Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort

BACKGROUND:Automated volumetry software (AVS) has recently become widely available to neuroradiologists. MRI volumetry with AVS may support the diagnosis of dementias by identifying regional atrophy. Moreover, automatic classifiers using machine learning techniques have recently emerged as promising approaches to assist diagnosis. However, the performance of both AVS and automatic classifiers has been evaluated mostly in the artificial setting of research datasets.OBJECTIVE:Our aim was to evaluate the performance of two AVS and an automatic classifier in the clinical routine condition of a memory clinic.METHODS:We studied 239 patients with cognitive troubles from a single memory center cohort. Using clinical routine T1-weighted MRI, we evaluated the classification performance of: 1) univariate volumetry using two AVS (volBrain and Neuroreader$^{TM}$); 2) Support Vector Machine (SVM) automatic classifier, using either the AVS volumes (SVM-AVS), or whole gray matter (SVM-WGM); 3) reading by two neuroradiologists. The performance measure was the balanced diagnostic accuracy. The reference standard was consensus diagnosis by three neurologists using clinical, biological (cerebrospinal fluid) and imaging data and following international criteria.RESULTS:Univariate AVS volumetry provided only moderate accuracies (46% to 71% with hippocampal volume). The accuracy improved when using SVM-AVS classifier (52% to 85%), becoming close to that of SVM-WGM (52 to 90%). Visual classification by neuroradiologists ranged between SVM-AVS and SVM-WGM.CONCLUSION:In the routine practice of a memory clinic, the use of volumetric measures provided by AVS yields only moderate accuracy. Automatic classifiers can improve accuracy and could be a useful tool to assist diagnosis

A Comparison of Automated Segmentation and Manual Tracing of Magnetic Resonance Imaging to Quantify Lateral Ventricle Volumes

Objective: Ventricular volume measurements have been proposed as a useful biomarker for several neurological diseases. The goal of this study was to compare the performance of 3 fully-automated tools, volBrain (http://volbrain.upv.es), ALVIN (Automatic Lateral Ventricle Delineation) (https://sites.google.com/site/mrilateralventricle/), and MRICloud (http://mricloud.org), with expert hand tracing to quantify lateral ventricle (LV) volume using magnetic resonance images. Materials and Methods: The sample comprised 24 healthy subjects (age: 25.1±5.7 years, all male). Volumes derived from each automated measurement were compared to hand tracing results performed by 2 specialists to assess the percent volume difference using the intraclass correlation coefficient (ICC), concordance correlation coefficient (CCC), Dice index value, and Bland-Altman analysis. Results: The ICC agreement of the Manual_1 and Manual_2 was very good (0.979), and there was no statistically significant difference (p>0.001). The volume difference of all methods was similar. The CCC with MRICloud and ALVIN was higher than that of volBrain. Bland-Altman plots indicated that the 3 automated methods demonstrated acceptable agreement. Conclusion: Compared with hand tracing, the LV volumes generated by MRICloud were more accurate than those of volBrain and ALVIN. LV volume values can provide valuable data related to the volumetric dependencies of the anatomical structures in various clinical conditions that can now be easily obtained using automated tools

Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study

Atrofia; IRM; Esclerosis múltipleAtròfia; IRM; Esclerosi múltipleAtrophy; MRI; Multiple SclerosisBackground and rationale Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. Methods Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. Results In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings. Conclusion Automated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings.The study was funded by the Nauta fonds through a travel grant. The MS Center Amsteram is supported by the Dutch MS Research Foundation through a program grant (current grant 18-358f). D.B. is supported by project PI18/00823 from the “Fondo de Investigación Sanitaria Carlos III”. F.B. and O.C. are supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. The acquisition of data in London was funded by supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. A sincere thank you to Tom Verhoeven for his editing of the figures

Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study

Background and rationale: Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. Methods: Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. Results: In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings. Conclusion: Automated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings

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). 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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). 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INVESTIGATING DIFFUSION TENSOR IMAGING CORRELATES OF COGNITIVE IMPAIRMENT IN IDIOPATHIC NORMAL PRESSURE HYDROCEPHALUS AND ALZHEIMER\u27S DISEASE

Modest expansion of the human brain cerebrospinal fluid (CSF)-filled ventricles is normal with aging, and because of this, it can be difficult for physicians to accurately diagnose and treat enlarged ventricles (ventriculomegaly), called hydrocephalus1 (fluid or water in the brain) Ventriculomegaly occurs due to an obstruction (such as a blood clot or tumor), or a change in CSF absorption2. Primary hydrocephalus, also called idiopathic normal pressure hydrocephalus (iNPH), is non-obstructive and may be comorbid with other neurodegenerative diseases such as Alzheimer’s disease (AD) or frontotemporal dementia (FTD). Clinically, it can be difficult to tell whether the pathophysiological changes leading to cognitive impairment also led to the ventriculomegaly, as may occur in AD, versus whether the hydrocephalus itself is driving cognitive and motor impairment, i.e. iNPH. The goal of this thesis project is to investigate the relationship between iNPH and AD in order to better understand how they may contribute to each other, and to help clinicians distinguish between them. To do this, we compared cognitive performance and white matter integrity between patients with “pure” iNPH, “pure” Alzheimer’s disease (AD), and co-morbid iNPH + AD. Our results demonstrated that there are specific periventricular structures in the brain that are associated with cognitive impairment in AD versus iNPH. We conclude that the distribution pattern of AD vs. iNPH may be a valid tool to distinguish between these disorders, and may form the basis for subsequent studies that can further explicate the link between these often-overlapping etiologies

Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis : A multicenter study

Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ ρ =(-0.42)-(-0.76); p- values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings. Automated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings

Hippocampal volume in Provisional Tic Disorder predicts tic severity at 12-month follow-up

Previous studies have investigated differences in the volumes of subcortical structures (e.g., caudate nucleus, putamen, thalamus, amygdala, and hippocampus) between individuals with and without Tourette syndrome (TS), as well as the relationships between these volumes and tic symptom severity. These volumes may also predict clinical outcome in Provisional Tic Disorder (PTD), but that hypothesis has never been tested. This study aimed to examine whether the volumes of subcortical structures measured shortly after tic onset can predict tic symptom severity at one-year post-tic onset, when TS can first be diagnosed. We obtained T1-weighted structural MRI scans from 41 children with PTD (25 with prospective motion correction (vNavs)) whose tics had begun less than 9 months (mean 4.04 months) prior to the first study visit (baseline). We re-examined them at the 12-month anniversary of their first tic (follow-up), assessing tic severity using the Yale Global Tic Severity Scale. We quantified the volumes of subcortical structures using volBrain software. Baseline hippocampal volume was correlated with tic severity at the 12-month follow-up, with a larger hippocampus at baseline predicting worse tic severity at follow-up. The volumes of other subcortical structures did not significantly predict tic severity at follow-up. Hippocampal volume may be an important marker in predicting prognosis in Provisional Tic Disorder

Sparse Representation-Based Framework for Preprocessing Brain MRI

This thesis addresses the use of sparse representations, specifically Dictionary Learning and Sparse Coding, for pre-processing brain MRI, so that the processed image retains the fine details of the original image, to improve the segmentation of brain structures, to assess whether there is any relationship between alterations in brain structures and the behavior of young offenders. Denoising an MRI while keeping fine details is a difficult task; however, the proposed method, based on sparse representations, NLM, and SVD can filter noise while prevents blurring, artifacts, and residual noise. Segmenting an MRI is a non-trivial task; because normally the limits between regions in these images may be neither clear nor well defined, due to the problems which affect MRI. However, this method, from both the label matrix of the segmented MRI and the original image, yields a new improved label matrix in which improves the limits among regions.DoctoradoDoctor en Ingeniería de Sistemas y Computació