Human Brain Mapping最新文献

The prediction of subject traits using brain data is an important goal in neuroscience, with relevant applications in clinical research, as well as in the study of differential psychology and cognition. While previous prediction work has predominantly been done on neuroimaging data, our focus is on electroencephalography (EEG), a relatively inexpensive, widely available and non-invasive data modality. However, EEG data is complex and needs some form of feature extraction for subsequent prediction. This process is sometimes done manually, risking biases and suboptimal decisions. Here we investigate the use of data-driven Kernel methods for prediction from single channels using the EEG spectrogram, which reflects macro-scale neural oscillations in the brain. Specifically, we introduce the idea of reinterpreting the spectrogram of each channel as a probability distribution, so that we can leverage advanced machine learning techniques that can handle probability distributions with mathematical rigour and without the need for manual feature extraction. We explore how the resulting technique, Kernel mean embedding regression, compares to a standard application of Kernel ridge regression as well as to a non-Kernelised approach. Overall, we found that the Kernel methods exhibit improved performance thanks to their capacity to handle nonlinearities in the relation between the EEG spectrogram and the trait of interest. We leveraged this method to predict biological age in a multinational EEG data set, HarMNqEEG, showing the method's capacity to generalise across experiments and acquisition setups.

Data aggregation across multiple research centers is gaining importance in the context of MRI research, driving diverse high-dimensional datasets to form large-scale heterogeneous sample, increasing statistical power and relevance of machine learning and deep learning algorithm. Site-related effects have been demonstrated to introduce bias in MRI features and confound subsequent analyses. Although Combating Batch (ComBat) technique has been recently reported to successfully harmonize multi-scale neuroimaging features, its performance assessments are still limited and largely based on qualitative visualizations and statistical analyses. In this study, we stand out by using a robust cross-validation approach to assess ComBat performances applied on volume- and surface-based measures acquired across three sites. A machine learning approach based on Multi-Class Gaussian Process Classifier was applied to predict imaging site based on raw and harmonized brain features, providing quantitative insights into ComBat effectiveness, and verifying the association between biological covariates and harmonized brain features. Our findings showed differences in terms of ComBat performances across measures of regional brain morphology, demonstrating tissue specific site effect modeling. ComBat adjustment of site effects also varied across regional level of each specific volume-based and surface-based measures. ComBat effectively eliminates unwanted data site-related variability, by maintaining or even enhancing data association with biological factors. Of note, ComBat has demonstrated flexibility and robustness of application on unseen independent gray matter volume data from the same sites.

The human hippocampus is a key region in cognitive and emotional processing, but also a vulnerable and plastic region. Accordingly, there is a great interest in understanding how variability in the hippocampus' structure relates to variability in behavior in healthy and clinical populations. In this study, we aimed to link interindividual variability in subregional hippocampal networks (i.e., the brain grey matter networks of hippocampal subregions) to variability in behavioral phenotype. To do so, we used a multiblock multivariate approach mapping the association between grey matter volume in hippocampal subregions, grey matter volume in the whole brain regions, and behavioral variables in healthy adults. To ensure the robustness and generalizability of the findings, we implemented a cross-cohort discovery and validation framework. This framework utilized two independent cohorts: the Human Connectome Project Young Adult (HCP-YA) cohort and the Human Connectome Project Aging (HCP-A) cohort, enabling us to assess the replicability and generalizability of hippocampal–brain–behavior phenotypes across different age groups in the population. Our results highlighted a left anterior hippocampal morphological network including the left amygdala and the posterior midline structures whose expression relates to higher self-regulation, life satisfaction, and better performance at standard neuropsychological tests. The cross-cohort generalizability of the hippocampus–brain–behavior mapping demonstrates its relevance beyond a specific population sample. Our previous work in developmental populations showed that the hippocampus' head co-maturates with most of the brain during childhood. The current data-driven study further suggests that grey matter volume in the left hippocampal head network would be particularly relevant for self-regulation abilities in adults that influence a range of life outcomes. Future studies should thus investigate the factors influencing the development of this morphological network across childhood, as well as its relationship to neurocognitive phenotypes in various brain diseases.

Burman Ingeberg, M., E. Van Houten, and J. J. M. Zwanenburg. 2023. “Estimating the Viscoelastic Properties of the Human Brain at 7 T MRI Using Intrinsic MRE and Nonlinear Inversion.” Human Brain Mapping 44, no. 18: 6575–6591. https://doi.org/10.1002/hbm.26524.

In caption of Table 1, the text “WMT ROIs” should be replaced by “global ROIs”.

In the body of Table 1, the shear stiffness values in subcortical GM of 213 ± 21 should be replaced with 233 ± 24.

In the abbreviations of Table 3, the abbreviation of the precentral cortex is missing. Thus, “PRE, precentral cortex;” should be added after “POST, postcentral cortex;”

Due to the incorrect calculation of the repeatability coefficient, the following sections also contain errors.

We apologize for these mistakes.

Weighted MRI images are widely used in clinical as well as open-source neuroimaging databases. Weighted images such as T1-weighted, T2-weighted, and proton density-weighted (T1w, T2w, and PDw, respectively) are used for evaluating the brain's macrostructure; however, their values cannot be used for microstructural analysis, as they lack physical meaning. Quantitative MRI (qMRI) relaxation rate parameters (e.g., R1 and R2) do contain microstructural physical meaning. Nevertheless, qMRI is rarely done in large-scale clinical databases. Currently, the weighted images ratio T1w/T2w is used as a quantifier to approximate the brain's microstructure. In this paper, we test three additional quantifiers that approximate quantitative maps, which can help bring quantitative MRI to the clinic for easy use. Following the signal equations and using simple mathematical operations, we combine the T1w, T2w, and PDw images to estimate the R1 and R2 maps. We find that two of these quantifiers (T1w/PDw and T1w/ln(T2w)) can approximate R1, and that (ln(T2w/PDw)) can approximate R2, in 3 datasets that were tested. We find that this approach also can be applied to T2w scans taken from widely available DTI (Diffusion Tensor Imaging) datasets. We tested these quantifiers on both in vitro phantom and in vivo human datasets. We found that the quantifiers accurately represent the quantitative parameters across datasets. Finally, we tested the quantifiers within a clinical context, and found that they are robust across datasets. Our work provides a simple pipeline to enhance the usability and quantitative accuracy of MRI weighted images.

In neuroimaging research, volumetric data contribute valuable information for understanding brain changes during both healthy aging and pathological processes. Extracting these measures from images requires segmenting the regions of interest (ROIs), and many popular methods accomplish this by fusing labels from multiple expert-segmented images called atlases. However, post-segmentation, current practices typically treat each subject's measurement equally without incorporating any information about variation in their segmentation precision. This naïve approach hinders comparing ROI volumes between different samples to identify associations between tissue volume and disease or phenotype. We propose a novel method that estimates the variance of the measured ROI volume for each subject due to the multi-atlas segmentation procedure. We demonstrate in real data that weighting by these estimates markedly improves the power to detect a mean difference in hippocampal volume between controls and subjects with mild cognitive impairment or Alzheimer's disease.

In functional magnetic resonance imaging, the hemodynamic response function (HRF) is a stereotypical response to local changes in cerebral hemodynamics and oxygen metabolism due to briefly (< 4 s) evoked neural activity. Accordingly, the HRF is often used as an impulse response with the assumption of linearity in data analysis. In cognitive aging studies, it has been very common to interpret differences in brain activation as age-related changes in neural activity. Contrary to this assumption, however, evidence has accrued that normal aging may also significantly affect the vasculature, thereby affecting cerebral hemodynamics and metabolism, confounding interpretation of fMRI cognitive aging studies. In this study, use was made of a multisensory task to evoke the HRF in ~87% of cerebral cortex in cognitively intact adults with ages ranging from 22 to 75 years. This widespread activation enabled us to investigate age trends in the spatial distributions of HRF characteristics within the majority of cortical gray matter, which we termed as global age trends. The task evoked both positive and negative HRFs, which were characterized using model-free parameters in native-space coordinates. We found significant global age trends in the distributions of HRF parameters in terms of both amplitudes (e.g., peak amplitude and contrast-to-noise ratio) and temporal dynamics (e.g., full-width-at-half-maximum). Our findings offer insight into how age-dependent changes affect neurovascular coupling and show promise for use of HRF parameters as non-invasive indicators for age-related pathology.

Transcranial Direct Current Stimulation (tDCS) is a non-invasive brain stimulation technique used to modulates cortical brain activity. However, its effects on brain metabolites within the dorsolateral prefrontal cortex (DLPFC), a crucial area targeted for brain stimulation in mental disorders, remain unclear. This study aimed to investigate whether prefrontal tDCS over the left and right DLPFC modulates levels of key metabolites, including gamma-aminobutyric acid (GABA), glutamate (Glu), glutamine/glutamate (Glx), N-acetylaspartate (NAA), near to the target region and to explore potential sex-specific effects on these metabolite concentrations. A total of 41 healthy individuals (19 female, M_age = 25 years, SD = 3.15) underwent either bifrontal active (2 mA for 20 min) or sham tDCS targeting the left (anode: F3) and right (cathode: F4) DLPFC within a 3 Tesla MRI scanner. Magnetic resonance spectroscopy (MRS) was used to monitor neurometabolic changes before, during, and after 40 min of tDCS, with measurements of two 10-min intervals during stimulation. A single voxel beneath F3 was used for metabolic quantification. Results showed a statistically significant increase in Glx levels under active tDCS compared to the sham condition, particularly during the second 10-min window and persisting into the post-stimulation phase. No significant changes were observed in other metabolites, but consistent sex differences were detected. Specifically, females showed lower levels of NAA and GABA under active tDCS compared to the sham condition, while no significant changes were observed in males. E-field modeling showed no significant differences in field magnitudes between sexes, and the magnitude of the e-fields did not correlate with changes in Glx levels between active and sham stimulation during the second interval or post-stimulation. This study demonstrates that a single session of prefrontal tDCS significantly elevates Glx levels in the left DLPFC, with effects persisting post-stimulation. However, the observed sex differences in the neurochemical response to tDCS were not linked to specific stimulation intervals or variations in e-field magnitudes, highlighting the complexity of tDCS effects and the need for personalized neuromodulation strategies.

The human brain is organized as a hierarchical global network. Functional connectivity research reveals that sensory cortices are connected to corresponding association cortices via a series of intermediate nodes linked by synchronous neural activity. These sensory pathways and relay stations converge onto central cortical hubs such as the default-mode network (DMN). The DMN regions are believed to be critical for representing concepts and, hence, language acquisition and use. Although prior research has established that major senses are placed at a similar distance from the DMN—five to six connective steps—it is still unknown how the olfactory system functionally connects to the large-scale cortical hubs of the human brain. In this study, we investigated the connective distance from olfactory seed areas to the DMN. The connective distance involves a series of three to four intermediate steps. Furthermore, we parcellated the olfactory cortical subregions and found evidence of two distinct olfactory pathways. One emerges from the anterior olfactory nucleus and olfactory tubercle; it involves early access to the orbitofrontal cortex, known for processing reward and multisensory signals. The other emerges from the frontal and temporal regions of the piriform cortex, involving the anterior insula, intermediate frontal sulcus, and parietal operculum. The results were confirmed in a replication cohort. Our results provide evidence that olfaction has unique early access to the central cortical networks via dual pathways.

The posterior cerebellum and anterior basal ganglia are critical subcortical structures for learning and identifying dynamic action sequences, in concert with the neocortex. The present analysis investigates the role of action sequences during social mentalizing, termed here dynamic or sequential social mentalizing. Although the role of the cerebellum in dynamic social mentalizing was extensively investigated during the last decade, the basal ganglia were long ignored. We conducted an activation likelihood estimation coordinate-based meta-analysis of sequential social mentalizing tasks (with 485 participants in 17 studies). These tasks required participants to make social mentalizing inferences ranging from low-level goals to high-level beliefs and traits, while either memorizing, generating or predicting temporal sequences of the social actions involved (i.e., social sequencing condition), or not (i.e., social non-sequencing control condition), or did so for nonsocial objects (i.e., nonsocial sequencing control condition). The tasks also occasionally included inconsistencies in social behavior. Results revealed that the cerebellum exhibited a preference for social, sequencing, and inconsistent information, while the basal ganglia showed a preference for sequencing and inconsistency, without a general preference for social input. Meta-analytic connectivity analysis further showed evidence of coactivation between mentalizing areas of the cerebellum, basal ganglia and cerebral neocortex. The present work underscores the role of subcortical structures in social mentalizing about dynamic action sequences.