Recent developments in semi-global tractogram optimisation algorithms have opened the field of diffusion magnetic resonance imaging (MRI) to the possibility of performing quantitative assessment of structural fibre ‘connectivity’. The proper application of these methods in neuroscience research has, however, been limited by a lack of awareness, understanding, or appreciation for the consequences of these methods; furthermore, particular steps necessary to use these tools in an appropriate manner to fully exploit their quantitative properties have not yet been described. This article therefore serves three purposes: to increase awareness of the fact that there are existing tools that attempt to address the well-known non-quantitative nature of streamlines counts; to illustrate why these algorithms work the way they do to yield quantitative estimates of white matter ‘connectivity’ (in the form of total intra-axonal cross-sectional area: ‘fibre bundle capacity (FBC)’); and to explain how to properly utilise these results for quantitative tractography analysis across subjects.

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple “crossing” fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the “Fixel-Based Analysis” (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities.

White matter hyperintensities (WMH) are regions of high signal intensity typically identified on fluid attenuated inversion recovery (FLAIR). Although commonly observed in elderly individuals, they are more prevalent in Alzheimer’s disease (AD) patients. Given that WMH appear relatively homogeneous on FLAIR, they are commonly partitioned into location- or distance-based classes when investigating their relevance to disease. Since pathology indicates that such lesions are often heterogeneous, probing their microstructure in vivo may provide greater insight than relying on such arbitrary classification schemes. In this study, we investigated WMH in vivo using an advanced diffusion MRI method known as single-shell 3-tissue constrained spherical deconvolution (SS3T-CSD), which models white matter microstructure while accounting for grey matter and CSF compartments. Diffusion MRI data and FLAIR images were obtained from AD (n = 48) and healthy elderly control (n = 94) subjects. WMH were automatically segmented, and classified: (1) as either periventricular or deep; or (2) into three distance-based contours from the ventricles. The 3-tissue profile of WMH enabled their characterisation in terms of white matter-, grey matter-, and fluid-like characteristics of the diffusion signal. Our SS3T-CSD findings revealed substantial heterogeneity in the 3-tissue profile of WMH, both within lesions and across the various classes. Moreover, this heterogeneity information indicated that the use of different commonly used WMH classification schemes can result in different disease-based conclusions. We conclude that future studies of WMH in AD would benefit from inclusion of microstructural information when characterising lesions, which we demonstrate can be performed in vivo using SS3T-CSD.

White matter hyperintensities (WMH) are commonly observed in elderly individuals, and are typically more prevalent in Alzheimer’s disease subjects than in healthy subjects. These lesions can be identified on fluid attenuated inversion recovery (FLAIR) MRI, on which they are hyperintense compared to their surroundings. These MRI-visible lesions appear homogeneously hyperintense despite known heterogeneity in their pathological underpinnings, and are commonly regarded as surrogate markers of small vessel disease in in vivo studies. Consequently, the extent to which these lesions contribute to Alzheimer’s disease remains unclear, likely due to the somewhat limited way in which these lesions are assessed in vivo . Diffusion MRI is sensitive to white matter microstructure, and might thus be used to investigate microstructural changes within WMH. In this study, we applied a method called single-shell 3-tissue constrained spherical deconvolution, which models white matter microstructure while also accounting for other tissue compartments, to investigate WMH in vivo . Diffusion MRI data and FLAIR images were obtained from Alzheimer’s disease ( n = 48) and healthy elderly control ( n = 94) subjects from the Australian Imaging, Biomarkers and Lifestyle study of ageing. WMH were automatically segmented and classified as periventricular or deep lesions from FLAIR images based on their continuity with the lateral ventricles, and the 3-tissue profile of different classes of WMH was characterised by three metrics, which together characterised the relative tissue profile in terms of the white matter-, grey matter-, and fluid-like characteristics of the diffusion signal. Our findings revealed that periventricular and deep lesion classes could be distinguished from one another, and from normal-appearing white matter based on their 3-tissue profile, with substantially higher free water content in periventricular lesions than deep. Given the higher lesion load of periventricular lesions in Alzheimer’s disease patients, the 3-tissue profile of these WMH could be interpreted as reflecting the more deleterious pathological underpinnings that are associated with disease. However, when alternatively classifying lesion sub-regions in terms of distance contours from the ventricles to account for potential heterogeneity within confluent lesions, we found that the highest fluid content was present in lesion areas most proximal to the ventricles, which were common to both Alzheimer’s disease subjects and healthy controls. We argue that whatever classification scheme is used when investigating WMH, failure to account for heterogeneity within lesions may result in classification-scheme dependent conclusions. Future studies of WMH in Alzheimer’s Disease would benefit from inclusion of microstructural information when characterising lesions.

MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualisation, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software.

Alzheimer's disease is increasingly considered a large-scale network disconnection syndrome, associated with progressive aggregation of pathological proteins, cortical atrophy, and functional disconnections between brain regions. These pathological changes are posited to arise in a stereotypical spatiotemporal manner, targeting intrinsic networks in the brain, most notably the default mode network. While this network-specific disruption has been thoroughly studied with functional neuroimaging, changes to specific white matter fibre pathways within the brain's structural networks have not been closely investigated, largely due to the challenges of modelling complex white matter structure. Here, we applied a novel technique known as 'fixel-based analysis' to comprehensively investigate fibre tract-specific differences at a within-voxel level (called 'fixels') to assess potential axonal loss in subjects with Alzheimer's disease and mild cognitive impairment. We hypothesized that patients with Alzheimer's disease would exhibit extensive degeneration across key fibre pathways connecting default network nodes, while patients with mild cognitive impairment would exhibit selective degeneration within fibre pathways connecting regions previously identified as functionally implicated early in Alzheimer's disease. Diffusion MRI data from Alzheimer's disease (n = 49), mild cognitive impairment (n = 33), and healthy elderly control subjects (n = 95) were obtained from the Australian Imaging, Biomarkers and Lifestyle study of ageing. We assessed microstructural differences in fibre density, and macrostructural differences in fibre bundle morphology using fixel-based analysis. Whole-brain analysis was performed to compare groups across all white matter fixels. Subsequently, we performed a tract of interest analysis comparing fibre density and cross-section across 11 selected white matter tracts, to investigate potentially subtle degeneration within fibre pathways in mild cognitive impairment, initially by clinical diagnosis alone, and then by including amyloid status (i.e. a positive or negative amyloid PET scan). Our whole-brain analysis revealed significant white matter loss manifesting both microstructurally and macrostructurally in Alzheimer's disease patients, evident in specific fibre pathways associated with default mode network nodes. Reductions in fibre density and cross-section in mild cognitive impairment patients were only exhibited within the posterior cingulum when statistical analyses were limited to tracts of interest. Interestingly, these degenerative changes did not appear to be associated with high amyloid accumulation, given that amyloid-negative, but not positive, mild cognitive impairment subjects exhibited subtle focal left posterior cingulum deficits. The findings of this study demonstrated a stereotypical distribution of white matter degeneration in patients with Alzheimer's disease, which was in line with canonical findings from other imaging modalities, and with a network-based conceptualization of the disease.awx355media15726254535001.

White matter hyperintensities (WMH) appearing on T2-weighted FLAIR MRI in Alzheimer's disease (AD) patients are commonly regarded as a surrogate marker for small vessel disease (SVD). However, SVD is not likely to be the only cause of WMH expression in AD, and the histopathological characteristics of WMH are known to be heterogeneous. Advanced diffusion MRI enables investigation of diffusional properties within WMH that could provide in vivo visualisation of underlying microstructural heterogeneity. High angular resolution diffusion MRI data (2.3mm3 voxels, 60 directions, b=3000s/mm2) and FLAIR images (0.9x1x1mm3) were collected from 48 AD and 94 healthy elderly controls (HC) from AIBL (Australian Imaging, Biomarkers and Lifestyle study) on a 3T scanner. All data were preprocessed. WMH segmentations were automatically performed using the Hyperintensity Segmentation Tool (HIST), and automatically classified into “periventricular” (PVWMH) or “deep” (DWMH). We computed a measure of relative white matter/grey matter/CSF (WM-GM-CSF)-likeness of the diffusion signal by obtaining compartments for each voxel using single-shell-3-tissue constrained spherical deconvolution (SS3T-CSD) (Fig.1). WM-GM-CSF compartments were normalized to sum to 1, and computed within WMH and normal appearing WM (NAWM). Appearance of WMH on FLAIR, segmentation classifications, and tissue compartments from SS3T- CSD. Left: WMH are typically segmented from FLAIR images, where they appear hyperintense. Middle: Segmentations were classified into “periventricular” and “deep” WMH based on the minimum and average distance of a lesion from the ventricles (classified as periventricular if the minimum distance < 5.0 mm or average distance < 20.0 mm, and deep otherwise). Right: SS3T-CSD enables modelling of WM FODs and GM/CSF compartments. Heterogeneity with regard to the underlying WM-GM-CSF-likeness can be observed with SS3T-CSD. AD patients exhibited significantly greater PVWMH, but not DWMH volume compared to HC (Table 1). In AD, PVWMH and DWMH showed differing profiles in relative WM-GM-CSF mean tissue fractions, with higher CSF-likeness in PVWMH, and higher GM-likeness in DWMH (Fig.2). PVWMH and DWMH exhibited distinct clusters based on their relative tissue fraction profiles, as did NAWM (Fig.3). Moreover, heterogeneity was consistently observed within individual lesions (Fig.4). Boxplots showing relative WM-GM-CSF-like signal fractions within lesions and NAWM. The relative WM-GM-CSF-like signal fractions are displayed across all AD subjects (n=48) as the median, first and third quartiles, and 95% confidence interval of the median. Normal-appearing white matter (NAWM) exhibits high WM-like fraction as expected, with relatively low GM/CSF-like signal fractions. In contrast, WMH exhibit higher GM/CSF-like signal fractions. Moreover, periventricular and deep WMH can be distinguished by their relative signal fractions across this Alzheimer's disease group. This is similarly evident in Figure 3. Ternary plot exhibiting relative signal fractions within lesions and NAWM. For each subject, the periventricular WMH (red circles), deep WMH (green triangles), and NAWM (blue squares) are displayed on a ternary plot (created using ggtern package in R), with the location corresponding to the relative WM-GM-CSF fraction of the lesions (or normal WM). The relative tissue fraction is shown as a percentage along the left (WM-like), right (GM-like), and bottom (CSF-like) axes. Remarkably, the periventricular WMH, deep WMH, and NAWM appear in distinct clusters, exhibiting their different profiles with regard to relative tissue fractions obtained from SS3T-CSD diffusion data. Heterogeneity within WMH. WMH appear as a homogeneous lesion on FLAIR images from which they are most commonly segmented (segmentation outline shown). Tissue maps derived from SS3T-CSD show that there is heterogeneity within a single lesion with regard to the relative tissue components. The insets on the left display the heterogeneity of the relative tissue compartments within the same lesion segmentation. The underlying pathological changes within these lesions is also likely to be heterogeneous, of which the diffusional changes are likely reflective. PVWMH and DWMH exhibit distinct diffusional profiles in AD patients, likely due to differing pathological substrates. Given the higher PVWMH load in AD compared to HC, these lesions may be more closely associated with AD pathology, likely reflecting substantial myelin and axonal loss that manifests as increased CSF-likeness due to increased interstitial fluid. We additionally go beyond the binary lesion segmentation from FLAIR MRI to reveal microstructural heterogeneity within individual lesions non-invasively in vivo using this novel approach. This will enable investigation of WMHs as heterogeneous entities when probing associations with histopathology and clinical progression of AD, rather than as indiscriminate markers of SVD.

ObjectiveTo investigate whether genetics, underlying pathology, or repeated seizures contribute to atrophy in specific white matter tracts.MethodsMedically refractory unilateral temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS‐TLE, n = 26) was studied as an archetype of focal epilepsy, using fixel‐based analysis of diffusion‐weighted imaging. A genetic effect was assessed in first‐degree relatives of HS‐TLE subjects who did not have epilepsy themselves (HS‐1°Rel; n = 26). The role of disease process was uncovered by comparing HS‐TLE to unilateral TLE with normal clinical magnetic resonance imaging (MRI‐neg TLE; n = 26, matched for seizure severity). The effect of focal seizures was inferred from lateralized atrophy common to both HS‐TLE and MRI‐neg TLE, in comparison to healthy controls (n = 76).ResultsHS‐1 °Rel had bilaterally small hippocampi, but no focal white matter atrophy was detected, indicating a limited effect of genetics. HS‐TLE subjects had lateralized atrophy of most temporal lobe tracts, and hippocampal volumes in HS‐TLE correlated with parahippocampal cingulum and anterior commissure atrophy, indicating an effect of the underlying pathology. Ipsilateral atrophy of the tapetum, uncinate, and inferior fronto‐occipital fasciculus was found in both HS‐TLE and MRI‐neg TLE, suggesting a common lateralized effect of focal seizures. Both epilepsy groups had bilateral atrophy of the dorsal cingulum and corpus callosum fibers, which we interpret as a consequence of bilateral insults (potentially generalized seizures and/or medications).InterpretationUnderlying pathology, repeated focal seizures, and global insults each contribute to atrophy in specific tracts. Genetic factors make less of a contribution in this cohort. A multifactorial model of white matter atrophy in focal epilepsy is proposed. Ann Neurol 2017;81:240–250

Alzheimer's disease (AD) is increasingly considered a large-scale network disconnection syndrome, which is presumably associated with substantial disruption to white matter (WM) connectivity. Previous studies have investigated microstructural changes to WM with voxel-based analysis of diffusion-weighted metrics; however, such voxel-averaged studies cannot provide fibre-specific information as they fail to account for the multiple fibre orientations that are present within most WM voxels. Here, we perform fixel-based analysis (FBA; whereby “fixel” refers to a specific fibre population within a voxel) in AD and in mild cognitive impairment (MCI). Diffusion MRI data was acquired from AD patients (n=49), MCI patients (n=33) and healthy elderly control subjects (n=95), as part of the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing (see Table 1). A comprehensive metric encapsulating both microscopic changes in fibre density and macroscopic changes in fibre-bundle cross-section (FDC) was obtained for each white matter fixel, and compared across groups using FBA, both at the whole-brain level, and subsequently across specific fibre tracts-of-interest. We further investigated whether changes in FDC were related to amyloid accumulation in MCI patients, by subdividing this group and comparing Aβ+ (n=20) and Aβ- (n=13) MCI participants. Whole-brain FBA exhibited significant FDC decreases in AD patients compared to controls, across various fibre tracts (uncinate, inferior fronto-occipital (IFOF), left arcuate fasciculi, splenium and genu) (see figure 1). When statistical analyses were limited to these fibre tracts-of-interest, MCI patients exhibited significant FDC reductions in the bilateral posterior cingulum and right uncinate fasciculus (see figure 2). When MCI patients were subdivided by Aβ status, only the Aβ- MCI group exhibited significant FDC decrease, and only in the left posterior cingulum. These results suggest substantial reductions in structural connectivity of various WM pathways arise in AD, and exhibit the value of FBA in identifying changes within specific fibre pathways, even in crossing fibre regions. Furthermore, while disruptions to the posterior cingulum and uncinate are likely associated with early cognitive impairment, they do not appear to be associated with high Aβ accumulation. Further longitudinal studies are necessary to determine any relationship between specific changes in white matter connectivity and progression in AD. Significant FDC decreases in AD compared to healthy control subjects. Significant reductions in FDC (FWE-corrected p-value < 0.05) upon whole-brain FBA are shown from coronal, axial and sagittal views. Fixels are coloured by percentage decrease in the AD group compared to healthy controls (HC) as per the scale bar. Significant tracts from tract-of-interest analysis. Left panel: mean FDC (diamonds) and 95% confidence intervals (bars) for each tract-of-interest displayed for AD and MCI groups, displayed as percentage difference from healthy control subjects. Significant tracts (p<0.05) are displayed above dotted line in colour (bilateral posterior cingulum and right uncinate fasciculus), while non-significant findings shown in grey. The corticospinal tract (CST) is displayed at the bottom for comparison. Right panel: tracts-of-interest displayed and colour-coded to match left panel.

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

Long term irreversible disability in multiple sclerosis (MS) is thought to be primarily driven by axonal degeneration. Axonal degeneration leads to degenerative atrophy, therefore early markers of axonal degeneration are required to predict clinical disability and treatment efficacy. Given that additional pathologies such as inflammation, demyelination and oedema are also present in MS, it is essential to develop axonal markers that are not confounded by these processes. The present study investigated a novel method for measuring axonal degeneration in MS based on high angular resolution diffusion magnetic resonance imaging. Unlike standard methods, this novel method involved advanced acquisition and modelling for improved axonal sensitivity and specificity. Recent work has developed analytical methods, two novel axonal markers, fibre density and cross-section, that can be estimated for each fibre direction in each voxel (termed a “fixel”). This technique, termed fixel-based analysis, thus simultaneously estimates axonal density and white matter atrophy from specific white matter tracts. Diffusion-weighted imaging datasets were acquired for 17 patients with a history of acute unilateral optic neuritis (35.3 ± 10.2 years, 11 females) and 14 healthy controls (32.7 ± 4.8 years, 8 females) on a 3 T scanner. Fibre density values were compared to standard diffusion tensor imaging parameters (fractional anisotropy and mean diffusivity) in lesions and normal appearing white matter. Group comparisons were performed for each fixel to assess putative differences in fibre density and fibre cross-section. Fibre density was observed to have a comparable sensitivity to fractional anisotropy for detecting white matter pathology in MS, but was not affected by crossing axonal fibres. Whole brain fixel-based analysis revealed significant reductions in fibre density and fibre cross-section in the inferior fronto-occipital fasciculus (including the optic radiations) of patients compared to controls. We interpret this result to indicate that this fixel-based approach is able to detect early loss of fibre density and cross-section in the optic radiations in MS patients with a history of optic neuritis. Fibre-specific markers of axonal degeneration should be investigated further for use in early stage therapeutic trials, or to monitor axonal injury in early stage MS.

Hemidisconnections (i.e. hemispherectomies or hemispherotomies) invariably lead to contralateral hemiparesis. Many patients with a pre-existing hemiparesis, however, experience no deterioration in motor functions, and some can still grasp with their paretic hand after hemidisconnection. The scope of our study was to predict this phenomenon. Hypothesizing that preserved contralateral grasping ability after hemidisconnection can only occur in patients controlling their paretic hands via ipsilateral corticospinal projections already in the preoperative situation, we analysed the asymmetries of the brainstem (by manual magnetic resonance imaging volumetry) and of the structural connectivity of the corticospinal tracts within the brainstem (by magnetic resonance imaging diffusion tractography), assuming that marked hypoplasia or Wallerian degeneration on the lesioned side in patients who can grasp with their paretic hands indicate ipsilateral control. One hundred and two patients who underwent hemidisconnections between 0.8 and 36 years of age were included. Before the operation, contralateral hand function was normal in 3/102 patients, 47/102 patients showed hemiparetic grasping ability and 52/102 patients could not grasp with their paretic hands. After hemidisconnection, 20/102 patients showed a preserved grasping ability, and 5/102 patients began to grasp with their paretic hands only after the operation. All these 25 patients suffered from pre- or perinatal brain lesions. Thirty of 102 patients lost their grasping ability. This group included all seven patients with a post-neonatally acquired or progressive brain lesion who could grasp before the operation, and also all three patients with a preoperatively normal hand function. The remaining 52/102 patients were unable to grasp pre- and postoperatively. On magnetic resonance imaging, the patients with preserved grasping showed significantly more asymmetric brainstem volumes than the patients who lost their grasping ability. Similarly, these patients showed striking asymmetries in the structural connectivity of the corticospinal tracts. In summary, normal preoperative hand function and a post-neonatally acquired or progressive lesion predict a loss of grasping ability after hemidisconnection. A postoperatively preserved grasping ability is possible in hemiparetic patients with pre- or perinatal lesions, and this is highly likely when the brainstem is asymmetric and especially when the structural connectivity of the corticospinal tracts within the brainstem is asymmetric.

This paper describes how to compile and use the Insight Toolkit Kinetic Analysis library (itk::ka), to perform analysis of dynamic medical images, along with a brief overview of the library source code. Supplied, are a set of interfaces to facilitate application development, application implementing a standard kinetic analysis pipeline (koala), and some sample data. Only versions of ITK4 and higher are currently supported.

Voxel-based analysis of diffusion MRI data is increasingly popular. However, most white matter voxels contain contributions from multiple fibre populations (often referred to as crossing fibres), and therefore voxel-averaged quantitative measures (e.g. fractional anisotropy) are not fibre-specific and have poor interpretability. Using higher-order diffusion models, parameters related to fibre density can be extracted for individual fibre populations within each voxel ('fixels'), and recent advances in statistics enable the multi-subject analysis of such data. However, investigating within-voxel microscopic fibre density alone does not account for macroscopic differences in the white matter morphology (e.g. the calibre of a fibre bundle). In this work, we introduce a novel method to investigate the latter, which we call fixel-based morphometry (FBM). To obtain a more complete measure related to the total number of white matter axons, information from both within-voxel microscopic fibre density and macroscopic morphology must be combined. We therefore present the FBM method as an integral piece within a comprehensive fixel-based analysis framework to investigate measures of fibre density, fibre-bundle morphology (cross-section), and a combined measure of fibre density and cross-section. We performed simulations to demonstrate the proposed measures using various transformations of a numerical fibre bundle phantom. Finally, we provide an example of such an analysis by comparing a clinical patient group to a healthy control group, which demonstrates that all three measures provide distinct and complementary information. By capturing information from both sources, the combined fibre density and cross-section measure is likely to be more sensitive to certain pathologies and more directly interpretable.

A biological parameter that would be valuable to be able to extract from diffusion MRI data is the local white matter axonal density. Track-density imaging (TDI) has been used as if it could provide such a measure; however, this has been the subject of controversy, primarily due to the fact that track-count quantitation is highly sensitive to tracking biases and errors. The spherical-deconvolution informed filtering of tractograms (SIFT) post-processing method was recently introduced to minimise tractography biases, and thus provides a more biologically meaningful measure that could be used in track-count mapping (i.e. TDI following SIFT). The TDI intensity following SIFT ideally corresponds to the orientational average of the fibre orientation distribution (FOD), which corresponds to the total Apparent Fibre Density (AFDtotal) within the AFD framework; in fact, AFDtotal provides a direct measure of local fibre density at native resolution that does not rely on fibre-tracking. In this study, we demonstrate problems associated with quantitative TDI investigations, which can be avoided by using SIFT processing or directly by using AFDtotal maps. We also characterise the intra- and inter-subject reproducibility of TDI maps (with and without SIFT pre-processing) and AFDtotal maps. It is shown that SIFT improves the quantitative characteristics of TDI, but is still vastly inferior to the properties of the AFDtotal parameter itself, because the latter does not require tracking. While standard TDI might be preferable in applications when high anatomical contrast is required, particularly when combined with super-resolution, for voxel-wise quantitation of total tract density (i.e. without tract orientation information) at native resolution, the total AFD maps are preferable to TDI or other related track-count maps. Regardless of the track-count measure, it should be noted that all of these voxel-averaged approaches discard important information that is retained in fibre-specific approaches such as AFD.

In this work we investigate the structural connectivity of the anterior cingulate cortex (ACC) and its link with impaired executive function in children with unilateral cerebral palsy (UCP) due to periventricular white matter lesions. Fifty two children with UCP and 17 children with typical development participated in the study, and underwent diffusion and structural MRI. Five brain regions were identified for their high connectivity with the ACC using diffusion MRI fibre tractography: the superior frontal gyrus, medial orbitofrontal cortex, rostral middle frontal gyrus, precuneus and isthmus cingulate. Structural connectivity was assessed in pathways connecting these regions to the ACC using three diffusion MRI derived measures: fractional anisotropy (FA), mean diffusivity (MD) and apparent fibre density (AFD), and compared between participant groups. Furthermore we investigated correlations of these measures with executive function as assessed by the Flanker task. The ACC-precuneus tract had significantly different MD (p < 0.0001) and AFD (p = 0.0072) between groups, with post-hoc analysis showing significantly increased MD in the right hemisphere of children with left hemiparesis compared with controls. The ACC-superior frontal gyrus tract had significantly different FA (p = 0.0049) and MD (p = 0.0031) between groups. AFD in this tract (contralateral to side of hemiparesis; right hemisphere in controls) showed a significant relationship with Flanker task performance (p = 0.0045, β = -0.5856), suggesting that reduced connectivity correlates with executive dysfunction. Reduced structural integrity of ACC tracts appears to be important in UCP, in particular the connection to the superior frontal gyrus. Although damage to this area is heterogeneous it may be important in early identification of children with impaired executive function.

In brain regions containing crossing fibre bundles, voxel-average diffusion MRI measures such as fractional anisotropy (FA) are difficult to interpret, and lack within-voxel single fibre population specificity. Recent work has focused on the development of more interpretable quantitative measures that can be associated with a specific fibre population within a voxel containing crossing fibres (herein we use fixel to refer to a specific fibre population within a single voxel). Unfortunately, traditional 3D methods for smoothing and cluster-based statistical inference cannot be used for voxel-based analysis of these measures, since the local neighbourhood for smoothing and cluster formation can be ambiguous when adjacent voxels may have different numbers of fixels, or ill-defined when they belong to different tracts. Here we introduce a novel statistical method to perform whole-brain fixel-based analysis called connectivity-based fixel enhancement (CFE). CFE uses probabilistic tractography to identify structurally connected fixels that are likely to share underlying anatomy and pathology. Probabilistic connectivity information is then used for tract-specific smoothing (prior to the statistical analysis) and enhancement of the statistical map (using a threshold-free cluster enhancement-like approach). To investigate the characteristics of the CFE method, we assessed sensitivity and specificity using a large number of combinations of CFE enhancement parameters and smoothing extents, using simulated pathology generated with a range of test-statistic signal-to-noise ratios in five different white matter regions (chosen to cover a broad range of fibre bundle features). The results suggest that CFE input parameters are relatively insensitive to the characteristics of the simulated pathology. We therefore recommend a single set of CFE parameters that should give near optimal results in future studies where the group effect is unknown. We then demonstrate the proposed method by comparing apparent fibre density between motor neurone disease (MND) patients with control subjects. The MND results illustrate the benefit of fixel-specific statistical inference in white matter regions that contain crossing fibres.

Medical images provide two sources of quantitative information to investigate pathology: 1) image intensity, 2) image morphology. Degeneration of axons in Alzheimer's disease (AD) is often detected using surrogate MRI measures for white matter 'integrity' (e.g. Fractional Anisotropy, FA). These measures are based entirely on image intensity and discard useful information provided by morphology. Morphology information is pertinent to investigating AD, as cellular debris is cleared after degeneration and axon loss largely manifests as white matter atrophy. We applied a new diffusion MRI measure called Apparent Fibre Density (AFD) [1] to compare 46 AD patients with 94 age-matched healthy volunteers (Australian Imaging Biomarkers & Lifestyle study). AFD is proportional to the image intensity, which is related to the quantity of intra-axonal water, and is therefore sensitive to the number of axons within a voxel (density). To perform voxel-wise comparisons of AFD, images were non-linearly warped to a common template image. We incorporated morphology information by modulating the AFD in each voxel based on the change to a fibre bundle's cross-sectional area (atrophy). Modulation ensures the AFD in template space encapsulates both sources of axonal loss (axon density as measured by the MRI signal, and volume loss due to atrophy). We investigated whole-brain group AFD differences with and without modulation using novel tractography-based statistics. Significant AFD decreases in AD were observed in white matter bundles known to be involved in language and memory (Fig.1). As shown in columns 1 & 2, the group AFD difference is more extensive when morphology information is included via AFD modulation. Since AFD differences are associated with a voxel and direction (shown by the direction-colour-encoded tractogram in Fig.1) population differences can be attributed to a specific fibre bundle in regions with crossing-fibres (e.g. Arcuate Fasciculus). The absence of differences in fibre density (column 1) within regions previously detected using FA suggests that FA decreases detected in AD are largely caused by atrophy-induced partial volume changes. By incorporating morphology information, AFD is potentially a more sensitive and interpretable biomarker of axon degeneration compared to existing diffusion MRI measures based on image intensity alone.[1] Raffelt et al.2012,59(4):3976–94. Significant AFD decreases in AD vs healthy controls (p<0.05, corrected for multiple comparions). Left: Significant AFD decreases without modulation (a reduction in fibre axon density). Middle: Significant AFD decreases with modulation (axonal loss due to density of fibers within the bundle and reduction in the volume of the bundle itself). Right: Whole-brain population-average tractogram included as an antomical reference image. Tracks are colour-coded by direction (red: left-right, green: anterior-posterior, blue: inferior-superior).