Author name cluster

William J. Jagust

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19 papers

1 author row

YNIMG Journal 2023 Journal Article

Optimizing quantification of MK6240 tau PET in unimpaired older adults

Theresa M. Harrison
Tyler J. Ward
Alice Murphy
Suzanne L. Baker
Pablo A. Dominguez
Robert Koeppe
Prashanthi Vemuri
Samuel N. Lockhart

F]florbetaben [FBB]) and MRI data to evaluate neuroimaging biomarkers of AD and cerebrovascular pathophysiology in this at-risk sample. Here 481 participants (70.0±5.0; 66% F) with baseline MK6240, FBB and structural MRI scans were included. PET scans were coregistered to the structural MRI which was used to create FreeSurfer-defined reference regions and target regions of interest (ROIs). We also created off-target signal (OTS) ROIs to examine the magnitude and distribution of MK6240 OTS across the brain as well as relationships between OTS and age, sex, and race. OTS was unimodally distributed, highly correlated across OTS ROIs and related to younger age and sex but not race. Aiming to identify an optimal processing approach for MK6240 that would reduce the influence of OTS, we compared our previously validated MRI-guided standard PET processing and 6 alternative approaches. The alternate approaches included combinations of reference region erosion and meningeal OTS masking before spatial smoothing as well as partial volume correction. To compare processing approaches we examined relationships between target ROIs (entorhinal cortex (ERC), hippocampus or a temporal meta-ROI (MetaROI)) SUVR and age, sex, race, Aβ and a general cognitive status measure, the Modified Telephone Interview for Cognitive Status (TICSm). Overall, the processing approaches performed similarly, and none showed a meaningful improvement over standard processing. Across processing approaches we observed previously reported relationships with MK6240 target ROIs including positive associations with age, an Aβ+> Aβ- effect and negative associations with cognition. In sum, we demonstrated that different methods for minimizing effects of OTS, which is highly correlated across the brain within subject, produced no substantive change in our performance metrics. This is likely because OTS contaminates both reference and target regions and this contamination largely cancels out in SUVR data. Caution should be used when efforts to reduce OTS focus on target or reference regions in isolation as this may exacerbate OTS contamination in SUVR data.

YNIMG Journal 2022 Journal Article

Locus coeruleus catecholamines link neuroticism and vulnerability to tau pathology in aging

Jourdan H. Parent
Claire J. Ciampa
Theresa M. Harrison
Jenna N. Adams
Kailin Zhuang
Matthew J. Betts
Anne Maass
Joseph R. Winer

C]Pittsburgh compound B PET (n = 14 Aβ positive). Lower LC catecholamine synthesis capacity was associated with higher neuroticism, more depressive symptoms as measured by the Geriatric Depression Scale, and higher amygdala tau-PET binding. Exploratory analyses with other personality traits revealed that low trait conscientiousness was also related to both lower LC catecholamine synthesis capacity, and more depressive symptoms. A significant indirect path linked both high neuroticism and low conscientiousness to greater amygdala tau burden via their mutual association with low LC catecholamine synthesis capacity. Together, these findings reveal LC catecholamine synthesis capacity to be a promising marker of affective health and pathology burden in aging, and identifies candidate neurobiological mechanisms for the effect of personality on increased vulnerability to dementia.

YNICL Journal 2020 Journal Article

Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease

Joseph Giorgio
Susan M. Landau
William J. Jagust
Peter Tino
Zoe Kourtzi

Alzheimer's disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities that may lead to MCI diagnosis without progression to AD. Despite the importance of early diagnosis of AD for prognosis and personalised interventions, we still lack robust tools for predicting individual progression to dementia. Here, we propose a novel trajectory modelling approach based on metric learning (Generalised Metric Learning Vector Quantization) that mines multimodal data from MCI patients in the Alzheimer's disease Neuroimaging Initiative (ADNI) cohort to derive individualised prognostic scores of cognitive decline due to AD. We develop an integrated biomarker generation- using partial least squares regression- and classification methodology that extends beyond binary patient classification into discrete subgroups (i.e. stable vs. progressive MCI), determines individual profiles from baseline (i.e. cognitive or biological) data and predicts individual cognitive trajectories (i.e. change in memory scores from baseline). We demonstrate that a metric learning model trained on baseline cognitive data (memory, executive function, affective measurements) discriminates stable vs. progressive MCI individuals with high accuracy (81.4%), revealing an interaction between cognitive (memory, executive functions) and affective scores that may relate to MCI comorbidity (e.g. affective disturbance). Training the model to perform the same binary classification on biological data (mean cortical β-amyloid burden, grey matter density, APOE 4) results in similar prediction accuracy (81.9%). Extending beyond binary classifications, we develop and implement a trajectory modelling approach that shows significantly better performance in predicting individualised rate of future cognitive decline (i.e. change in memory scores from baseline), when the metric learning model is trained with biological (r = -0.68) compared to cognitive (r = -0.4) data. Our trajectory modelling approach reveals interpretable and interoperable markers of progression to AD and has strong potential to guide effective stratification of individuals based on prognostic disease trajectories, reducing MCI patient misclassification, that is critical for clinical practice and discovery of personalised interventions.

YNICL Journal 2019 Journal Article

Tau covariance patterns in Alzheimer's disease patients match intrinsic connectivity networks in the healthy brain

Rik Ossenkoppele
Leonardo Iaccarino
Daniel R. Schonhaut
Jesse A. Brown
Renaud La Joie
James P. O'Neil
Mustafa Janabi
Suzanne L. Baker

F]flortaucipir covariance patterns and intrinsic connectivity maps (range Spearman rho's: 0.32-0.78, p < .001), which corresponded with expected functional networks (range goodness-of-fit: 3.8-8.2). The topography of amyloid-β covariance patterns was more diffuse and less network-specific, while glucose hypometabolic patterns were more spatially restricted than tau but overlapped with functional networks. These findings suggest that the spatial patterns of tau and glucose hypometabolism observed in AD resemble the functional organization of the healthy brain, supporting the notion that tau pathology spreads through circumscribed brain networks and drives neurodegeneration.

YNICL Journal 2018 Journal Article

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

Leonardo Iaccarino
Gautam Tammewar
Nagehan Ayakta
Suzanne L. Baker
Alexandre Bejanin
Adam L. Boxer
Maria Luisa Gorno-Tempini
Mustafa Janabi

C-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.

YNICL Journal 2018 Journal Article

Metabolic brain networks in aging and preclinical Alzheimer's disease

Katelyn L. Arnemann
Franziska Stöber
Sharada Narayan
Gil D. Rabinovici
William J. Jagust

Metabolic brain networks can provide insight into the network processes underlying progression from healthy aging to Alzheimer's disease. We explore the effect of two Alzheimer's disease risk factors, amyloid-β and ApoE ε4 genotype, on metabolic brain networks in cognitively normal older adults (N = 64, ages 69–89) compared to young adults (N = 17, ages 20–30) and patients with Alzheimer's disease (N = 22, ages 69–89). Subjects underwent MRI and PET imaging of metabolism (FDG) and amyloid-β (PIB). Normal older adults were divided into four subgroups based on amyloid-β and ApoE genotype. Metabolic brain networks were constructed cross-sectionally by computing pairwise correlations of metabolism across subjects within each group for 80 regions of interest. We found widespread elevated metabolic correlations and desegregation of metabolic brain networks in normal aging compared to youth and Alzheimer's disease, suggesting that normal aging leads to widespread loss of independent metabolic function across the brain. Amyloid-β and the combination of ApoE ε4 led to less extensive elevated metabolic correlations compared to other normal older adults, as well as a metabolic brain network more similar to youth and Alzheimer's disease. This could reflect early progression towards Alzheimer's disease in these individuals. Altered metabolic brain networks of older adults and those at the highest risk for progression to Alzheimer's disease open up novel lines of inquiry into the metabolic and network processes that underlie normal aging and Alzheimer's disease.

YNIMG Journal 2017 Journal Article

Amyloid and tau PET demonstrate region-specific associations in normal older people

Samuel N. Lockhart
Michael Schöll
Suzanne L. Baker
Nagehan Ayakta
Kaitlin N. Swinnerton
Rachel K. Bell
Taylor J. Mellinger
Vyoma D. Shah

β-amyloid (Aβ) and tau pathology become increasingly prevalent with age, however, the spatial relationship between the two pathologies remains unknown. We examined local (same region) and non-local (different region) associations between these 2 aggregated proteins in 46 normal older adults using [18F]AV-1451 (for tau) and [11C]PiB (for Aβ) positron emission tomography (PET) and 1. 5T magnetic resonance imaging (MRI) images. While local voxelwise analyses showed associations between PiB and AV-1451 tracer largely in the temporal lobes, k-means clustering revealed that some of these associations were driven by regions with low tracer retention. We followed this up with a whole-brain region-by-region (local and non-local) partial correlational analysis. We calculated each participant's mean AV-1451 and PiB uptake values within 87 regions of interest (ROI). Pairwise ROI analysis demonstrated many positive PiB—AV-1451 associations. Importantly, strong positive partial correlations (controlling for age, sex, and global gray matter fraction, p<. 01) were identified between PiB in multiple regions of association cortex and AV-1451 in temporal cortical ROIs. There were also less frequent and weaker positive associations of regional PiB with frontoparietal AV-1451 uptake. Particularly in temporal lobe ROIs, AV-1451 uptake was strongly predicted by PiB across multiple ROI locations. These data indicate that Aβ and tau pathology show significant local and non-local regional associations among cognitively normal elderly, with increased PiB uptake throughout the cortex correlating with increased temporal lobe AV-1451 uptake. The spatial relationship between Aβ and tau accumulation does not appear to be specific to Aβ location, suggesting a regional vulnerability of temporal brain regions to tau accumulation regardless of where Aβ accumulates.

YNIMG Journal 2017 Journal Article

Comparison of multiple tau-PET measures as biomarkers in aging and Alzheimer's disease

Anne Maass
Susan Landau
Suzanne L. Baker
Andy Horng
Samuel N. Lockhart
Renaud La Joie
Gil D. Rabinovici
William J. Jagust

The recent development of tau-specific positron emission tomography (PET) tracers enables in vivo quantification of regional tau pathology, one of the key lesions in Alzheimer's disease (AD). Tau PET imaging may become a useful biomarker for clinical diagnosis and tracking of disease progression but there is no consensus yet on how tau PET signal is best quantified. The goal of the current study was to evaluate multiple whole-brain and region-specific approaches to detect clinically relevant tau PET signal. Two independent cohorts of cognitively normal adults and amyloid-positive (Aβ+) patients with mild cognitive impairment (MCI) or AD-dementia underwent [18F]AV-1451 PET. Methods for tau tracer quantification included: (i) in vivo Braak staging, (ii) regional uptake in Braak composite regions, (iii) several whole-brain measures of tracer uptake, (iv) regional uptake in AD-vulnerable voxels, and (v) uptake in a priori defined regions. Receiver operating curves characterized accuracy in distinguishing Aβ- controls from AD/MCI patients and yielded tau positivity cutoffs. Clinical relevance of tau PET measures was assessed by regressions against cognition and MR imaging measures. Key tracer uptake patterns were identified by a factor analysis and voxel-wise contrasts. Braak staging, global and region-specific tau measures yielded similar diagnostic accuracies, which differed between cohorts. While all tau measures were related to amyloid and global cognition, memory and hippocampal/entorhinal volume/thickness were associated with regional tracer retention in the medial temporal lobe. Key regions of tau accumulation included medial temporal and inferior/middle temporal regions, retrosplenial cortex, and banks of the superior temporal sulcus. Our data indicate that whole-brain tau PET measures might be adequate biomarkers to detect AD-related tau pathology. However, regional measures covering AD-vulnerable regions may increase sensitivity to early tau PET signal, atrophy and memory decline.

YNICL Journal 2017 Journal Article

Regional correlations between [11C]PIB PET and post-mortem burden of amyloid-beta pathology in a diverse neuropathological cohort

Sang Won Seo
Nagehan Ayakta
Lea T. Grinberg
Sylvia Villeneuve
Manja Lehmann
Bruce Reed
Charles DeCarli
Bruce L. Miller

Imaging-pathological correlation studies show that in vivo amyloid-β (Aβ) positron emission tomography (PET) strongly predicts the presence of significant Aβ pathology at autopsy. We sought to determine whether regional PiB-PET uptake would improve sensitivity for amyloid detection in comparison with global measures (experiment 1), and to estimate the relative contributions of different Aβ aggregates to in vivo PET signal (experiment 2). In experiment 1, 54 subjects with [11C] PiB-PET during life and postmortem neuropathologic examination (85. 2% with dementia, interval from PET to autopsy 3. 1±1. 9 years) were included. We assessed Thal amyloid phase (N=36) and CERAD score (N=54) versus both global and regional PiB SUVRs. In experiment 2 (N=42), PiB SUVR and post-mortem amyloid β burden was analyzed in five customized regions of interest matching regions sampled at autopsy. We assessed the relative contribution of neuritic plaques (NPs), diffuse plaques (DPs) and cerebral amyloid angiopathy (CAA) to regional PIB SUVR using multi-linear regression. In experiment 1, there were no differences in Area Under the Curve for amyloid phase≥A2 and CERAD score≥C2 between global and highest regional PiB SUVR (p =0. 186 and 0. 230). In experiment 2, when NPs, DPs, and/or CAA were included in the same model, moderate to severe NPs were independently correlated with PiB SUVR in all regions except for the inferior temporal and calcarine ROI (β=0. 414–0. 804, p <0. 05), whereas DPs were independently correlated with PiB SUVR in the angular gyrus ROI (β=0. 446, p =0. 010). CAA was also associated with PiB SUVR in the inferior temporal and calcarine ROI (β=0. 222–0. 355, p <0. 05). In conclusion, global PiB-PET SUVR performed as well as regional values for amyloid detection in our cohort. The substrate-specific binding of PiB might differ among the brain specific regions.

YNIMG Journal 2015 Journal Article

The mediational effects of FDG hypometabolism on the association between cerebrospinal fluid biomarkers and neurocognitive function

N. Maritza Dowling
Sterling C. Johnson
Carey E. Gleason
William J. Jagust

Positive cerebrospinal fluid (CSF) biomarkers of tau and amyloid beta42 suggest possible active underlying Alzheimer's disease (AD) including neurometabolic dysfunction and neurodegeneration leading to eventual cognitive decline. But the temporal relationship between CSF, imaging markers of neural function, and cognition has not been described. Using a statistical mediation model, we examined relationships between cerebrospinal fluid (CSF) analytes (hyperphosphorylated tau (p-Tau181p), β-amyloid peptides 1–42 (Aβ1–42), total tau (t-Tau), and their ratios); change in cognitive function; and change in [18F]fluorodeoxyglucose (FDG) uptake using positron emission tomography (PET). We hypothesized that a) abnormal CSF protein values at baseline, result in cognitive declines by decreasing neuronal glucose metabolism across time, and b) the role of altered glucose metabolism in the assumed causal chain varies by brain region and the nature of CSF protein alteration. Data from 412 individuals participating in Alzheimer's Disease Neuroimaging (ADNI) cohort studies were included in analyses. At baseline, individuals were cognitively normal (N=82), or impaired: 241 with mild cognitive impairment, and 89 with Alzheimer's disease. A parallel-process latent growth curve model was used to test mediational effects of changes in regional FDG-PET uptake over time in relation to baseline CSF biomarkers and changes in cognition, measured with the 13-item Alzheimer Disease's Assessment Scale–cognitive subscale (ADAS–Cog). Findings suggested a causal sequence of events; specifically, FDG hypometabolism acted as a mediator between antecedent CSF biomarker alterations and subsequent cognitive impairment. Higher baseline concentrations of t-Tau, and p-Tau181p were more predictive of decline in cerebral glucose metabolism than lower baseline concentrations of Aβ1–42. FDG-PET changes appeared to mediate t-Tau or t-Tau/Aβ1–42-associated cognitive change across all brain regions examined. Significant direct effects of alterations in Aβ1–42 levels on hypometabolism were observed in a single brain region: middle/inferior temporal gyrus. Results support a temporal framework model in which reduced CSF amyloid-related biomarkers occur earlier in the pathogenic pathway, ultimately leading to detrimental cognitive effects. Also consistent with this temporal framework model, baseline markers of neurofibrillary degeneration predicted changes in brain glucose metabolism in turn causing longitudinal cognitive changes, suggesting that tau-related burden precedes neurometabolic dysfunction. While intriguing, the hypothesized mediational relationships require further validation.

YNICL Journal 2014 Journal Article

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

Robert Laforce
Duygu Tosun
Pia Ghosh
Manja Lehmann
Cindee M. Madison
Michael W. Weiner
Bruce L. Miller
William J. Jagust

The relationships between clinical phenotype, β-amyloid (Aβ) deposition and neurodegeneration in Alzheimer's disease (AD) are incompletely understood yet have important ramifications for future therapy. The goal of this study was to utilize multimodality positron emission tomography (PET) data from a clinically heterogeneous population of patients with probable AD in order to: (1) identify spatial patterns of Aβ deposition measured by ((11)C)-labeled Pittsburgh Compound B (PiB-PET) and glucose metabolism measured by FDG-PET that correlate with specific clinical presentation and (2) explore associations between spatial patterns of Aβ deposition and glucose metabolism across the AD population. We included all patients meeting the criteria for probable AD (NIA-AA) who had undergone MRI, PiB and FDG-PET at our center (N = 46, mean age 63.0 ± 7.7, Mini-Mental State Examination 22.0 ± 4.8). Patients were subclassified based on their cognitive profiles into an amnestic/dysexecutive group (AD-memory; n = 27), a language-predominant group (AD-language; n = 10) and a visuospatial-predominant group (AD-visuospatial; n = 9). All patients were required to have evidence of amyloid deposition on PiB-PET. To capture the spatial distribution of Aβ deposition and glucose metabolism, we employed parallel independent component analysis (pICA), a method that enables joint analyses of multimodal imaging data. The relationships between PET components and clinical group were examined using a Receiver Operator Characteristic approach, including age, gender, education and apolipoprotein E ε4 allele carrier status as covariates. Results of the first set of analyses independently examining the relationship between components from each modality and clinical group showed three significant components for FDG: a left inferior frontal and temporoparietal component associated with AD-language (area under the curve [AUC] 0.82, p = 0.011), and two components associated with AD-visuospatial (bilateral occipito-parieto-temporal [AUC 0.85, p = 0.009] and right posterior cingulate cortex [PCC]/precuneus and right lateral parietal [AUC 0.69, p = 0.045]). The AD-memory associated component included predominantly bilateral inferior frontal, cuneus and inferior temporal, and right inferior parietal hypometabolism but did not reach significance (AUC 0.65, p = 0.062). None of the PiB components correlated with clinical group. Joint analysis of PiB and FDG with pICA revealed a correlated component pair, in which increased frontal and decreased PCC/precuneus PiB correlated with decreased FDG in the frontal, occipital and temporal regions (partial r = 0.75, p < 0.0001). Using multivariate data analysis, this study reinforced the notion that clinical phenotype in AD is tightly linked to patterns of glucose hypometabolism but not amyloid deposition. These findings are strikingly similar to those of univariate paradigms and provide additional support in favor of specific involvement of the language network, higher-order visual network, and default mode network in clinical variants of AD. The inverse relationship between Aβ deposition and glucose metabolism in partially overlapping brain regions suggests that Aβ may exert both local and remote effects on brain metabolism. Applying multivariate approaches such as pICA to multimodal imaging data is a promising approach for unraveling the complex relationships between different elements of AD pathophysiology.

YNIMG Journal 2013 Journal Article

Genetic effects on behavior are mediated by neurotransmitters and large-scale neural networks

Linh C. Dang
James P. O'Neil
William J. Jagust

Claims of gene–behavior associations are complex and sometimes difficult to replicate because these relationships involve many downstream endogenous and environmental processes that mediate genetic effects. Knowing these mediating processes is critical to understanding the links between genes and behavior and how these factors differ between people. We identified and characterized the effects of a gene on neurochemistry and neural networks to elucidate the mechanism, at the systems level, whereby genes influence cognition. Catechol-O-methyltransferase (COMT) degrades dopamine in the prefrontal cortex (PFC) and is polymorphic with alleles differing in enzymatic activity. We found that COMT genotype determined dopamine synthesis, such that individuals with greater COMT activity synthesized more dopamine. Dopamine synthesis in the midbrain and ventral striatum affected functional connectivity in the default mode network, likely through the mesocorticolimbic pathway, in an inverted-U pattern with greater functional connectivity in medial PFC associated with intermediate levels of COMT activity and dopamine. Greater functional connectivity correlated with greater deactivation during performance of a set-shifting task that engaged the PFC. Greater deactivation was in turn associated with better performance. The integration of these results yields a model whereby COMT affects prefrontal function by a mechanism involving dopaminergic modulation of the default mode network. The model features the well-known inverted-U function between dopamine and performance and supports the hypothesis that dopamine and the default mode network shift attentional resources to influence prefrontal cognition.

YNIMG Journal 2012 Journal Article

Brain imaging in the study of Alzheimer's disease

Eric M. Reiman
William J. Jagust

Over the last 20years, there has been extraordinary progress in brain imaging research and its application to the study of Alzheimer's disease (AD). Brain imaging researchers have contributed to the scientific understanding, early detection and tracking of AD. They have set the stage for imaging techniques to play growing roles in the clinical setting, the evaluation of disease-modifying treatments, and the identification of demonstrably effective prevention therapies. They have developed ground-breaking methods, including positron emission tomography (PET) ligands to measure fibrillar amyloid-β (Aβ) deposition, new magnetic resonance imaging (MRI) pulse sequences, and powerful image analysis techniques, to help in these endeavors. Additional work is needed to develop even more powerful imaging methods, to further clarify the relationship and time course of Aβ and other disease processes in the predisposition to AD, to establish the role of brain imaging methods in the clinical setting, and to provide the scientific means and regulatory approval pathway needed to evaluate the range of promising disease-modifying and prevention therapies as quickly as possible. Twenty years from now, AD may not yet be a distant memory, but the best is yet to come.

YNIMG Journal 2012 Journal Article

Not quite PIB-positive, not quite PIB-negative: Slight PIB elevations in elderly normal control subjects are biologically relevant

Elizabeth C. Mormino
Michael G. Brandel
Cindee M. Madison
Gil D. Rabinovici
Shawn Marks
Suzanne L. Baker
William J. Jagust

Researchers employing Pittsburgh Compound B positron emission tomography (PIB-PET) imaging have consistently indentified old normal control (oNC) subjects with elevated tracer uptake, suggesting the presence of beta-amyloid deposition in these individuals. However, a consensus regarding the level at which PIB reveals a biologically meaningful signal does not exist (ie. an appropriate cutoff value for PIB positivity remains unclear). In this exploratory study, we sought to investigate the range of PIB distribution volume ratio (DVR) values present in our oNC cohort (N=75, age range=58–97). oNC subjects were classified based on global PIB index values (average DVR across prefrontal, parietal, lateral temporal and cingulate cortices) by employing two approaches: (1) an iterative outlier approach that revealed a cutoff value of 1. 16 (IO-cutoff) and (2) an approach using data from a sample of young normal control subjects (N=11, age range=20–30) that yielded a cutoff value of 1. 08 (yNC-cutoff). oNC subjects falling above the IO-cutoff had values similar to AD subjects (“PIB+”, 15%). Subjects falling between the 2 cutoffs were considered to have ambiguous PIB status (“Ambig”, 20%) and the remaining oNC were considered “PIB-“ (65%). Additional measures capturing focal DVR magnitude and extent of elevated DVR values were consistent with the classification scheme using PIB index values, and revealed evidence for elevated DVR values in a subset of PIB- oNC subjects. Furthermore, there were a greater proportion of ambiguously elevated values compared to low values, and these elevated values were present in regions known to show amyloid deposition. The analyses presented in this study, in conjunction with recently published pathological data, suggest a biological relevance of slight PIB elevations in aging.

YNIMG Journal 2011 Journal Article

Characterizing Alzheimer's disease using a hypometabolic convergence index

Kewei Chen
Napatkamon Ayutyanont
Jessica B.S. Langbaum
Adam S. Fleisher
Cole Reschke
Wendy Lee
Xiaofen Liu
Dan Bandy

This article introduces a hypometabolic convergence index (HCI) for the assessment of Alzheimer's disease (AD); compares it to other biological, cognitive and clinical measures; and demonstrates its promise to predict clinical decline in mild cognitive impairment (MCI) patients using data from the AD Neuroimaging Initiative (ADNI). The HCI is intended to reflect in a single measurement the extent to which the pattern and magnitude of cerebral hypometabolism in an individual's fluorodeoxyglucose positron emission tomography (FDG-PET) image correspond to that in probable AD patients, and is generated using a fully automated voxel-based image-analysis algorithm. HCIs, magnetic resonance imaging (MRI) hippocampal volume measurements, cerebrospinal fluid (CSF) assays, memory test scores, and clinical ratings were compared in 47 probable AD patients, 21 MCI patients who converted to probable AD within the next 18months, 76 MCI patients who did not, and 47 normal controls (NCs) in terms of their ability to characterize clinical disease severity and predict conversion rates from MCI to probable AD. HCIs were significantly different in the probable AD, MCI converter, MCI stable and NC groups (p =9e−17) and correlated with clinical disease severity. Using retrospectively characterized threshold criteria, MCI patients with either higher HCIs or smaller hippocampal volumes had the highest hazard ratios (HRs) for 18-month progression to probable AD (7. 38 and 6. 34, respectively), and those with both had an even higher HR (36. 72). In conclusion, the HCI, alone or in combination with certain other biomarker measurements, has the potential to help characterize AD and predict subsequent rates of clinical decline. More generally, our conversion index strategy could be applied to a range of imaging modalities and voxel-based image-analysis algorithms.

YNIMG Journal 2011 Journal Article

β-Amyloid affects frontal and posterior brain networks in normal aging

Hwamee Oh
Elizabeth C. Mormino
Cindee Madison
Amynta Hayenga
Andre Smiljic
William J. Jagust

Although deposition of β-amyloid (Aβ), a pathological hallmark of Alzheimer's disease (AD), has also been reported in cognitively intact older people, its influence on brain structure and cognition during normal aging remains controversial. Using PET imaging with the radiotracer Pittsburgh compound B (PIB), structural MRI, and cognitive measures, we examined the relationships between Aβ deposition, gray matter volume, and cognition in older people without AD. Fifty-two healthy older participants underwent PIB–PET and structural MRI scanning and detailed neuropsychological tests. Results from the whole-brain voxel-based morphometry (VBM) analysis revealed that gray matter volume in the left inferior frontal cortex was negatively associated with amyloid deposition across all participants whereas reduced gray matter volume was shown in the posterior cingulate among older people with high amyloid deposition. When gray matter density measures extracted from these two regions were related to other brain regions by applying a structural covariance analysis, distinctive frontal and posterior brain networks were seen. Gray matter volume in these networks in relation to cognition, however, differed such that reduced frontal network gray matter volume was associated with poorer working memory performance while no relationship was found for the posterior network. The present findings highlight structural and cognitive changes in association with the level of Aβ deposition in cognitively intact normal elderly and suggest a differential role of Aβ-dependent gray matter loss in the frontal and posterior networks in cognition during normal aging.

YNIMG Journal 2010 Journal Article

Twelve-month metabolic declines in probable Alzheimer's disease and amnestic mild cognitive impairment assessed using an empirically pre-defined statistical region-of-interest: Findings from the Alzheimer's Disease Neuroimaging Initiative

Kewei Chen
Jessica B.S. Langbaum
Adam S. Fleisher
Napatkamon Ayutyanont
Cole Reschke
Wendy Lee
Xiaofen Liu
Dan Bandy

Alzheimer's disease (AD) is characterized by specific and progressive reductions in fluorodeoxyglucose positron emission tomography (FDG PET) measurements of the cerebral metabolic rate for glucose (CMRgl), some of which may precede the onset of symptoms. In this report, we describe twelve-month CMRgl declines in 69 probable AD patients, 154 amnestic mild cognitive impairment (MCI) patients, and 79 cognitively normal controls (NCs) from the AD Neuroimaging Initiative (ADNI) using statistical parametric mapping (SPM). We introduce the use of an empirically pre-defined statistical region-of-interest (sROI) to characterize CMRgl declines with optimal power and freedom from multiple comparisons, and we estimate the number of patients needed to characterize AD-slowing treatment effects in multi-center randomized clinical trials (RCTs). The AD and MCI groups each had significant twelve-month CMRgl declines bilaterally in posterior cingulate, medial and lateral parietal, medial and lateral temporal, frontal and occipital cortex, which were significantly greater than those in the NC group and correlated with measures of clinical decline. Using sROIs defined based on training sets of baseline and follow-up images to assess CMRgl declines in independent test sets from each patient group, we estimate the need for 66 AD patients or 217 MCI patients per treatment group to detect a 25% AD-slowing treatment effect in a twelve-month, multi-center RCT with 80% power and two-tailed alpha=0. 05, roughly one-tenth the number of the patients needed to study MCI patients using clinical endpoints. Our findings support the use of FDG PET, brain-mapping algorithms and empirically pre-defined sROIs in RCTs of AD-slowing treatments.

YNIMG Journal 2009 Journal Article

Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography images from the Alzheimer's Disease Neuroimaging Initiative (ADNI)

Jessica B.S. Langbaum
Kewei Chen
Wendy Lee
Cole Reschke
Dan Bandy
Adam S. Fleisher
Gene E. Alexander
Norman L. Foster

In mostly small single-center studies, Alzheimer's disease (AD) is associated with characteristic and progressive reductions in fluorodeoxyglucose positron emission tomography (PET) measurements of the regional cerebral metabolic rate for glucose (CMRgl). The AD Neuroimaging Initiative (ADNI) is acquiring FDG PET, volumetric magnetic resonance imaging, and other biomarker measurements in a large longitudinal multi-center study of initially mildly affected probable AD (pAD) patients, amnestic mild cognitive impairment (aMCI) patients, who are at increased AD risk, and cognitively normal controls (NC), and we are responsible for analyzing the PET images using statistical parametric mapping (SPM). Here we compare baseline CMRgl measurements from 74 pAD patients and 142 aMCI patients to those from 82 NC, we correlate CMRgl with categorical and continuous measures of clinical disease severity, and we compare apolipoprotein E (APOE) ɛ4 carriers to non-carriers in each of these subject groups. In comparison with NC, the pAD and aMCI groups each had significantly lower CMRgl bilaterally in posterior cingulate, precuneus, parietotemporal and frontal cortex. Similar reductions were observed when categories of disease severity or lower Mini-Mental State Exam (MMSE) scores were correlated with lower CMRgl. However, when analyses were restricted to the pAD patients, lower MMSE scores were significantly correlated with lower left frontal and temporal CMRgl. These findings from a large, multi-site study support previous single-site findings, supports the characteristic pattern of baseline CMRgl reductions in AD and aMCI patients, as well as preferential anterior CMRgl reductions after the onset of AD dementia.

YNIMG Journal 2007 Journal Article

Automated template-based PET region of interest analyses in the aging brain

Felice T. Sun
Roberta A. Schriber
Joel M. Greenia
Jiawei He
Amy Gitcho
William J. Jagust

The definition of regions of interest for PET data analysis poses a number of complex problems. While studies have shown that regions drawn on a template can be appropriate for extracting data for normal healthy subjects, it is unclear how these results can be applied to different populations. In this study, we focused on the aging population and examined how different parameters in the template data-extraction process may affect the accuracy of the results. We first present an automated method for extracting PET counts using a region-of-interest approach within a template framework. Then, we discuss two studies in which we measure the effects of varying specific parameters in this process. In study 1 we examined three parameters that may influence this process: choice of template, region, and threshold. In study 2 we focused on the hippocampus. We considered 6 different templates, and examined how well the subject-specific hippocampal masks overlapped with each other and with the template hippocampal masks after normalization. While the data in the older cohort are more variable than the normal population, the results suggest that using an appropriate template and selecting the correct parameters for the template-based ROI method can provide template-extracted counts that are highly correlated to counts extracted using subject-specific ROIs.