Hum Brain Mapp [JOURNAL]
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200 papers
Topologically Optimized Intrinsic Brain Networks.
Lewis N, Iraji A, Miller R … +2 more , Agcaoglu O, Calhoun V
Hum Brain Mapp · 2025 Dec · PMID 41272954 · Full text
The estimation of brain networks is instrumental in quantifying and evaluating brain function. Nevertheless, achieving precise estimations of subject-level networks has proven to be a formidable task. In response to this...
The estimation of brain networks is instrumental in quantifying and evaluating brain function. Nevertheless, achieving precise estimations of subject-level networks has proven to be a formidable task. In response to this challenge, researchers have developed group-inference frameworks that leverage robust group-level estimations as a common reference point to infer corresponding subject-level networks. Generally, existing approaches either leverage the common reference as a strict, voxel-wise spatial constraint (i.e., strong constraints at the voxel level) or impose no constraints. Here, we propose a targeted approach that harnesses the topological information of group-level networks to encode a high-level representation of spatial properties to be used as constraints, which we refer to as Topologically Optimized Intrinsic Brain Networks (TOIBN). Consequently, our method inherits the significant advantages of constraint-based approaches, such as enhancing estimation efficacy in noisy data or small sample sizes. On the other hand, our method provides a softer constraint than voxel-wise penalties, which can result in the loss of individual variation, increased susceptibility to model biases, and potentially missing important subject-specific information. Our analyses show that the subject maps from our method are less noisy and true to the group networks while promoting subject variability that can be lost from strict constraints. We also find that the topological properties resulting from the TOIBN maps are more expressive of differences between individuals with schizophrenia and controls in the default mode, subcortical, and visual networks.
The Functional Connectome Mediates Associations Between Fitness and Cognition Across the Adult Lifespan.
Merchant JS, Purcell JJ, Callow DD … +10 more , Won J, Kommula Y, Rosenberg S, Wei Y, Woodard JL, Nielson KA, Rao SM, Durgerian S, Chen S, Smith JC
Hum Brain Mapp · 2025 Nov · PMID 41243382 · Full text
Cardiorespiratory fitness (CRF) is increasingly recognized as essential for improving neural and cognitive function, especially in older age. Here, we examined a cross-sectional sample of the Human Connectome Project (HC...
Cardiorespiratory fitness (CRF) is increasingly recognized as essential for improving neural and cognitive function, especially in older age. Here, we examined a cross-sectional sample of the Human Connectome Project (HCP) Aging dataset (ages 36-100; N = 378) to test the hypothesis that functional connectivity profiles that are related to CRF are also associated with episodic memory and executive function across middle age and older adults. To test these hypotheses, we first used connectome-based predictive modeling (CPM) to identify functional connections (FCs) that were separately predictive of CRF (2 Minute Walk Test), episodic memory (Rey Auditory Verbal Learning Test), and executive function (Flanker task). Briefly, this involved using nine-fold cross-validation to identify the top 2% of FCs most correlated with the outcome measure (e.g., CRF), adjusting for age, education, and sex. We then examined the relationship between CRF (as the predictor), cognitive function (as outcome), and FCs (as mediator or moderator). First, there was a high degree of overlap between the CRF and the cognition measure functional connectivity profiles in both the ventral attentional and limbic networks. Second, although we did not observe moderation effects, we did observe that these functional connectivity profiles fully mediated the relationship between CRF and episodic memory and partially mediated the relationship between CRF and executive function. Together, these findings suggest that the connectivities within the ventral attention network (VAN), particularly with the right mid-insula, right inferior frontal gyrus, and the limbic network, are neural mechanisms that underlie the associations between CRF and cognition across the lifespan from middle to older adulthood. These findings provide insight into the potential targets (CRF) and biomarkers (functional connectivity profiles) of brain health for future interventions to improve or maintain neurocognitive health in aging.
Toward Personalized Neuroscience: Evaluating Individual-Level Information in Neural Mass Models.
Barkhau CBC, Pellengahr C, Wang Z … +26 more , Fisch L, Leenings R, Winter NR, Ernsting J, Konowski M, Grotegerd D, Meinert S, Hubbert JM, Krieger J, Borgers T, Flinkenflügel K, Leehr EJ, Stein F, Thomas-Odenthal F, Usemann P, Teutenberg L, Nenadic I, Straube B, Alexander N, Jansen A, Porschen C, Kircher T, Griffiths JD, Jamalabadi H, Dannlowski U, Hahn T
Hum Brain Mapp · 2025 Nov · PMID 41243355 · Full text
Macroscale brain modeling using neural mass models (NMMs) offers a framework for simulating human whole-brain dynamics. These models are pivotal for investigating the brain as a complex dynamic system, exploring phenomen...
Macroscale brain modeling using neural mass models (NMMs) offers a framework for simulating human whole-brain dynamics. These models are pivotal for investigating the brain as a complex dynamic system, exploring phenomena like bifurcations, oscillatory patterns, and responses to stimuli. While connectome-based NMMs allow for the creation of personalized NMMs, their utility in capturing individual-specific neural characteristics remains underexplored, with current studies constrained by small sample sizes and computational inefficiencies. To address these limitations, we employed an algorithmically differentiable version of the reduced Wong Wang (RWW) model, enabling efficient optimization for large datasets. Applying this to resting-state fMRI data from 1444 samples, we optimized models with varying parameter complexities (n = 4, 658, and 23,875), which were derived from creating biologically plausible model variants. The optimized models achieved 4%, 19%, and 56% variance explanation in empirical functional connectivity (FC), respectively. Subject identification accuracy, based on simulated FC patterns, improved from < 1% (n = 4) to almost 100% (n = 23,875). Despite this precision, individual-level correlations between model parameters and attributes like age, gender, or intelligence quotient were small (effect sizes: , standardized ). Machine learning analyses confirmed that these parameters lack the granularity to encode personal traits effectively. These findings suggest that, while current implementations of the RWW NMM can robustly replicate resting-state dynamics, the resulting parameters may lack the granularity required to map onto individual-specific behavioral metrics. This highlights a critical alignment problem: neural patterns and behavioral constructs such as intelligence may not correspond in a one-to-one fashion but instead represent higher-level abstractions. Bridging this gap will require the development of new tools capable of uncovering the underlying mapping manifolds, likely situated at the level of functional dynamics rather than isolated parameters. Future efforts should build on individual-level mechanistic modeling by exploring more expressive model classes and integrating richer sources of data, such as multimodal imaging or task-based paradigms, to better capture individual variability in both neural dynamics and behavioral traits. Such approaches may ultimately help to bridge the gap between model-based neural similarity and clinically meaningful personalization.
Lateralization Disruption and Dynamic Balance Alterations in Alzheimer's Disease: Impacts on Hemispheric Interaction and Cognitive Performance.
Wang J, Li Y, Yang Y … +6 more , Liang Z, Xie P, Li X, Guo Y, Li S, Chen X
Hum Brain Mapp · 2025 Nov · PMID 41235769 · Full text
Brain lateralization is considered evolutionarily adaptive. Impaired functional specialization is thought to cause abnormal lateralization in neurological disorders. However, the dynamic changes in brain laterality in Al...
Brain lateralization is considered evolutionarily adaptive. Impaired functional specialization is thought to cause abnormal lateralization in neurological disorders. However, the dynamic changes in brain laterality in Alzheimer's disease (AD) remain unclear. In this study, resting-state fMRI data and neuropsychological assessments from 109 participants (49 AD patients and 60 healthy controls [HC]) were used. Dynamic laterality time series were constructed by extracting the dynamic laterality index (DLI) within each sliding window. We assessed two key features: laterality reversal (LR), reflecting intra-hemispheric processing efficiency, and laterality fluctuation (LF), indicating inter-hemispheric communication. Group differences in dynamic laterality characteristics were analyzed using statistically rigorous methods, regressing out gender, age, years of education, and head movements. Spearman correlation analyses examined the relationship between laterality characteristics and cognitive performance. Our results showed that AD patients exhibited a more pronounced loss of left lateralization as well as stronger right lateralization, especially in the somatomotor network (SMN) and default mode network (DMN). Additionally, we observed decreased LR as well as increased LF with global trends in AD. These divergent changes disrupted the dynamical balance between intra- and inter-hemispheric information interaction. Notably, this imbalance depended on the degree of lateralization, and the higher order cognitive networks with high-level lateralization were more vulnerable. Importantly, the observed abnormal lateralization metrics were associated with worse cognitive impairment. Our study highlights a disruption of dynamic lateralization balance in higher order cortical networks in AD patients and reveals its potential role in the disease's pathophysiology.
Diffusion MRI Microstructure Markers of Changes in the Human Brain Across the Lifespan Using Constrained Spherical Deconvolution and Fixel-Based Techniques.
Newman BT, Van Horn JD, Druzgal TJ
Hum Brain Mapp · 2025 Nov · PMID 41235744 · Full text
Understanding how the brain develops, matures, ages, and declines is one of the fundamental questions facing neuroscience. This study uses 3-tissue constrained spherical deconvolution (3T-CSD) and more recently developed...
Understanding how the brain develops, matures, ages, and declines is one of the fundamental questions facing neuroscience. This study uses 3-tissue constrained spherical deconvolution (3T-CSD) and more recently developed fixel-based derivatives to examine the relationship between brain diffusion microstructure and chronological age. These metrics are able to quantify signal fraction measurements at the voxel-wise level from six different tissue microenvironments found in the brain: extracellular free water, intracellular isotropic, intracellular anisotropic, fiber density and cross-section, the g-ratio, and aggregate conduction velocity. This study analyzes the Nathanial Kline Institute's Rockland cohort, a large-scale community sample of brain MRI data across the lifespan (409 subjects, ages 5-85 years). Microstructural measurements were taken in a number of structures throughout the white matter, subcortical gray matter, and lobar cortical regions while additionally evaluating lateral differences in microstructural measurements. The general trajectory of signal fraction measurements was a positive relationship with age and extracellular signal fraction and g-ratio, a negative relationship between age and intracellular isotropic signal fraction, fiber density and cross-section, and conduction velocity, and an inverted U-shaped trajectory for the intracellular anisotropic signal fraction. In individual sub-areas, these trends tended to still be present, with some notable exceptions. However, there were large differences in microstructure measurements between individual structures, including significant lateral differences between hemispheres for each of the subcortical gray matter structures and for each of the cortical regions. These results demonstrate that 3T-CSD and fixel-based metrics are able to describe age-related change across the brain and lifespan. By using a healthy population cohort this study can be used as a point of comparison for analysis of microstructure changes in the presence of pathology or with behavior. Finally, the detailed analysis of lateralized ROI results can inform diffusion microstructure studies examining cortical and subcortical regions.
Correction to "Evaluation of Thresholding Methods for Activation Likelihood Estimation Meta-Analysis via Large-Scale Simulations".
Hum Brain Mapp · 2025 Nov · PMID 41231446 · Full text
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Association of Choroid Plexus Dysfunction and Cognitive Decline in Preeclampsia: Using T1WI Imaging, Quantitative Susceptibility Mapping and Deep-Learning-Based Segmentation.
Chen B, Li M, Yang L … +8 more , Chen T, Zhang Q, Cheng Z, Huo X, Zhang F, Chen Y, Liang P, Guo L
Hum Brain Mapp · 2025 Nov · PMID 41231434 · Full text
Preeclampsia is a severe pregnancy complication that can cause brain injury, yet early detection of related cognitive deficits remains challenging. Therefore, in order to investigate alterations in choroid plexus volume...
Preeclampsia is a severe pregnancy complication that can cause brain injury, yet early detection of related cognitive deficits remains challenging. Therefore, in order to investigate alterations in choroid plexus volume (CPV) and susceptibility values of the choroid plexus (ChP) obtained from quantitative susceptibility mapping (QSM) in preeclampsia patients, we enrolled 281 participants, comprising 98 nonpregnant healthy controls (NPHC), 85 pregnant healthy controls (PHC), and 98 patients with preeclampsia. All participants were scanned on a 1.5 T MR scanner. The results of clinical characteristics and cognitive tests were collected from all the participants. One-way ANOVA tests were used to analyze the differences in CPV and susceptibility values of ChP among the three groups. Multiple linear regression analysis was used to find the factors that influenced CPV and its susceptibility values, as well as cognitive decline. Additionally, receiver operating characteristic (ROC) analysis was employed to evaluate the diagnostic performance of the two imaging measures. Preeclampsia patients exhibited smaller CPV and higher susceptibility values compared to the other groups (p < 0.001; p < 0.001). Significant negative correlations were observed between body mass index (BMI), mean arterial pressure and CPV/eTIV (β = -0.100, 95% CI = -0.158 ~ -0.042, p = 0.001; β = -0.022, 95% CI = -0.033 ~ -0.011, p < 0.001). Additionally, significant positive correlations were observed between BMI (β = 0.455, 95% CI = 0.125 ~ 0.786, p = 0.007), mean arterial pressure (β = 0.170, 95% CI = 0.107 ~ 0.232, p < 0.001), hemoglobin (β = 0.152, 95% CI = 0.051 ~ 0.254, p = 0.003) and susceptibility values of ChP. Furthermore, CPV/eTIV and susceptibility values of ChP could be independent contributing factors of scores of TMT. The combination of CPV, susceptibility values of ChP, BMI and gestational week could distinguish preeclampsia from pregnant groups (AUC = 0.787, 95% CI = 0.722-0.853, p < 0.001) as well as distinguish individuals with cognitive decline from preeclampsia patients (AUC = 0.737, 95% CI = 0.621-0.844, p < 0.001). These findings indicate that smaller CPV and higher susceptibility values characterize preeclampsia and may serve as auxiliary indices for its diagnosis and related cognitive decline.
Utility of Harmonisation for Fixel-Based Metrics in Travelling Subjects and Alzheimer's Disease Data.
Zou R, Kamagata K, Mito R … +11 more , Takabayashi K, Andica C, Uchida W, Guo S, Kitagawa T, Fujita S, Uematsu A, Maikusa N, Koike S, Aoki S, Alzheimer's Disease Neuroimaging Initiative
Hum Brain Mapp · 2025 Nov · PMID 41222163 · Full text
Fixel-based analysis (FBA) is an advanced diffusion MRI analysis technique that facilitates the evaluation of white matter microstructure within 'fixels' (specific fibre populations within a voxel). In recent years, FBA...
Fixel-based analysis (FBA) is an advanced diffusion MRI analysis technique that facilitates the evaluation of white matter microstructure within 'fixels' (specific fibre populations within a voxel). In recent years, FBA has gained prominence for its ability to better characterise fibre tract-specific changes than the more conventional diffusion MRI approaches and has shown promise in elucidating the pathophysiology of psychiatric and neurological diseases. However, FBA has been predominantly limited to single-centre studies, minimising the generalisability of the technique. In this study, the popular ComBat harmonisation technique was adapted for whole-brain FBA of diffusion MRI data. The study evaluates the effectiveness of ComBat in harmonising FBA metrics of fibre density, fibre cross-section and the combined metric of fibre density and cross-section in a large travelling subject dataset (n = 49, scan = 162). Participants were scanned across multiple centres, using different scanner models and imaging protocols, and FBA metrics were compared under these varying conditions before and after harmonisation. In addition, the impact of ComBat harmonisation on disease-related findings was evaluated in an independent multi-centre Alzheimer's disease (AD) dataset, by comparing the same fixel-based measures in patients with AD (n = 27) to those in cognitively normal control participants (n = 29) before and after ComBat harmonisation. We demonstrated that ComBat harmonisation effectively mitigated variability across scanner sites, scanner models, and protocols, in the travelling subject dataset, thus enhancing the comparability of FBA metrics. Notably, ComBat harmonisation improved the detection of AD-related changes in the fornix, a critical white matter tract associated with cognitive function, and strengthened the correlations between FBA metrics and cognitive scores. These results underscore the potential of ComBat harmonisation in enhancing the reliability of multi-centre neuroimaging studies, supporting the use of harmonisation techniques for accurate detection of disease-specific changes in neurodegenerative conditions. The ability to perform ComBat harmonisation within the whole-brain FBA pipeline may help further this fibre-specific technique into large-scale multi-centre studies.
Spatial Dissociation of Atrophy and Hypophysiology in Alzheimer's Disease: Implications for Biomarkers.
Dang A, Wang D, Towne JM … +3 more , Seshadri S, Habes M, Fox PT
Hum Brain Mapp · 2025 Nov · PMID 41222140 · Full text
The Amyloid/Tau/Neurodegeneration (A/T/N) biomarker framework is used for in vivo pathological profiling of Alzheimer's disease. A binary (+/-) label is assigned for each pathology (A, T, N) based on its presence or abse...
The Amyloid/Tau/Neurodegeneration (A/T/N) biomarker framework is used for in vivo pathological profiling of Alzheimer's disease. A binary (+/-) label is assigned for each pathology (A, T, N) based on its presence or absence as determined by imaging or fluid biomarkers. Using imaging, neurodegeneration is confirmed by either structural MRI (indicating atrophy) or 18F-FDG PET (indicating hypometabolism), implicitly assuming that both modalities identify equivalent pathology. Preliminary evidence suggests that atrophy and hypometabolism do not spatially co-localize and are likely caused by distinct underlying pathologies. Further, while MRI is widely available, many sites lack access to PET. Recent evidence indicates that hemodynamic indices, identified via functional MRI or SPECT, may be spatially concordant with hypometabolism identified by 18F-FDG-PET and able to serve as reliable proxies. Coordinate-based meta-analyses of voxel-based morphometry and voxel-based physiology reports in Alzheimer's disease, collectively analyzing 139 articles (4218 individuals) were performed to test the following hypotheses: (1) that atrophy and hypometabolism in AD are spatially dissociated; (2) that hemodynamic indices exhibit the same spatial distributions as hypometabolism identified by PET; (3) that regions of atrophy and hypometabolism involve different functional systems and cognitive operations. Results confirmed all three hypotheses. Separation of atrophy and hypometabolism into two distinct subcategories of neurodegeneration as well as the development of regionally specific biomarkers for each are in order.
Age-Related Changes in the Amygdala From In Utero to Early Childhood: Association With Social and Cognitive Outcomes.
Mueller ME, Nichols ES, Al-Saoud S … +5 more , de Vrijer B, McKenzie CA, Eagleson R, de Ribaupierre S, Duerden EG
Hum Brain Mapp · 2025 Nov · PMID 41216902 · Full text
The amygdala is a key element of the limbic system. It is involved in emotional processing controlling responses to social, affective, or motivational stimuli, facilitating interaction with the surrounding environment. G...
The amygdala is a key element of the limbic system. It is involved in emotional processing controlling responses to social, affective, or motivational stimuli, facilitating interaction with the surrounding environment. Given its importance in typical development, understanding early normative growth patterns in the amygdala can provide insights into the origins of emotional and social behaviors. This study examined age-related differences in amygdala volume from the fetal to early childhood period across six cohorts from in utero to the first 4 years of life, and explored the association with social, cognitive and communication outcomes. Amygdala volumes were analyzed in 471 participants ranging from 27 to 195 weeks postmenstrual age (PMA). The results of the mixed model analyses indicated that volume significantly increased in size with age (t(534) = 7.92, p < 0.001), with the left amygdala demonstrating stronger age-related changes (p < 0.001) and with males showing steeper age-related changes compared to females (p = 0.012). In a subset of participants with developmental outcome data (n = 30), significant associations were observed for the left amygdala, with larger volumes predicting receptive communication (B = -0.02, p = 0.017), including an interaction with sex (B = 0.03, p = 0.028). Additional effects were found for social-emotional (B = -0.03, p < 0.001) and cognitive outcomes (B = -0.02, p < 0.001), with interactions by sex and age (all p < 0.05), whereas no significant findings were detected for the right amygdala. These results characterize age-related changes in amygdala volumes early in development and highlight the role of sex and laterality, while underscoring its importance in social communication during early development.
Wired to Regulate: Brain Connectivity Predicts Emotion Regulation Capacity and Tendency.
Morawetz C, Hajrić M, Rammensee RA … +2 more , Berboth S, Basten U
Hum Brain Mapp · 2025 Nov · PMID 41204880 · Full text
Emotion regulation relies on the flexible coordination of neural networks involved in strategy selection and implementation. While previous studies have focused on task-related brain activity, the role of intrinsic, rest...
Emotion regulation relies on the flexible coordination of neural networks involved in strategy selection and implementation. While previous studies have focused on task-related brain activity, the role of intrinsic, resting-state connectivity in shaping regulatory tendency in strategy selection and capacity in strategy implementation remains less well understood. Using spectral Dynamic Causal Modeling (spDCM) of resting-state fMRI data, we examined how effective connectivity within four emotion-related brain networks predicts individual differences in the capacity to implement and the tendency to select reappraisal versus distraction for high-intensity emotional stimuli. Forty healthy adults completed two emotion regulation tasks and a 10-min resting-state fMRI scan. We found that distinct and partially overlapping network dynamics predicted both strategy-specific regulation capacity and reappraisal tendency. Notably, the fronto-parietal and parieto-limbic networks were central to both capacity and tendency. In addition, fronto-lateral and limbic networks significantly contributed to the prediction of strategy-specific measures: Reappraisal capacity was associated with broader and more inhibitory connectivity, whereas distraction capacity was related to more localized and mixed excitatory/inhibitory connectivity patterns. Crucially, the connections most predictive of distraction and reappraisal capacity were distinct rather than shared, underscoring the importance of strategy-specific neural adaptations. These findings suggest that intrinsic brain network configurations influence the individual capacity to implement specific strategies and the tendency to select one strategy over the other.
Brain Connectivity Gradients Alterations in Discordant Cerebrospinal Fluid Profile for Alzheimer's Disease Biomarkers.
Pini L, Tuzzato C, Griffa A … +7 more , Brusini L, Cruciani F, Allali G, Corbetta M, Menegaz G, Boscolo Galazzo I, Alzheimer's Disease Neuroimaging Initiative
Hum Brain Mapp · 2025 Nov · PMID 41204877 · Full text
Alzheimer's disease (AD) is a heterogeneous disorder characterized by brain accumulation of amyloid-beta (Aß, simplified as A for the AD biological model) and tau (T) proteins, with Aß emerging first. However, a signific...
Alzheimer's disease (AD) is a heterogeneous disorder characterized by brain accumulation of amyloid-beta (Aß, simplified as A for the AD biological model) and tau (T) proteins, with Aß emerging first. However, a significant proportion of individuals exhibit discordant biomarkers' profiles, such as elevated phosphorylated tau181 (p-tau181) with normal Aß42 from cerebrospinal fluid (CSF), posing diagnostic and mechanistic challenges. This study investigated whether functional and structural brain connectivity can distinguish individuals with discordant CSF profiles (A-T+) from those with concordant patterns (A+T+), hypothesizing that distinct connectivity patterns may reflect early divergent pathophysiological processes. Data from cognitively unimpaired or mildly impaired individuals in the ADNI3 repository were analyzed, selecting those with resting-state functional MRI (rsfMRI) and/or diffusion MRI (dMRI) within 18 months of CSF testing for Aß and p-tau181. Participants were grouped into A-T+ or A+T+ groups. Structural and functional connectivity gradients were generated for each participant and summarized using a Euclidean distance measure from reference gradient templates derived from cognitively unimpaired individuals without pathology (A-T-). We applied linear mixed models and analysis of variance to assess connectivity-based gradient differences between A-T+ and A+T+ groups, adjusting for relevant variables. Classification analyzes using logistic regression and support vector machine, along with feature importance via the Boruta algorithm, evaluated the discriminative power of gradient connectivity profiles. Multimodal integration was performed using partial least square canonical analysis (PLSC), and relationships between gradients and cognition were assessed via UMAP-based dimensionality reduction and bootstrapped linear regressions. Results were compared with a classical network analysis examining within- and between-network connectivity differences. Among 424 participants, n = 67 were classified as A-T+, n = 106 as A+T+, and n = 56 as cognitively healthy A-T-. The remaining 195 participants (n = 86 A+T+ and n = 109 cognitively impaired A-T-) were not included. A-T+ individuals (age = 75 ± 8.2) exhibited less cognitive impairment but greater functional connectivity gradients' distance to the reference templates (false discovery rate-corrected p < 0.05) in the temporo-occipital axis compared to A+T+ (age = 76.1 ± 7.7). Structural connectivity differences were not significant. FC-based models classified A-T+ and A+T+ with good accuracy (AUC = 0.77), loading on the same temporo-occipital regions, unlike SC (AUC = 0.52). The posterior brain involvement in A-T+ was confirmed by PLSC analyzes. A+T+ individuals showed a significant relation between cognitive scores and functional connectivity, primarily mapping the default mode network (DMN). A shift was observed in relation to executive functions and functional connectivity in A-T+. Discordant CSF profiles (A-T+) exhibit distinct functional connectivity patterns, particularly in posterior brain regions, compared to concordant CSF patterns (A+T+), which are characterized by a significant cognitive-DMN connectivity association. These results suggest that CSF p-tau181 accumulation in the absence of Aß42 may be associated with specific functional trajectories, suggesting specific pathophysiological patterns.
Higher-Order Triadic Interactions: Insights Into the Multiscale Network Organization in Schizophrenia.
Li Q, Yu S, Malo J … +3 more , Pearlson GD, Wang YP, Calhoun VD
Hum Brain Mapp · 2025 Nov · PMID 41195768 · Full text
Complex biological systems, like the brain, exhibit intricate multiway and multiscale interactions that drive emergent behaviors. In psychiatry, neural processes extend beyond pairwise connectivity, involving higher-orde...
Complex biological systems, like the brain, exhibit intricate multiway and multiscale interactions that drive emergent behaviors. In psychiatry, neural processes extend beyond pairwise connectivity, involving higher-order interactions critical for understanding mental disorders. Conventional brain network studies focus on pairwise links, offering insights into basic connectivity but failing to capture the complexity of neural dysfunction in psychiatric conditions. This study seeks to address this gap by utilizing a matrix-based entropy functional for estimating total correlation, which serves as a mathematical framework for capturing multivariate information. We apply this framework to fMRI-ICA-derived multiscale brain networks, enabling the investigation of multivariate interaction patterns within the human brain across multiple scales. Additionally, this approach holds significant promise for psychiatric research on schizophrenia, offering a novel framework for investigating higher-order triadic brain network interactions associated with the disorder. By examining both triple interactions and the latent factors underlying the triadic relationships among intrinsic brain connectivity networks through tensor decomposition, our study presents a novel approach to understanding changes in higher-order brain networks in schizophrenia. This framework not only advances our understanding of complex brain functions but also opens new avenues for investigating the pathophysiology of schizophrenia, potentially informing more targeted diagnostic and therapeutic strategies. Moreover, this method for analyzing multiway interactions is applicable across signal analysis domains. In this study, we apply this approach to neural signals in schizophrenia, demonstrating its ability to reveal complex multiway interaction patterns and provide new insights into brain connectivity beyond traditional pairwise analyses in the context of brain disorders.
Investigating Disruptions in Information Flow due to Sickle Cell Disease Using Granger Causality.
Mossazghi N, Karim HT, Farhat N … +4 more , Santini T, Novelli EM, Ibrahim T, Wood S
Hum Brain Mapp · 2025 Nov · PMID 41195759 · Full text
Sickle cell disease (SCD) is an inherited blood disorder caused by a mutation in the beta-globin gene, resulting in chronic complications, including cognitive decline-particularly in executive functions. Neuroimaging stu...
Sickle cell disease (SCD) is an inherited blood disorder caused by a mutation in the beta-globin gene, resulting in chronic complications, including cognitive decline-particularly in executive functions. Neuroimaging studies have identified structural and functional abnormalities associated with SCD; however, the directionality of information flow between brain networks and how disruptions in these interactions contribute to cognitive deficits remains poorly understood. This study employed Granger causality (GC) analysis to investigate effective connectivity and information flow between brain regions and resting-state networks using ultra-high-field 7T MRI in adult patients with SCD (n = 51) and age-, sex-, and race-matched controls (n = 44). We first performed a whole-brain network analysis, followed by an examination of specific brain regions within the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), and ventral attention network (VAN). For each analysis, we computed both the magnitude and directionality of information flow to capture the strength and directional influence of connectivity between brain regions. While patients with SCD exhibited a higher magnitude of information flow compared to controls, this difference was only statistically significant when computed at the brain region level, not at the resting-state network level. In terms of directionality, afferent flow from DAN and VAN to ECN was significantly greater in patients with SCD than in controls. Subtype analysis revealed that patients with severe SCD demonstrated significantly higher magnitude of information flow than those with mild SCD and controls. We also observed subtype-specific differences in afferent flow to ECN: mild SCD patients showed significant flow from VAN, while severe SCD patients showed significant flow from DAN. Additionally, multiple regression analyzes assessing correlations between information flow and cognitive performance showed that controls had higher R values than patients with SCD, suggesting reduced network efficiency in SCD. This study is the first to apply GC-based effective connectivity analysis in SCD, revealing unique pathways of information exchange in patients with SCD, potentially as compensatory mechanisms for disease-related structural and functional disruptions. These findings provide novel insights into how SCD impacts brain network organization and cognitive function, emphasizing the importance of investigating network-level dynamics in this population.
Estimation of the Intracranial Volume Is Crucial in Multi-Site Studies: Reliability for Longitudinal Investigations and Traveling Subjects.
Koike S, Maikusa N, Cai L … +7 more , Ueda I, Shibukawa S, Aso T, Tanaka SC, Hayashi T, Japanese Strategic Research Program for the Promotion of Brain Science (SRPBS) DecNef Study Project Group, Brain/MINDS Beyond Human Brain MRI (BMB‐HBM) Study Project Group
Hum Brain Mapp · 2025 Nov · PMID 41190774 · Full text
Accurate estimation of the total intracranial volume (TIV) is essential in brain magnetic resonance imaging (MRI) studies, particularly for multi-site longitudinal investigations. This study assessed the validity and rel...
Accurate estimation of the total intracranial volume (TIV) is essential in brain magnetic resonance imaging (MRI) studies, particularly for multi-site longitudinal investigations. This study assessed the validity and reliability of segmentation-based TIV (sbTIV) implemented in FreeSurfer version 7.2 for large-scale multi-site MRI data, by comparing it with the widely used estimated TIV (eTIV). We analyzed 6524 structural MRI scans from two multi-site projects in Japan, consisting of 30 procedures across 21 sites, 13 MRI machine types, 3 vendors, and 4 protocol categories. We tested the intraclass correlation coefficients (ICCs) between eTIV and sbTIV for each procedure and identified procedural factors affecting these ICCs using a general linear model. Machine- and protocol-specific biases were considered by a traveling subject harmonization approach. To specifically examine the reliability and validity of the longitudinal scans, we employed a general linear mixed model (GLMM). Overall agreement between eTIV and sbTIV was good (ICC = 0.78) but varied across procedures (0.62-0.94). The 1.0 mm isotropic protocol showed the highest reliability. Notably, there was poor consistency in participants with eTIV values of 120,000 mm or smaller (ICC = 0.053). sbTIV demonstrated superior cross-procedural consistency in adolescent and adult longitudinal scans compared to eTIV. In longitudinal scans, sbTIV showed greater sex difference and sex-specific increase for adolescents, and greater consistency for adults, compared to eTIV. sbTIV offers more robust and reliable estimation compared to eTIV, particularly for multi-site longitudinal studies. These findings highlight the need for careful consideration when interpreting previous multi-site studies using eTIV.
Cerebellar White Matter Microstructure Is Associated With Age, Cerebrospinal Fluid Amyloid Beta Levels, and Cognition in Cognitively Unimpaired Older Adults.
Paitel ER, Pettigrew C, Callow DD … +6 more , Moghekar A, Miller MI, Faria AV, Oishi K, Albert M, Soldan A
Hum Brain Mapp · 2025 Nov · PMID 41178759 · Full text
Structural changes in the cerebellum contribute to cognitive decline due to aging and Alzheimer's disease (AD). However, it is unclear whether age and AD pathology are associated with structural alterations in the cerebe...
Structural changes in the cerebellum contribute to cognitive decline due to aging and Alzheimer's disease (AD). However, it is unclear whether age and AD pathology are associated with structural alterations in the cerebellum among cognitively unimpaired individuals and how these alterations relate to cognition. This study examined the association of age and cerebrospinal fluid (CSF) AD biomarkers (amyloid beta [Aβ/Aβ], phosphorylated tau [p-tau]) with cerebellar gray matter (GM) and white matter (WM) volumes and cerebellar WM microstructure, measured via magnetic resonance imaging (MRI) among 176 cognitively unimpaired middle-aged and older adults (mean age = 66.70, range = 34-89). Cognition was measured with executive function and visuospatial composite scores. Older age was associated with lower cerebellar GM and WM volumes (ps < 0.01) and greater mean diffusivity (MD) in the cerebellar peduncles (p < 0.01). In contrast, more abnormal Aβ levels were associated with lower MD in three regions of interest, including the middle cerebellar peduncle (MCP, p < 0.01), a composite of superior, middle, and inferior peduncles (p < 0.05), and within-cerebellar WM (p < 0.05). Patterns were similar when comparing biomarker positive versus negative groups, particularly for the MCP. Further, lower MD in the peduncles and cerebellar WM was associated with better executive function and visuospatial composite scores (ps < 0.05), whereas cerebellar volumetric measures were not related to cognition. Results suggest that older age is associated with microstructural and volumetric cerebellar GM and WM alterations. In contrast, Aβ levels are associated with WM microstructural properties in cognitively unimpaired individuals. These findings highlight the importance of cerebellar WM microstructure to cognition and are consistent with, and expand on, previous reports that have linked more abnormal amyloid levels to WM microstructure in cerebral tracts. They also suggest that cerebellar WM alterations may be markers of preclinical AD.
Association Between Intraindividual Variability in Cognitive Performance and White Matter Organisation in Chronic Mild Traumatic Brain Injury.
Burnett J, Cobden AL, Burmester A … +3 more , Akhlaghi H, Domínguez D JF, Caeyenberghs K
Hum Brain Mapp · 2025 Nov · PMID 41175081 · Full text
Mild traumatic brain injury (mTBI) can result in persistent cognitive deficits (particularly in attention, processing speed, and working memory), even years after the injury. The majority of behavioural studies have focu...
Mild traumatic brain injury (mTBI) can result in persistent cognitive deficits (particularly in attention, processing speed, and working memory), even years after the injury. The majority of behavioural studies have focussed on averaged cognitive performance scores, such as average reaction time or accuracy scores after mTBI. However, less is understood about how mTBI affects intraindividual variability (IIV) in cognitive performance across repeated sessions or measurement occasions over time. In this study, we investigate IIV in cognitive performance in chronic mTBI patients (n = 11) relative to healthy controls (n = 22). Participants underwent a single behavioural testing session (incorporating the Rivermead Post-Concussion Symptom Questionnaire and a computerised processing speed task) and a multi-shell diffusion MRI scan. This was followed by a 30-day ecological momentary assessment (EMA) protocol using a smartphone app which measured symptoms and cognitive performance on a daily basis. Our results revealed that mTBI patients exhibited higher IIV than controls in both single-session trial-by-trial and daily EMA measures. Higher daily IIV in cognitive performance coincided with higher daily fluctuations in post-concussive symptoms. Additionally, mTBI patients showed reduced white matter organization, as indexed by fixel-wise fibre density and fibre density cross-section, in the left superior longitudinal fasciculus-II compared to controls. Finally, trial-by-trial IIV was positively associated with white matter alterations in the SLF-II in mTBI. Our findings suggest that mTBI results in dynamic performance deficits that persist into the chronic phase of injury. In addition, the white matter organization of a major fronto-parietal tract seems to play an important role in supporting the consistency of cognitive performance over time, highlighting its potential as a biomarker for understanding cognitive dynamics in healthy adults and clinical populations.
Molecular Mechanisms Explaining Neuroanatomical Subtypes in Major Depressive Disorder: Insights From Cortical Morphometric Inverse Divergence.
Ge Y, Chen L, Bai Y … +5 more , Wei W, Shen Y, Li K, Wang M, Wang M
Hum Brain Mapp · 2025 Nov · PMID 41171150 · Full text
Major depressive disorder (MDD) exhibits substantial neurobiological heterogeneity that complicates treatment selection and mechanistic understanding. While conventional group-level analyses identify diverse structural a...
Major depressive disorder (MDD) exhibits substantial neurobiological heterogeneity that complicates treatment selection and mechanistic understanding. While conventional group-level analyses identify diverse structural alterations, they obscure clinically relevant individual differences. We employed heterogeneity through discriminant analysis (HYDRA) clustering to decompose morphometric inverse divergence (MIND) network patterns into distinct neuroanatomical subtypes and examined their molecular underpinnings. We analyzed MIND network data from 240 Japanese individuals with MDD and 367 healthy controls using unsupervised clustering. Subtype-specific alterations were mapped onto neurotransmitter receptor density distributions, and transcriptomic data from the Allen Human Brain Atlas were integrated using partial least squares regression. Two neuroanatomically distinct subtypes emerged. Subtype 1 (n = 78) exhibited widespread increases in MIND strength across all Yeo networks, with predominant serotonergic, dopaminergic, and GABAergic associations. Gene expression analysis revealed SST and CUX2 correlations, with enrichment for metal ion homeostasis and circadian rhythm pathways. Subtype 2 (n = 162) showed reduced MIND strength in dorsal attention, somatomotor, frontoparietal, limbic, and default networks, with glutamatergic, cannabinoid, and dopaminergic dysfunction. This subtype demonstrated negative CRH correlations and enrichment for glutamatergic signaling and calcium/cAMP-mediated processes. Our findings demonstrate systematic decomposition of MDD heterogeneity into distinct neuroanatomical subtypes with unique molecular signatures. The identification of subtype-specific neurotransmitter profiles and transcriptomic architectures provides mechanistic insights into MDD heterogeneity, offering potential for biomarker-guided treatment selection and personalized therapeutic approaches.
Topographic Variation in Human Neurotransmitter Receptor Densities Explains Differences in Intracranial EEG Spectra.
Stoof UM, Friston KJ, Tisdall M … +2 more , Cooray GK, Rosch RE
Hum Brain Mapp · 2025 Nov · PMID 41171139 · Full text
Brain function and its failures arise from dynamical patterns of neuronal activity shaped by synaptic neurotransmission. Both neurotransmitter receptor expression and neuronal population dynamics show a remarkable region...
Brain function and its failures arise from dynamical patterns of neuronal activity shaped by synaptic neurotransmission. Both neurotransmitter receptor expression and neuronal population dynamics show a remarkable regional variability across the human cortex. We leverage this functional specialisation to characterise the relationship between receptor architectonics and electrophysiological signals. Using dynamic causal modelling (DCM), we fitted neural mass models to a normative set of intracranial EEG data. Subsequently, Bayesian model comparison helped to evaluate whether models improved when equipped with constraints on synaptic connectivity, based on regional neurotransmitter receptor densities. The results show that dynamic causal models generated region-specific intracranial EEG spectra accurately. Incorporating prior information on normative receptor distributions further improved model evidence, indicating that regional variation in receptor density explains variations in synaptic connectivity and ensuing cortical population dynamics. The output is a cortical atlas of neurobiologically informed intracortical synaptic connectivity parameters. These can serve as empirical priors in future, patient-specific models. In summary, we show that molecular cortical characteristics-that is, receptor densities-enrich and inform generative, biophysically plausible models of coupled neuronal populations. This work helps to explain regional variations in human electrophysiology, provides a methodological foundation to integrate multimodal data, and serves as a normative resource for future DCM studies of electrophysiology.
A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography.
Lo Y, Chen Y, Liu D … +8 more , Zekelman L, Rushmore J, Rathi Y, Makris N, Golby AJ, Zhang F, Cai W, O'Donnell LJ
Hum Brain Mapp · 2025 Nov · PMID 41171024 · Full text
Recently, shape measures have emerged as promising descriptors of white matter tractography, offering complementary insights into anatomical variability and associations with cognitive and clinical phenotypes. However, c...
Recently, shape measures have emerged as promising descriptors of white matter tractography, offering complementary insights into anatomical variability and associations with cognitive and clinical phenotypes. However, conventional methods for computing shape measures are computationally expensive and time-consuming for large-scale datasets due to reliance on voxel-based representations. To address these limitations, we introduce Tract2Shape, a novel multimodal deep learning framework that integrates geometric streamline features (as point clouds) with scalar data descriptors (as tabular data) from tractography to predict 10 white matter tractography shape measures. We propose a Siamese architecture in which each subnetwork incorporates a dual-encoder design, enabling each encoder to learn modality-specific representations. To enhance model efficiency, we utilize a dimensionality reduction algorithm for the model to predict five primary shape components. The model is trained and evaluated on two independently acquired datasets: the Human Connectome Project minimally preprocessed young adults (HCP-YA) dataset and the Parkinson's Progression Markers Initiative (PPMI) dataset. Tract2Shape is trained and tested on the HCP-YA dataset, with performance compared against state-of-the-art models. To assess robustness and generalization, we further evaluate the model on the unseen PPMI dataset. Tract2Shape outperforms state-of-the-art deep learning models across all 10 shape measures, achieving the highest average Pearson's r and the lowest normalized mean squared error (nMSE) on the HCP-YA dataset. The ablation study shows that both multimodal input and PCA benefit performance. On the unseen testing PPMI dataset, Tract2Shape maintains a high Pearson's r and low nMSE, demonstrating strong generalizability in cross-dataset evaluation. In comparison with traditional voxel-representation-based shape computation, Tract2Shape achieves a 99.2% improvement in efficiency (< 0.1 s per subject). Tract2Shape enables fast, accurate, and generalizable prediction of white matter shape measures from tractography data, supporting scalable analysis across datasets. This framework lays a promising foundation for future large-scale white matter shape analysis.
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