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  • 1
    In: Alzheimer's & Dementia, Wiley, Vol. 18, No. S5 ( 2022-12)
    Abstract: Ex vivo magnetic resonance imaging (MRI) enables detailed characterization of neuroanatomy (Augustinack et al. 2013), such as hippocampal subfields in the medial temporal lobe (MTL) (Yushkevich et al. 2021, Ravikumar et al. 2021). However, automated cortical segmentation methods in ex vivo MRI are not well developed due to limited data availability and heterogeneity in scanners and acquisition. Here, we investigate a deep learning framework to parcellate the cortical mantle, compute thickness and link them with neuropathology ratings across 16 cortical regions in 7 Tesla MRIs of 38 ex vivo brain specimens spanning Alzheimer Disease and Related Dementias. Method A deep learning method, nnU‐Net (Isensee et al. 2021), was trained on manually segmented 3D image patches (Figure 1C) to obtain automated cortical segmentations across 38 subjects (Table 1). We identified 16 landmarks (Figure 1A) for localized quantitative signatures of cortical morphometry and used the pipeline in Wisse et al. 2021 to measure local thickness (Figure 1B). Associations were computed between cortical thickness from manual and automated segmentations via Pearson’s correlation and average fixed‐raters Intra‐class Correlation Coefficient (ICC) for 16 locations (Figure 3). We also correlated thickness from both automated and manual segmentations with neuropathological ratings of tau and neuronal loss in corresponding contralateral regions and global Braak staging (Figures 4 and 5). Result Figure 2 depicts cortical mantle segmentation across brain hemispheres. Figure 3 shows good agreement between ground truth and automated thickness, with 15 regions with significant associations (p 〈 0.05) and 8 regions having r 〉 0.6. We observe high ICC scores with 9 regions where ICC 〉 0.7, confirming that automated segmentations accurately measure thickness. Figure 4 shows significant correlations between thickness and Tau ratings for Brodmann Area 35 (BA35) and midfrontal regions and trends between neuronal loss and thickness in entorhinal cortex (ERC), anterior temporal pole and anterior insula. Figure 5 shows significant correlations between thickness and Braak staging in ventrolateral temporal cortex and ERC, with trends in other regions. Conclusion Our automated ex vivo neuroimaging framework accurately segments the cortical mantle, provides thickness measurements that concur with user‐supervised thickness and links morphometry with underlying neurodegeneration, thus suggesting the strengths of ex vivo MRI.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2201940-6
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  • 2
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2023
    In:  SLEEP Vol. 46, No. Supplement_1 ( 2023-05-29), p. A238-A239
    In: SLEEP, Oxford University Press (OUP), Vol. 46, No. Supplement_1 ( 2023-05-29), p. A238-A239
    Abstract: Vagus nerve stimulation (VNS) treatment for patients with intractable epilepsy has been shown to effectively lower seizure frequency and improve quality of life. However, due to stimulation of the vagus nerve, alterations in both central regulation of breathing and laryngeal muscle stimulation can precipitate obstructive sleep apnea (OSA). While there have been case reports describing OSA following VNS implantation, there has yet to be a detailed evaluation of this complication in a large cohort of VNS patients. The objective of this meta-analysis was to determine OSA rates in patients following VNS implantation. Methods English full-text articles were searched for on Pubmed, Scopus, and Embase databases. Articles had to follow patients before and after VNS implantation; report apnea-hypopnea index (AHI), respiratory disturbance index (RDI), or OSA rates following VNS implantation; be from a clinical trial, cohort, or case-control study. Two reviewers reviewed articles and a third settled disagreements. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies were used. Following Freeman-Tukey transformation, the generic inverse variance method with random effects model was used for meta-analysis. Results Ten studies, seven retrospective and three prospective, representing a cohort of 306 patients were included in this study. Pooled OSA rates following VNS implantation were 27.3%, 95% CI: 15.1 - 41.5%. Subgroup analysis found no difference in rates between studies of pediatric populations (22.1%, 95% CI: 8.2 - 40.5%) and adult populations (31.9%, 95% CI: 18.9 - 47.5%) following VNS implantation (P = 0.39). There was significant heterogeneity in pooled analysis (P & lt; 0.00001, I2 = 100%), but no inter-subgroup heterogeneity (I2 = 0%). Conclusion Obstructive sleep apnea is a common adverse effect following VNS treatment and patients should be monitored following implantation. There are no differences in OSA rates between pediatric and adult populations. Routine screening for OSA following VNS implantation may be a reasonable choice. Support (if any) No institutional or NIH funding was received for this project
    Type of Medium: Online Resource
    ISSN: 0161-8105 , 1550-9109
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2056761-3
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  • 3
    In: Alzheimer's & Dementia, Wiley, Vol. 18, No. S5 ( 2022-12)
    Abstract: Ex vivo magnetic resonance imaging (MRI) of the brain provides remarkable advantages over in vivo MRI for linking neuroanatomy and morphometry to underlying pathology (Yushkevich et al. 2021, Ravikumar et al. 2021). Subcortical structures show atrophy in certain neurodegenerative diseases, especially Frontotemporal Lobar Degeneration with TDP‐43 (FTLD‐TDP) and four‐repeat (4R) tauopathies (i.e., Corticobasal Degeneration, Progressive Supranuclear Palsy) (Miletić et al. 2022), yet few methods exist to measure subcortical atrophy in ex vivo MRI. We present a framework to quantify subcortical morphometry using 7 Tesla ex vivo MRI and distinguish atrophy patterns across neurodegenerative spectrums. Method A deep learning method, nnU‐Net (Isensee et al. 2021), was trained on manual segmentations from only 3 brain hemispheres to obtain automated segmentations of 4 subcortical structures (caudate, putamen, globus pallidus, thalamus) across 38 subjects spanning Alzheimer's Disease (AD), Lewy Body Disease (LBD), FTLD‐TDP, 4R tauopathies and miscellaneous tauopathies (Figure 1, Table 1). Subcortical volumes were extracted from automated segmentations. Cerebral cortical volume was computed via cortical segmentation method in Khandelwal et al. 2021. Regional volumes were evaluated via likelihood ratio tests (Figure 2), adjusted for covariates (age, sex and intracranial volume from in vivo MRI) and multiple tests. Separately, correlations were computed between subcortical volumes, cortical thicknesses at 18 landmark locations and neuropathological ratings (Khandelwal et al. 2021, Wisse et al. 2021, Figure 3). Result The pipeline validated regional volumetric relationships in neurodegeneration. Global cortex volume did not significantly differ among disease groups (Figure 2). Compared to AD, FTLD‐TDP had significantly lower putamen and thalamus volumes while 4R tauopathies had reduced putamen and caudate volumes ( P ’s 〈 0.05, adjusted for covariates/multiple comparisons). Before multiple tests correction, there were decreased covariate‐adjusted volumes in globus pallidus and caudate in FTLD‐TDP and thalamus in 4R tauopathy relative to AD. Subcortical volumes correlated with each other ( P ’s 〈 0.05) but not with cortical thickness, with trends in motor cortex (Figure 3). Subcortical volumes also trended with local tau pathology (Figure 4). Conclusion Our ex vivo neuroimaging framework differentiates subcortical atrophy patterns in FTLD‐TDP and 4R tauopathies compared to AD, highlighting utility in ex vivo imaging for diagnosing and investigating neurodegeneration.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2201940-6
    Location Call Number Limitation Availability
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