Correlation between amygdala BOLD activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression

Real-time fMRI neurofeedback (rtfMRI-nf) is an emerging approach for studies and novel treatments of major depressive disorder (MDD). EEG performed simultaneously with an rtfMRI-nf procedure allows an independent evaluation of rtfMRI-nf brain modulation effects. Frontal EEG asymmetry in the alpha band is a widely used measure of emotion and motivation that shows profound changes in depression. However, it has never been directly related to simultaneously acquired fMRI data. We report the first study investigating electrophysiological correlates of the rtfMRI-nf procedure, by combining rtfMRI-nf with simultaneous and passive EEG recordings. In this pilot study, MDD patients in the experimental group (n=13) learned to upregulate BOLD activity of the left amygdala using an rtfMRI-nf during a happy emotion induction task. MDD patients in the control group (n=11) were provided with a sham rtfMRI-nf. Correlations between frontal EEG asymmetry in the upper alpha band and BOLD activity across the brain were examined. Average individual changes in frontal EEG asymmetry during the rtfMRI-nf task for the experimental group showed a significant positive correlation with the MDD patients' depression severity ratings, consistent with an inverse correlation between the depression severity and frontal EEG asymmetry at rest. Temporal correlations between frontal EEG asymmetry and BOLD activity were significantly enhanced, during the rtfMRI-nf task, for the amygdala and many regions associated with emotion regulation. Our findings demonstrate an important link between amygdala BOLD activity and frontal EEG asymmetry. Our EEG asymmetry results suggest that the rtfMRI-nf training targeting the amygdala is beneficial to MDD patients, and that alpha-asymmetry EEG-nf would be compatible with the amygdala rtfMRI-nf. Combination of the two could enhance emotion regulation training and benefit MDD patients.Comment: 28 pages, 16 figures, to appear in NeuroImage: Clinica

Resting state functional thalamic connectivity abnormalities in patients with post-stroke sleep apnoea: a pilot case-control study

OBJECTIVE: Sleep apnoea is common after stroke, and has adverse effects on the clinical outcome of affected cases. Its pathophysiological mechanisms are only partially known. Increases in brain connectivity after stroke might influence networks involved in arousal modulation and breathing control. The aim of this study was to investigate the resting state functional MRI thalamic hyper connectivity of stroke patients affected by sleep apnoea (SA) with respect to cases not affected, and to healthy controls (HC). PATIENTS AND METHODS: A series of stabilized strokes were submitted to 3T resting state functional MRI imaging and full polysomnography. The ventral-posterior-lateral thalamic nucleus was used as seed. RESULTS: At the between groups comparison analysis, in SA cases versus HC, the regions significantly hyper-connected with the seed were those encoding noxious threats (frontal eye field, somatosensory association, secondary visual cortices). Comparisons between SA cases versus those without SA, revealed in the former group significantly increased connectivity with regions modulating the response to stimuli independently to their potentiality of threat (prefrontal, primary and somatosensory association, superolateral and medial-inferior temporal, associative and secondary occipital ones). Further significantly functionally hyper connections were documented with regions involved also in the modulation of breathing during sleep (pons, midbrain, cerebellum, posterior cingulate cortices), and in the modulation of breathing response to chemical variations (anterior, posterior and para-hippocampal cingulate cortices). CONCLUSIONS: Our preliminary data support the presence of functional hyper connectivity in thalamic circuits modulating sensorial stimuli, in patients with post-stroke sleep apnoea, possibly influencing both their arousal ability and breathing modulation during sleep

The BrainMap strategy for standardization, sharing, and meta-analysis of neuroimaging data

<p>Abstract</p> <p>Background</p> <p>Neuroimaging researchers have developed rigorous community data and metadata standards that encourage meta-analysis as a method for establishing robust and meaningful convergence of knowledge of human brain structure and function. Capitalizing on these standards, the BrainMap project offers databases, software applications, and other associated tools for supporting and promoting quantitative coordinate-based meta-analysis of the structural and functional neuroimaging literature.</p> <p>Findings</p> <p>In this report, we describe recent technical updates to the project and provide an educational description for performing meta-analyses in the BrainMap environment.</p> <p>Conclusions</p> <p>The BrainMap project will continue to evolve in response to the meta-analytic needs of biomedical researchers in the structural and functional neuroimaging communities. Future work on the BrainMap project regarding software and hardware advances are also discussed.</p

Visualizing data mining results with the Brede tools

A few neuroinformatics databases now exist that record results from neuroimaging studies in the form of brain coordinates in stereotaxic space. The Brede Toolbox was originally developed to extract, analyze and visualize data from one of them --- the BrainMap database. Since then the Brede Toolbox has expanded and now includes its own database with coordinates along with ontologies for brain regions and functions: The Brede Database. With Brede Toolbox and Database combined we setup automated workflows for extraction of data, mass meta-analytic data mining and visualizations. Most of the Web presence of the Brede Database is established by a single script executing a workflow involving these steps together with a final generation of Web pages with embedded visualizations and links to interactive three-dimensional models in the Virtual Reality Modeling Language. Apart from the Brede tools I briefly review alternate visualization tools and methods for Internet-based visualization and information visualization as well as portals for visualization tools

Testing for difference between two groups of functional neuroimaging experiments

We describe a meta-analytic method that tests for the di#erence between two groups of functional neuroimaging experiments. We use kernel density estimation in three-dimensional brain space to convert points representing focal brain activations into a voxel-based representation. We find the maximum in the subtraction between two probability densities and compare its value against a resampling distribution obtained by permuting the labels of the two groups. The method is applied on data from thermal pain studies where &quot;hot pain&quot; and &quot;cold pain&quot; form the two groups

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

On the neural origin of pseudoneglect: EEG-correlates of shifts in line bisection performance with manipulation of line length

Healthy participants tend to show systematic biases in spatial attention, usually to the left. However, these biases can shift rightward as a result of a number of experimental manipulations. Using electroencephalography (EEG) and a computerized line bisection task, here we investigated for the first time the neural correlates of changes in spatial attention bias induced by line-length (the so-called line-length effect). In accordance with previous studies, an overall systematic left bias (pseudoneglect) was present during long line but not during short line bisection performance. This effect of line-length on behavioral bias was associated with stronger right parieto-occipital responses to long as compared to short lines in an early time window (100–200 ms) post-stimulus onset. This early differential activation to long as compared to short lines was task-independent (present even in a non-spatial control task not requiring line bisection), suggesting that it reflects a reflexive attentional response to long lines. This was corroborated by further analyses source-localizing the line-length effect to the right temporo-parietal junction (TPJ) and revealing a positive correlation between the strength of this effect and the magnitude by which long lines (relative to short lines) drive a behavioral left bias across individuals. Therefore, stimulus-driven left bisection bias was associated with increased right hemispheric engagement of areas of the ventral attention network. This further substantiates that this network plays a key role in the genesis of spatial bias, and suggests that post-stimulus TPJ-activity at early information processing stages (around the latency of the N1 component) contributes to the left bias

Demonstration and validation of Kernel Density Estimation for spatial meta-analyses in cognitive neuroscience using simulated data

The data presented in this article are related to the research article entitled "Convergence of semantics and emotional expression within the IFG pars orbitalis" (Belyk et al., 2017) [1]. The research article reports a spatial meta-analysis of brain imaging experiments on the perception of semantic compared to emotional communicative signals in humans. This Data in Brief article demonstrates and validates the use of Kernel Density Estimation (KDE) as a novel statistical approach to neuroimaging data. First, we performed a side-by-side comparison of KDE with a previously published meta-analysis that applied activation likelihood estimation, which is the predominant approach to meta-analyses in cognitive neuroscience. Second, we analyzed data simulated with known spatial properties to test the sensitivity of KDE to varying degrees of spatial separation. KDE successfully detected true spatial differences in simulated data and displayed few false positives when no true differences were present. R code to simulate and analyze these data is made publicly available to facilitate the further evaluation of KDE for neuroimaging data and its dissemination to cognitive neuroscientists