Neuroimage. Reports最新文献

Empathy, which is important for social functioning and mental health, is recognized to have both cognitive and affective components. While several studies have demonstrated unique neural underpinnings of empathy components in adults, few have investigated this in young people. Investigating associations between empathy and brain functional connectivity during childhood is beneficial to begin to build a comprehensive picture of the neural correlates of empathy across the lifespan. One hundred and twelve children (52% female, mean age 10 years) underwent magnetic resonance imaging brain scans including a resting-state sequence and completed empathy self-report measures. Seed-to-whole-brain resting-state functional connectivity analyses demonstrated that higher affective empathy (affective sharing and empathic distress) was associated with weaker connectivity between key hubs of the default mode network (DMN) and other widespread areas in the brain. Analyses of resting-state networks demonstrated that higher cognitive empathy was associated with both stronger and weaker connectivity between dorsal and lateral regions of the DMN and regions outside of the DMN, including the pre- and postcentral gyrus, and the cerebellum. Higher affective sharing was associated with greater connectivity between the anterior salience network and the pre- and postcentral gyrus. However, these associations did not withstand correction for multiple models. While interpretations should be made cautiously, findings suggest wide-ranging and diffuse connectivity patterns may be related to several components of empathy in children. These findings could indicate a more complex picture of the neural correlates of empathy processes in childhood, with less specialization and more widespread involvement of regions and networks. Future studies should attempt to replicate these findings using different measurement approaches.

Background

Neurometabolic abnormalities and amyloid-beta plaque deposition are important early pathophysiologic changes in Alzheimer's disease (AD). This study investigated the relationship between high-energy phosphorus-containing metabolites, glucose uptake, and amyloid plaque using phosphorus magnetic resonance spectroscopy (³¹P-MRS) and positron emission tomography (PET).

Methods

We measured ³¹P-MRS, fluorodeoxyglucose (FDG)-PET, and Pittsburgh Compound B (PiB)-PET in a cohort of 20 cognitively normal middle-aged adults at risk for AD. We assessed ³¹P-MRS reliability by scanning a separate cohort of 13 healthy volunteers twice each. We calculated the coefficient-of-variation (CV) of metabolite ratios phosphocreatine-to-adenosine triphosphate (PCr/α-ATP), inorganic phosphate (Pi)-to-α-ATP, and phosphomonoesters-to-phosphodiesters (PME/PDE), and pH in pre-defined brain regions. We performed linear regression analysis to determine the relationship between ³¹P measurements and tracer uptake, and Dunn's multiple comparison tests to investigate regional differences in phosphorus metabolism. Finally, we performed linear regression analysis on ³¹P-MRS measurements in both cohorts to investigate the relationship of phosphorus metabolism with age.

Results

Most regional ³¹P metabolite ratio and pH inter- and intra-day CVs were well below 10%. There was an inverse relationship between FDG-SUV levels and metabolite ratios PCr/α-ATP, Pi/α-ATP, and PME/PDE in several brain regions in the AD risk group. There were also several regional differences among ³¹P metabolites and pH in the AD risk group including elevated PCr/α-ATP, depressed PME/PDE, and elevated pH in the temporal cortices. Increased PCr/α-ATP throughout the brain was associated with aging.

Conclusions

Phosphorus spectroscopy in the brain can be performed with high repeatability. Phosphorus metabolism varies with region and age, and is related to glucose uptake in adults at risk for AD. Phosphorus spectroscopy may be a valuable approach to study early changes in brain energetics in high-risk populations.

A reproducibility crisis has been reported across many research fields, including neuroimaging, reaching up to 70% of studies. Neuroimaging data, such as magnetic resonance imaging (MRI), requires pre-processing to allow for inter-subject comparison, increase signal contrast and noise reduction. As manual MRI pre-processing is time consuming and requires expertise, multiple automatic pre-processing frameworks have been proposed. However, neuroimaging studies often report divergent results, even for similar populations, thus it is important to determine whether this occurs as a result of different processing tools. Two of the most used tools are FreeSurfer and the Computational Anatomy Toolbox (CAT12). In this study we assessed the reproducibility between these two automatic pre-processing frameworks for structural MRI and test-retest reliability within framework on estimation of cortical thickness. Our results show that the reproducibility between the frameworks is lower at the region-of-interest (ROI) level than at individual level. Furthermore, we found that the reproducibility was lower in paediatric samples than in adults. Finally, an acquisition site effect was also identified. Given the widespread use of these frameworks in basic and clinical neuroscience, the results of multicentric cross-sectional studies must be interpreted with caution, particularly with paediatric samples. The observed reproducibility issue might be one of the sources of discrepancies reported in neuroimaging studies. On a positive note, framework test-retest reliability within subject is high, suggesting that inconsistency of results may be less concerning in longitudinal studies. The code is available at: https://cibit-uc.github.io/fs-cat12-cortical-thickness-reproducibility.

Human decision making can be influenced by presenting different options with positive or negative connotations. This phenomenon is termed the framing effect. Neuroeconomic studies have found that the amygdala plays a significant role in the framing effect, as it incorporates emotional (or intuitive) information into the decision process, which may lead to bias or irrationality. However, previous studies have focused only on the gain domain, in which the initial state is positive (appetitive). The mechanisms underlying the framing effect in the loss (aversive) domain are less well understood, despite their importance for evolution and survival. In this study, we first replicated the findings of De Martino et al. on the framing effect in the gain domain. We then searched for similarities and differences between the gain and loss domains using a similar experimental design combined with functional magnetic resonance imaging. Behaviorally, the participants showed comparable effects for both frame types, suggesting a frame-indiscriminate decision bias. In contrast, at the neuronal level, we found that the amygdala specifically represented the framing effect in the gain domain, and its connectivity to the ventromedial prefrontal cortex was positively modulated by the framing bias, similar to the findings of De Martino et al. However, the striatum was found to represent the framing effect in the loss domain. Striatal connectivity to the dorsomedial prefrontal cortex was similarly affected by the framing bias, suggesting domain-specific neural substrates. Our study emphasizes the importance of distinguishing between gain and loss domains when studying decision making, and highlights the governing role of the cortical–striatal–limbic network in the framing effect.

This paper aims to establish how frontal midline theta and theta-gamma coupling are altered by memory load as well as by recall performance during a verbal digit span task using an open EEG dataset. Traditional behavioral scores, as well as new measure were used, as the recall accuracy as a function of the order of the digits followed a sigmoid curve. The new behavioral score was strongly correlated with traditional measures. The EEG results show that neither the resting state theta nor the theta during the digit span task is predictive of the task performance. However, there was a strong positive correlation between cognitive load and theta power as well as a negative correlation between cognitive load and theta peak frequency during the task. These results are in line with earlier studies indicating that frontal midline theta is modulated by cognitive load rather than memory performance. Furthermore, there was an increased alpha-beta cross frequency coupling during resting state but that was not correlated with the performance of the upcoming memory task. During the task the cross-frequency coupling was minimal, and it did not correlate with memory performance nor with task load, that raises the possibility that EEG is not sensitive enough for cross-frequency coupling measures.

Neuroimaging studies of autism spectrum disorder (ASD) have been predominantly unimodal. While many fMRI studies have reported atypical activity patterns for diverse tasks, the MEG literature in ASD remains comparatively small. Our group recently reported atypically increased event-related theta power in individuals with ASD during lexicosemantic processing. The current multimodal study examined the relationship between fMRI BOLD signal and anatomically-constrained MEG (aMEG) theta power. Thirty-three adolescents with ASD and 23 typically developing (TD) peers took part in both fMRI and MEG scans, during which they distinguished between standard words (SW), animal words (AW), and pseudowords (PW). Regions-of-interest (ROIs) were derived based on task effects detected in BOLD signal and aMEG theta power. BOLD signal and theta power were extracted for each ROI and word condition. Compared to TD participants, increased theta power in the ASD group was found across several time windows and regions including left fusiform and inferior frontal, as well as right angular and anterior cingulate gyri, whereas BOLD signal was significantly increased in the ASD group only in right anterior cingulate gyrus. No significant correlations were observed between BOLD signal and theta power. Findings suggest that the common interpretation of increases in BOLD signal and theta power as ‘activation’ require careful differentiation, as these reflect largely distinct aspects of regional brain activity. Some group differences in dynamic neural processing detected with aMEG that are likely relevant for lexical processing may be obscured by the hemodynamic signal source and low temporal resolution of fMRI.

Recent ERP research using a gaze-contingent paradigm suggests the face-sensitive N170 component is modulated by the presence of a face outline, the number of parafoveal facial features, and the type of feature in parafovea (Parkington and Itier, 2019). The present study re-analyzed these data using robust mass univariate statistics available through the LIMO toolbox, allowing the examination of the ERP signal across all electrodes and time points. We replicated the finding that the presence of a face outline significantly reduced ERP latencies and amplitudes, suggesting it is an important cue to the prototypical face template. However, we found that this effect began around 114 ms, and was maximal during the P1-N170 and N170-P2 intervals. The number of features present in parafovea also impacted the entire waveform, with systematic reductions in amplitude and latency as the number of features increased. This effect was maximal around 120 ms during the P1-N170 interval and around 170 ms between the N170 and P2. The ERP response was also modulated by feature type; contrary to previous findings this effect was maximal around 200 ms and the P2 peak. Although we provide partial replication of the previous results on the N170, the effects were more temporally distributed in the present analysis. These effects were generally maximal before and after the N170 and were the weakest at the N170 peak itself. This re-analysis demonstrates that classical ERP analysis can obscure important aspects of face processing beyond the N170 peak, and that tools like mass univariate statistics are needed to shed light on the whole time-course of face processing.

Neuroimaging research has begun to implicate alterations of brain reward systems in chronic pain. Previously, using functional magnetic resonance imaging (fMRI) and a monetary incentive delay (MID) task, Martucci et al. (2018) showed that neural responses to reward anticipation and outcome are altered in fibromyalgia. In the present study, we aimed to test the replicability of these altered neural responses to reward in a separate fibromyalgia cohort. In addition, the present study was conducted at a distinct U.S. location but involved a similar study design. For the present study, 20 patients with fibromyalgia and 20 healthy controls participated in MID task fMRI scan procedures and completed clinical/psychological questionnaires. fMRI analyses comparing patient and control groups revealed a consistent trend of main results which were largely similar to the prior reported results. Specifically, in the replication fibromyalgia cohort, medial prefrontal cortex (MPFC) response was reduced during gain anticipation and was increased during no-loss (non-punishment) outcome compared to controls. Also consistent with previous findings, the nucleus accumbens response to gain anticipation did not differ in patients vs. controls. Further, results from similarly-designed behavioral, correlational, and exploratory analyses were complementary to previous findings. Finally, a novel network-based functional connectivity analysis of the MID task fMRI data across patients vs. controls implied enhanced connectivity within the default mode network in participants with fibromyalgia. Together, based on replicating prior univariate results and new network-based functional connectivity analyses of MID task fMRI data, we provide further evidence of altered brain reward responses, particularly in the MPFC response to reward outcomes, in patients with fibromyalgia.

Background

The growth in multi-center neuroimaging studies generated a need for methods that mitigate the differences in hardware and acquisition protocols across sites i.e., scanner effects. ComBat harmonization methods have shown promise but have not yet been tested on all the data types commonly studied with magnetic resonance imaging (MRI). This study aimed to validate neuroCombat, longCombat and gamCombat on both structural and diffusion metrics in both cross-sectional and longitudinal data.

Methods

We used a travelling subject design whereby 73 healthy volunteers contributed 161 scans across two sites and four machines using one T1 and five diffusion MRI protocols. Scanner was defined as a composite of site, machine and protocol. A common pipeline extracted two structural metrics (volumes and cortical thickness) and two diffusion tensor imaging metrics (mean diffusivity and fractional anisotropy) for seven regions of interest including gray and (except for cortical thickness) white matter regions.

Results

Structural data exhibited no significant scanner effect and therefore did not benefit from harmonization in our particular cohort. Indeed, attempting harmonization obscured the true biological effect for some regions of interest. Diffusion data contained marked scanner effects and was successfully harmonized by all methods, resulting in smaller scanner effects and better detection of true biological effects. LongCombat less effectively reduced the scanner effect for cross-sectional white matter data but had a slightly lower probability of incorrectly finding group differences in simulations, compared to neuroCombat and gamCombat. False positive rates for all methods and all metrics did not significantly exceed 5%.

Conclusions

Statistical harmonization of structural data is not always necessary and harmonization in the absence of a scanner effect may be harmful. Harmonization of diffusion MRI data is highly recommended with neuroCombat, longCombat and gamCombat performing well in cross-sectional and longitudinal settings.

The mammillary bodies may be small, but they have an important role in encoding complex memories. Mammillary body pathology often occurs following thiamine deficiency but there is increasing evidence that the mammillary bodies are also compromised in other neurological conditions and in younger ages groups. For example, the mammillary bodies are frequently affected in neonates with hypoxic-ischemic encephalopathy. At present, there is no normative data for the mammillary bodies in younger groups making it difficult to identify abnormalities in neurological disorders. To address this, the present study set out to develop a normative dataset for neonates and for children to young adult. A further aim was to determine whether there were laterality or sex differences in mammillary body volumes. Mammillary body volumes were obtained from MRI scans from 506 participants across two datasets. Measures for neonates were acquired from the Developing Human Connectome Project database (156 male; 100 female); volumes for individuals aged 6–24 were acquired from the NICHE database (166 males; 84 females). Volume measurements were acquired using a semi-automated multi-atlas segmentation approach. Mammillary body volumes increased up to approximately 15 years-of-age. The left mammillary body was marginally, but significantly, larger than the right in the neonates with a similar pattern in older children/young adults. In neonates, the mammillary bodies in males were slightly bigger than females but no sex differences were present in older children/young adults. Given the increasing presentation of mammillary body pathology in neonates and children, these normative data will enable better assessment of the mammillary bodies in healthy and at-risk populations.