As the most common type of dementia, Alzheimer's disease (AD) is a neurodegenerative disorder initially manifested by impaired memory performances. While the diagnosis information indicates a dichotomous status of a patient, memory scores have the potential to capture the continuous nature of the disease progression and may provide more insights into the underlying mechanism. In this work, we performed a targeted genetic study of memory scores on an AD cohort to identify the associations between a set of genes highly expressed in the hippocampal region and seven cognitive scores related to episodic memory. Both main effects and interaction effects of the targeted genetic markers on these correlated memory scores were examined. In addition to well-known AD genetic markers APOE and TOMM40, our analysis identified a new risk gene NAV2 through the gene-level main effect analysis. NAV2 was found to be significantly and consistently associated with all seven episodic memory scores. Genetic interaction analysis also yielded a few promising hits warranting further investigation, especially for the RAVLT list B Score.

Amyloid imaging is a valuable tool for research and diagnosis in dementing disorders. As positron emission tomography (PET) scanners have limited spatial resolution, measured signals are distorted by partial volume effects. Various techniques have been proposed for correcting partial volume effects, but there is no consensus as to whether these techniques are necessary in amyloid imaging, and, if so, how they should be implemented. We evaluated a two-component partial volume correction technique and a regional spread function technique using both simulated and human Pittsburgh compound B (PiB) PET imaging data. Both correction techniques compensated for partial volume effects and yielded improved detection of subtle changes in PiB retention. However, the regional spread function technique was more accurate in application to simulated data. Because PiB retention estimates depend on the correction technique, standardization is necessary to compare results across groups. Partial volume correction has sometimes been avoided because it increases the sensitivity to inaccuracy in image registration and segmentation. However, our results indicate that appropriate PVC may enhance our ability to detect changes in amyloid deposition.

Cognitive decline is among the most feared treatment-related outcomes of older adults with cancer. The majority of older patients with breast cancer self-report cognitive problems during and after chemotherapy. Prior neuroimaging research has been performed mostly in younger patients with cancer. The purpose of this study was to evaluate longitudinal changes in brain volumes and cognition in older women with breast cancer receiving adjuvant chemotherapy.

It has been proposed that the signal distribution on tau positron emission tomography (PET) images could be used to define pathologic stages similar to those seen in neuropathology.

Imaging genetic studies typically focus on identifying single-nucleotide polymorphism (SNP) markers associated with imaging phenotypes. Few studies perform regression of SNP values on phenotypic measures for examining how the SNP values change when phenotypic measures are varied. This alternative approach may have a potential to help us discover important imaging genetic associations from a different perspective. In addition, the imaging markers are often measured over time, and this longitudinal profile may provide increased power for differentiating genotype groups. How to identify the longitudinal phenotypic markers associated to disease sensitive SNPs is an important and challenging research topic.

The cerebrospinal fluid (CSF) levels of total tau (t-tau) and Aβ1-42 are potential early diagnostic markers for probable Alzheimer's disease (AD). The influence of genetic variation on these CSF biomarkers has been investigated in candidate or genome-wide association studies (GWAS). However, the investigation of statistically modest associations in GWAS in the context of biological networks is still an under-explored topic in AD studies. The main objective of this study is to gain further biological insights via the integration of statistical gene associations in AD with physical protein interaction networks.

Neuroimaging genetics identifies the relationships between genetic variants (i.e., the single nucleotide polymorphisms) and brain imaging data to reveal the associations from genotypes to phenotypes. So far, most existing machine-learning approaches are widely used to detect the effective associations between genetic variants and brain imaging data at one time-point. However, those associations are based on static phenotypes and ignore the temporal dynamics of the phenotypical changes. The phenotypes across multiple time-points may exhibit temporal patterns that can be used to facilitate the understanding of the degenerative process. In this article, we propose a novel temporally constrained group sparse canonical correlation analysis (TGSCCA) framework to identify genetic associations with longitudinal phenotypic markers.

Alzheimer's disease (AD) patients display hippocampal atrophy, memory impairment, and cognitive decline. New neurons are generated throughout adulthood in 2 regions of the brain implicated in AD, the dentate gyrus of the hippocampus and the subventricular zone of the olfactory bulb. Disruption of this process contributes to neurodegenerative diseases including AD, and many of the molecular players in AD are also modulators of adult neurogenesis. However, the genetic mechanisms underlying adult neurogenesis in AD have been underexplored. To address this gap, we performed a gene-based association analysis in cognitively normal and impaired participants using neurogenesis pathway-related candidate genes curated from existing databases, literature mining, and large-scale genome-wide association study findings. A gene-based association analysis identified adenosine A2a receptor (ADORA2A) as significantly associated with hippocampal volume and the association between rs9608282 within ADORA2A and hippocampal volume was replicated in the meta-analysis after multiple comparison adjustments (p = 7.88 × 10-6). The minor allele of rs9608282 in ADORA2A is associated with larger hippocampal volumes and better memory.

We investigated the genome-wide distribution of CNVs in the Alzheimer's disease (AD) Neuroimaging Initiative (ADNI) sample (146 with AD, 313 with Mild Cognitive Impairment (MCI), and 181 controls). Comparison of single CNVs between cases (MCI and AD) and controls shows overrepresentation of large heterozygous deletions in cases (p-value<0.0001). The analysis of CNV-Regions identifies 44 copy number variable loci of heterozygous deletions, with more CNV-Regions among affected than controls (p=0.005). Seven of the 44 CNV-Regions are nominally significant for association with cognitive impairment. We validated and confirmed our main findings with genome re-sequencing of selected patients and controls. The functional pathway analysis of the genes putatively affected by deletions of CNV-Regions reveals enrichment of genes implicated in axonal guidance, cell-cell adhesion, neuronal morphogenesis and differentiation. Our findings support the role of CNVs in AD, and suggest an association between large deletions and the development of cognitive impairment.

Imaging traits provide a powerful and biologically relevant substrate to examine the influence of genetics on the brain. Interest in genome-wide, brain-wide search for influential genetic variants is growing, but has mainly focused on univariate, SNP-based association tests. Moving to gene-based multivariate statistics, we can test the combined effect of multiple genetic variants in a single test statistic. Multivariate models can reduce the number of statistical tests in gene-wide or genome-wide scans and may discover gene effects undetectable with SNP-based methods. Here we present a gene-based method for associating the joint effect of single nucleotide polymorphisms (SNPs) in 18,044 genes across 31,662 voxels of the whole brain in 731 elderly subjects (mean age: 75.56±6.82SD years; 430 males) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. Using the voxel-level volume difference values as the phenotype, we selected the most significantly associated gene (out of 18,044) at each voxel across the brain. No genes identified were significant after correction for multiple comparisons, but several known candidates were re-identified, as were other genes highly relevant to brain function. GAB2, which has been previously associated with late-onset AD, was identified as the top gene in this study, suggesting the validity of the approach. This multivariate, gene-based voxelwise association study offers a novel framework to detect genetic influences on the brain.

Proteins, widely studied as potential biomarkers, play important roles in numerous physiological functions and diseases. Genetic variation may modulate corresponding protein levels and point to the role of these variants in disease pathophysiology. Effects of individual single nucleotide polymorphisms (SNPs) within a gene were analyzed for corresponding plasma protein levels using genome-wide association study (GWAS) genotype data and proteomic panel data with 132 quality-controlled analytes from 521 Caucasian participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Linear regression analysis detected 112 significant (Bonferroni threshold p=2.44×10(-5)) associations between 27 analytes and 112 SNPs. 107 out of these 112 associations were tested in the Indiana Memory and Aging Study (IMAS) cohort for replication and 50 associations were replicated at uncorrected p<0.05 in the same direction of effect as those in the ADNI. We identified multiple novel associations including the association of rs7517126 with plasma complement factor H-related protein 1 (CFHR1) level at p<1.46×10(-60), accounting for 40 percent of total variation of the protein level. We serendipitously found the association of rs6677604 with the same protein at p<9.29×10(-112). Although these two SNPs were not in the strong linkage disequilibrium, 61 percent of total variation of CFHR1 was accounted for by rs6677604 without additional variation by rs7517126 when both SNPs were tested together. 78 other SNP-protein associations in the ADNI sample exceeded genome-wide significance (5×10(-8)). Our results confirmed previously identified gene-protein associations for interleukin-6 receptor, chemokine CC-4, angiotensin-converting enzyme, and angiotensinogen, although the direction of effect was reversed in some cases. This study is among the first analyses of gene-protein product relationships integrating multiplex-panel proteomics and targeted genes extracted from a GWAS array. With intensive searches taking place for proteomic biomarkers for many diseases, the role of genetic variation takes on new importance and should be considered in interpretation of proteomic results.

The underlying genetic etiology of late onset Alzheimer's disease (LOAD) remains largely unknown, likely due to its polygenic architecture and a lack of sophisticated analytic methods to evaluate complex genotype-phenotype models. The aim of the current study was to overcome these limitations in a bi-multivariate fashion by linking intermediate magnetic resonance imaging (MRI) phenotypes with a genome-wide sample of common single nucleotide polymorphism (SNP) variants. We compared associations between 94 different brain regions of interest derived from structural MRI scans and 533,872 genome-wide SNPs using a novel multivariate statistical procedure, parallel-independent component analysis, in a large, national multi-center subject cohort. The study included 209 elderly healthy controls, 367 subjects with amnestic mild cognitive impairment and 181 with mild, early-stage LOAD, all of them Caucasian adults, from the Alzheimer's Disease Neuroimaging Initiative cohort. Imaging was performed on comparable 1.5 T scanners at over 50 sites in the USA/Canada. Four primary "genetic components" were associated significantly with a single structural network including all regions involved neuropathologically in LOAD. Pathway analysis suggested that each component included several genes already known to contribute to LOAD risk (e.g. APOE4) or involved in pathologic processes contributing to the disorder, including inflammation, diabetes, obesity and cardiovascular disease. In addition significant novel genes identified included ZNF673, VPS13, SLC9A7, ATP5G2 and SHROOM2. Unlike conventional analyses, this multivariate approach identified distinct groups of genes that are plausibly linked in physiologic pathways, perhaps epistatically. Further, the study exemplifies the value of this novel approach to explore large-scale data sets involving high-dimensional gene and endophenotype data.

Structural brain networks were constructed based on diffusion tensor imaging (DTI) data of 59 young healthy male adults. The networks had 68 nodes, derived from FreeSurfer parcellation of the cortical surface. By means of streamline tractography, the edge weight was defined as the number of streamlines between two nodes normalized by their mean volume. Specifically, two weighting schemes were adopted by considering various biases from fiber tracking. The weighting schemes were tested for possible bias toward the physical size of the nodes. A novel thresholding method was proposed using the variance of number of streamlines in fiber tracking. The backbone networks were extracted and various network analyses were applied to investigate the features of the binary and weighted backbone networks. For weighted networks, a high correlation was observed between nodal strength and betweenness centrality. Despite similar small-worldness features, binary networks and weighted networks are distinctive in many aspects, such as modularity and nodal betweenness centrality. Inter-subject variability was examined for the weighted networks, along with the test-retest reliability from two repeated scans on 44 of the 59 subjects. The inter-/intra-subject variability of weighted networks was discussed in three levels - edge weights, local metrics, and global metrics. The variance of edge weights can be very large. Although local metrics show less variability than the edge weights, they still have considerable amounts of variability. Weighting scheme one, which scales the number of streamlines by their lengths, demonstrates stable intra-class correlation coefficients against thresholding for global efficiency, clustering coefficient and diversity. The intra-class correlation analysis suggests the current approach of constructing weighted network has a reasonably high reproducibility for most global metrics.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a multi-center study assessing neuroimaging in diagnosis and longitudinal monitoring. Amnestic Mild Cognitive Impairment (MCI) often represents a prodromal form of dementia, conferring a 10-15% annual risk of converting to probable AD. We analyzed baseline 1.5T MRI scans in 693 participants from the ADNI cohort divided into four groups by baseline diagnosis and one year MCI to probable AD conversion status to identify neuroimaging phenotypes associated with MCI and AD and potential predictive markers of imminent conversion. MP-RAGE scans were analyzed using publicly available voxel-based morphometry (VBM) and automated parcellation methods. Measures included global and hippocampal grey matter (GM) density, hippocampal and amygdalar volumes, and cortical thickness values from entorhinal cortex and other temporal and parietal lobe regions. The overall pattern of structural MRI changes in MCI (n=339) and AD (n=148) compared to healthy controls (HC, n=206) was similar to prior findings in smaller samples. MCI-Converters (n=62) demonstrated a very similar pattern of atrophic changes to the AD group up to a year before meeting clinical criteria for AD. Finally, a comparison of effect sizes for contrasts between the MCI-Converters and MCI-Stable (n=277) groups on MRI metrics indicated that degree of neurodegeneration of medial temporal structures was the best antecedent MRI marker of imminent conversion, with decreased hippocampal volume (left > right) being the most robust. Validation of imaging biomarkers is important as they can help enrich clinical trials of disease modifying agents by identifying individuals at highest risk for progression to AD.

We investigated the association between olfactory identification and Alzheimer's disease biomarkers, including amyloid, tau, and neurodegeneration.

Neuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. In recent studies, in order to detect complex multi-SNP-multi-QT associations, bi-multivariate techniques such as various structured sparse canonical correlation analysis (SCCA) algorithms have been proposed and used in imaging genetics studies. However, associations between genetic markers and imaging QTs identified by existing bi-multivariate methods may not be all disease specific. To bridge this gap, we propose an analytical framework, based on three-way sparse canonical correlation analysis (T-SCCA), to explore the intrinsic associations among genetic markers, imaging QTs, and clinical scores of interest. We perform an empirical study using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from AD risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging scans, and cognitive and diagnostic outcomes. The proposed T-SCCA model not only outperforms the traditional SCCA method in terms of identifying strong associations, but also discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding.

Vascular endothelial growth factor (VEGF) is associated with the clinical manifestation of Alzheimer's disease (AD). However, the role of the VEGF gene family in neuroprotection is complex due to the number of biological pathways they regulate. This study explored associations between brain expression of VEGF genes with cognitive performance and AD pathology. Genetic, cognitive, and neuropathology data were acquired from the Religious Orders Study and Rush Memory and Aging Project. Expression of ten VEGF ligand and receptor genes was quantified using RNA sequencing of prefrontal cortex tissue. Global cognitive composite scores were calculated from 17 neuropsychological tests. β-amyloid and tau burden were measured at autopsy. Participants (n = 531) included individuals with normal cognition (n = 180), mild cognitive impairment (n = 148), or AD dementia (n = 203). Mean age at death was 89 years and 37% were male. Higher prefrontal cortex expression of VEGFB, FLT4, FLT1, and PGF was associated with worse cognitive trajectories (p ≤ 0.01). Increased expression of VEGFB and FLT4 was also associated with lower cognition scores at the last visit before death (p ≤ 0.01). VEGFB, FLT4, and FLT1 were upregulated among AD dementia compared with normal cognition participants (p ≤ 0.03). All four genes associated with cognition related to elevated β-amyloid (p ≤ 0.01) and/or tau burden (p ≤ 0.03). VEGF ligand and receptor genes, specifically genes relevant to FLT4 and FLT1 receptor signaling, are associated with cognition, longitudinal cognitive decline, and AD neuropathology. Future work should confirm these observations at the protein level to better understand how changes in VEGF transcription and translation relate to neurodegenerative disease.

Neurofilament light chain (NfL) is a protein that is selectively expressed in neurons. Increased levels of NfL measured in either cerebrospinal fluid or blood is thought to be a biomarker of neuronal damage in neurodegenerative diseases. However, there have been limited investigations relating NfL to the concurrent measures of white matter (WM) decline that it should reflect. White matter damage is a common feature of Alzheimer's disease. We hypothesized that serum levels of NfL would associate with WM lesion volume and diffusion tensor imaging (DTI) metrics cross-sectionally in 117 autosomal dominant mutation carriers (MC) compared to 84 non-carrier (NC) familial controls as well as in a subset (N = 41) of MC with longitudinal NfL and MRI data. In MC, elevated cross-sectional NfL was positively associated with WM hyperintensity lesion volume, mean diffusivity, radial diffusivity, and axial diffusivity and negatively with fractional anisotropy. Greater change in NfL levels in MC was associated with larger changes in fractional anisotropy, mean diffusivity, and radial diffusivity, all indicative of reduced WM integrity. There were no relationships with NfL in NC. Our results demonstrate that blood-based NfL levels reflect WM integrity and supports the view that blood levels of NfL are predictive of WM damage in the brain. This is a critical result in improving the interpretability of NfL as a marker of brain integrity, and for validating this emerging biomarker for future use in clinical and research settings across multiple neurodegenerative diseases.

Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease impacting an estimated 44 million adults worldwide. The causal pathology of AD (accumulation of amyloid-beta and tau), precedes hallmark symptoms of dementia by more than a decade, necessitating development of early diagnostic markers of disease onset, particularly for new drugs that aim to modify disease processes. To evaluate differentially methylated positions (DMPs) as novel blood-based biomarkers of AD, we used a subset of 653 individuals with peripheral blood (PB) samples in the Alzheimer's disease Neuroimaging Initiative (ADNI) consortium. The selected cohort of AD, mild cognitive impairment (MCI), and age-matched healthy controls (CN) all had imaging, genetics, transcriptomics, cerebrospinal protein markers, and comprehensive clinical records, providing a rich resource of concurrent multi-omics and phenotypic information on a well-phenotyped subset of ADNI participants.

Alzheimer's disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans.