Human Brain Mapping最新文献

The brain develops most rapidly during pregnancy and early neonatal months. While prior electrophysiological studies have shown that aperiodic brain activity undergoes changes across infancy to adulthood, the role of gestational duration in aperiodic and periodic activity remains unknown. In this study, we aimed to bridge this gap by examining the associations between gestational duration and aperiodic and periodic activity in the EEG power spectrum in both neonates and toddlers. This cross-sectional study involved EEG data from 73 neonates (postnatal age 1–5 days, 40 females) and 56 toddlers (postnatal age of 2.9–3.2 years, 28 females) from the FinnBrain Birth Cohort Study. EEG power spectra were parameterized to aperiodic and periodic components using the SpecParam tool. We tested the associations between gestational duration as well as postnatal age and SpecParam parameters in neonates and toddlers while including birth weight and child sex as covariates. For neonates, multilevel models were employed, considering different data acquisitions (sleep and auditory paradigm + sleep), while in toddlers, regression models were used as only data from the auditory paradigm was available. We found that longer gestational duration was associated with a steeper power spectrum across EEG frequencies both in neonates and toddlers. Effect was especially strong in toddlers (β = 0.45, p = 0.004), while in neonates, it remained nearly statistically significant (p = 0.061). In neonates, a quadratic association between gestational duration and beta center frequency (12.5–30 Hz) was found. In toddlers, beta center frequencies were overall higher in females compared to males. Offset (calculated as the power of the aperiodic curve at 2.5 Hz) and theta center frequency had negative associations with postnatal age in neonates, but not in toddlers.

Our results suggest that gestational duration may have significant and relatively long-lasting effects on brain physiology. The possible behavioral and cognitive consequences of these changes are enticing topics for future research.

Working memory (WM) plays a crucial role in human cognition. Previous candidate and genome-wide association studies have reported many genetic variations associated with WM. However, little research has examined genetic basis of WM by using transcriptome, even though it reflects gene function more directly than does the genome. Here we propose a new approach to exploring the genetic mechanisms of WM by integrating connectome, transcriptome, and genome data in a high-quality dataset comprising 481 Chinese healthy adults. First, relevance vector regression was used to define WM-related brain regions. Second, genes differentially expressed within these regions were identified using the Allen Human Brain Atlas (AHBA) dataset. Finally, two independent datasets were used to validate these genes' contributions to WM. With this method, we identified 24 novel genes and 20 of them were confirmed in the large-scale datasets of ABCD and UK Biobank. These novel genes were enriched in the cellular component of collagen-containing extracellular matrix and the CCL18 signaling pathway. Our method offers an effective approach to integrating multimodal gene discovery and demonstrates the superiority of expression data. This new method and the newly identified genes deserve more attention in the future.

Perception and production of music and speech rely on auditory–motor coupling, a mechanism which has been linked to temporally precise oscillatory coupling between auditory and motor regions of the human brain, particularly in the beta frequency band. Recently, brain imaging studies using magnetoencephalography (MEG) have also shown that accurate auditory temporal predictions specifically depend on phase coherence between auditory and motor cortical regions. However, it is not yet clear whether this tight oscillatory phase coupling is an intrinsic feature of the auditory–motor loop, or whether it is only elicited by task demands. Further, we do not know if phase synchrony is uniquely enhanced in the auditory–motor system compared to other sensorimotor modalities, or to which degree it is amplified by musical training. In order to resolve these questions, we measured the degree of phase locking between motor regions and auditory or visual areas in musicians and non-musicians using resting-state MEG. We derived phase locking values (PLVs) and phase transfer entropy (PTE) values from 90 healthy young participants. We observed significantly higher PLVs across all auditory–motor pairings compared to all visuomotor pairings in all frequency bands. The pairing with the highest degree of phase synchrony was right primary auditory cortex with right ventral premotor cortex, a connection which has been highlighted in previous literature on auditory–motor coupling. Additionally, we observed that auditory–motor and visuomotor PLVs were significantly higher across all structures in the right hemisphere, and we found the highest differences between auditory and visual PLVs in the theta, alpha, and beta frequency bands. Last, we found that the theta and beta bands exhibited a preference for a motor-to-auditory PTE direction and that the alpha and gamma bands exhibited the opposite preference for an auditory-to-motor PTE direction. Taken together, these findings confirm our hypotheses that motor phase synchrony is significantly enhanced in auditory compared to visual cortical regions at rest, that these differences are highest across the theta-beta spectrum of frequencies, and that there exist alternating information flow loops across auditory–motor structures as a function of frequency. In our view, this supports the existence of an intrinsic, time-based coupling for low-latency integration of sounds and movements which involves synchronized phasic activity between primary auditory cortex with motor and premotor cortical areas.

Phenotypic and genetic relationships between white matter microstructure (i.e., fractional anisotropy [FA]) and peripheral inflammatory responses (i.e., circulating cytokines) have important implications for health and disease. However, it is unclear whether previously discovered genetic correlations between the two traits are due to tissue-specific white matter architecture or increased free water in the extracellular space. We applied a two-compartment model to diffusion tensor imaging (DTI) data and estimated tissue-specific white matter microstructure (FA_T) and free water volume (FW). We then quantified their heritability and their genetic correlations with two peripherally circulating proinflammatory cytokines (IL-8 and TNFα), and compared these correlations to those obtained using traditional FA measures from one-compartment DTI models. All DTI and cytokine measures were significantly moderately heritable. We confirmed phenotypic and genetic correlations between circulating cytokine levels and single-compartment FA across the brain (IL-8: ρ_p = −0.16, FDRp = 4.8 × 10⁻⁰⁷; ρ_g = −0.37 (0.12), FDRp = 0.01; TNFα: ρ_p = −0.15, FDRp = 2.4 × 10⁻⁰⁷; ρ_g = −0.34 (0.12), p = 0.01). However, this relationship no longer reached significance when FA measures were derived using the two-compartment DTI model (IL-8: ρ_p = −0.04, FDRp = 0.17; ρ_g = −0.14 (0.13), FDRp = 0.29; TNFα: ρ_p = −0.05, FDRp = 0.10; ρ_g = −0.22 (0.13), FDRp = 0.10). There were significant phenotypic and genetic correlations between FW and both IL-8 (ρ_p = 0.19, FDRp = 2.1 × 10⁻¹⁰; ρ_g = 0.34 (0.11), FDRp = 0.01) and TNFα (ρ_p = 0.16, FDRp = 1.89 × 10⁻⁰⁷; ρ_g = 0.30 (0.12), FDRp = 0.02). These results have important implications for understanding the mechanisms linking the two phenomena, but they also serve as a cautionary note for those examining associations between white matter integrity using single-compartment models and inflammatory processes.

Disruptions to brain networks, measured using structural (sMRI), diffusion (dMRI), or functional (fMRI) MRI, have been shown in people with multiple sclerosis (PwMS), highlighting the relevance of regions in the core of the connectome but yielding mixed results depending on the studied connectivity domain. Using a multilayer network approach, we integrated these three modalities to portray an enriched representation of the brain's core-periphery organization and explore its alterations in PwMS. In this retrospective cross-sectional study, we selected PwMS and healthy controls with complete multimodal brain MRI acquisitions from 13 European centers within the MAGNIMS network. Physical disability and cognition were assessed with the Expanded Disability Status Scale (EDSS) and the symbol digit modalities test (SDMT), respectively. SMRI, dMRI, and resting-state fMRI data were parcellated into 100 cortical and 14 subcortical regions to obtain networks of morphological covariance, structural connectivity, and functional connectivity. Connectivity matrices were merged in a multiplex, from which regional coreness—the probability of a node being part of the multiplex core—and coreness disruption index (κ)—the global weakening of the core-periphery structure—were computed. The associations of κ with disease status (PwMS vs. healthy controls), clinical phenotype, level of physical disability (EDSS ≥ 4 vs. EDSS < 4), and cognitive impairment (SDMT z-score < −1.5) were tested within a linear model framework. Using random forest permutation feature importance, we assessed the relative contribution of κ in the multiplex and single-layer domains, in addition to conventional MRI measures (brain and lesion volumes), in predicting disease status, physical disability, and cognitive impairment. We studied 1048 PwMS (695F, mean ± SD age: 43.3 ± 11.4 years) and 436 healthy controls (250F, mean ± SD age: 38.3 ± 11.8 years). PwMS showed significant disruption of the multiplex core-periphery organization (κ = −0.14, Hedges' g = 0.49, p < 0.001), correlating with clinical phenotype (F = 3.90, p = 0.009), EDSS (Hedges' g = 0.18, p = 0.01), and SDMT (Hedges' g = 0.30, p < 0.001). Multiplex κ was the only connectomic measure adding to conventional MRI in predicting disease status and cognitive impairment, while physical disability also depended on single-layer contributions. In conclusion, we show that multilayer networks represent a biologically and clinically meaningful framework to model multimodal MRI data, with disruption of the core-periphery structure emerging as a potential connectomic biomarker for disease severity and cognitive impairment in PwMS.

Owing to the high cost of modern magnetic resonance imaging (MRI) systems, their use in clinical care and neurodevelopmental research is limited to hospitals and universities in high income countries. Ultra-low-field systems with significantly lower scanning costs present a promising avenue towards global MRI accessibility; however, their reduced SNR compared to 1.5 or 3 T systems limits their applicability for research and clinical use. In this paper, we describe a deep learning-based super-resolution approach to generate high-resolution isotropic T₂-weighted scans from low-resolution paediatric input scans. We train a ‘multi-orientation U-Net’, which uses multiple low-resolution anisotropic images acquired in orthogonal orientations to construct a super-resolved output. Our approach exhibits improved quality of outputs compared to current state-of-the-art methods for super-resolution of ultra-low-field scans in paediatric populations. Crucially for paediatric development, our approach improves reconstruction of deep brain structures with the greatest improvement in volume estimates of the caudate, where our model improves upon the state-of-the-art in: linear correlation (r = 0.94 vs. 0.84 using existing methods), exact agreement (Lin's concordance correlation = 0.94 vs. 0.80) and mean error (0.05 cm³ vs. 0.36 cm³). Our research serves as proof-of-principle of the viability of training deep-learning based super-resolution models for use in neurodevelopmental research and presents the first model trained exclusively on paired ultra-low-field and high-field data from infants.

Despite transverse relaxation rate (R₂) being one of the fundamental contrasts in MRI, most investigations of brain R₂ and cognition have been cross-sectional and conducted in predominantly non-Latino White adults. We investigated the profile of R₂ as related to cognition in 212 older African Americans (~75 years of age) with longitudinal 3T MRI scans and cognitive test data to determine how changes in R₂ are associated with changes in cognition. For each participant, the slopes of global cognitive and five cognitive domain scores were each separately combined with voxel-specific slopes of R₂ in whole brain voxelwise analyses. Participants with less negative rates of R₂ change within left basal ganglia and centrum semiovale, bilateral hippocampal complex and temporal gyri, parietooccipital white matter, as well as posterior cingulate displayed less negative slopes in global cognition. Similar associations were seen for regional R₂ change and episodic memory (most robustly within bilateral hippocampi) as well as semantic memory (left greater than right hemisphere involvement). Results suggest a relatively wide distribution of regional associations between rates of changes in R₂ and changes in global cognition for older African Americans; a profile that became more regionally specific when considering individual cognitive domains. Relative preservation of tissue integrity across grey and white matter, and in key regions associated with specific cognitive domains, is associated with slower cognitive decline for older African Americans. These results may lay the foundation for more directed work to support healthy brain aging in older African Americans.

White matter hyperintensities (WMH) of presumed vascular origin are a magnetic resonance imaging (MRI)-based biomarker of cerebral small vessel disease (CSVD). WMH are associated with cognitive decline and increased risk of stroke and dementia, and are commonly observed in aging, vascular cognitive impairment, and neurodegenerative diseases. The reliable and rapid measurement of WMH in large-scale multisite clinical studies with heterogeneous patient populations remains challenging, where the diversity of imaging characteristics across studies adds additional complexity to this task. We present segcsvd_WMH, a convolutional neural network-based tool developed to provide reliable and accurate WMH quantification across diverse clinical datasets. segcsvd_WMH was developed using a large dataset consisting of over 700 fluid-attenuated inversion recovery MRI scans from seven multisite studies, spanning a wide range of clinical populations, WMH burdens, and imaging protocols. Model training incorporated anatomical information through a novel hierarchical segmentation approach, together with extensive data augmentation techniques to improve performance across varied imaging conditions. Benchmarked against three widely available segmentation tools, segcsvd_WMH demonstrated superior accuracy, achieving mean Dice score improvements of 7.8% ± 9.7% over HyperMapp3r, 21.8% ± 8.6% over SAMSEG, and 43.5% ± 7.1% over WMH-SynthSeg across four diverse test datasets. segcsvd_WMH also maintained consistently high Dice scores across these test datasets (mean DSC = 0.86 ± 0.08), and exhibited strong, stable correlations with periventricular, deep, and total WMH ground truth volumes (mean r = 0.99 ± 0.01). Additionally, segcsvd_WMH was robust to low and moderate levels of simulated MRI spike noise artifacts and maintained strong performance across a range of binary segmentation thresholds and WMH burden levels. These findings suggest that segcsvd_WMH may provide more accurate and robust WMH segmentation performance for heterogeneous clinical datasets characterized by varying degrees of CSVD severity.