Parallel ICA of FDG-PET and PiB-PET in three conditions with underlying Alzheimer's pathology

Revealing Alzheimer's Disease Dementia Patterns in [18F]Florbetapir PET with Independent Component Analysis

This study investigates Alzheimer's Disease (AD) dementia through [18F]Florbetapir ([18F]FBP) Positron Emission Tomography (PET) imaging. We employ Independent Component Analysis (ICA) to identify shared latent patterns across controls and individuals with Dementia. The dataset comprises PET brain images from 440 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). After visual inspection, nine independent components (IC) were selected, including visual, salience, default mode, cerebellum, left and right temporal, motor, frontal, and subcortical/brainstem. A Generalized Linear Model (GLM) analysis was performed on the IC weights to evaluate group differences. Salience, default mode, left and right temporal, and frontal components displayed a significant group effect with increased weights in the AD dementia group. Notably, the salience and frontal components demonstrated a significant interaction effect of diagnosis with age. This study emphasizes the potential of ICA in conjunction with [18F]FBP PET imaging to provide valuable insights into the neurobiology of AD dementia.

Molecular neuroimaging in dominantly inherited versus sporadic early-onset Alzheimer's disease

Approximately 5% of Alzheimer's disease patients develop symptoms before age 65 (early-onset Alzheimer's disease), with either sporadic (sporadic early-onset Alzheimer's disease) or dominantly inherited (dominantly inherited Alzheimer's disease) presentations. Both sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease are characterized by brain amyloid-β accumulation, tau tangles, hypometabolism and neurodegeneration, but differences in topography and magnitude of these pathological changes are not fully elucidated. In this study, we directly compared patterns of amyloid-β plaque deposition and glucose hypometabolism in sporadic early-onset Alzheimer's disease and dominantly inherited Alzheimer's disease individuals. Our analysis included 134 symptomatic sporadic early-onset Alzheimer's disease amyloid-Positron Emission Tomography (PET)-positive cases from the University of California, San Francisco, Alzheimer's Disease Research Center (mean ± SD age 59.7 ± 5.6 years), 89 symptomatic dominantly inherited Alzheimer's disease cases (age 45.8 ± 9.3 years) and 102 cognitively unimpaired non-mutation carriers from the Dominantly Inherited Alzheimer Network study (age 44.9 ± 9.2). Each group underwent clinical and cognitive examinations, 11C-labelled Pittsburgh Compound B-PET and structural MRI. 18F-Fluorodeoxyglucose-PET was also available for most participants. Positron Emission Tomography scans from both studies were uniformly processed to obtain a standardized uptake value ratio (PIB50-70 cerebellar grey reference and FDG30-60 pons reference) images. Statistical analyses included pairwise global and voxelwise group comparisons and group-independent component analyses. Analyses were performed also adjusting for covariates including age, sex, Mini-Mental State Examination, apolipoprotein ε4 status and average composite cortical of standardized uptake value ratio. Compared with dominantly inherited Alzheimer's disease, sporadic early-onset Alzheimer's disease participants were older at age of onset (mean ± SD, 54.8 ± 8.2 versus 41.9 ± 8.2, Cohen's d = 1.91), with more years of education (16.4 ± 2.8 versus 13.5 ± 3.2, d = 1) and more likely to be apolipoprotein ε4 carriers (54.6% ε4 versus 28.1%, Cramer's V = 0.26), but similar Mini-Mental State Examination (20.6 ± 6.1 versus 21.2 ± 7.4, d = 0.08). Sporadic early-onset Alzheimer's disease had higher global cortical Pittsburgh Compound B-PET binding (mean ± SD standardized uptake value ratio, 1.92 ± 0.29 versus 1.58 ± 0.44, d = 0.96) and greater global cortical 18F-fluorodeoxyglucose-PET hypometabolism (mean ± SD standardized uptake value ratio, 1.32 ± 0.1 versus 1.39 ± 0.19, d = 0.48) compared with dominantly inherited Alzheimer's disease. Fully adjusted comparisons demonstrated relatively higher Pittsburgh Compound B-PET standardized uptake value ratio in the medial occipital, thalami, basal ganglia and medial/dorsal frontal regions in dominantly inherited Alzheimer's disease versus sporadic early-onset Alzheimer's disease. Sporadic early-onset Alzheimer's disease showed relatively greater 18F-fluorodeoxyglucose-PET hypometabolism in Alzheimer's disease signature temporoparietal regions and caudate nuclei, whereas dominantly inherited Alzheimer's disease showed relatively greater hypometabolism in frontal white matter and pericentral regions. Independent component analyses largely replicated these findings by highlighting common and unique Pittsburgh Compound B-PET and 18F-fluorodeoxyglucose-PET binding patterns. In summary, our findings suggest both common and distinct patterns of amyloid and glucose hypometabolism in sporadic and dominantly inherited early-onset Alzheimer's disease.

Explainable AI-based Deep-SHAP for mapping the multivariate relationships between regional neuroimaging biomarkers and cognition

BACKGROUND

Mild cognitive impairment (MCI)/Alzheimer's disease (AD) is associated with cognitive decline beyond normal aging and linked to the alterations of brain volume quantified by magnetic resonance imaging (MRI) and amyloid-beta (Aβ) quantified by positron emission tomography (PET). Yet, the complex relationships between these regional imaging measures and cognition in MCI/AD remain unclear. Explainable artificial intelligence (AI) may uncover such relationships.

METHOD

We integrate the AI-based deep learning neural network and Shapley additive explanations (SHAP) approaches and introduce the Deep-SHAP method to investigate the multivariate relationships between regional imaging measures and cognition. After validating this approach on simulated data, we apply it to real experimental data from MCI/AD patients.

RESULTS

Deep-SHAP significantly predicted cognition using simulated regional features and identified the ground-truth simulated regions as the most significant multivariate predictors. When applied to experimental MRI data, Deep-SHAP revealed that the insula, lateral occipital, medial frontal, temporal pole, and occipital fusiform gyrus are the primary contributors to global cognitive decline in MCI/AD. Furthermore, when applied to experimental amyloid Pittsburgh compound B (PiB)-PET data, Deep-SHAP identified the key brain regions for global cognitive decline in MCI/AD as the inferior temporal, parahippocampal, inferior frontal, supratemporal, and lateral frontal gray matter.

CONCLUSION

Deep-SHAP method uncovered the multivariate relationships between regional brain features and cognition, offering insights into the most critical modality-specific brain regions involved in MCI/AD mechanisms.

Multiparametric hippocampal signatures for early diagnosis of Alzheimer's disease using 18F-FDG PET/MRI Radiomics

PURPOSE

This study aimed to explore the utility of hippocampal radiomics using multiparametric simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) for early diagnosis of Alzheimer's disease (AD).

METHODS

A total of 53 healthy control (HC) participants, 55 patients with amnestic mild cognitive impairment (aMCI), and 51 patients with AD were included in this study. All participants accepted simultaneous PET/MRI scans, including 18F-fluorodeoxyglucose (18F-FDG) PET, 3D arterial spin labeling (ASL), and high-resolution T1-weighted imaging (3D T1WI). Radiomics features were extracted from the hippocampus region on those three modal images. Logistic regression models were trained to classify AD and HC, AD and aMCI, aMCI and HC respectively. The diagnostic performance and radiomics score (Rad-Score) of logistic regression models were evaluated from 5-fold cross-validation.

RESULTS

The hippocampal radiomics features demonstrated favorable diagnostic performance, with the multimodal classifier outperforming the single-modal classifier in the binary classification of HC, aMCI, and AD. Using the multimodal classifier, we achieved an area under the receiver operating characteristic curve (AUC) of 0.98 and accuracy of 96.7% for classifying AD from HC, and an AUC of 0.86 and accuracy of 80.6% for classifying aMCI from HC. The value of Rad-Score differed significantly between the AD and HC (p < 0.001), aMCI and HC (p < 0.001) groups. Decision curve analysis showed superior clinical benefits of multimodal classifiers compared to neuropsychological tests.

CONCLUSION

Multiparametric hippocampal radiomics using PET/MRI aids in the identification of early AD, and may provide a potential biomarker for clinical applications.

Electroacupuncture stimulation improves cognitive ability and regulates metabolic disorders in Alzheimer's disease model mice: new insights from brown adipose tissue thermogenesis

Background

Metabolic defects play a crucial role in Alzheimer's disease (AD) development. Brown adipose tissue (BAT) has been identified as a novel potential therapeutic target for AD due to its unique role in energy metabolism. Electroacupuncture (EA) shows promise in improving cognitive ability and brain glucose metabolism in AD, but its effects on peripheral and central metabolism are unclear.

Methods

In this study, SAMP8 mice (AD model) received EA stimulation at specific acupoints. Cognitive abilities were evaluated using the Morris water maze test, while neuronal morphology and tau pathology were assessed through Nissl staining and immunofluorescence staining, respectively. Metabolic variations and BAT thermogenesis were measured using ELISA, HE staining, Western blotting, and infrared thermal imaging.

Results

Compared to SAMR1 mice, SAMP8 mice showed impaired cognitive ability, neuronal damage, disrupted thermoregulation, and metabolic disorders with low BAT activity. Both the EA and DD groups improved cognitive ability and decreased tau phosphorylation (p<0.01 or p<0.05). However, only the EA group had a significant effect on metabolic disorders and BAT thermogenesis (p<0.01 or p<0.05), while the DD group did not.

Conclusion

These findings indicate that EA not only improves the cognitive ability of SAMP8 mice, but also effectively regulates peripheral and central metabolic disorders, with this effect being significantly related to the activation of BAT thermogenesis.

Neuroimaging statistical approaches for determining neural correlates of Alzheimer's disease via positron emission tomography imaging

Alzheimer's disease (AD) is a degenerative disorder involving significant memory loss and other cognitive deficits, manifesting as a progression from normal cognitive functioning to mild cognitive impairment to AD. The sooner an accurate diagnosis of probable AD is made, the easier it is to manage symptoms and plan for future therapy. Functional neuroimaging stands to be a useful tool in achieving early diagnosis. Among the many neuroimaging modalities, positron emission tomography (PET) provides direct regional assessment of, among others, brain metabolism, cerebral blood flow, amyloid deposition-all quantities of interest in the characterization of AD. However, there are analytic challenges in identifying early indicators of AD from these high-dimensional imaging data sets, and it is unclear whether early indicators of AD are more likely to emerge in localized patterns of brain activity or in patterns of correlation between distinct brain regions. Early PET-based analyses of AD focused on alterations in metabolic activity at the voxel-level or in anatomically defined regions of interest. Other approaches, including seed-voxel and multivariate techniques, seek to characterize metabolic connectivity by identifying other regions in the brain with similar patterns of activity across subjects. We briefly review various neuroimaging statistical approaches applied to determine changes in metabolic activity or metabolic connectivity associated with AD. We then present an approach that provides a unified statistical framework for addressing both metabolic activity and connectivity. Specifically, we apply a Bayesian spatial hierarchical framework to longitudinal metabolic PET scans from the Alzheimer's Disease Neuroimaging Initiative.

Longitudinal associations between β-amyloid and cortical thickness in mild cognitive impairment

How beta-amyloid accumulation influences brain atrophy in Alzheimer's disease remains contentious with conflicting findings. We aimed to elucidate the correlations of regional longitudinal atrophy with cross-sectional regional and global amyloid in individuals with mild cognitive impairment and no cognitive impairment. We hypothesized that greater cortical thinning over time correlated with greater amyloid deposition, particularly within Alzheimer's disease characteristic regions in mild cognitive impairment, and weaker or no correlations in those with no cognitive impairment. 45 patients with mild cognitive impairment and 12 controls underwent a cross-sectional [¹¹C]-Pittsburgh Compound B PET and two retrospective longitudinal structural imaging (follow-up: 23.65 ± 2.04 months) to assess global/regional amyloid and regional cortical thickness, respectively. Separate linear mixed models were constructed to evaluate relationships of either global or regional amyloid with regional cortical thinning longitudinally. In patients with mild cognitive impairment, regional amyloid in the right banks of the superior temporal sulcus was associated with longitudinal cortical thinning in the right medial orbitofrontal cortex (P = 0.04 after False Discovery Rate correction). In the mild cognitive impairment group, greater right banks amyloid burden and less cortical thickness in the right medial orbitofrontal cortex showed greater visual and verbal memory decline over time, which was not observed in controls. Global amyloid was not associated with longitudinal cortical thinning in any locations in either group. Our findings indicate an increasing influence of amyloid on neurodegeneration and memory along the preclinical to prodromal spectrum. Future multimodal studies that include additional biomarkers will be well-suited to delineate the interplay between various pathological processes and amyloid and memory decline, as well as clarify their additive or independent effects along the disease deterioration.

ExHiF: Alzheimer's disease detection using exemplar histogram-based features with CT and MR images

PURPOSE

The classification of medical images is an important priority for clinical research and helps to improve the diagnosis of various disorders. This work aims to classify the neuroradiological features of patients with Alzheimer's disease (AD) using an automatic hand-modeled method with high accuracy.

MATERIALS AND METHOD

This work uses two (private and public) datasets. The private dataset consists of 3807 magnetic resonance imaging (MRI) and computer tomography (CT) images belonging to two (normal and AD) classes. The second public (Kaggle AD) dataset contains 6400 MR images. The presented classification model comprises three fundamental phases: feature extraction using an exemplar hybrid feature extractor, neighborhood component analysis-based feature selection, and classification utilizing eight different classifiers. The novelty of this model is feature extraction. Vision transformers inspire this phase, and hence 16 exemplars are generated. Histogram-oriented gradients (HOG), local binary pattern (LBP) and local phase quantization (LPQ) feature extraction functions have been applied to each exemplar/patch and raw brain image. Finally, the created features are merged, and the best features are selected using neighborhood component analysis (NCA). These features are fed to eight classifiers to obtain highest classification performance using our proposed method. The presented image classification model uses exemplar histogram-based features; hence, it is called ExHiF.

RESULTS

We have developed the ExHiF model with a ten-fold cross-validation strategy using two (private and public) datasets with shallow classifiers. We have obtained 100% classification accuracy using cubic support vector machine (CSVM) and fine k nearest neighbor (FkNN) classifiers for both datasets.

CONCLUSIONS

Our developed model is ready to be validated with more datasets and has the potential to be employed in mental hospitals to assist neurologists in confirming their manual screening of AD using MRI/CT images.

Transferability of Alzheimer's disease progression subtypes to an independent population cohort

In the past, methods to subtype or biotype patients using brain imaging data have been developed. However, it is unclear whether and how these trained machine learning models can be successfully applied to population cohorts to study the genetic and lifestyle factors underpinning these subtypes. This work, using the Subtype and Stage Inference (SuStaIn) algorithm, examines the generalisability of data-driven Alzheimer's disease (AD) progression models. We first compared SuStaIn models trained separately on Alzheimer's disease neuroimaging initiative (ADNI) data and an AD-at-risk population constructed from the UK Biobank dataset. We further applied data harmonization techniques to remove cohort effects. Next, we built SuStaIn models on the harmonized datasets, which were then used to subtype and stage subjects in the other harmonized dataset. The first key finding is that three consistent atrophy subtypes were found in both datasets, which match the previously identified subtype progression patterns in AD: 'typical', 'cortical' and 'subcortical'. Next, the subtype agreement was further supported by high consistency in individuals' subtypes and stage assignment based on the different models: more than 92% of the subjects, with reliable subtype assignment in both ADNI and UK Biobank dataset, were assigned to an identical subtype under the model built on the different datasets. The successful transferability of AD atrophy progression subtypes across cohorts capturing different phases of disease development enabled further investigations of associations between AD atrophy subtypes and risk factors. Our study showed that (1) the average age is highest in the typical subtype and lowest in the subcortical subtype; (2) the typical subtype is associated with statistically more-AD-like cerebrospinal fluid biomarkers values in comparison to the other two subtypes; and (3) in comparison to the subcortical subtype, the cortical subtype subjects are more likely to associate with prescription of cholesterol and high blood pressure medications. In summary, we presented cross-cohort consistent recovery of AD atrophy subtypes, showing how the same subtypes arise even in cohorts capturing substantially different disease phases. Our study opened opportunities for future detailed investigations of atrophy subtypes with a broad range of early risk factors, which will potentially lead to a better understanding of the disease aetiology and the role of lifestyle and behaviour on AD.

What is New in Nuclear Medicine Imaging for Dementia

Advances in the molecular biology, pathology and genetics of Alzheimer's disease (AD) and other degenerative dementias have led to the development of biomarkers specific to these diseases and radiotracers that are used in nuclear medicine. Imaging and non-imaging markers have enabled very early recognition of these diseases and have caused significant changes in their definitions. Amyloid positron emission tomography (PET) and tau PET, which are molecular imaging methods, [F18]fluorodeoxyglucose (FDG) PET showing the glucose metabolism pattern in the brain, dopamine transporter single photon emission computerized tomography (SPECT) that marks dopaminergic terminals are valuable tools for early recognition and differentiation of AD and its atypical variants, frontotemporal dementias and dementia with Lewy bodies. These imaging methods, which have different advantages over each other, have different indications for use and sometimes provide complementary information. In addition, research on radiotracers targeting neuroinflammation, astrocytes, synaptic density, and cholinergic terminals is ongoing. In this review, routinely used and newly developed nuclear imaging methods in AD and other neurodegenerative dementias, the agents used and their diagnostic features will be presented together with case examples.

Modulations of static and dynamic functional connectivity among brain networks by electroacupuncture in post-stroke aphasia

Introduction

Post-stroke aphasia (PSA) is a language disorder caused by left hemisphere stroke. Electroacupuncture (EA) is a minimally invasive therapeutic option for PSA treatment. Tongli (HT5) and Xuanzhong (GB39), two important language-associated acupoints, are frequently used in the rehabilitation of patients with PSA. Preliminary evidence indicated functional activation in distributed cortical areas upon HT5 and GB39 stimulation. However, research on the modulation of dynamic and static functional connectivity (FC) in the brain by EA in PSA is lacking.

Method

This study aimed to investigate the PSA-related effects of EA stimulation at HT5 and GB39 on neural processing. Thirty-five participants were recruited, including 19 patients with PSA and 16 healthy controls (HCs). The BOLD signal was analyzed by static independent component analysis, generalized psychophysiological interactions, and dynamic independent component analysis, considering variables such as age, sex, and years of education.

Results

The results revealed that PSA showed activated clusters in the left putamen, left postcentral gyrus (PostCG), and left angular gyrus in the salience network (SN) compared to the HC group. The interaction effect on temporal properties of networks showed higher variability of SN (F = 2.23, positive false discovery rate [pFDR] = 0.017). The interaction effect on static FC showed increased functional coupling between the right calcarine and right lingual gyrus (F = 3.16, pFDR = 0.043). For the dynamic FC, at the region level, the interaction effect showed lower variability and higher frequencies of circuit 3, with the strongest connections between the supramarginal gyrus and posterior cingulum (F = 5.42, pFDR = 0.03), middle cingulum and PostCG (F = 5.27, pFDR = 0.036), and triangle inferior frontal and lingual gyrus (F = 5.57, pFDR = 0.026). At the network level, the interaction effect showed higher variability in occipital network-language network (LN) and cerebellar network (CN) coupling, with stronger connections between the LN and CN (F = 4.29, pFDR = 0.042). Dynamic FC values between the triangle inferior frontal and lingual gyri were anticorrelated with transcribing, describing, and dictating scores in the Chinese Rehabilitation Research Center for Chinese Standard Aphasia Examination.

Discussion

These findings suggest that EA stimulation may improve language function, as it significantly modulated the nodes of regions/networks involved in the LN, SN, CN, occipital cortex, somatosensory regions, and cerebral limbic system.

Regional amyloid correlates of cognitive performance in ageing and mild cognitive impairment

Beta-amyloid deposition is one of the earliest pathological markers associated with Alzheimer's disease. Mild cognitive impairment in the setting of beta-amyloid deposition is considered to represent a preclinical manifestation of Alzheimer's disease. In vivo imaging studies are unique in their potential to advance our understanding of the role of beta-amyloid deposition in cognitive deficits in Alzheimer's disease and in mild cognitive impairment. Previous work has shown an association between global cortical measures of beta-amyloid deposition ('amyloid positivity') in mild cognitive impairment with greater cognitive deficits and greater risk of progression to Alzheimer's disease. The focus of the present study was to examine the relationship between the regional distribution of beta-amyloid deposition and specific cognitive deficits in people with mild cognitive impairment and cognitively normal elderly individuals. Forty-seven participants with multi-domain, amnestic mild cognitive impairment (43% female, aged 57-82 years) and 37 healthy, cognitively normal comparison subjects (42% female, aged 55-82 years) underwent clinical and neuropsychological assessments and high-resolution positron emission tomography with the radiotracer 11C-labelled Pittsburgh compound B to measure beta-amyloid deposition. Brain-behaviour partial least-squares analysis was conducted to identify spatial patterns of beta-amyloid deposition that correlated with the performance on neuropsychological assessments. Partial least-squares analysis identified a single significant (P < 0.001) latent variable which accounted for 80% of the covariance between demographic and cognitive measures and beta-amyloid deposition. Performance in immediate verbal recall (R = -0.46 ± 0.07, P < 0.001), delayed verbal recall (R = -0.39 ± 0.09, P < 0.001), immediate visual-spatial recall (R = -0.39 ± 0.08, P < 0.001), delayed visual-spatial recall (R = -0.45 ± 0.08, P < 0.001) and semantic fluency (R = -0.33 ± 0.11, P = 0.002) but not phonemic fluency (R = -0.05 ± 0.12, P < 0.705) negatively covaried with beta-amyloid deposition in the identified regions. Partial least-squares analysis of the same cognitive measures with grey matter volumes showed similar associations in overlapping brain regions. These findings suggest that the regional distribution of beta-amyloid deposition and grey matter volumetric decreases is associated with deficits in executive function and memory in mild cognitive impairment. Longitudinal analysis of these relationships may advance our understanding of the role of beta-amyloid deposition in relation to grey matter volumetric decreases in cognitive decline.

rPOP: Robust PET-only processing of community acquired heterogeneous amyloid-PET data

The reference standard for amyloid-PET quantification requires structural MRI (sMRI) for preprocessing in both multi-site research studies and clinical trials. Here we describe rPOP (robust PET-Only Processing), a MATLAB-based MRI-free pipeline implementing non-linear warping and differential smoothing of amyloid-PET scans performed with any of the FDA-approved radiotracers (¹⁸F-florbetapir/FBP, ¹⁸F-florbetaben/FBB or ¹⁸F-flutemetamol/FLUTE). Each image undergoes spatial normalization based on weighted PET templates and data-driven differential smoothing, then allowing users to perform their quantification of choice. Prior to normalization, users can choose whether to automatically reset the origin of the image to the center of mass or proceed with the pipeline with the image as it is. We validate rPOP with n = 740 (514 FBP, 182 FBB, 44 FLUTE) amyloid-PET scans from the Imaging Dementia-Evidence for Amyloid Scanning - Brain Health Registry sub-study (IDEAS-BHR) and n = 1,518 scans from the Alzheimer's Disease Neuroimaging Initiative (n = 1,249 FBP, n = 269 FBB), including heterogeneous acquisition and reconstruction protocols. After running rPOP, a standard quantification to extract Standardized Uptake Value ratios and the respective Centiloids conversion was performed. rPOP-based amyloid status (using an independent pathology-based threshold of ≥24.4 Centiloid units) was compared with either local visual reads (IDEAS-BHR, n = 663 with complete valid data and reads available) or with amyloid status derived from an MRI-based PET processing pipeline (ADNI, thresholds of >20/>18 Centiloids for FBP/FBB). Finally, within the ADNI dataset, we tested the linear associations between rPOP- and MRI-based Centiloid values. rPOP achieved accurate warping for N = 2,233/2,258 (98.9%) in the first pass. Of the N = 25 warping failures, 24 were rescued with manual reorientation and origin reset prior to warping. We observed high concordance between rPOP-based amyloid status and both visual reads (IDEAS-BHR, Cohen's k = 0.72 [0.7-0.74], ∼86% concordance) or MRI-pipeline based amyloid status (ADNI, k = 0.88 [0.87-0.89], ∼94% concordance). rPOP- and MRI-pipeline based Centiloids were strongly linearly related (R²:0.95, p<0.001), with this association being significantly modulated by estimated PET resolution (β= -0.016, p<0.001). rPOP provides reliable MRI-free amyloid-PET warping and quantification, leveraging widely available software and only requiring an attenuation-corrected amyloid-PET image as input. The rPOP pipeline enables the comparison and merging of heterogeneous datasets and is publicly available at https://github.com/leoiacca/rPOP.

Posterior Cingulate Cortex Hypometabolism in Non-Amnestic Variants of Alzheimer's Disease

BACKGROUND

Hypometabolism of the posterior cingulate cortex (PCC) is an important diagnostic feature of late-onset, amnestic Alzheimer's disease (AD) measured with 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). However, it is unclear whether PCC hypometabolism has diagnostic value in young-onset, non-amnestic variants of AD, which exhibit less pathology in the hippocampus and default mode network.

OBJECTIVE

Evaluate the prevalence and diagnostic value of PCC hypometabolism in non-amnestic variants of AD.

METHODS

We retrospectively identified 60 patients with young-onset, atypical dementia who have undergone a detailed clinical evaluation, FDG-PET, and an amyloid biomarker (amyloid-PET or cerebrospinal fluid analysis). We quantitatively analyzed regional hypometabolism in 70 regions of interest (ROI) using the MIMneuro® software.

RESULTS

Based on a cut-off of z-score < -1.5 for significant PCC hypometabolism, the prevalence of PCC hypometabolism in non-amnestic variants of AD was 65% compared to 28% in clinical variants of frontotemporal dementia (FTD). The ROI with the maximal hypometabolism was the dominant middle temporal gyrus in the language variant of AD (mean z score -2.28), middle occipital gyrus in PCA (-3.24), middle temporal gyrus in frontal AD (-2.70), and angular gyrus in corticobasal syndrome due to AD (-2.31). The PCC was not among the 10 most discriminant regions between non-amnestic variants of AD versus clinical variants of FTD.

CONCLUSION

We conclude that PCC hypometabolism is not a discriminant feature to distinguish non-amnestic variants of AD from clinical variants of FTD-and should be interpreted with caution in patients with young-onset, non-amnestic dementia.

Alzheimer disease

Alzheimer disease (AD) is biologically defined by the presence of β-amyloid-containing plaques and tau-containing neurofibrillary tangles. AD is a genetic and sporadic neurodegenerative disease that causes an amnestic cognitive impairment in its prototypical presentation and non-amnestic cognitive impairment in its less common variants. AD is a common cause of cognitive impairment acquired in midlife and late-life but its clinical impact is modified by other neurodegenerative and cerebrovascular conditions. This Primer conceives of AD biology as the brain disorder that results from a complex interplay of loss of synaptic homeostasis and dysfunction in the highly interrelated endosomal/lysosomal clearance pathways in which the precursors, aggregated species and post-translationally modified products of Aβ and tau play important roles. Therapeutic endeavours are still struggling to find targets within this framework that substantially change the clinical course in persons with AD.

Spatially Distributed Amyloid-β Reduces Glucose Metabolism in Mild Cognitive Impairment

Background:

Several positron emission tomography (PET) studies have explored the relationship between amyloid-β (Aβ), glucose metabolism, and the APOEɛ4 genotype. It has been reported that APOEɛ4, and not aggregated Aβ, contributes to glucose hypometabolism in pre-clinical stages of Alzheimer’s disease (AD) pathology.

Objective:

We hypothesize that typical measurements of Aβ taken either from composite regions-of-interest with relatively high burden actually cover significant patterns of the relationship with glucose metabolism. In contrast, spatially weighted measures of Aβ are more related to glucose metabolism in cognitively normal (CN) aging and mild cognitive impairment (MCI).

Methods:

We have generated a score of amyloid burden based on a joint singular value decomposition (SVD) of the cross-correlation structure between glucose metabolism, as measured by [¹⁸F]2-fluoro-2-deoxyglucose (FDG) PET, and Aβ, as measured by [¹⁸F]florbetapir PET, from the Alzheimer’s Disease Neuroimaging Initiative study. This SVD-based score reveals cortical regions where a reduced glucose metabolism is maximally correlated with distributed patterns of Aβ.

Results:

From an older population of CN and MCI subjects, we found that the SVD-based Aβ score was significantly correlated with glucose metabolism in several cortical regions. Additionally, the corresponding Aβ network has hubs that contribute to distributed glucose hypometabolism, which, in turn, are not necessarily foci of Aβ deposition.

Conclusions:

Our approach uncovered hidden patterns of the glucose metabolism-Aβ relationship. We showed that the SVD-based Aβ score produces a stronger relationship with decreasing glucose metabolism than either APOEɛ4 genotype or global measures of Aβ burden.

Electroacupuncture reduces scopolamine-induced amnesia via mediating the miR-210/SIN3A and miR-183/SIN3A signaling pathway

Background

The expression of SIN3A is closely correlated with electroacupuncture (EA) treatment efficacy of scopolamine-induced amnesia (SIA), but its underlying mechanisms remain to be further explored.

Methods

Quantitative real-time PCR was performed to analyze the expression of candidate microRNAs (miRNAs) and SIN3A mRNA in a rat model of SIA. Western blot was carried out to evaluate the differential expression of SIN3A proteins under different circumstances. Luciferase assay was used to explore the inhibitory role of certain miRNAs in SIN3A expression. A novel object recognition (NOR) test was performed to assess the memory function of SIA rats undergoing EA treatment. Immunohistochemistry was carried out to evaluate the expression of SIN3A in the hippocampus of SIA rats.

Results

Rno-miR-183-5p, rno-miR-34c-3p and rno-miR-210-3p were significantly up-regulated in SIA rats treated with EA. In addition, rno-miR-183-5p and rno-miR-210-3p exerted an inhibitory effect on SIN3A expression. EA treatment of SIA rats effectively restored the dysregulated expression of rno-miR-183-5p, rno-miR-210-3p and SIN3A. EA treatment also promoted the inhibited expression of neuronal IEGs including Arc, Egr1, Homer1 and Narp in the hippocampus of SIA rats. Accordingly, the NOR test also confirmed the effect of EA treatment on the improvement of memory in SIA rats.

Conclusion

In summary, the findings of this study demonstrated that scopolamine-induced amnesia was associated with downregulated expression of miR-210/miR-183 and upregulated expression of SIN3A. Furthermore, treatment with EA alleviated scopolamine-induced amnesia in rats and was associated with upregulated expression of miR-210/miR-183 and downregulated expression of SIN3A.

Alzheimer amyloid-β- peptide disrupts membrane localization of glucose transporter 1 in astrocytes: implications for glucose levels in brain and blood

Alzheimer’s disease (AD) is associated with disturbances in blood glucose regulation, and type-2 diabetes elevates the risk for dementia. A role for amyloid-β peptide (Aβ) in linking these age-related conditions has been proposed, tested primarily in transgenic mouse lines that overexpress mutated amyloid precursor protein (APP). Because APP has its own impacts on glucose regulation, we examined the BRI-Aβ42 line (“Aβ₄₂-tg”), which produces extracellular Aβ_1–42 in the CNS without elevation of APP. We also looked for interactions with diet-induced obesity (DIO) resulting from a high-fat, high-sucrose (“western”) diet. Aβ₄₂-tg mice were impaired in both spatial memory and glucose tolerance. Although DIO induced insulin resistance, Aβ_1–42 accumulation did not, and the impacts of DIO and Aβ on glucose tolerance were merely additive. Aβ₄₂-tg mice exhibited no significant differences from wild-type in insulin production, body weight, lipidemia, appetite, physical activity, respiratory quotient, an-/orexigenic factors, or inflammatory factors. These negative findings suggested that the phenotype in these mice arose from perturbation of glucose excursion in an insulin-independent tissue. To wit, cerebral cortex of Aβ₄₂-tg mice had reduced glucose utilization, similar to human patients with AD. This was associated with insufficient trafficking of glucose transporter 1 to the plasma membrane in parenchymal brain cells, a finding also documented in human AD tissue. Together, the lower cerebral metabolic rate of glucose and diminished function of parenchymal glucose transporter 1 indicate that aberrant regulation of blood glucose in AD likely reflects a central phenomenon, resulting from the effects of Aβ on cerebral parenchyma, rather than a generalized disruption of hypothalamic or peripheral endocrinology. The involvement of a specific glucose transporter in this deficit provides a new target for the design of AD therapies.

Sirtuin 3 attenuates amyloid-β induced neuronal hypometabolism

Alzheimer’s disease (AD) is manifested by regional cerebral hypometabolism. Sirtuin 3 (Sirt3) is localized in mitochondria and regulates cellular metabolism, but the role of Sirt3 in AD-related hypometabolism remains elusive. We used expression profiling and weighted gene co-expression network analysis (WGCNA) to analyze cortical neurons from a transgenic mouse model of AD (APPSwInd). Based on WGCNA results, we measured NAD⁺ level, NAD⁺/ NADH ratio, Sirt3 protein level and its deacetylation activity, and ATP production across both in vivo and in vitro models. To investigate the effect of Sirt3 on amyloid-β (Aβ)-induced mitochondria damage, we knocked down and over-expressed Sirt3 in hippocampal cells. WGCNA revealed Sirt3 as a key player in Aβ-related hypometabolism. In APP mice, the NAD⁺ level, NAD⁺/ NADH ratio, Sirt3 protein level and activity, and ATP production were all reduced compared to the control. As a result, learning and memory performance were impaired in 9-month-old APP mice compared to wild type controls. Using hippocampal HT22 cells model, Sirt3 overexpression increased Sirt3 deacetylation activity, rescued mitochondria function, and salvaged ATP production, which were damaged by Aβ. Sirt3 plays an important role in regulating Aβ-induced cerebral hypometabolism. This study suggests a potential direction for AD therapy.

The puzzle of preserved cognition in the oldest old

Although epidemiological studies predict an exponential increase in the prevalence of dementia with age, recent studies have demonstrated that the oldest old are actually less frequently affected by dementia than the younger elderly. To explain this, I suggest a parallel between brain ageing and Alzheimer's disease (AD) and assume that theories concerning the brain's vulnerability to AD and its individual variability may also explain why some of the oldest old remain cognitively efficient. Some theories argue that AD is due to the continuing presence of the immature neurones vulnerable to amyloid beta protein (Aß) that are normally involved in brain development and then removed as a result of cell selection by the proteins associated with both brain development and AD. If a dysfunction in cell selection allows these immature neurones to survive, they degenerate early as a result of the neurotoxic action of Aß accumulation, which their mature counterparts can withstand. Consequently, age at the time of onset of AD and its clinical presentations depend on the number and location of such immature cells. I speculate that the same mechanism is responsible for the variability of normal brain ageing: the oldest old with well-preserved cognitive function are people genetically programmed for extreme ageing who have benefited from better cell selection during prenatal and neonatal life and therefore have fewer surviving neurones vulnerable to amyloid-promoted degeneration, whereas the process of early life cell selection was less successful in the oldest old who develop dementia.

Brain network alterations in individuals with and without mild cognitive impairment: parallel independent component analysis of AV1451 and AV45 positron emission tomography

BACKGROUND

Amyloid β (Aβ) and tau proteins are considered as critical factors that affect Alzheimer's disease (AD) and mild cognitive impairment (MCI). Although many studies have conducted on these two proteins, little study has investigated the relationship between their spatial distributions. This study aims to explore the associations of spatial patterns between Aβ deposition and tau deposition in patients with MCI and normal control (NC).

METHODS

We used multimodality positron emission tomography (PET) data from a clinically heterogeneous population of patients with MCI and NC. All data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database containing information of 65 patients with MCI and 75 NC who both had undergone AV45 (Aβ) and AV1451 (tau) PET. To assess the spatial distribution of Aβ and tau deposition, we employed parallel independent component analysis (pICA), which enabled the joint analysis of multimodal imaging data. pICA was conducted to identify the significant difference and correlation relationship of brain networks between Aβ PET and tau PET in MCI and NC groups.

RESULTS

Our results revealed the strongly correlated network between Aβ PET and tau PET were colocalized with the default-mode network (DMN). Simultaneously, in comparison of the spatial distribution between Aβ PET and tau PET, it was found that the significant differences between MCI and NC were mainly distributed in DMN, cognitive control network and visual networks. The altered brain networks obtained from pICA analysis are consistent with the abnormalities of brain network in MCI patients.

CONCLUSIONS

Findings suggested the abnormal spatial distribution regions of tau PET were correlated with the abnormal spatial distribution regions of Aβ PET, and both of which were located in DMN network. This study revealed that combining pICA with multimodal imaging data is an effective approach for distinguishing MCI patients from NC group.

Regulation of AβPP Glycosylation Modification and Roles of Glycosylation on AβPP Cleavage in Alzheimer's Disease

The presence of senile plaques in the gray matter of the brain is one of the major pathologic features of Alzheimer's disease (AD), and amyloid-β (Aβ) is the main component of extracellular deposits of the senile plaques. Aβ derives from amyloid-β precursor protein (AβPP) cleaved by β-secretase (BACE1) and γ-secretase, and the abnormal cleavage of AβPP is an important event leading to overproduction and aggregation of Aβ species. After translation, AβPP undergoes post-translational modifications (PTMs) including glycosylation and phosphorylation in the endoplasmic reticulum (ER) and Golgi apparatus, and these modifications play an important role in regulating the cleavage of this protein. In this Review, we summarize research progress on the modification of glycosylation, especially O-GlcNAcylation and mucin-type O-linked glycosylation (also known as O-GalNAcylation), on the regulation of AβPP cleavage and on the influence of AβPP's glycosylation in the pathogenesis of AD.

A Case with Early Onset Alzheimer's Disease, Frontotemporal Hypometabolism, ApoE Genotype ɛ4/ɛ4 and C9ORF72 Intermediate Expansion: A Treviso Dementia (TREDEM) Registry Case Report

We report the case of a woman firstly referred to our Memory Clinic at the age of 61, following the development of cognitive complaints and difficulties in sustained attention. The investigation that was performed showed: predominant executive dysfunctions at the neuropsychological evaluation, with mild, partial and stable involvement of the memory domain; cortical and subcortical atrophy with well-preserved hippocampal structures at MRI; marked fronto-temporal and moderate parietal hypometabolism from 18F-FDG PET study with a sparing of the posterior cingulate and precuneus; positivity of amyloid-β at 18F-Flutemetamol PET; an hexanucleotide intermediate repeats expansion of C9ORF72 gene (12//38 repeats) and ApoE genotype ɛ4/ɛ4. The patient was diagnosed with probable early onset frontal variant of Alzheimer's disease (AD), presenting with a major executive function impairment. The lack of specific areas of brain atrophy, as well as the failure to meet the clinical criteria for any frontotemporal dementia, drove us to perform the aforementioned investigations, which yielded our final diagnosis. The present case highlights the need to take into consideration a diagnosis of frontal variant of AD when the metabolic and the clinical picture are somehow dissonant.

Neuroimaging in aging and neurologic diseases

Neuroimaging biomarkers for neurologic diseases are important tools, both for understanding pathology associated with cognitive and clinical symptoms and for differential diagnosis. This chapter explores neuroimaging measures, including structural and functional measures from magnetic resonance imaging (MRI) and molecular measures primarily from positron emission tomography (PET), in healthy aging adults and in a number of neurologic diseases. The spectrum covers neuroimaging measures from normal aging to a variety of dementias: late-onset Alzheimer's disease [AD; including mild cognitive impairment (MCI)], familial and nonfamilial early-onset AD, atypical AD syndromes, posterior cortical atrophy (PCA), logopenic aphasia (lvPPA), cerebral amyloid angiopathy (CAA), vascular dementia (VaD), sporadic and familial behavioral-variant frontotemporal dementia (bvFTD), semantic dementia (SD), progressive nonfluent aphasia (PNFA), frontotemporal dementia with motor neuron disease (FTD-MND), frontotemporal dementia with amyotrophic lateral sclerosis (FTD-ALS), corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), dementia with Lewy bodies (DLB), Parkinson's disease (PD) with and without dementia, and multiple systems atrophy (MSA). We also include a discussion of the appropriate use criteria (AUC) for amyloid imaging and conclude with a discussion of differential diagnosis of neurologic dementia disorders in the context of neuroimaging.

European Association of Nuclear Medicine and European Academy of Neurology recommendations for the use of brain 18 F-fluorodeoxyglucose positron emission tomography in neurodegenerative cognitive impairment and dementia: Delphi consensus

BACKGROUND AND PURPOSE

Recommendations for using fluorodeoxyglucose positron emission tomography (FDG-PET) to support the diagnosis of dementing neurodegenerative disorders are sparse and poorly structured.

METHODS

Twenty-one questions on diagnostic issues and on semi-automated analysis to assist visual reading were defined. Literature was reviewed to assess study design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiver operating characteristic curve, and positive/negative likelihood ratio of FDG-PET in detecting the target conditions. Using the Delphi method, an expert panel voted for/against the use of FDG-PET based on published evidence and expert opinion.

RESULTS

Of the 1435 papers, 58 papers provided proper quantitative assessment of test performance. The panel agreed on recommending FDG-PET for 14 questions: diagnosing mild cognitive impairment due to Alzheimer's disease (AD), frontotemporal lobar degeneration (FTLD) or dementia with Lewy bodies (DLB); diagnosing atypical AD and pseudo-dementia; differentiating between AD and DLB, FTLD or vascular dementia, between DLB and FTLD, and between Parkinson's disease and progressive supranuclear palsy; suggesting underlying pathophysiology in corticobasal degeneration and progressive primary aphasia, and cortical dysfunction in Parkinson's disease; using semi-automated assessment to assist visual reading. Panellists did not support FDG-PET use for pre-clinical stages of neurodegenerative disorders, for amyotrophic lateral sclerosis and Huntington disease diagnoses, and for amyotrophic lateral sclerosis or Huntington-disease-related cognitive decline.

CONCLUSIONS

Despite limited formal evidence, panellists deemed FDG-PET useful in the early and differential diagnosis of the main neurodegenerative disorders, and semi-automated assessment helpful to assist visual reading. These decisions are proposed as interim recommendations.

High and Low Levels of an NTRK2-Driven Genetic Profile Affect Motor- and Cognition-Associated Frontal Gray Matter in Prodromal Huntington's Disease

This study assessed how BDNF (brain-derived neurotrophic factor) and other genes involved in its signaling influence brain structure and clinical functioning in pre-diagnosis Huntington's disease (HD). Parallel independent component analysis (pICA), a multivariate method for identifying correlated patterns in multimodal datasets, was applied to gray matter concentration (GMC) and genomic data from a sizeable PREDICT-HD prodromal cohort (N = 715). pICA identified a genetic component highlighting NTRK2, which encodes BDNF's TrkB receptor, that correlated with a GMC component including supplementary motor, precentral/premotor cortex, and other frontal areas (p < 0.001); this association appeared to be driven by participants with high or low levels of the genetic profile. The frontal GMC profile correlated with cognitive and motor variables (Trail Making Test A (p = 0.03); Stroop Color (p = 0.017); Stroop Interference (p = 0.04); Symbol Digit Modalities Test (p = 0.031); Total Motor Score (p = 0.01)). A top-weighted NTRK2 variant (rs2277193) was protectively associated with Trail Making Test B (p = 0.007); greater minor allele numbers were linked to a better performance. These results support the idea of a protective role of NTRK2 in prodromal HD, particularly in individuals with certain genotypes, and suggest that this gene may influence the preservation of frontal gray matter that is important for clinical functioning.

Clinical utility of FDG-PET for the differential diagnosis among the main forms of dementia

AIM

To assess the clinical utility of FDG-PET as a diagnostic aid for differentiating Alzheimer's disease (AD; both typical and atypical forms), dementia with Lewy bodies (DLB), frontotemporal lobar degeneration (FTLD), vascular dementia (VaD) and non-degenerative pseudodementia.

METHODS

A comprehensive literature search was conducted using the PICO model to extract evidence from relevant studies. An expert panel then voted on six different diagnostic scenarios using the Delphi method.

RESULTS

The level of empirical study evidence for the use of FDG-PET was considered good for the discrimination of DLB and AD; fair for discriminating FTLD from AD; poor for atypical AD; and lacking for discriminating DLB from FTLD, AD from VaD, and for pseudodementia. Delphi voting led to consensus in all scenarios within two iterations. Panellists supported the use of FDG-PET for all PICOs-including those where study evidence was poor or lacking-based on its negative predictive value and on the assistance it provides when typical patterns of hypometabolism for a given diagnosis are observed.

CONCLUSION

Although there is an overall lack of evidence on which to base strong recommendations, it was generally concluded that FDG-PET has a diagnostic role in all scenarios. Prospective studies targeting diagnostically uncertain patients for assessing the added value of FDG-PET would be highly desirable.

[Recommendations of Swiss Memory Clinics for the Diagnosis of Dementia]

Recommendations of Swiss Memory Clinics for the Diagnosis of Dementia Abstract. The early diagnosis of subjectively perceived or externally anamnestically observed cognitive impairments is essential for proving neurodegenerative diseases or excluding treatable causes such as internal, neurological or psychiatric disorders. Only in this way is early treatment made possible. As part of the project 3.1 of the National Dementia Strategy 2014-2019 ('Development and expansion of regional and networked centres of competence for diagnostics'), the association Swiss Memory Clinics (SMC) set itself the goal of developing quality standards for dementia clarification and improving the community-based care in this field. In these recommendations, general guidelines of diagnostics and individual examination possibilities are presented, and standards for the related processes are suggested. Individual areas such as anamnesis, clinical examination, laboratory examination, neuropsychological testing and neuroradiological procedures are discussed in detail as part of standard diagnostics, and supplementary examination methods for differential diagnosis considerations are portrayed. The most important goals of the SMC recommendations for the diagnosis of dementia are to give all those affected access to high-quality diagnostics, if possible, to improve early diagnosis of dementia and to offer the basic service providers and the employees of Memory Clinics a useful instrument for the clarification.

[Recommendations of Swiss Memory Clinics for the Diagnosis of Dementia]

The early diagnosis of subjectively perceived or externally anamnestically observed cognitive impairments is essential for proving neurodegenerative diseases or excluding treatable causes such as internal, neurological or psychiatric disorders. Only in this way is early treatment made possible. As part of the project 3.1 of the National Dementia Strategy 2014–2019 («Development and expansion of regional and networked centres of competence for diagnostics»), the association Swiss Memory Clinics (SMC) set itself the goal of developing quality standards for dementia clarification and improving the community-based care in this field. In these recommendations, general guidelines of diagnostics and individual examination possibilities are presented, and standards for the related processes are suggested. Individual areas such as anamnesis, clinical examination, laboratory examination, neuropsychological testing and neuroradiological procedures are discussed in detail as part of standard diagnostics, and supplementary examination methods for differential diagnosis considerations are portrayed. The most important goals of the SMC recommendations for the diagnosis of dementia are to give all those affected access to high-quality diagnostics, if possible, to improve early diagnosis of dementia and to offer the basic service providers and the employees of Memory Clinics a useful instrument for the clarification.

Brain Network Alterations in Alzheimer's Disease Identified by Early-Phase PIB-PET

The aim of this study was to identify the brain networks from early-phase ¹¹C-PIB (perfusion PIB, pPIB) data and to compare the brain networks of patients with differentiating Alzheimer's disease (AD) with cognitively normal subjects (CN) and of mild cognitively impaired patients (MCI) with CN. Forty participants (14 CN, 12 MCI, and 14 AD) underwent ¹¹C-PIB and ¹⁸F-FDG PET/CT scans. Parallel independent component analysis (pICA) was used to identify correlated brain networks from the ¹¹C-pPIB and ¹⁸F-FDG data, and a two-sample t-test was used to evaluate group differences in the corrected brain networks between AD and CN, and between MCI and CN. Our study identified a brain network of perfusion (early-phase ¹¹C-PIB) that highly correlated with a glucose metabolism (¹⁸F-FDG) brain network and colocalized with the default mode network (DMN) in an AD-specific neurodegenerative cohort. Particularly, decreased ¹⁸F-FDG uptake correlated with a decreased regional cerebral blood flow in the frontal, parietal, and temporal regions of the DMN. The group comparisons revealed similar spatial patterns of the brain networks derived from the ¹¹C-pPIB and ¹⁸F-FDG data. Our findings indicate that ¹¹C-pPIB derived from the early-phase ¹¹C-PIB could provide complementary information for ¹⁸F-FDG examination in AD.

Amyloid PET Positivity in Different Primary Progressive Aphasia Phenotypes

PURPOSE

Primary progressive aphasia (PPA) is a neurological syndrome in which language functions become progressively impaired with relative sparing of memory and other instrumental functions. The pathologic causes of PPA are heterogeneous, but studies suggest that logopenic PPA (LPA) is underpinned by Alzheimer disease (AD) pathology in a high proportion of cases. The purposes of this descriptive and retrospective study were to characterize F-florbetapir PET imaging in a group of patients with a clinical syndrome of PPA, to determine the value of clinical characterization based on language phenotype in predicting the underlying pathology of PPA with F-florbetapir, and to quantify amyloid load in PPA subjects classified as "positive" F-florbetapir scans. Then, we compare the quantification and distribution of F-florbetapir uptake with those of typical, predominantly amnestic AD patients.

METHODS

We conducted a PET study with F-florbetapir in a cohort of 12 right-handed patients diagnosed with PPA: 3 patients with semantic-variant PPA, 5 with nonfluent PPA, 1 with LPA, and 3 unclassifiable patients. We evaluated amyloid deposition between APP groups and 11 patients with typical amnestic AD.

RESULTS

Among the 12 patients with PPA syndrome, 8 (66.7%) were considered as amyloid positive. One of the 3 patients with semantic-variant PPA was F-florbetapir positive. In contrast, 4 of the 5 nonfluent-variant PPA, 2 of the 3 unclassifiable cases and the single patient with LPA were F-florbetapir positive. A significantly higher F-florbetapir uptake was observed in PPA F-florbetapir-positive patients compared with typical AD patients. This difference was observed in all regions of interest, except in posterior cingulate and temporal cortex.

CONCLUSIONS

These results suggest that F-florbetapir PET may be useful in a routine clinical procedure to improve the reliability of identifying AD pathology in patients with PPA syndrome, with different clinical subtypes of the PPA syndrome.

The Brain's Structural Connectome Mediates the Relationship between Regional Neuroimaging Biomarkers in Alzheimer's Disease

Alzheimer's disease (AD), one of the most common causes of dementia in adults, is a progressive neurodegenerative disorder exhibiting well-defined neuropathological hallmarks. It is known that disease pathology involves misfolded amyloid-β (Aβ) and tau proteins, and exhibits a relatively stereotyped progression over decades. The relationship between AD neuropathological hallmarks (Aβ, hypometabolism, and tau proteins) and imaging biomarkers (MRI, AV-45/FDG-PET) is not fully understood. In addition, biomarker pathologies are oftentimes discordant, wherein it may show varying levels of abnormality across brain regions. Evidence based on recent elucidation of trans-neuronal "prion-like" transmission and other available data already suggests that disease spread follows the brain's fiber connectivity network. Thereby, the brain's connectome information can be used to predict the process of disease spread in AD. A recently established mathematical model of AD pathology spread using a connectome-based network diffusion model was successful in encapsulating neurodegenerative progression. Motivated by these network-based findings, the current study explores whether and how network connectivity mediates the interactions between various AD biomarkers. We hypothesized that the structural connectivity matrix will mediate the cross-sectional association between regional AD-associated hypometabolism and Aβ deposition. Given recent reports of inherent or lifetime activity of brain regions as strong predictors of Aβ deposition in patients, we also tested whether healthy metabolism exerts a network-mediated effect on Aβ deposition and hypometabolism in AD patients. We found that regional Aβ deposition is best predicted by a linear combination of both regional healthy local metabolism and connectome-mediated regional healthy metabolism.

The Use of 18F-FDG PET in the Diagnostic Workup of Alzheimer's Dementia

The diagnosis of dementia probably due to Alzheimer's disease is still primarily a clinical one. In cases that remain clinically unclear, however, biomarkers for amyloid deposition and neuronal injury can help to identify the underlying cause. One biomarker even for early neuronal injury in the stage of mild cognitive impairment is cerebral glucose hypometabolism measured by ¹⁸F-FDG PET. Distinct patterns of hypometabolism can be seen, for example, in dementia due to Alzheimer's disease, frontotemporal lobar degeneration, and dementia with Lewy bodies. This makes it possible to distinguish between different neurodegenerative diseases as well as major depressive disorder. While the sensitivity of ¹⁸F-FDG PET to detect Alzheimer's disease is high, specificity is low and the additional use of biomarkers for amyloid deposition might be beneficial in some cases. In conclusion, ¹⁸F-FDG PET is a useful tool when the cause for dementia remains unclear and different diagnosis would lead to different treatment approaches. Due to the lack of treatment options in pre-dementia stages, the use of ¹⁸F-FDG PET is currently not recommended for these cases in a purely clinical setting.

Clinical validity of brain fluorodeoxyglucose positron emission tomography as a biomarker for Alzheimer's disease in the context of a structured 5-phase development framework

The use of Alzheimer's disease (AD) biomarkers is supported in diagnostic criteria, but their maturity for clinical routine is still debated. Here, we evaluate brain fluorodeoxyglucose positron emission tomography (FDG PET), a measure of cerebral glucose metabolism, as a biomarker to identify clinical and prodromal AD according to the framework suggested for biomarkers in oncology, using homogenous criteria with other biomarkers addressed in parallel reviews. FDG PET has fully achieved phase 1 (rational for use) and most of phase 2 (ability to discriminate AD subjects from healthy controls or other forms of dementia) aims. Phase 3 aims (early detection ability) are partly achieved. Phase 4 studies (routine use in prodromal patients) are ongoing, and only preliminary results can be extrapolated from retrospective observations. Phase 5 studies (quantify impact and costs) have not been performed. The results of this study show that specific efforts are needed to complete phase 3 evidence, in particular comparing and combining FDG PET with other biomarkers, and to properly design phase 4 prospective studies as a basis for phase 5 evaluations.

Activation of brain glucose metabolism ameliorating cognitive impairment in APP/PS1 transgenic mice by electroacupuncture

An essential feature of Alzheimer's disease (AD) is implicated in brain energy metabolic impairment that is considered underlying pathogenesis of cognitive impairment. Therefore, therapeutic interventions to allay cognitive deficits that target energy metabolism may be an efficacy strategy in AD. In this study, we found that electroacupuncture (EA) at the DU20 acupoint obviously increased glucose metabolism in specific brain regions such as cortex, hippocampus, cingulate gyrus, basal forebrain septum, brain stem, and cerebellum in APP/PS1 transgenic mice by animal ¹⁸F-Fluoro-2-deoxy-D-Glucose (¹⁸F-FDG)/positron emission tomography (PET) imaging, accompanied by cognitive improvements in the spatial reference learning and memory and memory flexibility and novel object recognition performances. Further evidence shown energy metabolism occurred in neurons or non-neuronal cells of the cortex and hippocampus in terms of the co-location of GLUT3/NeuN and GLUT1/GFAP. Simultaneously, metabolic homeostatic factors were critical for glucose metabolism, including phosphorylated adenosine monophosphate-activated protein kinase (AMPK) and AKT serine/threonine kinase. Furthermore, EA-induced phosphorylated AMPK and AKT inhibited the phosphorylation level of the mammalian target of rapamycin (mTOR) to decrease the accumulation of amyloid-beta (Aβ) in the cortex and hippocampus. These findings are concluded that EA is a potential therapeutic target for delaying memory decline and Aβ deposition of AD. The AMPK and AKT are implicated in the EA-induced cortical and hippocampal energy metabolism, which served as a contributor to improving cognitive function and Aβ deposition in a transgenic mouse model of AD.

Local and distant relationships between amyloid, tau and neurodegeneration in Alzheimer's Disease

The relationships between β-amyloid (Aβ), tau and neurodegeneration within Alzheimer's Disease pathogenesis are not fully understood. To explore these associations in vivo, we evaluated 30 Aβ PET-positive patients (mean ± sd age 62.4 ± 8.3) with mild probable AD and 12 Aβ PET-negative healthy controls (HC) (mean ± sd age 77.3 ± 6.9) as comparison. All participants underwent 3 T MRI, ¹¹C-PiB (Aβ) PET and ¹⁸F-AV1451 (tau) PET. Multimodal correlation analyses were run at both voxel- and region-of-interest levels. ¹¹C-PiB retention in AD showed the most diffuse uptake pattern throughout association neocortex, whereas ¹⁸F-AV1451 and gray matter volume reduction (GMR) showed a progressive predilection for posterior cortices (p<0.05 Family-Wise Error-[FWE]-corrected). Voxel-level analysis identified negative correlations between ¹⁸F-AV1451 and gray matter peaking in medial and infero-occipital regions (p<0.01 False Discovery Rate-[FDR]-corrected). ¹⁸F-AV1451 and ¹¹C-PiB were positively correlated in right parietal and medial/inferior occipital regions (p<0.001 uncorrected). ¹¹C-PiB did not correlate with GMR at the voxel-level. Regionally, ¹⁸F-AV1451 was largely associated with local/adjacent GMR whereas frontal ¹¹C-PiB correlated with GMR in posterior regions. These findings suggest that, in mild AD, tau aggregation drives local neurodegeneration, whereas the relationships between Aβ and neurodegeneration are not region specific and may be mediated by the interaction between Aβ and tau.

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Tau tangles show tight and local associations with gray matter volume.

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Amyloid plaques show long-distance and indirect effects on gray matter volume.

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Local relationships between tau and amyloid may evolve and vary by disease stage.

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Amyloid accumulates homogeneously and uniformly across association cortices.

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Tau accumulation begins locally and spreads to functionally connected regions.

Visual Dysfunction in Posterior Cortical Atrophy

Posterior cortical atrophy (PCA) is a syndromic diagnosis. It is characterized by progressive impairment of higher (cortical) visual function with imaging evidence of degeneration affecting the occipital, parietal, and posterior temporal lobes bilaterally. Most cases will prove to have Alzheimer pathology. The aim of this review is to summarize the development of the concept of this disorder since it was first introduced. A critical discussion of the evolving diagnostic criteria is presented and the differential diagnosis with regard to the underlying pathology is reviewed. Emphasis is given to the visual dysfunction that defines the disorder, and the classical deficits, such as simultanagnosia and visual agnosia, as well as the more recently recognized visual field defects, are reviewed, along with the evidence on their neural correlates. The latest developments on the imaging of PCA are summarized, with special attention to its role on the differential diagnosis with related conditions.

Imaging and fluid biomarkers in frontotemporal dementia

Frontotemporal dementia (FTD), the second most common type of presenile dementia, is a heterogeneous neurodegenerative disease characterized by progressive behavioural and/or language problems, and includes a range of clinical, genetic and pathological subtypes. The diagnostic process is hampered by this heterogeneity, and correct diagnosis is becoming increasingly important to enable future clinical trials of disease-modifying treatments. Reliable biomarkers will enable us to better discriminate between FTD and other forms of dementia and to predict disease progression in the clinical setting. Given that different underlying pathologies probably require specific pharmacological interventions, robust biomarkers are essential for the selection of patients with specific FTD subtypes. This Review emphasizes the increasing availability and potential applications of structural and functional imaging biomarkers, and cerebrospinal fluid and blood fluid biomarkers in sporadic and genetic FTD. The relevance of new MRI modalities - such as voxel-based morphometry, diffusion tensor imaging and arterial spin labelling - in the early stages of FTD is discussed, together with the ability of these modalities to classify FTD subtypes. We highlight promising new fluid biomarkers for staging and monitoring of FTD, and underline the importance of large, multicentre studies of individuals with presymptomatic FTD. Harmonization in the collection and analysis of data across different centres is crucial for the implementation of new biomarkers in clinical practice, and will become a great challenge in the next few years.

Early-Onset Alzheimer Disease

Early-onset Alzheimer disease (EOAD), with onset in individuals younger than 65 years, although overshadowed by the more common late-onset AD (LOAD), differs significantly from LOAD. EOAD comprises approximately 5% of AD and is associated with delays in diagnosis, aggressive course, and age-related psychosocial needs. One source of confusion is that a substantial percentage of EOAD are phenotypic variants that differ from the usual memory-disordered presentation of typical AD. The management of EOAD is similar to that for LOAD, but special emphasis should be placed on targeting the specific cognitive areas involved and more age-appropriate psychosocial support and education.

Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of 18F-FDG PET Data

Brain connectivity has been assessed in several neurodegenerative disorders investigating the mutual correlations between predetermined regions or nodes. Selective breakdown of brain networks during progression from normal aging to Alzheimer disease dementia (AD) has also been observed. Methods: We implemented independent-component analysis of ¹⁸F-FDG PET data in 5 groups of subjects with cognitive states ranging from normal aging to AD-including mild cognitive impairment (MCI) not converting or converting to AD-to disclose the spatial distribution of the independent components in each cognitive state and their accuracy in discriminating the groups. Results: We could identify spatially distinct independent components in each group, with generation of local circuits increasing proportionally to the severity of the disease. AD-specific independent components first appeared in the late-MCI stage and could discriminate converting MCI and AD from nonconverting MCI with an accuracy of 83.5%. Progressive disintegration of the intrinsic networks from normal aging to MCI to AD was inversely proportional to the conversion time. Conclusion: Independent-component analysis of ¹⁸F-FDG PET data showed a gradual disruption of functional brain connectivity with progression of cognitive decline in AD. This information might be useful as a prognostic aid for individual patients and as a surrogate biomarker in intervention trials.

Visual Ratings of Medial Temporal Lobe Atrophy Correlate with CSF Tau Indices in Clinical Variants of Early-Onset Alzheimer Disease

BACKGROUND/AIMS

Prior studies of late-onset Alzheimer disease (AD) have reported that cerebrospinal fluid (CSF) tau levels correlate with hippocampal/medial temporal lobe atrophy. These findings suggest that CSF tau indices in AD may reflect tau-related neurodegeneration in the medial temporal lobe. However, it remains uncertain whether elevated CSF tau levels in the clinically heterogeneous subtypes of early-onset AD (EOAD; amnestic, posterior cortical atrophy [PCA], and logopenic progressive aphasia [LPA]) are attributable to similar underlying mechanisms.

METHODS

We identified 41 EOAD patients (18 amnestic, 14 with LPA, and 9 with PCA) with CSF and brain MRI data. Semiquantitative ratings were used to assess medial temporal lobe atrophy and PCA, which were compared to CSF biomarker indices.

RESULTS

Lower CSF tau levels were seen in PCA relative to amnestic EOAD and LPA, but similar ratings for medial temporal lobe atrophy and PCA were seen across the groups. After adjustments for demographics and cognitive performance, both total (p = 0.004) and hyperphosphorylated (p = 0.026) tau levels correlated with medial temporal lobe atrophy across this EOAD cohort.

CONCLUSIONS

These results replicate prior findings in late-onset AD and support the hypothesis that CSF tau levels primarily reflect tau-related neurodegenerative changes in the hippocampus/medial temporal lobe across the clinical subtypes of EOAD.

Amyloid pet in primary progressive aphasia: case series and systematic review of the literature

Primary progressive aphasia (PPA) is considered a heterogeneous syndrome, with different clinical subtypes and neuropathological causes. Novel PET biomarkers may help to predict the underlying neuropathology, but many aspects remain unclear. We studied the relationship between amyloid PET and PPA variant in a clinical series of PPA patients. A systematic review of the literature was performed. Patients with PPA were assessed over a 2-year period and classified based on language testing and the International Consensus Criteria as non-fluent/agrammatic (nfvPPA), semantic (svPPA), logopenic variant (lvPPA) or as unclassifiable (ucPPA). All patients underwent a Florbetapir (18-F) PET scan and images were analysed by two nuclear medicine physicians, using a previously validated reading method. Relevant studies published between January 2004 and January 2016 were identified by searching Medline and Web of Science databases. Twenty-four PPA patients were included (13 women, mean age 68.8, SD 8.3 years; range 54-83). Overall, 13/24 were amyloid positive: 0/2 (0%) nfvPPA, 0/4 (0%) svPPA, 10/14 (71.4%) lvPPA and 3/4 (75%) ucPPA (p = 0.028). The systematic review identified seven relevant studies, six including all PPA variants and one only lvPPA. Pooling all studies together, amyloid PET positivity was 122/224 (54.5%) for PPA, 14/52 (26.9%) for nfvPPA, 6/47 (12.8%) for svPPA, 101/119 for lvPPA (84.9%) and 12/22 (54.5%) for ucPPA. Amyloid PET may help to identify the underlying neuropathology in PPA. It could be especially useful in ucPPA, because in these cases it is more difficult to predict pathology. ucPPA is frequently associated with amyloid pathology.

Selective vulnerability in neurodegeneration: insights from clinical variants of Alzheimer's disease

Selective vulnerability in the nervous system refers to the fact that subpopulations of neurons in different brain systems may be more or less prone to abnormal function or death in response to specific types of pathological states or injury. The concept has been used extensively as a potential way of explaining differences in degeneration patterns and the clinical presentation of different neurodegenerative diseases. Yet the increasing complexity of molecular histopathology at the cellular level in neurodegenerative disorders frequently appears at odds with phenotyping based on clinically-directed, macroscopic regional brain involvement. While cross-disease comparisons can provide insights into the differential vulnerability of networks and neuronal populations, we focus here on what is known about selective vulnerability-related factors that might explain the differential phenotypic expressions of the same disease-in this case, typical and atypical forms of Alzheimer's disease. Whereas considerable progress has been made in this area, much is yet to be elucidated; further studies comparing different phenotypic variants aimed at identifying both vulnerability and resilience factors may provide valuable insights into disease pathogenesis, and suggest novel targets for therapy.

Multimodal Image Analysis in Alzheimer's Disease via Statistical Modelling of Non-local Intensity Correlations

The joint analysis of brain atrophy measured with magnetic resonance imaging (MRI) and hypometabolism measured with positron emission tomography with fluorodeoxyglucose (FDG-PET) is of primary importance in developing models of pathological changes in Alzheimer's disease (AD). Most of the current multimodal analyses in AD assume a local (spatially overlapping) relationship between MR and FDG-PET intensities. However, it is well known that atrophy and hypometabolism are prominent in different anatomical areas. The aim of this work is to describe the relationship between atrophy and hypometabolism by means of a data-driven statistical model of non-overlapping intensity correlations. For this purpose, FDG-PET and MRI signals are jointly analyzed through a computationally tractable formulation of partial least squares regression (PLSR). The PLSR model is estimated and validated on a large clinical cohort of 1049 individuals from the ADNI dataset. Results show that the proposed non-local analysis outperforms classical local approaches in terms of predictive accuracy while providing a plausible description of disease dynamics: early AD is characterised by non-overlapping temporal atrophy and temporo-parietal hypometabolism, while the later disease stages show overlapping brain atrophy and hypometabolism spread in temporal, parietal and cortical areas.