Electroencephalography-based classification of Alzheimer's disease spectrum during computer-based cognitive testing.

Alzheimer's disease (AD) is a progressive disease leading to cognitive decline, and to prevent it, researchers seek to diagnose mild cognitive impairment (MCI) early. Particularly, non-amnestic MCI (naMCI) is often mistaken for normal aging as the representative symptom of AD, memory decline, is absent. Subjective cognitive decline (SCD), an intermediate step between normal aging and MCI, is crucial for prediction or early detection of MCI, which determines the presence of AD spectrum pathology. We developed a computer-based cognitive task to classify the presence or absence of AD pathology and stage within the AD spectrum, and attempted to perform multi-stage classification through electroencephalography (EEG) during resting and memory encoding state. The resting and memory-encoding states of 58 patients (20 with SCD, 10 with naMCI, 18 with aMCI, and 10 with AD) were measured and classified into four groups. We extracted features that could reflect the phase, spectral, and temporal characteristics of the resting and memory-encoding states. For the classification, we compared nine machine learning models and three deep learning models using Leave-one-subject-out strategy. Significant correlations were found between the existing neurophysiological test scores and performance of our computer-based cognitive task for all cognitive domains. In all models used, the memory-encoding states realized a higher classification performance than resting states. The best model for the 4-class classification was cKNN. The highest accuracy using resting state data was 67.24%, while it was 93.10% using memory encoding state data. This study involving participants with SCD, naMCI, aMCI, and AD focused on early Alzheimer's diagnosis. The research used EEG data during resting and memory encoding states to classify these groups, demonstrating the significance of cognitive process-related brain waves for diagnosis. The computer-based cognitive task introduced in the study offers a time-efficient alternative to traditional neuropsychological tests, showing a strong correlation with their results and serving as a valuable tool to assess cognitive impairment with reduced bias.

Differences in functional network between focal onset nonconvulsive status epilepticus and toxic metabolic encephalopathy: application to machine learning models for differential diagnosis.

We aimed to compare network properties between focal-onset nonconvulsive status epilepticus (NCSE) and toxic/metabolic encephalopathy (TME) during periods of periodic discharge using graph theoretical analysis, and to evaluate the applicability of graph measures as markers for the differential diagnosis between focal-onset NCSE and TME, using machine learning algorithms. Electroencephalography (EEG) data from 50 focal-onset NCSE and 44 TMEs were analyzed. Epochs with nonictal periodic discharges were selected, and the coherence in each frequency band was analyzed. Graph theoretical analysis was performed to compare brain network properties between the groups. Eight different traditional machine learning methods were implemented to evaluate the utility of graph theoretical measures as input features to discriminate between the two conditions. The average degree (in delta, alpha, beta, and gamma bands), strength (in delta band), global efficiency (in delta and alpha bands), local efficiency (in delta band), clustering coefficient (in delta band), and transitivity (in delta band) were higher in TME than in NCSE. TME showed lower modularity (in delta band) and assortativity (in alpha, beta, and gamma bands) than NCSE. Machine learning algorithms based on EEG global graph measures classified NCSE and TME with high accuracy, and gradient boosting was the most accurate classification model with an area under the receiver operating characteristics curve of 0.904. Our findings on differences in network properties may provide novel insights that graph measures reflecting the network properties could be quantitative markers for the differential diagnosis between focal-onset NCSE and TME.

Enhanced functional connectivity in the reward circuitry in healthy adults with weekend catch-up sleep.

We aimed to identify structural and functional changes in healthy adults with catch-up sleep (CUS), we applied seed-based functional connectivity (FC) analysis using resting-state functional magnetic resonance imaging (MRI). We hypothesized that deficits in reward processing could be a fundamental mechanism underlying the motivation of taking CUS. Then, 55 healthy adults voluntarily (34 with CUS and 21 without CUS) participated in this study. Voxel-based morphometry was performed to explore region of gray matter volume (GMV) difference between CUS and non-CUS groups. Between-group comparison of FC was then carried out using resting-state functional MRI analysis seeding at the region of volume difference. Moreover, the region of volume difference and the strength of FC were correlated with scores of questionnaires for reward-seeking behavior and clinical variables. CUS group had a higher reward-seeking tendency, and increased GMV in the bilateral nucleus accumbens and right superior frontal gyrus relative to non-CUS group. FC analysis seeding at the bilateral accumbens revealed increases of FC in the bilateral medial prefrontal cortex in CUS group compared to non-CUS group. The questionnaire scores reflecting the reward-seeking tendency were correlated with the FC strength between bilateral accumbens and medial prefrontal cortex. Our results indicate that CUS is associated with reward-seeking tendency and increased GMV and FC in regions responsible for reward network. Our findings suggest that enhanced reward network could be the crucial mechanism underlying taking CUS and might be implicated in the detrimental effects of circadian misalignment.

Inherent seizure susceptibility in patients with antihistamine-induced acute symptomatic seizure: a resting-state EEG analysis.

We compared neural activities and network properties between the antihistamine-induced seizures (AIS) and seizure-free groups, with the hypothesis that patients with AIS might have inherently increased neural activities and network properties that are easily synchronized. Resting-state electroencephalography (EEG) data were collected from 27 AIS patients and 30 healthy adults who had never had a seizure. Power spectral density analysis was used to compare neural activities in each localized region. Functional connectivity (FC) was measured using coherence, and graph theoretical analyses were performed to compare network properties between the groups. Machine learning algorithms were applied using measurements found to be different between the groups in the EEG analyses as input features. Compared with the seizure-free group, the AIS group showed a higher spectral power in the entire regions of the delta, theta, and beta bands, as well as in the frontal areas of the alpha band. The AIS group had a higher overall FC strength, as well as a shorter characteristic path length in the theta band and higher global efficiency, local efficiency, and clustering coefficient in the beta band than the seizure-free group. The Support Vector Machine, k-Nearest Neighbor, and Random Forest models distinguished the AIS group from the seizure-free group with a high accuracy of more than 99%. The AIS group had seizure susceptibility considering both regional neural activities and functional network properties. Our findings provide insights into the underlying pathophysiological mechanisms of AIS and may be useful for the differential diagnosis of new-onset seizures in the clinical setting.

Differentiating amnestic from non-amnestic mild cognitive impairment subtypes using graph theoretical measures of electroencephalography.

The purpose of this study was to explore different patterns of functional networks between amnestic mild cognitive impairment (aMCI) and non-aMCI (naMCI) using electroencephalography (EEG) graph theoretical analysis. The data of 197 drug-naïve individuals who complained cognitive impairment were reviewed. Resting-state EEG data was acquired. Graph analyses were performed and compared between aMCI and naMCI, as well as between early and late aMCI. Correlation analyses were conducted between the graph measures and neuropsychological test results. Machine learning algorithms were applied to determine whether the EEG graph measures could be used to distinguish aMCI from naMCI. Compared to naMCI, aMCI showed higher modularity in the beta band and lower radius in the gamma band. Modularity was negatively correlated with scores on the semantic fluency test, and the radius in the gamma band was positively correlated with visual memory, phonemic, and semantic fluency tests. The naïve Bayes algorithm classified aMCI and naMCI with 89% accuracy. Late aMCI showed inefficient and segregated network properties compared to early aMCI. Graph measures could differentiate aMCI from naMCI, suggesting that these measures might be considered as predictive markers for progression to Alzheimer's dementia in patients with MCI.

Drinking coffee enhances neurocognitive function by reorganizing brain functional connectivity.

The purpose of this study was to identify the mechanisms underlying effects of coffee on cognition in the context of brain networks. Here we investigated functional connectivity before and after drinking coffee using graph-theoretic analysis of electroencephalography (EEG). Twenty-one healthy adults voluntarily participated in this study. The resting-state EEG data and results of neuropsychological tests were consecutively acquired before and 30 min after coffee consumption. Graph analyses were performed and compared before and after coffee consumption. Correlation analyses were conducted to assess the relationship between changes in graph measures and those in cognitive function tests. Functional connectivity (FC) was reorganized toward more efficient network properties after coffee consumption. Performance in Digit Span tests and Trail Making Test Part B improved after coffee consumption, and the improved performance in executive function was correlated with changes in graph measures, reflecting a shift toward efficient network properties. The beneficial effects of coffee on cognitive function might be attributed to the reorganization of FC toward more efficient network properties. Based on our findings, the patterns of network reorganization could be used as quantitative markers to elucidate the mechanisms underlying the beneficial effects of coffee on cognition, especially executive function.

Regional Gray Matter Volume Changes in Parkinson's Disease with Orthostatic Hypotension.

Neurodegenerative change in the central nervous system has been suggested as one of the pathophysiological mechanisms of autonomic nervous system dysfunction in Parkinson's disease (PD). We analyzed gray matter (GM) volume changes and clinical parameters in patients with PD to investigate any involvement in the brain structures responsible for autonomic control in patients with PD having orthostatic hypotension (OH). Voxel-based morphometry was applied to compare regional GM volumes between PD patients with and without OH. Multivariate logistic regression analysis using a hierarchical model was carried out to identify clinical factors independently contributing to the regional GM volume changes in PD patients with OH. The Sobel test was used to analyze mediation effects between the independent contributing factors to the GM volume changes. PD patients with OH had more severe autonomic dysfunction and reduction in volume in the right inferior temporal cortex than those without OH. The right inferior temporal volume was positively correlated with the Qualitative Scoring MMSE Pentagon Test (QSPT) score, reflecting visuospatial/visuoperceptual function, and negatively correlated with the Composite Autonomic Severity Score (CASS). The CASS and QSPT scores were found to be factors independently contributing to regional volume changes in the right inferior temporal cortex. The QSPT score was identified as a mediator in which regional GM volume predicts the CASS. Our findings suggest that a decrease in the visuospatial/visuoperceptual process may be involved in the presentation of autonomic nervous system dysfunction in PD patients.

Antihistamines as a common cause of new-onset seizures: a single-center observational study.

OBJECTIVE: Scarce data are available regarding the proportion of drugs that have provoked new-onset seizures. The aim of this study was to investigate the types of causative drugs of drug-induced new-onset seizures in a relatively large population of patients who were admitted to our epilepsy monitoring unit. METHODS: Using a hospital-based database, patients with new-onset seizures were selected and the underlying etiology of new-onset seizures was reviewed. Based on the etiologic conditions, acute symptomatic seizure was classified into 7 groups of provocation factors: drug, alcohol, encephalitis, stroke, hypoxic injury, metabolic, and unclassified. Causative drugs for new-onset seizures were further investigated. RESULTS: Altogether, 363 patients with new-onset seizures were reviewed in this study. The most common cause of new-onset seizures was epilepsy, followed by syncope, acute symptomatic seizure, and others. Drugs were found to be the most common provocation factor for acute symptomatic seizures. The most common causative drug was antihistamine, followed by stimulants, antibiotics, and other drugs. Most patients with antihistamine-induced seizures had normal renal function and were under treatment at the therapeutic dose. CONCLUSION: In our population, antihistamine accounted for the highest proportion of drug-induced seizures. Considering that antihistamines are widely used as over-the-counter drugs around the world, they should be considered a possible cause of new-onset seizures.

Heart-Rate-Based Machine-Learning Algorithms for Screening Orthostatic Hypotension.

BACKGROUND AND PURPOSE: Many elderly patients are unable to actively stand up by themselves and have contraindications to performing the head-up tilt test (HUTT). We aimed to develop screening algorithms for diagnosing orthostatic hypotension (OH) before performing the HUTT. METHODS: This study recruited 663 patients with orthostatic intolerance (78 with and 585 without OH, as confirmed by the HUTT) and compared their clinical characteristics. Univariate and multivariate analyses were performed to investigate potential predictors of an OH diagnosis. Machine-learning algorithms were applied to determine whether the accuracy of OH prediction could be used for screening OH without performing the HUTT. RESULTS: Differences between expiration and inspiration (E-I differences), expiration:inspiration ratios (E:I ratios), and Valsalva ratios were smaller in patients with OH than in those without OH. The univariate analysis showed that increased age and baseline systolic blood pressure (BP) as well as decreased E-I difference, E:I ratio, and Valsalva ratio were correlated with OH. In the multivariate analysis, increased baseline systolic BP and decreased Valsalva ratio were found to be independent predictors of OH. Using those variables as input features, the classification accuracies of the support vector machine, k-nearest neighbors, and random forest methods were 84.4%, 84.4%, and 90.6%, respectively. CONCLUSIONS: We have identified clinical parameters that are strongly associated with OH. Machine-learning analysis using those parameters was highly accurate in differentiating OH from non-OH patients. These parameters could be useful screening factors for OH in patients who are unable to perform the HUTT.

Patterns of Orthostatic Blood Pressure Changes in Patients with Orthostatic Hypotension.

BACKGROUND AND PURPOSE: The objective of this study was to determine the patterns of blood pressure (BP) changes during the head-up tilt (HUT) test, particularly in terms of its clinical significance for patients with orthostatic hypotension (OH). METHODS: OH was divided into four categories based on systolic BP changes occurring within the first 10 minutes of the HUT test: sustained orthostatic hypotension (SOH), progressive orthostatic hypotension (POH), orthostatic hypotension with partial recovery (OHPR), and transient orthostatic hypotension (TOH). RESULTS: In total, 151 patients were analyzed: 65 with SOH, 38 with POH, 21 with OHPR, and 27 with TOH. POH patients exhibited the greatest reduction in systolic BP after HUT and were also the most likely to develop symptoms requiring early termination of the HUT test (42.1%, p<0.001). Additionally, SOH patients exhibited smaller heart-rate variation with deep breathing values (p=0.003) and Valsalva ratios (p=0.022) compared to POH patients. The sweat volume was greatest in OHPR patients. CONCLUSIONS: Clinical characteristics, including the findings of autonomic function tests, differed between the OH patient groups. This might reflect differences in the underlying pathophysiologic mechanisms. Determining the patterns of BP changes during the HUT test may facilitate the development of effective management strategies in patients with OH.