Human Brain Mapping最新文献

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.

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.

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.

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.

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_t) 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 (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_t 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.

Computer-assisted online interaction (CAOI) has become predominant in daily life and is increasingly supplanting offline verbal interaction (FVI). Previous research has shown that face-to-face verbal interaction (VI) exhibits significant differences in interpersonal neural synchronization (INS) compared to computer-assisted online VI. However, the differences between various forms of FVI and CAOI remain unclear. In this work, we designed different forms of naturalistic VI tasks between dual persons and adopted electroencephalography (EEG) hyperscanning to simultaneously record neural activities from both participants. The experiment included three conditions: online versus offline, with versus without feedback, with versus without visual information or eye contact. Thirty-one pairs of labmates with ordinary levels of intimacy were recruited as subjects. To analyze the impacts of these VI forms on INS, we calculated intersubject correlation at both scalp and cortex levels and constructed brain-to-brain networks based on intersubject functional connectivity using the phase lag index at the scalp level and the phase locking value at the cortex level. We found that interactions with feedback exhibit higher synchronization than interactions without feedback. VIs with visual information or eye contact are more effective in eliciting stronger INS. Additionally, compared to FVI, CAOI exhibits weakened neural synchronization. Intriguingly, online text-based interaction also results in high neural coupling. Our study reveals significant differences in various CAOIs and FVIs concerning typical factors, providing crucial insights into the mechanisms of INS during online interactions.

The neural basis of multitasking costs is subject to continuing debate. Cognitive theories assume that the overlap of task representations may lead to between-task crosstalk in concurrent task processing and thus requires cognitive control. Recent research suggests that modality-based crosstalk contributes to multitasking costs, involving central overlap of modality-specific representations. Consistently increased costs for specific modality pairings (visual-vocal and auditory-manual vs. visual-manual and auditory-vocal) were demonstrated (modality-compatibility effect), which were recently linked to representational overlap in the auditory cortex. However, it remains unclear whether modality-based crosstalk emerges from overlapping patterns of global brain connectivity and whether resolving it requires additional involvement of cognitive control as reflected in the fronto-parietal control network. This preregistered functional imaging study investigates these questions in 64 healthy, young human adults. Specifically, we focus on the modality-compatibility effect in multitasking by employing functional connectivity (FC) analysis. First, we tested the FC similarity FC dissimilarity between the single-task networks. Second, we compared the strength of the control network in whole-brain FC between dual tasks. We found no evidence for differences in FC dissimilarities of single-task networks between modality pairings and no additional involvement of the control network during dual tasks by comparing the global connectivity. However, unregistered post hoc connectivity analysis revealed the first evidence for a correlation of the (behavioral) modality-compatibility effect with local FC. This effect was locally restricted to FC between lateral frontal and sensory auditory regions, consistent with the modality-based crosstalk assumption. More generally, the findings suggest that robust behavioral differences in multitasking are not necessarily related to global functional connectivity differences but might be related to functionally specific local connectivity changes.