Investigating white matter fibre density and morphology using fixel-based analysis

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

Connectivity-enhanced diffusion analysis reveals white matter density disruptions in first episode and chronic schizophrenia.

Reduced fractional anisotropy (FA) is a well-established correlate of schizophrenia, but it remains unclear whether these tensor-based differences are the result of axon damage and/or organizational changes and whether the changes are progressive in the adult course of illness. Diffusion MRI data were collected in 81 schizophrenia patients (54 first episode and 27 chronic) and 64 controls. Analysis of FA was combined with "fixel-based" analysis, the latter of which leverages connectivity and crossing-fiber information to assess both fiber bundle density and organizational complexity (i.e., presence and magnitude of off-axis diffusion signal). Compared with controls, patients with schizophrenia displayed clusters of significantly lower FA in the bilateral frontal lobes, right dorsal centrum semiovale, and the left anterior limb of the internal capsule. All FA-based group differences overlapped substantially with regions containing complex fiber architecture. FA within these clusters was positively correlated with principal axis fiber density, but inversely correlated with both secondary/tertiary axis fiber density and voxel-wise fiber complexity. Crossing fiber complexity had the strongest (inverse) association with FA (r = -0.82). When crossing fiber structure was modeled in the MRtrix fixel-based analysis pipeline, patients exhibited significantly lower fiber density compared to controls in the dorsal and posterior corpus callosum (central, postcentral, and forceps major). Findings of lower FA in patients with schizophrenia likely reflect two inversely related signals: reduced density of principal axis fiber tracts and increased off-axis diffusion sources. Whereas the former confirms at least some regions where myelin and or/axon count are lower in schizophrenia, the latter indicates that the FA signal from principal axis fiber coherence is broadly contaminated by macrostructural complexity, and therefore does not necessarily reflect microstructural group differences. These results underline the need to move beyond tensor-based models in favor of acquisition and analysis techniques that can help disambiguate different sources of white matter disruptions associated with schizophrenia

Visual dysfunction predicts cognitive impairment and white matter degeneration in Parkinson's disease

Visual dysfunction predicts dementia in Parkinsons disease (PD), but whether this translates to structural change is not known. We aimed to identify longitudinal white matter changes in patients with Parkinsons disease and low visual function and also in those who developed mild cognitive impairment (MCI). We used fixel-based analysis to examine longitudinal white matter change in PD. Diffusion MRI and clinical assessments were performed in 77 patients at baseline (22 low visual function /55 intact vision; and 13 MCI, 13 MCI converters /51 normal cognition) and 25 controls and again after 18 months. We compared micro-structural changes in fibre density, macro-structural changes in fibre bundle cross-section (FC) and combined fibre density and cross-section across white matter, adjusting for age, gender and intracranial volume. Patients with Parkinsons and visual dysfunction showed worse cognitive performance at follow up and were more likely to develop MCI compared with those with normal vision (p=0.008). Parkinsons with poor visual function showed diffuse micro-structural and macro-structural changes at baseline, whereas those with MCI showed fewer baseline changes. At follow-up, Parkinsons with low visual function showed widespread macrostructural changes, involving the fronto-occipital fasciculi, external capsules, and middle cerebellar peduncles bilaterally. No longitudinal change was seen in baseline MCI or in MCI converters, even when the two groups were combined. Parkinsons patients with poor visual function show increased white matter damage over time, providing further evidence for visual function as a marker of imminent cognitive decline

Recent advances in diffusion neuroimaging: applications in the developing preterm brain

Measures obtained from diffusion-weighted imaging provide objective indices of white matter development and injury in the developing preterm brain. To date, diffusion tensor imaging (DTI) has been used widely, highlighting differences in fractional anisotropy (FA) and mean diffusivity (MD) between preterm infants at term and healthy term controls; altered white matter development associated with a number of perinatal risk factors; and correlations between FA values in the white matter in the neonatal period and subsequent neurodevelopmental outcome. Recent developments, including neurite orientation dispersion and density imaging (NODDI) and fixel-based analysis (FBA), enable white matter microstructure to be assessed in detail. Constrained spherical deconvolution (CSD) enables multiple fibre populations in an imaging voxel to be resolved and allows delineation of fibres that traverse regions of fibre-crossings, such as the arcuate fasciculus and cerebellar-cortical pathways. This review summarises DTI findings in the preterm brain and discusses initial findings in this population using CSD, NODDI, and FBA

Microstructural Alterations in Hippocampal Subfields Mediate Age-Related Memory Decline in Humans.

Aging, even in the absence of clear pathology of dementia, is associated with cognitive decline. Neuroimaging, especially diffusion-weighted imaging, has been highly valuable in understanding some of these changes in live humans, non-invasively. Traditional tensor techniques have revealed that the integrity of the fornix and other white matter tracts significantly deteriorates with age, and that this deterioration is highly correlated with worsening cognitive performance. However, traditional tensor techniques are still not specific enough to indict explicit microstructural features that may be responsible for age-related cognitive decline and cannot be used to effectively study gray matter properties. Here, we sought to determine whether recent advances in diffusion-weighted imaging, including Neurite Orientation Dispersion and Density Imaging (NODDI) and Constrained Spherical Deconvolution, would provide more sensitive measures of age-related changes in the microstructure of the medial temporal lobe. We evaluated these measures in a group of young (ages 20-38 years old) and older (ages 59-84 years old) adults and assessed their relationships with performance on tests of cognition. We found that the fiber density (FD) of the fornix and the neurite density index (NDI) of the fornix, hippocampal subfields (DG/CA3, CA1, and subiculum), and parahippocampal cortex, varied as a function of age in a cross-sectional cohort. Moreover, in the fornix, DG/CA3, and CA1, these changes correlated with memory performance on the Rey Auditory Verbal Learning Test (RAVLT), even after regressing out the effect of age, suggesting that they were capturing neurobiological properties directly related to performance in this task. These measures provide more details regarding age-related neurobiological properties. For example, a change in fiber density could mean a reduction in axonal packing density or myelination, and the increase in NDI observed might be explained by changes in dendritic complexity or even sprouting. These results provide a far more comprehensive view than previously determined on the possible system-wide processes that may be occurring because of healthy aging and demonstrate that advanced diffusion-weighted imaging is evolving into a powerful tool to study more than just white matter properties

Longitudinal thalamic white and gray matter changes associated with visual hallucinations in Parkinson’s disease

Objective: Visual hallucinations are common in Parkinson’s disease (PD) and associated with worse outcomes. Large-scale network imbalance is seen in PD-associated hallucinations, but mechanisms remain unclear. As the thalamus is critical in controlling cortical networks, structural thalamic changes could underlie network dysfunction in PD hallucinations. Methods: We used whole-brain fixel-based analysis and cortical thickness measures to examine longitudinal white and grey matter changes in 76 patients with PD (15 hallucinators, 61 non-hallucinators) and 26 controls at baseline, and after 18 months. We compared white matter and cortical thickness, adjusting for age, gender, time-between-scans and intracranial volume. To assess thalamic changes, we extracted volumes for 50 thalamic subnuclei (25 each hemisphere) and mean fibre crosssection (FC) for white matter tracts originating in each subnucleus and examined longitudinal change in PDhallucinators versus non-hallucinators. Results: PD hallucinators showed white matter changes within the corpus callosum at baseline and extensive posterior tract involvement over time. Less extensive cortical thickness changes were only seen after followup. White matter connections from the right medial mediodorsal magnocellular thalamic nucleus showed reduced FC in PD hallucinators at baseline followed by volume reductions longitudinally. After follow-up, almost all thalamic subnuclei showed tract losses in PD hallucinators compared with non-hallucinators. Interpretation: PD hallucinators show white matter loss particularly in posterior connections and in thalamic nuclei, over time with relatively preserved cortical thickness. The right medial mediodorsal thalamic nucleus shows both connectivity and volume loss in PD hallucinations. Our findings provide mechanistic insights into the drivers of network imbalance in PD hallucinations and potential therapeutic targets

White matter fiber density abnormalities in cognitively normal adults at risk for late-onset Alzheimer's disease

Tau accumulation affecting white matter tracts is an early neuropathological feature of late-onset Alzheimer's disease (LOAD). There is a need to ascertain methods for the detection of early LOAD features to help with disease prevention efforts. The microstructure of these tracts and anatomical brain connectivity can be assessed by analyzing diffusion MRI (dMRI) data. Considering that family history increases the risk of developing LOAD, we explored the microstructure of white matter through dMRI in 23 cognitively normal adults who are offspring of patients with Late-Onset Alzheimer's Disease (O-LOAD) and 22 control subjects (CS) without family history of AD. We also evaluated the relation of white matter microstructure metrics with cortical thickness, volumetry, in vivo amyloid deposition (with the help of PiB positron emission tomography -PiB-PET) and regional brain metabolism (as FDG-PET) measures. Finally we studied the association between cognitive performance and white matter microstructure metrics. O-LOAD exhibited lower fiber density and fractional anisotropy in the posterior portion of the corpus callosum and right fornix when compared to CS. Among O-LOAD, reduced fiber density was associated with lower amyloid deposition in the right hippocampus, and greater cortical thickness in the left precuneus, while higher mean diffusivity was related with greater cortical thickness of the right superior temporal gyrus. Additionally, compromised white matter microstructure was associated with poorer semantic fluency. In conclusion, white matter microstructure metrics may reveal early differences in O-LOAD by virtue of parental history of the disorder, when compared to CS without a family history of LOAD. We demonstrate that these differences are associated with lower fiber density in the posterior portion of the corpus callosum and the right fornix.Fil: Sanchez, Stella Maris. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Instituto de Neurociencias - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Neurociencias; ArgentinaFil: Duarte Abritta, Barbara Micaela. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Instituto de Neurociencias - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Neurociencias; ArgentinaFil: Abulafia, Carolina Andrea. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Instituto de Neurociencias - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Neurociencias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas; ArgentinaFil: de Pino, Gabriela. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Instituto de Neurociencias - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Neurociencias; ArgentinaFil: Bocaccio, Hernan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Instituto de Neurociencias - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Neurociencias; ArgentinaFil: Castro, Mariana Nair. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Fisiología, Biología Molecular y Celular; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Departamento de Salud Mental; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Instituto de Neurociencias - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Neurociencias; ArgentinaFil: Sevlever, Gustavo. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Fonzo, Greg A.. University of Texas at Austin; Estados UnidosFil: Nemeroff, Charles B.. University of Texas at Austin; Estados UnidosFil: Gustafson, Deborah. State University of New York; Estados Unidos. University of Skövde; SueciaFil: Guinjoan, Salvador Martín. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Fisiología, Biología Molecular y Celular; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Departamento de Ciencias Fisiológicas. Laboratorio de Neurofisiología; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Departamento de Salud Mental; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Instituto de Neurociencias - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Neurociencias; ArgentinaFil: Villarreal, Mirta Fabiana. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia. Instituto de Neurociencias - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Neurociencias; Argentin

Impact of b-value on estimates of apparent fibre density

Recent advances in diffusion magnetic resonance imaging (dMRI) analysis techniques have improved our understanding of fibre-specific variations in white matter microstructure. Increasingly, studies are adopting multi-shell dMRI acquisitions to improve the robustness of dMRI-based inferences. However, the impact of b-value choice on the estimation of dMRI measures such as apparent fibre density (AFD) derived from spherical deconvolution is not known. Here, we investigate the impact of b-value sampling scheme on estimates of AFD. First, we performed simulations to assess the correspondence between AFD and simulated intra-axonal signal fraction across multiple b-value sampling schemes. We then studied the impact of sampling scheme on the relationship between AFD and age in a developmental population (n=78) aged 8-18 (mean=12.4, SD=2.9 years) using hierarchical clustering and whole brain fixel-based analyses. Multi-shell dMRI data were collected at 3.0T using ultra-strong gradients (300 mT/m), using 6 diffusion-weighted shells ranging from 0 – 6000 s/mm2. Simulations revealed that the correspondence between estimated AFD and simulated intra-axonal signal fraction was improved with high b-value shells due to increased suppression of the extra-axonal signal. These results were supported by in vivo data, as sensitivity to developmental age-relationships was improved with increasing b-value (b=6000 s/mm2, median R2 = .34; b=4000 s/mm2, median R2 = .29; b=2400 s/mm2, median R2 = .21; b=1200 s/mm2, median R2 = .17) in a tract-specific fashion. Overall, estimates of AFD and age-related microstructural development were better characterised at high diffusion-weightings due to improved correspondence with intra-axonal properties

Timing of selective basal ganglia white matter loss in premanifest Huntington’s disease

OBJECTIVES: To investigate the timeframe prior to symptom onset when cortico-basal ganglia white matter (white matter) loss begins in premanifest Huntington's disease (preHD), and which striatal and thalamic sub-region white matter tracts are most vulnerable. METHODS: We performed fixel-based analysis, which allows resolution of crossing white matter fibres at the voxel level, on diffusion tractography derived white matter tracts of striatal and thalamic sub-regions in two independent cohorts; TrackON-HD, which included 72 preHD (approx. 11 years before disease onset) and 85 controls imaged at three time points over two years; and the HD young adult study (HD-YAS), which included 54 preHD (approx. 25 years before disease onset) and 53 controls, imaged at one time point. Group differences in fibre density and cross section (FDC) were investigated. RESULTS: We found no significant group differences in cortico-basal ganglia sub-region FDC in preHD gene carriers 25 years before onset. In gene carriers 11 years before onset, there were reductions in striatal (limbic and caudal motor) and thalamic (premotor, motor and sensory) FDC at baseline, with no significant change over 2 years. Caudal motor-striatal, pre-motor-thalamic, and primary motor-thalamic FDC at baseline, showed significant correlations with the Unified Huntington's disease rating scale (UHDRS) total motor score (TMS). Limbic cortico-striatal FDC and apathy were also significantly correlated. CONCLUSIONS: Our findings suggest that limbic and motor white matter tracts to the striatum and thalamus are most susceptible to early degeneration in HD but that approximately 25 years from onset, these tracts appear preserved. These findings may have importance in determining the optimum time to initiate future disease modifying therapies in HD

Individualised profiling of white matter organisation in moderate-to-severe traumatic brain injury patients

Background and purpose Approximately 65% of moderate-to-severe traumatic brain injury (m-sTBI) patients present with poor long-term behavioural outcomes, which can significantly impair activities of daily living. Numerous diffusion-weighted MRI studies have linked these poor outcomes to decreased white matter integrity of several commissural tracts, association fibres and projection fibres in the brain. However, most studies have focused on group-based analyses, which are unable to deal with the substantial between-patient heterogeneity in m-sTBI. As a result, there is increasing interest and need in conducting individualised neuroimaging analyses. Materials and methods Here, we generated a detailed subject-specific characterisation of microstructural organisation of white matter tracts in 5 chronic patients with m-sTBI (29 – 49y, 2 females), presented as a proof-of-concept. We developed an imaging analysis framework using fixel-based analysis and TractLearn to determine whether the values of fibre density of white matter tracts at the individual patient level deviate from the healthy control group (n = 12, 8F, Mage = 35.7y, age range 25 – 64y). Results Our individualised analysis revealed unique white matter profiles, confirming the heterogenous nature of m-sTBI and the need of individualised profiles to properly characterise the extent of injury. Future studies incorporating clinical data, as well as utilising larger reference samples and examining the test–retest reliability of the fixel-wise metrics are warranted. Conclusions Individualised profiles may assist clinicians in tracking recovery and planning personalised training programs for chronic m-sTBI patients, which is necessary to achieve optimal behavioural outcomes and improved quality of life