Hum Brain Mapp [JOURNAL]
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200 papers
From Low Field to High Value: Robust Cortical Mapping From Low-Field MRI.
Gopinath K, Sorby-Adams A, Williams-Ramirez J … +13 more , Zemlyanker D, Guo J, Hunt D, Mac Donald CL, Keene CD, Coalson T, Glasser MF, Van Essen D, Rosen MS, Puonti O, Kimberly WT, Iglesias JE, Alzheimer's Disease Neuroimaging Initiative
Hum Brain Mapp · 2026 May · PMID 42050779 · Full text
Three-dimensional reconstruction of cortical surfaces from MRI for subsequent morphometric analysis is fundamental for understanding brain structure. While high-field Magnetic Resonance Imaging (HF-MRI) is the standard i...
Three-dimensional reconstruction of cortical surfaces from MRI for subsequent morphometric analysis is fundamental for understanding brain structure. While high-field Magnetic Resonance Imaging (HF-MRI) is the standard in research and clinical settings, its relatively limited availability hinders widespread use. Low-field MRI (LF-MRI), particularly portable systems, offers a cost-effective and accessible alternative. However, existing cortical surface analysis tools, such as FreeSurfer, are optimized for high-resolution HF-MRI and struggle with the lower signal-to-noise ratio (SNR) and resolution of LF-MRI. In this work, we present a machine learning method for 3D reconstruction and analysis of portable LF-MRI scans over a range of contrasts and resolutions. Our method works "out of the box" and does not require retraining. It leverages a 3D U-Net trained on synthetic LF-MRI data to predict signed distance functions of the cortical surfaces, followed by geometric processing to ensure topologically accurate reconstructions. We evaluate our approach using paired HF-/LF-MRI scans of the same 15 subjects and 50 subjects from the ULF-EnC dataset. The results show that our method robustly recovers surfaces across LF-MRI acquisitions, with accuracy depending on MRI contrast mechanism (T1 vs. T2), slice anisotropy (axial vs. isotropic), and resolution. A 3 mm isotropic T2-weighted scan acquired in under 4 min, which is comparable in duration to typical HF-MRI acquisitions, yields strong agreement with HF-derived surfaces: surface area correlates at , cortical parcellations reach a Dice coefficient of , and gray matter volume achieves . Cortical thickness remains more challenging but achieves correlations up to , reflecting the difficulties of achieving sub-mm precision with ~3 × 3 × 3 mm voxels. Our results also show that recon-any performs robustly across other sequences and contrasts, though thickness estimates are particularly sensitive and degrade substantially with anisotropic or low-resolution scans. We also validate our method on challenging postmortem LF-MRI scans, further illustrating its robustness. Our method represents a significant step toward making cortical surface analysis feasible for portable LF-MRI systems. The tool is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/ReconAny.
Trait-Relevant Tasks Improve Personality Prediction From Structural-Functional Brain Network Coupling.
Popp JL, Thiele JA, Faskowitz J … +3 more , Seguin C, Sporns O, Hilger K
Hum Brain Mapp · 2026 May · PMID 42050765 · Full text
Personality traits capture stable patterns of behavior and thought, and neurobiological correlates were identified in structural and functional brain networks. Here, we investigate whether the coupling between structural...
Personality traits capture stable patterns of behavior and thought, and neurobiological correlates were identified in structural and functional brain networks. Here, we investigate whether the coupling between structural and functional brain networks (SC-FC coupling), during resting state and seven tasks of varying trait-relevance, is associated with individual differences in the Big Five personality traits. We used diffusion-weighted and functional magnetic resonance imaging from 764 participants of the Human Connectome Project and modelled individual differences in SC-FC coupling with similarity and communication measures. These measures approximate functional interactions unfolding on top of the structural connectome and were set in relation to individual variations in personality traits. Small but significant associations in the main analysis were only observed during trait-relevant tasks: for agreeableness during social cognition, and conscientiousness could be predicted from task-general coupling patterns. We conclude that optimizing trait-relevance of tasks during neuroscientific measurement presents a promising means to increase effect sizes in studies on brain-behavior associations.
Neural Substrates of Attentional Control During Emotional Processing: Evidence From rTMS-fMRI Targeting the Frontal Eye Field.
Malsert J, Rochas V, Rihs T … +2 more , Pichon S, Vuilleumier P
Hum Brain Mapp · 2026 May · PMID 42047223 · Full text
Past research has provided conflicting evidence concerning whether emotional processing in the amygdala arises independent of selective attention to threat-related stimuli or instead depends on attentional resources and...
Past research has provided conflicting evidence concerning whether emotional processing in the amygdala arises independent of selective attention to threat-related stimuli or instead depends on attentional resources and top-down voluntary control. Here, we combine repetitive transcranial magnetic stimulation (rTMS) targeting the right frontal eye field (FEF) with functional magnetic resonance imaging (fMRI) to examine how perturbing top-down attentional control is associated with changes in neural responses to emotional stimuli in visual cortex and amygdala. Participants performed a matching task in which they had to judge whether task-relevant image pairs were similar or different while ignoring task-irrelevant pairs. On each trial, one pair showed houses and the other pair displayed either neutral or fearful faces. The task was performed in two sessions following either rTMS or no TMS, in counterbalanced order. Behavioral results revealed that right FEF perturbation selectively slowed responses to neutral but not fearful faces. ROI analyses revealed selective changes in fusiform face area (FFA) responses to neutral faces following FEF rTMS, while responses to fearful faces were relatively preserved; in parallel, amygdala responses to fearful faces remained intact or showed increased activation. A control group undergoing the same protocol with rTMS applied to the vertex (VTX) showed no significant changes in behavioral performance or neural activation patterns. Together, these findings suggest that neural responses to emotionally salient stimuli may be less dependent on top-down attentional modulation than responses to neutral stimuli, consistent with models proposing partially distinct contributions of attentional and emotional processing networks.
Selective Modulation of Evidence Accumulation by Hippocampal Theta Oscillations During Mnemonic Decision-Making.
Robins PL, Gilbert JR, Luber B … +5 more , Mustafa N, Bharti E, Stout JD, Carver FW, Deng ZD
Hum Brain Mapp · 2026 May · PMID 42047212 · Full text
A core function of episodic memory is to distinguish between overlapping experiences by converting similar inputs into distinct, non-overlapping representations, a process termed pattern separation. While anatomical mode...
A core function of episodic memory is to distinguish between overlapping experiences by converting similar inputs into distinct, non-overlapping representations, a process termed pattern separation. While anatomical models emphasize the role of specific hippocampal subfields, particularly the dentate gyrus, CA3, and CA1, less is known about how these computations unfold over time and influence memory-based decisions. Here, we use source-localized magnetoencephalography and computational modeling to examine how theta oscillations from the hippocampus as a whole are related to evidence accumulation during mnemonic discrimination. Participants performed the Mnemonic Similarity Task, in which they classified Repeat, Lure, and Foil images as "Old," "Similar," or "New." Event-related spectral and source activity confirmed reliable hippocampal engagement during the task despite its anatomical depth. We fit a hierarchical Linear Ballistic Accumulator model to behavioral data, estimating trial-by-trial drift rates as a latent index of mnemonic evidence accumulation, and examined whether hippocampal theta power predicted these dynamics. Left hippocampal theta was negatively associated with drift toward "New" responses on lure trials, while right hippocampal theta was positively associated with drift toward "Similar" responses on foil trials. These effects suggest that hippocampal theta selectively indexes partial-match sensitivity, with consequences that are beneficial or costly depending on whether the stimulus has an encoded memory counterpart. However, direct comparisons between hemispheres did not yield credible differences. These findings offer preliminary evidence that trial-level hippocampal theta fluctuations are related to the dynamics of memory-guided decision-making and demonstrate the feasibility of linking deep-source MEG recordings to computational models of evidence accumulation.
Longitudinal Multimodal Neuroimaging After Traumatic Brain Injury.
Radanovic A, Jamison KW, Kang Y … +3 more , Tozlu C, Shah SA, Kuceyeski A
Hum Brain Mapp · 2026 Apr · PMID 42046146 · Full text
Traumatic brain injury is a major cause of long-term cognitive impairment, yet the mechanisms underlying recovery remain poorly understood. Neuroimaging methods such as diffusion magnetic resonance imaging (MRI), functio...
Traumatic brain injury is a major cause of long-term cognitive impairment, yet the mechanisms underlying recovery remain poorly understood. Neuroimaging methods such as diffusion magnetic resonance imaging (MRI), functional MRI (fMRI), and positron emission tomography (PET) provide insight into micro- and macro-scale changes post-traumatic brain injury (TBI), but the relationships between regional cellular and functional alterations remain unclear. In this exploratory study, we conducted a longitudinal, multimodal neuroimaging analysis quantifying TBI-related pathologies in four biomarkers, namely flumazenil PET derived binding potential, diffusion MRI (dMRI)-derived structural connectivity, and resting-state fMRI-derived functional connectivity and fractional amplitude of low-frequency fluctuations in individuals with complicated mild-to-severe brain injury at the subacute (4-6 months post-injury) and chronic (1-year post-injury) stages. The TBI sample consisted of 41 fMRI, 40 dMRI, and nine PET subjects, with 16 fMRI and dMRI and seven PET longitudinal measurements. The control sample consisted of 14 dMRI and fMRI and 19 PET subjects scanned at a single time point for comparison with TBI subjects at both time points. Most of the PET and MRI subjects are overlapping in both TBI and control groups. Brain injury related regional pathologies, and their changes over time in TBI subjects, were correlated across the four biomarkers. Our results reveal complex, dynamic changes over time. We found that flumazenil-PET binding potential was significantly reduced in frontal and thalamic regions in brain-injured subjects, consistent with neural loss and dysfunction, with partial recovery over time. Functional hyperconnectivity was observed in brain injured subjects initially but declined while remaining elevated compared to non-injured controls, whereas cortical structural hypoconnectivity persisted. Importantly, we observed that brain injury-related alterations across MRI modalities became more strongly correlated with flumazenil-PET at the chronic stage. Regions with chronic reductions in flumazenil-PET binding also showed weaker structural node strength and lower amplitude of low-frequency fluctuations, a relationship that was not found at the subacute stage. This observation could suggest a progressive convergence of structural and functional disruptions with neuronal dysfunction and loss over time. Additionally, regions with declining structural node strength also exhibited decreases in functional node strength, while these same regions showed increased amplitude of low-frequency fluctuations over time. This pattern suggests that heightened intrinsic regional activity may serve as a compensatory mechanism in regions increasingly disconnected due to progressive axonal degradation. Altogether, these findings advance our understanding of how multimodal neuroimaging captures the evolving interplay between neuronal integrity, structural connectivity, and functional dynamics after brain injury. Given the exploratory nature of this study, stemming from the modest sample size, future work in larger cohorts will be essential to validate and refine these preliminary associations as well as the inclusion of multiple measures of healthy controls. Clarifying these interrelationships could inform prognostic models and enhance knowledge of degenerative, compensatory, and recovery mechanisms in traumatic brain injury.
Explainable AI Insights Into EEG Classification and Its Alignment to Neural Correlates.
Eilts H, Ivucic G, Koenen N … +3 more , Wright MN, Schultz T, Putze F
Hum Brain Mapp · 2026 Apr · PMID 42037083 · Full text
While deep learning has drastically improved the performance of electroencephalography (EEG) analysis, it remains unclear what these models, such as EEGNet, learn from the data and how their learned features relate to ne...
While deep learning has drastically improved the performance of electroencephalography (EEG) analysis, it remains unclear what these models, such as EEGNet, learn from the data and how their learned features relate to neuroscientific concepts. In this work, we introduce a comprehensive interpretability framework for deep learning models of neural data based on Concept Relevance Propagation (CRP), an extension of layer-wise relevance propagation that enables the analysis of abstract concepts encoded by individual neurons and filters. We apply CRP to individual filters of convolutional neural networks (EEGNet) trained using leave-one-out cross-validation. To identify common classification strategies across models, we guide the selection of representative data for individual filters using relevance maximization, reduce dimensionality via UMAP, and identify clusters of filters encoding similar concepts through density-based clustering. To gain insight into the neural correlates of these tasks, we analyze the learned features across multiple data domains without requiring model retraining. We integrate a virtual inspection layer to project explanations into the frequency domain, enabling the simultaneous analysis of spatial, temporal, and spectral aspects using topographic maps, functional grouping, and independent component analysis (ICA). Using three EEG classification tasks-auditory attention, internal/external attention, and motor imagery-we demonstrate that our approach reveals interpretable, task-relevant neural patterns that generalize across participants. Overall, this framework provides a step toward understanding the models itself and gaining insights into the tasks in terms of neuroscience.
The Neural Processes Underpinning Flexible Semantic Retrieval in Visual and Auditory Modalities.
Shao X, Zhang M, Wang X … +5 more , Gouws A, Jackson RL, Smallwood J, Krieger-Redwood K, Jefferies E
Hum Brain Mapp · 2026 Apr · PMID 42017780 · Full text
Contemporary accounts of semantic cognition propose that conceptual knowledge is supported by a heteromodal conceptual store and controlled retrieval processes. However, it remains unclear how the neural basis of semanti...
Contemporary accounts of semantic cognition propose that conceptual knowledge is supported by a heteromodal conceptual store and controlled retrieval processes. However, it remains unclear how the neural basis of semantic control varies across modalities. Recent models of cortical organisation suggest that control networks are distributed along a unimodal-to-heteromodal cortical gradient, with the semantic control network (SCN) located in more heteromodal cortex than the domain-general multiple demand network (MDN). We used fMRI to examine how these networks respond to semantic control demands in visual and auditory tasks. Participants judged the semantic relatedness of spoken and written word pairs. On half of the trials, a task cue specified the semantic feature to guide retrieval; on the remaining trials, no such cue was given. The SCN showed greater activation when task knowledge was available, consistent with a role in the top-down control of semantic retrieval across modalities. In contrast, the MDN showed greater activation for spoken words, likely reflecting increased demands in speech perception. These findings demonstrate a dissociation between control networks, with SCN involvement modulated by task structure and MDN activity influenced by perceptual difficulty.
Dynamic Fusion of Genomics and Functional Network Connectivity in UK Biobank Reveals Schizophrenia-Related SNP Manifolds.
Chen J, Iraji A, Fu Z … +5 more , Duda M, Andrés-Camazón P, Thapaliya B, Liu J, Calhoun VD
Hum Brain Mapp · 2026 Apr · PMID 42010738 · Full text
Many mental disorders show strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) has shown high sensitivity to brain changes related to mental disorders. However, previous studies linking...
Many mental disorders show strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) has shown high sensitivity to brain changes related to mental disorders. However, previous studies linking dFNC to genetics largely follow a paradigm to identify associations between one set of genetic factors and multiple sets of connectivity features from different dFNC states, ignoring the potential variability in genetic correlates across states. We propose a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically tuned genetic manifolds. A sliding window approach was utilized to estimate four dFNC states and compute subject-level state-average dFNC (sa-dFNC) features. The sa-dFNC features of each state were combined with schizophrenia risk single nucleotide polymorphisms (SNPs) within a jICA fusion framework, resulting in four parallel fusions in 32,861 individuals of the UK Biobank cohort. The extracted four sets of joint SNP-dFNC components were further validated for clinical relevance in a combined schizophrenia cohort of 820 individuals (348 patients). The similarity of SNP-dFNC components across four parallel fusions was evaluated as a measure of state variability. We observed a mixture of "state-invariant" and "state-variant" components for SNP and dFNC modalities. Particularly, the schizophrenia-related state-variant SNP components, or manifolds, complemented each other by capturing different SNPs involved in the same biological functions, revealing a partition of genomic risk particularly elicited by the dynamics of brain function. By augmenting the SNP factors to state-variant manifolds, this dynamic fusion framework promises additional insights into the underlying genetic risk of disease-related alterations in dynamic brain function.
Distinct Neonatal Brain Anatomy Is Associated With Cross-Disorder Genetic Risk for Psychiatric Disorders.
Dang X, Su R, Wu D … +1 more , Li M
Hum Brain Mapp · 2026 Apr · PMID 42003227 · Full text
Psychiatric disorders share a complex polygenic architecture, yet how this genetic liability relates to early brain development remains unclear. This study investigated the associations between cross-disorder polygenic r...
Psychiatric disorders share a complex polygenic architecture, yet how this genetic liability relates to early brain development remains unclear. This study investigated the associations between cross-disorder polygenic risk and neonatal brain anatomy. We derived three latent psychiatric factors using GenomicSEM, reflecting shared genetic liability among related conditions: A neurodevelopmental factor, a compulsive factor, and a mood-psychosis factor. We then calculated respective polygenic risk scores (PRS) in 336 neonates from the Developing Human Connectome Project. We found that cross-disorder PRSs (neurodevelopmental and mood-psychosis factors) showed significantly broader associations with neonatal brain volumes than disorder-specific PRSs. These associations were highly robust, as confirmed through validation analyses using updated GWAS data and an alternative PRS method (PRS-CS). These cross-disorder PRSs were strongly correlated with smaller global brain size. After accounting for this global effect, associations with a subset of brain regions remained detectable. In exploratory analyses, the neurodevelopmental factor was reproducibly linked to heightened alertness at 18 months. Our results reveal that shared genetic risk for psychiatric disorders manifests as both global and regionally specific variations in brain anatomy at birth, highlighting the value of cross-disorder genetic models for elucidating early neurodevelopmental vulnerability.
Linking GABAergic and Dopaminergic Neurotransmission: Effects on Neural Activity During Human Speech Control.
Battistella G, Simonyan K
Hum Brain Mapp · 2026 Apr · PMID 41999063 · Full text
Decades of research were instrumental in identifying the brain function controlling speech production. However, our understanding of the regulation of neuronal excitability via neurotransmission during speaking remains s...
Decades of research were instrumental in identifying the brain function controlling speech production. However, our understanding of the regulation of neuronal excitability via neurotransmission during speaking remains scant as the role of the inhibitory GABAergic system in controlling speech production is unknown. Using PET with [C]flumazenil radioligand combined with PET with [C]raclopride and functional MRI in healthy humans, we investigated the GABAergic neurotransmission and its relationship with dopaminergic function and brain activity during speaking and at the resting state. We demonstrate significant associations between neural activity and GABA receptor binding during speaking, with positive associations found in the inferior and superior parietal cortices, inferior frontal gyrus, supplementary motor area, superior temporal gyrus, putamen, and negative associations identified in the left inferior and middle frontal gyri. Neural activity was related to the interaction between the GABAergic neurotransmission and nigrostriatal dopamine release in the left associative caudate nucleus and sensorimotor putamen. Conversely, significant correlations between GABAergic neurotransmission and resting-state activity were limited to the primary visual cortex and the cerebellar lobule VI. These data provide the first direct evidence of the specific interactions of GABAergic and dopaminergic transmission with neural activity controlling the production of speech in healthy humans. Our findings suggest that GABAergic modulation of brain activity is exerted at different stages of speech control, from auditory perception to motor production, whereas dopaminergic function is important for maintaining the balance between excitation and inhibition within the speech motor circuitry.
Structuro-Functional Differentiation and Coupling of Gyri and Sulci in the Neonatal Cortex.
Mao W, He Z, Jin X … +5 more , Hamouda E, Ou X, Kendrick KM, Zhang T, Jiang X
Hum Brain Mapp · 2026 Apr · PMID 41992829 · Full text
Brain structure and function undergo rapid development during the neonatal period. Investigating neonatal brain structure, function, and their relationship is therefore crucial for understanding how the brain matures int...
Brain structure and function undergo rapid development during the neonatal period. Investigating neonatal brain structure, function, and their relationship is therefore crucial for understanding how the brain matures into a complex structuro-functional system. As fundamental anatomical units of the cerebral cortex, gyri and sulci provide novel and valuable insights for such investigations. However, gyro-sulcal differentiation and their structuro-functional developmental relationship in neonates remain poorly explored. To address this gap, we used multi-modal MRI data (structural T2w, diffusion-weighted, and resting state functional MRI) from 438 neonatal brains in the public dHCP dataset. We systematically examined differences in functional connectivity (FC) and structural connectivity (SC) between gyri and sulci from 38 to 44 weeks postmenstrual age, as well as their FC-SC coupling characteristics. From 38 to 44 weeks, both FC and SC were consistently strongest between gyro-gyral regions and weakest between sulco-sulcal regions, demonstrating that gyri act as global information processing hubs while sulci serve as local functional units in the neonatal brain. FC-SC coupling exhibited distinct patterns across cortical lobes and over time, with a characteristic shift from coupling to decoupling around 41 weeks in most regions. This study provides a foundation for understanding early developmental mechanisms of brain structure-function relationships and establishes a normative reference of gyro-sulcal differentiation as well as FC-SC coupling in the neonatal period. These findings may inform future investigations of atypical neurodevelopment and contribute to the identification of early biomarkers for neurodevelopmental disorders.
Transdiagnostic Profiles of BOLD Signal Variability in Autism and Schizophrenia Spectrum Disorders: Associations With Cognition and Functioning.
Secara MT, Khan Z, Rashidi A … +13 more , Oliver LD, Yu JC, Foussias G, Dickie EW, Szatmari P, Desarkar P, Lai MC, Baracchini G, Malhotra AK, Buchanan RW, Voineskos AN, Ameis SH, Hawco C
Hum Brain Mapp · 2026 Apr · PMID 41987679 · Full text
Autism spectrum disorder (autism) and schizophrenia spectrum disorders (schizophrenia) exhibit overlapping social and neurocognitive impairment and considerable neurobiological heterogeneity. Blood-oxygen-level-dependent...
Autism spectrum disorder (autism) and schizophrenia spectrum disorders (schizophrenia) exhibit overlapping social and neurocognitive impairment and considerable neurobiological heterogeneity. Blood-oxygen-level-dependent (BOLD) signal variability captures the brain's moment-to-moment fluctuations, offering a dynamic marker of neural flexibility that is sensitive to cognitive capacity. This study aimed to examine intra-regional BOLD signal variability during rest and task across schizophrenia, autism, and typically developing controls (TDC) to explore transdiagnostic patterns of brain signal variability and their relationship with cognitive and functional outcomes. Intra-regional BOLD variability, measured by mean squared successive difference (MSSD), was obtained from resting-state and empathic accuracy task fMRI in 176 SSD, 89 autism, and 149 TDC participants. ANCOVAs, controlling for age, sex, and motion, assessed group differences in intra-regional and network-level BOLD variability and dimensional associations with social cognition, neurocognition, social functioning, and symptom severity. Both autism and schizophrenia exhibited lower BOLD signal variability than TDC across rest and task, with reduced variability observed in somatomotor, visual, and auditory networks (pFDR < 0.01). Greater network variability was positively associated with better social cognitive, neurocognitive, and functional scores across the sample. Resting-state variability showed stronger group-based differences and cognitive associations than task-based variability. BOLD signal variability is positively associated with social cognition, neurocognition, and social functioning across groups, suggesting that variability impacts cognitive efficiency and behavior. Reduced variability in autism and schizophrenia may indicate similar patterns of neural rigidity among these related conditions, positioning BOLD variability as a potential biomarker for neural flexibility and a valuable target for future transdiagnostic clinical interventions.
Traces of Parenthood but Not Pregnancy Loss in UK Biobank Structural Brain MRI Data.
Fischer JL, Skalkidou A, Derntl B … +1 more , Kaufmann T
Hum Brain Mapp · 2026 Apr · PMID 41968278 · Full text
Pregnancy induces neuroanatomical changes in the human brain. Previous studies detected both traces of motherhood decades after childbirth and adaptations in fathers. It is unclear which effects can be attributed to pers...
Pregnancy induces neuroanatomical changes in the human brain. Previous studies detected both traces of motherhood decades after childbirth and adaptations in fathers. It is unclear which effects can be attributed to persisting traces of pregnancy and which are effects of parenthood. We investigated effects of past birth and of pregnancy loss in women, and effects of fatherhood in men, using univariate and machine learning analyses on 205 regional brain volumes. A group of mothers and an age-matched sample of nulliparous women (N = 4357 per group, mean age 63 years) from the UK Biobank, with no past pregnancy losses, showed significant volumetric group differences in 14 regions at Bonferroni-adjusted α = 0.05. Likewise, we identified 18 significant group differences between age-matched samples of fathers and non-fathers of the same size (mean age 63.4), with 9 regions overlapping between sexes. Brain-wide association statistics for past live birth in mothers and those for fatherhood correlated (r = 0.55). XGBoost machine learning models trained to classify parenthood status separately in both datasets showed performance that was low, but significantly above chance (10-fold cross validation: AUC = 0.56, p < 1e-5 Motherhood classifier, AUC = 0.54, p < 1e-5, Fatherhood classifier, 10 k permutations). We tested the motherhood classification model on an independent test sample comprising four age-matched groups: 1. women who have never been pregnant, 2. women with past pregnancy loss but no live births, 3. women with live births but no pregnancy loss, and 4. women who experienced both. Class probability was significantly associated with live births, but not past loss. These findings may suggest that neuroanatomical patterns of past childbirth partly also reflect traces of parenthood and not solely persisting traces of past pregnancy, although a more detailed characterization of pregnancy loss data would be needed for full confirmation of this interpretation. Therefore, further research is needed to quantify the extent and understand the nature of these changes, particularly considering the known vulnerability for mental disorders associated with reproductive events.
Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET.
Moon J, Kim S, Chung H … +2 more , Jang I, Alzheimer's Disease Neuroimaging Initiative
Hum Brain Mapp · 2026 Apr · PMID 41968275 · Full text
Positron emission tomography (PET) provides an in vivo molecular marker for various diseases, including Alzheimer's disease and related dementias (ADRD). PET has become increasingly integrated into diagnostic decision-ma...
Positron emission tomography (PET) provides an in vivo molecular marker for various diseases, including Alzheimer's disease and related dementias (ADRD). PET has become increasingly integrated into diagnostic decision-making, disease staging, and clinical trial enrichment. However, its widespread use remains constrained by high costs, government regulations, and the invasiveness of radiotracer injection. Modern diagnostic frameworks emphasize the importance of multimodal biomarker assessment, such as the "amyloid/tau/neurodegeneration" (A/T/N) framework for Alzheimer's disease; however, they are constrained by these barriers. Medical image synthesis or translation offers a potential solution by enabling the reconstruction of unavailable modalities. The clinical utility of PET depends on accurately capturing regional uptake patterns rather than exact voxel-wise intensities, motivating the use of perceptual loss functions to assess higher-level semantic features in generative models. While 2D, 3D, and 2.5D perceptual losses are utilized in 3D synthesis, each encounters challenges, including limited volumetric context, the scarcity of pretrained 3D models, and difficulty balancing optimization across anatomical planes. In this work, we address cross-modal synthesis of tau PET from structural magnetic resonance imaging (MRI), generating 3D pseudo-[F]flortaucipir standardized uptake value ratio (SUVR) maps from 3D T1-weighted MR images. We propose a cyclic 2.5D perceptual loss that cyclically optimizes the axial, coronal, and sagittal planes over training phases, thereby enhancing volumetric consistency. Furthermore, we standardize PET SUVRs by scanner manufacturer, reducing inter-manufacturer variability and better preserving high-uptake regions. We evaluate the proposed approach on cohorts spanning the ADRD spectrum using data from the Alzheimer's Disease Neuroimaging Initiative and the Standardized Centralized Alzheimer's Disease and Related Dementias Neuroimaging cohort. Our approach is broadly applicable across various generative frameworks and achieves high quantitative and qualitative performance on diverse architectures, including U-Net, UNETR, SwinUNETR, CycleGAN, and Pix2Pix. Notably, it achieves better agreement between synthesized SUVRs and measured PET scans in key brain regions relevant to Alzheimer-type tau pathology. The code is publicly available at https://github.com/labhai/Cyclic-2.5D-Perceptual-Loss.
Voluntary Attention Selectively Modulates Omission Responses.
Dercksen TT, Widmann A, Wetzel N
Hum Brain Mapp · 2026 Apr · PMID 41957935 · Full text
Predictive coding conceptualizes attention as a weighting of prediction error signals. However, empirical findings on how attention influences common markers of prediction error have been inconsistent, likely because the...
Predictive coding conceptualizes attention as a weighting of prediction error signals. However, empirical findings on how attention influences common markers of prediction error have been inconsistent, likely because these markers are typically derived from stimulus-evoked responses. To avoid stimulus-related confounds and isolate effects related purely to prediction, we investigated how attention modulates brain responses to unexpected stimulus omissions. Using visual-auditory couplings where the auditory stimulus was occasionally omitted, we recorded EEG responses that revealed a multistage omission response-from early sensory to later higher-level prediction error activity. Voluntary attention was manipulated along two dimensions: (1) toward the visual or auditory modality, and (2) toward the moment of stimulus presentation or sustained over time. Early sensory prediction error, reflected by the omission N1, was unaffected by any manipulation of attention. In contrast, later high-level prediction error processing, reflected by omission P3 responses, was strongly affected by directing attention: robust responses were elicited when attention was directed to the auditory modality-where the prediction had been violated-but these were markedly reduced or absent when attention was directed to the visual modality. These results suggest an attentional system that does not affect low-level sensory prediction error but is capable of influencing distinct stages in the processing hierarchy in service of task performance. This first investigation of how attention affects different stages of omission activity suggests that voluntary attention may modulate prediction error processing via specific neurotransmitter systems and demonstrates this approach's potential for reliably studying precision-weighting in the brain.
Investigating Emotional Reactivity in Experienced Users of Psychedelics: A Cross-Sectional fMRI Study.
Orłowski P, Domagalik A, Bola M
Hum Brain Mapp · 2026 Apr · PMID 41954041 · Full text
Classic psychedelics profoundly alter emotional states, inducing intense acute experiences lasting hours, followed by subtler, longer-lasting changes in emotional reactivity that can persist for weeks. While experimental...
Classic psychedelics profoundly alter emotional states, inducing intense acute experiences lasting hours, followed by subtler, longer-lasting changes in emotional reactivity that can persist for weeks. While experimental and clinical studies document these prolonged effects, the highly context-dependent nature of psychedelic experiences leaves open the question of whether naturalistic, nonclinical use similarly modulates emotional processing. To investigate this, we conducted a preregistered, cross-sectional fMRI study comparing experienced psychedelic users (≥ 10 lifetime uses; N = 33) with closely matched nonusers (N = 34). Participants performed an emotional face recognition task, and we examined behavioral performance and neural responses to angry, happy, and fearful facial expressions. Behavioral results revealed that psychedelic users recognized angry expressions more quickly and accurately, indicating enhanced processing efficiency for threat-related stimuli. Consistent with this, whole-brain fMRI analyses showed reduced activation to anger in key limbic and salience network regions. Psychedelic users also exhibited heightened responses to happy expressions in parietal and sensorimotor cortices-aligning with prior clinical observations-as well as increased precuneus activation to fearful expressions. Region-of-interest analyses further demonstrated reduced differentiation between emotional categories in two default mode network nodes: the frontal medial cortex and parahippocampal gyrus. These findings provide a nuanced characterization of neurofunctional changes in emotional processing linked to repeated naturalistic psychedelic use. By bridging clinical and real-world contexts, this work deepens our understanding of the potential long-term consequences of psychedelics and complements existing evidence from controlled therapeutic settings.
Bridging Histology and Tractography: First In Vivo Visualization of Short-Range Prefrontal Connections Informed by Primate Tract-Tracing.
Amandola M, Kim ME, Rheault F … +2 more , Landman B, Schilling K
Hum Brain Mapp · 2026 Apr · PMID 41947581 · Full text
Decades of histological research in non-human primates have revealed a dense web of short-range connections underpinning prefrontal cortex (PFC) function. However, translating this anatomical ground-truth to the living h...
Decades of histological research in non-human primates have revealed a dense web of short-range connections underpinning prefrontal cortex (PFC) function. However, translating this anatomical ground-truth to the living human brain has been a major challenge, leaving our understanding of the PFC's intrinsic wiring incomplete. These short-range fibers are difficult to resolve with non-invasive methods like diffusion tractography, which are often hampered by false positives. Here, we provide the first systematic in vivo visualization of these pathways in the human brain. By informing high-resolution probabilistic tractography with established tract-tracing findings, we mapped 91 histologically-defined short-range connections within and between five major PFC subdivisions in 1003 individuals (547 F, 456 M). Our anatomically-informed approach successfully reconstructed these intricate connections with high precision (> 80%) and accuracy (> 70%) relative to histological findings. The resulting tracts not only captured broad organizational principles but also replicated fine-grained patterns previously only seen in invasive studies. Furthermore, these connections showed high test-retest reliability within individuals alongside significant variability between them, highlighting a stable yet unique anatomical fingerprint. Ultimately, this study shows how linking histology to tractography provides a powerful framework to advance our understanding of the human connectome and opens avenues to investigate local circuitry that underpins cognition and disease.
Spatial Entropy of Brain Network Landscapes: A Novel Method to Assess Spatial Disorder in Brain Networks.
McIntyre CC, O'Donnell SM, Khodaei M … +3 more , Lyday RG, Burdette JH, Laurienti PJ
Hum Brain Mapp · 2026 Apr · PMID 41947544 · Full text
In this work, we introduce a method for mapping the spatial entropy of functional brain network community structure images in brain space. Entropy maps indicate the extent to which the network communities present in a lo...
In this work, we introduce a method for mapping the spatial entropy of functional brain network community structure images in brain space. Entropy maps indicate the extent to which the network communities present in a local area are ordered or disordered. We demonstrate how spatial entropy can be quantified for each voxel in the brain according to the network community affiliations of surrounding voxels. This process results in interpretable maps of brain network entropy. We show that local entropy decreases in predictable brain regions during working memory and music-listening tasks. We suggest that these regional entropy reductions reflect self-organization of neural processes in support of functionally localized cognitive tasks. In summary, we propose a method that allows group-level comparison of the brain network community structure identified in individuals. Analyses in this work provide a framework for future analyses of spatial entropy in complex networks that can be mapped to Euclidean space-both within the brain and in other contexts.
Resting-State and Task Functional Magnetic Resonance Imaging Network Topology Metrics With no Threshold Selection to Predict Cognition.
Billaud CHA, Yu J
Hum Brain Mapp · 2026 Apr · PMID 41947425 · Full text
Network topology measures characterise brain networks' organisation. Graph theoretical approaches have shown fMRI topology metrics' association with cognitive performance. Because arbitrary connectivity threshold selecti...
Network topology measures characterise brain networks' organisation. Graph theoretical approaches have shown fMRI topology metrics' association with cognitive performance. Because arbitrary connectivity threshold selection biases such metrics, alternatives including the minimum spanning tree (MST) and novel measures following principles of persistent homology were proposed. The present study compared alternative and graph theoretical metrics in association with cognition for resting-state and task-fMRI. Functional connectivity matrices were computed from Human Connectome Project (Young Adult) fMRI scans during resting-state, working memory (WM), gambling, language, motor, relational processing, social cognition, and movie-watching conditions. Global efficiency, clustering coefficient (at three thresholds), diameter, leaf fraction (LF), backbone strength (BS), and cycle strength were measured. Each was tested in association with cognitive test scores. ResultsBS significantly predicted general cognitive performance, specifically progressive matrices score, composite fluid and crystallised cognition, vocabulary, spatial orientation, and WM. Diameter significantly predicted WM. WM task BS outperformed the predictive performance of graph theory measures, but not at rest, where MST LF outperformed other measures. Stronger associations were observed between cognitive test scores and topology measures derived from task-based fMRI, especially the N-Back task, as opposed to resting-state fMRI. Among task-based topology measures, BS was the most strongly related to cognition.
Nonlinear Asymmetric Blood Oxygenation Level Dependent Responses in Somatosensory Cortex.
Wang F, Yang PF, Mishra A … +2 more , Chen LM, Gore JC
Hum Brain Mapp · 2026 Apr · PMID 41947417 · Full text
Blood oxygenation level dependent (BOLD) responses in fMRI have previously been shown to be nonlinear with regard to changes in stimulus parameters, and as a result they may be asymmetric when comparing stimulus increase...
Blood oxygenation level dependent (BOLD) responses in fMRI have previously been shown to be nonlinear with regard to changes in stimulus parameters, and as a result they may be asymmetric when comparing stimulus increases with decreases from an initial condition. We measured BOLD responses to varying vibrotactile stimuli of the hand digits in a monkey, including both increases and decreases in intensity and duration, relative to different levels of initial activation. Across variations in stimulus duration and intensity, positive and negative BOLD responses were asymmetric and nonlinear. Moreover, the asymmetry between positive and negative responses was manifest at different levels of baseline activation. The results confirm that the use of a common hemodynamic response function for increases and decreases in activity may underestimate the magnitude of decreases in activation. Electrophysiological recordings from multi-electrode arrays also revealed nonlinear and asymmetric features in multi-unit activities, linking neural firing properties to the nonlinear BOLD profiles.
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