Brain Topography最新文献

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.

The purpose of this study was to investigate the effects of dual-task standing on postural-related muscle activity and cortical activation in both young and older groups. Fourteen older adults and thirteen young adults were recruited. Participants performed single-task and dual-task standing. The surface electromyographic signals of tibialis anterior, solus, rectus femoris, and biceps femoris at left and right sides were recorded. Simultaneously, cortical activation in the dorsolateral prefrontal and motor cortices were measured. Two-way MANOVAs with repeated measures and Pearson correlation analyses were employed for the statistical analysis. Our results indicated that only the older group presented greater right (p = .002) and left (p = .003) ankle muscle co-activation index, and greater cortical activation in the dorsolateral prefrontal cortex (p < .001), premotor motor cortex (p = .011), supplementary motor area (p = .043), and primary motor cortex (p = .028) in the left hemisphere during dual-task compared to single-task standing. Additionally, the older group showed negative correlations, whereas the young group showed positive correlations between cortical activation and average linear envelope of muscle activity during the single-task standing. Furthermore, the older group showed more significant positive correlations between cortical activation and average linear envelope of muscle activity than the young group during dual-task standing. These observations suggest that age-related overactivation of the prefrontal-motor cortex may lead to redundant ankle joint muscle response during dual-task standing.

Different levels of reduced consciousness characterise human sleep stages at the behavioural level. On electroencephalography (EEG), the identification of sleep stages predominantly relies on localised oscillatory power within distinct frequency bands. Several theoretical frameworks converge on the central significance of long-range information sharing in maintaining consciousness, which experimentally manifests as high functional connectivity (FC) between distant brain regions. Here, we test the hypothesis that EEG-FC reflects sleep stages and hence changes in consciousness. We retrospectively investigated sleep EEG recordings in 14 participants undergoing all stages of non-rapid eye movement (NREM) sleep. We quantified FC with six phase coupling metrics and used the FC coefficients between electrode pairs as features for a gradient boosting classifier trained to distinguish between sleep stages. To characterise FC during each stage of NREM sleep, we compared these metrics regarding their classification accuracy and analysed the ranked feature importance across all electrode pairs. We observed frequency-specific differences in FC between sleep stages for all metrics except the imaginary part of coherence. Alpha coupling decreased from wake to sleep stages N1 and N2, whereas delta coupling increased in deep sleep (N3). FC-based sleep classifiers yielded 51% (phase locking index) to 73% (phase locking value) classification accuracy. Distributed FC patterns in the alpha band ranked highest in terms of feature importance. In a limited sample of 14 subjects, we demonstrated that FC computed from phase information changes significantly across sleep stages. The finding that EEG phase patterns are indicative of sleep stages supports the hypothesis that long-range and spatially distributed phase coupling within frequency bands, especially within the alpha band, is an electrophysiological correlate of consciousness across sleep stages.

The term Super-Recognizer (SR), which describes individuals with supposedly superior facial recognition abilities, may be something of a misnomer. In the same way that blind individuals would not be considered prosopagnosic, SR diagnoses should emphasise at least face identity processing (FIP) specificity, if not recognition in particular. However, SRs tend to be diagnosed with face-specific behavioral tasks, probing either perception and/or recognition, and leaving the neural basis and mechanisms underlying their abilities largely unexplored. The present study therefore sought to determine whether any common FIP subprocesses, among a sample of stringently and comparably diagnosed SRs, would distinguish them from neurotypical controls. To this end, we conducted three Fast Periodic Visual Stimulation (FPVS) EEG experiments in a group of Berlin Police officers identified as SRs using the only existing formal diagnostic framework for lab-based SR identification (Ramon in Neuropsychologia 158:107809, https://doi.org/10.1016/j.neuropsychologia.2021.107809 , 2021) that aligns with the seminal study of SRs (Russell et al. in Psychon Bull Rev 16(2):252-257, https://doi.org/10.3758/PBR.16.2.252 , 2009). These experiments aimed to isolate FIP from behavioral and general perceptual factors in terms of both the consistency and speed of face identity discrimination and categorization. Broadly, the results of all three experiments provided two key findings. First, whichever factors distinguish SRs from controls, they are not face-specific. Second, SRs are not all cut from the same cloth. Rather, the factors distinguishing SRs from controls seem to be individual-specific, warranting more nuanced and bespoke testing criteria for their deployment in practical applications.