•Sensitivity analysis allows the simulation of tDCS with uncertain conductivities.•White matter lesions (WML) have no global influence on the electric field in tDCS.•In subjects with a high lesion ...load, a local influence can be observed.•In low to medium lesion load subjects, explicit modeling of WML is not required.
Transcranial direct current stimulation (tDCS) is a promising tool to enhance therapeutic efforts, for instance, after a stroke. The achieved stimulation effects exhibit high inter-subject variability, primarily driven by perturbations of the induced electric field (EF). Differences are further elevated in the aging brain due to anatomical changes such as atrophy or lesions. Informing tDCS protocols by computer-based, individualized EF simulations is a suggested measure to mitigate this variability.
While brain anatomy in general and specifically atrophy as well as stroke lesions are deemed influential on the EF in simulation studies, the influence of the uncertainty in the change of the electrical properties of the white matter due to white matter lesions (WMLs) has not been quantified yet.
A group simulation study with 88 subjects assigned into four groups of increasing lesion load was conducted. Due to the lack of information about the electrical conductivity of WMLs, an uncertainty analysis was employed to quantify the variability in the simulation when choosing an arbitrary conductivity value for the lesioned tissue.
The contribution of WMLs to the EF variance was on average only one tenth to one thousandth of the contribution of the other modeled tissues. While the contribution of the WMLs significantly increased (p≪.01) in subjects exhibiting a high lesion load compared to low lesion load subjects, typically by a factor of 10 and above, the total variance of the EF didnot change with the lesion load.
Our results suggest that WMLs do not perturb the EF globally and can thus be omitted when modeling subjects with low to medium lesion load. However, for high lesion load subjects, the omission of WMLs may yield less robust local EF estimations in the vicinity of the lesioned tissue. Our results contribute to the efforts of accurate modeling of tDCS for treatment planning.
The subthalamic nucleus (STN) is a small but vitally important structure in the basal ganglia. Because of its small volume, and its localization in the basal ganglia, the STN can best be visualized ...using ultra-high resolution 7 Tesla (T) magnetic resonance imaging (MRI). In the present study, first we individually segmented 7T MRI STN masks to generate atlas probability maps. Secondly, the individually segmented STN masks and the probability maps were used to derive cortico-subthalamic white matter tract strength. Tract strength measures were then taken to test two functional STN hypotheses which account for the efficiency in stopping a motor response: the right inferior fronto-subthalamic (rIFC-STN) hypothesis and the posterior medial frontal cortex-subthalamic (pMFC-STN) hypothesis. Results of two independent experiments show that increased white matter tract strength between the pMFC and STN results in better stopping behaviour.
•Created STN atlas probability maps based on ultra-high resolution 7T MRI images.•Individually segmented STN masks and probability maps were used to test cortico-subthalamic white matter strength.•Tract strength measures were taken to test two functional STN hypotheses to account for the efficacy to withhold a motor response.•Results of two independent experiments show that increased white matter tract strength between the pMFC and STN results in better stopping behaviour. Display omitted
► Created STN atlas probability maps based on ultra-high resolution 7T MRI images. ► Individually segmented STN masks and probability maps were used to test cortico-subthalamic white matter strength. ► Tract strength measures were taken to investigate the efficacy to withhold a motor response. ► Results show increased white matter tract strength between the pFMC and STN to account for better stopping behaviour.
In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of ...five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website22The Challenge Evaluation Website is: http://smart-stats-tools.org/lesion-challenge-2015 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.
•Public lesion data base of 21 training data sets and 61 testing data sets.•Fully automated evaluation website.•Comparison between 14 state-of-the-art algorithms and 2 manual delineators.
We describe a new fully automatic method for the segmentation of brain images that contain multiple sclerosis white matter lesions. Multichannel magnetic resonance images are used to delineate ...multiple sclerosis lesions while segmenting the brain into its major structures. The method is an atlas-based segmentation technique employing a topological atlas as well as a statistical atlas. An advantage of this approach is that all segmented structures are topologically constrained, thereby allowing subsequent processing such as cortical unfolding or diffeomorphic shape analysis techniques. Evaluation with both simulated and real data sets demonstrates that the method has an accuracy competitive with state-of-the-art MS lesion segmentation methods, while simultaneously segmenting the whole brain.
How, and to what extent do size and shape of a voxel measured with magnetic resonance imaging (MRI) affect the ability to visualize small brain nuclei? Despite general consensus that voxel geometry ...affects volumetric properties of regions of interest, particularly those of small brain nuclei, no quantitative data on the influence of voxel size and shape on labeling accuracy is available. Using simulations, we investigated the selective influence of voxel geometry by reconstructing simulated ellipsoid structures with voxels varying in shape and size. For each reconstructed ellipsoid, we calculated differences in volume and similarity between the labeled volume and the predefined dimensions of the ellipsoid. Probability functions were derived from one or two individual raters and a simulated ground truth for reference. As expected, larger voxels (i.e., coarser resolution) and increasing anisotropy results in increased deviations of both volume and shape measures, which is of particular relevance for small brain structures. Our findings clearly illustrate the anatomical inaccuracies introduced by the application of large and/or anisotropic voxels. To ensure deviations occur within the acceptable range (Dice coefficient scores; DCS > 0.75, corresponding to < 57% volume deviation), the volume of isotropic voxels should not exceed 5% of the total volume of the region of interest. When high accuracy is required (DCS > 0.90, corresponding to a < 19% volume deviation), the volumes of isotropic voxels should not exceed 0.08%, of the total volume. Finally, when large anisotropic factors (>3) are used, and the ellipsoid is orthogonal to the slice axes, having its long axis in the imaging plane, the voxel volume should not exceed 0.005% of the total volume. This allows sufficient compensation of anisotropy effects, in order to reach accuracy in the acceptable range (DCS > 0.75, corresponding to >57% volume deviation).
The subthalamic nucleus (STh) is a small subcortical structure which is involved in regulating motor as well as cognitive functions. Due to its small size and close proximity to other small ...subcortical structures, it has been a challenge to localize and visualize it using magnetic resonance imaging (MRI). Currently there are several standard atlases available that are used to localize the STh in functional MRI studies and clinical procedures such as deep brain stimulation (DBS). DBS is an increasingly common neurosurgical procedure that has been successfully used to alleviate motor symptoms present in Parkinson's disease. However, current atlases are based on low sample sizes and restricted age ranges (Schaltenbrand and Wahren, 1977), and hence the use of these atlases effectively ignores the substantial structural brain changes that are associated with aging. In the present study, ultra-high field 7 tesla (T) magnetic resonance imaging (MRI) in humans was used to visualize and segment the STh in young, middle-aged, and elderly participants. The resulting probabilistic atlas maps for all age groups show that the STh shifts in the lateral direction with increasing age. In sum, the results of the present study suggest that age has to be taken into account in atlases for the optimal localization of the STh in healthy and diseased brains.
We conducted a comparative analysis of primate cerebral size and neocortical folding using magnetic resonance imaging data from 65 individuals belonging to 34 different species. We measured several ...neocortical folding parameters and studied their evolution using phylogenetic comparative methods. Our results suggest that the most likely model for neuroanatomical evolution is one where differences appear randomly (the Brownian Motion model), however, alternative models cannot be completely ruled out. We present estimations of the ancestral primate phenotypes as well as estimations of the rates of phenotypic change. Based on the Brownian Motion model, the common ancestor of primates may have had a folded cerebrum similar to that of a small lemur such as the aye-aye. Finally, we observed a non-linear relationship between fold wavelength and fold depth with cerebral volume. In particular, gyrencephalic primate neocortices across different groups exhibited a strikingly stable fold wavelength of about 12 mm (±20%), despite a 20-fold variation in cerebral volume. We discuss our results in the context of current theories of neocortical folding.
The focus of this article is to compare twenty normative and open-access neuroimaging databases based on quantitative measures of image quality, namely, signal-to-noise (SNR) and contrast-to-noise ...ratios (CNR). We further the analysis through discussing to what extent these databases can be used for the visualization of deeper regions of the brain, such as the subcortex, as well as provide an overview of the types of inferences that can be drawn. A quantitative comparison of contrasts including T1-weighted (T1w) and T2-weighted (T2w) images are summarized, providing evidence for the benefit of ultra-high field MRI. Our analysis suggests a decline in SNR in the caudate nuclei with increasing age, in T1w, T2w, qT1 and qT2* contrasts, potentially indicative of complex structural age-dependent changes. A similar decline was found in the corpus callosum of the T1w, qT1 and qT2* contrasts, though this relationship is not as extensive as within the caudate nuclei. These declines were accompanied by a declining CNR over age in all image contrasts. A positive correlation was found between scan time and the estimated SNR as well as a negative correlation between scan time and spatial resolution. Image quality as well as the number and types of contrasts acquired by these databases are important factors to take into account when selecting structural data for reuse. This article highlights the opportunities and pitfalls associated with sampling existing databases, and provides a quantitative backing for their usage.
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