Frequency–amplitude coupling: a new approach for decoding of attended features in covert visual attention task

Dynamic brain functional states associated with inhibition control under different altitudes

Chronic exposure to the hypobaric hypoxia environment of plateau could influence human cognitive behaviours which are supported by dynamic brain connectivity states. Until now, how functional connectivity (FC) of the brain network changes with altitudes is still unclear. In this article, we used EEG data of the Go/NoGo paradigm from Weinan (347 m) and Nyingchi (2950 m). A combination of dynamic FC (dFC) and the K-means cluster was employed to extract dynamic FC states which were later distinguished by graph metrics. Besides, temporal properties of networks such as fractional windows (FW), transition numbers (TN) and mean dwell time (MDT) were calculated. Finally, we successfully extracted two different states from dFC matrices where State 1 was verified to have higher functional integration and segregation. The dFC states dynamically switched during the Go/NoGo tasks and the FW of State 1 showed a rise in the high-altitude participants. Also, in the regional analysis, we found higher state deviation in the fronto-parietal cortices and enhanced FC strength in the occipital lobe. These results demonstrated that long-term exposure to the high-altitude environment could lead brain networks to reorganize as networks with higher inter- and intra-networks information transfer efficiency, which could be attributed to a compensatory mechanism to the compromised brain function due to the plateau environment. This study provides a new perspective in considering how the plateau impacted cognitive impairment.

Measuring functional connectivity in frequency-domain helps to better characterize brain function

Resting-state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence-based and correlation-based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging.

Re-emergent Tremor in Parkinson's Disease: Evidence of Pathologic β and Prokinetic γ Activity

BACKGROUND

Re-emergent tremor is characterized as a continuation of resting tremor and is often highly therapy refractory. This study examines variations in brain activity and oscillatory responses between resting and re-emergent tremors in Parkinson's disease.

METHODS

Forty patients with Parkinson's disease (25 males, mean age, 66.78 ± 5.03 years) and 40 age- and sex-matched healthy controls were included in the study. Electroencephalogram and electromyography signals were simultaneously recorded during resting and re-emergent tremors in levodopa on and off states for patients and mimicked by healthy controls. Brain activity was localized using the beamforming technique, and information flow between sources was estimated using effective connectivity. Cross-frequency coupling was used to assess neuronal oscillations between tremor frequency and canonical frequency oscillations.

RESULTS

During levodopa on, differences in brain activity were observed in the premotor cortex and cerebellum in both the patient and control groups. However, Parkinson's disease patients also exhibited additional activity in the primary sensorimotor cortex. On withdrawal of levodopa, different source patterns were observed in the supplementary motor area and basal ganglia area. Additionally, levodopa was found to suppress the strength of connectivity (P < 0.001) between the identified sources and influence the tremor frequency-related coupling, leading to a decrease in β (P < 0.001) and an increase in γ frequency coupling (P < 0.001).

CONCLUSIONS

Distinct variations in cortical-subcortical brain activity are evident in tremor phenotypes. The primary sensorimotor cortex plays a crucial role in the generation of re-emergent tremor. Moreover, oscillatory neuronal responses in pathological β and prokinetic γ activity are specific to tremor phenotypes. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Automatic and Early Detection of Parkinson's Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method

Parkinson's disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their 60-80% inability to produce dopamine, an organic chemical responsible for controlling a person's movement. This condition causes PD symptoms to appear. Diagnosis involves many physical and psychological tests and specialist examinations of the patient's nervous system, which causes several issues. The methodology method of early diagnosis of PD is based on analysing voice disorders. This method extracts a set of features from a recording of the person's voice. Then machine-learning (ML) methods are used to analyse and diagnose the recorded voice to distinguish Parkinson's cases from healthy ones. This paper proposes novel techniques to optimize the techniques for early diagnosis of PD by evaluating selected features and hyperparameter tuning of ML algorithms for diagnosing PD based on voice disorders. The dataset was balanced by the synthetic minority oversampling technique (SMOTE) and features were arranged according to their contribution to the target characteristic by the recursive feature elimination (RFE) algorithm. We applied two algorithms, t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), to reduce the dimensions of the dataset. Both t-SNE and PCA finally fed the resulting features into the classifiers support-vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multilayer perception (MLP). Experimental results proved that the proposed techniques were superior to existing studies in which RF with the t-SNE algorithm yielded an accuracy of 97%, precision of 96.50%, recall of 94%, and F1-score of 95%. In addition, MLP with the PCA algorithm yielded an accuracy of 98%, precision of 97.66%, recall of 96%, and F1-score of 96.66%.

Interhemispheric neural characteristics of noxious mechano-nociceptive stimulation in the anterior cingulate cortex

Background

Pain is an unpleasant sensory and emotional experience. One of the most critical regions of the brain for pain processing is the anterior cingulate cortex (ACC). Several studies have examined the role of this region in thermal nociceptive pain. However, studies on mechanical nociceptive pain have been very limited to date. Although several studies have investigated pain, the interactions between the two hemispheres are still not clear. This study aimed to investigate nociceptive mechanical pain in the ACC bilaterally.

Methods

Local field potential (LFP) signals were recorded from seven male Wistar rats' ACC regions of both hemispheres. Mechanical stimulations with two intensities, high-intensity noxious (HN) and non-noxious (NN) were applied to the left hind paw. At the same time, the LFP signals were recorded bilaterally from awake and freely moving rats. The recorded signals were analyzed from different perspectives, including spectral analysis, intensity classification, evoked potential (EP) analysis, and synchrony and similarity of two hemispheres.

Results

By using spectro-temporal features and support vector machine (SVM) classifier, HN vs. no-stimulation (NS), NN vs. NS, and HN vs. NN were classified with accuracies of 89.6, 71.1, and 84.7%, respectively. Analyses of the signals from the two hemispheres showed that the EPs in the two hemispheres were very similar and occurred simultaneously; however, the correlation and phase locking value (PLV) between the two hemispheres changed significantly after HN stimulation. These variations persisted for up to 4 s after the stimulation. In contrast, variations in the PLV and correlation for NN stimulation were not significant.

Conclusions

This study showed that the ACC area was able to distinguish the intensity of mechanical stimulation based on the power activities of neural responses. In addition, our results suggest that the ACC region is activated bilaterally due to nociceptive mechanical pain. Additionally, stimulations above the pain threshold (HN) significantly affect the synchronicity and correlation between the two hemispheres compared to non-noxious stimuli.

Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke

Evaluating the impact of stroke on the human brain based on electroencephalogram (EEG) remains a challenging problem. Previous studies are mainly analyzed within frequency bands. This article proposes a multi-granularity analysis framework, which uses multiple brain networks assembled with intra-frequency and cross-frequency phase-phase coupling to evaluate the stroke impact in temporal and spatial granularity. Through our experiments on the EEG data of 11 patients with left ischemic stroke and 11 healthy controls during the mental rotation task, we find that the brain information interaction is highly affected after stroke, especially in delta-related cross-frequency bands, such as delta-alpha, delta-low beta, and delta-high beta. Besides, the average phase synchronization index (PSI) of the right hemisphere between patients with stroke and controls has a significant difference, especially in delta-alpha (p = 0.0186 in the left-hand mental rotation task, p = 0.0166 in the right-hand mental rotation task), which shows that the non-lesion hemisphere of patients with stroke is also affected while it cannot be observed in intra-frequency bands. The graph theory analysis of the entire task stage reveals that the brain network of patients with stroke has a longer feature path length and smaller clustering coefficient. Besides, in the graph theory analysis of three sub-stags, the more stable significant difference between the two groups is emerging in the mental rotation sub-stage (500–800 ms). These findings demonstrate that the coupling between different frequency bands brings a new perspective to understanding the brain's cognitive process after stroke.

Prefrontal Lesions Disrupt Posterior Alpha-Gamma Coordination of Visual Working Memory Representations

How does the human brain prioritize different visual representations in working memory (WM)? Here, we define the oscillatory mechanisms supporting selection of "where" and "when" features from visual WM storage and investigate the role of pFC in feature selection. Fourteen individuals with lateral pFC damage and 20 healthy controls performed a visuospatial WM task while EEG was recorded. On each trial, two shapes were presented sequentially in a top/bottom spatial orientation. A retro-cue presented mid-delay prompted which of the two shapes had been in either the top/bottom spatial position or first/second temporal position. We found that cross-frequency coupling between parieto-occipital alpha (α; 8-12 Hz) oscillations and topographically distributed gamma (γ; 30-50 Hz) activity tracked selection of the distinct cued feature in controls. This signature of feature selection was disrupted in patients with pFC lesions, despite intact α-γ coupling independent of feature selection. These findings reveal a pFC-dependent parieto-occipital α-γ mechanism for the rapid selection of visual WM representations.