Journal of Alzheimer's Disease - Volume Pre-press, issue Pre-press

Authors: Strohmaier, Urs | Keller, Felix | Kilimann, Ingo | Michalowsky, Bernhard | Wucherer, Diana | Zwingmann, Ina | Teipel, Stefan | Hoffmann, Wolfgang | Thyrian, Jochen René

Article Type: Research Article

Abstract: Background: The current guidelines imply that basic medical diagnostics for dementia should be provided by general practitioners in cooperation with other specialists such as neurologists and psychiatrists. Objectives: The aims of this paper were to 1) compare the dementia patients of general practice residents whose care is co-managed by neurology/psychiatry residents with those whose care is not; 2) identify the patient variables associated with the utilization of neurological and psychiatric specialists; and 3) describe the frequency of imaging use a for dementia patients in primary care. Methods: The analyses utilized data from 485 individuals who screened …positive for dementia in primary care (PWD). Clinical variables and the utilization of specialists were assessed via medical records and face-to-face interviews. The factors associated with the utilization of specialists were assessed using multivariate linear regression and included age, sex, relationship status, cognitive impairment, depression, activities of daily living, and formal diagnosis of dementia. Results: Our results show that 89 out of 485 study participants (18.4%) were referred to specialists 12 months prior to assessment. Of these 89 individuals, 14.6% (n = 13) did not receive imaging diagnostics, while 39.3% (n = 35) received brain imaging by CT scan and 46.1% (n = 41) by MRI. PWD referred to specialists differed from those not referred, in age, relationship status, and the presence of a formal diagnosis. Our multivariate analysis revealed that younger age (OR = 0.95; 95% -confidence interval 0.90–0.99; p = 0.04) and higher functional impairment (OR = 1.15; 95% -confidence interval 1.02–1.30; p = 0.02) were associated with a visit to a specialist. Discussion: Only 1 out of every 4 to 5 individuals who have screened positive for dementia have visited a specialist in psychiatry or neurology. While in general, women utilized specialists less often than men, younger and more functionally impaired patients were more likely to be sent to a specialist by their treating general practitioner. Almost 90% of the patients sent to a specialist received cranial neuroimaging, suggesting high adherence to diagnostic guidelines in specialized care. Show more

DOI: 10.3233/JAD-180196

Citation: Journal of Alzheimer's Disease, vol. Pre-press, no. Pre-press, pp. 1-8, 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. Pre-press, no. Pre-press, pp. 1-17, 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. Pre-press, no. Pre-press, pp. 1-18, 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. Pre-press, no. Pre-press, pp. 1-15, 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. Pre-press, no. Pre-press, pp. 1-11, 2018

Authors: Gómez, Carlos | Juan-Cruz, Celia | Poza, Jesús | Ruiz-Gómez, Saúl J. | Gomez-Pilar, Javier | Núñez, Pablo | García, María | Fernández, Alberto | Hornero, Roberto

Article Type: Research Article

Abstract: Neuroimaging techniques have demonstrated over the years their ability to characterize the brain abnormalities associated with different neurodegenerative diseases. Among all these techniques, magnetoencephalography (MEG) stands out by its high temporal resolution and noninvasiveness. The aim of the present study is to explore the coupling patterns of resting-state MEG activity in subjects with mild cognitive impairment (MCI). To achieve this goal, five minutes of spontaneous MEG activity were acquired with a 148-channel whole-head magnetometer from 18 MCI patients and 26 healthy controls. Inter-channel relationships were investigated by means of two complementary coupling measures: coherence and Granger causality. Coherence is a …classical method of functional connectivity, while Granger causality quantifies effective (or causal) connectivity. Both measures were calculated in the five conventional frequency bands: delta (δ , 1–4 Hz), theta (θ , 4–8 Hz), alpha (α, 8–13 Hz), beta (β, 13–30 Hz), and gamma (γ , 30–45 Hz). Our results showed that connectivity values were lower for MCI patients than for controls in all frequency bands. However, only Granger causality revealed statistically significant differences between groups (p -values < 0.05, FDR corrected Mann-Whitney U-test), mainly in the beta band. Our results support the role of MCI as a disconnection syndrome, which elicits early alterations in effective connectivity patterns. These findings can be helpful to identify the neural substrates involved in prodromal stages of dementia. Show more

Keywords: Coherence, connectivity, Granger causality, magnetoencephalography (MEG), mild cognitive impairment neuroimaging

DOI: 10.3233/JAD-170475

Citation: Journal of Alzheimer's Disease, vol. Pre-press, no. Pre-press, pp. 1-12, 2017

Authors: Martinez Martinez, Pilar | Mielke, Michelle

Article Type: Editorial

DOI: 10.3233/JAD-170735

Citation: Journal of Alzheimer's Disease, vol. Pre-press, no. Pre-press, pp. 1-4, 2017

Authors: Zhang, Yudong | Wang, Shuihua | Sui, Yuxiu | Yang, Ming | Liu, Bin | Cheng, Hong | Sun, Junding | Jia, Wenjuan | Phillips, Preetha | Gorriz, Juan Manuel

Article Type: Research Article

Abstract: Background: The number of patients with Alzheimer’s disease is increasing rapidly every year. Scholars often use computer vision and machine learning methods to develop an automatic diagnosis system. Objective: In this study, we developed a novel machine learning system that can make diagnoses automatically from brain magnetic resonance images. Methods: First, the brain imaging was processed, including skull stripping and spatial normalization. Second, one axial slice was selected from the volumetric image, and stationary wavelet entropy (SWE) was done to extract the texture features. Third, a single-hidden-layer neural network was used as the classifier. Finally, …a predator-prey particle swarm optimization was proposed to train the weights and biases of the classifier. Results: Our method used 4-level decomposition and yielded 13 SWE features. The classification yielded an overall accuracy of 92.73±1.03%, a sensitivity of 92.69±1.29%, and a specificity of 92.78±1.51%. The area under the curve is 0.95±0.02. Additionally, this method only cost 0.88 s to identify a subject in online stage, after its volumetric image is preprocessed. Conclusion: In terms of classification performance, our method performs better than 10 state-of-the-art approaches and the performance of human observers. Therefore, this proposed method is effective in the detection of Alzheimer’s disease. Show more

Keywords: Alzheimer’s disease, detection, particle swarm optimization, predator-prey model, single-hidden-layer neural network, stationary wavelet entropy

DOI: 10.3233/JAD-170069

Citation: Journal of Alzheimer's Disease, vol. Pre-press, no. Pre-press, pp. 1-15, 2017

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. Pre-press, no. Pre-press, pp. 1-11, 2017

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. Pre-press, no. Pre-press, pp. 1-16, 2017