Clinical Neurophysiology最新文献

Objective: The application of signal analysis techniques to electroencephalographic (EEG) recordings from epilepsy patients shows that epilepsy involves not only altered neuronal synchronization but also the reorganization of functional EEG networks. This study aims to assess the large-scale phase-locking of such functional networks and how individual network nodes contribute to this collective dynamics.

Methods: We analyze the EEG recorded before, during and after seizures from sixteen patients with pharmacoresistant focal-onset epilepsy. The data is filtered to low (4-30 Hz) and high (80-150 Hz) frequencies. We define the multivariate phase-locking measure and the univariate phase-locking contribution measure. Surrogate signals are used to estimate baseline results expected under the null hypothesis that the EEG is a correlated linear stochastic process.

Results: On average, nodes from inside and outside the seizure onset zone (SOZ) increase and decrease, respectively, the large-scale phase-locking. This difference becomes most evident in a joint analysis of low and high frequencies.

Conclusions: Nodes inside and outside the SOZ play opposite roles for the large-scale phase-locking in functional EEG network in epilepsy patients.

Significance: The application of the phase-locking contribution measure to EEG recordings from epilepsy patients can potentially help in localizing the SOZ.

Objective: To apply machine learning approaches on EEG event-related oscillations (ERO) to discriminate preclinical Alzheimer's disease (AD) from age- and sex-matched controls.

Methods: Twenty-two cognitively normal preclinical AD participants with elevated amyloid and 21 cognitively normal controls without elevated amyloid completed n-back working memory tasks (n = 0, 1, 2). The absolute and relative power of ERO was extracted using the discrete wavelet transform in the delta, theta, alpha, and beta bands. Four machine learning methods were employed, and classification performance was assessed using three metrics.

Results: The low-frequency bands produced higher discriminative performances compared to high-frequency bands. The 2-back task yielded the best classification capability among the three tasks. The highest area under the curve value (0.86) was achieved in the 2-back delta band nontarget condition data. The highest accuracy (80.47%) was obtained in the 2-back delta and theta bands nontarget data. The highest F1 score (0.82) was in the 2-back theta band nontarget data. The support vector machine achieved the highest performance among tested classifiers.

Conclusion: This study demonstrates the promise of using machine learning on EEG ERO from working memory tasks to detect preclinical AD.

Significance: EEG ERO may reveal pathophysiological differences in the earliest stage of AD when no cognitive impairments are apparent.

Objective: There is a need for improved diagnostic tools in Amyotrophic Lateral Sclerosis (ALS). Our objective was to assess muscle ultrasound as a diagnostic tool in patients with ALS and determine a simplified screening protocol to aid implementation in clinical practice.

Methods: Ultrasound of bulbar and limb muscles was prospectively performed on all patients referred to a single centre with suspected ALS. Clinical measures of disease severity and upper motor neuron impairment were also recorded. Receiver operating characteristic (ROC) curves were calculated to assess the diagnostic utility of muscle ultrasound.

Results: 94 patients initially suspected of ALS were recruited to this observational cohort study. Forty-four were subsequently diagnosed as ALS and 50 as disease mimics. ALS patients demonstrated a higher frequency and more generalised distribution of fasciculations compared to mimics. A simplified 5 muscle screening protocol exhibited an AUC of 0.94 (95 %CI 0.89-0.99) in discriminating ALS from mimics. The presence of ≥ 3 fasciculating muscles detected using this screening protocol was 89 % sensitive and 88 % specific for the diagnosis of ALS.

Conclusions: Muscle ultrasound, screening as few as 5 muscles, has diagnostic utility in ALS.

Significance: Muscle ultrasound enhances clinical diagnosis in ALS.