Conference PaperPDF Available

A novel iterative approach to reap the benefits of multi-tissue CSD from just single-shell (+b=0) diffusion MRI data



Constrained spherical deconvolution (CSD) is a robust approach to resolve the fibre orientation distribution (FOD) from diffusion MRI data. However, the FOD from CSD only aims to represent "pure" white matter (WM) and is inappropriate/distorted in regions of (partial voluming with) grey matter (GM) or cerebrospinal fluid (CSF). Multi-shell multi-tissue CSD was proposed to solve this issue by estimating WM/GM/CSF components, but requires multi-shell data to do so. In this work, we provide the first proof that similar results can also be obtained from only simple single-shell (+b=0) data, and propose a novel specialised optimiser that achieves this goal.
Proc. Intl. Soc. Mag. Reson. Med. 24 (2016) 3010
A novel iterative approach to reap the benefits of multi-tissue CSD
from just single-shell (+b=0) diffusion MRI data
Thijs Dhollander1 and Alan Connelly1,2
1The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
2The Florey Department of Neuroscience, University of Melbourne, Melbourne, Australia
Constrained spherical deconvolution (CSD) is a robust approach to resolve the fibre orientation distribution (FOD)
from diffusion MRI data. However, the FOD from CSD only aims to represent "pure" white matter (WM) and is
inappropriate/distorted in regions of (partial voluming with) grey matter (GM) or cerebrospinal fluid (CSF). Multi-
shell multi-tissue CSD was proposed to solve this issue by estimating WM/GM/CSF components, but requires
multi-shell data to do so. In this work, we provide the first proof that similar results can also be obtained from
only simple single-shell (+b=0) data, and propose a novel specialised optimiser that achieves this goal.
Constrained spherical deconvolution (CSD) is a robust approach to resolve the fibre orientation distribution (FOD)
from diffusion MRI (dMRI) data[1]. The FOD from single-shell single-tissue (SSST)-CSD only models white matter
(WM); it will be distorted/inappropriate when other tissue types are (partially) present; i.e., grey matter (GM) and
cerebrospinal fluid (CSF). Multi-shell multi-tissue (MSMT)-CSD was proposed to solve this issue[2], but requires multi-
shell data. We aim to achieve similar results/benefits, by using only single-shell (+b=0) data.
Data acquisition & preprocessing
Single subject dMRI data were acquired on a Siemens 3T scanner, with voxel size 2.5×2.5×2.5mm³, and a multi-shell
scheme (b=0,1000,2000,3000s/mm² respectively for 5,17,31,50 directions + additional b=0 volume with reversed-
phase encoding). The data were corrected for susceptibility-induced distortions[3], eddy-current-induced distortions
and motion[4], and bias-fields[5].
We use these terms to refer to subsets of the data:
MS-data (multi-shell data): all images over all 3 dMRI shells + b=0 data.
SS-data (single-shell data): the 50 directions at b=3000s/mm².
B0-data: b=0 images.
SS+B0-data: combination of the latter 2. (often informally called "single-shell" data)
Conservative regions or individual voxels deemed to contain "pure" samples of single-fibre-WM,GM,CSF were
selected to estimate the tissue response functions (guided by FA and ADC maps). MSMT-CSD results are shown in
Fig.1, first column. The WM-tissue outcome is presented using FOD-based directionally-encoded colour (DEC),
weighted by the WM-FOD integral[6]. SSST-CSD results are shown in the second column: WM is overestimated,
because GM/CSF parts are not estimated. Both results match the findings of [2].
Proc. Intl. Soc. Mag. Reson. Med. 24 (2016) 3010
Naive multi-tissue approaches for SS+B0-data
First naive approach: applying MSMT-CSD directly to SS+B0-data. Even under non-negativity constraints, given
isotropy of GM and only two b-values, GM can be (and is) fitted by a WM+CSF mixture (Fig.1, third column). The
WM is still grossly overestimated; most fundamental problems of SSST-CSD results remain.
Second naive approach: applying MSMT-CSD, but using only WM+GM. This yields a more aggressive "cleanup" of
WM (Fig.1, fourth column). CSF gets fitted as "hyper-GM" (far beyond the 0-to-1 range): the only/best means to fit
its high B0-data. But this also (partially) happens in WM+CSF mixtures, resulting in an overly aggressive cleanup;
e.g., enlarged ventricles, eroded nearby WM... even at the cost of GM not being able to represent the non-B0 WM
Iterative 2-shell 3-tissue (2S3T)-CSD for SS+B0-data
The naive approaches' results provide important insights. GM sits "in the range between WM and CSF". Fitting only
WM+GM provides an underestimate of WM. A similar property holds for fitting only CSF+GM: this yields an
underestimate of CSF.
This inspired us to design a specialised optimiser to tease out the WM-GM-CSF parts from SS+B0-data. Without
going into details, the overall strategy is:
1.Initialise WM to 0.
2.Fit only CSF+GM, given WM as prior constraint. This yields an underestimate of CSF.
3.Fit only WM+GM, given CSF as prior constraint. Since CSF is an underestimate, the resulting WM will be as well.
This marks the end of an iteration, yielding underestimates of CSF/WM, and consequently an overestimate of GM.
The next iteration is initialised with the current (under)estimate of WM.
In the theory of MSMT-CSD[2], the B0-data are regarded like any other b-value/shell; so "formally", SS+B0-data is a
case of 2-shell data (even though often informally called "single-shell" data). Retaining consistency, we refer to our
specialised strategy as a 2-shell 3-tissue CSD approach; 2S3T-CSD for short.
Results & discussion
We performed 2S3T-CSD on the SS+B0-data for 4 iterations (this took 13 minutes for the whole volume, on a
standard desktop computer). The final result is shown in Fig.1, fifth column. Note how closely the outcome
resembles the MSMT-CSD (on MS-data) result. Fig.2 shows the WM-GM-CSF estimates after each iteration. Note
how, even after iteration #1, the WM estimate is already informed by the initial (under)estimate of CSF; e.g., the
fornix starts to reappear. Over iterations, the WM/CSF are recovered. This is most apparent at, e.g., the ventricle
borders, where excess GM is swiftly eliminated; but also happens in other regions. Figs.3-4 present tractography
results, to further support the benefits of 2S3T-CSD for SS+B0-data. Fig.5 shows further 2S3T-CSD results, offering all
typical outputs previously only offered by MSMT-CSD[2].
Informed CSD[7] attempts this as well, but requires acquisition of a high-resolution T1-image, subvoxel-accuracy
registration and intricate spatial segmentation. 2S3T-CSD leverages the full potential of dMRI.
While it was initially believed that multiple tissue types could not be distinguished using single-shell data[2], we
hereby provide the first proof of the contrary. Leveraging (relative) properties of the 3 common brain tissue types
(WM,GM,CSF), we obtain close to the same results/benefits as MSMT-CSD, yet from single-shell (+b=0) data and
without any external spatial/anatomical priors, using a novel iterative method: 2-shell 3-tissue CSD (2S3T-CSD).
Proc. Intl. Soc. Mag. Reson. Med. 24 (2016) 3010
Fig.1: results from different techniques on SS(+B0)-data, compared to MSMT-CSD on MS-data. The WM results are
presented using FOD-based DEC maps (intensity = WM-FOD integral). All images are windowed equally; from 0
(black) to 1 (white or full DEC intensity). The 2S3T-CSD results are very similar to MSMT-CSD on MS-data.
Proc. Intl. Soc. Mag. Reson. Med. 24 (2016) 3010
Fig.2: evolution of 2S3T-CSD over iterations. The result at iteration #4 is also presented in Fig.1. Initially, the
WM/CSF parts are always a "safe" underestimate, and the GM an overestimate; especially in WM/CSF partial
volumed voxels (e.g., ventricle borders and fornix). The "excess GM" is eliminated in favour of WM/CSF.
Proc. Intl. Soc. Mag. Reson. Med. 24 (2016) 3010
Fig.3: tractography results (2mm slab) after MSMT-CSD on MS-data, versus SSST-CSD and 2S3T-CSD on SS(+B0)-data.
A low (0.06) FOD threshold was used; this allows to exploit a maximum amount of information from MSMT-CSD
results. SSST-CSD yields many false positives, while 2S3T-CSD closely replicates the quality previously ony expected
from MSMT-CSD.
Fig.4: detail of tractography results in a coronal slab, comparing MSMT-CSD (on MS-data) and 2S3T-CSD (on SS+B0-
data). Both results show similar qualities: little to no spurious tracks, while large coherent bundles of tracks nicely
fan out into cortical regions. 2S3T-CSD achieves this using data from a standard "single-shell" (+b=0) acquisition.
Proc. Intl. Soc. Mag. Reson. Med. 24 (2016) 3010
Fig.5: a range of 2S3T-CSD results. Upper row: tissue maps of several axial slices. Middle row: WM FOD-based DEC
maps of the same slices. Bottom row: WM-FODs in part of a coronal slice, overlaid on the FOD-based DEC (left) and
tissue (right) maps. Clean FODs nicely "penetrate" into the cortex.
Proc. Intl. Soc. Mag. Reson. Med. 24 (2016) 3010
[1] Tournier JD, Calamante F, Connelly A. Robust determination of the fibre orientation distribution in diffusion MRI:
Non-negativity constrained super-resolved spherical deconvolution. NeuroImage 2007;35(4):1459-1472.
[2] Jeurissen B, Tournier JD, Dhollander T, Connelly A, Sijbers J. Multi-tissue constrained spherical deconvolution for
improved analysis of multi-shell diffusion MRI data. NeuroImage 2014;103:411-426.
[3] Andersson JLR, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images:
application to diffusion tensor imaging. NeuroImage 2003;20(2):870-888.
[4] Andersson JLR, Xu J, Yacoub E, Auerbach E, Moeller S, Ugurbil K. A comprehensive Gaussian Process framework
for correcting distortions and movements in diffusion images. Proc ISMRM 2012;20:2426.
[5] Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC. N4ITK: Improved N3 bias correction.
IEEE TMI 2010;29(6):1310-1320.
[6] Dhollander T, Smith RE, Tournier JD, Jeurissen B, Connelly A. Time to move on: an FOD-based DEC map to replace
DTI's trademark DEC FA. Proc ISMRM 2015;23:1027.
[7] Roine T, Jeurissen B, Perrone D, Aelterman J, Philips W, Leemans A, Sijbers J. Informed constrained spherical
deconvolution (iCSD). Med Image Anal 2015;24(1):269-281.
... For the diffusion scans, images were preprocessed using established methods to remove artifacts 53 , the white matter fiber orientation distribution was resolved using single-shell constrained spherical deconvolution 54 , and a whole brain connectome was created using probabilistic tractography and the AAL atlas parcellation 55,56 . Tracts from each participant's connectome were used in analysis if they terminated in the right or left amygdala and in the ROI overlapping the search region defined in the fMRI analysis. ...
... For a full description of preprocessing steps as well as reliability of metrics derived from this diffusion processing pipeline see Newman et., al 2020 53 . The white matter fiber orientation distribution (FOD) was then resolved at the voxel-wise level by processing the outermost b-value shell (b=3000s/mm 2 ) using single-shell constrained spherical deconvolution, a technique to separate directional axonal signal from intracellular and extracellular isotropic diffusion 54 . Probabilistic tractography was performed by applying the iFOD2 algorithm which propagates streamlines between voxels based on the direction and amplitude of the underlying FOD 87 . ...
Full-text available
Functional connectivity between the amygdala and the medial prefrontal cortex (mPFC) has been identified as a neural substrate of emotion regulation that undergoes changes throughout development. Amygdala-mPFC connectivity has been well studied in adolescents and adults, with a mature profile typically emerging at 10 years of age. Maternal bonding in childhood has been shown to buffer amygdala reactivity and to influence the trajectory of amygdala-mPFC coupling, which in turn may impact socio-emotional dysfunction later in life. The oxytocinergic system is critical in the development of social behavior and maternal bonding. Early life parental care influences the methylation status of the oxytocin receptor (OXTRm) in animal models and humans, and higher OXTRm is associated with lower amygdala-PFC functional connectivity in adults. Using a neuroimaging-epigenetic approach, we investigated OXTRm as a biological marker of functional connectivity maturation in middle childhood. We find that higher levels of OXTRm are associated with a more adult-like functional connectivity profile. We also find that lower OXTRm blunts the association between amygdala-mPFC connectivity and future internalizing behaviors in early adolescence. These findings implicate OXTRm as a biological marker at the interface of the social environment and amygdala-mPFC coupling in emotional and behavioral regulation. Ultimately, identification of neurobiological markers may lead to earlier detection of children at risk for socio-emotional dysfunction.
... Individual and cohort-averaged tissue response functions for Grey Matter (GM), cerebrospinal fluid, and single-fiber WM compartments were calculated (Dhollander et al., 2016) and used to compute WM Fiber Orientation Distribution (FOD) functions within the healthy tissue of each subject. The latter step was performed using SS3T-CSD (Dhollander and Connelly, 2016). ...
... Individual and cohort-averaged tissue response functions for Grey Matter (GM), cerebrospinal fluid, and single-fiber WM compartments were calculated (Dhollander et al., 2016) and used to compute WM Fiber Orientation Distribution (FOD) functions within the healthy tissue of each subject. The latter step was performed using SS3T-CSD (Dhollander and Connelly, 2016). ...
Full-text available
Introduction Neonatal arterial ischemic stroke (NAIS) has been shown to affect white matter (WM) microstructure beyond the lesion. Here, we employed fixel-based analysis, a technique which allows to model and interpret WM alterations in complex arrangements such as crossing fibers, to further characterize the long-term effects of NAIS on the entire WM outside the primary infarct area. Materials and Methods 32 children (mean age 7.3 years (SD 0.4), 19 male) with middle cerebral artery NAIS (18 left hemisphere, 14 right hemisphere) and 31 healthy controls (mean age 7.7 years (SD 0.6), 16 male) underwent diffusion MRI scans and clinical examination for manual dexterity. Microstructural and macrostructural properties of the WM were investigated in a fixel-based whole-brain analysis, which allows to detect fiber-specific effects. Additionally, tract-averaged fixel metrics in interhemispheric tracts, and their correlation with manual dexterity, were examined. Results Significantly reduced microstructural properties were identified, located within the parietal and temporal WM of the affected hemisphere, as well as within their interhemispheric connecting tracts. Tract-averaged fixel metrics showed moderate, significant correlation with manual dexterity of the affected hand. No increased fixel metrics or contralesional alterations were observed. Discussion Our results show that NAIS leads to long-term alterations in WM microstructure distant from the lesion site, both within the parietal and temporal lobes as well as in their interhemispheric connections. The functional significance of these findings is demonstrated by the correlations with manual dexterity. The localization of alterations in structures highly connected to the lesioned areas shift our perception of NAIS from a focal towards a developmental network injury.
... However, as demonstrated in the results, this method is limited and has not been evaluated on tractography yet. Also, WM, GM, and CSF masks can be extracted with a single-shell 3-tissue fODF version developed by Dhollander and Connelly (2016) called the SS3T-CSD method. However, SS3T-CSD signal fraction maps have not been confronted with ACT. ...
Full-text available
Modern tractography algorithms such as anatomically-constrained tractography (ACT) are based on segmentation maps of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). These maps are generally estimated from a T1-weighted (T1w) image and then registered in diffusion weighted images (DWI) space. Registration of T1w to diffusion space and partial volume estimation are challenging and rarely voxel-perfect. Diffusion-based segmentation would, thus, potentially allow not to have higher quality anatomical priors injected in the tractography process. On the other hand, even if FA-based tractography is possible without T1 registration, the literature shows that this technique suffers from multiple issues such as holes in the tracking mask and a high proportion of generated broken and anatomically implausible streamlines. Therefore, there is an important need for a tissue segmentation algorithm that works directly in the native diffusion space. We propose DORIS, a DWI-based deep learning segmentation algorithm. DORIS outputs 10 different tissue classes including WM, GM, CSF, ventricles, and 6 other subcortical structures (putamen, pallidum, hippocampus, caudate, amygdala, and thalamus). DORIS was trained and validated on a wide range of subjects, including 1,000 individuals from 22 to 90 years old from clinical and research DWI acquisitions, from 5 public databases. In the absence of a “true” ground truth in diffusion space, DORIS used a silver standard strategy from Freesurfer output registered onto the DWI. This strategy is extensively evaluated and discussed in the current study. Segmentation maps provided by DORIS are quantitatively compared to Freesurfer and FSL-fast and the impacts on tractography are evaluated. Overall, we show that DORIS is fast, accurate, and reproducible and that DORIS-based tractograms produce bundles with a longer mean length and fewer anatomically implausible streamlines.
... First, 3-tissue response functions representing single-fibre WM, grey matter (GM) and CSF were estimated from the data themselves using a robust and fully automated unsupervised method (Dhollander and Connelly 2016). Next, MSMT-CSD was performed to obtain WM-like fibre orientation distributions (WM-FODs) (Supplementary Fig. 1) as well as GM-like and CSF-like compartments in all voxels (Tournier et al. 2007(Tournier et al. , 2019. ...
Full-text available
The superior longitudinal fasciculus (SLF) is a complex associative tract comprising three distinct subdivisions in the frontoparietal cortex, each of which has its own anatomical connectivity and functional roles. However, many studies on white matter development, hampered by limitations of data quality and tractography methods, treated the SLF as a single entity. The exact anatomical trajectory and developmental status of each sub-bundle of the human SLF in neonates remain poorly understood. Here, we compared the morphological and microstructural characteristics of each branch of the SLF at two ages using diffusion MRI data from 40 healthy neonates and 40 adults. A multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) algorithm was used to ensure the successful separation of the three SLF branches (SLF I, SLF II and SLF III). Then, between-group differences in the diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) metrics were investigated in all the SLF branches. Meanwhile, Mahalanobis distances based on all the diffusion metrics were computed to quantify the maturation of neonatal SLF branches, considering the adult brain as the reference. The SLF branches, excluding SLF II, had similar fibre morphology and connectivity between the neonatal and adult groups. The Mahalanobis distance values further supported the notion of heterogeneous maturation among SLF branches. The greatest Mahalanobis distance was observed in SLF II, possibly indicating that it was the least mature. Our findings provide a new anatomical basis for the early diagnosis and treatment of diseases caused by abnormal neonatal SLF development.
... was carried out. Next, the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) response functions were obtained using the Dhollander(Dhollander & Connelly, 2016) algorithm which were in turn used for estimating fiber orientation distributions (FOD) in the WM, GM and CSF tissues from diffusion data using spherical deconvolution. Then, anatomically constrained tractography(Smith et al., 2012) was carried out using the WM FOD. ...
Full-text available
Machine learning methods have increasingly been used to map out brain‐behavior associations (BBA), and to predict out‐of‐scanner behavior of unseen subjects. Given the brain changes that occur in the context of aging, the accuracy of these predictions is likely to depend on how similar the training and testing data sets are in terms of age. To this end, we examined how well BBAs derived from an age‐group generalize to other age‐groups. We partitioned the CAM‐CAN data set (N = 550) into the young, middle, and old age‐groups, then used the young and old age‐groups to construct prediction models for 11 behavioral outcomes using multimodal neuroimaging features (i.e., structural and resting‐state functional connectivity, and gray matter volume/cortical thickness). These models were then applied to all three age‐groups to predict their behavioral scores. When the young‐derived models were used, a graded pattern of age‐generalization was generally observed across most behavioral outcomes—predictions are the most accurate in the young subjects in the testing data set, followed by the middle and then old‐aged subjects. Conversely, when the old‐derived models were used, the disparity in the predictive accuracy across age‐groups was mostly negligible. These findings hold across different imaging modalities. These results suggest the asymmetric age‐generalization of BBAs—old‐derived BBAs generalized well to all age‐groups, however young‐derived BBAs generalized poorly beyond their own age‐group. We examined how well brain‐behavior associations derived from an age‐group generalize to other age‐groups. Our results showed that these associations derived from an old‐aged neuroimaging data set generalized well to the young individuals; however, the reverse is not true—Young‐derived brain‐behavior associations generalized poorly to older adults.
... Briefly, the following was performed in each subject's native brain space: (i) up-sampling of DWI to 1 mm 3 , (ii) calculation of fiber orientation distribution (FOD) images using multi-shell multi-tissue constrained spherical deconvolution (CSD) and group-averaged 'Dhollander' tissue response functions (white matter, grey matter, CSF) 22 ; (iii) global intensity normalization of subject-specific FOD images; (iv) whole-brain probabilistic tractography (20 million streamlines) using iFOD2 22 , hybrid surfacevolume anatomically constrained tractography (ACT) 24 , and dynamic seeding; and (v) streamline weighting using spherically-informed filtering of tractograms (SIFT2) 25 Accepted Article acquired. DWI pre-processing and VTA structural connectivity analysis followed all steps described above for the HCP dataset, with the following exceptions due to differences in DWI acquisition: (i) EPI distortion correction was performed without field-map or reverse phase-encoding data, using the 'synthetic b0' approach implemented by Synb0-DISCO software 30 ; and (ii) FOD images were generated using Single-Shell 3-Tissue CSD 23 , rather than multi-shell multi-tissue CSD 22 . ...
Full-text available
Objective: Deep brain stimulation (DBS) can reduce seizures in Lennox-Gastaut syndrome (LGS). However, little is known about the optimal target and whether efficacy depends on connectivity of the stimulation site. Using outcome data from the ESTEL trial, we aimed to determine the optimal target and connectivity for DBS in LGS. Methods: A total of 20 patients underwent bilateral DBS of the thalamic centromedian nucleus (CM). Outcome was percentage seizure reduction from baseline after 3 months of DBS, defined using three measures (monthly seizure diaries, 24-hour scalp electroencephalography [EEG], and a novel diary-EEG composite). Probabilistic stimulation mapping identified thalamic locations associated with higher/lower efficacy. Two substitute diffusion MRI datasets (a normative dataset from healthy subjects and a "disease-matched" dataset from a separate group of LGS patients) were used to calculate structural connectivity between DBS sites and a map of areas known to express epileptic activity in LGS, derived from our previous EEG-fMRI research. Results: Results were similar across the three outcome measures. Stimulation was most efficacious in the anterior and inferolateral "parvocellular" CM border, extending into the ventral lateral nucleus (posterior subdivision). There was a positive association between diary-EEG composite seizure reduction and connectivity to areas of a priori EEG-fMRI activation, including premotor and prefrontal cortex, putamen, and pontine brainstem. In contrast, outcomes were not associated with baseline clinical variables. Interpretation: Efficacious CM-DBS for LGS is linked to stimulation of the parvocellular CM and the adjacent ventral lateral nucleus, and is associated with connectivity to, and thus likely modulation of, the "secondary epileptic network" underlying the shared electroclinical manifestations of LGS. ANN NEUROL 2022.
... Data were then upsampled to a voxel size of 1.5 x 1.5 x 1.5 mm 3 to improve spatial anatomical contrast and single-shell 3-tissue constrained spherical deconvolution (SS3T-CSD) was conducted to obtain WM Fiber Orientation Directions (FODs) for each subject (Dhollander & Connelly, 2016). We further applied group-level intensity normalization to ensure that FOD amplitudes were directly comparable across subjects within each separate (scanning site) dataset. ...
Full-text available
Background Corpus callosum anomalies are commonly noted in autism spectrum disorder (ASD). Given the complexity of its microstructural architecture, with crossing fibers projecting throughout, we applied fixel-based analysis to probe white matter micro- and macrostructure within this region. As ASD is a neurodevelopmental condition with noted abnormalities in brain growth, age was also investigated. Methods Data for participants with (N=54) and without (N=50) ASD, aged 5-34 years, were obtained from the Autism Brain Imaging Data Exchange-II (ABIDE-II). Within each site, indices of fiber density (FD), fiber cross-section (FC), and combined fiber density and cross-section (FDC) were compared between those groups. Results Young adolescents with ASD (age = 11.19 ± 7.54) showed reduced macroscopic FC and FDC compared to age-matched neurotypical controls (age = 10.04 ± 4.40). Reduced FD and FDC was noted in a marginally older ASD (age 13.87 ± 3.15) cohort compared to matched controls (age = 13.85 ± 2.90). Among the oldest cohorts, a non-significant trend indicated reduced FD in older adolescents/young adults with ASD (age = 17.07 ± 3.56) compared to controls (age = 16.55 ± 2.95). There was a positive correlation between age and callosal mean FC and FDC in the youngest cohort. When stratified by diagnosis, this finding remained only for the ASD sample. Conclusion White matter aberration appears greatest among younger ASD cohorts. In older adolescents and young adults, less of the corpus callosum seems affected. This supports the suggestion that some early neuropathophysiological indicators in ASD may dissipate with age.
Background Tensor-based investigations suggest that delayed or disrupted white matter (WM) development may relate to adverse behavioral outcomes in individuals born very preterm (VP), however, metrics derived from such models lack specificity. Here, we applied a fixel-based analysis framework to examine WM microstructural and macrostructural correlates of concurrent internalizing and externalizing problems in VP and full-term (FT) children at 7 and 13 years. Methods Diffusion imaging data were collected in a longitudinal cohort of VP and FT individuals (130 VP, 29 FT at 7 years; 125 VP, 44 FT at 13 years). Fixel-based measures of fiber density (FD), fiber-bundle cross-section (FC) and fiber density and cross-section (FDC) were extracted from 21 WM tracts previously implicated in psychopathology. Internalizing and externalizing symptoms were assessed using the Strengths and Difficulties Questionnaire parent report at 7 and 13 years. Results At age 7 years, widespread reductions in FC and FDC and tract-specific reductions in FD were related to more internalizing and externalizing symptoms irrespective of birth group. At age 13 years, fixel-based measures were not related to internalizing symptoms, while tract-specific reductions in FD, FC, FDC measures were related to more externalizing symptoms in the FT group only. Conclusions Age-specific neurobiological markers of internalizing and externalizing problems identified in this study extend previous tensor-based findings to inform pathophysiological models of behavior problems and provide the foundation for investigations into novel preventative and therapeutic intervention to mitigate risk in VP and other high-risk infant populations.
Full-text available
Sports-related concussion, a form of mild traumatic brain injury, is characterised by transient disturbances of brain function. There is increasing evidence that functional brain changes may be driven by subtle abnormalities in white matter microstructure, and diffusion MRI has been instrumental in demonstrating these white matter abnormalities in vivo. However, the reported location and direction of the observed white matter changes in mild traumatic brain injury are variable, likely attributable to the inherent limitations of the white matter models used. This cross-sectional study applies an advanced and robust technique known as fixel-based analysis to investigate fibre tract-specific abnormalities in professional Australian Football League players with a recent mild traumatic brain injury. We used the fixel-based analysis framework to identify common abnormalities found in specific fibre tracts in participants with an acute injury (≤ 12 days after injury; n = 14). We then assessed whether similar changes exist in subacute injury (> 12 days and < 3 months after injury; n = 15). The control group was 29 neurologically healthy control participants. We assessed microstructural differences in fibre density and fibre bundle morphology and performed whole-brain fixel-based analysis to compare groups. Subsequent tract-of-interest analyses were performed within five selected white matter tracts to investigate the relationship between the observed tract-specific abnormalities and days since injury and the relationship between these tract-specific changes with cognitive abnormalities. Our whole-brain analyses revealed significant increases in fibre density and bundle cross-section in the acute mild traumatic brain injury group when compared to controls. The acute mild traumatic brain injury group showed even more extensive differences when compared to the subacute injury group than to controls. The fibre structures affected in acute concussion included the corpus callosum, left prefrontal and left parahippocampal white matter. The fibre density and cross-sectional increases were independent of time since injury in the acute injury group, and were not associated with cognitive deficits. Overall, this study demonstrates that acute mild traumatic brain injury is characterised by specific white matter abnormalities, which are compatible with tract-specific cytotoxic oedema. These potential oedematous changes were absent in our subacute mild traumatic brain injury participants, suggesting that they may normalise within 12 days after injury, although subtle abnormalities may persist in the subacute stage. Future longitudinal studies are needed to elucidate individualised recovery after brain injury.
There is substantial variation between healthy individuals in the number of retinal ganglion cells (RGC) in the eye, with commensurate variation in the number of axons in the optic tracts. Fixel-based analysis of diffusion MR produces estimates of fiber density (FD) and cross section (FC). Using these fixel measurements along with retinal imaging, we asked if individual differences in RGC tissue volume are correlated with individual differences in FD and FC measurements obtained from the optic tracts, and subsequent structures along the cortical visual pathway. We find that RGC endowment is correlated with optic tract FC, but not with FD. RGC volume had a decreasing relationship with measurements from subsequent regions of the visual system (LGN volume, optic radiation FC/FD, and V1 surface area). However, we also found that the variations in each visual area were correlated with the variations in its immediately adjacent visual structure. We only observed these serial correlations when FC is used as the measure of interest for the optic tract and radiations, but no significant relationship was found when FD represented these white matter structures. From these results, we conclude that the variations in RGC endowment, LGN volume, and V1 surface area are better predicted by the overall cross section of the optic tract and optic radiations as compared to the intra-axonal restricted signal component of these white matter pathways. Additionally, the presence of significant correlations between adjacent, but not distant, anatomical structures suggests that there are multiple, local sources of anatomical variation along the visual pathway.
Conference Paper
Full-text available
The "traditional" directionally-encoded colour (DEC) FA map is an icon of DTI, but is also affected by its inherent flaws. The first eigenvector is known to be ill-defined in regions of crossing fibres, resulting in misleading specific DEC values as well as "false edges" in the overall map. Additionally, the FA shows naturally low values in these regions. In a clinical setting, this might potentially lead to false positive findings; but also to false negative ones in case these false features mask out or otherwise distract from real pathological features. We propose an FOD-based DEC map that solves these issues.
Full-text available
Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. However, the voxel sizes used in DW-MRI are relatively large, making DW-MRI prone to significant partial volume effects (PVE). These PVEs can be caused both by complex (e.g. crossing) WM fiber configurations and non-WM tissue, such as gray matter (GM) and cerebrospinal fluid. High angular resolution diffusion imaging methods have been developed to correctly characterize complex WM fiber configurations, but significant non-WM PVEs are also present in a large proportion of WM voxels. In constrained spherical deconvolution (CSD), the full fiber orientation distribution function (fODF) is deconvolved from clinically feasible DW data using a response function (RF) representing the signal of a single coherently oriented population of fibers. Non-WM PVEs cause a loss of precision in the detected fiber orientations and an emergence of false peaks in CSD, more prominently in voxels with GM PVEs. We propose a method, informed CSD (iCSD), to improve the estimation of fODFs under non-WM PVEs by modifying the RF to account for non-WM PVEs locally. In practice, the RF is modified based on tissue fractions estimated from high-resolution anatomical data. Results from simulation and in-vivo bootstrapping experiments demonstrate a significant improvement in the precision of the identified fiber orientations and in the number of false peaks detected under GM PVEs. Probabilistic whole brain tractography shows fiber density is increased in the major WM tracts and decreased in subcortical GM regions. The iCSD method significantly improves the fiber orientation estimation at the WM-GM interface, which is especially important in connectomics, where the connectivity between GM regions is analyzed.
Constrained spherical deconvolution (CSD) has become one of the most widely used methods to extract white matter (WM) fibre orientation information from diffusion-weighted MRI (DW-MRI) data, overcoming the crossing fibre limitations inherent in the diffusion tensor model. It is routinely used to obtain high quality fibre orientation distribution function (fODF) estimates and fibre tractograms and is increasingly used to obtain apparent fibre density (AFD) measures. Unfortunately, CSD typically only supports data acquired on a single shell in q-space. With multi-shell data becoming more and more prevalent, there is a growing need for CSD to fully support such data. Furthermore, CSD can only provide high quality fODF estimates in voxels containing WM only. In voxels containing other tissue types such as grey matter (GM) and cerebrospinal fluid (CSF), the WM response function may no longer be appropriate and spherical deconvolution produces unreliable, noisy fODF estimates. The aim of this study is to incorporate support for multi-shell data into the CSD approach as well as to exploit the unique b-value dependencies of the different tissue types to estimate a multi-tissue ODF. The resulting approach is dubbed multi-shell, multi-tissue CSD (MSMT-CSD) and is compared to the state-of-the-art single-shell, single-tissue CSD (SSST-CSD) approach. Using both simulations and real data, we show that MSMT-CSD can produce reliable WM/GM/CSF volume fraction maps, directly from the DW data, whereas SSST-CSD has a tendency to overestimate the WM volume in voxels containing GM and/or CSF. In addition, compared to SSST-CSD, MSMT-CSD can substantially increase the precision of the fODF fibre orientations and reduce the presence ofspurious fODF peaks in voxels containing GM and/or CSF. Both effects translate into more reliable AFD measures and tractography results with MSMT-CSD compared to SSST-CSD.
Diffusion tensor imaging is often performed by acquiring a series of diffusion-weighted spin-echo echo-planar images with different direction diffusion gradients. A problem of echo-planar images is the geometrical distortions that obtain near junctions between tissues of differing magnetic susceptibility. This results in distorted diffusion-tensor maps. To resolve this we suggest acquiring two images for each diffusion gradient; one with bottom-up and one with top-down traversal of k-space in the phase-encode direction. This achieves the simultaneous goals of providing information on the underlying displacement field and intensity maps with adequate spatial sampling density even in distorted areas. The resulting DT maps exhibit considerably higher geometric fidelity, as assessed by comparison to an image volume acquired using a conventional 3D MR technique.
A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B -spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as ??N4ITK,?? available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized <sup>3</sup>He lung image data, and 9.4T postmortem hippocampus data.
Diffusion-weighted (DW) MR images contain information about the orientation of brain white matter fibres that potentially can be used to study human brain connectivity in vivo using tractography techniques. Currently, the diffusion tensor model is widely used to extract fibre directions from DW-MRI data, but fails in regions containing multiple fibre orientations. The spherical deconvolution technique has recently been proposed to address this limitation. It provides an estimate of the fibre orientation distribution (FOD) by assuming the DW signal measured from any fibre bundle is adequately described by a single response function. However, the deconvolution is ill-conditioned and susceptible to noise contamination. This tends to introduce artefactual negative regions in the FOD, which are clearly physically impossible. In this study, the introduction of a constraint on such negative regions is proposed to improve the conditioning of the spherical deconvolution. This approach is shown to provide FOD estimates that are robust to noise whilst preserving angular resolution. The approach also permits the use of super-resolution, whereby more FOD parameters are estimated than were actually measured, improving the angular resolution of the results. The method provides much better defined fibre orientation estimates, and allows orientations to be resolved that are separated by smaller angles than previously possible. This should allow tractography algorithms to be designed that are able to track reliably through crossing fibre regions.