Human Brain Mapping最新文献

Structural head MRIs are a crucial ingredient in MEG/EEG source imaging; they are used to define a realistically shaped volume conductor model, constrain the source space, and visualize the source estimates. However, individual MRIs are not always available, or they may be of insufficient quality for segmentation, leading to the use of a generic template MRI, matched MRI, or the application of a spherical conductor model. Such approaches deviate the model geometry from the true head structure and limit the accuracy of the forward solution. Here, we implemented an easy-to-use tool, pseudo-MRI engine, which utilizes the head-shape digitization acquired during a MEG/EEG measurement for warping an MRI template to fit the subject's head. To this end, the algorithm first removes outlier digitization points, densifies the point cloud by interpolation if needed, and finally warps the template MRI and its segmented surfaces to the individual head shape using the thin-plate-spline method. To validate the approach, we compared the geometry of segmented head surfaces, cortical surfaces, and canonical brain regions in the real and pseudo-MRIs of 25 subjects. We also tested the MEG source reconstruction accuracy with pseudo-MRIs against that obtained with the real MRIs from individual subjects with simulated and real MEG data. We found that the pseudo-MRI enables comparable source localization accuracy to the one obtained with the subject's real MRI. The study indicates that pseudo-MRI can replace the need for individual MRI scans in MEG/EEG source imaging for applications that do not require subcentimeter spatial accuracy.

This study examined the statistical underpinnings of dynamic functional connectivity in mental disorders, using resting-state fMRI signals. Notably, there has been an absence of research demonstrating the non-stationarity of the empirical probability distribution of functional connectivity. This gap has prompted debate on the existence of dynamic functional connectivity, leading skeptics to question its relevance and the reliability of research findings. Our aim was to fill this gap by conducting a comprehensive empirical distribution analysis of functional connectivity, using Pearson's correlation as a measure. We conducted our analysis on a set of preprocessed resting-state fMRI samples obtained from 186 subjects selected from the UCLA Consortium for Neuropsychiatric Phenomics dataset. Departing from conventional methods that aggregated signals over voxels within a region of interest, our approach leveraged individual voxel signals. Specifically, our approach offered a precise characterization of the empirical probability distribution of resting-state fMRI signals by evaluating the temporal variations and non-stationarity in dynamic functional connectivity, as measured by Pearson's correlation. Our study investigated functional connectivity patterns across 49 regions of interest, comparing healthy control subjects with patients diagnosed with ADHD, bipolar disorder, and schizophrenia. Our analysis revealed that (1) the empirical distribution of the correlation coefficient exhibited non-stationarity, (2) the beta distribution was an accurate approximation of the exact correlation coefficient distribution, and (3) the empirical distribution of means derived from the fitted beta distributions, unraveled distinctive dynamic functional connectivity patterns with potential as biomarkers associated with different mental disorders. A key contribution of our study was the presentation of the first comprehensive empirical distribution analysis of dynamic functional connectivity, thus providing compelling evidence for its existence. Overall, our study presented an innovative statistical approach that advances our understanding of the dynamic nature of functional connectivity patterns derived from resting-state fMRI. Our examination of the empirical distribution of dynamic functional connectivity provided solid evidence supporting its existence. The distinctive dynamic functional connectivity patterns we identified across various mental disorders hold promise as potential biomarkers for further development.

Neuroimaging research has increasingly used decoding techniques, in which multivariate statistical methods identify patterns in neural data that allow the classification of experimental conditions or participant groups. Typically, the features used for decoding are spatial in nature, including voxel patterns and electrode locations. However, the strength of many neurophysiological recording techniques such as electroencephalography or magnetoencephalography is in their rich temporal, rather than spatial, content. The present report introduces the time-GAL toolbox, which implements a decoding method based on time information in electrophysiological recordings. The toolbox first quantifies the decodable information contained in neural time series. This information is then used in a subsequent step, generalization across location (GAL), which characterizes the relationship between sensor locations based on their ability to cross-decode. Two datasets are used to demonstrate the usage of the toolbox, involving (1) event-related potentials in response to affective pictures and (2) steady-state visual evoked potentials in response to aversively conditioned grating stimuli. In both cases, experimental conditions were successfully decoded based on the temporal features contained in the neural time series. Spatial cross-decoding occurred in regions known to be involved in visual and affective processing. We conclude that the approach implemented in the time-GAL toolbox holds promise for analyzing neural time series from a wide range of paradigms and measurement domains providing an assumption-free method to quantifying differences in temporal patterns of neural information processing and whether these patterns are shared across sensor locations.

Functional brain fingerprinting has emerged as an influential tool to quantify reliability in neuroimaging studies and to identify cognitive biomarkers in both healthy and clinical populations. Recent studies have revealed that brain fingerprints reside in the timescale-specific functional connectivity of particular brain regions. However, the impact of the acquisition's temporal resolution on fingerprinting remains unclear. In this study, we examine for the first time the reliability of functional fingerprinting derived from resting-state functional MRI (rs-fMRI) with different whole-brain temporal resolutions (TR = 0.5, 0.7, 1, 2, and 3 s) in a cohort of 20 healthy volunteers. Our findings indicate that subject identifiability within a fixed TR is successful across different temporal resolutions, with the highest identifiability observed at TR 0.5 and 3 s (TR(s)/identifiability(%): 0.5/64; 0.7/47; 1/44; 2/44; 3/56). We discuss this observation in terms of protocol-specific effects of physiological noise aliasing. We further show that, irrespective of TR, associative brain areas make an higher contribution to subject identifiability (functional connections with highest mean ICC: within subcortical network [SUB; ICC = 0.0387], within default mode network [DMN; ICC = 0.0058]; between DMN and somato-motor [SM] network [ICC = 0.0013]; between ventral attention network [VA] and DMN [ICC = 0.0008]; between VA and SM [ICC = 0.0007]), whereas sensory-motor regions become more influential when integrating data from different TRs (functional connections with highest mean ICC: within fronto-parietal network [ICC = 0.382], within dorsal attention network [DA; ICC = 0.373]; within SUB [ICC = 0.367]; between visual network [VIS] and DA [ICC = 0.362]; within VIS [ICC = 0.358]). We conclude that functional connectivity fingerprinting derived from rs-fMRI holds significant potential for multicentric studies also employing protocols with different temporal resolutions. However, it remains crucial to consider fMRI signal's sampling rate differences in subject identifiability between data samples, in order to improve reliability and generalizability of both whole-brain and specific functional networks' results. These findings contribute to a better understanding of the practical application of functional connectivity fingerprinting, and its implications for future neuroimaging research.

Pattern separation and pattern completion in the hippocampus play a critical role in episodic learning and memory. However, there is limited empirical evidence supporting the role of the hippocampal circuit in these processes during complex continuous experiences. In this study, we analyzed high-resolution fMRI data from the “Forrest Gump” open-access dataset (16 participants) using a sliding-window temporal autocorrelation approach to investigate whether the canonical hippocampal circuit (DG-CA3-CA1-SUB) shows evidence consistent with the occurrence of pattern separation or pattern completion during a naturalistic audio movie task. Our results revealed that when processing continuous naturalistic stimuli, the DG-CA3 pair exhibited evidence consistent with the occurrence of the pattern separation process, whereas both the CA3-CA1 and CA1-SUB pairs showed evidence consistent with pattern completion. Moreover, during the latter half of the audio movie, we observed evidence consistent with a reduction in pattern completion in the CA3-CA1 pair and an increase in pattern completion in the CA1-SUB pair. Overall, these findings improve our understanding of the evidence related to the occurrence of pattern separation and pattern completion processes during natural experiences.

Accurate metacognitive judgments about an individual's performance in a mental task require the brain to have access to representations of the quality and difficulty of first-order cognitive processes. However, little is known about how accurate metacognitive judgments are implemented in the brain. Here, we combine brain stimulation with functional neuroimaging to determine the neural and psychological mechanisms underlying the frontopolar cortex's (FPC) role in metacognition. Specifically, we evaluate two-layer neural architectures positing that FPC enables metacognitive judgments by communicating with brain regions encoding first-order decision difficulty. In support of two-layer architectures of metacognition, we found that high-intensity transcranial alternating current stimulation (tACS; 4 mA peak-to-peak) over FPC impaired metacognitive accuracy; at the neural level, this impairment was reflected by reduced coupling between FPC and dorsolateral prefrontal cortex (DLPFC), particularly during difficult metacognitive judgments. We also evaluated conceptual accounts assuming that metacognition relies on self-directed mentalizing. However, we observed no influence of FPC tACS on mentalizing performance and only a weak overlap of the networks underlying metacognition and mentalizing. Together, our findings put the FPC at the center of a two-layer architecture that enables accurate evaluations of cognitive processes, mainly via the FPC's connectivity with regions encoding first-level task difficulty, with little contributions from mentalizing-related processes.

Over a third of minor stroke patients experience post-stroke cognitive impairment (PSCI), but no validated tools exist to identify at-risk patients early. This study investigated whether disconnection features derived from infarcts and white matter hyperintensities (WMH) could serve as markers for short- and long-term cognitive decline in first-ever minor ischemic stroke patients. First-ever minor ischemic stroke patients (NIHSS ≤ 7) were prospectively followed at 72-h, 6 months, and 36 months post-stroke with cognitive tests and brain MRI. Infarct and WMH volumes were semi-automatically assessed on DWI and FLAIR sequences. Bayesian tract-based disconnection models estimated remote pathological effects of infarcts and WMH. Associations between disconnection features and cognitive outcomes were analyzed using canonical correlation analyses, adjusted for age, education, and multiple comparisons. Among 105 patients (31% female, mean age 63 ± 12 years), infarct volume averaged 10.28 ± 17.10 cm³ and predominantly involved the middle cerebral artery territory (83%). WMH burden was higher in frontal periventricular white matter. Infarct-based features did not significantly relate to PCSI. However, a WMH-derived disconnection factor, involving commissural and frontal tracts, and the right superior longitudinal fasciculus, was significantly associated with PSCI at 6 months (OR = 9.96, p value = 0.02) and 36 months (OR = 12.27, p value = 0.006), particularly in executive/attention, language, and visuospatial domains. This factor, unrelated to WMH volume, outperformed demographic and clinical predictors of PSCI. WMH-induced disconnection may be associated with short- and long-term PSCI in minor stroke. Routine MR-derived features could identify at-risk patients for rehabilitation trials.

Neurodegeneration is presumed to be the pathological process measure most proximal to clinical symptom onset in Alzheimer Disease (AD). Structural MRI is routinely collected in research and clinical trial settings. Several quantitative MRI-based measures of atrophy have been proposed, but their low correspondence with each other has been previously documented. The purpose of this study was to identify which commonly used structural MRI measure (hippocampal volume, cortical thickness in AD signature regions, or brain age gap [BAG]) had the best correspondence with the Clinical Dementia Rating (CDR) in an ethno-racially diverse sample. 2870 individuals recruited by the Healthy and Aging Brain Study—Health Disparities completed both structural MRI and CDR evaluation. Of these, 1887 individuals were matched on ethno-racial identity (Mexican American [MA], non-Hispanic Black [NHB], and non-Hispanic White [NHW]) and CDR (27% CDR > 0). We estimated brain age using two pipelines (DeepBrainNet, BrainAgeR) and then calculated BAG as the difference between the estimated brain age and chronological age. We also quantified their hippocampal volumes using HippoDeep and cortical thicknesses (both an AD-specific signature and average whole brain) using FreeSurfer. We used ordinal regression to evaluate associations between neuroimaging measures and CDR and to test whether these associations differed between ethno-racial groups. Higher BAG (p_DeepBrainNet = 0.0002; p_BrainAgeR = 0.00117) and lower hippocampal volume (p = 0.0015) and cortical thickness (p < 0.0001) were associated with worse clinical status (higher CDR). AD signature cortical thickness had the strongest relationship with CDR (AIC_DeepBrainNet = 2623, AIC_{whole cortex} = 2588, AIC_BrainAgeR = 2533, AIC_Hippocampus = 2293, AIC_{Signature Cortical Thickness} = 1903). The relationship between CDR and atrophy measures differed between ethno-racial groups for both BAG estimates and hippocampal volume, but not for cortical thickness. We interpret the lack of an interaction between ethno-racial identity and AD signature cortical thickness on CDR as evidence that cortical thickness effectively captures sources of disease-related atrophy that may differ across racial and ethnic groups. Cortical thickness had the strongest association with CDR. These results suggest that cortical thickness may be a more sensitive and generalizable marker of neurodegeneration than hippocampal volume or BAG in ethno-racially diverse cohorts.

Converging lines of research indicate that inhibitory control is likely to be compromised in contexts that place competing demands on emotional, motivational, and cognitive systems, potentially leading to damaging impulsive behavior. The objective of this study was to identify the neural impact of three challenging contexts that typically compromise self-regulation and weaken impulse control. Participants included 66 healthy adults (M/SD_age = 29.82/10.21 years old, 63.6% female) who were free of psychiatric disorders and psychotropic medication use. Participants completed a set of novel Go/NoGo (GNG) paradigms in the scanner, which manipulated contextual factors to induce (i) aversive emotions, (ii) appetitive drive, or (iii) concurrent working memory load. Voxelwise analysis of neural activation during each of these tasks was compared to that of a neutral GNG task. Findings revealed differential inhibition-related activation in the aversive emotions and appetitive drive GNG tasks relative to the neutral task in frontal, parietal and temporal cortices, suggesting emotional and motivational contexts may suppress activation of these cortical regions during inhibitory control. In contrast, the GNG task with a concurrent working memory load showed widespread increased activation across the cortex compared to the neutral task, indicative of enhanced recruitment of executive control regions. Results suggest the neural circuitry recruited for inhibitory control varies depending on the concomitant emotional, motivational, and cognitive demands of a given context. This battery of GNG tasks can be used by researchers interested in studying unique patterns of neural activation associated with inhibitory control across three clinically relevant contexts that challenge self-regulation and confer risk for impulsive behavior.

Population receptive field (pRF) mapping is a quantitative functional MRI (fMRI) analysis method that links visual field positions with specific locations in the visual cortex. A common preprocessing step in pRF analyses involves projecting volumetric fMRI data onto the cortical surface, typically leading to upsampling of the data. This process may introduce biases in the resulting pRF parameters. Using publicly available analysis containers, we compared pRF maps generated from the original volumetric with those from upsampled surface data. Our results show substantial increases in pRF coverage in the central visual field of upsampled datasets. These effects were consistent across early visual cortex areas V1-3. Further analysis indicates that this bias is primarily driven by the nonlinear relationship between cortical distance and visual field eccentricity, known as cortical magnification. Our results underscore the importance of understanding and addressing biases introduced by processing steps to ensure accurate interpretation of pRF mapping data, particularly in cross-study comparisons.