Human Brain Mapping最新文献

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.

The loss of smell is common in older age, reducing quality of life and often precedes the onset of cognitive decline and dementia. While age-related olfactory loss has been linked to cortical thinning and volume reductions in key olfactory areas, associations between white-matter (WM) integrity and olfaction are poorly understood. Here, we studied individuals aged 25–85 years from a population-based cohort study with diffusion weighted imaging, together with self-reported olfactory impairment, odor identification and odor threshold measures at baseline (N = 248) and follow-up 5 years later (N = 192). Performance on the odor identification and threshold tests were lower in older adults and declined longitudinally. Older individuals also reported more olfaction complaints, and such complaints increased over time. Results from general linear models showed no cross-sectional associations between WM integrity and olfaction. However, results from non-competitive random forest models identified several tracts as significant contributors to odor identification and subjective olfactory impairment, including the fornix, cingulum and uncinate fasciculus. Moreover, longitudinal analyses showed that olfactory threshold decline was associated with decline in WM integrity in the body of corpus callosum. Taken together, the results support a link between white-matter integrity and olfaction and provide initial evidence for its interplay with age.

The accurate estimation of fiber orientation distribution functions (fODFs) in diffusion magnetic resonance imaging (MRI) is crucial for understanding early brain development and its potential disruptions. Although supervised deep learning (DL) models have shown promise in fODF estimation from neonatal diffusion MRI (dMRI) data, the out-of-domain (OOD) performance of these models remains largely unexplored, especially under diverse domain shift scenarios. This study evaluated the robustness of three state-of-the-art DL architectures: multilayer perceptron (MLP), transformer, and U-Net/convolutional neural network (CNN) on fODF predictions derived from dMRI data. Using 488 subjects from the developing Human Connectome Project (dHCP) and the Baby Connectome Project (BCP) datasets, we reconstructed reference fODFs from the full dMRI series using single-shell three-tissue constrained spherical deconvolution (SS3T-CSD) and multi-shell multi-tissue CSD (MSMT-CSD) to generate reference fODF reconstructions for model training, and systematically assessed the impact of age, scanner/protocol differences, and input dimensionality on model performance. Our findings reveal that U-Net consistently outperformed other models when fewer diffusion gradient directions were used, particularly with the SS3T-CSD-derived ground truth, which showed superior performance in capturing crossing fibers. However, as the number of input diffusion gradient directions increased, MLP and the transformer-based model exhibited steady gains in accuracy. Nevertheless, performance nearly plateaued from 28 to 45 input directions in all models. Age-related domain shifts showed asymmetric patterns, being less pronounced in late developmental stages (late neonates, and babies), with SS3T-CSD demonstrating greater robustness to variability compared to MSMT-CSD. To address inter-site domain shifts, we implemented two adaptation strategies: the Method of Moments (MoM) and fine-tuning. Both strategies achieved significant improvements () in over 95% of tested configurations, with fine-tuning consistently yielding superior results and U-Net benefiting the most from increased target subjects. This study represents the first systematic evaluation of OOD settings in DL applications to fODF estimation, providing critical insights into model robustness and adaptation strategies for diverse clinical and research applications.

Early life stress (ELS) has profound implications for developmental trajectories, yet the neural mechanisms underlying its long-term effects remain incompletely understood. In the present study, we examined whether interindividual similarity in ELS exposure aligns with similarity in neural representations and behavioral task performance in early adulthood. Leveraging a 20-year longitudinal dataset of Finnish families, we evaluated 87 young adults who underwent functional magnetic resonance imaging (fMRI) during an emotional go/no-go task. Intersubject representational similarity analysis (IS-RSA) was used to assess the associations between pairwise similarities in prospectively or retrospectively measured ELS, neural representations in 360 cortical regions, and task performance. We incorporated multidimensional scaling and Procrustes analysis to visualize interindividual differences in neural representational spaces. Prospective ELS—but not Retrospective ELS—was significantly associated with neural representational similarity across 40 cortical regions, including the anterior insula, frontal operculum, and anterior cingulate cortex. These findings highlight the systematic and chronic effects of more moderate ELS on brain development and emphasize the value of prospective measurements and advanced similarity analyses in capturing the nuanced influences of ELS. By integrating spatial and shape analytical techniques, the present study provides new insights into the long-term neurobiological correlates of ELS and introduces novel methodological tools for neurodevelopmental research.

Understanding the presymptomatic biology in those at high risk of developing amyotrophic lateral sclerosis (ALS) is essential for the development of preventative therapeutic interventions. Approximately 10% of ALS is associated with a C9orf72 expansion or pathogenic variants in SOD1. Magnetoencephalography (MEG), combined with machine learning algorithms, can model brain network dynamics in such at-risk populations to develop pathogenic biomarkers. Individuals with symptomatic ALS (symALS, n = 61), asymptomatic C9orf72 carriers (aC9, n = 16), or pathological SOD1 carriers (aSOD, n = 12), and healthy controls (n = 84) underwent resting-state MEG recordings. Extracted metrics included regional oscillatory power, connectivity, and spectral shape. ‘DyNeMo’ was trained to identify six functional dynamic brain networks. Metrics were compared between groups. A classifier was trained to distinguish asymptomatic gene carriers from controls. Compared to controls, beta frequency power was decreased in both symALS and aC9 groups. The aC9 group showed a marked slowing of frontal oscillatory activity, while the aSOD group showed a marked acceleration. Dynamic network coactivation was dramatically disrupted in aC9, more than in both symALS and aSOD. The classifier accurately distinguished genetically at-risk groups from controls (receiver-operator-characteristic area-under-curve 0.89). The cerebral network dynamics of aC9 are markedly different from both aSOD and symALS, supporting the concept of profoundly different upstream pathways in SOD1 ALS, sparing wider cortical pathology when compared to C9orf72 ALS. aC9 changes may reflect chronic adaptive changes relating to neurodevelopmental factors or underpin aspects of system vulnerability that define penetrance variability. MEG metrics might provide important biomarkers of prevention therapy efficacy and phenoconversion in at-risk populations.

Advances in fetal three-dimensional (3D) ultrasound (US) and magnetic resonance imaging (MRI) have revolutionized the study of fetal brain development, enabling detailed analysis of brain structures and growth. Despite their complementary capabilities, these modalities capture fundamentally different physical signals, potentially leading to systematic differences in image-derived phenotypes (IDPs). Here, we evaluate the agreement of IDPs between US and MRI by comparing the volumes of eight brain structures from 90 subjects derived using deep-learning algorithms from majority same-day imaging (days between scans: mean = 1.2, mode = 0 and max = 4). Excellent agreement (intra-class correlation coefficient, $��\mathrm{ICC}�>�0.75�$) was observed for the cerebellum, cavum septum pellucidum, thalamus, white matter and deep grey matter volumes, with significant correlations for most structures, except the ventricular system. Bland–Altman analysis revealed some systematic biases: intracranial and cortical plate volumes were larger on US than MRI, by an average of $��35��{\mathrm{cm}}^3�$ and $��4.1��{\mathrm{cm}}^3�$, respectively. Finally, we found the labels of the brainstem and ventricular system were not comparable between the modalities. These findings highlight the necessity of structure-specific adjustments when interpreting fetal brain IPDs across modalities and underscore the complementary roles of US and MRI in advancing fetal neuroimaging.

We previously reported that a unilateral dorsal column lesion (DCL) at the cervical C4 level primarily reduces inter-horn resting-state functional connectivity (rsFC) measured by functional Magnetic Resonance Imaging (fMRI) in segments below the lesion. This study compares changes in rsFC from fMRI with changes in local field potential (LFP) coherence over an extended post-injury period. High-resolution fMRI and LFP data were acquired bilaterally in healthy monkeys and at 3- and 6-months post-lesion. At 3 months post-injury, tactile-stimulus-evoked LFP power in the dorsal horn was significantly weaker than in the healthy cord and non-lesion side. LFP coherences increased on the lesion side for the dorsal-to-intermediate zone (D-IGM) and dorsal-to-ventral (D-V) pairs but decreased for the non-lesion side D-IGM. By 6 months, stimulus-evoked LFP power on the lesion side remained low. LFP coherences between dorsal-to-dorsal (D-D), ventral-to-ventral (V-V), and D-V pairs on both the lesion and non-lesion sides were significantly reduced relative to the healthy cord. Low-frequency (delta, theta, and alpha) D-IGM coherences on the lesion side, and high-frequency (beta and gamma) coherences on the non-lesion side, were also significantly weakened. Across specific inter-horn pairs and time points, changes in LFP coherences and rsFC measures were weakly correlated. Measurements of inter-horn correlations two segments caudal to the lesion level at C7 revealed distance-dependent intraspinal connectivity changes following DCL. Post-mortem histology confirmed a complete DCL in most animals (7/9). The extent of the disruption of ascending sensory afferents, as assessed histologically, did not appear to correlate with the degree of LFP power reduction or rsFC changes at post-injury time points. In summary, we observed temporally and spatially heterogeneous changes of fMRI correlations and LFP coherences within intraspinal circuits. fMRI rsFC and LFP coherences were not always concordant, with discrepancies depending on specific gray-matter horns and intermediate-zone pairs.