Brain Topography最新文献

Optically pumped magnetometers (OPMs) represent a significant advancement in magnetoencephalography (MEG), offering high sensitivity without cryogenic cooling and enabling flexible sensor placement. In this pilot study, we evaluated whether a small, zero-contact 16-channel OPM array can capture movement-related beta-band modulation (event-related desynchronization/synchronization; ERD/ERS) in healthy participants and explored feasibility in a single patient with amyotrophic lateral sclerosis (ALS). MEG responses to visually cued active and passive finger movements were recorded in a magnetically shielded room with the OPM array and separately with 306-channel superconducting quantum interference device (SQUID). Time-frequency analyses focused on beta-band activity across baseline, ERD, and ERS periods. In healthy participants, both OPM and SQUID successfully captured movement-related beta oscillations, with no significant differences between active and passive conditions or between measurement systems, based on non-parametric tests. In the ALS patient, movement-related responses were attenuated and more affected by artifacts in the OPM data compared with SQUID, limiting interpretability. Although movement artifacts were noted, the OPM system provided group-level results in healthy controls comparable to SQUID-based MEG, demonstrating its viability and potential for rapid, flexible deployment. These findings indicate that a compact zero-contact OPM array can reliably measure movement-related cortical beta activity and may offer a cost-effective alternative to cryogenic MEG systems. In ALS, however, the present results should be interpreted strictly as a feasibility demonstration, and larger patient cohorts will be required to establish reliability and clinical utility.

Transcranial electrical stimulation (tES) is one of the most widely used non-invasive brain stimulation (NIBS) methods employed to investigate the causal relationship between brain regions and cognitive functions. tES has been utilized in numerous studies to explore the role of brain regions involved in decision-making. The present study aims to review tES studies conducted to examine the causal relationship of neural regions involved in ambiguous and risky decision-making. A systematic review was conducted based on the PRISMA guidelines (PROSPERO record: CRD42024571443). A systematic search was performed in the PubMed database from 1985 to 2024. The search results were screened for eligibility based on inclusion and exclusion criteria. In addition to the qualitative synthesis, a random-effects meta-analysis was performed on eligible studies reporting sufficient statistical detail. This systematic review examined 19 studies involving 837 participants (351 men and 486 women) with an average age of 23.15 years, investigating the role of different brain regions in risky and ambiguous decision-making. Qualitative synthesis showed that the DLPFC has the strongest association with risky decision-making, particularly with significant changes observed after anodal stimulation in the right hemisphere and cathodal stimulation in the left hemisphere. Limited evidence also suggested roles for frontal asymmetry, the right orbitofrontal cortex (rOFC), and the dorsal anterior cingulate cortex (dACC) in risky decision-making. In the domain of ambiguous decision-making, only a few studies demonstrated the effect of anodal stimulation of the right DLPFC (RDLPFC). The only study using transcranial alternating current stimulation (tACS) also showed a connection between the beta frequency band and the DLPFC in risky decision-making. Complementing these results, the quantitative synthesis of 6 studies (14 effect sizes, n = 378) showed no significant overall effect of tES on decision-making, though subgroup analyses revealed polarity- and site-specific patterns, with cathodal stimulation of the dACC showing the most robust effect. The combined qualitative and quantitative evidence supports a causal role of prefrontal and cingulate regions in risky decision-making, with effects shaped by stimulation polarity and cortical target. Given the limited number of studies conducted, future research should focus on ambiguous decision-making. The use of neuroimaging techniques and simulations may enhance the results obtained.

Neurofeedback (NFB) therapy based on spectral neuromarkers of Attention-Deficit/Hyperactivity Disorder (ADHD) has faced challenges regarding its efficacy and replicability. In this study, we investigate whether a microstate-based EEG marker, recently implicated in ADHD, could serve as a novel target for neurofeedback. Emerging research suggests that ADHD patients often exhibit an excess of microstate D, a state characterized by fronto-central cortical activity linked to attentional functions. This study aims to assess whether neurofeedback training can effectively modulate microstate D in adult ADHD patients, along with its short-term neurobehavioral correlates. We employed a within-subject, crossover design with 19 adults with ADHD, who participated in two counterbalanced neurofeedback sessions: one aimed at upregulating microstate D percent time coverage, and the other at downregulating it. While patients were able to volitionally increase microstate D during the upregulation session, no significant change was observed during the downregulation session. Direct comparison between the two sessions revealed that online control of microstate D was specific to the closed-loop feedback, rather than merely task engagement. No short-term effects of the neurofeedback sessions were observed. No moderate nor major adverse effects were reported. Despite lack of statistical power, this study provides controlled indicationfor the specificity and safety of neurofeedback training based on microstate D in adult ADHD patients. Although the short-term design did not yield clinical improvements, the findings demonstrate the feasibility of microstate-based neurofeedback protocols in a clinical population and offer valuable technical and methodological insights for designing futur studies.

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.