Increased cerebral activity during microsleeps reflects an unconscious drive to re-establish consciousness.

BACKGROUND: Microsleeps are brief instances of sleep, causing complete lapses in responsiveness and partial or total extended closure of both eyes. Microsleeps can have devastating consequences, particularly in the transportation sector. STUDY OBJECTIVES: Questions remain regarding the neural signature and underlying mechanisms of microsleeps. This study aimed to gain a better understanding of the physiological substrates of microsleeps, which might lead to a better understanding of the phenomenon. METHODS: Data from an earlier study, involving 20 healthy non-sleep-deprived subjects, were analysed. Each session lasted 50 min and required subjects to perform a 2-D continuous visuomotor tracking task. Simultaneous data collection included tracking performance, eye-video, EEG, and fMRI. A human expert visually inspected each participant's tracking performance and eye-video recordings to identify microsleeps. Our interest was in microsleeps of ≥4-s duration, leaving us with a total of 226 events from 10 subjects. The microsleep events were divided into four 2-s segments (pre, start, end, and post) (with a gap in the middle, between start and end segments, for microsleeps >4 s), then each segment was analysed relative to its prior segment by examining changes in source-reconstructed EEG power in the delta, theta, alpha, beta, and gamma bands. RESULTS: EEG power increased in the theta and alpha bands between the pre and start of microsleeps. There was also increased power in the delta, beta, and gamma bands between the start and end of microsleeps. Conversely, there was a reduction in power between the end and post of microsleeps in the delta and alpha bands. These findings support previous findings in the delta, theta, and alpha bands. However, increased power in the beta and gamma bands has not been previously reported. CONCLUSIONS: We contend that increased high-frequency activity during microsleeps reflects unconscious 'cognitive' activity aimed at re-establishing consciousness following falling asleep during an active task.

Progressive MRI brain volume changes in ovine models of CLN5 and CLN6 neuronal ceroid lipofuscinosis.

Neuronal ceroid lipofuscinoses (Batten disease) are a group of inherited lysosomal storage disorders characterized by progressive neurodegeneration leading to motor and cognitive dysfunction, seizure activity and blindness. The disease can be caused by mutations in 1 of 13 ceroid lipofuscinosis neuronal (CLN) genes. Naturally occurring sheep models of the CLN5 and CLN6 neuronal ceroid lipofuscinoses recapitulate the clinical disease progression and post-mortem pathology of the human disease. We used longitudinal MRI to assess global and regional brain volume changes in CLN5 and CLN6 affected sheep compared to age-matched controls over 18 months. In both models, grey matter volume progressively decreased over time, while cerebrospinal fluid volume increased in affected sheep compared with controls. Total grey matter volume showed a strong positive correlation with clinical scores, while cerebrospinal fluid volume was negatively correlated with clinical scores. Cortical regions in affected animals showed significant atrophy at baseline (5 months of age) and progressively declined over the disease course. Subcortical regions were relatively spared with the exception of the caudate nucleus in CLN5 affected animals that degenerated rapidly at end-stage disease. Our results, which indicate selective vulnerability and provide a timeline of degeneration of specific brain regions in two sheep models of neuronal ceroid lipofuscinoses, will provide a clinically relevant benchmark for assessing therapeutic efficacy in subsequent trials of gene therapy for CLN5 and CLN6 disease.

Cognitive and arginine metabolic correlates of temporal dysfunction in the MIA rat model of schizophrenia risk.

As a hallmark characteristic of schizophrenia, abnormal perception of time is thought to arise from cognitive impairment; however, the absence of translational models indexing this pathological relationship creates barriers to understanding the functional and biological bases of timing impairments. Here, we investigate the relationship between timing and cognition using the maternal immune activation (MIA) rat model of schizophrenia. We additionally investigate the role of prefrontal cortex L-arginine metabolism in these processes via high-performance liquid chromatography and liquid chromatography/mass spectrometry. Results revealed that MIA rats exhibit greater underestimation of interval durations (2-8 s); greater underestimation corresponded with declines in sustained attention capacity. Working memory impairments were not found to contribute to timing deficits. These findings represent the first direct identification of a timing-attention relationship within rodents and are discussed with respect to the dopamine hypothesis of temporal pace. We also found that MIA exposure altered aspects of arginine metabolism as observed in schizophrenia, and we present preliminary evidence suggesting that these changes have functional consequences for cognition. These findings support the MIA rat model as a valuable tool for future investigations exploring the biological instantiation of interrelated timing and cognitive deficits in schizophrenia. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Extracellular vesicle biomarkers for cognitive impairment in Parkinson's disease.

Besides motor symptoms, many individuals with Parkinson's disease develop cognitive impairment perhaps due to coexisting α-synuclein and Alzheimer's disease pathologies and impaired brain insulin signalling. Discovering biomarkers for cognitive impairment in Parkinson's disease could help clarify the underlying pathogenic processes and improve Parkinson's disease diagnosis and prognosis. This study used plasma samples from 273 participants: 103 Parkinson's disease individuals with normal cognition, 121 Parkinson's disease individuals with cognitive impairment (81 with mild cognitive impairment, 40 with dementia) and 49 age- and sex-matched controls. Plasma extracellular vesicles enriched for neuronal origin were immunocaptured by targeting the L1 cell adhesion molecule, then biomarkers were quantified using immunoassays. α-Synuclein was lower in Parkinson's disease compared to control individuals (P = 0.004) and in cognitively impaired Parkinson's disease individuals compared to Parkinson's disease with normal cognition (P < 0.001) and control (P < 0.001) individuals. Amyloid-ß42 did not differ between groups. Phosphorylated tau (T181) was higher in Parkinson's disease than control individuals (P = 0.003) and in cognitively impaired compared to cognitively normal Parkinson's disease individuals (P < 0.001) and controls (P < 0.001). Total tau was not different between groups. Tyrosine-phosphorylated insulin receptor substrate-1 was lower in Parkinson's disease compared to control individuals (P = 0.03) and in cognitively impaired compared to cognitively normal Parkinson's disease individuals (P = 0.02) and controls (P = 0.01), and also decreased with increasing motor symptom severity (P = 0.005); serine312-phosphorylated insulin receptor substrate-1 was not different between groups. Mechanistic target of rapamycin was not different between groups, whereas phosphorylated mechanistic target of rapamycin trended lower in cognitively impaired compared to cognitively normal Parkinson's disease individuals (P = 0.05). The ratio of α-synuclein to phosphorylated tau181 was lower in Parkinson's disease compared to controls (P = 0.001), in cognitively impaired compared to cognitively normal Parkinson's disease individuals (P < 0.001) and decreased with increasing motor symptom severity (P < 0.001). The ratio of insulin receptor substrate-1 phosphorylated serine312 to insulin receptor substrate-1 phosphorylated tyrosine was higher in Parkinson's disease compared to control individuals (P = 0.01), in cognitively impaired compared to cognitively normal Parkinson's disease individuals (P = 0.02) and increased with increasing motor symptom severity (P = 0.003). α-Synuclein, phosphorylated tau181 and insulin receptor substrate-1 phosphorylated tyrosine contributed in diagnostic classification between groups. These findings suggest that both α-synuclein and tau pathologies and impaired insulin signalling underlie Parkinson's disease with cognitive impairment. Plasma neuronal extracellular vesicles biomarkers may inform cognitive prognosis in Parkinson's disease.

Investigating the neural signature of microsleeps using EEG.

A microsleep (MS) is a complete lapse of responsiveness due to an episode of brief sleep (≲ 15 s) with eyes partially or completely closed. MSs are highly correlated with the risk of car accidents, severe injuries, and death. To investigate EEG changes during MSs, we used a 2D continuous visuomotor tracking (CVT) task and eye-video to identify MSs in 20 subjects performing the 50-min task. Following pre-processing, FFT spectral analysis was used to calculate the activity in the EEG delta, theta, alpha, beta, and gamma bands, followed by eLORETA for source reconstruction. A group statistical analysis was performed to compare the change in activity over EEG bands of an MS to its baseline. After correction for multiple comparisons, we found maximum increases in delta, theta, and alpha activities over the frontal lobe, and beta over the parietal and occipital lobes. There were no significant changes in the gamma band, and no significant decreases in any band. Our results are in agreement with previous studies which reported increased alpha activity in MSs. However, this is the first study to have reported increased beta activity during MSs, which, due to the usual association of beta activity with wakefulness, was unexpected.

Classification of alcoholic EEG signals using wavelet scattering transform-based features.

Following the research question and the relevant dataset, feature extraction is the most important component of machine learning and data science pipelines. The wavelet scattering transform (WST) is a recently developed knowledge-based feature extraction technique and is structurally like a convolutional neural network (CNN). It preserves information in high-frequency, is insensitive to signal deformations, and generates low variance features of real-valued signals generally required in classification tasks. With data from a publicly-available UCI database, we investigated the ability of WST-based features extracted from multichannel electroencephalogram (EEG) signals to discriminate 1.0-s EEG records of 20 male subjects with alcoholism and 20 male healthy subjects. Using record-wise 10-fold cross-validation, we found that WST-based features, inputted to a support vector machine (SVM) classifier, were able to correctly classify all alcoholic and normal EEG records. Similar performances were achieved with 1D CNN. In contrast, the highest independent-subject-wise mean 10-fold cross-validation performance was achieved with WST-based features fed to a linear discriminant (LDA) classifier. The results achieved with two 10-fold cross-validation approaches suggest that the WST together with a conventional classifier is an alternative to CNN for classification of alcoholic and normal EEGs. WST-based features from occipital and parietal regions were the most informative at discriminating between alcoholic and normal EEG records.

Neuropsychiatric Symptoms Are Associated with Dementia in Parkinson's Disease but Not Predictive of it.

BACKGROUND: Neuropsychiatric symptoms in Parkinson's disease (PD) may increase dementia (PDD) risk. The predictive value of these symptoms, however, has not been compared to clinical and demographic predictors of future PDD. OBJECTIVES: Determine if neuropsychiatric symptoms are useful markers of PDD risk. METHODS: 328 PD participants completed baseline neuropsychiatric and MDS-Task Force-Level II assessments. Of these, 202 non-demented individuals were followed-up over a four-years period to detect conversion to PDD; 51 developed PDD. ROC analysis tested associations between baseline neuropsychiatric symptoms and future PDD. The probability of developing PDD was also modeled as a function of neuropsychiatric inventory (NPI)-total score, PD Questionnaire (PDQ)-hallucinations, PDQ-anxiety, and contrasted to cognitive ability, age, and motor function. Leave-one-out information criterion was used to evaluate which models provided useful information when predicting future PDD. RESULTS: The PDD group experienced greater levels of neuropsychiatric symptoms compared to the non-PDD groups at baseline. Few differences were found between the PD-MCI and PD-N groups. Six neuropsychiatric measures were significantly, but weakly, associated with future PDD. The strongest was NPI-total score: AUC = 0.66 [0.57-0.75]. There was, however, no evidence it contained useful out-of-sample predictive information of future PDD (delta ELPD = 1.8 (SD 2.5)); Similar results held for PDQ-hallucinations and PDQ-anxiety. In contrast, cognitive ability (delta ELPD = 36 (SD 8)) and age (delta ELPD = 11 (SD 5)) provided useful predictive information of future PDD. CONCLUSIONS: Cognitive ability and age strongly out-performed neuropsychiatric measures as markers of developing PDD within 4 years. Therefore, neuropsychiatric symptoms do not appear to be useful markers of PDD risk.

Neural Correlates of Attention Lapses During Continuous Tasks.

Attention lapses (ALs) are common phenomenon, which can affect our performance and productivity by slowing or suspending responsiveness. Occurrence of ALs during continuous monitoring tasks, such as driving or operating machinery, can lead to injuries and fatalities. However, we have limited understanding of what happens in the brain when ALs intrude during such continuous tasks. Here, we analyzed fMRI data from a study, in which participants performed a continuous visuomotor tracking task during fMRI scanning. A total of 68 ALs were identified from 20 individuals, using visual rating of tracking performance and video-based eye-closure. ALs were found to be associated with increased BOLD fMRI activity partially in the executive control network, and sensorimotor network. Surprisingly, we found no evidence of deactivations.

Childbirth and Delayed Parkinson's Onset: A Reproducible Nonbiological Artifact of Societal Change.

BACKGROUND: Uncontrolled studies have reported associations between later Parkinson's disease onset in women and a history of giving birth, with age at onset delayed by nearly 3 years per child. We tested this association in two independent data sets, but, as a control to test for nonbiological explanations, also included men with PD. METHODS: We analyzed valid cases from the Parkinson's Progressive Markers Initiative incident sample (145 women, 276 men) and a prevalent sample surveyed by the New Zealand Brain Research Institute (210 women, 394 men). RESULTS: The association was present in both women and men in the Parkinson's Progressive Markers Initiative study, and absent in both in the New Zealand Brain Research Institute study. This is consistent with generational differences common to men and women, which confound with age at onset in incident-dominant samples. CONCLUSIONS: Despite being replicable in certain circumstances, associations between childbirth and later PD onset are an artifact of generational cohort differences. © 2020 International Parkinson and Movement Disorder Society.

Test-retest reliability and sample size estimates after MRI scanner relocation.

OBJECTIVE: Many factors can contribute to the reliability and robustness of MRI-derived metrics. In this study, we assessed the reliability and reproducibility of three MRI modalities after an MRI scanner was relocated to a new hospital facility. METHODS: Twenty healthy volunteers (12 females, mean age (standard deviation) â€‹= â€‹41 (11) years, age range [25-66]) completed three MRI sessions. The first session (S1) was one week prior to the 3T GE HDxt scanner relocation. The second (S2) occurred nine weeks after S1 and at the new location; a third session (S3) was acquired 4 weeks after S2. At each session, we acquired structural T1-weighted, pseudo-continuous arterial spin labelled, and diffusion tensor imaging sequences. We used longitudinal processing streams to create 12 summary MRI metrics, including total gray matter (GM), cortical GM, subcortical GM, white matter (WM), and lateral ventricle volume; mean cortical thickness; total surface area; average gray matter perfusion, and average diffusion tensor metrics along principal white matter pathways. We compared mean MRI values and variance at the old scanner location to multiple sessions at the new location using Bayesian multi-level regression models. K-fold cross validation allowed identification of important predictors. Whole-brain analyses were used to investigate any regional differences. Furthermore, we calculated within-subject coefficient of variation (wsCV), intraclass correlation coefficient (ICC), and dice similarity index (SI) of cortical segmentations across scanner relocation and within-site. Additionally, we estimated sample sizes required to robustly detect a 4% difference between two groups across MRI metrics. RESULTS: All global MRI metrics exhibited little mean difference and small variability (bar cortical gray matter perfusion) both across scanner relocation and within-site repeat. T1- and DTI-derived tissue metrics showed â€‹< â€‹|0.3|% mean difference and <1.2% variance across scanner location and <|0.4|% mean difference and <0.8% variance within the new location, with between-site intraclass correlation coefficient (ICC) â€‹> â€‹0.80 and within-subject coefficient of variation (wsCV) â€‹< â€‹1.4%. Mean cortical gray matter perfusion had the highest between-session variability (6.7% [0.3, 16.7], estimate [95% uncertainty interval]), and hence the smallest ICC (0.71 [0.44,0.92]) and largest wsCV (13.4% [5.4, 18.1]). No global metric exhibited evidence of a meaningful mean difference between scanner locations. However, surface area showed evidence of a mean difference within-site repeat (between S2 and S3). Whole-brain analyses revealed no significant areas of difference between scanner relocation or within-site. For all metrics, we found no support for a systematic difference in variance across relocation sites compared to within-site test-retest reliability. Necessary sample sizes to detect a 4% difference between two independent groups varied from a maximum of n â€‹= â€‹362 per group (cortical gray matter perfusion), to total gray matter volume (n â€‹= â€‹114), average fractional anisotropy (n â€‹= â€‹23), total gray matter volume normalized by intracranial volume (n â€‹= â€‹19), and axial diffusivity (n â€‹= â€‹3 per group). CONCLUSION: Cortical gray matter perfusion was the most variable metric investigated (necessitating large sample sizes to identify group differences), with other metrics showing substantially less variability. Scanner relocation appeared to have a negligible effect on variability of the global MRI metrics tested. This manuscript reports within-site test-retest variability to act as a tool for calculating sample size in future investigations. Our results suggest that when all other parameters are held constant (e.g., sequence parameters and MRI processing), the effect of scanner relocation is indistinguishable from within-site variability, but may need to be considered depending on the question being investigated.

Detection and Prediction of Microsleeps from EEG using Spatio-Temporal Patterns.

A microsleep is a brief lapse in performance due to an involuntary sleep-related loss of consciousness. These episodes are of particular importance in occupations requiring extended unimpaired visuomotor performance, such as driving. Detection and even prediction of microsleeps has the potential to prevent catastrophic events and fatal accidents. In this study, we examined detection and prediction of microsleeps using EEG data of 8 subjects who performed two 1-h sessions of continuous 1-D tracking. A regularized spatio-temporal filtering and classification (RSTFC) method was used to extract features from 5-s EEG segments. These features were then used to train three different linear classifiers: linear discriminant analysis (LDA), sparse Bayesian learning (SBL), and variational Bayesian logistic regression (VBLR). The performance of microsleep state detection and prediction was evaluated using leave-one-subject-out cross-validation. The detection performance measures were AUCROC 0.96, AUCPR 0.52, and phi 0.47. As expected, prediction of microsleep states with a 0.25-s ahead prediction time resulted in slightly lower performances compared to the detection. Prediction performance measures were substantially higher than those achieved with log-power spectral features, i.e., AUCROC 0.95 (cf. 0.90), AUCPR 0.50 (cf. 0.36), and phi 0.46 (cf. 0.34).

Deep Learning with Convolutional Neural Network for detecting microsleep states from EEG: A comparison between the oversampling technique and cost-based learning.

Any occupation which involves critical decision making in real-time requires attention and concentration. When repetitive and expanded working periods are encountered, it can result in microsleeps. Microsleeps are complete lapses in which a subject involuntarily stops responding to the task that they are currently performing due to temporary interruptions in visual-motor and cognitive coordination. Microsleeps can last up to 15 s while performing a particular task. In this study, the ability of a convolutional neural network (CNN) to detect microsleep states from 16-channel EEG data from 8 subjects, performing a 1D visuomotor was explored. The data were highly imbalanced. When averaged across 8 subjects there were 17 responsive states for every microsleep state. Two approaches were used to handle the CNN training with data imbalance - oversampling the minority class and cost-based learning. The EEG was analysed using a 4-s epoch with a step size of 0.25 s. Leave-one-subject-out cross-validation was used to evaluate the performance. The performance measures used for assessing the detection capability of the CNN were: sensitivity, precision, phi, geometric mean (GM), AUCROC, and AUCPR. The performance measures obtained using the oversampling and cost-based learning methods were: AUCROC = 0.90/0.90, AUCPR = 0.41/0.41 and a phi = 0.42/0.40, respectively. Although the performances were similar, the cost-based learning method had a considerably shorter training time than the oversampling method.

Predicting Microsleep States Using EEG Inter-Channel Relationships.

A microsleep is a brief and an involuntary sleep-related loss of consciousness of up to 15 s. We investigated the performances of seven pairwise inter-channel relationships-covariance, Pearson's correlation coefficient, wavelet cross-spectral power, wavelet coherence, joint entropy, mutual information, and phase synchronization index-in continuous prediction of microsleep states from EEG. These relationships were used as the feature sets of a linear discriminant analysis (LDA) and a linear support vector machine classifiers. Priors for both classifiers were incorporated to address the class imbalance in the training data sets. Each feature set was extracted from a 5-s window of EEG with the step of 0.25 s and was demeaned with respect to the mean of first 2 min. The sequential forward selection (SFS) method, based on a serial combination of the correlation coefficient, Fisher score-based filter, and an LDA-based wrapper, was used to select features from each training set. The comparison was based on 16-channel EEG data from eight subjects who had performed a 1-D visuomotor task for two 1-h sessions. The prediction performances were evaluated using leave-one-subject-out cross-validation. For both classifiers, non-normalized feature sets were found to perform better than normalized feature sets. Furthermore, demeaning the non-normalized features considerably improved the prediction performance. Overall, the LDA classifier with joint entropy features resulted in the best average prediction performances (phi, AUCPR, and AUCROC) of (0.47, 0.50, and 0.95). Joint entropy between O1 and O2 from theta frequency band was the most informative feature.

Ensemble learning based on overlapping clusters of subjects to predict microsleep states from EEG.

Microsleeps are brief and involuntary instances of complete loss of sleep-related consciousness. We present a novel approach of creating overlapping clusters of subjects and training of an ensemble classifier to enhance the prediction of microsleep states from EEG. Overlapping clusters are created using Kullback-Leibler divergence between responsive state features of each pair of training subjects. Highly correlated features within each overlapping cluster are discarded. The remaining features are selected via Fisher score based ranking followed by an average of 5-fold cross-validation areas under the curves of receiver operating characteristics (AUCRoc) of a linear discriminant analysis (LDA) classifier. The decisions of LDA classifiers on overlapping clusters are fused using weighted average. We evaluated this new approach on 16-channel EEG data from 8 subjects who had performed a 1-D visuomotor task for two l-h sessions. Joint entropy features were extracted from a 5-s window of EEG with steps of 0.25 s Test performances were evaluated using leave-one-subject-out cross-validation. Our ensemble of overlapping clusters of subjects achieved a mean prediction performance, phi, of 0.42 compared with 0.39 for a single LDA classifier and 0.37 for generalized stacking.

Bayesian multi-subject factor analysis to predict microsleeps from EEG power spectral features.

Prediction of an imminent microsleep has the potential to save lives and prevent catastrophic accidents. A microsleep is a brief episode of unintentional unconsciousness and, hence, loss of responsiveness. In this study, prediction of imminent microsleeps using EEG data from 8 subjects was examined. A novel Bayesian algorithm was proposed to identify common components of pre-microsleep activity in the EEG in all subjects and predict microsleeps 0.25 s ahead. To avoid overfitting, this model incorporates sparsity-promoting priors to automatically find the minimum number of components. Due to intractability of full Bayesian treatment, variational Bayesian was integrated to approximate posterior probabilities. To predict microsleeps, EEG log-power spectral features were extracted from a 5-s window. Bayesian multi-subject factor analysis was used to extract common microsleep patterns and transform all features into lower-dimension common-space features. Discrimination between responsive and microsleep instances was done with a single linear discriminant analysis (LDA) classifier. Performance of the proposed method was evaluated using leave-one-subject-out cross-validation. Our prediction system achieved moderate AUCROC and GM of 0.90 and 0.80, respectively, but with a relatively low precision of 0.29.

Prediction of microsleeps from EEG: Preliminary results.

Brief episodes of momentarily falling asleep - microsleeps - can have fatal consequences, especially in the transportation sector. In this study, the EEG data of eight subjects, while performing a 1-D tracking task, were used to predict imminent microsleeps. A novel algorithm was developed to improve the accuracy of microsleep identification from two independent measures: tracking performance and face-video. The uncertain labels of gold-standard were then pruned out. Additionally, the state of microsleep at 0.25 s ahead was continuously predicted. Log-power spectral features were then extracted from EEG data. The most relevant features were selected by mutual information. Leave-one-subject-out was performed to test the classifier on an independent subject and this procedure was done for all the subjects. Two oversampling methods, synthetic minority oversampling technique (SMOTE) and adaptive sampling (ADASYN), were utilized to improve the training in the presence of imbalanced data. The best average area under the curve of receiver operating characteristic (AUCroc) of 0.90 was achieved using SMOTE oversampling over a 5.25 s window length, with a corresponding geometric mean (GM) of 0.74. ADASYN oversampling achieved the best sensitivity of 0.76 (cf. 0.70 for SMOTE), but with a lower specificity of 0.77 (cf. 0.86 for SMOTE).