Hum Brain Mapp [JOURNAL]
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200 papers
Temporally Defined Brain Network Activation Associated With Slowed Information Processing Speed in Multiple Sclerosis.
Burta O, Akbarian F, Rossi C … +5 more , Vidaurre D, D'hooghe MB, D'Haeseleer M, Nagels G, Van Schependom J
Hum Brain Mapp · 2026 Feb · PMID 41709103 · Full text
Information processing speed (IPS) is a core cognitive deficit in people with multiple sclerosis (PwMS). Previous efforts have associated IPS performance to frontal regions, but were constrained by limited temporal resol...
Information processing speed (IPS) is a core cognitive deficit in people with multiple sclerosis (PwMS). Previous efforts have associated IPS performance to frontal regions, but were constrained by limited temporal resolution. In this work, we employed a data-driven method, the time delay embedded-hidden Markov model (TDE-HMM), to identify task-specific states that are spectrally defined with distinct temporal and spatial profiles. We used magnetoencephalographic (MEG) data recorded while healthy controls and PwMS performed a cognitive task designed to capture IPS, the Symbol Digit Modalities Test (SDMT). The TDE-HMM identified five task-relevant states, supporting a tri-factor contribution to IPS: sensory speed (occipital visual detection and processing), cognitive speed (prefrontal executive and frontoparietal attention shift), and motor speed (sensorimotor). We observed reduced prefrontal activation in PwMS, while peak features across prefrontal, frontoparietal, and occipital networks were associated with task reaction time and clinical SDMT performance. This work can drive future research for MS treatments targeting IPS improvements.
The Relationship Between Hippocampal Cerebrovascular Reactivity and Brain Structure in Older Age.
Wang C, Jobbins L, Reid G … +9 more , Hobden G, Patel R, Mackay CE, Ebmeier KP, Pinto J, Bulte D, Kivimäki M, Singh-Manoux A, Suri S
Hum Brain Mapp · 2026 Feb · PMID 41708486 · Full text
Cerebral autoregulatory mechanisms such as cerebrovascular reactivity (CVR) are impaired in dementia. However, their associations with brain structure, especially in the hippocampus, remain unclear. We investigated assoc...
Cerebral autoregulatory mechanisms such as cerebrovascular reactivity (CVR) are impaired in dementia. However, their associations with brain structure, especially in the hippocampus, remain unclear. We investigated associations between hippocampal CVR and hippocampal volume, white matter microstructural integrity, and white matter hyperintensities in older adults. 163 participants from the Heart and Brain Study received multimodal MRI scans, including T1-weighted structural imaging, diffusion tensor imaging, fluid-attenuated inversion recovery imaging at Wave 1 (2012-2014, mean age 68.2, SD 4.4 years) and Wave 2 (2019-2023, mean age 76.9, SD 4.5 years). Participants also received BOLD fMRI scans with a 5% CO inhalation challenge to measure CVR at Wave 2. Linear regression was used to examine the cross-sectional associations of hippocampal CVR with brain structure at Wave 2 as well as with changes in brain structure between Waves 1 and 2. Lower hippocampal CVR was associated with lower left hippocampal volume, as well as lower fractional anisotropy, higher mean, radial, and axial diffusivity in the corpus callosum, internal capsule, and fornix at Wave 2. Lower hippocampal CVR was also associated with greater changes in white matter integrity in the corpus callosum, internal capsule, and cingulum bundle between Waves 1 and 2. There were no significant associations between hippocampal CVR and white matter hyperintensities. Our findings highlight hippocampal CVR as a potential imaging marker associated with structural brain changes relevant to cognitive decline. Further longitudinal studies are needed to clarify the directionality of this association.
Emotion Processing in Schizophrenia: Insights From a Brain Imaging Study Comparing Patients, Siblings, and Healthy Controls.
Fiorito AM, Kheireddine A, Han H … +5 more , Morel-Prieur C, Oriol N, Schneider FC, Sescousse G, Fakra E
Hum Brain Mapp · 2026 Feb · PMID 41708484 · Full text
Abnormal facial emotion recognition has been proposed as a potential endophenotype for schizophrenia, as both patients with schizophrenia and their healthy relatives show difficulties in recognizing negative facial emoti...
Abnormal facial emotion recognition has been proposed as a potential endophenotype for schizophrenia, as both patients with schizophrenia and their healthy relatives show difficulties in recognizing negative facial emotions. In psychiatric disorders, brain functioning is considered highly informative for endophenotype research. However, recent studies have raised questions about our current understanding of the neural correlates of emotion recognition across the schizophrenia spectrum, pointing out two major limitations in previous research. First, individual fMRI studies and meta-analyses have predominantly used neutral stimuli as a comparator in emotional tasks. Yet, recent evidence indicates that neutral stimuli are not perceived as truly neutral by patients with schizophrenia or their first-degree relatives, thereby calling into question the interpretability of earlier findings. Second, few studies have explored brain connectivity in response to negative emotional faces in healthy relatives of patients, even though emotions are processed by a complex network of interconnected brain structures. This study aims to investigate the neural mechanisms underlying emotion processing in patients with schizophrenia and their siblings, focusing on brain activation and functional connectivity during the perception of negatively valenced facial expressions. We employed a more ecologically valid task compared with previous studies, incorporating emotional faces within an emotionally charged context. By employing a well-matched control condition and examining connectivity patterns within emotion-processing networks, we seek to address the limitations of prior research. 118 participants (37 patients with schizophrenia, 39 siblings, and 42 healthy controls) underwent functional magnetic resonance imaging (fMRI) scanning while performing an emotional task involving negatively valenced faces and control conditions. Behavioral performance was assessed using the Balanced Integration Score (BIS) to evaluate speed-accuracy tradeoffs. fMRI data were analyzed for brain activation, using a complementary approach based on frequentist and Bayesian statistics, as well as for functional connectivity, using generalized psychophysiological interaction (gPPI) analysis with the right and left amygdala as seed regions. Behavioral analyses revealed significant group differences, with both patients and their siblings displaying lower BIS scores compared with controls. Exploratory between-group fMRI analyses revealed that, compared with controls, siblings exhibited decreased brain responses to negative faces in a cluster encompassing the right superior temporal gyrus and the right postcentral gyrus. Bayesian analyses showed that, as compared with controls, patients seem to display similarly impaired activation in the right postcentral gyrus. In most of the rest of the brain, including the amygdala, Bayesian analyses indicated an absence of group differences for both patients and siblings compared with controls. Functional connectivity analyses revealed that siblings had stronger task-related connectivity between the right amygdala and the right cuneus compared with controls. Our findings provide evidence that while the amygdala appears to respond to negative faces similarly among controls, siblings, and patients, an endophenotypic pattern may be present in the right postcentral gyrus. Furthermore, these results suggest that activation-based analyses may not fully capture the neural abnormalities associated with emotion processing in relatives of patients with schizophrenia. We hypothesize that the increased connectivity between the right amygdala and the right cuneus may reflect a compensatory mechanism in siblings. CLINICAL REGISTRATION: https://clinicaltrials.gov/study/NCT02834208?term=schizoimagen&rank=1.
Reproducibility and Reliability of Free-Water-Corrected Diffusion Tensor Imaging of the Brain: Revisited.
Pieciak T, París G, Aja-Fernández S … +1 more , Tristán-Vega A
Hum Brain Mapp · 2026 Feb · PMID 41706439 · Full text
Diffusion tensor imaging (DTI) corrected for the free-water (FW) enables the separation of a hindered Gaussian-like profile from an isotropic component, which represents diffusion found in cerebrospinal and interstitial...
Diffusion tensor imaging (DTI) corrected for the free-water (FW) enables the separation of a hindered Gaussian-like profile from an isotropic component, which represents diffusion found in cerebrospinal and interstitial fluids within the extracellular space of grey and white matter. The assessment of the reproducibility and reliability properties of FW-corrected DTI is a crucial factor in demonstrating the potential clinical utility of this refinement, particularly considering the examinations across multiple medical centres. This paper explores the variability, reliability, and separability properties of free-water volume fraction (FWVF) and FW-corrected DTI-based measures in healthy human brain white matter using publicly available test-retest databases acquired in (1) intra-scanner, (2) intra-scanner longitudinal and (3) inter-scanner settings under varying acquisition schemes. Three different estimation techniques to retrieve the FW-corrected DTI parameters tailored to single- or multiple-shell diffusion-sensitising magnetic resonance (MR) acquisitions are investigated: (i) a direct optimization of bi-tensor signal representation in the variational framework, (ii) the region contraction-based approach and (iii) the spherical means technique combined with a correction of diffusion-weighted MR signal prior to DTI estimation. We found the previous suggestion that the FW correction to DTI in a single-shell diffusion-weighted MR acquisition improves the repeatability of DTI-based measures may be data- and methodology-dependent, and does not generalise to multiple-shell scenarios. The study also confirms that the single-shell variational FW-correction method fails to retrieve meaningful information from the mean diffusivity (MD) parameter. In contrast, the combined FW-correction scheme reduces the biological variability of MD, regardless of whether DTI is estimated from single- or multiple-shell data, given that the FWVF used for the correction in both cases is derived from multiple-shell acquisitions. Our experiments have shown that the most reliable and repeatable/reproducible measures, while preserving a moderate separability property, are fractional anisotropy and axial diffusivity estimated in a multiple-shell variant under a combined FW-correction scheme. On the contrary, our results show evidence that the least reliable measures are the mean diffusivity estimated using any FW-correction procedure, as well as the FWVF parameter itself. These results can be used to establish the direction for selecting the most attractive FW-correction DTI scheme for clinical applications in terms of the variability-reliability-separability criterion.
Leveraging Longitudinal Data to Improve BrainChart Calibration for Small Study Sample Sizes.
Adamson C, Moran C, Brown A … +7 more , Collyer TA, Sakowski SA, Srikanth V, Northam EA, Feldman EL, Cameron FJ, Beare R
Hum Brain Mapp · 2026 Feb · PMID 41705288 · Full text
Longitudinal brain imaging studies offer valuable insight into trajectories of brain structures over time; however, changes in acquisition scanners and protocols can introduce biases in the resulting measures. Although B...
Longitudinal brain imaging studies offer valuable insight into trajectories of brain structures over time; however, changes in acquisition scanners and protocols can introduce biases in the resulting measures. Although BrainChart provides a framework for calibrating FreeSurfer-derived brain structure measurements between studies or time points, population samples with small subject numbers ≤ 100 are known to give unstable sample effect parameter estimates. Under the assumption that centiles of control subjects are stable across time points, we present a method to improve reliability of population sample effect parameter estimation for time points in longitudinal studies that have small numbers of control participants but include a nested sample with repeat scans of some control participants at multiple time points. We verify the accuracy of this method using both simulated and real datasets, which demonstrate comparable estimates close to the ground truth and improved confidence intervals at smaller sample sizes than the original method. Additionally, our approach confirms accuracy across both time points and scanners. The software, which is available on GitHub, offers a means to extend the value of brain imaging data acquired using variable scanners and/or protocols for longitudinal studies, thus maximizing the value of brain imaging data in established cohorts.
In Humans, fMRI Reveals That Striosome-Like and Matrix-Like Striatal Voxels Are Engaged in Different Phases of Movement.
Sadiq A, Waugh JL
Hum Brain Mapp · 2026 Feb · PMID 41703732 · Full text
The striatum is organized into two neurochemically and anatomically distinct compartments, the striosome and matrix, that play specialized roles in motor and cognitive functions. While extensive animal research has eluci...
The striatum is organized into two neurochemically and anatomically distinct compartments, the striosome and matrix, that play specialized roles in motor and cognitive functions. While extensive animal research has elucidated compartment-specific contributions to reward, learning and motor control, direct evidence for compartment specialization in humans is lacking. We defined human striatal voxels as striosome-like or matrix-like based on biases in structural (diffusion) connectivity. Then we investigated functional activation patterns in those compartment-like voxels using task-based functional MRI (tfMRI) during pre-movement cue and five motor conditions (left/right hand, left/right foot, and tongue movements). Functional activation was strikingly segregated: striosome-like voxels were preferentially engaged during the cue phase, while matrix-like voxels dominated activation during motor execution, especially for tongue and foot movement. Motor tasks elicited robust bilateral striatal activation, with contralateral activation dominating during limb movements. Activation was more lateralized in matrix-like than in striosome-like voxels. Both striosome-like and matrix-like voxels exhibited strong activation at the onset of task execution (e.g., within the first few seconds post-cue). However, activation in matrix-like voxels declined modestly over the course of the movement phase, while striosomal activation dropped sharply at task termination, suggesting a role in behavioral transitions. These findings are consistent with the role of the striosome in anticipatory evaluation and dopaminergic modulation, and matrix specialization for executing automatized routines. This study provides the first task-based fMRI evidence of temporally and functionally distinct striatal compartment dynamics in humans, offering novel insights into striatal microcircuitry in motivated behavior and the planning and execution of movements.
Voxel-Wise Whole-Brain Analysis in Quantitative Susceptibility Mapping: A Narrative Review.
Tang Y, Zhong H, Li G … +2 more , Wang Y, Li J
Hum Brain Mapp · 2026 Feb · PMID 41699814 · Full text
Quantitative susceptibility mapping (QSM) is a post-processing magnetic resonance imaging technique that quantifies the magnetic susceptibility of biological tissue and provides insights into factors such as iron deposit...
Quantitative susceptibility mapping (QSM) is a post-processing magnetic resonance imaging technique that quantifies the magnetic susceptibility of biological tissue and provides insights into factors such as iron deposition, hemorrhage, calcification, myelin content, and oxygen extraction fraction. A variety of analytical approaches have been developed to interpret QSM data from different perspectives, including region-of-interest-based, depth-wise, surface-based, network-based, and voxel-wise methods. Among these, voxel-wise analysis has gained increasing prominence due to its ability to perform a detailed examination of the entire brain without anatomically predefined regions. This approach is especially valuable for investigating neurological pathologies and aging-related changes, including neurodegenerative and neuropsychiatric disorders. This article aims to comprehensively summarize voxel-wise analysis in QSM by outlining key methodological considerations and clinical applications. Moreover, it offers practical data processing recommendations to advance the reproducibility and transparency of voxel-wise QSM research.
Cerebellar Tissue Properties Alterations in Fibromyalgia: A T1w/T2w Ratio Study.
Quidé Y, Gustin SM
Hum Brain Mapp · 2026 Feb · PMID 41693260 · Full text
Fibromyalgia is associated with elevated levels of comorbid anxiety and depression, together impacting brain morphology possibly reflecting common underlying biological processes. The present study aims to determine the...
Fibromyalgia is associated with elevated levels of comorbid anxiety and depression, together impacting brain morphology possibly reflecting common underlying biological processes. The present study aims to determine the difference in regional myelination in females with fibromyalgia compared with females who do not experience chronic pain and to determine the role of the severity of comorbid anxiety and depressive symptoms experienced to mediate this difference in brain myelination. Thirty-three females with and 33 females without (controls) fibromyalgia were included, for which the severity of depressive and anxiety symptoms was recorded using the Hamilton Anxiety/Depression Rating scales (HAMA/HAMD). Whole-brain three-dimensional T1-weighted (T1w) and T2-weighted (T2w) magnetic resonance imaging scans were collected, and T1w/T2w ratio (myelin maps) was derived. Mediation analyses were performed with anxiety and depressive symptoms as mediators of the T1w/T2w ratio differences among the groups. Compared with the control group, the fibromyalgia group had lower T1w/T2w values in the left cerebellar lobule VI (pFWEc = 0.030) and left cerebellar lobule VIII (pFWEc = 0.029). These T1w/T2w values were significantly negatively associated with the severity of anxiety and depressive symptoms (all p < 0.001). Mediation analyses indicated that the severity of anxiety (but not depressive) symptoms mediated the group difference in T1w/T2w values in cerebellar lobule VI (p = 0.012), but not VIII (p = 0.813). Lowered cerebellar myelination may reflect chronic states of low-grade inflammation, resulting from the long-term consequences of living with fibromyalgia and related anxiety and depressive symptoms. This remains speculative, and future studies integrating peripheral biological markers of inflammation are warranted to confirm this interpretation.
Phase-Specific Dual-Site Beta Transcranial Alternating Current Stimulation Differentially Influences Functional Connectivity Associated With Motor Inhibition Performance.
Zhu T, Sack AT, Leunissen I
Hum Brain Mapp · 2026 Feb · PMID 41693256 · Full text
Inhibitory control relies on coordinated beta-band activity within a fronto-basal ganglia network, which implements inhibition via downstream effects on (pre)motor areas. However, the causal role of beta synchrony in mot...
Inhibitory control relies on coordinated beta-band activity within a fronto-basal ganglia network, which implements inhibition via downstream effects on (pre)motor areas. However, the causal role of beta synchrony in motor inhibition remains unclear. In this study, we employed dual-site transcranial alternating current stimulation (tACS) targeting the right inferior frontal gyrus (rIFG) and left primary motor cortex (lM1) to directly manipulate phase relationships in the beta band and assess their effects on both functional connectivity and motor inhibition. Fifty-two healthy participants received in-phase, anti-phase, and sham stimulation while performing a stop-signal task. Connectivity between rIFG and lM1 increased following in-phase stimulation and decreased after anti-phase stimulation. No significant group-level effects on stop-signal task performance were observed. Exploratory Δ-Δ correlations indicated that individuals with larger connectivity increases during in-phase stimulation tended to show greater improvements in inhibitory performance, whereas greater connectivity decreases during anti-phase stimulation were associated with faster go responses. Crucially, ANCOVA analyses revealed significant stimulation-dependent changes in the slope of the connectivity-behavior relationship, demonstrating that tACS altered how beta synchrony predicted inhibitory and motor performance despite unchanged mean behavior. These findings suggest that dual-site beta-tACS can bidirectionally modulate rIFG-M1 connectivity in a phase-dependent manner and selectively alter how beta synchrony predicts stopping and motor execution. This mechanistic insight may inform future research exploring dual-site beta-tACS as a tool to probe or potentially normalize inhibitory network dynamics in disorders characterized by impaired inhibition.
The Tap-to-Safety Task: A Novel fMRI Paradigm Assessing Repetitive Threat-Neutralization.
Berg H, Rozniarek R, Robinson A … +7 more , Kuplicki R, Rostel A, Mungunkhet N, Smith R, Simpson HB, Paulus MP, Aupperle RL
Hum Brain Mapp · 2026 Feb · PMID 41693243 · Full text
Existing experimental threat-related paradigms focus primarily on active or passive avoidance behavior, but do not model the common behavioral pattern of repetitive, effortful actions aimed at neutralizing perceived thre...
Existing experimental threat-related paradigms focus primarily on active or passive avoidance behavior, but do not model the common behavioral pattern of repetitive, effortful actions aimed at neutralizing perceived threats. Here, we describe and provide initial validation for the Tap-to-Safety (TTS) Task, a novel human paradigm designed to experimentally elicit repetitive threat-neutralization behavior during functional magnetic resonance imaging (fMRI). Adult participants completed the TTS Task; one sample completed the task online and an additional sample completed the task in person, with a subsample completing fMRI. Task stimuli included a threat cue (CS+) paired with an aversive unconditioned stimulus (US), safety cues (CS-) never paired with the US, and safe generalization stimuli (GSs) varying in similarity to the CS+. During an extinction phase, the CS+ was no longer paired with the US. Trials included passive viewing trials, without a neutralization option; and choice trials, in which participants could tap a button repeatedly to gain protection from the US (i.e., repetitive threat-neutralization) while reducing accumulation of reward points. Linear mixed-effects models (LMEs) were used to assess behavioral and neural responses. For fMRI analyses in a subset of participants, a priori regions of interest (ROIs) were used with Bonferroni correction. In-person results (n = 49) demonstrated increased threat expectancy, anxiety, and repetitive threat-neutralization behavior that were higher to the threat cue than to safety cues (ps < 0.001, η > 0.42), and generalized across safe stimuli resembling the threat cue (ps < 0.001, η > 0.42). During extinction, risk and anxiety ratings gradually decreased (ps < 0.015, η > 0.01), whereas neutralization behavior persisted (p = 0.10). Greater trial-wise neutralization predicted lower post-neutralization threat expectancy and anxiety ratings (ps < 0.005, η > 0.11). Behavioral results were largely replicated in an online sample (n = 88). Analyses of fMRI data (n = 31) indicated that neural activity pre-neutralization in anterior insula, dorsal anterior cingulate cortex (dACC), and dorsal striatum scaled with threat relevance of stimuli (ps < 0.001, η > 0.30) and with magnitude of neutralization (ps < 0.003, η > 0.06). These findings support the use of the TTS Task for quantifying the behavioral and neural mechanisms of repetitive threat-neutralization. Results point to a key role of the salience network and dorsal striatum. Future research in clinical populations is warranted.
Parsing Reward Processing Substages With Multimodal EEG-fMRI.
Lau KJ, Roach BJ, Abram S … +7 more , Nicholas SC, Holroyd CB, Shamshiri EA, Paulus M, Ford JM, Mathalon DH, Fryer SL
Hum Brain Mapp · 2026 Feb · PMID 41681123 · Full text
Multimodal imaging studies that combine temporally and spatially precise methods can enhance understanding of reward neurocircuitry by revealing how signals specifically time-locked to reward processing substages relate...
Multimodal imaging studies that combine temporally and spatially precise methods can enhance understanding of reward neurocircuitry by revealing how signals specifically time-locked to reward processing substages relate to functional network-level activity. Prior reward studies that require decision making and/or motor responses to obtain rewards may complicate efforts to isolate basic reward responses from higher-order functions that support reward attainment. Here, we take an integrated, multimodal approach to evaluate anticipatory and consummatory reward processing substages, while minimizing demands on higher-order cognitive and motor processes. Functional magnetic resonance imaging (fMRI) and electrophysiological (EEG) data were separately recorded from 52 adults playing a simple, slot machine task, with reward outcomes independent of performance. Joint Independent Component Analyses (jICAs) were conducted with EEG-based event-related potential (ERP) difference waveforms and fMRI contrast images specific to reward anticipation and outcome processing substages. Resulting joint independent components (JICs) segregated reward processing substages, indicating significant co-modulation between temporal ERP and spatial fMRI signals (p < 0.001). During Reward Anticipation, a JIC with the temporal signature of the stimulus preceding negativity (SPN) ERP component covaried with fMRI activation in bilateral supplementary motor areas (pre-SMA/SMA) and inferior fronto-insular salience network regions implicated in attentional orienting and shifting. During Reward Feedback, JICs with the temporal signature of the reward positivity (RewP) ERP component covaried with fMRI activation in dorsal anterior cingulate cortex (dACC), ventral striatum, SMA, and inferior frontal cortex, extending to the insula. Further, trait reward sensitivity correlated with jICA-informed Win > Loss brain activations during Reward Feedback (p = 0.016). Our findings demonstrate that temporally precise electrophysiological and spatially rich hemodynamic measures of reward processing converge to map onto specific substages of reward-related brain processes. ERP and fMRI signaling during reward feedback support covariation of the RewP with dACC-striatal reward networks while the SPN covaried with fMRI signal in pre-SMA/SMA and inferior fronto-insular regions implicated in motor planning, salience, and attention.
Selective Disruption of Salience-Network Anterior Insula Connectivity in Misophonia: A Disorder-Specific Neural Signature.
Hansen HA, Norris JE, Bain CM … +2 more , Ethridge LE, Tardif CL
Hum Brain Mapp · 2026 Feb · PMID 41676968 · Full text
Misophonia, a disorder characterized by extreme aversion to certain sounds, affects 5%-20% of the general population, yet mechanisms are still largely unknown. Recent neuroimaging studies have reported abnormal functiona...
Misophonia, a disorder characterized by extreme aversion to certain sounds, affects 5%-20% of the general population, yet mechanisms are still largely unknown. Recent neuroimaging studies have reported abnormal functional connectivity of the anterior insula to various limbic, salience, and motor regions in smaller samples of misophonic individuals versus controls, suggesting potential differences in underlying attentional or emotional processes. These findings prompt questions about the insular connectivity profile in larger samples of adults, what patterns emerge when the samples span a wider range of misophonia severity, and how these patterns may or may not overlap with other co-occurring disorders. To address these questions, we analyzed resting-state functional magnetic resonance imaging data from the open Welsh Advanced Neuroimaging Database (N = 162) comprising participants recruited from the general adult population and assessed for sensory sensitivity, anxiety, depression, and autistic traits. A misophonia severity score was derived from the sensory sensitivity data using a model trained on a second adult self-report sample from Oklahoma (N = 777). Using anterior insula as a seed for a whole-brain seed-to-voxel connectivity analysis, the derived misophonia severity scores were found to be significantly related to connectivity from the insula to clusters overlapping the planum temporale, operculum, precentral gyrus, and supplementary motor area. Notably, this insular connectivity profile was unique to the anterior insula of the salience network and was not observed when dividing the sample into misophonia (patient) versus control groups, or when grouping participants as a function of anxiety, depression, or autistic traits. These results underline the importance of the salience-network anterior insula in understanding misophonic aversion and provide tentative evidence of neurological differences between misophonia and anxiety, depression, and autism. This work aids in our understanding of neural mechanisms of misophonia and emphasizes the benefit of treating misophonia as a continuous spectrum disorder to better reflect the variability of symptoms in the real world.
Diffusion Tensor MRI and Spherical-Deconvolution-Based Tractography on an Ultra-Low Field Portable MRI System.
Gholam J, Schmid P, Ametepe J … +11 more , Plumley A, Beltrachini L, Padormo F, Teixeira R, O'Halloran R, Petkov K, Engel K, Williams SCR, Deoni S, Cercignani M, Jones DK
Hum Brain Mapp · 2026 Feb · PMID 41645829 · Full text
Ultra-low-field (ULF) MRI is emerging as an alternative modality to high field (HF) MRI due to its lower cost, minimal siting requirements, portability and enhanced accessibility-factors that enable large-scale deploymen...
Ultra-low-field (ULF) MRI is emerging as an alternative modality to high field (HF) MRI due to its lower cost, minimal siting requirements, portability and enhanced accessibility-factors that enable large-scale deployment. Although ULF-MRI exhibits a lower signal-to-noise ratio (SNR), advanced imaging and data-driven denoising methods enabled by high-performance computing have made contrasts like diffusion-weighted imaging (DWI) feasible at ULF. This study investigates the potential and limitations of ULF tractography, using data acquired on a 0.064 T commercially available mobile point-of-care MRI scanner. The results demonstrate that most major white matter bundles can be successfully retrieved in healthy adult brains within clinically tolerable scan times. This study also examines the recovery of diffusion tensor imaging (DTI)-derived scalar maps, including fractional anisotropy and mean diffusivity. Strong correspondence is observed between scalar maps obtained with ULF-MRI and those acquired at high field strengths. Furthermore, fibre orientation distribution functions reconstructed from ULF data show good agreement with high-field references, supporting the feasibility of using ULF-MRI for reliable tractography. These findings open new opportunities to use ULF-MRI in studies of brain health, development and disease progression-particularly in populations traditionally underserved due to geographic or economic constraints. The results show that robust assessments of white matter microstructure can be achieved with ULF-MRI, effectively democratising microstructural MRI and extending advanced imaging capabilities to a broader range of research and clinical settings where resources are typically limited.
The Neural Organization of Visual Information in the Auditory Cortex of the Congenitally Deaf.
Tal Z, Sayal J, Fang F … +3 more , Bi Y, Almeida J, Fracasso A
Hum Brain Mapp · 2026 Feb · PMID 41645742 · Full text
Using fMRI and pRF modeling, we show that visual spatial information is represented in the auditory cortex of congenital deaf individuals through deactivation signals. These negative BOLD responses suggest a novel mechan...
Using fMRI and pRF modeling, we show that visual spatial information is represented in the auditory cortex of congenital deaf individuals through deactivation signals. These negative BOLD responses suggest a novel mechanism of cross-modal plasticity.
Mapping the Causal Roles of Non-Primary Motor Areas in Human Reach Planning and Execution.
Haddadshargh G, de Freitas RM, Mak J … +7 more , Boos A, Fang X, Collinger JL, McKernan G, Zhan L, Liu F, Wittenberg GF
Hum Brain Mapp · 2026 Feb · PMID 41641924 · Full text
Non-primary motor areas, including dorsal premotor cortex (PMd), ventral premotor cortex (PMv), and posterior parietal cortex (PPC), contribute to movement planning, but how these regions differentially shape kinematic f...
Non-primary motor areas, including dorsal premotor cortex (PMd), ventral premotor cortex (PMv), and posterior parietal cortex (PPC), contribute to movement planning, but how these regions differentially shape kinematic features of goal-directed movements, and how this specialization is associated with functional connectivity within the frontoparietal network, remains of interest, particularly in relation to recovery after stroke. We used functional magnetic resonance imaging (fMRI), transcranial magnetic stimulation (TMS), and kinematic assessments to explore how these areas influence reaching performance in neurologically intact adults. Participants performed a goal-directed planar reaching task using the KINARM exoskeleton robot. Brief TMS pulse trains were initiated before movement onset to perturb cortical activity at subthreshold and suprathreshold intensities targeting bilateral PMd, PMv, and dorsomedial superior parietal lobule (SPL) within PPC. Resting-state fMRI quantified functional connectivity among these regions to assess whether connectivity explains stimulation-induced kinematic changes. Relative to the control target within the postcentral sulcus (PCS), subthreshold stimulation of contralateral PMd and PMv reduced reach efficiency and smoothness, while suprathreshold stimulation of contralateral PPC increased deviation error and reduced smoothness. Among ipsilateral targets, PMd showed consistent TMS-induced effects, and was the only target where resting-state connectivity predicted behavioral response. Stronger interhemispheric connectivity in the primary motor cortex and weaker interhemispheric PPC connectivity were associated with greater reductions in straightness and smoothness during subthreshold ipsilateral PMd stimulation. We found that perturbation of premotor and parietal targets led to distinct kinematic effects that varied by site, intensity, and laterality, with premotor stimulation showing the most consistent disruptions at subthreshold intensity and bilateral effects, whereas parietal effects were observed primarily for contralateral stimulation at suprathreshold intensity, and differences in network organization explain variability in behavioral response. Identifying contributions of cortical areas and connectivity patterns may help personalize interventions after stroke. Trial Registration: This study was registered at ClinicalTrials.gov under ID NCT04286516.
segcsvd: A Convolutional Neural Network-Based Tool for Quantification of Enlarged Perivascular Spaces (PVS) on T-Weighted Images.
Gibson E, Ramirez J, Woods LA … +22 more , Berberian S, Ottoy J, Scott CJM, Yhap V, Gao F, Coello RD, Valdes Hernandez M, Lang AE, Tartaglia CM, Kumar S, Binns MA, Bartha R, Symons S, Swartz RH, Masellis M, Singh N, MacIntosh BJ, Wardlaw JM, Black SE, ONDRI Investigators, ADNI, CAHHM Investigators, CAIN Investigators, colleagues from the Foundation Leducq Transatlantic Network of Excellence, Lim ASP, Goubran M
Hum Brain Mapp · 2026 Feb · PMID 41641899 · Full text
Enlarged perivascular spaces (PVS) are imaging markers of cerebral small vessel disease (CSVD) that are associated with age, disease phenotypes, and overall health. Quantification of PVS is challenging but necessary to e...
Enlarged perivascular spaces (PVS) are imaging markers of cerebral small vessel disease (CSVD) that are associated with age, disease phenotypes, and overall health. Quantification of PVS is challenging but necessary to expand an understanding of their role in cerebrovascular pathology. Accurate and automated segmentation of PVS on T-weighted images would be valuable given the widespread use of T-weighted imaging protocols in multisite clinical and research datasets. We introduce segcsvd, a convolutional neural network (CNN)-based tool for automated PVS segmentation on T-weighted images. segcsvd was developed using a novel hierarchical approach that builds on existing tools and incorporates robust training strategies to enhance the accuracy and consistency of PVS segmentation. Performance was evaluated using a comprehensive evaluation strategy that included comparison to existing benchmark methods, ablation-based validation, accuracy validation against manual ground truth annotations, correlation with age-related PVS burden as a biological benchmark, and extensive robustness testing. segcsvd achieved strong object-level performance for basal ganglia PVS (DSC = 0.78), exhibiting both high sensitivity (SNS = 0.80) and precision (PRC = 0.78). Although voxel-level precision was lower (PRC = 0.57), manual correction improved this by only ~3%, indicating that the additional voxels reflected primary boundary- or extent-related differences rather than correctable false positive error. For non-basal ganglia PVS, segcsvd outperformed benchmark methods, exhibiting higher voxel-level performance across several metrics (DSC = 0.60, SNS = 0.67, PRC = 0.57, NSD = 0.77), despite overall lower performance relative to basal ganglia PVS. Additionally, the association between age and segmentation-derived measures of PVS burden was consistently stronger and more reliable for segcsvd compared to benchmark methods across three cohorts (test6, ADNI, CAHHM), providing further evidence of the accuracy and consistency of its segmentation output. segcsvd demonstrates robust performance across diverse imaging conditions and improved sensitivity to biologically meaningful associations, supporting its utility as a T-based PVS segmentation tool.
Disrupted Frontoparietal Dynamics in Neurofibromatosis Type 1: Reduced Sensitivity and Atypical Modulation During Working Memory.
Litwińczuk MC, Garg S, Lea-Carnall C … +1 more , Trujillo-Barreto NJ
Hum Brain Mapp · 2026 Feb · PMID 41631643 · Full text
Neurofibromatosis type 1 (NF1) is a rare, single-gene neurodevelopmental disorder. Atypical brain activation patterns have been linked to working memory difficulties in individuals with NF1. This work investigates the al...
Neurofibromatosis type 1 (NF1) is a rare, single-gene neurodevelopmental disorder. Atypical brain activation patterns have been linked to working memory difficulties in individuals with NF1. This work investigates the alterations in frontoparietal effective connectivity in regions with atypical activation during working memory performance, with particular attention to self-connections (intrinsic inhibitory influences each region exerts on itself). Forty-three adolescents with NF1 and 26 age-matched neurotypical controls completed functional magnetic resonance imaging scans during a verbal working memory task. Dynamic causal models (DCMs) were estimated for the bilateral frontoparietal network (dorsolateral and ventrolateral prefrontal cortices (dlPFC and vlPFC), superior and inferior parietal gyri (SPG and IPG)). The parametric empirical Bayes approach with Bayesian model reduction was used to test the hypothesis that NF1 diagnosis would be characterised by greater inhibitory intrinsic (self-) connections. Leave-one-out cross-validation (LOO-CV) was performed to test the generalisability of group differences. NF1 participants demonstrated greater endogenous self-connectivity of left dlPFC and IPG. The DCM that best explained the effects of working memory showed that the NF1 group has increased intrinsic connectivity of left vlPFC but weaker intrinsic connectivity of left dlPFC, left SPG and right IPG. The parameters of these connections showed a modest but positive predictive correlation coefficient of 0.34 (p = 0.002) with diagnosis status, suggesting a predictive value. Overall, increased endogenous self-connectivity of left dlPFC and IPG in NF1 suggests reduced overall sensitivity of these regions to inputs. Working memory evoked different patterns of input processing in NF1 that cannot be characterised by increased inhibition alone. Instead, modulatory connectivity related to working memory showed less inhibitory self-connectivity of left dlPFC, left SPG and right IPG and more inhibitory intrinsic connectivity of left vlPFC in NF1. This discrepancy between endogenous and modulatory connectivity suggests that overall NF1 participants are responsive to cognitive task-related inputs but may show atypical adaptation to the task demands of working memory.
Investigating the Utility of Explainable Artificial Intelligence for Neuroimaging-Based Dementia Diagnosis and Prognosis.
Martin SA, Zhao A, Qu J … +5 more , Imms P, Irimia A, Barkhof F, Cole JH, Alzheimer's Disease Neuroimaging Initiative
Hum Brain Mapp · 2026 Feb · PMID 41626721 · Full text
Artificial intelligence and neuroimaging enable accurate dementia prediction but often involve 'black box' models that can be difficult to trust. Explainable artificial intelligence (XAI) aims to provide insights into th...
Artificial intelligence and neuroimaging enable accurate dementia prediction but often involve 'black box' models that can be difficult to trust. Explainable artificial intelligence (XAI) aims to provide insights into the model's decisions; however, choosing the most appropriate method is non-trivial and often context-specific. We used T1-weighted MRI to train models on two tasks: Alzheimer's disease (AD) classification (diagnosis) and predicting conversion from mild-cognitive impairment (MCI) to all-cause dementia (prognosis). We applied eleven XAI methods across two popular image classification architectures, producing visualisations of the most salient regions. We also propose a framework for interpreting explanations produced by different XAI methods and predictive models. Models achieved balanced accuracies of 81% and 67% for diagnosis and prognosis. XAI outputs highlighted brain regions relevant to AD with strong convergence across gradient-based techniques. LIME produced explanations that were most similar across architectures. Mean saliency enhanced MCI prognosis prediction when included as an additional input feature. XAI can be used to verify that models are utilising relevant features and to generate valuable measures for further analysis.
Synaptic Tuning of Brain Rhythms: From Chemical Signalling to Cortical Oscillations.
Baud MO, Van De Ville D
Hum Brain Mapp · 2026 Feb · PMID 41626713 · Full text
Stoof et al. investigate why distinct brain regions exhibit characteristic oscillatory frequencies, such as occipital alpha and frontal beta rhythms. Their work elegantly links openly available intracranial EEG spectra f...
Stoof et al. investigate why distinct brain regions exhibit characteristic oscillatory frequencies, such as occipital alpha and frontal beta rhythms. Their work elegantly links openly available intracranial EEG spectra from 106 epilepsy patients to synaptic receptor densities from available autoradiography maps in three healthy donors. In the framework of dynamic causal modelling, they show that regional oscillations emerge from balanced combinations of excitatory (AMPAR, NMDAR) and inhibitory receptors (GABAR A or B), while neuromodulatory receptors exert subtler influences.
Accelerated Diffusion Basis Spectrum Imaging With Tensor Computations.
Utt KL, Blum JS, Rim D … +1 more , Song SK
Hum Brain Mapp · 2026 Feb · PMID 41622723 · Full text
This paper introduces an advanced framework for accelerated processing of diffusion-weighted imaging (DWI) data that utilizes an entire-image modeling approach to optimize the estimation of diffusion parameters from DWIs...
This paper introduces an advanced framework for accelerated processing of diffusion-weighted imaging (DWI) data that utilizes an entire-image modeling approach to optimize the estimation of diffusion parameters from DWIs by mapping input diffusion data to predicted signals and estimating parameter values via a stochastic gradient descent optimizer (Adam). To validate this approach, we applied this framework to diffusion basis spectrum imaging (DBSI) and analyzed in vivo human brain and ex vivo mouse brain DWIs. Results demonstrate significant improvements to computational speed and signal-to-noise ratio (SNR) in estimated parameter maps compared to standard DBSI. Our approach is applicable to any diffusion signal representation and enables rapid and reliable signal partitioning in complex microstructural environments, demonstrating the potential of this framework for future neuroimaging research.
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