Investigating Heritage Language Processing: Meaning Composition in Chinese Classifier-Noun Phrasal Contexts

The aim of the present study was to investigate how Chinese-Malay bilingual speakers with Chinese as heritage language process semantic congruency effects in Chinese and how their brain activities compare to those of monolingual Chinese speakers using electroencephalography (EEG) recordings. To this end, semantic congruencies were manipulated in Chinese classifier-noun phrases, resulting in four conditions: (i) a strongly constraining/high-cloze, plausible (SP) condition, (ii) a weakly constraining/low-cloze, plausible (WP) condition, (iii) a strongly constraining/implausible (SI) condition, and (iv) a weakly constraining/implausible (WI) condition. The analysis of EEG data focused on two event-related potential components, i.e., the N400, which is known for its sensitivity to semantic fit of a target word to its context, and a post-N400 late positive complex (LPC), which is linked to semantic integration after prediction violations and retrospective, evaluative processes. We found similar N400/LPC effects in response to the manipulations of semantic congruency in the mono- and bilingual groups, with a gradient N400 pattern (WI/SI > WP > SP), a larger frontal LPC in response to WP compared to SP, SI, and WI, as well as larger centro-parietal LPCs in response to WP compared to SI and WI, and a larger centro-parietal LPC for SP compared to SI. These results suggest that, in terms of event-related potential (ERP) data, Chinese-Malay early bilingual speakers predict and integrate upcoming semantic information in Chinese classifier-noun phrase to the same extent as monolingual Chinese speakers. However, the global field power (GFP) data showed significant differences between SP and WP in the N400 and LPC time windows in bilinguals, whereas no such effects were observed in monolinguals. This finding was interpreted as showing that bilinguals differ from their monolingual peers in terms of global field power intensity of the brain by processing plausible classifier-noun pairs with different congruency effects.

Decreased Global EEG Synchronization in Amyloid Positive Mild Cognitive Impairment and Alzheimer’s Disease Patients—Relationship to APOE ε4

The apolipoprotein E (APOE) ε4 allele is a risk factor for Alzheimer’s disease (AD) that has been linked to changes in brain structure and function as well as to different biological subtypes of the disease. The present study aimed to investigate the association of APOE ε4 genotypes with brain functional impairment, as assessed by quantitative EEG (qEEG) in patients on the AD continuum. The study population included 101 amyloid positive patients diagnosed with mild cognitive impairment (MCI) (n = 50) and AD (n = 51) that underwent resting-state EEG recording and CSF Aβ42 analysis. In total, 31 patients were APOE ε4 non-carriers, 42 were carriers of one, and 28 were carriers of two APOE ε4 alleles. Quantitative EEG analysis included computation of the global field power (GFP) and global field synchronization (GFS) in conventional frequency bands. Amyloid positive patients who were carriers of APOE ε4 allele(s) had significantly higher GFP beta and significantly lower GFS in theta and beta bands compared to APOE ε4 non-carriers. Increased global EEG power in beta band in APOE ε4 carriers may represent a brain functional compensatory mechanism that offsets global EEG slowing in AD patients. Our findings suggest that decreased EEG measures of global synchronization in theta and beta bands reflect brain functional deficits related to the APOE ε4 genotype in patients that are on a biomarker-verified AD continuum.

Synaptic Molecular and Neurophysiological Markers Are Independent Predictors of Progression in Alzheimer’s Disease

Background: Cerebrospinal fluid (CSF) neurogranin and quantitative electroencephalography (qEEG) are potential molecular and functional markers of synaptic pathology in Alzheimer’s disease (AD). Synaptic markers have emerged as candidate prognostic indicators of AD since synaptic degeneration was shown to be an early event and the best correlate of cognitive deficits in patients along the disease continuum. Objective: The present study investigated the association between CSF neurogranin and qEEG measures as well as their potential to predict clinical deterioration in mild cognitive impairment (MCI) patients. Methods: Patients diagnosed with MCI (n = 99) underwent CSF conventional AD biomarkers and neurogranin analysis and resting-state EEG recordings. The study population was further stratified into stable (n = 41) and progressive MCI (n = 31), based on the progression to AD dementia during two years follow-up. qEEG analysis included computation of global field power and global field synchronization in four conventional frequency bands. Results: CSF neurogranin levels were associated with theta power and synchronization in the progressive MCI group. CSF neurogranin and qEEG measures were significant predictors of progression to AD dementia, independent of baseline amyloid status in MCI patients. A combination of CSF neurogranin with global EEG power in theta and global EEG synchronization in beta band exhibited the highest classification accuracy as compared to either of these markers alone. Conclusion: qEEG and CSF neurogranin are independent predictors of progression to AD dementia in MCI patients. Molecular and neurophysiological synaptic markers may have additive value in a multimodal diagnostic and prognostic approach to dementia.

Dual-Threshold-Based Microstate Analysis on Characterizing Temporal Dynamics of Affective Process and Emotion Recognition From EEG Signals

Recently, emotion classification from electroencephalogram (EEG) data has attracted much attention. As EEG is an unsteady and rapidly changing voltage signal, the features extracted from EEG usually change dramatically, whereas emotion states change gradually. Most existing feature extraction approaches do not consider these differences between EEG and emotion. Microstate analysis could capture important spatio-temporal properties of EEG signals. At the same time, it could reduce the fast-changing EEG signals to a sequence of prototypical topographical maps. While microstate analysis has been widely used to study brain function, few studies have used this method to analyze how brain responds to emotional auditory stimuli. In this study, the authors proposed a novel feature extraction method based on EEG microstates for emotion recognition. Determining the optimal number of microstates automatically is a challenge for applying microstate analysis to emotion. This research proposed dual-threshold-based atomize and agglomerate hierarchical clustering (DTAAHC) to determine the optimal number of microstate classes automatically. By using the proposed method to model the temporal dynamics of auditory emotion process, we extracted microstate characteristics as novel temporospatial features to improve the performance of emotion recognition from EEG signals. We evaluated the proposed method on two datasets. For public music-evoked EEG Dataset for Emotion Analysis using Physiological signals, the microstate analysis identified 10 microstates which together explained around 86% of the data in global field power peaks. The accuracy of emotion recognition achieved 75.8% in valence and 77.1% in arousal using microstate sequence characteristics as features. Compared to previous studies, the proposed method outperformed the current feature sets. For the speech-evoked EEG dataset, the microstate analysis identified nine microstates which together explained around 85% of the data. The accuracy of emotion recognition achieved 74.2% in valence and 72.3% in arousal using microstate sequence characteristics as features. The experimental results indicated that microstate characteristics can effectively improve the performance of emotion recognition from EEG signals.

Your Brain Knows if You're Iron Deficient: Distinct Brain Dynamics in Iron Deficient vs. Sufficient Females in a Visual Category Learning Task

Objectives The present study sought to determine the possibility of identifying someone as either iron deficient or sufficient based solely on brain activity, and, if possible, how quickly (based on processing time on a cognitive task) this could be done. Methods Both iron sufficient(IS) and iron deficient non-anemic (IDNA) females (mean age 21.1 y) learned two visual categorization tasks while concurrent EEG was acquired. Both tasks involved classifying gray-scale gabor patches on the basis of spatial frequency and orientation; one task used an easily-verbalized rule (rule-based, RB), the other required complex integration of the information (II). Moving windows (20 ms width) of EEG data from 100 electrodes were used to predict the participant's iron status using logistic regression; model form was determined using stepwise variable selection. The outcome variable was the area under the curve (AUC) of the receiver operating characteristic for the classification. We set a criterion of AUC ≥ 0.80 for successful classification performance. Results For both tasks, successful classification was possible before 200 ms of processing on the basis of fewer than 12 electrodes. Classification in the RB task suggested some early right lateralization in the selection of electrodes, which became more central as processing proceeded. Classification in the II task did not suggest lateralization, although there was some change to more central electrodes as processing proceeded. At each 20 ms time window, for each of the selected set of electrodes, a measure of neural efficiency was calculated as the ratio of the hazard function of the reaction time distribution and the global field power of the selected set of electrodes. This ratio can be interpreted as the amount of work accomplished per unit of energy expended. In all cases, neural efficiency for the IS females exceeded that for the IDNA females, suggesting that the efficiency with which neural energy is expended in cognitive work differs as a function of iron status. Conclusions Iron deficiency without anemia results in distinct patterns of brain activity early in processing that reflect reduced levels of neural efficiency, relative to females who are iron sufficient. Funding Sources OU Office of the Vice President for Research.

Breath hold task induces temporal heterogeneity in electroencephalographic regional field power in healthy subjects

While the brainstem is in charge of the automatic control of ventilation, the cortex is involved in the voluntary control of breathing but also receives inputs from the brainstem, which influence the perception of breathing and the arousal state and sleep architecture in conditions of hypoxia/hypercapnia. We evaluated in eleven healthy subjects the effects of breath hold (BH: 30 seconds of apneas and 30 seconds of normal breathing) and BH-related CO2/O2 changes on electroencephalogram (EEG) global field power (GFP) and regional field power (RFP) in 9 different areas (3 rostrocaudal sections -anterior, central, posterior- and 3 sagittal sections -left, middle, right) in the δ and α bands, by cross correlation analysis. No significant differences were observed in GFP and RFP when comparing free breathing (FB) with the BH task. Within the BH task, the shift from apnea to normal ventilation was accompanied by an increase in the δ power and a decrease in the α power. The end-tidal pressure of CO2 (PETCO2) was positively correlated with the δ-band and negatively with the α- band with a positive time shift, while an opposite behaviour was found for the end-tidal pressure of O2 (PETO2). Notably, the time shift between PETCO2/PETO2 signals and cortical activity at RFP was heterogenous and seems to follow a hierarchical activation with the δ-band responding earlier than the α band. Overall, these findings suggest that the effect of BH on the cortex may follow specific ascending pathways from the brainstem and be related to chemoreflex stimulation.

Improving Cognitive Workload in Radiation Therapists: A Pilot EEG Neurofeedback Study

Radiation therapy therapists (RTTs) face challenging daily tasks that leave them prone to high attrition and burnout and subsequent deficits in performance. Here, we employed an accelerated alpha-theta neurofeedback (NF) protocol that is implementable in a busy medical workplace to test if 12 RTTs could learn the protocol and exhibit behavior and brain performance-related benefits. Following the 3-week protocol, participants showed a decrease in subjective cognitive workload and a decrease in response time during a performance task, as well as a decrease in desynchrony of the alpha electroencephalogram (EEG) band. Additionally, novel microstate analysis for neurofeedback showed a significant decrease in global field power (GFP) following neurofeedback. These results suggest that the RTTs successfully learned the protocol and improved in perceived cognitive workload following 3 weeks of neurofeedback. In sum, this study presents promising behavioral improvements as well as brain performance-related evidence of neurophysiological changes following neurofeedback, supporting the feasibility of implementing neurofeedback in a busy workplace and encouraging the further study of neurofeedback as a tool to mitigate burnout.

EEG microstates as biomarker for psychosis in ultra-high-risk patients

Resting-state EEG microstates are brief (50–100 ms) periods, in which the spatial configuration of scalp global field power remains quasi-stable before rapidly shifting to another configuration. Changes in microstate parameters have been described in patients with psychotic disorders. These changes have also been observed in individuals with a clinical or genetic high risk, suggesting potential usefulness of EEG microstates as a biomarker for psychotic disorders. The present study aimed to investigate the potential of EEG microstates as biomarkers for psychotic disorders and future transition to psychosis in patients at ultra-high-risk (UHR). We used 19-channel clinical EEG recordings and orthogonal contrasts to compare temporal parameters of four normative microstate classes (A–D) between patients with first-episode psychosis (FEP; n = 29), UHR patients with (UHR-T; n = 20) and without (UHR-NT; n = 34) later transition to psychosis, and healthy controls (HC; n = 25). Microstate A was increased in patients (FEP & UHR-T & UHR-NT) compared to HC, suggesting an unspecific state biomarker of general psychopathology. Microstate B displayed a decrease in FEP compared to both UHR patient groups, and thus may represent a state biomarker specific to psychotic illness progression. Microstate D was significantly decreased in UHR-T compared to UHR-NT, suggesting its potential as a selective biomarker of future transition in UHR patients.

Electrophysiological correlates of visual backward masking in patients with bipolar disorder

BackgroundIn visual backward masking (VBM), a target is followed by a mask that decreases target discriminability. Schizophrenia patients (SZ) show strong and reproducible masking impairments, which are associated with reduced EEG amplitudes. Patients with bipolar disorder (BP) show masking deficits, too. Here, we investigated the neural EEG correlates of VBM in BP.Methods122 SZ, 94 unaffected controls, and 38 BP joined a standard VBM experiment. 123 SZ, 94 unaffected controls and 16 BP joined a corresponding EEG experiment, analyzed in terms of the global field power.ResultsAs in previous studies, SZ and BP show strong masking deficits. Importantly and similarly to SZ, BP show decreased global field power amplitudes at approximately 200 ms after the target onset, compared to controls.ConclusionsThese results suggest that VBM deficits are not specific for schizophrenia but for a broader range of functional psychoses. Potentially, both SZ and BP show deficient target enhancement.

Defining Clusters of Young Autistic and Typically-Developing Children Based on Loudness-Dependent Auditory Electrophysiological Responses

Background: Autistic individuals exhibit atypical patterns of sensory processing that are known to be related to quality of life, but which are also highly heterogeneous. Previous investigations of this heterogeneity have ordinarily used questionnaires and have rarely investigated sensory processing in Typical Development (TD) alongside Autism Spectrum Development (ASD). Methods: The present study used hierarchical clustering in a large sample to identify subgroups of young autistic and typically-developing children based the normalized global field power (GFP) of their event-related potentials (ERPs) to auditory stimuli of four different loudness intensities (50, 60, 70, 80 dB SPL): that is, based on an index of the relative strengths of their neural responses across these loudness conditions. Results: Four clusters of participants were defined. Normalized GFP responses to sounds of different intensities differed strongly across clusters. There was considerable overlap in cluster assignments of autistic and typically-developing participants, but autistic participants were more likely to display a pattern of relatively linear increases in response strength accompanied by a disproportionately strong response to 70 dB stimuli. Autistic participants displaying this pattern trended towards obtaining higher scores on assessments of cognitive abilities. There was also a trend for typically-developing participants to disproportionately fall into a cluster characterized by disproportionately/nonlinearly strong 60 dB responses. Greater auditory distractibility was reported among autistic participants in a cluster characterized by disproportionately strong responses to the loudest (80 dB) sounds, and furthermore, relatively strong responses to loud sounds were correlated with both auditory distractibility and noise distress. This appears to provide evidence of coinciding behavioural and neural sensory atypicalities. Limitations : Replication may be needed to verify exploratory results. This analysis may ignore some variability related to classical ERP latencies and topographies. The sensory questionnaire employed was not specifically designed for use in autism. Variability in sensory responses unrelated to loudness is ignored, leaving much room for additional research. Conclusions: Taken together, these data demonstrate the broader benefits of using electrophysiology to explore individual differences. They illuminate different neural response patterns and suggest relationships between sensory neural responses and sensory behaviours, cognitive abilities, and autism diagnostic status.