Brain Topography最新文献

{"title":"Conductivity Deviations as Virtual Sources in Magnetoencephalography.","authors":"Seppo P Ahlfors, Seok Lew, Matti S Hämäläinen, Risto J Ilmoniemi, Yoshio Okada","doi":"10.1007/s10548-026-01192-9","DOIUrl":"10.1007/s10548-026-01192-9","url":null,"abstract":"<p><p>Magnetoencephalography (MEG) is a method to study electrical activity in the brain. MEG signals are modeled by primary currents, which represent neuronal activity, and associated passive volume currents, which depend on the conductivity distribution within the body. The effect of conductivity inhomogeneities can be described as if additional virtual source currents were present. Virtual sources help to understand conductivity effects independently from the sensor array properties. The Volume Current Formulation (VCF) of the virtual sources focuses on altered patterns of volume currents, whereas in the Secondary Current Formulation (SCF) the virtual sources are at locations where conductivity changes. We derived and compared these formulations for deviations from a reference conductivity distribution. In VCF, the virtual sources are located only where the conductivity deviation is non-zero, but their orientation and magnitude depend on the local electric field. In contrast, in SCF, both the location and the orientation of the virtual sources are determined by the conductivity distribution, typically by the anatomical tissue boundaries. In SCF, however, all conductivity boundaries, including those in the reference distribution, generally need to be considered. For spherically symmetric reference conductivity, in VCF the radial component of a virtual source does not contribute to any component of the magnetic field, whereas in SCF the radial component of a virtual source does not contribute to the radial component but contributes to the tangential components of the magnetic field. Complementary descriptions using VCF and SCF were illustrated in a model for fontanels in infants.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"39 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13109224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Altered Structural Brain Connectivity in Postherpetic Neuralgia and its Association with Neuropathic Pain Severity: A Diffusion Tensor Imaging Study.","authors":"Daeseok Oh, Dong Ah Lee, Ho-Joon Lee, Soon Ho Cheong, Sang Eun Lee, Kang Min Park","doi":"10.1007/s10548-026-01204-8","DOIUrl":"10.1007/s10548-026-01204-8","url":null,"abstract":"","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"39 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Sleep Deprivation and Neuromodulatory Interventions on Olfactory-Related Brain Function.","authors":"Haiqi Xiang, Qiongfang Cao, Peng Zhang, Wenhao Lv, Tong Li, Fangfang Liu, Tiecheng Zhang, Xiechuan Weng, Fan Xu","doi":"10.1007/s10548-026-01194-7","DOIUrl":"10.1007/s10548-026-01194-7","url":null,"abstract":"<p><p>Sleep deprivation (SD) has been shown to impair sensory functions, including olfactory processing, which may be related to changes in brain activity. This study aims to investigate the effects of SD, short-term sleep recovery (RS), and neuromodulation interventions on the functional activity of olfactory-related brain regions. We hypothesize that these interventions could restore some of the neural activity disrupted by SD, particularly in areas associated with olfactory processing. In this study, 90 male participants first underwent a baseline sleep period of at least 8 h, followed by 36 h of sleep deprivation (SD36h). The participants were randomly assigned to four groups: control group (CON, n = 30, age 20.8 ± 1.6 years), magnetic stimulation group (MS, n = 30, age 21.3 ± 1.6 years), electrical stimulation group (ES, n = 17, age 20.3 ± 0.7 years), and combined stimulation group (CS, n = 13, age 20.8 ± 1.0 years). After SD36h, participants underwent an 8-hour restorative sleep period, before and after completing the 8-hour RS period, additionally received 30 min of MS(MS was applied over the occipital region using a rhythmic gamma magnetic-field device), ES(ES was delivered as tDCS targeting the left DLPFC, anode at F3, cathode at FP1, 2 mA), and CS. All participants underwent three resting-state functional MRI (fMRI) scans: at baseline, after sleep deprivation, and after intervention. This experiment analyzed the Amplitude of Low-Frequency Fluctuations (ALFF) and functional connectivity (FC) in olfactory-related regions. ALFF and FC were analyzed using a group (CON/MS/ES/CS) × time (baseline/SD36h/RS) repeated-measures ANOVA with FDR correction, followed by FDR-corrected post-hoc tests for significant main effects. No group × time interaction survived FDR correction for ALFF or FC. For ALFF, a main group effect was observed in Hippocampus_L and Olfactory_R, with the ES (and CS in Olfactory_R) showing lower ALFF than CON and/or MS. For time effects, ALFF in Hippocampus_L was reduced at SD36h and remained lower after RS relative to baseline, while Olfactory_L ALFF was lower after RS than baseline. For FC, main group effects were found in Hippocampus_L and Fusiform_R, with ES and CS showing higher FC than CON and MS. A main time effect cluster with a peak in Cingulum_Mid_R showed reduced FC after SD36h and increased FC after RS, returning to a level not significantly different from baseline. SD disrupts the ALFF and FC in olfactory-related brain regions, while RS can partially restore these functions. Short-term neuromodulation interventions, such as electrical stimulation and combined stimulation, may improve network-level connectivity but are insufficient to fully restore local neural activity.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"39 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative Synthesis of Fractional Anisotropy Maps from T1 MRI Using Transfer Learning for White Matter Assessment in Stroke.","authors":"Gyubin Kwon, Hyunjin Kim, Hongmin Kim, Jungsoo Lee","doi":"10.1007/s10548-026-01202-w","DOIUrl":"10.1007/s10548-026-01202-w","url":null,"abstract":"<p><p>Assessment of white matter integrity is critical for predicting functional recovery after ischemic stroke. However, conventional magnetic resonance imaging (MRI) cannot capture tract-specific microstructural changes, and diffusion tensor imaging (DTI) is limited by prolonged acquisition times. This study aimed to synthesize fractional anisotropy (FA) maps from routinely acquired T1-weighted (T1) MRI using 2.5D inputs within a generative adversarial network (GAN) framework. Specifically, our primary objective was to evaluate the relative efficacy of a proposed transfer learning strategy compared to single-domain training approaches. T1-FA paired data from 375 cognitively normal participants (832 images) from the Alzheimer's Disease Neuroimaging Initiative served as the non-lesion dataset, while longitudinal MRI data from 69 ischemic stroke patients (236 images) were from a single-center cohort. Three models were evaluated: the non-lesion-trained (NLT) model trained on non-lesion data, the lesion-trained (LT) model trained on stroke data, and the NLT model further fine-tuned on the stroke dataset (NLT + LF). Model performance was evaluated using voxel-wise errors (mean absolute error (MAE) and root mean square error (RMSE)), structural similarity (peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM)), spatial overlap (Dice coefficient (Dice)), and distributional similarity (Kullback-Leibler divergence). Bonferroni-corrected paired t-tests showed that NLT + LF showed significantly better performance than NLT across all evaluated regions, including the whole brain, white matter, and lesions (all p < 0.001). Compared with LT, NLT + LF showed superior performance for all metrics at p < 0.001, except for lesion-region Dice and RMSE, which remained significant at p < 0.01. The preservation of lesion-relevant features and anatomical fidelity, together with the capture of degeneration patterns, accompanied these gains. Overall, the proposed NLT + LF approach improved lesion-specific representation and established that high-fidelity FA maps can be reliably synthesized from T1 MRI. This transfer learning framework offers a practical alternative to DTI for clinical stroke assessment.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"39 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Quantitative Comparison of Two Methods for Higher-Order EEG Microstate Syntax Analysis.","authors":"Frederic von Wegner, Gesine Hermann, Inken Tödt, Inga Karin Todtenhaupt, Helmut Laufs","doi":"10.1007/s10548-026-01196-5","DOIUrl":"10.1007/s10548-026-01196-5","url":null,"abstract":"<p><p>Entropy rate (ER) and sample entropy (SE) are two metrics that have been used to quantify the syntactic complexity of electroencephalography (EEG) microstate sequences. We here present a theoretical and numerical comparison of these two metrics and apply them to a resting-state EEG dataset from individuals with Alzheimer's disease (AD) and a control group. We first derive theoretical ER and SE estimates for first-order discrete Markov processes, providing a null hypothesis for statistical testing of higher-order syntax properties. Under the first-order syntax null hypothesis, we find a close mathematical relationship between both metrics that can be expressed by the microstate transition probability matrix. An inequality is derived that shows ER to be an upper bound to SE under the Markov approximation. We quantify accuracy and precision of the theoretical ER and SE estimates on EEG microstate sequences from the healthy control group. We then show that ER and SE identify significant higher-order syntax properties in microstate sequences from the control and AD groups. We investigate continuous and jump microstate sequences. In the former, each time point is labelled with the best matching microstate label, and in the latter, duplicate labels are removed, exclusively retaining transitions between non-identical microstates. Group comparison demonstrates that continuous microstate sequences from the AD group have lower entropy values (ER, SE), whereas jump sequences from the AD group have higher entropy values compared to control. Finally, we introduce a new syntax metric that normalizes ER and SE values with respect to their first-order syntax levels, to assess differences that only depend on syntax order. This metric revealed no differences between control and AD groups for either continuous or jump microstate sequences. This study provides further insights into higher-order microstate syntax and how it can be quantified with respect to the underlying first-order syntax. Similarities and differences between ER and SE as syntax metrics are highlighted and exemplified on experimental data. Our results show that (i) EEG microstate sequences from control and AD subjects show higher-order syntax properties across the tested syntax levels, (ii) continuous and jump sequences from control and AD groups are syntactically different, and (iii) differences between the control and AD groups disappear when higher-order syntax properties are normalized to the group-specific Markov level.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"39 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13109229/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"White Matter Structural Biomarkers Derived from Diffusion Tensor Imaging to Estimate Upper-Extremity Motor Function in Stroke Using Machine Learning.","authors":"Martín Emiliano Rodríguez-García, Ruben I Carino-Escobar, Paul Carrillo-Mora, Norma Marín-Arriaga, Ana G Ramirez-Nava, Claudia Hernandez-Arenas, Ma Del Refugio Pacheco-Gallegos, Jimena Quinzaños-Fresnedo, Jessica Cantillo-Negrete","doi":"10.1007/s10548-026-01203-9","DOIUrl":"10.1007/s10548-026-01203-9","url":null,"abstract":"","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"39 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147790194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Source Localization of Auditory Evoked Fields using Reciprocal BEM-FMM.","authors":"Derek A Drumm, Guillermo Nuñez Ponasso, Alexander Linke, Sirshendu Ganguly, Abbie Wang, Gregory M Noetscher, Burkhard Maess, Thomas R Knösche, Jens Haueisen, Jeffrey David Lewine, Christopher C Abbott, Sergey N Makaroff, Zhi-De Deng","doi":"10.1007/s10548-026-01190-x","DOIUrl":"10.1007/s10548-026-01190-x","url":null,"abstract":"<p><p>Precise localization of auditory evoked fields (AEFs) from magnetoencephalography (MEG) data is very important for the functional understanding of the auditory cortex in medicine and cognitive neuroscience. The numerical solution of the field equations in the human head using the boundary element method (BEM) is a powerful tool for achieving this. The spatial resolution of the BEM is crucial for the achievable accuracy of localized neural sources. However, in classical BEM (as implemented, e.g., in MNE-Python), very high resolutions are impractical due to the associated prohibitive computational effort. In contrast, our recently introduced reciprocal boundary element fast multipole method (reciprocal BEM-FMM) allows for hitherto unprecedented spatial resolution of forward models. In this work, we employ reciprocal BEM-FMM to construct high-resolution forward models to localize AEFs. Simulated AEFs were generated using a direct BEM-FMM approach on realistic 40-tissue Sim4Life segmentations. Comparative analyses from simulated data demonstrate that high-resolution BEM-FMM forward models yield statistically superior source estimates relative to the 3-layer BEM. We also compare BEM-FMM forward models with source dipole resolution varying from 25,000 to 3,200,000 sources, and find that resolutions above 200,000 sources are sufficient for achieving accurate, high-resolution source estimates. We therefore recommend using the high-resolution reciprocal BEM-FMM to utilize high spatial anatomical precision for the modeling of neural activity.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"39 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13035606/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147582986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG Microstate Correlates of Emotional Processing in Patients with Alcohol use Disorder and Healthy Controls: An Exploratory Resting-EEG Study.","authors":"Oliver B Affentranger, Thomas Koenig, Franz Moggi, Leila M Soravia, Ingo Butzke, Hallie M Batschelet, Raphaela M Tschuemperlin, Maria Stein","doi":"10.1007/s10548-026-01180-z","DOIUrl":"10.1007/s10548-026-01180-z","url":null,"abstract":"<p><p>Emotion processing is a key domain in alcohol use disorder (AUD). Given the neuronal basis of emotion regulation and the neurophysiological deviations observed in AUD patients the present exploratory study investigates EEG Microstate (MS) correlates of emotional processing in patients with AUD and healthy controls (HC). Emotions, moods and emotion regulation competencies were assessed in 29 patients with AUD and 22 HC using self-report. Additionally, a 64-channel resting-state electroencephalography (EEG) was recorded for each individual and a microstate analysis yielding 7 classes (A-G) was performed. The effects of the factors group (patients with AUD vs. HC), emotion/emotion regulation (from self-report scales) and MS class (A-G) on the MS feature contribution were analyzed using linear models. Three-way interactions were observed for \"awareness of feelings\" (Bonferroni-corrected; F(6, 329) = 3.75, p = .001) and \"shame\" (F(6, 322) = 2.42, p = .026). Level of awareness of feelings correlated positively with MS C in HC, while an inverse correlation was found for MS D. No such association between awareness of feelings and MS C and D was observed in patients with AUD who rather displayed significantly higher overall MS C values than HC (t_Ratio(343) = -3.13, p = .002). The present study provides initial evidence that neuronal activity, as reflected in MSs, differs in relation to emotion processing between patients with AUD and healthy individuals. The results indicate that MS C and D might be neurophysiological markers for awareness of feelings in HC, while such an association is blunted in patients with AUD.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"39 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147522757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG Unpredictability in the Resting State of Major Depressive Disorder: A Multidomain EEG Analysis.","authors":"Kassra Ghassemkhani, Blake T Dotta","doi":"10.1007/s10548-026-01176-9","DOIUrl":"10.1007/s10548-026-01176-9","url":null,"abstract":"<p><p>The use of scalp electroencephalography (EEG) provides a cost-effective utility in identifying features and treatment outcomes for Major Depressive Disorder (MDD). We utilised a publicly available dataset to explore spectral features, complexity, and large-scale dynamics in the eyes closed resting state of MDD compared to controls. Relative band power revealed significantly higher beta power (13-30 Hz) in MDD compared to controls, further, a significantly reduced aperiodic exponent was observed. Upon observing multiscale entropy and Higuchi fractal dimension we observed significantly higher values in MDD with both metrics. To explore this further, we observed the temporal dynamics of brain states through microstate analysis and the transmission of information through network topology. We found reduced stability for the temporal components of brain microstates in MDD compared to controls. Further, small worldness index values were significantly lower in MDD through the phase locking value (PLV) as well, indicating a greater deviation towards the topology of random networks. Through rank ordering the features extracted with the area under the receiver operating characteristic curve (ROC), and found relative beta power, HFD, aperiodic exponent, and short-scale entropy to be the best predictors for MDD. From the data presented, it is clear that EEG activity is not only unpredictable at the level of the channel but also in the domain of communication between regions.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"39 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147522813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agreement between FreeSurfer and CAT12 Cortical Thickness Measurements: A Discovery and a Replication Study in Children/Adolescents.","authors":"Cameron C McKay, Marissa Laws, Gabrielle-Ann A Torre, Ashley S VanMeter, Guinevere F Eden","doi":"10.1007/s10548-026-01191-w","DOIUrl":"10.1007/s10548-026-01191-w","url":null,"abstract":"<p><p>Cortical thickness can be derived from magnetic resonance imaging scans using the software tools FreeSurfer or the Computational Anatomy Toolbox (CAT12). Two studies in children/adolescents have compared cortical thickness estimates generated by these two programs and reported positive correlations. However, these studies, as well as similar studies in adults, found that the magnitude of cortical thickness generated by these two programs for the most part was not comparable, with one program or the other generating relatively lower values, and the direction of this effect varying across and within studies. Here, we tackled this issue by using, for the first time, discovery and replication samples of children/adolescents. For both samples (age range 6-16 years), we observed strong positive correlations between cortical thickness values generated with FreeSurfer and with CAT12 at both the whole-cortex and regional levels. We also found that FreeSurfer yielded relatively lower cortical thickness values than CAT12, both at the level of the whole cortex and in ≥ 94% of the regions investigated. In supplementary analyses, we further evaluated several factors that could influence inter-software convergence and found that age, image quality, and scanner manufacturer were associated with the magnitude of agreement between the two programs. Given this unequivocal difference in magnitude across both samples, we conclude that cortical thickness values can only be compared amongst studies on children/adolescents if the studies implemented the same software program.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"39 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147522746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}