Neuroimage. Reports最新文献

Mild cognitive impairment (MCI) and hearing loss (HL) have been separately associated with increased dementia risk. These highly co-occurring dementia risk factors are associated with aberrant functional brain connectivity. In individuals with HL aberrant functional connectivity has been associated with cognitive impairment. In individuals with MCI, aberrant brain connectivity has been associated with severity of cognitive impairment and conversion to dementia. Despite the high prevalence of HL in individuals with MCI, the relationship between the two is understudied, especially in the context of functional connectivity.

Participants

include 94 older adults with MCI. Hearing measures include pure-tone hearing thresholds and speech-reception thresholds. In analyses with pure-tone hearing loss, participants were classified as having normal hearing (n = 60) or hearing loss (n = 34) based on a pure-tone screening protocol. We used independent component analyses on resting-state functional magnetic resonance imaging data to identify the default-mode network (DMN). Analysis of variance with covariate control was used to investigate DMN connectivity differences between the normally hearing individuals with MCI and individuals with MCI and pure-tone HL. A linear regression was used to investigate the relationship between speech-reception threshold and DMN connectivity in the whole sample.

Compared to those with MCI and normal hearing, those with MCI and pure-tone HL had decreased connectivity between the DMN and the caudate and thalamus. There was no difference in DMN connectivity as a function of speech-reception threshold.

Our findings indicate that in individuals with MCI, HL is associated with decreased connectivity between the DMN and regions important for sensory and cognitive processing. This suggests that the increased risk for cognitive decline and dementia in individuals with HL may be due to decreased DMN connectivity in individuals with HL.

3D-Edge Enhancing Gradient Echo MRI (EDGE) is a promising method for identifying focal cortical dysplasia (FCD) because of its superior contrast-to-noise ratio. Gains in signal-to-noise ratio (SNR) and spatial resolution using 7T MRI should further enhance the utility of EDGE; however, the exquisite sensitivity to the optimal inversion time makes translation to 7T challenging given the extreme nonuniformities in the transmit field (B1+). By utilizing a combined MP2RAGE-EDGE sequence, the efficiency of the acquisition can be optimized to improve B1+ transmit uniformity and SNR. Using this approach, we report the first experience of using 7T EDGE MRI for the diagnosis of FCD in epilepsy, showing the feasibility of achieving high spatial resolution, SNR, and image contrast uniformity. EDGE-MP2RAGE sequence improves the detection of gray-white matter junction abnormalities associated with FCD and is a valuable, time-efficient addition to a 7T MR protocol in patients with focal epilepsy.

Objective

Patients with left perisylvian gliomas might undergo language mapping with nTMS in preparation for awake brain surgery. Action naming is an important addition to the presurgical language mapping protocol. However, it has not yet been determined whether specific action stimuli can influence mapping outcomes in terms of number and/or localisation of induced errors.

Methods

We investigated this question by employing tractography-based nTMS language mapping of the left arcuate fasciculus (AF) with two types of verbs: transitive and intransitive. Data were collected from 22 patients with a left perisylvian glioma.

Results

Our results demonstrated that nTMS induced a higher error rate with transitive rather than intransitive verbs, specifically during stimulation of the posterior temporal terminations of the left AF (transitive error rate: 8.3%; intransitive error rate: 4.8%). The effect was absent when gliomas displaced the temporal terminations of the AF. Also, nTMS triggered a higher number of semantic errors with transitive (vs intransitive) actions during stimulation of the posterior temporal terminations of the AF (semantic error rate – transitives: 3.3%; semantic error rate – intransitives: 0%).

Conclusion

Our work highlights that clinical outcomes of language mapping with nTMS are affected by the choice of linguistic stimuli. Transitive verbs may be suited to achieve optimal nTMS mapping outcomes in posterior temporal areas of the left AF in this population. Displacement of white matter terminations due to the tumor can affect these results, and semantic errors may indicate core language processes that can be mapped when administering transitive verbs.

Performance monitoring is essential for successful action execution and previous studies have suggested that frontomedial theta (FMT) activity in scalp-recorded EEG reflects need for control signaling in response to negative outcomes. However, these studies have overlooked the fact that anticipating the most probable outcome is often possible. To optimize action execution, it is necessary for the time-critical performance monitoring system to utilize continuously updated information to adjust actions in time. This study used a combination of mobile EEG and virtual reality to investigate how the performance monitoring system adapts to continuously updated information during brief phases of outcome evaluation that follow action execution. In two virtual shooting tasks, participants were either able to observe the projectile and hence anticipate the outcome or not. We found that FMT power increased in response to missing shots in both tasks, but this effect was suppressed when participants were able to anticipate the outcome. Specifically, the suppression was linearly related to the duration of the anticipatory phase. Our results suggest that the performance monitoring system dynamically integrates incoming information to evaluate the most likely outcome of an action as quickly as possible. This dynamic mode of performance monitoring provides significant advantages over idly waiting for an action outcome before getting engaged. Early and adaptive performance monitoring not only helps prevent negative outcomes but also improves overall performance. Our findings highlight the crucial role of dynamic integration of incoming information in the performance monitoring system, providing insights for real-time decision-making and action control.

Introduction

Though empathy, compassion, and Theory of Mind (ToM) are related, they have been distinguished conceptually and empirically across behavioural and neuroimaging experiments. The EmpaToM task was the first realistic paradigm developed for use in functional Magnetic Resonance Imaging (fMRI), which can reliably detect and distinguish three different types of neural pathways crucial for understanding others with a single well-controlled task. Though the paradigm holds the potential for use in research settings as well as clinical practice, it has thus far only been validated in German speaking populations, using stimuli in German language, restricting its usability across countries. We present an English-language translation of the original paradigm here.

Method

Thirty-two English speaking adults underwent fMRI scanning, during which we collected neural and behavioural data as in the original validation of the EmpaToM task.

Results

Apart from minor differences, these results replicated the main behavioural and neural findings observed during the validation of the German paradigm. Participants reported increased negative affect and activity in brain regions previously associated with empathy when observing video clips with negative vs neutral valence. They further reported increased compassion. The pattern of neural activity differentiating empathy from compassion was largely consistent with previous research. Increased activity in regions previously associated with ToM were observed in response to stimuli with ToM vs factual reasoning content.

Conclusion

We therefore conclude that the English version of the EmpaToM task can be used to reliably assess empathy, compassion, and ToM on a behavioural as well as neuronal level across English speaking countries and institutions.

Background

The Brain Health Index (BHI) is an automated approach to quantifying brain integrity, combining different types of structural magnetic resonance imaging (MRI). Normative values derived from generally healthy individuals provide a vital baseline for understanding neurodegenerative change. Although commonplace in other areas of medicine, these are not always established when proposing new analytical approaches using MRI. The scale and quality of the UK Biobank imaging cohort (approximately N = 50k, as of 2022) allows for derivation of such values, and the wealth of additional lifestyle, physiological and demographic data enables validation of BHI through comparison with more established variables which may affect brain health.

Aim

This study aimed to: 1) establish normative BHI values in a cohort of ‘healthy’ participants, and 2) explore associations between BHI and risk factors for brain health.

Methods

The BHI was computed using voxel-based Gaussian mixture model cluster analysis of T1 and T2 FLAIR MRI in a sub-cohort of UK Biobank participants. From these data, normative score curves – with bounds described as 1, 2 and 3 standard deviations from the mean – were produced for males and females, using regression analyses to measure the scale of the BHI values as a function of age. Additional Pearson’s correlation testing was used to examine known risk factors to brain health and their relationship to BHI scores, with t-tests and ANOVAs used to determine between-group differences in BHI scoring.

Results

Data from 2,990 participants (50.07% male, 97.05% Caucasian, 43.6% with degree-level education) were used to derive normative BHI curves from 48 to 77 years old. BHI scores were higher in female than male participants (95% CI: 0.0103 to 0.0162, p <0.001, Cohen’s d = 0.0416), males with a degree (95% CI: 0.000 to 0.009; p < 0.05; Cohen’s d = 0.044), and lower in people with type 2 diabetes mellitus (95% CI: 0.018 to 0.033; p <0.001; Cohen’s d = 0.0417), hypertension (95% CI: 0.008 to 0.018; p <0.001; Cohen’s d = 0.0419), and regular smokers (95% CI: 0.009 to 0.017, p <0.001, Cohen’s d = 0.041). BHI scores were higher in those with lower waist-to-hip ratios (WHR; males: R² = 0.02121, F(1, 1466) = 31.77, p <0.001; females: R² = 0.02201, F(1, 1454) = 32.72, p <0.001), and lower pulse pressure (males: R² = 0.06261, F(1, 1215) = 81.16, p <0.001; females: R² = 0.07616, F(1, 1205) = 99.34, p <0.001).

Conclusions

BHI score curves may provide useful reference values for future clinical research. More work is required to determine normative values in more diverse populations.

Sex differences in the size of specific brain structures have been extensively studied, but careful and reproducible statistical hypothesis testing to identify them produced overall small effect sizes and differences in brains of males and females. On the other hand, multivariate statistical or machine learning methods that analyze MR images of the whole brain have reported respectable accuracies for the task of distinguishing brains of males from brains of females. However, most existing studies lacked a careful control for brain volume differences between sexes and, if done, their accuracy often declined to 70% or below. This raises questions about the relevance of accuracies achieved without careful control of overall volume.

We examined how accurately sex can be classified from gray matter properties of the human brain when matching on overall brain volume. We tested, how robust machine learning classifiers are when predicting cross-cohort, i.e. when they are used on a different cohort than they were trained on. Furthermore, we studied how their accuracy depends on the size of the training set and attempted to identify brain regions relevant for successful classification. MRI data was used from two population-based data sets of 3298 mostly older adults from the Study of Health in Pomerania (SHIP) and 399 mostly younger adults from the Human Connectome Project (HCP), respectively. We benchmarked two multivariate methods, logistic regression and a 3D convolutional neural network.

We show that male and female brains of the same intracranial volume can be distinguished with >92% accuracy with logistic regression on a dataset of 1166 matched individuals. The same model also reached 85% accuracy on a different cohort without retraining. The accuracy for both methods increased with the training cohort size up to and beyond 3000 individuals, suggesting that classifiers trained on smaller cohorts likely have an accuracy disadvantage. We found no single outstanding brain region necessary for successful classification, but important features appear rather distributed across the brain.

As direct evaluation of a mouse model of human neurodevelopment, adolescent and young adult mice and humans underwent MR diffusion tensor imaging to quantify age-related differences in microstructural integrity of brain white matter fibers. Fractional anisotropy (FA) was greater in older than younger mice and humans. Despite the cross-species commonality, the underlying developmental mechanism differed: whereas evidence for greater axonal extension contributed to higher FA in older mice, evidence for continuing myelination contributed to higher FA in human adolescent development. These differences occurred in the context of species distinctions in overall brain growth: whereas the continued growth of the brain and skull in the murine model can accommodate volume expansion into adulthood, human white matter volume and myelination continue growth into adulthood within a fixed intracranial volume. Appreciation of the similarities and differences in developmental mechanism can enhance the utility of animal models of brain white matter structure, function, and response to exogenous manipulation.

The representation of upper limb muscles in the motor cortex is not clear-cut. The motor cortex contains areas that, when stimulated, may activate different muscles simultaneously, hence they seem to overlap. We expected the cortical representations of synergistic muscle pairs to overlap more than those of non-synergistic muscles. To test this, we used navigated transcranial magnetic stimulation to probe eight hand and forearm muscles of twenty healthy participants. We transformed the cortical representations of muscles to a template MRI to allow for group analysis. We found that the amount of overlap in cortical representations differed significantly between within-hand and within-forearm muscle combinations. Most synergistic muscle pairs, both within the hand, within the forearm and between them, had a larger overlap than non-synergistic muscle pairs. Our study demonstrates the largely overlapping nature of cortical representations of upper limb muscles. It is noteworthy that the overlap is elevated in muscles that usually act in a synergistic manner.

Defensive stress reactions, such as freezing and active fight-or-flight, are relevant for coping with threat. Action-preparatory activity supporting these reactions, including the amygdala, has been posited as a potential marker for stress-resilience. We considered the successive COVID-19 lockdowns as two pervasive stressors, to prospectively investigate the predictive value of neural threat-responses towards symptom development. Five years prior to the COVID-19 pandemic, 17-year-old adolescents (n = 64, Baseline-17) performed the fMRI-adapted Go/Nogo Under Threat (GUNT) task, where threat-anticipatory freezing reactions and transition to action are evoked to avoid a shock. A majority (n = 44) made themselves available for follow-up assessments before COVID (Baseline-20, age 20), during the first COVID-19 lockdown in the Netherlands (LD1, age 22.5), and during a second lockdown (LD2, age 23). The GUNT task quantified neural (thalamic, subcortical, amygdala) and physiological (bradycardia) markers of threat-anticipatory freezing and transition to action (mediated by anterior cingulate cortex). Threat-anticipatory amygdala responses (Baseline-17) were linked to stressor resilience, as quantified by self-reported anxiety symptoms between LD1 and LD2. However, stronger amygdala responses to low threat cues (Baseline-17) were associated with stronger anxiety symptoms. These effects occurred over and above early-life stress, COVID-19 stress burden, and overall symptom changes between age 17 and 20. These findings suggest that amygdala responses to acute threat provide a marker for resilience against real-life stressors, with adequate threat discrimination signaling resilience and stronger amygdala responses to low threat predicting vulnerability. The findings support the notion that neural responses to threat are instrumental for adaptive coping with pervasive stress.