Hum Brain Mapp [JOURNAL]
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200 papers
A Systematic Evaluation of the Performance of Multiple Brain Age Algorithms in Two Cohorts of Youth.
Michael C, Jones NS, Hanson JL … +6 more , Westerman HB, Klump KL, Mitchell C, Monk CS, Burt SA, Hyde LW
Hum Brain Mapp · 2026 Feb · PMID 41614428 · Full text
The brain matures rapidly during childhood and adolescence. The environment may calibrate the pace of this process to shape cognition and mental health. Extending its utility as a risk marker from older to younger popula...
The brain matures rapidly during childhood and adolescence. The environment may calibrate the pace of this process to shape cognition and mental health. Extending its utility as a risk marker from older to younger populations, brain age has been proposed to capture relative brain maturity in youth. Multiple algorithms have been developed to estimate brain age in predominantly White advantaged adults. Whether these models are useful in youth, particularly in more representative cohorts, remains unclear. Here, we systematically compare five influential algorithms (Drobinin, Whitmore, Pyment, Kaufmann, Centile) in two population-based youth cohorts as a benchmark for future applied research. We examined (a) prediction accuracy (correlation with chronological age, mean absolute error), (b) sensitivity to scanning parameters (acquisition sequence, image quality), demographics (sex, puberty), and genetic similarity (intraclass correlations in pairs of monozygotic twins), and (c) strength of convergence between algorithms. In our primary sample of twins recruited from birth records to represent families in disadvantaged neighborhoods (N = 593; 9-19 years), three algorithms (Drobinin, Pyment, Centile) exhibited strong predictions from structural MRI data (correlations with chronological age = 0.51-0.68, mean absolute error = 1.60-3.02). These algorithms also generated correlated brain age values and gaps, and the expected pattern of strong but not identical intraclass correlations in monozygotic twins. Pyment exhibited the strongest correlation with age and was not sensitive to acquisition sequence, image quality, sex, and puberty. In a second sample of predominantly Black, low-income youth with a narrow age range (N = 198; 15-17 years), these five algorithms exhibited weak predictions. This study raises critical questions about what "brain age" means, how it can best be estimated depending on the research question and study population, and whether it can be universally applied across samples with heterogeneous backgrounds and age ranges that are narrow or misaligned with the training data.
Neurostructural Substrates of Hierarchical Dimensions of Internalizing Symptoms in Youth.
Durham EL, Moore TM, Ellis KE … +5 more , Wang S, Jeong HJ, Reimann GE, Archer C, Kaczkurkin AN
Hum Brain Mapp · 2026 Feb · PMID 41614244 · Full text
Internalizing symptoms are common in childhood and linked to meaningful differences in brain structure, yet their organization and neurobiological correlates during this developmental period remain poorly understood. An...
Internalizing symptoms are common in childhood and linked to meaningful differences in brain structure, yet their organization and neurobiological correlates during this developmental period remain poorly understood. An increasing number of studies conceptualize internalizing psychopathology as dimensional, transdiagnostic, and hierarchical, yet the factor structure of these symptoms in youth remains to be clearly defined. Additionally, the neurostructural underpinnings of internalizing factors warrants further investigation in younger samples. Using a large sample (N = 11,868) of 9- to 10-year-old children from the Adolescent Brain Cognitive Development (ABCD Study), we examined the factor structure of internalizing symptoms and identified associated neurostructural correlates, focusing on regional gray matter volume, cortical thickness, and cortical surface area. Higher-order modeling was used, in which the correlations among first-order factors for distress, cognitive, fear, and somatic symptoms were accounted for by a higher-order general internalizing factor. After controlling for age, sex, income, parental education, and site/MRI scanner, we found that general internalizing, distress, and cognitive symptoms were associated with smaller gray matter volume and cortical surface area across most regions. Fear symptoms showed a more localized pattern of smaller surface area in the parietal, temporal, and insular cortices. Cortical thickness and somatic symptoms showed less consistent associations. These findings contribute to the growing literature on dimensional models of internalizing psychopathology in youth by linking higher- and lower-order internalizing symptom factors to distinct patterns of neurostructural variation. Our results support the utility of hierarchical dimensional approaches for elucidating the neural substrates of internalizing symptoms during middle childhood.
Characterization of the Central Sulcus Pli-De-Passage Fronto-Pariétal Moyen in > 1000 Human Brains.
Muellen AM, Schweizer R
Hum Brain Mapp · 2026 Feb · PMID 41614230 · Full text
The pli-de-passage fronto-pariétal moyen (PPfpm), a deep cerebral fold of the human brain, presents as a common though small elevation at the central sulcus (CS) fundus where it connects the pre- and postcentral gyri at...
The pli-de-passage fronto-pariétal moyen (PPfpm), a deep cerebral fold of the human brain, presents as a common though small elevation at the central sulcus (CS) fundus where it connects the pre- and postcentral gyri at the level of the sensorimotor hand area. Given the PPfpm's location, single case-reports of its association with the functional sensorimotor hand area, and evidence linking it to the somato-cognitive action network, it holds potential as an anatomical landmark for the sensorimotor region. To characterize the macroscopic morphology of the PPfpm and evaluate its relevance as a reliable and easily detectable anatomical landmark, methods for observer-independent characterization of cortical sulci and structures were adapted and developed to investigate the PPfpm in a large dataset. For 1112 subjects from the Human Connectome Project Young Adult S1200 Release, CS depth profiles were computed from structural magnetic resonance imaging (MRI) data, and an algorithm was developed to automatically extract the PPfpm from these depth profiles. Based on the extraction of two key features approximating the PPfpm at its peak height (PPfpm-I) and its lateral end (PPfpm-II), a principal description of the PPfpm's position and extent as influenced by hemisphere, handedness, and sex was conducted. Analyses revealed the PPfpm as a near-universal cerebral fold in the adult human brain, consistently located at mid-height within the CS with a strong association to the CS sulcal pits. Though commonly of small extent, the PPfpm can be reliably identified in CS depth profiles and in structural MRI data. By providing a systematic, modern macroanatomical characterization of the PPfpm in a large cohort with rigorous quality control, the present study demonstrates the potential of the PPfpm to serve as a robust anatomical landmark for the sensorimotor hand and digit area of the human brain.
Molecular and Transcriptional Signatures of Gray Matter Volume Alterations Associated With Depressive Symptoms in Mild Cognitive Impairment.
Xu H, Li Y, Li C … +7 more , Xu F, Liu Y, Yan K, Chen S, Song W, Luo Y, Li Y
Hum Brain Mapp · 2026 Feb · PMID 41589420 · Full text
Depressive symptoms are common in individuals with mild cognitive impairment (MCI) and may contribute to an increased risk of dementia. However, the neuroanatomical correlates and underlying pathophysiological mechanisms...
Depressive symptoms are common in individuals with mild cognitive impairment (MCI) and may contribute to an increased risk of dementia. However, the neuroanatomical correlates and underlying pathophysiological mechanisms of depressive symptoms in MCI remain largely unknown. We aimed to elucidate alterations in gray matter volume and the related molecular and genetic bases in MCI patients with depressive symptoms. A total of 177 participants were enrolled, comprising 57 MCI patients with depressive symptoms (D-MCI), 60 MCI patients without depressive symptoms (nD-MCI), and 60 healthy controls (HCs). Gray matter morphological differences among groups were examined using voxel-based morphometry. The associations between depressive symptom-related morphological alterations and functional characteristics, neurotransmitter distributions, and gene expression profiles were further investigated. Group comparisons revealed depressive symptom-related morphological alterations in the inferior frontal gyrus, precentral gyrus, and anterior cingulate cortex, with the associated functional terms strongly linked to "emotions" and "affective." These alterations were further correlated with serotonergic, dopaminergic, and GABAergic systems and the expression of specific genes implicated in synaptic function and excitatory neurons. This study demonstrated the molecular and transcriptional underpinnings of brain morphological alterations linked to depressive symptoms in MCI, which may provide deeper insight into this condition.
Effective Connectivity Reveals Dual-Route Mechanism of Visual Prediction Precision via Insula and Pulvinar.
Tao L, Steward T, Corbett J … +3 more , Glarin RK, Sava TV, Garrido MI
Hum Brain Mapp · 2026 Feb · PMID 41578854 · Full text
The brain's ability to weight predictions by their precision is a central mechanism in predictive processing, enabling optimal integration of prior expectations with incoming sensory input. Despite its theoretical signif...
The brain's ability to weight predictions by their precision is a central mechanism in predictive processing, enabling optimal integration of prior expectations with incoming sensory input. Despite its theoretical significance, the neural circuitry that implements precision-weighted prediction remains unclear. Using 7-Tesla fMRI and dynamic causal modelling (DCM), this study investigated how the brain encodes the precision of predictions during a visual cueing task with high- and low-precision conditions. We focused on the key regions implicated in predictive processing: the insular cortex, the pulvinar nucleus of the thalamus and primary visual cortex (V1). Behaviourally, participants showed significantly greater accuracy in the high-precision condition (p < 0.001), confirming effective task manipulation. DCM analyses revealed that high-precision predictions elicited excitatory modulation of connectivity from the insula to V1 (P = 0.95), alongside inhibitory influences from the insula to the pulvinar (P = 0.99) and from the pulvinar to V1 (P = 0.89). Furthermore, leave-one-out cross validation revealed that individual differences in behavioural sensitivity to precision were positively predicted by pulvinar-to-insula connectivity (r = 0.36, p = 0.026) and negatively predicted by the connectivity between pulvinar and V1 (pulvinar to V1: r = 0.35, p = 0.033; V1 to pulvinar: r = 0.37, p = 0.026), highlighting the behavioural relevance of these pathways. Together, these findings suggest a dual-route mechanism whereby the insula directly enhances top-down predictions in V1 while indirectly dampening bottom-up sensory input via the pulvinar. This mechanism may facilitate Bayesian integration under uncertainty and offers new hypotheses into how precision weighting may be disrupted in neuropsychiatric conditions.
Differential Neural Dynamics in Psychomotor Retardation and Agitation of Depression.
Liang Q, Xu Z, Chen S … +8 more , Lin S, Lin X, Li Y, Zhang Y, Peng B, Hou G, Qiu Y, Northoff G
Hum Brain Mapp · 2026 Feb · PMID 41578838 · Full text
Psychomotor disturbances like agitation and retardation are key symptoms of major depressive disorder (MDD). Despite their clinical significance, the underlying neural mechanisms, for example, motor or psychomotor, remai...
Psychomotor disturbances like agitation and retardation are key symptoms of major depressive disorder (MDD). Despite their clinical significance, the underlying neural mechanisms, for example, motor or psychomotor, remain yet elusive. This study aimed to investigate whether psychomotor agitation and retardation in MDD are associated with alterations in brain dynamics. A total of 119 patients with MDD and 94 HCs were recruited and undertaken fMRI testing. Brain dynamics was measured by the time delays, the lag propagation of global to somatomotor network (SMN) resting state functional connectivity (FC, e.g., lag propagation). Lag propagation of global to SMN FC was delayed in retarded MDD compared to both agitated MDD (t = 3.256, pFDR = 0.006) and HC (t = 2.493, pFDR = 0.041). Further, we observed a significant correlation of the severity of agitation and retardation, measured by the Hamilton depression scale, with global to local SMN's time delays, respectively (agitation: r = -0.19, p = 0.04; retardation: r = 0.32, p = 0.03). Finally, early global to SMN delays predicted a close association of agitation and anxiety levels (F = 5.18, p = 0.025). In contrast to these results in global-to-SMN dynamics, no significant delay changes were observed in the local intra-network SMN dynamics. Together, our findings show distinct neural dynamics in MDD psychomotor retardation, for example, delayed, and agitation, for example, early in global to local SMN functional connectivity. This supports the psychomotor over the motor model of psychomotor retardation which carries major implications for clinical diagnosis and therapy.
Pubertal Hormones and the Early Adolescent Female Brain: A Multimodality Brain MRI Study.
Khetan M, Vijayakumar N, Tian YE … +4 more , Herting MM, O'Connell M, Seal M, Whittle S
Hum Brain Mapp · 2026 Feb · PMID 41556185 · Full text
Puberty is a critical developmental process that is associated with changes in pubertal (or steroid) hormone levels, which are believed to influence adolescent behaviour via their effects on the developing brain. So far,...
Puberty is a critical developmental process that is associated with changes in pubertal (or steroid) hormone levels, which are believed to influence adolescent behaviour via their effects on the developing brain. So far, there are limited and inconsistent findings regarding the relationship between steroid hormones and brain structure and function in adolescent females, with many existing studies employing small sample sizes. Thus, in this study, we explored the association between oestradiol (E2), testosterone (Tes), and dehydroepiandrosterone (DHEA) and brain structure (gray matter volume, sulcal depth, cortical thickness, and white matter microstructure) and function (resting-state connectivity, emotional n-back task-related function) in 3024 adolescent females (age 8.92-13.33 years, mean age (SD) = 10.37 (0.94) years) from the Adolescent Brain Cognitive Development (ABCD) Study. We used elastic-net regression with cross-validation to investigate associations between hormones and brain phenotypes derived from multiple imaging modalities. We found that structural brain features, including cortical thickness, sulcal depth, and white matter microstructure, and resting state connectivity between cortical networks and subcortical regions, were important features associated with hormones. E2 was most strongly associated with prefrontal and premotor regions involved in working memory and emotion processing, while Tes and DHEA were most strongly associated with parietal and occipital regions involved in visuospatial functioning. All three hormones were also associated with prefrontal, temporoparietal junction, and insula cortices. Thus, using an advanced methodological approach, this study suggests both unique and overlapping neural correlates of pubertal hormones in adolescent females and sheds light on the mechanisms by which puberty influences adolescent development and behaviour.
Fast Interneuron Dysfunction in Laminar Neural Mass Model Reproduces Alzheimer's Oscillatory Biomarkers.
Sanchez-Todo R, Mercadal B, Lopez-Sola E … +3 more , Guasch-Morgades M, Deco G, Ruffini G
Hum Brain Mapp · 2026 Jan · PMID 41532391 · Full text
Early-stage AD involves cortical hyperexcitability, progressing to oscillatory slowing and hypoactivity. These changes are linked to parvalbumin-positive ( ) interneuron dysfunction and neuronal loss driven by amyloid-b...
Early-stage AD involves cortical hyperexcitability, progressing to oscillatory slowing and hypoactivity. These changes are linked to parvalbumin-positive ( ) interneuron dysfunction and neuronal loss driven by amyloid-beta ( ) and hyperphosphorylated tau (hp- ), though underlying mechanisms remain unclear. To investigate this relationship, we employed a Laminar Neural Mass Model integrating excitatory and inhibitory populations. Synaptic coupling from interneurons to pyramidal cells was progressively reduced to mimic -induced neurotoxicity. Additional parameter variations simulated alternate mechanisms, including hp-tau pathology. Simulated dipole activity was analyzed in the time-frequency domain and compared to the literature. Simulating interneuron dysfunction reproduced AD's biphasic progression: early hyperexcitability with elevated gamma and alpha power, followed by oscillatory slowing and reduced spectral power. Alternative mechanisms, such as increased excitatory drive, did not replicate this trajectory. To account for late-stage hypoactivity and reduced firing rates, we incorporated pyramidal cell disruption consistent with hp- neurotoxicity. While not essential for local oscillatory changes, this addition aligns the model with empirical markers of advanced AD and supports whole-brain modeling. These findings highlight interneuron dysfunction as a primary mechanism of early electrophysiological disruption in AD, with pyramidal cell loss contributing to late-stage hypoactivity, offering a mechanistic model for excitation-inhibition imbalance across progression.
Neural Dynamics of Social Cognition: A Single-Trial Computational Analysis of Learning Under Uncertainty.
Charlton CE, Hauke DJ, Litvak V … +6 more , Wobmann M, de Bock R, Andreou C, Borgwardt S, Roth V, Diaconescu AO
Hum Brain Mapp · 2026 Jan · PMID 41531418 · Full text
Understanding others' intentions amidst uncertainty is critical for effective social interactions, yet the neural mechanisms underlying this process are not fully understood. Here, we combined computational modeling and...
Understanding others' intentions amidst uncertainty is critical for effective social interactions, yet the neural mechanisms underlying this process are not fully understood. Here, we combined computational modeling and single-trial EEG analysis to examine how the brain dynamically updates beliefs about others' intentions in volatile social contexts. A total of 43 healthy volunteers engaged in a deception-free advice-taking task, featuring alternating stable and volatile phases that systematically manipulated the reliability of an adviser's intentions. Using the hierarchical Gaussian filter (HGF), a Bayesian model of learning, we quantified trial-by-trial updates of participants' beliefs and their neural correlates. EEG amplitudes systematically varied according to task volatility, engaging neural regions associated with uncertainty processing such as the fusiform gyrus and posterior cingulate cortex. Sensor-level EEG analyses confirmed a temporal sequence consistent with the hierarchical computations predicted by the HGF, whereby lower-level prediction errors were processed earlier than higher-order volatility-related signals. Moreover, individual differences in these hierarchical neural processes correlated significantly with psychosocial functioning, suggesting that disruptions in Bayesian belief updating may underlie functional impairments in clinical populations. Collectively, our results reveal novel neural evidence for hierarchical Bayesian inference during social learning, highlighting its critical role in adaptive social behavior and potential relevance to mental health.
Crossroads in the Learning Brain: The Neural Overlap Between Arithmetic and Phonological Processing.
Alvarez-Rivero A, Peters L, Joanisse MF … +2 more , Gaab N, Ansari D
Hum Brain Mapp · 2026 Jan · PMID 41528045 · Full text
Robust behavioral evidence suggests an association between reading and math performance. Moreover, previous neuroimaging evidence suggests that arithmetic fact retrieval is supported by similar areas along the perisylvia...
Robust behavioral evidence suggests an association between reading and math performance. Moreover, previous neuroimaging evidence suggests that arithmetic fact retrieval is supported by similar areas along the perisylvian language network as those typically involved in phonological processing. However, the neural correlates of these abilities have been mostly studied in isolation, and therefore remains unclear whether these abilities recruit functionally overlapping brain areas. We addressed this question by using functional magnetic resonance imaging to measure brain activity during an arithmetic and a word rhyming task. We then used both a test of univariate overlap and a rigorous pattern similarity analysis to provide a more nuanced assessment of brain-level associations across both domains. We identified clusters of significant overlap along the left inferior frontal gyrus, the left inferior temporal gyrus, and the right posterior cerebellum in adults; as well as multiple clusters along the left frontal gyrus in children. Moreover, we found significant similarity between the patterns corresponding to both abilities along the clusters of overlap. However, contrary to our expectations, we observed higher similarity between phonological processing and large problems than small problems, which grants the need for further research about the role of arithmetic strategies in this relationship. Our findings represent a contribution to the literature examining the potential links between the brain regions supporting arithmetic and word reading by providing direct, within-participant statistical evidence of the long-hypothesized overlap between these processes at the neural level.
Feasibility and Validity of Ultra-Low-Field MRI for Measurement of Regional Infant Brain Volumes in Structures Associated With Antenatal Maternal Anemia.
Ringshaw JE, Bourke NJ, Zieff MR … +14 more , Wedderburn CJ, Casella C, Bradford LE, Williams SR, Herr D, Miles M, O'Muircheartaigh J, Bennallick C, Deoni S, Stein DJ, Alexander DC, Jones DK, Williams SCR, Donald KA
Hum Brain Mapp · 2026 Jan · PMID 41521841 · Full text
The availability of ultra-low-field (ULF) magnetic resonance imaging (MRI) has the potential to improve neuroimaging accessibility in low-resource settings. However, the utility of ULF MRI in detecting child brain change...
The availability of ultra-low-field (ULF) magnetic resonance imaging (MRI) has the potential to improve neuroimaging accessibility in low-resource settings. However, the utility of ULF MRI in detecting child brain changes associated with anemia is unknown. The aim of this study was to assess the comparability of 3T high-field (HF) and 64mT ULF volumes in infants for brain regions associated with antenatal maternal anemia. This neuroimaging substudy is nested within Khula South Africa, a population-based birth cohort. Pregnant women were enrolled antenatally and postnatally, and mother-child dyads (n = 394) were followed prospectively at approximately 3, 6, 12, and 18 months. A subgroup of infants was scanned on 3T and 64mT MRI systems across study visits and images were segmented using MiniMORPH. Correlations and concordance coefficients were used to cross-validate HF and ULF infant brain volumes for the caudate nucleus, putamen, and corpus callosum. Seventy-eight children (53.85% male) had paired HF (mean [SD] age = 9.64 [5.26] months) and ULF (mean [SD] age = 9.47 [5.32] months) datasets. Results indicated strong agreement between systems for intracranial volume (ICV; r = 0.96, ρ = 0.95) and brain regions of interest in anemia including the caudate nucleus (r = 0.89, ρ = 0.86), putamen (r = 0.97, ρ = 0.96) and corpus callosum (r = 0.87, ρ = 0.79). This cross-validation study demonstrates excellent correspondence between 3T and 64mT volumes for infant brain regions implicated in antenatal maternal anemia. Findings validate the use of ULF MRI for pediatric neuroimaging on anemia in Africa.
Sex Classification Based on the Functional Connectivity Patterns of the Language Network: A Resting State fMRI Study.
Lajoie X, DeRoy C, Bedetti C … +6 more , Houzé B, Clarke N, Hétu S, Picard MÈ, Bellec L, Brambati SM
Hum Brain Mapp · 2026 Jan · PMID 41518149 · Full text
Research on sex differences in the brain is essential for a better understanding of how the brain develops and ages, and how neurological and psychiatric conditions can impact men and women differently. While numerous st...
Research on sex differences in the brain is essential for a better understanding of how the brain develops and ages, and how neurological and psychiatric conditions can impact men and women differently. While numerous studies have focused on sex differences in brain structures, few have examined the characteristics of functional networks, particularly the language network. Although previous research suggests similar overall language performance across sexes, differences may still exist in the brain networks that underlie language processing. In addition, prior studies on sex differences in language have predominantly relied on task-based fMRI, which may fail to capture subtle differences in underlying functional activity. In this study, we applied a machine learning approach to classify participants' sex based on resting-state functional connectivity patterns of the language network in healthy young adults (270 men and 288 women; age: 22-36 years), and to identify the most predictive functional connectivity features. The classifier achieved 91.3% accuracy, with key discriminant features anchored to the left opercular part of the inferior frontal gyrus, the left planum temporale, and the left anterior middle temporal gyrus. These regions show distinctive connectivity patterns with heteromodal association cortices, including the occipital poles, angular gyrus, posterior cingulate gyrus, and intraparietal sulcus. Although there was an overlap between men and women, men displayed stronger functional connectivity values in these regions. These findings highlight sex-related differences in functional connectivity patterns of the language network at rest, underscoring the importance of considering sex as a variable in future research on language and brain function.
Functional Impact Score of Mitochondrial Variants and Its Relationship With Functional Connectivity of the Brain: Potential Origins of Premature Aging in Young Adulthood.
Mareckova K, Mendes-Silva AP, Mareček R … +5 more , Jordánek T, Pačínková A, Klánová J, Gonçalves VF, Nikolova YS
Hum Brain Mapp · 2026 Jan · PMID 41479401 · Full text
Alterations in mitochondrial DNA (mtDNA) have been associated with worse cognitive abilities in older adults and premature epigenetic aging in young adulthood. However, it is not clear how mitochondrial dysfunction affec...
Alterations in mitochondrial DNA (mtDNA) have been associated with worse cognitive abilities in older adults and premature epigenetic aging in young adulthood. However, it is not clear how mitochondrial dysfunction affects brain function in young adulthood and whether cognition-related networks might be most affected. We tested whether mtDNA functional impact (FI) score might map onto specific patterns of between-network functional connectivity in young adults from the European Longitudinal Study of Pregnancy and Childhood (ELSPAC). We also tested whether these relationships might be mediated by accelerated epigenetic aging, calculated using Horvath's epigenetic clock, CheekAge clock, and AltumAge clock. General connectivity method was used as a reliable marker of individual differences in brain function. We showed that a greater mtDNA FI score was associated with lower connectivity between the dorsal attention and language networks (beta = -0.41, p = 0.0007, AdjR = 0.15) and that there was suggestive evidence that this relationship might be mediated by accelerated epigenetic aging calculated using Horvath's epigenetic clock in young adulthood (ab = -0.061, SE = 0.04, 95% CI [-0.163; 0.001], 90% CI [-0.142; -0.002]). These findings were independent of sex, current BMI, and current substance use. Overall, we conclude that individuals with a greater mtDNA FI score might be at greater risk of experiencing worse attention to relevant linguistic inputs, greater difficulties with speech comprehension, and verbal working memory.
Decoding Effector-Specific Parametric Grip-Force Anticipation From fMRI-Data.
Caccialupi G, Schmidt TT, Blankenburg F
Hum Brain Mapp · 2026 Jan · PMID 41469773 · Full text
Planning motor-actions involves the neuronal representation of key parameters such as force and timing prior to execution. Functional magnetic resonance imaging (fMRI) studies have shown that activity in premotor and par...
Planning motor-actions involves the neuronal representation of key parameters such as force and timing prior to execution. Functional magnetic resonance imaging (fMRI) studies have shown that activity in premotor and parietal areas covaries with these parameters during motor-preparation. While previous research has demonstrated that parametric codes reflect graded grip-force intensities before and after their transformation into motor-codes, it remains unclear whether these representations are encoded in effector-specific brain-regions. To address this, we conducted an fMRI-study using a delayed grip-force task in which participants prepared one of four force-intensities with either their right or left cued-hand, with the hand to-be-used being switched in 50% of the trials midway through the delay. Using time-resolved multivoxel pattern analysis (MVPA) with a searchlight approach, we identified brain-regions encoding anticipated grip-force intensities of the cued-hand across the two 6-s delay-periods. In addition, cross-decoding analyses tested whether force-intensities were represented in an effector-specific or effector-independent format. We found above-chance decoding in two lateralized networks: the contralateral intraparietal sulcus (r-/l-IPS), as well as the lateral occipitotemporal cortex (r-/l-LOTC) during the first, and the contralateral primary motor cortices (r-/l-M1) during the second delay. These results indicate effector-specific coding of anticipated grip-force intensities, which is revealed by systematic lateralization of decoding-accuracy depending on the hand to-be-used. Cross-decoding corroborated effector-specific representation in these regions. Together, our results show that contralateral IPS and LOTCs encode effector-specific parametric information prior to M1s, likely reflecting a transformation process in which the intended grip-force intensity is selected, maintained, and then converted into detailed movement-plans.
Decoding the Self: Single-Trial Prediction of Self-Boundary Meditation States From Magnetoencephalography Recordings.
Röhr H, Atad DA, Trautwein FM … +6 more , Mediano PAM, Dor-Ziderman Y, Schweitzer Y, Berkovich-Ohana A, Schmidt S, van Vugt MK
Hum Brain Mapp · 2026 Jan · PMID 41451954 · Full text
The sense of self is a multidimensional feature of human experience. Different dimensions of self-experience can change drastically during altered states of consciousness induced through meditation or psychedelic drugs,...
The sense of self is a multidimensional feature of human experience. Different dimensions of self-experience can change drastically during altered states of consciousness induced through meditation or psychedelic drugs, as well as in a variety of mental disorders. Some experienced meditation practitioners are able to modulate their sense of self deliberately, which allows for a direct comparison between an active and suspended sense of self. Meditation therefore has the potential to serve as a model-system for alterations in the sense of self. The current study aims to identify a neural marker of such meditation-induced alterations in the sense of self based on magnetoencephalography (MEG) recordings of meditation practitioners (N = 41). Participants alternated between a state of reduced sense of self, termed self-boundary dissolution, a resting state and a control meditation state of maintaining their sense of self. Machine learning methods were used to find multivariate patterns of brain activity which distinguish these states on a single-trial basis. Source band power and Lempel-Ziv complexity features allowed to predict the mental state from MEG recordings with significantly above-chance accuracy (> 0.5). The highest performance was obtained for the self-boundary dissolution versus rest classification based on Lempel-Ziv complexity, which showed an average accuracy of ~0.64 when training and testing were performed on data from the same individual (within-participant prediction) and ~0.57 when models trained on one group of individuals were tested on different participants (across-participant prediction). Potential applications include decoded neurofeedback, for example, for clinical treatments of disorders of the sense of self, or for assistance in meditation training.
Characterizing Spatial Associations Between GluCEST MRI and Neurotransmitter Receptor Density in the Human Cortex.
Pecsok MK, Shafiei G, Atkins A … +15 more , Calkins ME, Gur RC, Reddy Nanga RP, Reddy R, Matyi MA, Stifelman J, Robinson H, Baller EB, Shinohara RT, Ruparel K, Linn KA, Wolf DH, Satterthwaite TD, McMillan CT, Roalf D
Hum Brain Mapp · 2025 Dec · PMID 41437520 · Full text
Glutamate-weighted Chemical Exchange Saturation Transfer (GluCEST) captures in vivo glutamate (Glu) levels with high spatial resolution and has been used to assess glutamatergic function in healthy and clinical populatio...
Glutamate-weighted Chemical Exchange Saturation Transfer (GluCEST) captures in vivo glutamate (Glu) levels with high spatial resolution and has been used to assess glutamatergic function in healthy and clinical populations. While GluCEST is well-validated against proton magnetic resonance spectroscopy (H-MRS), its correspondence with local expression of glutamatergic neurotransmitter receptors remains unclear. Recent initiatives, such as Neuromaps, have collated positron emission tomography (PET) data into curated, publicly available databases, providing a novel opportunity to establish convergence in the regional distribution of GluCEST and normative receptor density maps. Here, we examine the spatial correspondence between GluCEST signal and PET-based cortical receptor density levels of N-methyl-D-aspartate (NMDA), metabotropic glutamate receptor 5 (mGluR5), and gamma-aminobutyric acid A (GABA). A cohort of 86 participants (age: 22.7 years [3.7 years], 45% female) included 34 individuals with no psychiatric history, 31 participants with significant sub-threshold psychosis symptoms, and 21 participants with first-episode psychosis. All participants underwent 7T GluCEST imaging. Data were processed using in-house and field-standard pipelines. Mean receptor density levels were computed using the Neuromaps PET receptor density data. GluCEST and Neuromaps data were parcellated using the Cammoun 500 atlas. Pearson correlations assessed the correspondence between GluCEST signal and PET-based receptor density, and spin tests were used for empirical significance testing of the spatial correlations across all parcels. Sensitivity analyses examined the effect of age, sex, and diagnosis and other covariates. Exploratory analyses assessed regional variability across cytoarchitecturally defined von Economo regions and overall trends with gene expression. Analyses were performed in Python and R. GluCEST signal converged with the regional distribution of both NMDA (r = 0.23, p = 0.039) and GABA (r = 0.35, p = 0.004). There was no significant effect for mGluR5 (r = 0.09, p > 0.05). Exploratory analyses indicated that cytoarchitecturally defined von Economo regions showed variable GluCEST-receptor association patterns across the cortex and that gene expression patterns generally correspond with receptor density findings. Our findings reveal a positive spatial association between GluCEST signal in a transdiagnostic cohort and atlas-based PET-derived cortical receptor density of NMDA and GABA, and a nominal positive association with mGluR5. The association between GluCEST and NMDA suggests that regions with dense ionotropic Glu receptors exhibit higher Glu levels, while the coupling between GluCEST and GABA may reflect tight regulation of excitation-inhibition balance. Regional differences in these associations point to the potential influence of local cytoarchitectural specialization on Glu-receptor dynamics. These results advance our understanding of the neurobiological basis of GluCEST and highlight its potential utility as a non-invasive tool for probing receptor-mediated glutamatergic neurotransmission.
Disrupted Energetic and Entropic Landscape in Individuals With Mild Cognitive Impairment: Insights From Network Control Theory.
Neumann D, Razlighi QR, Stern Y … +4 more , Devanand DP, Jamison KW, Kuceyeski A, Tozlu C
Hum Brain Mapp · 2025 Dec · PMID 41427478 · Full text
The energetic and entropic organization of the brain's functional activity in mild cognitive impairment (MCI) has yet to be fully characterized. Network Control Theory (NCT) is a multi-modal approach that captures altera...
The energetic and entropic organization of the brain's functional activity in mild cognitive impairment (MCI) has yet to be fully characterized. Network Control Theory (NCT) is a multi-modal approach that captures alterations in the brain's energetic landscape by combining the brain's functional activity and the structural connectome. Entropy is another complementary metric that can quantify the complexity and predictability in a neural time series, offering insights into the brain's dynamic functional activity. Our study aims to explore the differences in the brain's energetic and entropic landscape between people with MCI and healthy controls (HC). Four hundred ninety-nine HC and 55 MCI patients were included. First, k-means clustering was applied to functional MRI (fMRI) time series to identify commonly recurring brain activity states. Second, NCT was used to calculate the minimum energy required to transition between these brain activity states, otherwise known as transition energy (TE). The entropy of the fMRI time series as well as PET-derived amyloid beta (Aβ) and tau deposition were measured for each brain region. The TE and entropy were compared between MCI and HC at the network, regional, and global levels using linear models where age, sex, and intracranial volume were added as covariates. The association of TE and entropy with Aβ and tau deposition was investigated in MCI patients using linear models where age, sex, and intracranial volume were controlled. Commonly recurring brain activity states included those with high (+) and low (-) amplitude activity in visual (+/-), default mode (+/-), and dorsal attention (+/-) networks. Compared to HC, MCI patients required lower transition energy in the limbic network (adjusted p = 0.028). Decreased global entropy was observed in MCI patients compared to HC (p = 7.29e-7). There was a positive association between TE and entropy in the frontoparietal network (p = 7.03e-3). Increased global Aβ was associated with higher global entropy in MCI patients (ρ = 0.632, p = 0.041). Lower TE in the limbic network in MCI patients may indicate either neurodegeneration-related neural loss and atrophy or a potential functional upregulation mechanism in this early stage of cognitive impairment. Future studies that include people with Alzheimer's Disease (AD) are needed to better characterize the changes in the energetic landscape in the later stages of cognitive impairment.
The Role of the Right Language Network and the Multiple-Demand Network in Verbal Semantics: Insights From an Activation Likelihood Estimation Meta-Analysis of 561 Functional Neuroimaging Studies.
Demirkan E, Branzi FM
Hum Brain Mapp · 2025 Dec · PMID 41422378 · Full text
Language processing has been traditionally associated with a network of fronto-parietal and temporal regions in the left hemisphere. Nevertheless, the 'right language network' (frontal, temporal and parietal regions homo...
Language processing has been traditionally associated with a network of fronto-parietal and temporal regions in the left hemisphere. Nevertheless, the 'right language network' (frontal, temporal and parietal regions homologous to the left language network) and the 'multiple-demand network' (MDN) are often involved in verbal semantic processing as well; however their role remains poorly understood. This is in part due to the inconsistent engagement of these latter two networks across linguistic tasks. To explore the factors driving the recruitment of the right language network and MDN during verbal semantic processing, we conducted a large-scale Activation Likelihood Estimation meta-analysis of neuroimaging studies. We examined whether the right language network is influenced by verbal stimulus type (sentences/narratives versus single words/word pairs) and whether this may be due to differences in semantic control demands and/or the presence of social content in the stimuli. Additionally, we investigated whether MDN recruitment depends on external task demands rather than semantic control demands. Our main findings revealed greater engagement of the right language network during the semantic processing of sentence/narrative stimuli, with distinct regions reflecting different functions: increased semantic control demands recruit the right inferior frontal gyrus. Instead, social content processing during a semantic task engages the right anterior temporal lobe, as well as the right posterior middle temporal gyrus. Finally, semantic processing engages the MDN, but only when external task (rather than semantic) demands increase.
Abstinence Alters Triple Network Dynamics in Moderate-to-Heavy Drinkers.
Khodaei M, Peterson-Sockwell H, McIntyre CC … +4 more , Lyday RG, Simpson SL, Laurienti PJ, Shappell HM
Hum Brain Mapp · 2025 Dec · PMID 41414867 · Full text
Alcohol misuse is a significant public health concern, yet little is known about the neural dynamics associated with habitual heavy drinking, particularly during abstinence. The Triple Network Model, comprising the salie...
Alcohol misuse is a significant public health concern, yet little is known about the neural dynamics associated with habitual heavy drinking, particularly during abstinence. The Triple Network Model, comprising the salience network (SN), default mode network (DMN), and central executive network (CEN), provides a framework for understanding large-scale brain network dysfunction associated with heavy alcohol use. Using resting-state fMRI and a Hidden Semi-Markov Model (HSMM), we examined dynamic brain state changes in moderate-to-heavy drinkers (n = 38) across two conditions: typical drinking and alcohol abstinence. Our findings revealed six distinct brain states, with significant differences in state occupancy, transitions, and duration between drinking conditions. Abstinence was associated with decreased time spent in a DMN-dominant state, a lower probability of transitioning to a state with high SN activation, and more frequent but shorter durations in a state without a distinct dominant network. These results suggest alcohol abstinence alters the temporal dynamics of these brain networks, potentially disrupting attention shifting and cognitive control mechanisms that may contribute to relapse risk. Understanding these neural adaptations will provide critical insight into the neurobiology of habitual heavy drinking and inform potential targets for future interventions.
Free Water Corrected Diffusion Magnetic Resonance Imaging Reveals Microstructural Alterations in Corpus Callosum Subregions of Preschool Children With Autism.
Cao D, Ni L, Qi Q … +9 more , Zhou L, Wang J, Li Y, Zhang W, Wei J, Luo Y, Wang Y, Zhang F, Li S
Hum Brain Mapp · 2025 Dec · PMID 41413942 · Full text
Autism spectrum disorder (ASD) is associated with white matter microstructural abnormalities, particularly in the corpus callosum (CC). This study employed free water corrected diffusion magnetic resonance imaging (fwc-d...
Autism spectrum disorder (ASD) is associated with white matter microstructural abnormalities, particularly in the corpus callosum (CC). This study employed free water corrected diffusion magnetic resonance imaging (fwc-dMRI) to investigate CC subregion-specific microstructural alterations in preschool children with ASD, which mitigates partial volume effects from extracellular free water. Sixty-one ASD children (6.03 ± 1.08 years) and 62 typically developing (TD) controls (6.49 ± 1.45 years) were enrolled in this study. In the ASD group, the symptom severity was assessed by the Autism Behavior Checklist (ABC). Fwc-dMRI technique, a bi-tensor tractography method, was used to investigate the white matter microstructure, which models free water and brain tissues through isotropic and anisotropic tensors to eliminate the partial volume effects caused by extracellular free water. The CC was segmented into seven subregions automatically according to its alignment to the cortex by a robust machine learning approach based on an anatomically curated white matter atlas. Fwc-dMRI-derived metrics were extracted for each CC subregion. Then we compared diffusion metrics between the two groups, and the correlation between the fractional anisotropy tissue (FA) and the scores of the ABC scale was analyzed in ASD. Significant group differences were localized to CC6 (temporal lobe projections), showing reduced FA (t = -3.251, p < 0.01) and elevated radial diffusivity tissue (t = 3.632, p < 0.01), and CC1 (orbital lobe projections), exhibiting decreased free water (t = -3.068, p < 0.05). FA in CC2-5 negatively correlated with ABC scores (r = -0.36 to -0.52, p < 0.01), linking frontoparietal connectivity to the symptom severity of ASD. Fwc-dMRI identified distinct microstructural disruptions in CC subregions, implicating dysmyelination in temporal pathways (CC6) and abnormal axonal development in frontal projections (CC1). These findings highlight fwc-dMRI's potential for early ASD diagnosis and intervention monitoring.
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