Reduction in Constitutively Activated Auditory Brainstem Microglia in Aging and Alzheimer's Disease.

Background: Alzheimer's disease (AD) pathology is considered to begin in the brainstem, and cerebral microglia are known to play a critical role in AD pathogenesis, yet little is known about brainstem microglia in AD. Translocator protein (TSPO) PET, sensitive to activated microglia, shows high signal in dorsal brainstem in humans, but the precise location and clinical correlates of this signal are unknown. Objective: To define age and AD associations of brainstem TSPO PET signal in humans. Methods: We applied new probabilistic maps of brainstem nuclei to quantify PET-measured TSPO expression over the whole brain including brainstem in 71 subjects (43 controls scanned using 11C-PK11195; 20 controls and 8 AD subjects scanned using 11C-PBR28). We focused on inferior colliculi (IC) because of visually-obvious high signal in this region, and potential relevance to auditory dysfunction in AD. We also assessed bilateral cortex. Results: TSPO expression was normally high in IC and other brainstem regions. IC TSPO was decreased with aging (p = 0.001) and in AD subjects versus controls (p = 0.004). In cortex, TSPO expression was increased with aging (p = 0.030) and AD (p = 0.033). Conclusions: Decreased IC TSPO expression with aging and AD-an opposite pattern than in cortex-highlights underappreciated regional heterogeneity in microglia phenotype, and implicates IC in a biological explanation for strong links between hearing loss and AD. Unlike in cerebrum, where TSPO expression is considered pathological, activated microglia in IC and other brainstem nuclei may play a beneficial, homeostatic role. Additional study of brainstem microglia in aging and AD is needed.

Remote Associations Between Tau and Cortical Amyloid-ß Are Stage-Dependent.

Background: Histopathologic studies of Alzheimer's disease (AD) suggest that extracellular amyloid-ß (Aß) plaques promote the spread of neurofibrillary tau tangles. However, these two proteinopathies initiate in spatially distinct brain regions, so how they interact during AD progression is unclear. Objective: In this study, we utilized Aß and tau positron emission tomography (PET) scans from 572 older subjects (476 healthy controls (HC), 14 with mild cognitive impairment (MCI), 82 with mild AD), at varying stages of the disease, to investigate to what degree tau is associated with cortical Aß deposition. Methods: Using multiple linear regression models and a pseudo-longitudinal ordering technique, we investigated remote tau-Aß associations in four pathologic phases of AD progression based on tau spread: 1) no-tau, 2) pre-acceleration, 3) acceleration, and 4) post-acceleration. Results: No significant tau-Aß association was detected in the no-tau phase. In the pre-acceleration phase, the earliest stage of tau deposition, associations emerged between regional tau in medial temporal lobe (MTL) (i.e., entorhinal cortex, parahippocampal gyrus) and cortical Aß in lateral temporal lobe regions. The strongest tau-Aß associations were found in the acceleration phase, in which tau in MTL regions was strongly associated with cortical Aß (i.e., temporal and frontal lobes regions). Strikingly, in the post-acceleration phase, including 96% of symptomatic subjects, tau-Aß associations were no longer significant. Conclusions: The results indicate that associations between tau and Aß are stage-dependent, which could have important implications for understanding the interplay between these two proteinopathies during the progressive stages of AD.

Topographical Overlapping of the Amyloid-ß and Tau Pathologies in the Default Mode Network Predicts Alzheimer's Disease with Higher Specificity.

BACKGROUND: While amyloid-ß (Aß) plaques and tau tangles are the well-recognized pathologies of Alzheimer's disease (AD), they are more often observed in healthy individuals than in AD patients. This discrepancy makes it extremely challenging to utilize these two proteinopathies as reliable biomarkers for the early detection as well as later diagnosis of AD. OBJECTIVE: We hypothesize and provide preliminary evidence that topographically overlapping Aß and tau within the default mode network (DMN) play more critical roles in the underlying pathophysiology of AD than each of the tau and/or Aß pathologies alone. METHODS: We used our newly developed quantification methods and publicly available neuroimaging data from 303 individuals to provide preliminary evidence of our hypothesis. RESULTS: We first showed that the probability of observing overlapping Aß and tau is significantly higher within than outside the DMN. We then showed evidence that using Aß and tau overlap can increase the reliability of the prediction of healthy individuals converting to mild cognitive impairment (MCI) and to a lesser degree converting from MCI to AD. Finally, we provided evidence that while the initial accumulations of Aß and tau seems to be started independently in the healthy participants, the accumulations of the two pathologies interact in the MCI and AD groups. CONCLUSION: These findings shed some light on the complex pathophysiology of AD and suggest that overlapping Aß and tau pathologies within the DMN might be a more reliable biomarker of AD for early detection and later diagnosis of the disease.

Prediction and Modeling of Neuropsychological Scores in Alzheimer's Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks.

Background: In recent years, predicting and modeling the progression of Alzheimer's disease (AD) based on neuropsychological tests has become increasingly appealing in AD research. Objective: In this study, we aimed to predict the neuropsychological scores and investigate the non-linear progression trend of the cognitive declines based on multimodal neuroimaging data. Methods: We utilized unimodal/bimodal neuroimaging measures and a non-linear regression method (based on artificial neural networks) to predict the neuropsychological scores in a large number of subjects (n = 1143), including healthy controls (HC) and patients with mild cognitive impairment non-converter (MCI-NC), mild cognitive impairment converter (MCI-C), and AD. We predicted two neuropsychological scores, i.e., the clinical dementia rating sum of boxes (CDRSB) and Alzheimer's disease assessment scale cognitive 13 (ADAS13), based on structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) biomarkers. Results: Our results revealed that volumes of the entorhinal cortex and hippocampus and the average fluorodeoxyglucose (FDG)-PET of the angular gyrus, temporal gyrus, and posterior cingulate outperform other neuroimaging features in predicting ADAS13 and CDRSB scores. Compared to a unimodal approach, our results showed that a bimodal approach of integrating the top two neuroimaging features (i.e., the entorhinal volume and the average FDG of the angular gyrus, temporal gyrus, and posterior cingulate) increased the prediction performance of ADAS13 and CDRSB scores in the converting and stable stages of MCI and AD. Finally, a non-linear AD progression trend was modeled to describe the cognitive decline based on neuroimaging biomarkers in different stages of AD. Conclusion: Findings in this study show an association between neuropsychological scores and sMRI and FDG-PET biomarkers from normal aging to severe AD.

Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI.

Accurate prediction of the early stage of Alzheimer's disease (AD) is important but very challenging. The goal of this study was to utilize predictors for diagnosis conversion to AD based on integrating resting-state functional MRI (rs-fMRI) connectivity analysis and structural MRI (sMRI). We included 177 subjects in this study and aimed at identifying patients with mild cognitive impairment (MCI) who progress to AD, MCI converter (MCI-C), patients with MCI who do not progress to AD, MCI non-converter (MCI-NC), patients with AD, and healthy controls (HC). The graph theory was used to characterize different aspects of the rs-fMRI brain network by calculating measures of integration and segregation. The cortical and subcortical measurements, e.g., cortical thickness, were extracted from sMRI data. The rs-fMRI graph measures were combined with the sMRI measures to construct input features of a support vector machine (SVM) and classify different groups of subjects. Two feature selection algorithms [i.e., the discriminant correlation analysis (DCA) and sequential feature collection (SFC)] were used for feature reduction and selecting a subset of optimal features. Maximum accuracy of 67 and 56% for three-group ("AD, MCI-C, and MCI-NC" or "MCI-C, MCI-NC, and HC") and four-group ("AD, MCI-C, MCI-NC, and HC") classification, respectively, were obtained with the SFC feature selection algorithm. We also identified hub nodes in the rs-fMRI brain network which were associated with the early stage of AD. Our results demonstrated the potential of the proposed method based on integration of the functional and structural MRI for identification of the early stage of AD.

Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI.

Structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) have provided promising results in the diagnosis of Alzheimer's disease (AD), though the utility of integrating sMRI with rs-fMRI has not been explored thoroughly. We investigated the performances of rs-fMRI and sMRI in single modality and multi-modality approaches for classifying patients with mild cognitive impairment (MCI) who progress to probable AD-MCI converter (MCI-C) from those with MCI who do not progress to probable AD-MCI non-converter (MCI-NC). The cortical and subcortical measurements, e.g. cortical thickness, extracted from sMRI and graph measures extracted from rs-fMRI functional connectivity were used as features in our algorithm. We trained and tested a support vector machine to classify MCI-C from MCI-NC using rs-fMRI and sMRI features. Our algorithm for classifying MCI-C and MCI-NC utilized a small number of optimal features and achieved accuracies of 89% for sMRI, 93% for rs-fMRI, and 97% for the combination of sMRI with rs-fMRI. To our knowledge, this is the first study that investigated integration of rs-fMRI and sMRI for identification of the early stage of AD. Our findings shed light on integration of sMRI with rs-fMRI for identification of the early stages of AD.

Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM.

BACKGROUND: We investigated identifying patients with mild cognitive impairment (MCI) who progress to Alzheimer's disease (AD), MCI converter (MCI-C), from those with MCI who do not progress to AD, MCI non-converter (MCI-NC), based on resting-state fMRI (rs-fMRI). NEW METHOD: Graph theory and machine learning approach were utilized to predict progress of patients with MCI to AD using rs-fMRI. Eighteen MCI converts (average age 73.6 years; 11 male) and 62 age-matched MCI non-converters (average age 73.0 years, 28 male) were included in this study. We trained and tested a support vector machine (SVM) to classify MCI-C from MCI-NC using features constructed based on the local and global graph measures. A novel feature selection algorithm was developed and utilized to select an optimal subset of features. RESULTS: Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively. Furthermore, results of our statistical analyses were used to identify the affected brain regions in AD. COMPARISON WITH EXISTING METHOD(S): To the best of our knowledge, this is the first study that combines the graph measures (constructed based on rs-fMRI) with machine learning approach and accurately classify MCI-C from MCI-NC. CONCLUSION: Results of this study demonstrate potential of the proposed approach for early AD diagnosis and demonstrate capability of rs-fMRI to predict conversion from MCI to AD by identifying affected brain regions underlying this conversion.