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De Francesco S, Crema C, Archetti D, Muscio C, Reid RI, Nigri A, Bruzzone MG, Tagliavini F, Lodi R, D'Angelo E, Boeve B, Kantarci K, Firbank M, Taylor JP, Tiraboschi P, Redolfi A. Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA. Sci Rep 2023; 13:17355. [PMID: 37833302 PMCID: PMC10575864 DOI: 10.1038/s41598-023-43706-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023] Open
Abstract
Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis.
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Affiliation(s)
- Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
| | - Claudio Crema
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Damiano Archetti
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Cristina Muscio
- ASST Bergamo Ovest, Bergamo, Italy
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic and Foundation, Rochester, Minnesota, USA
| | - Anna Nigri
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Maria Grazia Bruzzone
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Fabrizio Tagliavini
- Scientific Directorate, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | - Brad Boeve
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Michael Firbank
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle Upon Tyne, UK
| | - John-Paul Taylor
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle Upon Tyne, UK
| | - Pietro Tiraboschi
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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De Francesco S, Galluzzi S, Vanacore N, Festari C, Rossini PM, Cappa SF, Frisoni GB, Redolfi A. Norms for Automatic Estimation of Hippocampal Atrophy and a Step Forward for Applicability to the Italian Population. Front Neurosci 2021; 15:656808. [PMID: 34262425 PMCID: PMC8273578 DOI: 10.3389/fnins.2021.656808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 06/03/2021] [Indexed: 11/23/2022] Open
Abstract
INTRODUCTION Hippocampal volume is one of the main biomarkers of Alzheimer's Dementia (AD). Over the years, advanced tools that performed automatic segmentation of Magnetic Resonance Imaging (MRI) T13D scans have been developed, such as FreeSurfer (FS) and ACM-Adaboost (AA). Hippocampal volume is considered abnormal when it is below the 5th percentile of the normative population. The aim of this study was to set norms, established from the Alzheimer's Disease Neuroimaging Initiative (ADNI) population, for hippocampal volume measured with FS v.6.0 and AA tools in the neuGRID platform (www.neugrid2.eu) and demonstrate their applicability for the Italian population. METHODS Norms were set from a large group of 545 healthy controls belonging to ADNI. For each pipeline, subjects with segmentation errors were discarded, resulting in 532 valid segmentations for FS and 421 for AA (age range 56-90 years). The comparability of ADNI and the Italian Brain Normative Archive (IBNA), representative of the Italian general population, was assessed testing clinical variables, neuropsychological scores and normalized hippocampal volumes. Finally, percentiles were validated using the Italian Alzheimer's disease Repository Without Borders (ARWiBo) as external independent data set to evaluate FS and AA generalizability. RESULTS Hippocampal percentiles were checked with the chi-square goodness of fit test. P-values were not significant, showing that FS and AA algorithm distributions fitted the data well. Clinical, neuropsychological and volumetric features were similar in ADNI and IBNA (p > 0.01). Hippocampal volumes measured with both FS and AA were associated with age (p < 0.001). The 5th percentile thresholds, indicating left/right hippocampal atrophy were respectively: (i) below 3,223/3,456 mm3 at 56 years and 2,506/2,415 mm3 at 90 years for FS; (ii) below 4,583/4,873 mm3 at 56 years and 3,831/3,870 mm3 at 90 years for AA. The average volumes computed on 100 cognitively intact healthy controls (CN) selected from ARWiBo were close to the 50th percentiles, while those for 100 AD patients were close to the abnormal percentiles. DISCUSSION Norms generated from ADNI through the automatic FS and AA segmentation tools may be used as normative references for Italian patients with suspected AD.
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Affiliation(s)
- Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Samantha Galluzzi
- Laboratory of Alzheimer’s Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Nicola Vanacore
- National Center for Disease Prevention and Health Promotion, National Institute of Health, Rome, Italy
| | - Cristina Festari
- Laboratory of Alzheimer’s Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Paolo Maria Rossini
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | - Stefano F. Cappa
- IRCCS Mondino Foundation, Pavia, Italy
- IUSS Cognitive Neuroscience (ICoN) Center, University School for Advanced Studies, Pavia, Italy
| | - Giovanni B. Frisoni
- Laboratory of Alzheimer’s Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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Redolfi A, De Francesco S, Palesi F, Galluzzi S, Muscio C, Castellazzi G, Tiraboschi P, Savini G, Nigri A, Bottini G, Bruzzone MG, Ramusino MC, Ferraro S, Gandini Wheeler-Kingshott CAM, Tagliavini F, Frisoni GB, Ryvlin P, Demonet JF, Kherif F, Cappa SF, D'Angelo E. Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts. Front Neurol 2020; 11:1021. [PMID: 33071930 PMCID: PMC7538836 DOI: 10.3389/fneur.2020.01021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 08/04/2020] [Indexed: 12/13/2022] Open
Abstract
Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify-CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from "slight" to "significant" in 80% of the cases. Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology.
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Affiliation(s)
- Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | - Samantha Galluzzi
- Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Cristina Muscio
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Gloria Castellazzi
- IRCCS Mondino Foundation, Pavia, Italy
- NMR Research Unit, Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, London, United Kingdom
- Department of Computer, Electrical and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Pietro Tiraboschi
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | - Anna Nigri
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Gabriella Bottini
- Neuropsychology Center, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Maria Grazia Bruzzone
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Matteo Cotta Ramusino
- IRCCS Mondino Foundation, Pavia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Stefania Ferraro
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
- NMR Research Unit, Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, London, United Kingdom
| | - Fabrizio Tagliavini
- Division of Neurology V/Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Giovanni B. Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Leenaards Memory Center, Center Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Jean-François Demonet
- Department of Clinical Neurosciences, Leenaards Memory Center, Center Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Ferath Kherif
- Department of Clinical Neurosciences, Leenaards Memory Center, Center Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Stefano F. Cappa
- IRCCS Mondino Foundation, Pavia, Italy
- University School of Advanced Studies, Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
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Khlif MS, Werden E, Egorova N, Boccardi M, Redolfi A, Bird L, Brodtmann A. Assessment of longitudinal hippocampal atrophy in the first year after ischemic stroke using automatic segmentation techniques. NEUROIMAGE-CLINICAL 2019; 24:102008. [PMID: 31711030 PMCID: PMC6849411 DOI: 10.1016/j.nicl.2019.102008] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 08/21/2019] [Accepted: 09/17/2019] [Indexed: 12/11/2022]
Abstract
First-year hippocampal atrophy in stroke is more accelerated ipsi-lesionally. Volume estimation is not impacted by hemisphere side, study group, or scan timepoint. Segmentation method-hippocampal size interaction determines volume estimation. FreeSurfer/Subfields and fsl/FIRST segmentations agreed best with manual tracing.
We assessed first-year hippocampal atrophy in stroke patients and healthy controls using manual and automated segmentations: AdaBoost, FIRST (fsl/v5.0.8), FreeSurfer/v5.3 and v6.0, and Subfields (in FreeSurfer/v6.0). We estimated hippocampal volumes in 39 healthy controls and 124 stroke participants at three months, and 38 controls and 113 stroke participants at one year. We used intra-class correlation, concordance, and reduced major axis regression to assess agreement between automated and ‘Manual’ estimations. A linear mixed-effect model was used to characterize hippocampal atrophy. Overall, hippocampal volumes were reduced by 3.9% in first-ever stroke and 9.2% in recurrent stroke at three months post-stroke, with comparable ipsi-and contra-lesional reductions in first-ever stroke. Mean atrophy rates between time points were 0.5% for controls and 1.0% for stroke patients (0.6% contra-lesionally, 1.4% ipsi-lesionally). Atrophy rates in left and right-hemisphere strokes were comparable. All methods revealed significant volume change in first-ever and ipsi-lesional stroke (p < 0.001). Hippocampal volume estimation was not impacted by hemisphere, study group, or scan time point, but rather, by the interaction between the automated segmentation method and hippocampal size. Compared to Manual, Subfields and FIRST recorded the lowest bias. FreeSurfer/v5.3 overestimated volumes the most for large hippocampi, while FIRST was the most accurate in estimating small volumes. AdaBoost performance was average. Our findings suggest that first-year ipsi-lesional hippocampal atrophy rate especially in first-ever stroke, is greater than atrophy rates in healthy controls and contra-lesional stroke. Subfields and FIRST can complementarily be effective in characterizing the hippocampal atrophy in healthy and stroke cohorts.
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Affiliation(s)
- Mohamed Salah Khlif
- The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Emilio Werden
- The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Natalia Egorova
- The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia; Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Australia
| | - Marina Boccardi
- LANVIE-Laboratory of Neuroimaging of Aging, University of Geneva, Geneva, Switzerland; Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Alberto Redolfi
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE), IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Laura Bird
- The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Amy Brodtmann
- The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia; Department of Neurology, Austin Health, Heidelberg, Victoria, Australia
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Archetti D, Ingala S, Venkatraghavan V, Wottschel V, Young AL, Bellio M, Bron EE, Klein S, Barkhof F, Alexander DC, Oxtoby NP, Frisoni GB, Redolfi A. Multi-study validation of data-driven disease progression models to characterize evolution of biomarkers in Alzheimer's disease. NEUROIMAGE-CLINICAL 2019; 24:101954. [PMID: 31362149 PMCID: PMC6675943 DOI: 10.1016/j.nicl.2019.101954] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 06/24/2019] [Accepted: 07/19/2019] [Indexed: 11/18/2022]
Abstract
Understanding the sequence of biological and clinical events along the course of Alzheimer's disease provides insights into dementia pathophysiology and can help participant selection in clinical trials. Our objective is to train two data-driven computational models for sequencing these events, the Event Based Model (EBM) and discriminative-EBM (DEBM), on the basis of well-characterized research data, then validate the trained models on subjects from clinical cohorts characterized by less-structured data-acquisition protocols. Seven independent data cohorts were considered totalling 2389 cognitively normal (CN), 1424 mild cognitive impairment (MCI) and 743 Alzheimer's disease (AD) patients. The Alzheimer's Disease Neuroimaging Initiative (ADNI) data set was used as training set for the constriction of disease models while a collection of multi-centric data cohorts was used as test set for validation. Cross-sectional information related to clinical, cognitive, imaging and cerebrospinal fluid (CSF) biomarkers was used. Event sequences obtained with EBM and DEBM showed differences in the ordering of single biomarkers but according to both the first biomarkers to become abnormal were those related to CSF, followed by cognitive scores, while structural imaging showed significant volumetric decreases at later stages of the disease progression. Staging of test set subjects based on sequences obtained with both models showed good linear correlation with the Mini Mental State Examination score (R2EBM = 0.866; R2DEBM = 0.906). In discriminant analyses, significant differences (p-value ≤ 0.05) between the staging of subjects from training and test sets were observed in both models. No significant difference between the staging of subjects from the training and test was observed (p-value > 0.05) when considering a subset composed by 562 subjects for which all biomarker families (cognitive, imaging and CSF) are available. Event sequence obtained with DEBM recapitulates the heuristic models in a data-driven fashion and is clinically plausible. We demonstrated inter-cohort transferability of two disease progression models and their robustness in detecting AD phases. This is an important step towards the adoption of data-driven statistical models into clinical domain. Data-driven event sequences describe evolution of relevant biomarkers in AD. Agreement between event sequences and heuristic AD progression models Accuracy in classifying subjects from clinical cohorts up to 91% Staging of subjects and MMSE scores of individuals show linear relation. Transferability of AD progression models based on research data to clinical cohorts
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Affiliation(s)
- Damiano Archetti
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands.
| | - Vikram Venkatraghavan
- Biomedical Imaging Group Rotterdam, Depts. of Medical Informatics & Radiology, Erasmus MC, The Netherlands.
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands.
| | - Alexandra L Young
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK.
| | - Maura Bellio
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK.
| | - Esther E Bron
- Biomedical Imaging Group Rotterdam, Depts. of Medical Informatics & Radiology, Erasmus MC, The Netherlands.
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Depts. of Medical Informatics & Radiology, Erasmus MC, The Netherlands.
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands; Institutes of Neurology and Healthcare Engineering, UCL, London, UK.
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK.
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK.
| | - Giovanni B Frisoni
- University of Geneva, Geneva, Switzerland; IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
| | - Alberto Redolfi
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
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Falgàs N, Sánchez-Valle R, Bargalló N, Balasa M, Fernández-Villullas G, Bosch B, Olives J, Tort-Merino A, Antonell A, Muñoz-García C, León M, Grau O, Castellví M, Coll-Padrós N, Rami L, Redolfi A, Lladó A. Hippocampal atrophy has limited usefulness as a diagnostic biomarker on the early onset Alzheimer's disease patients: A comparison between visual and quantitative assessment. NEUROIMAGE-CLINICAL 2019; 23:101927. [PMID: 31491836 PMCID: PMC6627030 DOI: 10.1016/j.nicl.2019.101927] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 06/11/2019] [Accepted: 06/30/2019] [Indexed: 11/24/2022]
Abstract
NIA-AA diagnostic criteria include volumetric or visual rating measures of hippocampal atrophy (HA) as a diagnostic biomarker of Alzheimer's disease (AD). We aimed to determine its utility as a diagnostic biomarker for early onset Alzheimer's disease (EOAD) by assessing Medial Temporal Atrophy (MTA) and hippocampal volume (HV) determination. MTA score and HV quantified by FreeSurfer were assessed in 140 (aged ≤65) subjects with biomarker supported diagnosis: 38 amnesic (A-EOAD), 20 non-amnesic (NA-EOAD), 30 late onset AD (LOAD), 20 fronto-temporal dementia (FTD) and 32 healthy controls (HC). The results showed that the proportion of MTA ≥ 1.5 was higher on LOAD and FTD than EOAD and HC but none of the MTA thresholds (≥1, ≥1.5 and ≥ 2) showed acceptable diagnostic accuracy. LOAD had lower HV than the other groups. A-EOAD HV was lower than NA-EOAD and HC but equal to FTD. The 6258 mm3 cut-off showed good diagnostic accuracy between A-EOAD and HC. Both tools showed a moderate inverse correlation. In conclusion, MTA has a limited diagnostic utility as an EOAD biomarker as it does not discriminate AD from FTD or HC in initial symptomatic stages. HV may discriminate A-EOAD from HC but not from FTD. FTD had higher MTA scores than AD patients. MTA scores visual assessment had low diagnostic performance in EOAD. Amnesic EOAD patients had lower hippocampal volume than the non-amnesic ones. Quantitative assessment only discriminate between amnesic EOAD from controls.
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Affiliation(s)
- Neus Falgàs
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Raquel Sánchez-Valle
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Núria Bargalló
- Imaging Diagnostic Center, Hospital Clínic, Barcelona, Spain; Magnetic Resonance Image Core Facility, IDIBAPS, Spain
| | - Mircea Balasa
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain; Atlantic Fellow for Equity in Brain Health, Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Guadalupe Fernández-Villullas
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Beatriz Bosch
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Jaume Olives
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Adrià Tort-Merino
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Anna Antonell
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Cristina Muñoz-García
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - María León
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Oriol Grau
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Magdalena Castellví
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Nina Coll-Padrós
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Lorena Rami
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain
| | - Alberto Redolfi
- Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Albert Lladó
- Alzheimer's disease and other cognitive disorders Unit, Neurology department, IDIBAPS, Hospital Clínic, Barcelona, Spain.
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7
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Bos I, Vos S, Vandenberghe R, Scheltens P, Engelborghs S, Frisoni G, Molinuevo JL, Wallin A, Lleó A, Popp J, Martinez-Lage P, Baird A, Dobson R, Legido-Quigley C, Sleegers K, Van Broeckhoven C, Bertram L, ten Kate M, Barkhof F, Zetterberg H, Lovestone S, Streffer J, Visser PJ. The EMIF-AD Multimodal Biomarker Discovery study: design, methods and cohort characteristics. Alzheimers Res Ther 2018; 10:64. [PMID: 29980228 PMCID: PMC6035398 DOI: 10.1186/s13195-018-0396-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 06/08/2018] [Indexed: 02/04/2023]
Abstract
BACKGROUND There is an urgent need for novel, noninvasive biomarkers to diagnose Alzheimer's disease (AD) in the predementia stages and to predict the rate of decline. Therefore, we set up the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study. In this report we describe the design of the study, the methods used and the characteristics of the participants. METHODS Participants were selected from existing prospective multicenter and single-center European studies. Inclusion criteria were having normal cognition (NC) or a diagnosis of mild cognitive impairment (MCI) or AD-type dementia at baseline, age above 50 years, known amyloid-beta (Aβ) status, availability of cognitive test results and at least two of the following materials: plasma, DNA, magnetic resonance imaging (MRI) or cerebrospinal fluid (CSF). Targeted and untargeted metabolomic and proteomic analyses were performed in plasma, and targeted and untargeted proteomics were performed in CSF. Genome-wide SNP genotyping, next-generation sequencing and methylation profiling were conducted in DNA. Visual rating and volumetric measures were assessed on MRI. Baseline characteristics were analyzed using ANOVA or chi-square, rate of decline analyzed by linear mixed modeling. RESULTS We included 1221 individuals (NC n = 492, MCI n = 527, AD-type dementia n = 202) with a mean age of 67.9 (SD 8.3) years. The percentage Aβ+ was 26% in the NC, 58% in the MCI, and 87% in the AD-type dementia groups. Plasma samples were available for 1189 (97%) subjects, DNA samples for 929 (76%) subjects, MRI scans for 862 (71%) subjects and CSF samples for 767 (63%) subjects. For 759 (62%) individuals, clinical follow-up data were available. In each diagnostic group, the APOE ε4 allele was more frequent amongst Aβ+ individuals (p < 0.001). Only in MCI was there a difference in baseline Mini Mental State Examination (MMSE) score between the A groups (p < 0.001). Aβ+ had a faster rate of decline on the MMSE during follow-up in the NC (p < 0.001) and MCI (p < 0.001) groups. CONCLUSIONS The characteristics of this large cohort of elderly subjects at various cognitive stages confirm the central roles of Aβ and APOE ε4 in AD pathogenesis. The results of the multimodal analyses will provide new insights into underlying mechanisms and facilitate the discovery of new diagnostic and prognostic AD biomarkers. All researchers can apply for access to the EMIF-AD MBD data by submitting a research proposal via the EMIF-AD Catalog.
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Affiliation(s)
- Isabelle Bos
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
- Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Universiteitssingel 40, Box 34, P.O. Box 616, 6200 MD Maastricht, the Netherlands
| | - Stephanie Vos
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Rik Vandenberghe
- University Hospital Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, the Netherlands
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium
- Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
- University of Antwerp, Antwerp, Belgium
| | - Giovanni Frisoni
- University of Geneva, Geneva, Switzerland
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - José Luis Molinuevo
- Alzheimer’s Disease & Other Cognitive Disorders Unit, Hospital Clínic—IDIBAPS, Barcelona, Spain
- Barcelona Beta Brain Research Center, Fundació Pasqual Maragall, Barcelona, Spain
| | - Anders Wallin
- Institute of Neuroscience and Physiology, Moelndal, Sweden
| | - Alberto Lleó
- Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Julius Popp
- Geriatric Psychiatry, Department of Mental Health and Psychiatry, Geneva University Hospitals, Geneva, Switzerland
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Pablo Martinez-Lage
- Center for Research and Advanced Therapies, CITA—Alzheimer Foundation, San Sebastian, Spain
| | | | - Richard Dobson
- King’s College London, London, UK
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK
- Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | | | - Kristel Sleegers
- Neurodegenerative Brain Diseases Group, VIB—Department of Molecular Genetics, Antwerp, Belgium
- Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Christine Van Broeckhoven
- Neurodegenerative Brain Diseases Group, VIB—Department of Molecular Genetics, Antwerp, Belgium
- Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany
- School of Public Health, Imperial College London, London, UK
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Mara ten Kate
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute, London, UK
| | | | - Johannes Streffer
- Experimental Medicine, Janssen Pharmaceutical Companies, Beerse, Belgium
| | - Pieter Jelle Visser
- Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
- Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, the Netherlands
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Redolfi A, Manset D, Barkhof F, Wahlund LO, Glatard T, Mangin JF, Frisoni GB. Head-to-head comparison of two popular cortical thickness extraction algorithms: a cross-sectional and longitudinal study. PLoS One 2015; 10:e0117692. [PMID: 25781983 PMCID: PMC4364123 DOI: 10.1371/journal.pone.0117692] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 12/29/2014] [Indexed: 11/19/2022] Open
Abstract
Background and Purpose The measurement of cortical shrinkage is a candidate marker of disease progression in Alzheimer’s. This study evaluated the performance of two pipelines: Civet-CLASP (v1.1.9) and Freesurfer (v5.3.0). Methods Images from 185 ADNI1 cases (69 elderly controls (CTR), 37 stable MCI (sMCI), 27 progressive MCI (pMCI), and 52 Alzheimer (AD) patients) scanned at baseline, month 12, and month 24 were processed using the two pipelines and two interconnected e-infrastructures: neuGRID (https://neugrid4you.eu) and VIP (http://vip.creatis.insa-lyon.fr). The vertex-by-vertex cross-algorithm comparison was made possible applying the 3D gradient vector flow (GVF) and closest point search (CPS) techniques. Results The cortical thickness measured with Freesurfer was systematically lower by one third if compared to Civet’s. Cross-sectionally, Freesurfer’s effect size was significantly different in the posterior division of the temporal fusiform cortex. Both pipelines were weakly or mildly correlated with the Mini Mental State Examination score (MMSE) and the hippocampal volumetry. Civet differed significantly from Freesurfer in large frontal, parietal, temporal and occipital regions (p<0.05). In a discriminant analysis with cortical ROIs having effect size larger than 0.8, both pipelines gave no significant differences in area under the curve (AUC). Longitudinally, effect sizes were not significantly different in any of the 28 ROIs tested. Both pipelines weakly correlated with MMSE decay, showing no significant differences. Freesurfer mildly correlated with hippocampal thinning rate and differed in the supramarginal gyrus, temporal gyrus, and in the lateral occipital cortex compared to Civet (p<0.05). In a discriminant analysis with ROIs having effect size larger than 0.6, both pipelines yielded no significant differences in the AUC. Conclusions Civet appears slightly more sensitive to the typical AD atrophic pattern at the MCI stage, but both pipelines can accurately characterize the topography of cortical thinning at the dementia stage.
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Affiliation(s)
- Alberto Redolfi
- Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- * E-mail:
| | - David Manset
- Gnúbila France, Imp Pres d’en Bas, Argonay, France
| | - Frederik Barkhof
- Department of Radiology, VU University Medical Center, Amsterdam, The Netherlands
| | - Lars-Olof Wahlund
- Department of Neurobiology, Caring Sciences & Society, Division of Clinical Geriatrics Novum, Karolinska Institutet, Stockholm, Stockholm, Sweden
| | - Tristan Glatard
- CREATIS, CNRS, INSERM, University of Lyon, Lyon, France
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | | | - Giovanni B. Frisoni
- Laboratory of Epidemiology & Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Laboratory of Neuroimaging of Aging, Memory Clinic and LANVIE, University Hospitals and University of Geneva, Geneva, Switzerland
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Abstract
One of the most fascinating challenges in neuroscience is the reconstruction of the connectivity map of the brain. Recent years have seen a rapid expansion in the field of connectomics, whose aim is to trace this map and understand its relationship with neural computation. Many different approaches, ranging from electron and optical microscopy to magnetic resonance imaging, have been proposed to address the connectomics challenge on various spatial scales and in different species. Here, we review the main technological advances in the microscopy techniques applied to connectomics, highlighting the potential and limitations of the different methods. Finally, we briefly discuss the role of connectomics in the Human Brain Project, the Future and Emerging Technologies (FET) Flagship recently approved by the European Commission.
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D'Angelo E, Solinas S, Garrido J, Casellato C, Pedrocchi A, Mapelli J, Gandolfi D, Prestori F. Realistic modeling of neurons and networks: towards brain simulation. FUNCTIONAL NEUROLOGY 2014; 28:153-66. [PMID: 24139652 DOI: 10.11138/fneur/2013.28.3.153] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Realistic modeling is a new advanced methodology for investigating brain functions. Realistic modeling is based on a detailed biophysical description of neurons and synapses, which can be integrated into microcircuits. The latter can, in turn, be further integrated to form large-scale brain networks and eventually to reconstruct complex brain systems. Here we provide a review of the realistic simulation strategy and use the cerebellar network as an example. This network has been carefully investigated at molecular and cellular level and has been the object of intense theoretical investigation. The cerebellum is thought to lie at the core of the forward controller operations of the brain and to implement timing and sensory prediction functions. The cerebellum is well described and provides a challenging field in which one of the most advanced realistic microcircuit models has been generated. We illustrate how these models can be elaborated and embedded into robotic control systems to gain insight into how the cellular properties of cerebellar neurons emerge in integrated behaviors. Realistic network modeling opens up new perspectives for the investigation of brain pathologies and for the neurorobotic field.
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