Brain Functional Connectivity Research Articles

Classification of EEG evoked in 2D and 3D virtual reality: traditional machine learning versus deep learning

Backgrounds. Virtual reality (VR) simulates real-life events and scenarios and is widely utilized in education, entertainment, and medicine. VR can be presented in two dimensions (2D) or three dimensions (3D), with 3D VR offering a more realistic and immersive experience. Previous research has shown that electroencephalogram (EEG) profiles induced by 3D VR differ from those of 2D VR in various aspects, including brain rhythm power, activation, and functional connectivity. However, studies focused on classifying EEG in 2D and 3D VR contexts remain limited.Methods. A 56-channel EEG was recorded while visual stimuli were presented in 2D and 3D VR. The recorded EEG signals were classified using two machine learning approaches: traditional machine learning and deep learning. In the traditional approach, features such as power spectral density (PSD) and common spatial patterns (CSP) were extracted, and three classifiers-support vector machines (SVM), K-nearest neighbors (KNN), and random forests (RF)-were used. For the deep learning approach, a specialized convolutional neural network, EEGNet, was employed. The classification performance of these methods was then compared.Results. In terms of accuracy, precision, recall, and F1-score, the deep learning method outperformed traditional machine learning approaches. Specifically, the classification accuracy using the EEGNet deep learning model reached up to 97.86%.Conclusions. EEGNet-based deep learning significantly outperforms conventional machine learning methods in classifying EEG signals induced by 2D and 3D VR. Given EEGNet's design for EEG-based brain-computer interfaces (BCI), this superior classification performance suggests that it can enhance the application of 3D VR in BCI systems.

Age-related differences of the time-varying features in the brain functional connectivity and cognitive aging.

Brain functional modular organization changes with age. Considering the brain as a dynamic system, recent studies have suggested that time-varying connectivity provides more information on brain functions. However, the spontaneous reconfiguration of modular brain structures over time during aging remains poorly understood. In this study, we investigated the age-related dynamic modular reconfiguration using resting-state functional MRI data (615 participants, aged 18-88 years) from Cam-CAN. We employed a graph-based modularity analysis to investigate modular variability and the transition of nodes from one module to another in modular brain networks across the adult lifespan. Results showed that modular structure exhibits both linear and nonlinear age-related trends. The modular variability is higher in early and late adulthood, with higher modular variability in the association networks and lower modular variability in the primary networks. In addition, the whole-brain transition matrix showed that the times of transition from other networks to the dorsal attention network were the largest. Furthermore, the modular structure was closely related to the number of cognitive components and memory-related cognitive performance, suggesting a potential contribution to flexibility cognitive function. Our findings highlighted the notable dynamic characteristics in large-scale brain networks across the adult lifespan, which enhanced our understanding of the neural substrate in various cognitions during aging. These findings also provided further evidence that dedifferentiation and compensation are the outcomes of functional brain interactions.

Working memory related functional connectivity in adult ADHD and its amenability to training: A randomized controlled trial

BackgroundWorking memory (WM) deficits are among the most prominent cognitive impairments in attention deficit hyperactivity disorder (ADHD). While functional connectivity is a prevailing approach in brain imaging of ADHD, alterations in WM-related functional brain networks and their malleability by cognitive training are not well known. We examined whole-brain functional connectivity differences between adults with and without ADHD during n-back WM tasks and rest at pretest, as well as the effects of WM training on functional and structural brain connectivity in the ADHD group. MethodsForty-two adults with ADHD and 36 neurotypical controls performed visuospatial and verbal n-back tasks during functional magnetic resonance imaging (fMRI). In addition, seven-minute resting state fMRI data and diffusion-weighted MR images were collected from all participants. The adults with ADHD continued into a 5-week randomized controlled WM training trial (experimental group training on a dual n-back task, n = 21; active control group training on Bejeweled II video game, n = 21), followed by a posttraining MRI. Brain connectivity was examined with Network-Based Statistic. ResultsAt the pretest, adults with ADHD had decreased functional connectivity compared with the neurotypical controls during both n-back tasks in networks encompassing fronto-parietal, temporal, occipital, cerebellar, and subcortical brain regions. Furthermore, WM-related connectivity in widespread networks was associated with performance accuracy in a continuous performance test. Regarding resting state connectivity, no group differences or associations with task performance were observed. WM training did not modulate functional or structural connectivity compared with the active controls. ConclusionOur results indicate large-scale abnormalities in functional brain networks underlying deficits in verbal and visuospatial WM commonly faced in ADHD. Training-induced plasticity in these networks may be limited

Resting-State Functional Magnetic Resonance Imaging Reveals the Effects of rTMS on Neural Activity and Brain Connectivity After Experimental Stroke.

Limited understanding of neurobiological mechanisms of repetitive transcranial magnetic stimulation (rTMS) prevents us from choosing optimal therapeutic regimen for patients to improve therapeutic efficiency. Resting-state functional magnetic resonance imaging (rs-fMRI) has been demonstrated to obtain comparable functional readouts across species. Intermittent and continuous theta burst stimulation were used to stimulate ipsilesional and contralesional hemisphere, respectively, during the subacute phase after stroke. We used a rat middle cerebral artery occlusion stroke model. The amplitude of low-frequency fluctuations and functional connectivity analyses of rs-fMRI were chosen to detect neuron activity and functional connectivity. The expression of neuron activation marker c-Fos and axonal plasticity marker GAP43 was examined by an immunochemistry method to corroborate the results of rs-fMRI. iTBS altered the long-term neuronal activity in bilateral sensorimotor cortex, whereas cTBS influenced immediate neuronal activity of bilateral sensorimotor cortex. In addition, cTBS enhanced interhemispheric and intrahemisheric functional connectivity in contralesional hemisphere, accompanied by axonal and dendritic remodeling in the perilesional cortical areas and contralesional homologous areas after large stroke. rTMS exerted complex effects on brain structural and functional connectivity in addition to affecting cortical excitability. cTBS promoted the compensatory effect of contralesional hemisphere after stroke with large lesions.

Study of the Brain Functional Connectivity Processes During Multi-Movement States of the Lower Limbs

Study of the Brain Functional Connectivity Processes During Multi-Movement States of the Lower Limbs

Longitudinal functional brain connectivity maturation in premature newborn infants: modulatory influence of early music enrichment

Abstract Premature birth affects brain maturation, illustrated by altered brain functional connectivity at term equivalent age (TEA) and alters neurobehavioral outcome. To correct early developmental differences and improve neurological outcome, music during the NICU stay has been proposed as an auditory enrichment with modulatory effects on functional and structural brain development, but longitudinal effects of such interventions have not been studied so far. We longitudinally investigated resting-state functional connectivity (RS-FC) maturation in preterm infants (n=43). Data-driven Independent Component Analyses (ICA) were performed on scans obtained at 33- and 40-weeks gestational age (GA), determining the presence of distinct RSNs. Connectome analysis ´accordance measure´ quantitively examined the RS-FC both at 33- and 40-weeks GA. Further comparing the internetwork RS-FC at 33- and 40 weeks GA provided a circuitry of interest (COI) for significant maturational changes in which the effects on the RS-FC of a music intervention was tested. The connectome analyses resulted in a COI of RS-FC connections significantly maturing from 33 to 40 weeks GA, namely between the thalamic/brainstem and prefrontal-limbic, salience, sensorimotor, auditory and prefrontal cortical networks; between the prefrontal-limbic and cerebellar, visual and left hemispheric precuneus networks; between the salience and visual, and cerebellar networks; and between the sensorimotor and auditory, and posterior cingulate/precuneus networks. The infants exposed to music exhibited significantly increased maturation in RS-FC between the thalamic/brainstem and salience networks, compared to controls. This study exemplifies that preterm infant RS-FC maturation is modulated through NICU music exposure, highlighting the importance of environmental enrichment for neurodevelopment in premature newborns.

Atypical Brain Connectivity During Pragmatic and Semantic Language Processing in Children with Autism

Background/Objectives: Children with Autism Spectrum Disorder (ASD) face challenges in social communication due to difficulties in considering context, processing information, and interpreting social cues. This study aims to explore the neural processes related to pragmatic language communication in children with ASD and address the research question of how functional brain connectivity operates during complex pragmatic language tasks. Methods: We examined differences in brain functional connectivity between children with ASD and typically developing peers while they engaged in video recordings of spoken language tasks. We focused on two types of speech acts: semantic and pragmatic. Results: Our results showed differences between groups during the pragmatic and semantic language processing, indicating more idiosyncratic connectivity in children with ASD in the Left Somatomotor and Left Limbic networks, suggesting that these networks play a role in task-dependent functional connectivity. Additionally, these functional differences were mainly localized to the left hemisphere.

Age-associated alteration of innate defensive response to a looming stimulus and brain functional connectivity pattern in mice

Innate defensive behaviors are essential for species survival. While these behaviors start to develop early in an individual’s life, there is still much to be understood about how they evolve with advancing age. Considering that aging is often accompanied by various cognitive and physical declines, we tested the hypothesis that innate fear behaviors and underlying cerebral mechanisms are modified by aging. In our study we investigated this hypothesis by examining how aged mice respond to a looming visual threat compared to their younger counterparts. Our findings indicate that aged mice exhibit a different fear response than young mice when facing this imminent threat. Specifically, unlike young mice, aged mice tend to predominantly display freezing behavior without seeking shelter. Interestingly, this altered behavioral response in aged mice is linked to a distinct pattern of functional brain connectivity compared to young mice. Notably, our data highlights a lack of a consistent brain activation following the fear response in aged mice, suggesting that innate defensive behaviors undergo changes with aging.

Effects of Manual Acupuncture Versus Sham Acupuncture in patients with Post-Stroke Depression: A Randomized Clinical Trial.

Post-stroke depression (PSD) is a prevalent psychiatric complication in stroke patients, severely reducing quality of life and delaying social recovery in stroke survivors. Clinical studies have shown that acupuncture can be used as an alternative approach for PSD. The aim of this study was to examine the safety, efficacy, and electroencephalogram (EEG) mechanism of acupuncture in treating PSD patients. From October 28, 2022 to May 16, 2023, this single-center, single-blind, randomized clinical trial was conducted at the Second Affiliated Hospital of Nanchang University. A total of 56 eligible subjects were assigned in a random manner, with an equal distribution between two groups: the manual acupuncture (MA) group and the sham acupuncture (SA) group. The primary outcome was the Hamilton Depression Scale-24 (HAMD-24); the secondary outcomes included the Pittsburgh Sleep Quality Index (PSQI), the National Institutes of Health Stroke Scale (NIHSS), the Barthel index, EEG power spectrum, and EEG imaginary coherent (iCOH). Compared to the SA group, the MA group exhibited significant improvements in HAMD-24, NIHSS, PSIQ, and Barthel index at week 6. The total improvement rate was 85.71% in the MA group and 28.57% in the SA group. After 6weeks of treatment, the alpha and beta bands power spectrum increased significantly, while the delta and theta bands power spectrum decreased significantly in the MA group compared to the SA group. The iCOH analysis showed that the MA group had significantly higher functional connectivity in the four bands than the SA group. Acupuncture might be regarded as an adjunctive treatment for PSD patients with improvements in their neurological deficits, sleep quality, and depression. Meanwhile, the mechanism of acupuncture in treating PSD patients may be through decreasing the slow wave power spectrum and increasing the fast wave power spectrum, and enhancing brain functional connectivity. Chinese Clinical Trial Registry (ChiCTR2200065112/2022-10-28).

Functional connectivity density of brain in children with primary nocturnal enuresis: results from a resting-state fMRI study.

Primary nocturnal enuresis (PNE) is a disease characterized by involuntary urination during sleep after the age of five, causing inconvenience and psychological burden to children and their families. The onset of PNE is related to many factors, and in recent years, delayed central nervous system maturation has been considered one of the important causes of PNE. Previous studies have demonstrated functional changes in multiple brain regions in children with PNE. However, these studies either focused on changes in local brain regions or the functional connection (FC) between specific brain regions, and there is currently a lack of research on the whole brain FC in children with PNE. This study analyzed functional connectivity density (FCD) across the entire brain based on voxels and comprehensively evaluated the global FCD (gFCD), local FCD (lFCD), and long-range FCD (lrFCD). Decreased gFCD and lFCD were found in the left temporal lobe and the right posterior cerebellum in the children with PNE compared with the HC. The FCD values in these regions were negatively correlated with the scores of hyperactivity/impulsivity in the children with PNE. This study may help to reveal the neural mechanisms underlying the onset of PNE in children from a new perspective.

Exploring Electrophysiological Responses to Hypnosis in Patients with Fibromyalgia

Background/Objectives: Hypnosis shows great potential for managing patients suffering from fibromyalgia and chronic pain. Several studies have highlighted its efficacy in improving pain, quality of life, and reducing psychological distress. Despite its known feasibility and efficacy, the mechanisms of action remain poorly understood. Building on these insights, this innovative study aims to assess neural activity during hypnosis in fibromyalgia patients using high-density electroencephalography (EEG) and self-reported measures. Methods: Thirteen participants with fibromyalgia were included in this study. EEG recordings were done during resting state and hypnosis conditions. After both conditions, levels of pain, comfort, absorption, and dissociation were assessed using a numerical rating scale. Time perception was collected via an open-ended question. The study was prospectively registered in the ClinicalTrials.gov public registry (NCT04263324). Results: Neural oscillations showed increased theta power during hypnosis in the left parietal and occipital electrodes, increased beta power in the frontal and left temporal electrodes, and increased slow-gamma power in the frontal and left parietal electrodes. Functional connectivity using pairwise-phase consistency measures showed decreased connectivity in the frontal electrodes during hypnosis. Graph-based measures, the node strength, and the cluster coefficient were lower in frontal electrodes in the slow-gamma bands during hypnosis compared to resting state. Key findings indicate significant changes in neural oscillations and brain functional connectivity, suggesting potential electrophysiological markers of hypnosis in this patient population.

Disentangling the impact of motion artifact correction algorithms on functional near-infrared spectroscopy-based brain network analysis.

Functional near-infrared spectroscopy (fNIRS) has been widely used to assess brain functional networks due to its superior ecological validity. Generally, fNIRS signals are sensitive to motion artifacts (MA), which can be removed by various MA correction algorithms. Yet, fNIRS signals may also undergo varying degrees of distortion due to MA correction, leading to notable alternation in functional connectivity (FC) analysis results. We aimed to investigate the effect of different MA correction algorithms on the performance of brain FC and topology analyses. We evaluated various MA correction algorithms on simulated and experimental datasets, including principal component analysis, spline interpolation, correlation-based signal improvement, Kalman filtering, wavelet filtering, and temporal derivative distribution repair (TDDR). The mean FC of each pre-defined network, receiver operating characteristic (ROC), and graph theory metrics were investigated to assess the performance of different algorithms. Although most algorithms did not differ significantly from each other, the TDDR and wavelet filtering turned out to be the most effective methods for FC and topological analysis, as evidenced by their superior denoising ability, the best ROC, and an enhanced ability to recover the original FC pattern. The findings of our study elucidate the varying impact of MA correction algorithms on brain FC analysis, which could serve as a reference for choosing the most appropriate method for future FC research. As guidance, we recommend using TDDR or wavelet filtering to minimize the impact of MA correction in brain network analysis.

Resting state network changes induced by experimental inaudible infrasound exposure and associations with self-reported noise sensitivity and annoyance.

The effects of prolonged infrasound (IS) exposure on brain function and behavior are largely unknown, with only one prior study investigating functional connectivity (FC) changes. In a long-term randomized-controlled trial, 38 participants were exposed to inaudible airborne IS (6Hz, 80-90 dB) or sham devices for four weeks (8h/night). We assessed FC changes in resting-state networks (auditory, default mode (DMN), sensorimotor (SMN), and executive control (ECN)), and explored IS 'sensitivity' as a predictor of identified significant FC changes. We also examined correlations between somatic symptoms and FC. IS exposure led to decreased FC in the right precuneus (DMN) and increased FC in the Vermis IV and V (SMN). In the ECN, we observed increased FC in the right frontal middle gyrus (BA8) and the right inferior parietal lobe, and decreased FC in another region of the right frontal middle gyrus. Changes in the ECN (right inferior parietal lobe) were negatively associated with self-reported annoyance from IS/low-frequency noise. A significant negative association was found between FC changes in the DMN (right precuneus) and somatic symptoms. Our study is the first to investigate prolonged IS exposure effects on brain FC, revealing changes in the vDMN, SMN, and ECN, but not in the auditory network. Future studies should assess annoyance and sensitivity markers, fine-grained measures of somatic symptoms, and stratify samples by sensitivity to uncover individual differences in response to IS.

Dynamic patterns of functional connectivity in the human brain underlie individual memory formation

Remembering our everyday experiences involves dynamically coordinating information distributed across different brain regions. Investigating how momentary fluctuations in connectivity in the brain are relevant for episodic memory formation, however, has been challenging. Here we leverage the high temporal precision of intracranial EEG to examine sub-second changes in functional connectivity in the human brain as 20 participants perform a paired associates verbal memory task. We first identify potential functional connections by selecting electrode pairs across the neocortex that exhibit strong correlations with a consistent time delay across random recording segments. We then find that successful memory formation during the task involves dynamic sub-second changes in functional connectivity that are specific to each word pair. These patterns of dynamic changes are reinstated when participants successfully retrieve the word pairs from memory. Therefore, our data provide direct evidence that specific patterns of dynamic changes in human brain connectivity are associated with successful memory formation.

Contrasting topologies of synchronous and asynchronous functional brain networks

ABSTRACT We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy and compared aFN topology with the correlation-based synchronous functional networks (sFNs), which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and Neurodevelopment in Adolescence study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks. After adjusting for differences in network density, aFNs had higher global efficiency but lower local efficiency than the sFNs. In the aFNs, nodes with the highest outgoing global efficiency tended to be in the brainstem and orbitofrontal cortex. aFN nodes with the highest incoming global efficiency tended to be members of the default mode network in sFNs. Age interacted with node global efficiency in aFNs and node local efficiency in sFNs to influence connection probability. We conclude that the sFN and aFN both offer information about functional brain connectivity, which the other type of network does not.

Multi-omics insights into the microbiota-gut-brain axis and cognitive improvement post-bariatric surgery

BackgroundAlthough numerous studies have shown that bariatric surgery results in sustained weight loss and modifications in gut microbiota composition and cognitive function, the exact underlying mechanisms are unclear. This study aimed to investigate the effects of bariatric surgery on cognitive function through the microbiota-gut-brain axis (MGBA).MethodsDemographic data, serum samples, fecal samples, cognitive assessment scales, and resting-state functional connectivity magnetic resonance imaging (rs-fMRI) scans were obtained from 39 obese patients before and after (6 months) laparoscopic sleeve gastrectomy (LSG). PCA analysis, OPLS-DA analysis, and permutation tests were used to conduct fecal 16 S microbiota profiling, serum metabolomics, and neuroimaging analyses, and a bariatric surgery-specific rs-fMRI brain functional connectivity network was constructed. Spearman correlation analysis and Co-inertia analysis were employed to correlate significant alterations in cognitive assessment scales and resting-state functional connectivity difference networks with differential serum metabolites and 16 S microbiota data to identify key gut microbiota and serum metabolic factors.ResultsLSG significantly reduced the weight of obese patients, with reductions of up to 28%. Furthermore, cognitive assessment scale measurements revealed that LSG enhanced cognitive functions, including memory (HVLT, p = 0.000) and executive function (SCWT, p = 0.008). Also, LSG significantly altered gut microbiota composition (p = 0.001), with increased microbial abundance and diversity (p < 0.05). Moreover, serum metabolite levels were significantly altered, revealing intergroup differences in 229 metabolites mapped to 72 metabolic pathways (p < 0.05, VIP > 1). Spearman correlation analysis among cognitive assessment scales, gut microbiota species, and serum metabolites revealed correlations with 68 gut microbiota species and 138 serum metabolites (p < 0.05). Furthermore, pairwise correlations were detected between gut microbiota and serum metabolites (p < 0.05). Functional neuroimaging analysis revealed that LSG increased functional connectivity in cognitive-related frontotemporal networks (FPN, p < 0.01). Additionally, normalization of the default mode network (DMN) and salience network (SN) connectivity was observed after LSG (p < 0.001). Further canonical correlation and correlation analysis suggested that the cognitive-related brain network changes induced by LSG were associated with key gut microbiota species (Akkermansia, Blautia, Collinsella, Phascolarctobacterium, and Ruminococcus, p < 0.05) and neuroactive metabolites (Glycine, L-Serine, DL-Dopa, SM (d18:1/24:1(15Z), p < 0.05).ConclusionThese findings indicate the pathophysiological role of the microbiota-gut-brain axis in enhancing cognitive function after bariatric surgery, and the study provides a basis for clinical dietary adjustments, probiotic supplementation, and guidance for bariatric surgery, but further research is still needed.Trial registrationChinese Clinical Trial Registry, ChiCTR2100049403. Registered 02 August 2021, https://www.chictr.org.cn/.

Patients with temporomandibular disorders and chronic pain of myofascial origin display reduced alpha power density and altered small-world properties of brain networks

BACKGROUND: Chronic pain is one of the most common symptoms of temporomandibular disorders (TMD). Although its pathophysiology is still a challenge, TMD has been associated with changes in central nervous system activity related to pain modulatory capacity. OBJECTIVE: To assess the cortical activity of patients with temporomandibular disorders and chronic pain of myofascial origin using quantitative electroencephalography (qEEG) in different mental states. METHOD: This study consists of a cross-sectional study. Individuals with TMD and chronic pain and healthy controls were evaluated using qEEG in four consecutive conditions, all with closed eyes: 1) initial resting condition; 2) non-painful motor imagery task of hand movement; 3) painful motor imagery task of clenching the teeth; 4) final resting condition. RESULTS: Participants with TMD and chronic pain overall presented decreased alpha power density during baseline at rest, non-painful and painful motor imagery tasks when compared to healthy controls. Furthermore, functional brain connectivity was distinct between groups, with TMD and chronic pain showing lower small-world values for the delta (all conditions), theta (painful and non-painful motor imagery task), and alpha bands (painful motor imagery task), and an increase in the beta band (all conditions). CONCLUSION: These results suggest that TMD and chronic pain could be associated with maladaptive plasticity in the brain, which may correspond to a reduced ability to modify brain activity during different mental tasks, including painful and non-painful motor imagery.

Correlations between alterations in global brain functional connectivity in patients with major depressive disorder and their genetic characteristics

This study aims to elucidate the neuroimaging changes associated with major depressive disorder (MDD) and their relationship with genetic characteristics. We conducted a global-brain functional connectivity (GFC) and genetic-neuroimaging correlation analysis on 42 MDD patients and 42 healthy controls (HCs), exploring the correlation between GFC abnormalities and clinical variables. Results showed that compared to HCs, MDD patients had significantly decreased GFC values in the bilateral posterior cingulate cortex/precuneus and increased GFC values in the left and right cerebellum Crus I/II. Additionally, a negative correlation was observed between the GFC values of the left cerebellum Crus I/II and subjective support scores, as well as social support revalued scale total scores. We identified genes associated with GFC changes in MDD, which are enriched in biological processes such as synaptic transmission and ion transport. Our findings indicate the presence of abnormal GFC values in severe depression, complementing the pathological research on the condition. Furthermore, this study provides preliminary evidence for the correlation between social support levels and brain functional connectivity, offering insights into the potential association between GFC changes and gene expression in MDD patients.

Privacy-Preserving Visualization of Brain Functional Connectivity.

Privacy protection is important in visualization due to the risk of leaking personal sensitive information. In this paper, we study the problem of privacy-preserving visualizations using differential privacy, employing biomedical data from neuroimaging as a use case. We investigate several approaches based on perturbing correlation values and characterize their privacy cost and the impact of pre- and post-processing. To obtain a better privacy/visual utility tradeoff, we propose workflows for connectogram and seed-based connectivity visualizations, respectively. These workflows successfully generate visualizations similar to their non-private counterparts. Experiments show that qualitative assessments can be preserved while guaranteeing privacy. These results show that differential privacy is a promising method for protecting sensitive information in data visualization.

A Brain Network Analysis Model for Motion Sickness in Electric Vehicles Based on EEG and fNIRS Signal Fusion

Motion sickness is a common issue in electric vehicles, significantly impacting passenger comfort. This study aims to develop a functional brain network analysis model by integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals to evaluate motion sickness symptoms. During real-world testing with the Feifan F7 series of new energy-electric vehicles from SAIC Motor Corp, data were collected from 32 participants. The EEG signals were divided into four frequency bands: delta-range, theta-range, alpha-range, and beta-range, and brain oxygenation variation was calculated from the fNIRS signals. Functional connectivity between brain regions was measured to construct functional brain network models for motion sickness analysis. A motion sickness detection model was developed using a graph convolutional network (GCN) to integrate EEG and fNIRS data. Our results show significant differences in brain functional connectivity between participants in motion and non-motion sickness states. The model that combined fNIRS data with high-frequency EEG signals achieved the best performance, improving the F1 score by 11.4% compared to using EEG data alone and by 8.2% compared to using fNIRS data alone. These results highlight the effectiveness of integrating EEG and fNIRS signals using GCN for motion sickness detection. They demonstrate the model’s superiority over single-modality approaches, showcasing its potential for real-world applications in electric vehicles.