Understanding the role of self-attention in a Transformer model for the discrimination of SCD from MCI using resting-state EEG.

The identification of EEG biomarkers to discriminate Subjective Cognitive Decline (SCD) from Mild Cognitive Impairment (MCI) conditions is a complex task which requires great clinical effort and expertise. We exploit the self-attention component of the Transformer architecture to obtain physiological explanations of the model's decisions in the discrimination of 56 SCD and 45 MCI patients using resting-state EEG. Specifically, an interpretability workflow leveraging attention scores and time-frequency analysis of EEG epochs through Continuous Wavelet Transform is proposed. In the classification framework, models are trained and validated with 5-fold cross-validation and evaluated on a test set obtained by selecting 20% of the total subjects. Ablation studies and hyperparameter tuning tests are conducted to identify the optimal model configuration. Results show that the best performing model, which achieves acceptable results both on epochs' and patients' classification, is capable of finding specific EEG patterns that highlight changes in the brain activity between the two conditions. We demonstrate the potential of attention weights as tools to guide experts in understanding which disease-relevant EEG features could be discriminative of SCD and MCI.

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.

Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings.

INTRODUCTION: Early identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern. METHODS: We developed a cortical model of AD-related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n = 17), subjective cognitive decline (SCD, n = 58), and mild cognitive impairment (MCI, n = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings. RESULTS: Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50). DISCUSSION: Personalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non-invasive EEG recordings. Highlights: Personalized cortical model estimating structural alterations from EEG recordings.Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%)Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%)Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y.

The Italian version of Edinburgh Handedness Inventory: Translation, transcultural adaptation, and validation in healthy subjects.

Lateralization is a key aspect of brain architecture and handedness is its primary manifestation. The Edinburgh Handedness Inventory (EHI) and the laterality quotient (LQ) assess the direction and consistency of handedness and require translation and cross-cultural adaptation to guarantee construct validity. We developed a standardized Italian EHI version. The developed Italian version was tested on 202 Italian subjects, classified into three hand types based on their LQs: right, mixed, and left. The frequency of left-handedness in Italians and other populations was compared to previous data. LQs from the twenty- and the ten-item original inventories were also compared. We conducted a factorial analysis. Mcdonald's Omega tested internal consistency. The prevalence of left-handedness was 6.4%, consistent with prior findings in Italian samples and other EHI translations. Age was the only socio-demographic variable that significantly affected the LQ. The internal consistency of the Italian EHI was excellent. Handedness is a feature of several cognitive functions and some neuropsychological diseases; it is influenced by socio-demographic and cultural factors and the instrument used to assess it. To provide a consistent and comparable evaluation of the construct, we recommend using this validated Italian translation of the EHI.

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.

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.

Heads-Up Micronavigation Reliability of Preoperative Transcranial Magnetic Stimulation Maps for the Motor Function: Comparison With Direct Cortical Stimulation.

BACKGROUND AND OBJECTIVES: We aimed to assess the reliability of preoperative navigated transcranial magnetic stimulation (nTMS) maps for motor function as visualized intraoperatively with augmented reality heads-up display and to assess its accuracy via direct point-by-point comparison with the gold-standard direct cortical stimulation (DCS). METHODS: From January 2022 to January 2023, candidates for surgical removal of lesions involving the motor pathways underwent preoperative nTMS assessment to obtain cortical maps of motor function. Intraoperatively and before tumor removal, nTMS maps were superimposed on the cortical surface, and DCS was performed on positive points with increasing current intensity until obtaining a positive response at 16 mA. The outcome of each stimulation was recorded to obtain discrimination metrics. RESULTS: Twelve patients were enrolled (5 females [42%] vs 7 males [58%], mean age 62.9 ± 12.8 years), for a total of 304 investigated points. Agreement between nTMS and DCS was moderate (κ = 0.43, P < .005), with 0.66 (0.53-0.78) sensitivity, 0.87 (0.82-0.90) specificity, 0.50 (0.39-0.62) positive predictive values, 0.93 (0.89-0.95) negative predictive value, and 0.83 (0.79-0.87) accuracy. A loss of accuracy was observed with higher DCS current intensities. CONCLUSION: We performed a point-by-point validation of preoperative nTMS maps for motor function using augmented reality visualization. The high negative predictive value and low positive predictive values highlight nTMS reliability to visualize safe cortical zones but not to identify critical functional areas, confirming previous findings of nTMS maps for the language function and suggesting the need for combined use of nTMS maps and DCS for optimal maximal safe resection.

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.

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.

Is the Patient State Index a reliable parameter as guide to anaesthesiology in cranial neurosurgery? A first intraoperative study and a literature review.

BACKGROUND: Patient State Index (PSI) and Suppression Ratio (SR) are two indices calculated by quantitative analysis of EEG used to estimate the depth of anaesthesia but their validation in neurosurgery must be done. Our aim was to investigate the congruity PSI and SR with raw EEG monitoring in neurosurgery. METHODS: We included 34 patients undergoing elective cranial neurosurgery. Each patient was monitored by a SedLine device (PSI and SR) and by raw EEG. To appraise the agreement between PSI, SR and EEG Suppr%, Bland-Altman analysis was used. We also correlated the PSI and SR recorded at different times during surgery to the degree of suppression of the raw EEG data by Spearman's rank correlation coefficient. For a comparison with previous data we made an international literature review according to PRISMA protocol. RESULTS: At all recording times, we found that there is a strong agreement between PSI and raw EEG. We also found a significant correlation for both PSI and SR with the EEG suppression percentage (p < 0.05), but with a broad dispersion of the individual values within the confidence interval. CONCLUSION: The Masimo SedLine processed EEG monitoring system can be used as a guide in the anaesthetic management of patients during elective cranial neurosurgery, but the anaesthesiologist must be aware that previous correlations between PSI and SR with the suppression percentage may not always be valid in all individual patients. The use of an extended visual raw EEG evaluated by an expert electroencephalographer might help to provide better guidance.

Depressive Symptoms Moderate the Association Between Functional Level at Admission to Intensive Post-Stroke Rehabilitation and Effectiveness of the Intervention.

INTRODUCTION: Previous studies showed that depression acts as an independent factor in functional recovery after stroke. In a prospective cohort of patients admitted to intensive inpatient rehabilitation after a stroke, we aimed to test depression as a moderator of the relationship between the functional level at admission and the effectiveness of rehabilitation at discharge. METHODS: All patients admitted to within 30 days from an ischemic or hemorrhagic stroke to 4 intensive rehabilitation units were prospectively screened for eligibility to a multicenter prospective observational study. Enrolled patients underwent an evidence-based rehabilitation pathway. We used clinical data collected at admission (T0) and discharge (T1). The outcome was the effectiveness of recovery at T1 on the modified Barthel Index (proportion of achieved over potential functional improvement). Moderation analysis was performed by using the PROCESS macro for SPSS using the bootstrapping procedure. RESULTS: Of 278 evaluated patients, 234 were eligible and consented to enrolment; 81 patients were able to answer to the Hospital Anxiety and Depression Scale (HADS) and were included in this analysis. The relationship between the functional status at admission and rehabilitation effectiveness was significant only in persons with fewer depressive symptoms; depression (HADS cut-off score: 5.9) moderated this relationship (P = .047), independent from age and neurological impairment. CONCLUSIONS: Our results suggest that depression moderates between the functional status at admission and the functional recovery after post-stroke rehabilitation. This approach facilitates the identification of subgroups of individuals who may respond differently to stroke rehabilitation based on depression.

PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer's Disease With machine learning: the PREVIEW study protocol.

BACKGROUND: As disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are becoming a reality, there is an urgent need to select cost-effective tools that can accurately identify patients in the earliest stages of the disease. Subjective Cognitive Decline (SCD) is a condition in which individuals complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer's pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of AD. We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features derived from easily accessible, cost-effective and non-invasive assessment to accurately detect SCD patients who will progress to AD dementia. METHODS: We will include patients who self-referred to our memory clinic and are diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits, APOE and BDNF genotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of Aß42, t-tau, and p-tau concentration and Aß42/Aß40 ratio. Recruited patients will have follow-up neuropsychological examinations every two years. Collected data will be used to train a machine learning algorithm to define the risk of being carriers of AD and progress to dementia in patients with SCD. DISCUSSION: This is the first study to investigate the application of machine learning to predict AD in patients with SCD. Since all the features we will consider can be derived from non-invasive and easily accessible assessments, our expected results may provide evidence for defining cost-effective and globally scalable tools to estimate the risk of AD and address the needs of patients with memory complaints. In the era of DMTs, this will have crucial implications for the early identification of patients suitable for treatment in the initial stages of AD. TRIAL REGISTRATION NUMBER (TRN): NCT05569083.

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.

Intraoperative Mapping of the Sensory Root of the Trigeminal Nerve in Patients with Pontocerebellar Angle Pathology.

OBJECTIVE: The aim of the present study was to determine the position of the 3 sensory branches of the trigeminal nerve in the preganglionic tract using intraoperative neurophysiological mapping. METHODS: We included consecutive adult patients who underwent neurosurgical treatment of cerebellopontine angle lesions. The trigeminal nerve was antidromically stimulated at 3 sites along its circumference with different stimulus intensities at a distance of ≤1 cm from the brainstem. The sensory nerve action potentials (SNAPs) were recorded from each main trigeminal branch (V1 [ophthalmic branch], V2 [maxillary branch], and V3 [mandibular branch]). RESULTS: We analyzed 13 patients. The stimulation points at which we obtained the greatest number of congruous and exclusive SNAPs (SNAPs only on the stimulated branch) was the stimulation point for V3 (20.7%). The stimulation intensity at which we obtained the highest number of congruent and exclusive SNAPs with the stimulated branch was 0.5 mA. CONCLUSIONS: Using our recording conditions, trigeminal stimulation is a reliable technique for mapping the V3 and V1 branches using an intensity not exceeding 0.5. However, reliable identification of the fibers of V2 is more difficult. Stimulation of the trigeminal nerve can be a reliable technique to identify the V3 and V1 branches if rhizotomy of these branches is necessary.

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.

Spatial and Temporal Gait Characteristics in Patients Admitted to a Neuro-Rehabilitation Department with Age-Related White Matter Changes: A Gait Analysis and Clinical Study.

BACKGROUND: Patients with age-related white matter changes (ARWMC) frequently present a gait disorder, depression and cognitive impairment. Our aims are to define which alterations in the gait parameters are associated with motor or neuro-psychological impairment and to assess the role of motor, mood or cognitive dysfunction in explaining the variance of the gait parameters. METHODS: Patients with gait disorders admitted to a Neuro-rehabilitation Department, affected by vascular leukoencephalopathy who had ARWMC confirmed by a brain MRI, were consecutively enrolled, classified by a neuroradiological scale (Fazekas 1987) and compared to healthy controls. We excluded subjects unable to walk independently, subjects with hydrocephalus or severe aphasia, with orthopaedic and other neurological pathologies conditioning the walking pattern. Patients and controls were assessed by clinical and functional scales (Mini Mental State Examination, Geriatric Depression Scale, Nevitt Motor Performance Scale, Berg Balance Scale, Functional Independence Measure), and computerised gait analysis was performed to assess the spatial and temporal gait parameters in a cross-sectional study. RESULTS: We recruited 76 patients (48 males, aged 78.3 ± 6.2 years) and 14 controls (6 males, aged 75.8 ± 5 years). In the multiple regression analysis, the gait parameter with overall best model summary values, associated with the ARWMC severity, was the stride length even after correction for age, sex, weight and height (R2 = 0.327). The motor performances justified at least in part of the gait disorder (R2 change = 0.220), but the mood state accounted independently for gait alterations (R2 change = 0.039). The increase in ARWMC severity, the reduction of motor performance and a depressed mood state were associated with a reduction of stride length (R = 0.766, R2 = 0.587), reduction of gait speed (R2 = 0.573) and an increase in double support time (R2 = 0.421). CONCLUSION: The gait disorders in patients with ARWMC are related to motor impairment, but the presence of depression is an independent factor for determining gait alterations and functional status. These data pave the way for longitudinal studies, including gait parameters, to quantitatively assess gait changes after treatment or to monitor the natural progression of the gait disorders.

Oral and fecal microbiota perturbance in cocaine users: Can rTMS-induced cocaine abstinence support eubiosis restoration?

The effects of cocaine on microbiota have been scarcely explored. Here, we investigated the gut (GM) and oral (OM) microbiota composition of cocaine use disorder (CUD) patients and the effects of repetitive transcranial magnetic stimulation (rTMS). 16S rRNA sequencing was used to characterize GM and OM, whereas PICRUST2 assessed functional changes in microbial communities, and gas-chromatography was used to evaluate fecal short and medium chain fatty acids. CUD patients reported a significant decrease in alpha diversity and modification of the abundances of several taxa in both GM and OM. Furthermore, many predicted metabolic pathways were differentially expressed in CUD patients' stool and saliva samples, as well as reduced levels of butyric acid that appear restored to normal amounts after rTMS treatment. In conclusion, CUD patients showed a profound dysbiotic fecal and oral microbiota composition and function and rTMS-induced cocaine abstinence determined the restoration of eubiotic microbiota.

Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer's Disease continuum?

Alzheimer's disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by mild cognitive impairment (MCI), the initial stage of clinical cognitive decline. However, the neural correlates of these prodromic stages are not completely clear yet. Recent studies suggest that EEG analysis tools characterizing the cortical activity as a whole, such as microstates and cortical regions connectivity, might support a characterization of SCD and MCI conditions. Here we test this approach by performing a broad set of analyses to identify the prominent EEG markers differentiating SCD (n = 57), MCI (n = 46) and healthy control subjects (HC, n = 19). We found that the salient differences were in the temporal structure of the microstates patterns, with MCI being associated with less complex sequences due to the altered transition probability, frequency and duration of canonic microstate C. Spectral content of EEG, network connectivity, and spatial arrangement of microstates were instead largely similar in the three groups. Interestingly, comparing properties of EEG microstates in different cerebrospinal fluid (CSF) biomarkers profiles, we found that canonic microstate C displayed significant differences in topography in AD-like profile. These results show that the progression of dementia might be associated with a degradation of the cortical organization captured by microstates analysis, and that this leads to altered transitions between cortical states. Overall, our approach paves the way for the use of non-invasive EEG recordings in the identification of possible biomarkers of progression to AD from its prodromal states.

Heart rate variability for the evaluation of patients with disorders of consciousness.

OBJECTIVE: Clinical responsiveness of patients with a Disorder of Consciousness (DoC) correlates to sympathetic/parasympathetic homeostatic balance. Heart Rate Variability (HRV) metrics result in non-invasive proxies of modulation capabilities of visceral states. In this work, our aim was to evaluate whether HRV measures could improve the differential diagnosis between Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS) with respect to multivariate models based on standard clinical electroencephalography (EEG) labeling only in a rehabilitation setting. METHODS: A prospective observational study was performed consecutively enrolling 82 DoC patients. Polygraphic recordings were performed. HRV-metrics and EEG descriptors derived from the American Clinical Neurophysiology Society's Standardized Critical Care terminology were included. Descriptors entered univariate and then multivariate logistic regressions with the target set to the UWS/MCS diagnosis. RESULTS: HRV measures resulted significantly different between UWS and MCS patients, with higher values being associated with better consciousness levels. Specifically, adding HRV-related metrics to ACNS EEG descriptors increased the Nagelkerke R2 from 0.350 (only EEG descriptors) to 0.565 (HRV-EEG combination) with the outcome set to the consciousness diagnosis. CONCLUSIONS: HRV changes across the lowest states of consciousness. Rapid changes in heart rate, occurring in better consciousness levels, confirm the mutual correlation between visceral state functioning patterns and consciousness alterations. SIGNIFICANCE: Quantitative analysis of heart rate in patients with a DoC paves the way for the implementation of low-cost pipelines supporting medical decisions within multimodal consciousness assessments.

The role of the electroencephalogram and evoked potentials after cardiac arrest.

PURPOSE OF REVIEW: In comatose cardiac arrest survivors, the electroencephalogram (EEG) is the most widely used test to assess the severity of hypoxic-ischemic brain injury (HIBI) and guide antiseizure treatment. However, a wide variety of EEG patterns are described in literature. Moreover, the value of postarrest seizure treatment is uncertain. Absent N20 waves of short-latency somatosensory-evoked potentials (SSEPs) are a specific predictor of irreversible HIBI. However, the prognostic significance of the N20 amplitude is less known. RECENT FINDINGS: The increasing adoption of standardized EEG pattern classification identified suppression and burst-suppression as 'highly-malignant' EEG patterns, accurately predicting irreversible HIBI. Conversely, continuous normal-voltage EEG is a reliable predictor of recovery from postarrest coma. A recent trial on EEG-guided antiseizure treatment in HIBI was neutral but suggested potential benefits in specific subgroups. A prognostic approach based on the amplitude rather than on the presence/absence of the N20 SSEP wave recently showed greater sensitivity for poor outcome prediction and added potential for predicting recovery. SUMMARY: Standardized EEG terminology and quantitative approach to SSEP are promising for improving the neuroprognostic accuracy of these tests. Further research is needed to identify the potential benefits of antiseizure treatment after cardiac arrest.