Human Brain Mapping最新文献

{"title":"Pattern of Deep Grey Matter Undersizing Boosts MRI-Based Diagnostic Classifiers in Fetal Alcohol Spectrum Disorders","authors":"Eliot Kerdreux, Justine Fraize, Alexandra Ntorkou, Pauline Garzón, Richard Delorme, Monique Elmaleh-Berges, Edouard Duchesnay, Lucie Hertz-Pannier, Yann Leprince, Jean-François Mangin, David Germanaud","doi":"10.1002/hbm.70233","DOIUrl":"https://doi.org/10.1002/hbm.70233","url":null,"abstract":"<p>In fetal alcohol spectrum disorders (FASD), brain growth deficiency is a hallmark of subjects with both fetal alcohol syndrome (FAS) and nonsyndromic FASD (NS-FASD, that is, those without specific diagnostic features). Although previous studies have suggested that the deep grey matter is heterogeneously affected at the group level, it has not yet been established within proper scaling modeling, nor has it been given a place in the FASD diagnostic criteria where neuroanatomical features still contribute almost nothing to diagnostic specificity. We segmented a 1.5T T1-weighted brain MRI dataset of 90 monocentric FASD patients (53 FAS, 37 NS-FASD) and 95 typically developing controls (ages 6–20), using volBrain-vol2Brain as reference, and both Freesurfer-SAMSEG and FSL-FIRST to estimate result robustness. The segmentation resulted in seven anatomical volumes: total brain (TBV), total deep grey matter, caudate, putamen, globus pallidus, thalamus, and accumbens. After adjusting for confounds, we fitted the scaling relationship between deep grey matter nuclei volumes (V<sub>i</sub>) and TBV (V<sub>i</sub> = <i>b</i> × TBV<sup>a</sup>) and evaluated the effect of FAS on scaling. We then estimated the volumetric deviation from typical scaling (<sub>v</sub>DTS) for each deep grey nucleus volume in the FAS sample. Finally, we tested the improvement of FAS versus control classifiers based on total deep grey matter <sub>v</sub>DTS or total brain deviation from typical volume, by adding the five nuclear <sub>v</sub>DTS, both in terms of performance and generalizability to NS-FASD. Scaling was significantly different between the FAS and control groups for all deep grey matter nuclei (<i>p</i> < 0.05). We confirmed the undersizing of total deep grey matter in FAS (<sub>v</sub>DTS = −6%) and identified a pattern of volumetric undersizing, most pronounced in the caudate (−13%) and globus pallidus (−11%), less so in the thalamus (−4%) and putamen (−2%) and sparing the accumbens (0%). These findings were consistent across segmentation tools, despite variations in magnitude. The pattern-based classifier was more efficient than the one based on total deep grey matter alone (<i>p</i> < 0.001) and identified 32.4% of the NS-FASD as having a FAS-like deep grey matter phenotype, compared to 18.9% with the classifier based on total deep grey matter alone (<i>p</i> = 0.113). Added to a classifier based on TBV only, the pattern improved the performance (<i>p</i> = 0.033) of the model and increased identification of NS-FASD with a FAS-like neuroanatomical phenotype from 37.8% to 62.2% (<i>p</i> = 0.002). This study details the volumetric undersizing of deep grey matter in a large series of FASD patients. It reveals a differential pattern of vulnerability to prenatal alcohol exposure partially convergent across automatic segmentation tools. It also strongly suggests that this pattern of volumetric undersizing in the deep grey matter may contribute to a neuroanatomica","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three Latent Factors in Major Depressive Disorder Base on Functional Connectivity Show Different Treatment Preferences","authors":"Xinyi Wang,&nbsp;Xinruo Wei,&nbsp;Junneng Shao,&nbsp;Li Xue,&nbsp;Zhilu Chen,&nbsp;Zhijian Yao,&nbsp;Qing Lu","doi":"10.1002/hbm.70215","DOIUrl":"https://doi.org/10.1002/hbm.70215","url":null,"abstract":"<p>The heterogeneity of major depressive disorder (MDD) complicates the selection of effective treatments. While more studies have identified cluster-based MDD subtypes, they often overlook individual variability within subtypes. To address this, we applied latent dirichlet allocation to decompose resting-state functional connectivity (FC) into latent factors. It allows patients to express varying degrees of FC across multiple factors, retaining inter-individual variability. We enrolled 226 patients and 100 healthy controls to identify latent factors and examine their distinct patterns of hyper- and hypo-connectivity. We investigated the association between these connectivity patterns and treatment preferences. Additionally, we compared demographic characteristics, clinical symptoms, and longitudinal symptom improvements across the identified factors. We identified three factors. Factor 1, characterized by inter-network hyperconnectivity of the default mode network (DMN), was associated with treatment response to antidepressant monotherapy. Additionally, factor 1 was more frequently expressed by younger and highly educated patients, with significant improvements in cognitive symptoms. Conversely, factor 3, characterized by inter-networks and intra-networks hypoconnectivity of DMN, was associated with treatment response when combining antidepressants with stimulation therapy. Factor 2, characterized by global hypoconnectivity without DMN, was associated with higher baseline depression severity and anxiety symptoms. These three factors showed distinct treatment preferences and clinical characteristics. Importantly, our results suggested that patients with DMN hyperconnectivity benefited from monotherapy, while those with DMN hypoconnectivity benefited from combined treatments. Our approach allows for a unique composition of factors in each individual, potentially facilitating the development of more personalized treatment-related biomarkers.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70215","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep-Diffeomorphic Networks for Conditional Brain Templates","authors":"Luke Whitbread,&nbsp;Stephan Laurenz,&nbsp;Lyle J. Palmer,&nbsp;Mark Jenkinson,&nbsp;The Alzheimer's Disease Neuroimaging Initiative","doi":"10.1002/hbm.70229","DOIUrl":"https://doi.org/10.1002/hbm.70229","url":null,"abstract":"&lt;p&gt;Deformable brain templates are an important tool in many neuroimaging analyses. Conditional templates (e.g., age-specific templates) have advantages over single population templates by enabling improved registration accuracy and capturing common processes in brain development and degeneration. Conventional methods require large, evenly spread cohorts to develop conditional templates, limiting their ability to create templates that could reflect richer combinations of clinical and demographic variables. More recent deep-learning methods, which can infer relationships in very high-dimensional spaces, open up the possibility of producing conditional templates that are jointly optimised for these richer sets of conditioning parameters. We have built on recent deep-learning template generation approaches using a diffeomorphic (topology-preserving) framework to create a purely geometric method of conditional template construction that learns diffeomorphisms between: (i) a global or group template and conditional templates, and (ii) conditional templates and individual brain scans. We evaluated our method, as well as other recent deep-learning approaches, on a data set of cognitively normal (CN) participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI), using age as the conditioning parameter of interest. We assessed the effectiveness of these networks at capturing age-dependent anatomical differences. Our results demonstrate that while the assessed deep-learning methods have a number of strengths, they require further refinement to capture morphological changes in ageing brains with an acceptable degree of accuracy. The volumetric output of our method, and other recent deep-learning approaches, across four brain structures (grey matter, white matter, the lateral ventricles and the hippocampus), was measured and showed that although each of the methods captured some changes well, each method was unable to accurately track changes in all of the volumes. However, as our method is purely geometric, it was able to produce T1-weighted conditional templates with high spatial fidelity and with consistent topology as age varies, making these conditional templates advantageous for spatial registrations. The use of diffeomorphisms in these deep-learning methods represents an important strength of these approaches, as they can produce conditional templates that can be explicitly linked, geometrically, across age as well as to fixed, unconditional templates or brain atlases. The use of deep learning in conditional template generation provides a framework for creating templates for more complex sets of conditioning parameters, such as pathologies and demographic variables, in order to facilitate a broader application of conditional brain templates in neuroimaging studies. This can aid researchers and clinicians in their understanding of how brain structure changes over time and under various interventions, with the ultimate goal of improving the ca","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70229","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human Cortico-Cerebellar Dynamics During Motor Error Processing After Stroke","authors":"Nitesh Singh Malan,&nbsp;Raghavan Gopalakrishnan,&nbsp;David Cunningham,&nbsp;Olivia Hogue,&nbsp;Kenneth B. Baker,&nbsp;Andre G. Machado","doi":"10.1002/hbm.70227","DOIUrl":"https://doi.org/10.1002/hbm.70227","url":null,"abstract":"<p>The cerebellum acts as a forward internal model to predict motor outcomes, compare them with sensory feedback, and generate prediction errors that refine prediction accuracy. Our physiological understanding of cerebellar function during motor control derives predominantly from animal experiments and clinical observations in patients with disorders of the cerebellum or its connections with the cerebrum and spinal cord. Here, we report a human electrophysiology-based investigation of cerebello-thalamo-cortical pathway activity during motor error detection and correction. Participants performed a computerized motor oddball task while synchronized electrophysiological recordings were collected from cerebellar dentate (DN) using depth electrodes and scalp electroencephalography (EEG). The task involved moving a 2-D ball on a screen toward a predetermined target at 40% (standard trials) or 20% (oddball trials) of their maximum voluntary contraction. Six participants completed an average of 239 trials, with oddball trials randomly occurring with a 30% frequency. At the cortex, oddball trials exhibited significantly greater centro-parietal error positivity and fronto-centro-parietal desynchronization during error correction, predominantly in the alpha and low beta frequency bands. DN examination also revealed greater alpha and low beta desynchronization during error correction. Lastly, oddball trials showed significantly greater cortico-cerebellar coherence during error correction in the same frequency bands with bidirectional interaction between the cortex and DN. These findings expand on the cortico-cerebello-cortical physiology of human motor control and provide cues for designing interventions aimed at alleviating the functional burdens of acquired injuries of the central nervous system.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic Links Between Subcortical Brain Morphometry and Suicide Attempt Risk in Children and Adults","authors":"Zuriel Ceja,&nbsp;Luis M. García-Marín,&nbsp;I-Tzu Hung,&nbsp;Sarah E. Medland,&nbsp;Alexis C. Edwards,&nbsp;Miguel E. Rentería,&nbsp;Jill A. Rabinowitz","doi":"10.1002/hbm.70220","DOIUrl":"https://doi.org/10.1002/hbm.70220","url":null,"abstract":"<p>Genome-wide association studies (GWAS) have uncovered genetic variants associated with suicide attempt (SA) risk and regional brain volumes (RBVs). However, the extent of their genetic overlap remains unclear. To address this, we investigated whether the genetic architecture of SA and various RBVs (i.e., caudate nucleus, hippocampus, brainstem, ventral diencephalon, thalamus, globus pallidus, putamen, nucleus accumbens, amygdala and intracranial volume (ICV)) was shared. We leveraged GWAS summary statistics from the largest available datasets on SA (<i>N</i> = 958,896) and intracranial and subcortical RBVs (<i>N</i> = 74,898). Using linkage disequilibrium score regression, we estimated genome-wide genetic correlations between SA and individual RBVs. GWAS-pairwise analyses identified genomic segments associated with both SA and RBVs, followed by functional annotation. Additionally, we examined whether polygenic scores (PGS) for SA were associated with ICV and subcortical brain structure phenotypes in youth of European ancestry (<i>N</i> = 5276) in the Adolescent Brain Cognitive Development (ABCD) study. Linkage disequilibrium score regression results indicated a significant genetic correlation between SA and ICV (rG = −0.10, <i>p</i>-value = 1.9 × 10–3). GWAS-pairwise analyses and functional annotation revealed 10 genomic segments associated with SA and at least one RBV (thalamus, putamen and caudate nucleus). After adjusting for multiple tests, PGS association analysis indicated that a higher PGS for SA was significantly associated with a smaller volume of the right nucleus accumbens (<i>b</i> = −7.05, <i>p</i> = 0.018). Our findings highlight a negative genetic correlation between SA and ICV amongst adults and suggest different neural correlates associated with genetic risk for SA across developmental periods. This study advances our understanding of the shared genetic underpinnings of SA and brain structure, potentially informing future research and clinical interventions.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 7","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70220","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain Morphometry in Infants Later Diagnosed With Autism is Related to Later Language Skills","authors":"Luke E. Moraglia,&nbsp;Bernadette Weigman,&nbsp;Hervé Abdi,&nbsp;Martin Styner,&nbsp;Sun Hyung Kim,&nbsp;Catherine A. Burrows,&nbsp;Mark D. Shen,&nbsp;Shruthi Ravi,&nbsp;Jason J. Wolff,&nbsp;Stephen R. Dager,&nbsp;Heather C. Hazlett,&nbsp;Juhi Pandey,&nbsp;Robert T. Schultz,&nbsp;Jessica B. Girault,&nbsp;Kelly N. Botteron,&nbsp;Natasha Marrus,&nbsp;Annette M. Estes,&nbsp;Tanya St. John,&nbsp;Guoyan Zheng,&nbsp;Joseph Piven,&nbsp;Meghan R. Swanson,&nbsp;the IBIS Network","doi":"10.1002/hbm.70221","DOIUrl":"https://doi.org/10.1002/hbm.70221","url":null,"abstract":"<p>Autism spectrum disorder (ASD) presents early in life with distinct social and language differences. This study explores the association between infant brain morphometry and language abilities using an infant-sibling design. Participants included infants who had an older sibling with autism (high likelihood, HL) who were later diagnosed with autism (HL-ASD; <i>n</i> = 31) and two non-autistic control groups: HL-Neg (HL infants not diagnosed with autism; <i>n</i> = 126) and LL-Neg (typically developing infants who did not have an older sibling with autism; <i>n</i> = 77). Using a whole-brain approach, we measured cortical thickness and surface area at 6 and 12 months and expressive and receptive language abilities at 24 months. Partial least squares correlation analyses were computed separately for each of the three groups. Results from the HL-ASD group indicated negative associations between surface area in the left inferior frontal gyrus and 24-month language abilities. Notably, regions outside the standard adult language network were also associated with language in the HL-ASD group. Results in the HL-ASD group highlight the distinct processing guiding development of surface area and cortical thickness; associations were mostly negative for surface area at 6 months but mostly positive for cortical thickness at the same time point. Results from this data-driven study align with the theory of interactive specialization—a theory highlighting the dynamic nature of the infant brain—and advocate for a whole-brain approach in investigating early brain-behavior neurodevelopment in ASD.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 7","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70221","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143926130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Topography of Functional Organization of Beat Perception in Human Premotor Cortex: Causal Evidence From a Transcranial Magnetic Stimulation (TMS) Study","authors":"Giorgio Lazzari,&nbsp;Giulio Costantini,&nbsp;Stefania La Rocca,&nbsp;Andrea Massironi,&nbsp;Luigi Cattaneo,&nbsp;Virginia Penhune,&nbsp;Carlotta Lega","doi":"10.1002/hbm.70225","DOIUrl":"https://doi.org/10.1002/hbm.70225","url":null,"abstract":"<p>Humans can flexibly extract a regular beat from complex rhythmic auditory patterns, as often occurs in music. Contemporary models of beat perception suggest that the premotor cortex (PMC) and the supplementary motor area (SMA) are integral to this process. However, how these motor planning regions actively contribute to beat perception, along with any potential hemispheric specialization, remains open questions. Therefore, following the validation of stimuli in a behavioral experiment (Experiment I, <i>N</i> = 29, 12 males, mean age = 23.8 ± 0.7 years), we employed transcranial magnetic stimulation (TMS) to test the causal contribution of these regions to beat perception. In Experiment II (<i>N</i> = 40, 16 males, mean age = 23.2 ± 2.37 years), we applied online repetitive TMS (rTMS) over a defined grid encompassing the right rostral and caudal dPMC, SMA, and pre-SMA, and a sham control location. Results showed that stimulation of the caudal portion of right dPMC selectively affected beat perception compared to all other regions. In Experiment III (preregistered, <i>N</i> = 42, 17 males, mean age = 23.5 ± 2.61 years), we tested the lateralization of this contribution by applying rTMS over right and left caudal dPMC. Our results showed that only stimulation over right, but not left, dPMC modulated beat perception. Finally, across all three experiments, individual differences in musical reward predicted beat perception sensitivity. Together, these results support the causal role of the right dPMC in generating internal action predictions and perceptual expectations regarding ongoing sequential events, in line with recent models emphasizing the role of the dorsal auditory stream in beat-based temporal perception. These findings offer valuable insights into the functional organization of the premotor cortex, contributing to a deeper understanding of the neural mechanisms involved in human rhythm perception.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 7","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143926131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Juvenile Myoclonic Epilepsy Imaging Endophenotypes and Relationship With Cognition and Resting-State EEG","authors":"Aaron F. Struck,&nbsp;Camille Garcia-Ramos,&nbsp;Klevest Gjini,&nbsp;Jana E. Jones,&nbsp;Vivek Prabhakaran,&nbsp;Nagesh Adluru,&nbsp;Bruce P. Hermann","doi":"10.1002/hbm.70226","DOIUrl":"https://doi.org/10.1002/hbm.70226","url":null,"abstract":"<p>Structural neuroimaging studies of patients with Juvenile Myoclonic Epilepsy (JME) typically present two findings: 1-volume reduction of subcortical gray matter structures, and 2-abnormalities of cortical thickness. The general trend has been to observe increased cortical thickness primarily in medial frontal regions, but heterogeneity across studies is common, including reports of decreased cortical thickness. These differences have not been explained. The cohort of patients investigated here originates from the Juvenile Myoclonic Epilepsy Connectome Project, which included comprehensive neuropsychological testing, 3 T MRI, and high-density 256-channel EEG. 64 JME patients aged 12–25 and 41 age and sex-matched healthy controls were included. Data-driven approaches were used to compare cortical thickness and subcortical volumes between the JME and control participants. After differences were identified, supervised machine learning was used to confirm their classification power. K-means clustering was used to generate imaging endophenotypes, which were then correlated with cognition, EEG frequency band lagged coherence from resting state high-density EEG, and white and grey matter based spatial statistics from diffusion imaging. The volumes of subcortical gray matter structures, particularly the thalamus and the motor-associated thalamic nuclei (ventral anterior), were found to be smaller in JME. In addition, the right hemisphere (primarily) sulcal pre-motor cortex was abnormally thicker in an age-dependent manner in JME with an asymmetry in the pre-motor cortical findings. These results suggested that for some patients JME may be an asymmetric disease, at least at the cortical level. Cluster analysis revealed three discrete imaging endophenotypes (left, right, symmetric). Clinically, the groups were not substantially different except for cognition, where left hemisphere disease was linked with a lower performance on a general cognitive factor (“g”). HD-EEG demonstrated statistically significant differences between imaging endophenotypes. Tract-based spatial statistics showed significant changes between endophenotypes as well. The left dominant disease group exhibited diffuse white matter changes. JME patients present with heterogeneity in underlying imaging endophenotypes that are defined by the presence and laterality of asymmetric abnormality at the level of the pre-motor sulcal cortex; these endophenotypes are linked to orderly relationships with cognition, EEG, and white matter pathology. The relationship of JME's adolescent onset, age-dependent cortical thickness loss, and seizure upon awakening all suggest that synaptic pruning may be a key element in the pathogenesis of JME. Individualized treatment approaches for neuromodulation are needed to target the most relevant cortical and subcortical structures as well as develop disease-modifying and neuroprotective strategies.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 7","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70226","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143930347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Decoding the Spatiotemporal Dynamics of Neural Response Similarity in Auditory Processing: A Multivariate Analysis Based on OPM-MEG”","authors":"","doi":"10.1002/hbm.70228","DOIUrl":"https://doi.org/10.1002/hbm.70228","url":null,"abstract":"<p>Liu, C., Y. Ma, X. Liang, M. Xiang, H. Wu, and X. Ning. 2025. “Decoding the Spatiotemporal Dynamics of Neural Response Similarity in Auditory Processing: A Multivariate Analysis Based on OPM-MEG.” <i>Human Brain Mapping</i> 46, no. 4: e70175. https://doi.org/10.1002/hbm.70175.</p><p>One of the corresponding author's name was inadvertently misspelled as “Xiaoling Ning”. It should be corrected to “Xiaolin Ning”.</p><p>The online version of the article has been corrected accordingly.</p><p>We apologize for this error.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 7","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping Brain Lesions to Conduction Delays: The Next Step for Personalized Brain Models in Multiple Sclerosis","authors":"C. Mazzara,&nbsp;A. Ziaeemehr,&nbsp;E. Troisi Lopez,&nbsp;L. Cipriano,&nbsp;M. Angiolelli,&nbsp;M. Sparaco,&nbsp;M. Quarantelli,&nbsp;C. Granata,&nbsp;G. Sorrentino,&nbsp;M. Hashemi,&nbsp;V. Jirsa,&nbsp;P. Sorrentino","doi":"10.1002/hbm.70219","DOIUrl":"https://doi.org/10.1002/hbm.70219","url":null,"abstract":"<p>Multiple sclerosis (MS) is a clinically heterogeneous, multifactorial autoimmune disorder affecting the central nervous system. Structural damage to the myelin sheath, resulting in the consequent slowing of the conduction velocities, is a key pathophysiological mechanism. In fact, the conduction velocities are closely related to the degree of myelination, with thicker myelin sheaths associated to higher conduction velocities. However, how the intensity of the structural lesions of the myelin translates to slowing of nerve conduction delays is not known. In this work, we use large-scale brain models and Bayesian model inversion to estimate how myelin lesions translate to longer conduction delays across the damaged tracts. A cohort of 38 subjects (20 healthy and 18 with MS) underwent MEG recordings during an eyes-closed resting-state condition, along with MRI acquisitions and detailed white matter tractography analysis. We observed that MS patients consistently showed decreased power within the alpha frequency band (8–13 Hz) as compared to the healthy group. We also derived a lesion matrix indicating the percentage of lesions for each tract in every patient. Using large-scale brain modeling, the neural activity of each region was represented as a Stuart-Landau oscillator operating in a regime showing damped oscillations, and the regions were coupled according to subject-specific connectomes. We propose a linear formulation to the relationship between the conduction delays and the amount of structural damage in each white matter tract. Dependent upon the parameter <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>γ</mi>\u0000 </mrow>\u0000 <annotation>$$ upgamma $$</annotation>\u0000 </semantics></math>, this function translates lesions into edge-specific conduction delays (leading to shifts in the power spectra). Using deep neural density estimators, we found that the estimation of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>γ</mi>\u0000 </mrow>\u0000 <annotation>$$ upgamma $$</annotation>\u0000 </semantics></math> showed a strong correlation with the alpha peak in MEG recordings. The most probable inferred <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>γ</mi>\u0000 </mrow>\u0000 <annotation>$$ upgamma $$</annotation>\u0000 </semantics></math> for each subject is inversely proportional to the observed peaks, while power peaks themselves do not correlate with total lesion volume. Furthermore, the estimated parameters were predictive (cross-sectionally) of individual clinical disability. This study represents the initial exploration showcasing the location-specific impact of myelin lesions on conduction delays, thereby enhancing the customization of models for individuals with multiple sclerosis.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 7","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}