Human Brain Mapping最新文献

{"title":"Dynamic and Static Structure–Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease","authors":"Han Wu,&nbsp;Yinping Lu,&nbsp;Luyao Wang,&nbsp;Jinglong Wu,&nbsp;Ying Liu,&nbsp;Zhilin Zhang","doi":"10.1002/hbm.70202","DOIUrl":"https://doi.org/10.1002/hbm.70202","url":null,"abstract":"<p>The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure–function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to the overall structure–function interaction, whereas dynamic SFC refers to transient coupling variations. In this study, we aimed to assess the potential of combining static and dynamic SFC with machine learning (ML) for the early detection of AD. We analyzed a discovery cohort and an external validation cohort, including AD, mild cognitive impairment (MCI), and healthy control (HC) groups. Then, we quantified differences between static SFC and dynamic SFC at different stages of AD progression. Feature selection was performed using ElasticNet. A Gaussian naive Bayes (GNB) classifier was used to test the ability of SFC to classify AD stages. We also analyzed the correlations between SFC features and early AD physiological biomarkers. Static SFC increased with AD progression, whereas dynamic SFC showed greater variability and decreased stability. Using SFC features selected by ElasticNet, the GNB classifier achieved high performance in differentiating between the HC and MCI stages (area under the curve [AUC] = 91.1%) and between the MCI and AD stages (AUC = 89.03%). Significant correlations were found between SFC features and physiological biomarkers. The combined use of SFC features and ML has strong potential value for the accurate classification of AD stages and significant potential value for the early detection of AD. This study demonstrates that combining static and dynamic SFC with ML provides a novel perspective for understanding the mechanisms of AD and contributes to improving its early detection.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793287","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":"Effective Motor Skill Learning Induces Inverted-U Load-Dependent Activation in Contralateral Pre-Motor and Supplementary Motor Area","authors":"Xiaolu Wang,&nbsp;Xuan Liang,&nbsp;Yixuan Ku,&nbsp;Yinwei Zhan,&nbsp;Rong Song","doi":"10.1002/hbm.70208","DOIUrl":"https://doi.org/10.1002/hbm.70208","url":null,"abstract":"<p>Motor learning involves complex interactions between the cognitive and sensorimotor systems, which are susceptible to different levels of task load. While the mechanism underlying load-dependent regulations in cognitive functions has been extensively investigated, their influence on downstream execution in motor skill learning remains less understood. The current study extends the understanding of whether and how learning alters the load-dependent activation pattern by a longitudinal functional near-infrared spectroscopy (fNIRS) study in which 30 healthy participants (15 females) engaged in extensive practice on a two-dimensional continuous hand tracking task with varying task difficulty. We proposed the index of difficulty (ID) as a quantitative measure of task difficulty, which was monotonically associated with a psychometric measure of subjective workload. As learning progressed, participants exhibited enhanced behavioral and metacognitive performance. Behavioral improvements were accompanied by plastic changes in the inferior prefrontal cortex, reflecting a shift in control strategy during motor learning. Most importantly, we found robust evidence of the learning-induced alteration in load-dependent cortical activation patterns, indicating that effective motor skill learning may lead to the emergence of an inverted-U relationship between cortical activation and load level in the contralateral pre-motor and supplementary motor areas. Our findings provide new insights into the learning-induced plasticity in brain and behavior, highlighting the load-dependent contributions in motor skill learning.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70208","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143778453","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":"Cortical Oscillatory Activity and Motor Control in Pediatric Stroke Patients With Hemidystonia","authors":"Prisca Hsu,&nbsp;Cecilia Jobst,&nbsp;Silvia L. Isabella,&nbsp;Trish Domi,&nbsp;Robyn Westmacott,&nbsp;Nomazulu Dlamini,&nbsp;Douglas Cheyne","doi":"10.1002/hbm.70204","DOIUrl":"https://doi.org/10.1002/hbm.70204","url":null,"abstract":"<p>Dystonia is a movement disorder characterized by repetitive muscle contractions, twisting movements, and abnormal posture, affecting 20% of pediatric arterial ischemic stroke (AIS) survivors. Recent studies have reported that children with dystonia are at higher risk of cognitive deficits. The connection between impaired motor outcomes and cognitive impairment in dystonia is not fully understood; dystonia might affect motor control alone, or it could also contribute to cognitive impairment through disruptions in higher-order motor processes. To assess the functional correlates underlying motor control in children with dystonia, we used magnetoencephalography (MEG) to measure frontal theta (4–8 Hz), motor beta (15–30 Hz), and sensorimotor gamma (60–90 Hz) activity during a “go”/“no-go” task. Beamformer-based source analysis was carried out on 19 post-stroke patients: nine with dystonia (mean age = 13.78, SD = 2.82, 8 females), 10 without dystonia (mean age = 12.90, SD = 3.54, 4 females), and 17 healthy controls (mean age = 12.82, SD = 2.72, 8 females). To evaluate inhibitory control, frontal theta activity was analyzed during correct “no-go” (successful withhold) trials. To assess motor execution and sensorimotor integration, movement time-locked beta and sensorimotor gamma activity were analyzed during correct “go” trials. Additionally, the Delis-Kaplan Executive Function System (DKEFS) color-word interference task was used as a non-motor, inhibitory control task to evaluate general cognitive inhibition abilities. During affected hand use, dystonia patients had higher “no-go” error rates (failed withhold) compared to all other groups. Dystonia patients also exhibited higher frontal theta power during correct withhold responses for both affected and unaffected hands compared to healthy controls. Furthermore, dystonia patients exhibited decreased movement-evoked gamma power and gamma peak frequency compared to non-dystonia patients and healthy controls. Movement-related beta desynchronization (ERD) activity was increased in non-dystonia patients for both hands compared to healthy participants. These results confirm that post-stroke dystonia is associated with impaired frontally mediated inhibitory control, as reflected by increased frontal theta power. Post-stroke dystonia patients also exhibited reduced motor gamma activity during movement, reflecting altered sensorimotor integration. The increased beta ERD activity in non-dystonia patients may suggest compensatory sensorimotor plasticity not observed in dystonia patients. These findings suggest that differences in motor outcomes in childhood stroke result from a combination of cognitive and motor deficits.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70204","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143778454","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":"The Impact of Atlas Parcellation on Functional Connectivity Analysis Across Six Psychiatric Disorders","authors":"Xiaoya Wu,&nbsp;Chuang Liang,&nbsp;Juan Bustillo,&nbsp;Peter Kochunov,&nbsp;Xuyun Wen,&nbsp;Jing Sui,&nbsp;Rongtao Jiang,&nbsp;Xiao Yang,&nbsp;Zening Fu,&nbsp;Daoqiang Zhang,&nbsp;Vince D. Calhoun,&nbsp;Shile Qi","doi":"10.1002/hbm.70206","DOIUrl":"https://doi.org/10.1002/hbm.70206","url":null,"abstract":"<p>Neuropsychiatric disorders are associated with altered functional connectivity (FC); however, the reported regional patterns of functional alterations suffered from low replicability and high variability. This is partly because of differences in the atlas and delineation techniques used to measure FC-related deficits within/across disorders. We systematically investigated the impact of the brain parcellation approach on the FC-based brain network analysis. We focused on identifying the replicable FCs using three structural brain atlases, including Automated Anatomical Labeling (AAL), Brainnetome atlas (BNA) and HCP_MMP_1.0, and four functional brain parcellation approaches: Yeo-Networks (Yeo), Gordon parcel (Gordon) and two Schaefer parcelletions, among correlation, group difference, and classification tasks in six neuropsychiatric disorders: attention deficit and hyperactivity disorder (ADHD, <i>n</i> = 340), autism spectrum disorder (ASD, <i>n</i> = 513), schizophrenia (SZ, <i>n</i> = 200), schizoaffective disorder (SAD, <i>n</i> = 142), bipolar disorder (BP, <i>n</i> = 172), and major depression disorder (MDD, <i>n</i> = 282). Our cross-atlas/disorder analyses demonstrated that frontal-related FC deficits were reproducible in all disorders, independent of the atlasing approach; however, replicable FC extraction in other areas and the classification accuracy were affected by the parcellation schema. Overall, functional atlases with finer granularity performed better in classification tasks. Specifically, the Schaefer atlases generated the most repeatable FC deficit patterns across six illnesses. These results indicate that frontal-related FCs may serve as potential common and robust neuro-abnormalities across 6 psychiatric disorders. Furthermore, in order to improve the replicability of rsfMRI-based FC analyses, this study suggests the use of functional templates at larger granularity.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749644","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":"Phase Locking of 40 Hz Auditory Steady State Responses Is Modulated by Sensory Predictability and Linked to Cerebellar Myelination","authors":"Kit Melissa Larsen,&nbsp;Kiran Thapaliya,&nbsp;Markus Barth,&nbsp;Chin-Husan Sophie Lin,&nbsp;Hartwig R. Siebner,&nbsp;Marta I. Garrido","doi":"10.1002/hbm.70178","DOIUrl":"https://doi.org/10.1002/hbm.70178","url":null,"abstract":"<p>40 Hz auditory steady-state responses (ASSR) can be evoked by brief auditory clicks delivered at 40 Hz. While the neuropharmacology behind the generation of ASSR is well examined, the link between ASSR and microstructural properties of the brain is unclear. Further, whether the 40 Hz ASSR can be manipulated through processes involving top-down control, such as prediction, is currently unknown. We recorded EEG in 50 neurotypical participants while they engaged in a 40 Hz auditory steady-state paradigm. We manipulated the predictability of the stimuli to test the modulatory effect of prediction on 40 Hz steady-state responses. Further, we acquired T1w and T2w structural MRI on the same individuals and used the T1/T2 ratio as a proxy to determine myelination content in gray matter. The phase locking of the 40 Hz ASSR was indeed modulated by prediction, suggesting that prediction violation directly affects phase locking to the 40 Hz ASSR. We found that the prediction violation of the phase locking at 40 Hz (gamma) was associated with the degree of gray matter myelination in the right cerebellum, such that greater myelin led to less desynchronization induced by prediction violations. We demonstrate that prediction violations modulate steady-state activity at 40 Hz and suggest that the efficiency of this process is promoted by greater cerebellar myelin. Our findings provide a structural-functional relationship for myelin and phase locking of auditory oscillatory activity. These results introduce a framework for investigating the interaction of predictive processes and ASSR in disorders where these processes are impaired, such as in psychosis.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143749643","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":"The Shape of the Brain's Connections Is Predictive of Cognitive Performance: An Explainable Machine Learning Study","authors":"Yui Lo,&nbsp;Yuqian Chen,&nbsp;Dongnan Liu,&nbsp;Wan Liu,&nbsp;Leo Zekelman,&nbsp;Jarrett Rushmore,&nbsp;Fan Zhang,&nbsp;Yogesh Rathi,&nbsp;Nikos Makris,&nbsp;Alexandra J. Golby,&nbsp;Weidong Cai,&nbsp;Lauren J. O'Donnell","doi":"10.1002/hbm.70166","DOIUrl":"https://doi.org/10.1002/hbm.70166","url":null,"abstract":"<p>The shape of the brain's white matter connections is relatively unexplored in diffusion magnetic resonance imaging (dMRI) tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tractography-derived shape may relate to the brain's functional variability across individuals. This work explores the potential of leveraging tractography fiber cluster shape measures to predict subject-specific cognitive performance. We implement two machine learning models (1D-CNN and Least Absolute Shrinkage and Selection Operator [LASSO]) to predict individual cognitive performance scores. We study a large-scale database from the Human Connectome Project Young Adult study (<i>n</i> = 1065). We apply an atlas-based fiber cluster parcellation (953 fiber clusters) to the dMRI tractography of each individual. We compute 15 shape, microstructure, and connectivity features for each fiber cluster. Using these features as input, we train a total of 210 models (using fivefold cross-validation) to predict 7 different NIH Toolbox cognitive performance assessments. We apply an explainable AI technique, SHapley Additive exPlanations (SHAP), to assess the importance of each fiber cluster for prediction. Our results demonstrate that fiber cluster shape measures are predictive of individual cognitive performance. The studied shape measures, such as irregularity, diameter, total surface area, volume, and branch volume, are generally as effective for prediction as traditional microstructure and connectivity measures. The 1D-CNN model generally outperforms the LASSO method for prediction. Further interpretation and analysis using SHAP values from the 1D-CNN suggest that fiber clusters with features highly predictive of cognitive ability are widespread throughout the brain, including fiber clusters from the superficial association, deep association, cerebellar, striatal, and projection pathways. This study demonstrates the strong potential of shape descriptors to enhance the study of the brain's white matter and its relationship to cognitive function.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70166","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707217","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":"Evaluating Traditional, Deep Learning and Subfield Methods for Automatically Segmenting the Hippocampus From MRI","authors":"Sabrina Sghirripa,&nbsp;Gaurav Bhalerao,&nbsp;Ludovica Griffanti,&nbsp;Grace Gillis,&nbsp;Clare Mackay,&nbsp;Natalie Voets,&nbsp;Stephanie Wong,&nbsp;Mark Jenkinson,&nbsp;For the Alzheimer's Disease Neuroimaging Initiative","doi":"10.1002/hbm.70200","DOIUrl":"https://doi.org/10.1002/hbm.70200","url":null,"abstract":"<p>Given the relationship between hippocampal atrophy and cognitive impairment in various pathological conditions, hippocampus segmentation from MRI is an important task in neuroimaging. Manual segmentation, though considered the gold standard, is time-consuming and error-prone, leading to the development of numerous automatic segmentation methods. However, no study has yet independently compared the performance of traditional, deep learning-based and hippocampal subfield segmentation methods within a single investigation. We evaluated 10 automatic hippocampal segmentation methods (FreeSurfer, SynthSeg, FastSurfer, FIRST, e2dhipseg, Hippmapper, Hippodeep, FreeSurfer-Subfields, HippUnfold and HSF) across 3 datasets with manually segmented hippocampus labels. Performance metrics included overlap with manual labels, correlations between manual and automatic volumes, volume similarity, diagnostic group differentiation and systematically located false positives and negatives. Most methods, especially deep learning-based ones that were trained on manual labels, performed well on public datasets but showed more error and variability on clinical data. Many methods tended to over-segment, particularly at the anterior hippocampus border, but were able to distinguish between healthy controls, MCI, and dementia patients based on hippocampal volume. Our findings highlight the challenges in hippocampal segmentation from MRI and the need for more publicly accessible datasets with manual labels across diverse ages and pathological conditions.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70200","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707628","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":"Prediction of Verbal Abilities From Brain Connectivity Data Across the Lifespan Using a Machine Learning Approach","authors":"Deborah Früh,&nbsp;Camilla Mendl-Heinisch,&nbsp;Nora Bittner,&nbsp;Susanne Weis,&nbsp;Svenja Caspers","doi":"10.1002/hbm.70191","DOIUrl":"https://doi.org/10.1002/hbm.70191","url":null,"abstract":"<p>Compared to nonverbal cognition such as executive or memory functions, language-related cognition generally appears to remain more stable until later in life. Nevertheless, different language-related processes, for example, verbal fluency versus vocabulary knowledge, appear to show different trajectories across the life span. One potential explanation for differences in verbal functions may be alterations in the functional and structural network architecture of different large-scale brain networks. For example, differences in verbal abilities have been linked to the communication within and between the frontoparietal (FPN) and default mode network (DMN). It, however, remains open whether brain connectivity within these networks may be informative for language performance at the individual level across the life span. Further information in this regard may be highly desirable as verbal abilities allow us to participate in daily activities, are associated with quality of life, and may be considered in preventive and interventional setups to foster cognitive health across the life span. So far, mixed prediction results based on resting-state functional connectivity (FC) and structural connectivity (SC) data have been reported for language abilities across different samples, age groups, and machine-learning (ML) approaches. Therefore, the current study set out to investigate the predictability of verbal fluency and vocabulary knowledge based on brain connectivity data in the DMN, FPN, and the whole brain using an ML approach in a lifespan sample (<i>N</i> = 717; age range: 18–85) from the 1000BRAINS study. Prediction performance was, thereby, systematically compared across (i) verbal [verbal fluency and vocabulary knowledge] and nonverbal abilities [processing speed and visual working memory], (ii) modalities [FC and SC data], (iii) feature sets [DMN, FPN, DMN-FPN, and whole brain], and (iv) samples [total, younger, and older aged group]. Results from the current study showed that verbal abilities could not be reliably predicted from FC and SC data across feature sets and samples. Thereby, no predictability differences emerged between verbal fluency and vocabulary knowledge across input modalities, feature sets, and samples. In contrast to verbal functions, nonverbal abilities could be moderately predicted from connectivity data, particularly SC, in the total and younger age group. Satisfactory prediction performance for nonverbal cognitive functions based on currently chosen connectivity data was, however, not encountered in the older age group. Current results, hence, emphasized that verbal functions may be more difficult to predict from brain connectivity data in domain-general cognitive networks and the whole brain compared to nonverbal abilities, particularly executive functions, across the life span. Thus, it appears warranted to more closely investigate differences in predictability between different cognitive functions and age groups.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689753","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":"Temporal Structure of Music Improves the Cortical Encoding of Speech","authors":"Laura Fernández-Merino,&nbsp;Mikel Lizarazu,&nbsp;Nicola Molinaro,&nbsp;Marina Kalashnikova","doi":"10.1002/hbm.70199","DOIUrl":"https://doi.org/10.1002/hbm.70199","url":null,"abstract":"<p>Long- and short-term musical training has been proposed to improve the efficiency of cortical tracking of speech, which refers to the synchronization of brain oscillations and the acoustic temporal structure of external stimuli. Here, we study how musical sequences with different rhythm structures can guide the temporal dynamics of auditory oscillations synchronized with the speech envelope. For this purpose, we investigated the effects of prior exposure to rhythmically structured musical sequences on cortical tracking of speech in Basque–Spanish bilingual adults (Experiment 1; <i>N</i> = 33, 22 female, Mean age = 25 years). We presented participants with sentences in Basque and Spanish preceded by musical sequences that differed in their rhythmical structure. The rhythmical structure of the musical sequences was created to (1) reflect and match the syllabic structure of the sentences, (2) reflect a regular rhythm but not match the syllabic structure of the sentences, and (3) follow an irregular rhythm. Participants' brain responses were recorded using electroencephalography, and speech-brain coherence in the delta and theta bands was calculated. Results showed stronger speech-brain coherence in the delta band in the first condition, but only for Spanish stimuli. A follow-up experiment including a subset of the initial sample (Experiment 2; <i>N</i> = 20) was conducted to investigate whether language-specific stimuli properties influenced the Basque results. Similar to Experiment 1, we found stronger speech-brain coherence in the delta and theta bands when the sentences were preceded by musical sequences that matched their syllabic structure. These results suggest that not only the regularity in music is crucial for influencing cortical tracking of speech, but so is adjusting this regularity to optimally reflect the rhythmic characteristics of listeners' native language(s). Despite finding some language-specific differences across frequencies, we showed that rhythm, inherent in musical signals, guides the adaptation of brain oscillations, by adapting the temporal dynamics of the oscillatory activity to the rhythmic scaffolding of the musical signal.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689752","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":"A New Multiple Imputation Method for High-Dimensional Neuroimaging Data","authors":"Tong Lu,&nbsp;Peter Kochunov,&nbsp;Chixiang Chen,&nbsp;Hsin-Hsiung Huang,&nbsp;L. Elliot Hong,&nbsp;Shuo Chen","doi":"10.1002/hbm.70161","DOIUrl":"10.1002/hbm.70161","url":null,"abstract":"<p>Missing data are a prevalent challenge in neuroimaging, with significant implications for downstream statistical analysis. Neglecting this issue can introduce bias and lead to erroneous inferential conclusions, making it crucial to employ appropriate statistical methods for handling missing data. Although the multiple imputation is a widely used technique, its application in neuroimaging is severely hindered by the high dimensionality of neuroimaging data, and the substantial computational demands. To tackle the critical computational challenges, we propose a novel approach, <b>H</b>igh d<b>i</b>mensional <b>M</b>ultiple Imput<b>a</b>tion (HIMA), based on Bayesian models specifically designed for large-scale neuroimaging datasets. HIMA introduces a new computational strategy to sample large covariance matrices based on a robustly estimated posterior mode, significantly improving both computational efficiency and numerical stability. To assess the effectiveness of HIMA, we conducted extensive simulation studies and real-data analysis from a Schizophrenia brain imaging dataset with around 1000 voxels. HIMA showcases a remarkable reduction of computational burden, for example, 1 hour by HIMA versus 800 hours by classic multiple imputation packages. HIMA also demonstrates improved precision and stability of imputed data.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 5","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669498","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}