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Marchese SM, Palesi F, Nigri A, Bruzzone MG, Pantaleoni C, Gandini Wheeler-Kingshott CAM, D’Arrigo S, D’Angelo E, Cavallari P. Structural and connectivity parameters reveal spared connectivity in young patients with non-progressive compared to slow-progressive cerebellar ataxia. Front Neurol 2023; 14:1279616. [PMID: 37965172 PMCID: PMC10642782 DOI: 10.3389/fneur.2023.1279616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/12/2023] [Indexed: 11/16/2023] Open
Abstract
Introduction Within Pediatric Cerebellar Ataxias (PCAs), patients with non-progressive ataxia (NonP) surprisingly show postural motor behavior comparable to that of healthy controls, differently to slow-progressive ataxia patients (SlowP). This difference may depend on the building of compensatory strategies of the intact areas in NonP brain network. Methods Eleven PCAs patients were recruited: five with NonP and six with SlowP. We assessed volumetric and axonal bundles alterations with a multimodal approach to investigate whether eventual spared connectivity between basal ganglia and cerebellum explains the different postural motor behavior of NonP and SlowP patients. Results Cerebellar lobules were smaller in SlowP patients. NonP patients showed a lower number of streamlines in the cerebello-thalamo-cortical tracts but a generalized higher integrity of white matter tracts connecting the cortex and the basal ganglia with the cerebellum. Discussion This work reveals that the axonal bundles connecting the cerebellum with basal ganglia and cortex demonstrate a higher integrity in NonP patients. This evidence highlights the importance of the cerebellum-basal ganglia connectivity to explain the different postural motor behavior of NonP and SlowP patients and support the possible compensatory role of basal ganglia in patients with stable cerebellar malformation.
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Affiliation(s)
- Silvia Maria Marchese
- Human Physiology Section of the DePT, Università degli Studi di Milano, Milan, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Unit of Digital Neuroscience, IRCCS Mondino Foundation, Pavia, Italy
| | - Anna Nigri
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico “Carlo Besta”, Milan, Italy
| | - Maria Grazia Bruzzone
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico “Carlo Besta”, Milan, Italy
| | - Chiara Pantaleoni
- Department of Pediatric Neuroscience, 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
- Unit of Digital Neuroscience, IRCCS Mondino Foundation, Pavia, Italy
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Stefano D’Arrigo
- Department of Pediatric Neuroscience, Fondazione IRCCS Istituto Neurologico "Carlo Besta", Milan, Italy
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Unit of Digital Neuroscience, IRCCS Mondino Foundation, Pavia, Italy
| | - Paolo Cavallari
- Human Physiology Section of the DePT, Università degli Studi di Milano, Milan, Italy
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Lorenzi RM, Geminiani A, Zerlaut Y, De Grazia M, Destexhe A, Gandini Wheeler-Kingshott CAM, Palesi F, Casellato C, D'Angelo E. A multi-layer mean-field model of the cerebellum embedding microstructure and population-specific dynamics. PLoS Comput Biol 2023; 19:e1011434. [PMID: 37656758 PMCID: PMC10501640 DOI: 10.1371/journal.pcbi.1011434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 09/14/2023] [Accepted: 08/15/2023] [Indexed: 09/03/2023] Open
Abstract
Mean-field (MF) models are computational formalism used to summarize in a few statistical parameters the salient biophysical properties of an inter-wired neuronal network. Their formalism normally incorporates different types of neurons and synapses along with their topological organization. MFs are crucial to efficiently implement the computational modules of large-scale models of brain function, maintaining the specificity of local cortical microcircuits. While MFs have been generated for the isocortex, they are still missing for other parts of the brain. Here we have designed and simulated a multi-layer MF of the cerebellar microcircuit (including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells) and validated it against experimental data and the corresponding spiking neural network (SNN) microcircuit model. The cerebellar MF was built using a system of equations, where properties of neuronal populations and topological parameters are embedded in inter-dependent transfer functions. The model time constant was optimised using local field potentials recorded experimentally from acute mouse cerebellar slices as a template. The MF reproduced the average dynamics of different neuronal populations in response to various input patterns and predicted the modulation of the Purkinje Cells firing depending on cortical plasticity, which drives learning in associative tasks, and the level of feedforward inhibition. The cerebellar MF provides a computationally efficient tool for future investigations of the causal relationship between microscopic neuronal properties and ensemble brain activity in virtual brain models addressing both physiological and pathological conditions.
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Affiliation(s)
| | - Alice Geminiani
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Yann Zerlaut
- Institut du Cerveau-Paris Brain Institute-ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | | | | | - Claudia A M Gandini Wheeler-Kingshott
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, UCL, London, United Kingdom
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Claudia Casellato
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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3
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Borrelli P, Savini G, Cavaliere C, Palesi F, Grazia Bruzzone M, Aquino D, Biagi L, Bosco P, Carne I, Ferraro S, Giulietti G, Napolitano A, Nigri A, Pavone L, Pirastru A, Redolfi A, Tagliavini F, Tosetti M, Salvatore M, Gandini Wheeler-Kingshott CAM, Aiello M. Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network. Phys Med 2023; 112:102610. [PMID: 37331082 DOI: 10.1016/j.ejmp.2023.102610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 03/20/2023] [Accepted: 05/30/2023] [Indexed: 06/20/2023] Open
Abstract
PURPOSE The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers. METHODS Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A "traveling brains" dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability. RESULTS The results show an inter-center and inter-subject variability of <10%, except for "clustering coefficient" (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners' hardware. CONCLUSIONS The results show low variability of connectivity topological metrics across sites running a harmonised protocol.
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Affiliation(s)
| | | | | | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, Università degli Studi di Pavia, Pavia, Italy
| | - Maria Grazia Bruzzone
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Domenico Aquino
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Laura Biagi
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Paolo Bosco
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Irene Carne
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Stefania Ferraro
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Giovanni Giulietti
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy; SAIMLAL Department, Sapienza University of Rome, Rome, Italy
| | - Antonio Napolitano
- Medical Physics, IRCCS Istituto Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Anna Nigri
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | | | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizio Tagliavini
- Scientific Direction, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Michela Tosetti
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | | | - Claudia A M Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, Università degli Studi di Pavia, Pavia, Italy; NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
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4
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Monteverdi A, Palesi F, Schirner M, Argentino F, Merante M, Redolfi A, Conca F, Mazzocchi L, Cappa SF, Cotta Ramusino M, Costa A, Pichiecchio A, Farina LM, Jirsa V, Ritter P, Gandini Wheeler-Kingshott CAM, D’Angelo E. Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias. Front Aging Neurosci 2023; 15:1204134. [PMID: 37577354 PMCID: PMC10419271 DOI: 10.3389/fnagi.2023.1204134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 07/10/2023] [Indexed: 08/15/2023] Open
Abstract
Introduction Neural circuit alterations lay at the core of brain physiopathology, and yet are hard to unveil in living subjects. The Virtual Brain (TVB) modeling, by exploiting structural and functional magnetic resonance imaging (MRI), yields mesoscopic parameters of connectivity and synaptic transmission. Methods We used TVB to simulate brain networks, which are key for human brain function, in Alzheimer's disease (AD) and frontotemporal dementia (FTD) patients, whose connectivity and synaptic parameters remain largely unknown; we then compared them to healthy controls, to reveal novel in vivo pathological hallmarks. Results The pattern of simulated parameter differed between AD and FTD, shedding light on disease-specific alterations in brain networks. Individual subjects displayed subtle differences in network parameter patterns that significantly correlated with their individual neuropsychological, clinical, and pharmacological profiles. Discussion These TVB simulations, by informing about a new personalized set of networks parameters, open new perspectives for understanding dementias mechanisms and design personalized therapeutic approaches.
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Affiliation(s)
- Anita Monteverdi
- Unit of Digital Neuroscience, IRCCS Mondino Foundation, Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Michael Schirner
- Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
| | - Francesca Argentino
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Mariateresa Merante
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Laura Mazzocchi
- Advanced Imaging and Artificial Intelligence Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Stefano F. Cappa
- IRCCS Mondino Foundation, Pavia, Italy
- University Institute of Advanced Studies (IUSS), Pavia, Italy
| | | | - Alfredo Costa
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Anna Pichiecchio
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Advanced Imaging and Artificial Intelligence Center, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, INSERM, INS, Aix Marseille University, Marseille, France
| | - Petra Ritter
- Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Unit of Digital Neuroscience, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Egidio D’Angelo
- Unit of Digital Neuroscience, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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5
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Bosco P, Lancione M, Retico A, Nigri A, Aquino D, Baglio F, Carne I, Ferraro S, Giulietti G, Napolitano A, Palesi F, Pavone L, Savini G, Tagliavini F, Bruzzone MG, Gandini Wheeler-Kingshott CAM, Tosetti M, Biagi L. Quality assessment, variability and reproducibility of anatomical measurements derived from T1-weighted brain imaging: The RIN-Neuroimaging Network case study. Phys Med 2023; 110:102577. [PMID: 37126963 DOI: 10.1016/j.ejmp.2023.102577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 03/01/2023] [Accepted: 04/05/2023] [Indexed: 05/03/2023] Open
Abstract
Initiatives for the collection of harmonized MRI datasets are growing continuously, opening questions on the reliability of results obtained in multi-site contexts. Here we present the assessment of the brain anatomical variability of MRI-derived measurements obtained from T1-weighted images, acquired according to the Standard Operating Procedures, promoted by the RIN-Neuroimaging Network. A multicentric dataset composed of 77 brain T1w acquisitions of young healthy volunteers (mean age = 29.7 ± 5.0 years), collected in 15 sites with MRI scanners of three different vendors, was considered. Parallelly, a dataset of 7 "traveling" subjects, each undergoing three acquisitions with scanners from different vendors, was also used. Intra-site, intra-vendor, and inter-site variabilities were evaluated in terms of the percentage standard deviation of volumetric and cortical thickness measures. Image quality metrics such as contrast-to-noise and signal-to-noise ratio in gray and white matter were also assessed for all sites and vendors. The results showed a measured global variability that ranges from 11% to 19% for subcortical volumes and from 3% to 10% for cortical thicknesses. Univariate distributions of the normalized volumes of subcortical regions, as well as the distributions of the thickness of cortical parcels appeared to be significantly different among sites in 8 subcortical (out of 17) and 21 cortical (out of 68) regions of i nterest in the multicentric study. The Bland-Altman analysis on "traveling" brain measurements did not detect systematic scanner biases even though a multivariate classification approach was able to classify the scanner vendor from brain measures with an accuracy of 0.60 ± 0.14 (chance level 0.33).
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Affiliation(s)
- Paolo Bosco
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Marta Lancione
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Alessandra Retico
- Pisa Division, INFN - National Institute for Nuclear Physics, Pisa, Italy
| | - Anna Nigri
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Domenico Aquino
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | - Irene Carne
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Stefania Ferraro
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy; MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Giovanni Giulietti
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy; SAIMLAL Department, Sapienza University of Rome, Rome, Italy
| | - Antonio Napolitano
- Medical Physics, IRCCS Istituto Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Fulvia Palesi
- Neuroradiology Unit, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | - Giovanni Savini
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Fabrizio Tagliavini
- Scientific Direction, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Maria Grazia Bruzzone
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- Neuroradiology Unit, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy; NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square, Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Michela Tosetti
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy.
| | - Laura Biagi
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
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Müller EJ, Palesi F, Hou KY, Tan J, Close T, Wheeler-Kingschott CAMG, D’Angelo E, Calamante F, Shine JM. Parallel processing relies on a distributed, low-dimensional cortico-cerebellar architecture. Netw Neurosci 2023. [DOI: 10.1162/netn_a_00308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Abstract
A characteristic feature of human cognition is our ability to ‘multi-task’ – performing two or more tasks in parallel – particularly when one task is well-learned. How the brain supports this capacity remains poorly understood. Most past studies have focussed on identifying the areas of the brain – typically the dorsolateral prefrontal cortex – that are required to navigate information processing bottlenecks. In contrast, we take a systems neuroscience approach to test the hypothesis that the capacity to conduct effective parallel processing relies on a distributed architecture that interconnects the cerebral cortex with the cerebellum. The latter structure contains over half of the neurons in the adult human brain, and is well-suited to support the fast, effective, dynamic sequences required to perform tasks relatively automatically. By delegating stereotyped within-task computations to the cerebellum, the cerebral cortex can be freed up to focus on the more challenging aspects of performing the tasks in parallel. To test this hypothesis, we analysed task-based fMRI data from 50 participants who performed a task in which they either balanced an avatar on a screen (‘Balance’), performed serial-7 subtractions (‘Calculation’) or performed both in parallel (‘Dual-Task’). Using a set of approaches that include dimensionality reduction, structure-function coupling and time-varying functional connectivity, we provide robust evidence in support of our hypothesis. We conclude that distributed interactions between the cerebral cortex and cerebellum are crucially involved in parallel processing in the human brain.
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Affiliation(s)
- Eli J. Müller
- Complex Systems Research Group, The University of Sydney, Sydney, NSW, Australia
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Kevin Y. Hou
- Complex Systems Research Group, The University of Sydney, Sydney, NSW, Australia
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Joshua Tan
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Thomas Close
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW, Australia
- Sydney Imaging, The University of Sydney, Sydney, NSW, Australia
- National Imaging Facility, Sydney, NSW, Australia
| | - Claudia A. M. Gandini Wheeler-Kingschott
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Faculty of Brain Sciences, UCL Queen Square Institute of Neurology, UCL, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Fernando Calamante
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW, Australia
- Sydney Imaging, The University of Sydney, Sydney, NSW, Australia
| | - James M. Shine
- Complex Systems Research Group, The University of Sydney, Sydney, NSW, Australia
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
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Lancione M, Bosco P, Costagli M, Nigri A, Aquino D, Carne I, Ferraro S, Giulietti G, Napolitano A, Palesi F, Pavone L, Pirastru A, Savini G, Tagliavini F, Bruzzone MG, Gandini Wheeler-Kingshott CA, Tosetti M, Biagi L. Multi-centre and multi-vendor reproducibility of a standardized protocol for quantitative susceptibility Mapping of the human brain at 3T. Phys Med 2022; 103:37-45. [DOI: 10.1016/j.ejmp.2022.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/12/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
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Gaviraghi M, Ricciardi A, Palesi F, Brownlee W, Vitali P, Prados F, Kanber B, Gandini Wheeler-Kingshott CAM. A generalized deep learning network for fractional anisotropy reconstruction: Application to epilepsy and multiple sclerosis. Front Neuroinform 2022; 16:891234. [PMID: 35991288 PMCID: PMC9390860 DOI: 10.3389/fninf.2022.891234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/28/2022] [Indexed: 11/14/2022] Open
Abstract
Fractional anisotropy (FA) is a quantitative map sensitive to microstructural properties of tissues in vivo and it is extensively used to study the healthy and pathological brain. This map is classically calculated by model fitting (standard method) and requires many diffusion weighted (DW) images for data quality and unbiased readings, hence needing the acquisition time of several minutes. Here, we adapted the U-net architecture to be generalized and to obtain good quality FA from DW volumes acquired in 1 minute. Our network requires 10 input DW volumes (hence fast acquisition), is robust to the direction of application of the diffusion gradients (hence generalized), and preserves/improves map quality (hence good quality maps). We trained the network on the human connectome project (HCP) data using the standard model-fitting method on the entire set of DW directions to extract FA (ground truth). We addressed the generalization problem, i.e., we trained the network to be applicable, without retraining, to clinical datasets acquired on different scanners with different DW imaging protocols. The network was applied to two different clinical datasets to assess FA quality and sensitivity to pathology in temporal lobe epilepsy and multiple sclerosis, respectively. For HCP data, when compared to the ground truth FA, the FA obtained from 10 DW volumes using the network was significantly better (p <10-4) than the FA obtained using the standard pipeline. For the clinical datasets, the network FA retained the same microstructural characteristics as the FA calculated with all DW volumes using the standard method. At the subject level, the comparison between white matter (WM) ground truth FA values and network FA showed the same distribution; at the group level, statistical differences of WM values detected in the clinical datasets with the ground truth FA were reproduced when using values from the network FA, i.e., the network retained sensitivity to pathology. In conclusion, the proposed network provides a clinically available method to obtain FA from a generic set of 10 DW volumes acquirable in 1 minute, augmenting data quality compared to direct model fitting, reducing the possibility of bias from sub-sampled data, and retaining FA pathological sensitivity, which is very attractive for clinical applications.
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Affiliation(s)
- Marta Gaviraghi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Antonio Ricciardi
- NMR Research Unit, Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London (UCL), London, United Kingdom
| | - Fulvia Palesi
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Wallace Brownlee
- NMR Research Unit, Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London (UCL), London, United Kingdom
| | - Paolo Vitali
- Department of Radiology, IRCCS Policlinico San Donato, Milan, Italy
- Department of Biomedical Sciences for Health, Universitá degli Studi di Milano, Milan, Italy
| | - Ferran Prados
- NMR Research Unit, Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London (UCL), London, United Kingdom
- Department of Medical Physics and Bioengineering, Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom
- E-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Baris Kanber
- NMR Research Unit, Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London (UCL), London, United Kingdom
| | - Claudia A. M. Gandini Wheeler-Kingshott
- NMR Research Unit, Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London (UCL), London, United Kingdom
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Centre, IRCCS Mondino Foundation, Pavia, Italy
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9
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Monteverdi A, Palesi F, Costa A, Vitali P, Pichiecchio A, Cotta Ramusino M, Bernini S, Jirsa V, Gandini Wheeler-Kingshott CAM, D’Angelo E. Subject-specific features of excitation/inhibition profiles in neurodegenerative diseases. Front Aging Neurosci 2022; 14:868342. [PMID: 35992607 PMCID: PMC9391060 DOI: 10.3389/fnagi.2022.868342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 07/05/2022] [Indexed: 11/26/2022] Open
Abstract
Brain pathologies are characterized by microscopic changes in neurons and synapses that reverberate into large scale networks altering brain dynamics and functional states. An important yet unresolved issue concerns the impact of patients' excitation/inhibition profiles on neurodegenerative diseases including Alzheimer's Disease, Frontotemporal Dementia, and Amyotrophic Lateral Sclerosis. In this work, we used The Virtual Brain (TVB) simulation platform to simulate brain dynamics in healthy and neurodegenerative conditions and to extract information about the excitatory/inhibitory balance in single subjects. The brain structural and functional connectomes were extracted from 3T-MRI (Magnetic Resonance Imaging) scans and TVB nodes were represented by a Wong-Wang neural mass model endowing an explicit representation of the excitatory/inhibitory balance. Simulations were performed including both cerebral and cerebellar nodes and their structural connections to explore cerebellar impact on brain dynamics generation. The potential for clinical translation of TVB derived biophysical parameters was assessed by exploring their association with patients' cognitive performance and testing their discriminative power between clinical conditions. Our results showed that TVB biophysical parameters differed between clinical phenotypes, predicting higher global coupling and inhibition in Alzheimer's Disease and stronger N-methyl-D-aspartate (NMDA) receptor-dependent excitation in Amyotrophic Lateral Sclerosis. These physio-pathological parameters allowed us to perform an advanced analysis of patients' conditions. In backward regressions, TVB-derived parameters significantly contributed to explain the variation of neuropsychological scores and, in discriminant analysis, the combination of TVB parameters and neuropsychological scores significantly improved the discriminative power between clinical conditions. Moreover, cluster analysis provided a unique description of the excitatory/inhibitory balance in individual patients. Importantly, the integration of cerebro-cerebellar loops in simulations improved TVB predictive power, i.e., the correlation between experimental and simulated functional connectivity in all pathological conditions supporting the cerebellar role in brain function disrupted by neurodegeneration. Overall, TVB simulations reveal differences in the excitatory/inhibitory balance of individual patients that, combined with cognitive assessment, can promote the personalized diagnosis and therapy of neurodegenerative diseases.
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Affiliation(s)
- Anita Monteverdi
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Alfredo Costa
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Paolo Vitali
- Department of Radiology, IRCCS Policlinico San Donato, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Anna Pichiecchio
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Advanced Imaging and Radiomic Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Matteo Cotta Ramusino
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Sara Bernini
- Dementia Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, INSERM, INS, Aix-Marseille University, Marseille, France
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, University College London (UCL) Queen Square Institute of Neurology, London, United Kingdom
| | - Egidio D’Angelo
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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10
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Nigri A, Ferraro S, Gandini Wheeler-Kingshott CAM, Tosetti M, Redolfi A, Forloni G, D'Angelo E, Aquino D, Biagi L, Bosco P, Carne I, De Francesco S, Demichelis G, Gianeri R, Lagana MM, Micotti E, Napolitano A, Palesi F, Pirastru A, Savini G, Alberici E, Amato C, Arrigoni F, Baglio F, Bozzali M, Castellano A, Cavaliere C, Contarino VE, Ferrazzi G, Gaudino S, Marino S, Manzo V, Pavone L, Politi LS, Roccatagliata L, Rognone E, Rossi A, Tonon C, Lodi R, Tagliavini F, Bruzzone MG. Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN-Neuroimaging Network. Front Neurol 2022; 13:855125. [PMID: 35493836 PMCID: PMC9047871 DOI: 10.3389/fneur.2022.855125] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures.
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Affiliation(s)
- Anna Nigri
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Stefania Ferraro
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Michela Tosetti
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Gianluigi Forloni
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Egidio D'Angelo
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Domenico Aquino
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Laura Biagi
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Paolo Bosco
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Irene Carne
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Greta Demichelis
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Ruben Gianeri
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | - Edoardo Micotti
- Laboratory of Biology of Neurodegenerative Disorders, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Antonio Napolitano
- Medical Physics, IRCCS Istituto Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Fulvia Palesi
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | - Giovanni Savini
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Elisa Alberici
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Carmelo Amato
- Unit of Neuroradiology, Oasi Research Institute-IRCCS, Troina, Italy
| | - Filippo Arrigoni
- Neuroimaging Unit, Scientific Institute, IRCCS E. Medea, Bosisio Parini, Italy
| | | | - Marco Bozzali
- Neuroimaging Laboratory, Santa Lucia Foundation, IRCCS, Rome, Italy
| | | | | | - Valeria Elisa Contarino
- Unità di Neuroradiologia, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Simona Gaudino
- Istituto di Radiologia, UOC Radiologia e Neuroradiologia, IRCCS Fondazione Policlinico Universitario Agostino Gemelli, Rome, Italy
| | - Silvia Marino
- IRCCS Centro Neurolesi “Bonino-Pulejo”, Messina, Italy
| | - Vittorio Manzo
- Department of Radiology, Istituto Auxologico Italiano, IRCCS, Milan, Italy
| | | | - Letterio S. Politi
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Luca Roccatagliata
- Neuroradiologia IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Dipartimento di Scienze della Salute Università di Genova, Genoa, Italy
| | - Elisa Rognone
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
| | - Andrea Rossi
- Dipartimento di Scienze della Salute Università di Genova, Genoa, Italy
- UO Neuroradiologia, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Caterina Tonon
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Raffaele Lodi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Fabrizio Tagliavini
- Scientific Direction, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Maria Grazia Bruzzone
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
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11
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Palesi F, Ferrante M, Gaviraghi M, Misiti A, Savini G, Lascialfari A, D'Angelo E, Gandini Wheeler‐Kingshott CAM. Motor and higher-order functions topography of the human dentate nuclei identified with tractography and clustering methods. Hum Brain Mapp 2021; 42:4348-4361. [PMID: 34087040 PMCID: PMC8356999 DOI: 10.1002/hbm.25551] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 05/11/2021] [Accepted: 05/17/2021] [Indexed: 01/29/2023] Open
Abstract
Deep gray matter nuclei are the synaptic relays, responsible to route signals between specific brain areas. Dentate nuclei (DNs) represent the main output channel of the cerebellum and yet are often unexplored especially in humans. We developed a multimodal MRI approach to identify DNs topography on the basis of their connectivity as well as their microstructural features. Based on results, we defined DN parcellations deputed to motor and to higher-order functions in humans in vivo. Whole-brain probabilistic tractography was performed on 25 healthy subjects from the Human Connectome Project to infer DN parcellations based on their connectivity with either the cerebral or the cerebellar cortex, in turn. A third DN atlas was created inputting microstructural diffusion-derived metrics in an unsupervised fuzzy c-means classification algorithm. All analyses were performed in native space, with probability atlas maps generated in standard space. Cerebellar lobule-specific connectivity identified one motor parcellation, accounting for about 30% of the DN volume, and two non-motor parcellations, one cognitive and one sensory, which occupied the remaining volume. The other two approaches provided overlapping results in terms of geometrical distribution with those identified with cerebellar lobule-specific connectivity, although with some differences in volumes. A gender effect was observed with respect to motor areas and higher-order function representations. This is the first study that indicates that more than half of the DN volumes is involved in non-motor functions and that connectivity-based and microstructure-based atlases provide complementary information. These results represent a step-ahead for the interpretation of pathological conditions involving cerebro-cerebellar circuits.
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Affiliation(s)
- Fulvia Palesi
- Department of Brain and Behavioral SciencesUniversity of PaviaPavia
| | | | - Marta Gaviraghi
- Department of Electrical, Computer, and Biomedical EngineeringUniversity of PaviaPaviaItaly
| | - Anastasia Misiti
- Department of Electrical, Computer, and Biomedical EngineeringUniversity of PaviaPaviaItaly
| | - Giovanni Savini
- Department of NeuroradiologyIRCCS Humanitas Research HospitalMilanItaly
| | | | - Egidio D'Angelo
- Department of Brain and Behavioral SciencesUniversity of PaviaPavia
- Brain Connectivity CenterIRCCS Mondino FoundationPavia
| | - Claudia A. M. Gandini Wheeler‐Kingshott
- Department of Brain and Behavioral SciencesUniversity of PaviaPavia
- Brain Connectivity CenterIRCCS Mondino FoundationPavia
- NMR Research Unit, Queen Square MS Centre, Department of NeuroinflammationUCL Queen Square Institute of NeurologyLondon
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12
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Pizzarotti B, Palesi F, Vitali P, Castellazzi G, Anzalone N, Alvisi E, Martinelli D, Bernini S, Cotta Ramusino M, Ceroni M, Micieli G, Sinforiani E, D'Angelo E, Costa A, Gandini Wheeler-Kingshott CAM. Frontal and Cerebellar Atrophy Supports FTSD-ALS Clinical Continuum. Front Aging Neurosci 2020; 12:593526. [PMID: 33324193 PMCID: PMC7726473 DOI: 10.3389/fnagi.2020.593526] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 11/02/2020] [Indexed: 11/13/2022] Open
Abstract
Background Frontotemporal Spectrum Disorder (FTSD) and Amyotrophic Lateral Sclerosis (ALS) are neurodegenerative diseases often considered as a continuum from clinical, epidemiologic, and genetic perspectives. We used localized brain volume alterations to evaluate common and specific features of FTSD, FTSD-ALS, and ALS patients to further understand this clinical continuum. Methods We used voxel-based morphometry on structural magnetic resonance images to localize volume alterations in group comparisons: patients (20 FTSD, seven FTSD-ALS, and 18 ALS) versus healthy controls (39 CTR), and patient groups between themselves. We used mean whole-brain cortical thickness ( C T ¯ ) to assess whether its correlations with local brain volume could propose mechanistic explanations of the heterogeneous clinical presentations. We also assessed whether volume reduction can explain cognitive impairment, measured with frontal assessment battery, verbal fluency, and semantic fluency. Results Common (mainly frontal) and specific areas with reduced volume were detected between FTSD, FTSD-ALS, and ALS patients, confirming suggestions of a clinical continuum, while at the same time defining morphological specificities for each clinical group (e.g., a difference of cerebral and cerebellar involvement between FTSD and ALS). C T ¯ values suggested extensive network disruption in the pathological process, with indications of a correlation between cerebral and cerebellar volumes and C T ¯ in ALS. The analysis of the neuropsychological scores indeed pointed toward an important role for the cerebellum, along with fronto-temporal areas, in explaining impairment of executive, and linguistic functions. Conclusion We identified common elements that explain the FTSD-ALS clinical continuum, while also identifying specificities of each group, partially explained by different cerebral and cerebellar involvement.
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Affiliation(s)
- Beatrice Pizzarotti
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Paolo Vitali
- Radiology Unit, IRCCS Mondino Foundation, Pavia, Italy.,Department of Radiology, IRCCS Policlinico San Donato, Milan, Italy
| | - Gloria Castellazzi
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.,IRCCS Mondino Foundation, Pavia, Italy
| | - Nicoletta Anzalone
- Neuroradiology Unit, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Elena Alvisi
- Department of Neurology and Laboratory Neuroscience, IRCCS Italian Auxological Institute, Milan, Italy
| | - Daniele Martinelli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Headache Science and Neurorehabilitation, IRCCS Mondino Foundation, Pavia, Italy
| | - Sara Bernini
- Laboratory of Neuropsychology, IRCCS Mondino Foundation, Pavia, Italy
| | - Matteo Cotta Ramusino
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Mauro Ceroni
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Department of Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Giuseppe Micieli
- Department of Emergency Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Elena Sinforiani
- Laboratory of Neuropsychology, IRCCS Mondino Foundation, Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Alfredo Costa
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.,NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
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13
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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14
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Palesi F, Lorenzi RM, Casellato C, Ritter P, Jirsa V, Gandini Wheeler-Kingshott CA, D’Angelo E. The Importance of Cerebellar Connectivity on Simulated Brain Dynamics. Front Cell Neurosci 2020; 14:240. [PMID: 32848628 PMCID: PMC7411185 DOI: 10.3389/fncel.2020.00240] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 07/09/2020] [Indexed: 11/14/2022] Open
Abstract
The brain shows a complex multiscale organization that prevents a direct understanding of how structure, function and dynamics are correlated. To date, advances in neural modeling offer a unique opportunity for simulating global brain dynamics by embedding empirical data on different scales in a mathematical framework. The Virtual Brain (TVB) is an advanced data-driven model allowing to simulate brain dynamics starting from individual subjects' structural and functional connectivity obtained, for example, from magnetic resonance imaging (MRI). The use of TVB has been limited so far to cerebral connectivity but here, for the first time, we have introduced cerebellar nodes and interconnecting tracts to demonstrate the impact of cerebro-cerebellar loops on brain dynamics. Indeed, the matching between the empirical and simulated functional connectome was significantly improved when including the cerebro-cerebellar loops. This positive result should be considered as a first step, since issues remain open about the best strategy to reconstruct effective structural connectivity and the nature of the neural mass or mean-field models generating local activity in the nodes. For example, signal processing is known to differ remarkably between cortical and cerebellar microcircuits. Tackling these challenges is expected to further improve the predictive power of functional brain activity simulations, using TVB or other similar tools, in explaining not just global brain dynamics but also the role of cerebellum in determining brain states in physiological conditions and in the numerous pathologies affecting the cerebro-cerebellar loops.
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Affiliation(s)
- Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Claudia Casellato
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Petra Ritter
- Brain Simulation Section, Department of Neurology with Experimental Neurology, Charité – Universitätsmedizin Berlin and Berlin Institute of Health, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes – Inserm UMR1106, Aix-Marseille Université, Marseille, France
| | - Claudia A.M. Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, United Kingdom
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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15
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Vitali P, Pan MI, Palesi F, Germani G, Faggioli A, Anzalone N, Francaviglia P, Minafra B, Zangaglia R, Pacchetti C, Gandini Wheeler-Kingshott CAM. Substantia Nigra Volumetry with 3-T MRI in De Novo and Advanced Parkinson Disease. Radiology 2020; 296:401-410. [PMID: 32544035 DOI: 10.1148/radiol.2020191235] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Background Magnetization transfer-prepared T1-weighted MRI can depict a hyperintense subregion of the substantia nigra involved in the degeneration process of Parkinson disease. Purpose To evaluate quantitative measurement of substantia nigra volume by using MRI to support clinical diagnosis and staging of Parkinson disease. Materials and Methods In this prospective study, a high-spatial-resolution magnetization transfer-prepared T1-weighted volumetric sequence was performed with a 3-T MRI machine between January 2014 and October 2015 for participants with de novo Parkinson disease, advanced Parkinson disease, and healthy control participants. A reproducible semiautomatic quantification analysis method that entailed mesencephalic intensity as an internal reference was used for hyperintense substantia nigra volumetry normalized to intracranial volume. A general linear model with age and sex as covariates was used to compare the three groups. Results Eighty participants were evaluated: 20 healthy control participants (mean age ± standard deviation, 56 years ± 11; 11 women), 29 participants with de novo Parkinson disease (64 years ± 10; 19 men), and 31 participants with advanced Parkinson disease (60 years ± 9; 16 women). Volumetric measurement of hyperintense substantia nigra from magnetization transfer-prepared T1-weighted MRI helped differentiate healthy control participants from participants with advanced Parkinson disease (mean difference for ipsilateral side, 64 mm3 ± 14, P < .001; mean difference for contralateral side, 109 mm3 ± 14, P < .001) and helped distinguish healthy control participants from participants with de novo Parkinson disease (mean difference for ipsilateral side, 45 mm3 ± 15, P < .01; mean difference for contralateral side, 66 mm3 ± 15, P < .001) and participants with de novo Parkinson disease from those with advanced Parkinson disease (mean difference for ipsilateral side, 20 mm3 ± 13, P = .40; mean difference for contralateral side, 43 mm3 ± 13, P = .004). Conclusion Magnetization transfer-prepared T1-weighted MRI volumetry of the substantia nigra helped differentiate the stages of Parkinson disease. © RSNA, 2020 Online supplemental material is available for this article.
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Affiliation(s)
- Paolo Vitali
- From the Department of Neuroradiology, Brain MRI 3T Research Center (P.V., G.G., A.F., C.A.M.G.W.), Brain Connectivity Centre (F.P.), and Parkinson's Disease and Movement Disorders Unit (B.M., R.Z., C.P.), IRCCS Mondino Foundation, Pavia, Italy; Departments of Neurology (M.I.P.) and Brain and Behavioural Sciences (F.P., C.A.M.G.W.), University of Pavia, Pavia, Italy; Neuroradiology Unit, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy (N.A.); Department of Radiology, Acqui Terme Hospital, Acqui Terme, Italy (P.F.); and NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, England (C.A.M.G.W.)
| | - Marina I Pan
- From the Department of Neuroradiology, Brain MRI 3T Research Center (P.V., G.G., A.F., C.A.M.G.W.), Brain Connectivity Centre (F.P.), and Parkinson's Disease and Movement Disorders Unit (B.M., R.Z., C.P.), IRCCS Mondino Foundation, Pavia, Italy; Departments of Neurology (M.I.P.) and Brain and Behavioural Sciences (F.P., C.A.M.G.W.), University of Pavia, Pavia, Italy; Neuroradiology Unit, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy (N.A.); Department of Radiology, Acqui Terme Hospital, Acqui Terme, Italy (P.F.); and NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, England (C.A.M.G.W.)
| | - Fulvia Palesi
- From the Department of Neuroradiology, Brain MRI 3T Research Center (P.V., G.G., A.F., C.A.M.G.W.), Brain Connectivity Centre (F.P.), and Parkinson's Disease and Movement Disorders Unit (B.M., R.Z., C.P.), IRCCS Mondino Foundation, Pavia, Italy; Departments of Neurology (M.I.P.) and Brain and Behavioural Sciences (F.P., C.A.M.G.W.), University of Pavia, Pavia, Italy; Neuroradiology Unit, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy (N.A.); Department of Radiology, Acqui Terme Hospital, Acqui Terme, Italy (P.F.); and NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, England (C.A.M.G.W.)
| | - Giancarlo Germani
- From the Department of Neuroradiology, Brain MRI 3T Research Center (P.V., G.G., A.F., C.A.M.G.W.), Brain Connectivity Centre (F.P.), and Parkinson's Disease and Movement Disorders Unit (B.M., R.Z., C.P.), IRCCS Mondino Foundation, Pavia, Italy; Departments of Neurology (M.I.P.) and Brain and Behavioural Sciences (F.P., C.A.M.G.W.), University of Pavia, Pavia, Italy; Neuroradiology Unit, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy (N.A.); Department of Radiology, Acqui Terme Hospital, Acqui Terme, Italy (P.F.); and NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, England (C.A.M.G.W.)
| | - Arianna Faggioli
- From the Department of Neuroradiology, Brain MRI 3T Research Center (P.V., G.G., A.F., C.A.M.G.W.), Brain Connectivity Centre (F.P.), and Parkinson's Disease and Movement Disorders Unit (B.M., R.Z., C.P.), IRCCS Mondino Foundation, Pavia, Italy; Departments of Neurology (M.I.P.) and Brain and Behavioural Sciences (F.P., C.A.M.G.W.), University of Pavia, Pavia, Italy; Neuroradiology Unit, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy (N.A.); Department of Radiology, Acqui Terme Hospital, Acqui Terme, Italy (P.F.); and NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, England (C.A.M.G.W.)
| | - Nicoletta Anzalone
- From the Department of Neuroradiology, Brain MRI 3T Research Center (P.V., G.G., A.F., C.A.M.G.W.), Brain Connectivity Centre (F.P.), and Parkinson's Disease and Movement Disorders Unit (B.M., R.Z., C.P.), IRCCS Mondino Foundation, Pavia, Italy; Departments of Neurology (M.I.P.) and Brain and Behavioural Sciences (F.P., C.A.M.G.W.), University of Pavia, Pavia, Italy; Neuroradiology Unit, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy (N.A.); Department of Radiology, Acqui Terme Hospital, Acqui Terme, Italy (P.F.); and NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, England (C.A.M.G.W.)
| | - Pietro Francaviglia
- From the Department of Neuroradiology, Brain MRI 3T Research Center (P.V., G.G., A.F., C.A.M.G.W.), Brain Connectivity Centre (F.P.), and Parkinson's Disease and Movement Disorders Unit (B.M., R.Z., C.P.), IRCCS Mondino Foundation, Pavia, Italy; Departments of Neurology (M.I.P.) and Brain and Behavioural Sciences (F.P., C.A.M.G.W.), University of Pavia, Pavia, Italy; Neuroradiology Unit, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy (N.A.); Department of Radiology, Acqui Terme Hospital, Acqui Terme, Italy (P.F.); and NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, England (C.A.M.G.W.)
| | - Brigida Minafra
- From the Department of Neuroradiology, Brain MRI 3T Research Center (P.V., G.G., A.F., C.A.M.G.W.), Brain Connectivity Centre (F.P.), and Parkinson's Disease and Movement Disorders Unit (B.M., R.Z., C.P.), IRCCS Mondino Foundation, Pavia, Italy; Departments of Neurology (M.I.P.) and Brain and Behavioural Sciences (F.P., C.A.M.G.W.), University of Pavia, Pavia, Italy; Neuroradiology Unit, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy (N.A.); Department of Radiology, Acqui Terme Hospital, Acqui Terme, Italy (P.F.); and NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, England (C.A.M.G.W.)
| | - Roberta Zangaglia
- From the Department of Neuroradiology, Brain MRI 3T Research Center (P.V., G.G., A.F., C.A.M.G.W.), Brain Connectivity Centre (F.P.), and Parkinson's Disease and Movement Disorders Unit (B.M., R.Z., C.P.), IRCCS Mondino Foundation, Pavia, Italy; Departments of Neurology (M.I.P.) and Brain and Behavioural Sciences (F.P., C.A.M.G.W.), University of Pavia, Pavia, Italy; Neuroradiology Unit, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy (N.A.); Department of Radiology, Acqui Terme Hospital, Acqui Terme, Italy (P.F.); and NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, England (C.A.M.G.W.)
| | - Claudio Pacchetti
- From the Department of Neuroradiology, Brain MRI 3T Research Center (P.V., G.G., A.F., C.A.M.G.W.), Brain Connectivity Centre (F.P.), and Parkinson's Disease and Movement Disorders Unit (B.M., R.Z., C.P.), IRCCS Mondino Foundation, Pavia, Italy; Departments of Neurology (M.I.P.) and Brain and Behavioural Sciences (F.P., C.A.M.G.W.), University of Pavia, Pavia, Italy; Neuroradiology Unit, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy (N.A.); Department of Radiology, Acqui Terme Hospital, Acqui Terme, Italy (P.F.); and NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, England (C.A.M.G.W.)
| | - Claudia A M Gandini Wheeler-Kingshott
- From the Department of Neuroradiology, Brain MRI 3T Research Center (P.V., G.G., A.F., C.A.M.G.W.), Brain Connectivity Centre (F.P.), and Parkinson's Disease and Movement Disorders Unit (B.M., R.Z., C.P.), IRCCS Mondino Foundation, Pavia, Italy; Departments of Neurology (M.I.P.) and Brain and Behavioural Sciences (F.P., C.A.M.G.W.), University of Pavia, Pavia, Italy; Neuroradiology Unit, San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy (N.A.); Department of Radiology, Acqui Terme Hospital, Acqui Terme, Italy (P.F.); and NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, England (C.A.M.G.W.)
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16
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Castellazzi G, Cuzzoni MG, Cotta Ramusino M, Martinelli D, Denaro F, Ricciardi A, Vitali P, Anzalone N, Bernini S, Palesi F, Sinforiani E, Costa A, Micieli G, D'Angelo E, Magenes G, Gandini Wheeler-Kingshott CAM. A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features. Front Neuroinform 2020; 14:25. [PMID: 32595465 PMCID: PMC7300291 DOI: 10.3389/fninf.2020.00025] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 04/24/2020] [Indexed: 12/28/2022] Open
Abstract
Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD–AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a “mixed VD–AD dementia” (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations.
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Affiliation(s)
- Gloria Castellazzi
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, London, United Kingdom.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.,Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Matteo Cotta Ramusino
- Laboratory of Neuropsychology and Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Daniele Martinelli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Headache Center, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Antonio Ricciardi
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Paolo Vitali
- Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy.,Radiology Unit, IRCCS Policlinico San Donato, Milan, Italy
| | - Nicoletta Anzalone
- Scientific Institute H.S. Raffaele Vita e Salute University, Milan, Italy
| | - Sara Bernini
- Laboratory of Neuropsychology and Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Elena Sinforiani
- Laboratory of Neuropsychology and Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Alfredo Costa
- Laboratory of Neuropsychology and Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Giuseppe Micieli
- Department of Emergency Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Giovanni Magenes
- Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square MS Centre, UCL Queen Square Institute of Neurology, London, United Kingdom.,Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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17
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Casiraghi L, Alahmadi AAS, Monteverdi A, Palesi F, Castellazzi G, Savini G, Friston K, Gandini Wheeler-Kingshott CAM, D'Angelo E. I See Your Effort: Force-Related BOLD Effects in an Extended Action Execution-Observation Network Involving the Cerebellum. Cereb Cortex 2020; 29:1351-1368. [PMID: 30615116 PMCID: PMC6373696 DOI: 10.1093/cercor/bhy322] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 11/28/2018] [Indexed: 12/11/2022] Open
Abstract
Action observation (AO) is crucial for motor planning, imitation learning, and social interaction, but it is not clear whether and how an action execution–observation network (AEON) processes the effort of others engaged in performing actions. In this functional magnetic resonance imaging (fMRI) study, we used a “squeeze ball” task involving different grip forces to investigate whether AEON activation showed similar patterns when executing the task or observing others performing it. Both in action execution, AE (subjects performed the visuomotor task) and action observation, AO (subjects watched a video of the task being performed by someone else), the fMRI signal was detected in cerebral and cerebellar regions. These responses showed various relationships with force mapping onto specific areas of the sensorimotor and cognitive systems. Conjunction analysis of AE and AO was repeated for the “0th” order and linear and nonlinear responses, and revealed multiple AEON nodes remapping the detection of actions, and also effort, of another person onto the observer’s own cerebrocerebellar system. This result implies that the AEON exploits the cerebellum, which is known to process sensorimotor predictions and simulations, performing an internal assessment of forces and integrating information into high-level schemes, providing a crucial substrate for action imitation.
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Affiliation(s)
- Letizia Casiraghi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Adnan A S Alahmadi
- Diagnostic Radiography Technology Department, Faculty of Applied Medical Science, King Abdulaziz University (KAU), Jeddah 80200-21589, Saudi Arabia.,NMR Research Unit, Queen Square Multiple Sclerosis (MS) Centre, Department of Neuroinflammation, Institute of Neurology, University College London (UCL), London, UK
| | - Anita Monteverdi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Fulvia Palesi
- Brain MRI 3T Center, Neuroradiology Unit, IRCCS Mondino Foundation, Pavia, PV, Italy
| | - Gloria Castellazzi
- NMR Research Unit, Queen Square Multiple Sclerosis (MS) Centre, Department of Neuroinflammation, Institute of Neurology, University College London (UCL), London, UK.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Giovanni Savini
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Physics, University of Milan, Milan, Italy
| | - Karl Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London (UCL), London, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,NMR Research Unit, Queen Square Multiple Sclerosis (MS) Centre, Department of Neuroinflammation, Institute of Neurology, University College London (UCL), London, UK.,Brain MRI 3T Mondino Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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18
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Lorenzi RM, Palesi F, Castellazzi G, Vitali P, Anzalone N, Bernini S, Cotta Ramusino M, Sinforiani E, Micieli G, Costa A, D’Angelo E, Gandini Wheeler-Kingshott CAM. Unsuspected Involvement of Spinal Cord in Alzheimer Disease. Front Cell Neurosci 2020; 14:6. [PMID: 32082122 PMCID: PMC7002560 DOI: 10.3389/fncel.2020.00006] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 01/10/2020] [Indexed: 11/13/2022] Open
Abstract
Objective: Brain atrophy is an established biomarker for dementia, yet spinal cord involvement has not been investigated to date. As the spinal cord is relaying sensorimotor control signals from the cortex to the peripheral nervous system and vice-versa, it is indeed a very interesting question to assess whether it is affected by atrophy due to a disease that is known for its involvement of cognitive domains first and foremost, with motor symptoms being clinically assessed too. We, therefore, hypothesize that in Alzheimer's disease (AD), severe atrophy can affect the spinal cord too and that spinal cord atrophy is indeed an important in vivo imaging biomarker contributing to understanding neurodegeneration associated with dementia. Methods: 3DT1 images of 31 AD and 35 healthy control (HC) subjects were processed to calculate volume of brain structures and cross-sectional area (CSA) and volume (CSV) of the cervical cord [per vertebra as well as the C2-C3 pair (CSA23 and CSV23)]. Correlated features (ρ > 0.7) were removed, and the best subset identified for patients' classification with the Random Forest algorithm. General linear model regression was used to find significant differences between groups (p ≤ 0.05). Linear regression was implemented to assess the explained variance of the Mini-Mental State Examination (MMSE) score as a dependent variable with the best features as predictors. Results: Spinal cord features were significantly reduced in AD, independently of brain volumes. Patients classification reached 76% accuracy when including CSA23 together with volumes of hippocampi, left amygdala, white and gray matter, with 74% sensitivity and 78% specificity. CSA23 alone explained 13% of MMSE variance. Discussion: Our findings reveal that C2-C3 spinal cord atrophy contributes to discriminate AD from HC, together with more established features. The results show that CSA23, calculated from the same 3DT1 scan as all other brain volumes (including right and left hippocampi), has a considerable weight in classification tasks warranting further investigations. Together with recent studies revealing that AD atrophy is spread beyond the temporal lobes, our result adds the spinal cord to a number of unsuspected regions involved in the disease. Interestingly, spinal cord atrophy explains also cognitive scores, which could significantly impact how we model sensorimotor control in degenerative diseases with a primary cognitive domain involvement. Prospective studies should be purposely designed to understand the mechanisms of atrophy and the role of the spinal cord in AD.
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Affiliation(s)
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Neuroradiology Unit, Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Gloria Castellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Paolo Vitali
- Neuroradiology Unit, Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Sara Bernini
- Laboratory of Neuropsychology, IRCCS Mondino Foundation, Pavia, Italy
| | - Matteo Cotta Ramusino
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Elena Sinforiani
- Laboratory of Neuropsychology, IRCCS Mondino Foundation, Pavia, Italy
| | - Giuseppe Micieli
- Department of Emergency Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Alfredo Costa
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center (BCC), IRCCS Mondino Foundation, Pavia, Italy
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
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19
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Nath V, Schilling KG, Parvathaneni P, Huo Y, Blaber JA, Hainline AE, Barakovic M, Romascano D, Rafael-Patino J, Frigo M, Girard G, Thiran JP, Daducci A, Rowe M, Rodrigues P, Prchkovska V, Aydogan DB, Sun W, Shi Y, Parker WA, Ould Ismail AA, Verma R, Cabeen RP, Toga AW, Newton AT, Wasserthal J, Neher P, Maier-Hein K, Savini G, Palesi F, Kaden E, Wu Y, He J, Feng Y, Paquette M, Rheault F, Sidhu J, Lebel C, Leemans A, Descoteaux M, Dyrby TB, Kang H, Landman BA. Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge. J Magn Reson Imaging 2020; 51:234-249. [PMID: 31179595 PMCID: PMC6900461 DOI: 10.1002/jmri.26794] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 05/06/2019] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Fiber tracking with diffusion-weighted MRI has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria. PURPOSE To assess the variability for an algorithm in group studies reproducibility is of critical context. However, reproducibility does not assess the validity of the brain connections. Phantom studies provide concrete quantitative comparisons of methods relative to absolute ground truths, yet do no capture variabilities because of in vivo physiological factors. The ISMRM 2017 TraCED challenge was created to fulfill the gap. STUDY TYPE A systematic review of algorithms and tract reproducibility studies. SUBJECTS Single healthy volunteers. FIELD STRENGTH/SEQUENCE 3.0T, two different scanners by the same manufacturer. The multishell acquisition included b-values of 1000, 2000, and 3000 s/mm2 with 20, 45, and 64 diffusion gradient directions per shell, respectively. ASSESSMENT Nine international groups submitted 46 tractography algorithm entries each consisting 16 tracts per scan. The algorithms were assessed using intraclass correlation (ICC) and the Dice similarity measure. STATISTICAL TESTS Containment analysis was performed to assess if the submitted algorithms had containment within tracts of larger volume submissions. This also serves the purpose to detect if spurious submissions had been made. RESULTS The top five submissions had high ICC and Dice >0.88. Reproducibility was high within the top five submissions when assessed across sessions or across scanners: 0.87-0.97. Containment analysis shows that the top five submissions are contained within larger volume submissions. From the total of 16 tracts as an outcome relatively the number of tracts with high, moderate, and low reproducibility were 8, 4, and 4. DATA CONCLUSION The different methods clearly result in fundamentally different tract structures at the more conservative specificity choices. Data and challenge infrastructure remain available for continued analysis and provide a platform for comparison. LEVEL OF EVIDENCE 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:234-249.
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Affiliation(s)
- Vishwesh Nath
- Computer Science, Vanderbilt University, Nashville, TN, USA
| | | | | | - Yuankai Huo
- Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Justin A. Blaber
- Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | | | | | | | | | | | | | | | | | | | | | | | - Dogu B. Aydogan
- Keck School of Medicine, University of Southern California (NICR), Los Angeles CA, USA
| | - Wei Sun
- Keck School of Medicine, University of Southern California (NICR), Los Angeles CA, USA
| | - Yonggang Shi
- Keck School of Medicine, University of Southern California (NICR), Los Angeles CA, USA
| | - William A. Parker
- Center for Biomedical Image Computing and Analytics, Dept of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN)
| | - Abdol A. Ould Ismail
- Center for Biomedical Image Computing and Analytics, Dept of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN)
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics, Dept of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN)
| | - Ryan P. Cabeen
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute
| | - Arthur W. Toga
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute
| | - Allen T. Newton
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
| | - Jakob Wasserthal
- Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Peter Neher
- Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Klaus Maier-Hein
- Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Fulvia Palesi
- Brain Connectivity Center, C. Mondino National Neurological Institute (EFG), Pavia, Italy
| | - Enrico Kaden
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Ye Wu
- Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China
| | - Jianzhong He
- Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China
| | - Yuanjing Feng
- Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China
| | - Michael Paquette
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, 2500 Boul. Université, J1K 2R1, Sherbrooke, Canada
| | - Francois Rheault
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, 2500 Boul. Université, J1K 2R1, Sherbrooke, Canada
| | - Jasmeen Sidhu
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, 2500 Boul. Université, J1K 2R1, Sherbrooke, Canada
| | | | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, 2500 Boul. Université, J1K 2R1, Sherbrooke, Canada
| | - Tim B. Dyrby
- Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital, Hvidovre, Denmark
| | - Hakmook Kang
- Biostatistics, Vanderbilt University, Nashville, TN, USA
| | - Bennett A. Landman
- Computer Science, Vanderbilt University, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Electrical Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
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20
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Redolfi A, De Francesco S, Palesi F, Kherif F, Muscio C, Nigri A, Bottini G, Frisoni GB, Tagliavini F, Bruzzone MG, Ryvlin P, Démonet JF, Cappa S, D'Angelo E. P1-333: MEDICAL INFORMATICS PLATFORM (MIP): A VALIDATION STUDY ACROSS CLINICAL ITALIAN COHORTS. Alzheimers Dement 2019. [DOI: 10.1016/j.jalz.2019.06.888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Alberto Redolfi
- Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE; IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli; Brescia Italy
| | - Silvia De Francesco
- 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
| | - Ferath Kherif
- Department of Clinical Neurosciences, Leenaards Memory Centre; Centre Hospitalier Universitaire Vaudois and University of Lausanne; Lausanne Switzerland
| | - Cristina Muscio
- Division of Neurology V/Neuropathology; Fondazione IRCCS Istituto Neurologico Carlo Besta; Milan Italy
| | - Anna Nigri
- Division of Neurology V/Neuropathology; Fondazione IRCCS Istituto Neurologico Carlo Besta; Milan Italy
| | - Gabriella Bottini
- Neuropsychology Center; ASST Grande Ospedale Metropolitano Niguarda; Milan Italy
| | - Giovanni B. Frisoni
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging; University Hospitals and University of Geneva; Geneva Switzerland
- IRCCS Centro San Giovanni di Dio Fatebenefratelli; Brescia Italy
| | - Fabrizio Tagliavini
- Division of Neurology V/Neuropathology; Fondazione IRCCS Istituto Neurologico Carlo Besta; Milan Italy
| | - Maria Grazia Bruzzone
- Division of Neurology V/Neuropathology; Fondazione IRCCS Istituto Neurologico Carlo Besta; Milan Italy
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Leenaards Memory Centre; Centre Hospitalier Universitaire Vaudois and University of Lausanne; Lausanne Switzerland
| | - Jean-François Démonet
- Department of Clinical Neurosciences, Leenaards Memory Centre; Centre Hospitalier Universitaire Vaudois and University of Lausanne; Lausanne Switzerland
| | - Stefano Cappa
- IRCCS San Giovanni di Dio Fatebenefratelli; Brescia Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences; University of Pavia; Pavia Italy
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21
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Redolfi A, De Francesco S, Palesi F, Kherif F, Muscio C, Nigri A, Bottini G, Frisoni GB, Tagliavini F, Bruzzone MG, Ryvlin P, Démonet JF, Cappa S, D'Angelo E. IC-P-045: MEDICAL INFORMATICS PLATFORM (MIP): A VALIDATION STUDY ACROSS CLINICAL ITALIAN COHORTS. Alzheimers Dement 2019. [DOI: 10.1016/j.jalz.2019.06.4207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Alberto Redolfi
- Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE; IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli; Brescia Italy
| | - Silvia De Francesco
- 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
| | - Ferath Kherif
- Department of Clinical Neurosciences, Leenaards Memory Centre; Centre Hospitalier Universitaire Vaudois and University of Lausanne; Lausanne Switzerland
| | - Cristina Muscio
- Division of Neurology/Neuropathology; Fondazione IRCCS Istituto Neurologico Carlo Besta; Milan Italy
| | - Anna Nigri
- Division of Neurology/Neuropathology; Fondazione IRCCS Istituto Neurologico Carlo Besta; Milan Italy
| | - Gabriella Bottini
- Neuropsychology Center; ASST Grande Ospedale Metropolitano Niguarda; Milan Italy
| | - Giovanni B. Frisoni
- IRCCS 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
| | - Fabrizio Tagliavini
- Division of Neurology/Neuropathology; Fondazione IRCCS Istituto Neurologico Carlo Besta; Milan Italy
| | - Maria Grazia Bruzzone
- Division of Neurology/Neuropathology; Fondazione IRCCS Istituto Neurologico Carlo Besta; Milan Italy
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Leenaards Memory Centre; Centre Hospitalier Universitaire Vaudois and University of Lausanne; Lausanne Switzerland
| | - Jean-François Démonet
- Department of Clinical Neurosciences, Leenaards Memory Centre; Centre Hospitalier Universitaire Vaudois and University of Lausanne; Lausanne Switzerland
| | - Stefano Cappa
- IRCCS San Giovanni di Dio Fatebenefratelli; Brescia Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences; University of Pavia; Pavia Italy
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22
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Vitali P, Palesi F, Cotta Ramusino M, Pan M, Costa A, Gandini Wheeler-Kingshott C, Ceroni M, Micieli G, Anzalone N, Giaccone G, Tagliavini F, Geschwind M. Early cortical and late striatal diffusion restriction on 3T MRI in a long-lived sporadic creutzfeldt-jakob disease case. J Magn Reson Imaging 2019; 50:1659-1662. [PMID: 30912188 DOI: 10.1002/jmri.26711] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Revised: 02/22/2019] [Accepted: 02/22/2019] [Indexed: 12/29/2022] Open
Affiliation(s)
- Paolo Vitali
- Neuroradiology, Brain MRI 3T Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Fulvia Palesi
- Neuroradiology, Brain MRI 3T Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Matteo Cotta Ramusino
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Department of Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Marina Pan
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Alfredo Costa
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Department of Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Claudia Gandini Wheeler-Kingshott
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
| | - Mauro Ceroni
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Department of Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Nicoletta Anzalone
- Brain MRI 3T Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Neuroradiology, San Raffaele Hospital, Milan, Italy
| | - Giorgio Giaccone
- Division of Neuropathology, Neurology 5, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Fabrizio Tagliavini
- Division of Neuropathology, Neurology 5, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Michael Geschwind
- Department of Neurology, University of California San Francisco, San Francisco, California, USA
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23
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Gandini Wheeler-Kingshott CAM, Riemer F, Palesi F, Ricciardi A, Castellazzi G, Golay X, Prados F, Solanky B, D'Angelo EU. Challenges and Perspectives of Quantitative Functional Sodium Imaging (fNaI). Front Neurosci 2018; 12:810. [PMID: 30473659 PMCID: PMC6237845 DOI: 10.3389/fnins.2018.00810] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 10/17/2018] [Indexed: 12/31/2022] Open
Abstract
Brain function has been investigated via the blood oxygenation level dependent (BOLD) effect using magnetic resonance imaging (MRI) for the past decades. Advances in sodium imaging offer the unique chance to access signal changes directly linked to sodium ions (23Na) flux across the cell membrane, which generates action potentials, hence signal transmission in the brain. During this process 23Na transiently accumulates in the intracellular space. Here we show that quantitative functional sodium imaging (fNaI) at 3T is potentially sensitive to 23Na concentration changes during finger tapping, which can be quantified in gray and white matter regions key to motor function. For the first time, we measured a 23Na concentration change of 0.54 mmol/l in the ipsilateral cerebellum, 0.46 mmol/l in the contralateral primary motor cortex (M1), 0.27 mmol/l in the corpus callosum and -11 mmol/l in the ipsilateral M1, suggesting that fNaI is sensitive to distributed functional alterations. Open issues persist on the role of the glymphatic system in maintaining 23Na homeostasis, the role of excitation and inhibition as well as volume distributions during neuronal activity. Haemodynamic and physiological signal recordings coupled to realistic models of tissue function will be critical to understand the mechanisms of such changes and contribute to meeting the overarching challenge of measuring neuronal activity in vivo.
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Affiliation(s)
- Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.,Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Brain MRI 3T Mondino Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Frank Riemer
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.,Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Fulvia Palesi
- Neuroradiology Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Antonio Ricciardi
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.,Center for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Gloria Castellazzi
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Xavier Golay
- NMR Research Unit, Queen Square MS Centre, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Ferran Prados
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.,Center for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.,Universitat Oberta de Catalunya, Barcelona, Spain
| | - Bhavana Solanky
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Egidio U D'Angelo
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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24
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Castellazzi G, Bruno SD, Toosy AT, Casiraghi L, Palesi F, Savini G, D'Angelo E, Wheeler-Kingshott CAMG. Prominent Changes in Cerebro-Cerebellar Functional Connectivity During Continuous Cognitive Processing. Front Cell Neurosci 2018; 12:331. [PMID: 30327590 PMCID: PMC6174227 DOI: 10.3389/fncel.2018.00331] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 09/10/2018] [Indexed: 01/07/2023] Open
Abstract
While task-dependent responses of specific brain areas during cognitive tasks are well established, much less is known about the changes occurring in resting state networks (RSNs) in relation to continuous cognitive processing. In particular, the functional involvement of cerebro-cerebellar loops connecting the posterior cerebellum to associative cortices, remains unclear. In this study, 22 healthy volunteers underwent a multi-session functional magnetic resonance imaging (fMRI) protocol composed of four consecutive 8-min resting state fMRI (rs-fMRI) scans. After a first control scan, participants listened to a narrated story for the entire duration of the second rs-fMRI scan; two further rs-fMRI scans followed the end of story listening. The story plot was purposely designed to stimulate specific cognitive processes that are known to involve the cerebro-cerebellar loops. Almost all of the identified 15 RSNs showed changes in functional connectivity (FC) during and for several minutes after the story. The FC changes mainly occurred in the frontal and prefrontal cortices and in the posterior cerebellum, especially in Crus I-II and lobule VI. The FC changes occurred in cerebellar clusters belonging to different RSNs, including the cerebellar network (CBLN), sensory networks (lateral visual network, LVN; medial visual network, MVN) and cognitive networks (default mode network, DMN; executive control network, ECN; right and left ventral attention networks, RVAN and LVAN; salience network, SN; language network, LN; and working memory network, WMN). Interestingly, a k-means analysis of FC changes revealed clustering of FCN, ECN, and WMN, which are all involved in working memory functions, CBLN, DMN, and SN, which play a key-role in attention switching, and RSNs involved in visual imagery. These results show that the cerebellum is deeply entrained in well-structured network clusters, which reflect multiple aspects of cognitive processing, during and beyond the conclusion of auditory stimulation.
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Affiliation(s)
- Gloria Castellazzi
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, Institute of Neurology, University College London, London, United Kingdom.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Stefania D Bruno
- Blackheath Brain Injury Rehabilitation Centre, London, United Kingdom
| | - Ahmed T Toosy
- NMR Research Unit, Department of Brain Repair and Rehabilitation, Queen Square MS Centre, Institute of Neurology, University College London, London, United Kingdom
| | - Letizia Casiraghi
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Fulvia Palesi
- Brain MRI 3T Center, Neuroradiology Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Giovanni Savini
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Physics, University of Milan, Milan, Italy
| | - Egidio D'Angelo
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Claudia Angela Michela Gandini Wheeler-Kingshott
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, Institute of Neurology, University College London, London, United Kingdom.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain MRI 3T Center, IRCCS Mondino Foundation, Pavia, Italy
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25
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Palesi F, De Rinaldis A, Vitali P, Castellazzi G, Casiraghi L, Germani G, Bernini S, Anzalone N, Ramusino MC, Denaro FM, Sinforiani E, Costa A, Magenes G, D'Angelo E, Gandini Wheeler-Kingshott CAM, Micieli G. Specific Patterns of White Matter Alterations Help Distinguishing Alzheimer's and Vascular Dementia. Front Neurosci 2018; 12:274. [PMID: 29922120 PMCID: PMC5996902 DOI: 10.3389/fnins.2018.00274] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 04/09/2018] [Indexed: 12/19/2022] Open
Abstract
Alzheimer disease (AD) and vascular dementia (VaD) together represent the majority of dementia cases. Since their neuropsychological profiles often overlap and white matter lesions are observed in elderly subjects including AD, differentiating between VaD and AD can be difficult. Characterization of these different forms of dementia would benefit by identification of quantitative imaging biomarkers specifically sensitive to AD or VaD. Parameters of microstructural abnormalities derived from diffusion tensor imaging (DTI) have been reported to be helpful in differentiating between dementias, but only few studies have used them to compare AD and VaD with a voxelwise approach. Therefore, in this study a whole brain statistical analysis was performed on DTI data of 93 subjects (31 AD, 27 VaD, and 35 healthy controls—HC) to identify specific white matter patterns of alteration in patients affected by VaD and AD with respect to HC. Parahippocampal tracts were found to be mainly affected in AD, while VaD showed more spread white matter damages associated with thalamic radiations involvement. The genu of the corpus callosum was predominantly affected in VaD, while the splenium was predominantly affected in AD revealing the existence of specific patterns of alteration useful in distinguishing between VaD and AD. Therefore, DTI parameters of these regions could be informative to understand the pathogenesis and support the etiological diagnosis of dementia. Further studies on larger cohorts of subjects, characterized for brain amyloidosis, will allow to confirm and to integrate the present findings and, furthermore, to elucidate the mechanisms of mixed dementia. These steps will be essential to translate these advances to clinical practice.
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Affiliation(s)
- Fulvia Palesi
- Department of Physics, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Andrea De Rinaldis
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Paolo Vitali
- Brain MRI 3T Mondino Research Center, IRCCS Mondino Foundation, Pavia, Italy.,Neuroradiology Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Gloria Castellazzi
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Letizia Casiraghi
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Giancarlo Germani
- Brain MRI 3T Mondino Research Center, IRCCS Mondino Foundation, Pavia, Italy.,Neuroradiology Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Sara Bernini
- Alzheimer's Disease Assessment Unit, Laboratory of Neuropsychology, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Matteo Cotta Ramusino
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Alzheimer's Disease Assessment Unit, Laboratory of Neuropsychology, IRCCS Mondino Foundation, Pavia, Italy
| | - Federica M Denaro
- Department of Emergency Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Elena Sinforiani
- Alzheimer's Disease Assessment Unit, Laboratory of Neuropsychology, IRCCS Mondino Foundation, Pavia, Italy
| | - Alfredo Costa
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Alzheimer's Disease Assessment Unit, Laboratory of Neuropsychology, IRCCS Mondino Foundation, Pavia, Italy
| | - Giovanni Magenes
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Egidio D'Angelo
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- Brain MRI 3T Mondino Research Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Giuseppe Micieli
- Department of Emergency Neurology, IRCCS Mondino Foundation, Pavia, Italy
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26
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Palesi F, Tournier JD, Calamante F, Muhlert N, Castellazzi G, Chard D, D'Angelo E, Wheeler-Kingshott CG. Reconstructing contralateral fiber tracts: methodological aspects of cerebello-thalamocortical pathway reconstruction. Funct Neurol 2017; 31:229-238. [PMID: 28072383 DOI: 10.11138/fneur/2016.31.4.229] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The identification of pathways connecting the cerebral cortex with subcortical structures is critical to understanding how large-scale brain networks operate. The cerebellum, for example, is known to project numerous axonal bundles to thecerebral cortex passing through the thalamus. This paper focuses on the technical details of cerebello-thalamo-cortical pathway reconstruction using advanced diffusion MRI techniques in humans in vivo. Pathways reconstructed using seed/target placement on super-resolution maps, created with track density imaging (TDI), were compared with those reconstructed by defining regions of interest (ROIs) on non-diffusion weighted images (b0). We observed that the reconstruction of the pathways was more anatomically accurate when using ROIs placed on TDI rather than on b0 maps, while inter-subject variability and reproducibility were similar between the two methods. Diffusion indices along pathways showed a position-dependent specificity that will need to be taken into consideration in future clinical investigations.
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27
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Castellazzi G, Palesi F, Bruno S, Toosy A, D‘Angelo E, Gandini Wheeler-Kingshott C. Resting state fMRI during continuous cognitive processing reveals dynamical changes of brain networks involving cerebral cortex and cerebellum. Front Cell Neurosci 2017. [DOI: 10.3389/conf.fncel.2017.37.00009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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28
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Palesi F, Castellazzi G, Casiraghi L, Sinforiani E, Vitali P, Gandini Wheeler-Kingshott CAM, D'Angelo E. Exploring Patterns of Alteration in Alzheimer's Disease Brain Networks: A Combined Structural and Functional Connectomics Analysis. Front Neurosci 2016; 10:380. [PMID: 27656119 PMCID: PMC5013043 DOI: 10.3389/fnins.2016.00380] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Accepted: 08/04/2016] [Indexed: 11/13/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a severe derangement of cognitive functions, primarily memory, in elderly subjects. As far as the functional impairment is concerned, growing evidence supports the "disconnection syndrome" hypothesis. Recent investigations using fMRI have revealed a generalized alteration of resting state networks (RSNs) in patients affected by AD and mild cognitive impairment (MCI). However, it was unclear whether the changes in functional connectivity were accompanied by corresponding structural network changes. In this work, we have developed a novel structural/functional connectomic approach: resting state fMRI was used to identify the functional cortical network nodes and diffusion MRI to reconstruct the fiber tracts to give a weight to internodal subcortical connections. Then, local and global efficiency were determined for different networks, exploring specific alterations of integration and segregation patterns in AD and MCI patients compared to healthy controls (HC). In the default mode network (DMN), that was the most affected, axonal loss, and reduced axonal integrity appeared to compromise both local and global efficiency along posterior-anterior connections. In the basal ganglia network (BGN), disruption of white matter integrity implied that main alterations occurred in local microstructure. In the anterior insular network (AIN), neuronal loss probably subtended a compromised communication with the insular cortex. Cognitive performance, evaluated by neuropsychological examinations, revealed a dependency on integration and segregation of brain networks. These findings are indicative of the fact that cognitive deficits in AD could be associated not only with cortical alterations (revealed by fMRI) but also with subcortical alterations (revealed by diffusion MRI) that extend beyond the areas primarily damaged by neurodegeneration, toward the support of an emerging concept of AD as a "disconnection syndrome." Since only AD but not MCI patients were characterized by a significant decrease in structural connectivity, integrated structural/functional connectomics could provide a useful tool for assessing disease progression from MCI to AD.
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Affiliation(s)
- Fulvia Palesi
- Department of Physics, University of PaviaPavia, Italy; Brain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy
| | - Gloria Castellazzi
- Brain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy; Department of Electrical, Computer and Biomedical Engineering, University of PaviaPavia, Italy
| | - Letizia Casiraghi
- Brain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy; Department of Brain and Behavioural Sciences, University of PaviaPavia, Italy
| | - Elena Sinforiani
- Neuroradiology Unit, C. Mondino National Neurological Institute Pavia, Italy
| | - Paolo Vitali
- Neuroradiology Unit, C. Mondino National Neurological InstitutePavia, Italy; Brain MRI 3T Mondino Research Center, C. Mondino National Neurological InstitutePavia, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- Department of Brain and Behavioural Sciences, University of PaviaPavia, Italy; Brain MRI 3T Mondino Research Center, C. Mondino National Neurological InstitutePavia, Italy; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of NeurologyLondon, UK
| | - Egidio D'Angelo
- Brain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy; Department of Brain and Behavioural Sciences, University of PaviaPavia, Italy
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Palesi F, Tournier JD, Calamante F, Muhlert N, Castellazzi G, Chard D, D'Angelo E, Wheeler-Kingshott CAM. Contralateral cerebello-thalamo-cortical pathways with prominent involvement of associative areas in humans in vivo. Brain Struct Funct 2015; 220:3369-84. [PMID: 25134682 PMCID: PMC4575696 DOI: 10.1007/s00429-014-0861-2] [Citation(s) in RCA: 111] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2014] [Accepted: 07/21/2014] [Indexed: 12/26/2022]
Abstract
In addition to motor functions, it has become clear that in humans the cerebellum plays a significant role in cognition too, through connections with associative areas in the cerebral cortex. Classical anatomy indicates that neo-cerebellar regions are connected with the contralateral cerebral cortex through the dentate nucleus, superior cerebellar peduncle, red nucleus and ventrolateral anterior nucleus of the thalamus. The anatomical existence of these connections has been demonstrated using virus retrograde transport techniques in monkeys and rats ex vivo. In this study, using advanced diffusion MRI tractography we show that it is possible to calculate streamlines to reconstruct the pathway connecting the cerebellar cortex with contralateral cerebral cortex in humans in vivo. Corresponding areas of the cerebellar and cerebral cortex encompassed similar proportion (about 80%) of the tract, suggesting that the majority of streamlines passing through the superior cerebellar peduncle connect the cerebellar hemispheres through the ventrolateral thalamus with contralateral associative areas. This result demonstrates that this kind of tractography is a useful tool to map connections between the cerebellum and the cerebral cortex and moreover could be used to support specific theories about the abnormal communication along these pathways in cognitive dysfunctions in pathologies ranging from dyslexia to autism.
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Affiliation(s)
- Fulvia Palesi
- Department of Physics, University of Pavia, Via Bassi 6, 27100, Pavia, Italy.
- Brain Connectivity Center, C. Mondino National Neurological Institute, Via Mondino 2, 27100, Pavia, Italy.
| | - Jacques-Donald Tournier
- The Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia.
- Department of Medicine, Austin Health and Northern Health, University of Melbourne, Studley Road, Heidelberg, Australia.
| | - Fernando Calamante
- The Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia.
- Department of Medicine, Austin Health and Northern Health, University of Melbourne, Studley Road, Heidelberg, Australia.
| | - Nils Muhlert
- Department of Neuroinflammation, NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK.
- Department of Psychology, Cardiff University, Cardiff, CF10 2AT, UK.
| | - Gloria Castellazzi
- Brain Connectivity Center, C. Mondino National Neurological Institute, Via Mondino 2, 27100, Pavia, Italy.
- Department of Industrial and Information Engineering, University of Pavia, Via Ferrata 1, 27100, Pavia, Italy.
| | - Declan Chard
- Department of Neuroinflammation, NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK.
- National Institute for Health Research, University College London Hospitals Biomedical Research Centre, 149 Tottenham Court Road, London, W1T 7DN, UK.
| | - Egidio D'Angelo
- Brain Connectivity Center, C. Mondino National Neurological Institute, Via Mondino 2, 27100, Pavia, Italy.
- Department of Brain and Behavioural Sciences, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy.
| | - Claudia A M Wheeler-Kingshott
- Department of Neuroinflammation, NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK.
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Pardini M, Sethi V, Muhlert N, Yaldizli Ö, Palesi F, Altmann D, Ron M, Wheeler-Kingshott C, Miller D, Chard D. NETWORK EFFICIENCY AND COGNITIVE DEFICITS IN MS. J Neurol Neurosurg Psychiatry 2014. [DOI: 10.1136/jnnp-2014-309236.141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Castellazzi G, Palesi F, Casali S, Vitali P, Sinforiani E, Wheeler-Kingshott CAM, D'Angelo E. A comprehensive assessment of resting state networks: bidirectional modification of functional integrity in cerebro-cerebellar networks in dementia. Front Neurosci 2014; 8:223. [PMID: 25126054 PMCID: PMC4115623 DOI: 10.3389/fnins.2014.00223] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2014] [Accepted: 07/07/2014] [Indexed: 01/26/2023] Open
Abstract
In resting state fMRI (rs-fMRI), only functional connectivity (FC) reductions in the default mode network (DMN) are normally reported as a biomarker for Alzheimer's disease (AD). In this investigation we have developed a comprehensive strategy to characterize the FC changes occurring in multiple networks and applied it in a pilot study of subjects with AD and Mild Cognitive Impairment (MCI), compared to healthy controls (HC). Resting state networks (RSNs) were studied in 14 AD (70 ± 6 years), 12 MCI (74 ± 6 years), and 16 HC (69 ± 5 years). RSN alterations were present in almost all the 15 recognized RSNs; overall, 474 voxels presented a reduced FC in MCI and 1244 in AD while 1627 voxels showed an increased FC in MCI and 1711 in AD. The RSNs were then ranked according to the magnitude and extension of FC changes (gFC), putting in evidence 6 RSNs with prominent changes: DMN, frontal cortical network (FCN), lateral visual network (LVN), basal ganglia network (BGN), cerebellar network (CBLN), and the anterior insula network (AIN). Nodes, or hubs, showing alterations common to more than one RSN were mostly localized within the prefrontal cortex and the mesial-temporal cortex. The cerebellum showed a unique behavior where voxels of decreased gFC were only found in AD while a significant gFC increase was only found in MCI. The gFC alterations showed strong correlations (p < 0.001) with psychological scores, in particular Mini-Mental State Examination (MMSE) and attention/memory tasks. In conclusion, this analysis revealed that the DMN was affected by remarkable FC increases, that FC alterations extended over several RSNs, that derangement of functional relationships between multiple areas occurred already in the early stages of dementia. These results warrant future work to verify whether these represent compensatory mechanisms that exploit a pre-existing neural reserve through plasticity, which evolve in a state of lack of connectivity between different networks with the worsening of the pathology.
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Affiliation(s)
- Gloria Castellazzi
- Department of Industrial and Information Engineering, University of PaviaPavia, Italy
- Brain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy
| | - Fulvia Palesi
- Brain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy
- Department of Physics, University of PaviaPavia, Italy
| | - Stefano Casali
- Brain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy
- Department of Brain and Behavioral Sciences, University of PaviaPavia, Italy
| | - Paolo Vitali
- Brain MRI 3T Mondino Research Center, C. Mondino National Neurological InstitutePavia, Italy
| | - Elena Sinforiani
- Neurology Unit, C. Mondino National Neurological InstitutePavia, Italy
| | | | - Egidio D'Angelo
- Brain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy
- Department of Brain and Behavioral Sciences, University of PaviaPavia, Italy
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Caverzasi E, Pichiecchio A, Poloni GU, Calligaro A, Pasin M, Palesi F, Castellazzi G, Pasquini M, Biondi M, Barale F, Bastianello S. Magnetic resonance spectroscopy in the evaluation of treatment efficacy in unipolar major depressive disorder: a review of the literature. Funct Neurol 2012; 27:13-22. [PMID: 22687162 PMCID: PMC3812759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
More and more neuroimaging studies are using in vivo proton magnetic resonance spectroscopy (1H-MRS) to explore correlates of response to therapy in major depressive disorder (MDD). Their aim is to further understanding of the effects of neurotransmitter changes in areas involved in MDD and the mechanisms underlying a good treatment response. We set out to summarise the literature from the past fifteen years on biochemical correlates of treatment response in MDD patients, reflected in pre- and post-therapy changes in 1H-MRS measurements. Our literature search identified fifteen articles reporting 1H-MRS studies in MDD treatment; no study used 1P-MRS. Despite the wide diversity of 1H-MRS methods applied, brain regions studied, and metabolite changes found, there emerged strong evidence of a correlation between changes in neurometabolite concentrations, in particular glutamate, N-acetylaspartate and choline, and a good treatment response to pharmacotherapy or antidepressant stimulation techniques.
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Pichiecchio A, Tavazzi E, Poloni G, Ponzio M, Palesi F, Pasin M, Piccolo L, Tosello D, Romani A, Bergamaschi R, Piccolo G, Bastianello S. Advanced magnetic resonance imaging of neuromyelitis optica: a multiparametric approach. Mult Scler 2011; 18:817-24. [PMID: 22183930 DOI: 10.1177/1352458511431072] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Several authors have used advanced magnetic resonance imaging (MRI) techniques to investigate whether patients with neuromyelitis optica (NMO) have occult damage in normal-appearing brain tissue, similarly to multiple sclerosis (MS). To date, the literature contains no data derived from the combined use of several advanced MRI techniques in the same NMO subjects. OBJECTIVE We set out to determine whether occult damage could be detected in the normal-appearing brain tissue of a small group of patients with NMO using a multiparametric MRI approach. METHODS Eight female patients affected by NMO (age range 44-58 years) and seven sex- and age-matched healthy controls were included. The techniques used on a 1.5 T MRI imaging scanner were magnetization transfer imaging, diffusion tensor imaging, tract-based spatial statistics, spectroscopy and voxel-based morphometry in order to analyse normal-appearing white matter and normal-appearing grey matter. RESULTS Structural and metabolic parameters showed no abnormalities in normal-appearing white matter of patients with NMO. Conversely, tract-based spatial statistics demonstrated a selective alteration of the optic pathways and the lateral geniculate nuclei. Diffusion tensor imaging values in the normal-appearing grey matter were found to be significantly different in the patients with NMO versus the healthy controls. Moreover, voxel-based morphometry analysis demonstrated a significant density and volume reduction of the sensorimotor cortex and the visual cortex. CONCLUSIONS Our data disclosed occult structural damage in the brain of patients with NMO, predominantly involving regions connected with motor and visual systems. This damage seems to be the direct consequence of transsynaptic degeneration triggered by lesions of the optic nerve and spine.
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Affiliation(s)
- A Pichiecchio
- Neuroradiology Department, IRCCS National Neurological Institute IRCCS C Mondino, Pavia, Italy.
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Maccabelli G, Pichiecchio A, Guala A, Ponzio M, Palesi F, Maranzana RT D, Poloni GU, Bastianello S, Danesino C. Advanced magnetic resonance imaging in benign hereditary chorea: Study of two familial cases. Mov Disord 2010; 25:2670-4. [DOI: 10.1002/mds.23281] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
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