Brain Topography最新文献

Functional brain network (FBN) derived from functional Magnetic Resonance Imaging (fMRI) has promising prospects in clinical research, but fMRI is not a routine acquisition data, which limits its popularity in clinical applications. Therefore, it is imperative to generate FBN based on routine clinical structural MRI brain network. In this study, a BrainNet-GAN model was proposed for generating FBN from radiomics-based morphological brain network (radMBN) derived from routinely acquired T1-weighted image (T1WI). BrainNet-GAN integrated two Multi-Channel Multi-Scale Adaptive (Multi²Ada) generators and two (Local_to_Global) discriminators. In the generator, Graph Convolutional Network (GCN) was used inside each channel to aggregate multi-scale information between direct or indirect neighbors of nodes, and the output of each channel was adaptively fused through several sets of learnable coefficients; In the discriminator, Multi-channel GCN was used to aggregate local nodes information, and a feature selection module was designed to establish correlations between feature maps at different channels. Additionally, a Multi-Angle Multi-Constraint (MAMC) loss function was proposed, which could guide the learning process of the model from different aspects. Experiments with 2116 subjects in two publicly available datasets showed that BrainNet-GAN model exhibited promising performance on the task of generating FBN. Meanwhile, the individual-level brain network visualization was displayed with high consistency in generated FBN and target FBN. Further, the Top 10 brain regions identified by four graph-theory analysis metrics also exhibited with consistency. The proposed BrainNet-GAN model demonstrated superior performance in generating FBN based on radMBN, which could facilitate the application of FBN in clinical practice.

Developmental dyslexia (DD) is a common reading disorder with neurological underpinnings; however, it remains unclear whether Chinese children with DD exhibit spectral power or network topology abnormalities. This study investigated spectral power and brain network topology abnormalities using electroencephalography (EEG) during resting states and a one-back Chinese-Korean character task in 85 Hong Kong Chinese children with DD and 51 typically developing peers (ages 7-11). EEG signals were transformed using the Fast Fourier Transform to estimate spectral power. Functional connectivity matrices were derived using the phase-lag index, and network topology was assessed via minimum spanning tree (MST) analysis. The results suggested that children with DD showed reduced alpha power over central, frontal, temporal, parietal, and occipital scalp areas at rest, and over central and frontal areas during the task. MST results revealed decreased beta band integration at rest but increased alpha band integration during the one-back task. Familiar Chinese stimuli elicited greater alpha and beta power and lower beta band integration compared to unfamiliar Korean stimuli. Moreover, resting-state beta band integration correlated positively with reading fluency in children with DD. These findings point to inhibitory control deficits and cortical hyperactivation in Chinese DD, reflected in disrupted large-scale network topology, and highlight the alpha band as a potential biomarker. They also demonstrate that language familiarity modulates neural efficiency and recruits compensatory networks. Overall, the study provides new insights into the neural basis of reading difficulties in Chinese children with DD.

Apathy is a cognitive, behavioral, and emotional disorder marked by a decrease in goal-directed activities as well as affective flattening. This multifaceted disorder has been described in Parkinson's disease as a highly common neuropsychiatric feature. The pathophysiology that underlies apathy, however, is still not entirely understood. The major goal of this study was to determine the microstate correlations of apathy in Parkinson's disease. This study involved patients with the diagnosis of idiopathic Parkinson's disease. Based on the Apathy Evaluation Scale criteria, Parkinson's disease groups were divided into two main groups- apathetic and non-apathetic. Patients underwent clinical, motor, and demographic characteristics as well as neuropsychometric evaluations. Spontaneous EEG brain activity was recorded, and a microstate analysis was conducted. The clinical and motor functions of the apathetic and non-apathetic groups did not differ significantly; nevertheless, the apathetic group performed worse on several executive function and memory tests. A comparison of EEG microstates between the apathetic and non-apathetic groups found that the apathetic group had an increase in the duration and coverage of microstates B and E, whereas the frequency of Microstate D decreased. Additionally, in patients with apathy, an increased transition was observed from Microstate A > B, C > E and C > G. Our findings suggest that the increased transitions from Microstate A to B and from C to E and G, along with an increase in Microstates E and B and a decrease in Microstate D, may reflect changes in the activity or functional connectivity of several large-scale brain circuits in Parkinsonian apathy. On the other hand, Microstate E could be the fundamental microstate reflecting changes associated with the Default Mode Network in Parkinsonian apathy.

Interoception, the process of sensing and interpreting internal bodily signals, plays a crucial role in emotional regulation, decision-making, and overall well-being. This study aimed to investigate the relationship between self-reported interoceptive processes, assessed through the Body Perception Questionnaire (BPQ), and psychophysiological measures of interoception, including cardiac autonomic markers (HF-HRV and RMSSD), cortical processing of cardiac signals (heartbeat-evoked potentials, HEPs), and EEG microstates. We recorded EEG and ECG from 64 healthy volunteers during open-eyed resting state. A positive association was found between the Subdiaphragmatic Reactivity subscale of the BPQ and the coverage of microstate A, a spatial configuration linked to the activation of temporal brain regions, arousal, and sensory processing. No associations were observed between BPQ scores and cardiac measures or HEP amplitudes, suggesting that subjective reports may not align with psychophysiological indices of interoception. Associations were found between HEP amplitudes and microstates A and B, as well as between HRV measures and microstate D, highlighting potential links between autonomic functioning and brain activity during resting state. Although the BPQ is a widely used tool to assess interoceptive sensibility, it may not fully capture the complexity of this construct. These findings provide insight into the complex interplay between self-reported interoception and psychophysiological markers, while emphasizing the need for further research to clarify these relationships and their implications for emotional and cognitive processing.

The ability to maintain our body's balance and stability in space is crucial for performing daily activities. Effective postural control (PC) strategies rely on integrating visual, vestibular, and proprioceptive sensory inputs. While neuroimaging has revealed key areas involved in PC-including brainstem, cerebellum, and cortical networks-the rapid neural mechanisms underlying dynamic postural tasks remain less understood. Therefore, we used EEG microstate analysis within the BioVRSea experiment to explore the temporal brain dynamics that support PC. This complex paradigm simulates maintaining an upright posture on a moving platform, integrated with virtual reality (VR), to replicate the sensation of balancing on a boat. Data were acquired from 266 healthy subjects using a 64-channel EEG system. Using a modified k-means method, five EEG microstate maps were identified to best model the paradigm. Differences in each microstate maps feature (occurrence, duration, and coverage) between experimental phases were analyzed using a linear mixed model, revealing significant differences between microstates within the experiment phases. The temporal parameters of microstate C showed significantly higher levels in all experimental phases compared to other microstate maps, whereas microstate B displayed an opposite pattern, consistently showing lower levels. This study marks the first attempt to use microstate analysis during a dynamic task, demonstrating the decisive role of microstate C and, conversely, microstate B in differentiating the PC phases. These results demonstrate the utility of microstate technique in studying temporal brain dynamics during PC, with potential applications in the early detection of neurodegenerative diseases.

There is growing interest in the neural network of pragmatic language and its potential overlap with the Theory of Mind (ToM) network. However, no Spanish-adapted fMRI tasks were used for studying sarcasm, the subtype of pragmatic language most related to ToM. Furthermore, stimuli used in prior studies often impose high cognitive demands, confounding its sarcasm brain representation with the executive network. We investigate the neural correlates of sarcasm in Spanish using a novel experimental paradigm designed to minimize cognitive load and enhance ecological validity. Eighteen healthy, right-handed participants underwent a 3T fMRI session with a sarcasm comprehension task. Brain activations analysed with SPM12 were calculated for sarcasm vs. literal contrast. Sarcasm activated the left temporo-parietal junction, Medial Prefrontal Cortex (BA 10), Left Inferior Frontal Gyrus (BA 45), Left Medial and Superior Temporal Gyrus (BA 21 & 22), and Left Temporal Pole (BA 38). Sarcasm comprehension involves an extensive fronto-temporal-parietal network, with prominent activation of ToM-related areas. These findings suggest an overlap between sarcasm and ToM networks, emphasizing the role of the medial prefrontal cortex in pragmatic language, the left inferior frontal gyrus in semantic integration, and the role of a left-lateralized frontotemporal network for sarcasm processing.

Type 1 Diabetes Mellitus (T1DM) progression has a direct impact on brain microstructural integrity and typical functional organization from the early stages of neurodevelopment. Diffusion Tensor Imaging (DTI) is a neuroimaging method that has proven sensitive to changes in white matter microstructure. Using diffusion-weighted probabilistic tractography methods, we aim to evaluate the white matter integrity and anatomical relationships within the Default Mode Network (DMN) brain regions, which have been proven to be particularly affected by T1DM in a group of eighteen carefully selected clinically well-controlled young T1DM patients versus eighteen healthy matched controls according to sex, age, and education level. Results showed no relevant differences in the anatomical distribution of DMN between the groups. However, the transitivity graph metric was significantly lower in T1DM patients, who also showed weaker connectivity between the left ventral prefrontal cortex and the left medial temporal gyrus, representing the anatomical trajectory of the arcuate fasciculus. Considering that neural myelination is affected by language input and the critical role of language-related structures on brain development, the current findings denote early ill-driven brain modifications to better adapt to the increasing daily demands.

Hemodynamic responses in the dorsolateral prefrontal cortex (DLPFC) during gait initiation could influence anticipatory postural adjustments (APAs). However, how DLPFC during motor preparation modulates APA integration remains unknown. Seventeen right-handed participants completed two sessions of the rapid arm raising task and simultaneously received the real and sham repetitive transcranial magnetic stimulation (rTMS) over the left DLPFC during the motor preparation period before arm raising. The rTMS protocol involves 10 Hz stimulation at an intensity of 110% of the resting motor threshold. The activations of DLPFC, supplementary motor area (SMA), and primary motor cortex (M1) were recorded using the functional near-infrared spectroscopy (fNIRS) during the rapid arm raising task. The APAs were assessed by recording the latency and amplitude of the postural muscles using the surface electromyography. Compared with sham stimulation, the activation of DLPFC (t = -2.97, p = 0.033), SMA (t = -2.141, p = 0.048) and M1 (t = -2.787, p = 0.013) was significantly decreased during real rTMS. It was also observed that the latency was reduced (t = -2.209, p = 0.041) and the amplitude was decreased (t = -2.696, p = 0.010) during real rTMS in the superficial lumbar multifidus. The DLPFC activation was positively correlated with those of M1 (r = 0.569, p = 0.017) and SMA (r = 0.595, p = 0.012) in the real rTMS session. Finally, the oxygenated hemoglobin concentration in the DLPFC and M1 significantly correlated with the muscle amplitude (r = 0.646, p = 0.007 and r = 0.589, p = 0.013, respectively). The association between DLPFC and the APAs was totally mediated by M1. rTMS over the DLPFC during motor preparation could enhance the neural efficiency of the M1, and subsequently facilitate the integration of APAs with voluntary movement.

Electroencephalography (EEG) recordings are widely used in neuroscience to identify healthy individual brain rhythms and to detect alterations associated with various brain diseases. However, understanding the cellular origins of scalp EEG signals and their spatiotemporal changes during the resting state (RS) in humans remains challenging, as cellular-level recordings are typically restricted to animal models. The objective of this study was to simulate individual-specific spatiotemporal features of RS EEG and measure the degree of similarity between real and simulated EEG. Using a physiologically grounded whole-brain computational model (based on known neuronal subtypes and their structural and functional connectivity) that simulates interregional cortical circuitry activity, realistic individual EEG recordings during RS of three healthy subjects were created. The model included interconnected neural mass modules simulating activities of different neuronal subtypes, including pyramidal cells and four types of GABAergic interneurons. High-definition EEG and source localization were used to delineate the cortical extent of alpha and beta-gamma rhythms. To evaluate the realism of the simulated EEG, we developed a similarity index based on cross-correlation analysis in the frequency domain across various bipolar channels respecting standard longitudinal montage. Alpha oscillations were produced by strengthening the somatostatin-pyramidal loop in posterior regions, while beta-gamma oscillations were generated by increasing the excitability of parvalbumin-interneurons on pyramidal neurons in anterior regions. The generation of realistic individual RS EEG rhythms represents a significant advance for research fields requiring data augmentation, including brain-computer interfaces and artificial intelligence training.

This study investigates changes in resting-state networks (RSNs) associated with tobacco addiction (TA) and whether these changes reflect alterations in neurotransmitter systems. A total of 90 patients with TA and 46 healthy controls (HCs) matched for age, education, and body mass index undergo functional magnetic resonance imaging (fMRI) scans. Independent component analysis (ICA) is employed to extract RSNs based on a customized network template using the HCP ICA MATCHING toolbox. Additionally, a correlation study is conducted to examine the relationship between changes in functional connectivity (FC) within RSNs and positron emission tomography and single photon emission computed tomography-derived maps, aiming to identify specific neurotransmitter system changes underlying abnormal FC in TA. Compared to HCs, the TA group exhibits decreased FC values in the left precentral gyrus of the sensorimotor network B and in the right calcarine of the visual network B. Furthermore, changes in FC within the visual network B are associated with the 5-hydroxytryptamine system (1a) and opioid receptor system (Kappa) maps. Post-hoc power analysis confirms the adequacy of the sample size, with effect sizes (d) all greater than 0.9, supporting the robustness of the findings. Patients with TA show reduced intranetwork connectivity in the sensorimotor network B and visual network B, which may reflect underlying molecular changes. These findings improve understanding of the neurobiological aspects of TA.