Neuroimage. Reports最新文献

Language is an essential higher cognitive function in humans and is often affected by psychiatric and neurological disorders. Objective measures like the verbal fluency test are often used to determine language dysfunction. Recent applications of computational approaches broaden insights into language-related functions. In addition, individuals diagnosed with a psychiatric or neurological disorder also often report subjective difficulties in language-related functions. Therefore, we investigated the association between objective and subjective measures of language functioning, on the one hand, and inter-individual structural variations in language-related brain areas, on the other hand.

We performed a Latent Semantic analysis (LSA) on a semantic verbal fluency task in 101 healthy adult participants. To investigate if these objective measures are associated with a subjective one, we examined assessed subjective natural tendency of interest in language-related activity with a study-specific questionnaire. Lastly, a voxel-based brain morphometry (VBM) was conducted to reveal associations between objective (LSA) measures and structural changes in language-related brain areas.

We found a positive correlation between the LSA measure cosine similarity and the subjective interest in language. Furthermore, we found that higher cosine similarity corresponds to higher gray matter volume in the right cerebellum. The results suggest that people with higher interests in language access semantic knowledge in a more organized way exhibited by higher cosine similarity and have larger gray matter volume in the right cerebellum, when compared to people with lower interests.

In conclusion, we demonstrate that there is inter-individual diverseness of accessing the semantic knowledge space and that it is associated with subjective language interests as well as structural differences in the right cerebellum.

Individuals with acute and chronic traumatic brain injury (TBI) are associated with unique white matter (WM) structural abnormalities, including fractional anisotropy (FA) differences. Our research group previously used FA as a feature in a linear support vector machine (SVM) pattern classifier, observing high classification between individuals with and without acute TBI (i.e., an area under the curve [AUC] value of 75.50%). However, it is not known whether FA could similarly classify between individuals with and without history of chronic TBI. Here, we attempted to replicate our previous work with a new sample, investigating whether FA could similarly classify between incarcerated men with (n = 80) and without (n = 80) self-reported history of chronic TBI. Additionally, given limitations associated with FA, including underestimation of FA values in WM tracts containing crossing fibers, we extended upon our previous study by incorporating neurite orientation dispersion and density imaging (NODDI) metrics, including orientation dispersion (ODI) and isotropic volume (Viso). A linear SVM based classification approach, similar to our previous study, was incorporated here to classify between individuals with and without self-reported chronic TBI using FA and NODDI metrics as separate features. Overall classification rates were similar when incorporating FA and NODDI ODI metrics as features (AUC: 82.50%). Additionally, NODDI-based metrics provided the highest sensitivity (ODI: 85.00%) and specificity (Viso: 82.50%) rates. The current study serves as a replication and extension of our previous study, observing that multiple diffusion MRI metrics can reliably classify between individuals with and without self-reported history of chronic TBI.

Background

Genome-wide association studies (GWAS) have identified large numbers of genetic variants associated with cognition. However, little is known about how these genetic discoveries impact cognitive aging.

Methods

We conducted polygenic-index (PGI) analysis of cognitive performance in n = 168 European-ancestry adults aged 20–80. We computed PGIs based on GWAS of cognitive performance in young/middle-aged and older adults. We tested associations of the PGI with cognitive performance, as measured through neuropsychological evaluation. We explored whether these associations were accounted for by magnetic resonance imaging (MRI) measures of brain-aging phenotypes: total gray matter volume (GM), cortical thickness (CT), and white matter hyperintensities burden (WMH).

Results

Participants with higher PGI values performed better on cognitive tests (B = 0.627, SE = 0.196, p = 0.002) (age, sex, and principal components as covariates). Associations remained significant with inclusion of covariates for MRI measures of brain aging; B = 0.439, SE: 0.198, p = 0.028). PGI associations were stronger in young and middle-aged (age<65) as compared to older adults. For further validation, linear regression for Cog PGI and cognition in the fully adjusted model and adding the interaction between age group and Cog PGI, showed significant results (B = 0.892, SE: 0.325, p = 0.007) driven by young and middle-aged adults (B = −0.403, SE: 0.193, p = 0.039). In ancillary analysis, the Cognitive PGI was not associated with any of the brain measures.

Conclusions

Genetics discovered in GWAS of cognition are associated with cognitive performance in healthy adults across age, but most strongly in young and middle-aged adults. Associations were not explained by brain-structural markers of brain aging. Genetics uncovered in GWAS of cognitive performance may contribute to individual differences established relatively early in life and may not reflect genetic mechanisms of cognitive aging.

Deep learning has been demonstrated effective in many neuroimaging applications. However, in many scenarios, the number of imaging sequences capturing information related to small vessel disease lesions is insufficient to support data-driven techniques. Additionally, cohort-based studies may not always have the optimal or essential imaging sequences for accurate lesion detection. Therefore, it is necessary to determine which imaging sequences are crucial for precise detection. This study introduces a deep learning framework to detect enlarged perivascular spaces (ePVS) and aims to find the optimal combination of MRI sequences for deep learning-based quantification. We implemented an effective lightweight U-Net adapted for ePVS detection and comprehensively investigated different combinations of information from SWI, FLAIR, T1-weighted (T1w), and T2-weighted (T2w) MRI sequences. The experimental results showed that T2w MRI is the most important for accurate ePVS detection, and the incorporation of SWI, FLAIR and T1w MRI in the deep neural network had minor improvements in accuracy and resulted in the highest sensitivity and precision (sensitivity = 0.82, precision = 0.83). The proposed method achieved comparable accuracy at a minimal time cost compared to manual reading. The proposed automated pipeline enables robust and time-efficient readings of ePVS from MR scans and demonstrates the importance of T2w MRI for ePVS detection and the potential benefits of using multimodal images. Furthermore, the model provides whole-brain maps of ePVS, enabling a better understanding of their clinical correlates compared to the clinical rating methods within only a couple of brain regions.

Resting state functional connectivity (RS-FC) studies of tinnitus over the years have produced inconsistent results. While findings can be organized into broad categories, such as increased correlations between auditory and limbic areas in tinnitus patients and a disrupted default mode network, there has been little one-to-one correspondence of results across RS-FC studies of tinnitus. While some of this variation can be explained by the heterogeneity of the tinnitus population, including tinnitus severity, the sources of variability in RS-FC of tinnitus patients are unclear. To directly assess the reliability of RS-FC measures in tinnitus, both tinnitus and control participants from two different sites (University of Illinois at Urbana-Champaign, or UIUC, and the Wilford Hall Ambulatory Surgical Center, or WHASC, at the Lackland Airforce Base in San Antonio, Texas) participated in two resting state MRI scans separated by exactly one week. Seed-to-seed analysis assessing correlations between the fMRI activity of 27 regions in the default mode, dorsal attention, auditory, visual, salience, and emotional processing networks were examined in control and tinnitus participants separately for each site. Additionally, heart rate and respiration measures were collected at UIUC, and the effect of extra physiological corrections using these measures on reliability was examined within the UIUC participants. Intra-class correlation coefficients (ICCs) were used as the measure of reliability. Overall, RS-FC in a seed-to-seed analysis was as reliable in tinnitus participants as it was in control participants in the seed regions examined. As previously shown in studies of participants with normal hearing sensitivity, intra-network reliability was higher than inter-network reliability. Related to this, stronger correlations between two seed regions were predictive of stronger reliability of the connectivity between those regions. These effects were seen in both control and tinnitus populations. Additional physiological corrections did not have a significant impact on the ICC values. The current study demonstrates that, on a whole-brain level, RS-FC assessed via seed-to-seed analysis is reliable in tinnitus participants. We therefore must look to other sources as potential causes of discrepancies across studies, such as variability within analysis techniques or within the behavioral characteristics of tinnitus participants.

Background

The abilities of individual neuroimaging methods to resolve spatial and temporal contributions of brain regions during cognitive processes are limited. Co-processing of functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) may overcome some of the limitations by utilizing Multiple Sparse Priors (MSP) in a Bayesian framework that takes advantage of the temporal resolution of MEG and spatial resolution of fMRI.

Methods

24 healthy participants were recruited to perform a paired-associate verbal learning task during fMRI and MEG scans. FMRI data were processed within Group ICA fMRI Toolbox. Independent components (ICs) were temporally sorted by task time series (|r|>0.30 threshold identified task-related ICs). Task-positive (“generate”) ICs were retained as spatial priors for MEG analyses. MEG data were processed by an event-related potential (ERP) approach and with a theta power approach. MEG source reconstructions were constrained within the task-positive ICs for both ERP and theta-power approaches.

Results

For fMRI, five networks were identified as task-related. Four ICs underlying active generation spanned bilateral parietal, orbitofrontal, medial frontal and superior temporal regions, and occipital lobe. FMRI-constrained MEG source reconstructions using the ERP approach yielded early visual cortex activity followed by left inferior frontal gyrus (IFG) and orbito-frontal cortex (OFC) recruitment to coalesce in the left inferior temporal lobe. For the theta approach, MEG source reconstructions showed a progression of activity from bilateral temporal areas to left OFC and middle temporal gyrus, followed by right IFG.

Discussion

MSP analyses informed by fMRI produced more focused regional activity than reconstructions without priors suggesting this approach may result in identifying more relevant semantic information during active generation. Constraining MEG source reconstruction to fMRI priors during active generation indicates fronto-temporal and fronto-parietal networks are interconnected across time and space.

White matter integrity and cognition have been found to decline with advancing adult age. Aerobic exercise may be effective in counteracting these declines. Generally, white matter integrity has been quantified using a volumetric measure (WMV) and with tensor-based parameters, such as fractional anisotropy (FA) and mean diffusivity (MD), the validity of which appears to be compromised in the presence of crossing fibers. Fixel-based analysis techniques claim to overcome this problem by yielding estimates of fiber density (FD), cross-section (FC), and their product (FDC) in multiple directions per voxel. In a sample of 61 healthy older adults (63–76 years old), we quantified changes in white matter integrity following an aerobic exercise intervention with the commonly used volumetric and tensor-based metrics (WMV, FA, MD) and with fixel-based metrics (FD, FC, FDC). We investigated the associations of changes in these white matter parameters with changes in cardiovascular fitness and Digit Symbol Substitution task (DSST) performance, a marker of perceptual speed. In line with previous findings, we observed maintained WMV in the corpus callosum of exercisers, and positive change-change correlations between WMV and fitness, and between WMV and perceptual speed. For FA and MD, group differences in change opposite to those hypothesized were found in the corpus callosum, posterior corona radiata, and superior longitudinal fasciculus at an uncorrected significance threshold. Likewise, regions in superficial WM in the prefrontal cortex showed group differences in FD and FDC change, uncorrected, with more positive change in controls and more negative change in exercisers. Finally, changes in FD and FDC were found to be inversely correlated to changes in fitness and DSST performance. The present results corroborate previous findings of WMV changes, but cast doubt on current physiological interpretations of both tensor-based and fixel-based indicators of white matter properties in the context of exercise intervention studies.

Several practices contribute to low replication rates in neuroimaging; unreported analytical flexibility, publication biases, the lack of data sharing, the use of underpowered samples, and many more. In the current study, we tried to replicate emotional interference during motor response inhibition in a sample of N = 57 healthy students at a different study site. The failure to inhibit impulses in highly emotional situations is a characteristic of many mental disorders. Apart from this, exaggerated emotional responses can debilitate social and cognitive functioning in healthy participants. Functional magnetic resonance imaging was acquired while participants performed a combined stop signal and Simon task with neutral and negative, visual primes. Negative, visual primes led to prolonged reaction times and stopping latencies. FMRI data showed that negative, visual primes led to increased activation in the bilateral amygdala and enhanced visual processing. Consistent with other studies on emotional interference, we observed decreased activation in regions of the central-executive network and reduced deactivation in the ventromedial prefrontal cortex for negative trials. Replicating the results of our former studies (Kampa et al., 2018, 2020), we found an interaction effect in the right amygdala. Concurrent inhibition of motor responses thus seemed to downregulate the processing of negative stimuli in the right amygdala. We performed exploratory brain-behavior correlation analyses to test if increased right amygdala activation to negative primes was associated with a decrement in performance. We found no such relationship; however, because of the low statistical power, we cannot decide conclusively on the relationship between the right amygdala, and behavioral interference. We discuss potential solutions for overcoming the power problem associated with brain-behavior correlation analyses.

Language is central to human life; however, how our brains derive meaning from language is still not well understood. A commonly studied electrophysiological measure of on-line meaning related processing is the N400 component, the computational basis of which is still actively debated. Here, we test one of the recently proposed, computationally explicit hypotheses on the N400 – namely, that it reflects surprise with respect to a probabilistic representation of the semantic features of the current stimulus in a given context. We devise a Bayesian sequential learner model to derive trial-by-trial semantic surprise in a semantic oddball like roving paradigm experiment, where single nouns from different semantic categories are presented in sequences. Using experimental data from 40 subjects, we show that model-derived semantic surprise significantly predicts the N400 amplitude, substantially outperforming a non-probabilistic baseline model. Investigating the temporal signature of the effect, we find that the effect of semantic surprise on the EEG is restricted to the time window of the N400. Moreover, comparing the topography of the semantic surprise effect to a conventional ERP analysis of predicted vs. unpredicted words, we find that the semantic surprise closely replicates the N400 topography. Our results make a strong case for the role of probabilistic semantic representations in eliciting the N400, and in language comprehension in general.

Obtaining high quality images of the spinal cord with MRI is difficult, partly due to the fact that the spinal cord is surrounded by a number of structures that have differing magnetic susceptibility. This causes inhomogeneities in the magnetic field, which in turn lead to image artifacts. In order to address this issue, linear compensation gradients can be employed. The latter can be generated using an MRI scanner's first order gradient coils and adjusted on a per-slice basis, in order to correct for through-plane (“z”) magnetic field gradients. This approach is referred to as z-shimming. The aim of this study is two-fold. The first aim was to replicate aspects of a previous study wherein z-shimming was found to improve image quality in T2*-weighted echo-planar imaging. Our second aim was to improve upon the z-shimming approach by including in-plane compensation gradients and adjusting the compensation gradients during the image acquisition process so that they take into account respiration-induced magnetic field variations. We refer to this novel approach as realtime dynamic shimming. Measurements performed in a group of 12 healthy volunteers at 3 T show improved signal homogeneity along the spinal cord when using z-shimming. Signal homogeneity may be further improved by including realtime compensation for respiration-induced field gradients and by also doing this for gradients along the in-plane axes.