Multi-scale graph-based grading for Alzheimer's disease prediction

[EN] The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer¿s disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.This work benefited from the support of the project DeepvolBrain of the French National Research Agency (ANR-18-CE450013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project. Moreover, we thank the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and the CNRS. Finally, this work was also supported by the NIH grants R01-NS094456 and U01-NS106845. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01-AG024904) and by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Biogen; Bristol-Myes Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffman-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Pharmaceutical Research & Development LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org).The grantee organization is the Northern California Institute of Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.Hett, K.; Ta, V.; Oguz, I.; Manjón Herrera, JV.; Coupé, P.; Alzheimers Disease Neuroimaging Initiative (2021). Multi-scale graph-based grading for Alzheimer's disease prediction. Medical Image Analysis. 67:1-13. https://doi.org/10.1016/j.media.2020.1018501136

Simultaneous segmentation and grading of anatomical structures for patient's classification: application to Alzheimer's Disease

Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI).In this paper, we propose an innovative approach to robustly and accurately detect Alzheimer's disease (AD) based on the distinction of specific atrophic patterns of anatomical structures such as hippocampus (HC) and entorhinal cortex (EC). The proposed method simultaneously performs segmentation and grading of structures to efficiently capture the anatomical alterations caused by AD. Known as SNIPE (Scoring by Non-local Image Patch Estimator), the novel proposed grading measure is based on a nonlocal patch-based frame-work and estimates the similarity of the patch surrounding the voxel under study with all the patches present in different training populations. In this study, the training library was composed of two populations: 50 cognitively normal subjects (CN) and 50 patients with AD, randomly selected from the ADNI database. During our experiments, the classification accuracy of patients (CN vs. AD) using several biomarkers was compared: HC and EC volumes, the grade of these structures and finally the combination of their volume and their grade. Tests were completed in a leave-one-out framework using discriminant analysis. First, we showed that biomarkers based on HC provide better classification accuracy than biomarkers based on EC. Second, we demonstrated that structure grading is a more powerful measure than structure volume to distinguish both populations with a classification accuracy of 90%. Finally, by adding the ages of subjects in order to better separate age-related structural changes from disease-related anatomical alterations, SNIPE obtained a classification accuracy of 93%Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Insti- tute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30AG010129, K01 AG030514, and the Dana Foundation.Coupé, P.; Eskildsen, SF.; Manjón Herrera, JV.; Fonov, VS.; Collins, DL.; Alzheimer's Dis Neuroimaging (2012). Simultaneous segmentation and grading of anatomical structures for patient's classification: application to Alzheimer's Disease. NeuroImage. 59(4):3736-3747. https://doi.org/10.1016/j.neuroimage.2011.10.080S3736374759

Structural MRI used to predict conversion from mild cognitive impairment to Alzheimer's disease at different rates

BACKGROUND: Early detection of individuals at risk for converting to Alzheimer’s disease (AD) can potentially lead to more efficient treatment and better disease management. A well-known approach has aimed at identifying individuals at the prodromal stage of dementia; namely, Mild Cognitive Impairment (MCI). Past studies showed that MCI subjects often have accelerated rates of conversion to AD, or to other types of dementia compared to healthy controls (HCs). However, with more investigations of the MCI population, it became evident that a high level of heterogeneity exists within this group: many remain clinically stable even after 10 years. MCI subtypes defined by the conventional classification criteria showed inconsistent results for determining an individual's risk of AD. As another approach, neuroimaging techniques such as magnetic resonance imaging (MRI) are able to successfully identify neurological changes during early AD. MRI markers including morphological, connectional and abnormal signal patterns in the brain have been shown to have good sensitivity for classifying AD. Based on these findings, recent studies started implementing these imaging markers to create computer-aided classification models for predicting the risk of conversion to AD. Most of these studies enrolled MCI subjects who remained stable or converted to AD within 3 years, and generated computer-aided classification models to predict conversion using various imaging markers and clinical data. To our knowledge, no classification models proposed achieved an accuracy of higher than 80% for predicting MCI-AD conversion earlier than 3 years with only using structural MRI features. In this paper, we tested the prediction range beyond 3 years, and suggested new candidate imaging measures for earlier prediction. METHODS: The subjects included in the current study are n=51 MCI non-converter, n=157 MCI converter (115 fast converters and 42 slow converters) and n=38 AD, selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Using subjects' baseline T1-weighted MRI scans, we combined conventional morphometric measures (e.g. cortical thickness, surface area, volume, etc.) with novel intensity measures to differentiate MCI converters from non-converters. We additionally applied a machine learning approach to classify MCI subgroups by combining features in multiple measurement domains. RESULTS: Based on group comparison using independent t-test, we found that while MCI fast converters (conversion within 0-2 years) were highly distinct from MCI non-converters across many cortical and subcortical regions, MCI slow converters (conversion within 3-5 years) demonstrated more focal differences from MCI non-converters mainly in the temporal regions and hippocampal subfields. We identified unique imaging features associated with each converter group and had improved classification performance on both MCI converter groups by adding those markers. The best performing classifiers combined conventional imaging features, novel intensity features and neuropsychological features. For our best performing classification models, we were able to classify MCI fast converters (0-2 years) from non-converter with an average accuracy of 86.1%, sensitivity of 85.5%, and specificity of 89.8%, and to classify MCI slow converters (3-5 years) from non-converters with an accuracy of 80.5%, sensitivity of 75.7%, and specificity of 82.3%. CONCLUSION: Our results demonstrated the potential of the suggested approach for predicting the conversion from MCI to AD at an even earlier time point (3-5 years) before the onset of AD. The combination of standard morphometric features and proposed novel intensity features improved the sensitivity of using T1-weighted MRI for describing the heterogeneity between MCI subgroups

Neuronal changes in the hippocampus of post-stroke survivors

PhD ThesisBackground: Delayed post-stroke dementia (PSD) affects up to 50% of all stroke survivors, developing months or years after the initial stroke. However, the underlying mechanisms which cause PSD are unclear. Hippocampal atrophy is associated with PSD and vascular dementia, and hippocampal neurons are known to be particularly vulnerable in stroke and cerebrovascular disease. This work aimed to identify neuropathological characteristics and mechanisms contributing to cognitive decline in post-stroke survivors, focusing on the involvement of regional specific hippocampal neurons. Methods: Post-mortem brain tissue from the prospective CogFAST study was analyzed to compare pathological changes in stroke survivors who developed PSD with those who maintained normal cognitive function (PSND). Tissue from elderly controls and pathologically defined dementia groups lzheimer’s disease ( D) vascular dementia (VaD), mixed AD with VaD (MD); were also analysed for comparison with different disease aetiologies. Histological and immunohistochemical staining with quantitative image analysis and 3D morphometric analysis was carried out in paraffin-embedded sections, and protein immunoblotting was used in frozen hippocampal tissue. Key findings: Neuronal volumes in hippocampal subfields CA1-4 were reduced in PSD, VaD and AD subjects compared to elderly controls and PSND. Neuronal volume was also related to post-stroke cognitive function. There were no differences in dendritic length-density, hippocampal myelin loss, or autophagy markers between PSD and PSND. However, neuronal volumes were related to hippocampal tau pathology burden, reactive astrocyte density and myelin density in the alveus. Interestingly, the PSND subjects had greater burden of hippocampal amyloid-β than PSD. There were no quantitative differences in markers for astrocytes or microglia between the post-stroke groups. Conclusion: These findings suggest that neuronal volume loss is associated with post- stroke and ageing-related dementia. There were no relationships between the observed neuronal changes and AD pathology in stroke survivors, suggesting an important role for cerebrovascular disease processes.Medical Research Counci

Development of Anatomical and Functional Magnetic Resonance Imaging Measures of Alzheimer Disease

Alzheimer disease is considered to be a progressive neurodegenerative condition, clinically characterized by cognitive dysfunction and memory impairments. Incorporating imaging biomarkers in the early diagnosis and monitoring of disease progression is increasingly important in the evaluation of novel treatments. The purpose of the work in this thesis was to develop and evaluate novel structural and functional biomarkers of disease to improve Alzheimer disease diagnosis and treatment monitoring. Our overarching hypothesis is that magnetic resonance imaging methods that sensitively measure brain structure and functional impairment have the potential to identify people with Alzheimer’s disease prior to the onset of cognitive decline. Since the hippocampus is considered to be one of the first brain structures affected by Alzheimer disease, in our first study a reliable and fully automated approach was developed to quantify medial temporal lobe atrophy using magnetic resonance imaging. This measurement of medial temporal lobe atrophy showed differences (pnovel biomarker of brain activity was developed based on a first-order textural feature of the resting state functional magnetic resonance imagining signal. The mean brain activity metric was shown to be significantly lower (pp18F labeled fluorodeoxyglucose positron emission tomography. In the final study, we examine whether combined measures of gait and cognition could predict medial temporal lobe atrophy over 18 months in a small cohort of people (N=22) with mild cognitive impairment. The results showed that measures of gait impairment can help to predict medial temporal lobe atrophy in people with mild cognitive impairment. The work in this thesis contributes to the growing evidence the specific magnetic resonance imaging measures of brain structure and function can be used to identify and monitor the progression of Alzheimer’s disease. Continued refinement of these methods, and larger longitudinal studies will be needed to establish whether the specific metrics of brain dysfunction developed in this thesis can be of clinical benefit and aid in drug development

Neuroimaging correlates of cognitive impairment and dementia in Parkinson's disease.

There has been a gradual shift in the definition of Parkinson's disease, from a movement disorder to a neurodegenerative condition affecting multiple cognitive domains. Mild cognitive impairment (PD-MCI) is a frequent comorbidity in PD that is associated with progression to dementia (PDD) and debilitating consequences for patients and caregivers. At present, the pathophysiology underpinning cognitive impairment in PD is not established, although emerging evidence has suggested that multi-modal imaging biomarkers could be useful in the early diagnosis of PD-MCI and PDD, thereby identifying at-risk patients to enable treatment at the earliest stage possible. Structural MRI studies have revealed prominent grey matter atrophy and disruptions of white matter tracts in PDD, although findings in non-demented PD have been more variable. There is a need for further longitudinal studies to clarify the spatial and temporal progression of morphological changes in PD, as well as to assess their underlying involvement in the evolution of cognitive deficits. In this review, we discuss the aetiology and neuropsychological profiles of PD-MCI and PDD, summarize the putative imaging substrates in light of evidence from multi-modal neuroimaging studies, highlight limitations in the present literature, and suggest recommendations for future research.This work 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 the Newcastle University. Elijah Mak was in receipt of a Gates Cambridge PhD studentship.This is the accepted manuscript. The final version is available at http://www.sciencedirect.com/science/article/pii/S1353802015002151

STUDYING VASCULAR MORPHOLOGIES IN THE AGED HUMAN BRAIN USING LARGE AUTOPSY DATASETS

Cerebrovascular disease is a major cause of dementia in elderly individuals, especially Black/African Americans. Within my dissertation, we focused on two vascular morphologies that affect small vessels: brain arteriolosclerosis (B-ASC) and multi-vascular profiles (MVPs). B-ASC is characterized by degenerative thickening of the wall of brain arterioles. The risk factors, cognitive sequelae, and co-pathologies of B-ASC are not fully understood. To address this, we used multimodal data from the National Alzheimer’s Coordinating Center, Alzheimer’s Disease Neuroimaging Initiative, and brain-banked tissue samples from the University of Kentucky Alzheimer’s Disease Center (UK-ADC) brain repository. We analyzed two age at death groups separately: \u3c 80 years and ≥ 80 years. Hypertension was a risk factor in the \u3c 80 years at death group. In addition, an ABCC9 gene variant (rs704180), previously associated with aging-related hippocampal sclerosis, was associated with B-ASC in the ≥ 80 years at death group. With respect to cognition as determined by test scores, severe B-ASC was associated with worse global cognition in both age groups. With brain-banked tissue samples, we described B-ASC’s relationship to hippocampal sclerosis of aging (HS-Aging), a pathology characterized by neuronal cell loss in the hippocampal region not due to Alzheimer’s disease. We also studied MVPs, which are characterized by multiple small vessel lumens within a single vascular (Virchow-Robin) space. Little information exists on the frequency, risk factors, and co-pathologies of MVPs. Therefore, we used samples and data from the UK-ADC, University of Kentucky pathology department, and University of Pittsburgh pathology department to address this information. We only found MVPs to be correlated with age. Lastly, given the high prevalence of cerebrovascular disease and dementia in Black/African Americans, we discussed the challenges and considerations for studying Blacks/African Americans in these contexts

Pathological and cognitive alterations in mouse models of traumatic brain injury and hypoperfusion

Intact white matter is critical for normal cognitive function. In traumatic brain injury (TBI), chronic cerebral hypoperfusion and Alzheimer’s disease (AD) damage to white matter is associated with cognitive impairment. However, these conditions are associated with grey matter damage or with other pathological states and the contribution of white matter damage in isolation to their pathogenesis is not known. Furthermore, TBI is a risk factor for AD and cerebral hypoperfusion is an early feature of AD. It is hypothesised that white matter damage following TBI or chronic cerebral hypoperfusion will be associated with cognitive deficits and that white matter changes after injury contribute to AD pathogenesis. To investigate this, this thesis examined the contribution of white matter damage to cognitive deficits after TBI and chronic cerebral hypoperfusion and furthermore, investigated the role of white matter damage in the relationship between TBI and AD. Three studies addressed these aims. In the first, mild TBI was induced in wild-type mice and the effects on axons, myelin and neuronal cell bodies examined at time points from 4 hours to 6 weeks after injury. Spatial reference learning and memory was tested at 3 and 6 weeks after injury. Injured mice showed axonal damage in the cingulum, close to the injury site in the hours after injury and at 6 weeks, damage in the thalamus and external capsule were apparent. Injured and sham animals had comparable levels of neuronal damage and no change was observed in myelin. Injured animals showed impaired spatial reference learning at 3 weeks after injury, demonstrating that selective axonal damage is sufficient to impair cognition. In the second study mild TBI was induced in a transgenic mouse model of AD and the effects on white matter pathology and AD-related proteins examined 24 hours after injury. There was a significant increase in axonal damage in the cingulum and external capsule and parallel accumulations of amyloid were observed in these regions. There were no changes in tau or in overall levels of AD-related proteins. This suggests that axonal damage may have a role in mediating the link between TBI and AD. The third study used a model of chronic cerebral hypoperfusion in wild type mice and investigated white matter changes after one and two months of hypoperfusion as well as a comprehensive assessment of learning and memory. Chronic cerebral hypoperfusion resulted in diffuse myelin damage in the absence of ischaemic neuronal damage at both 1 and 2 months after induction of hypoperfusion. Hypoperfused animals also showed minimal axonal damage and microglial activation. Cognitive testing revealed a selective impairment in spatial working memory but not spatial reference or episodic memory in hypoperfused animals, showing that modest reductions in blood flow have effects on white matter sufficient to cause cognitive impairment. These results demonstrate that selective damage to white matter components can have a long-term impact on cognitive function as well as on the development of AD. This suggests that minimisation of axonal damage after TBI is a target for reducing subsequent risk of AD and that repair or prevention of white matter damage is a promising strategy for rescuing cognitive function in individuals who have experienced mild TBI or chronic cerebral hypoperfusion

Profile, determinants and mechanisms of cerebral injury and cognitive impairment following stroke

PhD ThesisOne in three people over a life time will develop a stroke, dementia or both but little is known about stroke - related cognitive impairment despite current epidemiologic transition in sub - Saharan Africa. The CogFAST Study was established in Newcastle to unmask risk factors, pathological substrates and cellular mechanisms underlying cerebral injury and cognitive impairment following stroke. The overall aim of this thesis was to establish a comparative cohort in Nigerian African stroke survivors and explore mechanisms in post - mortem brains accrued from the Newcastle cohort. Two hundred and twenty Nigerian African stroke survivors were screened three months after index stroke out of whom 143 eligible participants underwent cognitive assessment in comparison with 74 stroke - free healthy controls. We found a high frequency (49.3%) of early vascular cognitive impairment and significant association with older age and low education. Pre-stroke daily fish intake and moderate – to - heavy physical activity were inversely associated. The frequency of vascular cognitive impairment no dementia (vCIND) in the cohort (39.9%) was relatively higher than earlier report from Newcastle (32%) but neuroimaging studies revealed significant findings of MTLA and correlative white matter changes in tandem with previous reports from the Newcastle cohort. Given these, we investigated neurodegenerative hippocampal Alzheimer pathology and synaptic changes, as well as frontal and temporal white matter abnormalities in post - mortem brain tissue from the Newcastle cohort. We found increased Alzheimer pathology in the post - stroke groups but largely this did not differ between the demented (PSD) and non - demented (PSND) sub - groups. However, we found significantly higher hippocampal expression of synaptic markers (vesicular glutamate transporter – 1 and Drebrin) but lower expression of microglial, astrocytic and axonal injury markers in PSND compared to PSD subjects. The protective effect of educational attainment, pre-stroke physical activity and fish intake have public brain health implications.ORS Award from Newcastle University, a Research Fellowship from the International Brain Research Organization (IBRO) and laboratory visit support from the International Society of Neurochemistry (ISN)