Brain Topography最新文献

Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs) represent an innovative measure for examining brain connectivity and developing biomarkers of psychiatric conditions. Minimizing TEP variability across studies and participants, which may stem from methodological choices, is therefore vital. By combining classic peak analysis and microstate investigation, we tested how TMS pulse waveform and current direction may affect cortico-cortical circuit engagement when targeting the primary motor cortex (M1). We aim to disentangle whether changing these parameters affects the degree of activation of the same neural circuitry or may lead to changes in the pathways through which the induced activation spreads. Thirty-two healthy participants underwent a TMS-EEG experiment in which the pulse waveform (monophasic, biphasic) and current direction (posterior-anterior, anterior-posterior, latero-medial) were manipulated. We assessed the latency and amplitude of M1-TEP components and employed microstate analyses to test differences in topographies. Results revealed that TMS parameters strongly influenced M1-TEP components' amplitude but had a weaker role over their latencies. Microstate analysis showed that the current direction in monophasic stimulations changed the pattern of evoked microstates at the early TEP latencies, as well as their duration and global field power. This study shows that the current direction of monophasic pulses may modulate cortical sources contributing to TEP signals, activating neural populations and cortico-cortical paths more selectively. Biphasic stimulation reduces the variability associated with current direction and may be better suited when TMS targeting is blind to anatomical information.

Negative symptoms represent pervasive symptoms in schizophrenia (SZ) and major depressive disorder (MDD). Empirical findings suggest that disrupted striatal function contributes significantly to negative symptoms. However, the changes in striatal functional connectivity in relation to these negative symptoms, in the transdiagnostic context, remain unclear. The present study aimed to capture the shared neural mechanisms underlying negative symptoms in SZ and MDD. Resting-state functional magnetic resonance imaging data were obtained from 60 patients with SZ and MDD (33 with SZ and 27 with MDD) exhibiting predominant negative symptoms, and 52 healthy controls (HC). Negative symptoms and hedonic capacity were assessed using the Scale for Assessment of Negative Symptoms (SANS) and the Temporal Experience of Pleasure Scale (TEPS), respectively. Signal extraction for time series from 12 subregions of the striatum was carried out to examine the group differences in resting-state functional connectivity (rsFC) between striatal subregions and the whole brain. We observed significantly decreased rsFC between the right dorsal rostral putamen (DRP) and the right pallidum, the bilateral rostral putamen and the contralateral putamen, as well as between the dorsal caudal putamen and the right middle frontal gyrus in both patients with SZ and MDD. The right DRP-right pallidum rsFC was positively correlated with the level of negative symptoms in SZ. However, patients with SZ showed increased rsFC between the dorsal striatum and the left precentral gyrus, the right middle temporal gyrus, and the right lingual gyrus compared with those with MDD. Our findings expand on the understanding that reduced putaminal rsFC contributes to negative symptoms in both SZ and MDD. Abnormal functional connectivity of the putamen may represent a partially common neural substrate for negative symptoms in SZ and MDD, supporting that the comparable clinical manifestations between the two disorders are underpinned by partly shared mechanisms, as proposed by the transdiagnostic Research Domain Criteria.

Virtual reality (VR) is an immersive technology capable of simulating alternate realities, however, it often leads to cybersickness, causing discomfort for users. We conducted an experiment using a group of 30 participants (aged 25 ± 2.1 years) to see the alpha and delta wave changes for three conditions: Blank, Video, and Video Pause, with electroencephalography (EEG) recordings. The experiments were repeated three times (Trial 1, Trial 2, and Trial 3). The results showed a significant increase in delta wave power for Video compared with the Blank (p < 0.05). Video Pause showed a significant decrease compared to Video. Alpha waves significantly decreased during the Video compared with Blank (p < 0.05). Alpha waves during Video Pause showed a significant increase compared to Video (p < 0.05). Our study showed consistent alterations in alpha and delta waves across various visual stimuli for inducing cybersickness, and we observed that the decrease in alpha waves may be significantly associated with cybersickness rather than visual stimuli. These findings have implications for advancing cybersickness research.

Electroencephalographic (EEG) oscillations occur across a wide range of spatial and spectral scales, and analysis of neural rhythmic variability have attracted recent attention as markers of development, intelligence, cognitive states and neural disorders. Nonnegative matrix factorization (NMF) has been successfully applied to multi-subject electroencephalography (EEG) spectral analysis. However, existing group NMF methods have not explicitly optimized the individual-level EEG components derived from group-level components. To preserve EEG characteristics at the individual level while establishing correspondence of patterns across participants, we present a novel framework for obtaining subject-specific EEG components, which we term group-information guided NMF (GIGNMF). In this framework, group information captured by standard NMF at the group level is utilized as guidance to compute individual subject-specific components through a multi-objective optimization strategy. Specifically, we propose a three-stage framework: first, group-level consensus EEG patterns are derived using standard group NMF tools; second, an optimal procedure is implemented to determine the number of components; and finally, the group-level EEG patterns serve as references in a new one-unit NMF employing a multi-objective optimization solver. We test the performance of the algorithm on both synthetic signals and real EEG recordings obtained from Alzheimer's disease data. Our results highlight the feasibility of using GIGNMF to identify EEG spatiotemporal patterns and present novel individual electrophysiological characteristics that enhance our understanding of cognitive function and contribute to clinical neuropathological diagnosis.

EEG is the most powerful tool for epilepsy discharge detection in brain. Visual evaluation is hard in long term monitoring EEG data as huge amount of data needs to be inspected. Considering the fast and efficient results from deep learning networks especially convolutional networks, and its capability for detection of complex epileptic wave forms, inspired us to evaluate YOLO network for spike detection solution.The most used versions of YOLO (V3, V4 and V7) were evaluated for various epileptic signals. The epileptic discharge wave-forms were first labeled to 9 different signal types, but classified to four group combinations based on their features. EEG data from 20 patients were used under guidance of expert epileptologist. The YOLO networks were all trained for four various class-grouping strategies. The most suitable network to recommend was found to be YOLO-V4, for all four classifying methods giving average sensitivity, specificity, and accuracy of 96.7, 94.3, and 92.8, respectively. YOLO networks have shown promising results in detection of epileptic signals, which by adding some extra measurements this can become a great assistant tool for epileptologists. In addition, besides YOLO's High speed and accuracy in detection of epileptic signals in EEG, it can classify these signals to different morphologies.

Functional magnetic resonance imaging (fMRI) is a widely used tool to investigate the functional brain responses in living humans. Valid comparisons of fMRI results depend on consistency of the blood-oxygen-level-dependent (BOLD) hemodynamic response function (HRF). Although common statistical approaches assume a single HRF across the entire brain, the HRF differs across individuals, regions of the brain, and cortical depth. Here, we measure HRF properties in primary visual cortex (V1) using 7 T fMRI with ultra-high spatiotemporal resolution line-scanning (250 μm in laminar direction, sampled every 105 ms). Line-scanning allowed us to investigate age-related HRF changes as a function of cortical depth. Eleven young and eleven middle-aged healthy participants participated in the experiments. We estimated the HRFs using a smooth basis function deconvolution approach. We also compared the results with conventional resolutions. From these HRFs, we extracted properties related to response magnitude and temporal dynamics. The cortical depth dependent HRFs were similar to the HRFs extracted using conventional resolutions validating the cortical depth dependent approach. We found that the properties of the HRF in the two age groups are similar across cortical depth. In other words, the variance between participants is larger than the variance between age groups. This suggests that middle-aged individuals can participate in cortical depth dependent studies free of bias in HRF properties.

Neurological disorders are a major global health concern that have a substantial impact on death rates and quality of life. accurately identifying a number of diseases Due to inherent data uncertainties and Electroencephalogram (EEG) pattern overlap, conventional EEG diagnosis methods frequently encounter difficulties. This paper proposes a novel framework that integrates FLSNN to enhance the accuracy and robustness of multiple neurological disorder disease detection from EEG signals. In multiple neurological disorders, the primary motivation is to overcome the limitations of existing methods that are unable to handle the complex and overlapping nature of EEG signals. The key aim is to provide a unified, automated solution for detecting multiple neurological disorders such as epilepsy, Parkinson's, Alzheimer's, schizophrenia, and stroke in a single framework. In the Fuzzy Logic and Spiking Neural Networks (FLSNN) framework, EEG data is preprocessed to eliminate noise and artifacts, while a fuzzy logic model is applied to handling uncertainties prior to applying spike neural networking to analyze the temporal and dynamics of the signals. Processes EEG data three times faster than traditional techniques. This framework achieves 97.46% accuracy in binary classification and 98.87% accuracy in multi-class classification, indicating increased efficiency. This research provides a significant advancement in the diagnosis of multiple neurological disorders using EEG and enhances both the quality and speed of diagnostics from the EEG signal and the advancement of AI-based medical diagnostics. at https://github.com/jainshraddha12/FLSNN , the source code will be available to the public.

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.