Human Brain Mapping最新文献

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.

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.

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.

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_p = 0.95), alongside inhibitory influences from the insula to the pulvinar (P_p = 0.99) and from the pulvinar to V1 (P_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.

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^SM (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.

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.

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.

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_ccc = 0.95) and brain regions of interest in anemia including the caudate nucleus (r = 0.89, ρ_ccc = 0.86), putamen (r = 0.97, ρ_ccc = 0.96) and corpus callosum (r = 0.87, ρ_ccc = 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.

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.