Human Brain Mapping最新文献

Human arousal is essential to survival and mediated by the ascending arousal network (AAN) and its connections. It spans from the brainstem to the diencephalon, basal forebrain, and cerebral cortex. Despite advances in mapping the AAN in adults, it is unexplored in fetal and early infant life, especially with high-resolution magnetic resonance imaging techniques. In this study, we conducted—for the first time—high-resolution ex vivo diffusion MRI-based analysis of the AAN in seven fetal, infant, and adult brains, incorporating probabilistic tractography and quantifying connectivity using graph theory. We observed that AAN structural connectivity becomes increasingly integrated during development, progressively reaching rostrally during the first postconceptional year. We quantitatively identified the dorsal raphe (DR) nucleus and ventral tegmental area (VTA) as AAN connectivity hubs already in the fetus persisting into adulthood. The DR appears to form a local hub of short-range connectivities, while the VTA evolves as a long-range global hub. The identified connectivity maps advance our understanding of AAN architecture changes due to normative human brain development, as well as disorders of arousal, such as coma and sudden infant death syndrome.

Transcranial magnetic stimulation (TMS) guided by magnetic resonance imaging (MRI) has significantly advanced the treatment of mood disorders by enabling precise targeting of brain circuits implicated in their pathophysiology. The integration of neuronavigation systems, which utilize real-time MRI-based coil positioning, has improved spatial targeting accuracy, individualization, and therapeutic outcomes. Despite these advancements, achieving optimal stimulation efficacy requires careful consideration of MRI techniques, including anatomical imaging, functional MRI (fMRI), and connectivity-based methods. Anatomical MRI provides a reliable structural foundation for neuronavigation but lacks specificity regarding functional neural networks implicated in mood disorders. In contrast, fMRI, through task-based and resting-state paradigms, enhances target selection precision by identifying patient-specific neural activity and functional connectivity patterns, although this approach is vulnerable to variability and imaging artifacts. Connectivity-based MRI neuronavigation represents a promising advancement by explicitly targeting disrupted neural networks. This review critically examines recent technological and methodological progress in MRI-guided neuronavigation for TMS, addressing current challenges such as image acquisition quality, co-registration accuracy, artifact mitigation, and practical constraints in clinical settings. Finally, it discusses emerging opportunities and innovations poised to enhance neuronavigation precision, foster wider clinical adoption, and ultimately improve therapeutic outcomes in interventional psychiatry for mood disorders.

Recent studies suggest that it is possible to predict an individual brain's spatial activation pattern in response to a paradigm from their functional connectivity at rest (rsFC). However, it is unclear whether this prediction works across the brain. We here aim to understand whether individual task activation can be best predicted in local regions that are highly specialised to the task at hand or whether there are domain-independent regions in the brain that carry most information about the individual. To answer this question, we used fMRI data from participants at rest and during an auditory oddball paradigm. We then predicted individual differences in brain responses to melodic deviants from their rsFC both across the whole brain and within the auditory cortices. Predictability was consistently higher in sensory association cortices: In the local (auditory cortex) parcellation, the best predicted area was the right superior temporal gyrus (STG), an auditory association area, while in the global parcellation, the best predicted network was the bilateral visual association cortex. Our results indicate that individual differences can be predicted in paradigm-relevant areas or general areas with high inter-individual variability. Predicting individual task activation from rsFC may be of clinical relevance in cases where patients are unable to carry out a certain task, such as, to inform surgical targets.

Attentional control (AC) plays a causal role in various mental disorders and, within the metacognitive model, contributes to maladaptive repetitive cognitive processes such as rumination and worry. The Attention Training Technique (ATT), an auditory psychotherapeutic intervention, improves AC and is associated with the efficiency of large-scale fronto-parietal control networks (FPN). This study investigates the neural correlates of ATT by applying a newly tailored fMRI paradigm, focusing on FPN engagement and its relationship with AC. We adapted ATT to examine neural responses during ATT compared to passive listening in ATT-naïve participants (N = 43) and ensured the robustness of results by validating the findings in a second independent sample (N = 28). To optimize the paradigm, we compared two ATT conditions, rapidly switching (ATT_switch) and selectively focusing (ATT_focus) attention, against multiple passive-listening control conditions, to probe ATT-related FPN activation. We also tested whether trial-wise subjective effort and self/external focus ratings differentiated ATT from control trials, parametrically modulated FPN activation, and whether ATT-related FPN activation correlated with trait AC. ATT versus control conditions activated the FPN (p_FWE < 0.05). This effect was present in both ATT conditions, with stronger activation in the ATT_switch versus ATT_focus condition, and independent of the specific control condition. Ratings of self/external focus and effort significantly differentiated ATT from control conditions (all p < 0.001) and parametrically modulated FPN activation (p_FWE < 0.05). All effects were replicated in the second sample. Across both samples, FPN activation in ATT versus control conditions and trial-wise ratings related to trait AC. Using a novel fMRI paradigm in two independent samples, we demonstrate that the ATT is associated with activation of the FPN, a key network for AC and mental health. The relationship between FPN activation and self-report measures supports the relevance of the data for understanding ATT and its links to clinical phenotypes.

The locus coeruleus (LC) is a nucleus within the brainstem associated with physiological arousal and altered structure and function in the context of neurological conditions. Pathologies related to difficulties with attention have previously been associated with abnormalities in neurotransmitter production and sensitivity, suggesting the possibility of abnormality in neurotransmitter-producing neural regions. One such region is the LC, associated with norepinephrine production. To examine the possibility that LC alteration is associated with inattentive symptom reporting, regression analyses were performed using neuromelanin contrast ratios and volume in a sample of 141 individuals age-ranged from 8 to 54. Mediation modeling was subsequently performed to examine the relationship between neuromelanin contrast and volume in regard to inattentive behavior. We found that the structural integrity value of the LC, especially in the right hemisphere, showed a significant negative association with the level of the individual's inattentiveness score. LC volume was also significantly positively associated with inattention, and this finding was also lateralized to the right LC. Interestingly, an inverse association was found between structural integrity and volume. These findings support the relationship between LC and attention-related behavior through both neuromelanin-sensitive and structural imaging, with important implications for the association between regional structure and function.

The hippocampus is a key structure in cognition. Although much research has focused on defining the functions of its anatomically distinct subfields, the communication among these subfields within the hippocampal circuit, supported by white matter pathways, is theoretically key to emergent cognitive function. Yet, hippocampal white matter connections in humans have not been fully explored in vivo. By leveraging diffusion-weighted imaging and a large healthy sample (N = 653), we developed a processing pipeline for in vivo quantification of human hippocampal pathways. We provided evidence for monosynaptic and trisynaptic pathway-related connections in humans, supporting the described hippocampal circuit in ex vivo and animal studies. In addition to hemispheric and sex differences, the individual variability in hippocampal pathways was linked to cognitive abilities. Thus, in vivo characterization of human hippocampal pathways highlights the individual differences within these structures and paves the way for their implications in cognition, aging, and diseases.

Over time, we develop event schemas or scripts that shape our expectations about what typically happens in certain contexts. However, even after forming a memory about a certain event, we are often exposed to related information about that same event at later points in time. This additional information sometimes causes one to have to re-evaluate the interpretation of the original event. Over a two-day fNIRS experiment, participants were exposed to events that were subsequently updated with schema-congruent or schema-incongruent additional details. These schema-incongruent additional details make those events more fitting to another schema than originally was the case, meaning that participants would need to dissociate that event from the original schema and re-integrate it with another schema. The fNIRS results showed higher oxygenated hemoglobin (HbO) concentrations in the prefrontal cortex (PFC)—which indicates enhanced PFC activation—for events updated with schema-congruent compared to schema-incongruent details. When specifically looking at those events that were updated with schema-incongruent details, our results suggest that dissociating an event from the original schema and re-integrating it with another schema was accompanied by an initial PFC decrease early in the trial followed by a PFC increase later in the trial. This was a distinctly different pattern compared to trials in which participants failed to re-integrate the event with another schema, which showed delayed PFC increase with lower amplitude and no initial PFC decrease. Our findings provide insight into how people process and integrate conflicting information.

The estimation of brain networks is instrumental in quantifying and evaluating brain function. Nevertheless, achieving precise estimations of subject-level networks has proven to be a formidable task. In response to this challenge, researchers have developed group-inference frameworks that leverage robust group-level estimations as a common reference point to infer corresponding subject-level networks. Generally, existing approaches either leverage the common reference as a strict, voxel-wise spatial constraint (i.e., strong constraints at the voxel level) or impose no constraints. Here, we propose a targeted approach that harnesses the topological information of group-level networks to encode a high-level representation of spatial properties to be used as constraints, which we refer to as Topologically Optimized Intrinsic Brain Networks (TOIBN). Consequently, our method inherits the significant advantages of constraint-based approaches, such as enhancing estimation efficacy in noisy data or small sample sizes. On the other hand, our method provides a softer constraint than voxel-wise penalties, which can result in the loss of individual variation, increased susceptibility to model biases, and potentially missing important subject-specific information. Our analyses show that the subject maps from our method are less noisy and true to the group networks while promoting subject variability that can be lost from strict constraints. We also find that the topological properties resulting from the TOIBN maps are more expressive of differences between individuals with schizophrenia and controls in the default mode, subcortical, and visual networks.

Cardiorespiratory fitness (CRF) is increasingly recognized as essential for improving neural and cognitive function, especially in older age. Here, we examined a cross-sectional sample of the Human Connectome Project (HCP) Aging dataset (ages 36–100; N = 378) to test the hypothesis that functional connectivity profiles that are related to CRF are also associated with episodic memory and executive function across middle age and older adults. To test these hypotheses, we first used connectome-based predictive modeling (CPM) to identify functional connections (FCs) that were separately predictive of CRF (2 Minute Walk Test), episodic memory (Rey Auditory Verbal Learning Test), and executive function (Flanker task). Briefly, this involved using nine-fold cross-validation to identify the top 2% of FCs most correlated with the outcome measure (e.g., CRF), adjusting for age, education, and sex. We then examined the relationship between CRF (as the predictor), cognitive function (as outcome), and FCs (as mediator or moderator). First, there was a high degree of overlap between the CRF and the cognition measure functional connectivity profiles in both the ventral attentional and limbic networks. Second, although we did not observe moderation effects, we did observe that these functional connectivity profiles fully mediated the relationship between CRF and episodic memory and partially mediated the relationship between CRF and executive function. Together, these findings suggest that the connectivities within the ventral attention network (VAN), particularly with the right mid-insula, right inferior frontal gyrus, and the limbic network, are neural mechanisms that underlie the associations between CRF and cognition across the lifespan from middle to older adulthood. These findings provide insight into the potential targets (CRF) and biomarkers (functional connectivity profiles) of brain health for future interventions to improve or maintain neurocognitive health in aging.