Citations

PurposeThe homeostasis of intracranial pressure (ICP) is of paramount importance for maintaining normal brain function. A noninvasive technique capable of making direct measurements of ICP currently does not exist. MR elastography (MRE) is capable of noninvasively measuring brain tissue stiffness in vivo, and may act as a surrogate to measure ICP. The objective of this study was to investigate the impact of changing ICP on brain stiffness using MRE in a swine model.MethodsBaseline MRE measurements were obtained, and then catheters were surgically placed into the left and right lateral ventricles of three animals. ICP was systematically increased over the range of 0 to 55 millimeters mercury (mmHg), and stiffness measurements were made using brain MRE at vibration frequencies of 60 hertz (Hz), 90 Hz, 120 Hz, and 150 Hz.ResultsA significant linear correlation between stiffness and ICP in the cross‐subject comparison was observed for all tested vibrational frequencies (P ≤ 0.01). The 120 Hz (0.030 ± 0.004 kilopascal (kPa)/mmHg, P < 0.0001) and 150 Hz (0.031 ± 0.008 kPa/mmHg, P = 0.01) vibrational frequencies had nearly identical slopes, which were approximately two‐ to three‐fold higher than the 90 Hz (0.017 ± 0.002 kPa/mmHg, P < 0.0001) and 60 Hz (0.009 ± 0.002 kPa/mmHg, P = 0.001) slopes, respectively.ConclusionIn this study, MRE demonstrated the potential for noninvasive measurement of changes in ICP. Magn Reson Med 79:1043–1051, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

Background: There is growing interest in white matter (WM) imaging with positron emission tomography (PET). Objectives: We studied the association of cognitive function in late multiple sclerosis (MS) with cortical and WM Pittsburgh compound-B PET (PiB-PET) binding. Methods: In the population-based Mayo Clinic Study of Aging, 24 of 4869 participants had MS (12 underwent PiB-PET). Controls were age and sex matched (5:1). We used automated or semi-automated processing for quantitative image analyses and conditional logistic regression for group differences. Results: MS patients had lower memory ( p = 0.03) and language ( p = 0.02) performance; smaller thalamic volumes ( p = 0.003); and thinner temporal ( p = 0.001) and frontal ( p = 0.045) cortices on magnetic resonance imaging (MRI) than controls. There was no difference in global cortical PiB standardized uptake value ratios between MS and controls ( p = 0.35). PiB uptake was lower in areas of WM hyperintensities compared to normal-appearing white matter (NAWM) in MS ( p = 0.0002). Reduced PiB uptake in both the areas of WM hyperintensities ( r = 0.65; p = 0.02) and NAWM ( r = 0.69; p = 0.01) was associated with decreased visuospatial performance in MS. Conclusion: PiB uptake in the cortex in late MS is not different from normal age-matched controls. PiB uptake in the WM in late MS may be a marker of the large network structures’ integrity such as those involved in visuospatial performance.

AbstractQuantitative measurement of β‐amyloid from amyloid PET scans typically relies on localizing target and reference regions by image registration to MRI. In this work, we present a series of simulations where 50 small random perturbations of starting location and orientation were applied to each subject's PET scan, and rigid registration usingspm_coregwas performed between each perturbed PET scan and its corresponding MRI. We then measured variation in the output PET‐MRI registrations and how this variation affected the resulting SUVR measurements. We performed these experiments using scans of 1196 participants, half using 18F florbetapir and half using 11C PiB. From these experiments, we measured the magnitude of the imprecision in the rigid registration steps used to localize measurement regions, and how this contributes to the overall imprecision in SUVR measurements. Unexpectedly, we found for both tracers that the imprecision in these measurements depends on the degree of amyloid tracer uptake, and thus also indirectly on Alzheimer's disease clinical status. We then examined common choices of reference regions, and we show that SUVR measurements using supratentorial white matter references are relatively resistant to this source of error. We also show that the use of partial volume correction further magnifies the effects of registration imprecision on SUVR measurements. Together, these results suggest that this rigid registration step is an attractive target for future work in improving measurement techniques.Hum Brain Mapp 38:3323–3336, 2017. ©2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. Methods We used standard searches to find publications using ADNI data. Results (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β‐amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white‐matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion‐like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by “classic” AD pathology but also by normal biomarkers, accelerated decline, and suspected non‐Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. Discussion Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.

In the history of medicine, one means to progress is when we make the decision that our assumptions and definitions of disease are no longer consistent with the scientific evidence, and no longer serve our health care needs. The arc of scientific progress is now requiring a change in how we diagnose Alzheimer's disease. Both the National Institute on Aging—Alzheimer's Association (NIA‐AA) 2011 workgroup and the International Work Group (IWG) have proposed guidelines that use detectable measures of biological changes in the brain, commonly known as biological markers, or biomarkers, as part of the diagnosis. This Special Report examines how the development and validation of Alzheimer's disease biomarkers—including those detectable in the blood or cerebral spinal fluid, or through neuroimaging—is a top research priority. This has the potential to markedly change how we diagnose Alzheimer's disease and, as a result, how we count the number of people with this disease. As research advances a biomarker‐based method for diagnosis and treatment at the earliest stages of Alzheimer's disease, we envision a future in which Alzheimer's disease is placed in the same category as other chronic diseases, such as cardiovascular disease or diabetes, which can be readily identified with biomarkers and treated before irrevocable disability occurs.

Abstract Introduction Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) have previously been reported to be associated with increased risk of Alzheimer's disease (AD). We are using biomarkers to study Vietnam Veterans with/without mild cognitive impairment with a history of at least one TBI and/or ongoing PTSD to determine whether these contribute to the development of AD. Methods Potential subjects identified by Veterans Administration records underwent an initial telephone screen. Consented subjects underwent clinical evaluation, lumbar puncture, structural magnetic resonance imaging, and amyloid positron emission tomography (PET) scans. Results We observed worse cognitive functioning in PTSD and TBI + PTSD groups, worse global cognitive functioning in the PTSD group, lower superior parietal volume in the TBI + PTSD group, and lower amyloid positivity in the PTSD group, but not the TBI group compared to controls without TBI/PTSD. Medial temporal lobe atrophy was not increased in the PTSD and/or TBI groups. Discussion Preliminary results do not indicate that TBI or PTSD increase the risk for AD measured by amyloid PET. Additional recruitment, longitudinal follow‐up, and tau‐PET scans will provide more information in the future.

PurposeIn diffusion MRI (dMRI), fractional anisotropy derived from the single‐tensor model (FADTI) is the most widely used metric to characterize white matter (WM) microarchitecture, despite known limitations in regions with crossing fibers. Due to time constraints when scanning patients in clinical settings, high angular resolution diffusion imaging acquisition protocols, often used to overcome these limitations, are still rare in clinical population studies. However, the tensor distribution function (TDF) may be used to model multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors.MethodsWe compared the ability of standard FADTIand TDF‐derived FA (FATDF), calculated from a range of dMRI angular resolutions (41, 30, 15, and 7 gradient directions), to profile WM deficits in 251 individuals from the Alzheimer's Disease Neuroimaging Initiative and to detect associations with 1) Alzheimer's disease diagnosis, 2) Clinical Dementia Rating scores, and 3) average hippocampal volume.ResultsAcross angular resolutions and statistical tests, FATDFshowed larger effect sizes than FADTI, particularly in regions preferentially affected by Alzheimer's disease, and was less susceptible to crossing fiber anomalies.ConclusionThe TDF “corrected” form of FA may be a more sensitive and accurate alternative to the commonly used FADTI, even in clinical quality dMRI data. Magn Reson Med 78:2322–2333, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

AbstractIntroductionBiomarkers for Alzheimer's disease (AD) pathophysiology have been developed that focus on various levels of brain organization. However, no robust biomarker of large‐scale network failure has been developed. Using the recently introduced cascading network failure model of AD, we developed the network failure quotient (NFQ) as a biomarker of this process.MethodsWe developed and optimized the NFQ using our recently published analyses of task‐free functional magnetic resonance imaging data in clinically normal (n = 43) and AD dementia participants (n = 28) from the Alzheimer's Disease Neuroimaging Initiative. The optimized NFQ (oNFQ) was then validated in a cohort spanning the AD spectrum from the Mayo Clinic (n = 218).ResultsThe oNFQ (d = 1.25, 95% confidence interval [1.25, 1.26]) had the highest effect size for differentiating persons with AD dementia from clinically normal participants. The oNFQ measure performed similarly well on the validation Mayo Clinic sample (d = 1.44, 95% confidence interval [1.43, 1.44]). The oNFQ was also associated with other available key biomarkers in the Mayo cohort.DiscussionThis study demonstrates a measure of functional connectivity, based on a cascading network failure model of AD, and was highly successful in identifying AD dementia. A robust biomarker of the large‐scale effects of AD pathophysiology will allow for richer descriptions of the disease process and its modifiers, but is not currently suitable for discriminating clinical diagnostic categories. The large‐scale network level may be one of the earliest manifestations of AD, making this an attractive target for continued biomarker development to be used in prevention trials.