyps and oedema (47% and 53%, respectively). The results on each lesion and the different patterns of the model on M and F are in line with clinical observations, obtaining better results on F and detection of sensitive polyps in M. Conclusions: This study showed moderately accurate detection of four types of BLVF using acoustic signals. The analysis of the diagnostic results on gender subgroups highlights different behaviours of the diagnostic model.

Neural coding of autonomic functions in different states of consciousness.

Detecting signs of residual neural activity in patients with altered states of consciousness is a crucial issue for the customization of neurorehabilitation treatments and clinical decision-making. With this large observational prospective study, we propose an innovative approach to detect residual signs of consciousness via the assessment of the amount of autonomic information coded within the brain. The latter was estimated by computing the mutual information (MI) between preprocessed EEG and ECG signals, to be then compared across consciousness groups, together with the absolute power and an international qualitative labeling. One-hundred seventy-four patients (73 females, 42%) were included in the study (median age of 65 years [IQR = 20], MCS +: 29, MCS -: 23, UWS: 29). Electroencephalography (EEG) information content was found to be mostly related to the coding of electrocardiography (ECG) activity, i.e., with higher MI (p < 0.05), in Unresponsive Wakefulness Syndrome and Minimally Consciousness State minus (MCS -). EEG-ECG MI, besides clearly discriminating patients in an MCS - and +, significantly differed between lesioned areas (sides) in a subgroup of unilateral hemorrhagic patients. Crucially, such an accessible and non-invasive measure of residual consciousness signs was robust across electrodes and patient groups. Consequently, exiting from a strictly neuro-centric consciousness detection approach may be the key to provide complementary insights for the objective assessment of patients' consciousness levels and for the patient-specific planning of rehabilitative interventions.

EEG fractal dimensions predict high-level behavioral responses in minimally conscious patients.

Objective.Brain-injured patients may enter a state of minimal or inconsistent awareness termed minimally conscious state (MCS). Such patient may (MCS+) or may not (MCS-) exhibit high-level behavioral responses, and the two groups retain two inherently different rehabilitative paths and expected outcomes. We hypothesized that brain complexity may be treated as a proxy of high-level cognition and thus could be used as a neural correlate of consciousness.Approach.In this prospective observational study, 68 MCS patients (MCS-: 30; women: 31) were included (median [IQR] age 69 [20]; time post-onset 83 [28]). At admission to intensive rehabilitation, 30 min resting-state closed-eyes recordings were performed together with consciousness diagnosis following international guidelines. The width of the multifractal singularity spectrum (MSS) was computed for each channel time series and entered nested cross-validated interpretable machine learning models targeting the differential diagnosis of MCS±.Main results.Frontal MSS widths (p< 0.05), as well as the ones deriving from the left centro-temporal network (C3:p= 0.018, T3:p= 0.017; T5:p= 0.003) were found to be significantly higher in the MCS+ cohort. The best performing solution was found to be the K-nearest neighbor model with an aggregated test accuracy of 75.5% (median [IQR] AuROC for 100 executions 0.88 [0.02]). Coherently, the electrodes with highest Shapley values were found to be Fz and Cz, with four out the first five ranked features belonging to the fronto-central network.Significance.MCS+ is a frequent condition associated with a notably better prognosis than the MCS-. High fractality in the left centro-temporal network results coherent with neurological networks involved in the language function, proper of MCS+ patients. Using EEG-based interpretable algorithm to complement differential diagnosis of consciousness may improve rehabilitation pathways and communications with caregivers.

Cognitive reserve index and long-term disability in patients with severe traumatic brain injury discharged from the intensive rehabilitation unit.

Objectives: The "cognitive reserve" (CR) theory posits that higher premorbid cognitive activities can mitigate the effects of brain damage. This study aimed to investigate the association between CR and long-term functional autonomy in patients surviving a severe traumatic brain injury (sTBI). Setting: Data were collected from the database of inpatients with severe acquired brain injury in a rehabilitation unit admitted from August 2012 to May 2020. Participants: Patients that had incurred an sTBI, aged 18+ years, completing the phone Glasgow Outcome Scale-Expanded at follow-up (pGOS-E) in absence of previous brain trauma or neurological disease, or cognitive disorders were included. Patients with severe brain injury from non-traumatic etiologies were not included in the study. Design: In this longitudinal study, all patients underwent a multidimensional assessment including the cognitive reserve index questionnaire (CRIq), the coma recovery scale-revised, the level of cognitive functioning, the Disability Rating Scale (DRS), and the Galveston Orientation and Amnesia Test at admission. At discharge, functional scales were administered again together with the Glasgow Outcome Scale. The pGOS-E was assessed at follow-up. Main measures: pGOS-E. Results: A total of 106 patients/caregivers underwent the pGOS-E after 5.8 [3.6] years from the event. Among them, 46 (43.4%) died after discharge, and 60 patients [men: 48 (80%); median age: 54 years; median time post-onset: 37 days; median education level: 10 years; median CRIq total score: 91] were included in the analysis exploring the association between pGOS-E and demographic data, cognitive reserve surrogates, and clinical variables at admission and discharge from the rehabilitation unit. A younger age (B = -0.035, p = 0.004) and a lower DRS category at discharge (B = -0.392, p = 0.029) were significantly related to a higher long-term functional autonomy in the multivariate analysis. Conclusion: Long-term functional autonomy was not influenced by CR as assessed through the educational level and the CRIq.