Human Brain Mapping最新文献

Temporal interference (TI) stimulation is a novel neuromodulation technique that overcomes the depth limitations of traditional transcranial electrical stimulation while avoiding the invasiveness of deep brain stimulation. Our previous behavioral research has demonstrated the effects of multi-target TI stimulation in enhancing working memory (WM) performance, however, the neural mechanisms of this special form of envelope modulation remain unclear. To address this issue, here we designed this randomized, double-blind, crossover study, which consisted of a task-based functional magnetic resonance imaging (fMRI) experiment, to explore how offline TI stimulation modulated brain activity and behavioral performance in healthy adults. We conducted a 2 × 2 within-subjects design with two factors: stimulation (TI vs. Sham) and time (pre vs. post). Participants received two stimulation protocols in a random order: TI (beat frequency: 6 Hz, targeting middle frontal gyrus [MFG] and inferior parietal lobule [IPL]) and sham stimulation. Neuroimaging data of a WM task with different cognitive loads were acquisited immediately before and after stimulation. We found TI stimulation significantly improved d′ in the high-demand WM task. Whole-brain analysis showed the significant time-by-stimulation interactions in two main clusters in IPL and precuneus with lower activation after TI stimulation. The generalized psychophysiological interaction (gPPI) analysis revealed a significant interaction in task-modulated connectivity between MFG and IPL, with improvement observed after TI stimulation. Notably, this increasing functional connectivity induced by TI stimulation was positively correlated with better behavioral performance. Overall, our findings show specific effects of TI stimulation on brain activation and functional connectivity in the frontoparietal network and may contribute to provide new perspectives for future neuromodulation applications.

Congenital heart disease (CHD) is the most common congenital anomaly, leading to an increased risk of neurodevelopmental abnormalities in many children with CHD. Understanding the neurological mechanisms behind these neurodevelopmental disorders is crucial for implementing early interventions and treatments. In this study, we recruited 83 infants aged 12–26.5 months with complex CHD, along with 86 healthy controls (HCs). We collected multimodal data to explore the abnormal patterns of cerebral cortex development and explored the complex interactions among blood oxygen-carrying capacity, cortical development, and gross motor skills. We found that, compared to healthy infants, those with complex CHD exhibit significant reductions in cortical surface area development, particularly in the default mode network. Most of these developmentally abnormal brain regions are significantly correlated with the blood oxygen-carrying capacity and gross motor skills of infants with CHD. Additionally, we further discovered that the blood oxygen-carrying capacity of infants with CHD can indirectly predict their gross motor skills through cortical structures, with the left middle temporal area and left inferior temporal area showing the greatest mediation effects. This study identified biomarkers for neurodevelopmental disorders and highlighted blood oxygen-carrying capacity as an indicator of motor development risk, offering new insights for the clinical management CHD.

Head motion during diffusion magnetic resonance imaging (MRI) scans can cause numerous artifacts and biases subsequent quantification. However, a thorough characterization of motion across multiple scans, cohorts, and consortiums has not been performed. To address this, we designed a study with three aims. First, we aimed to characterize subject motion across several large cohorts, utilizing 13 cohorts comprised of 16,995 imaging sessions (age 0.1–100 years, mean age = 63 years; 7220 females; 3175 cognitively impaired adults; 471 developmentally delayed children) to describe the magnitude and directions of subject movement. Second, we aimed to investigate whether state-of-the-art diffusion preprocessing pipelines mitigate biases in quantitative measures of microstructure and connectivity by taking advantage of datasets with scan-rescan acquisitions and ask whether there are detectable differences between the same subjects when scans and rescans have differing levels of motion. Third, we aimed to investigate whether there are structural connectivity differences between movers and non-movers. We found that (1) subjects typically move 1–2 mm/min with most motion as translation in the anterior–posterior direction and rotation around the right–left axis; (2) Modern preprocessing pipelines can effectively mitigate motion to the point where biases are not detectable with current analysis techniques; and (3) There are no apparent differences in microstructure or macrostructural connections in participants who exhibit high motion versus those that exhibit low motion. Overall, characterizing motion magnitude and directions, as well as motion correlates, informs and improves motion mitigation strategies and image processing pipelines.

Schizophrenia (SZ) patients exhibit abnormal static and dynamic functional connectivity across various brain domains. We present a novel approach based on static and dynamic inter-network connectivity entropy (ICE), which represents the entropy of a given network's connectivity to all the other brain networks. This novel approach enables the investigation of how connectivity strength is heterogeneously distributed across available targets in both SZ patients and healthy controls. We analyzed fMRI data from 151 SZ patients and 160 demographically matched healthy controls (HC). Our assessment encompassed both static and dynamic ICE, revealing significant differences in the heterogeneity of connectivity levels across available functional brain networks between SZ patients and HC. These networks are associated with subcortical (SC), auditory (AUD), sensorimotor (SM), visual (VIS), cognitive control (CC), default mode network (DMN), and cerebellar (CB) functional brain domains. Elevated ICE observed in individuals with SZ suggests that patients exhibit significantly higher randomness in the distribution of time-varying connectivity strength across functional regions from each source network, compared to HC. C-means fuzzy clustering analysis of functional ICE correlation matrices revealed that SZ patients exhibit significantly higher occupancy weights in clusters with weak, low-scale functional entropy correlation, while the control group shows greater occupancy weights in clusters with strong, large-scale functional entropy correlation. K-means clustering analysis on time-indexed ICE vectors revealed that cluster with highest ICE have higher occupancy rates in SZ patients whereas clusters characterized by lowest ICE have larger occupancy rates for control group. Furthermore, our dynamic ICE approach revealed that in HC, the brain primarily communicates through complex, less structured connectivity patterns, with occasional transitions into more focused patterns. Individuals with SZ are significantly less likely to attain these more focused and structured transient connectivity patterns. The proposed ICE measure presents a novel framework for gaining deeper insight into mechanisms of healthy and diseased brain states and represents a useful step forward in developing advanced methods to help diagnose mental health conditions.

High gamma activity (HGA) is a crucial biomarker for functional brain mapping, particularly in sensorimotor areas, to preserve functionality after brain surgeries. HGA mapping paradigms typically involve multiple task blocks alternating with resting (R) conditions, where each block comprises consecutive tasks under nonresting (NR) conditions. However, the repetitive nature of these tasks may lead to attenuation due to repetition suppression, potentially compromising the accuracy of HGA mapping. This study tests the hypothesis that repetitive grasping paradigms result in attenuated HGA over time in sensorimotor areas. It explores the temporal and spatial characteristics of this attenuation to optimize electrocorticography (ECoG) HGA protocols and enhance result interpretation. Eleven consecutive patients who underwent surgical treatment of intractable epilepsy or malignant glioma were included in this study. Intracranial electrode locations on the pre- and postcentral gyrus were considered regions of interest (ROI). Each patient performed ten blocks of ten consecutive grasping trials. The mean z-scored HGA (60–170 Hz) across these trials was calculated, and attenuation was analyzed using the Kruskal–Wallis test. Obtained signals were also divided into three grouped periods for R and NR groups to assess short-term attenuation within movement blocks and long-term attenuation over multiple blocks. Electrode locations were mapped to the MNI152 (Montreal Neurological Institute) brain template to investigate the spatial distribution of attenuation. Distances from each electrode to the hand-knob region were compared between attenuated and nonattenuated electrodes. A total of 568 electrodes from 11 patients were analyzed, including 139 electrodes within the ROI. Thus, 60 electrodes demonstrated significant HGAs during the grasping task (p < 0.05). Sensorimotor HGA z-scores significantly attenuated over time during both consecutive grasping trials and repeated blocks. Short-term attenuation (25%, 15/60 electrodes in ROI) was more pronounced than long-term attenuation (15%, 9/60 electrodes in ROI). Notably, three patients undergoing intraoperative mapping demonstrated less short-term attenuation compared to long-term attenuation. Spatially, attenuated electrodes clustered around the hand-knob region of the precentral gyrus and adjacent areas of the postcentral gyrus. However, no significant differences were observed in the distances from electrodes to the hand-knob region between attenuated and nonattenuated electrodes. The present study showed that repetitive grasping tasks attenuated the HGA of significant electrodes in the sensorimotor area over time. Considering the findings with the characteristics can further improve the usability of ECoG mapping in terms of more precise results in the most reasonable mapping time.

Measuring propagation of perturbations across the human brain and their transmission delays is critical for network neuroscience, but it is a challenging problem that still requires advancement. Here, we compare results from a recently introduced, noninvasive technique of functional delays estimation from source-reconstructed electro/magnetoencephalography, to the corresponding findings from a large dataset of cortico-cortical evoked potentials estimated from intracerebral stimulations of patients suffering from pharmaco-resistant epilepsies. The two methods yield significantly similar probabilistic connectivity maps and signal propagation delays, in both cases characterized with Pearson correlations greater than 0.5 (when grouping by stimulated parcel is applied for delays). This similarity suggests a correspondence between the mechanisms underpinning the propagation of spontaneously generated scale-free perturbations (i.e., neuronal avalanches observed in resting state activity studied using magnetoencephalography) and the spreading of cortico-cortical evoked potentials. This manuscript provides evidence for the accuracy of the estimate of functional delays obtained noninvasively from reconstructed sources.

Conversely, our findings show that estimates obtained from externally induced perturbations in patients capture physiological activities in healthy subjects. In conclusion, this manuscript constitutes a mutual validation between two modalities, broadening their scope of applicability and interpretation. Importantly, the capability to measure delays noninvasively (as per MEG) paves the way for the inclusion of functional delays in personalized large-scale brain models as well as in diagnostic and prognostic algorithms.

Alcohol Use Disorder (AUD), a prevalent and potentially severe psychiatric condition, is one of the leading causes of morbidity and mortality. This systematic review investigates the relationship between AUD and resting-state functional connectivity (rsFC) derived from functional magnetic resonance imaging data. Following the PRISMA guidelines, a comprehensive search yielded 248 papers, and a screening process identified 39 studies with 73 relevant analyses. Using the automated anatomical labeling atlas for whole-brain parcellation, relevance maps were generated to quantify associations between brain regions and their connections with AUD. These outcomes are based on the frequency with which significant findings are reported in the literature, to deal with the challenge of methodological diversity between analyses, including sample sizes, types of independent rsFC features, and AUD measures. The analysis focuses on whole-brain studies to mitigate selection biases associated with seed-based approaches. The most frequently reported regions include the middle and superior frontal gyri, the anterior cingulate cortex, and the insula. The generated relevance maps can serve as a valuable tool for formulating hypotheses and advancing our understanding of AUD's neural correlates in the future. This work also provides a template on how to quantitatively summarize a diverse literature, which could be applied to more specific aspects of AUD, including craving, relapse, binge drinking, or other diseases.

Aging and neurodegeneration impact structural brain integrity and can result in changes to behavior and cognition. Personality, a relatively stable trait in adults as compared to behavior, in part relies on normative individual differences in cellular organization of the cerebral cortex, but links between brain structure and personality expression have been mixed. One key finding is that personality has been shown to be a risk factor in the development of Alzheimer's disease, highlighting a structure–trait relationship. Magnetic resonance elastography (MRE) has been used to noninvasively study age-related changes in tissue mechanical properties because of its high sensitivity to both the microstructural health and the structure–function relationship of the tissue. Recent advancements in MRE methodology have allowed for reliable property recovery of cortical subregions, which had previously presented challenges due to the complex geometry and overall thin structure. This study aimed to quantify age-related changes in cortical mechanical properties and the relationship of these properties to measures of personality in an older adult population (N = 57; age 60–85 years) for the first time. Mechanical properties including shear stiffness and damping ratio were calculated for 30 bilateral regions of the cortex across all four lobes, and the NEO Personality Inventory (NEO-PI) was used to measure neuroticism and conscientiousness in all participants. Shear stiffness and damping ratio were found to vary widely across regions of the cortex, upward of 1 kPa in stiffness and by 0.3 in damping ratio. Shear stiffness changed regionally with age, with some regions experiencing accelerated degradation compared to neighboring regions. Greater neuroticism (i.e., the tendency to experience negative emotions and vulnerability to stress) was associated with high damping ratio, indicative of poorer tissue integrity, in the rostral middle frontal cortex and the precentral gyrus. This study provides evidence of structure–trait correlates between physical mechanical properties and measures of personality in older adults and adds to the supporting literature that neurotic traits may impact brain health in cognitively normal aging.

Alzheimer's disease (AD) brain markers are needed to select people with early-stage AD for clinical trials and as quantitative endpoint measures in trials. Using 10 clinical cohorts (N = 9140) and the community volunteer UK Biobank (N = 37,664) we performed region of interest (ROI) and vertex-wise analyses of grey-matter structure (thickness, surface area and volume). We identified 94 trait-ROI significant associations, and 307 distinct cluster of vertex-associations, which partly overlap the ROI associations. For AD versus controls, smaller hippocampus, amygdala and of the medial temporal lobe (fusiform and parahippocampal gyri) was confirmed and the vertex-wise results provided unprecedented localisation of some of the associated region. We replicated AD associated differences in several subcortical (putamen, accumbens) and cortical regions (inferior parietal, postcentral, middle temporal, transverse temporal, inferior temporal, paracentral, superior frontal). These grey-matter regions and their relative effect sizes can help refine our understanding of the brain regions that may drive or precede the widespread brain atrophy observed in AD. An AD grey-matter score evaluated in independent cohorts was significantly associated with cognition, MCI status, AD conversion (progression from cognitively normal or MCI to AD), genetic risk, and tau concentration in individuals with none or mild cognitive impairments (AUC in 0.54–0.70, p-value < 5e-4). In addition, some of the grey-matter regions associated with cognitive impairment, progression to AD (‘conversion’), and cognition/functional scores were also associated with AD, which sheds light on the grey-matter markers of disease stages, and their relationship with cognitive or functional impairment. Our multi-cohort approach provides robust and fine-grained maps the grey-matter structures associated with AD, symptoms, and progression, and calls for even larger initiatives to unveil the full complexity of grey-matter structure in AD.

Poirot, M. G., D. E. Boucherie, M. W. A. Caan, et al. 2025. “Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group.” Human Brain Mapping 46, no. 1: e70053. https://doi.org/10.1002/hbm.70053.

The ITEA (Reference number 21016 DAIsy) funding program was missing from the Acknowledgments section in the originally published version. Here is the corrected text:

This work was done with the ENIGMA major depressive disorder (ENIGMA-MDD) working group and was supported by the Eurostars (Reference number 113351 DEPREDICT) and ITEA (Reference number 21016 DAIsy) funding programs.

The online version has been corrected accordingly.

We apologize for this error.