Validation of NODDI estimation of dispersion anisotropy in V1 of the human neocortex

This work validates the estimation of neurite dispersion anisotropy in the brain, using Bingham-NODDI [1], an extension of the diffusion MRI technique called NODDI [2]. The original NODDI provides indices of neurite (axons and dendrites) morphology that are sensitive and specific to microstructural changes [3-7]. Bingham-NODDI additionally allows the estimation of neurite dispersion anisotropy, which can enhance the accuracy of tractography algorithms [8]. The in vivo feasibility of Bingham-NODDI has been evaluated in [1]. The present study validates its indices using high-resolution ex vivo imaging data of the human primary visual cortex (V1), a well characterised region of the neocortex known to include fibres that fan or bend into the cortical layers

SWI-informed diffusion tensor tractography

Introduction In diffusion tensor tractography (DTT), white matter structure is inferred in vivo by reconstructing fiber tracts from diffusion weighted images (DWI). Recently [1], white matter structure has also been shown at 7T using susceptibility weighted imaging (SWI) [2]. Most notably, SWI shows excellent contrast between the highly myelinated optic radiation (OR) and the surrounding white matter [3]. Because DTT attempts to reconstruct tracts from voxels orders of magnitude larger than the underlying substrate, it suffers from partial volume effects in voxels that contain multiple or incoherently oriented tracts, resulting in false positive and false negative tracts. Tractography might therefore benefit from the combination of the diffusion tensor with the white matter contrasts in SWI which can be obtained at a much higher resolution. We have adapted a DTT algorithm to include the structure tensor [4] of the SWI magnitude in order to improve tractography in locations where DWI and SWI provide complementary information. Methods DWI (3T; 32-ch coil; twice-refocused spin echo EPI; 61+7 gradient directions; b=1000 s/mm2; TR=8300 ms; TE=95 ms; matrix size=110x110; FOV=220x220 mm; slice thickness=2.0 mm; number of slices=64) and SWI (7T; 8-ch coil; sagittal orientation; TR=36 ms; TE=25 ms; flip angle=15°; matrix size=448x336; FOV=224x168 mm; slice thickness=0.5 mm; number of slices=208; BW=120 Hz/px; acquisition time=20 min) were recorded from a healthy volunteer. The DWI mean b0 image and SWI were bias field corrected and then coregistered with FSL using the normalized mutual information algorithm and weighting volumes. The diffusion tensor and structure tensor fields were reconstructed from the DWI and SWI volumes, respectively. The structure tensor was calculated as the partial derivatives [dxx,dxy,dxz; dyx,dyy,dyz; dzx,dzy,dzz] of the SWI magnitude and every structure tensor component was smoothed (FWHM = 2.5 mm). Tractography was performed using Camino (PICo; 5000 iterations; curvature threshold = 80º; FA threshold = 0.10; step size = 0.50 mm). The structure tensor information was incorporated by requiring that the tracking direction be in the plane orthogonal to the first eigenvector of the structure tensor (ST ϵ1). This plane is assumed to be aligned to the direction of the tract that causes the intensity variation. The tracking direction within this plane is determined as the projection of the diffusion tensor onto the plane. To avoid adapted tracking directions where the structure tensor was non-informative, it was used only if the first eigenvalue of the structure tensor (ST λ1) > 100. For evaluating the performance, seeds were placed in the OR posterior to the point where it merged with the splenium of the corpus callosum (SCC). Waypoints were created anterior to the split in both the OR and SCC. Fractions of streamlines crossing these waypoints were extracted for both DTT and SWI-informed DTT. Results and Discussion Although the main tracts were similar for both DTT and SWI-informed DTT, the algorithms often showed very different branching patterns and more subtle differences in the course of the tracts. Examples are provided in Fig 1&2. In Fig 1 a putatively more accurate tracking of the OR using SWI-informed DTT compared to DTT is shown after seeding in the posterior OR. The seed at the merging of the OR and the SCC resulted in markedly different results for DTT and SWI-informed DTT (Fig 2). Frontal branches emerged for SWI-informed DTT, but not for DTT. A presumably non-veridical split is seen in the SCC for SWI-informed DTT (black arrow), where the structure tensor seems to cause a bias towards the borders of the tract. Fractions of streamlines entering OR/SCC were 0.026 for DTT vs. 0.336 for SWI-informed DTT. Conclusions A modification of a method was proposed to overcome some limitations of diffusion tensor tractography. It was shown that the contrast within the white matter in susceptibility weighted images can provide additional information for tractography algorithms, leading to increased sensitivity at specific locations. To have an unambiguous validation of the findings of SWI-informed DTT, an ex vivo validation of white matter connectivity has to be performed. We have shown that SWI-informed DTT reveals white matter fiber tracts that were not found using standard DTT

Entropy and Complexity Analyses in Alzheimer’s Disease: An MEG Study

Alzheimer’s disease (AD) is one of the most frequent disorders among elderly population and it is considered the main cause of dementia in western countries. This irreversible brain disorder is characterized by neural loss and the appearance of neurofibrillary tangles and senile plaques. The aim of the present study was the analysis of the magnetoencephalogram (MEG) background activity from AD patients and elderly control subjects. MEG recordings from 36 AD patients and 26 controls were analyzed by means of six entropy and complexity measures: Shannon spectral entropy (SSE), approximate entropy (ApEn), sample entropy (SampEn), Higuchi’s fractal dimension (HFD), Maragos and Sun’s fractal dimension (MSFD), and Lempel-Ziv complexity (LZC). SSE is an irregularity estimator in terms of the flatness of the spectrum, whereas ApEn and SampEn are embbeding entropies that quantify the signal regularity. The complexity measures HFD and MSFD were applied to MEG signals to estimate their fractal dimension. Finally, LZC measures the number of different substrings and the rate of their recurrence along the original time series. Our results show that MEG recordings are less complex and more regular in AD patients than in control subjects. Significant differences between both groups were found in several brain regions using all these methods, with the exception of MSFD (p-value < 0.05, Welch’s t-test with Bonferroni’s correction). Using receiver operating characteristic curves with a leave-one-out cross-validation procedure, the highest accuracy was achieved with SSE: 77.42%. We conclude that entropy and complexity analyses from MEG background activity could be useful to help in AD diagnosis

Validation of NODDI estimation of dispersion anisotropy in V1 of the human neocortex

This work validates the estimation of neurite dispersion anisotropy in the brain, using Bingham-NODDI [1], an extension of the diffusion MRI technique called NODDI [2]. The original NODDI provides indices of neurite (axons and dendrites) morphology that are sensitive and specific to microstructural changes [3-7]. Bingham-NODDI additionally allows the estimation of neurite dispersion anisotropy, which can enhance the accuracy of tractography algorithms [8]. The in vivo feasibility of Bingham-NODDI has been evaluated in [1]. The present study validates its indices using high-resolution ex vivo imaging data of the human primary visual cortex (V1), a well characterised region of the neocortex known to include fibres that fan or bend into the cortical layers

Tractography demonstrates dentate-rubro-thalamic tract disruption in an adult with cerebellar mutism.

A 55-year-old female is presented with transient cerebellar mutism caused by a well-circumscribed left pontine infarction due to postoperative basilar perforator occlusion. Although conventional T2 imaging shows a well-demarcated lesion confined to the pontine region, diffusion tensor imaging shows an asymmetry in fractional anisotropy in the superior cerebellar peduncle. This supports the general hypothesis that cerebellar mutism is caused by functional disruption of the dentate-rubro-thalamic tract. Correlating postoperative anatomic changes to a heterogenic clinical syndrome remains challenging, however

Structure Tensor Informed Fiber Tractography (STIFT) by combining gradient echo MRI and diffusion weighted imaging

Structural connectivity research in the human brain in vivo relies heavily on fiber tractography in diffusion-weighted MRI (DWI). The accurate mapping of white matter pathways would gain from images with a higher resolution than the typical ~ 2 mm isotropic DWI voxel size. Recently, high field gradient echo MRI (GE) has attracted considerable attention for its detailed anatomical contrast even within the white and gray matter. Susceptibility differences between various fiber bundles give a contrast that might provide a useful representation of white matter architecture complementary to that offered by DWI.\ud \ud In this paper, Structure Tensor Informed Fiber Tractography (STIFT) is proposed as a method to combine DWI and GE. A data-adaptive structure tensor is calculated from the GE image to describe the morphology of fiber bundles. The structure tensor is incorporated in a tractography algorithm to modify the DWI-based tracking direction according to the contrast in the GE image.\ud \ud This GE structure tensor was shown to be informative for tractography. From closely spaced seedpoints (0.5 mm) on both sides of the border of 1) the optic radiation and inferior longitudinal fasciculus 2) the cingulum and corpus callosum, STIFT fiber bundles were clearly separated in white matter and terminated in the anatomically correct areas. Reconstruction of the optic radiation with STIFT showed a larger anterior extent of Meyer's loop compared to a standard tractography alternative. STIFT in multifiber voxels yielded a reduction in crossing-over of streamlines from the cingulum to the adjacent corpus callosum, while tracking through the fiber crossings of the centrum semiovale was unaffected.\ud \ud The STIFT method improves the anatomical accuracy of tractography of various fiber tracts, such as the optic radiation and cingulum. Furthermore, it has been demonstrated that STIFT can differentiate between kissing and crossing fiber configurations. Future investigations are required to establish the applicability in more white matter pathways\u

The spatial correspondence and genetic influence of interhemispheric connectivity with white matter microstructure

Microscopic features (that is, microstructure) of axons affect neural circuit activity through characteristics such as conduction speed. To what extent axonal microstructure in white matter relates to functional connectivity (synchrony) between brain regions is largely unknown. Using MRI data in 11,354 subjects, we constructed multivariate models that predict functional connectivity of pairs of brain regions from the microstructural signature of white matter pathways that connect them. Microstructure-derived models provided predictions of functional connectivity that explained 3.5% of cross-subject variance on average (ranging from 1–13%, or r = 0.1–0.36) and reached statistical significance in 90% of the brain regions considered. The microstructure–function relationships were associated with genetic variants, co-located with genes DAAM1 and LPAR1, that have previously been linked to neural development. Our results demonstrate that variation in white matter microstructure predicts a fraction of functional connectivity across individuals, and that this relationship is underpinned by genetic variability in certain brain areas

The spatial correspondence and genetic influence of interhemispheric connectivity with white matter microstructure

Microscopic features (that is, microstructure) of axons affect neural circuit activity through characteristics such as conduction speed. To what extent axonal microstructure in white matter relates to functional connectivity (synchrony) between brain regions is largely unknown. Using MRI data in 11,354 subjects, we constructed multivariate models that predict functional connectivity of pairs of brain regions from the microstructural signature of white matter pathways that connect them. Microstructure-derived models provided predictions of functional connectivity that explained 3.5% of cross-subject variance on average (ranging from 1-13%, or r = 0.1-0.36) and reached statistical significance in 90% of the brain regions considered. The microstructure-function relationships were associated with genetic variants, co-located with genes DAAM1 and LPAR1, that have previously been linked to neural development. Our results demonstrate that variation in white matter microstructure predicts a fraction of functional connectivity across individuals, and that this relationship is underpinned by genetic variability in certain brain areas