Evolution of white matter damage in amyotrophic lateral sclerosis

Objective To characterize disease evolution in amyotrophic lateral sclerosis using an event‐based model designed to extract temporal information from cross‐sectional data. Conventional methods for understanding mechanisms of rapidly progressive neurodegenerative disorders are limited by the subjectivity inherent in the selection of a limited range of measurements, and the need to acquire longitudinal data. Methods The event‐based model characterizes a disease as a series of events, each comprising a significant change in subject state. The model was applied to data from 154 patients and 128 healthy controls selected from five independent diffusion MRI datasets acquired in four different imaging laboratories between 1999 and 2016. The biomarkers modeled were mean fractional anisotropy values of white matter tracts implicated in amyotrophic lateral sclerosis. The cerebral portion of the corticospinal tract was divided into three segments. Results Application of the model to the pooled datasets revealed that the corticospinal tracts were involved before other white matter tracts. Distal corticospinal tract segments were involved earlier than more proximal (i.e., cephalad) segments. In addition, the model revealed early ordering of fractional anisotropy change in the corpus callosum and subsequently in long association fibers. Interpretation These findings represent data‐driven evidence for early involvement of the corticospinal tracts and body of the corpus callosum in keeping with conventional approaches to image analysis, while providing new evidence to inform directional degeneration of the corticospinal tracts. This data‐driven model provides new insight into the dynamics of neuronal damage in amyotrophic lateral sclerosis

A T1 and DTI fused 3D corpus callosum analysis in MCI subjects with high and low cardiovascular risk profile

abstract: Understanding the extent to which vascular disease and its risk factors are associated with prodromal dementia, notably Alzheimer's disease (AD), may enhance predictive accuracy as well as guide early interventions. One promising avenue to determine this relationship consists of looking for reliable and sensitive in-vivo imaging methods capable of characterizing the subtle brain alterations before the clinical manifestations. However, little is known from the imaging perspective about how risk factors such as vascular disease influence AD progression. Here, for the first time, we apply an innovative T1 and DTI fusion analysis of 3D corpus callosum (CC) on mild cognitive impairment (MCI) populations with different levels of vascular profile, aiming to de-couple the vascular factor in the prodromal AD stage. Our new fusion method successfully increases the detection power for differentiating MCI subjects with high from low vascular risk profiles, as well as from healthy controls. MCI subjects with high and low vascular risk profiles showed differed alteration patterns in the anterior CC, which may help to elucidate the inter-wired relationship between MCI and vascular risk factors.The final version of this article, as published in NeuroImage: Clinical, can be viewed online at: http://www.sciencedirect.com/science/article/pii/S2213158216302649?via%3Dihu

Investigating neurodegeneration after traumatic brain injury: a longitudinal study of axonal injury

Traumatic brain injury (TBI) is associated with neurodegeneration and dementia, with Alzheimer’s disease (AD) reported to be more prevalent post-injury. Traumatic axonal injury (TAI) is suspected to trigger progressive neurodegeneration, with axonal damage leading to proteinopathies of tau and amyloid, also features of AD. However, while axonal injury has been difficult to assess clinically, advances in biomarkers now make this more amenable to quantification. This thesis uses advanced fluid and imaging biomarkers to investigate TAI longitudinally and assess how this relates to neurodegeneration post-TBI. I assess biomarkers in plasma and cerebral microdialysate after acute moderate-severe injuries, relating changes to diffusion tensor imaging (DTI) MRI measures of TAI, brain volumetric change and clinical outcomes. In a separate cohort in the chronic phase I assess how DTI measures predict neurodegeneration in comparison with other possible predictors, and characterise the neurodegenerative consequences of injury in comparison with AD and atrophy in healthy ageing. I found that axonal markers neurofilament light (NfL) and tau were markedly increased in concentration within brain extracellular fluid early post-TBI, correlating closely with plasma levels. Subacute plasma NfL related to DTI measures of TAI, predicted clinical outcomes and white matter neurodegeneration, with peak tau predicting grey matter atrophy. In the chronic phase, I found that DTI predicts the extent and pattern of brain atrophy and explains substantially more variance than clinical severity measures. Comparing post-traumatic atrophy with AD and ageing, I show that post-traumatic atrophy patterns are distinctive and reminiscent of axonal injury spatially. These findings provide evidence of axonal injury as a trigger of progressive neurodegeneration and show this can be sensitively measured with fluid and neuroimaging tools both early and late after single moderate-severe injury. These approaches have the potential to improve clinical diagnosis of TAI and its sequelae, prognostication, and facilitate trials of anti-neurodegeneration treatments.Open Acces

Impact of asthma on the brain: evidence from diffusion MRI, CSF biomarkers and cognitive decline

Chronic systemic inflammation increases the risk of neurodegeneration, but the mechanisms remain unclear. Part of the challenge in reaching a nuanced understanding is the presence of multiple risk factors that interact to potentiate adverse consequences. To address modifiable risk factors and mitigate downstream effects, it is necessary, although difficult, to tease apart the contribution of an individual risk factor by accounting for concurrent factors such as advanced age, cardiovascular risk, and genetic predisposition. Using a case-control design, we investigated the influence of asthma, a highly prevalent chronic inflammatory disease of the airways, on brain health in participants recruited to the Wisconsin Alzheimer's Disease Research Center (31 asthma patients, 186 non-asthma controls, aged 45-90 years, 62.2% female, 92.2% cognitively unimpaired), a sample enriched for parental history of Alzheimer's disease. Asthma status was determined using detailed prescription information. We employed multi-shell diffusion weighted imaging scans and the three-compartment neurite orientation dispersion and density imaging model to assess white and gray matter microstructure. We used cerebrospinal fluid biomarkers to examine evidence of Alzheimer's disease pathology, glial activation, neuroinflammation and neurodegeneration. We evaluated cognitive changes over time using a preclinical Alzheimer cognitive composite. Using permutation analysis of linear models, we examined the moderating influence of asthma on relationships between diffusion imaging metrics, CSF biomarkers, and cognitive decline, controlling for age, sex, and cognitive status. We ran additional models controlling for cardiovascular risk and genetic risk of Alzheimer's disease, defined as a carrier of at least one apolipoprotein E (APOE) ε4 allele. Relative to controls, greater Alzheimer's disease pathology (lower amyloid-β42/amyloid-β40, higher phosphorylated-tau-181) and synaptic degeneration (neurogranin) biomarker concentrations were associated with more adverse white matter metrics (e.g. lower neurite density, higher mean diffusivity) in patients with asthma. Higher concentrations of the pleiotropic cytokine IL-6 and the glial marker S100B were associated with more salubrious white matter metrics in asthma, but not in controls. The adverse effects of age on white matter integrity were accelerated in asthma. Finally, we found evidence that in asthma, relative to controls, deterioration in white and gray matter microstructure was associated with accelerated cognitive decline. Taken together, our findings suggest that asthma accelerates white and gray matter microstructural changes associated with aging and increasing neuropathology, that in turn, are associated with more rapid cognitive decline. Effective asthma control, on the other hand, may be protective and slow progression of cognitive symptoms

Profiles of white matter tract pathology in frontotemporal dementia.

Despite considerable interest in improving clinical and neurobiological characterisation of frontotemporal dementia and in defining the role of brain network disintegration in its pathogenesis, information about white matter pathway alterations in frontotemporal dementia remains limited. Here we investigated white matter tract damage using an unbiased, template-based diffusion tensor imaging (DTI) protocol in a cohort of 27 patients with the behavioral variant of frontotemporal dementia (bvFTD) representing both major genetic and sporadic forms, in relation both to healthy individuals and to patients with Alzheimer's disease. Widespread white matter tract pathology was identified in the bvFTD group compared with both healthy controls and Alzheimer's disease group, with prominent involvement of uncinate fasciculus, cingulum bundle and corpus callosum. Relatively discrete and distinctive white matter profiles were associated with genetic subgroups of bvFTD associated with MAPT and C9ORF72 mutations. Comparing diffusivity metrics, optimal overall separation of the bvFTD group from the healthy control group was signalled using radial diffusivity, whereas optimal overall separation of the bvFTD group from the Alzheimer's disease group was signalled using fractional anisotropy. Comparing white matter changes with regional grey matter atrophy (delineated using voxel based morphometry) in the bvFTD cohort revealed co-localisation between modalities particularly in the anterior temporal lobe, however white matter changes extended widely beyond the zones of grey matter atrophy. Our findings demonstrate a distributed signature of white matter alterations that is likely to be core to the pathophysiology of bvFTD and further suggest that this signature is modulated by underlying molecular pathologies. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc

A comprehensive review of transcranial magnetic stimulation in secondary dementia

Although primary degenerative diseases are the main cause of dementia, a non-negligible proportion of patients is affected by a secondary and potentially treatable cognitive disorder. Therefore, diagnostic tools able to early identify and monitor them and to predict the response to treatment are needed. Transcranial magnetic stimulation (TMS) is a non-invasive neurophysiological technique capable of evaluating in vivo and in "real time" the motor areas, the cortico-spinal tract, and the neurotransmission pathways in several neurological and neuropsychiatric disorders, including cognitive impairment and dementia. While consistent evidence has been accumulated for Alzheimer's disease, other degenerative cognitive disorders, and vascular dementia, to date a comprehensive review of TMS studies available in other secondary dementias is lacking. These conditions include, among others, normal-pressure hydrocephalus, multiple sclerosis, celiac disease and other immunologically mediated diseases, as well as a number of inflammatory, infective, metabolic, toxic, nutritional, endocrine, sleep-related, and rare genetic disorders. Overall, we observed that, while in degenerative dementia neurophysiological alterations might mirror specific, and possibly primary, neuropathological changes (and hence be used as early biomarkers), this pathogenic link appears to be weaker for most secondary forms of dementia, in which neurotransmitter dysfunction is more likely related to a systemic or diffuse neural damage. In these cases, therefore, an effort toward the understanding of pathological mechanisms of cognitive impairment should be made, also by investigating the relationship between functional alterations of brain circuits and the specific mechanisms of neuronal damage triggered by the causative disease. Neurophysiologically, although no distinctive TMS pattern can be identified that might be used to predict the occurrence or progression of cognitive decline in a specific condition, some TMS-associated measures of cortical function and plasticity (such as the short-latency afferent inhibition, the short-interval intracortical inhibition, and the cortical silent period) might add useful information in most of secondary dementia, especially in combination with suggestive clinical features and other diagnostic tests. The possibility to detect dysfunctional cortical circuits, to monitor the disease course, to probe the response to treatment, and to design novel neuromodulatory interventions in secondary dementia still represents a gap in the literature that needs to be explored

Methods for the analysis and characterization of brain morphology from MRI images

Brain magnetic resonance imaging (MRI) is an imaging modality that produces detailed images of the brain without using any ionizing radiation. From a structural MRI scan, it is possible to extract morphological properties of different brain regions, such as their volume and shape. These measures can both allow a better understanding of how the brain changes due to multiple factors (e.g., environmental and pathological) and contribute to the identification of new imaging biomarkers of neurological and psychiatric diseases. The overall goal of the present thesis is to advance the knowledge on how brain MRI image processing can be effectively used to analyze and characterize brain structure. The first two works presented in this thesis are animal studies that primarily aim to use MRI data for analyzing differences between groups of interest. In Paper I, MRI scans from wild and domestic rabbits were processed to identify structural brain differences between these two groups. Domestication was found to significantly reshape brain structure in terms of both regional gray matter volume and white matter integrity. In Paper II, rat brain MRI scans were used to train a brain age prediction model. This model was then tested on both controls and a group of rats that underwent long-term environmental enrichment and dietary restriction. This healthy lifestyle intervention was shown to significantly affect the predicted brain age trajectories by slowing the rats’ aging process compared to controls. Furthermore, brain age predicted on young adult rats was found to have a significant effect on survival. Papers III to V are human studies that propose deep learning-based methods for segmenting brain structures that can be severely affected by neurodegeneration. In particular, Papers III and IV focus on U-Net-based 2D segmentation of the corpus callosum (CC) in multiple sclerosis (MS) patients. In both studies, good segmentation accuracy was obtained and a significant correlation was found between CC area and the patient’s level of cognitive and physical disability. Additionally, in Paper IV, shape analysis of the segmented CC revealed a significant association between disability and both CC thickness and bending angle. Conversely, in Paper V, a novel method for automatic segmentation of the hippocampus is proposed, which consists of embedding a statistical shape prior as context information into a U-Net-based framework. The inclusion of shape information was shown to significantly improve segmentation accuracy when testing the method on a new unseen cohort (i.e., different from the one used for training). Furthermore, good performance was observed across three different diagnostic groups (healthy controls, subjects with mild cognitive impairment and Alzheimer’s patients) that were characterized by different levels of hippocampal atrophy. In summary, the studies presented in this thesis support the great value of MRI image analysis for the advancement of neuroscientific knowledge, and their contribution is mostly two-fold. First, by applying well-established processing methods on datasets that had not yet been explored in the literature, it was possible to characterize specific brain changes and disentangle relevant problems of a clinical or biological nature. Second, a technical contribution is provided by modifying and extending already-existing brain image processing methods to achieve good performance on new datasets

Longitudinal changes in subcortical morphology in Huntington Disease and the relationship with clinical, motor and neurocognitive outcomes

Huntington disease (HD) is a devastating inherited neurodegenerative disease which causes progressive motor, psychiatric and cognitive disturbances as well as neurodegeneration. Mapping the spatiotemporal progression of neuroanatomical change in HD is fundamental to developing biomeasures suitable for prognostication and to aid in development and testing of potential treatments. The neostriatum is central to HD and is known to start to degenerate more than a decade before observable motor onset. It is central to a number of frontostriatal re-entrant circuits which regulate motor control and other forms of behaviour. Changes in striatal morphology can consequently be correlated with observable clinical, motor and cognitive outcomes. However, the neostriatum is merely one part of the "hubs and spokes" of neural circuitry and neurodegeneration in HD also occurs in other areas of the brain. The hippocampus has been less fully studied in HD and has implications for neural plasticity, particularly given neurogenesis continues into adulthood in this region. Furthermore, thickness of the corpus callosum may be used as a proxy for cortical changes that are known to occur later in HD. This thesis uses data from the IMAGE-HD study to characterise neuroanatomical changes in HD, with the aim to improve knowledge of HD-associated neurodegenerative pathways and to provide further insight to relate quantitative measures of morphology to function. A number of analytical techniques are used to investigate changes in size and shape of neuroanatomical structures and to correlate these with clinical, motor and neurocognitive outcomes. This thesis demonstrates that shape changes in the neostriatum in HD and pre-symptomatic HD correlate with functional measures subserved by corticostriatal circuits, and identifies significant longitudinal differences in putaminal and caudate shape. Only the putamen has a significant group by time interaction, suggesting that it is a better marker for longitudinal change in pre-symptomatic HD and HD. While HD has its most marked effects on the neostriatum, it also has more subtle effects on other subcortical areas. This thesis shows surface contraction occurring in HD in the hippocampus compared to controls, although without correlations to functional measures or significant longitudinal change. Unlike these "hubs", this thesis finds that the large "spoke" of the corpus callosum is not impacted early in the HD process but becomes affected after symptom onset, highlighting the spread of neurodegeneration in other structures. This is the first time that such robust statistical analysis of longitudinal shape change in HD has been able to be performed and shows the neostriatum, particularly the putamen, as a potentially useful structural basis for the characterisation of an endophenotype of HD. This thesis provides a more comprehensive picture of neuroanatomical change in HD by using a "hubs and spokes" approach to analyse key areas, increasing knowledge about neurodegenerative pathways and functional outcomes

Diagnosis of Neurogenetic Disorders: Contribution of Next Generation Sequencing and Deep Phenotyping

The contribution of genomic variants to the aetiopathogenesis of both paediatric and adult neurological disease is being increasingly recognized. The use of next-generation sequencing has led to the discovery of novel neurodevelopmental disorders, as exemplified by the deciphering developmental disorders (DDD) study, and provided insight into the aetiopathogenesis of common adult neurological diseases. Despite these advances, many challenges remain. Correctly classifying the pathogenicity of genomic variants from amongst the large number of variants identified by next-generation sequencing is recognized as perhaps the major challenge facing the field. Deep phenotyping (e.g., imaging, movement analysis) techniques can aid variant interpretation by correctly classifying individuals as affected or unaffected for segregation studies. The lack of information on the clinical phenotype of novel genetic subtypes of neurological disease creates limitations for genetic counselling. Both deep phenotyping and qualitative studies can capture the clinical and patient’s perspective on a disease and provide valuable information. This Special Issue aims to highlight how next-generation sequencing techniques have revolutionised our understanding of the aetiology of brain disease and describe the contribution of deep phenotyping studies to a variant interpretation and understanding of natural history