Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease

Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, most existing sparse learning-based studies only adopt cross-sectional analysis methods, where the sparse model is learned using data from a single time-point. Actually, multiple time-points of data are often available in brain imaging applications, which can be used in some longitudinal analysis methods to better uncover the disease progression patterns. Accordingly, in this paper we propose a novel temporally-constrained group sparse learning method aiming for longitudinal analysis with multiple time-points of data. Specifically, we learn a sparse linear regression model by using the imaging data from multiple time-points, where a group regularization term is first employed to group the weights for the same brain region across different time-points together. Furthermore, to reflect the smooth changes between data derived from adjacent time-points, we incorporate two smoothness regularization terms into the objective function, i.e., one fused smoothness term which requires that the differences between two successive weight vectors from adjacent time-points should be small, and another output smoothness term which requires the differences between outputs of two successive models from adjacent time-points should also be small. We develop an efficient optimization algorithm to solve the proposed objective function. Experimental results on ADNI database demonstrate that, compared with conventional sparse learning-based methods, our proposed method can achieve improved regression performance and also help in discovering disease-related biomarkers

Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation

To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject's cortical atrophy pattern with that of a representative AD patient cohort. In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia. Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively. In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p < 0.001) and first year visits (p < 0.001) relative to non-converters. Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p = 0.042), first year (p = 0.028), and third year visits (p = 0.027). The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level.ope

Multimodal manifold-regularized transfer learning for MCI conversion prediction

As the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has high chance to convert to AD. Effective prediction of such conversion from MCI to AD is of great importance for early diagnosis of AD and also for evaluating AD risk pre-symptomatically. Unlike most previous methods that used only the samples from a target domain to train a classifier, in this paper, we propose a novel multimodal manifold-regularized transfer learning (M2TL) method that jointly utilizes samples from another domain (e.g., AD vs. normal controls (NC)) as well as unlabeled samples to boost the performance of the MCI conversion prediction. Specifically, the proposed M2TL method includes two key components. The first one is a kernel-based maximum mean discrepancy criterion, which helps eliminate the potential negative effect induced by the distributional difference between the auxiliary domain (i.e., AD and NC) and the target domain (i.e., MCI converters (MCI-C) and MCI non-converters (MCI-NC)). The second one is a semi-supervised multimodal manifold-regularized least squares classification method, where the target-domain samples, the auxiliary-domain samples, and the unlabeled samples can be jointly used for training our classifier. Furthermore, with the integration of a group sparsity constraint into our objective function, the proposed M2TL has a capability of selecting the informative samples to build a robust classifier. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database validate the effectiveness of the proposed method by significantly improving the classification accuracy of 80.1 % for MCI conversion prediction, and also outperforming the state-of-the-art methods

Domain Transfer Learning for MCI Conversion Prediction

Machine learning methods have been increasingly used to predict the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD), by classifying MCI converters (MCI-C) from MCI non-converters (MCI-NC). However, most of existing methods construct classifiers using only data from one particular target domain (e.g., MCI), and ignore data in the other related domains (e.g., AD and normal control (NC)) that could provide valuable information to promote the performance of MCI conversion prediction. To this end, we develop a novel domain transfer learning method for MCI conversion prediction, which can use data from both the target domain (i.e., MCI) and the auxiliary domains (i.e., AD and NC). Specifically, the proposed method consists of three key components: 1) a domain transfer feature selection (DTFS) component that selects the most informative feature-subset from both target domain and auxiliary domains with different imaging modalities, 2) a domain transfer sample selection (DTSS) component that selects the most informative sample-subset from the same target and auxiliary domains with different data modalities, and 3) a domain transfer support vector machine (DTSVM) classification component that fuses the selected features and samples to separate MCI-C and MCI-NC patients. We evaluate our method on 202 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with MRI, FDG-PET and CSF data. The experimental results show that the proposed method can classify MCI-C patients from MCI-NC patients with an accuracy of 79.4%, with the aid of additional domain knowledge learned from AD and NC

Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data.

Item does not contain fulltextPatterns of brain atrophy measured by magnetic resonance structural imaging have been utilized as significant biomarkers for diagnosis of Alzheimer's disease (AD). However, brain atrophy is variable across patients and is non-specific for AD in general. Thus, automatic methods for AD classification require a large number of structural data due to complex and variable patterns of brain atrophy. In this paper, we propose an incremental method for AD classification using cortical thickness data. We represent the cortical thickness data of a subject in terms of their spatial frequency components, employing the manifold harmonic transform. The basis functions for this transform are obtained from the eigenfunctions of the Laplace-Beltrami operator, which are dependent only on the geometry of a cortical surface but not on the cortical thickness defined on it. This facilitates individual subject classification based on incremental learning. In general, methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. Adopting a vertex-wise cortical thickness representation, our method can still achieve robustness to noise by filtering out high frequency components of the cortical thickness data while reflecting their spatial variation. This compromise leads to high accuracy in AD classification. We utilized MR volumes provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) to validate the performance of the method. Our method discriminated AD patients from Healthy Control (HC) subjects with 82% sensitivity and 93% specificity. It also discriminated Mild Cognitive Impairment (MCI) patients, who converted to AD within 18 months, from non-converted MCI subjects with 63% sensitivity and 76% specificity. Moreover, it showed that the entorhinal cortex was the most discriminative region for classification, which is consistent with previous pathological findings. In comparison with other classification methods, our method demonstrated high classification performance in both categories, which supports the discriminative power of our method in both AD diagnosis and AD prediction

Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data

Patterns of brain atrophy measured by magnetic resonance structural imaging have been utilized as significant biomarkers for diagnosis of Alzheimer's disease (AD). However, brain atrophy is variable across patients and is non-specific for AD in general. Thus, automatic methods for AD classification require a large number of structural data due to complex and variable patterns of brain atrophy. In this paper, we propose an incremental method for AD classification using cortical thickness data. We represent the cortical thickness data of a subject in terms of their spatial frequency components, employing the manifold harmonic transform. The basis functions for this transform are obtained from the eigenfunctions of the Laplace-Beltrami operator, which are dependent only on the geometry of a cortical surface but not on the cortical thickness defined on it. This facilitates individual subject classification based on incremental learning. In general, methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. Adopting a vertex-wise cortical thickness representation, our method can still achieve robustness to noise by filtering out high frequency components of the cortical thickness data while reflecting their spatial variation. This compromise leads to high accuracy in AD classification. We utilized MR volumes provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) to validate the performance of the method. Our method discriminated AD patients from Healthy Control (HC) subjects with 82% sensitivity and 93% specificity. It also discriminated Mild Cognitive Impairment (MCI) patients, who converted to AD within 18 months, from non-converted MCI subjects with 63% sensitivity and 76% specificity. Moreover, it showed that the entorhinal cortex was the most discriminative region for classification, which is consistent with previous pathological findings. In comparison with other classification methods, our method demonstrated high classification performance in both categories, which supports the discriminative power of our method in both AD diagnosis and AD predictio

Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data.

Patterns of brain atrophy measured by magnetic resonance structural imaging have been utilized as significant biomarkers for diagnosis of Alzheimer's disease (AD). However, brain atrophy is variable across patients and is non-specific for AD in general. Thus, automatic methods for AD classification require a large number of structural data due to complex and variable patterns of brain atrophy. In this paper, we propose an incremental method for AD classification using cortical thickness data. We represent the cortical thickness data of a subject in terms of their spatial frequency components, employing the manifold harmonic transform. The basis functions for this transform are obtained from the eigenfunctions of the Laplace–Beltrami operator, which are dependent only on the geometry of a cortical surface but not on the cortical thickness defined on it. This facilitates individual subject classification based on incremental learning. In general, methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. Adopting a vertex-wise cortical thickness representation, our method can still achieve robustness to noise by filtering out high frequency components of the cortical thickness data while reflecting their spatial variation. This compromise leads to high accuracy in AD classification. We utilized MR volumes provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) to validate the performance of the method. Our method discriminated AD patients from Healthy Control (HC) subjects with 82% sensitivity and 93% specificity. It also discriminated Mild Cognitive Impairment (MCI) patients, who converted to AD within 18 months, from non-converted MCI subjects with 63% sensitivity and 76% specificity. Moreover, it showed that the entorhinal cortex was the most discriminative region for classification, which is consistent with previous pathological findings. In comparison with other classification methods, our method demonstrated high classification performance in both categories, which supports the discriminative power of our method in both AD diagnosis and AD prediction

Cytoplasmic protein aggregates interfere with nucleo-cytoplasmic transport of protein and RNA

Protein misfolding and aggregation are linked to various forms of dementia and amyloidoses, such as Alzheimer’s, Parkinson’s, and Creutzfeldt Jakob diseases. Although the primary misfolding proteins are disease-specific and structurally diverse, the related disorders share numerous symptoms and cellular malfunctions. A sustainable cure remains so far out of reach. The highly complex nature of the associated cellular deficiencies challenges our understanding of primary causes and consequences in the disease progression. To focus on the toxic properties and pathogenic gain-of-function mechanisms of misfolded structures in cells, we applied a set of artificial β proteins directly folding into amyloid-like oligomers and fibrils. Amyloid-related proteotoxicity appeared sequence-dependently in human, murine neuronal, fungal, and bacterial cells. The interplay between elevated surface hydrophobicity and structural disorder among the β proteins and their cellular interactors was critical for toxicity. Small distributed oligomers correlated to elevated toxicity. Protein-rich plaques or misfolded assemblies appear in patients often simultaneously in different cellular compartments and in the extracellular space. To analyze site-specific toxicities and vulnerabilities, we targeted the β proteins specifically into distinct compartments. Aggregation in the cytoplasm was highly toxic and interfered with active nucleo-cytoplamsic transport in both directions, including the translocation of NF-κB and mRNA. We compared our results to human disease-associated mutants of Huntingtin, TDP-43, and Parkin, causing comparable transport defects. Remarkably, toxicity of the β proteins was strongly reduced when targeted to the nucleus. Aggregates localized in dense nucleolar foci caused no transport inhibition. Only protein aggregation in the cytoplasm led to sequestration and mislocalization of numerous proteins with extended disordered regions, including factors involved in nucleo-cytoplasmic transport of proteins and mRNA (importin α and THOC proteins). Nuclear β proteins in contrast behaved very inert, potentially being shielded by nucleolar factors such as nucleophosmin (NPM-1). In presence of cytoplasmic aggregation vital signaling processes were impaired, further destabilizing cellular homoeostasis. The mRNA accumulated in enlarged “nuclear RNA bodies”. Depletion of cytoplasmic mRNA consequently resulted in a reduction of protein synthesis. Impairment of nucleo-cytoplasmic transport caused by cytoplasmic protein aggregation may thus seriously aggravate the cellular pathology initiated by misfolding and aggregation in human amyloid diseases. Our findings suggest that novel therapeutic strategies may improve nucleocytoplasmic transport, utilize the nuclear proteostasis for aggregate removal, or increase the cellular resilience towards misfolded structures in general.Proteinmissfaltung und -aggregation wird mit neurodegenerativen Krankheiten wie Alzheimer, Parkinson und der Creutzfeldt-Jakob-Krankheit, sowie mit systemischen Amyloidosen in Verbindung gebracht. Auch wenn sich anfangs die Hauptbestandteile der Proteinaggregate krankheitsspezifisch unterscheiden, so kommt es bei den verschiedenen Demenzerkrankungen doch oft zu ähnlichen Symptomen und zellulären Fehlfunktionen. Eine nachhaltige Heilung ist bisher nicht möglich. Die Komplexität der auftretenden zellulären Fehlfunktionen erschwert eine klare Unterscheidung von primären Ursachen sowie deren Folgen und Nebenwirkungen. Um uns auf die toxischen Eigenschaften und die toxische Wirkung von missgefalteten Strukturen in Zellen zu konzentrieren, setzen wir eine Reihe von künstlichen β Proteinen ein, welche direkt amyloide Oligomere und Aggregate bilden. Die Toxizität der β Proteine trat sequenzabhängig in menschlichen, neuronalen, Pilz- und Bakterienzellen auf. Erhöhte Hydrophobie an der Proteinoberfläche und unstrukturierte Sequenzbereiche wurden als kritische strukturelle Merkmale der β Proteine und ihrer zellulären Interaktionspartner im Zusammenhang zur Toxizität identifiziert. Auch korrelierten kleinere, über das Zytoplasma verteilte Oligomere mit hoher Toxizität. Proteinaggregate treten in Patienten in verschiedenen Kompartimenten der Zelle und im extrazellulären Raum auf, oft an mehreren Stellen gleichzeitig. Um die Toxizität in verschiedenen Kompartimenten und deren Sensibilitäten zu untersuchen, schickten wir die β Proteine mittels Signalsequenzen gezielt in bestimmte zelluläre Kompartimente. Aggregation im Zytoplasma war hochtoxisch und störte den aktiven Transport zwischen Zytoplasma und Zellkern, einschließlich der Translokation von NF-κB und mRNA. Wir reproduzierten unsere Ergebnisse mit krankheitsassoziierten Mutanten von Huntingtin, TDP-43 und Parkin, welche vergleichbare Transportdefekte verursachten. Bemerkenswerterweise reduzierte sich die Toxizität der β Proteine stark, wenn sie in den Zellkern geschickt wurden. Hier sammelten sich die β Proteine in dichten Aggregaten in den Nukleoli. Dabei traten keine Transportprobleme auf. Nur Proteinaggregation im Zytoplasma verursachte (Ko-)Aggregation und Fehllokalisation zahlreicher zellulärer Proteine, besonders von solchen mit längeren unstrukturierten Bereichen. Dazu zählten auch Faktoren, welche den Transport von Proteinen und mRNA zwischen Zytoplasma und Zellkern vermitteln (Importin α und THOC Proteine). Die β Proteine im Zellkern verhielten sich im Gegensatz sehr unauffällig. Anscheinend wurden sie zusätzlich durch nukleoläre Faktoren wie Nukleophosmin (NPM-1) abgeschirmt. Aggregation im Zytoplasma beeinträchtigte die Übermittlung lebenswichtiger zellulärer Signale, was die zelluläre Homöostase weiter destabilisierte. Die mRNA hat sich dabei in vergrößerten „nukleären RNA Körperchen“ angesammelt. Die fehlende mRNA im Zytoplasma führte zu einer Abnahme der Proteinsynthese. Die von Proteinaggregaten verursachten Defekte im molekularen Transport zwischen Zytoplasma und Zellkern könnten so ernsthaft zur Verschlimmerung der zellulären Funktionsfähigkeit in neurodegenerativen und anderen Proteinfehlfaltungserkrankungen beitragen. Neue Therapieansätze könnten in einer Verbesserung des Kerntransports, in einer Verminderung von Aggregaten durch Proteostasissysteme im Zellkern, oder in einer generellen Stärkung der zellulären Resilienz gegenüber fehlgefalteten Proteinen zu finden sein