A novel deep learning based hippocampus subfield segmentation method

[EN] The automatic assessment of hippocampus volume is an important tool in the study of several neurodegenerative diseases such as Alzheimer's disease. Specifically, the measurement of hippocampus subfields properties is of great interest since it can show earlier pathological changes in the brain. However, segmentation of these subfields is very difficult due to their complex structure and for the need of high-resolution magnetic resonance images manually labeled. In this work, we present a novel pipeline for automatic hippocampus subfield segmentation based on a deeply supervised convolutional neural network. Results of the proposed method are shown for two available hippocampus subfield delineation protocols. The method has been compared to other state-of-the-art methods showing improved results in terms of accuracy and execution time.This research was supported by the Spanish DPI2017-87743-R grant from the Ministerio de Economia, Industria y Competitividad of Spain. This study has been also carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project) and Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57). The authors gratefully acknowledge the support of NVIDIA Corporation with their donation of the TITAN X GPU used in this research.Manjón Herrera, JV.; Romero, JE.; Coupe, P. (2022). A novel deep learning based hippocampus subfield segmentation method. Scientific Reports. 12(1):1-9. https://doi.org/10.1038/s41598-022-05287-81912

HIPS: A new hippocampus subfield segmentation method

[EN] The importance of the hippocampus in the study of several neurodegenerative diseases such as Alzheimer's disease makes it a structure of great interest in neuroimaging. However, few segmentation methods have been proposed to measure its subfields due to its complex structure and the lack of high resolution magnetic resonance (MR) data. In this work, we present a new pipeline for automatic hippocampus subfield segmentation using two available hippocampus subfield delineation protocols that can work with both high and standard resolution data. The proposed method is based on multi-atlas label fusion technology that benefits from a novel multi-contrast patch match search process (using high resolution T1-weighted and T2-weighted images). The proposed method also includes as post-processing a new neural network-based error correction step to minimize systematic segmentation errors. The method has been evaluated on both high and standard resolution images and compared to other state-of-the-art methods showing better results in terms of accuracy and execution time.This research was supported by Spanish UPV2016-0099 and TIN2013-43457-R grants from UPV and the Ministerio de Economia y Competitividad. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project "Defi imag'In". We also want to thank Javier Juan Albarracin for his valuable contribution to the development of this method.Romero Gómez, JE.; Coupe, P.; Manjón Herrera, JV. (2017). HIPS: A new hippocampus subfield segmentation method. NeuroImage. 163:286-295. https://doi.org/10.1016/j.neuroimage.2017.09.049S28629516

Lifespan Changes of the Human Brain In Alzheimer's Disease

[EN] Brain imaging studies have shown that slow and progressive cerebral atrophy characterized the development of Alzheimer's Disease (AD). Despite a large number of studies dedicated to AD, key questions about the lifespan evolution of AD biomarkers remain open. When does the AD model diverge from the normal aging model? What is the lifespan trajectory of imaging biomarkers for AD? How do the trajectories of biomarkers in AD differ from normal aging? To answer these questions, we proposed an innovative way by inferring brain structure model across the entire lifespan using a massive number of MRI (N = 4329). We compared the normal model based on 2944 control subjects with the pathological model based on 3262 patients (AD + Mild cognitive Impaired subjects) older than 55 years and controls younger than 55 years. Our study provides evidences of early divergence of the AD models from the normal aging trajectory before 40 years for the hippocampus, followed by the lateral ventricles and the amygdala around 40 years. Moreover, our lifespan model reveals the evolution of these biomarkers and suggests close abnormality evolution for the hippocampus and the amygdala, whereas trajectory of ventricular enlargement appears to follow an inverted U-shape. Finally, our models indicate that medial temporal lobe atrophy and ventricular enlargement are two mid-life physiopathological events characterizing AD brain.This work benefited from the support of the project DeepVolBrain of the French National Research Agency (ANR-18-CE45-0013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project. Moreover, we thank the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX- 03-02, HL-MRI Project), Cluster of excellence CPU and the CNRS. This study has been also supported by the DPI2017-87743-R grant from the Spanish Ministerio de Economia, Industria y Competitividad. Moreover, this work is based on multiple samples. We wish to thank all investigators of these projects who collected these datasets and made them freely accessible. The C-MIND data used in the preparation of this article were obtained from the C-MIND Data Repository (accessed in Feb 2015) created by the C-MIND study of Normal Brain Development. This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by Cincinnati Children's Hospital Medical Center and UCLA and supported by the National Institute of Child Health and Human Development (Contract #s HHSN275200900018C). A listing of the participating sites and a complete listing of the study investigators can be found at https://research.cchmc.org/c-mind. The NDAR data used in the preparation of this manuscript were obtained from the NIH-supported National Database for Autism Research (NDAR). NDAR is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in autism. The NDAR dataset includes data from the NIH Pediatric MRI Data Repository created by the NIH MRI Study of Normal Brain Development. This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by the Brain Development Cooperative Group and supported by the National Institute of Child Health and Human Development, the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke (Contract #s N01- HD02-3343, N01-MH9-0002, and N01-NS-9-2314, -2315, -2316, -2317, -2319 and -2320). A listing of the participating sites and a complete listing of the study investigators can be found at http://pediatricmri.nih.gov/nihpd/info/participating_centers.html. The ADNI data used in the preparation of this manuscript were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). The ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics NV, Johnson & Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffmann-La Roche, Schering-Plough, Synarc Inc., as well as nonprofit partners, the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to the ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org).Coupé, P.; Manjón Herrera, JV.; Lanuza, E.; Catheline, G. (2019). Lifespan Changes of the Human Brain In Alzheimer's Disease. Scientific Reports. 9:1-12. https://doi.org/10.1038/s41598-019-39809-8S1129Lobo, A. et al. Prevalence of dementia and major subtypes in Europe: a collaborative study of population-based cohorts. Neurology 54, S4 (2000).Barnes, J. et al. Alzheimer’s disease first symptoms are age dependent: evidence from the NACC dataset. Alzheimer’s & dementia 11, 1349–1357 (2015).Jack, C. R. et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. The Lancet Neurology 12, 207–216 (2013).Nestor, P. J., Scheltens, P. & Hodges, J. R. Advances in the early detection of Alzheimer’s disease. Nature medicine 10 (2004).Davatzikos, C., Fan, Y., Wu, X., Shen, D. & Resnick, S. M. Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiology of aging 29, 514–523 (2008).Bakkour, A., Morris, J. C. & Dickerson, B. C. The cortical signature of prodromal AD Regional thinning predicts mild AD dementia. Neurology 72, 1048–1055 (2009).Chan, D. et al. Change in rates of cerebral atrophy over time in early-onset Alzheimer’s disease: longitudinal MRI study. The Lancet 362, 1121–1122 (2003).Ridha, B. H. et al. Tracking atrophy progression in familial Alzheimer’s disease: a serial MRI study. The Lancet Neurology 5, 828–834 (2006).Sala-Llonch, R., Bartrés-Faz, D. & Junqué, C. Reorganization of brain networks in aging: a review of functional connectivity studies. Frontiers in psychology 6 (2015).Bateman, R. J. et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. New England Journal of Medicine 367, 795–804 (2012).Dickerson, B. et al. Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults. Neurology 76, 1395–1402 (2011).Miller, M. I. et al. The diffeomorphometry of temporal lobe structures in preclinical Alzheimer’s disease. NeuroImage: Clinical 3, 352–360 (2013).Bernard, C. et al. Time course of brain volume changes in the preclinical phase of Alzheimer’s disease. Alzheimer’s & Dementia 10, 143–151. e141 (2014).den Heijer, T. et al. A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline. Brain 133, 1163–1172 (2010).Coupé, P. et al. Detection of Alzheimer’s disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis. Hum Brain Mapp 36, 4758–4770, https://doi.org/10.1002/hbm.22926 (2015).Albert, M. et al. Predicting progression from normal cognition to mild cognitive impairment for individuals at 5 years. Brain (2018).Poldrack, R. A. & Gorgolewski, K. J. Making big data open: data sharing in neuroimaging. Nature neuroscience 17, 1510–1517 (2014).Solomon, A. et al. Serum cholesterol changes after midlife and late-life cognition twenty-one-year follow-up study. Neurology 68, 751–756 (2007).Debette, S. et al. Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline. Neurology 77, 461–468 (2011).Tolppanen, A.-M. et al. Midlife and late-life body mass index and late-life dementia: results from a prospective population-based cohort. Journal of Alzheimer’s Disease 38, 201–209 (2014).Coupe, P., Catheline, G., Lanuza, E. & Manjon, J. V. & Alzheimer’s Disease Neuroimaging, I. Towards a unified analysis of brain maturation and aging across the entire lifespan: A MRI analysis. Hum Brain Mapp 38, 5501–5518, https://doi.org/10.1002/hbm.23743 (2017).Villemagne, V. L. et al. Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. The Lancet Neurology 12, 357–367 %@1474–4422 (2013).Villemagne, V. L. et al. Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease. Annals of neurology 69, 181–192 (2011).Poulin, S. P. et al. Amygdala atrophy is prominent in early Alzheimer’s disease and relates to symptom severity. Psychiatry Research: Neuroimaging 194, 7–13 (2011).Jack, C. R. et al. Medial temporal atrophy on MRI in normal aging and very mild Alzheimer’s disease. Neurology 49, 786–794 (1997).Apostolova, L. G. et al. Hippocampal atrophy and ventricular enlargement in normal aging, mild cognitive impairment and Alzheimer’s disease. Alzheimer disease and associated disorders 26, 17 (2012).Nestor, S. M. et al. Ventricular enlargement as a possible measure of Alzheimer’s disease progression validated using the Alzheimer’s disease neuroimaging initiative database. Brain 131, 2443–2454 (2008).Petersen, R. C. et al. Alzheimer’s disease Neuroimaging Initiative (ADNI) clinical characterization. Neurology 74, 201–209 (2010).Marcus, D. S. et al. Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of cognitive neuroscience 19, 1498–1507 (2007).Manjon, J. V. & Coupe, P. volBrain: An Online MRI Brain Volumetry System. Front Neuroinform 10, 30, https://doi.org/10.3389/fninf.2016.00030 (2016).Manjon, J. V., Coupe, P., Marti-Bonmati, L., Collins, D. L. & Robles, M. Adaptive non-local means denoising of MR images with spatially varying noise levels. J Magn Reson Imaging 31, 192–203, https://doi.org/10.1002/jmri.22003 (2010).Tustison, N. J. et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29, 1310–1320, https://doi.org/10.1109/TMI.2010.2046908 (2010).Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54, 2033–2044 (2011).Ashburner, J. & Friston, K. J. Unified segmentation. Neuroimage 26, 839–851, https://doi.org/10.1016/j.neuroimage.2005.02.018 (2005).Manjón, J. V., Tohka, J. & Robles, M. Improved estimates of partial volume coefficients from noisy brain MRI using spatial context. Neuroimage 53, 480–490 (2010).Manjon, J. V. et al. Nonlocal intracranial cavity extraction. Int J Biomed Imaging 2014, 820205, https://doi.org/10.1155/2014/820205 (2014).Coupe, P. et al. Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. Neuroimage 54, 940–954, https://doi.org/10.1016/j.neuroimage.2010.09.018 (2011).Frisoni, G. B. et al. The EADC-ADNI Harmonized Protocol for manual hippocampal segmentation on magnetic resonance: evidence of validity. Alzheimer’s & Dementia 11, 111–125 (2015).Solow, R. M. A contribution to the theory of economic growth. The quarterly journal of economics 70, 65–94 %@1531–4650 (1956).Coupe, P. et al. Scoring by nonlocal image patch estimator for early detection of Alzheimer’s disease. Neuroimage Clin 1, 141–152, https://doi.org/10.1016/j.nicl.2012.10.002 (2012).Cuingnet, R. et al. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56, 766–781, https://doi.org/10.1016/j.neuroimage.2010.06.013 (2011).Eskildsen, S. F. et al. Prediction of Alzheimer’s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. Neuroimage 65, 511–521 (2013).Eskildsen, S. F. et al. Structural imaging biomarkers of Alzheimer’s disease: predicting disease progression. Neurobiology of aging 36, S23–S31 (2015).Tong, T. et al. A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer’s Disease. IEEE Transactions on Biomedical Engineering 64, 155–165 (2017).Wolz, R. et al. Multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. PLoS One 6, e25446, https://doi.org/10.1371/journal.pone.0025446 (2011).Bron, E. E. et al. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. Neuroimage 111, 562–579, https://doi.org/10.1016/j.neuroimage.2015.01.048 (2015).Chaddad, A., Desrosiers, C., Hassan, L. & Tanougast, C. Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder. BMC Neurosci 18, 52, https://doi.org/10.1186/s12868-017-0373-0 (2017).Chaddad, A., Desrosiers, C. & Toews, M. Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age. Sci Rep 7, 45639, https://doi.org/10.1038/srep45639 (2017).Apostolova, L. G. et al. Subregional hippocampal atrophy predicts Alzheimer’s dementia in the cognitively normal. Neurobiology of aging 31, 1077–1088 (2010).Younes, L., Albert, M., Miller, M. I. & Team, B. R. Inferring changepoint times of medial temporal lobe morphometric change in preclinical Alzheimer’s disease. NeuroImage: Clinical 5, 178–187 (2014).Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta neuropathologica 82, 239–259 (1991).Badea, A. et al. The fornix provides multiple biomarkers to characterize circuit disruption in a mouse model of Alzheimer’s disease. NeuroImage 142, 498–511 (2016).Micotti, E. et al. Striatum and entorhinal cortex atrophy in AD mouse models: MRI comprehensive analysis. Neurobiology of aging 36, 776–788 (2015).Whitwell, J. L. et al. MRI correlates of neurofibrillary tangle pathology at autopsy A voxel-based morphometry study. Neurology 71, 743–749 (2008).Iaccarino, L. et al. Local and distant relationships between amyloid, tau and neurodegeneration in Alzheimer’s Disease. NeuroImage: Clinical 17, 452–464 (2018).Das, S. R. et al. Longitudinal and cross-sectional structural magnetic resonance imaging correlates of AV-1451 uptake. Neurobiology of aging 66, 49–58 (2018).Knopman, D. S. et al. Joint associations of β-amyloidosis and cortical thickness with cognition. Neurobiology of aging 65, 121–131 (2018).Doré, V. et al. Cross-sectional and longitudinal analysis of the relationship between Aβ deposition, cortical thickness, and memory in cognitively unimpaired individuals and in Alzheimer disease. JAMA neurology 70, 903–911 (2013).Jack, C. R. et al. A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 87, 539–547 (2016).Cavedo, E. et al. Local amygdala structural differences with 3T MRI in patients with Alzheimer disease. Neurology 76, 727–733 (2011).Qiu, A., Fennema-Notestine, C., Dale, A. M., Miller, M. I. & Alzheimer’s Disease Neuroimaging, I. Regional shape abnormalities in mild cognitive impairment and Alzheimer’s disease. Neuroimage 45, 656–661 (2009).Lin, T.-W. et al. Neurodegeneration in amygdala precedes hippocampus in the APPswe/PS1dE9 mouse model of Alzheimer’s disease. Current Alzheimer Research 12, 951–963 (2015).Phelps, E. A. Human emotion and memory: interactions of the amygdala and hippocampal complex. Current opinion in neurobiology 14, 198–202 (2004).Kumfor, F. et al. Degradation of emotion processing ability in corticobasal syndrome and Alzheimer’s disease. Brain 137, 3061–3072 (2014).De Olmos, J. S. In The Human Nervous System (Second Edition) Ch. 22, 739–868 (2004).Tabert, M. H. et al. A 10‐item smell identification scale related to risk for Alzheimer’s disease. Annals of neurology 58, 155–160 (2005).Serby, M., Larson, P. & Kalkstein, D. The nature and course of olfactory deficits in Alzheimer’s disease. The American journal of psychiatry 148, 357 (1991).Djordjevic, J., Jones-Gotman, M., De Sousa, K. & Chertkow, H. Olfaction in patients with mild cognitive impairment and Alzheimer’s disease. Neurobiology of aging 29, 693–706 (2008).Price, J. L., Davis, P., Morris, J. & White, D. The distribution of tangles, plaques and related immunohistochemical markers in healthy aging and Alzheimer’s disease. Neurobiology of aging 12, 295–312 (1991).Ohm, T. & Braak, H. Olfactory bulb changes in Alzheimer’s disease. Acta neuropathologica 73, 365–369 (1987).Carmichael, O. T. et al. Cerebral ventricular changes associated with transitions between normal cognitive function, mild cognitive impairment, and dementia. Alzheimer disease and associated disorders 21, 14 (2007).Prince, M., Bryce, R. & Ferri, C. World Alzheimer Report 2011: The benefits of early diagnosis and intervention. (Alzheimer’s Disease International, 2011).De Jong, L. W. et al. Strongly reduced volumes of putamen and thalamus in Alzheimer’s disease: an MRI study. Brain 131, 3277–3285 (2008).Braak, H. & Braak, E. Alzheimer’s disease affects limbic nuclei of the thalamus. Acta neuropathologica 81, 261–268 (1991).Fjell, A. M. et al. Critical ages in the life course of the adult brain: nonlinear subcortical aging. Neurobiol Aging 34, 2239–2247, https://doi.org/10.1016/j.neurobiolaging.2013.04.006 (2013).Fotenos, A. F., Snyder, A. Z., Girton, L. E., Morris, J. C. & Buckner, R. L. Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 64, 1032–1039 (2005).Fjell, A. M. et al. One-year brain atrophy evident in healthy aging. Journal of Neuroscience 29, 15223–15231 (2009).Jack, C. R. et al. Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology 62, 591–600 (2004).Barnes, J. et al. A meta-analysis of hippocampal atrophy rates in Alzheimer’s disease. Neurobiology of aging 30, 1711–1723 (2009).McDonald, C. R. et al. Regional rates of neocortical atrophy from normal aging to early Alzheimer disease. Neurology 73, 457–465 (2009).Sankar, T. et al. Your algorithm might think the hippocampus grows in Alzheimer’s disease: Caveats of longitudinal automated hippocampal volumetry. Human Brain Mapping 38, 2875–2896 (2017).Small, B. J., Fratiglioni, L., Viitanen, M., Winblad, B. & Bäckman, L. The course of cognitive impairment in preclinical Alzheimer disease: three-and 6-year follow-up of a population-based sample. Archives of neurology 57, 839–844 (2000).La Rue, A. & Jarvik, L. F. Cognitive function and prediction of dementia in old age. The International Journal of Aging and Human Development 25, 79–89 (1987).Elias, M. F. et al. The preclinical phase of Alzheimer disease: a 22-year prospective study of the Framingham Cohort. Archives of neurology 57, 808–813 (2000).Snowdon, D. A. et al. Linguistic ability in early life and cognitive function and Alzheimer’s disease in late life: Findings from the Nun Study. Jama 275, 528–532 (1996).Dubois, B. et al. Preclinical Alzheimer’s disease: definition, natural history, and diagnostic criteria. Alzheimer’s & Dementia 12, 292–323 (2016).Krell-Roesch, J. et al. Leisure-Time Physical Activity and the Risk of IncidentDementia: The Mayo Clinic Study of Aging. Journal of Alzheimer’s Disease, 1–7 (2018).Rusanen, M., Kivipelto, M., Quesenberry, C. P., Zhou, J. & Whitmer, R. A. Heavy smoking in midlife and long-term risk of Alzheimer disease and vascular dementia. Archives of internal medicine 171, 333–339 (2011)

Towards a Unified Analysis of Brain Maturation and Aging across the Entire Lifespan: A MRI Analysis

"This is the peer reviewed version of the following article: Coupé, Pierrick, Gwenaelle Catheline, Enrique Lanuza, and José Vicente Manjón. 2017. Towards a Unified Analysis of Brain Maturation and Aging across the Entire Lifespan: A MRI Analysis. Human Brain Mapping 38 (11). Wiley: 5501 18. doi:10.1002/hbm.23743, which has been published in final form at https://doi.org/10.1002/hbm.23743. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] There is no consensus in literature about lifespan brain maturation and senescence, mainly because previous lifespan studies have been performed on restricted age periods and/or with a limited number of scans, making results instable and their comparison very difficult. Moreover, the use of nonharmonized tools and different volumetric measurements lead to a great discrepancy in reported results. Thanks to the new paradigm of BigData sharing in neuroimaging and the last advances in image processing enabling to process baby as well as elderly scans with the same tool, new insights on brain maturation and aging can be obtained. This study presents brain volume trajectory over the entire lifespan using the largest age range to date (from few months of life to elderly) and one of the largest number of subjects (N=2,944). First, we found that white matter trajectory based on absolute and normalized volumes follows an inverted U-shape with a maturation peak around middle life. Second, we found that from 1 to 8-10 y there is an absolute gray matter (GM) increase related to body growth followed by a GM decrease. However, when normalized volumes were considered, GM continuously decreases all along the life. Finally, we found that this observation holds for almost all the considered subcortical structures except for amygdala which is rather stable and hippocampus which exhibits an inverted U-shape with a longer maturation period. By revealing the entire brain trajectory picture, a consensus can be drawn since most of the previously discussed discrepancies can be explained. Hum Brain Mapp 38:5501-5518, 2017. (C) 2017 Wiley Periodicals, Inc.French State (French National Research Ageny in the frame of the Investments for the future Program IdEx Bordeaux); Contract grant number: ANR-10-IDEX-03-02, HL-MRI Project; Contract grant sponsor: Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57); Contract grant sponsor: CNRS ("Defi imag'In and the dedicated volBrain support); Contract grant sponsor: Ministerio de Economia y competitividad (Spanish); Contract grant number: TIN2013-43457-R; Contract grant sponsor: National Institute of Child Health and Human Development; Contract grant number: HHSN275200900018C; Contract grant sponsors: National Institute of Child Health and Human Development, the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke; Contract grant numbers: N01- HD02-3343, N01-MH9-0002, and N01-NS-9-2314, -2315, -2316, -2317, -2319 and -2320; Contract grant sponsor: National Institutes of Health; Contract grant number: U01 AG024904; Contract grant sponsor: National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering (ADNI); Contract grant sponsor: NIH; Contract grant number: P30AG010129, K01 AG030514; Contract grant sponsor: Dana Foundation; Contract grant sponsor: OASIS project (OASIS data); Contract grant numbers: P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, R01 MH56584; Contract grant sponsor: Common-wealth Scientific Industrial Research Organization (a publicly funded government research organization); Contract grant sponsor: Science Industry Endowment Fund, National Health and Medical Research Council of Australia; Contract grant number: 1011689; Contract grant sponsors: Alzheimer's Association, Alzheimer's Drug Discovery Foundation, and an anonymous foundation; Contract grant sponsor: Human Brain Project; Contract grant number: PO1MHO52176-11 (ICBM, P.I. Dr John Mazziotta); Contract grant sponsor: Canadian Institutes of Health Research; Contract grant number: MOP-34996; Contract grant sponsor: U.K. Engineering and Physical Sciences Research Council (EPSRC); Contract grant number: GR/S21533/02; Contract grant sponsor: ABIDE funding resources; Contract grant sponsor: NIMH; Contract grant number: K23MH087770; Contract grant sponsor: Leon Levy Foundation; Contract grant sponsor: NIMH award to MPM; Contract grant number: R03MH096321Coupé, P.; Catheline, G.; Lanuza, E.; Manjón Herrera, JV. (2017). Towards a Unified Analysis of Brain Maturation and Aging across the Entire Lifespan: A MRI Analysis. Human Brain Mapping. 38(11):5501-5518. https://doi.org/10.1002/hbm.23743S550155183811Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26(3), 839-851. doi:10.1016/j.neuroimage.2005.02.018Aubert-Broche, B., Fonov, V. S., García-Lorenzo, D., Mouiha, A., Guizard, N., Coupé, P., … Collins, D. L. (2013). A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood. NeuroImage, 82, 393-402. doi:10.1016/j.neuroimage.2013.05.065Avants, 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.2010.09.025Becker, J. B., & Hu, M. (2008). Sex differences in drug abuse. Frontiers in Neuroendocrinology, 29(1), 36-47. doi:10.1016/j.yfrne.2007.07.003(2011). Total and Regional Brain Volumes in a Population-Based Normative Sample from 4 to 18 Years: The NIH MRI Study of Normal Brain Development. Cerebral Cortex, 22(1), 1-12. doi:10.1093/cercor/bhr018Carne, R. P., Vogrin, S., Litewka, L., & Cook, M. J. (2006). Cerebral cortex: An MRI-based study of volume and variance with age and sex. Journal of Clinical Neuroscience, 13(1), 60-72. doi:10.1016/j.jocn.2005.02.013Coffey, C. E., Lucke, J. F., Saxton, J. A., Ratcliff, G., Unitas, L. J., Billig, B., & Bryan, R. N. (1998). Sex Differences in Brain Aging. Archives of Neurology, 55(2), 169. doi:10.1001/archneur.55.2.169Coupé, P., Manjón, J. V., Fonov, V., Pruessner, J., Robles, M., & Collins, D. L. (2011). Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage, 54(2), 940-954. doi:10.1016/j.neuroimage.2010.09.018Ducharme, S., Albaugh, M. D., Nguyen, T.-V., Hudziak, J. J., Mateos-Pérez, J. M., Labbe, A., … Karama, S. (2016). Trajectories of cortical thickness maturation in normal brain development — The importance of quality control procedures. NeuroImage, 125, 267-279. doi:10.1016/j.neuroimage.2015.10.010Eriksson, P. S., Perfilieva, E., Björk-Eriksson, T., Alborn, A.-M., Nordborg, C., Peterson, D. A., & Gage, F. H. (1998). Neurogenesis in the adult human hippocampus. Nature Medicine, 4(11), 1313-1317. doi:10.1038/3305Fjell, A. M., Westlye, L. T., Amlien, I., Espeseth, T., Reinvang, I., Raz, N., … Walhovd, K. B. (2009). High Consistency of Regional Cortical Thinning in Aging across Multiple Samples. Cerebral Cortex, 19(9), 2001-2012. doi:10.1093/cercor/bhn232Fjell, A. M., Westlye, L. T., Grydeland, H., Amlien, I., Espeseth, T., Reinvang, I., … Walhovd, K. B. (2013). Critical ages in the life course of the adult brain: nonlinear subcortical aging. Neurobiology of Aging, 34(10), 2239-2247. doi:10.1016/j.neurobiolaging.2013.04.006Fjell, A. M., McEvoy, L., Holland, D., Dale, A. M., & Walhovd, K. B. (2014). What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus. Progress in Neurobiology, 117, 20-40. doi:10.1016/j.pneurobio.2014.02.004Fox, N. C., Warrington, E. K., Freeborough, P. A., Hartikainen, P., Kennedy, A. M., Stevens, J. M., & Rossor, M. N. (1996). Presymptomatic hippocampal atrophy in Alzheimer’s disease. Brain, 119(6), 2001-2007. doi:10.1093/brain/119.6.2001Giedd, J. N., & Rapoport, J. L. (2010). Structural MRI of Pediatric Brain Development: What Have We Learned and Where Are We Going? Neuron, 67(5), 728-734. doi:10.1016/j.neuron.2010.08.040Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., … Rapoport, J. L. (1999). Brain development during childhood and adolescence: a longitudinal MRI study. Nature Neuroscience, 2(10), 861-863. doi:10.1038/13158Gilmore, J. H., Lin, W., Prastawa, M. W., Looney, C. B., Vetsa, Y. S. K., Knickmeyer, R. C., … Gerig, G. (2007). Regional Gray Matter Growth, Sexual Dimorphism, and Cerebral Asymmetry in the Neonatal Brain. Journal of Neuroscience, 27(6), 1255-1260. doi:10.1523/jneurosci.3339-06.2007Gilmore, J. H., Shi, F., Woolson, S. L., Knickmeyer, R. C., Short, S. J., Lin, W., … Shen, D. (2011). Longitudinal Development of Cortical and Subcortical Gray Matter from Birth to 2 Years. Cerebral Cortex, 22(11), 2478-2485. doi:10.1093/cercor/bhr327Good CD Johnsrude IS Ashburner J Henson RN Fristen KJ Frackowiak RS 2002Green, P. S., & Simpkins, J. W. (2000). Neuroprotective effects of estrogens: potential mechanisms of action. International Journal of Developmental Neuroscience, 18(4-5), 347-358. doi:10.1016/s0736-5748(00)00017-4Groeschel, S., Vollmer, B., King, M. D., & Connelly, A. (2010). Developmental changes in cerebral grey and white matter volume from infancy to adulthood. International Journal of Developmental Neuroscience, 28(6), 481-489. doi:10.1016/j.ijdevneu.2010.06.004Gur, R. C., Mozley, P. D., Resnick, S. M., Gottlieb, G. L., Kohn, M., Zimmerman, R., … Berretta, D. (1991). Gender differences in age effect on brain atrophy measured by magnetic resonance imaging. Proceedings of the National Academy of Sciences, 88(7), 2845-2849. doi:10.1073/pnas.88.7.2845Hedman, A. M., van Haren, N. E. M., Schnack, H. G., Kahn, R. S., & Hulshoff Pol, H. E. (2011). Human brain changes across the life span: A review of 56 longitudinal magnetic resonance imaging studies. Human Brain Mapping, 33(8), 1987-2002. doi:10.1002/hbm.21334Holland, D., Chang, L., Ernst, T. M., Curran, M., Buchthal, S. D., Alicata, D., … Dale, A. M. (2014). Structural Growth Trajectories and Rates of Change in the First 3 Months of Infant Brain Development. JAMA Neurology, 71(10), 1266. doi:10.1001/jamaneurol.2014.1638Hu, S., Pruessner, J. C., Coupé, P., & Collins, D. L. (2013). Volumetric analysis of medial temporal lobe structures in brain development from childhood to adolescence. NeuroImage, 74, 276-287. doi:10.1016/j.neuroimage.2013.02.032Huttenlocher, P. R., & Dabholkar, A. S. (1997). Regional differences in synaptogenesis in human cerebral cortex. The Journal of Comparative Neurology, 387(2), 167-178. doi:10.1002/(sici)1096-9861(19971020)387:23.0.co;2-zJack, C. R., Petersen, R. C., Xu, Y. C., Waring, S. C., O’Brien, P. C., Tangalos, E. G., … Kokmen, E. (1997). Medial temporal atrophy on MRI in normal aging and very mild Alzheimer’s disease. Neurology, 49(3), 786-794. doi:10.1212/wnl.49.3.786Jernigan, T. L., Baaré, W. F. C., Stiles, J., & Madsen, K. S. (2011). Postnatal brain development. Gene Expression to Neurobiology and Behavior: Human Brain Development and Developmental Disorders, 77-92. doi:10.1016/b978-0-444-53884-0.00019-1Lebel, C., Gee, M., Camicioli, R., Wieler, M., Martin, W., & Beaulieu, C. (2012). Diffusion tensor imaging of white matter tract evolution over the lifespan. NeuroImage, 60(1), 340-352. doi:10.1016/j.neuroimage.2011.11.094Lenroot, R. K., & Giedd, J. N. (2010). Sex differences in the adolescent brain. Brain and Cognition, 72(1), 46-55. doi:10.1016/j.bandc.2009.10.008Lenroot, R. K., Gogtay, N., Greenstein, D. K., Wells, E. M., Wallace, G. L., Clasen, L. S., … Giedd, J. N. (2007). Sexual dimorphism of brain developmental trajectories during childhood and adolescence. NeuroImage, 36(4), 1065-1073. doi:10.1016/j.neuroimage.2007.03.053Makropoulos, A., Gousias, I. S., Ledig, C., Aljabar, P., Serag, A., Hajnal, J. V., … Rueckert, D. (2014). Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain. IEEE Transactions on Medical Imaging, 33(9), 1818-1831. doi:10.1109/tmi.2014.2322280Makropoulos, A., Aljabar, P., Wright, R., Hüning, B., Merchant, N., Arichi, T., … Rueckert, D. (2016). Regional growth and atlasing of the developing human brain. NeuroImage, 125, 456-478. doi:10.1016/j.neuroimage.2015.10.047Manjón, 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., Tohka, J., & Robles, M. (2010). Improved estimates of partial volume coefficients from noisy brain MRI using spatial context. NeuroImage, 53(2), 480-490. doi:10.1016/j.neuroimage.2010.06.046Manjón, 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:10.1155/2014/820205Mills, K. L., Goddings, A.-L., Herting, M. M., Meuwese, R., Blakemore, S.-J., Crone, E. A., … Tamnes, C. K. (2016). Structural brain development between childhood and adulthood: Convergence across four longitudinal samples. NeuroImage, 141, 273-281. doi:10.1016/j.neuroimage.2016.07.044Ostby, Y., Tamnes, C. K., Fjell, A. M., Westlye, L. T., Due-Tonnessen, P., & Walhovd, K. B. (2009). Heterogeneity in Subcortical Brain Development: A Structural Magnetic Resonance Imaging Study of Brain Maturation from 8 to 30 Years. Journal of Neuroscience, 29(38), 11772-11782. doi:10.1523/jneurosci.1242-09.2009Pfefferbaum, A., Rohlfing, T., Rosenbloom, M. J., Chu, W., Colrain, I. M., & Sullivan, E. V. (2013). Variation in longitudinal trajectories of regional brain volumes of healthy men and women (ages 10 to 85years) measured with atlas-based parcellation of MRI. NeuroImage, 65, 176-193. doi:10.1016/j.neuroimage.2012.10.008Phelps, E. A., & LeDoux, J. E. (2005). Contributions of the Amygdala to Emotion Processing: From Animal Models to Human Behavior. Neuron, 48(2), 175-187. doi:10.1016/j.neuron.2005.09.025Poldrack, R. A., & Gorgolewski, K. J. (2014). Making big data open: data sharing in neuroimaging. Nature Neuroscience, 17(11), 1510-1517. doi:10.1038/nn.3818Potvin, O., Mouiha, A., Dieumegarde, L., & Duchesne, S. (2016). Normative data for subcortical regional volumes over the lifetime of the adult human brain. NeuroImage, 137, 9-20. doi:10.1016/j.neuroimage.2016.05.016Potvin, O., Dieumegarde, L., & Duchesne, S. (2017). Freesurfer cortical normative data for adults using Desikan-Killiany-Tourville and ex vivo protocols. NeuroImage, 156, 43-64. doi:10.1016/j.neuroimage.2017.04.035Raznahan, A., Shaw, P., Lalonde, F., Stockman, M., Wallace, G. L., Greenstein, D., … Giedd, J. N. (2011). How Does Your Cortex Grow? Journal of Neuroscience, 31(19), 7174-7177. doi:10.1523/jneurosci.0054-11.2011Shaw, P., Kabani, N. J., Lerch, J. P., Eckstrand, K., Lenroot, R., Gogtay, N., … Wise, S. P. (2008). Neurodevelopmental Trajectories of the Human Cerebral Cortex. Journal of Neuroscience, 28(14), 3586-3594. doi:10.1523/jneurosci.5309-07.2008Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003). Mapping cortical change across the human life span. Nature Neuroscience, 6(3), 309-315. doi:10.1038/nn1008Spalding, K. L., Bergmann, O., Alkass, K., Bernard, S., Salehpour, M., Huttner, H. B., … Frisén, J. (2013). Dynamics of Hippocampal Neurogenesis in Adult Humans. Cell, 153(6), 1219-1227. doi:10.1016/j.cell.2013.05.002Stiles, J., & Jernigan, T. L. (2010). The Basics of Brain Development. Neuropsychology Review, 20(4), 327-348. doi:10.1007/s11065-010-9148-4Suzuki, M. (2004). Male-specific Volume Expansion of the Human Hippocampus during Adolescence. Cerebral Cortex, 15(2), 187-193. doi:10.1093/cercor/bhh121Tustison, 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.2010.2046908Van Praag, H., Schinder, A. F., Christie, B. R., Toni, N., Palmer, T. D., & Gage, F. H. (2002). Functional neurogenesis in the adult hippocampus. Nature, 415(6875), 1030-1034. doi:10.1038/4151030aVijayakumar, N., Allen, N. B., Youssef, G., Dennison, M., Yücel, M., Simmons, J. G., & Whittle, S. (2016). Brain development during adolescence: A mixed-longitudinal investigation of cortical thickness, surface area, and volume. Human Brain Mapping, 37(6), 2027-2038. doi:10.1002/hbm.23154Walhovd, K. B., Westlye, L. T., Amlien, I., Espeseth, T., Reinvang, I., Raz, N., … Fjell, A. M. (2011). Consistent neuroanatomical age-related volume differences across multiple samples. Neurobiology of Aging, 32(5), 916-932. doi:10.1016/j.neurobiolaging.2009.05.013Wang, L., Shi, F., Li, G., Gao, Y., Lin, W., Gilmore, J. H., & Shen, D. (2014). Segmentation of neonatal brain MR images using patch-driven level sets. NeuroImage, 84, 141-158. doi:10.1016/j.neuroimage.2013.08.008Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006). User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. NeuroImage, 31(3), 1116-1128. doi:10.1016/j.neuroimage.2006.01.015Ziegler, G., Dahnke, R., Jäncke, L., Yotter, R. A., May, A., & Gaser, C. (2011). Brain structural trajectories over the adult lifespan. Human Brain Mapping, 33(10), 2377-2389. doi:10.1002/hbm.2137

Adaptive fusion of texture-based grading for Alzheimer's disease classification

[EN] Alzheimer's disease is a neurodegenerative process leading to irreversible mental dysfunctions. To date, diagnosis is established after incurable brain structure alterations. The development of new biomarkers is crucial to perform an early detection of this disease. With the recent improvement of magnetic resonance imaging, numerous methods were proposed to improve computer-aided detection. Among these methods, patch-based grading framework demonstrated state-of-the-art performance. Usually, methods based on this framework use intensity or grey matter maps. However, it has been shown that texture filters improve classification performance in many cases. The aim of this work is to improve performance of patch-based grading framework with the development of a novel texture-based grading method. In this paper, we study the potential of multi-directional texture maps extracted with 3D Gabor filters to improve patch-based grading method. We also proposed a novel patch-based fusion scheme to efficiently combine multiple grading maps. To validate our approach, we study the optimal set of filters and compare the proposed method with different fusion schemes. In addition, we also compare our new texture-based grading biomarker with state-of-the-art methods. Experiments show an improvement of AD detection and prediction accuracy. Moreover, our method obtains competitive performance with 91.3% of accuracy and 94.6% of area under a curve for AD detection. (C) 2018 Elsevier Ltd. All rights reserved.This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (HL-MRI ANR-10-IDEX-03-02), Cluster of excellence CPU and TRAIL (BigDataBrain ANR-10-LABX-57).Hett, K.; Ta, V.; Manjón Herrera, JV.; Coupe, P. (2018). Adaptive fusion of texture-based grading for Alzheimer's disease classification. Computerized Medical Imaging and Graphics. 70:8-16. https://doi.org/10.1016/j.compmedimag.2018.08.002S8167

MRI white matter lesion segmentation using an ensemble of neural networks and overcomplete patch-based voting

[EN] Accurate quantification of white matter hyperintensities (WMH) from Magnetic Resonance Imaging (MRI) is a valuable tool for the analysis of normal brain ageing or neurodegeneration. Reliable automatic extraction of WMH lesions is challenging due to their heterogeneous spatial occurrence, their small size and their diffuse nature. In this paper, we present an automatic method to segment these lesions based on an ensemble of overcomplete patch-based neural networks. The proposed method successfully provides accurate and regular segmentations due to its overcomplete nature while minimizing the segmentation error by using a boosted ensemble of neural networks. The proposed method compared favourably to state of the art techniques using two different neurodegenerative datasets. (C) 2018 Elsevier Ltd. All rights reserved.This research has been done thanks to the Australian distinguished visiting professor grant from the CSIRO (Commonwealth Scientific and Industrial Research Organisation) and the Spanish "Programa de apoyo a la investigacion y desarrollo (PAID-00-15)" of the Universidad Politecnica de Valencia. This research was partially supported by the Spanish grant TIN2013-43457-R from the Ministerio de Economia y competitividad. This study has been carried out also with support from the French State, managed by the French National Research Ageny in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project Defi imag'In. Some of the data used in this work was collected by the AIBL study group. Funding for the AIBL study is provided by the CSIRO Flagship Collaboration Fund and the Science and Industry Endowment Fund (SIEF) in partnership with Edith Cowan University (ECU), Mental Health Research Institute (MHRI), Alzheimer's Australia (AA), National Ageing Research Institute (NARI), Austin Health, Macquarie University, CogState Ltd, Hollywood Private Hospital, and Sir Charles Gairdner Hospital.Manjón Herrera, JV.; Coupe, P.; Raniga, P.; Xia, Y.; Desmond, P.; Fripp, J.; Salvado, O. (2018). MRI white matter lesion segmentation using an ensemble of neural networks and overcomplete patch-based voting. Computerized Medical Imaging and Graphics. 69:43-51. https://doi.org/10.1016/j.compmedimag.2018.05.001S43516

Multi-scale graph-based grading for Alzheimer's disease prediction

[EN] The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer¿s disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.This work benefited from the support of the project DeepvolBrain of the French National Research Agency (ANR-18-CE450013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project. Moreover, we thank the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and the CNRS. Finally, this work was also supported by the NIH grants R01-NS094456 and U01-NS106845. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01-AG024904) and by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Biogen; Bristol-Myes Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffman-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Pharmaceutical Research & Development LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org).The grantee organization is the Northern California Institute of Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.Hett, K.; Ta, V.; Oguz, I.; Manjón Herrera, JV.; Coupé, P.; Alzheimers Disease Neuroimaging Initiative (2021). Multi-scale graph-based grading for Alzheimer's disease prediction. Medical Image Analysis. 67:1-13. https://doi.org/10.1016/j.media.2020.1018501136

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

Non-local intracranial cavity extraction

[EN] Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may help to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging. In this paper, we present a new approach to perform ICV extraction based on the use of a library of prelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden.The authors want to thank the IXI (EPSRC GR/S21533/02) and OASIS (P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, and R01 MH56584) dataset promoters for making available this valuable resource to the scientific community which surely will boost the research in brain imaging. This work has been supported by the Spanish Grant TIN2011-26727 from Ministerio de Ciencia e Innovacion. This work was funded in part by operating grants ´ from the Canadian Institutes of Health Research, les Fonds de la recherche sante du Quebec, MINDLab UNIK initiative at ´ Aarhus University, funded by the Danish Ministry of Science, Technology and Innovation, Grant agreement no. 09-065250.Manjón Herrera, JV.; Eskildsen, SF.; Coupé, P.; Romero Gómez, JE.; Collins, L.; Robles Viejo, M. (2014). Non-local intracranial cavity extraction. International Journal of Biomedical Imaging. 2014:1-11. https://doi.org/10.1155/2014/820205S111201

Fully automated delineation of the optic radiation for surgical planning using clinically feasible sequences

[EN] Quadrantanopia caused by inadvertent severing of Meyer's Loop of the optic radiation is a well-recognised complication of temporal lobectomy for conditions such as epilepsy. Dissection studies indicate that the anterior extent of Meyer's Loop varies considerably between individuals. Quantifying this for individual patients is thus an important step to improve the safety profile of temporal lobectomies. Previous attempts to delineate Meyer's Loop using diffusion MRI tractography have had difficulty estimating its full anterior extent, required manual ROI placement, and/or relied on advanced diffusion sequences that cannot be acquired routinely in most clinics. Here we present CONSULT: a pipeline that can delineate the optic radiation from raw DICOM data in a completely automated way via a combination of robust pre-processing, segmentation, and alignment stages, plus simple improvements that bolster the efficiency and reliability of standard tractography. We tested CONSULT on 696 scans of predominantly healthy participants (539 unique brains), including both advanced acquisitions and simpler acquisitions that could be acquired in clinically acceptable timeframes. Delineations completed without error in 99.4% of the scans. The distance between Meyer's Loop and the temporal pole closely matched both averages and ranges reported in dissection studies for all tested sequences. Median scan-rescan error of this distance was 1¿mm. When tested on two participants with considerable pathology, delineations were successful and realistic. Through this, we demonstrate not only how to identify Meyer's Loop with clinically feasible sequences, but also that this can be achieved without fundamental changes to tractography algorithms or complex post-processing methods.Advance Queensland, Grant/Award Number: R-09964-01; Fundacion Merck Salud; Proyecto Societat Catalana Neurologia; Ministerio de Economia, Industria y Competitividad of Spain, Grant/Award Number: DPI2017-87743-R; Red Espanola de Esclerosis Multiple, Grant/Award Numbers: RD12/0032/0002, RD12/0060/01-02, RD16/0015/0002, RD16/0015/0003; Spanish Government; Instituto de Salud Carlos III, Grant/Award Numbers: FIS 2015 PI15/00061, FIS 2015 - PI15/00587, FIS 2018 - PI18/01030Reid, LB.; Martínez-Heras, E.; Manjón Herrera, JV.; Jeffree, RL.; Alexander, H.; Trinder, J.; Solana, E.... (2021). Fully automated delineation of the optic radiation for surgical planning using clinically feasible sequences. Human Brain Mapping. 42(18):5911-5926. https://doi.org/10.1002/hbm.25658S59115926421