Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC

Location of pathogenic variants in PSEN1 impacts progression of cognitive, clinical, and neurodegenerative measures in autosomal-dominant Alzheimer's disease

Although pathogenic variants in PSEN1 leading to autosomal-dominant Alzheimer disease (ADAD) are highly penetrant, substantial interindividual variability in the rates of cognitive decline and biomarker change are observed in ADAD. We hypothesized that this interindividual variability may be associated with the location of the pathogenic variant within PSEN1. PSEN1 pathogenic variant carriers participating in the Dominantly Inherited Alzheimer Network (DIAN) observational study were grouped based on whether the underlying variant affects a transmembrane (TM) or cytoplasmic (CY) protein domain within PSEN1. CY and TM carriers and variant non-carriers (NC) who completed clinical evaluation, multimodal neuroimaging, and lumbar puncture for collection of cerebrospinal fluid (CSF) as part of their participation in DIAN were included in this study. Linear mixed effects models were used to determine differences in clinical, cognitive, and biomarker measures between the NC, TM, and CY groups. While both the CY and TM groups were found to have similarly elevated Aβ compared to NC, TM carriers had greater cognitive impairment, smaller hippocampal volume, and elevated phosphorylated tau levels across the spectrum of pre-symptomatic and symptomatic phases of disease as compared to CY, using both cross-sectional and longitudinal data. As distinct portions of PSEN1 are differentially involved in APP processing by γ-secretase and the generation of toxic β-amyloid species, these results have important implications for understanding the pathobiology of ADAD and accounting for a substantial portion of the interindividual heterogeneity in ongoing ADAD clinical trials

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

Network anatomy in logopenic variant of primary progressive aphasia

The logopenic variant of primary progressive aphasia (lvPPA) is a neurodegenerative syndrome characterized linguistically by gradual loss of repetition and naming skills resulting from left posterior temporal and inferior parietal atrophy. Here, we sought to identify which specific cortical loci are initially targeted by the disease (epicenters) and investigate whether atrophy spreads through predetermined networks. First, we used cross-sectional structural MRI data from individuals with lvPPA to define putative disease epicenters using a surface-based approach paired with an anatomically fine-grained parcellation of the cortical surface (i.e., HCP-MMP1.0 atlas). Second, we combined cross-sectional functional MRI data from healthy controls and longitudinal structural MRI data from individuals with lvPPA to derive the epicenter-seeded resting-state networks most relevant to lvPPA symptomatology and ascertain whether functional connectivity in these networks predicts longitudinal atrophy spread in lvPPA. Our results show that two partially distinct brain networks anchored to the left anterior angular and posterior superior temporal gyri epicenters were preferentially associated with sentence repetition and naming skills in lvPPA. Critically, the strength of connectivity within these two networks in the neurologically-intact brain significantly predicted longitudinal atrophy progression in lvPPA. Taken together, our findings indicate that atrophy progression in lvPPA, starting from inferior parietal and temporoparietal junction regions, predominantly follows at least two partially nonoverlapping pathways, which may influence the heterogeneity in clinical presentation and prognosis

Data-driven staging of genetic frontotemporal dementia using multi-modal MRI

Frontotemporal dementia in genetic forms is highly heterogeneous and begins many years to prior symptom onset, complicating disease understanding and treatment development. Unifying methods to stage the disease during both the presymptomatic and symptomatic phases are needed for the development of clinical trials outcomes. Here we used the contrastive trajectory inference (cTI), an unsupervised machine learning algorithm that analyzes temporal patterns in high-dimensional large-scale population datasets to obtain individual scores of disease stage. We used cross-sectional MRI data (gray matter density, T1/T2 ratio as a proxy for myelin content, resting-state functional amplitude, gray matter fractional anisotropy, and mean diffusivity) from 383 gene carriers (269 presymptomatic and 115 symptomatic) and a control group of 253 noncarriers in the Genetic Frontotemporal Dementia Initiative. We compared the cTI-obtained disease scores to the estimated years to onset (age—mean age of onset in relatives), clinical, and neuropsychological test scores. The cTI based disease scores were correlated with all clinical and neuropsychological tests (measuring behavioral symptoms, attention, memory, language, and executive functions), with the highest contribution coming from mean diffusivity. Mean cTI scores were higher in the presymptomatic carriers than controls, indicating that the method may capture subtle pre-dementia cerebral changes, although this change was not replicated in a subset of subjects with complete data. This study provides a proof of concept that cTI can identify data-driven disease stages in a heterogeneous sample combining different mutations and disease stages of genetic FTD using only MRI metrics

Data-driven staging of genetic frontotemporal dementia using multi-modal MRI

Frontotemporal dementia in genetic forms is highly heterogeneous and begins many years to prior symptom onset, complicating disease understanding and treatment development. Unifying methods to stage the disease during both the presymptomatic and symptomatic phases are needed for the development of clinical trials outcomes. Here we used the contrastive trajectory inference (cTI), an unsupervised machine learning algorithm that analyzes temporal patterns in high-dimensional large-scale population datasets to obtain individual scores of disease stage. We used cross-sectional MRI data (gray matter density, T1/T2 ratio as a proxy for myelin content, resting-state functional amplitude, gray matter fractional anisotropy, and mean diffusivity) from 383 gene carriers (269 presymptomatic and 115 symptomatic) and a control group of 253 noncarriers in the Genetic Frontotemporal Dementia Initiative. We compared the cTI-obtained disease scores to the estimated years to onset (age-mean age of onset in relatives), clinical, and neuropsychological test scores. The cTI based disease scores were correlated with all clinical and neuropsychological tests (measuring behavioral symptoms, attention, memory, language, and executive functions), with the highest contribution coming from mean diffusivity. Mean cTI scores were higher in the presymptomatic carriers than controls, indicating that the method may capture subtle pre-dementia cerebral changes, although this change was not replicated in a subset of subjects with complete data. This study provides a proof of concept that cTI can identify data-driven disease stages in a heterogeneous sample combining different mutations and disease stages of genetic FTD using only MRI metrics.© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC

Biomarker profiling beyond amyloid and tau: cerebrospinal fluid markers, hippocampal atrophy, and memory change in cognitively unimpaired older adults

Brain changes occurring in aging can be indexed by biomarkers. We used cluster analysis to identify subgroups of cognitively unimpaired individuals (n ¼ 99, 64e93 years) with different profiles of the cerebrospinal fluid biomarkers beta amyloid 1e42 (Ab42), phosphorylated tau (P-tau), total tau, chitinase-3-like protein 1 (YKL-40), fatty acid binding protein 3 (FABP3), and neurofilament light (NFL). Hippocampal volume and memory were assessed across multiple follow-up examinations covering up to 6.8 years. Clustering revealed one group (39%) with more pathological concentrations of all biomarkers, which could further be divided into one group (20%) characterized by tauopathy and high FABP3 and one (19%) by brain b-amyloidosis, high NFL, and slightly higher YKL-40. The clustering approach clearly outperformed classification based on Ab42 and P-tau alone in prediction of memory decline, with the individuals with most tauopathy and FABP3 showing more memory decline, but not more hippocampal volume change. The results demonstrate that older adults can be classified based on biomarkers beyond amyloid and tau, with improved prediction of memory decline

Clinical and neuroimaging prognostic markers in Alzheimer's Disease and Lewy Body Dementia: The role of muscle status and nutrition

Alzheimer's Disease and Lewy body dementia are the two most common neurodegenerative dementias. They have a progressive course with devastating consequences for the people living with these diseases and their families, but there are large individual variations. Finding early markers and markers of progression and prognosis could promote actions to improve the quality of life of the people affected with these diseases. Nutrition and muscle status are closely related and have systemic functions and interactions that affect the brain. This thesis describes the role of nutritional and muscle status biomarkers in the prognosis of people diagnosed with mild Alzheimer's disease, Lewy body dementia, and mild subjective cognitive decline. Methods For the aim of this thesis, I used data from 2 community-based prospective Norwegian multicenter cohort studies: DemVest (The Dementia Study of Western Norway) and DDI (Dementia Disease Initiation). In DemVest, patients with mild dementia, defined as a Mini-Mental Status Examination (MMSE) score; equal or higher to 20 or Clinical Dementia Rating (CDR) global score equal to 1, with different types of dementia, were included. The DDI study was designed to investigate early cognitive impairment and dementia markers. DDI participants included in this thesis were those classified as having Subjective cognitive decline (SCD) according to the SCD-I framework. Comprehensive clinical assessments, including measures of cognition, daily functioning and anthropometric measurement, blood samples, and brain MRI were performed in both studies. Brain morphology was studied using FreeSurfer segmentation and muscle morphology using slice O-Matic software. Results This thesis findings first indicate that nutritional status has an essential role in the 5-year prognosis of people living with dementia in the capacity to perform daily life activities and mortality. Second, the quality of the muscle, here the muscle of the tongue, and its amount of fat infiltration were associated with malnutrition onset in people with dementia. Finally, in patients with SCD, muscle function measured with the timed up and go test (TUG) was associated with cognitive decline. TUG, in addition, was associated with cortical thickness in areas related with cognitive and motor functioning. Conclusion Nutritional and muscular status predict prognosis in people with SCD and with dementia. These findings suggest that interventions focused on these areas may improve outcomes such as cognition, function, and survival in these groups

The aging frontal lobe in health and disease : a structural magnetic resonance imaging study

Cortical and subcortical regions of the brain decrease in volume in normal as well as pathological aging. Previous studies indicate that certain parts of the brain, like the prefrontal cortex, may be particularly vulnerable to age-related processes which are manifested by significant volume loss in this region. Cortical volume loss may be further enhanced by different kinds of pathology in the brain. The purpose of this study was to further investigate regional volumetric changes of the frontal lobe in normal aging and in aging patients with dementia. In study I-III patients with frontotemporal lobar degeneration (FTLD), Alzheimer’s disease (AD) and healthy controls are investigated. Cortical atrophy is related to clinical symptoms (study I), discussed in relation to gross morphology and cytoarchitecture (study II), and compared with the atrophy in the hippocampus (study III). In study IV a large number of normal elderly participants are investigated. Age-related volume loss in the limbic system (the dorsal anterior cingulate cortex and the hippocampus) is compared with atrophy of a region of the prefrontal cortex (the orbitofrontal cortex). Volumetric data of frontal and temporal cortical regions and the hippocampus was acquired by manual delineation on structural magnetic resonance images. Results of study I and III reveal that the clinical symptoms displayed by the subtypes of FTLD are commonly reflected in a specific pattern of atrophy in frontotemporal cortices as well as in the hippocampus. Study II suggests that the surface morphology of sulci and gyri may be unreliable landmarks for cyto-architectonic regions of the frontal cortex. Study IV finally indicates that a common characteristic of limbic regions may be that age-related volume loss is delayed in comparison to regions of the prefrontal cortex. Results also suggest that the dorsal anterior cingulate is more resistant to age-related volume loss than hippocampus, which implies that age-related volume loss occurs at different rates for different regions also within the limbic system