Adam Mezher

Brain Amyloid-β Burden Is Associated with Disruption of Intrinsic Functional Connectivity within the Medial Temporal Lobe in Cognitively Normal Elderly

The medial temporal lobe is implicated as a key brain region involved in the pathogenesis of Alzheimers disease (AD) and consequent memory loss. Tau tangle aggregation in this region may develop concurrently with cortical Aβ deposition in preclinical AD, but the pathological relationship between tau and Aβ remains unclear. We used task-free fMRI with a focus on the medical temporal lobe, together with Aβ PET imaging, in cognitively normal elderly human participants. We found that cortical Aβ load was related to disrupted intrinsic functional connectivity of the perirhinal cortex, which is typically the first brain region affected by tauopathies in AD. There was no concurrent association of cortical Aβ load with cognitive performance or brain atrophy. These findings suggest that dysfunction in the medial temporal lobe may represent a very early sign of preclinical AD and may predict future memory loss.

MRI-based brain atrophy rates in ADNI phase 2: acceleration and enrichment considerations for clinical trials

The goal of this work was to assess statistical power to detect treatment effects in Alzheimer’s disease (AD) clinical trials using magnetic resonance imaging (MRI)–derived brain biomarkers. We used unbiased tensor-based morphometry (TBM) to analyze n = 5,738 scans, from Alzheimer’s Disease Neuroimaging Initiative 2 participants scanned with both accelerated and nonaccelerated T1-weighted MRI at 3T. The study cohort included 198 healthy controls, 111 participants with significant memory complaint, 182 with early mild cognitive impairment (EMCI) and 177 late mild cognitive impairment (LMCI), and 155 AD patients, scanned at screening and 3, 6, 12, and 24 months. The statistical power to track brain change in TBM-based imaging biomarkers depends on the interscan interval, disease stage, and methods used to extract numerical summaries. To achieve reasonable sample size estimates for potential clinical trials, the minimal scan interval was 6 months for LMCI and AD and 12 months for EMCI. TBM-based imaging biomarkers were not sensitive to MRI scan acceleration, which gave results comparable with nonaccelerated sequences. ApoE status and baseline amyloid-beta positron emission tomography data improved statistical power. Among healthy, EMCI, and LMCI participants, sample size requirements were significantly lower in the amyloid+/ApoE4+ group than for the amyloid−/ApoE4− group. ApoE4 strongly predicted atrophy rates across brain regions most affected by AD, but the remaining 9 of the top 10 AD risk genes offered no added predictive value in this cohort.

Disrupted rich club network in behavioral variant frontotemporal dementia and early-onset Alzheimer's disease

In network analysis, the so‐called “rich club” describes the core areas of the brain that are more densely interconnected among themselves than expected by chance, and has been identified as a fundamental aspect of the human brain connectome. This is the first in‐depth diffusion imaging study to investigate the rich club along with other organizational changes in the brains anatomical network in behavioral frontotemporal dementia (bvFTD), and a matched cohort with early‐onset Alzheimers disease (EOAD). Our study sheds light on how bvFTD and EOAD affect connectivity of white matter fiber pathways in the brain, revealing differences and commonalities in the connectome among the dementias. To analyze the breakdown in connectivity, we studied three groups: 20 bvFTD, 23 EOAD, and 37 healthy elderly controls. All participants were scanned with diffusion‐weighted magnetic resonance imaging (MRI), and based on whole‐brain probabilistic tractography and cortical parcellations, we analyzed the rich club of the brains connectivity network. This revealed distinct patterns of disruption in both forms of dementia. In the connectome, we detected less disruption overall in EOAD than in bvFTD [false discovery rate (FDR) critical Pperm = 5.7 × 10−3, 10,000 permutations], with more involvement of richly interconnected areas of the brain (chi‐squared P = 1.4 × 10−4)—predominantly posterior cognitive alterations. In bvFTD, we found a greater spread of disruption including the rich club (FDR critical Pperm = 6 × 10−4), but especially more peripheral alterations (chi‐squared P = 6.5 × 10−3), particularly in medial frontal areas of the brain, in line with the known behavioral socioemotional deficits seen in these patients. Hum Brain Mapp 37:868–883, 2016.

Test-retest reliability of longitudinal task-based fMRI: Implications for developmental studies

Great advances have been made in functional Magnetic Resonance Imaging (fMRI) studies, including the use of longitudinal design to more accurately identify changes in brain development across childhood and adolescence. While longitudinal fMRI studies are necessary for our understanding of typical and atypical patterns of brain development, the variability observed in fMRI blood-oxygen-level dependent (BOLD) signal and its test-retest reliability in developing populations remain a concern. Here we review the current state of test-retest reliability for child and adolescent fMRI studies (ages 5–18 years) as indexed by intraclass correlation coefficients (ICC). In addition to highlighting ways to improve fMRI test-retest reliability in developmental cognitive neuroscience research, we hope to open a platform for dialogue regarding longitudinal fMRI study designs, analyses, and reporting of results.

Collaboration for a systematic comparison of different techniques to measure subfield volumes: Announcement and first results

Background: The hippocampus consists of several histologically and functionally distinct subfields that are differently affected by neurodegenerative processes. Particularly in the early stages subfield-specific atrophy measurements often provide superior diagnostic information compared to global hippocampal atrophy measurements. Consequently several approaches to investigate subfield specific atrophy using in vivo MR imaging have been developed and have been successfully used in small populations. However a systematic performance comparison in a common, large, and well defined data set with different degrees of hippocampal atrophy has yet to be done. The overall goal of this study is therefore to compare the performance of six representative manual and automated T1 and T2 based subfield labeling techniques in a sub-set of the ADNI2 population with mild hippocampal atrophy (MCI, eMCI, amyloid-pos and amyloid-neg controls).

Information-theoretic characterization of blood panel predictors for brain atrophy and cognitive decline in the elderly

Cognitive decline in old age is tightly linked with brain atrophy, causing significant burden. It is critical to identify which biomarkers are most predictive of cognitive decline and brain atrophy in the elderly. In 566 older adults from the Alzheimers Disease Neuroimaging Initiative (ADNI), we used a novel unsupervised machine learning approach to evaluate an extensive list of more than 200 potential brain, blood and cerebrospinal fluid (CSF)-based predictors of cognitive decline. The method, called CorEx, discovers groups of variables with high multivariate mutual information and then constructs latent factors that explain these correlations. The approach produces a hierarchical structure and the predictive power of biological variables and latent factors are compared with regression. We found that a group of variables containing the well-known AD risk gene APOE and CSF tau and amyloid levels were highly correlated. This latent factor was the most predictive of cognitive decline and brain atrophy.

Spectral graph theory and graph energy metrics show evidence for the alzheimer's disease disconnection syndrome in APOE-4 risk gene carriers

Our understanding of network breakdown in Alzheimers disease (AD) is likely to be enhanced through advanced mathematical descriptors. Here, we applied spectral graph theory to provide novel metrics of structural connectivity based on 3-Tesla diffusion weighted images in 42 AD patients and 50 healthy controls. We reconstructed connectivity networks using whole-brain tractography and examined, for the first time here, cortical disconnection based on the graph energy and spectrum. We further assessed supporting metrics - link density and nodal strength - to better interpret our results. Metrics were analyzed in relation to the well-known APOE-4 genetic risk factor for late-onset AD. The number of disconnected cortical regions increased with the number of copies of the APOE-4 risk gene in people with AD. Each additional copy of the APOE-4 risk gene may lead to more dysfunctional networks with weakened or abnormal connections, providing evidence for the previously hypothesized “disconnection syndrome”.

Information-Theoretic Clustering of Neuroimaging Metrics Related to Cognitive Decline in the Elderly

As Alzheimer’s disease progresses, there are changes in metrics of brain atrophy and network breakdown derived from anatomical or diffusion MRI. Neuroimaging biomarkers of cognitive decline are crucial to identify, but few studies have investigated how sets of biomarkers cluster in terms of the information they provide. Here, we evaluated more than 700 frequently studied diffusion and anatomical measures in 247 elderly participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used a novel unsupervised machine learning technique - CorEx - to identify groups of measures with high multivariate mutual information; we computed latent factors to explain correlations among them. We visualized groups of measures discovered by CorEx in a hierarchical structure and determined how well they predict cognitive decline. Clusters of variables significantly predicted cognitive decline, including measures of cortical gray matter, and correlated measures of brain networks derived from graph theory and spectral graph theory.

Relative value of diverse brain MRI and blood-based biomarkers for predicting cognitive decline in the elderly

Cognitive decline accompanies many debilitating illnesses, including Alzheimer’s disease (AD). In old age, brain tissue loss also occurs along with cognitive decline. Although blood tests are easier to perform than brain MRI, few studies compare brain scans to standard blood tests to see which kinds of information best predict future decline. In 504 older adults from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we first used linear regression to assess the relative value of different types of data to predict cognitive decline, including 196 blood panel biomarkers, 249 MRI biomarkers obtained from the FreeSurfer software, demographics, and the AD-risk gene APOE. A subset of MRI biomarkers was the strongest predictor. There was no specific blood marker that increased predictive accuracy on its own, we found that a novel unsupervised learning method, CorEx, captured weak correlations among blood markers, and the resulting clusters offered unique predictive power.

Systematic comparison of different techniques to measure hippocampal subfield volumes in ADNI2

Objective Subfield-specific measurements provide superior information in the early stages of neurodegenerative diseases compared to global hippocampal measurements. The overall goal was to systematically compare the performance of five representative manual and automated T1 and T2 based subfield labeling techniques in a sub-set of the ADNI2 population. Methods The high resolution T2 weighted hippocampal images (T2-HighRes) and the corresponding T1 images from 106 ADNI2 subjects (41 controls, 57 MCI, 8 AD) were processed as follows. A. T1-based: 1. Freesurfer + Large-Diffeomorphic-Metric-Mapping in combination with shape analysis. 2. FreeSurfer 5.1 subfields using in-vivo atlas. B. T2-HighRes: 1. Model-based subfield segmentation using ex-vivo atlas (FreeSurfer 6.0). 2. T2-based automated multi-atlas segmentation combined with similarity-weighted voting (ASHS). 3. Manual subfield parcellation. Multiple regression analyses were used to calculate effect sizes (ES) for group, amyloid positivity in controls, and associations with cognitive/memory performance for each approach. Results Subfield volumetry was better than whole hippocampal volumetry for the detection of the mild atrophy differences between controls and MCI (ES: 0.27 vs 0.11). T2-HighRes approaches outperformed T1 approaches for the detection of early stage atrophy (ES: 0.27 vs.0.10), amyloid positivity (ES: 0.11 vs 0.04), and cognitive associations (ES: 0.22 vs 0.19). Conclusions T2-HighRes subfield approaches outperformed whole hippocampus and T1 subfield approaches. None of the different T2-HghRes methods tested had a clear advantage over the other methods. Each has strengths and weaknesses that need to be taken into account when deciding which one to use to get the best results from subfield volumetry.