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  • Neuroinformatics
  • Open Access
  • Canadian Institutes of Health Research
  • English

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Rao, Anil; Monteiro, Joao M.; Mourao-Miranda, Janaina;

    When training predictive models from neuroimaging data, we typically have available non-imaging variables such as age and gender that affect the imaging data but which we may be uninterested in from a clinical perspective. Such variables are commonly referred to as ‘confounds’. In this work, we firstly give a working definition for confound in the context of training predictive models from samples of neuroimaging data. We define a confound as a variable which affects the imaging data and has an association with the target variable in the sample that differs from that in the population-of-interest, i.e., the population over which we intend to apply the estimated predictive model. The focus of this paper is the scenario in which the confound and target variable are independent in the population-of-interest, but the training sample is biased due to a sample association between the target and confound. We then discuss standard approaches for dealing with confounds in predictive modelling such as image adjustment and including the confound as a predictor, before deriving and motivating an Instance Weighting scheme that attempts to account for confounds by focusing model training so that it is optimal for the population-of-interest. We evaluate the standard approaches and Instance Weighting in two regression problems with neuroimaging data in which we train models in the presence of confounding, and predict samples that are representative of the population-of-interest. For comparison, these models are also evaluated when there is no confounding present. In the first experiment we predict the MMSE score using structural MRI from the ADNI database with gender as the confound, while in the second we predict age using structural MRI from the IXI database with acquisition site as the confound. Considered over both datasets we find that none of the methods for dealing with confounding gives more accurate predictions than a baseline model which ignores confounding, although including the confound as a predictor gives models that are less accurate than the baseline model. We do find, however, that different methods appear to focus their predictions on specific subsets of the population-of-interest, and that predictive accuracy is greater when there is no confounding present. We conclude with a discussion comparing the advantages and disadvantages of each approach, and the implications of our evaluation for building predictive models that can be used in clinical practice. Highlights • Definition of confound given from the point of view of predictive modelling. • Instance Weighting derived for dealing with confounding with continuous targets. • None of the evaluated methods performs better than a model that ignores confounding. • Different methods favourably predicted different strata of population-of-interest. • Predictive accuracy over population-of-interest reduced in presence of confounding.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Article . 2017
    Data sources: PubMed Central
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    NeuroImage
    Article . 2017
    License: CC BY
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    MPG.PuRe
    Article . 2017
    Data sources: MPG.PuRe
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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      Europe PubMed Central
      Article . 2017
      Data sources: PubMed Central
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      NeuroImage
      Article . 2017
      License: CC BY
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      MPG.PuRe
      Article . 2017
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Thibeau-Sutre, Elina;

    L’objectif de cette thèse était la validation de l’existence ainsi que la découverte de nouveaux sous-types au sein de la maladie d’Alzheimer, première cause de démence au monde. Afin d’explorer son hétérogénéité, nous avons employé des méthodes d’apprentissage profond appliquées à une modalité de neuroimagerie, l’imagerie par résonance magnétique structurelle.Cependant, la découverte de biais méthodologiques importants dans de nombreuses études de notre domaine, ainsi que l’absence de consensus de la communauté sur la manière d’interpréter les résultats des méthodes d’apprentissage profond a fait en partie dévier la thèse de son objectif principal pour s’orienter d’avantage vers des problématiques de validation, de robustesse et d’interprétabilité de l’apprentissage profond. Ainsi, trois études expérimentales ont été menées pour s’assurer de la capacité des réseaux profonds de correctement détecter la maladie. La première est une étude expérimentale de méthodes d’apprentissage profond pour la classification de la maladie d’Alzheimer et a permis d’établir une juste comparaison des méthodes. La seconde étude a permis de constater un manque de robustesse de la classification avec l’apprentissage profond en termes de motifs d’atrophie découverts à l’aide de méthodes d’interprétabilité. Enfin, la dernière étude propose une méthode de découverte de sous-types aidée par l’augmentation de données. Bien que fonctionnant sur des données synthétiques, celle-ci ne généralise pas aux données réelles.Une contribution majeure de la thèse est la librairie ClinicaDL, grâce à laquelle les résultats expérimentaux de la thèse ont été produits de manière à être reproductibles. The goal of this PhD was the validation of the existence and the discovery of new subtypes of Alzheimer’s disease, the first cause of dementia worldwide. Indeed, despite its discovery more than a century ago, this disease is still not well defined and existing treatments are only weakly effective, possibly because several phenotypes exist within the disease. In order to explore its heterogeneity, we employed deep learning methods applied to a neuroimaging modality, structural magnetic resonance imaging.However, the discovery of important methodological biases in many studies in our field, as well as the lack of consensus regarding deep learning interpretability, partly changed the main objective of the PhD to focus more on issues of validation, robustness and interpretability of deep learning. Then, to correctly assess the ability of deep learning to detect Alzheimer’s disease, three experimental studies were conducted. The first one is a study of deep learning methods for Alzheimer’s classification and allowed a fair comparison of the methods. The second study found a lack of robustness of classification with deep learning in terms of atrophy patterns discovered using interpretability methods. Finally, the last study proposed a subtype discovery method aided by data augmentation. Although it works on synthetic data, it does not generalize to real data.Experimental results of this PhD were obtained thanks to ClinicaDL, one major contribution of this PhD. It is an open source Python library that was used to improve the reproducibility of deep learning experiments.

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    Hyper Article en Ligne
    Other literature type . 2021
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      Other literature type . 2021
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    Authors: Emon, MA; Heinson, A; Wu, Peiqian; Domingo-Fernandez, D; +6 Authors

    International audience; One of the visions of precision medicine has been to re-define disease taxonomies based on molecular characteristics rather than on phenotypic evidence. However, achieving this goal is highly challenging, specifically in neurology. Our contribution is a machine-learning based joint molecular subtyping of Alzheimer's (AD) and Parkinson's Disease (PD), based on the genetic burden of 15 molecular mechanisms comprising 27 proteins (e.g. APOE) that have been described in both diseases. We demonstrate that our joint AD/PD clustering using a combination of sparse autoencoders and sparse non-negative matrix factorization is reproducible and can be associated with significant differences of AD and PD patient subgroups on a clinical, pathophysiological and molecular level. Hence, clusters are disease-associated. To our knowledge this work is the first demonstration of a mechanism based stratification in the field of neurodegenerative diseases. Overall, we thus see this work as an important step towards a molecular mechanism-based taxonomy of neurological disorders, which could help in developing better targeted therapies in the future by going beyond classical phenotype based disease definitions.

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    Europe PubMed Central
    Article . 2020
    Data sources: PubMed Central
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    e-Prints Soton
    Article . 2020 . Peer-reviewed
    Data sources: e-Prints Soton
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    Scientific Reports
    Article . 2020
    Data sources: DOAJ-Articles
    Fraunhofer-ePrints
    Other literature type . 2020
    Data sources: Fraunhofer-ePrints
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      Europe PubMed Central
      Article . 2020
      Data sources: PubMed Central
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      e-Prints Soton
      Article . 2020 . Peer-reviewed
      Data sources: e-Prints Soton
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      Scientific Reports
      Article . 2020
      Data sources: DOAJ-Articles
      Fraunhofer-ePrints
      Other literature type . 2020
      Data sources: Fraunhofer-ePrints
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    Authors: Carrasco, Andres; Brown, Trecia A.; Lomber, Stephen G.;

    Assemblies of vertically connected neurons in the cerebral cortex form information processing units (columns) that participate in the distribution and segregation of sensory signals. Despite well-accepted models of columnar architecture, functional mechanisms of inter-laminar communication remain poorly understood. Hence, the purpose of the present investigation was to examine the effects of sensory information features on columnar response properties. Using acute recording techniques, extracellular response activity was collected from the right hemisphere of eight mature cats (felis catus). Recordings were conducted with multichannel electrodes that permitted the simultaneous acquisition of neuronal activity within primary auditory cortex columns. Neuronal responses to simple (pure tones), complex (noise burst and frequency modulated sweeps), and ecologically relevant (con-specific vocalizations) acoustic signals were measured. Collectively, the present investigation demonstrates that despite consistencies in neuronal tuning (characteristic frequency), irregularities in discharge activity between neurons of individual A1 columns increase as a function of spectral (signal complexity) and temporal (duration) acoustic variations. Multi-unit responses to acoustic signals within A1 columnsThe data set consists of eight multi-unit electrophysiology experiments located within a single .zip file. Acoustic feature (signal type and duration) are in subfolders where data rasters for each recording session conducted can be found. Columns represent time and rows trial number. Data is presented as Matlab files.DRYAD.zip

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    Dataset . 2014
    Data sources: B2FIND
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    ZENODO
    Dataset . 2015
    License: CC 0
    Data sources: ZENODO
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      DANS-EASY
      Dataset . 2014
      Data sources: B2FIND
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      ZENODO
      Dataset . 2015
      License: CC 0
      Data sources: ZENODO
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    Authors: Ursula I. Tuor; Melissa Morgunov; Manasi Sule; Min Qiao; +4 Authors

    Ischemically damaged brain can be accompanied by secondary degeneration of associated axonal connections e.g. Wallerian degeneration. Diffusion tensor imaging (DTI) is widely used to investigate axonal injury but the cellular correlates of many of the degenerative changes remain speculative. We investigated the relationship of DTI of directly damaged cerebral cortex and secondary axonal degeneration in the cerebral peduncle with cellular alterations in pan-axonal neurofilament staining, myelination, reactive astrocytes, activation of microglia/macrophages and neuronal cell death. DTI measures (axial, radial and mean diffusivity, and fractional anisotropy (FA)) were acquired at hyperacute (3 h), acute (1 and 2 d) and chronic (1 and 4 week) times after transient cerebral hypoxia with unilateral ischemia in neonatal rats. The tissue pathology underlying ischemic and degenerative responses had a complex relationship with DTI parameters. DTI changes at hyperacute and subacute times were smaller in magnitude and tended to be transient and/or delayed in cerebral peduncle compared to cerebral cortex. In cerebral peduncle by 1 d post-insult, there were reductions in neurofilament staining corresponding with decreases in parallel diffusivity which were more sensitive than mean diffusivity in detecting axonal changes. Ipsilesional reductions in FA within cerebral peduncle were robust in detecting both early and chronic degenerative responses. At one or four weeks post-insult, radial diffusivity was increased ipsilaterally in the cerebral peduncle corresponding to pathological evidence of a lack of ontogenic myelination in this region. The detailed differences in progression and magnitude of DTI and histological changes reported provide a reference for identifying the potential contribution of various cellular responses to FA, and, parallel, radial, and mean diffusivity. Highlights • Diffusion tensor imaging (DTI) widely used; cellular correlates often speculative • Studied longitudinal DTI and histological changes following hypoxia–ischemia • Compared neonatal cortex changes to those in degenerating cerebral peduncle • DTI and cellular changes were often transient or delayed in cerebral peduncle. • This provides a reference for potential cellular contributions to DTI changes.

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    Europe PubMed Central
    Article . 2014
    Data sources: PubMed Central
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    NeuroImage: Clinical
    Article . 2014
    Data sources: DOAJ-Articles
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      Europe PubMed Central
      Article . 2014
      Data sources: PubMed Central
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      NeuroImage: Clinical
      Article . 2014
      Data sources: DOAJ-Articles
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    Authors: Bercier, Valérie;

    La sclérose latérale amyotrophique (SLA) est une pathologie neurodégénerative progressive se déclarant vers 50-60 ans. Elle est majoritairement de nature sporadique son incidence est estimée à 1 :1000. La SLA mène à une paralysie progressive et entraine généralement à la mort des patients de 2 à 5 ans suivant le diagnostic aux suite d’une fonte musculaire importante liée à la perte des neurones moteurs. Au cours des années, plusieurs mutations ont été identifiées autant chez les patients atteints de SLA sporadique que de SLA familiale. Ces mutations interfèrent avec la fonction de gènes variés, tels que DCTN1, codant pour la protéine dynactine1, sous-unité du complexe multimoléculaire dynactine. Ce complexe sert d’adaptateur au moteur moléculaire dynéine, chargé du transport axonal rétrograde, où sa fonction permettrait de régir l’activité du complexe moteur et sa capacité à lier divers cargos. Nous avons donc entrepris la caractérisation d’une lignée de poissons zèbre mutants pour dynactin1a (nommés mikre okom632, mokm632), plus particulière en terme du développement d’un type de neurone moteur primaire (les CaPs), afin de déterminer l’effet de la perte de fonction de ce gène sur l’axonogenèse, la formation et la stabilisation de la jonction neuromusculaire, sur le comportement de l’embryon, ainsi que sur le transport axonal.Nous suggérons que dynactin1 favorise la stabilité synaptique, où une perte de fonction de ce gène entraine des défauts de croissance, des anomalies éléctrophysiologiques et un comportement anormal. Ce rôle semble être indépendant des fonctions connues de régulateur du moteur dynéine. Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease, which is mainly sporadic in nature. This progressive pathology has an estimated incidence of 1:1000 and generally leads to death within 2-5 years of diagnosis due to muscle wasting and severe motor neuron loss. Over the last years, mutations have been identified in both sporadic and familial ALS patients, interfering with the function of many genes, including DCTN1, which encodes for a subunit of the motor protein complex subunit dynactin. The dynactin complex serves as an adaptor for the dynein motor complex, responsible for retrograde axonal transport, and it is believed to regulate dynein activity and the binding capacity for cargos. We set out to characterize a mutant zebrafish line for dynactn1a (named mikre okom632, mokm632), looking specifically at caudal primary motor neurons (CaPs), with regard to axonal development, formation and stability of the neuromuscular junction (NMJ) and the behavioral phenotype produced in embryos, as well as axonal transport metrics. We suggest a role for dynactin1 in synapse stability, where the loss-of-function of this gene leads to growth defects, electrophysiological abnormalities and behavioral deficits. This role appears to be independent of its known function as a regulator of dynein, its implication in axonal transport, or its regulation of microtubule dynamics. With this study, we hope to elucidate key molecular mechanisms in ALS etiology by revealing the role of dynactin1 in NMJ development and maintenance.

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    Authors: Florian Udo Fischer; Dominik Wolf; Armin Scheurich; Andreas Fellgiebel;

    Surrogates of whole-brain white matter (WM) networks reconstructed using diffusion tensor imaging (DTI) are novel markers of structural brain connectivity. Global connectivity of networks has been found impaired in clinical Alzheimer's disease (AD) compared to cognitively healthy aging. We hypothesized that network alterations are detectable already in preclinical AD and investigated major global WM network properties. Other structural markers of neurodegeneration typically affected in prodromal AD but seeming largely unimpaired in preclinical AD were also examined. 12 cognitively healthy elderly with preclinical AD as classified by florbetapir-PET (mean age 73.4 ± 4.9) and 31 age-matched controls without cerebral amyloidosis (mean age 73.1 ± 6.7) from the ADNI were included. WM networks were reconstructed from DTI using tractography and graph theory. Indices of network capacity and the established imaging markers of neurodegeneration hippocampal volume, and cerebral glucose utilization as measured by fludeoxyglucose-PET were compared between the two groups. Additionally, we measured surrogates of global WM integrity (fractional anisotropy, mean diffusivity, volume). We found an increase of shortest path length and a decrease of global efficiency in preclinical AD. These results remained largely unchanged when controlling for WM integrity. In contrast, neither markers of neurodegeneration nor WM integrity were altered in preclinical AD subjects. Our results suggest an impairment of WM networks in preclinical AD that is detectable while other structural imaging markers do not yet indicate incipient neurodegeneration. Moreover, these findings are specific to WM networks and cannot be explained by other surrogates of global WM integrity. Highlights • We reconstruct WM networks using DTI fiber tractography and graph theory. • We investigate global network properties in cognitively healthy elderly. • Global network integration is decreased in subjects with amyloidosis. • In contrast, no neurodegeneration is found (FDG, hippocampus). • WM network topology may be altered already in preclinical AD.

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    Europe PubMed Central
    Article . 2015
    Data sources: PubMed Central
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    NeuroImage: Clinical
    Article . 2015
    Data sources: DOAJ-Articles
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    https://doi.org/10.25358/opens...
    Other literature type . 2015
    License: CC BY NC ND
    Data sources: Datacite
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      Europe PubMed Central
      Article . 2015
      Data sources: PubMed Central
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      NeuroImage: Clinical
      Article . 2015
      Data sources: DOAJ-Articles
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      https://doi.org/10.25358/opens...
      Other literature type . 2015
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    Authors: Madan, Christopher R.;
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    Frontiers in Human Neuroscience
    2017 . Peer-reviewed
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      Frontiers in Human Neuroscience
      2017 . Peer-reviewed
      Data sources: Frontiers
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    Authors: Mingasson, Tom; Duval, Tanguy; Stikov, Nikola; Cohen-Adad, Julien;

    HIGHLIGHTS AxonPacking: Open-source software for simulating white matter microstructure.Validation on a theoretical disk packing problem.Reproducible and stable for various densities and diameter distributions.Can be used to study interplay between myelin/fiber density and restricted fraction.Quantitative Magnetic Resonance Imaging (MRI) can provide parameters that describe white matter microstructure, such as the fiber volume fraction (FVF), the myelin volume fraction (MVF) or the axon volume fraction (AVF) via the fraction of restricted water (fr). While already being used for clinical application, the complex interplay between these parameters requires thorough validation via simulations. These simulations required a realistic, controlled and adaptable model of the white matter axons with the surrounding myelin sheath. While there already exist useful algorithms to perform this task, none of them combine optimisation of axon packing, presence of myelin sheath and availability as free and open source software. Here, we introduce a novel disk packing algorithm that addresses these issues. The performance of the algorithm is tested in term of reproducibility over 50 runs, resulting density, and stability over iterations. This tool was then used to derive multiple values of FVF and to study the impact of this parameter on fr and MVF in light of the known microstructure based on histology sample. The standard deviation of the axon density over runs was lower than 10−3 and the expected hexagonal packing for monodisperse disks was obtained with a density close to the optimal density (obtained: 0.892, theoretical: 0.907). Using an FVF ranging within [0.58, 0.82] and a mean inter-axon gap ranging within [0.1, 1.1] μm, MVF ranged within [0.32, 0.44] and fr ranged within [0.39, 0.71], which is consistent with the histology. The proposed algorithm is implemented in the open-source software AxonPacking (https://github.com/neuropoly/axonpacking) and can be useful for validating diffusion models as well as for enabling researchers to study the interplay between microstructure parameters when evaluating qMRI methods.

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    Frontiers in Neuroinformatics
    2017 . Peer-reviewed
    Data sources: Frontiers
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      Frontiers in Neuroinformatics
      2017 . Peer-reviewed
      Data sources: Frontiers
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    Authors: Martin, Allan R.; De Leener, Benjamin; Cohen-Adad, Julien; Cadotte, David W.; +7 Authors

    Objectives: Degenerative cervical myelopathy (DCM) involves extrinsic spinal cord compression causing tissue injury and neurological dysfunction. Asymptomatic spinal cord compression (ASCC) is more common but its significance is poorly defined. This study investigates if: 1) ASCC can be automatically diagnosed using spinal cord shape analysis; 2) multiparametric quantitative MRI can detect similar spinal cord tissue injury as previously observed in DCM. Design: Prospective observational longitudinal cohort study. Setting: Single centre, tertiary care and research institution. Participants: 40 neurologically intact subjects (19 female, 21 male) divided into groups with and without ASCC. Interventions: None. Outcome Measures: Clinical assessments: modified Japanese Orthopedic Association (mJOA) score and physical examination. 3T MRI assessments: automated morphometric analysis compared with consensus ratings of spinal cord compression, and measures of tissue injury: cross-sectional area (CSA), diffusion fractional anisotropy (FA), magnetization transfer ratio (MTR), and T2-weighted imaging white to grey matter signal intensity ratio (T2WI WM/GM) extracted from rostral (C1-3), caudal (C6-7), and maximally compressed levels (MCL). Results: ASCC was present in 20/40 subjects. Diagnosis with automated shape analysis showed area under the curve > 97%. Five MRI metrics showed differences suggestive of tissue injury in ASCC compared with uncompressed subjects (p<0.05), while a composite of all 10 measures (average of z scores) showed highly significant differences (p=0.002). At follow-up (median 21 months), two ASCC subjects developed DCM. Conclusions: ASCC appears to be common and can be accurately and objectively diagnosed with automated morphometric analysis. Quantitative MRI appears to detect subclinical tissue injury in ASCC prior to the onset of neurological symptoms and signs. These findings require further validation, but offer the intriguing possibility of pre-symptomatic diagnosis and treatment of DCM and other spinal pathologies. Registration: Not registered. Demographic, morphometric, and quantitative MRI dataData includes anonymized subject ID, presence of spinal cord compression, age, sex, follow-up mJOA score, spinal cord morphometric parameters of compression ratio, solidity, and relative rotation, measured at C2-3, C3-4, C4-5, C5-6, and C6-7, and quantitative MRI measures of cross sectional area, magnetization transfer ratio, fractional anisotropy, and T2*-weighted white matter to grey matter signal intensity ratio, measured at rostral (C1-3), maximally compressed level, and caudal (C6-7) levels.qMRI_subclinical_tissue_injury_public_data.xlsx

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    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
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    DRYAD; NARCIS
    Dataset . 2018
    License: CC 0
    Data sources: Datacite; NARCIS
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    ZENODO
    Dataset . 2018
    License: CC 0
    Data sources: ZENODO
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      DANS-EASY
      Dataset . 2018
      Data sources: B2FIND
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      DRYAD; NARCIS
      Dataset . 2018
      License: CC 0
      Data sources: Datacite; NARCIS
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      ZENODO
      Dataset . 2018
      License: CC 0
      Data sources: ZENODO
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    Authors: Rao, Anil; Monteiro, Joao M.; Mourao-Miranda, Janaina;

    When training predictive models from neuroimaging data, we typically have available non-imaging variables such as age and gender that affect the imaging data but which we may be uninterested in from a clinical perspective. Such variables are commonly referred to as ‘confounds’. In this work, we firstly give a working definition for confound in the context of training predictive models from samples of neuroimaging data. We define a confound as a variable which affects the imaging data and has an association with the target variable in the sample that differs from that in the population-of-interest, i.e., the population over which we intend to apply the estimated predictive model. The focus of this paper is the scenario in which the confound and target variable are independent in the population-of-interest, but the training sample is biased due to a sample association between the target and confound. We then discuss standard approaches for dealing with confounds in predictive modelling such as image adjustment and including the confound as a predictor, before deriving and motivating an Instance Weighting scheme that attempts to account for confounds by focusing model training so that it is optimal for the population-of-interest. We evaluate the standard approaches and Instance Weighting in two regression problems with neuroimaging data in which we train models in the presence of confounding, and predict samples that are representative of the population-of-interest. For comparison, these models are also evaluated when there is no confounding present. In the first experiment we predict the MMSE score using structural MRI from the ADNI database with gender as the confound, while in the second we predict age using structural MRI from the IXI database with acquisition site as the confound. Considered over both datasets we find that none of the methods for dealing with confounding gives more accurate predictions than a baseline model which ignores confounding, although including the confound as a predictor gives models that are less accurate than the baseline model. We do find, however, that different methods appear to focus their predictions on specific subsets of the population-of-interest, and that predictive accuracy is greater when there is no confounding present. We conclude with a discussion comparing the advantages and disadvantages of each approach, and the implications of our evaluation for building predictive models that can be used in clinical practice. Highlights • Definition of confound given from the point of view of predictive modelling. • Instance Weighting derived for dealing with confounding with continuous targets. • None of the evaluated methods performs better than a model that ignores confounding. • Different methods favourably predicted different strata of population-of-interest. • Predictive accuracy over population-of-interest reduced in presence of confounding.

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    Europe PubMed Central
    Article . 2017
    Data sources: PubMed Central
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    NeuroImage
    Article . 2017
    License: CC BY
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    MPG.PuRe
    Article . 2017
    Data sources: MPG.PuRe
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      Europe PubMed Central
      Article . 2017
      Data sources: PubMed Central
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      NeuroImage
      Article . 2017
      License: CC BY
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      MPG.PuRe
      Article . 2017
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    Authors: Thibeau-Sutre, Elina;

    L’objectif de cette thèse était la validation de l’existence ainsi que la découverte de nouveaux sous-types au sein de la maladie d’Alzheimer, première cause de démence au monde. Afin d’explorer son hétérogénéité, nous avons employé des méthodes d’apprentissage profond appliquées à une modalité de neuroimagerie, l’imagerie par résonance magnétique structurelle.Cependant, la découverte de biais méthodologiques importants dans de nombreuses études de notre domaine, ainsi que l’absence de consensus de la communauté sur la manière d’interpréter les résultats des méthodes d’apprentissage profond a fait en partie dévier la thèse de son objectif principal pour s’orienter d’avantage vers des problématiques de validation, de robustesse et d’interprétabilité de l’apprentissage profond. Ainsi, trois études expérimentales ont été menées pour s’assurer de la capacité des réseaux profonds de correctement détecter la maladie. La première est une étude expérimentale de méthodes d’apprentissage profond pour la classification de la maladie d’Alzheimer et a permis d’établir une juste comparaison des méthodes. La seconde étude a permis de constater un manque de robustesse de la classification avec l’apprentissage profond en termes de motifs d’atrophie découverts à l’aide de méthodes d’interprétabilité. Enfin, la dernière étude propose une méthode de découverte de sous-types aidée par l’augmentation de données. Bien que fonctionnant sur des données synthétiques, celle-ci ne généralise pas aux données réelles.Une contribution majeure de la thèse est la librairie ClinicaDL, grâce à laquelle les résultats expérimentaux de la thèse ont été produits de manière à être reproductibles. The goal of this PhD was the validation of the existence and the discovery of new subtypes of Alzheimer’s disease, the first cause of dementia worldwide. Indeed, despite its discovery more than a century ago, this disease is still not well defined and existing treatments are only weakly effective, possibly because several phenotypes exist within the disease. In order to explore its heterogeneity, we employed deep learning methods applied to a neuroimaging modality, structural magnetic resonance imaging.However, the discovery of important methodological biases in many studies in our field, as well as the lack of consensus regarding deep learning interpretability, partly changed the main objective of the PhD to focus more on issues of validation, robustness and interpretability of deep learning. Then, to correctly assess the ability of deep learning to detect Alzheimer’s disease, three experimental studies were conducted. The first one is a study of deep learning methods for Alzheimer’s classification and allowed a fair comparison of the methods. The second study found a lack of robustness of classification with deep learning in terms of atrophy patterns discovered using interpretability methods. Finally, the last study proposed a subtype discovery method aided by data augmentation. Although it works on synthetic data, it does not generalize to real data.Experimental results of this PhD were obtained thanks to ClinicaDL, one major contribution of this PhD. It is an open source Python library that was used to improve the reproducibility of deep learning experiments.

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    Hyper Article en Ligne
    Other literature type . 2021
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      Other literature type . 2021
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    Authors: Emon, MA; Heinson, A; Wu, Peiqian; Domingo-Fernandez, D; +6 Authors

    International audience; One of the visions of precision medicine has been to re-define disease taxonomies based on molecular characteristics rather than on phenotypic evidence. However, achieving this goal is highly challenging, specifically in neurology. Our contribution is a machine-learning based joint molecular subtyping of Alzheimer's (AD) and Parkinson's Disease (PD), based on the genetic burden of 15 molecular mechanisms comprising 27 proteins (e.g. APOE) that have been described in both diseases. We demonstrate that our joint AD/PD clustering using a combination of sparse autoencoders and sparse non-negative matrix factorization is reproducible and can be associated with significant differences of AD and PD patient subgroups on a clinical, pathophysiological and molecular level. Hence, clusters are disease-associated. To our knowledge this work is the first demonstration of a mechanism based stratification in the field of neurodegenerative diseases. Overall, we thus see this work as an important step towards a molecular mechanism-based taxonomy of neurological disorders, which could help in developing better targeted therapies in the future by going beyond classical phenotype based disease definitions.

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    Europe PubMed Central
    Article . 2020
    Data sources: PubMed Central
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    e-Prints Soton
    Article . 2020 . Peer-reviewed
    Data sources: e-Prints Soton
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    Scientific Reports
    Article . 2020
    Data sources: DOAJ-Articles
    Fraunhofer-ePrints
    Other literature type . 2020
    Data sources: Fraunhofer-ePrints
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      Europe PubMed Central
      Article . 2020
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      e-Prints Soton
      Article . 2020 . Peer-reviewed
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      Scientific Reports
      Article . 2020
      Data sources: DOAJ-Articles
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    Authors: Carrasco, Andres; Brown, Trecia A.; Lomber, Stephen G.;

    Assemblies of vertically connected neurons in the cerebral cortex form information processing units (columns) that participate in the distribution and segregation of sensory signals. Despite well-accepted models of columnar architecture, functional mechanisms of inter-laminar communication remain poorly understood. Hence, the purpose of the present investigation was to examine the effects of sensory information features on columnar response properties. Using acute recording techniques, extracellular response activity was collected from the right hemisphere of eight mature cats (felis catus). Recordings were conducted with multichannel electrodes that permitted the simultaneous acquisition of neuronal activity within primary auditory cortex columns. Neuronal responses to simple (pure tones), complex (noise burst and frequency modulated sweeps), and ecologically relevant (con-specific vocalizations) acoustic signals were measured. Collectively, the present investigation demonstrates that despite consistencies in neuronal tuning (characteristic frequency), irregularities in discharge activity between neurons of individual A1 columns increase as a function of spectral (signal complexity) and temporal (duration) acoustic variations. Multi-unit responses to acoustic signals within A1 columnsThe data set consists of eight multi-unit electrophysiology experiments located within a single .zip file. Acoustic feature (signal type and duration) are in subfolders where data rasters for each recording session conducted can be found. Columns represent time and rows trial number. Data is presented as Matlab files.DRYAD.zip

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    DANS-EASY
    Dataset . 2014
    Data sources: B2FIND
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    ZENODO
    Dataset . 2015
    License: CC 0
    Data sources: ZENODO
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