Sequelae of premature birth in young adults

Background and Purpose Qualitative studies about the abnormalities appreciated on routine magnetic resonance imaging (MRI) sequences in prematurely born adults are lacking. This article aimed at filling this knowledge gap by (1) qualitatively describing routine imaging findings in prematurely born adults, (2) evaluating measures for routine image interpretation and (3) investigating the impact of perinatal variables related to premature birth. Methods In this study two board-certified radiologists assessed T1-weighted and FLAIR-weighted images of 100 prematurely born adults born very preterm (VP <32 weeks) and/or at very low birth weight (VLBW <1500 g) and 106 controls born at full term (FT) (mean age 26.8 ± 0.7 years). The number of white matter lesions (WML) was counted according to localization. Lateral ventricle volume (LVV) was evaluated subjectively and by measurements of Evans’ index (EI) and frontal-occipital-horn ratio (FOHR). Freesurfer-based volumetry served as reference standard. Miscellaneous incidental findings were noted as free text. Results The LVV was increased in 24.7% of VP/VLBW individuals and significantly larger than in FT controls. This was best identified by measurement of FOHR (AUC = 0.928). Ventricular enlargement was predicted by low gestational age (odds ratio: 0.71, 95% CI 0.51–0.98) and presence of neonatal intracranial hemorrhage (odds ratio: 0.26, 95% CI 0.07–0.92). The numbers of deep and periventricular WML were increased while subcortical WMLs were not. Conclusion Enlargement of the LVV and deep and periventricular WMLs are typical sequelae of premature birth that can be appreciated on routine brain MRI. To increase sensitivity of abnormal LVV detection, measurement of FOHR seems feasible in clinical practice

Lesion size and long-term cognitive outcome after pediatric stroke: A comparison between two techniques to assess lesion size.

BACKGROUND There is little consensus on how lesion size impacts long-term cognitive outcome after pediatric arterial ischemic stroke (AIS). This study, therefore, compared two techniques to assessed lesion size in the chronic phase after AIS and determined their measurement agreement in relation to cognitive functions in patients after pediatric stroke. METHODS Twenty-five patients after pediatric AIS were examined in the chronic phase (>2 years after stroke) in respect to intelligence, memory, executive functions, visuo-motor functions, motor abilities, and disease-specific outcome. Lesion size was measured using the ABC/2 formula and segmentation technique (3D Slicer). Correlation analysis determined the association between volumetry techniques and outcome measures in respect to long-term cognitive outcome. RESULTS The measurements from the ABC/2 and segmentation technique were strongly correlated (r = 0.878, p < .001) and displayed agreement in particular for small lesions. Lesion size from both techniques was significantly correlated with disease-specific outcome (p < .001) and processing speed (p < .005) after controlling for age at stroke and multiple comparison. CONCLUSION The two techniques showed convergent validity and were both significantly correlated with long-term outcome after pediatric AIS. Compared to the time-consuming segmentation technique, ABC/2 facilitates clinical and research work as it requires relatively little time and is easy to apply

Serial MRI Features of Canine GM1 Gangliosidosis: A Possible Imaging Biomarker for Diagnosis and Progression of the Disease

GM1 gangliosidosis is a fatal neurodegenerative lysosomal storage disease caused by an autosomal recessively inherited deficiency of β-galactosidase activity. Effective therapies need to be developed to treat the disease. In Shiba Inu dogs, one of the canine GM1 gangliosidosis models, neurological signs of the disease, including ataxia, start at approximately 5 months of age and progress until the terminal stage at 12 to 15 months of age. In the present study, serial MR images were taken of an affected dog from a model colony of GM1 gangliosidosis and 4 sporadic clinical cases demonstrating the same mutation in order to characterize the MRI features of this canine GM1 gangliosidosis. By 2 months of age at the latest and persisting until the terminal stage of the disease, the MR findings consistently displayed diffuse hyperintensity in the white matter of the entire cerebrum on T2-weighted images. In addition, brain atrophy manifested at 9 months of age and progressed thereafter. Although a definitive diagnosis depends on biochemical and genetic analyses, these MR characteristics could serve as a diagnostic marker in suspect animals with or without neurological signs. Furthermore, serial changes in MR images could be used as a biomarker to noninvasively monitor the efficacy of newly developed therapeutic strategies

Preterm white matter injury : ultrasound diagnosis and classification

White matter injury (WMI) is the most frequent form of preterm brain injury. Cranial ultrasound (CUS) remains the preferred modality for initial and sequential neuroimaging in preterm infants, and is reliable for the diagnosis of cystic periventricular leukomalacia. Although magnetic resonance imaging is superior to CUS in detecting the diffuse and more subtle forms of WMI that prevail in very premature infants surviving nowadays, recent improvement in the quality of neonatal CUS imaging has broadened the spectrum of preterm white matter abnormalities that can be detected with this technique. We propose a structured CUS assessment of WMI of prematurity that seeks to account for both cystic and non-cystic changes, as well as signs of white matter loss and impaired brain growth and maturation, at or near term equivalent age. This novel assessment system aims to improve disease description in both routine clinical practice and clinical research. Whether this systematic assessment will improve prediction of outcome in preterm infants with WMI still needs to be evaluated in prospective studies

Arterial Spin-Labeling Perfusion Metrics in Pediatric Posterior Fossa Tumor Surgery

BACKGROUND AND PURPOSE: Pediatric posterior fossa tumors often present with hydrocephalus; postoperatively, up to 25% of patients develop cerebellar mutism syndrome. Arterial spin-labeling is a noninvasive means of quantifying CBF and bolus arrival time. The aim of this study was to investigate how changes in perfusion metrics in children with posterior fossa tumors are modulated by cerebellar mutism syndrome and hydrocephalus requiring pre-resection CSF diversion. MATERIALS AND METHODS: Forty-four patients were prospectively scanned at 3 time points (preoperatively, postoperatively, and at 3-month follow-up) with single- and multi-inflow time arterial spin-labeling sequences. Regional analyses of CBF and bolus arrival time were conducted using coregistered anatomic parcellations. ANOVA and multivariable, linear mixed-effects modeling analysis approaches were used. The study was registered at clinicaltrials.gov (NCT03471026). RESULTS: CBF increased after tumor resection and at follow-up scanning (P = .045). Bolus arrival time decreased after tumor resection and at follow-up scanning (P = .018). Bolus arrival time was prolonged (P = .058) following the midline approach, compared with cerebellar hemispheric surgical approaches to posterior fossa tumors. Multivariable linear mixed-effects modeling showed that regional perfusion changes were more pronounced in the 6 children who presented with symptomatic obstructive hydrocephalus requiring pre-resection CSF diversion, with hydrocephalus lowering the baseline mean CBF by 20.5 (standard error, 6.27) mL/100g/min. Children diagnosed with cerebellar mutism syndrome (8/44, 18.2%) had significantly higher CBF at follow-up imaging than those who were not (P = .040), but no differences in pre- or postoperative perfusion parameters were seen. CONCLUSIONS: Multi-inflow time arterial spin-labeling shows promise as a noninvasive tool to evaluate cerebral perfusion in the setting of pediatric obstructive hydrocephalus and demonstrates increased CBF following resolution of cerebellar mutism syndrome

Volumetric analysis of arteriovenous malformation using computed tomographic angiography

Thesis (M.A.)--Boston UniversityAn arteriovenous malformation (AVM) is an abnormal collection of blood vessels in which arterial blood flows directly into the draining vein without the normal interposed capillaries. It is an important and growing public healthcare problem affecting millions of Americans and many more people internationally. There are several potential treatment options for the AVM, and the best treatment depends on the maximum length of nidus based on the Spetzler- Martin grading system. However, this grading system is insensitive to volume, because it was designed on the basis of two dimensional digital subtraction angiography images. Here, we report a method using computed tomographic angiography to measure the volume of AVM nidus, as a means for noninvasively assessment. The initial results show statistically significant differences between healthy and AVM subject groups in the direct comparisons of the volume (cm3) through the method we suggested (2.456 ± 1.482, 12.478 ± 5.743 and 53.963 ± 9.338 (mean ± stdev.); Normal (No AVM), Small (< 3cm), Medium (3 ~ 6 cm) respectively; P < 0.005 for all), and they also show the exponential correlation between the AVM volume and the maximum length of a nidus (trend-line: y = 4.4183e0.536x with R2 = 0.945). These results provide more accurate volumetric information. Therefore, this noninvasive imaging-based method is a promising means to measure the volume of AVM using clinically available imaging tools

Brain growth and development in fetuses with congenital heart disease

Introduction and Objectives: In the current era of excellent surgical results for congenital heart disease (CHD), focus has become directed on quality of life for these children. Previous studies have shown that neurodevelopmental outcome in CHD is impaired. The mechanisms are incompletely understood but there is increasing evidence that the origins of this are in fetal life. This thesis aims to describe the in utero brain growth in a cohort of fetuses with CHD and relate this to the circulatory abnormalities and fetal Doppler parameters. Methods: Pregnant women with a fetus with CHD were prospectively recruited. The congenital heart defect was phenotyped using fetal echocardiography and patients subdivided into three physiological groups on the basis of the anticipated abnormality of cerebral blood flow and oxygen delivery: (1) isolated reduced flow to the brain; 2) reduced oxygen saturation of cerebral blood flow; (3) combination of reduced oxygen and flow. Fetal brain MRI was performed. In addition to standard biometric measurements, snapshot to volume reconstruction (SVR) was used to construct a 3D data set from the oversampled raw data. From these 3D volumes the total brain volume and ventricular volumes were measured by manual segmentation. Serial measurements of fetal growth were also made and umbilical artery and middle cerebral artery Doppler parameters were analysed. Results: 29 women were included; comparison was made with 83 normal MRI controls. Fetuses with CHD were found to have smaller brain volumes compared to controls when adjusting for advancing gestation (p<0.01). This difference becomes more pronounced with advancing gestation, suggesting a slower rate of in utero brain growth. Measurements of growth found that the fetuses with CHD were smaller throughout gestation with a highly significant difference at the later growth scan. (p<0.001). Cerebral and umbilical artery Doppler data showed evidence of reduced cerebrovascular resistance in fetuses with CHD but did not show a difference in the umbilical artery Doppler. Conclusion: Fetuses with CHD have evidence of impaired brain growth with advancing pregnancy and an increased rate of overall growth restriction. Doppler evidence of cerebral vasodilation supports the mechanism of reduced oxygen delivery as an underlying cause.Open Acces

Hippocampal volumes in patients with bipolar-schizophrenic spectrum disorders and their unaffected first-degree relatives

BACKGROUND: schizophrenic and bipolar disorders are complex and disabling psychiatric diseases whose classical nosography and classification are still under challenging debate aiming to overcome the traditional “Kraepelinian Dichotomy”. For the past hundred years most clinical work and research in psychiatry has proceeded under the assumption that schizophrenia and bipolar disorderaredistinctentities with separate underlying disease processes and treatments. In more recent years there has been increasing evidence for phenomenological, biological and genetic overlap between the two disorders (Potash and Bienvenu 2009). Nowadays, the categorical approach to psychiatric nosography is in contrast with the recent neurobiological, neuropsychological and genetic findings in affective and schizophrenic disorders. Further, symptoms and signs constituting bipolar and schizophrenic disorders are continuously, not dichotomously, distributed; there may be no point of “real cleavage” (Phelps et al. 2008). This recognition has led some clinicians and researchers to call for a diagnostic model that, moving to a “dimensional perspective”, formally recognizes a continuous spectrum from schizophrenic to bipolar (and recurrent depressive) disorders. Kelsoe argued that the existing data coming from various fields of research in bipolar and schizophrenic disorders may best fit a model in which different set of genes predispose to overlapping phenotypes in a continuum. Given the apparent overlap of regions of the genome implicated in bipolar disorder with those for schizophrenia (Kelsoe 1999; Berrettini 2000), the data suggest the possibility that a common polygenic background predisposes to both bipolar disorder and schizophrenia, according to the so-called “multiple threshold model” (Kelsoe 2003). As highlighted by Craddock and Owen, the recent findings are compatible with a model of functional psychosis in which susceptibility to a spectrum of clinical phenotypes is under the influence of overlapping sets of genes, which, together with environmental and epigenetic factors, determine an individual’s expression of illness (Craddock and Owen 2005). A lot of interest is focusing on brain structural abnormalities in patients suffering from schizophrenia and bipolar disorder. A huge amount of neuroimaging studies has been published so far, however the literature is heterogeneous and there is still some degree of uncertainty concerning what key regions are involved in the pathogenesis of such disorders. Schizophrenia and Bipolar Disorder have a number of overlapping symptoms and risk factors, but it is not yet clear if the disorders are characterized by similar deviations in brain morphometry or whether any such deviations reflect the impact of shared susceptibility genes on brain structure. To date there is no consensus about whether, and to what extent, gray matter loss in Schizophrenia is mirrored in Bipolar Disorder and what is the effect of medication or other confounding factors. Studies in family members of patients, who share the risk of the disease but not the confounding factors, may help elucidate whether abnormalities in brain structures are shared by both illnesses. AIM OF THE STUDY: to investigate hippocampal gray matter volume differences in a group of patients with bipolar-schizophrenic spectrum disorders, a group of their unaffected first-degree relatives, and a group of healthy control subjects. METHODS: a total of 104 subjects - 36 schizophrenic or schizoaffective (SZ), 27 bipolar (BP), 2 major depression, 8 unaffected relatives (UR), and 31 healthy controls (HC) - underwent 1,5 T MRI scanning, with volumetric T1 3D acquisition protocol, at the Neuroradiology Unit of Conegliano Hospital. We calculate bilateral hippocampal gray matter volume (HV) and total cerebral volume (TCV) in a sample of 31 SZ, 27 BP, 8 UR and 26 HC, with a stereological method using ANALYZE 10.0 software. RESULTS: we found statistically significant reductions in bilateral HV in the BP-SZ patients compared to HC; the direct comparison between patient groups identified statistically significant reduction in the right HV of SZ, but no significant differences for left HV or TCV (however statistical significance was lost after normalization); statistically significant reduction in the left HV and a trend towards statistical significance for right HV in the UR compared to HC (a trend towards statistically significant reduction in bilateral HV persisted after normalization). CONCLUSION: it might be speculated that the alterations of the gray matter volume in the hippocampus highlighted in our study could be interpreted as a possible structural “biological marker” in the schizophrenic-bipolar spectrum

Mr volumetry of total ventricular volume and total brain volume in normal adult population in Kelantan

Background: This study documented the normal value of total ventricular volume in adult Malay Kelantan population, the normal ventricular brain ratio and the differences in between genders. Methodology: 58 healthy subjects aged 40years and above were included whom undergone MRI brain examinations in HUSM. Axial and sagittal T1-weighted images retrieved from PACS were analysed. The brain and ventricular outlines were delineated using OSIRIX software. The sum of the ventricular area multiplied by 0.7 were calculated as the total ventricular volume in each patient. The ventricular brain ratio obtained by dividing the total ventricular volume with the brain volume and multiplied by 100. The volumes of ventricle and brain were analyzed using paired t-test. Independent t-test was used to analyze ventricular of both male and female. P value of less than 0.05 (p<0.05) was taken as significant. Results were expressed as mean ±standard deviation (SD). Result: The mean total ventricular volume was 21.67cm3 (12.82), while that for male and female was 28.14cm3 (15.61) and 16.02cm3 (5.50) respectively. Mean total ventricular to brain ratio (in percentage) for all subjects was 1.71 (0.89). Mean VBR for male subjects was 2.09 (1.11) and 1.37 (0.44) for female. There was significant difference of the mean total ventricular volume and VBR between male and female subjects (p value <0.05). Conclusion: This study has obtained the normal mean value for the total ventricular volume and ventricular brain ratio in adult Kelantan population, which are statistically significant difference between difference genders

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)