Hum Brain Mapp [JOURNAL]
Sun
200 papers
Differences of Gradient Connectivity and Ventricle Volumes in Long-Term Sensorineural Hearing Loss Related Cognitive Impairment Correlate With Transcriptional Signatures.
Xu XM, Fang ZH, Xia Y … +6 more , Li S, Feng Y, Wu Y, Salvi R, Yin X, Chen YC
Hum Brain Mapp · 2026 Apr · PMID 41913592 · Full text
Long-term sensorineural hearing loss (SNHL) is a prevalent condition associated with an increased risk of cognitive impairment. This study aimed to investigate the relationship among gradient connectivity, ventricle volu...
Long-term sensorineural hearing loss (SNHL) is a prevalent condition associated with an increased risk of cognitive impairment. This study aimed to investigate the relationship among gradient connectivity, ventricle volumes, and transcriptional signatures in individuals experiencing cognitive deficits related to long-term SNHL. This study enrolled 81 patients with long-term SNHL and 78 healthy controls (HCs). All participants underwent audiological tests, neuropsychological assessment, and MRI scanning. Connectome gradient analysis and ventricular volume measurements were performed. Additionally, regional gene expression and neurotransmitter receptor data were integrated. Correlation analysis was conducted to examine associations between neuroimaging metrics and cognitive performance. Patients with SNHL had significantly higher hearing thresholds and worse cognitive performance than HCs. The principal gradient was compressed in the SNHL group, with significant differences in the default mode network and dorsal attention network. Enlarged volumes of the choroid plexus and lateral ventricles were also observed in the SNHL group. Correlation analysis revealed significant associations among ventricle volumes, gradient connectivity, and cognitive performance. Transcriptomic analysis revealed 496 genes associated with regions showing an increased principal gradient and 321 genes linked to regions with a decreased gradient. Enrichment analyses indicated these genes were implicated in synaptic plasticity, neurotransmitter regulation, energy metabolism, and neurodegenerative pathways. This study provides new insights into the multifaceted nature of SNHL-related cognitive impairments, suggesting that gradient connectivity, ventricle volumes, and transcriptional signatures are interconnected and may serve as potential biomarkers for monitoring cognitive decline in individuals with long-term SNHL. Future research should focus on elucidating the causal pathways and underlying biological mechanisms connecting these multimodal factors.
Multi-Centre Reproducibility of DTI and NODDI in White Matter Tracts Segmented Using TractFinder Across Three MRI Scanners of the Same Model.
Sierhej A, Correia MM, Evans CJ … +5 more , Seunarine KK, Clayden JD, Smith NAS, Hall MG, Clark CA
Hum Brain Mapp · 2026 Apr · PMID 41913049 · Full text
Quantitative imaging biomarkers (QIBs) are objective measures derived from quantitative imaging that can differentiate pathological changes from healthy biological processes. Diffusion MRI parameters derived from Diffusi...
Quantitative imaging biomarkers (QIBs) are objective measures derived from quantitative imaging that can differentiate pathological changes from healthy biological processes. Diffusion MRI parameters derived from Diffusion Tensor Imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI) could serve as potential QIBs for studying both healthy neurodevelopment and various neurological conditions. However, quantitative neuroimaging studies often require large datasets collected across multiple scanners, which introduces variability. To ensure the reliability of multi-centre studies, the inter-centre reproducibility of DTI and NODDI parameters must be thoroughly assessed before data collection begins. Discrepancies between results reported by previous studies can be explained by other sources of variability. The inter-scanner reproducibility of diffusion parameters needs to be determined when the other sources of variability, such as differences in acquisition parameters, processing and ROI segmentation are controlled for. We assess the reproducibility of DTI and NODDI parameters in clinically relevant white matter (WM) tracts across three scanners of the same model, ensuring consistency in the acquisition scheme and pre-processing pipelines. WM tract regions of interest (ROIs) are automatically segmented to standardise the analysis. Additionally, we investigate ROI and signal-to-noise ratio differences to better understand the sources of variability in diffusion parameters. According to the Koo and Li classification system, our results demonstrate excellent reproducibility for fractional anisotropy and mean diffusivity across scanners of the same model (ICC ≥ 0.964) when using identical acquisition schemes, pre-processing pipelines and automated ROI segmentation. NODDI orientation dispersion index and neurite density index exhibit a similar level of reproducibility (ICC ≥ 0.942 and ICC ≥ 0.911, respectively), while free water fraction (FWF) has ICC ≥ 0.862. However, statistically significant variability was observed in the FWF, specifically within the left inferior fronto-occipital fasciculus (CoV 9.43%) and optic radiation (CoV 9.95%), even when scanning the same cohort across sites. If there is an error in the signal fraction in one compartment in the NODDI model, the signal fractions from other compartments may likely be misestimated. The reproducibility and variability of diffusion parameters reported in this study provide guidance for future QIB research involving datasets derived from multiple scanners. These findings can help determine whether observed changes in diffusion parameters reflect meaningful biological differences or are highly influenced by measurement variability.
Dynamic Alterations of Functional Systems in Alzheimer's Disease: A Co-Activation Pattern Analysis.
Wang P, Xue M, Mao Y … +3 more , Wang C, Yao X, Biswal BB
Hum Brain Mapp · 2026 Apr · PMID 41889068 · Full text
While resting-state brain dysfunctions have been extensively investigated in Alzheimer's disease (AD), the dynamic alterations of functional systems remain poorly understood. We employed co-activation pattern (CAP) analy...
While resting-state brain dysfunctions have been extensively investigated in Alzheimer's disease (AD), the dynamic alterations of functional systems remain poorly understood. We employed co-activation pattern (CAP) analysis to characterize the functional-state alterations in 243 participants using resting-state fMRI data and applied graph theory analysis to estimate corresponding topological properties. The CAP analysis identified five distinct brain states across groups: State 1 (limbic network dominated), State 2 (dorsal attention network (DAN) and central executive network dominated), State 3 (default mode network and central executive network dominated), State 4 (somatomotor network and ventral attention network dominated), and State 5 (DAN, sensorimotor, and visual networks dominated). Compared to cognitively unimpaired individuals, State 3 demonstrated significantly reduced persistence and resilience in both mild cognitive impairment (MCI) and AD groups. Additionally, both clinical groups (MCI and AD) exhibited decreased transitions from State 2 to State 5 and reduced self-transitions within State 3. Graph theory analysis revealed that compared to cognitively unimpaired individuals, MCI and AD individuals had increased node degree centrality and node efficiency, alongside decreased node local efficiency in regions within the default mode network (DAN) and visual network, which corresponded well with CAP analysis results. Our findings provide a multiscale framework linking dynamic state instability to static network reorganization, advancing understanding of the dynamic functional alterations underlying cognitive decline in AD spectrum disorders.
Effects of Age on Resting-State Cortical Networks.
Gohil C, Kohl O, Pitt J … +6 more , van Es MWJ, Quinn AJ, Vidaurre D, Turner MR, Nobre AC, Woolrich MW
Hum Brain Mapp · 2026 Apr · PMID 41889066 · Full text
Understanding how ageing affects brain function remains a central challenge in neuroscience. Electrophysiological brain imaging techniques provide a near-direct measure of neuronal activity, which is useful for character...
Understanding how ageing affects brain function remains a central challenge in neuroscience. Electrophysiological brain imaging techniques provide a near-direct measure of neuronal activity, which is useful for characterising neurophysiological health. They offer us the ability to track large-scale networks of functional activity with high temporal precision. The effects of healthy ageing on these networks remain poorly understood, in part due to small sample sizes and limited control for confounding factors in previous studies. Here, we analysed resting-state source-reconstructed magnetoencephalography (MEG) data from a large cross-sectional cohort of healthy adults ( = 612, 18-88 years old) to characterise the effect of age using not only time-averaged (static), but also transient (dynamic) network activity. We examined time-averaged power and coherence across canonical frequency bands ( , , , , ), as well as transient network dynamics identified using Hidden Markov Modelling. We included many confounding variables known to be affected by age, such as brain volume, as well as head size and position, which have previously been overlooked. Ageing was associated with frequency-specific changes in oscillatory power, with decreases in low-frequency ( , ) power and increases in high-frequency ( ) power. Coherence increased across all frequency bands and was positively associated with cognitive performance. Transient network analyses additionally revealed that frontal network occurrences declined with age, with evidence suggesting a compensatory role in supporting cognition. These findings provide a more comprehensive electrophysiological signature for healthy ageing and establish a baseline for detecting pathological change.
Efficacy of Optically Pumped Magnetometers in Detecting Activity From the Cerebellar Cortex.
Roos S, Hämäläinen M, Iivanainen J
Hum Brain Mapp · 2026 Apr · PMID 41888639 · Full text
The cerebral cortex has been extensively studied using magnetoencephalography (MEG), but the cerebellum has received less attention, partly due to technical limitations. Recent advances in high-resolution anatomical mode...
The cerebral cortex has been extensively studied using magnetoencephalography (MEG), but the cerebellum has received less attention, partly due to technical limitations. Recent advances in high-resolution anatomical modeling enable surface-based analysis of cerebellar activity. At the same time, MEG technology has evolved, with on-scalp systems employing optically pumped magnetometers (OPMs) emerging as an alternative for conventional superconducting quantum interference device (SQUID)-based systems. In contrast to rigid one-size-fits-all SQUID sensor helmets, OPMs allow flexible positioning of the sensors on the participant's scalp to provide improved coverage of the cerebellum. To assess the benefits provided by OPMs in detecting cerebellar activity, we conducted simulations using a high-resolution model of the human cerebellum, where we compared OPM arrays consisting either of single-axis or triaxial sensors to commercial SQUID sensor arrays. We show that both OPM types measure stronger net signals from across the cerebellum compared to the SQUID-based systems. OPMs also reduce signal correlations between the cerebral and cerebellar cortices, improving source separability. Increasing the number of OPM sensors leads to larger gains in total information capacity compared to SQUIDs. In all metrics, triaxial OPMs outperformed single-axis configurations. These results suggest that already a 102-sensor, triaxial OPM-based on-scalp MEG system could substantially improve noninvasive electrophysiological studies of the human cerebellum.
Watching Together, Yet Feeling Apart: Interpersonal Neural Synchronization as a Marker of Symptom Severity in Children With Autism Spectrum Disorder During Naturalistic Viewing.
Liu J, Wu Y, Wang Z … +8 more , Deng Y, Jin Z, Sun X, Jiang Y, Wang G, Zhang M, Jiang F, Wang G
Hum Brain Mapp · 2026 Apr · PMID 41888090 · Full text
Social interaction impairments in autism spectrum disorder (ASD) have been widely attributed to deficits in empathy, including both cognitive and affective components. However, the underlying neural mechanisms remain inc...
Social interaction impairments in autism spectrum disorder (ASD) have been widely attributed to deficits in empathy, including both cognitive and affective components. However, the underlying neural mechanisms remain incompletely understood. In the current study, we used functional near-infrared spectroscopy (fNIRS) hyperscanning to simultaneously measure brain activities in children with ASD or typical development (TD) and their mothers while they co-viewed the animated film Partly Cloudy. Results revealed that compared to TD children, children with ASD exhibited significantly reduced interpersonal neural synchronization (INS), that is, desynchronization, in the frontopolar area and right dorsolateral prefrontal cortex (DLPFC) during scenes involving theory of mind (ToM) and pain-related events. Notably, TD children showed enhanced INS in the right DLPFC, with significant group differences. Moreover, the right DLPFC INS negatively correlated with ASD symptom severity and mediated the relationship between group and symptom severity. Inter-brain functional connectivity analysis revealed that, during empathy-related events, children's frontopolar area/right DLPFC and maternal brain regions were reduced in ASD but enhanced in TD dyads, with significant group-level contrasts. Finally, using support vector machine (SVM) classification, we found that INS features in the right DLPFC during empathy scenes could accurately distinguish between ASD and TD children. This study offers novel insights into the neural basis of empathy deficits in ASD by examining mother-child INS in a naturalistic setting. These findings provide preliminary insights into the neural mechanisms of INS in ASD and may inform future research on parent-child neural synchrony and its potential relevance for intervention strategies.
Introducing Structural Reliance: A New Method to Assess Structure-Function Coupling in the Brain.
Madden D, Laurienti PJ, Shappell HM … +1 more , Bahrami M
Hum Brain Mapp · 2026 Apr · PMID 41884953 · Full text
The relationship between the structural connectome and functional activity in the brain is highly complex, and understanding of the connection between the two is limited. Previous work has shown a marginal reliance of fu...
The relationship between the structural connectome and functional activity in the brain is highly complex, and understanding of the connection between the two is limited. Previous work has shown a marginal reliance of functional brain activity on underlying structural connections, indicating significant flexibility of neural communication. Here, we introduce a new method to quantify structure-function coupling and compare it with a standard coupling technique by evaluating the structure-function relationship across numerous fMRI task paradigms. Through this comparison, we investigate how structure-function relationships change during different cognitive demands and we evaluate how they relate to behavior. The new method introduced here, structural reliance, exhibits different structure-function correspondence patterns throughout the brain, and it generally outperforms the standard coupling measure in coupling-based behavioral measure predictions.
Precision Imaging for Intraindividual Investigation of the Reward Response.
Mattoni M, Wang S, Sharp CJ … +2 more , Olino TM, Smith DV
Hum Brain Mapp · 2026 Apr · PMID 41877495 · Full text
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 b...
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.
Contributions of Gray Matter Microstructure to Differences in Fluid Cognition and Episodic Memory Across the Healthy Adult Lifespan.
Merenstein JL, Bennett IJ, Madden DJ
Hum Brain Mapp · 2026 Apr · PMID 41873035 · Full text
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 hav...
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 > 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.
Testing for Network Specificity in Brain-Behavior Associations Using Ordinal Dominance Curves.
Hillman N, Weinstein SM, Bagautdinova J … +11 more , Sun KY, Cieslak M, Salo T, Fan Y, Keller AS, Alexander-Bloch AF, Vandekar SN, Raznahan A, Satterthwaite TD, Shou H, Shinohara RT
Hum Brain Mapp · 2026 Apr · PMID 41872989 · Full text
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 asso...
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.
Revisiting Amplitude of Low-Frequency Fluctuations (ALFF) in Resting-State fMRI: Clarifications and Improvements.
Huang L, Jabakhanji R, Vigotsky AD … +3 more , Branco P, Baliki MN, Apkarian AV
Hum Brain Mapp · 2026 Apr · PMID 41867070 · Full text
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 ban...
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 z-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) z-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.
Distinct Resting-State Connectomes for Face and Scene Perception Predict Individual Task Performance.
Soyuhos O, Scarpa A, Baldauf D
Hum Brain Mapp · 2026 Apr · PMID 41867056 · Full text
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. U...
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.
Deep Learning Empowered Microstructure Codebook: New Paradigm for Multi-Parameter Tissue Characterization Estimation.
Wang T, Wan Z, Cao S … +5 more , Yu J, He Y, Xie Y, Zhang F, Wu Y
Hum Brain Mapp · 2026 Apr · PMID 41857810 · Full text
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 learnin...
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.
Brain Age Estimation on T2-FLAIR Scans for Application to Multiple Sclerosis.
Colman J, Pontillo G, Goodkin O … +31 more , Foster MA, Mahmoudi N, Wattjes MP, Brunetti A, Gonzalez-Escamilla G, Groppa S, Høgestøl EA, Westlye LT, Messina S, Palace J, Cortese R, De Stefano N, Rovira À, Sastre-Garriga J, Ropele S, Enzinger C, Rocca MA, Filippi M, Bellenberg B, Lukas C, Calabrese M, Castellaro M, Uher T, Vaneckova M, Toosy A, Ciccarelli O, Yousry T, Prados F, Barkhof F, Cole JH, MAGNIMS Study Group
Hum Brain Mapp · 2026 Apr · PMID 41853885 · Full text
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 acquir...
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, R = 0.944, 5x ensemble MAE = 2.81, R = 0.955), which were comparable to those obtained with the T1w-based model (test set MAE = 3.34 years, R = 0.942, 5x ensemble MAE = 2.84, R = 0.955, p = 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, p < 0.0001). As with T1 brain-PAD, FLAIR brain-PAD correlated with MS disease duration (R = 0.24, p < 0.0001) and EDSS (R = 0.30, p < 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.
Sub-Networks of the Brain R Structural Connectome Integrity Matrix (SCIM) and Older Adult Cognitive and Motor Functioning.
Lamar M, Wagner M, Leurgans SE … +7 more , Fan W, Zhang S, Poole V, Barnes LL, Estrella ML, Schneider JA, Arfanakis K
Hum Brain Mapp · 2026 Mar · PMID 41847971 · Full text
Neuroimaging the structural connectome provides a window into the aging brain; however, few studies address the complexities of mapping late-life structural connectivity. We defined white matter connections via a structu...
Neuroimaging the structural connectome provides a window into the aging brain; however, few studies address the complexities of mapping late-life structural connectivity. We defined white matter connections via a structural connectivity-based atlas and extracted transverse relaxation rates (R) forming a structural connectome integrity matrix to neuroimaging data of 1239 participants (age ~ 79 ± 7). Factor analyses revealed four Sub-Networks (SN) characterized by edge integrity as follows: SN-1 involved most frontal nodes, all parietal nodes, and key subcortical (basal ganglia) structures; SN-2 involved most albeit slightly different frontal nodes than SN-1, nearly all temporal and key subcortical (limbic) nodes; SN-3 was primarily characterized by edges involving select parietal and temporal and all occipital nodes; SN-4 was confined to cerebellum, basal ganglia and limbic nodes. A linear mixed-effects regression model containing weighted composite scores representing each Sub-Network and adjusting for relevant confounders demonstrated associations of lower R in SN-1, SN-2, and SN-4 with lower baseline global cognition and lower R in SN-2 and SN-3 with faster declines in global cognition. Sub-Networks were also differentially associated with domain-specific cognitive functions at baseline and over time. Nearly all Sub-Networks negatively associated with global motor function, dexterity and gait speed at baseline, but only SN-1 and SN-4 were associated with change in motor functioning, specifically gait speed, over time. Our approach to the aging structural connectome provides an assessment of its R integrity and underlying sub-networks as related to critical behaviors associated with normal and pathological aging.
Evidence of White Matter Neuroinflammation in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Diffusion-Based Neuroinflammation Imaging Study.
Yu Q, Kothe K, Kwiatek RA … +4 more , Del Fante P, Bonner A, Calhoun VD, Shan ZY
Hum Brain Mapp · 2026 Mar · PMID 41834684 · Full text
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...
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.
Somatosensory Evoked BOLD-Signals With Ultra-High Temporal Resolution.
Wesolek S, Nierhaus T, Ostwald D … +1 more , Blankenburg F
Hum Brain Mapp · 2026 Mar · PMID 41834676 · Full text
The blood oxygen level-dependent (BOLD) signal has been instrumental in characterizing brain activity. While the spatial resolution of fMRI continues to improve, relatively few methods have focused on enhancing and lever...
The blood oxygen level-dependent (BOLD) signal has been instrumental in characterizing brain activity. While the spatial resolution of fMRI continues to improve, relatively few methods have focused on enhancing and leveraging temporal resolution to investigate the spatiotemporal dynamics of the hemodynamic response. In this study, we applied a reordering method to achieve ultra-high temporal resolution (60 ms) in data acquired during a somatosensory stimulation paradigm. We then used a finite impulse response model (FIR) for each participant (N = 31) to preserve the temporal dynamics in the statistical analysis. At the group level, we employed an ANOVA combined with 4D nonparametric permutation testing to identify significant signal changes in time across the whole brain. Our results characterize the hemodynamic response in terms of both its temporal and spatial patterns and reveal distinct differences in response shapes within the somatosensory system. This method introduces a time-resolved approach to BOLD signal analysis, drawing inspiration from grand-average techniques commonly used in EEG research.
Listening Without the Noise: Near-Silent Looping Star fMRI Reveals Neural Processing of Degraded Speech.
Ritter CJ, Hüsser AM, Jakab A … +6 more , Wiesinger F, Solana AB, Fernandez B, Swanborough H, O'Gorman Tuura R, Hervais-Adelman A
Hum Brain Mapp · 2026 Mar · PMID 41821155 · Full text
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 speec...
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 > 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 > baseline and moderately degraded > 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.
Factors Contributing to Short-Term Structural Variability in a Longitudinal MRI Dataset.
Kalc P, Ter Veer M, Dahnke R … +3 more , Ziegler G, Kühn S, Gaser C
Hum Brain Mapp · 2026 Mar · PMID 41817026 · Full text
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 identi...
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.
The Regulation of Vasomotor and Cardiorespiratory Pulsations Is Disrupted in Primary Central Nervous System Lymphoma: A Case-Control fMRI Study.
Poltojainen V, Järvelä M, Keinänen N … +13 more , Bode MK, Isokangas JM, Kuitunen H, Nikkinen J, Korhonen V, Huotari N, Raitamaa L, Kananen J, Helakari H, Korhonen TK, Tetri S, Kuittinen O, Kiviniemi V
Hum Brain Mapp · 2026 Mar · PMID 41814627 · Full text
Primary central nervous system lymphoma (PCNSL) alters (peri)vascular structures while increasing vasomotor and cardiorespiratory pulsations within the brain. Vasomotor pulsations may arise from amplitude modulations of...
Primary central nervous system lymphoma (PCNSL) alters (peri)vascular structures while increasing vasomotor and cardiorespiratory pulsations within the brain. Vasomotor pulsations may arise from amplitude modulations of respiratory (RPE) and cardiovascular (CHE) pulsations while cardiovascular fluctuations may be modulated by respiration through cardiorespiratory amplitude modulation (CREM). In this study, we examined glymphatic cerebrospinal fluid convection in brains of PCNSL patients by assessing these waves. Thirty PCNSL patients (median 66y; 9 females) and 40 healthy age-matched controls (median 62y; 29 females) were scanned using an fMRI-based MREG sequence. Respective MREG fluctuation amplitudes (AF; AF; AF) were compared between groups using nonparametric permutation. Regional amplitudes were compared using Mann-Whitney analysis and Cox survival analysis. Subject-specific pulsations were analyzed through Z-score mapping. AF and AF were significantly elevated across PCNSL brains, with lesser increases in AF. However, only significant increases in AF remained after correcting for sex and head displacement. AF showed a link to mortality as it was markedly elevated in deceased patients. While elevations in all pulsations were present within (peri)tumoral regions, AF elevations extended into extra-tumoral white matter and grey matter. Thus, altered cardiorespiratory fluctuations give rise to dysfunctional vasomotor and CSF pulsations in PCNSL, predicting impaired glymphatic function.
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