Longitudinal diffusion tensor imaging in dementia with Lewy bodies and Alzheimer's disease.

OBJECTIVE: Changes in the white matter of dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) have been reported using diffusion weighted MRI, though few longitudinal studies have been done. METHODS: We performed diffusion weighted MRI twice, a year apart on 23 AD, 14 DLB, and 32 healthy control subjects. Mean diffusivity (MD) and fractional anisotropy (FA) were calculated. RESULTS: In AD, there were widespread regions where the longitudinal MD increase was greater than in controls, and small areas in the parietal and temporal lobes where it was greater in AD than DLB. In AD, decrease in brain volume correlated with increased MD. There were no significant differences in progression between DLB and controls. CONCLUSIONS: In AD the white matter continues to degenerate during the disease process, whereas in DLB, changes in the white matter structure are a relatively early feature. Different mechanisms are likely to underpin changes in diffusivity.The study was supported by the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge, and the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University. Elijah Mak was in receipt of a Gates Cambridge PhD studentship.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.parkreldis.2016.01.00

Reducing CSF partial volume effects to enhance diffusion tensor imaging metrics of brain microstructure

Technological advances over recent decades now allow for in vivo observation of human brain tissue through the use of neuroimaging methods. While this field originated with techniques capable of capturing macrostructural details of brain anatomy, modern methods such as diffusion tensor imaging (DTI) that are now regularly implemented in research protocols have the ability to characterize brain microstructure. DTI has been used to reveal subtle micro-anatomical abnormalities in the prodromal phase ofº various diseases and also to delineate “normal” age-related changes in brain tissue across the lifespan. Nevertheless, imaging artifact in DTI remains a significant limitation for identifying true neural signatures of disease and brain-behavior relationships. Cerebrospinal fluid (CSF) contamination of brain voxels is a main source of error on DTI scans that causes partial volume effects and reduces the accuracy of tissue characterization. Several methods have been proposed to correct for CSF artifact though many of these methods introduce new limitations that may preclude certain applications. The purpose of this review is to discuss the complexity of signal acquisition as it relates to CSF artifact on DTI scans and review methods of CSF suppression in DTI. We will then discuss a technique that has been recently shown to effectively suppress the CSF signal in DTI data, resulting in fewer errors and improved measurement of brain tissue. This approach and related techniques have the potential to significantly improve our understanding of “normal” brain aging and neuropsychiatric and neurodegenerative diseases. Considerations for next-level applications are discussed

Reducing CSF Partial Volume Effects to Enhance Diffusion Tensor Imaging Metrics of Brain Microstructure

Technological advances over recent decades now allow for in vivo observation of human brain tissue through the use of neuroimaging methods. While this field originated with techniques capable of capturing macrostructural details of brain anatomy, modern methods such as diffusion tensor imaging (DTI) that are now regularly implemented in research protocols have the ability to characterize brain microstructure. DTI has been used to reveal subtle micro-anatomical abnormalities in the prodromal phase ofº various diseases and also to delineate “normal” age-related changes in brain tissue across the lifespan. Nevertheless, imaging artifact in DTI remains a significant limitation for identifying true neural signatures of disease and brain-behavior relationships. Cerebrospinal fluid (CSF) contamination of brain voxels is a main source of error on DTI scans that causes partial volume effects and reduces the accuracy of tissue characterization. Several methods have been proposed to correct for CSF artifact though many of these methods introduce new limitations that may preclude certain applications. The purpose of this review is to discuss the complexity of signal acquisition as it relates to CSF artifact on DTI scans and review methods of CSF suppression in DTI. We will then discuss a technique that has been recently shown to effectively suppress the CSF signal in DTI data, resulting in fewer errors and improved measurement of brain tissue. This approach and related techniques have the potential to significantly improve our understanding of “normal” brain aging and neuropsychiatric and neurodegenerative diseases. Considerations for next-level applications are discussed

Generalized Richardson-Lucy (GRL) for analyzing multi-shell diffusion MRI data

Spherical deconvolution is a widely used approach to quantify fiber orientation distribution from diffusion MRI data. The damped Richardson-Lucy (dRL) is developed to perform robust spherical deconvolution on single shell diffusion MRI data. While the dRL algorithm could in theory be directly applied to multi-shell data, it is not optimised to model the signal from multiple tissue types. In this work, we introduce a new framework based on dRL - dubbed Generalized Richardson Lucy (GRL) - that uses multi-shell data in combination with user-chosen tissue models to disentangle partial volume effects and increase the accuracy in FOD estimation. The optimal weighting of multi-shell data in the fit and the robustness to noise and partial volume effects of GRL was studied with synthetic data. Subsequently, we investigated the performances of GRL in comparison to dRL on a high-resolution diffusion MRI dataset from the Human Connectome Project and on an MRI dataset acquired at 3T on a clinical scanner. The feasibility of including intra-voxel incoherent motion (IVIM) effects in the modelling was studied on a third dataset. Results of simulations show that GRL can robustly disentangle different tissue types at SNR above 20 and improves the angular accuracy of the FOD estimation. On real data, GRL provides signal fraction maps that are physiologically plausible and consistent between datasets. When considering IVIM effects, high blood pseudo-diffusion fraction is observed in the medial temporal lobe and in the sagittal sinus. In comparison to dRL, GRL provides sharper FODs and less spurious peaks in presence of partial volume effects and results in a better tract termination at the grey/white matter interface or at the outer cortical surface. In conclusion, GRL offers a new modular and flexible framework to perform spherical deconvolution of multi-shell data

CSF contamination contributes to apparent microstructural alterations in mild cognitive impairment

Diffusion MRI is used widely to probe microstructural alterations in neurological and psychiatric disease. However, ageing and neurodegeneration are also associated with atrophy, which leads to artefacts through partial volume effects due to cerebrospinal-fluid contamination (CSFC). The aim of this study was to explore the influence of CSFC on apparent microstructural changes in mild cognitive impairment (MCI) at several spatial levels: individually reconstructed tracts; at the level of a whole white matter skeleton (tract-based spatial statistics); and histograms derived from all white matter. 25 individuals with MCI and 20 matched controls underwent diffusion MRI. We corrected for CSFC using a post-acquisition voxel-by-voxel approach of free-water elimination. Tracts varied in their susceptibility to CSFC. The apparent pattern of tract involvement in disease shifted when correction was applied. Both spurious group differences, driven by CSFC, and masking of true differences were observed. Tract-based spatial statistics were found to be robust across much of the skeleton but with some localised CSFC effects. Diffusivity measures were affected disproportionately in MCI, and group differences in fornix microstructure were exaggerated. Group differences in white matter histogram measures were also partly driven by CSFC. For diffusivity measures, up to two thirds of observed group differences were due to CSFC. Our results demonstrate that CSFC has an impact on quantitative differences between MCI and controls. Furthermore, it affects the apparent spatial pattern of white matter involvement. Free-water elimination provides a step towards disentangling intrinsic and volumetric alterations in individuals prone to atrophy

Imaging markers of cerebral small vessel disease

Vascular cognitive impairment (VCI) is the second most common cause of cognitive impairment in the elderly population and it very often co-occurs with impairment resulting from other neurodegenerative pathologies. Cognitive impairment due to vascular pathology is potentially treatable; i.e. the progression could be slowed or even stopped by managing the underlying vascular disease. However, there is no specific treatment available for VCI up to date. One of the main reasons for this is an insufficient understanding of the disease pathophysiology. Cerebral small vessel disease is the primary pathology leading to VCI and therefore its study provides the chance to elucidate the mechanisms leading from vascular pathology to cognitive impairment. Understanding the underlying disease mechanisms is crucial for diagnosis, prevention and managing the disease. For this purpose, markers play an important role, as they indicate which disease processes are at play within the brain. This PhD-work aimed at finding optimal imaging markers for diagnosing cerebral small vessel diseases and estimating the vascular disease burden in the brain. Advances in brain imaging tools, in particular diffusion tensor imaging (DTI), have enabled the exploration of microstructural changes in the human brain, which precede the occurrence of lesions that are visible on conventional MRI. The first project focused on developing and establishing a DTI-based imaging marker for small vessel disease that is quantitative, reliable, and fully automated. This marker (peak width of skeletonized mean diffusivity, PSMD) was then systematically investigated - along with conventional imaging markers - in patients with hereditary and sporadic SVD, memory clinic patients as well as in patients with Alzheimer pathology. The results showed that PSMD outperformed the conventional markers in explaining the cognitive impairment scores. Furthermore, in longitudinal analysis, PSMD was more sensitive to disease related changes than any other imaging markers, which resulted in low sample size estimations for a hypothetical clinical trial. Additionally. PSMD showed very high interscanner reproducibility suggesting that it might be especially useful in multicenter studies. Interestingly, increases in PSMD were mostly linked to vascular but not to neurodegenerative disease. Therefore, PSMD could be a valuable tool to disentangle effects caused by these different pathologies, a common challenge in understanding cognitive impairment. This suggests that the newly established marker PSMD could be easily applied to large samples and may be of great utility for both research studies and clinical use. The second project focused on the evaluation of cortical superficial siderosis (cSS) as a potential new marker for cerebral small vessel diseases. cSS emerged recently as a marker for cerebral amyloid angiopathy (CAA). However, the presence of cSS is associated with many other signs of cSVD, such as cerebral microbleeds (CMB) and white matter hyperintensities (WMH), and therefore its specificity for CAA was questionable. The results of the second project revealed that the distribution patterns and frequency of CMB and WMH overlap between different subtypes of cSVD. This clearly demonstrated that these imaging features have limited discriminative value. More importantly, the presence of cSS was found to be strongly indicative of CAA. To summarize, the key findings reported in this PhD-work have important implications for diagnosing patients with cerebral small vessel disease, disentangling underlying pathologies, as well as for managing and treating the disease. The newly established imaging marker PSMD can be utilized to select the target population for clinical studies and may function as a surrogate marker for treatment effects. PSMD can be further used to identify patients who have a low disease burden as targets for prevention and early treatment

Diffusion imaging markers of cerebral small vessel disease

Diffusion magnetic resonance imaging (MRI) is widely used as a research tool to assess (subtle) alterations of the cerebral white matter. Measures derived from diffusion MRI appear to be valuable markers for cerebral small vessel disease (SVD). However, SVD is frequently co-occurring with Alzheimer’s disease (AD), and disturbed white matter integrity and altered diffusion measures are considered key findings in both conditions. Yet, the contribution of SVD and AD to diffusion alterations is unclear, which hampers the interpretation of research studies in patients with mixed disease, e.g. memory clinic patients. Study 1 of this thesis aimed to clarify the effect of SVD and AD on diffusion measures by including multiple (memory clinic) samples covering the entire spectrum of SVD, mixed disease, and AD. We calculated diffusion measures from diffusion tensor imaging (DTI) and free water imaging. Within each sample of the disease spectrum, we applied simple regression analyses and multivariable random forest analyses between AD biomarkers (amyloid-beta, tau), conventional MRI markers of SVD, and global diffusion measures. Furthermore, we investigated regional associations between tau on positron emission tomography (PET) and diffusion measures in voxel-wise analyses. Our main findings are that conventional MRI markers of SVD were strongly associated with diffusion measures and showed a higher contribution than AD biomarkers in multivariable analyses across all memory clinic samples. Regional analyses between tau PET and diffusion measures were not significant. We conclude that SVD rather than AD determines diffusion alterations in memory clinic patients. Our findings validate diffusion measures as markers for SVD. Study 2 applied diffusion MRI markers to study gait impairment in SVD. Gait impairment is a commonly reported clinical deficit in SVD patients, but the underlying mechanisms are still debated. The proposed mechanisms include SVD-related white matter alterations resulting in impaired supraspinal locomotor control, cognitive deficits (e.g. planning and execution of movements), and factors independent of SVD, such as age-related instability (e.g. joint wear, sarcopenia) and comorbidities (e.g. neurodegenerative pathology). A reason for the lack of knowledge on gait impairment in SVD is that studies in elderly, sporadic SVD patients are typically confounded by effects of normal-aging and age-related comorbidities. Therefore, Study 2 of this thesis aimed to study the effect of pure SVD on gait performance in a relatively young sample of genetically defined SVD patients without age-related confounding. We performed comprehensive gait assessment using an electronic walkway to obtain multiple spatio-temporal gait parameters standardized based on data from healthy controls. Importantly, we tested the association between diffusion MRI markers of SVD-related white matter alterations and gait performance, since (strategic) white matter alterations are discussed as a major cause of gait decline in the elderly. Furthermore, we assessed the relation between cognitive deficits and gait performance. Our main finding is that, despite severe white matter alterations in pure SVD patients, gait performance was relatively preserved. Cognitive deficits in our study participants were not related to gait impairment. Thus, our results query isolated white matter alterations, in the absence of comorbidities, as a main factor of gait impairment in SVD and suggest that their combination with age-related comorbidities and/or normal-aging may play a crucial role in gait decline. In conclusion, diffusion measures are valid MRI markers of SVD-related white matter alterations. They have significant value both in future research on altered white matter and potentially also in the diagnostic work-up of memory clinic patients, to differentiate between vascular and neurodegenerative disease. Researchers may select target populations for clinical trials based on diffusion measures, e.g. to identify patients with a low SVD burden as targets for prevention and early intervention in SVD. Clinicians and researchers should always consider SVD as the origin of diffusion alterations in patients with mixed pathology. The field of application of diffusion measures is wide and may provide new insights into effects of subtle white matter alterations on clinical deficits, as shown in Study 2 on gait impairment in pure SVD. Future studies should investigate measures from advanced diffusion models and diffusion-based brain network analysis, to further elucidate the mechanisms of clinical deficits in SVD patients

Diffusion imaging markers of cerebral small vessel disease

Diffusion magnetic resonance imaging (MRI) is widely used as a research tool to assess (subtle) alterations of the cerebral white matter. Measures derived from diffusion MRI appear to be valuable markers for cerebral small vessel disease (SVD). However, SVD is frequently co-occurring with Alzheimer’s disease (AD), and disturbed white matter integrity and altered diffusion measures are considered key findings in both conditions. Yet, the contribution of SVD and AD to diffusion alterations is unclear, which hampers the interpretation of research studies in patients with mixed disease, e.g. memory clinic patients. Study 1 of this thesis aimed to clarify the effect of SVD and AD on diffusion measures by including multiple (memory clinic) samples covering the entire spectrum of SVD, mixed disease, and AD. We calculated diffusion measures from diffusion tensor imaging (DTI) and free water imaging. Within each sample of the disease spectrum, we applied simple regression analyses and multivariable random forest analyses between AD biomarkers (amyloid-beta, tau), conventional MRI markers of SVD, and global diffusion measures. Furthermore, we investigated regional associations between tau on positron emission tomography (PET) and diffusion measures in voxel-wise analyses. Our main findings are that conventional MRI markers of SVD were strongly associated with diffusion measures and showed a higher contribution than AD biomarkers in multivariable analyses across all memory clinic samples. Regional analyses between tau PET and diffusion measures were not significant. We conclude that SVD rather than AD determines diffusion alterations in memory clinic patients. Our findings validate diffusion measures as markers for SVD. Study 2 applied diffusion MRI markers to study gait impairment in SVD. Gait impairment is a commonly reported clinical deficit in SVD patients, but the underlying mechanisms are still debated. The proposed mechanisms include SVD-related white matter alterations resulting in impaired supraspinal locomotor control, cognitive deficits (e.g. planning and execution of movements), and factors independent of SVD, such as age-related instability (e.g. joint wear, sarcopenia) and comorbidities (e.g. neurodegenerative pathology). A reason for the lack of knowledge on gait impairment in SVD is that studies in elderly, sporadic SVD patients are typically confounded by effects of normal-aging and age-related comorbidities. Therefore, Study 2 of this thesis aimed to study the effect of pure SVD on gait performance in a relatively young sample of genetically defined SVD patients without age-related confounding. We performed comprehensive gait assessment using an electronic walkway to obtain multiple spatio-temporal gait parameters standardized based on data from healthy controls. Importantly, we tested the association between diffusion MRI markers of SVD-related white matter alterations and gait performance, since (strategic) white matter alterations are discussed as a major cause of gait decline in the elderly. Furthermore, we assessed the relation between cognitive deficits and gait performance. Our main finding is that, despite severe white matter alterations in pure SVD patients, gait performance was relatively preserved. Cognitive deficits in our study participants were not related to gait impairment. Thus, our results query isolated white matter alterations, in the absence of comorbidities, as a main factor of gait impairment in SVD and suggest that their combination with age-related comorbidities and/or normal-aging may play a crucial role in gait decline. In conclusion, diffusion measures are valid MRI markers of SVD-related white matter alterations. They have significant value both in future research on altered white matter and potentially also in the diagnostic work-up of memory clinic patients, to differentiate between vascular and neurodegenerative disease. Researchers may select target populations for clinical trials based on diffusion measures, e.g. to identify patients with a low SVD burden as targets for prevention and early intervention in SVD. Clinicians and researchers should always consider SVD as the origin of diffusion alterations in patients with mixed pathology. The field of application of diffusion measures is wide and may provide new insights into effects of subtle white matter alterations on clinical deficits, as shown in Study 2 on gait impairment in pure SVD. Future studies should investigate measures from advanced diffusion models and diffusion-based brain network analysis, to further elucidate the mechanisms of clinical deficits in SVD patients

Imaging markers of cerebral small vessel disease

Vascular cognitive impairment (VCI) is the second most common cause of cognitive impairment in the elderly population and it very often co-occurs with impairment resulting from other neurodegenerative pathologies. Cognitive impairment due to vascular pathology is potentially treatable; i.e. the progression could be slowed or even stopped by managing the underlying vascular disease. However, there is no specific treatment available for VCI up to date. One of the main reasons for this is an insufficient understanding of the disease pathophysiology. Cerebral small vessel disease is the primary pathology leading to VCI and therefore its study provides the chance to elucidate the mechanisms leading from vascular pathology to cognitive impairment. Understanding the underlying disease mechanisms is crucial for diagnosis, prevention and managing the disease. For this purpose, markers play an important role, as they indicate which disease processes are at play within the brain. This PhD-work aimed at finding optimal imaging markers for diagnosing cerebral small vessel diseases and estimating the vascular disease burden in the brain. Advances in brain imaging tools, in particular diffusion tensor imaging (DTI), have enabled the exploration of microstructural changes in the human brain, which precede the occurrence of lesions that are visible on conventional MRI. The first project focused on developing and establishing a DTI-based imaging marker for small vessel disease that is quantitative, reliable, and fully automated. This marker (peak width of skeletonized mean diffusivity, PSMD) was then systematically investigated - along with conventional imaging markers - in patients with hereditary and sporadic SVD, memory clinic patients as well as in patients with Alzheimer pathology. The results showed that PSMD outperformed the conventional markers in explaining the cognitive impairment scores. Furthermore, in longitudinal analysis, PSMD was more sensitive to disease related changes than any other imaging markers, which resulted in low sample size estimations for a hypothetical clinical trial. Additionally. PSMD showed very high interscanner reproducibility suggesting that it might be especially useful in multicenter studies. Interestingly, increases in PSMD were mostly linked to vascular but not to neurodegenerative disease. Therefore, PSMD could be a valuable tool to disentangle effects caused by these different pathologies, a common challenge in understanding cognitive impairment. This suggests that the newly established marker PSMD could be easily applied to large samples and may be of great utility for both research studies and clinical use. The second project focused on the evaluation of cortical superficial siderosis (cSS) as a potential new marker for cerebral small vessel diseases. cSS emerged recently as a marker for cerebral amyloid angiopathy (CAA). However, the presence of cSS is associated with many other signs of cSVD, such as cerebral microbleeds (CMB) and white matter hyperintensities (WMH), and therefore its specificity for CAA was questionable. The results of the second project revealed that the distribution patterns and frequency of CMB and WMH overlap between different subtypes of cSVD. This clearly demonstrated that these imaging features have limited discriminative value. More importantly, the presence of cSS was found to be strongly indicative of CAA. To summarize, the key findings reported in this PhD-work have important implications for diagnosing patients with cerebral small vessel disease, disentangling underlying pathologies, as well as for managing and treating the disease. The newly established imaging marker PSMD can be utilized to select the target population for clinical studies and may function as a surrogate marker for treatment effects. PSMD can be further used to identify patients who have a low disease burden as targets for prevention and early treatment