Human Brain Mapping最新文献

{"title":"Precision Imaging for Intraindividual Investigation of the Reward Response","authors":"Matthew Mattoni,&nbsp;Shenghan Wang,&nbsp;Cooper J. Sharp,&nbsp;Thomas M. Olino,&nbsp;David V. Smith","doi":"10.1002/hbm.70512","DOIUrl":"10.1002/hbm.70512","url":null,"abstract":"<p>The reliance of fMRI research on between-person comparisons is limited by low test–retest reliability and an inability to explain within-person processes. Intraindividual studies are needed to understand how changes in brain functioning relate to changes in behavior. Here, we present open data and analysis of a novel intensively sampled fMRI study. This precision imaging dataset includes 44 sessions acquired across four participants at a twice-weekly rate. In each session, participants completed multiple reward-related tasks, mood and alertness ratings, and a behavioral mood manipulation. We examined how the reward response reflects between-person or within-person variance. Trial-level models suggested dramatically more trials than typically collected are needed to maximize reliability within runs and individuals. Test-retest reliability of the reward response was very low and not explained by measurement error, suggesting low power for between-person comparisons without large amounts of data. At an intraindividual level, mood and alertness explained up to 37% of the intraindividual variance of the anticipatory reward response. Finally, we found that while reliability or brain-behavior associations were not improved by multi-echo denoising, a multivariate reward signature had stronger intraindividual behavioral associations than a univariate anatomical mask. Together, results suggest that the BOLD reward response is not a stable trait-like marker, but moderated by state-like factors. More broadly, BOLD activation to reward tasks—and likely other fMRI tasks—presents substantial opportunity for within-person study to complement the traditional focus on between-person study. We conclude with a discussion of considerations for intensive longitudinal neuroimaging designs.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70512","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147511834","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":"Distinct Resting-State Connectomes for Face and Scene Perception Predict Individual Task Performance","authors":"Orhan Soyuhos,&nbsp;Aurelia Scarpa,&nbsp;Daniel Baldauf","doi":"10.1002/hbm.70498","DOIUrl":"10.1002/hbm.70498","url":null,"abstract":"<p>Face and scene perception rely on distinct neural networks centered on the Fusiform Face Area (FFA) and Parahippocampal Place Area (PPA). However, how these regions interact with broader brain networks remains unclear. Using resting-state fMRI and MEG data, we mapped the spatial and frequency-specific functional connectivity of the FFA and PPA. We found that the FFA showed predominant fMRI connectivity with lateral occipitotemporal, inferior temporal, and temporoparietal regions, while the PPA connected more strongly with ventral medial visual, posterior cingulate, and entorhinal-perirhinal areas. MEG analyses further revealed this network segregation was reflected in beta and gamma bands. Importantly, connectome-based predictive modeling showed that the strength of these intrinsic fMRI connectivity patterns predicted individual reaction times on corresponding face and scene perception tasks. Our findings demonstrate that the FFA and PPA anchor distinct intrinsic networks with unique spatio-temporal profiles that provide a functional architecture supporting their specialized roles in face and scene perception.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70498","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147498728","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":"Revisiting Amplitude of Low-Frequency Fluctuations (ALFF) in Resting-State fMRI: Clarifications and Improvements","authors":"Lejian Huang,&nbsp;Rami Jabakhanji,&nbsp;Andrew D. Vigotsky,&nbsp;Paulo Branco,&nbsp;Marwan N. Baliki,&nbsp;A. Vania Apkarian","doi":"10.1002/hbm.70506","DOIUrl":"10.1002/hbm.70506","url":null,"abstract":"<p>The amplitude of low-frequency fluctuations (ALFF) and its related measure, fractional ALFF (fALFF), are widely used resting-state fMRI techniques for quantifying spontaneous neural activity within specific frequency bands. However, inconsistencies in the definition and implementation of ALFF have led to confusion in the field. In this study, we provide a mathematical clarification of ALFF and fALFF by introducing two variants: the arithmetic mean-defined ALFF/fALFF (amALFF/amfALFF) and the quadratic mean-defined ALFF/fALFF (qmALFF/qmfALFF). We examine the relationships between mean BOLD intensity (MBI), amALFF, and qmALFF across both subjects and voxels using two independent datasets mapped onto different brain templates. Additionally, we investigate the impact of <i>z</i>-scoring the original BOLD signal on ALFF and fALFF metrics. Finally, we evaluate the validity and test–retest reliability of (f)ALFF using a dataset with two runs at voxel, parcellation, and cortical level. Our key findings include: (1) MBI is positively correlated with both amALFF and qmALFF, highlighting the need for normalization to subject-level means; (2) normalized qmALFF and qmfALFF are highly correlated with normalized amALFF and amfALFF, respectively, at both the subject and voxel levels; (3) <i>z</i>-scoring the BOLD signal does not affect amfALFF or qmfALFF, but it substantially alters amALFF and qmALFF; (4) ALFF exhibits higher reliability than fALFF and both perform best at the parcellation level compared to voxel and cortical (subject) levels. Based on these findings, we present a comprehensive flowchart of the (f)ALFF algorithm implemented in the temporal domain. The full procedure is implemented in R, and the corresponding script is available at: https://github.com/lejianhuang/ALFF.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147498698","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":"Testing for Network Specificity in Brain-Behavior Associations Using Ordinal Dominance Curves","authors":"Noah Hillman,&nbsp;Sarah M. Weinstein,&nbsp;Joëlle Bagautdinova,&nbsp;Kevin Y. Sun,&nbsp;Matthew Cieslak,&nbsp;Taylor Salo,&nbsp;Yong Fan,&nbsp;Arielle S. Keller,&nbsp;Aaron F. Alexander-Bloch,&nbsp;Simon N. Vandekar,&nbsp;Armin Raznahan,&nbsp;Theodore D. Satterthwaite,&nbsp;Haochang Shou,&nbsp;Russell T. Shinohara","doi":"10.1002/hbm.70493","DOIUrl":"10.1002/hbm.70493","url":null,"abstract":"<p>Interpreting brain-behavior relationships through the lens of anatomical parcellations or functional networks is commonplace in human brain mapping. However, statistical approaches for testing whether brain–behavior associations are stronger (i.e., enriched) within a region of interest remain underdeveloped. Here, we propose a permutation-based approach for network enrichment testing using ordinal dominance curves (NETDOM). In simulation studies, we demonstrate that NETDOM properly controls the type I error rate—unlike other prominent enrichment methods—while exhibiting increased statistical power when enrichment occurs in a subset of in-network locations. Using data from two large-scale neurodevelopmental cohorts, we illustrate that NETDOM effectively detects enriched associations between structural and functional brain measures and neurocognitive performance.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70493","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147503649","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":"Contributions of Gray Matter Microstructure to Differences in Fluid Cognition and Episodic Memory Across the Healthy Adult Lifespan","authors":"Jenna L. Merenstein,&nbsp;Ilana J. Bennett,&nbsp;David J. Madden","doi":"10.1002/hbm.70511","DOIUrl":"10.1002/hbm.70511","url":null,"abstract":"<p>Cognitive decline, in healthy older adults without cognitive impairment or dementia, has been associated with numerous microstructural alterations in brain tissue using magnetic resonance imaging (MRI). Prior studies have primarily linked age-related cognitive decline to alterations in white matter tissue, but methodological advances in diffusion-weighted imaging (dMRI) data acquisition and modeling now allow for these analyses to be extended to gray matter tissue. Here, using a sample of 152 healthy adults (18–88 years of age), we used a multicompartment dMRI model to assess (1) age-related differences in gray matter microstructure of functionally defined networks and (2) whether microstructural alterations accounted for age-related differences in episodic memory and speed-dependent fluid cognition. We observed significant age-related alterations in gray matter tissue in the form of nonlinear, age-related increases and decreases in intracellular and dispersed diffusion, respectively, and linear increases in free diffusion. Free diffusion exhibited the most pronounced age-related effects, especially for frontoparietal relative to occipital regions. Dispersed diffusion in the dorsal attention network statistically mediated age-related differences in episodic memory performance. Moreover, higher intracellular diffusion in the default mode and ventral attention networks was related to worse fluid cognition performance, but only for adults &gt; 51 years of age. These results suggest that healthy aging is accompanied by distinct profiles of gray matter microstructural alterations that negatively affect memory and speed-dependent cognition, the latter of which is more pronounced after midlife.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70511","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147503718","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 Age Estimation on T2-FLAIR Scans for Application to Multiple Sclerosis","authors":"Jordan Colman,&nbsp;Giuseppe Pontillo,&nbsp;Olivia Goodkin,&nbsp;Michael A. Foster,&nbsp;Nima Mahmoudi,&nbsp;Mike P. Wattjes,&nbsp;Arturo Brunetti,&nbsp;Gabriel Gonzalez-Escamilla,&nbsp;Sergiu Groppa,&nbsp;Einar August Høgestøl,&nbsp;Lars T. Westlye,&nbsp;Silvia Messina,&nbsp;Jacqueline Palace,&nbsp;Rosa Cortese,&nbsp;Nicola De Stefano,&nbsp;Àlex Rovira,&nbsp;Jaume Sastre-Garriga,&nbsp;Stefan Ropele,&nbsp;Christian Enzinger,&nbsp;Maria A. Rocca,&nbsp;Massimo Filippi,&nbsp;Barbara Bellenberg,&nbsp;Carsten Lukas,&nbsp;Massimiliano Calabrese,&nbsp;Marco Castellaro,&nbsp;Tomas Uher,&nbsp;Manuela Vaneckova,&nbsp;Ahmed Toosy,&nbsp;Olga Ciccarelli,&nbsp;Tarek Yousry,&nbsp;Ferran Prados,&nbsp;Frederik Barkhof,&nbsp;James H. Cole,&nbsp;MAGNIMS Study Group","doi":"10.1002/hbm.70425","DOIUrl":"10.1002/hbm.70425","url":null,"abstract":"<p>The brain-predicted age difference (brain-PAD) is associated with measures of clinical interest in people with multiple sclerosis (pwMS). Most brain age models rely on 3D T1-weighted scans, which are not routinely acquired in MS clinical practice, limiting their potential for clinical translation. We aimed to develop a model predicting brain age using T2-FLAIR, the core sequence for MS diagnosis and monitoring, and validate the resulting brain-PAD values as a biomarker of MS severity and progression. We collected 3D T2-FLAIR and 3D T1-weighted brain MRI scans to compose (i) a multicentre cohort of healthy participants for brain age modeling, and (ii) a single-centre cohort of pwMS and healthy controls for external validation. We trained and evaluated 3D convolutional neural network models predicting brain age from T2-FLAIR or T1-weighted images. Models were compared using t-tests based on bootstrapped standard errors. Saliency maps were obtained with the SmoothGrad method to visualize regions that were most important for the predictions. Finally, using a linear model framework, we clinically validated the resulting brain-PAD metric by assessing its relationship with diagnosis (MS versus healthy controls), clinical phenotype, disease duration, and physical disability as measured with the Expanded Disability Status Scale (EDSS), adjusting for age and sex. The Inception-ResNet-V2 model based on T2-FLAIR scans yielded accurate brain age predictions (test set MAE = 3.31 years, R² = 0.944, 5x ensemble MAE = 2.81, R² = 0.955), which were comparable to those obtained with the T1w-based model (test set MAE = 3.34 years, R² = 0.942, 5x ensemble MAE = 2.84, R² = 0.955, <i>p</i> = 0.91). Brain age predictions were mostly driven by subcortical regions, particularly the thalamus. T2-FLAIR-based brain-PAD was higher in pwMS than healthy controls (7.07 vs −0.50 years, <i>p</i> &lt; 0.0001). As with T1 brain-PAD, FLAIR brain-PAD correlated with MS disease duration (<i>R</i> = 0.24, <i>p</i> &lt; 0.0001) and EDSS (<i>R</i> = 0.30, <i>p</i> &lt; 0.0001). Brain age predictions relying on T2-FLAIR scans are as accurate as those derived from T1-weighted scans and could be used as an easily obtainable biomarker of MS severity and progression in clinical practice.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70425","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147480571","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 Learning Empowered Microstructure Codebook: New Paradigm for Multi-Parameter Tissue Characterization Estimation","authors":"Tenglong Wang,&nbsp;Zhonghua Wan,&nbsp;Shuxin Cao,&nbsp;Jiahao Yu,&nbsp;Yifei He,&nbsp;Yu Xie,&nbsp;Fan Zhang,&nbsp;Ye Wu","doi":"10.1002/hbm.70513","DOIUrl":"10.1002/hbm.70513","url":null,"abstract":"<p>Diffusion MRI (dMRI) enables the examination of microstructural profiles and tissue changes using specific microstructural modeling, but it requires long acquisition times and dense q-space sampling. Current deep learning-based methods are also limited by their inability to generalize across protocols and extend to new microstructural indices. This work introduces a novel framework that addresses these limitations by learning a microstructural codebook, facilitating accurate, rapid, and multi-parameter microstructure imaging. Our approach integrates the spherical mean technique (SMT) with a hybrid Mamba-CNN architecture and learnable tissue-compartment kernels, effectively capturing multiscale spatial dependencies while linking spherical mean signals to biophysical microstructure models. This design enhances both interpretability and adaptability, enabling robust estimation of 24 microstructural metrics derived from 8 widely used biophysical diffusion models, even under undersampled acquisition conditions. Notably, the framework demonstrates strong generalization across diverse acquisition protocols and enables seamless adaptation to novel microstructural indices with minimal fine-tuning, underscoring its flexibility and practical utility. Extensive experiments on multiple datasets confirm the method's superior accuracy, generalization, and transferability. This work presents a codebook-driven framework for microstructure imaging that bridges biophysical modeling and deep learning to enable more interpretable and adaptable dMRI analysis. The code is available at https://github.com/1nlandempire/Microstructure-codebook-imaging.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 5","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147485488","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":"Evidence of White Matter Neuroinflammation in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Diffusion-Based Neuroinflammation Imaging Study","authors":"Qiang Yu,&nbsp;Kiana Kothe,&nbsp;Richard A. Kwiatek,&nbsp;Peter Del Fante,&nbsp;Anya Bonner,&nbsp;Vince D. Calhoun,&nbsp;Zack Y. Shan","doi":"10.1002/hbm.70505","DOIUrl":"10.1002/hbm.70505","url":null,"abstract":"<p>Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disorder with suspected neuroinflammatory pathophysiology. However, previous diffusion tensor imaging (DTI) studies have reported inconsistent white matter abnormalities in ME/CFS, and specific white matter inflammatory changes remain poorly characterised. This study employed an advanced diffusion-based neuroinflammation imaging (NII) model to investigate white matter neuroinflammation in ME/CFS. Diffusion MRI data from 67 ME/CFS patients (median age, 38; and 54 women) and 67 rigorously matched healthy controls (HCs) (median age 38; and 52 women) were analysed. Seven NII-derived metrics were computed: hindered water ratio (NII-HR), restricted fraction (NII-RF), fibre fraction (NII-FF), axial diffusivity (NII-AD), radial diffusivity (NII-RD), mean diffusivity (NII-MD) and fractional anisotropy (NII-FA). Conventional DTI metrics were also calculated. Tract-based spatial statistics were used to perform voxel-wise group comparisons, and multiple regression analysis was conducted to examine the relationship between NII/DTI metrics and clinical measures of mental health, physical health, sleep quality, disability, disease severity and disease duration. Compared to HCs, ME/CFS patients exhibited widespread white matter abnormalities, including significantly lower NII-HR and NII-RF, and significantly higher NII-FF, NII-AD, NII-MD and NII-FA across association, commissural and projection fibres. Additionally, some regions showed decreased NII-AD and NII-MD in ME/CFS. Lower NII-RF, NII-AD and NII-MD in ME/CFS were significantly associated with worse mental health, while lower NII-RF was also associated with a higher level of disability. Among ME/CFS patients, higher NII-FF was associated with lower disease severity. Conventional DTI showed minimal group differences and no significant clinical associations. This study provides in vivo evidence of white matter neuroinflammation in ME/CFS, characterised by cerebral edema (reduced NII-HR), cellular infiltration (reduced NII-RF) and axonal reorganisation (increased NII-FF). This suggests NII-derived indices may serve as sensitive biomarkers for neuroinflammation in ME/CFS.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 4","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70505","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147463356","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":"Factors Contributing to Short-Term Structural Variability in a Longitudinal MRI Dataset","authors":"Polona Kalc,&nbsp;Mayla ter Veer,&nbsp;Robert Dahnke,&nbsp;Gabriel Ziegler,&nbsp;Simone Kühn,&nbsp;Christian Gaser","doi":"10.1002/hbm.70500","DOIUrl":"10.1002/hbm.70500","url":null,"abstract":"<p>When planning longitudinal magnetic resonance imaging (MRI) studies, it is advisable to consider various (confounding) factors that could influence brain structural changes over time. The goal of this study was to identify factors that contribute to intraindividual variability of brain structure within a short period of time. We employed multilevel sparse partial least squares regression to investigate the changes in regional gray matter volume in the longitudinal Day2day MRI dataset. The findings suggest that the changes in regional GM volume estimations were primarily driven by image quality, while the outdoor temperature and time since baseline appeared as the main predictors of volumetric changes in insular and diencephalic brain regions. We additionally investigated factors associated with variability in image quality. The findings underscore the importance of maintaining adequate participant arousal during scanning.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 4","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70500","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147432561","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":"Listening Without the Noise: Near-Silent Looping Star fMRI Reveals Neural Processing of Degraded Speech","authors":"Christopher J. Ritter,&nbsp;Alejandra M. Hüsser,&nbsp;András Jakab,&nbsp;Florian Wiesinger,&nbsp;Ana Beatriz Solana,&nbsp;Brice Fernandez,&nbsp;Huw Swanborough,&nbsp;Ruth O’Gorman Tuura,&nbsp;Alexis Hervais-Adelman","doi":"10.1002/hbm.70501","DOIUrl":"10.1002/hbm.70501","url":null,"abstract":"<p>Excessive acoustic noise during fMRI poses challenges for auditory cognitive neuroscience. We aimed to evaluate a near-silent fMRI sequence, Looping Star (LS), for measuring brain responses to acoustically degraded speech. We hypothesized that LS would maintain BOLD sensitivity comparable to conventional Gradient-echo EPI and potentially reveal enhanced activation in brain regions associated with effortful speech processing under degraded listening conditions. Ten healthy adult native German speakers underwent fMRI while passively listening to blocks of spoken sentences under four conditions: clear speech, moderately degraded speech (spectral noise vocoding at ~50% intelligibility), unintelligibly degraded speech and clear speech mixed with EPI-like scanner acoustic noise. Each participant was scanned in four runs (two using standard single-echo EPI and two using the near-silent multi-echo LS sequence) in an alternating order. All fMRI timeseries (single-echo EPI, single-echo LS and echo-combined LS) detected robust bilateral auditory cortex activation for all Sound &gt; silent baseline. Echo-combined LS, however, yielded larger activation clusters and higher peak effect sizes, extending beyond auditory cortex regions. Notably, only echo-combined LS revealed significant activation in the left inferior frontal gyrus (inferior frontal gyrus, pars opercularis) and adjacent regions when comparing all speech stimuli to baseline, whereas EPI and single-echo LS showed no such frontal activation. Direct between-sequence comparisons demonstrated significantly greater BOLD responses in echo-combined LS compared to EPI in key regions (including left insula, left precentral gyrus and left inferior frontal gyrus) for the all Sound &gt; baseline and moderately degraded &gt; baseline contrasts. These findings underscore the promise of near-silent fMRI for auditory neuroscience: by virtually eliminating scanner noise, LS fMRI can reveal neural responses to degraded speech that might be masked during loud EPI scans.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"47 4","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70501","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443482","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}