Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification

Numerous studies have proposed biomarkers based on magnetic resonance imaging (MRI) to detect and predict the risk of evolution toward Alzheimer’s disease (AD). Most of these methods have focused on the hippocampus, which is known to be one of the earliest structures impacted by the disease. To date, patch-based grading approaches provide among the best biomarkers based on the hippocampus. However, this structure is complex and is divided into different subfields, not equally impacted by AD. Former in-vivo imaging studies mainly investigated structural alterations of these subfields using volumetric measurements and microstructural modifications with mean diffusivity measurements. The aim of our work is to improve the current classification performances based on the hippocampus with a new multimodal patch-based framework combining structural and diffusivity MRI. The combination of these two MRI modalities enables the capture of subtle structural and microstructural alterations. Moreover, we propose to study the efficiency of this new framework applied to the hippocampal subfields. To this end, we compare the classification accuracy provided by the different hippocampal subfields using volume, mean diffusivity, and our novel multimodal patch-based grading framework combining structural and diffusion MRI. The experiments conducted in this work show that our new multimodal patch-based method applied to the whole hippocampus provides the most discriminating biomarker for advanced AD detection while our new framework applied into subiculum obtains the best results for AD prediction, improving by two percentage points the accuracy compared to the whole hippocampus

DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation

Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These techniques have even outperformed human experts in controlled evaluation conditions such as Longitudinal MS Lesion Segmentation Challenge (ISBI Challenge). However state-of-the-art approaches trained to perform well on highly-controlled datasets fail to generalize on clinical data from unseen datasets. Instead of proposing another improvement of the segmentation accuracy, we propose a novel method robust to domain shift and performing well on unseen datasets, called DeepLesionBrain (DLB). This generalization property results from three main contributions. First, DLB is based on a large group of compact 3D CNNs. This spatially distributed strategy ensures a robust prediction despite the risk of generalization failure of some individual networks. Second, DLB includes a new image quality data augmentation to reduce dependency to training data specificity (e.g., acquisition protocol). Finally, to learn a more generalizable representation of MS lesions, we propose a hierarchical specialization learning (HSL). HSL is performed by pre-training a generic network over the whole brain, before using its weights as initialization to locally specialized networks. By this end, DLB learns both generic features extracted at global image level and specific features extracted at local image level. DLB generalization was validated in cross-dataset experiments on MSSEG'16, ISBI challenge, and in-house datasets. During experiments, DLB showed higher segmentation accuracy, better segmentation consistency and greater generalization performance compared to state-of-the-art methods. Therefore, DLB offers a robust framework well-suited for clinical practice

Differential aquaporin 4 expression during edema build-up and resolution phases of brain inflammation

<p>Abstract</p> <p>Background</p> <p>Vasogenic edema dynamically accumulates in many brain disorders associated with brain inflammation, with the critical step of edema exacerbation feared in patient care. Water entrance through blood-brain barrier (BBB) opening is thought to have a role in edema formation. Nevertheless, the mechanisms of edema resolution remain poorly understood. Because the water channel aquaporin 4 (AQP4) provides an important route for vasogenic edema resolution, we studied the time course of AQP4 expression to better understand its potential effect in countering the exacerbation of vasogenic edema.</p> <p>Methods</p> <p>Focal inflammation was induced in the rat brain by a lysolecithin injection and was evaluated at 1, 3, 7, 14 and 20 days using a combination of in vivo MRI with apparent diffusion coefficient (ADC) measurements used as a marker of water content, and molecular and histological approaches for the quantification of AQP4 expression. Markers of active inflammation (macrophages, BBB permeability, and interleukin-1β) and markers of scarring (gliosis) were also quantified.</p> <p>Results</p> <p>This animal model of brain inflammation demonstrated two phases of edema development: an initial edema build-up phase during active inflammation that peaked after 3 days (ADC increase) was followed by an edema resolution phase that lasted from 7 to 20 days post injection (ADC decrease) and was accompanied by glial scar formation. A moderate upregulation in AQP4 was observed during the build-up phase, but a much stronger transcriptional and translational level of AQP4 expression was observed during the secondary edema resolution phase.</p> <p>Conclusions</p> <p>We conclude that a time lag in AQP4 expression occurs such that the more significant upregulation was achieved only after a delay period. This change in AQP4 expression appears to act as an important determinant in the exacerbation of edema, considering that AQP4 expression is insufficient to counter the water influx during the build-up phase, while the second more pronounced but delayed upregulation is involved in the resolution phase. A better pathophysiological understanding of edema exacerbation, which is observed in many clinical situations, is crucial in pursuing new therapeutic strategies.</p

Regional hippocampal vulnerability in early multiple sclerosis: a dynamic pathological spreading from dentate gyrus to CA1

"This is the peer reviewed version of the following article: Planche, V., Koubiyr, I., Romero, J. E., Manjon, J. V., Coupé, P., Deloire, M., ... & Tourdias, T. (2018). Regional hippocampal vulnerability in early multiple sclerosis: Dynamic pathological spreading from dentate gyrus to CA 1. Human brain mapping, 39(4), 1814-1824., which has been published in final form at https://doi.org/10.1002/hbm.23970. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] Background: Whether hippocampal subfields are differentially vulnerable at the earliest stages of multiple sclerosis (MS) and how this impacts memory performance is a current topic of debate. Method: We prospectively included 56 persons with clinically isolated syndrome (CIS) suggestive of MS in a 1-year longitudinal study, together with 55 matched healthy controls at baseline. Participants were tested for memory performance and scanned with 3T MRI to assess the volume of 5 distinct hippocampal subfields using automatic segmentation techniques. Results: At baseline, CA4/dentate gyrus was the only hippocampal subfield with a volume significantly smaller than controls (p < .01). After one year, CA4/dentate gyrus atrophy worsened (-6.4%, p < .0001) and significant CA1 atrophy appeared (both in the stratum-pyramidale and the stratum radiatum-lacunosum-moleculare, -5.6%, p < .001 and -6.2%, p < .01, respectively). CA4/dentate gyrus volume at baseline predicted CA1 volume one year after CIS (R-2 = 0.44 to 0.47, p < .001, with age, T2 lesion-load, and global brain atrophy as covariates). The volume of CA4/dentate gyrus at baseline was associated with MS diagnosis during follow-up, independently of T2-lesion load and demographic variables (p < .05). Whereas CA4/dentate gyrus volume was not correlated with memory scores at baseline, CA1 atrophy was an independent correlate of episodic verbal memory performance one year after CIS (beta = 0.87, p < .05). Conclusion: The hippocampal degenerative process spread from dentate gyrus to CA1 at the earliest stage of MS. This dynamic vulnerability is associated with MS diagnosis after CIS and will ultimately impact hippocampal-dependent memory performance.ARSEP Foundation; Bordeaux University Hospital; TEVA Laboratories; French Agence Nationale de la Recherche, Grant/Award Numbers: ANR-10-LABX-57, ANR-10-LABX-43, ANR-10-IDEX-03-02, ANR-10-COHO-002; UPV, Grant/Award Numbers: UPV2016-0099, TIN2013-43457-R; Ministerio de Economia y competitividadPlanche, V.; Koubiyr, I.; Romero Gómez, JE.; Manjón Herrera, JV.; Coupe, P.; Deloire, M.; Dousset, V.... (2018). Regional hippocampal vulnerability in early multiple sclerosis: a dynamic pathological spreading from dentate gyrus to CA1. Human Brain Mapping. 39(4):1814-1824. https://doi.org/10.1002/hbm.23970S18141824394Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage, 54(3), 2033-2044. doi:10.1016/j.neuroimage.2010.09.025Bakker, A., Kirwan, C. B., Miller, M., & Stark, C. E. L. (2008). Pattern Separation in the Human Hippocampal CA3 and Dentate Gyrus. Science, 319(5870), 1640-1642. doi:10.1126/science.1152882Coupé, P., Manjón, J. V., Chamberland, M., Descoteaux, M., & Hiba, B. (2013). Collaborative patch-based super-resolution for diffusion-weighted images. NeuroImage, 83, 245-261. doi:10.1016/j.neuroimage.2013.06.030De Stefano, N., Airas, L., Grigoriadis, N., Mattle, H. P., O’Riordan, J., Oreja-Guevara, C., … Kieseier, B. C. (2014). Clinical Relevance of Brain Volume Measures in Multiple Sclerosis. CNS Drugs, 28(2), 147-156. doi:10.1007/s40263-014-0140-zDu, A. T., Schuff, N., Kramer, J. H., Ganzer, S., Zhu, X. P., Jagust, W. J., … Weiner, M. W. (2004). Higher atrophy rate of entorhinal cortex than hippocampus in AD. Neurology, 62(3), 422-427. doi:10.1212/01.wnl.0000106462.72282.90Dutta, R., Chang, A., Doud, M. K., Kidd, G. J., Ribaudo, M. V., Young, E. A., … Trapp, B. D. (2011). Demyelination causes synaptic alterations in hippocampi from multiple sclerosis patients. Annals of Neurology, 69(3), 445-454. doi:10.1002/ana.22337De Flores, R., La Joie, R., & Chételat, G. (2015). Structural imaging of hippocampal subfields in healthy aging and Alzheimer’s disease. Neuroscience, 309, 29-50. doi:10.1016/j.neuroscience.2015.08.033Fraser, M. A., Shaw, M. E., & Cherbuin, N. (2015). A systematic review and meta-analysis of longitudinal hippocampal atrophy in healthy human ageing. NeuroImage, 112, 364-374. doi:10.1016/j.neuroimage.2015.03.035Frisoni, G. B., Ganzola, R., Canu, E., Rub, U., Pizzini, F. B., Alessandrini, F., … Thompson, P. M. (2008). Mapping local hippocampal changes in Alzheimer’s disease and normal ageing with MRI at 3 Tesla. Brain, 131(12), 3266-3276. doi:10.1093/brain/awn280Gold, S. M., Kern, K. C., O’Connor, M.-F., Montag, M. J., Kim, A., Yoo, Y. S., … Sicotte, N. L. (2010). Smaller Cornu Ammonis 2–3/Dentate Gyrus Volumes and Elevated Cortisol in Multiple Sclerosis Patients with Depressive Symptoms. Biological Psychiatry, 68(6), 553-559. doi:10.1016/j.biopsych.2010.04.025Habbas, S., Santello, M., Becker, D., Stubbe, H., Zappia, G., Liaudet, N., … Volterra, A. (2015). Neuroinflammatory TNFα Impairs Memory via Astrocyte Signaling. Cell, 163(7), 1730-1741. doi:10.1016/j.cell.2015.11.023Hulst, H. E., Schoonheim, M. M., Van Geest, Q., Uitdehaag, B. M., Barkhof, F., & Geurts, J. J. (2015). Memory impairment in multiple sclerosis: Relevance of hippocampal activation and hippocampal connectivity. Multiple Sclerosis Journal, 21(13), 1705-1712. doi:10.1177/1352458514567727Jack, C. R., Petersen, R. C., Xu, Y., O’Brien, P. C., Smith, G. E., Ivnik, R. J., … Kokmen, E. (2000). Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology, 55(4), 484-490. doi:10.1212/wnl.55.4.484Jack, C. R., Barkhof, F., Bernstein, M. A., Cantillon, M., Cole, P. E., DeCarli, C., … Foster, N. L. (2011). Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer’s disease. Alzheimer’s & Dementia, 7(4), 474-485.e4. doi:10.1016/j.jalz.2011.04.007Kerchner, G. A., Bernstein, J. D., Fenesy, M. C., Deutsch, G. K., Saranathan, M., Zeineh, M. M., & Rutt, B. K. (2013). Shared Vulnerability of Two Synaptically-Connected Medial Temporal Lobe Areas to Age and Cognitive Decline: A Seven Tesla Magnetic Resonance Imaging Study. Journal of Neuroscience, 33(42), 16666-16672. doi:10.1523/jneurosci.1915-13.2013La Joie, R., Fouquet, M., Mézenge, F., Landeau, B., Villain, N., Mevel, K., … Chételat, G. (2010). Differential effect of age on hippocampal subfields assessed using a new high-resolution 3T MR sequence. NeuroImage, 53(2), 506-514. doi:10.1016/j.neuroimage.2010.06.024Longoni, G., Rocca, M. A., Pagani, E., Riccitelli, G. C., Colombo, B., Rodegher, M., … Filippi, M. (2013). Deficits in memory and visuospatial learning correlate with regional hippocampal atrophy in MS. Brain Structure and Function, 220(1), 435-444. doi:10.1007/s00429-013-0665-9Manjón, J. V., & Coupé, P. (2016). volBrain: An Online MRI Brain Volumetry System. Frontiers in Neuroinformatics, 10. doi:10.3389/fninf.2016.00030Manjón, J. V., Coupé, P., Martí-Bonmatí, L., Collins, D. L., & Robles, M. (2009). Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging, 31(1), 192-203. doi:10.1002/jmri.22003Manjón, J. V., Eskildsen, S. F., Coupé, P., Romero, J. E., Collins, D. L., & Robles, M. (2014). Nonlocal Intracranial Cavity Extraction. International Journal of Biomedical Imaging, 2014, 1-11. doi:10.1155/2014/820205Maruszak, A., & Thuret, S. (2014). Why looking at the whole hippocampus is not enough—a critical role for anteroposterior axis, subfield and activation analyses to enhance predictive value of hippocampal changes for Alzheimer’s disease diagnosis. Frontiers in Cellular Neuroscience, 8. doi:10.3389/fncel.2014.00095Miller, D. H., Chard, D. T., & Ciccarelli, O. (2012). Clinically isolated syndromes. The Lancet Neurology, 11(2), 157-169. doi:10.1016/s1474-4422(11)70274-5Morra, J. H., Tu, Z., Apostolova, L. G., Green, A. E., Avedissian, C., … Madsen, S. K. (2009). Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls. Human Brain Mapping, 30(9), 2766-2788. doi:10.1002/hbm.20708Ny�l, L. G., & Udupa, J. K. (1999). On standardizing the MR image intensity scale. Magnetic Resonance in Medicine, 42(6), 1072-1081. doi:10.1002/(sici)1522-2594(199912)42:63.0.co;2-mPapadopoulos, D., Dukes, S., Patel, R., Nicholas, R., Vora, A., & Reynolds, R. (2009). Substantial Archaeocortical Atrophy and Neuronal Loss in Multiple Sclerosis. Brain Pathology, 19(2), 238-253. doi:10.1111/j.1750-3639.2008.00177.xPérez-Miralles, F., Sastre-Garriga, J., Tintoré, M., Arrambide, G., Nos, C., Perkal, H., … Montalban, X. (2013). Clinical impact of early brain atrophy in clinically isolated syndromes. Multiple Sclerosis Journal, 19(14), 1878-1886. doi:10.1177/1352458513488231Planche, V., Ruet, A., Coupé, P., Lamargue-Hamel, D., Deloire, M., Pereira, B., … Tourdias, T. (2016). Hippocampal microstructural damage correlates with memory impairment in clinically isolated syndrome suggestive of multiple sclerosis. Multiple Sclerosis Journal, 23(9), 1214-1224. doi:10.1177/1352458516675750Planche, V., Panatier, A., Hiba, B., Ducourneau, E.-G., Raffard, G., Dubourdieu, N., … Tourdias, T. (2017). Selective dentate gyrus disruption causes memory impairment at the early stage of experimental multiple sclerosis. Brain, Behavior, and Immunity, 60, 240-254. doi:10.1016/j.bbi.2016.11.010Planche, V., Ruet, A., Charré-Morin, J., Deloire, M., Brochet, B., & Tourdias, T. (2017). Pattern separation performance is decreased in patients with early multiple sclerosis. Brain and Behavior, 7(8), e00739. doi:10.1002/brb3.739Polman, C. H., Reingold, S. C., Banwell, B., Clanet, M., Cohen, J. A., Filippi, M., … Wolinsky, J. S. (2011). Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria. Annals of Neurology, 69(2), 292-302. doi:10.1002/ana.22366Rocca, M. A., Longoni, G., Pagani, E., Boffa, G., Colombo, B., Rodegher, M., … Filippi, M. (2015). In vivo evidence of hippocampal dentate gyrus expansion in multiple sclerosis. Human Brain Mapping, 36(11), 4702-4713. doi:10.1002/hbm.22946Romero, J. E., Coupe, P., & Manjón, J. V. (2016). High Resolution Hippocampus Subfield Segmentation Using Multispectral Multiatlas Patch-Based Label Fusion. Lecture Notes in Computer Science, 117-124. doi:10.1007/978-3-319-47118-1_15Romero, J. E., Coupé, P., & Manjón, J. V. (2017). HIPS: A new hippocampus subfield segmentation method. NeuroImage, 163, 286-295. doi:10.1016/j.neuroimage.2017.09.049Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., … Mühlau, M. (2012). An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage, 59(4), 3774-3783. doi:10.1016/j.neuroimage.2011.11.032Sicotte, N. L., Kern, K. C., Giesser, B. S., Arshanapalli, A., Schultz, A., Montag, M., … Bookheimer, S. Y. (2008). Regional hippocampal atrophy in multiple sclerosis. Brain, 131(4), 1134-1141. doi:10.1093/brain/awn030Small, S. A. (2014). Isolating Pathogenic Mechanisms Embedded within the Hippocampal Circuit through Regional Vulnerability. Neuron, 84(1), 32-39. doi:10.1016/j.neuron.2014.08.030Stark, S. M., Yassa, M. A., Lacy, J. W., & Stark, C. E. L. (2013). A task to assess behavioral pattern separation (BPS) in humans: Data from healthy aging and mild cognitive impairment. Neuropsychologia, 51(12), 2442-2449. doi:10.1016/j.neuropsychologia.2012.12.014Thompson, P. M., Hayashi, K. M., de Zubicaray, G. I., Janke, A. L., Rose, S. E., Semple, J., … Toga, A. W. (2004). Mapping hippocampal and ventricular change in Alzheimer disease. NeuroImage, 22(4), 1754-1766. doi:10.1016/j.neuroimage.2004.03.040Tustison, N. J., Avants, B. B., Cook, P. A., Yuanjie Zheng, Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 29(6), 1310-1320. doi:10.1109/tmi.2010.2046908Wang, L., Swank, J. S., Glick, I. E., Gado, M. H., Miller, M. I., Morris, J. C., & Csernansky, J. G. (2003). Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging☆. NeuroImage, 20(2), 667-682. doi:10.1016/s1053-8119(03)00361-6West, M. ., Coleman, P. ., Flood, D. ., & Troncoso, J. . (1994). Differences in the pattern of hippocampal neuronal loss in normal ageing and Alzheimer’s disease. The Lancet, 344(8925), 769-772. doi:10.1016/s0140-6736(94)92338-8Winterburn, J. L., Pruessner, J. C., Chavez, S., Schira, M. M., Lobaugh, N. J., Voineskos, A. N., & Chakravarty, M. M. (2013). A novel in vivo atlas of human hippocampal subfields using high-resolution 3T magnetic resonance imaging. NeuroImage, 74, 254-265. doi:10.1016/j.neuroimage.2013.02.003Wisse, L. E. M., Daugherty, A. M., Olsen, R. K., Berron, D., Carr, V. A., … Stark, C. E. L. (2016). A harmonized segmentation protocol for hippocampal and parahippocampal subregions: Why do we need one and what are the key goals? Hippocampus, 27(1), 3-11. doi:10.1002/hipo.22671Yushkevich, P. A., Amaral, R. S. C., Augustinack, J. C., Bender, A. R., Bernstein, J. D., Boccardi, M., … Zeineh, M. M. (2015). Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: Towards a harmonized segmentation protocol. NeuroImage, 111, 526-541. doi:10.1016/j.neuroimage.2015.01.00

Brain Communications

Brain charts for the human lifespan have been recently proposed to build dynamic models of brain anatomy in normal aging and various neurological conditions. They offer new possibilities to quantify neuroanatomical changes from preclinical stages to death, where longitudinal MRI data are not available. In this study, we used brain charts to model the progression of brain atrophy in progressive supranuclear palsy – Richardson syndrome (PSPRS). We combined multiple datasets (n=8170 quality controlled MRI of healthy subjects from 22 cohorts covering the entire lifespan, and n=62 MRI of PSP-RS patients from the 4 Repeat Tauopathy Neuroimaging Initiative) to extrapolate lifetime volumetric models of healthy and PSP-RS brain structures. We then mapped in time and space the sequential divergence between healthy and PSP-RS charts. We found six major consecutive stages of atrophy progression: (i) ventral diencephalon (including subthalamic nuclei, substantia nigra, and red nuclei), (ii) pallidum, (iii) brainstem, striatum and amygdala, (iv) thalamus, (v) frontal lobe and (vi) occipital lobe. The three structures with most severe atrophy over time were the thalamus, followed by the pallidum and the brainstem. These results match the neuropathological staging of tauopathy progression in PSP-RS, where the pathology is supposed to start in the pallido-nigro-luysian system and spreads rostrally via the striatum and the amygdala to the cerebral cortex, and caudally to the brainstem. This study supports the use of brain charts for the human lifespan to study the progression of neurodegenerative diseases, especially in the absence of specific biomarkers as in PSP.Apprentissage profond pour la volumétrie cérébrale : vers le BigData en neuroscienceInitiative d'excellence de l'Université de Bordeau

Brain Commun.

The chronological progression of brain atrophy over decades, from pre-symptomatic to dementia stages, has never been formally depicted in Alzheimer's disease. This is mainly due to the lack of cohorts with long enough MRI follow-ups in cognitively unimpaired young participants at baseline. To describe a spatiotemporal atrophy staging of Alzheimer's disease at the whole-brain level, we built extrapolated lifetime volumetric models of healthy and Alzheimer's disease brain structures by combining multiple large-scale databases (n = 3512 quality controlled MRI from 9 cohorts of subjects covering the entire lifespan, including 415 MRI from ADNI1, ADNI2 and AIBL for Alzheimer's disease patients). Then, we validated dynamic models based on cross-sectional data using external longitudinal data. Finally, we assessed the sequential divergence between normal aging and Alzheimer's disease volumetric trajectories and described the following staging of brain atrophy progression in Alzheimer's disease: (i) hippocampus and amygdala; (ii) middle temporal gyrus; (iii) entorhinal cortex, parahippocampal cortex and other temporal areas; (iv) striatum and thalamus and (v) middle frontal, cingular, parietal, insular cortices and pallidum. We concluded that this MRI scheme of atrophy progression in Alzheimer's disease was close but did not entirely overlap with Braak staging of tauopathy, with a 'reverse chronology' between limbic and entorhinal stages. Alzheimer's disease structural progression may be associated with local tau accumulation but may also be related to axonal degeneration in remote sites and other limbic-predominant associated proteinopathies. © 2022 The Author(s). Published by Oxford University Press on behalf of the Guarantors of Brain.Apprentissage profond pour la volumétrie cérébrale : vers le BigData en neuroscienceTranslational Research and Advanced Imaging Laborator

Mult Scler

Background: Investigating the degeneration of specific thalamic nuclei in multiple sclerosis (MS) remains challenging. Methods: White-matter-nulled (WMn) MPRAGE, MP-FLAIR, and standard T1-weighted magnetic resonance imaging (MRI) were performed on MS patients (n = 15) and matched controls (n = 12). Thalamic lesions were counted in individual sequences and lesion contrast-to-noise ratio (CNR) was measured. Volumes of 12 thalamic nuclei were measured using an automatic segmentation pipeline specifically developed for WMn-MPRAGE. Results: WMn-MPRAGE showed more thalamic MS lesions (n = 35 in 9 out of 15 patients) than MP-FLAIR (n = 25) and standard T1 (n = 23), which was associated with significant improvement of CNR (p < 0.0001). MS patients had whole thalamus atrophy (p = 0.003) with lower volumes found for the anteroventral (p < 0.001), the pulvinar (p < 0.0001), and the habenular (p = 0.004) nuclei. Conclusion: WMn-MPRAGE and automatic thalamic segmentation can highlight thalamic MS lesions and measure patterns of focal thalamic atrophy. © The Author(s), 2019.Translational Research and Advanced Imaging LaboratoryBordeaux Region Aquitaine Initiative for Neuroscienc

Neuroimage

The thalamus is a central integration structure in the brain, receiving and distributing information among the cerebral cortex, subcortical structures, and the peripheral nervous system. Prior studies clearly show that the thalamus atrophies in cognitively unimpaired aging. However, the thalamus is comprised of multiple nuclei involved in a wide range of functions, and the age-related atrophy of individual thalamic nuclei remains unknown. Using a recently developed automated method of identifying thalamic nuclei (3T or 7T MRI with white-matter-nulled MPRAGE contrast and THOMAS segmentation) and a cross-sectional design, we evaluated the age-related atrophy rate for 10 thalamic nuclei (AV, CM, VA, VLA, VLP, VPL, pulvinar, LGN, MGN, MD) and an epithalamic nucleus (habenula). We also used T1-weighted images with the FreeSurfer SAMSEG segmentation method to identify and measure age-related atrophy for 11 extra-thalamic structures (cerebral cortex, cerebral white matter, cerebellar cortex, cerebellar white matter, amygdala, hippocampus, caudate, putamen, nucleus accumbens, pallidum, and lateral ventricle). In 198 cognitively unimpaired participants with ages spanning 20–88 years, we found that the whole thalamus atrophied at a rate of 0.45% per year, and that thalamic nuclei had widely varying age-related atrophy rates, ranging from 0.06% to 1.18% per year. A functional grouping analysis revealed that the thalamic nuclei involved in cognitive (AV, MD; 0.53% atrophy per year), visual (LGN, pulvinar; 0.62% atrophy per year), and auditory/vestibular (MGN; 0.64% atrophy per year) functions atrophied at significantly higher rates than those involved in motor (VA, VLA, VLP, and CM; 0.37% atrophy per year) and somatosensory (VPL; 0.32% atrophy per year) functions. A proximity-to-CSF analysis showed that the group of thalamic nuclei situated immediately adjacent to CSF atrophied at a significantly greater atrophy rate (0.59% atrophy per year) than that of the group of nuclei located farther from CSF (0.36% atrophy per year), supporting a growing hypothesis that CSF-mediated factors contribute to neurodegeneration. We did not find any significant hemispheric differences in these rates of change for thalamic nuclei. Only the CM thalamic nucleus showed a sex-specific difference in atrophy rates, atrophying at a greater rate in male versus female participants. Roughly half of the thalamic nuclei showed greater atrophy than all extra-thalamic structures examined (0% to 0.54% per year). These results show the value of white-matter-nulled MPRAGE imaging and THOMAS segmentation for measuring distinct thalamic nuclei and for characterizing the high and heterogeneous atrophy rates of the thalamus and its nuclei across the adult lifespan. Collectively, these methods and results advance our understanding of the role of thalamic substructures in neurocognitive and disease-related changes that occur with aging. © 2022Initiative d'excellence de l'Université de Bordeau

AJNR Am J Neuroradiol

Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning-based reconstruction could provide a relevant strategy while shortening the imaging examination. Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning-based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed. Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning-based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning-based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning-based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence. Denoising using deep learning-based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.Translational Research and Advanced Imaging Laborator