Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available.
multivariate analysis; multiple comparisons; multimodality imaging; diffusion tensor imaging; structural magnetic resonance imaging; perfusion weighted magnetic resonance imaging; Alzheimer's disease
Treatment of Alzheimer’s disease (AD) is significantly hampered by the lack of easily accessible biomarkers that can detect disease presence and predict disease risk reliably. Fluid biomarkers of AD currently provide indications of disease stage; however, they are not robust predictors of disease progression or treatment response, and most are measured in cerebrospinal fluid, which limits their applicability. With these aspects in mind, the aim of this article is to underscore the concerted efforts of the Blood-Based Biomarker Interest Group, an international working group of experts in the field. The points addressed include: (1) the major challenges in the development of blood-based biomarkers of AD, including patient heterogeneity, inclusion of the “right” control population, and the blood– brain barrier; (2) the need for a clear definition of the purpose of the individual markers (e.g., prognostic, diagnostic, or monitoring therapeutic efficacy); (3) a critical evaluation of the ongoing biomarker approaches; and (4) highlighting the need for standardization of preanalytical variables and analytical methodologies used by the field.
APOE ε4’s role as a modulator of the relationship between soluble plasma beta-amyloid (Aβ) and fibrillar brain Aβ measured by Pittsburgh Compound-B positron emission tomography ([11C]PiB PET) has not been assessed.
Ninety-six Alzheimer’s Disease Neuroimaging Initiative participants with [11C]PiB scans and plasma Aβ1-40 and Aβ1-42 measurements at time of scan were included. Regional and voxel-wise analyses of [11C]PiB data were used to determine the influence of APOE ε4 on association of plasma Aβ1-40, Aβ1-42, and Aβ1-40/Aβ1-42 with [11C]PiB uptake.
In APOE ε4− but not ε4+ participants, positive relationships between plasma Aβ1-40/Aβ1-42 and [11C]PiB uptake were observed. Modeling the interaction of APOE and plasma Aβ1-40/Aβ1-42 improved the explained variance in [11C]PiB binding compared to using APOE and plasma Aβ1-40/Aβ1-42 as separate terms.
The results suggest that plasma Aβ is a potential Alzheimer’s disease biomarker and highlight the importance of genetic variation in interpretation of plasma Aβ levels.
Alzheimer’s disease (AD); mild cognitive impairment (MCI); Alzheimer’s Disease Neuroimaging Initiative (ADNI); beta-amyloid (Aβ); plasma beta-amyloid; positron emission tomography (PET); Pittsburgh Compound-B ([11C]PiB); Apolipoprotein E (APOE)
The objective of this study was to define whether vascular risk factors interact with β-amyloid (Aβ) in producing changes in brain structure that could underlie the increased risk of Alzheimer disease (AD).
Sixty-six cognitively normal and mildly impaired older individuals with a wide range of vascular risk factors were included in this study. The presence of Aβ was assessed using [11C]Pittsburgh compound B–PET imaging, and cortical thickness was measured using 3-tesla MRI. Vascular risk was measured with the Framingham Coronary Risk Profile Index.
Individuals with high levels of vascular risk factors have thinner frontotemporal cortex independent of Aβ. These frontotemporal regions are also affected in individuals with Aβ deposition, but the latter show additional thinning in parietal cortices. Aβ and vascular risk were found to interact in posterior (especially in parietal) brain regions, where Aβ has its greatest effect. In this way, the negative effect of Aβ in posterior regions is increased by the presence of vascular risk.
Aβ and vascular risk interact to enhance cortical thinning in posterior brain regions that are particularly vulnerable to AD. These findings give insight concerning the mechanisms whereby vascular risk increases the likelihood of developing AD and supports the therapeutic intervention of controlling vascular risk for the prevention of AD.
Diffusion spectrum imaging (DSI) is a generalization of diffusion tensor imaging to map fibrous structure of white matter and potentially very sensitive to alterations of the cingulum bundles in dementia. In this in-vivo 4T study, DSI parameters especially spatial resolution and diffusion encoding bandwidth were optimized on humans to segment the cingulum bundles for tract level measurements of diffusion. The careful tailoring of the DSI acquisitions in conjunction with fiber tracking provided an optimal DSI setting for a reliable quantification of the cingulum bundle tracts. The optimization of tracking the cingulum bundle was verified using fiber tract quantifications, including coefficients of variability of DSI measurements along the fibers between and within healthy subjects in back-to-back studies and variogram analysis of spatial correlations between diffusion orientation distribution functions (ODF) along the cingulum bundle tracts. The results demonstrate identification of the cingulum bundle in human brain is reproducible using an optimized DSI parameter for maximum b-value and high spatial resolution of the DSI acquisition with a feasible acquisition time of whole brain in clinical practice. This optimized DSI setting should be useful for detecting alterations along the cingulum bundle in Alzheimer disease and related neurodegenerative disorders.
MR diffusion; Diffusion spectrum imaging; optimization; cingulum bundle; Alzheimer
Modern machine learning algorithms are increasingly being used in neuroimaging studies, such as the prediction of Alzheimer’s disease (AD) from structural MRI. However, finding a good representation for multivariate brain MRI features in which their essential structure is revealed and easily extractable has been difficult. We report a successful application of a machine learning framework that significantly improved the use of brain MRI for predictions. Specifically, we used the unsupervised learning algorithm of locally linear embedding (LLE) to transform multivariate MRI data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions, while also utilizing the global nonlinear data structure. The embedded brain features were then used to train a classifier for predicting future conversion to AD based on a baseline MRI. We tested the approach on 413 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) who had baseline MRI scans and complete clinical follow-ups over 3 years with following diagnoses: Cognitive normal (CN; n= 137), stable mild cognitive impairment (s-MCI; n=93), MCI converters to AD (c-MCI, n=97), and AD (n=86). We found classifications using embedded MRI features generally outperformed (p < 0.05) classifications using the original features directly. Moreover, the improvement from LLE was not limited to a particular classifier but worked equally well for regularized logistic regressions, support vector machines, and linear discriminant analysis. Most strikingly, using LLE significantly improved (p = 0.007) predictions of MCI subjects who converted to AD and those who remained stable (accuracy/sensitivity/specificity: = 0.68/0.80/0.56). In contrast, predictions using the original features performed not better than by chance (accuracy/sensitivity/specificity: = 0.56/0.65/0.46). In conclusion, LLE is a very effective tool for classification studies of AD using multivariate MRI data. The improvement in predicting conversion to AD in MCI could have important implications for health management and for powering therapeutic trials by targeting non-demented subjects who later convert to AD.
Alzheimer’s disease; locally linear embedding; statistical learning; classification of AD; MRI
Autosomal dominant Alzheimer disease (ADAD) is caused by rare genetic
mutations in three specific genes, in contrast to late-onset Alzheimer
Disease (LOAD), which has a more polygenetic risk profile.
Design, Setting, and Participants
We analyzed functional connectivity in multiple brain resting state
networks (RSNs) in a cross-sectional cohort of ADAD (N=79) and LOAD (N=444)
human participants using resting state functional connectivity MRI
(rs-fcMRI) at multiple international academic sites.
Main Outcomes and Measures
For both types of AD, we quantified and compared functional
connectivity changes in RSNs as a function of dementia severity as measured
by clinical dementia rating (CDR). In ADAD, we qualitatively investigated
functional connectivity changes with respect to estimated years from onset
of symptoms within five RSNs.
Functional connectivity decreases with increasing CDR were similar
for both LOAD and ADAD in multiple RSNs. Ordinal logistic regression models
constructed in each type of AD accurately predicted CDR stage in the other,
further demonstrating similarity of functional connectivity loss in each
disease type. Among ADAD participants, functional connectivity in multiple
RSNs appeared qualitatively lower in asymptomatic mutation carriers near
their anticipated age of symptom onset compared to asymptomatic mutation
Conclusions and Relevance
rs-fcMRI changes with progressing AD severity are similar between
ADAD and LOAD. Rs-fcMRI may be a useful endpoint for LOAD and ADAD therapy
trials. ADAD disease process may be an effective model for LOAD disease
Resting-state functional connectivity; autosomal dominant Alzheimer's disease; late-onset Alzheimer's disease; default mode network; apolipoprotein E (APOE)
Multimodal brain image analysis : third International Workshop, MBIA 2013, held in conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013 : proceedings / Li Shen, Tianming Liu, Pew-Thian Yap, Heng Huang, Dinggang Shen, Carl-Fre.
Alzheimer's disease (AD) is the most common cause of dementia in older adults. By the time an individual has been diagnosed with AD, it may be too late for potential disease modifying therapy to strongly influence outcome. Therefore, it is critical to develop better diagnostic tools that can recognize AD at early symptomatic and especially pre-symptomatic stages. Mild cognitive impairment (MCI), introduced to describe a prodromal stage of AD, is presently classified into early and late stages (E-MCI, L-MCI) based on severity. Using a graph-based semi-supervised learning (SSL) method to integrate multimodal brain imaging data and select valid imaging-based predictors for optimizing prediction accuracy, we developed a model to differentiate E-MCI from healthy controls (HC) for early detection of AD. Multimodal brain imaging scans (MRI and PET) of 174 E-MCI and 98 HC participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were used in this analysis. Mean targeted region-of-interest (ROI) values extracted from structural MRI (voxel-based morphometry (VBM) and FreeSurfer V5) and PET (FDG and Florbetapir) scans were used as features. Our results show that the graph-based SSL classifiers outperformed support vector machines for this task and the best performance was obtained with 66.8% cross-validated AUC (area under the ROC curve) when FDG and FreeSurfer datasets were integrated. Valid imaging-based phenotypes selected from our approach included ROI values extracted from temporal lobe, hippocampus, and amygdala. Employing a graph-based SSL approach with multimodal brain imaging data appears to have substantial potential for detecting E-MCI for early detection of prodromal AD warranting further investigation.
Mild Cognitive Impairment; Multimodal Brain Imaging Data; Data Integration; Graph-based Semi-Supervised Learning; Alzheimer's Disease
To improve signal-to-noise ratio (SNR) for diffusion-weighted MR images.
A new method is proposed for denoising diffusion-weighted magnitude images. The proposed method formulates the denoising problem as an maximum a posteriori estimation problem based on Rician/noncentral χ likelihood models, incorporating an edge prior and a low-rank model. The resulting optimization problem is solved efficiently using a half-quadratic method with an alternating minimization scheme.
The performance of the proposed method has been validated using simulated and experimental data. Diffusion-weighted images and noisy data were simulated based on the diffusion tensor imaging (DTI) model and Rician/noncentral χ distributions. The simulation study (with known gold standard) shows substantial improvements in SNR and diffusion tensor es-timation after denoising. In-vivo diffusion imaging data at different b-values were acquired. Based on the experimental data, qualitative improvement in image quality and quantitative im-provement in diffusion tensor estimation were demonstrated. Additionally, the proposed method is shown to outperform one of the state-of-the-art non-local means based denoising algorithms, both qualitatively and quantitatively.
The SNR of diffusion-weighted images can be effectively improved with rank and edge constraints, resulting in an improvement in diffusion parameter estimation accuracy.
Diffusion-weighted imaging; diffusion tensor imaging; Rician distribution; noncentral χ distribution; low-rank approximation; edge constraints
Prevalence and risk factors for focal hemosiderin deposits are important considerations when planning amyloid–modifying trials for treatment and prevention of Alzheimer’s disease (AD).
Subjects were cognitively normal (n=171), early-mild cognitive impairment (MCI) (n=240), late-MCI (n=111) and AD (n=40) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Microhemorrhages and superficial siderosis were assessed at baseline and on all available MRIs at 3, 6 and 12 months. β-amyloid load was assessed with 18F-florbetapir PET.
Prevalence of superficial siderosis was 1% and prevalence of microhemorrhages was 25% increasing with age (p<0.001) and β-amyloid load (p<0.001). Topographic densities of microhemorrhages were highest in the occipital lobes and lowest in the deep/infratentorial regions. A greater number of microhemorrhages at baseline was associated with a greater annualized rate of additional microhemorrhages by last follow-up (rank correlation=0.49;P<0.001).
Focal hemosiderin deposits are relatively common in the ADNI cohort and are associated with β-amyloid load.
ADNI; microhemorrhage; superficial siderosis; MRI; Amyloid; PET; Florbetapir; Alzheimer’s disease; mild cognitive impairment; early mild cognitive impairment
To investigate the associations among β-amyloid (Aβ), cortical thickness, and episodic memory in a cohort of cognitively normal to mildly impaired individuals at increased risk of vascular disease.
In 67 subjects specifically recruited to span a continuum of cognitive function and vascular risk, we measured brain Aβ deposition using [11C] Pittsburgh compound B–PET imaging and cortical thickness using MRI. Episodic memory was tested using a standardized composite score of verbal memory, and vascular risk was quantified using the Framingham Coronary Risk Profile index.
Increased Aβ was associated with cortical thinning, notably in frontoparietal regions. This relationship was strongest in persons with high Aβ deposition. Increased Aβ was also associated with lower episodic memory performance. Cortical thickness was found to mediate the relationship between Aβ and memory performance. While age had a marginal effect on these associations, the relationship between Aβ and cortical thickness was eliminated after controlling for vascular risk except when examined in only Pittsburgh compound B–positive subjects, in whom Aβ remained associated with thinner cortex in precuneus and occipital lobe. In addition, only the precuneus was found to mediate the relationship between Aβ and memory after controlling for vascular risk.
These results suggest strong links among Aβ, cortical thickness, and memory. They highlight that, in individuals without dementia, vascular risk also contributes to cortical thickness and influences the relationships among Aβ, cortical thickness, and memory.
A positive family history (FH) raises the risk for late-onset Alzheimer's disease though, other than the known risk conferred by apolipoprotein ε4 (ApoE4), much of the genetic variance remains unexplained. We examined the effect of family history on longitudinal regional brain atrophy rates in 184 subjects (42% FH+, mean age 79.9) with mild cognitive impairment (MCI) enrolled in a national biomarker study. An automated image analysis method was applied to T1-weighted MR images to measure atrophy rates for 20 cortical and subcortical regions. Mixed-effects linear regression models incorporating repeated-measures to control for within-subject variation over multiple time points tested the effect of FH over a follow-up of up to 48 months. Most of the 20 regions showed significant atrophy over time. Adjusting for age and gender, subjects with a positive FH had greater atrophy of the amygdala (p < 0.01), entorhinal cortex (p < 0.01), hippocampus (p < 0.053) and cortical gray matter (p < 0.009). However, when E4 genotype was added as a covariate, none of the FH effects remained significant. Analyses by ApoE genotype showed that the effect of FH on amygdala atrophy rates was numerically greater in ε3 homozygotes than in E4 carriers, but this difference was not significant. FH+ subjects had numerically greater 4-year cognitive decline and conversion rates than FH− subjects but the difference was not statistically significant after adjusting for ApoE and other variables. We conclude that a positive family history of AD may influence cortical and temporal lobe atrophy in subjects with mild cognitive impairment, but it does not have a significant additional effect beyond the known effect of the E4 genotype.
•Family history of AD is associated with accelerated medial temporal atrophy in MCI.•Family history effect on medial temporal lobe atrophy is largely due to ApoE4 effect•Family history is not related to brain atrophy rate in ApoE3 homozygotes.
Hippocampus; Amygdala; Entorhinal cortex; Memory; Dementia; Family history
Deposition of amyloid-β (Aβ) in the cerebral cortex is thought to be a pivotal event in Alzheimer’s disease (AD) pathogenesis with a significant genetic contribution. Molecular imaging can provide an early noninvasive phenotype but small samples have prohibited genome-wide association studies (GWAS) of cortical Aβ load until now. We employed florbetapir (18F) positron emission tomography (PET) imaging to assess brain Aβ levels in vivo for 555 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). More than six million common genetic variants were tested for association to quantitative global cortical Aβ load controlling for age, gender, and diagnosis. Independent genome-wide significant associations were identified on chromosome 19 within APOE (rs429358, p = 5.5 × 10−14) and on chromosome 3 upstream of BCHE (rs509208, p = 2.7 × 10−8) in a region previously associated with serum butyrylcholinesterase activity. Together, these loci explained 15% of the variance in cortical Aβ levels in this sample (APOE 10.7%, BCHE 4.3%). Suggestive associations were identified within ITGA6, near EFNA5, EDIL3, ITGA1, PIK3R1, NFIB, and ARID1B, and between NUAK1 and C12orf75. These results confirm the association of APOE with Aβ deposition and represent the largest known effect of BCHE on an AD-related phenotype. Butyrylcholinesterase has been found in senile plaques and this new association of genetic variation at the BCHE locus with Aβ burden in humans may have implications for potential disease-modifying effects of butyrylcholinesterase-modulating agents in the AD spectrum.
Alzheimer’s disease (AD); amyloid; apolipoprotein E (APOE); butyrylcholinesterase (BCHE); florbetapir (AV-45); genome-wide association study (GWAS)
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers Dis 2006;9(Suppl 3):151–3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers combine optimum features from multiple modalities, including MRI, [18F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects, and are leading candidates for the detection of AD in its preclinical stages; (5) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.
Alzheimer's disease; Mild cognitive impairment; Amyloid; Tau; Biomarker
Multimodal brain image analysis : third International Workshop, MBIA 2013, held in conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013 : proceedings / Li Shen, Tianming Liu, Pew-Thian Yap, Heng Huang, Dinggang Shen, Carl-Fre.
Ventricular volume (VV) is a powerful global indicator of brain tissue loss on MRI in normal aging and dementia. VV is used by radiologists in clinical practice and has one of the highest obtainable effect sizes for tracking brain change in clinical trials, but it is crucial to relate VV to structural alterations underlying clinical symptoms. Here we identify patterns of thinner cortical gray matter (GM) associated with dynamic changes in lateral VV at 1-year (N=677) and 2-year (N=536) intervals, in the ADNI cohort. People with faster VV loss had thinner baseline cortical GM in temporal, inferior frontal, inferior parietal, and occipital regions (controlling for age, sex, diagnosis). These findings show the patterns of relative cortical atrophy that predict later ventricular enlargement, further validating the use of ventricular segmentations as biomarkers. We may also infer specific patterns of regional cortical degeneration (and perhaps functional changes) that relate to VV expansion.
imaging biomarkers*; brain imaging; magnetic resonance imaging; quantitative image analysis; statistical analysis; temporal/longitudinal image series analysis
Brain connectivity analyses show considerable promise for understanding how our neural pathways gradually break down in aging and Alzheimer's disease (AD). Even so, we know very little about how the brain's networks change in AD, and which metrics are best to evaluate these changes. To better understand how AD affects brain connectivity, we analyzed anatomical connectivity based on 3-T diffusion-weighted images from 111 subjects (15 with AD, 68 with mild cognitive impairment, and 28 healthy elderly; mean age, 73.7±7.6 SD years). We performed whole brain tractography based on the orientation distribution functions, and compiled connectivity matrices showing the proportions of detected fibers interconnecting 68 cortical regions. We computed a variety of measures sensitive to anatomical network topology, including the structural backbone—the so-called “k-core”—of the anatomical network, and the nodal degree. We found widespread network disruptions, as connections were lost in AD. Among other connectivity measures showing disease effects, network nodal degree, normalized characteristic path length, and efficiency decreased with disease, while normalized small-worldness increased, in the whole brain and left and right hemispheres individually. The normalized clustering coefficient also increased in the whole brain; we discuss factors that may cause this effect. The proportions of fibers intersecting left and right cortical regions were asymmetrical in all diagnostic groups. This asymmetry may intensify as disease progressed. Connectivity metrics based on the k-core may help understand brain network breakdown as cognitive impairment increases, revealing how degenerative diseases affect the human connectome.
Alzheimer's disease; asymmetry; brain connectivity; diffusion tensor imaging; efficiency; k-core; mild cognitive impairment; nodal degree; small-world; tractography
Cortical atrophy has been associated with late life depression (LLD) and recent findings suggest that reduced right hemisphere cortical thickness is associated with familial risk for major depressive disorder but cortical thickness abnormalities in LLD have not been explored. Further, cortical atrophy has been posited as a contributor to poor antidepressant treatment response in LLD but the impact of cortical thickness on psychotherapy response is unknown. This study was conducted to evaluate patterns of cortical thickness in LLD and in relation to psychotherapy treatment outcomes.
Participants included 22 individuals with LLD and 12 age matched comparison subjects. LLD participants completed 12 weeks of psychotherapy and treatment response was defined as a 50% reduction in depressive symptoms. All participants participated in Magnetic Resonance Imaging (MRI) of the brain and cortical mapping of grey matter tissue thickness was calculated.
LLD individuals demonstrated thinner cortex than controls prominently in the right frontal, parietal, and temporal brain regions. Eleven participants (50%) exhibited positive psychotherapy response after 12 weeks of treatment. Psychotherapy non-responders demonstrated thinner cortex in bilateral posterior cingulate and parahippocampal cortices, left paracentral, precuneus, cuneus, and insular cortices, and the right medial orbito-frontal and lateral occipital cortices relative to treatment responders.
Our findings suggest more distributed right hemisphere cortical abnormalities in LLD than have been previously reported. Additionally, our findings suggest that reduced bilateral cortical thickness may be an important phenotypic marker of individuals at higher risk for poor response to psychotherapy.
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
Alzheimer's disease; Copy Number Variable Regions (CNV-Regions); Copy Number Variations (CNVs); Genome-wide scan; Next Generation Sequencing (NGS)
The newly proposed National Institute on Aging-Alzheimer’s Association (NIA-AA) criteria for mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) suggest a combination of clinical features and biomarker measures, but their performance in the community is not known.
The Mayo Clinic Study of Aging (MCSA) is a population-based longitudinal study of non-demented subjects in Olmsted County, Minnesota. A sample of 154 MCI subjects from the MCSA was compared to a sample of 58 amnestic MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI 1) to assess the applicability of the criteria in both settings and to assess their outcomes.
In the MCSA, 14% and in ADNI 1 16% of subjects were biomarker negative. In addition, 14% of the MCSA and 12% of ADNI 1 subjects had evidence for amyloid deposition only, while 43% of MCSA and 55% of ADNI 1 subjects had evidence for amyloid deposition plus neurodegeneration (MRI atrophy, FDG PET hypometabolism or both). However, a considerable number of subjects had biomarkers inconsistent with the proposed AD model, e.g., 29% of MCSA subjects and 17% of the ADNI 1 subjects had evidence for neurodegeneration without amyloid deposition. These subjects may not be on an AD pathway. Neurodegeneration appears to be a key factor in predicting progression relative to amyloid deposition alone.
The NIA-AA criteria apply to most MCI subjects in both the community and clinical trials settings however, a sizeable proportion of subjects had conflicting biomarkers which may be very important and need to be explored.
ApoliopoproteinE ε4 (ApoE-ε4) polymorphism is the most well known genetic risk factor for developing Alzheimers Disease. The exact mechanism through which ApoE ε4 increases AD risk is not fully known, but may be related to decreased clearance and increased oligomerization of Aβ. By making measurements of white matter integrity via diffusion MR and correlating the metrics in a voxel-based statistical analysis with ApoE-ε4 genotype (whilst controlling for vascular risk factor, gender, cognitive status and age) we are able to identify changes in white matter associated with carrying an ApoE ε4 allele. We found potentially significant regions (Puncorrected < 0.05) near the hippocampus and the posterior cingulum that were independent of voxels that correlated with age or clinical dementia rating (CDR) status suggesting that ApoE may affect cognitive decline via a pathway in dependent of normal aging and acute insults that can be measured by CDR and Framingham Coronary Risk Score (FCRS).
DTI; Diffusion MRI; MRI; Alzheimer’s Disease; Dementia; ApoE; Neuroimaging
The SNP-SNP interactome has rarely been explored in the context of neuroimaging genetics mainly due to the complexity of conducting ∼1011 pairwise statistical tests. However, recent advances in machine learning, specifically the iterative sure independence screening (SIS) method, have enabled the analysis of datasets where the number of predictors is much larger than the number of observations. Using an implementation of the SIS algorithm (called EPISIS), we used exhaustive search of the genome-wide, SNP-SNP interactome to identify and prioritize SNPs for interaction analysis. We identified a significant SNP pair, rs1345203 and rs1213205, associated with temporal lobe volume. We further examined the full-brain, voxelwise effects of the interaction in the ADNI dataset and separately in an independent dataset of healthy twins (QTIM). We found that each additional loading in the epistatic effect was associated with ∼5% greater brain regional brain volume (a protective effect) in both the ADNI and QTIM samples.
epistasis; interaction; genome; sure independence; tensor-based morphometry
Reduced cerebrospinal fluid (CSF) β-amyloid42 (Aβ42) and increased florbetapir positron emission tomography (PET) uptake reflects brain Aβ accumulation. These biomarkers are correlated with each other and altered in Alzheimer's disease (AD), but no study has directly compared their diagnostic performance.
We examined healthy controls (CN, N = 169) versus AD dementia patients (N = 118), and stable (sMCI; no dementia, followed up for at least 2 years, N = 165) versus progressive MCI (pMCI; conversion to AD dementia, N = 59). All subjects had florbetapir PET (global and regional; temporal, frontal, parietal, and cingulate) and CSF Aβ42 measurements at baseline. We compared area under the curve (AUC), sensitivity, and specificity (testing a priori and optimized cutoffs). Clinical diagnosis was the reference standard.
CSF Aβ42 and (global or regional) PET florbetapir did not differ in AUC (CN vs. AD, CSF 84.4%; global PET 86.9%; difference [95% confidence interval] −6.7 to 1.5). CSF Aβ42 and global PET florbetapir did not differ in sensitivity, but PET had greater specificity than CSF in most comparisons. Sixteen CN progressed to MCI and AD (six Aβ negative, seven Aβ positive, and three PET positive but CSF negative).
The overall diagnostic accuracies of CSF Aβ42 and PET florbetapir were similar, but PET had greater specificity. This was because some CN and sMCI subjects appear pathological using CSF but not using PET, suggesting that low CSF Aβ42 not always translates to cognitive decline or brain Aβ accumulation. Other factors, including costs and side effects, may also be considered when determining the optimal modality for different applications.
Droplet-based microfluidic techniques can form and process micrometer scale droplets at thousands per second. Each droplet can house an individual biochemical reaction, allowing millions of reactions to be performed in minutes with small amounts of total reagent. This versatile approach has been used for engineering enzymes, quantifying concentrations of DNA in solution, and screening protein crystallization conditions. Here, we use it to read the sequences of DNA molecules with a FRET-based assay. Using probes of different sequences, we interrogate a target DNA molecule for polymorphisms. With a larger probe set, additional polymorphisms can be interrogated as well as targets of arbitrary sequence.
Diffusion imaging and brain connectivity analyses can monitor white matter deterioration, revealing how neural pathways break down in aging and Alzheimer's disease (AD). Here we tested how AD disrupts the ‘rich club’ effect – a network property found in the normal brain – where high-degree nodes in the connectivity network are more heavily interconnected with each other than expected by chance. We analyzed 3-Tesla whole-brain diffusionweighted images (DWI) from 66 subjects (22 AD/44 normal elderly). We performed whole-brain tractography based on the orientation distribution functions. Connectivity matrices were compiled, representing the proportion of detected fibers interconnecting 68 cortical regions. As expected, AD patients had a lower nodal degree (average number of connections) in cortical regions implicated in the disease. Unexpectedly, the normalized rich club coefficient was higher in AD. AD disrupts cortical networks by removing connections; when these networks are thresholded, organizational properties are disrupted leading to additional new biomarkers of AD.
Delta opioid receptors are implicated in a variety of psychiatric and neurological disorders. These receptors play a key role in the reinforcing properties of drugs of abuse, and polymorphisms in OPRD1 (the gene encoding delta opioid receptors) are associated with drug addiction. Delta opioid receptors are also involved in protecting neurons against hypoxic and ischemic stress. Here, we first examined a large sample of 738 elderly participants with neuroimaging and genetic data from the Alzheimer’s Disease Neuroimaging Initiative. We hypothesized that common variants in OPRD1 would be associated with differences in brain structure, particularly in regions relevant to addictive and neurodegenerative disorders. One very common variant (rs678849) predicted differences in regional brain volumes. We replicated the association of this single-nucleotide polymorphism with regional tissue volumes in a large sample of young participants in the Queensland Twin Imaging study. Although the same allele was associated with reduced volumes in both cohorts, the brain regions affected differed between the two samples. In healthy elderly, exploratory analyses suggested that the genotype associated with reduced brain volumes in both cohorts may also predict cerebrospinal fluid levels of neurodegenerative biomarkers, but this requires confirmation. If opiate receptor genetic variants are related to individual differences in brain structure, genotyping of these variants may be helpful when designing clinical trials targeting delta opioid receptors to treat neurological disorders.
neuroimaging; genetics; neurodegeneration; drug addiction; opiates