Impact of self-esteem and sex on stress reactions.

Positive self-evaluation is a major psychological resource modulating stress coping behavior. Sex differences have been reported in self-esteem as well as stress reactions, but so far their interactions have not been investigated. Therefore, we investigated sex-specific associations of self-esteem and stress reaction on behavioral, hormonal and neural levels. We applied a commonly used fMRI-stress task in 80 healthy participants. Men compared to women showed higher activation during stress in hippocampus, precuneus, superior temporal gyrus (STG) and insula. Furthermore, men outperformed women in the stress task and had higher cortisol and testosterone levels than women after stress. Self-esteem had an impact on precuneus, insula and STG activation during stress across the whole group. During stress, men recruit regions associated with emotion and stress regulation, self-referential processing and cognitive control more strongly than women. Self-esteem affects stress processing, however in a sex-independent fashion: participants with lower self-esteem show higher activation of regions involved in emotion and stress regulation, self-referential processing and cognitive control. Taken together, our data suggest that men are more engaged during the applied stress task. Across women and men, lower self-esteem increases the effort in emotion and stress processing and cognitive control, possibly leading to self-related thoughts in stressful situations.

Oppositional COMT Val158Met effects on resting state functional connectivity in adolescents and adults.

Prefrontal dopamine levels are relatively increased in adolescence compared to adulthood. Genetic variation of COMT (COMT Val158Met) results in lower enzymatic activity and higher dopamine availability in Met carriers. Given the dramatic changes of synaptic dopamine during adolescence, it has been suggested that effects of COMT Val158Met genotypes might have oppositional effects in adolescents and adults. The present study aims to identify such oppositional COMT Val158Met effects in adolescents and adults in prefrontal brain networks at rest. Resting state functional connectivity data were collected from cross-sectional and multicenter study sites involving 106 healthy young adults (mean age 24 ± 2.6 years), gender matched to 106 randomly chosen 14-year-olds. We selected the anterior medial prefrontal cortex (amPFC) as seed due to its important role as nexus of the executive control and default mode network. We observed a significant age-dependent reversal of COMT Val158Met effects on resting state functional connectivity between amPFC and ventrolateral as well as dorsolateral prefrontal cortex, and parahippocampal gyrus. Val homozygous adults exhibited increased and adolescents decreased connectivity compared to Met homozygotes for all reported regions. Network analyses underscored the importance of the parahippocampal gyrus as mediator of observed effects. Results of this study demonstrate that adolescent and adult resting state networks are dose-dependently and diametrically affected by COMT genotypes following a hypothetical model of dopamine function that follows an inverted U-shaped curve. This study might provide cues for the understanding of disease onset or dopaminergic treatment mechanisms in major neuropsychiatric disorders such as schizophrenia and attention deficit hyperactivity disorder.

fMRI measurements of amygdala activation are confounded by stimulus correlated signal fluctuation in nearby veins draining distant brain regions.

Imaging the amygdala with functional MRI is confounded by multiple averse factors, notably signal dropouts due to magnetic inhomogeneity and low signal-to-noise ratio, making it difficult to obtain consistent activation patterns in this region. However, even when consistent signal changes are identified, they are likely to be due to nearby vessels, most notably the basal vein of rosenthal (BVR). Using an accelerated fMRI sequence with a high temporal resolution (TR = 333 ms) combined with susceptibility-weighted imaging, we show how signal changes in the amygdala region can be related to a venous origin. This finding is confirmed here in both a conventional fMRI dataset (TR = 2000 ms) as well as in information of meta-analyses, implying that "amygdala activations" reported in typical fMRI studies are likely confounded by signals originating in the BVR rather than in the amygdala itself, thus raising concerns about many conclusions on the functioning of the amygdala that rely on fMRI evidence alone.

Reduced default mode network suppression during a working memory task in remitted major depression.

Insufficient default mode network (DMN) suppression was linked to increased rumination in symptomatic Major Depressive Disorder (MDD). Since rumination is known to predict relapse and a more severe course of MDD, we hypothesized that similar DMN alterations might also exist during full remission of MDD (rMDD), a condition known to be associated with increased relapse rates specifically in patients with adolescent onset. Within a cross-sectional functional magnetic resonance imaging study activation and functional connectivity (FC) were investigated in 120 adults comprising 78 drug-free rMDD patients with adolescent- (n = 42) and adult-onset (n = 36) as well as 42 healthy controls (HC), while performing the n-back task. Compared to HC, rMDD patients showed diminished DMN deactivation with strongest differences in the anterior-medial prefrontal cortex (amPFC), which was further linked to increased rumination response style. On a brain systems level, rMDD patients showed an increased FC between the amPFC and the dorsolateral prefrontal cortex, which constitutes a key region of the antagonistic working-memory network. Both whole-brain analyses revealed significant differences between adolescent-onset rMDD patients and HC, while adult-onset rMDD patients showed no significant effects. Results of this study demonstrate that reduced DMN suppression exists even after full recovery of depressive symptoms, which appears to be specifically pronounced in adolescent-onset MDD patients. Our results encourage the investigation of DMN suppression as a putative predictor of relapse in clinical trials, which might eventually lead to important implications for antidepressant maintenance treatment.

Big Data Approaches for the Analysis of Large-Scale fMRI Data Using Apache Spark and GPU Processing: A Demonstration on Resting-State fMRI Data from the Human Connectome Project.

Technologies for scalable analysis of very large datasets have emerged in the domain of internet computing, but are still rarely used in neuroimaging despite the existence of data and research questions in need of efficient computation tools especially in fMRI. In this work, we present software tools for the application of Apache Spark and Graphics Processing Units (GPUs) to neuroimaging datasets, in particular providing distributed file input for 4D NIfTI fMRI datasets in Scala for use in an Apache Spark environment. Examples for using this Big Data platform in graph analysis of fMRI datasets are shown to illustrate how processing pipelines employing it can be developed. With more tools for the convenient integration of neuroimaging file formats and typical processing steps, big data technologies could find wider endorsement in the community, leading to a range of potentially useful applications especially in view of the current collaborative creation of a wealth of large data repositories including thousands of individual fMRI datasets.

Identification of Voxels Confounded by Venous Signals Using Resting-State fMRI Functional Connectivity Graph Community Identification.

Identifying venous voxels in fMRI datasets is important to increase the specificity of fMRI analyses to microvasculature in the vicinity of the neural processes triggering the BOLD response. This is, however, difficult to achieve in particular in typical studies where magnitude images of BOLD EPI are the only data available. In this study, voxelwise functional connectivity graphs were computed on minimally preprocessed low TR (333 ms) multiband resting-state fMRI data, using both high positive and negative correlations to define edges between nodes (voxels). A high correlation threshold for binarization ensures that most edges in the resulting sparse graph reflect the high coherence of signals in medium to large veins. Graph clustering based on the optimization of modularity was then employed to identify clusters of coherent voxels in this graph, and all clusters of 50 or more voxels were then interpreted as corresponding to medium to large veins. Indeed, a comparison with SWI reveals that 75.6±5.9% of voxels within these large clusters overlap with veins visible in the SWI image or lie outside the brain parenchyma. Some of the remaining differences between the two modalities can be explained by imperfect alignment or geometric distortions between the two images. Overall, the graph clustering based method for identifying venous voxels has a high specificity as well as the additional advantages of being computed in the same voxel grid as the fMRI dataset itself and not needing any additional data beyond what is usually acquired (and exported) in standard fMRI experiments.

On the generalizability of resting-state fMRI machine learning classifiers.

Machine learning classifiers have become increasingly popular tools to generate single-subject inferences from fMRI data. With this transition from the traditional group level difference investigations to single-subject inference, the application of machine learning methods can be seen as a considerable step forward. Existing studies, however, have given scarce or no information on the generalizability to other subject samples, limiting the use of such published classifiers in other research projects. We conducted a simulation study using publicly available resting-state fMRI data from the 1000 Functional Connectomes and COBRE projects to examine the generalizability of classifiers based on regional homogeneity of resting-state time series. While classification accuracies of up to 0.8 (using sex as the target variable) could be achieved on test datasets drawn from the same study as the training dataset, the generalizability of classifiers to different study samples proved to be limited albeit above chance. This shows that on the one hand a certain amount of generalizability can robustly be expected, but on the other hand this generalizability should not be overestimated. Indeed, this study substantiates the need to include data from several sites in a study investigating machine learning classifiers with the aim of generalizability.

The spectral diversity of resting-state fluctuations in the human brain.

In order to assess whole-brain resting-state fluctuations at a wide range of frequencies, resting-state fMRI data of 20 healthy subjects were acquired using a multiband EPI sequence with a low TR (354 ms) and compared to 20 resting-state datasets from standard, high-TR (1800 ms) EPI scans. The spatial distribution of fluctuations in various frequency ranges are analyzed along with the spectra of the time-series in voxels from different regions of interest. Functional connectivity specific to different frequency ranges (<0.1 Hz; 0.1-0.25 Hz; 0.25-0.75 Hz; 0.75-1.4 Hz) was computed for both the low-TR and (for the two lower-frequency ranges) the high-TR datasets using bandpass filters. In the low-TR data, cortical regions exhibited highest contribution of low-frequency fluctuations and the most marked low-frequency peak in the spectrum, while the time courses in subcortical grey matter regions as well as the insula were strongly contaminated by high-frequency signals. White matter and CSF regions had highest contribution of high-frequency fluctuations and a mostly flat power spectrum. In the high-TR data, the basic patterns of the low-TR data can be recognized, but the high-frequency proportions of the signal fluctuations are folded into the low frequency range, thus obfuscating the low-frequency dynamics. Regions with higher proportion of high-frequency oscillations in the low-TR data showed flatter power spectra in the high-TR data due to aliasing of the high-frequency signal components, leading to loss of specificity in the signal from these regions in high-TR data. Functional connectivity analyses showed that there are correlations between resting-state signal fluctuations of distant brain regions even at high frequencies, which can be measured using low-TR fMRI. On the other hand, in the high-TR data, loss of specificity of measured fluctuations leads to lower sensitivity in detecting functional connectivity. This underlines the advantages of low-TR EPI sequences for resting-state and potentially also task-related fMRI experiments.

Dynamic contrast-enhanced magnetic resonance imaging of breast tumors at 3 and 7 T: a comparison.

OBJECTIVES: The objective of this study was to compare the image quality, contrast enhancement behavior, and diagnostic value of bilateral 3-dimensional dynamic contrast-enhanced breast magnetic resonance imaging (MRI), with high spatial and temporal resolution, at 3 and 7 T, in the same patient group. MATERIALS AND METHODS: Twenty-four consecutive patients (mean [SD] age, 57 [17] years) were included in this prospective institutional review board-approved study. Written informed consent was obtained from all patients. T1-weighted 3-dimensional sequences (time-resolved angiography with stochastic trajectories) were optimized at 3 and 7 T, with high temporal (both 14 seconds) and spatial resolution (1.1 × 1.1 × 1.1 mm [3 T], 0.7 × 0.7 × 0.7 mm [7 T]): echo time/repetition time, 2.84/6.01 milliseconds (3 T) and 2.5/4.75 milliseconds (7 T); acquisition time, 9 minutes (3 T/7 T). Dotarem (gadoterate meglumine, Guerbet, Roissy CdG, France) contrast agent was injected intravenously as a bolus (0.2 mL/kg of body weight) after 3 baseline images. The images were rated according to breast imaging-reporting and data system by 2 radiologists in consensus. Signal-to-noise ratio and average enhancement ratios were measured quantitatively by means of region of interest analysis. In addition, B1 mapping was done in the same 5 healthy subjects at both field strengths. RESULTS: Twenty-eight enhancing lesions were detected in the 24 patients at both field strengths (16 malignant, 12 benign). At 7 T, higher contrast than that at 3 T and good image quality were achieved. With the high spatial isotropic resolution of 0.7 mm at 7 T, images with more detailed information could be acquired when compared with those acquired at 3 T. Sensitivity was 93.75% and 100%, at 3 and 7 T, respectively. Specificity was 91.67% at both field strengths. The signal-to-noise ratio at both field strengths was comparable, but at 7 T, the spatial resolution was 3.2-times higher than that at 3 T. A signal-to-noise ratio decrease toward prepectoral breast regions due to B1 inhomogeneities was observed at both field strengths but was stronger at 7 T (51%) than at 3 T (19%)(P = 0.0002). At 7 T, B1+ dropped by 20.7% and 32.8% in the prepectoral and lateral region of the breast in healthy subjects. CONCLUSIONS: Our comparison study shows that 7-T DCE-MRI provides simultaneous high temporal and spatial resolution that is significantly improved compared with lower field strengths, but further technical improvements are necessary to overcome B1 inhomogeneity problems at 7 T to fully unfold the potential of breast MRI at 7 T.

Scanning fast and slow: current limitations of 3 Tesla functional MRI and future potential.

Functional MRI at 3T has become a workhorse for the neurosciences, e.g., neurology, psychology, and psychiatry, enabling non-invasive investigation of brain function and connectivity. However, BOLD-based fMRI is a rather indirect measure of brain function, confounded by physiology related signals, e.g., head or brain motion, brain pulsation, blood flow, intermixed with susceptibility differences close or distant to the region of neuronal activity. Even though a plethora of preprocessing strategies have been published to address these confounds, their efficiency is still under discussion. In particular, physiological signal fluctuations closely related to brain supply may mask BOLD signal changes related to "true" neuronal activation. Here we explore recent technical and methodological advancements aimed at disentangling the various components, employing fast multiband vs. standard EPI, in combination with fast temporal ICA. Our preliminary results indicate that fast (TR <0.5 s) scanning may help to identify and eliminate physiologic components, increasing tSNR and functional contrast. In addition, biological variability can be studied and task performance better correlated to other measures. This should increase specificity and reliability in fMRI studies. Furthermore, physiological signal changes during scanning may then be recognized as a source of information rather than a nuisance. As we are currently still undersampling the complexity of the brain, even at a rather coarse macroscopic level, we should be very cautious in the interpretation of neuroscientific findings, in particular when comparing different groups (e.g., age, sex, medication, pathology, etc.). From a technical point of view our goal should be to sample brain activity at layer specific resolution with low TR, covering as much of the brain as possible without violating SAR limits. We hope to stimulate discussion toward a better understanding and a more quantitative use of fMRI.

Beyond Noise: Using Temporal ICA to Extract Meaningful Information from High-Frequency fMRI Signal Fluctuations during Rest.

Analysis of resting-state networks using fMRI usually ignores high-frequency fluctuations in the BOLD signal - be it because of low TR prohibiting the analysis of fluctuations with frequencies higher than 0.25 Hz (for a typical TR of 2 s), or because of the application of a bandpass filter (commonly restricting the signal to frequencies lower than 0.1 Hz). While the standard model of convolving neuronal activity with a hemodynamic response function suggests that the signal of interest in fMRI is characterized by slow fluctuation, it is in fact unclear whether the high-frequency dynamics of the signal consists of noise only. In this study, 10 subjects were scanned at 3 T during 6 min of rest using a multiband EPI sequence with a TR of 354 ms to critically sample fluctuations of up to 1.4 Hz. Preprocessed data were high-pass filtered to include only frequencies above 0.25 Hz, and voxelwise whole-brain temporal ICA (tICA) was used to identify consistent high-frequency signals. The resulting components include physiological background signal sources, most notably pulsation and heart-beat components, that can be specifically identified and localized with the method presented here. Perhaps more surprisingly, common resting-state networks like the default-mode network also emerge as separate tICA components. This means that high-frequency oscillations sampled with a rather T1-weighted contrast still contain specific information on these resting-state networks to consistently identify them, not consistent with the commonly held view that these networks operate on low-frequency fluctuations alone. Consequently, the use of bandpass filters in resting-state data analysis should be reconsidered, since this step eliminates potentially relevant information. Instead, more specific methods for the elimination of physiological background signals, for example by regression of physiological noise components, might prove to be viable alternatives.

RESCALE: Voxel-specific task-fMRI scaling using resting state fluctuation amplitude.

The BOLD signal measured in fMRI studies depends not only on neuronal activity, but also on other parameters like tissue vascularization, which may vary between subjects and between brain regions. A correction for variance from vascularization effects can thus lead to improved group statistics by reducing inter-subject variability. The fractional amplitude of low-frequency fluctuations (fALFF) as determined in a resting-state scan has been shown to be dependent on vascularization. Here we present a correction method termed RESCALE (REsting-state based SCALing of parameter Estimates) that uses local information to compute a voxel-wise scaling factor based on the correlation structure of fALFF and task activation parameter estimates from within a cube of 3 × 3 × 3 surrounding that voxel. The scaling method was used on a visuo-motor paradigm and resulted in a consistent increase in t-values in all task-activated cortical regions, with increases in peak t-values of 37.0% in the visual cortex and 12.7% in the left motor cortex. The RESCALE method as proposed herein can be easily applied to all task-based fMRI group studies provided that resting-state data for the same subject group is also acquired.

Fully exploratory network independent component analysis of the 1000 functional connectomes database.

The 1000 Functional Connectomes Project is a collection of resting-state fMRI datasets from more than 1000 subjects acquired in more than 30 independent studies from around the globe. This large, heterogeneous sample of resting-state data offers the unique opportunity to study the consistencies of resting-state networks at both subject and study level. In extension to the seminal paper by Biswal et al. (2010), where a repeated temporal concatenation group independent component analysis (ICA) approach on reduced subsets (using 20 as a pre-specified number of components) was used due to computational resource limitations, we herein apply Fully Exploratory Network ICA (FENICA) to 1000 single-subject independent component analyses. This, along with the possibility of using datasets of different lengths without truncation, enabled us to benefit from the full dataset available, thereby obtaining 16 networks consistent over the whole group of 1000 subjects. Furthermore, we demonstrated that the most consistent among these networks at both subject and study level matched networks most often reported in the literature, and found additional components emerging in prefrontal and parietal areas. Finally, we identified the influence of scan duration on the number of components as a source of heterogeneity between studies.

A highly parallelized framework for computationally intensive MR data analysis.

OBJECT: The goal of this study was to develop a comprehensive magnetic resonance (MR) data analysis framework for handling very large datasets with user-friendly tools for parallelization and to provide an example implementation. MATERIALS AND METHODS: Commonly used software packages (AFNI, FSL, SPM) were connected via a framework based on the free software environment R, with the possibility of using Nvidia CUDA GPU processing integrated for high-speed linear algebra operations in R. Three hundred single-subject datasets from the 1,000 Functional Connectomes project were used to demonstrate the capabilities of the framework. RESULTS: A framework for easy implementation of processing pipelines was developed and an R package for the example implementation of Fully Exploratory Network ICA was compiled. Test runs on data from 300 subjects demonstrated the computational advantages of a processing pipeline developed using the framework compared to non-parallelized processing, reducing computation time by a factor of 15. CONCLUSION: The feasibility of computationally intensive exploratory analyses allows broader access to the tools for discovery science.