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Ann C. McKee

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4 papers

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YNICL Journal 2019 Journal Article

Positron emission tomography of tau in Iraq and Afghanistan Veterans with blast neurotrauma

Meghan E. Robinson
Ann C. McKee
David H. Salat
Ann M. Rasmusson
Lauren J. Radigan
Ciprian Catana
William P. Milberg
Regina E. McGlinchey

Military personnel are often exposed to multiple instances of various types of head trauma. As a result, there has been increasing concern recently over identifying when head trauma has resulted in a brain injury and what, if any, long-term consequences those brain injuries may have. Efforts to develop equipment to protect soldiers from these long-term consequences will first require understanding the types of head trauma that are likely responsible. In this study, we sought to identify the types of head trauma most likely to lead to the deposition of tau, a protein identified as a likely indicator of long-term negative consequences of brain injury. To define the types of head trauma in a military population, we applied a factor analysis to interviews from a larger cohort of 428 Veterans enrolled in the Translational Research Center for Traumatic Brain Injury and Stress Disorders. Three factors were identified: Blast Exposure, Symptom Duration, and Blunt Concussion. Sixteen male Veterans from this study and one additional male civilian (aged 25-69, mean 35.2 years) underwent simultaneous positron emission tomography/magnetic resonance imaging using a tracer that binds to tau protein, the ligand T807/AV-1451 (Flortaucipir). Standard uptake value ratios to the isthmus of the cingulate were calculated from a 20-minute time frame 70 min post-injection. We found that tracer uptake throughout the brain was associated with Blast Exposure factor beta weights, but not with either Symptom Duration or Blunt Concussion. Associations with uptake were located primarily in the cerebellar, occipital, inferior temporal and frontal regions. The data suggest that in this small, relatively young cohort of Veterans, elevated T807/AV-1451 uptake is associated with exposure to blast neurotrauma. These findings are unanticipated, as they do not match histopathological descriptions of tau pathology associated with head trauma. Continued work will be necessary to understand the nature of the regional T807/AV-1451 uptake and any associations with clinical symptoms.

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YNIMG Journal 2017 Journal Article

Quantitative validation of a nonlinear histology-MRI coregistration method using generalized Q-sampling imaging in complex human cortical white matter

Mihika Gangolli
Laurena Holleran
Joong Hee Kim
Thor D. Stein
Victor Alvarez
Ann C. McKee
David L. Brody

Advanced diffusion MRI methods have recently been proposed for detection of pathologies such as traumatic axonal injury and chronic traumatic encephalopathy which commonly affect complex cortical brain regions. However, radiological-pathological correlations in human brain tissue that detail the relationship between the multi-component diffusion signal and underlying pathology are lacking. We present a nonlinear voxel based two dimensional coregistration method that is useful for matching diffusion signals to quantitative metrics of high resolution histological images. When validated in ex vivo human cortical tissue at a 250×250×500 μm spatial resolution, the method proved robust in correlations between generalized q-sampling imaging and histologically based white matter fiber orientations, with r=0. 94 for the primary fiber direction and r=0. 88 for secondary fiber direction in each voxel. Importantly, however, the correlation was substantially worse with reduced spatial resolution or with fiber orientations derived using a diffusion tensor model. Furthermore, we have detailed a quantitative histological metric of white matter fiber integrity termed power coherence capable of distinguishing architecturally complex but intact white matter from disrupted white matter regions. These methods may allow for more sensitive and specific radiological-pathological correlations of neurodegenerative diseases affecting complex gray and white matter.

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YNIMG Journal 2015 Journal Article

A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI

Juan Eugenio Iglesias
Jean C. Augustinack
Khoa Nguyen
Christopher M. Player
Allison Player
Michelle Wright
Nicole Roy
Matthew P. Frosch

Automated analysis of MRI data of the subregions of the hippocampus requires computational atlases built at a higher resolution than those that are typically used in current neuroimaging studies. Here we describe the construction of a statistical atlas of the hippocampal formation at the subregion level using ultra-high resolution, ex vivo MRI. Fifteen autopsy samples were scanned at 0. 13mm isotropic resolution (on average) using customized hardware. The images were manually segmented into 13 different hippocampal substructures using a protocol specifically designed for this study; precise delineations were made possible by the extraordinary resolution of the scans. In addition to the subregions, manual annotations for neighboring structures (e. g. , amygdala, cortex) were obtained from a separate dataset of in vivo, T1-weighted MRI scans of the whole brain (1mm resolution). The manual labels from the in vivo and ex vivo data were combined into a single computational atlas of the hippocampal formation with a novel atlas building algorithm based on Bayesian inference. The resulting atlas can be used to automatically segment the hippocampal subregions in structural MRI images, using an algorithm that can analyze multimodal data and adapt to variations in MRI contrast due to differences in acquisition hardware or pulse sequences. The applicability of the atlas, which we are releasing as part of FreeSurfer (version 6. 0), is demonstrated with experiments on three different publicly available datasets with different types of MRI contrast. The results show that the atlas and companion segmentation method: 1) can segment T1 and T2 images, as well as their combination, 2) replicate findings on mild cognitive impairment based on high-resolution T2 data, and 3) can discriminate between Alzheimer's disease subjects and elderly controls with 88% accuracy in standard resolution (1mm) T1 data, significantly outperforming the atlas in FreeSurfer version 5. 3 (86% accuracy) and classification based on whole hippocampal volume (82% accuracy).

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YNIMG Journal 2013 Journal Article

Predicting the location of human perirhinal cortex, Brodmann's area 35, from MRI

Jean C. Augustinack
Kristen E. Huber
Allison A. Stevens
Michelle Roy
Matthew P. Frosch
André J.W. van der Kouwe
Lawrence L. Wald
Koen Van Leemput

The perirhinal cortex (Brodmann's area 35) is a multimodal area that is important for normal memory function. Specifically, perirhinal cortex is involved in the detection of novel objects and manifests neurofibrillary tangles in Alzheimer's disease very early in disease progression. We scanned ex vivo brain hemispheres at standard resolution (1mm×1mm×1mm) to construct pial/white matter surfaces in FreeSurfer and scanned again at high resolution (120μm×120μm×120μm) to determine cortical architectural boundaries. After labeling perirhinal area 35 in the high resolution images, we mapped the high resolution labels to the surface models to localize area 35 in fourteen cases. We validated the area boundaries determined using histological Nissl staining. To test the accuracy of the probabilistic mapping, we measured the Hausdorff distance between the predicted and true labels and found that the median Hausdorff distance was 4. 0mm for the left hemispheres (n=7) and 3. 2mm for the right hemispheres (n=7) across subjects. To show the utility of perirhinal localization, we mapped our labels to a subset of the Alzheimer's Disease Neuroimaging Initiative dataset and found decreased cortical thickness measures in mild cognitive impairment and Alzheimer's disease compared to controls in the predicted perirhinal area 35. Our ex vivo probabilistic mapping of the perirhinal cortex provides histologically validated, automated and accurate labeling of architectonic regions in the medial temporal lobe, and facilitates the analysis of atrophic changes in a large dataset for earlier detection and diagnosis.

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