Human Brain Mapping最新文献

The growing availability of large neuroimaging datasets, such as the UK Biobank, provides new opportunities to improve robustness and reproducibility in brain imaging research. However, little is known about the extent to which MRI processing pipelines influence results. Using 39,655 T1-weighted MRI scans from the UK Biobank, we systematically compared five widely used gray-matter representations derived from three major software packages: FSL (volume-based), CAT12/SPM (volume- and surface-based), and FreeSurfer (cortical and subcortical surface-based). We assessed their impact on morphometricity (trait variance explained by brain features), susceptibility to imaging confounders, false positives, association findings, and prediction accuracy across 29 diverse traits, including lifestyle, metabolic, and disease-related variables. We found that all pipelines were sensitive to imaging confounders such as head motion, brain position, and signal-to-noise ratio, and many produced non-normal voxel or vertex distributions. FSL and FreeSurfer generally yielded higher morphometricity estimates, but each captured partially unique signals, leading to inconsistencies in brain regions identified across methods. Volume-based approaches tended to outperform surface-based ones, detecting more significant clusters, achieving higher replication rates, and producing stronger predictive performance. Small clusters (single voxels or vertices) were less reliable, suggesting caution in their interpretation. Among all methods, FSLVBM emerged as the most consistent all-rounder, maximizing morphometricity, replicability, and predictive accuracy. Our results highlight the strengths and limitations of commonly used processing pipelines, offering benchmarks to guide researchers in method selection. They further suggest that combining multiple pipelines may improve brain-based prediction by leveraging unique, complementary signals, and that careful treatment of imaging confounders is essential for robust large-scale neuroimaging analyses.

Self-enhancement motivates individuals to prefer positive or expected social feedback over negative or unexpected feedback, thereby eliciting corresponding emotional experiences. Emotion regulation strategies that aim to reduce negative experiences and enhance positive ones often face the dilemma of prioritizing one outcome at the expense of the other. Modest individuals, characterized by the low self-focus perspective, may demonstrate advantages in managing emotional experiences derived from self-relevant social feedback. In this study, participants with high and low levels of modesty were scanned with functional magnetic resonance imaging while receiving social feedback of different valences and congruencies, with feedback indicating whether others liked participants. Results showed that highly modest individuals were less likely to use expressive suppression as an emotion regulation strategy. At the neural level, trait modesty modulated brain activity in the inferior parietal lobe and left superior temporal gyrus under unexpected conditions compared to expected conditions, as well as in the ventral anterior cingulate cortex, ventral medial prefrontal cortex, dorsal anterior cingulate cortex, and dorsolateral prefrontal cortex under acceptance versus rejection conditions. Psychophysiological interaction analysis and brain-behavior correlation analyses further explored the mechanisms of modesty, helping individuals reduce negative experiences and enhance positive experiences. Our findings reveal the cognitive processing patterns and brain activity of modest individuals when dealing with social feedback and provide insights into how individuals can better cope with social feedback.

The posterior middle temporal gyrus (pMTG) has been implicated in sensorimotor control of speech production, but the causality underlying this relationship remains largely unclear. The present event-related potential study employed dual-site continuous theta burst stimulation (c-TBS) over the left and right pMTGs concurrently to investigate their causal roles and interhemispheric interactions in vocal feedback control. Following bilateral c-TBS, unilateral c-TBS paired with contralateral sham stimulation, or bilateral sham stimulation over the left and right pMTGs, 24 healthy young adults produced sustained vocalizations while exposed to unexpected pitch perturbations (±200 cents) in auditory feedback. Compared to sham stimulation, c-TBS over the left, right, or bilateral pMTG significantly reduced the magnitudes and shortened the latencies of vocal compensations, paralleled by enhanced P2 responses that received contributions from distinct fronto-tempo-parietal networks. In contrast, reduced N1 responses were observed only following bilateral pMTG stimulation. Our findings not only provide the first causal evidence for bilateral pMTG involvement in vocal feedback control but also reveal a phase-specific interhemispheric interaction, transitioning from bilateral coordination during early error detection to unilateral sufficiency during later motor correction. These insights pave new avenues for developing novel multi-site neuromodulation protocols to optimize speech rehabilitation.

Changes in cortical gray matter are a key feature of neurodegenerative diseases that have been linked with concussion and repetitive head impacts (RHIs). Prior evidence implicates prior concussion and RHI in reduced cortical thickness or volume in temporal and frontal regions, with results largely restricted to older retired contact sport athletes. Fewer studies have investigated similar associations in younger athletes or applied approaches to capture more subtle differences in gray matter earlier in the lifespan. The current study assessed the association of concussion and RHI with cortical macrostructure (cortical thickness, cortical surface area), and cortical microstructure (cortical mean diffusivity), the latter of which has been suggested to be an earlier marker of gray matter abnormalities in neurodegenerative diseases. A total of 207 otherwise healthy collegiate-aged athletes completed semistructured interviews for concussion and sport participation history, as well as a magnetic resonance imaging session including anatomical and diffusion imaging (N = 205 with available diffusion data). Cortical surface area and cortical thickness were estimated using FreeSurfer; cortical mean diffusivity was calculated with correction for partial volume. Bayesian multilevel modeling was conducted on regions of interest derived from Desikan–Killiany Atlas parcellations to determine the association of the number of prior concussions and RHI (included in the same models) with each metric, controlling for sex, age, and intracranial volume (area only). There was strong evidence for a positive association between the number of prior concussions and cortical mean diffusivity throughout most of the cortex. In addition, there was strong evidence for a positive association of the number of prior concussions with cortical surface area across several regions. For cortical thickness, there was strong evidence of inverse associations between the number of prior concussions and anterior and medial temporal cortical regions only. In contrast, only weak to no evidence of associations between years of contact sport exposure, a proxy for RHI, and any cortical surface metric was observed. These results demonstrate that cortical diffusivity may represent a more sensitive metric of subtle, early structural changes associated with repetitive neurotrauma, and highlight the importance of efforts to reduce concussion risk in sport.

Preclinical detection of Alzheimer's disease (AD) is crucial to efficiently recruit clinical trial participants for examining AD-modifying drugs and ultimately yield clinical benefits for at-risk individuals. Cerebral amyloidosis precedes synaptic dysfunction and neurodegeneration markers, followed by the onset of AD-related cognitive impairment. To improve early AD-biomarker detection accuracy, patient data is, however, often collected via invasive procedures such as a lumbar puncture or intravenous injection of active radiopharmaceuticals. This coupled health risk is small yet significant and can be avoided by generating equally predictive or superior AD-risk staging proxy biomarkers derived from noninvasive neuroimaging modalities. In addition, using neuroimaging can provide richer insights into regional distributions of brain biomarkers of AD. Motivated by that, here we train neural networks to optimally generate latent structural MRI (sMRI) representations as proxies for cerebrospinal fluid (CSF) biomarker status on multiple classification and prediction contexts, an approach that we demonstrate has the potential to be clinically useful in screening and diagnosing AD and predicting AD progression. We found that the amygdala, hippocampus, parahippocampus, posterior and middle cingulate gyrus, middle and inferior temporal gyrus, angular gyrus, precuneus, and inferior parietal lobe regions revealed maximum attribution, thereby implying the highest prognostic value for AD risk. The proposed approach of predicting amyloid and/or tau pathology biomarkers from MRI data and subsequently transferring the MRI-derived amyloid and/or tau pathology models to predict future risk of AD progression may be useful to assist in disease screening, triage of patients for invasive testing, and efficiently determining suitability for clinical trial recruitment.

When recalling what you ate for breakfast last Wednesday, you might not remember the exact meal, but you may confidently select the items you typically eat. Here, semantic knowledge (i.e., what you usually eat) contributes to the reconstructive process of episodic memory retrieval (i.e., what you actually ate). In the current fMRI study, we used a highly realistic virtual environment to test this influence of semantic knowledge on episodic memory retrieval. During the task, 60 participants actively (task-relevant) or passively (task-irrelevant) encountered everyday objects that were either congruent (i.e., rubber duck in the bathroom) or incongruent (i.e., a toaster in the bathroom) with their expected location. Thereby, we created conflicting information between the episodic memory trace (toaster in the bathroom) and semantic information (toaster in the kitchen) during retrieval. Using multivariate analyses, we analyzed the neural basis of this semantic bias. Further, we administered cortisol, typically associated with impaired episodic memory retrieval, to half of the participants prior to retrieval, thereby manipulating the balance between correct episodic and incorrect semantic retrieval. In the lateral occipital cortex (LOC), incongruent task-relevant objects showed greater similarity to their congruent semantic counterparts than did task-irrelevant objects. Notably, spatial memory tended to be reflected in similarity patterns in the LOC. Strikingly, incongruent objects showed a higher pattern reorganization (i.e., pre-/post-encoding similarity) compared to congruent objects, reflecting a difference in neural representation for objects encountered in conflict with prior knowledge. In contrast to our hypotheses, cortisol prior to retrieval had no effect on semantic bias. However, cortisol influenced neural pattern similarity: we found higher pattern reorganization within the posterior hippocampus in the cortisol group. Similarly, we found higher confidence to be linked with similarity patterns in the LOC and lingual gyrus in the placebo, but not in the cortisol group. This indicates an effect of cortisol on memory trace reinstatement during retrieval. Our findings on incongruent object processing contribute to the understanding of how the human brain constructs past episodes from episodic memory traces, suggesting an influence of prior semantic knowledge, reflected in neural similarity patterns.

Fluctuating timescales are present in nature and are commonly observed in music, movies, brain activity, and speech. In human speech, semantic timescales span from single words to complete sentences and vary throughout conversation. Similarly, the brain's intrinsic neuronal timescales (INT), reflected in temporally correlated activity, carry information across time. How are these semantic and neuronal timescales related? Our combined semantic input and functional magnetic resonance imaging (fMRI) study using the 7 Tesla Human Connectome Project movie-watching dataset reveals information transfer from speech's semantic timescales to the brain's INT. We extracted two semantic time-series, sentence similarity and word depth, using Sentence-BERT (SBERT) and WordNet, respectively. The timescales of both semantic signals and the brain's activity were quantified using the autocorrelation window (ACW), with a dynamic, time-varying analysis approach. This allows testing for information transfer from the simultaneously varying semantic timescales to the brain's varying timescales via Transfer Entropy (TE). We report three main findings: (1) Sentence similarity and word depth time-series exhibit high and systematic fluctuations over time. (2) Dynamic ACW analysis captures the dominant timescales in both semantic input (sentence similarity and word depth) and the brain's continuously varying INT. (3) Significant TE from the varying semantic timescales to the brain's simultaneously varying INT. We also demonstrate that the information transfer only emerges on the level of timescales, and is absent when comparing the two raw semantic input time-series with the BOLD signal, respectively. Conclusively, we demonstrate the key role of timescales in the information transfer from semantic inputs to the brain's neural activity.

It is increasingly established that the organization of the brain into functional resting-state networks allows efficient integration and processing of information. Functional hubs anchoring such networks are characterized by a high degree of communication, which relies on efficient utilization of glucose. Alzheimer's disease (AD) disrupts the balance between glucose metabolism and intrinsic functional connectivity (FC). We hypothesized that this critical coupling would also be weakened in frontotemporal dementia (FTD), particularly within the salience network, given its association with the disease. Towards this goal, behavioral variant FTD (bvFTD) patients (n = 21) and healthy participants (n = 18) underwent simultaneous FDG-PET and functional MRI imaging in a hybrid PET/MR system, with an additional cohort completing the MRI component only. PET images were converted into standardized uptake value ratios (SUVr), and local FC was quantified using regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF), two metrics that have been demonstrated to be related to FDG-PET uptake. The interplay between FC and glucose metabolism was investigated within the salience and default mode networks. The bvFTD group showed network-level functional breakdown and significantly weakened metabolism/FC coupling, especially in the dorsal anterior insula and posterior cingulate cortex. Importantly, reduced coupling in the posterior cingulate cortex was associated with cognitive and behavioral symptoms in patients. Though significant, the reduction in whole-brain metabolic/FC coupling in bvFTD was not as strong as reported previously for AD. These results highlight the vulnerability of functional hubs to neurodegenerative disease. Aberrant regional disruptions in the coupling between metabolism and neuronal activity may drive network-level dysfunction and contribute to functional impairments characteristic of the disease.

Creativity means the formation of novel and useful associations. Meanwhile, the role of the hippocampus in episodic memory and some forms of creative thinking has been identified, but it remains unclear how the hippocampus participates in the formation of memory for creative associations. In particular, considering creative associations are often formed on the basis of old ones, it is important to identify how the hippocampus and its associated neural network represent the interactions between the new and old associations during the encoding of creative associations. Thus, using the subsequent memory effect (SME) paradigm, the present study asked participants to learn a set of creative combinations (a common object paired with a creative alternate use, for example, basketball-buoy, which means a basketball is used as a buoy) during fMRI scanning. Moreover, we also quantified the degree of pre-existing semantic connections individually according to subjective ratings of inherent semantic relatedness between the objects and their alternate uses in the relatedness judgment task, resulting in a 2 (memory: remembered vs. forgotten) by 2 (semantic relatedness: remote vs. close) factorial design. Multivariate analysis revealed higher inter-item hippocampal pattern similarity for remembered relative to forgotten trials in both close relatedness and remote relatedness conditions, indicating that hippocampal representations become less separable supporting successful memory for creative associations. However, univariate analyses of the hippocampus and its neural network showed that enhanced hippocampal activation was associated with successful encoding in the remote relatedness but not close relatedness condition, whereas increased hippocampal functional connectivity with prefrontal and parietal cortices contributed to successful memory in the close relatedness but not remote relatedness condition. These observations suggest that hippocampal-dependent processes and distributed hippocampal network patterns selectively support successful memory for creative associations with either remote or close inherent semantic relatedness, which implies the interactions between pre-existing semantic connections and newly formed creative associations.

The thalamus is connected to the cerebral cortex and subcortical regions, serving as a node within cognitive networks. It is a heterogeneous structure formed of functionally distinct nuclei with unique connectivity patterns. However, their contributions to cognitive functioning within networks is poorly understood. Recent animal research suggests that thalamic nuclei such as the mediodorsal nucleus play critical roles in goal-directed behaviour. Our aim was to investigate how functional integration of thalamic nuclei within cortical and subcortical networks changes whilst transitioning from more controlled goal-directed behaviour towards more automatic or habitual behaviour in humans. We analysed functional magnetic resonance imaging (fMRI) data from a stimulus–response learning study to investigate functional connectivity (FC) changes across learning between thalamic nuclei with cortical networks and subcortical structures in 52 healthy subjects. We also defined additional regions-of-interest (ROIs) individually in native space, segmenting the thalamus into 47 nuclei and segmenting 38 subregions within the basal ganglia and hippocampus. Additionally, we defined 12 cerebral cortex ROIs via maximum-probability network templates. Associative S-R learning-related connectivity changes were examined via ROI-to-ROI functional network analysis. Our results showed that learning was associated with: (1) decreasing FC between the frontoparietal network and higher order thalamic nuclei; (2) increasing FC between the cingulo-opercular network and pulvinar nuclei; (3) decreasing FC between the default mode network (DMN) and right mediodorsal nuclei; (4) increasing FC between the DMN and left mediodorsal nuclei; (5) changes in functional connectivity between thalamic nuclei and putamen subregions, and (6) increasing intrathalamic FC. Together, this suggests that several thalamic nuclei are involved in the learning-related transition from controlled to more automatic behaviour.