Brain tumor surgery guided by navigated transcranial magnetic stimulation mapping for arithmetic calculation.

OBJECTIVE: The onco-functional balance represents the primary goal in neuro-oncology. The increasing use of navigated transcranial magnetic stimulation (nTMS) allows the noninvasive characterization of cortical functional anatomy, and its reliability for motor and language mapping has previously been validated. Calculation and arithmetic processing has not been studied with nTMS so far. In this study, the authors present their preliminary data concerning nTMS calculation. METHODS: The authors designed a monocentric prospective study, adopting an internal protocol to use nTMS for preoperative planning, including arithmetic processing. When awake surgery was possible, according to the patients' conditions, nTMS points were used to guide direct cortical stimulation (DCS), i.e., the gold standard for cortical mapping. Navigated TMS-based tractography was used for surgical planning. Statistical analyses on the nTMS and DCS points were performed. RESULTS: From February 2021 to October 2023, 61 procedures for nTMS calculation mapping were performed. The clinical evaluation, including pre- and postoperative evaluations (3 months after surgery), demonstrated a good clinical outcome with preservation of arithmetic function and recovery (92.8% of patients). Between the awake and asleep surgery groups, the postoperative clinical results were comparable at the 3-month follow-up, with > 90% of the patients achieving improved calculation function. The surgical strategy adopted was aimed at sparing nTMS positive points in asleep procedures, whereas nTMS and DCS positive points were not removed in awake procedures. Overall, 62% of the positive points for calculation functions were exposed by craniotomy and 85% were spared during surgery. None of the patients developed nTMS-related seizures. Diffusion tensor imaging fiber tracking based on nTMS positive points for calculation was used. The white matter fiber tracts involved in calculation functions were the arcuate fasciculus (56%) and frontal aslant tract (22%). When nTMS and DCS points were compared in awake surgery (n = 10 patients), a sensitivity of 31.71%, specificity of 85.76%, positive predictive value of 22.41%, negative predictive value of 90.64%, and accuracy of approximately 69% were achieved. CONCLUSIONS: Based on the authors' preliminary data, nTMS can be an advantageous tool to study cognitive functions, aimed at minimizing neurological impairment. The postoperative clinical outcome for patients who underwent operation with nTMS was very good. Considering these results, nTMS has proved to be a feasible method to map cognitive areas including those for calculation functions. Further analyses are needed to validate these data. Finally, other cognitive functions (e.g., visuospatial) may be explored with nTMS.

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.

Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction.

Patients with Disorder of Consciousness (DoC) entering Intensive Rehabilitation Units after a severe Acquired Brain Injury have a highly variable evolution of the state of consciousness which is a complex aspect to predict. Besides clinical factors, electroencephalography has clearly shown its potential into the identification of prognostic biomarkers of consciousness recovery. In this retrospective study, with a dataset of 271 patients with DoC, we proposed three different Elastic-Net regressors trained on different datasets to predict the Coma Recovery Scale-Revised value at discharge based on data collected at admission. One dataset was completely EEG-based, one solely clinical data-based and the last was composed by the union of the two. Each model was optimized, validated and tested with a robust nested cross-validation pipeline. The best models resulted in a median absolute test error of 4.54 [IQR = 4.56], 3.39 [IQR = 4.36], 3.16 [IQR = 4.13] for respectively the EEG, clinical and hybrid model. Furthermore, the hybrid model for what concerns overcoming an unresponsive wakefulness state and exiting a DoC results in an AUC of 0.91 and 0.88 respectively. Small but useful improvements are added by the EEG dataset to the clinical model for what concerns overcoming an unresponsive wakefulness state. Data-driven techniques and namely, machine learning models are hereby shown to be capable of supporting the complex decision-making process the practitioners must face.

Multiple Electroencephalogram Recordings for Monitoring the Evolution of Neurological Complications during Baclofen Withdrawal Syndrome.

Baclofen withdrawal syndrome represents a clinical emergency that can lead to life-threatening complications. It is often a diagnostic challenge because of its nonspecific nature of presentation and degree of symptom overlap with other clinical diseases. Electroencephalography (EEG) might provide important supporting evidence when neurological complications are involved. We present the case of a 55-year-old woman with sudden onset of motor manifestations at the limbs and an altered mental status 24 hours after cessation of intrathecal baclofen administration, following the removal of the pump due to infection, in whom a computed tomography did not show any acute-onset brain injuries, and multiple EEG recordings were performed. The first EEG showed the presence of bilateral sharply contoured waves, in the absence of epileptic discharges and seizures. No correlation between motor manifestations and EEG changes were detected. This EEG pattern was considered to be the expression of an overexcitation of the central nervous system (CNS) due to the loss of baclofen inhibitory effects, excluding an epileptic origin of motor manifestations. Another EEG, performed 24 hours later, showed the presence of triphasic waves with severe generalised slowing, suggesting the presence of encephalopathy. The last EEG, performed 48 hours after the previous recording, when a recovered state of consciousness was already present, showed regression of the triphasic waves and a reorganisation of the background activity. In our case, repeated EEG evaluation allowed monitoring the evolution of acute encephalopathy developed during baclofen withdrawal syndrome, from the initial phase of CNS hyperexcitability, through the phase of metabolic encephalopathy, and to its resolution. This modality allowed for optimising the diagnostic-therapeutic management of the patient during her stay in the intensive care unit.

Clinical, Neurophysiological, and Genetic Predictors of Recovery in Patients With Severe Acquired Brain Injuries (PRABI): A Study Protocol for a Longitudinal Observational Study.

Background: Due to continuous advances in intensive care technology and neurosurgical procedures, the number of survivors from severe acquired brain injuries (sABIs) has increased considerably, raising several delicate ethical issues. The heterogeneity and complex nature of the neurological damage of sABIs make the detection of predictive factors of a better outcome very challenging. Identifying the profile of those patients with better prospects of recovery will facilitate clinical and family choices and allow to personalize rehabilitation. This paper describes a multicenter prospective study protocol, to investigate outcomes and baseline predictors or biomarkers of functional recovery, on a large Italian cohort of sABI survivors undergoing postacute rehabilitation. Methods: All patients with a diagnosis of sABI admitted to four intensive rehabilitation units (IRUs) within 4 months from the acute event, aged above 18, and providing informed consent, will be enrolled. No additional exclusion criteria will be considered. Measures will be taken at admission (T0), at three (T1) and 6 months (T2) from T0, and follow-up at 12 and 24 months from onset, including clinical and functional data, neurophysiological results, and analysis of neurogenetic biomarkers. Statistics: Advanced machine learning algorithms will be cross validated to achieve data-driven prediction models. To assess the clinical applicability of the solutions obtained, the prediction of recovery milestones will be compared to the evaluation of a multiprofessional, interdisciplinary rehabilitation team, performed within 2 weeks from admission. Discussion: Identifying the profiles of patients with a favorable prognosis would allow customization of rehabilitation strategies, to provide accurate information to the caregivers and, possibly, to optimize rehabilitation outcomes. Conclusions: The application and validation of machine learning algorithms on a comprehensive pool of clinical, genetic, and neurophysiological data can pave the way toward the implementation of tools in support of the clinical prognosis for the rehabilitation pathways of patients after sABI.

Predictors of Function, Activity, and Participation of Stroke Patients Undergoing Intensive Rehabilitation: A Multicenter Prospective Observational Study Protocol.

Background: The complex nature of stroke sequelae, the heterogeneity in rehabilitation pathways, and the lack of validated prediction models of rehabilitation outcomes challenge stroke rehabilitation quality assessment and clinical research. An integrated care pathway (ICP), defining a reproducible rehabilitation assessment and process, may provide a structured frame within investigated outcomes and individual predictors of response to treatment, including neurophysiological and neurogenetic biomarkers. Predictors may differ for different interventions, suggesting clues to personalize and optimize rehabilitation. To date, a large representative Italian cohort study focusing on individual variability of response to an evidence-based ICP is lacking, and predictors of individual response to rehabilitation are largely unexplored. This paper describes a multicenter study protocol to prospectively investigate outcomes and predictors of response to an evidence-based ICP in a large Italian cohort of stroke survivors undergoing post-acute inpatient rehabilitation. Methods: All patients with diagnosis of ischemic or hemorrhagic stroke confirmed both by clinical and brain imaging evaluation, admitted to four intensive rehabilitation units (adopting the same stroke rehabilitation ICP) within 30 days from the acute event, aged 18+, and providing informed consent will be enrolled (expected sample: 270 patients). Measures will be taken at admission (T0), at discharge (T1), and at follow-up 6 months after a stroke (T2), including clinical data, nutritional, functional, neurological, and neuropsychological measures, electroencephalography and motor evoked potentials, and analysis of neurogenetic biomarkers. Statistics: In addition to classical multivariate logistic regression analysis, advanced machine learning algorithms will be cross-validated to achieve data-driven prognosis prediction models. Discussion: By identifying data-driven prognosis prediction models in stroke rehabilitation, this study might contribute to the development of patient-oriented therapy and to optimize rehabilitation outcomes. Clinical Trial Registration: ClinicalTrials.gov, NCT03968627. https://www.clinicaltrials.gov/ct2/show/NCT03968627?term=Cecchi&cond=Stroke&draw=2&rank=2.