Human Brain Mapping最新文献

The ability to integrate semantic information into the context of a sentence is essential for human communication. Several studies have shown that the predictability of a final keyword based on the sentence context influences semantic integration on the behavioral, neurophysiological, and neural level. However, the architecture of the underlying network interactions for semantic integration across the lifespan remains unclear. In this study, 32 healthy participants (30–75 years) performed an auditory cloze probability task during functional magnetic resonance imaging (fMRI), requiring lexical decisions on the sentence's final words. Semantic integration demands were implicitly modulated by presenting sentences with expected, unexpected, anomalous, or pseudoword endings. To elucidate network interactions supporting semantic integration, we combined univariate task-based fMRI analyses with seed-based connectivity and between-network connectivity analyses. Behavioral data revealed typical semantic integration effects, with increased integration demands being associated with longer response latencies and reduced accuracy. Univariate results demonstrated increased left frontal and temporal brain activity for sentences with higher integration demands. Between-network interactions highlighted the role of task-positive and default mode networks for sentence processing with increased semantic integration demands. Furthermore, increasing integration demands led to a higher number of behaviorally relevant network interactions, suggesting that the increased between-network coupling becomes more relevant for successful task performance as integration demands increase. Our findings elucidate the complex network interactions underlying semantic integration across the aging continuum. Stronger interactions between various task-positive and default mode networks correlated with more efficient processing of sentences with increased semantic integration demands. These results may inform future studies with healthy old and clinical populations.

Intracortical microstimulation (ICMS) is a method for restoring sensation to people with paralysis as part of a bidirectional brain–computer interface (BCI) to restore upper limb function. Evoking tactile sensations of the hand through ICMS requires precise targeting of implanted electrodes. Here we describe the presurgical imaging procedures used to generate functional maps of the hand area of the somatosensory cortex and subsequent planning that guided the implantation of intracortical microelectrode arrays. In five participants with cervical spinal cord injury, across two study locations, this procedure successfully enabled ICMS-evoked sensations localized to at least the first four digits of the hand. The imaging and planning procedures developed through this clinical trial provide a roadmap for other BCI studies to ensure the successful placement of stimulation electrodes.

The thalamus is a collection of gray matter nuclei that play a crucial role in sensorimotor processing and modulation of cortical activity. Characterizing thalamic nuclei non-invasively with structural MRI is particularly relevant for patient populations with Parkinson's disease, epilepsy, dementia, and schizophrenia. However, severe head motion in these populations poses a significant challenge for in vivo mapping of thalamic nuclei. Recent advancements have leveraged the compressed sensing (CS) framework to accelerate structural MRI acquisition times in MPRAGE sequence variants, while fast segmentation tools like FastSurfer have reduced processing times in neuroimaging research. In this study, we evaluated thalamic nuclei segmentations derived from six different MPRAGE variants with varying degrees of CS acceleration (from about 9 to about 1-min acquisitions). Thalamic segmentations were initialized from either FastSurfer or FreeSurfer, and the robustness of the thalamic nuclei segmentation tool to different initialization inputs was evaluated. Our findings show minimal sequence effects with no systematic bias, and low volume variability across sequences for the whole thalamus and major thalamic nuclei. Notably, CS-accelerated sequences produced less variable volumes compared to non-CS sequences. Additionally, segmentations of thalamic nuclei initialized from FastSurfer and FreeSurfer were highly comparable. We provide the first evidence supporting that a good segmentation quality of thalamic nuclei with CS T1-weighted image acceleration in a clinical 3T MRI system is possible. Our findings encourage future applications of fast T1-weighted MRI to study deep gray matter. CS-accelerated sequences and rapid segmentation methods are promising tools for future studies aiming to characterize thalamic nuclei in vivo at 3T in both healthy individuals and clinical populations.

It is now understood that brain metastases do not occur randomly but have distinct spatial patterns depending on the origin of the cancer. According to the “seed and soil” hypothesis, the final colonization of metastatic cells is the result of their adaptation to the altered environment. To investigate the most favorable microenvironment for brain metastasis, we analyzed neuroimaging data from 177 patients with breast cancer brain metastasis and 548 patients with lung cancer brain metastasis to create a replicable probabilistic map of metastatic locations. Additionally, we used population-based data from open repositories to generate brain atlases of diverse microenvironment features, including gene expression, functional connectivity, glucose metabolism, and neurotransmitter transporters/receptors. We then compared the spatial correlation between brain metastasis frequency and these features, after which we constructed a general linear model to identify the most significant variables that contributed to tumor location predilection. Our findings revealed that brain metastases from breast cancer and lung cancer had distinct radiographic characteristics and distribution patterns. Breast cancer tended to metastasize in brain regions with decreased expression of genes associated with immunity and metabolism and reduced levels of connectomic hubness and glucose metabolism. In contrast, lung cancer had a higher probability of metastasizing in regions with active metabolism. Moreover, neurotransmitter systems play various roles in determining tumor location. These results provide new insights into the adaptation of metastatic cells to the brain microenvironment and illustrate how factors on diverse biological scales can affect the colonization of brain metastases.

Not harming others is widely regarded as a fundamental tenet of human morality. Harm aversion based on the consequences of an action is called utilitarianism while focusing on the action itself is associated with deontology. This study investigated how interoceptive processing affects the neural processing of utilitarian and deontological moral decision-making. The study utilized the heartbeat-evoked potential (HEP), an averaged electrophysiological component from electroencephalogram (EEG) to gauge cardiac interoceptive processing. Twenty-seven participants were asked to make utilitarian and deontological decisions for personal and impersonal moral dilemmas (18 for each) with direct and indirect harm actions, respectively, while their EEG and electrocardiogram were being recorded. We found no difference in HEPs between personal and impersonal moral dilemmas. In contrast, differential HEPs were observed between utilitarian and deontological moral decision-making, regardless of type of dilemmas. Significant differences were observed over centro-posterior electrodes between 110 and 172 milliseconds after R-peaks during the Scenario Phase, and over right fronto-temporal electrodes between 314 and 404 milliseconds after R-peaks in the Decision Phase. We confirmed that these differences in HEP amplitude between deontological and utilitarian decisions did not stem from cardiac artifacts. These findings reveal that the brain utilizes interoceptive information to make subsequent moral decisions.

Diffusion-weighted imaging (DWI) has been frequently used to examine age-related deterioration of white matter microstructure and its relationship to cognitive decline. However, typical tensor-based analytical approaches are often difficult to interpret due to the challenge of decomposing and (mis)interpreting the impact of crossing fibers within a voxel. We hypothesized that a novel analytical approach capable of resolving fiber-specific changes within each voxel (i.e., fixel-based analysis [FBA])—would show greater sensitivity relative to the traditional tensor-based approach for assessing relationships between white matter microstructure, age, and cognitive performance. To test our hypothesis, we studied 636 cognitively normal adults aged 65–80 years (mean age = 69.8 years; 71% female) using diffusion-weighted MRI. We analyzed fixels (i.e., fiber-bundle elements) to test our hypotheses. A fixel provides insight into the structural integrity of individual fiber populations in each voxel in the presence of multiple crossing fiber pathways, allowing for potentially increased specificity over other diffusion measures. Linear regression was used to investigate associations between each of three fixel metrics (fiber density, cross-section, and density × cross-section) with age and cognitive performance. We then compared and contrasted the FBA results to a traditional tensor-based approach examining voxel-wise fractional anisotropy. In a whole-brain analysis, significant associations were found between fixel-based metrics and age after adjustments for sex, education, total brain volume, site, and race. We found that increasing age was associated with decreased fiber density and cross-section, namely in the fornix, striatal, and thalamic pathways. Further analysis revealed that lower fiber density and cross-section were associated with poorer performance in measuring processing speed and attentional control. In contrast, the tensor-based analysis failed to detect any white matter tracts significantly associated with age or cognition. Taken together, these results suggest that FBAs of DWI data may be more sensitive for detecting age-related white matter changes in an older adult population and can uncover potentially clinically important associations with cognitive performance.

Learning new categories is fundamental to cognition, occurring in daily life through various sensory modalities. However, it is not well known how acquiring new categories can modulate the brain networks. Resting-state functional connectivity is an effective method for detecting short-term brain alterations induced by various modality-based learning experiences. Using fMRI, our study investigated the intricate link between novel category learning and brain network reorganization. Eighty-four adults participated in an object categorization experiment utilizing visual (n = 41, with 20 females and a mean age of 23.91 ± 3.11 years) or tactile (n = 43, with 21 females and a mean age of 24.57 ± 2.58 years) modalities. Resting-state networks (RSNs) were identified using independent component analysis across the group of participants, and their correlation with individual differences in object category learning across modalities was examined using dual regression. Our results reveal an increased functional connectivity of the frontoparietal network with the left superior frontal gyrus in visual category learning task and with the right superior occipital gyrus and the left middle temporal gyrus after tactile category learning. Moreover, the somatomotor network demonstrated an increased functional connectivity with the left parahippocampus exclusively after tactile category learning. These findings illuminate the neural mechanisms of novel category learning, emphasizing distinct brain networks' roles in diverse modalities. The dynamic nature of RSNs emphasizes the ongoing adaptability of the brain, which is essential for efficient novel object category learning. This research provides valuable insights into the dynamic interplay between sensory learning, brain plasticity, and network reorganization, advancing our understanding of cognitive processes across different modalities.

Deep brain stimulation (DBS) of the anterior limb of the internal capsule (ALIC) is a circuit-based treatment for severe, refractory obsessive-compulsive disorder (OCD). The therapeutic effects of DBS are hypothesized to be mediated by direct modulation of a distributed cortico-striato-thalmo-cortical network underlying OCD symptoms. However, the exact underlying mechanism by which DBS exerts its therapeutic effects still remains unclear. In five participants receiving DBS for severe, refractory OCD (3 responders, 2 non-responders), we conducted a DBS On/Off cycling paradigm during the acquisition of functional MRI (23 fMRI runs) to determine the network effects of stimulation across a variety of bipolar configurations. We also performed tractography using diffusion-weighted imaging (DWI) to relate the functional impact of DBS to the underlying structural connectivity between active stimulation contacts and functional brain networks. We found that therapeutic DBS had a distributed effect, suppressing BOLD activity within regions such as the orbitofrontal cortex, dorsomedial prefrontal cortex, and subthalamic nuclei compared to non-therapeutic configurations. Many of the regions suppressed by therapeutic DBS were components of the default mode network (DMN). Moreover, the estimated stimulation field from the therapeutic configurations exhibited significant structural connectivity to core nodes of the DMN. Based upon these findings, we hypothesize that the suppression of the DMN by ALIC DBS is mediated by interruption of communication through structural white matter connections surrounding the DBS active contacts.

Age-related atrophy of the human hippocampus and the enthorinal cortex starts accelerating at around age 60. Due to the contributions of these regions to many cognitive functions seamlessly used in everyday life, this can heavily impact the lives of elderly people. The hippocampus is not a unitary structure, and mechanisms of its age-related decline appear to differentially affect its subfields. Human and animal studies have suggested that altered sleep is associated with hippocampal atrophy. Yet, we know little about subfield specific effects of altered sleep in healthy aging and their effect on cognition. Here, in a sample of 118 older middle-aged and older adults (M_age = 63.25 y, range: 50–80 y), we examined the association between highly reliable hippocampal subfield and entorhinal cortex volumetry (n = 112), sleep measures derived from multi-night recordings of portable electroencephalography (n = 61) and episodic memory (n = 117). Objective sleep efficiency—but not self-report measures of sleep—was associated with entorhinal cortex volume when controlling for age. Age-related differences in subfield volumes were associated with objective sleep efficiency, but not with self-report measures of sleep. Moreover, participants characterized by a common multivariate pattern of subfield volumes that contributed to positive sleep–subfield volume associations, showed lower rates of forgetting. Our results showcase the benefit of objective sleep measures in identifying potential contributors of age-related differences in brain-behavior couplings.