Human Brain Mapping最新文献

The human structural connectome has a complex internal community organization, characterized by a high degree of overlap and related to functional and cognitive phenomena. We explored connectivity properties in connectome networks and showed that $��k�$-clique percolation of an anomalously high order is characteristic of the human structural connectome. The resulting structural organization maintains a high local density of connectivity distributed throughout the connectome while preserving the overall sparsity of the network. To analyze these findings, we proposed a novel model for the emergence of high-order clique percolation during network formation with a phase transition dynamic under constraints on connection length. Investigating the structural basis of functional brain subnetworks, we identified a direct relationship between their interaction and the formation of clique clusters within their structural connections. Based on these findings, we hypothesize that the percolating clique cluster serves as a distributed bridge between interacting functional subnetworks, showing the complex, complementary nature of their structural connections. We also examined the difference between individual-specific and common structural connections and found that the latter plays a sustaining role in the connectivity of structural communities. At the same time, the superiority of individual connections, in contrast to common ones, creates variability in the interaction of functional brain subnetworks.

The pituitary and pineal glands are two small yet critical brain structures that help to modulate the human endocrine system. Unfortunately, very little research has been devoted to segmenting the pineal gland, and existing methods for pituitary segmentation focus only on the entire gland without distinguishing between its two lobes. To fill this gap, this work presents the first deep-learning-based tool for segmentation of both the pineal and pituitary glands in T1-weighted MRI. A five-fold cross-validation study was conducted on a manually labeled training dataset and produced segmentations with accuracy comparable to similar methods for segmenting other small brain structures. Model performance was then tested in three publicly available datasets using a total of n = 816 subjects, the results of which were both highly reproducible and robust to differences in MRI scanners and acquisition protocols. Finally, an analysis was performed to identify group differences related to sex and the diagnosis of schizophrenia and showed that volumes measured from the output segmentations were effective at discerning sex- and disease case-related differences in the pituitary and pineal glands.

The functional organization of the human spinal cord has primarily been derived from clinical observations and invasive electrophysiological studies. Recent methodological advances opened the possibility of studying the neuronal activity of the spinal cord in humans using noninvasive functional magnetic resonance imaging (fMRI). Here, we took advantage of fMRI to map the patterns of activity elicited by muscle-specific proprioceptive information along the whole cervical cord. We quantified the fMRI signals of the cervical spinal cord in 24 healthy participants who received mechanical muscle tendon vibration to stimulate proprioceptive afferents. The wrist flexor, biceps, and anterior deltoid muscles were independently stimulated while the upper limbs were stationary to avoid movement artifacts. To account for anatomical variability among participants, we optimized activity pattern localization by identifying individual rootlets and determining corresponding spinal levels using a trained deep-learning model. Distinct activation patterns emerged based on the stimulated muscle and body side, which coincided with well-established myotome maps. Concretely, the vibration-induced proprioceptive stimuli activity circumscribed to the ipsilateral ventral horn with a rostrocaudal distribution that reflected the proximo-distal location of the stimulated muscles. This spatial organization supported the proprioceptive origin of the response. This study demonstrates that muscle tendon vibration combined with spinal cord fMRI enables the noninvasive identification of upper-limb myotomes within the cervical spinal cord, offering new possibilities for studying the functional organization of the spinal cord and for clinical applications.

Large-scale functional brain networks have most commonly been evaluated using static methods that assess patterns of activation or functional connectivity over an extended period. However, this approach does not capture time-varying features of functional networks, such as variability in functional connectivity or transient network states that form and dissolve over time. Addressing this gap, dynamic methods for analyzing functional magnetic resonance imaging (fMRI) data estimate time-varying properties of brain functioning. In the context of resting-state neuroimaging, dynamic methods can reveal spontaneously occurring network configurations and temporal properties of such networks. Patterns of network functioning over time during the resting state may be indicative of individual differences in cognitive-affective processes such as rumination, or the tendency to engage in a pattern of repetitive negative thinking. We first introduce the current landscape of dynamic methods and then review an emerging body of literature applying these methods to the study of rumination and depression to illustrate how dynamic methods may be used to study clinical and cognitive phenomena. An emerging body of research suggests that rumination is related to altered functional flexibility of brain networks that overlap with the canonical default mode network. An important future direction for dynamic fMRI analyses is to explore associations with more specific features of cognition.

When operating on gliomas near critical language regions, surgeons risk either leaving residual tumor or inducing permanent postoperative language deficits (PLDs). Despite the advent of intraoperative mapping techniques, subjective judgments frequently determine important surgical decisions. We aim to inform data-driven surgery by constructing a non-invasive mapping approach that quantitatively predicts the impact of individual surgical decisions on long-term language function. This study included 79 consecutive patients undergoing resection of language-eloquent gliomas. Patients underwent preoperative navigated transcranial magnetic stimulation (TMS) language mapping to identify language-positive sites (“TMS points”) and their associated white matter tracts (“TMS tracts”) as well as formal language evaluations pre-and postoperatively. The resection of regions identified by preoperative mapping was correlated with permanent postoperative language deficits (PLDs). Resected tract segments (RTS) were normalized to MNI space for comparison with normative data. The resection of TMS points did not predict PLDs. However, a TMS point subgroup defined by white matter connectivity significantly predicted PLDs (OR = 8.74, p < 0.01) and demonstrated a canonical distribution of cortical language sites at a group level. TMS tracts recapitulated normative patterns of white matter connectivity defined by the Human Connectome Project. Subcortical resection of TMS tracts predicted PLDs independently of cortical resection (OR = 60, p < 0.001). In patients with PLDs, RTS showed significantly stronger co-localization with normative language-associated tracts compared to RTS in patients without PLDs (p < 0.05). Resecting patient-specific co-localizations between TMS tracts and normative tracts in native space predicted PLDs with an accuracy of 94% (OR = 134, p < 0.001). Prospective application of this data in a patient with glioblastoma precisely predicted the results of intraoperative language mapping with direct subcortical stimulation. Long-term postoperative language deficits result from resecting patient-specific white matter segments. We integrate these findings into a personalized tool that uses TMS language mappings, diffusion tractography, and population-level connectivity to preoperatively predict the long-term linguistic impact of individual surgical decisions.

While it is well accepted that the human brain shifts between internal and external monitoring both during tasks and at rest, no task-switching studies have focused on brain changes when switching from and to self-referential processing. Using a cued task-switching design, we explored the preparatory fMRI activation associated with switching not only within externally oriented tasks, but also within self-referential tasks, as well as between these two domains. We found that preparing to perform internal tasks activated the default mode network, while preparing for external tasks activated regions of the dorsal attention network (DAN). Switch preparation activated left-lateralised DAN regions with ventrolateral peaks as well as dorsal precuneus, posterior cingulate and supplementary motor area. These results show a dynamic pattern of communication across networks associated with external and internal domain processing and common preparatory activation in working memory and executive control regions. In particular, the dorsal precuneus was consistently engaged in task-switch preparation, suggesting a key role of this region in cognitive control, in the context of switching across external and internal domains.

With an aging global population, cognitive decline in older adults presents significant healthcare challenges. Emerging evidence suggests that physical activity can support cognitive health by promoting plasticity, functional reorganization, and structural adaptation of the brain. In the FIT4BRAIN study, we examined the effects of multi-component physical activity on cognitive and brain health. Here, we report the results on one of the secondary outcomes, namely changes in brain age (BrainAGE), which estimates the difference between chronological and predicted brain age based on structural MRI data, and changes in brain structure, assessed through voxel-based morphometry (VBM). Ninety-two healthy older adults were randomized into a multi-component physical activity group, performing aerobic, coordination, and balance exercises, or an active control group engaging in non-aerobic relaxation exercises and educational content (physical activity group (PAG): 36 participants; active control group (CON): 33 participants). Of these, 69 participants underwent MRI assessment and were included in the present analyses. BrainAGE analyses revealed a greater decrease in the physical activity group compared to the control group, indicating a beneficial effect of physical activity on brain aging. Subgroup analyses based on baseline cardiorespiratory fitness (CRF) further revealed that participants with lower CRF showed greater benefits, consistent with VBM findings of structural changes in the same subgroup. These results underscore BrainAGE as a sensitive biomarker for intervention outcomes and suggest that stratification by baseline fitness level may help identify differences in the benefits of physical activity on brain health.

Neuromodulation of subcortical network hubs by pharmacologic, electrical, or ultrasonic stimulation is a promising therapeutic strategy for patients with disorders of consciousness (DoC). However, optimal subcortical targets for therapeutic stimulation are not well established. Here, we leveraged 7 Tesla resting-state functional MRI (rs-fMRI) data from 168 healthy subjects from the Human Connectome Project to map the subcortical connectivity of six canonical cortical networks that modulate higher-order cognition and function: the default mode, executive control, salience, dorsal attention, visual, and somatomotor networks. Based on spatiotemporally overlapped networks generated by the Nadam-Accelerated SCAlable and Robust (NASCAR) tensor decomposition method, our goal was to identify subcortical hubs that are functionally connected to multiple cortical networks. We found that the ventral tegmental area (VTA) in the midbrain and the central lateral and parafascicular nuclei of the thalamus—regions that have historically been targeted by neuromodulatory therapies to restore consciousness—are subcortical hubs widely connected to multiple cortical networks. Further, we identified a subcortical hub in the pontomesencephalic tegmentum that overlapped with multiple reticular and extrareticular arousal nuclei and that encompassed a well-established “hot spot” for coma-causing brainstem lesions. Multiple hubs within the brainstem arousal nuclei and thalamic intralaminar nuclei were functionally connected to both the default mode and salience networks, emphasizing the importance of these cortical networks in integrative subcortico-cortical signaling. Additional subcortical connectivity hubs were observed within the caudate head, putamen, amygdala, hippocampus, and bed nucleus of the stria terminalis, regions classically associated with modulation of cognition, behavior, and sensorimotor function. Collectively, these results suggest that multiple subcortical hubs in the brainstem tegmentum, thalamus, basal ganglia, and medial temporal lobe modulate cortical function in the human brain. Our findings strengthen the evidence for targeting subcortical hubs in the VTA, thalamic intralaminar nuclei, and pontomesencephalic tegmentum to restore consciousness in patients with DoC. We release the subcortical connectivity maps to support ongoing efforts at therapeutic neuromodulation of consciousness.

Although substantial progress has been made in mapping the connectivity of cortical networks responsible for conscious awareness, neuroimaging analysis of subcortical networks that modulate arousal (i.e., wakefulness) has been limited by a lack of robust segmentation procedures for ascending arousal network (AAN) nuclei in the brainstem. Automated segmentation of brainstem AAN nuclei is an essential step toward elucidating the physiology of human consciousness and the pathophysiology of disorders of consciousness. We created a probabilistic atlas of 10 AAN nuclei built on diffusion MRI scans of 5 ex vivo human brain specimens imaged at 750 μm isotropic resolution. The neuroanatomic boundaries of AAN nuclei were manually annotated with reference to 200 μm 7 Tesla MRI scans in all five specimens and nucleus-specific immunostains in two of the scanned specimens. We then developed a Bayesian segmentation algorithm that utilizes the probabilistic atlas as a generative model and automatically identifies AAN nuclei in a resolution- and contrast-adaptive manner. The segmentation method displayed high accuracy when applied to in vivo T1 MRI scans of healthy individuals and patients with traumatic brain injury, as well as high test–retest reliability across T1 and T2 MRI contrasts. Finally, we show through classification and correlation assessments that the algorithm can detect volumetric changes and differences in magnetic susceptibility within AAN nuclei in patients with Alzheimer's disease and traumatic coma, respectively. We release the probabilistic atlas and Bayesian segmentation tool to advance the study of human consciousness and its disorders.

Trial Registration: ClinicalTrials.gov: NCT03504709

The behavioural variant of frontotemporal dementia (bvFTD) is a younger-onset dementia syndrome characterised by early atrophy of frontoinsular cortices, manifesting in profound socioemotional disturbances. Converging evidence from correlational, data-driven, and computational approaches indicates large-scale network degeneration in bvFTD. While the insula is consistently implicated, it remains unclear whether insular atrophy causally impacts progressive large-scale structural network alterations in bvFTD. Eighty-two patients with clinically probable bvFTD were classified as very mild/mild (n = 35), moderate (n = 30), and severe (n = 17) using the CDR plus NACC FTLD. Grey matter volume comparison between the entire bvFTD group and a healthy control group matched for age and education identified the left anterior insula as the initial maximal site of atrophy in bvFTD. To determine potential causal effects of insular atrophy on network-based dysfunction in bvFTD, a voxel-wise causal structural covariance network (CaSCN) was constructed based on pseudo-time-series morphometric data using the left anterior insula as the seed region. Sex, age, years of education, total intracranial volume (TIV), and scanning site were included as covariates, along with the difference between the sum of boxes score for the CDR plus NACC FTLD across the two pseudo–time points. Finally, an event-based model (EBM) was applied to confirm the sequence of regional atrophy precipitated by left anterior insula atrophy, which emerged in the CaSCN analysis. BvFTD patients in the very mild/mild disease subgroup showed predominant atrophy of frontotemporal (e.g., insula, middle frontal gyrus), limbic (e.g., hippocampus, amygdala), and subcortical (e.g., putamen, nucleus accumbens) structures. Widespread grey matter atrophy was evident in the moderate bvFTD subgroup, extending to the middle cingulate, paracingulate gyri, and the thalamus, which progressed to posterior brain regions, including the fusiform gyrus and the cerebellum in the severe subgroup. Importantly, the CaSCN and event-based model analysis reinforced the disease-staging results by revealing progression of atrophy from the initial seed region of the left anterior insula to the orbitofrontal cortex, putamen/nucleus accumbens, anterior cingulate cortex, dorsolateral prefrontal cortex, inferior temporal gyrus, and supramarginal gyrus, before progressing posteriorly to the lingual gyrus. Using causal structural covariance network analysis and event-based modelling, our findings indicate a causal role for the left anterior insula in driving the spread of pathology in bvFTD through well-delineated functional brain networks known to support higher-order cognitive and socioemotional processing. By capturing the direction of atrophy progression, our findings hold utility for potentially monitoring and tracking the efficacy of novel therapeutics on brain function in bvFTD.