Fibre-specific laterality of white matter in left and right language dominant people

Language is the most commonly described lateralised cognitive function, relying more on the left hemisphere compared to the right hemisphere in over 90% of the population. Most research examining the structure-function relationship of language lateralisation only included people showing a left language hemisphere dominance. In this work, we applied a state-of-the-art "fixel-based" analysis approach, allowing statistical analysis of white matter micro- and macrostructure on a fibre-specific level in a sample of participants with left and right language dominance (LLD and RLD). Both groups showed a similar extensive pattern of white matter lateralisation including a comparable leftwards lateralisation of the arcuate fasciculus, regardless of their functional language lateralisation. These results suggest that lateralisation of language functioning and the arcuate fasciculus are driven by independent biases. Finally, a significant group difference of lateralisation was detected in the forceps minor, with a leftwards lateralisation in LLD and rightwards lateralisation for the RLD group

Modeling brain dynamics after tumor resection using The Virtual Brain

Brain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, simulation of the activity of neural populations connected according to the white matter fibers, producing personalized brain network models, has been introduced as a promising tool for this purpose. The Virtual Brain provides a robust open source framework to implement these models. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first "virtual neurosurgery", mimicking patient's actual surgery by removing white matter fibers in the resection mask and simulating again neural activity on this new connectome. We find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. Concerning the virtual neurosurgery analyses, use of the pre-surgery model implemented on the virtually resected structural connectome resulted in improved similarity with post-surgical empirical functional connectivity in some patients, but negligible improvement in others. These findings reveal interesting avenues for increasing interactions between computational neuroscience and neuro-oncology, as well as important limitations that warrant further investigation

DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli

DISTILLER, a data integration framework for the inference of transcriptional module networks, is presented and used to investigate the condition dependency and modularity in Escherichia coli networks

Accounting for Complex Structure in Diffusion Weighted Imaging Data using Volume Fraction Representations (Rekening houden met complexe structuur in diffusie gewogen beeldvorming data door gebruik van volume fractie representaties)

The domain of diffusion weighted imaging (DWI) has come a long way since its initial development in the mid-1980s. Over the years, we have gained a better understanding of the accompanying techniques and necessary processing steps involved, furthermore leading to a wealth of new insights in the complex workings of the (human) brain. The introduction of diffusion tensor imaging (DTI) has played a crucial role in thisprocess, as it provided the first model intended to deal with anisotropic diffusion; a particular feature observed in the white matter (WM), asopposed to the other most common 'tissue' types found in the human brain, i.e. the gray matter (GM) and cerebrospinal fluid (CSF). During the last decade, however, we have come to realize that the DTI model is severely lacking in its possibilities to represent voxels that contain so called 'crossing fibers', a general name that is often used to refer to a range of complex geometric fiber configurations caused by the partial volume effect. In this PhD thesis, we intended to design certain representations of (information extracted from) DWI data that take into account the aforementioned variety of complex geometrical configurations. Our proposed novel representations aim to offer a greater flexibility that should inherently render many existing difficult problems (e.g. segmentation and registration) trivial; yet make as little assumptions aspossible on the nature of the data or the properties of the underlying structures. A first major contribution is a generic framework for multi-shell multi-tissue (MSMT) representations, and a specific implementation tailored to represent WM, GM and CSF in the human brain. This representation was specifically designed to render the retransformation problem trivial. The latter was easily solved by a newly introduced preservation of principal volume fractions (PPVF) retransformation strategy. A second major contribution is a more tangible track orientation distribution (TOD) representation for complex fiber track distributions. Our newly developed method to obtain such a TOD, is termed track orientation density imaging (TODI). This technique allowed us to gain further understanding in the amplitude of a short-tracks TOD, which can now be interpreted as a measure of track-like local support (TLS). Furthermore, we showed that employing the latter for TOD-based tractography results in guiding the tracks along directions that are morelikely to correspond to continuous structure over a longer distance; i.e. track-like structure! Both representations (MSMT and the TOD) forthcoming from these major contributions, also allow for increased insightin many other aspects of the data they describe, and provide a large range of opportunities for future research.Dhollander T., ''Accounting for complex structure in diffusion weighted imaging data using volume fraction representations'', Proefschrift voorgedragen tot het behalen van het doctoraat in de ingenieurswetenschappen, KU Leuven, April 2014, Leuven, Belgium.nrpages: 212status: publishe

Evaluating the performance of 3-tissue constrained spherical deconvolution pipelines for within-tumor tractography

AbstractThe use of diffusion MRI (dMRI) for assisting in the planning of neurosurgery has become increasingly common practice, allowing to non-invasively map white matter pathways via tractography techniques. Limitations of earlier pipelines based on the diffusion tensor imaging (DTI) model have since been revealed and improvements were made possible by constrained spherical deconvolution (CSD) pipelines. CSD allows to resolve a full white matter (WM) fiber orientation distribution (FOD), which can describe so-called “crossing fibers”: complex local geometries of WM tracts, which DTI fails to model. This was found to have a profound impact on tractography results, with substantial implications for presurgical decision making and planning. More recently, CSD itself has been extended to allow for modeling of other tissue compartments in addition to the WM FOD, typically resulting in a 3-tissue CSD model. It seems likely this may improve the capability to resolve WM FODs in the presence of infiltrating tumor tissue. In this work, we evaluated the performance of 3-tissue CSD pipelines, with a focus on within-tumor tractography. We found that a technique named single-shell 3-tissue CSD (SS3T-CSD) successfully allowed tractography within infiltrating gliomas, without increasing existing single-shell dMRI acquisition requirements.</jats:p

Structural perisylvian asymmetry in naturally occurring atypical language dominance

Functional and anatomical hemispheric asymmetries abound in the neural language system, yet the relationship between them remains elusive. One attractive proposal is that structural interhemispheric differences reflect or even drive functional language laterality. However, studies on structure-function couplings either find that left and right language dominant individuals display similar leftward structural asymmetry or yield inconsistent results. The current study aimed to replicate and extend prior work by comparing structural asymmetries between neurologically healthy left-handers with right hemispheric language dominance (N = 24) and typically lateralized left-handed controls (N = 39). Based on structural MRI data, anatomical measures of six 'language-related' perisylvian structures were derived, including the surface area of five gray matter regions with known language functions and the FDC (combined fiber density and fiber-bundle cross-sectional area) of the arcuate fasciculus. Only the surface area of the pars triangularis and the anterior insula differed significantly between participant groups, being on average leftward asymmetric in those with typical dominance, but right lateralized in volunteers with atypical language specialization. However, these findings did not survive multiple testing correction and the asymmetry of these structures demonstrated much inter-individual variability in either subgroup. By integrating our findings with those reported previously we conclude that while some perisylvian anatomical asymmetries may differ subtly between typical and atypical speech dominants at the group level, they serve as poor participant-specific predictors of hemispheric language specialization

Groupwise deformable registration of fiber track sets using track orientation distributions

Diffusion-weighted imaging (DWI) and tractography allow to study the macroscopic structure of white matter in vivo. We present a novel method for deformable registration of unsegmented full-brain fiber track sets extracted from DWI data. Our method attempts to align the track orientation distributions (TODs) of multiple subjects, rather than individual tracks. As such, it can handle complex track configurations and allows for multi-resolution registration. We validated the registration method on synthetically deformed DWI data and on data of 15 healthy subjects, and achieved sub-voxel accuracy in dense white matter structures. This work is, to the best of our knowledge, the first demonstration of direct registration of probabilistic tractography data.status: publishe

Feasibility of atlas-based segmentation of the brain in the presence of tumor by a weighted least-squares demons algorithm

Haeck T., Dhollander T., Maes F., Sunaert S., Suetens P., ''Feasibility of atlas-based segmentation of the brain in the presence of tumor by a weighted least-squares demons algorithm'', ISMRM 21st annual meeting & exhibition, April 20-26, 2013, Salt Lake City, Utah, USA.status: publishe

On the estimation of the fiber response function for constrained spherical deconvolution

Christiaens D., Dhollander T., Maes F., Sunaert S., Suetens P., ''On the estimation of the fiber response function for constrained spherical deconvolution'', 5th annual meeting of the ISMRM Benelux Chapter, January 14, 2013, Rotterdam, The Netherlands.status: publishe

Dynamic analysis of fMRI activation during epileptic spikes can help identify the seizure origin

Objective We use the dynamic electroencephalography-functional magnetic resonance imaging (EEG-fMRI) method to incorporate variability in the amplitude and field of the interictal epileptic discharges (IEDs) into the fMRI analysis. We ask whether IED variability analysis can (a) identify additional activated brain regions during the course of IEDs, not seen in standard analysis; and (b) demonstrate the origin and spread of epileptic activity. We explore whether these functional changes recapitulate the structural connections and propagation of epileptic activity during seizures. Methods Seventeen patients with focal epilepsy and at least 30 IEDs of a single type during simultaneous EEG-fMRI were studied. IED variability and EEG source imaging (ESI) analysis extracted time-varying dynamic changes. General linear modeling (GLM) generated static functional maps. Dynamic maps were compared to static functional maps. The dynamic sequence from IED variability was compared to the ESI results. In a subset of patients, we investigated structural connections between active brain regions using diffusion-based fiber tractography. Results IED variability distinguished the origin of epileptic activity from its propagation in 15 of 17 (88%) patients. This included two cases where no result was obtained from the standard GLM analysis. In both of these cases, IED variability revealed activation in line with the presumed epileptic focus. Two cases showed no result from either method. Both had very high spike rates associated with dysplasia in the postcentral gyrus. In all 15 cases with dynamic activation, the observed dynamics were concordant with ESI. Fiber tractography identified specific white matter pathways between brain regions that were active at IED onset and propagation. Significance Dynamic techniques involving IED variability can provide additional power for EEG-fMRI analysis, compared to standard analysis, revealing additional biologically plausible information in cases with no result from the standard analysis and gives insight into the origin and spread of IEDs