Brain Topography最新文献

Timely identification of Parkinson's disease and schizophrenia is crucial for the effective management and enhancement of patients' quality of life. The utilization of electroencephalogram (EEG) monitoring applications has proven instrumental in diagnosing various brain disorders. Prior research has predominantly relied on predefined knowledge of physiological alterations associated with different diseases, employing feature extraction to discern brain conditions. This study introduces SwiftBrainNet, a neural network designed for the classification of Parkinson's disease and schizophrenia using short resting-state EEG segments. SwiftBrainNet aims to minimize reliance on manual feature extraction, relying solely on short EEG segments. Functioning as a single-input, dual-output neural network, SwiftBrainNet incorporates a deep supervisory mechanism facilitated by an auxiliary decoder, which enhances its classification performance by guiding the network in extracting shallow features. Our study conducts a clinical application-oriented experiment that uses continuous multi-segment EEG voting classification. This experiment demonstrates a noticeable improvement in accuracy compared to leave-one-out cross-validation (LOOCV), especially when combined with our data augmentation techniques. These findings underscore the method's practical value in clinical settings, where continuous data frames and enhanced generalization across subjects can significantly improve diagnostic accuracy. Additionally, the high accuracy observed in subject-dependent classification with very short data segments suggests that SwiftBrainNet might capture subject-specific EEG patterns, which could be further explored to enhance disease-related feature learning. This paper provides new evidence supporting the use of short-term EEG data for neurodiagnostic applications, making SwiftBrainNet a promising tool for the early detection of neurological disorders.

Loud speech and whispered speech are two distinct speech modes that are part of daily verbal exchanges, but that involve a different employment of the speech apparatus. However, a clear account of whether and when the motor speech (or phonetic) encoding of these speech modes differs from standard speech has not been provided yet. Here, we addressed this question using Electroencephalography (EEG)/Event related potential (ERP) approaches during a delayed production task to contrast the production of speech sequences (pseudowords) when speaking normally or under a specific speech mode: loud speech in experiment 1 and whispered speech in experiment 2. Behavioral results demonstrated that non-standard speech modes entail a behavioral encoding cost in terms of production latency. Standard speech and speech modes' ERPs were characterized by the same sequence of microstate maps, suggesting that the same brain processes are involved to produce speech under a specific speech mode. Only loud speech entailed electrophysiological modulations relative to standard speech in terms of waveform amplitudes but also temporal distribution and strength of neural recruitment of the same sequence of microstates during a large time window (from approximatively - 220 ms to - 100 ms) preceding the vocal onset. Alternatively, the electrophysiological activity of whispered speech was similar in nature to standard speech. On the whole, speech modes and standard speech seem to be encoded through the same brain processes but the degree of adjustments required seem to vary subsequently across speech modes.

The selection of an appropriate virtual reference schema is pivotal in determining the outcomes of event-related potential (ERP) studies, particularly within the widely utilized Talk/Listen ERP paradigm, which is employed to non-invasively explore the corollary discharge phenomenon in the speech-auditory system. This research centers on examining the effects of prevalent EEG reference schemas-linked mastoids (LM), common average reference (CAR), and reference electrode standardization technique (REST)-through statistical analysis, statistical parametric scalp mapping (SPSM), and source localization techniques. Our ANOVA findings indicate significant main effects for both the reference and the experimental condition on the amplitude of N1 ERPs. Depending on the reference used, the polarity and amplitude of the N1 ERPs demonstrate systematic variations: LM is associated with pronounced frontocentral activity, whereas both CAR and REST exhibit patterns of frontocentral and occipitotemporal activity. The significance of SPSM results is confined to regions exhibiting prominent N1 activity for each reference schema. Source analysis provides corroborative evidence more aligned with the SPSM results for CAR and REST than for LM, suggesting that results under CAR and REST are more objective and reliable. Therefore, the CAR and REST reference are recommended for future studies involving Talk/Listen ERP paradigms.

Alterations in the temporal characteristics of EEG microstates in patients with schizophrenia (SCZ) have been repeatedly found in previous studies. Nevertheless, altered temporal characteristics of EEG microstates in auditory verbal hallucinations (AVHs) SCZ are still unknown. This study aimed to investigate whether SCZ patients with sAVHs exhibit abnormal EEG microstates. We analyzed high-density electroencephalography data that from 79 SCZ patients, including 38 severe AVHs patients (sAVH group), 17 moderate auditory verbal hallucinations patients (mid-AVH group), and 24 without auditory verbal hallucinations patients (non-AVH group). Microstates were compared between three groups. Microstate C exhibited significant differences in duration and coverage and microstate B exhibited significant differences in occurrence between patients with sAVHs and without AVHs. There was a significant negative correlation between the coverage in microstate C and the severity of sAVH. Microstate C in duration, microstate B in occurrence were efficient in detecting sAVH patients. The decreased class C microstates in duration and coverage and increased class B microstates in occurrence may contribute to the severity of symptoms in AVH patients. Furthermore, we have identified that microstates C could serve as potential neurophysiological markers for detecting AVHs in SCZ patients. These results can provide potential avenues for therapeutic intervention of AVHs.

PHN is one of the most common clinical complications of herpes zoster (HZ), the pathogenesis of which is unclear and poorly treated clinically, and many studies now suggest that postherpetic neuralgia (PHN) pain may be related to central neurologic mechanisms. This study aimed to investigate the white matter structural networks and changes in the organization of the rich-club in HZ and PHN. Diffusion imaging (DTI) data from 89 PHN patients, 76 HZ patients, and 66 healthy controls (HCs) were used to construct corresponding structural networks. Using graph-theoretic analysis, changes in the overall and local characteristics of the structural networks and rich-club organization were analyzed, and their correlations with clinical scales were analyzed. Compared with HCs, PHN patients had reduced global efficiency (Eg), reduced local efficiency (Eloc), a reduced clustering coefficient (Cp), a longer characteristic path length (Lp), and reduced nodal efficiency (Ne) in several brain regions, including the right posterior cingulate gyrus, the right supraoccipital gyrus, the bilateral postcentral gyrus, and the right precuneus; HZ patients had reduced Eg, a longer Lp, and reduced right orbital frontalis suprachiasmatic Ne. Moreover, HZ and PHN patients showed a significant reduction in the strength of rich-club connections. Compared with HZ patients, the intensities of the rich-club and feeder connections were lower in the PHN patients. Moreover, the changes in the structural networks and rich-club organization topology indices of the patients in the HZ and PHN patients were significantly correlated with disease duration, pain scores, and emotional changes. The structural networks of HZ and PHN patients exhibited reduced network transmission efficiency and rich-club connectivity, possibly due to structural damage to the white matter, and this was more obvious in PHN patients. The rich-club connectivity of HZ patients showed incomplete compensation in the acute pain stage.

A major question in cognitive neuroscience is understanding the neural basis of mental imagery, particularly in cases of its absence, known as aphantasia. While research in this field has focused on the role of sensory domains, we propose that the key to understanding imagery lies in the intertwining of sensory processing and autonomic responses. Interoception plays a crucial role in mental imagery by anchoring experiences in first-person physiological signals, providing a self-referential perspective, and grounding the imagery in the body while also enabling its emotional aspects. Moreover, interoception contributes to the sense of agency and volitional control, as well as body schema-hallmarks of voluntary mental imagery. Therefore, imagery should be approached as an integrated phenomenon that combines sensory-specific information with interoceptive signals. At the neural level, this process engages the insula and anterior cingulate cortex (ACC), regions vital for synthesizing information across cognitive, emotional, and physical domains, as well as for supporting self-awareness. From this perspective, aphantasia may reflect a suboptimal functioning of the insula/ACC, which can account for its associations with deficits in autobiographical memory, emotion perception, and conditions such as autism and dyspraxia.

Over recent years, electroencephalographic (EEG) microstates have been increasingly used to investigate, at a millisecond scale, the temporal dynamics of large-scale brain networks. By studying their topography and chronological sequence, microstates research has contributed to the understanding of the brain's functional organization at rest and its alteration in neurological or mental disorders. Artifact removal strategies, which differ from study to study, may alter microstates topographies and features, possibly reducing the generalizability and comparability of results across research groups. The aim of this work was therefore to test the reliability of the microstate extraction process and the stability of microstate features against different strategies of EEG data preprocessing with Independent Component Analysis (ICA) to remove artifacts embedded in the data. A normative resting state EEG dataset was used where subjects alternate eyes-open (EO) and eyes-closed (EC) periods. Four strategies were tested: (i) avoiding ICA preprocessing altogether, (ii) removing ocular artifacts only, (iii) removing all reliably identified physiological/non physiological artifacts, (iv) retaining only reliably identified brain ICs. Results show that skipping the removal of ocular artifacts affects the stability of microstate evaluation criteria, microstate topographies and greatly reduces the statistical power of EO/EC microstate features comparisons, however differences are not as prominent with more aggressive preprocessing. Provided a good-quality dataset is recorded, and ocular artifacts are removed, microstates topographies and features can capture brain-related physiological data and are robust to artifacts, independently of the level of preprocessing, paving the way to automatized microstate extraction pipelines.

EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, accurately classifying imagined speech signals remains challenging due to their complex and non-stationary nature. Existing approaches often struggle with low signal-to-noise ratios and high inter-subject variability. A proposed method named imagined speech functional connectivity graph (ISFCG) is implemented to deal with these issues. The functional connectivity graphs capture the complex relationships between brain regions during imagined speech tasks. These graphs are then used to extract features that serve as inputs to various machine-learning models. The ISFCG provides an alternative representation of imagined speech signals, focusing on brain connectivity features to enhance the analysis and classification process. Also, a convolutional neural network (CNN) is proposed to learn features from these complex graphs, leading to improved classification accuracy. Experimental results on a benchmark dataset demonstrate the effectiveness of our method.

Electroencephalography microstates (EEG-MS) show promise to be a neurobiological biomarker in stroke. Thus, the aim of the study was to identify biomarkers to discriminate stroke patients from healthy individuals based on EEG-MS and clinical features using a machine learning approach. Fifty-four participants (27 stroke patients and 27 healthy age and sex-matched controls) were recruited. We recorded EEG-MS using 32 channels during eyes-closed and eyes-open conditions and analyzed the four classical EEG-MS maps (A, B, C, D). Clinical information and motor aspects were evaluated. A machine learning method using k-means algorithms to discriminate stroke patients from healthy subjects showed that the most influential parameters in clustering were balance scores and microstate parameters (duration and coverage of microstate A, duration, coverage and occurrence of microstates C and global variance explained). To evaluate the quality of clustering, the Silhouette score was applied and the score was close to 0.20, indicating that the clusters overlap. These results are encouraging and support the usefulness of these methods for classifying stroke patients in order to contribute to the development of therapeutic strategies, improve the clinical management of these patients, and consequently reduce the associated costs.

Relational integration is a key subcomponent of working memory and a strong predictor of fluid intelligence. Both relational integration and fluid intelligence share a common neural foundation, particularly involving the frontoparietal network. This study utilized a randomized controlled experiment to examine the effect of relational integration training on brain networks using electroencephalogram (EEG) and microstate analysis. Participants were randomly assigned to either a relational integration training group (n = 29) or an active control group (n = 28) for one month. The Sandia matrices task assessed fluid intelligence, while rest-EEG was recorded during pre- and post-tests. Microstate analysis revealed that, for microstate D, the training group demonstrated a significant increase in occurrence and contribution following the intervention compared to the control group. Additionally, microstate D occurrence was negatively correlated with reaction times (RTs). Post-training, the training group showed a lower occurrence and contribution of microstate C compared to the control group. Regarding transfer probability, the training group exhibited a decrease between microstates A and B, and an increase between microstates C and D. In contrast, the control group showed increased transfer probability between microstates A, B, and C, and a decrease between microstate D and other microstates (B and A). These findings indicate that relational integration training influences frontoparietal networks associated with fluid intelligence. The current study suggests that relational integration training is an effective intervention for enhancing fluid intelligence.