Human Brain Mapping最新文献

Auditory perception is established through experience-dependent stimuli exposure during sensitive developmental periods; however, little is known regarding the structural development of the central auditory pathway in humans. The present study characterized the regional developmental trajectories of the ascending auditory pathway from the brainstem to the auditory cortex from infancy through adolescence using a novel diffusion MRI-based tractography approach and along-tract analyses. We used diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) to quantify the magnitude and timing of auditory pathway microstructural maturation. We found spatially varying patterns of white matter maturation along the length of the tract, with inferior brainstem regions developing earlier than thalamocortical projections and left hemisphere tracts developing earlier than the right. These results help to characterize the processes that give rise to functional auditory processing and may provide a baseline for detecting abnormal development.

Neurodevelopmental disorders (NDDs) can severely impact functioning yet effective treatments are limited. Greater insight into the neurobiology underlying NDDs is critical to the development of successful treatments. Using a genetics-first approach, we investigated the potential of advanced diffusion-weighted imaging (DWI) techniques to characterize the neural microstructure unique to neurofibromatosis type 1 (NF1) and Noonan syndrome (NS). In this prospective study, children with NF1, NS, and typical developing (TD) were scanned using a multi-shell DWI sequence optimized for neurite orientation density and dispersion imaging (NODDI) and diffusion kurtosis imaging (DKI). Region-of-interest and tract-based analysis were conducted on subcortical regions and white matter tracts. Analysis of covariance, principal components, and linear discriminant analysis compared between three groups. 88 participants (M_age = 9.36, SD_age = 2.61; 44 male) were included: 31 NS, 25 NF1, and 32 TD. Subcortical regions differed between NF1 and NS, particularly in the thalamus where the neurite density index (NDI; estimated difference 0.044 [95% CI: −0.034, 0.053], d = 2.36), orientation dispersion index (ODI; estimate 0.018 [95% CI: 0.010, 0.026], d = 1.39), and mean kurtosis (MK; estimate 0.049 [95% CI: 0.025, 0.072], d = 1.39) were lower in NF1 compared with NS (all p < 0.0001). Reduced NDI was found in NF1 and NS compared with TD in all 39 white matter tracts investigated (p < 0.0001). Reduced MK was found in a majority of the tracts in NF1 and NS relative to TD, while fewer differences in ODI were observed. The middle cerebellar peduncle showed lower NDI (estimate 0.038 [95% CI: 0.021, 0.056], p < 0.0001) and MK (estimate 0.057 [95% CI: 0.026, 0.089], p < 0.0001) in NF1 compared to NS. Multivariate analyses distinguished between groups using NDI, ODI, and MK measures. Principal components analysis confirmed that the clinical groups differ most from TD in white matter tract-based NDI and MK, whereas ODI values appear similar across the groups. The subcortical regions showed several differences between NF1 and NS, to the extent that a linear discriminant analysis could classify participants with NF1 with an accuracy rate of 97%. Differences in neural microstructure were detected between NF1 and NS, particularly in subcortical regions and the middle cerebellar peduncle, in line with pre-clinical evidence. Advanced DWI techniques detected subtle alterations not found in prior work using conventional diffusion tensor imaging.

Large-scale brain network function is critical for healthy cognition, yet links between such network function, neurochemistry, and smaller-scale neurocircuitry are unclear. Here, we evaluated 59 healthy individuals using resting-state fMRI to determine how network-level temporal dynamics were impacted by two well-characterized pharmacotherapies targeting catecholamines: methylphenidate (20 mg) and haloperidol (2 mg)—administered via randomized, double-blind, placebo-controlled design. Network temporal dynamic changes were tested for links with drug-induced alterations in complex corticostriatal connections as this circuit is a primary site of action for both drugs. Methylphenidate increased time in the default mode network state (DMN p < 0.001) and dorsal attention network state (DAN p < 0.001) and reduced time in the frontoparietal network state (p < 0.01). Haloperidol increased time in a sensory motor-DMN state (p < 0.01). The magnitude of change in network dynamics induced by methylphenidate vs. placebo correlated with the magnitude of methylphenidate-induced rearrangement of complex corticostriatal connectivity (R = 0.32, p = 0.014). Haloperidol did not alter complex corticostriatal connectivity. Methylphenidate enhanced time in network states involved in internal and external attention (DMN and DAN, respectively), aligning with methylphenidate's established role in attention. Methylphenidate also significantly changed complex corticostriatal connectivity by altering the relative strength between multiple corticostriatal connections, indicating that methylphenidate may shift which corticostriatal connections are prioritized relative to others. Findings show that these corticostriatal circuit changes are linked with large-scale network temporal dynamics. Collectively, these findings provide a deeper understanding of large-scale network function, set a stage for mechanistic understanding of network engagement, and provide useful information to guide medication use based on network-level effects.

Trial Registration: Registry name: ClinicalTrials.gov; URL: Brain Networks and Addiction Susceptibility—Full Text View—ClinicalTrials.gov; URL Plain text: https://classic.clinicaltrials.gov/ct2/show/NCT01924468; Identifier: NCT01924468

Subthalamic (STN) deep brain stimulation (DBS) in Parkinson's disease (PD) patients not only improves kinematic parameters of movement but also modulates cognitive control in the motor and non-motor domain, especially in situations of high conflict. The objective of this study was to investigate the relationship between DBS-induced changes in functional connectivity at rest and modulation of response- and movement inhibition by STN-DBS in a visuomotor task involving high conflict. During DBS ON and OFF conditions, we conducted a visuomotor task in 14 PD patients who previously underwent resting-state functional MRI (rs-fMRI) acquisitions DBS ON and OFF as part of a different study. In the task, participants had to move a cursor with a pen on a digital tablet either toward (automatic condition) or in the opposite direction (controlled condition) of a target. STN-DBS induced modulation of resting-state functional connectivity (RSFC) as a function of changes in behavior ON versus OFF DBS was estimated using link-wise network-based statistics. Behavioral results showed diminished reaction time adaptation and higher pen-to-target movement velocity under DBS. Reaction time reduction was associated with attenuated functional connectivity between cortical motor areas, basal ganglia, and thalamus. On the other hand, increased movement velocity ON DBS was associated with stronger pallido-thalamic connectivity. These findings suggest that decoupling of a motor cortico-basal ganglia network underlies impaired inhibitory control in PD patients undergoing subthalamic DBS and highlight the concept of functional network modulation through DBS.

Tau pathology spread into neocortex indicates a transition from healthy aging to Alzheimer's disease (AD). Connectivity between tau epicenters and later accumulating regions of cortex has been proposed as a mechanism of tau spread, but how this relationship changes with greater AD pathology burden or genotype is not understood. We investigated tau accumulation in two key regions, precuneus and inferior temporal cortex, using resting state functional connectivity (rsFC) and longitudinal PET imaging from a multicohort sample of cognitively unimpaired older adults. We examined how baseline tau PET, Aβ PET, and ApoE4 genotype status interact with rsFC between hippocampus and these downstream regions to predict rate of tau accumulation in neocortex. We found that the 3-way interaction between connectivity, baseline tau, and baseline Aβ or ApoE4 status was associated with neocortical tau accumulation in precuneus and inferior temporal cortex. In addition, baseline tau, Aβ, and ApoE4 status also moderated the association between connectivity and rate of memory decline. Together, these results suggest that the extent and distribution of future tau accumulation may be predicted by the interaction of baseline connectivity, AD pathology, and genetic risk.

Cognitive reappraisal, an effective emotion regulation strategy, is influenced by various individual factors. Although previous studies have established a link between negative emotion differentiation (NED) and cognitive reappraisal, the underlying neural mechanisms remain largely unknown. Using electroencephalography, this study investigates the influence and neural basis of NED in cognitive reappraisal by integrating aspects of event-related potentials, neural oscillation rhythms, and cross-frequency coupling. The findings revealed that individuals with high NED demonstrated a significant decrease in parietal late positive potential amplitudes during cognitive reappraisal, suggesting enhanced cognitive reappraisal abilities. Moreover, high NED individuals displayed increased γ synchronization, parietal α–γ coupling, and frontal θ–γ coupling when reappraising negative emotions than those with low emotion differentiation ability. Machine learning analysis of these neural indicators highlighted the superior classification and predictive accuracy of multimodal indicators for NED as opposed to unimodal indicators. Overall, this multimodal evidence provides a comprehensive interpretation of the neurophysiological mechanisms through which NED influences cognitive reappraisal and provides preliminary empirical support for personalized cognitive reappraisal interventions to alleviate emotional problems.

Previous studies have found that betrayal increases negative attentional bias and hinders executive control. However, it remains unclear how betrayal influences emotional modulation of executive control. What's more, according to the dual mechanisms of control model, executive control can be divided into reactive and proactive control. It also requires clarification whether both aspects of executive control are affected equally by betrayal in emotional contexts. Thus, the present study aims to provide insight into how betrayal influences the emotional modulation of executive control. Betrayal was induced using a repeated trust game in two experiments. Eighty-two participants (40 for Experiment 1 and 42 for Experiment 2) completed emotional executive control tasks while event-related potentials were recorded. In Experiment 1, an emotional Go/No-go task was used to explore the impact of betrayal on the emotional modulation of executive control. The results indicated that betrayal resulted in inefficient top-down attention processing towards negative stimuli and impaired executive control over these stimuli. This was evidenced by higher N2a and N2b amplitudes in the angry Go condition, and smaller angry No-go P3 amplitudes in individuals who experienced betrayal compared to the control group. In Experiment 2, a modified emotional Stroop task was employed to measure proactive and reactive control in emotional contexts. The results indicated that betrayal impaired only reactive control towards negative stimuli and did not affect proactive control. This was evidenced by the betrayed group exhibiting smaller SP amplitudes under the happy incongruent condition in the most congruent context, with no significant difference observed in the most incongruent context. In summary, betrayal decreases the efficiency of top-down attentional processing directed towards negative stimuli and hampers executive control over negative stimuli. Moreover, this impairment appears to be confined to reactive control strategy.

The human pulvinar is considered a prototypical associative thalamic nucleus as it represents a key node in several cortico-subcortical networks. Through this extensive connectivity to widespread brain areas, it has been suggested that the pulvinar may play a central role in modulating cortical oscillatory dynamics of complex cognitive and executive functions. Additionally, derangements of pulvinar activity are involved in different neuropsychiatric conditions including Lewy-body disease, Alzheimer's disease, and schizophrenia. Anatomical investigations in nonhuman primates have demonstrated a topographical organization of cortico-pulvinar connectivity along its dorsoventral and rostrocaudal axes; this specific organization shows only partial overlap with the traditional subdivision into subnuclei (anterior, lateral, medial, and inferior) and is thought to coordinate information processing within specific brain networks. However, despite its relevance in mediating higher-order cognitive functions, such a structural and functional organization of the pulvinar in the human brain remains poorly understood. Track-weighted dynamic functional connectivity (tw-dFC) is a recently developed technique that combines structural and dynamic functional connectivity, allowing the identification of white matter pathways underlying the fluctuations observed in functional connectivity between brain regions over time. Herein, we applied a data-driven parcellation approach to reveal topographically organized connectivity clusters within the human pulvinar complex, in two large cohorts of healthy human subjects. Unsupervised clustering of tw-dFC time series within the pulvinar complex revealed dorsomedial, dorsolateral, ventral anterior, and ventral posterior connectivity clusters. Each of these clusters shows functional coupling to specific, widespread cortico-subcortical white matter brain networks. Altogether, our findings represent a relevant step towards a better understanding of pulvinar anatomy and function, and a detailed characterization of his role in healthy and pathological conditions.

The human brain undergoes major developmental changes during pregnancy. Three-dimensional (3D) ultrasound images allow for the opportunity to investigate typical prenatal brain development on a large scale. Transabdominal ultrasound can be challenging due to the small fetal brain and its movement, as well as multiple sweeps that may not yield high-quality images, especially when brain structures are unclear. By applying the latest developments in artificial intelligence for automated image processing allowing automated training of brain anatomy in these images retrieving reliable quantitative brain measurements becomes possible at a large scale. Here, we developed a convolutional neural network (CNN) model for automated segmentation of fetal intracranial volume (ICV) from 3D ultrasound. We applied the trained model in a large longitudinal population sample from the YOUth Baby and Child cohort measured at 20- and 30-week of gestational age to investigate biological sex differences in fetal ICV as a proof-of-principle and validation for our automated method (N = 2235 individuals with 43492 ultrasounds). A total of 168 annotated, randomly selected, good quality 3D ultrasound whole-brain images were included to train a 3D CNN for automated fetal ICV segmentation. A data augmentation strategy provided physical variation to train the network. K-fold cross-validation and Bayesian optimization were used for network selection and the ensemble-based system combined multiple networks to form the final ensemble network. The final ensemble network produced consistent and high-quality segmentations of ICV (Dice Similarity Coefficient (DSC) > 0.93, Hausdorff Distance (HD): HD_voxel < 4.6 voxels, and HD_physical < 1.4 mm). In addition, we developed an automated quality control procedure to include the ultrasound scans that successfully predicted ICV from all 43492 3D ultrasounds available in all individuals, no longer requiring manual selection of the best scan for analysis. Our trained model automatically retrieved ultrasounds with brain data and estimated ICV and ICV growth in 7672 (18%) of ultrasounds in 1762 participants that passed the automatic quality control procedure. Boys had significantly larger ICV at 20-weeks (81.7 ± 0.4 mL vs. 80.8 ± 0.5 mL; B = 2.86; p = 5.7e-14) and 30-weeks (257.0 ± 0.9 mL vs. 245.1 ± 0.9 mL; B = 12.35; p = 8.2e-27) of pregnancy, and more pronounced ICV growth than girls (delta growth 0.12 mL/day; p = 1.8e-5). Our automated artificial intelligence approach provides an opportunity to investigate fetal brain development on a much larger scale and to answer fundamental questions related to prenatal brain development.

White matter (WM) tracts shape the brain's dynamical activity and their damage (e.g., white matter hyperintensities, WMH) yields relevant functional alterations, ultimately leading to cognitive symptoms. The mechanisms linking the structural damage caused by WMH to the arising alterations of brain dynamics is currently unknown. To estimate the impact of WMH on brain dynamics, we combine neural-mass whole-brain modeling with a virtual-lesioning (disconnectome) approach informed by empirical data. We account for the heterogeneous effects of WMH either on inter-regional communication (i.e., edges) or on dynamics (i.e., nodes) and create models of their local versus global, and edge versus nodal effects using a large fMRI dataset comprising 188 non-demented individuals (120 cognitively normal, 68 with mild cognitive impairment) with varying degrees of WMH. We show that, although WMH mainly determine local damage to specific WM tracts, these lesions yield relevant global dynamical effects by reducing the overall synchronization of the brain through a reduction of global coupling. Alterations of local nodal dynamics through disconnections are less relevant and present only at later stages of WMH damage. Exploratory analyses suggest that education might play a beneficial role in counteracting the reduction in global coupling associated with WMH. This study provides generative models linking the structural damage caused by WMH to alterations in brain dynamics. These models might be used to evaluate the detrimental effects of WMH on brain dynamics in a subject-specific manner. Furthermore, it validates the use of whole-brain modeling for hypothesis-testing of structure–function relationships in diseased states characterized by empirical disconnections.