what is the role of post acute eeg in prediction of late neurological outcome in severe disorders of consciousness

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What is new about somatosensory evoked potentials as neurological predictors of comatose survivors after cardiac arrest

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PROGNOSTIC VALUE OF MOTOR EVOKED POTENTIALS IN MOTOR FUNCTION RECOVERY OF UPPER LIMB AFTER STROKE

Objective: To determine the prognostic value of clinical assessment and motor evoked potentials for upper limb strength and functional recovery after acute stroke, and to establish the possible use of motor evoked potentials in rehabilitation. Design: A prospective study. Subjects: Fifty-two patients with hemiparesis were enrolled one month post-stroke; 38 patients concluded the study at 12 months. Methods: Motor evoked potentials were recorded at baseline and after one month. Upper limb muscular strength (Medical Research Council Scale, MRC) and functional tests (Frenchay Arm Test, Barthel Index) were used as dependent outcome variables 12 months later. Motor evoked potentials were classified as present or absent. Predictive values of motor evoked potentials and MRC were evaluated. Results: At 12 months, patients with baseline recordable motor evoked potentials showed a good functional recovery (positive predictive value 94%). The absence of motor evoked potentials did not exclude muscular strength recovery (negative predictive value 95%). Motor evoked potentials had a higher positive predictive value than MRC only in patients with MRC < 2. Conclusion: Motor evoked potentials could be a supportive tool to increase the prognostic accuracy of upper limb motor and functional outcome in hemiparetic patients, especially those with severe initial paresis (MRC < 2) and/or with motor evoked potentials absent in the post-stroke acute phase

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals.Approach. EEG recordings of 17 healthy controls (HCs), 56 subjective cognitive decline (SCD) and 45 mild cognitive impairment (MCI) subjects were acquired at resting state. After preprocessing, we selected sections corresponding to eyes-closed condition. Five different datasets were created by extracting delta, theta, alpha, beta and delta-to-theta frequency bands using bandpass filters. To classify SCDvsMCI and HCvsSCDvsMCI, we propose a framework based on the transformer architecture, which uses multi-head attention to focus on the most relevant parts of the input signals. We trained and validated the model on each dataset with a leave-one-subject-out cross-validation approach, splitting the signals into 10 s epochs. Subjects were assigned to the same class as the majority of their epochs. Classification performances of the transformer were assessed for both epochs and subjects and compared with other DL models.Main results. Results showed that the delta dataset allowed our model to achieve the best performances for the discrimination of SCD and MCI, reaching an Area Under the ROC Curve (AUC) of 0.807, while the highest results for the HCvsSCDvsMCI classification were obtained on alpha and theta with a micro-AUC higher than 0.74.Significance. We demonstrated that DL approaches can support the adoption of non-invasive and economic techniques as EEG to stratify patients in the clinical population at risk for AD. This result was achieved since the attention mechanism was able to learn temporal dependencies of the signal, focusing on the most discriminative patterns, achieving state-of-the-art results by using a deep model of reduced complexity. Our results were consistent with clinical evidence that changes in brain activity are progressive when considering early stages of AD

Brain death following ingestion of E‐cigarette liquid nicotine refill solution

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Does a combination of ≥2 abnormal tests vs. the ERC-ESICM stepwise algorithm improve prediction of poor neurological outcome after cardiac arrest? A post-hoc analysis of the ProNeCA multicentre study.

BACKGROUND Bilaterally absent pupillary light reflexes (PLR) or N20 waves of short-latency evoked potentials (SSEPs) are recommended by the 2015 ERC-ESICM guidelines as robust, first-line predictors of poor neurological outcome after cardiac arrest. However, recent evidence shows that the false positive rates (FPRs) of these tests may be higher than previously reported. We investigated if testing accuracy is improved when combining PLR/SSEPs with malignant electroencephalogram (EEG), oedema on brain computed tomography (CT), or early status myoclonus (SM). METHODS Post-hoc analysis of ProNeCA multicentre prognostication study. We compared the prognostic accuracy of the ERC-ESICM prognostication strategy vs. that of a new strategy combining ≥2 abnormal results from any of PLR, SSEPs, EEG, CT and SM. We also investigated if using alternative classifications for abnormal SSEPs (absent-pathological vs. bilaterally-absent N20) or malignant EEG (ACNS-defined suppression or burst-suppression vs. unreactive burst-suppression or status epilepticus) improved test sensitivity. RESULTS We assessed 210 adult comatose resuscitated patients of whom 164 (78%) had poor neurological outcome (CPC 3-5) at six months. FPRs and sensitivities of the ≥2 abnormal test strategy vs. the ERC-ESICM algorithm were 0[0-8]% vs. 7 [1-18]% and 49[41-57]% vs. 63[56-71]%, respectively (p < .0001). Using alternative SSEP/EEG definitions increased the number of patients with ≥2 concordant test results and the sensitivity of both strategies (67[59-74]% and 54[46-61]% respectively), with no loss of specificity. CONCLUSIONS In comatose resuscitated patients, a prognostication strategy combining ≥2 among PLR, SSEPs, EEG, CT and SM was more specific than the 2015 ERC-ESICM prognostication algorithm for predicting 6-month poor neurological outcome

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&nbsp;=&nbsp;57), MCI (n&nbsp;=&nbsp;46) and healthy control subjects (HC, n&nbsp;=&nbsp;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