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Predicting time to dementia using a quantitative template of disease progression [article]

Murat Bilgel, Bruno M Jedynak
<span title="2018-11-02">2018</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
[6, Fig. 4 ] and Bilgel et al. [7, Fig. 6, right] . In both applications, the progression score is an affine function of age for each subject, yet Eq. 1 provides a reasonable approximation.  ... 
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<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190429111948/https://www.biorxiv.org/content/biorxiv/early/2018/11/02/458273.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/02/c7/02c7f9555800f054fa571f1ac181b9f76986e51f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/458273"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Disentangling A Single MR Modality [article]

Lianrui Zuo, Yihao Liu, Yuan Xue, Shuo Han, Murat Bilgel, Susan M. Resnick, Jerry L. Prince, Aaron Carass
<span title="2022-05-10">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks. Current methods learn disentangled representations using either paired multi-modal images with the same underlying anatomy or auxiliary labels (e.g., manual delineations) to provide inductive bias for disentanglement. However, these requirements could significantly increase the time and cost in data collection and limit the applicability
more &raquo; ... these methods when such data are not available. Moreover, these methods generally do not guarantee disentanglement. In this paper, we present a novel framework that learns theoretically and practically superior disentanglement from single modality magnetic resonance images. Moreover, we propose a new information-based metric to quantitatively evaluate disentanglement. Comparisons over existing disentangling methods demonstrate that the proposed method achieves superior performance in both disentanglement and cross-domain image-to-image translation tasks.
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Deformation field correction for spatial normalization of PET images

Murat Bilgel, Aaron Carass, Susan M. Resnick, Dean F. Wong, Jerry L. Prince
<span title="">2015</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sa477uo7lveh7hchpikpixop5u" style="color: black;">NeuroImage</a> </i> &nbsp;
Bilgel et al. Page 24 Neuroimage. Author manuscript; available in PMC 2016 October 01. Bilgel et al. Page 27 Neuroimage. Author manuscript; available in PMC 2016 October 01.  ...  Bilgel et al. Page 18 Figure A.4. Neuroimage. Author manuscript; available in PMC 2016 October 01. Bilgel et al. Page 20 Neuroimage. Author manuscript; available in PMC 2016 October 01.  ...  Bilgel et al. Page 25 Neuroimage. Author manuscript; available in PMC 2016 October 01.  ... 
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Predicting time to dementia using a quantitative template of disease progression

Murat Bilgel, Bruno M. Jedynak
<span title="2019-02-28">2019</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/y5b3apqmfve23ds3kch2bvmcxa" style="color: black;">Alzheimer&#39;s and Dementia: Diagnosis, Assessment and Disease Monitoring</a> </i> &nbsp;
Bilgel and B.M. Jedynak / Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 11 (2019) 205-215  ...  Bilgel and B.M. Jedynak / Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 11 (2019) 205-215 .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.dadm.2019.01.005">doi:10.1016/j.dadm.2019.01.005</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30859120">pmid:30859120</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6396328/">pmcid:PMC6396328</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iumrdkpve5clloaznuz7csufmu">fatcat:iumrdkpve5clloaznuz7csufmu</a> </span>
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Automated anatomical labeling of the cerebral arteries using belief propagation

Murat Bilgel, Snehashis Roy, Aaron Carass, Paul A. Nyquist, Jerry L. Prince, David R. Haynor, Sebastien Ourselin
<span title="2013-03-13">2013</span> <i title="SPIE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ha25cznnjncxtjoykhsg6fz5ly" style="color: black;">Medical Imaging 2013: Image Processing</a> </i> &nbsp;
Labeling of cerebral vasculature is important for characterization of anatomical variation, quantification of brain morphology with respect to specific vessels, and inter-subject comparisons of vessel properties and abnormalities. We propose an automated method to label the anterior portion of cerebral arteries using a statistical inference method on the Bayesian network representation of the vessel tree. Our approach combines the likelihoods obtained from a random forest classifier trained
more &raquo; ... g vessel centerline features with a belief propagation method integrating the connection probabilities of the cerebral artery network. We evaluate our method on 30 subjects using a leave-one-out validation, and show that it achieves an average correct vessel labeling rate of over 92%.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.2006460">doi:10.1117/12.2006460</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24236229">pmid:24236229</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3824264/">pmcid:PMC3824264</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/miip/BilgelRCNP13.html">dblp:conf/miip/BilgelRCNP13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/v3rthg6mrzhizh3g43uew6qmne">fatcat:v3rthg6mrzhizh3g43uew6qmne</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20150625200734/http://iacl.ece.jhu.edu//proceedings/iacl/2013/BilxSPIE13-Automated_anatomical_labeling_of_cerebral_arteries.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a3/f7/a3f7341612980d906907beadce3eb48ea7574814.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.2006460"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3824264" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Trajectories of Alzheimer disease-related cognitive measures in a longitudinal sample

Murat Bilgel, Yang An, Andrew Lang, Jerry Prince, Luigi Ferrucci, Bruno Jedynak, Susan M. Resnick
<span title="2014-07-14">2014</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/skzqffpatvawfgf7uspguqs4yu" style="color: black;">Alzheimer&#39;s &amp; Dementia</a> </i> &nbsp;
The delineation of the relative temporal trajectories of specific cognitive measures associated with Alzheimer's disease (AD) is important for evaluating preclinical markers and monitoring disease progression. We characterized the temporal trajectories of measures of verbal episodic memory, short-term visual memory, and mental status using data from 895 participants in the Baltimore Longitudinal Study of Aging. The California Verbal Learning Test (CVLT) immediate recall was the first measure to
more &raquo; ... decline, followed by CVLT delayed recall. However, further along the disease progression scale, CVLT delayed recall and visual memory changed more rapidly than CVLT immediate recall. Our findings reconcile reports of early changes in immediate recall with greater reliance on delayed recall performance in clinical settings. Moreover, the utility of cognitive markers in evaluating AD progression depends on the stage of cognitive decline, suggesting that optimal endpoints in therapeutic trials may vary across different stages of the disease process.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jalz.2014.04.520">doi:10.1016/j.jalz.2014.04.520</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25035155">pmid:25035155</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4253313/">pmcid:PMC4253313</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/q7d62vos4fdmpizcrzmt3s3ike">fatcat:q7d62vos4fdmpizcrzmt3s3ike</a> </span>
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Temporal filtering of longitudinal brain magnetic resonance images for consistent segmentation

Snehashis Roy, Aaron Carass, Jennifer Pacheco, Murat Bilgel, Susan M. Resnick, Jerry L. Prince, Dzung L. Pham
<span title="">2016</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/47gmsc3hojdm5kdlgbg4peyvyi" style="color: black;">NeuroImage: Clinical</a> </i> &nbsp;
Longitudinal analysis of magnetic resonance images of the human brain provides knowledge of brain changes during both normal aging as well as the progression of many diseases. Previous longitudinal segmentation methods have either ignored temporal information or have incorporated temporal consistency constraints within the algorithm. In this work, we assume that some anatomical brain changes can be explained by temporal transitions in image intensities. Once the images are aligned in the same
more &raquo; ... ace, the intensities of each scan at the same voxel constitute a temporal (or 4D) intensity trend at that voxel. Temporal intensity variations due to noise or other artifacts are corrected by a 4D intensity-based filter that smooths the intensity values where appropriate, while preserving real anatomical changes such as atrophy. Here smoothing refers to removal of sudden changes or discontinuities in intensities. Images processed with the 4D filter can be used as a pre-processing step to any segmentation method. We show that such a longitudinal pre-processing step produces robust and consistent longitudinal segmentation results, even when applying 3D segmentation algorithms. We compare with stateof-the-art 4D segmentation algorithms. Specifically, we experimented on three longitudinal datasets containing 4-12 time-points, and showed that the 4D temporal filter is more robust and has more power in distinguishing between healthy subjects and those with dementia, mild cognitive impairment, as well as different phenotypes of multiple sclerosis.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.nicl.2016.02.005">doi:10.1016/j.nicl.2016.02.005</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26958465">pmid:26958465</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4773508/">pmcid:PMC4773508</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rte33fsizbcefnrw3zidf525li">fatcat:rte33fsizbcefnrw3zidf525li</a> </span>
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Temporal order of Alzheimer's disease-related cognitive marker changes in BLSA and WRAP longitudinal studies [article]

Murat Bilgel, Rebecca L Koscik, Yang An, Jerry L Prince, Susan M Resnick, Sterling C Johnson, Bruno M Jedynak
<span title="2016-10-14">2016</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Investigation of the temporal trajectories of currently used neuropsychological tests is critical to identifying earliest changing measures on the path to dementia due to Alzheimer's disease (AD). We used the Progression Score (PS) method to characterize the temporal trajectories of measures of verbal memory, executive function, attention, processing speed, language, and mental state using data spanning normal cognition, mild cognitive impairment (MCI), and AD from 1661 participants with a
more &raquo; ... of 7839 visits (age at last visit 77.6 SD 9.2) in the Baltimore Longitudinal Study of Aging and 1542 participants with a total of 4467 visits (age at last visit 59.9 SD 7.3) in the Wisconsin Registry for Alzheimer's Prevention. This method aligns individuals in time based on the similarity of their longitudinal measurements to reveal temporal trajectories. As a validation of our methodology, we explored the associations between the individualized cognitive progression scores (Cog‑PS) computed by our method and clinical diagnosis. Digit span tests were the first to show declines in both data sets, and were detected mainly among cognitively normal individuals. These were followed by tests of verbal memory, which were in turn followed by Trail Making Tests, Boston Naming Test, and Mini-Mental State Examination. Differences in Cog-PS across the clinical diagnosis groups were statistically significant, highlighting the potential use of Cog-PS as individualized indicators of disease progression. Identifying cognitive measures that are changing in preclinical AD can lead to the development of novel cognitive tests that are finely tuned to detecting earliest changes.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/081174">doi:10.1101/081174</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bq4zmq427nayldx2a26q3indhe">fatcat:bq4zmq427nayldx2a26q3indhe</a> </span>
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Bile Acids in Dementia: Brain Amyloid, White Matter Lesions, and Atrophy

Vijay Varma, Youjin Wang, Yang An, Sudhir Varma, Murat Bilgel, Jimit Doshi, Cristina Legido-Quigley, Madhav Thambisetty
<span title="2020-12-01">2020</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sbqurkblwjdvlglwhot67btpwy" style="color: black;">Innovation in aging</a> </i> &nbsp;
Bilgel, 1 Jimit Doshi, 4 Cristina Legido-Quigley, 5 and Madhav Thambisetty, 6 1.  ...  CEREBRAL MICROSTRUCTURE IN AGING USING ADVANCED QUANTITATIVE MRI BILE ACIDS IN DEMENTIA: BRAIN AMYLOID, WHITE MATTER LESIONS, AND ATROPHY Vijay Varma, 1 Youjin Wang, 2 Yang An, 1 Sudhir Varma, 3 Murat  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/geroni/igaa057.2772">doi:10.1093/geroni/igaa057.2772</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3ajbkoaz3zhc7lbs6fohyanz6m">fatcat:3ajbkoaz3zhc7lbs6fohyanz6m</a> </span>
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Vestibular Function and Beta-Amyloid Deposition in the Baltimore Longitudinal Study of Aging

Rebecca J. Kamil, Murat Bilgel, Dean F. Wong, Susan M. Resnick, Yuri Agrawal
<span title="2018-12-11">2018</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xyafp6gbr5et3ghi4kqddnyolm" style="color: black;">Frontiers in Aging Neuroscience</a> </i> &nbsp;
(cDVR; Klunk et al., 2004; Lopresti et al., 2005; Villeneuve et al., 2015; Bilgel et al., 2016) .  ...  Image Acquisition and Processing Dynamic PiB-PET scans were obtained using a GE Advance scanner in 3D mode directly after 15 mCi of [ 11 C]-PiB was injected intravenously (Bilgel et al., 2016) .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnagi.2018.00408">doi:10.3389/fnagi.2018.00408</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30618715">pmid:30618715</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6297212/">pmcid:PMC6297212</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nkkrrkufk5fgvmypjaw22pzehu">fatcat:nkkrrkufk5fgvmypjaw22pzehu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190301234151/http://pdfs.semanticscholar.org/9a82/39f3fc55f13190572c4e2818b3cad36032d1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9a/82/9a8239f3fc55f13190572c4e2818b3cad36032d1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnagi.2018.00408"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6297212" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Individual estimates of age at detectable amyloid onset for risk factor assessment

Murat Bilgel, Yang An, Yun Zhou, Dean F. Wong, Jerry L. Prince, Luigi Ferrucci, Susan M. Resnick
<span title="">2016</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/skzqffpatvawfgf7uspguqs4yu" style="color: black;">Alzheimer&#39;s &amp; Dementia</a> </i> &nbsp;
Bilgel et al. Page 14  ...  Figure 2 . 2 Figure 2. 1 Bilgel et al. Page 6 Alzheimers Dement. Author manuscript; available in PMC 2017 April 01. Alzheimers Dement. Author manuscript; available in PMC 2017 April 01.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jalz.2015.08.166">doi:10.1016/j.jalz.2015.08.166</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26588863">pmid:26588863</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4841700/">pmcid:PMC4841700</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5o42ezsqknf4vojgnhylfvyiwm">fatcat:5o42ezsqknf4vojgnhylfvyiwm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200210042038/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC4841700&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0b/5d/0b5d504cd270c764022f1a96052efc5017b6f832.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jalz.2015.08.166"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4841700" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Effects of amyloid pathology and neurodegeneration on cognitive change in cognitively normal adults

Murat Bilgel, Yang An, Jessica Helphrey, Wendy Elkins, Gabriela Gomez, Dean F Wong, Christos Davatzikos, Luigi Ferrucci, Susan M Resnick
<span title="2018-06-12">2018</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3yly6om4ynhcta4fdjenhrdk5y" style="color: black;">Brain</a> </i> &nbsp;
See Gonneaud and Chételat (doi:10.1093/brain/awy174) for a scientific commentary on this article. Understanding short-term cognitive decline in relation to Alzheimer's neuroimaging biomarkers in early stages of the development of neuropathology and neurodegeneration will inform participant recruitment and monitoring strategies in clinical trials aimed at prevention of cognitive impairment and dementia. We assessed associations among neuroimaging measures of cerebral amyloid pathology, a
more &raquo; ... Alzheimer's neuropathology, hippocampal atrophy, and prospective cognition among 171 cognitively normal Baltimore Longitudinal Study of Aging participants (baseline age 56-95 years, 48% female, 562 cognitive assessments, 3.7 years follow-up). We categorized each individual based on dichotomous amyloid pathology (A) and hippocampal neurodegeneration (N) status at baseline: AÀNÀ, A+NÀ, AÀN + , A + N + . We conducted linear mixed effects analyses to assess cross-sectional and longitudinal trends in cognitive test z-scores by amyloid and neurodegeneration group. To investigate the effects of amyloid dose and degree of hippocampal atrophy, we assessed the associations of continuous mean cortical amyloid level and hippocampal volume with cognitive performance among individuals with detectable amyloid pathology at baseline. Individuals with amyloidosis or hippocampal atrophy had steeper longitudinal declines in verbal episodic memory and learning compared to those with neither condition (A + NÀ versus AÀNÀ: b = À 0.069, P = 0.017; AÀN + versus AÀNÀ: b = À 0.081, P = 0.025). Among individuals with hippocampal atrophy, amyloid positivity was associated with steeper declines in verbal memory (b = À 0.123, P = 0.015), visual memory (b = À 0.121, P = 0.036), language (b = À 0.144, P = 0.0004), and mental status (b = À 0.242, P = 0.002). Similarly, among individuals with amyloidosis, hippocampal atrophy was associated with steeper declines in verbal memory (b = À 0.135, P = 0.004), visual memory (b = À 0.141, P = 0.010), language (b = À 0.108, P = 0.006), and mental status (b = À 0.165, P = 0.022). Presence of both amyloidosis and hippocampal atrophy was associated with greater declines than would be expected by their additive contributions in visual memory (b = À 0.139, P = 0.036), language (b = À 0.132, P = 0.005), and mental status (b = À 0.170, P = 0.049). Neither amyloidosis nor hippocampal atrophy was predictive of declines in executive function, processing speed, or visuospatial ability. Among individuals with amyloidosis, higher baseline amyloid level was associated with lower concurrent visual memory, steeper declines in language, visuospatial ability, and mental status, whereas greater hippocampal atrophy was associated with steeper declines in category fluency. Our results suggest that both amyloid pathology and neurodegeneration have disadvantageous, in part synergistic, effects on prospective cognition. These cognitive effects are detectable early among cognitively normal individuals with amyloidosis, who are in preclinical stages of Alzheimer's disease according to research criteria. Our findings highlight the importance of early intervention to target both amyloidosis and atrophy to preserve cognitive function before further damage occurs.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/brain/awy150">doi:10.1093/brain/awy150</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29901697">pmid:29901697</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6061788/">pmcid:PMC6061788</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/omn7l7eetfeuvphvyyvqqx2zey">fatcat:omn7l7eetfeuvphvyyvqqx2zey</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200210212718/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC6061788&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e9/3c/e93c84a05565b01444238d04e4a9851cd5fa7d99.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/brain/awy150"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> oup.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6061788" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Self-reported Sleep and β-Amyloid Deposition in Community-Dwelling Older Adults

Adam P. Spira, Alyssa A. Gamaldo, Yang An, Mark N. Wu, Eleanor M. Simonsick, Murat Bilgel, Yun Zhou, Dean F. Wong, Luigi Ferrucci, Susan M. Resnick
<span title="2013-10-21">2013</span> <i title="American Medical Association (AMA)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/y2buushac5gd7hqlt5nzlet74m" style="color: black;">JAMA Neurology</a> </i> &nbsp;
N umerous studies have linked disturbed sleep to cognitive impairment in older adults. Individuals with Alzheimer disease (AD) have been shown to spend more time in bed awake 1,2 and have more fragmented sleep than those without AD, 1-3 and studies of healthier older adults document associations between worse self-reported sleep and lower cognitive performance. 4,5 In addition, recent research demonstrates that poor sleep quality, measured using wrist actigraphy, is associated with lower
more &raquo; ... ve performance in community-dwelling older adults. 6 Although these findings indicate that sleep disturbance is associated with poor cognitive outcomes, whether poor sleep contributes to the neuropathological changes underlying cognitive decline remains unclear. β-Amyloid (Aβ) plaques are one of the hallmarks of AD, and fluctuations in Aβ peptide levels may be regulated by sleep/ wake patterns. Kang et al 7 demonstrated that levels of Aβ in brain interstitial fluid increased with time awake and decreased during sleep in wild-type mice and a mouse model of AD. The authors also demonstrated similar fluctuations in cerebrospinal fluid levels of Aβ in young humans. Sleep deprivation in the AD mouse model produced a substantial increase in Aβ plaque burden. 7 We are unaware of any published studies that have investigated whether sleep disturbance is associated with neuroimaging evidence of Aβ deposition in the brains of older living human participants. We used data from community-dwelling participants in the Baltimore Longitudinal Study of Aging (BLSA) to investigate whether self-reported sleep variables were associated with fibrillar Aβ burden, measured in vivo with positron emission tomography (PET) with the tracer carbon 11-labeled Pittsburgh IMPORTANCE Older adults commonly report disturbed sleep, and recent studies in humans and animals suggest links between sleep and Alzheimer disease biomarkers. Studies are needed that evaluate whether sleep variables are associated with neuroimaging evidence of β-amyloid (Aβ) deposition. OBJECTIVE To determine the association between self-reported sleep variables and Aβ deposition in community-dwelling older adults. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional study of 70 adults (mean age, 76 [range, 53-91] years) from the neuroimaging substudy of the Baltimore Longitudinal Study of Aging, a normative aging study. EXPOSURE Self-reported sleep variables. MAIN OUTCOMES AND MEASURES β-Amyloid burden, measured by carbon 11-labeled Pittsburgh compound B positron emission tomography distribution volume ratios (DVRs). RESULTS After adjustment for potential confounders, reports of shorter sleep duration were associated with greater Aβ burden, measured by mean cortical DVR (B = 0.08 [95% CI, 0.03-0.14]; P = .005) and precuneus DVR (B = 0.11 [0.03-0.18]; P = .007). Reports of lower sleep quality were associated with greater Aβ burden measured by precuneus DVR (B = 0.08 [0.01-0.15]; P = .03). CONCLUSIONS AND RELEVANCE Among community-dwelling older adults, reports of shorter sleep duration and poorer sleep quality are associated with greater Aβ burden. Additional studies with objective sleep measures are needed to determine whether sleep disturbance causes or accelerates Alzheimer disease.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1001/jamaneurol.2013.4258">doi:10.1001/jamaneurol.2013.4258</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24145859">pmid:24145859</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3918480/">pmcid:PMC3918480</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m3kvqauu3fckzigtaxenwmmwpq">fatcat:m3kvqauu3fckzigtaxenwmmwpq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190430215409/https://jamanetwork.com/journals/jamaneurology/articlepdf/1788611/noi130061.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a1/83/a18384f0ee5c6599dcd0094aab9c3a54cb577537.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1001/jamaneurol.2013.4258"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3918480" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Deformation Field Correction for Spatial Normalization of PET Images Using a Population-Derived Partial Least Squares Model [chapter]

Murat Bilgel, Aaron Carass, Susan M. Resnick, Dean F. Wong, Jerry L. Prince
<span title="">2014</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Spatial normalization of positron emission tomography (PET) images is essential for population studies, yet work on anatomically accurate PET-to-PET registration is limited. We present a method for the spatial normalization of PET images that improves their anatomical alignment based on a deformation correction model learned from structural image registration. To generate the model, we first create a population-based PET template with a corresponding structural image template. We register each
more &raquo; ... ET image onto the PET template using deformable registration that consists of an affine step followed by a diffeomorphic mapping. Constraining the affine step to be the same as that obtained from the PET registration, we find the diffeomorphic mapping that will align the structural image with the structural template. We train partial least squares (PLS) regression models within small neighborhoods to relate the PET intensities and deformation fields obtained from the diffeomorphic mapping to the structural image deformation fields. The trained model can then be used to obtain more accurate registration of PET images to the PET template without the use of a structural image. A cross validation based evaluation on 79 subjects shows that our method yields more accurate alignment of the PET images compared to deformable PETto-PET registration as revealed by 1) a visual examination of the deformed images, 2) a smaller error in the deformation fields, and 3) a greater overlap of the deformed anatomical labels with ground truth segmentations.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-10581-9_25">doi:10.1007/978-3-319-10581-9_25</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25383393">pmid:25383393</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4222176/">pmcid:PMC4222176</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kw4aji5z6nbs3iec7pwzhknoqy">fatcat:kw4aji5z6nbs3iec7pwzhknoqy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200210192639/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC4222176&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a7/36/a73613d9fc403cbbc84d1d5d1ec576c978bcaf02.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-10581-9_25"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4222176" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Lasting consequences of concussion on the aging brain: findings from the Baltimore Longitudinal Study of Aging

Danielle June, Owen A Williams, Chiung-Wei Huang, Yang An, Bennett A. Landman, Christos Davatzikos, Murat Bilgel, Susan M Resnick, Lori L Beason-Held
<span title="2020-07-20">2020</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sa477uo7lveh7hchpikpixop5u" style="color: black;">NeuroImage</a> </i> &nbsp;
Studies suggest that concussions may be related to increased risk of neurodegenerative diseases, such as Chronic Traumatic Encephalopathy and Alzheimer's Disease. Most neuroimaging studies show effects of concussions in frontal and temporal lobes of the brain, yet the long-term impacts of concussions on the aging brain have not been well studied. We examined neuroimaging data from 51 participants (mean age at first imaging visit=65.1±11.23) in the Baltimore Longitudinal Study of Aging who
more &raquo; ... ed a concussion in their medical history an average of 23 years prior to the first imaging visit, and compared them to 150 participants (mean age at first imaging visit=66.6±10.97) with no history of concussion. Participants underwent serial structural MRI over a mean of 5.17±6.14 years and DTI over a mean of 2.92±2.22 years to measure brain structure, as well as 15O-water PET over a mean of 5.33±2.19 years to measure brain function. A battery of neuropsychological tests was also administered over a mean of 11.62±7.41 years. Analyses of frontal and temporal lobe regions were performed to examine differences in these measures between the concussion and control groups at first imaging visit and in change over time. Compared to those without concussion, participants with a prior concussion had greater brain atrophy in temporal lobe white matter and hippocampus at first imaging visit, which remained stable throughout the follow-up visits. Those with prior concussion also showed differences in white matter microstructure using DTI, including increased radial and axial diffusivity in the fornix/stria terminalis, anterior corona radiata, and superior longitudinal fasciculus at first imaging visit. In 15O-water PET, higher resting cerebral blood flow was seen at first imaging visit in orbitofrontal and lateral temporal regions, and both increases and decreases were seen in prefrontal, cingulate, insular, hippocampal, and ventral temporal regions with longitudinal follow-up. There were no significant differences in neuropsychological performance between groups. Most of the differences observed between the concussed and non-concussed groups were seen at the first imaging visit, suggesting that concussions can produce long-lasting structural and functional alterations in temporal and frontal regions of the brain in older individuals. These results also suggest that many of the reported short-term effects of concussion may still be apparent later in life.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2020.117182">doi:10.1016/j.neuroimage.2020.117182</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32702483">pmid:32702483</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7848820/">pmcid:PMC7848820</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wmq2k5vasrdv7gbzix453yleie">fatcat:wmq2k5vasrdv7gbzix453yleie</a> </span>
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