Journal of Alzheimer's Disease - Volume 65, issue 3

Authors: Górriz, Juan Manuel | Iglesias-González, Eugenio | Ramirez, Javier

Article Type: Introduction

DOI: 10.3233/JAD-180654

Citation: Journal of Alzheimer's Disease, vol. 65, no. 3, pp. 693-695, 2018

Authors: Penny, Will | Iglesias-Fuster, Jorge | Quiroz, Yakeel T. | Lopera, Francisco Javier | Bobes, Maria A.

Article Type: Research Article

Abstract: Dynamic causal modeling (DCM) is a framework for making inferences about changes in brain connectivity using neuroimaging data. We fitted DCMs to high-density EEG data from subjects performing a semantic picture matching task. The subjects are carriers of the PSEN1 mutation, which leads to early onset Alzheimer’s disease, but at the time of EEG acquisition in 1999, these subjects were cognitively unimpaired. We asked 1) what is the optimal model architecture for explaining the event-related potentials in this population, 2) which connections are different between this Presymptomatic Carrier (PreC) group and a Non-Carrier (NonC) group performing the same task, …and 3) which network connections are predictive of subsequent Mini-Mental State Exam (MMSE) trajectories. We found 1) a model with hierarchical rather than lateral connections between hemispheres to be optimal, 2) that a pathway from right inferotemporal cortex (IT) to left medial temporal lobe (MTL) was preferentially activated by incongruent items for subjects in the PreC group but not the NonC group, and 3) that increased effective connectivity among left MTL, right IT, and right MTL was predictive of subsequent MMSE scores. Show more

Keywords: Autosomal dominant, dynamic causal modeling, EEG, effective connectivity, multivariate

DOI: 10.3233/JAD-170405

Citation: Journal of Alzheimer's Disease, vol. 65, no. 3, pp. 697-711, 2018

Authors: Martinez-Murcia, Francisco Jesus | Górriz, Juan Manuel | Ramírez, Javier | Segovia, Fermín | Salas-Gonzalez, Diego | Castillo-Barnes, Diego | Ortiz, Andrés | for the Alzheimer’s Disease Neuroimaging Initiative

Article Type: Research Article

Abstract: Background: The early diagnosis of Alzheimer’s Disease (AD), particularly in its prodromal stage, mild cognitive impairment (MCI), still remains a challenge. Many computational tools have been developed to successfully explore and predict the disease progression. In this context, the Spherical Brain Mapping (SBM) proved its ability in detecting differences between AD and aged subjects without symptoms of dementia. Being a very visual tool, its application in predicting MCI conversion to AD could be of great help to understand neurodegeneration and the disease progression. Objective: In this work, we aim at predicting the conversion of MCI affected subjects to …AD more than 6 months in advance of their conversion session and understanding the progression of the disease by predicting neuropsychological test outcomes from MRI data. Methods: In order to do so, SBM is applied to a series of MRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The resulting spherical brain maps show statistical and morphological information of the brain in a bidimensional plane, performing at the same time a significant feature reduction that provides a feature vector used in classification analysis. Results: The study achieves up to 92.3% accuracy in the AD versus normal controls (CTL) detection, and up to a 77.6% in detection a of MCI conversions when trained with AD and CTL subjects. The prediction of neuropsychological test outcomes achieved R2 rates up to more than 0.5. Significant regions according to t -test and correlation analysis match reported brain areas in the literature. Conclusion: The results prove that Spherical Brain Mapping offers good ability to predict conversion patterns and cognitive state, at the same time that provides an additional aid for visualizing a two-dimensional abstraction map of the brain. Show more

Keywords: Alzheimer’s disease, classification, cognitive dysfunction, disease progression, magnetic resonance imaging, regression analysis

DOI: 10.3233/JAD-170403

Citation: Journal of Alzheimer's Disease, vol. 65, no. 3, pp. 713-729, 2018

Authors: Dyrba, Martin | Grothe, Michel J. | Mohammadi, Abdolreza | Binder, Harald | Kirste, Thomas | Teipel, Stefan J. | for the Alzheimer’s Disease Neuroimaging Initiative

Article Type: Research Article

Abstract: Alzheimer’s disease (AD) is characterized by a cascade of pathological processes that can be assessed in vivo using different neuroimaging methods. Recent research suggests a systematic sequence of pathogenic events on a global biomarker level, but little is known about the associations and dependencies of distinct lesion patterns on a regional level. Markov random fields are a probabilistic graphical modeling approach that represent the interaction between individual random variables by an undirected graph. We propose the novel application of this approach to study the interregional associations and dependencies between multimodal imaging markers of AD pathology and to compare different …hypotheses regarding the spread of the disease. We retrieved multimodal imaging data from 577 subjects enrolled in the Alzheimer’s Disease Neuroimaging Initiative. Mean amyloid load (AV45-PET), glucose metabolism (FDG-PET), and gray matter volume (MRI) were calculated for the six principle nodes of the default mode network— a functional network of brain regions that appears to be preferentially targeted by AD. Multimodal Markov random field models were developed for three different hypotheses regarding the spread of the disease: the “intraregional evolution model”, the “trans-neuronal spread” hypothesis, and the “wear-and-tear” hypothesis. The model likelihood to reflect the given data was evaluated using tenfold cross-validation with 1,000 repetitions. The most likely graph structure contained the posterior cingulate cortex as main hub region with edges to various other regions, in accordance with the “wear-and-tear” hypothesis of disease vulnerability. Probabilistic graphical models facilitate the analysis of interactions between several variables in a network model and therefore afford great potential to complement traditional multiple regression analyses in multimodal neuroimaging research. Show more

Keywords: Alzheimer’s disease, AV45-PET, FDG-PET, Markov random field, mild cognitive impairment, multimodal imaging, probabilistic graphical model

DOI: 10.3233/JAD-161197

Citation: Journal of Alzheimer's Disease, vol. 65, no. 3, pp. 731-746, 2018

Authors: Powell, Fon | Tosun, Duygu | Sadeghi, Roksana | Weiner, Michael | Raj, Ashish | for the Alzheimer’s Disease Neuroimaging Initiative

Article Type: Research Article

Abstract: Models of Alzheimer’s disease (AD) hypothesize stereotyped progression via white matter (WM) fiber connections, most likely via trans-synaptic transmission of toxic proteins along neuronal pathways. An important question in the field is whether and how organization of fiber pathways is affected by disease. It remains unknown whether fibers act as conduits of degenerative pathologies, or if they also degenerate with the gray matter network. This work uses graph theoretic modeling in a longitudinal design to investigate the impact of WM network organization on AD pathology spread. We hypothesize if altered WM network organization mediates disease progression, then a previously published …network diffusion model will yield higher prediction accuracy using subject-specific connectomes in place of a healthy template connectome. Neuroimaging data in 124 subjects from ADNI were assessed. Graph topology metrics show preserved network organization in patients compared to controls. Using a published diffusion model, we further probe the effect of network alterations on degeneration spread in AD. We show that choice of connectome does not significantly impact the model’s predictive ability. These results suggest that, despite measurable changes in integrity of specific fiber tracts, WM network organization in AD is preserved. Further, there is no difference in the mediation of putative pathology spread between healthy and AD-impaired networks. This conclusion is somewhat at variance with previous results, which report global topological disturbances in AD. Our data indicates the combined effect of edge thresholding, binarization, and inclusion of subcortical regions to network graphs may be responsible for previously reported effects. Show more

Keywords: Alzheimer’s disease, atrophy, biomarkers, diffusion tensor imaging, longitudinal, magnetic resonance imaging, neural networks, structural connectivity

DOI: 10.3233/JAD-170798

Citation: Journal of Alzheimer's Disease, vol. 65, no. 3, pp. 747-764, 2018

Authors: Segovia, Fermín | Gómez-Río, Manuel | Sánchez-Vañó, Raquel | Górriz, Juan Manuel | Ramírez, Javier | Triviño-Ibáñez, Eva | Carnero-Pardo, Cristóbal | Martínez-Lozano, María Dolores | Sopena-Novales, Pablo

Article Type: Research Article

Abstract: Background: Biomarkers of neurodegeneration play a major role in the diagnosis of Alzheimer’s disease (AD). Information on both amyloid-β accumulation, e.g., from amyloid positron emission tomography (PET), and downstream neuronal injury, e.g., from 18 F-fluorodeoxyglucose (FDG) PET, would ideally be obtained in a single procedure. Objective: On the basis that the parallelism between brain perfusion and glucose metabolism is well documented, the objective of this work is to evaluate whether brain perfusion estimated in a dual-point protocol of 18 F-florbetaben (FBB) PET can be a surrogate of FDG PET in appropriate use criteria (AUC) for amyloid PET. …Methods: This study included 47 patients fulfilling international AUC for amyloid PET. FDG PET, early FBB (pFBB) PET (0–10 min post injection), and standard FBB (sFBB) PET (90–110 min post injection) scans were acquired. Results of clinical subjective reports and of quantitative region of interest (ROI)-based analyses were compared between procedures using statistical techniques such as Pearson’s correlation coefficients and t -tests. Results: pFBB and FDG visual reports on the 47 patients showed good agreement (k   >  0.74); ROI quantitative analysis indicated that both data modalities are highly correlated; and the t -test analysis does not reject the null hypothesis that data from pFBB and FDG examinations comes from independent random samples from normal distributions with equal means and variances. Conclusions: A good agreement was found between pFBB and FDG data as obtained by subjective visual and quantitative analyses. Dual-point FBB PET scans could offer complementary information (similar to that from FDG PET and FBB PET) in a single procedure, considering pFBB as a surrogate of FDG. Show more

Keywords: Alzheimer’s disease, amyloid PET, appropriate use criteria, brain perfusion, brain rCBF, brain rCMRglc, FDG PET, florbetaben PET, mild cognitive impairment, quantitative analysis

DOI: 10.3233/JAD-180232

Citation: Journal of Alzheimer's Disease, vol. 65, no. 3, pp. 765-779, 2018

Authors: Blautzik, Janusch | Kotz, Sebastian | Brendel, Matthias | Sauerbeck, Julia | Vettermann, Franziska | Winter, Yaroslav | Bartenstein, Peter | Ishii, Kazunari | Rominger, Axel | for the Alzheimer’s Disease Neuroimaging Initiative

Article Type: Research Article

Abstract: Body weight loss in late-life is known to occur at a very early stage of Alzheimer’s disease (AD). Apolipoprotein E4 (ApoE4) represents a major genetic risk factor for AD and is linked to an increased cortical amyloid-β (Aβ) accumulation. Since the relationship between body weight, ApoE4, and AD pathology is poorly investigated, we aimed to evaluate whether ApoE4 allelic status modifies the association of body mass index (BMI) with markers of AD pathology. A total of 368 Aβ-positive cognitively healthy or mild cognitive impaired subjects had undergone [18 F]-AV45-PET, [18 F]-FDG-PET, and T1w-MRI examinations. Composite cortical [18 F]-AV45 uptake and …[18 F]-FDG uptake in posterior cingulate cortex were calculated as surrogates of cortical Aβ load and glucose metabolism, respectively. Multiple linear regressions were performed to assess the relationships between these PET biomarkers with BMI, present cognitive performance, and cognitive changes over time. Multivariate analysis of covariance was conducted to test for statistical differences between ApoE4/BMI categories on the PET markers and cognitive scores. In carriers of the ApoE4 allele only, BMI was inversely associated with cortical amlyoid load (β= –0.193, p  < 0.005) and recent cognitive decline (β= –0.209, p  < 0.05), and positively associated with cortical glucose metabolism in an AD-vulnerable region (β= 0.145, p  < 0.05). ApoE4/BMI category analyses demonstrated lower Aβ load, higher posterior cingulate glucose metabolism, improved cognitive performance, and lower progression of cognitive decline in obese ApoE4 carriers. The effect of ApoE4 in promoting the accumulation of cortical amyoid, which may itself be a driver for weight loss, may be moderated by altering leptin signaling in the hypothalamus. Show more

Keywords: Alzheimer’s disease, amyloid-β PET, ApoE4, body mass index, FDG-PET, markers of AD pathology

DOI: 10.3233/JAD-170064

Citation: Journal of Alzheimer's Disease, vol. 65, no. 3, pp. 781-791, 2018

Authors: Brendel, Matthias | Sauerbeck, Julia | Greven, Sonja | Kotz, Sebastian | Scheiwein, Franziska | Blautzik, Janusch | Delker, Andreas | Pogarell, Oliver | Ishii, Kazunari | Bartenstein, Peter | Rominger, Axel | for the Alzheimer’s Disease Neuroimaging Initiative

Article Type: Research Article

Abstract: Late-life depression, even when of subsyndromal severity, has shown strong associations with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Preclinical studies have suggested that serotonin selective reuptake inhibitors (SSRIs) can attenuate amyloidogenesis. Therefore, we aimed to investigate the effect of SSRI medication on amyloidosis and grey matter volume in subsyndromal depressed subjects with MCI and AD during an interval of two years. 256 cognitively affected subjects (225 MCI/ 31 AD) undergoing [18 F]-AV45-PET and MRI at baseline and 2-year follow-up were selected from the ADNI database. Subjects with a positive depression item (DEP(+); n = 73) in the …Neuropsychiatric Inventory Questionnaire were subdivided to those receiving SSRI medication (SSRI(+); n = 24) and those without SSRI treatment (SSRI(−); n = 49). Longitudinal cognition (Δ-ADAS), amyloid deposition rate (standardized uptake value, using white matter as reference region (SUVRWM ), and changes in grey matter volume were compared using common covariates. Analyses were performed separately in all subjects and in the subgroup of amyloid-positive subjects. Cognitive performance in DEP(+)/SSRI(+) subjects (Δ-ADAS: –5.0%) showed less deterioration with 2-year follow-up when compared to DEP(+)/SSRI(−) subjects (Δ-ADAS: +18.6%, p < 0.05), independent of amyloid SUVRWM at baseline. With SSRI treatment, the progression of grey matter atrophy was reduced (−0.9% versus –2.7%, p < 0.05), notably in fronto-temporal cortex. A slight trend towards lower amyloid deposition rate was observed in DEP(+)/SSRI(+) subjects versus DEP(+)/SSRI(−). Despite the lack of effect to amyloid PET, SSRI medication distinctly rescued the declining cognitive performance in cognitively affected patients with depressive symptoms, and likewise attenuated grey matter atrophy. Show more

Keywords: Alzheimer’s disease, amyloid PET, depressive symptoms, grey matter volume, SSRI

DOI: 10.3233/JAD-170387

Citation: Journal of Alzheimer's Disease, vol. 65, no. 3, pp. 793-806, 2018

Authors: Kwak, Kichang | Yun, Hyuk Jin | Park, Gilsoon | Lee, Jong-Min | and for the Alzheimer’s Disease Neuroimaging Initiative

Article Type: Research Article

Abstract: Background: Alzheimer’s disease (AD) and mild cognitive impairment (MCI) are age-related neurodegenerative diseases characterized by progressive loss of memory and irreversible cognitive functions. The hippocampus, a brain area critical for learning and memory processes, is especially susceptible to damage at early stages of AD. Objective: We aimed to develop prediction model using a multi-modality sparse representation approach. Methods: We proposed a sparse representation approach to the hippocampus using structural T1-weighted magnetic resonance imaging (MRI) and 18-fluorodeoxyglucose-positron emission tomography (FDG-PET) to distinguish AD/MCI from healthy control subjects (HCs). We considered structural and function information for the hippocampus …and applied a sparse patch-based approach to effectively reduce the dimensions of neuroimaging biomarkers. Results: In experiments using Alzheimer’s Disease Neuroimaging Initiative data, our proposed method demonstrated more reliable than previous classification studies. The effects of different parameters on segmentation accuracy were also evaluated. The mean classification accuracy obtained with our proposed method was 0.94 for AD/HCs, 0.82 for MCI/HCs, and 0.86 for AD/MCI. Conclusion: We extracted multi-modal features from automatically defined hippocampal regions of training subjects and found this method to be discriminative and robust for AD and MCI classification. The extraction of features in T1 and FDG-PET images is expected to improve classification performance due to the relationship between brain structure and function. Show more

Keywords: Alzheimer’s disease, mild cognitive impairment, prediction model, sparse representation

DOI: 10.3233/JAD-170338

Citation: Journal of Alzheimer's Disease, vol. 65, no. 3, pp. 807-817, 2018

Authors: Ruiz, Elena | Ramírez, Javier | Górriz, Juan Manuel | Casillas, Jorge | the Alzheimer’s Disease Neuroimaging Initiative

Article Type: Research Article

Abstract: This paper proposes a novel fully automatic computer-aided diagnosis (CAD) system for the early detection of Alzheimer’s disease (AD) based on supervised machine learning methods. The novelty of the approach, which is based on histogram analysis, is twofold: 1) a feature extraction process that aims to detect differences in brain regions of interest (ROIs) relevant for the recognition of subjects with AD and 2) an original greedy algorithm that predicts the severity of the effects of AD on these regions. This algorithm takes account of the progressive nature of AD that affects the brain structure with different levels of severity, …i.e., the loss of gray matter in AD is found first in memory-related areas of the brain such as the hippocampus. Moreover, the proposed feature extraction process generates a reduced set of attributes which allows the use of general-purpose classification machine learning algorithms. In particular, the proposed feature extraction approach assesses the ROI image separability between classes in order to identify the ones with greater discriminant power. These regions will have the highest influence in the classification decision at the final stage. Several experiments were carried out on segmented magnetic resonance images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) in order to show the benefits of the overall method. The proposed CAD system achieved competitive classification results in a highly efficient and straightforward way. Show more

Keywords: Alzheimer’s disease, Alzheimer’s Disease Neuroimaging Initiative, classification, computer aided diagnosis, computer-assisted, histogram-based analysis, image processing, MRI, supervised learning

DOI: 10.3233/JAD-170514

Citation: Journal of Alzheimer's Disease, vol. 65, no. 3, pp. 819-842, 2018