Physiological, behavioral and subjective sadness reactivity in frontotemporal dementia subtypes.

Frontotemporal dementia (FTD), a neurodegenerative disease broadly characterized by socioemotional impairments, includes three clinical subtypes: behavioral variant FTD (bvFTD), semantic variant primary progressive aphasia (svPPA) and non-fluent variant primary progressive aphasia (nfvPPA). Emerging evidence has shown emotional reactivity impairments in bvFTD and svPPA, whereas emotional reactivity in nfvPPA is far less studied. In 105 patients with FTD (49 bvFTD, 31 svPPA and 25 nfvPPA) and 27 healthy controls, we examined three aspects of emotional reactivity (physiology, facial behavior and subjective experience) in response to a sad film. In a subset of the sample, we also examined the neural correlates of diminished aspects of reactivity using voxel-based morphometry. Results indicated that all three subtypes of FTD showed diminished physiological responding in respiration rate and diastolic blood pressure; patients with bvFTD and svPPA also showed diminished subjective experience, and no subtypes showed diminished facial behavior. Moreover, there were differences among the clinical subtypes in brain regions where smaller volumes were associated with diminished sadness reactivity. These results show that emotion impairments extend to sadness reactivity in FTD and underscore the importance of considering different aspects of sadness reactivity in multiple clinical subtypes for characterizing emotional deficits and associated neurodegeneration in FTD

Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia

Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying high-resolution images, potentially limiting the accuracy of EBM. Therefore, we propose a novel method that exploits high-dimensional voxel-wise imaging biomarkers: n-dimensional discriminative EBM (nDEBM). nDEBM is based on an insight that mixture modeling, which is a key element of conventional EBMs, can be replaced by a more scalable semi-supervised support vector machine (SVM) approach. This SVM is used to estimate the degree of abnormality of each region which is then used to obtain subject-specific disease progression patterns. These patterns are in turn used for estimating the mean ordering by fitting a generalized Mallows model. In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers' Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions. SImBioTE trains variational auto-encoders (VAE) in different brain regions independently to simulate images at varying stages of disease progression. We also validate nDEBM clinically using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In both experiments, nDEBM using high-dimensional features gave better performance than state-of-the-art EBM methods using regional volume biomarkers. This suggests that nDEBM is a promising approach for disease progression modeling.Comment: IPMI 201

Relationship between cerebrospinal fluid neurodegeneration biomarkers and temporal brain atrophy in cognitively healthy older adults

It is unclear whether cerebrospinal fluid (CSF) biomarkers of neurodegeneration predict brain atrophy in cognitively healthy older adults, whether these associations can be explained by phosphorylated tau181 (p-tau) and the 42 amino acid form of amyloid-ꞵ (Aꞵ42) biomarkers, and which neural substrates may drive these associations. We addressed these questions in two samples of cognitively healthy older adults who underwent longitudinal structural MRI up to 7 years and had baseline CSF levels of heart-type fatty-acid binding protein [FABP3], total-tau, neurogranin, and neurofilament light [NFL] (n=189, scans=721). The results showed that NFL, total-tau, and FABP3 predicted entorhinal thinning and hippocampal atrophy. Brain atrophy was not moderated by Aꞵ42 and the associations between NFL and FABP3 with brain atrophy were independent of p-tau. The spatial pattern of cortical atrophy associated with the biomarkers overlapped with neurogenetic profiles associated with expression in the axonal (total-tau, NFL) and dendritic (neurogranin) components. CSF biomarkers of neurodegeneration are useful for predicting specific features of brain atrophy in older adults, independently of amyloid and tau pathology biomarkers

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

Detection of emerging neurodegeneration using Bayesian linear mixed-effect modeling

Early detection of neurodegeneration, and prediction of when neurodegenerative diseases will lead to symptoms, are critical for developing and initiating disease modifying treatments for these disorders. While each neurodegenerative disease has a typical pattern of early changes in the brain, these disorders are heterogeneous, and early manifestations can vary greatly across people. Methods for detecting emerging neurodegeneration in any part of the brain are therefore needed. Prior publications have described the use of Bayesian linear mixed-effects (BLME) modeling for characterizing the trajectory of change across the brain in healthy controls and patients with neurodegenerative disease. Here, we use an extension of such a model to detect emerging neurodegeneration in cognitively healthy individuals at risk for dementia. We use BLME to quantify individualized rates of volume loss across the cerebral cortex from the first two MRIs in each person and then extend the BLME model to predict future values for each voxel. We then compare observed values at subsequent time points with the values that were expected from the initial rates of change and identify voxels that are lower than the expected values, indicating accelerated volume loss and neurodegeneration. We apply the model to longitudinal imaging data from cognitively normal participants in the Alzheimer\u27s Disease Neuroimaging Initiative (ADNI), some of whom subsequently developed dementia, and two cognitively normal cases who developed pathology-proven frontotemporal lobar degeneration (FTLD). These analyses identified regions of accelerated volume loss prior to or accompanying the earliest symptoms, and expanding across the brain over time, in all cases. The changes were detected in regions that are typical for the likely diseases affecting each patient, including medial temporal regions in patients at risk for Alzheimer\u27s disease, and insular, frontal, and/or anterior/inferior temporal regions in patients with likely or proven FTLD. In the cases where detailed histories were available, the first regions identified were consistent with early symptoms. Furthermore, survival analysis in the ADNI cases demonstrated that the rate of spread of accelerated volume loss across the brain was a statistically significant predictor of time to conversion to dementia. This method for detection of neurodegeneration is a potentially promising approach for identifying early changes due to a variety of diseases, without prior assumptions about what regions are most likely to be affected first in an individual

Reliability of spinal cord measures based on synthetic T1_{1}-weighted MRI derived from multiparametric mapping (MPM)

Short MRI acquisition time, high signal-to-noise ratio, and high reliability are crucial for image quality when scanning healthy volunteers and patients. Cross-sectional cervical cord area (CSA) has been suggested as a marker of neurodegeneration and potential outcome measure in clinical trials and is conventionally measured on T$_{1}$-weigthed 3D Magnetization Prepared Rapid Acquisition Gradient-Echo (MPRAGE) images. This study aims to reduce the acquisition time for the comprehensive assessment of the spinal cord, which is typically based on MPRAGE for morphometry and multi-parameter mapping (MPM) for microstructure. The MPRAGE is replaced by a synthetic T$_{1}$-w MRI (synT$_{1}$-w) estimated from the MPM, in order to measure CSA. SynT$_{1}$-w images were reconstructed using the MPRAGE signal equation based on quantitative maps of proton density (PD), longitudinal (R$_{1}$) and effective transverse (R$_{2}$*) relaxation rates. The reliability of CSA measurements from synT$_{1}$-w images was determined within a multi-center test-retest study format and validated against acquired MPRAGE scans by assessing the agreement between both methods. The response to pathological changes was tested by longitudinally measuring spinal cord atrophy following spinal cord injury (SCI) for synT$_{1}$-w and MPRAGE using linear mixed effect models. CSA measurements based on the synT$_{1}$-w MRI showed high intra-site (Coefficient of variation [CoV]: 1.43% to 2.71%) and inter-site repeatability (CoV: 2.90% to 5.76%), and only a minor deviation of -1.65 mm$^{2}$ compared to MPRAGE. Crucially, by assessing atrophy rates and by comparing SCI patients with healthy controls longitudinally, differences between synT$_{1}$-w and MPRAGE were negligible. These results demonstrate that reliable estimates of CSA can be obtained from synT$_{1}$-w images, thereby reducing scan time significantly

Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer's disease

Neurofilament light chain (NfL) is a promising fluid biomarker of disease progression for various cerebral proteopathies. Here we leverage the unique characteristics of the Dominantly Inherited Alzheimer Network and ultrasensitive immunoassay technology to demonstrate that NfL levels in the cerebrospinal fluid (n = 187) and serum (n = 405) are correlated with one another and are elevated at the presymptomatic stages of familial Alzheimer's disease. Longitudinal, within-person analysis of serum NfL dynamics (n = 196) confirmed this elevation and further revealed that the rate of change of serum NfL could discriminate mutation carriers from non-mutation carriers almost a decade earlier than cross-sectional absolute NfL levels (that is, 16.2 versus 6.8 years before the estimated symptom onset). Serum NfL rate of change peaked in participants converting from the presymptomatic to the symptomatic stage and was associated with cortical thinning assessed by magnetic resonance imaging, but less so with amyloid-β deposition or glucose metabolism (assessed by positron emission tomography). Serum NfL was predictive for both the rate of cortical thinning and cognitive changes assessed by the Mini-Mental State Examination and Logical Memory test. Thus, NfL dynamics in serum predict disease progression and brain neurodegeneration at the early presymptomatic stages of familial Alzheimer's disease, which supports its potential utility as a clinically useful biomarker