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Martinelli D, Pocora MM, De Icco R, Allena M, Vaghi G, Sances G, Castellazzi G, Tassorelli C. Searching for the Predictors of Response to BoNT-A in Migraine Using Machine Learning Approaches. Toxins (Basel) 2023; 15:364. [PMID: 37368665 DOI: 10.3390/toxins15060364] [Citation(s) in RCA: 1] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/19/2023] [Accepted: 05/24/2023] [Indexed: 06/29/2023] Open
Abstract
OnabotulinumtoxinA (BonT-A) reduces migraine frequency in a considerable portion of patients with migraine. So far, predictive characteristics of response are lacking. Here, we applied machine learning (ML) algorithms to identify clinical characteristics able to predict treatment response. We collected demographic and clinical data of patients with chronic migraine (CM) or high-frequency episodic migraine (HFEM) treated with BoNT-A at our clinic in the last 5 years. Patients received BoNT-A according to the PREEMPT (Phase III Research Evaluating Migraine Prophylaxis Therapy) paradigm and were classified according to the monthly migraine days reduction in the 12 weeks after the fourth BoNT-A cycle, as compared to baseline. Data were used as input features to run ML algorithms. Of the 212 patients enrolled, 35 qualified as excellent responders to BoNT-A administration and 38 as nonresponders. None of the anamnestic characteristics were able to discriminate responders from nonresponders in the CM group. Nevertheless, a pattern of four features (age at onset of migraine, opioid use, anxiety subscore at the hospital anxiety and depression scale (HADS-a) and Migraine Disability Assessment (MIDAS) score correctly predicted response in HFEM. Our findings suggest that routine anamnestic features acquired in real-life settings cannot accurately predict BoNT-A response in migraine and call for a more complex modality of patient profiling.
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Affiliation(s)
- Daniele Martinelli
- Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Maria Magdalena Pocora
- Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, 27100 Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
| | - Roberto De Icco
- Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, 27100 Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
| | - Marta Allena
- Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Gloria Vaghi
- Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, 27100 Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
| | - Grazia Sances
- Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Gloria Castellazzi
- Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Cristina Tassorelli
- Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, 27100 Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
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Wingrove J, Makaronidis J, Prados F, Kanber B, Yiannakas MC, Magee C, Castellazzi G, Grandjean L, Golay X, Tur C, Ciccarelli O, D'Angelo E, Gandini Wheeler-Kingshott CA, Batterham RL. Aberrant olfactory network functional connectivity in people with olfactory dysfunction following COVID-19 infection: an exploratory, observational study. EClinicalMedicine 2023; 58:101883. [PMID: 36883140 PMCID: PMC9980836 DOI: 10.1016/j.eclinm.2023.101883] [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: 10/03/2022] [Revised: 02/02/2023] [Accepted: 02/07/2023] [Indexed: 03/06/2023] Open
Abstract
BACKGROUND Olfactory impairments and anosmia from COVID-19 infection typically resolve within 2-4 weeks, although in some cases, symptoms persist longer. COVID-19-related anosmia is associated with olfactory bulb atrophy, however, the impact on cortical structures is relatively unknown, particularly in those with long-term symptoms. METHODS In this exploratory, observational study, we studied individuals who experienced COVID-19-related anosmia, with or without recovered sense of smell, and compared against individuals with no prior COVID-19 infection (confirmed by antibody testing, all vaccine naïve). MRI Imaging was carried out between the 15th July and 17th November 2020 at the Queen Square House Clinical Scanning Facility, UCL, United Kingdom. Using functional magnetic resonance imaging (fMRI) and structural imaging, we assessed differences in functional connectivity (FC) between olfactory regions, whole brain grey matter (GM) cerebral blood flow (CBF) and GM density. FINDINGS Individuals with anosmia showed increased FC between the left orbitofrontal cortex (OFC), visual association cortex and cerebellum and FC reductions between the right OFC and dorsal anterior cingulate cortex compared to those with no prior COVID-19 infection (p < 0.05, from whole brain statistical parametric map analysis). Individuals with anosmia also showed greater CBF in the left insula, hippocampus and ventral posterior cingulate when compared to those with resolved anosmia (p < 0.05, from whole brain statistical parametric map analysis). INTERPRETATION This work describes, for the first time to our knowledge, functional differences within olfactory areas and regions involved in sensory processing and cognitive functioning. This work identifies key areas for further research and potential target sites for therapeutic strategies. FUNDING This study was funded by the National Institute for Health and Care Research and supported by the Queen Square Scanner business case.
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Affiliation(s)
- Jed Wingrove
- Centre for Obesity Research, Department of Medicine, University College London, London, UK
- National Institute for Health and Care Research, Biomedical Research Centre at UCLH and UCL, London, UK
| | - Janine Makaronidis
- Centre for Obesity Research, Department of Medicine, University College London, London, UK
- National Institute for Health and Care Research, Biomedical Research Centre at UCLH and UCL, London, UK
| | - Ferran Prados
- National Institute for Health and Care Research, Biomedical Research Centre at UCLH and UCL, London, UK
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Baris Kanber
- National Institute for Health and Care Research, Biomedical Research Centre at UCLH and UCL, London, UK
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Marios C. Yiannakas
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Cormac Magee
- Centre for Obesity Research, Department of Medicine, University College London, London, UK
- National Institute for Health and Care Research, Biomedical Research Centre at UCLH and UCL, London, UK
| | - Gloria Castellazzi
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Brain Connectivity Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Louis Grandjean
- Department of Infection, Immunity & Inflammation, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Xavier Golay
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Carmen Tur
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Olga Ciccarelli
- National Institute for Health and Care Research, Biomedical Research Centre at UCLH and UCL, London, UK
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Egidio D'Angelo
- Brain Connectivity Research Centre, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Claudia A.M. Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Brain Connectivity Research Centre, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Rachel L. Batterham
- Centre for Obesity Research, Department of Medicine, University College London, London, UK
- National Institute for Health and Care Research, Biomedical Research Centre at UCLH and UCL, London, UK
- Corresponding author. Division of Medicine, University College London, Rayne Building, 5 University Street, London, WC1E 6JF, UK.
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Jandric D, Lipp I, Paling D, Rog D, Castellazzi G, Haroon H, Parkes L, Parker GJM, Tomassini V, Muhlert N. Mechanisms of Network Changes in Cognitive Impairment in Multiple Sclerosis. Neurology 2021; 97:e1886-e1897. [PMID: 34649879 PMCID: PMC8601205 DOI: 10.1212/wnl.0000000000012834] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [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: 02/05/2021] [Accepted: 09/13/2021] [Indexed: 12/14/2022] Open
Abstract
Background and Objectives Cognitive impairment in multiple sclerosis (MS) is associated with functional connectivity abnormalities. While there have been calls to use functional connectivity measures as biomarkers, there remains to be a full understanding of why they are affected in MS. In this cross-sectional study, we tested the hypothesis that functional network regions may be susceptible to disease-related “wear and tear” and that this can be observable on co-occurring abnormalities on other magnetic resonance metrics. We tested whether functional connectivity abnormalities in cognitively impaired patients with MS co-occur with (1) overlapping, (2) local, or (3) distal changes in anatomic connectivity and cerebral blood flow abnormalities. Methods Multimodal 3T MRI and assessment with the Brief Repeatable Battery of Neuropsychological tests were performed in 102 patients with relapsing-remitting MS and 27 healthy controls. Patients with MS were classified as cognitively impaired if they scored ≥1.5 SDs below the control mean on ≥2 tests (n = 55) or as cognitively preserved (n = 47). Functional connectivity was assessed with Independent Component Analysis and dual regression of resting-state fMRI images. Cerebral blood flow maps were estimated, and anatomic connectivity was assessed with anatomic connectivity mapping and fractional anisotropy of diffusion-weighted MRI. Changes in cerebral blood flow and anatomic connectivity were assessed within resting-state networks that showed functional connectivity abnormalities in cognitively impaired patients with MS. Results Functional connectivity was significantly decreased in the anterior and posterior default mode networks and significantly increased in the right and left frontoparietal networks in cognitively impaired relative to cognitively preserved patients with MS (threshold-free cluster enhancement corrected at p ≤ 0.05, 2 sided). Networks showing functional abnormalities showed altered cerebral blood flow and anatomic connectivity locally and distally but not in overlapping locations. Discussion We provide the first evidence that functional connectivity abnormalities are accompanied by local cerebral blood flow and structural connectivity abnormalities but also demonstrate that these effects do not occur in exactly the same location. Our findings suggest a possibly shared pathologic mechanism for altered functional connectivity in brain networks in MS.
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Affiliation(s)
- Danka Jandric
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - Ilona Lipp
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - David Paling
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - David Rog
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - Gloria Castellazzi
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - Hamied Haroon
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - Laura Parkes
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - Geoff J M Parker
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - Valentina Tomassini
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy
| | - Nils Muhlert
- From the Division of Neuroscience & Experimental Psychology (D.J., H.H., L.P., G.P., N.M.), School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Department of Neurophysics (I.L.), Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Royal Hallamshire Hospital (D.P.), Sheffield Teaching Hospitals, NHS UK; Salford Royal Hospital (D.R.), Salford Royal NHS Foundation Trust, NHS UK; NMR Research Unit (G.C.), Queens Square Multiple Sclerosis Centre, and Centre for Medical Image Computing (G.C., G.P.), Department of Computer Science and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London; Cardiff University Brain Research Imaging Centre (V.T.), Cardiff University, UK; Institute for Advanced Biomedical Technologies (ITAB) (V.T.), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara; and Multiple Sclerosis Centre (V.T.), Department of Neurology, SS Annunziata University Hospital, Chieti, Italy.
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Martinelli D, Arceri S, De Icco R, Allena M, Guaschino E, Ghiotto N, Castellazzi G, Cosentino G, Sances G, Tassorelli C. BONT-A efficacy in high frequency migraine: An open label, single arm, exploratory study applying the preempt paradigm. J Neurol Sci 2021. [DOI: 10.1016/j.jns.2021.117695] [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] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Martinelli D, Arceri S, De Icco R, Allena M, Guaschino E, Ghiotto N, Bitetto V, Castellazzi G, Cosentino G, Sances G, Tassorelli C. BoNT-A efficacy in high frequency migraine: an open label, single arm, exploratory study applying the PREEMPT paradigm. Cephalalgia 2021; 42:170-175. [PMID: 34404257 DOI: 10.1177/03331024211034508] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
INTRODUCTION In this open label, single-arm trial we evaluated the efficacy of onabotulinum toxin-A in the prevention of high-frequency episodic migraine (8-14 migraine days/month). METHODS We enrolled 32 high-frequency episodic migraine subjects (age 44.8 ± 11.9 years, 11.0 ± 2.2 migraine days, 11.5 ± 2.1 headache days, 7 females). After a 28-day baseline period, subjects underwent 4 subsequent onabotulinum toxin-A treatments according to the phase III research evaluating migraine prophylaxis therapy (PREEMPT) paradigm, 12-weeks apart. The primary outcome was the reduction of monthly migraine days from baseline in the 12-week period following the last onabotulinum toxin-A treatment. RESULTS Onabotulinum toxin-A reduced monthly migraine days by 3.68 days (-33.1%, p < 0.01). Thirty-nine percent of the patients experienced a ≥50% reduction in monthly migraine days. Onabotulinum toxin-A also reduced the number of headache days (-33.9%, p < 0.01) and the intake of acute medications (-22.9%, p = 0.03). Disability and quality of life (QoL) scores improved markedly (migraine disability assessment (MIDAS) -41.7%; migraine specific questionnaire (MSQ) -31.7%, p < 0.01). CONCLUSIONS The findings suggest that, when administered according to the PREEMPT paradigm, onabotulinum toxin-A is effective in the prevention of high-frequency episodic migraine.Trial Registration: NCT04578782.
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Affiliation(s)
- Daniele Martinelli
- Deptartment of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Sebastiano Arceri
- Deptartment of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Roberto De Icco
- Deptartment of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Marta Allena
- Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Elena Guaschino
- Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Natascia Ghiotto
- Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Vito Bitetto
- Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Gloria Castellazzi
- Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, Pavia, Italy.,NMR Research Unit, Department of Neuroinflammation, 61554UCL Queen Square Institute of Neurology, 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
| | - Giuseppe Cosentino
- Deptartment of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Grazia Sances
- Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Cristina Tassorelli
- Deptartment of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, Pavia, Italy
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Martinelli D, Castellazzi G, De Icco R, Bacila A, Allena M, Faggioli A, Sances G, Pichiecchio A, Borsook D, Gandini Wheeler-Kingshott CAM, Tassorelli C. Thalamocortical Connectivity in Experimentally-Induced Migraine Attacks: A Pilot Study. Brain Sci 2021; 11:165. [PMID: 33514029 PMCID: PMC7911420 DOI: 10.3390/brainsci11020165] [Citation(s) in RCA: 3] [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: 12/30/2020] [Revised: 01/19/2021] [Accepted: 01/20/2021] [Indexed: 11/17/2022] Open
Abstract
In this study we used nitroglycerin (NTG)-induced migraine attacks as a translational human disease model. Static and dynamic functional connectivity (FC) analyses were applied to study the associated functional brain changes. A spontaneous migraine-like attack was induced in five episodic migraine (EM) patients using a NTG challenge. Four task-free functional magnetic resonance imaging (fMRI) scans were acquired over the study: baseline, prodromal, full-blown, and recovery. Seed-based correlation analysis (SCA) was applied to fMRI data to assess static FC changes between the thalamus and the rest of the brain. Wavelet coherence analysis (WCA) was applied to test time-varying phase-coherence changes between the thalamus and salience networks (SNs). SCA results showed significantly FC changes between the right thalamus and areas involved in the pain circuits (insula, pons, cerebellum) during the prodromal phase, reaching its maximal alteration during the full-blown phase. WCA showed instead a loss of synchronisation between thalami and SN, mainly occurring during the prodrome and full-blown phases. These findings further support the idea that a temporal change in thalamic function occurs over the experimentally induced phases of NTG-induced headache in migraine patients. Correlation of FC changes with true clinical phases in spontaneous migraine would validate the utility of this model.
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Affiliation(s)
- Daniele Martinelli
- Headache Science Center, IRCCS Mondino Foundation, 27100 Pavia, Italy; (R.D.I.); (M.A.); (G.S.); (C.T.)
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (A.P.); (C.A.M.G.W.-K.)
| | - Gloria Castellazzi
- NMR Research Unit Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, London WC1N3BG, UK;
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
- IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Roberto De Icco
- Headache Science Center, IRCCS Mondino Foundation, 27100 Pavia, Italy; (R.D.I.); (M.A.); (G.S.); (C.T.)
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (A.P.); (C.A.M.G.W.-K.)
| | - Ana Bacila
- Center of Advance Imaging and Radiomics, IRCCS Mondino Foundation, 27100 Pavia, Italy; (A.B.); (A.F.)
| | - Marta Allena
- Headache Science Center, IRCCS Mondino Foundation, 27100 Pavia, Italy; (R.D.I.); (M.A.); (G.S.); (C.T.)
| | - Arianna Faggioli
- Center of Advance Imaging and Radiomics, IRCCS Mondino Foundation, 27100 Pavia, Italy; (A.B.); (A.F.)
| | - Grazia Sances
- Headache Science Center, IRCCS Mondino Foundation, 27100 Pavia, Italy; (R.D.I.); (M.A.); (G.S.); (C.T.)
| | - Anna Pichiecchio
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (A.P.); (C.A.M.G.W.-K.)
- Center of Advance Imaging and Radiomics, IRCCS Mondino Foundation, 27100 Pavia, Italy; (A.B.); (A.F.)
| | - David Borsook
- Centre for Pain and The Brain Boston Children’s Hospital and Massachussetts General Hospital (MGH) Harvard Medical School, Boston, MA 02115, USA;
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (A.P.); (C.A.M.G.W.-K.)
- NMR Research Unit Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, London WC1N3BG, UK;
| | - Cristina Tassorelli
- Headache Science Center, IRCCS Mondino Foundation, 27100 Pavia, Italy; (R.D.I.); (M.A.); (G.S.); (C.T.)
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy; (A.P.); (C.A.M.G.W.-K.)
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7
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Martinelli D, Arceri S, De Icco R, Allena M, Guaschino E, Ghiotto N, Castellazzi G, Cosentino G, Sances G, Tassorelli C. BoNT-A efficacy in high-frequency migraine: An open-label, single-arm, exploratory study applying the PREEMPT paradigm. Toxicon 2021. [DOI: 10.1016/j.toxicon.2020.11.438] [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] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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8
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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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9
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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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10
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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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11
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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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12
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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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13
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Savini G, Pardini M, Castellazzi G, Lascialfari A, Chard D, D'Angelo E, Gandini Wheeler-Kingshott CAM. Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis. Front Cell Neurosci 2019; 13:21. [PMID: 30853896 PMCID: PMC6396736 DOI: 10.3389/fncel.2019.00021] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [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: 07/02/2018] [Accepted: 01/17/2019] [Indexed: 01/21/2023] Open
Abstract
Cognitive impairment affects about 50% of multiple sclerosis (MS) patients, but the mechanisms underlying this remain unclear. The default mode network (DMN) has been linked with cognition, but in MS its role is still poorly understood. Moreover, within an extended DMN network including the cerebellum (CBL-DMN), the contribution of cortico-cerebellar connectivity to MS cognitive performance remains unexplored. The present study investigated associations of DMN and CBL-DMN structural connectivity with cognitive processing speed in MS, in both cognitively impaired (CIMS) and cognitively preserved (CPMS) MS patients. 68 MS patients and 22 healthy controls (HCs) completed a symbol digit modalities test (SDMT) and had 3T brain magnetic resonance imaging (MRI) scans that included a diffusion weighted imaging protocol. DMN and CBL-DMN tracts were reconstructed with probabilistic tractography. These networks (DMN and CBL-DMN) and the cortico-cerebellar tracts alone were modeled using a graph theoretical approach with fractional anisotropy (FA) as the weighting factor. Brain parenchymal fraction (BPF) was also calculated. In CIMS SDMT scores strongly correlated with the FA-weighted global efficiency (GE) of the network [GE(CBL-DMN): ρ = 0.87, R2 = 0.76, p < 0.001; GE(DMN): ρ = 0.82, R2 = 0.67, p < 0.001; GE(CBL): ρ = 0.80, R2 = 0.64, p < 0.001]. In CPMS the correlation between these measures was significantly lower [GE(CBL-DMN): ρ = 0.51, R2 = 0.26, p < 0.001; GE(DMN): ρ = 0.48, R2 = 0.23, p = 0.001; GE(CBL): ρ = 0.52, R2 = 0.27, p < 0.001] and SDMT scores correlated most with BPF (ρ = 0.57, R2 = 0.33, p < 0.001). In a multivariable regression model where SDMT was the independent variable, FA-weighted GE was the only significant explanatory variable in CIMS, while in CPMS BPF and expanded disability status scale were significant. No significant correlation was found in HC between SDMT scores, MRI or network measures. DMN structural GE is related to cognitive performance in MS, and results of CBL-DMN suggest that the cerebellum structural connectivity to the DMN plays an important role in information processing speed decline.
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Affiliation(s)
| | - Matteo Pardini
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Genoa, Italy.,Ospedale Policlinico S. Martino, Genoa, Italy
| | - Gloria Castellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.,NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, Institute of Neurology, University College London, London, United Kingdom
| | | | - Declan Chard
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, Institute of Neurology, University College London, London, United Kingdom.,National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, 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
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, Institute of Neurology, University College London, London, United Kingdom.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain MRI 3T Mondino Research Center, IRCCS Mondino Foundation, Pavia, Italy
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14
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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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15
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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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16
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Castellazzi G, Debernard L, Melzer TR, Dalrymple-Alford JC, D'Angelo E, Miller DH, Gandini Wheeler-Kingshott CAM, Mason DF. Functional Connectivity Alterations Reveal Complex Mechanisms Based on Clinical and Radiological Status in Mild Relapsing Remitting Multiple Sclerosis. Front Neurol 2018; 9:690. [PMID: 30177910 PMCID: PMC6109785 DOI: 10.3389/fneur.2018.00690] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [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: 01/24/2018] [Accepted: 07/30/2018] [Indexed: 11/13/2022] Open
Abstract
Resting state functional MRI (rs-fMRI) has provided important insights into functional reorganization in subjects with Multiple Sclerosis (MS) at different stage of disease. In this cross-sectional study we first assessed, by means of rs-fMRI, the impact of overall T2 lesion load (T2LL) and MS severity score (MSSS) on resting state networks (RSNs) in 62 relapsing remitting MS (RRMS) patients with mild disability (MSSS < 3). Independent Component Analysis (ICA) followed by dual regression analysis confirmed functional connectivity (FC) alterations of many RSNs in RRMS subjects compared to healthy controls. The anterior default mode network (DMNa) and the superior precuneus network (PNsup) showed the largest areas of decreased FC, while the sensory motor networks area M1 (SMNm1) and the medial visual network (MVN) showed the largest areas of increased FC. In order to better understand the nature of these alterations as well as the mechanisms of functional alterations in MS we proposed a method, based on linear regression, that takes into account FC changes and their correlation with T2LL and MSSS. Depending on the sign of the correlation between FC and T2LL, and furthermore the sign of the correlation with MSSS, we suggested the following possible underlying mechanisms to interpret altered FC: (1) FC reduction driven by MS lesions, (2) "true" functional compensatory mechanism, (3a) functional compensation attempt, (3b) "false" functional compensation, (4a) neurodegeneration, (4b) pre-symptomatic condition (damage precedes MS clinical manifestation). Our data shows areas satisfying 4 of these 6 conditions (i.e., 1,2,3b,4b), supporting the suggestion that increased FC has a complex nature that may exceed the simplistic assumption of an underlying compensatory mechanism attempting to limit the brain damage caused by MS progression. Exploring differences between RRMS subjects with short disease duration (MSshort) and RRMS with similar disability but longer disease duration (MSlong), we found that MSshort and MSlong were characterized by clearly distinct pattern of FC, involving predominantly sensory and cognitive networks respectively. Overall, these results suggest that the analysis of FC alterations in multiple large-scale networks in relation to radiological (T2LL) and clinical (MSSS, disease duration) status may provide new insights into the pathophysiology of relapse onset MS evolution.
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Affiliation(s)
- Gloria Castellazzi
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Institute of Neurology, London, United Kingdom.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Laetitia Debernard
- New Zealand Brain Research Institute, Christchurch, New Zealand.,Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Tracy R Melzer
- New Zealand Brain Research Institute, Christchurch, New Zealand.,Department of Medicine, University of Otago, Christchurch, New Zealand.,Brain Research New Zealand, Auckland, New Zealand
| | - John C Dalrymple-Alford
- New Zealand Brain Research Institute, Christchurch, New Zealand.,Brain Research New Zealand, Auckland, New Zealand.,Department of Psychology, University of Canterbury, Christchurch, New Zealand
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - David H Miller
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Institute of Neurology, London, United Kingdom.,New Zealand Brain Research Institute, Christchurch, New Zealand.,Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Institute of Neurology, London, United Kingdom.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain MRI 3T Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Deborah F Mason
- New Zealand Brain Research Institute, Christchurch, New Zealand.,Department of Medicine, University of Otago, Christchurch, New Zealand.,Department of Neurology, Christchurch Hospital, Christchurch, New Zealand
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17
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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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18
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Matrone G, Ramalli A, Savoia AS, Quaglia F, Castellazzi G, Morbini P, Piastra M. An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging. J Vis Exp 2017. [PMID: 28994803 DOI: 10.3791/55798] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
The possibility to perform an early and repeatable assessment of imaging performance is fundamental in the design and development process of new ultrasound (US) probes. Particularly, a more realistic analysis with application-specific imaging targets can be extremely valuable to assess the expected performance of US probes in their potential clinical field of application. The experimental protocol presented in this work was purposely designed to provide an application-specific assessment procedure for newly-developed US probe prototypes based on Capacitive Micromachined Ultrasonic Transducer (CMUT) technology in relation to brain imaging. The protocol combines the use of a bovine brain fixed in formalin as the imaging target, which ensures both realism and repeatability of the described procedures, and of neuronavigation techniques borrowed from neurosurgery. The US probe is in fact connected to a motion tracking system which acquires position data and enables the superposition of US images to reference Magnetic Resonance (MR) images of the brain. This provides a means for human experts to perform a visual qualitative assessment of the US probe imaging performance and to compare acquisitions made with different probes. Moreover, the protocol relies on the use of a complete and open research and development system for US image acquisition, i.e. the Ultrasound Advanced Open Platform (ULA-OP) scanner. The manuscript describes in detail the instruments and procedures involved in the protocol, in particular for the calibration, image acquisition and registration of US and MR images. The obtained results prove the effectiveness of the overall protocol presented, which is entirely open (within the limits of the instrumentation involved), repeatable, and covers the entire set of acquisition and processing activities for US images.
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Affiliation(s)
- Giulia Matrone
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia
| | | | | | | | - Gloria Castellazzi
- Brain Connectivity Center, BCC, Istituto Neurologico Nazionale Fondazione C. Mondino I.R.C.C.S
| | - Patrizia Morbini
- Department of Molecular Medicine - Unit of Pathology, University of Pavia, Foundation IRCCS Policlinico San Matteo
| | - Marco Piastra
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia;
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19
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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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20
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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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21
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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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22
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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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23
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Mazzotti M, Bartoli I, Castellazzi G, Marzani A. Computation of leaky guided waves dispersion spectrum using vibroacoustic analyses and the Matrix Pencil Method: a validation study for immersed rectangular waveguides. Ultrasonics 2014; 54:1895-1898. [PMID: 24890709 DOI: 10.1016/j.ultras.2014.05.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 05/07/2014] [Accepted: 05/07/2014] [Indexed: 06/03/2023]
Abstract
The paper aims at validating a recently proposed Semi Analytical Finite Element (SAFE) formulation coupled with a 2.5D Boundary Element Method (2.5D BEM) for the extraction of dispersion data in immersed waveguides of generic cross-section. To this end, three-dimensional vibroacoustic analyses are carried out on two waveguides of square and rectangular cross-section immersed in water using the commercial Finite Element software Abaqus/Explicit. Real wavenumber and attenuation dispersive data are extracted by means of a modified Matrix Pencil Method. It is demonstrated that the results obtained using the two techniques are in very good agreement.
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Affiliation(s)
- M Mazzotti
- Civil, Architectural & Environmental Engineering (CAEE) Department, Drexel University, 3141 Chestnut St., Philadelphia, PA 19104, USA
| | - I Bartoli
- Civil, Architectural & Environmental Engineering (CAEE) Department, Drexel University, 3141 Chestnut St., Philadelphia, PA 19104, USA.
| | - G Castellazzi
- Dipartimento di Ingegneria Civile, Chimica, Ambientale e dei Materiali (DICAM), Università degli Studi di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
| | - A Marzani
- Dipartimento di Ingegneria Civile, Chimica, Ambientale e dei Materiali (DICAM), Università degli Studi di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
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24
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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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25
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Shiroishi MS, Castellazzi G, Boxerman JL, D'Amore F, Essig M, Nguyen TB, Provenzale JM, Enterline DS, Anzalone N, Dörfler A, Rovira À, Wintermark M, Law M. Principles of T2*-weighted dynamic susceptibility contrast MRI technique in brain tumor imaging. J Magn Reson Imaging 2014; 41:296-313. [DOI: 10.1002/jmri.24648] [Citation(s) in RCA: 85] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2013] [Accepted: 04/03/2014] [Indexed: 01/17/2023] Open
Affiliation(s)
- Mark S. Shiroishi
- Keck School of Medicine; University of Southern California; Los Angeles California USA
| | - Gloria Castellazzi
- Department of Industrial and Information Engineering; University of Pavia; Pavia Italy
- Brain Connectivity Center, IRCCS “C. Mondino Foundation,”; Pavia Italy
| | - Jerrold L. Boxerman
- Warren Alpert Medical School of Brown University; Providence Rhode Island USA
| | - Francesco D'Amore
- Keck School of Medicine; University of Southern California; Los Angeles California USA
- Department of Neuroradiology; IRCCS “C. Mondino Foundation,” University of Pavia; Pavia Italy
| | - Marco Essig
- University of Manitoba's Faculty of Medicine; Winnipeg Manitoba Canada
| | - Thanh B. Nguyen
- Faculty of Medicine, Ottawa University; Ottawa Ontario Canada
| | - James M. Provenzale
- Duke University Medical Center; Durham North Carolina USA
- Emory University School of Medicine; Atlanta Georgia USA
| | | | | | - Arnd Dörfler
- University of Erlangen-Nuremberg, Erlangen; Germany
| | - Àlex Rovira
- Vall d'Hebron University Hospital; Barcelona Spain
| | - Max Wintermark
- School of Medicine; University of Virginia; Charlottesville Virginia USA
| | - Meng Law
- Keck School of Medicine; University of Southern California; Los Angeles California USA
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26
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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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27
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Stradiotti P, Curti A, Castellazzi G, Zerbi A. Metal-related artifacts in instrumented spine. Techniques for reducing artifacts in CT and MRI: state of the art. Eur Spine J 2009; 18 Suppl 1:102-8. [PMID: 19437043 DOI: 10.1007/s00586-009-0998-5] [Citation(s) in RCA: 168] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/14/2009] [Indexed: 11/30/2022]
Abstract
The projectional nature of radiogram limits its amount of information about the instrumented spine. MRI and CT imaging can be more helpful, using cross-sectional view. However, the presence of metal-related artifacts at both conventional CT and MRI imaging can obscure relevant anatomy and disease. We reviewed the literature about overcoming artifacts from metallic orthopaedic implants at high-field strength MRI imaging and multi-detector CT. The evolution of multichannel CT has made available new techniques that can help minimizing the severe beam-hardening artifacts. The presence of artifacts at CT from metal hardware is related to image reconstruction algorithm (filter), tube current (in mA), X-ray kilovolt peak, pitch, hardware composition, geometry (shape), and location. MRI imaging has been used safely in patients with orthopaedic metallic implants because most of these implants do not have ferromagnetic properties and have been fixed into position. However, on MRI imaging metallic implants may produce geometric distortion, the so-called susceptibility artifact. In conclusion, although 140 kV and high milliamperage second exposures are recommended for imaging patients with hardware, caution should always be exercised, particularly in children, young adults, and patients undergoing multiple examinations. MRI artifacts can be minimized by positioning optimally and correctly the examined anatomy part with metallic implants in the magnet and by choosing fast spin-echo sequences, and in some cases also STIR sequences, with an anterior to posterior frequency-encoding direction and the smallest voxel size.
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Affiliation(s)
- P Stradiotti
- IRCCS Istituto Ortopedico Galeazzi, via R. Galeazzi 4, Milan 20161, Italy
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28
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Castellazzi G, Vanel D, Le Cesne A, Le Pechoux C, Caillet H, Perona F, Bonvalot S. Can the MRI signal of aggressive fibromatosis be used to predict its behavior? Eur J Radiol 2009; 69:222-9. [DOI: 10.1016/j.ejrad.2008.10.012] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2008] [Accepted: 10/02/2008] [Indexed: 12/18/2022]
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