Human Brain Mapping最新文献

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.

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, AdjR² = 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.

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.

Early-stage AD involves cortical hyperexcitability, progressing to oscillatory slowing and hypoactivity. These changes are linked to parvalbumin-positive ( $\mathrm{PV}$ ) interneuron dysfunction and neuronal loss driven by amyloid-beta ( $\mathrm{A\beta }$ ) and hyperphosphorylated tau (hp- $\tau$ ), though underlying mechanisms remain unclear. To investigate this relationship, we employed a Laminar Neural Mass Model integrating excitatory and inhibitory populations. Synaptic coupling from $\mathrm{PV}$ interneurons to pyramidal cells was progressively reduced to mimic $\mathrm{A\beta }$ -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 $\mathrm{PV}$ 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- $\tau$ 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 $\mathrm{PV}$ 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.

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.

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.

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.

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.