Brain Topography最新文献

The functional brain networks related to procedural learning (PL) have never been explored in children with self-limited focal epilepsies of childhood (SeLFE), despite their role in the development of various sequence-related sensorimotor, language, and cognitive abilities that are impaired in this clinical population. Our study fills this gap by investigating PL and its interaction with the rapid reorganisation of resting-state functional connectivity (rsFC) in SeLFE. A serial reaction time task, preceded and followed by resting-state magnetoencephalography (MEG) recordings, was used to assess PL in 10 children with SeLFE and 28 age-, sex- and IQ-matched typically developing (TD) children. Pre- to post-learning rsFC changes were estimated using band-limited power envelope correlation, after regressing interictal epileptic discharges (IEDs) in SeLFE patients. rsFC maps were compared between groups and correlated with PL and IED frequency. Compared to TD peers, children with SeLFE showed atypical pre- to post-learning rsFC changes within widespread antero-posterior brain networks in theta, alpha and low beta bands, as well as reduced PL performance negatively correlated with sleep IED frequency. This MEG study is the first to demonstrate reduced PL abilities combined with atypical post-learning reorganisation of rsFC in children with SeLFE compared to TD peers. These results suggest that the pathophysiology of SeLFE, including the chronic repetition of IEDs during sleep across development, have a detrimental impact on the acquisition of PL brain-behaviour processes in these patients.

Major depressive disorder (MDD) is marked by cognitive and affective dysfunctions associated with altered prefrontal cortical activity. While high-definition transcranial direct current stimulation (HD-tDCS) shows promise in modulating these deficits, little is known about the differential effects of targeting specific prefrontal subregions. This study investigated whether HD-tDCS over the dorsolateral (DLPFC) or ventrolateral (VLPFC) prefrontal cortex produces distinct behavioural and neurophysiological effects in patients with MDD, focusing on cognitive control, mood, and functional brain connectivity. Twenty-six patients with MDD received ten sessions of HD-tDCS over the left DLPFC, left VLPFC, or sham stimulation. Assessments were performed pre-intervention, post-intervention, and at one-month follow-up. Measures included the Beck Depression Inventory (BDI), World Health Organization Quality of Life - BREF (WHOQOL-BREF), and performance on cognitive tasks. A subset underwent resting-state functional near-infrared spectroscopy (fNIRS) to assess changes in prefrontal connectivity. DLPFC stimulation led to early and sustained improvements in depressive symptoms, executive function (e.g., Trail Making Test, Wisconsin Card Sorting Task), and quality of life domains. VLPFC stimulation produced delayed improvements, particularly in inhibitory control (e.g., Attention Network Test). fNIRS revealed no significant within-group changes in global connectivity, but at follow-up, the DLPFC group showed greater prefrontal connectivity than both VLPFC and sham, suggesting lasting functional reorganization. VLPFC stimulation did not alter global connectivity, possibly reflecting more localized or subcortical effects. HD-tDCS can differentially modulate cognitive and affective processes in MDD. DLPFC stimulation promotes broader, earlier, and more durable effects, while VLPFC stimulation may exert more specific, delayed influences. Functional connectivity measures enhance interpretation of neuromodulatory outcomes in clinical research.

We introduce Automata Pattern (AutPat), a feature extractor for EEG, and embed it in an explainable feature engineering (XFE) pipeline. We evaluated AutPat on three tasks: EEG artifact classification, stress detection, and mental performance detection. The pipeline computes AutPat features from raw EEG, selects informative variables with cumulative weighted iterative neighborhood component analysis (CWINCA), and performs classification using a t-algorithm-based k-nearest neighbors (tkNN) classifier. For interpretability, we map the selected features to Directed Lobish (DLob) symbols and derive DLob strings and cortical connectome diagrams. The AutPat-based XFE achieved > 88% classification accuracy on all datasets. CWINCA reduced the feature space while maintaining accuracy, and the DLob layer yielded dataset-specific symbolic outputs and 8 × 8 connectome matrices. AutPat, combined with CWINCA and tkNN, provides a compact and accurate EEG pipeline with inherent symbolic explanations. The results indicate that AutPat-based XFE is a practical option for EEG analysis when both performance and interpretability are required.

Accurate localization and orientation estimation of neural sources are crucial in electroencephalography (EEG) source imaging, particularly for focal brain activities. This study introduces an enhanced method that integrates a Singular Value Decomposition (SVD)-based coordinate transform to improve the performance of Hierarchical Adaptive L1-Regression (HAL1R). By applying the SVD transform to the lead field matrix columns corresponding to individual source locations, we derive physiologically meaningful orientation bases that align with the brain's structural and functional properties. Enforcing sparsity into these bases mitigates orientation biases inherent in standard L1-norm algorithms applied in traditional Cartesian systems. Numerical simulations and somatosensory evoked potential (SEP) data validate the proposed approach, demonstrating improved localization stability and orientation accuracy compared to conventional methods, such as Adaptive Group LASSO, Unit Noise Gain (UNG) Beamformer, and Dipole Scanning (DS). The SVD-based HAL1R framework establishes a robust and generalizable methodology for EEG source imaging, enhancing its accuracy and utility in clinical and research settings, including pre-surgical planning and non-invasive cortical mapping.

The cerebellum plays a crucial role in the control of hand movements, enabling fine motor skills such as clapping and writing. Neurological diseases can affect the cerebellum, often leading to motor impairment. However, the cerebellar organisation of specific motor and sensory tasks in humans is under-explored in vivo compared to the neocortex, due to a lack of acquisition and analysis methods that effectively portray cerebellar activation in-vivo due to the cerebellum's thin and highly-foliated cortex. In the neocortex, by comparison, response differences between distinct motor and sensory tasks have been reported, implying an extensive sensorimotor organisation. Here, we studied the cerebellar functional responses during three distinct tasks: flexing, extending and stroking of digits 1, 3 and 5 using B1-shimmed 7T functional MRI. We analysed the data in the standard 3D-functional space and the surface space, respecting the dense foliation of the cerebellum. All tasks elicited individual digit responses, engaging the cerebellar cortex in distinct ways: Digit extension yielded larger, more bilateral activation clusters and less distinct progressions of digit representations in comparison to flexing and stroking tasks. The stroking responses were found more medial in the anterior lobe than the flexing and extending clusters. The anterior lobe clusters were larger for the extending and flexing tasks than for the stroking task. These results imply that the cerebellum is engaged differently when tasks with differing sensory/motor components are performed and that these differences exist on a (sub)millimetre scale, akin to the mesoscale organisation in the cerebral cortex.

The development of virtual reality technology has provided psychological research with powerful tools by presenting stimuli and constructing scenarios, and the combination of VR and neuroimaging techniques begins to provide particularly interesting insights into the experience of virtual events and scenarios, similar to real life. Here we combined VR with EEG technology, so to record and analyze EEG microstates evoked by VR experiences. Our findings suggest that microstates A, B, C, and D reflect cognitive activities during VR experience, while microstate E specifically corresponds to immersion and presence in VR. These findings provide crucial insights into the neural underpinnings of the experience of virtual reality.

Magnetoencephalography (MEG) and electroencephalography (EEG) provide complementary insights into brain activity, yet their distinct biophysical principles influence how normal neurophysiological patterns and artifacts are represented. This study presents a comprehensive qualitative and quantitative analysis of common physiological variants and artifacts in simultaneously recorded MEG and EEG data. We systematically examined patterns such as alpha spindles, sensorimotor rhythms, sleep-related waveforms (vertex waves, K-complexes, sleep spindles, and posterior slow waves of youth), as well as common artifacts including eye blinks, chewing, and movement-related interferences. By applying time-domain, time-frequency, and source-space analyses, we identified modality-specific differences in signal representation, source localization, and artifact susceptibility. Our results demonstrate that MEG provides a more spatially focal representation of physiological patterns, whereas EEG captures broader, radially oriented cortical activity. Mutual information analysis indicated that MEG-derived independent components exhibited greater topographical variability and higher information content for neurophysiological activity, while EEG components were more homogeneous. Signal-to-noise ratio (SNR) analysis confirmed that MEG planar gradiometers capture the highest total information, followed by magnetometers and then EEG. Notably, physiological signals such as vertex waves and K-complexes exhibited significantly higher total information in MEG, whereas EEG was more sensitive to high-amplitude artifacts, including swallowing and muscle activity. These findings highlight the distinct strengths and limitations of MEG and EEG, reinforcing the necessity of multimodal approaches in clinical and research applications to improve the accuracy of neurophysiological assessments.

Humans can quickly extract spatial regularities from sequences to reduce working memory (WM) load, yet the electrophysiological mechanisms remain unclear. Although previous studies have underscored the role of frontal-midline theta (FM-theta) in sequential WM processing, whether and how spatial regularity modulates FM-theta is unknown. To investigate this, we varied the spatial relation between successive items-more repetitions of the same displacement yielded fewer unique chunks and thus higher regularity-while sequence length stayed fixed. Participants were asked to encode, maintain and reproduce the temporal order of sequences utilizing their spatial structures. To enhance ecological validity, we further embedded the task in a complex scenario that included meaningful contexts, dispersed layouts, and variable stimulus sizes. Behavioral data revealed that sequences with higher regularity and the simple scenario yielded higher accuracy, confirming successful manipulations of regularity and scenario difficulty. The overall temporal dynamics of EEG data showed prominent theta enhancement and concurrent alpha/beta suppression during encoding and maintenance. Subsequent analyses across the 4-30 Hz and delay period demonstrated that theta power increased while alpha/beta power declined monotonically with sequence complexity. Notably, regularity-modulated alpha power differed in two scenarios. Moreover, the results found that only sequence regularity-not scenario difficulty-modulated fronto-posterior theta connectivity and slowed the FM-theta frequency. In sum, FM-theta, operating through long-range connectivity and frequency modulation, exclusively tracks spatial-regularity demands in sequential WM, while such neural mechanisms remain impervious to variations in scenario difficulty. These findings suggest that FM-theta may serve as a specific neural marker for spatial regularity processing, rather than a general index of task difficulty, thereby offering a concrete target for future neuromodulatory interventions.

Avoidant/Restrictive Food Intake Disorder (ARFID) is a psychiatric condition characterized by extreme food avoidance and limited food intake, leading to nutritional deficiencies, weight loss, and and/or social impairment. Despite its clinical significance, there is a notable lack of research on the neurobiological basis of ARFID. To address this gap, we examined resting-state EEG brain activity in children and adolescents with ARFID, using microstate analysis to capture spatial configurations associated with large-scale brain networks, offering a novel perspective on brain network abnormalities in this population. Eighteen patients with full/subthreshold ARFID (mean age 12.78 ± 3.57) were compared to 18 age- and sex-matched healthy controls (HC) (mean age 14.56 ± 2.85). EEG data were recorded during eyes-closed resting-state and examined using microstate analysis. Four microstate maps were identified (A, B, C, D). Significant differences were observed between groups for Map C, with the ARFID group showing higher values for mean duration compared to the HC group (U = 254, p = 0.003). Additionally, a trend towards decreased transition probabilities from microstate B to A (p = 0.018) and increased transition probabilities from B to C (p = 0.023) were found in ARFID. Source imaging analyses further revealed increased activation in the right posterior cingulate cortex (p = 0.005) and decreased activation in the right inferior occipital cortex (p = 0.003) in the ARFID group compared to HC. These results suggest distinct patterns of brain activity in children and adolescents with ARFID, particularly involving microstate C, which may reflect atypical Default Mode Network functioning. This study provides preliminary evidence of altered brain network dynamics in ARFID, contributing to a better understanding of its neurobiological basis and offering potential avenues for neurophysiological interventions.

Electroencephalography (EEG) and magnetoencephalography (MEG), two of the most widely used tools for studying human brain dynamics, are thought to have varying spatial resolutions. Here, we simultaneously recorded EEG and MEG data from 14 participants to directly compare their sensitivities - at both the sensor and source levels - to the auditory Mismatch Negativity (MMN in EEG and MMNm in MEG) elicited by pitch deviants. At the sensor level, we observed that negative components emerged in early (100-190 ms) and late (260-420 ms) latency windows. These responses displayed a fronto-central distribution in EEG and a centro-parietal distribution in MEG. MEG also yielded larger effect sizes than EEG, likely reflecting differences in signal-to-noise ratio between MEG and EEG. At the source level, our findings support the involvement of a fronto-temporal auditory MMN network. Both EEG and MEG identified generators in the superior temporal gyrus, Heschl's gyrus, interior frontal gyrus, and insular regions. Notably, EEG source localization revealed additional generators in the left superior temporal sulcus not detected by MEG, whereas MEG identified late components generators in the right hemisphere that were not observed with EEG. Taken together, these results suggest that EEG and MEG may provide complementary perspectives on auditory processing. However, given the inherent complexity of comparing data acquired with different methodologies and the limited sample size, our conclusions should be regarded as preliminary.