Human Brain Mapping最新文献

Neuroimaging research in the field of schizophrenia and other psychotic disorders has sought to investigate neuroanatomical markers, relative to healthy control groups. In recent decades, a large number of structural magnetic resonance imaging (MRI) studies have been funded and undertaken, but their small sample sizes and heterogeneous methods have led to inconsistencies across findings. To tackle this, efforts have been made to combine datasets across studies and sites. While notable recent multicentre initiatives and the resulting meta- and mega-analytical outputs have progressed the field, efforts have generally been restricted to MRI scans in one or two illness stages, often overlook patient heterogeneity, and study populations have rarely been globally representative of the diversity of patients who experience psychosis. Furthermore, access to these datasets is often restricted to consortia members who can contribute data, likely from research institutions located in high-income countries. The Psychosis MRI Shared Data Resource (Psy-ShareD) is a new open access structural MRI data sharing partnership that will host pre-existing structural T1-weighted MRI data collected across multiple sites worldwide, including the Global South. MRI T1 data included in Psy-ShareD will be available in image and feature-level formats, having been harmonised using state-of-the-art approaches. All T1 data will be linked to demographic and illness-related (diagnosis, symptoms, medication status) measures, and in a number of datasets, IQ and cognitive data, and medication history will also be available, allowing subgroup and dimensional analyses. Psy-ShareD will be free-to-access for all researchers. Importantly, comprehensive data catalogues, scientific support and training resources will be available to facilitate use by early career researchers and build capacity in the field. We are actively seeking new collaborators to contribute further T1 data. Collaborators will benefit in terms of authorships, as all publications arising from Psy-ShareD will include data contributors as authors.

Early efforts to understand the human cerebral cortex focused on localization of function, assigning functional roles to specific brain regions. More recent evidence depicts the cortex as a dynamic system, organized into flexible networks with patterns of spatiotemporal activity corresponding to attentional demands. In functional MRI (fMRI), dynamic analysis of such spatiotemporal patterns is highly promising for providing non-invasive biomarkers of neurodegenerative diseases and neural disorders. However, there is no established neurotypical spectrum to interpret the burgeoning literature of dynamic functional connectivity from fMRI across attentional states. In the present study, we apply dynamic analysis of network-scale spatiotemporal patterns in a range of fMRI datasets across numerous tasks including a left–right moving dot task, visual working memory tasks, congruence tasks, multiple resting state datasets, mindfulness meditators, and subjects watching TV. We find that cortical networks show shifts in dynamic functional connectivity across a spectrum that tracks the level of external to internal attention demanded by these tasks. Dynamics of networks often grouped into a single task positive network show divergent responses along this axis of attention, consistent with evidence that definitions of a single task positive network are misleading. Additionally, somatosensory and visual networks exhibit strong phase shifting along this spectrum of attention. Results were robust on a group and individual level, further establishing network dynamics as a potential individual biomarker. To our knowledge, this represents the first study of its kind to generate a spectrum of dynamic network relationships across such an axis of attention.

There is a growing interest in neuroscience for how individual-specific structural and functional features of the cortex relate to cognitive traits. This work builds on previous research which, by using classical high-dimensional approaches, has proven that the interindividual variability of functional connectivity (FC) profiles reflects differences in fluid intelligence. To provide an additional perspective into this relationship, the present study uses a recent framework for investigating cortical organization: functional gradients. This approach places local connectivity profiles within a common low-dimensional space whose axes are functionally interpretable dimensions. Specifically, this study uses a data-driven approach to model the association between FC variability and interindividual differences in intelligence. For one of these loci, in the right ventral-lateral prefrontal cortex (vlPFC), we describe an association between fluid intelligence and the relative functional distance of this area from sensory and high-cognition systems. Furthermore, the topological properties of this region indicate that, with decreasing functional affinity with high-cognition systems, vlPFC functional connections are more evenly distributed across all networks. Participating in multiple functional networks may reflect a better ability to coordinate sensory and high-order cognitive systems.

Response inhibition (RI) deficits are a core feature across diagnostic categories of mental disorders. However, it remains unclear whether the brain networks underlying different forms of RI deficits are disorder-shared or disorder-specific, and how they interact with aberrant brain connectivity across disorders. Connectome-based predictive modeling (CPM) provides a novel approach for exploring the brain networks associated with RI abnormalities across diagnostic categories of mental disorders. Publicly available resting-state functional magnetic resonance imaging data from individuals with schizophrenia (n = 47), bipolar disorder (n = 47), and attention-deficit/hyperactivity disorder (n = 40), as well as healthy controls (n = 121), were utilized to construct whole-brain network predictive models for different forms of RI (action cancellation and action restraint). The brain networks of different forms of RI were further compared with abnormal brain networks in the diagnostic groups. Action restraint and action cancellation exhibited both shared and distinct brain networks. There was a dissociation in the relationship between the brain networks underlying different forms of RI and the aberrant connectivity patterns observed across diagnostic categories. Our models successfully predicted action restraint performance across diagnostic categories, whereas the model failed to effectively predict action cancellation due to the influence of disease-related aberrant connectivity on the brain networks underlying action cancellation. Nevertheless, the action cancellation model demonstrated generalizability to novel, healthy participants (n = 220) from an independent dataset. Our study clarifies the complex relationship between deficits in RI and the neuropathology of mental disorders and provides a foundation for more accurate cognitive assessment and targeted interventions. Our findings highlight the importance of refining RI constructs and emphasize the value of applying connectome methods to reveal cross-diagnostic neural mechanisms.

The analysis tools and statistical methods used in large neuroimaging research studies differ from those applied in clinical contexts, making it unclear whether these techniques can be translated to a memory clinic setting. The Oxford Brain Health Clinic (OBHC) was established in 2020 to bridge this gap between research studies and memory clinics. We optimised the UK Biobank imaging framework for the memory clinic setting by integrating enhanced quality control (QC) processes (MRIQC, QUAD, and DSE decomposition) and supplementary dementia-informed analyses (lobar volumes, NBM volumes, WMH classification, PSMD, cortical diffusion MRI metrics, and tract volumes) into the analysis pipeline. We explored associations between resultant imaging-derived phenotypes (IDPs) and clinical phenotypes in the OBHC patient population (N = 213), applying hierarchical FDR correction to account for multiple testing. 14%–24% of scans were flagged by automated QC tools, but upon visual inspection, only 0%–2.4% of outputs were excluded. The pipeline successfully generated 5683 IDPs aligned with UK Biobank and 110 IDPs targeted towards dementia-related changes. We replicated established associations and found novel associations between brain metrics and age, cognition, and dementia-related diagnoses. The imaging protocol is feasible, acceptable, and yields high-quality data that is usable for both clinical and research purposes. We validated the use of this methodology in a real-world memory clinic population, which demonstrates the potential of this enhanced pipeline to bridge the gap between big data studies and clinical settings.

While the use of naturalistic stimuli such as movie clips for understanding individual differences and brain–behaviour relationships attracts increasing interest, the influence of stimulus selection remains largely unclear. By using machine learning to predict individual traits (phenotypes) from brain activity evoked during various movie clips, we show that different movie stimuli can result in distinct prediction performances. In brain regions related to lower-level processing of the stimulus, prediction to a certain degree benefits from stronger synchronisation of brain activity across subjects. By contrast, better predictions in frontoparietal brain regions are mainly associated with larger inter-subject variability. Furthermore, we demonstrate that while movie clips with rich social content in general achieve better predictions, the importance of specific movie features for prediction highly depends on the phenotype under investigation. Overall, our findings underscore the importance of careful stimulus selection and provide novel insights into stimulus selection for phenotype prediction in naturalistic conditions, opening new avenues for future research.

During the second and third trimesters of human gestation, the brain undergoes rapid neurodevelopment thanks to critical processes such as neuronal migration, radial glial scaffolding, and synaptic sprouting. Unfortunately, gathering high-quality MRI data on the healthy fetal brain is complex, making it challenging to understand this development. To address this issue, we conducted a study using motion-corrected diffusion tensor imaging (DTI) to analyze changes in the cortical gray matter (CP) and sub-cortical white matter (scWM) microstructure in 44 healthy fetuses between 23 and 36 weeks of gestational age. We automatically segmented these two tissues and parcellated them into eight regions based on anatomy, including the frontal, parietal, occipital, and temporal lobes, cingulate, sensory and motor cortices, and the insula. We were able to observe distinct patterns of diffusion MRI signals across these regions. Specifically, we found that in the CP, fractional anisotropy (FA) consistently decreased with age, while mean diffusivity (MD) followed a downward-open parabolic trend. Conversely, in the scWM, FA exhibited an upward-open parabolic trajectory, while MD followed a downward-open parabolic trend. Our study underscores the potential for diffusion as a biomarker for normal and abnormal neurodevelopment before birth, especially since most neurodiagnostic tools are not yet available at this stage.

Dopamine, a key neuromodulator in the central nervous system, regulates cortical excitability and plasticity by interacting with glutamate and GABA receptors, which are affected by dopamine receptor subtypes (D1- and D2-like). Non-invasive brain stimulation techniques can induce plasticity and monitor cortical facilitation and inhibition in humans. In a randomized, placebo-controlled, double-blinded study, we investigated how dopamine and D1- and D2-like receptors impact transcranial direct current stimulation (tDCS)-induced plasticity concerning glutamatergic and GABAergic mechanisms. Eighteen healthy volunteers received 1 mA anodal (13 min) and cathodal tDCS (9 min) over the left motor cortex combined with the dopaminergic agents l-dopa (general dopamine activation), bromocriptine (D2-like receptor agonist), combined D2 antagonism via sulpiride and general dopaminergic activation via l-dopa to activate D1-like receptors, and placebo medication. Glutamate-related cortical facilitation and GABA-related cortical inhibition were monitored using transcranial magnetic stimulation techniques, including I–O curve, intracortical facilitation (ICF), short-interval intracortical inhibition (SICI), and I-wave facilitation protocols. Our results indicate that anodal tDCS alone enhanced the I–O curve and ICF while decreasing SICI. Conversely, cathodal tDCS decreased the I-O curve and ICF while increasing SICI. General dopamine and D2 receptor activation combined with anodal tDCS decreased the I-O curve and ICF, but enhanced SICI compared to tDCS alone. When paired with cathodal tDCS, general Dopamine and D2-like receptor activity enhancement prolonged the cathodal tDCS effect on excitability. After anodal tDCS, D1-like receptor activation increased the I-O curve and ICF while reducing SICI. These effects were abolished with cathodal tDCS. Dopaminergic substances combined with anodal and cathodal tDCS did not have a significant effect on I-wave facilitation. These results suggest that D1-like receptor activation enhanced LTP-like plasticity and abolished LTD-like plasticity via glutamatergic NMDA receptor enhancement, while global dopaminergic and D2-like receptor enhancement weakened LTP-like but strengthened LTD-like plasticity primarily via glutamatergic NMDA receptor activity diminution.

Disorders of the central nervous system, including neurodegenerative diseases, frequently affect the brainstem and can present with focal atrophy. This study aimed to (1) optimize deep learning-based brainstem segmentation for a wide range of pathologies and T1-weighted image acquisition parameters, (2) conduct a systematic technical and clinical validation, (3) improve segmentation quality in the presence of brainstem lesions, and (4) make an optimized brainstem segmentation tool available for public use. An intentionally heterogeneous ground truth dataset (n = 257) was employed in the training of deep learning models based on multi-dimensional gated recurrent units (MD-GRU) or the nnU-Net method. Segmentation performance was evaluated against ground truth labels. FreeSurfer was used for benchmarking in subsequent validation. Technical validation, including scan-rescan repeatability (n = 46) and inter-scanner reproducibility (n = 20, 3 different scanners) in unseen data, was conducted in patients with cerebral small vessel disease. Clinical validation in unseen data was performed in 1-year follow-up data of 16 patients with multiple system atrophy, evaluating the annual percentage volume change. Two lesion filling algorithms were investigated to improve segmentation performance in 23 patients with multiple sclerosis. The MD-GRU and nnU-Net models demonstrated very good segmentation performance (median Dice coefficients ≥ 0.95 each) and outperformed a previously published model trained on a narrower dataset. Scan–rescan repeatability and inter-scanner reproducibility yielded similar Bland–Altman derived limits of agreement for longitudinal FreeSurfer (total brainstem volume repeatability/reproducibility 0.68/1.85), MD-GRU (0.72/1.46), and nnU-Net (0.48/1.52). All methods showed comparable performance in the detection of atrophy in the total brainstem (atrophy detected in 100% of patients) and its substructures. In patients with multiple sclerosis, lesion filling further improved the accuracy of brainstem segmentation. We enhanced and systematically validated two fully automated deep learning brainstem segmentation methods and released them publicly. This enables a broader evaluation of brainstem volume as a candidate biomarker for neurodegeneration.

Previous functional magnetic resonance imaging (fMRI) studies have shown that activity in premotor and parietal brain-regions covaries with the intensity of upcoming grip-force. However, it remains unclear how information about the intended grip-force intensity is initially represented and subsequently transformed into a motor code before motor execution. In this fMRI study, we used multivoxel pattern analysis (MVPA) to decode where and when information about grip-force intensities is parametrically coded in the brain. Human participants performed a delayed grip-force task in which one of four cued levels of grip-force intensity had to be maintained in working memory (WM) during a 9-s delay-period preceding motor execution. Using time-resolved MVPA with a searchlight approach and support vector regression, we tested which brain regions exhibit multivariate WM codes of anticipated grip-force intensities. During the early delay period, we observed above-chance decoding in the ventromedial prefrontal cortex (vmPFC). During the late delay period, we found a network of action-specific brain regions, including the bilateral intraparietal sulcus (IPS), left dorsal premotor cortex (l-PMd), and supplementary motor areas. Additionally, cross-regression decoding was employed to test for temporal generalization of activation patterns between early and late delay periods with those during cue presentation and motor execution. Cross-regression decoding indicated temporal generalization to the cue period in the vmPFC and to motor-execution in the l-IPS and l-PMd. Together, these findings suggest that the WM representation of grip-force intensities undergoes a transformation where the vmPFC encodes information about the intended grip-force, which is subsequently converted into a motor code in the l-IPS and l-PMd before execution.