Human Brain Mapping最新文献

{"title":"EEG Biomarkers for Affective Disorders Diagnosis: An Evaluation and Validation Study","authors":"Sha Zhao,&nbsp;Yitian Liu,&nbsp;Jiquan Wang,&nbsp;Ruizhe Zhang,&nbsp;Yilin Wang,&nbsp;Haiteng Jiang,&nbsp;Shijian Li,&nbsp;Tao Li,&nbsp;Gang Pan","doi":"10.1002/hbm.70628","DOIUrl":"10.1002/hbm.70628","url":null,"abstract":"<p>Electroencephalography (EEG) provides real-time, dynamic insights into brain function, making it a valuable tool for the screening, diagnosis, and treatment of affective disorders. The use of EEG-derived features as biomarkers for affective disorder diagnosis has attracted growing attention. In this work, we introduce 25 commonly used EEG features and categorize them into four domains: time-domain, frequency-domain, complexity, and connectivity. Each feature captures distinct aspects of emotional processing and serves as a potential biomarker for diagnosing affective disorders. To evaluate their diagnostic utility, we analyzed two independent resting-state EEG datasets. The first dataset comprised 84 healthy controls, 62 patients with schizophrenia (SZ), 43 patients with schizoaffective disorder (SAD), and 32 patients with bipolar disorder (BD), while the second included 28 healthy controls and 32 patients with major depressive disorder (MDD). These features are extracted and used for classification, allowing us to identify biomarkers with strong discriminative power. Finally, we provide a comprehensive discussion on key issues in the field, including multimodal biomarker integration, challenges in EEG-based diagnosis, variability in classification results, and factors influencing EEG feature extraction.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 13","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13547600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900437","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":"Right Hemisphere Functional Connectivity Increases in Proportion to Left Hemisphere Lesion Size, but Not Within the Language Network","authors":"Natalya Vladyko,&nbsp;Andrew T. DeMarco,&nbsp;Peter E. Turkeltaub","doi":"10.1002/hbm.70631","DOIUrl":"10.1002/hbm.70631","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Left hemisphere (LH) strokes often disrupt the language network (LN), causing aphasia. The right hemisphere (RH) has been proposed to contribute to post-stroke aphasia outcomes, but existing findings on RH functional connectivity (FC) changes after LH stroke are inconsistent. This study systematically investigates whether and how the RH FC differs in chronic stroke survivors compared to healthy controls, and whether these differences relate to lesion characteristics and language outcomes. Using a naturalistic movie-watching fMRI paradigm, we compared the RH FC in a large sample of chronic LH stroke survivors (<i>n</i> = 85) to neurologically healthy controls (<i>n</i> = 71). We first used a validated semantic decision fMRI task to identify a core LN and a non-canonical LN (labeled Semantic Network (SN)). We then mapped group FC differences within and between these LH networks and their RH homotopes. Compared to controls, stroke survivors had higher overall within-RH FC and lower within-LH and interhemispheric connectivity. These differences were greater with larger lesions. However, few of the RH FC differences were observed within the RH LN. Rather, most changes were between networks and were distributed widely in the RH. Greater RH connectivity was not associated with lesions or reduced FC in homotopic LH regions, although lesions of LH superior and middle temporal gyri were associated with increased FC between RH SN and LN. RH FC was not associated with behavioral outcomes. These findings show that LH lesions lead to increased RH FC, but the pattern of results suggests that these changes may not reflect meaningful functional reorganization that contributes to language function after stroke.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Trial Registration</h3>\u0000 \u0000 <p>clinicaltrials.gov NCT04991519</p>\u0000 </section>\u0000 </div>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 13","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13545644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148891389","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":"Investigating the Contribution of Molecular-Enriched Functional Connectivity to Brain-Age Analysis","authors":"Marco Pinamonti,&nbsp;Manuela Moretto,&nbsp;Valentina Sammassimo,&nbsp;Marco Castellaro,&nbsp;Mattia Veronese","doi":"10.1002/hbm.70627","DOIUrl":"10.1002/hbm.70627","url":null,"abstract":"<p>Brain-age prediction from neuroimaging data provides a proxy of biological aging, yet most models rely on structural magnetic resonance imaging (MRI), a modality that captures macroanatomy but offers limited biological specificity. We tested whether integrating molecular-enriched functional connectivity (FC) from resting-state functional MRI (rs-fMRI) data improves brain-age prediction and biological explainability. We analyzed MRI data of 2120 healthy adults (1243/877 F/M; 18–90 years) from three public datasets. Molecular-enriched connectivity maps were derived with Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) using receptor-density templates for the dopamine (DAT), norepinephrine (NET), and serotonin (SERT) transporter systems. Support vector regression models were applied to predict chronological age from molecular-enriched FC, structural morphometry, or both combined. The effect of multi-site variability was mitigated via ComBat harmonization with and without Empirical Bayes pooling. We additionally conducted a common-parcellation analysis to assess the impact of differing parcellations between modalities. Single-transporter molecular-enriched FC explained up to 51% of age variance. The most predictive transporter varied by dataset, with DAT dominating in the harmonized and common-parcellation settings. Combining the three molecular-enriched maps consistently improved prediction over any single map and increased explained variance up to 64%. Structural morphometry remained the strongest single modality overall. In the merged multi-site cohort using a common parcellation, adding transporter-enriched FC to structural features yielded a small but consistent reduction in prediction error (mean absolute error (MAE) from 6.02 to 5.81 years), supporting limited complementarity between the two modalities. Residual-level paired comparisons across repeated cross-validation confirmed that this improvement is statistically reliable but modest in magnitude. In contrast, when different parcellations were applied, incorporating molecular-enriched FC into brain age prediction resulted in a 2% higher MAE compared to structural morphometry alone, suggesting that parcellation mismatch may obscure the functional contributions. In conclusion, molecular-enriched FC is a feasible and biologically informative extension to brain-age modeling; however, its added predictive value over structural morphometry was modest and depended on harmonization and atlas alignment.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 13","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13535138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148872843","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":"Transient Elevation and Subsequent Normalization of Brain Age Gap During the Postpartum Period","authors":"Chang-hyun Park,&nbsp;Yunjin Bak,&nbsp;Sanghoon Han,&nbsp;Seung-Koo Lee,&nbsp;Na-Young Shin","doi":"10.1002/hbm.70608","DOIUrl":"https://doi.org/10.1002/hbm.70608","url":null,"abstract":"<p>The postpartum period involves substantial brain changes, but whether these represent pathological aging or adaptive plasticity remains unclear. We applied machine learning-based brain age prediction to quantify postpartum brain aging trajectories in a controlled longitudinal study. Brain age gap, the difference between MRI-predicted and chronological age, was assessed in 55 women (25 postpartum and 30 nulliparous controls). Postpartum women were assessed at approximately 3 and 12 months postpartum; controls were assessed at matched intervals. Postpartum women showed elevated brain age gap at 3 months (2.5 ± 2.7 years) compared with controls (0.3 ± 3.6 years; <i>p</i> = 0.01, Cohen's <i>d</i> = 0.69), which normalized by 12 months. Within-group analysis revealed significant brain age gap decrease in postpartum women (−3.3 ± 3.5 years; <i>p</i> = 0.008), while controls showed minimal change; the between-group difference in longitudinal change did not reach statistical significance (<i>p</i> = 0.12, Cohen's <i>d</i> = −0.58). Across all participants, brain age gap correlated with working memory performance (<i>ρ</i> = −0.30; <i>p</i> = 0.03), depressive symptoms (<i>ρ</i> = 0.38; <i>p</i> = 0.004), and subjective cognitive complaints (<i>ρ</i> = 0.28; <i>p</i> = 0.04) at baseline, providing preliminary evidence of clinical relevance. Explainability analysis using Shapley additive explanations identified subcortical structures as the network most selectively associated with the postpartum elevation in brain age gap at 3 months (<i>p</i> = 0.006, Cohen's <i>d</i> = 0.82), with normalization by 12 months. Together, these findings indicate transient brain age gap elevation in postpartum women without statistical confirmation of a trajectory differing from controls. With brain age gap providing a quantitative index of postpartum brain structural change, these preliminary findings are consistent with adaptive neuroplasticity, suggesting that such change may be temporary rather than reflecting permanent impairment.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 13","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70608","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148859920","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 “ComBat-Predict Enhances Generalizability of Neuroimaging Models to New Sites”","authors":"","doi":"10.1002/hbm.70625","DOIUrl":"https://doi.org/10.1002/hbm.70625","url":null,"abstract":"<p>\u0000 <span>Xin, Y.</span>, <span>M. Gardner</span>, <span>N. J. Tustison</span>, et al. <span>2026</span>. “ <span>ComBat-Predict Enhances Generalizability of Neuroimaging Models to New Sites</span>.” <i>Human Brain Mapping</i> <span>47</span>, no. <span>8</span>: e70546. https://doi.org/10.1002/hbm.70546.\u0000 </p><p>In the Acknowledgements section, the funding information was incomplete. The updated Acknowledgements section is as follows:</p><p>\u0000 <b>Acknowledgements</b>\u0000 </p><p>A.A.Chen’s work was partially supported by National Institutes of Health (NIH) Grant R01MH123550.</p><p>Y.X., A.B. and J.H.J. ’s work was supported in part by the National Institutes of Health/National Institute on Aging, under award number R01AG054159 (PI: Andreana Benitez).</p><p>The processing of the LBCC dataset was partially supported by NIH grants R01MH132934, R01MH133843, and R01MH134896. A complete list of funding sources that supported the collection and processing of data included in the LBCC can be found in Bethlehem et al, 2022.</p><p>We apologize for this error.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 13","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70625","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860193","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":"Cerebral Cortical Structural Variation and General Cognitive Ability: Evidence From Mendelian Randomization","authors":"Chun-Ju Chou,&nbsp;Mark Fiecas,&nbsp;Elisabetta C. del Re,&nbsp;Eero Vuoksimaa,&nbsp;Chi-Hua Chen","doi":"10.1002/hbm.70635","DOIUrl":"https://doi.org/10.1002/hbm.70635","url":null,"abstract":"<p>Understanding the cortical architecture underlying individual differences in general cognitive ability (GCA) remains a central question in cognitive neuroscience. Prior work has established associations between global brain size and GCA, yet the regional effects and directionality of these relationships remain debated. Using a genetically informed cortical parcellation in 11,289 UK Biobank participants, we examined associations between cortical surface area (SA), cortical thickness (CT), and GCA measured via verbal–numerical reasoning. Total SA showed a robust positive association with GCA. At the regional level, dorsolateral prefrontal and superior temporal SA exhibited the strongest positive associations, which persisted after adjustment for global SA. In contrast, CT showed comparatively modest associations. Using Mendelian randomization (MR) with genome-wide significant genetic instruments, we observed evidence consistent with a bidirectional relationship between total SA and GCA. At the regional level, dorsolateral prefrontal and temporal SA demonstrated evidence of MR-inferred directional effects on GCA, while GCA showed evidence of MR-inferred directional effects on total SA and perisylvian thickness. These findings support a polyregional SA architecture underlying GCA, with prominent contributions from prefrontal and temporal association cortices. Our results refine global brain–GCA models and highlight the value of genetically informed parcellation for identifying regional cortical contributions.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 13","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70635","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860203","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":"Morphometric Explanations for Deep Learning Based Neuroimaging Classifiers","authors":"Ankush Gajanan Arudkar,&nbsp;Bernard J. E. Evans,&nbsp;Sabrina Sghirripa,&nbsp;Mark Jenkinson,&nbsp;for the Alzheimer's Disease Neuroimaging Initiative","doi":"10.1002/hbm.70633","DOIUrl":"10.1002/hbm.70633","url":null,"abstract":"<p>Despite increasing accuracy of deep-learning-based medical imaging classifiers for detecting complex neurodegenerative disorders, an inherent lack of transparency and interpretability in their decisions poses a challenge to their clinical and research applications. While existing explainability methods provide insights into broader aspects of a classification decision, they do not provide a direct interpretation of how specific image information was used for a classification. Geometric, or morphological, brain changes are known to exist in multiple neurodegenerative disorders, and explaining how these changes are being used by a classifier would provide domain experts with an easily interpretable understanding of morphology contributing towards the classification decision. Our work proposes a counterfactual method to explain morphological features used by deep-learning classifiers for classifying image instances. Furthermore, to provide a more interpretable understanding of common morphological features used by a classifier for the population, we combine the instance explanations to describe average morphological brain changes used by the classifier in MNI152 space. The proposed method was tested using classifiers trained for detecting Parkinson's Disease (PD) and a simulated disease in T1-w MRIs. The counterfactual images generated by our method were shown to explain learnt disease-related image information, as the classification of more than 98% of images for the PD classifier was changed by modifying the explained aspects of the image morphology. By deriving geometric explanations used by deep-learning classifiers, future research can advance trustworthiness by validating the classifiers against existing disease knowledge and potentially harness them to explore novel disease processes. Our code is available at: https://github.com/ankusharudkar/Morphometric-Explanations.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 12","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70633","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148827677","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":"Characterising the Diffusion Functional Signature of Negative BOLD With Interleaved TMS-fMRI in the Human Brain","authors":"Inès de Riedmatten,&nbsp;Arthur P. C. Spencer,&nbsp;Roberto Martuzzi,&nbsp;Vincent Rochas,&nbsp;Jean-Baptiste Pérot,&nbsp;Filip Szczepankiewicz,&nbsp;Ileana O. Jelescu","doi":"10.1002/hbm.70629","DOIUrl":"10.1002/hbm.70629","url":null,"abstract":"<p>The coupling between brain excitatory activity and positive blood oxygen level-dependent (BOLD) responses is well-established. Although often associated with inhibition, negative BOLD remains partially understood. Moving away from neurovascular coupling, apparent diffusion coefficient (ADC)-fMRI provides a more direct measure of excitatory activity, possibly mediated by transient cellular deformations. Diffusion-weighted fMRI (dfMRI), from which ADC-fMRI derives, combines vascular and microstructural contributions. While decreases in ADC align with positive BOLD, the possible translation of negative BOLD into positive ADC and the ability of ADC-fMRI to capture inhibitory activity remain unexplored in humans. In this study, we used transcranial magnetic stimulation (TMS)-fMRI on the right primary motor cortex (M1) to selectively induce contralateral negative BOLD responses, whilst acquiring interleaved fMRI. TMS was applied at 5 Hz, 90% resting motor threshold, for both BOLD-fMRI and ADC-fMRI contrasts, in <i>n</i> = 12 and <i>n</i> = 11 healthy participants, respectively. We replicated previously reported negative BOLD clusters in the contralateral M1 and primary somatosensory cortex (S1). This was accompanied by a negative dfMRI response, but no ADC-fMRI response, indicating minimal microstructural fluctuations. In ipsilateral M1/S1, no BOLD response was detected while dfMRI revealed a positive cluster, suggesting different sensitivity to the excitatory/inhibitory balance. Overall, combining the findings from BOLD-fMRI and ADC-fMRI provides new insights into the vascular and neuronal responses underlying subthreshold TMS and negative BOLD.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 12","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13500454/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148808446","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":"Candidate Right Locus Coeruleus-Centered Symptom Pathways in Young Major Depressive Disorder: A Multimodal Magnetic Resonance Imaging Study","authors":"Zesong Yuan,&nbsp;Jing Wang,&nbsp;Danian Li,&nbsp;Baohua Cheng,&nbsp;Yingpeng Kuang,&nbsp;Rongyu Zhang,&nbsp;Nana Luo,&nbsp;Shijun Qiu,&nbsp;Yongsheng Chen,&nbsp;Yujie Liu","doi":"10.1002/hbm.70632","DOIUrl":"https://doi.org/10.1002/hbm.70632","url":null,"abstract":"<p>Major depressive disorder (MDD) involves network-level abnormalities that may reflect dysfunction in catecholaminergic nuclei, particularly the substantia nigra (SN) and locus coeruleus (LC), which regulate cortical gain and signal-to-noise ratio. However, whether MRI-derived SN/LC phenotypes are linked to depressive symptom severity through functional connectivity pathways in young individuals with MDD remains unclear. We studied 84 young participants (MDD, <i>n</i> = 46; controls, <i>n</i> = 38). MRI measures were extracted from the bilateral SN and LC using neuromelanin-sensitive magnetization transfer contrast (MTC) imaging and iron-sensitive quantitative susceptibility mapping (QSM). Seed-to-voxel analyses assessed associations between these phenotypes and resting-state functional connectivity. Graph-theoretical analyses extended functional connectivity findings to global network topology. Mediation analyses tested whether functional connectivity linked SN/LC MRI phenotypes to Hamilton Depression Rating Scale scores within the MDD group. Multiple comparisons were controlled using false discovery rate (FDR) at <i>q</i> &lt; 0.05. Compared with controls, the MDD group exhibited reduced bilateral SN volume on QSM and decreased LC signal intensity on MTC. Neuromelanin- and iron-sensitive phenotypes showed distributed functional connectivity associations involving frontal, sensorimotor, precuneus, and cerebellar regions. Bilateral SN susceptibility showed diagnosis-dependent associations with global efficiency. Mediation analyses identified four candidate right LC functional connectivity pathways linking SN/LC qMRI phenotypes to HAMD scores, mainly involving right LC connectivity with the left precuneus, right paracentral lobule, and cerebellum.SN/LC catecholaminergic MRI phenotypes are associated with depressive severity and right LC-centered functional connectivity, supporting candidate circuit-level associations linking brainstem neuromodulatory phenotypes to clinical expression in MDD.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 12","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70632","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784242","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":"Optimising 7T-fMRI for Imaging Regions of Magnetic Susceptibility","authors":"Saskia L. Frisby,&nbsp;Marta M. Correia,&nbsp;Minghao Zhang,&nbsp;Christopher T. Rodgers,&nbsp;Timothy T. Rogers,&nbsp;Matthew A. Lambon Ralph,&nbsp;Ajay D. Halai","doi":"10.1002/hbm.70621","DOIUrl":"10.1002/hbm.70621","url":null,"abstract":"<p>The temporal signal-to-noise ratio (tSNR) of functional magnetic resonance imaging (fMRI) is particularly poor in ventral anterior temporal and orbitofrontal regions because of B₀ and B₁⁺ magnetic field inhomogeneity, a problem that is exacerbated at higher field strengths. In this 7T-fMRI study we compared three methods of improving sensitivity in these areas: parallel transmit, which uses multiple transmit elements, controlled independently, to homogenise the flip angle experienced by the tissue; multi-echo, which entails collection of multiple volumes at different echo times following a single radiofrequency pulse; and multiband, in which multiple slices are acquired simultaneously. We found that parallel transmit and multi-echo increased the magnitude of the BOLD signal change, but only multi-echo increased BOLD magnitude in areas prone to susceptibility artefacts. Multiband and denoising of multi-echo data with independent components analysis (ICA) both improved precision of GLM fit. Exploratory results suggested that multi-echo and ICA denoising can both benefit multivariate analyses. In conclusion, a multi-echo, multiband sequence improved fMRI quality in areas prone to susceptibility artefacts while maintaining sensitivity across the whole brain. We recommend this approach for studies investigating the functional roles of ventral temporal and orbitofrontal regions with 7T fMRI.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 11","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13456059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148701024","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}