Human Brain Mapping最新文献

{"title":"Connectome-Based Predictive Modeling of Trait Mindfulness.","authors":"Isaac N Treves, Aaron Kucyi, Madelynn Park, Tammi R A Kral, Simon B Goldberg, Richard J Davidson, Melissa Rosenkranz, Susan Whitfield-Gabrieli, John D E Gabrieli","doi":"10.1002/hbm.70123","DOIUrl":"10.1002/hbm.70123","url":null,"abstract":"<p><p>Trait mindfulness refers to one's disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved. To address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome-based predictive modeling analysis in 367 meditation-naïve adults across three samples collected at different sites. In the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging. Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model. We present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 1","pages":"e70123"},"PeriodicalIF":3.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11711207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142948025","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":"Evaluation of Theta EEG Neurofeedback Procedure for Cognitive Training Using Simultaneous fMRI in Counterbalanced Active-Sham Study Design.","authors":"Vadim Zotev, Jessica R McQuaid, Cidney R Robertson-Benta, Anne K Hittson, Tracey V Wick, Upasana Nathaniel, Samuel D Miller, Josef M Ling, Harm J van der Horn, Andrew R Mayer","doi":"10.1002/hbm.70127","DOIUrl":"10.1002/hbm.70127","url":null,"abstract":"<p><p>Evaluation of mechanisms of action of EEG neurofeedback (EEG-nf) using simultaneous fMRI is highly desirable to ensure its effective application for clinical rehabilitation and therapy. Counterbalancing training runs with active neurofeedback and sham (neuro)feedback for each participant is a promising approach to demonstrate specificity of training effects to the active neurofeedback. We report the first study in which EEG-nf procedure is both evaluated using simultaneous fMRI and controlled via the counterbalanced active-sham study design. Healthy volunteers (n = 18) used EEG-nf to upregulate frontal theta EEG asymmetry (FTA) during fMRI while performing tasks that involved mental generation of a random numerical sequence and serial summation of numbers in the sequence. The FTA was defined as power asymmetry for channels F3 and F4 in [4-7] Hz band. Sham feedback was provided based on asymmetry of motion-related artifacts. The experimental procedure included two training runs with the active EEG-nf and two training runs with the sham feedback, in a randomized order. The participants showed significantly more positive FTA changes during the active EEG-nf conditions compared to the sham conditions, associated with significantly higher theta EEG power changes for channel F3. Temporal correlations between the FTA and fMRI activities of prefrontal, parietal, and occipital brain regions were significantly enhanced during the active EEG-nf conditions compared to the sham conditions. Temporal correlation between theta EEG power for channel F3 and fMRI activity of the left dorsolateral prefrontal cortex (DLPFC) was also significantly enhanced. Significant active-vs-sham difference in fMRI activations was observed for the left DLPFC. Our results demonstrate that mechanisms of EEG-nf training can be reliably evaluated using the counterbalanced active-sham study design and simultaneous fMRI.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 1","pages":"e70127"},"PeriodicalIF":3.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11711506/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142948077","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":"Novel Genes Associated With Working Memory Are Identified by Combining Connectome, Transcriptome, and Genome.","authors":"Xiaoyu Zhao, Ruochen Yin, Chuansheng Chen, Sebastian Markett, Xinrui Wang, Gui Xue, Qi Dong, Chunhui Chen","doi":"10.1002/hbm.70114","DOIUrl":"10.1002/hbm.70114","url":null,"abstract":"<p><p>Working memory (WM) plays a crucial role in human cognition. Previous candidate and genome-wide association studies have reported many genetic variations associated with WM. However, little research has examined genetic basis of WM by using transcriptome, even though it reflects gene function more directly than does the genome. Here we propose a new approach to exploring the genetic mechanisms of WM by integrating connectome, transcriptome, and genome data in a high-quality dataset comprising 481 Chinese healthy adults. First, relevance vector regression was used to define WM-related brain regions. Second, genes differentially expressed within these regions were identified using the Allen Human Brain Atlas (AHBA) dataset. Finally, two independent datasets were used to validate these genes' contributions to WM. With this method, we identified 24 novel genes and 20 of them were confirmed in the large-scale datasets of ABCD and UK Biobank. These novel genes were enriched in the cellular component of collagen-containing extracellular matrix and the CCL18 signaling pathway. Our method offers an effective approach to integrating multimodal gene discovery and demonstrates the superiority of expression data. This new method and the newly identified genes deserve more attention in the future.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 1","pages":"e70114"},"PeriodicalIF":3.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11705410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142948083","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":"Subtypes of Insomnia Disorder Identified by Cortical Morphometric Similarity Network.","authors":"Haobo Zhang, Haonan Sun, Jiaqi Li, Xu Lei","doi":"10.1002/hbm.70119","DOIUrl":"10.1002/hbm.70119","url":null,"abstract":"<p><p>Insomnia disorder (ID) is a highly heterogeneous psychiatric disease, and the use of neuroanatomical data to objectively define biological subtypes is essential. We aimed to examine the neuroanatomical subtypes of ID by morphometric similarity network (MSN) and the association between MSN changes and specific transcriptional expression patterns. We recruited 144 IDs and 124 healthy controls (HC). We performed heterogeneity through discriminant analysis (HYDRA) and identified subtypes within the MSN strength. Differences in MSN between subtypes and HC were compared, and clinical behavioral differences were compared between subtypes. In addition, we investigated the association between MSN changes and brain gene expression in different ID subtypes using partial least squares regression to assess genetic commonalities in psychiatric disorders and further performed functional enrichment analyses. Two distinct subtypes of ID were identified, each exhibiting different MSN changes compared to HC. Furthermore, subtype 1 is characterized by objective short sleep, impaired cognitive function, and some relationships with major depressive disorder and autism spectrum disorder (ASD). In contrast, subtype 2 has normal objective sleep duration but subjectively reports poor sleep and is only related to ASD. The pathogenesis of subtype 1 may be related to genes that regulate sleep rhythms and sleep-wake cycles. In contrast, subtype 2 is more due to adverse emotion perception and regulation. Overall, these findings provide insights into the neuroanatomical subtypes of ID, elucidating the relationships between structural and molecular aspects of the relevant subtypes.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 1","pages":"e70119"},"PeriodicalIF":3.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11712197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142948108","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":"Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group.","authors":"Maarten G Poirot, Daphne E Boucherie, Matthan W A Caan, Roberto Goya-Maldonado, Vladimir Belov, Emmanuelle Corruble, Romain Colle, Baptiste Couvy-Duchesne, Toshiharu Kamishikiryo, Hotaka Shinzato, Naho Ichikawa, Go Okada, Yasumasa Okamoto, Ben J Harrison, Christopher G Davey, Alec J Jamieson, Kathryn R Cullen, Zeynep Başgöze, Bonnie Klimes-Dougan, Bryon A Mueller, Francesco Benedetti, Sara Poletti, Elisa M T Melloni, Christopher R K Ching, Ling-Li Zeng, Joaquim Radua, Laura K M Han, Neda Jahanshad, Sophia I Thomopoulos, Elena Pozzi, Dick J Veltman, Lianne Schmaal, Paul M Thompson, Henricus G Ruhe, Liesbeth Reneman, Anouk Schrantee","doi":"10.1002/hbm.70053","DOIUrl":"https://doi.org/10.1002/hbm.70053","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that predict individual-level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA-MDD consortium (n = 262 MDD patients; age = 36.5 ± 15.3 years; 154 (59%) female; mean response rate = 57%). Treatment response was defined as a ≥ 50% reduction in symptom severity score after 4-12 weeks post-initiation of antidepressant treatment. Structural MRI was acquired before, or &lt; 14 days after, treatment initiation. The cortex was parcellated using FreeSurfer, from which cortical thickness and surface area were measured. We tested several machine learning pipeline configurations, which varied in (i) the way we presented the cortical data (i.e., average values per region of interest, as a vector containing voxel-wise cortical thickness and surface area measures, and as cortical thickness and surface area projections), (ii) whether we included clinical data, and the (iii) machine learning model (i.e., gradient boosting, support vector machine, and neural network classifiers) and (iv) cross-validation methods (i.e., k-fold and leave-one-site-out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10-fold cross-validation permutation test. Second, we compared if some machine learning pipeline configurations outperformed others. In an exploratory analysis, we repeated our first analysis in three subpopulations, namely patients (i) from a single site, (ii) with comparable response rates, and (iii) showing the least (first quartile) and the most (fourth quartile) treatment response, which we call the extreme (non-)responders subpopulation. Finally, we explored the effect of including subcortical volumetric data on model performance. Overall, performance predicting antidepressant treatment response was not significantly better than chance (balanced accuracy = 50.5%; p = 0.66) and did not vary with alternative pipeline configurations. Exploratory analyses revealed that performance across models was only significantly better than chance in the extreme (non-)responders subpopulation (balanced accuracy = 63.9%, p = 0.001). Including subcortical data did not alter the observed model performance. Cortical structural MRI alone could not reliably predict individual pharmacotherapeutic treatment response in MDD. None of the used machine learning pipeline configurations outperformed the others. In exploratory analyses, we found that predicting response in the extreme (non-)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibi","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 1","pages":"e70053"},"PeriodicalIF":3.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142931651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Altered Connectome Topology in Newborns at Risk for Cognitive Developmental Delay: A Cross-Etiologic Study.","authors":"Anna Speckert, Kelly Payette, Walter Knirsch, Michael von Rhein, Patrice Grehten, Raimund Kottke, Cornelia Hagmann, Giancarlo Natalucci, Ueli Moehrlen, Luca Mazzone, Nicole Ochsenbein-Kölble, Beth Padden, Beatrice Latal, Andras Jakab","doi":"10.1002/hbm.70084","DOIUrl":"10.1002/hbm.70084","url":null,"abstract":"<p><p>The human brain connectome is characterized by the duality of highly modular structure and efficient integration, supporting information processing. Newborns with congenital heart disease (CHD), prematurity, or spina bifida aperta (SBA) constitute a population at risk for altered brain development and developmental delay (DD). We hypothesize that, independent of etiology, alterations of connectomic organization reflect neural circuitry impairments in cognitive DD. Our study aim is to address this knowledge gap by using a multi-etiologic neonatal dataset to reveal potential commonalities and distinctions in the structural brain connectome and their associations with DD. We used diffusion tensor imaging of 187 newborns (42 controls, 51 with CHD, 51 with prematurity, and 43 with SBA). Structural weighted connectomes were constructed using constrained spherical deconvolution-based probabilistic tractography and the Edinburgh Neonatal Atlas. Assessment of brain network topology encompassed the analysis of global graph features, network-based statistics, and low-dimensional representation of global and local graph features. The Cognitive Composite Score of the Bayley scales of Infant and Toddler Development 3rd edition was used as outcome measure at corrected 2 years for the preterm born individuals and SBA patients, and at 1 year for the healthy controls and CHD. We detected differences in the connectomic structure of newborns across the four groups after visualizing the connectomes in a two-dimensional space defined by network integration and segregation. Further, analysis of covariance analyses revealed differences in global efficiency (p < 0.0001), modularity (p < 0.0001), mean rich club coefficient (p = 0.017), and small-worldness (p = 0.016) between groups after adjustment for postmenstrual age at scan and gestational age at birth. Moreover, small-worldness was significantly associated with poorer cognitive outcome, specifically in the CHD cohort (r = -0.41, p = 0.005). Our cross-etiologic study identified divergent structural brain connectome profiles linked to deviations from optimal network integration and segregation in newborns at risk for DD. Small-worldness emerges as a key feature, associating with early cognitive outcomes, especially within the CHD cohort, emphasizing small-worldness' crucial role in shaping neurodevelopmental trajectories. Neonatal connectomic alterations associated with DD may serve as a marker identifying newborns at-risk for DD and provide early therapeutic interventions. Trial Registration: ClinicalTrials.gov identifier: NCT00313946.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 1","pages":"e70084"},"PeriodicalIF":3.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11718324/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142948019","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":"White Matter Tract Crossing and Bottleneck Regions in the Fetal Brain.","authors":"Camilo Calixto, Matheus D Soldatelli, Bo Li, Lana Vasung, Camilo Jaimes, Ali Gholipour, Simon K Warfield, Davood Karimi","doi":"10.1002/hbm.70132","DOIUrl":"10.1002/hbm.70132","url":null,"abstract":"<p><p>There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been investigated for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 62 fetal brain scans and extracted a set of 51 distinct white matter tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20%-25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75% and 80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. These results highlight the relevance of these regions to key developmental processes, specifically, the dispersion of projection fibers, the protracted growth of commissural pathways, and the emergence of association tracts that contribute to the formation of complex intersection regions. These developmental interactions lead to a high prevalence of crossing fibers and bottleneck areas, reflecting the intricate organization required for establishing structural and functional connectivity. Additionally, our results highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 1","pages":"e70132"},"PeriodicalIF":3.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733681/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142983073","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":"Developmental Trajectories and Differences in Functional Brain Network Properties of Preterm and At-Term Neonates.","authors":"N López-Guerrero, Sarael Alcauter","doi":"10.1002/hbm.70126","DOIUrl":"10.1002/hbm.70126","url":null,"abstract":"<p><p>Premature infants, born before 37 weeks of gestation can have alterations in neurodevelopment and cognition, even when no anatomical lesions are evident. Resting-state functional neuroimaging of naturally sleeping babies has shown altered connectivity patterns, but there is limited evidence on the developmental trajectories of functional organization in preterm neonates. By using a large dataset from the developing Human Connectome Project, we explored the differences in graph theory properties between at-term (n = 332) and preterm (n = 115) neonates at term-equivalent age, considering the age subgroups proposed by the World Health Organization for premature birth. Leveraging the longitudinal follow-up for some preterm participants, we characterized the developmental trajectories for preterm and at-term neonates, for this purpose linear, quadratic, and log-linear mixed models were constructed with gestational age at scan as an independent fixed-effect variable and random effects were added for the intercept and subject ID. Significance was defined at p < 0.05, and the model with the lowest Akaike Information Criterion (AIC) was selected as the best model. We found significant differences between groups in connectivity strength, clustering coefficient, characteristic path length and global efficiency. Specifically, at term-equivalent ages, higher connectivity, clustering coefficient and efficiency are identified for neonates born at later postmenstrual ages. Similarly, the characteristic path length showed the inverse pattern. These results were consistent for a variety of connectivity thresholds at both the global (whole brain) and local level (brain regions). The brain regions with the greatest differences between groups include primary sensory and motor regions and the precuneus which may relate to the risk factors for sensorimotor and behavioral deficits associated with premature birth. Our results also show non-linear developmental trajectories for premature neonates, but decreased integration and segregation even at term-equivalent age. Overall, our results confirm altered functional connectivity, integration and segregation properties of the premature brain despite showing rapid maturation after birth.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 1","pages":"e70126"},"PeriodicalIF":3.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143004582","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":"Predicting Subject Traits From Brain Spectral Signatures: An Application to Brain Ageing","authors":"Cecilia Jarne,&nbsp;Ben Griffin,&nbsp;Diego Vidaurre","doi":"10.1002/hbm.70096","DOIUrl":"10.1002/hbm.70096","url":null,"abstract":"<p>The prediction of subject traits using brain data is an important goal in neuroscience, with relevant applications in clinical research, as well as in the study of differential psychology and cognition. While previous prediction work has predominantly been done on neuroimaging data, our focus is on electroencephalography (EEG), a relatively inexpensive, widely available and non-invasive data modality. However, EEG data is complex and needs some form of feature extraction for subsequent prediction. This process is sometimes done manually, risking biases and suboptimal decisions. Here we investigate the use of data-driven Kernel methods for prediction from single channels using the EEG spectrogram, which reflects macro-scale neural oscillations in the brain. Specifically, we introduce the idea of reinterpreting the spectrogram of each channel as a probability distribution, so that we can leverage advanced machine learning techniques that can handle probability distributions with mathematical rigour and without the need for manual feature extraction. We explore how the resulting technique, Kernel mean embedding regression, compares to a standard application of Kernel ridge regression as well as to a non-Kernelised approach. Overall, we found that the Kernel methods exhibit improved performance thanks to their capacity to handle nonlinearities in the relation between the EEG spectrogram and the trait of interest. We leveraged this method to predict biological age in a multinational EEG data set, HarMNqEEG, showing the method's capacity to generalise across experiments and acquisition setups.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"45 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142864074","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":"Assessment of ComBat Harmonization Performance on Structural Magnetic Resonance Imaging Measurements","authors":"Emma Tassi,&nbsp;Anna Maria Bianchi,&nbsp;Federico Calesella,&nbsp;Benedetta Vai,&nbsp;Marcella Bellani,&nbsp;Igor Nenadić,&nbsp;Fabrizio Piras,&nbsp;Francesco Benedetti,&nbsp;Paolo Brambilla,&nbsp;Eleonora Maggioni","doi":"10.1002/hbm.70085","DOIUrl":"10.1002/hbm.70085","url":null,"abstract":"<p>Data aggregation across multiple research centers is gaining importance in the context of MRI research, driving diverse high-dimensional datasets to form large-scale heterogeneous sample, increasing statistical power and relevance of machine learning and deep learning algorithm. Site-related effects have been demonstrated to introduce bias in MRI features and confound subsequent analyses. Although Combating Batch (ComBat) technique has been recently reported to successfully harmonize multi-scale neuroimaging features, its performance assessments are still limited and largely based on qualitative visualizations and statistical analyses. In this study, we stand out by using a robust cross-validation approach to assess ComBat performances applied on volume- and surface-based measures acquired across three sites. A machine learning approach based on Multi-Class Gaussian Process Classifier was applied to predict imaging site based on raw and harmonized brain features, providing quantitative insights into ComBat effectiveness, and verifying the association between biological covariates and harmonized brain features. Our findings showed differences in terms of ComBat performances across measures of regional brain morphology, demonstrating tissue specific site effect modeling. ComBat adjustment of site effects also varied across regional level of each specific volume-based and surface-based measures. ComBat effectively eliminates unwanted data site-related variability, by maintaining or even enhancing data association with biological factors. Of note, ComBat has demonstrated flexibility and robustness of application on unseen independent gray matter volume data from the same sites.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"45 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142863867","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}