Tau and atrophy: domain-specific relationships with cognition.

BackgroundLate-onset Alzheimer's disease (AD) is characterized by primary memory impairment, which then progresses towards severe deficits across cognitive domains. Here, we report how performance in cognitive domains relates to patterns of tau deposition and cortical thickness.MethodsWe analyzed data from 131 amyloid-β positive participants (55 cognitively normal, 46 mild cognitive impairment, 30 AD) of the Alzheimer's Disease Neuroimaging Initiative who underwent magnetic resonance imaging (MRI), flortaucipir (FTP) positron emission tomography, and neuropsychological testing. Surface-based vertex-wise and region-of-interest analyses were conducted between FTP and cognitive test scores, and between cortical thickness and cognitive test scores.ResultsFTP and thickness were differentially related to cognitive performance in several domains. FTP-cognition associations were more widespread than thickness-cognition associations. Further, FTP-cognition patterns reflected cortical systems that underlie different aspects of cognition.ConclusionsOur findings indicate that AD-related decline in domain-specific cognitive performance reflects underlying progression of tau and atrophy into associated brain circuits. They also suggest that tau-PET may have better sensitivity to this decline than MRI-derived measures of cortical thickness

Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.

Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies-amyloid plaques and cerebral amyloid angiopathy-in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability suggests a route to neuropathologic deep phenotyping

Amidst an amygdala renaissance in Alzheimer’s disease

Laura E. M. Wisse and David Berron contributed equally to this work. Accepted manuscripts: Accepted manuscripts are PDF versions of the author’s final manuscript, as accepted for publication by the journal but prior to copyediting or typesetting. They can be cited using the author(s), article title, journal title, year of online publication, and DOI. They will be replaced by the final typeset articles, which may therefore contain changes. The DOI will remain the same throughout.Supplementary material: Supplementary material is available at Brain online at: https://doi.org/10.1093/brain/awad411 .Copyright © The Author(s) 2023. The amygdala was highlighted as an early site for neurofibrillary tau tangle pathology in Alzheimer’s disease in the seminal Braak & Braak article (1991). This knowledge has, however, only received traction recently with advances in imaging and image analysis techniques. Here, we provide a cross-disciplinary overview of pathology and neuroimaging studies on the amygdala. These studies provide strong support for an early role of the amygdala in Alzheimer’s disease and the utility of imaging biomarkers of the amygdala in detecting early changes and predicting decline in cognitive functions and neuropsychiatric symptoms in early stages. We summarize the animal literature on connectivity of the amygdala, demonstrating that amygdala nuclei that show the earliest and strongest accumulation of neurofibrillary tangle pathology are those that are connected to brain regions that also show early neurofibrillary tangle accumulation. Additionally, we propose an alternative pathway of neurofibrillary tangle spreading within the medial temporal lobe between the amygdala and the anterior hippocampus. The proposed existence of this pathway is strengthened by novel experimental data on human functional connectivity. Finally, we summarize the functional roles of the amygdala, highlighting the correspondence between neurofibrillary tangle accumulation and symptomatic profiles in Alzheimer’s disease. In summary, these findings provide a new impetus for studying the amygdala in Alzheimer’s disease and a unique perspective to guide further study on neurofibrillary tangle spreading and the occurrence of neuropsychiatric symptoms in Alzheimer’s disease.This work is supported by grants from the Swedish Research Council (2022-00900) and the Crafoord Foundation (LW). This study is supported by MultiPark - A Strategic Research Area at Lund University (LW). This work is supported by the National Institutes of Health: U19-AG033655, P30-AG066507, P41-EB031771, R01-EB020062, T32-GM13677, U19-MH114821, R01-NS074980-10S1, RF1MH126732, RF1MH128875, F30AG077736 (MM and KS) and the Kavli Neuroscience Discovery Institute (MM and KS) as well as the Kavli Foundation and the KG. Jebsen Foundation (MPW). This work is further supported by the Elly Bergren Foundation and regional research support by the Division of Psychiatry, Habilitation and Medical aid, Region Skåne (MJ). This work was partly supported by the National Institute for Health and Care Research 23 Exeter Biomedical Research Centre

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

Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data

International audienceWe introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from series of high-dimensional brain images. The model is based on spatio-temporal matrix factorization, where inference on the sources is constrained by anatomically plausible statistical priors. To model realistic trajectories, the temporal sources are defined as monotonic and time-reparametrized Gaussian Processes. To account for the non-stationarity of brain images, we model the spatial sources as sparse codes convolved at multiple scales. The method was tested on synthetic data favourably comparing with standard blind source separation approaches. The application on large-scale imaging data from a clinical study allows to disentangle differential temporal progression patterns mapping brain regions key to neurodegeneration, while revealing a disease-specific time scale associated to the clinical diagnosis

Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data

We introduce a probabilistic generative model for disentangling spatio-temporal disease trajectories from series of high-dimensional brain images. The model is based on spatio-temporal matrix factorization, where inference on the sources is constrained by anatomically plausible statistical priors. To model realistic trajectories, the temporal sources are defined as monotonic and time-reparametrized Gaussian Processes. To account for the non-stationarity of brain images, we model the spatial sources as sparse codes convolved at multiple scales. The method was tested on synthetic data favourably comparing with standard blind source separation approaches. The application on large-scale imaging data from a clinical study allows to disentangle differential temporal progression patterns mapping brain regions key to neurodegeneration, while revealing a disease-specific time scale associated to the clinical diagnosis

Predicting patient outcome using radioclinical features selected with RENT for patients with colorectal cancer

Colorectal cancer remains a problem in medicine, costing countless lives each year. The growing amount of data available about these patients have piqued the interest of researchers, as they try to use machine learning to aid diagnosis, decision making, and treatment for these patients. Unfortunately, as the data sets grow, the risk of creating unstable and non-generalizable models increase. The research in this thesis has aimed at investigating how to implement a novel technique called RENT (Repeated Elastic Net Technique) for feature selection. The predictive problem was a binary classification problem on colorectal cancer patients to predict overall survival. The analysis applied repeated stratified k-fold cross-validation with four folds and five repeats to reduce the risk of random subsets causing non-generalizable results. Further, the analysis created 25 000 different RENT models to search through the hyperparameters to find high performance parameter combinations. Each of the 25 000 models were trained with six different Random Forest [RF] hyperparameter combinations and twelve logistic regression hyperparameter combinations, resulting in 450 000 different models. A high performing group of models was collected for one unique combination of hyperparameters. These models had the highest average test performance: accuracy 0.76 ± 0.07, MCC 0.47 ± 0.16, F1 positive class 0.57 ± 0.13, F1 negative class 0.83 ± 0.05, and AUC 0.69 ± 0.08. The results have also shown that the generalization error is lower for a RENT based RF model than non-RENT based RF model. The RENT analysis revealed that patients that died was overrepresented in a group of patients that were the most frequently predicted incorrectly. Finally, the RENT analysis has resulted in a distribution of features that were most frequently selected for high predictive ability. Most of the clinical features in this group has previously been reported as relevant by medical literature. The research and the corresponding framework show promising results to implement a brute-force approach to the RENT analysis, to ensure low generalization error and predictive interpretability. Further research with this framework can support medicine in validating feature importance for patient outcome. The framework could also prove useful in other research fields than medicine, given predictive problems with similar challenges.Tykktarmskreft er fortsatt et problem innen medisin, og koster utallige liv hvert år. Den økende mengden data som er tilgjengelig om disse pasientene har vekket interessen til forskerne, der flere prøver å bruke maskinlæring for å hjelpe diagnostisering, beslutningstaking og behandling for disse pasientene. Dessverre, ettersom datasettene vokser, øker også risikoen for å lage ustabile og ikke-generaliserbare modeller. Forskningen i denne oppgaven har tatt sikte på å undersøke hvordan man implementerer en ny teknikk kalt RENT (Repeated Elastic Net Technique) for variabel seleksjon. Det prediktive problemet var et binært klassifiseringsproblem på pasienter med tykk- og endetarmskreft for å forutsi samlet overlevelse. Analysen brukte gjentatt stratifisert k-foldet kryssvalidering med fire folder og fem repetisjoner for å redusere risikoen for at tilfeldige undergrupper av data fører til ikke-generaliserbare resultater. Videre beregnet analysen 25 000 forskjellige RENT-modeller for å søke gjennom hyperparametrene for å finne høyytelsesparameterkombinasjoner. Hver av de 25 000 modellene ble trent med seks forskjellige hyperparameterkombinasjoner for Random Forest [RF] og tolv hyperparameterkombinasjoner for logistisk regresjons, noe som resulterte i totalt 450 000 forskjellige modeller. En høytytende gruppe modeller ble samlet inn for én unik kombinasjon av hyperparametre. Disse modellene hadde den høyeste gjennomsnittlige testytelsen: «accuracy» 0,76 ± 0,07, MCC 0,47 ± 0,16, F1 positiv klasse 0,57 ± 0,13, F1 negativ klasse 0,83 ± 0,05 og AUC 0,69 ± 0,08. Resultatene har også vist at generaliseringsfeilen er lavere for en RENT-basert RF-modell enn ikke-RENT-basert RF-modell. RENT-analysen avdekket at pasienter som døde var overrepresentert i en pasientgruppe som oftest ble predikert feil. Til slutt har RENT-analysen resultert i en fordeling av variabler som oftest ble valgt for høy prediksjonsevne. De fleste av de kliniske trekkene i denne gruppen er tidligere rapportert som relevante av medisinsk litteratur. Forskningen og det tilhørende rammeverket viser lovende resultater for å implementere en brute-force-tilnærming til RENT-analysen, for å sikre lav generaliseringsfeil og prediktiv tolkbarhet. Ytterligere forskning med dette rammeverket kan bistå medisin i å validere variablers betydning for pasienters prognose. Rammeverket kan også vise seg nyttig innenfor andre forskningsfelt enn medisin, gitt prediktive problemer med lignende utfordringer.M-D

Place cell physiology in a transgenic mouse model of Alzheimer's disease

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by progressive cognitive impairments (Selkoe, 2001). Hippocampal place cells are a well understood candidate for the neural basis of one type of memory in rodents; these cells identify the animal's location in an environment and are crucial for spatial memory and navigation. This PhD project aims to clarify the mechanisms responsible for the cognitive deficits in AD at the hippocampal network level, by examining place cell physiology in a transgenic mouse model of AD. I have recorded place cells in tg2576 mice, and found that aged (16 months) but not young (3 months) transgenic mice show degraded neuronal representations of the environment. The level of place cell degradation correlates with the animals' (poorer) spatial memory as tested in a forced-choice spatial alternation T-maze task and with hippocampal, but not neocortical, amyloid plaque burden. Additionally, pilot data show that physiological changes of the hippocampus in tg2576 mice seem to start as early as 3 months, when no pathological and behavioural deficits are present. However, these changes are not obvious at the neuronal level, but only at the hippocampal network level, which represent hippocampal responses to environmental changes. Place cell recording provides a sensitive assay for measuring the amount and rate of functional deterioration in animal models of dementia as well as providing a quantifiable physiological indication of the beneficial effects of potential therapies