Brain Topography最新文献

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.

Unilateral neglect (UN) is a common post-stroke neurocognitive deficit linked to interhemispheric interactions, though mechanisms remain unclear. This study evaluated the 'bimodal balance recovery' model in UN, exploring its relationship with interhemispheric connectivity and proposing a stratification framework for patient categorization. Thirty stroke patients with UN and 15 healthy controls were recruited. Interhemispheric signal propagation (ISP) was assessed using transcranial magnetic stimulation-electroencephalography. UN severity was quantified using a battery of paper-and-pencil tasks, and overall patient functioning was evaluated using the Activities of Daily Living Scale, Fugl-Meyer Assessment, and Berg Balance Scale. Analyses of the Schenkenberg Line Bisection Test, Albert's Cancellation Task, and Ota's Cancellation Task indicated that quadratic models provided a better fit than linear regressions. A novel metric, the PenPCA Index, was derived using Principal Component Analysis (PCA) to assess the complex relationship between ISP and UN. This index demonstrated a bimodal relationship with ISP, effectively distinguishing between negative and positive correlations with the PenPCA Index. This study introduces the PenPCA Index, underscores the bimodal UN-ISP relationship, and offers a stratified assessment framework for stroke patients.

fMRI measures beyond zero-lag functional connectivity could serve as useful tools for understanding the distinct spatio-temporal dynamics characterizing psychiatric conditions. Therefore, the primary objective was to investigate whether and how state-dependence influences lag-structure in healthy controls (n = 95). Moreover, the study aimed to explore clinical-behavioral correlates of state-dependent lag-structure in three groups of psychiatric patients (35 ADHD, 38 Bipolar Disorder and 23 Schizophrenia patients, diagnosed according to DSM-IV-TR). Lag-structure was computed from cross-correlation coefficients in resting-state and stop-signal scans. Between and within-group differences were compared through non-parametric tests. Correlations with clinical-behavioral parameters were evaluated using linear regressions (Brief Psychiatric Rating Scale - BPRS, task reaction time). Compared to healthy controls, lag-structure within default-mode, executive-control and salience networks was generally increased in ADHD (min Z-score - 3.983), generally decreased in Schizophrenia (min Z-score - 3.716) and mixed increased/decreased in Bipolar patients (min Z-score - 3.912, max 4.739). Widespread state-dependent reductions of lag-structure were observed across all groups from rest to task (max Q-statistics: healthy controls 58; ADHD 22; Bipolar 26; Schizophrenia 17). Correlations with clinical-behavioral features (BPRS, reaction time) were positive in the executive-control network and negative in the bilateral thalamus for ADHD; negative in the cerebellum for Schizophrenia; positive in the right temporal gyri, amygdala, hippocampus, cerebellum for Bipolar Disorder (p = 0.05). In summary, according to these preliminary results, differences in lag-structure in comparison to healthy controls may be described as progressively increased in magnitude from ADHD to Bipolar Disorder and Schizophrenia, with specific clinical and behavioral correlates according to each diagnostic group.

Magnetoencephalography (MEG) is a valuable tool in the presurgical workup of refractory epilepsy patients. Ictal Magnetic Source Imaging (MSI) can more accurately localize the ictal onset zone, aiding presurgical planning. Nevertheless, the optimal approach for ictal MSI remains undetermined. To evaluate the effectiveness of distinct ictal MSI techniques, assessing their performance based on the ictal onset pattern (IOP). Design: Retrospective study. 16 ictal MEG events from 12 epilepsy patients were retrospectively analyzed. Techniques employed include the traditional sECD, and alternative approaches comprising the linearly constrained minimum variance (LCMV) beamforming, kurtosis beamforming, and dynamic statistical parametric mapping (dSPM). Seizures were classified into IOP groups: ictal discharge, rhythmic activity (RA), slow RA, and fast activity. Sublobar and lobar concordance and the minimum Euclidean distance (Dmin) were evaluated using SEEG data as ground truth. sECD fitting failed for three seizures, whereas alternative techniques demonstrated superiority. LCMV showed the highest sublobar concordance. No significant differences in Dmin across techniques were found. All techniques performed better in the ictal discharge group. Performance declined in the rhythmic activity IOP group, especially in lower frequencies, although LCMV performed better. ECD, beamforming, and dSPM are effective techniques for ictal MEG analysis. Beamforming techniques are particularly important when ECD is unsuitable. The IOP should be considered when selecting the appropriate ictal MSI technique. Optimizing MSI techniques and customizing them based on seizure characteristics can aid in invasive study planning and potentially improve post-surgical outcomes.

Transcranial Magnetic Stimulation (TMS), particularly Theta Burst Stimulation (TBS), is a non-invasive, non-convulsive neuromodulation technique that induces clinically relevant network modulations with long-term effects. Two TBS protocols- continuous TBS (cTBS) and intermittent TBS (iTBS)- have been approved as effective therapeutic interventions for neuropsychiatric disorders, including mood disorders. With this aim, we examined EEG microstate temporal dynamics during resting-state recordings across three sessions of TMS. Twenty-four participants underwent cTBS, iTBS, and sham stimulation in a pseudo-randomized order, each separated by at least one week. Six distinct microstates (A-F), associated with activity in specific neural networks, were identified across six frequency bands (broadband, δ, θ, α, β, and γ). Our findings reveal frequency band-specific modulation of EEG microstates B, C, E, and F, previously reported as biomarkers in mood disorders. Notably, C microstates showed increased stability, whereas microstates E and F showed decreased dynamics up to fifty-five minutes after TBS. Most importantly, a negative association was observed for microstate E occurrence, between before stimulation (pre-cTBS) and three post-standing time points (post1-cTBS, post2-cTBS, and post3-cTBS), suggesting that baseline microstate E characteristics may be related to individual variability in cTBS treatment response. These results further support the potential of TBS to induce clinically relevant neuroplastic changes, establishing a strong foundation for the development of band-specific EEG microstate markers for assessing treatment response and personalized closed-loop TMS-EEG protocols.

Easy access, overuse, and misuse of the internet have contributed to the rise of Problematic Internet Use (PIU). Despite a growing body of research linking excessive and addictive digital media use to adverse physical, psychological, social, and neurological consequences, identifying robust and widely accepted neurophysiological markers of PIU severity remains a significant challenge and a leading focus within the field. In this study, 156 healthy regular internet users (70 males; aged 18-35) were assessed using the PIUQ-9 questionnaire, along with measures of anxiety, depression, and obsessive-compulsive symptoms. Resting-state electroencephalogram (EEG) was recorded, and microstates (MS) of EEG were assessed. Psychological and neurophysiological profiles were examined both within the full sample and through comparisons between High and Low PIU groups. The microstate analysis resulted in extraction of 7 MS classes. A significant association between increased occurrence rate and time coverage of MS E - associated with interoception, salience and emotional processing, and PIU scores, both in the full sample and in High vs. Low group comparisons. Furthermore, parameters of MS B, MS C, and MS D showed significant negative associations with anxiety and obsessive-compulsive symptoms. These findings suggest that altered MS E dynamics, may represent a potential early functional biomarker of PIU, reflecting neural changes specifically associated with problematic internet behavior, rather than with psychological traits reflecting general psychopathology, particularly anxiety symptoms.

As a response to the environment and internal signals, brain networks reorganize on a sub-second scale. To capture this reorganization in patients with disorders of consciousness (DoC) and understand their residual brain activity, we investigated the dynamics of electroencephalography (EEG) microstates. EEG microstates are meta-stable topographies that last tens to a few hundreds of milliseconds and are hypothesized to reflect large-scale cortical networks. To obtain EEG‑microstate segmentation, EEG topographies per sample were clustered into four groups for the purpose of the present comparison with the existing four‑class literature. We then obtained a time series of maps with different frequencies of occurrence and duration. One such occurrence of a map with a given duration is called a microstate. The goal of this work was to study the static and dynamic properties of these topographical patterns in DoC patients. Using the microstate time series, we calculated static and dynamic markers. In contrast to the static, the dynamic metrics depend on the specific temporal sequences of the maps. The static measure map coverage showed differences between healthy controls and patients. In contrast, some dynamic markers captured inter-patient group differences. The dynamic markers we investigated are Mean Microstate Durations (MMD), Microstate Duration Variances (MDV), Microstate Transition Matrices (MTM), and Entropy Production (EP). The MMD and MDV decreased with the state of consciousness, whereas the MTM non-diagonal transitions and EP increased. In other words, DoC patients had slower and closer to equilibrium (time-reversible) brain dynamics. In conclusion, static and dynamic EEG microstate metrics differed across consciousness levels, with the latter having captured the subtler differences between groups of patients with DoC.