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Sandra E. Black

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15 papers
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15

YNICL Journal 2023 Journal Article

Alzheimer’s and vascular disease classification using regional texture biomarkers in FLAIR MRI

  • Karissa Chan
  • Corinne Fischer
  • Pejman Jabehdar Maralani
  • Sandra E. Black
  • Alan R. Moody
  • April Khademi

Interactions between subcortical vascular disease and dementia due to Alzheimer's disease (AD) are unclear, and clinical overlap between the diseases makes diagnosis challenging. Existing studies have shown regional microstructural changes specific to each disease, and that textures in fluid-attenuated inversion recovery (FLAIR) MRI images may characterize abnormalities in tissue microstructure. This work aims to investigate regional FLAIR biomarkers that can differentiate dementia cohorts with and without subcortical vascular disease. FLAIR and diffusion MRI (dMRI) volumes were obtained in 65 mild cognitive impairment (MCI), 21 AD, 44 subcortical vascular MCI (scVMCI), 22 Mixed etiology, and 48 healthy elderly patients. FLAIR texture and intensity biomarkers were extracted from the normal appearing brain matter (NABM), WML penumbra, blood supply territory (BST), and white matter tract regions of each patient. All FLAIR biomarkers were correlated to dMRI metrics in each region and global WML load, and biomarker means between groups were compared using ANOVA. Binary classifications were performed using Random Forest classifiers to investigate the predictive nature of the regional biomarkers, and SHAP feature analysis was performed to further investigate optimal regions of interest for differentiating disease groups. The regional FLAIR biomarkers were strongly correlated to MD, while all biomarker regions but white matter tracts were strongly correlated to WML burden. Classification between Mixed disease and healthy, AD, and scVMCI patients yielded accuracies of 97%, 81%, and 72% respectively using WM tract biomarkers. Classification between scVMCI and healthy, MCI, and AD patients yielded accuracies of 89%, 84%, and 79% respectively using penumbra biomarkers. Only the classification between AD and healthy patients had optimal results using NABM biomarkers. This work presents novel regional FLAIR biomarkers that may quantify white matter degeneration related to subcortical vascular disease, and which indicate that investigating degeneration in specific regions may be more important than assessing global WML burden in vascular disease groups.

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YNIMG Journal 2023 Journal Article

ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI

  • Lyndon Boone
  • Mahdi Biparva
  • Parisa Mojiri Forooshani
  • Joel Ramirez
  • Mario Masellis
  • Robert Bartha
  • Sean Symons
  • Stephen Strother

Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to their success in classification, segmentation, and detection challenges. A principal challenge in large-scale deployment of DNNs in neuroimage analysis is the potential for shifts in signal-to-noise ratio, contrast, resolution, and presence of artifacts from site to site due to variances in scanners and acquisition protocols. DNNs are famously susceptible to these distribution shifts in computer vision. Currently, there are no benchmarking platforms or frameworks to assess the robustness of new and existing models to specific distribution shifts in MRI, and accessible multi-site benchmarking datasets are still scarce or task-specific. To address these limitations, we propose ROOD-MRI: a novel platform for benchmarking the Robustness of DNNs to Out-Of-Distribution (OOD) data, corruptions, and artifacts in MRI. This flexible platform provides modules for generating benchmarking datasets using transforms that model distribution shifts in MRI, implementations of newly derived benchmarking metrics for image segmentation, and examples for using the methodology with new models and tasks. We apply our methodology to hippocampus, ventricle, and white matter hyperintensity segmentation in several large studies, providing the hippocampus dataset as a publicly available benchmark. By evaluating modern DNNs on these datasets, we demonstrate that they are highly susceptible to distribution shifts and corruptions in MRI. We show that while data augmentation strategies can substantially improve robustness to OOD data for anatomical segmentation tasks, modern DNNs using augmentation still lack robustness in more challenging lesion-based segmentation tasks. We finally benchmark U-Nets and vision transformers, finding robustness susceptibility to particular classes of transforms across architectures. The presented open-source platform enables generating new benchmarking datasets and comparing across models to study model design that results in improved robustness to OOD data and corruptions in MRI.

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YNIMG Journal 2021 Journal Article

Resting state fMRI scanner instabilities revealed by longitudinal phantom scans in a multi-center study

  • Aras Kayvanrad
  • Stephen R. Arnott
  • Nathan Churchill
  • Stefanie Hassel
  • Aditi Chemparathy
  • Fan Dong
  • Mojdeh Zamyadi
  • Tom Gee

Quality assurance (QA) is crucial in longitudinal and/or multi-site studies, which involve the collection of data from a group of subjects over time and/or at different locations. It is important to regularly monitor the performance of the scanners over time and at different locations to detect and control for intrinsic differences (e.g., due to manufacturers) and changes in scanner performance (e.g., due to gradual component aging, software and/or hardware upgrades, etc.). As part of the Ontario Neurodegenerative Disease Research Initiative (ONDRI) and the Canadian Biomarker Integration Network in Depression (CAN-BIND), QA phantom scans were conducted approximately monthly for three to four years at 13 sites across Canada with 3T research MRI scanners. QA parameters were calculated for each scan using the functional Biomarker Imaging Research Network's (fBIRN) QA phantom and pipeline to capture between- and within-scanner variability. We also describe a QA protocol to measure the full-width-at-half-maximum (FWHM) of slice-wise point spread functions (PSF), used in conjunction with the fBIRN QA parameters. Variations in image resolution measured by the FWHM are a primary source of variance over time for many sites, as well as between sites and between manufacturers. We also identify an unexpected range of instabilities affecting individual slices in a number of scanners, which may amount to a substantial contribution of unexplained signal variance to their data. Finally, we identify a preliminary preprocessing approach to reduce this variance and/or alleviate the slice anomalies, and in a small human data set show that this change in preprocessing can have a significant impact on seed-based connectivity measurements for some individual subjects. We expect that other fMRI centres will find this approach to identifying and controlling scanner instabilities useful in similar studies.

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YNIMG Journal 2020 Journal Article

Lactate topography of the human brain using hyperpolarized 13C-MRI

  • Casey Y. Lee
  • Hany Soliman
  • Benjamin J. Geraghty
  • Albert P. Chen
  • Kim A. Connelly
  • Ruby Endre
  • William J. Perks
  • Chris Heyn

Lactate is now recognized as an important intermediate in brain metabolism, but its role is still under investigation. In this work we mapped the distribution of lactate and bicarbonate produced from intravenously injected 13C-pyruvate over the whole brain using a new imaging method, hyperpolarized 13C MRI (N = 14, ages 23 to 77). Segmenting the 13C-lactate images into brain atlas regions revealed a pattern of lactate that was preserved across individuals. Higher lactate signal was observed in cortical grey matter compared to white matter and was highest in the precuneus, cuneus and lingual gyrus. Bicarbonate signal, indicating flux of [1–13C]pyruvate into the TCA cycle, also displayed consistent spatial distribution. One-way ANOVA to test for significant differences in lactate among atlas regions gave F = 87. 6 and p < 10−6. This report of a “lactate topography” in the human brain and its consistent pattern is evidence of region-specific lactate biology that is preserved across individuals.

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YNIMG Journal 2019 Journal Article

Resting state functional connectivity changes after MR-guided focused ultrasound mediated blood-brain barrier opening in patients with Alzheimer's disease

  • Ying Meng
  • Bradley J. MacIntosh
  • Zahra Shirzadi
  • Alex Kiss
  • Allison Bethune
  • Chinthaka Heyn
  • Karim Mithani
  • Clement Hamani

MR-guided focused ultrasound (MRgFUS) can temporarily permeabilize the blood-brain barrier (BBB), noninvasively, to allow therapeutics access to the central nervous system. However, its secondary and potential neuromodulation effects are not well understood. We aimed to characterize the functional impact of MRgFUS BBB opening in human subjects, based on the phase I trial in patients with Alzheimer's disease. We analyzed for changes in bilateral frontoparietal networks in resting state functional MRI from five subjects after BBB opening in the right frontal lobe. We found a transient functional connectivity decrease within only the ipsilateral frontoparietal network that was recovered by the next day. Additionally, baseline to month three comparisons did not reveal any significant differences from matched-controls from the Alzheimer's Disease Neuroimaging Initiative. Overall, MRgFUS may transiently affect neurologic function, but the functional organization is restored at one day and remains unchanged at three months. This first in human data has implications for the development of MRgFUS as a drug delivery platform to pathologic brain tissue and potential use for non-invasive neuromodulation.

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YNICL Journal 2019 Journal Article

The Canadian Dementia Imaging Protocol: Harmonization validity for morphometry measurements

  • Olivier Potvin
  • Isabelle Chouinard
  • Louis Dieumegarde
  • Robert Bartha
  • Pierre Bellec
  • D. Louis Collins
  • Maxime Descoteaux
  • Rick Hoge

The harmonized Canadian Dementia Imaging Protocol (CDIP) has been developed to suit the needs of a number of co-occurring Canadian studies collecting data on brain changes across adulthood and neurodegeneration. In this study, we verify the impact of CDIP parameters compliance on total brain volume variance using 86 scans of the same individual acquired on various scanners. Data included planned data collection acquired within the Consortium pour l'identification précoce de la maladie Alzheimer - Québec (CIMA-Q) and Canadian Consortium on Neurodegeneration in Aging (CCNA) studies, as well as opportunistic data collection from various protocols. For images acquired from Philips scanners, lower variance in brain volumes were observed when the stated CDIP resolution was set. For images acquired from GE scanners, lower variance in brain volumes were noticed when TE/TR values were within 5% of the CDIP protocol, compared to values farther from that criteria. Together, these results suggest that a harmonized protocol like the CDIP may help to reduce neuromorphometric measurement variability in multi-centric studies.

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YNICL Journal 2016 Journal Article

Impaired dynamic cerebrovascular response to hypercapnia predicts development of white matter hyperintensities

  • Kevin Sam
  • John Conklin
  • Kenneth R. Holmes
  • Olivia Sobczyk
  • Julien Poublanc
  • Adrian P. Crawley
  • Daniel M. Mandell
  • Lakshmikumar Venkatraghavan

PURPOSE: To evaluate the relationship between both dynamic and steady-state measures of cerebrovascular reactivity (CVR) and the progression of age-related white matter disease. METHODS: Blood oxygen level-dependent (BOLD) MRI CVR scans were acquired from forty-five subjects (age range: 50-90 years, 25 males) with moderate to severe white matter disease, at baseline and one-year follow-up. To calculate the dynamic (τ) and steady-state (ssCVR) components of the BOLD signal response, the PETCO2 signal waveform was convolved with an exponential decay function. The τ corresponding to the best fit between the convolved PETCO2 and BOLD signal defined the speed of response, and the slope of the regression between the convolved PETCO2 and BOLD signal defined ssCVR. ssCVR and τ were compared between normal-appearing white matter (NAWM) that remains stable over time and NAWM that progresses to white matter hyperintensities (WMHs). RESULTS: In comparison to contralateral NAWM, NAWM that progressed to WMH had significantly lower ssCVR values by mean (SD) 46.5 (7.6)%, and higher τ values by 31.9 (9.6)% (both P < 0.01). CONCLUSIONS: Vascular impairment in regions of NAWM that progresses to WMH consists not only of decreased magnitude of ssCVR, but also a pathological decrease in the speed of vascular response. These findings support the association between cerebrovascular dysregulation and the development of WMH.

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YNIMG Journal 2013 Journal Article

A direct morphometric comparison of five labeling protocols for multi-atlas driven automatic segmentation of the hippocampus in Alzheimer's disease

  • Sean M. Nestor
  • Erin Gibson
  • Fu-Qiang Gao
  • Alex Kiss
  • Sandra E. Black

Hippocampal volumetry derived from structural MRI is increasingly used to delineate regions of interest for functional measurements, assess efficacy in therapeutic trials of Alzheimer's disease (AD) and has been endorsed by the new AD diagnostic guidelines as a radiological marker of disease progression. Unfortunately, morphological heterogeneity in AD can prevent accurate demarcation of the hippocampus. Recent developments in automated volumetry commonly use multi-template fusion driven by expert manual labels, enabling highly accurate and reproducible segmentation in disease and healthy subjects. However, there are several protocols to define the hippocampus anatomically in vivo, and the method used to generate atlases may impact automatic accuracy and sensitivity — particularly in pathologically heterogeneous samples. Here we report a fully automated segmentation technique that provides a robust platform to directly evaluate both technical and biomarker performance in AD among anatomically unique labeling protocols. For the first time we test head-to-head the performance of five common hippocampal labeling protocols for multi-atlas based segmentation, using both the Sunnybrook Longitudinal Dementia Study and the entire Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) baseline and 24-month dataset. We based these atlas libraries on the protocols of (Haller et al. , 1997; Killiany et al. , 1993; Malykhin et al. , 2007; Pantel et al. , 2000; Pruessner et al. , 2000), and a single operator performed all manual tracings to generate de facto “ground truth” labels. All methods distinguished between normal elders, mild cognitive impairment (MCI), and AD in the expected directions, and showed comparable correlations with measures of episodic memory performance. Only more inclusive protocols distinguished between stable MCI and MCI-to-AD converters, and had slightly better associations with episodic memory. Moreover, we demonstrate that protocols including more posterior anatomy and dorsal white matter compartments furnish the best voxel-overlap accuracies (Dice Similarity Coefficient=0. 87–0. 89), compared to expert manual tracings, and achieve the smallest sample sizes required to power clinical trials in MCI and AD. The greatest distribution of errors was localized to the caudal hippocampus and the alveus-fimbria compartment when these regions were excluded. The definition of the medial body did not significantly alter accuracy among more comprehensive protocols. Voxel-overlap accuracies between automatic and manual labels were lower for the more pathologically heterogeneous Sunnybrook study in comparison to the ADNI-1 sample. Finally, accuracy among protocols appears to significantly differ the most in AD subjects compared to MCI and normal elders. Together, these results suggest that selection of a candidate protocol for fully automatic multi-template based segmentation in AD can influence both segmentation accuracy when compared to expert manual labels and performance as a biomarker in MCI and AD.

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YNICL Journal 2013 Journal Article

Quantified MRI and cognition in TBI with diffuse and focal damage

  • Brian Levine
  • Natasa Kovacevic
  • Elena Irina Nica
  • Michael L. Schwartz
  • Fuqiang Gao
  • Sandra E. Black

In patients with chronic-phase traumatic brain injury (TBI), structural MRI is readily attainable and provides rich anatomical information, yet the relationship between whole-brain structural MRI measures and neurocognitive outcome is relatively unexplored and can be complicated by the presence of combined focal and diffuse injury. In this study, sixty-three patients spanning the full range of TBI severity received high-resolution structural MRI concurrent with neuropsychological testing. Multivariate statistical analysis assessed covariance patterns between volumes of grey matter, white matter, and sulcal/subdural and ventricular CSF across 38 brain regions and neuropsychological test performance. Patients with diffuse and diffuse + focal injury were analyzed both separately and together. Tests of speeded attention, working memory, and verbal learning and memory robustly covaried with a distributed pattern of volume loss over temporal, ventromedial prefrontal, right parietal regions, and cingulate regions. This pattern was modulated by the presence of large focal lesions, but held even when analyses were restricted to those with diffuse injury. Effects were most consistently observed within grey matter. Relative to regional brain volumetric data, clinically defined injury severity (depth of coma at time of injury) showed only weak relation to neuropsychological outcome. The results showed that neuropsychological test performance in patients with TBI is related to a distributed pattern of volume loss in regions mediating mnemonic and attentional processing. This relationship holds for patients with and without focal lesions, indicating that diffuse injury alone is sufficient to cause significant neuropsychological disability in relation to regional volume loss. Quantified structural brain imaging data provides a highly sensitive index of brain integrity that is related to cognitive functioning in chronic phase TBI.

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