Seqian Wang

Amyloid-β and hyperphosphorylated tau synergy drives metabolic decline in preclinical Alzheimer’s disease

This study was designed to test the interaction between amyloid-β and tau proteins as a determinant of metabolic decline in preclinical Alzheimer’s disease (AD). We assessed 120 cognitively normal individuals with [18F]florbetapir positron emission tomography (PET) and cerebrospinal fluid (CSF) measurements at baseline, as well as [18F]fluorodeoxyglucose ([18F]FDG) PET at baseline and at 24 months. A voxel-based interaction model was built to test the associations between continuous measurements of CSF biomarkers, [18F]florbetapir and [18F]FDG standardized uptake value ratios (SUVR). We found that the synergistic interaction between [18F]florbetapir SUVR and CSF phosphorylated tau (p-tau) measurements, rather than the sum of their independent effects, was associated with a 24-month metabolic decline in basal and mesial temporal, orbitofrontal, and anterior and posterior cingulate cortices (P<0.001). In contrast, interactions using CSF amyloid-β1–42 and total tau biomarkers did not associate with metabolic decline over a time frame of 24 months. The interaction found in this study further support the framework that amyloid-β and hyperphosphorylated tau aggregates synergistically interact to cause downstream AD neurodegeneration. In fact, the regions displaying the metabolic decline reported here were confined to brain networks affected early by amyloid-β plaques and neurofibrillary tangles. Preventive clinical trials may benefit from using a combination of amyloid-β PET and p-tau biomarkers to enrich study populations of cognitively normal subjects with a high probability of disease progression in studies, using [18F]FDG as a biomarker of efficacy.

Synergistic interaction between amyloid and tau predicts the progression to dementia

Recent literature proposes that amyloid β (Aβ) and phosphorylated tau (p‐tau) synergism accelerates biomarker abnormalities in controls. Yet, it remains to be answered whether this synergism is the driving force behind Alzheimer disease (AD) dementia.

Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies.

Resting-state functional connectivity is a promising biomarker for Alzheimers disease. However, previous resting-state functional magnetic resonance imaging studies in Alzheimers disease and amnestic mild cognitive impairment (aMCI) have shown limited reproducibility as they have had small sample sizes and substantial variation in study protocol. We sought to identify functional brain networks and connections that could consistently discriminate normal aging from aMCI despite variations in scanner manufacturer, imaging protocol, and diagnostic procedure. We therefore combined four datasets collected independently, including 112 healthy controls and 143 patients with aMCI. We systematically tested multiple brain connections for associations with aMCI using a weighted average routinely used in meta-analyses. The largest effects involved the superior medial frontal cortex (including the anterior cingulate), dorsomedial prefrontal cortex, striatum, and middle temporal lobe. Compared with controls, patients with aMCI exhibited significantly decreased connectivity between default mode network nodes and between regions of the cortico-striatal-thalamic loop. Despite the heterogeneity of methods among the four datasets, we identified common aMCI-related connectivity changes with small to medium effect sizes and sample size estimates recommending a minimum of 140 to upwards of 600 total subjects to achieve adequate statistical power in the context of a multisite study with 5–10 scanning sites and about 10 subjects per group and per site. If our findings can be replicated and associated with other established biomarkers of Alzheimers disease (e.g., amyloid and tau quantification), then these functional connections may be promising candidate biomarkers for Alzheimers disease.

VoxelStats: A MATLAB Package for Multi-Modal Voxel-Wise Brain Image Analysis

In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab® and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.

Consistent inter-protocol differences in resting-state functional connectomes between normal aging and mild cognitive impairment

Resting-state functional connectivity is a promising biomarker for Alzheimer’s disease. However, previous resting-state functional magnetic resonance imaging studies in Alzheimer’s disease and mild cognitive impairment (MCI) have shown limited reproducibility as they have had small sample sizes and substantial variation in study protocol. We sought to identify functional brain networks and connections that could consistently discriminate normal aging from MCI despite variations in scanner manufacturer, imaging protocol, and diagnostic procedure. We therefore pooled four independent datasets, including 112 healthy controls and 143 patients with MCI, systematically testing multiple brain connections for consistent differences. The largest effects associated with MCI involved the ventromedial and dorsomedial prefrontal cortex, striatum, and middle temporal lobe. Compared with controls, patients with MCI exhibited significantly decreased connectivity within the frontal lobe, between frontal and temporal areas, and between regions of the cortico-striatal-thalamic loop. Despite the heterogeneity of methods among the four datasets, we identified robust MCI-related connectivity changes that appear to be promising candidate biomarkers for Alzheimer’s disease.

Should a global or a regional measure of amyloidosis be used in a longitudinal study

Figure 1. Vertex-based multivariate linear regression model showing the effect of amyloid load on the rate of hypo-metabolism in each disease stage, corrected for baseline glucose metabolism, age, gender and apoe genotype. Only LMCI and AD stages show positive effect from amyloid load on hypometabolism in temporo-parietal and precuneus regions. Sulantha S. Mathotaarachchi, Sara Mohades, Monica Shin, Thomas Beaudry, Andrea Lessa Benedet, Tharick Ali Pascoal, Seqian Wang, Sarinporn Manitsirikul, Maxime J. Parent, Min Su Kang, Vladimir Fonov, Chang Oh Chung, Sr., Serge Gauthier, Pedro RosaNeto, McGill University, Montreal, QC, Canada; McGill Centre for Studies in Aging, Montreal, QC, Canada; McGill Centre for Studies in Aging/Translational Neuroimaging Laboratory, Montreal, QC, Canada; McGill Centre for Studies in Aging, Verdun, QC, Canada; McGill University Centre for Studies in Aging, Verdun, QC, Canada; Image Processing Laboratory, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Centre for Studies on Prevention of Alzheimer’s Disease (StoP-AD Centre), Douglas Mental Health Institute, Montreal, QC, Canada; Douglas Hospital Research Centre, Montreal, QC, Canada; Translational Imaging Laboratory, Montreal, QC, Canada. Contact e-mail: sulantha.s@gmail.com

GLUCOSE METABOLISM IMPACT ON FUNCTIONAL CONNECTIVITY IN ALZHEIMER'S DISEASE

Canada; McGill Center for Studies in Aging/Translational Neuroimaging Laboratory, Montreal, Quebec, Canada; McGill Center for Studies in Aging, Montreal, Quebec, Canada; McGill University, Verdun, Quebec, Canada; McGill Center for Studies in Aging/Translational Neuroimaging Laboratory, Montreal, Quebec, Canada; McGill University, Montreal, Quebec, Canada; McGill Center for Studies in Aging, Montreal, Quebec, Canada. Contact e-mail: ju_sarinporn@yahoo.com

NEURODEGENERATION AND CORTICAL ATROPHY IN [18F]FLORBETAPIR ACCUMULATORS AND NON-ACCUMULATORS

BBSI n 18 22 26 23 49 104 LS Mean (SE) 13.17 (2.06) 13.46 (1.91) 15.72 (1.80) 16.03 (1.89) 15.77 (1.25) 14.5 (8.5) VBSI n 20 22 29 24 53 104 LS Mean (SE) 4.46 (0.59) 4.25 (0.57) 5.31 (0.54) 4.36 (0.55) 4.85 (0.35) 4.43 (3.17) LHBSI n 21 22 29 24 53 104 LS Mean (SE) 0.136 (0.020) 0.119 (0.020) 0.186 (0.018) 0.191 (0.019) 0.188 (0.013) 0.340 (0.190) RHBSI n 21 22 29 24 53 LS Mean (SE) 0.135 (0.020) 0.110 (0.020) 0.176 (0.018) 0.171 (0.019) 0.173 (0.013)

[18F]FLORBETAPIR ROC CURVE AT EVERY VOXEL REVELS A WIDE RANGE OF CORTICAL SUVR CUT-OFFS

the Centiloid transformation resulted in a reduced mean error of -0.52% in the AD+ and 1.12% in the HC-. Conclusions:Reliable Centiloid measures can be obtained for PiB images using the PETonlymethod CapAIBL using the recommended calibration method. Using the estimated slope and intercept to further correct the estimation of the Centiloid values might however be desirable to reduce the quantification errors.

NOVEL TOOLBOX FOR PERFORMING VOXEL-WISE GENERALIZED LINEAR REGRESSION WITH MULITPLE VOLUMETRIC COVARIATES IN LONGITUDINAL DATA

elwise analyses revealed widespread areas of hypometabolism (in lateral frontal, temporal, and parietal lobes) that were associated with less cognitive activity, and these regions overlapped partially the meta ROIs. Hippocampal volume was not correlated with cognitive activity in any age epoch. Conclusions: Cognitive engagement in early and midlife is associated with reduced amyloid, while current cognitive engagement is associated with reduced hypometabolism. These findings are consistent with proposed effects of early/midlife cognitive engagement on neural efficiency, but suggest that late life effects reflect other factors, such as cognitive reserve or reverse causation.