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Khamaysa M, El Mendili M, Marchand V, Querin G, Pradat PF. Quantitative spinal cord imaging: Early ALS diagnosis and monitoring of disease progression. Rev Neurol (Paris) 2024:S0035-3787(24)00657-X. [PMID: 39547910 DOI: 10.1016/j.neurol.2024.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 08/23/2024] [Accepted: 10/08/2024] [Indexed: 11/17/2024]
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the progressive degeneration of motor neurons in the cortex, brainstem, and spinal cord. This degeneration leads to muscular weakness, progressively impairing motor functions and ultimately resulting in respiratory failure. The clinical, genetic, and pathological heterogeneity of ALS, combined with the absence of reliable biomarkers, significantly challenge the efficacy of therapeutic trials. Despite these hurdles, neuroimaging, and particularly spinal cord imaging, has emerged as a promising tool. It provides insights into the involvement of both upper and lower motor neurons. Quantitative spinal imaging has the potential to facilitate early diagnosis, enable accurate monitoring of disease progression, and refine the design of clinical trials. In this review, we explore the utility of spinal cord imaging within the broader context of developing spinal imaging biomarkers in ALS. We focus on a both diagnostic and prognostic biomarker in ALS, highlighting its pivotal role in elucidating the disease's underlying pathology. We also discuss the existing limitations and future avenues for research, aiming to bridge the translational gap between academic research and its application in clinical practice and therapeutic trials.
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Affiliation(s)
- M Khamaysa
- Laboratoire d'Imagerie Biomédicale, Inserm, Sorbonne Université, CNRS, Paris, France
| | - M El Mendili
- Laboratoire d'Imagerie Biomédicale, Inserm, Sorbonne Université, CNRS, Paris, France
| | - V Marchand
- Laboratoire d'Imagerie Biomédicale, Inserm, Sorbonne Université, CNRS, Paris, France
| | - G Querin
- Département de Neurologie, Hôpital Pitié-Salpêtrière, Centre référent SLA, AP-HP, Paris, France
| | - P-F Pradat
- Laboratoire d'Imagerie Biomédicale, Inserm, Sorbonne Université, CNRS, Paris, France; Département de Neurologie, Hôpital Pitié-Salpêtrière, Centre référent SLA, AP-HP, Paris, France.
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2
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Tahedl M, Tan EL, Siah WF, Hengeveld JC, Doherty MA, McLaughlin RL, Hardiman O, Finegan E, Bede P. Radiological correlates of pseudobulbar affect: Corticobulbar and cerebellar components in primary lateral sclerosis. J Neurol Sci 2023; 451:120726. [PMID: 37421883 DOI: 10.1016/j.jns.2023.120726] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/02/2023] [Accepted: 06/28/2023] [Indexed: 07/10/2023]
Abstract
INTRODUCTION Pseudobulbar affect (PBA) is a distressing symptom of a multitude of neurological conditions affecting patients with a rage of neuroinflammatory, neurovascular and neurodegenerative conditions. It manifests in disproportionate emotional responses to minimal or no contextual stimulus. It has considerable quality of life implications and treatment can be challenging. METHODS A prospective multimodal neuroimaging study was conducted to explore the neuroanatomical underpinnings of PBA in patients with primary lateral sclerosis (PLS). All participants underwent whole genome sequencing and screening for C9orf72 hexanucleotide repeat expansions, a comprehensive neurological assessment, neuropsychological screening (ECAS, HADS, FrSBe) and PBA was evaluated by the emotional lability questionnaire. Structural, diffusivity and functional MRI data were systematically evaluated in whole-brain (WB) data-driven and region of interest (ROI) hypothesis-driven analyses. In ROI analyses, functional and structural corticobulbar connectivity and cerebello-medullary connectivity alterations were evaluated separately. RESULTS Our data-driven whole-brain analyses revealed associations between PBA and white matter degeneration in descending corticobulbar as well as in commissural tracts. In our hypothesis-driven analyses, PBA was associated with increased right corticobulbar tract RD (p = 0.006) and decreased FA (p = 0.026). The left-hemispheric corticobulbar tract, as well as functional connectivity, showed similar tendencies. While uncorrected p-maps revealed both voxelwise and ROI trends for associations between PBA and cerebellar measures, these did not reach significance to unequivocally support the "cerebellar hypothesis". CONCLUSIONS Our data confirm associations between cortex-brainstem disconnection and the clinical severity of PBA. While our findings may be disease-specific, they are consistent with the classical cortico-medullary model of pseudobulbar affect.
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Affiliation(s)
- Marlene Tahedl
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Ireland
| | - Ee Ling Tan
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Ireland
| | - We Fong Siah
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Ireland
| | | | - Mark A Doherty
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | | | - Orla Hardiman
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Ireland
| | - Eoin Finegan
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Ireland; Department of Neurology, St James's Hospital, Dublin, Ireland.
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Hippocampal Metabolic Alterations in Amyotrophic Lateral Sclerosis: A Magnetic Resonance Spectroscopy Study. Life (Basel) 2023; 13:life13020571. [PMID: 36836928 PMCID: PMC9965919 DOI: 10.3390/life13020571] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND Magnetic resonance spectroscopy (MRS) in amyotrophic lateral sclerosis (ALS) has been overwhelmingly applied to motor regions to date and our understanding of frontotemporal metabolic signatures is relatively limited. The association between metabolic alterations and cognitive performance in also poorly characterised. MATERIAL AND METHODS In a multimodal, prospective pilot study, the structural, metabolic, and diffusivity profile of the hippocampus was systematically evaluated in patients with ALS. Patients underwent careful clinical and neurocognitive assessments. All patients were non-demented and exhibited normal memory performance. 1H-MRS spectra of the right and left hippocampi were acquired at 3.0T to determine the concentration of a panel of metabolites. The imaging protocol also included high-resolution T1-weighted structural imaging for subsequent hippocampal grey matter (GM) analyses and diffusion tensor imaging (DTI) for the tractographic evaluation of the integrity of the hippocampal perforant pathway zone (PPZ). RESULTS ALS patients exhibited higher hippocampal tNAA, tNAA/tCr and tCho bilaterally, despite the absence of volumetric and PPZ diffusivity differences between the two groups. Furthermore, superior memory performance was associated with higher hippocampal tNAA/tCr bilaterally. Both longer symptom duration and greater functional disability correlated with higher tCho levels. CONCLUSION Hippocampal 1H-MRS may not only contribute to a better academic understanding of extra-motor disease burden in ALS, but given its sensitive correlations with validated clinical metrics, it may serve as practical biomarker for future clinical and clinical trial applications. Neuroimaging protocols in ALS should incorporate MRS in addition to standard structural, functional, and diffusion sequences.
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Barry RL, Torrado-Carvajal A, Kirsch JE, Arabasz GE, Albrecht DS, Alshelh Z, Pijanowski O, Lewis AJ, Keegan M, Reynolds B, Knight PC, Morrissey EJ, Loggia ML, Atassi N, Hooker JM, Babu S. Selective atrophy of the cervical enlargement in whole spinal cord MRI of amyotrophic lateral sclerosis. Neuroimage Clin 2022; 36:103199. [PMID: 36137496 PMCID: PMC9668597 DOI: 10.1016/j.nicl.2022.103199] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/26/2022] [Accepted: 09/13/2022] [Indexed: 12/14/2022]
Abstract
Amyotrophic lateral sclerosis (ALS) is a deadly neurodegenerative disorder affecting motor neurons in the spinal cord and brain. Studies have reported on atrophy within segments of the cervical cord, but we are not aware of previous investigations of the whole spinal cord. Herein we present our findings from a 3T MRI study involving 32 subjects (15 ALS participants and 17 healthy controls) characterizing cross-sectional area along the entire cord. We report atrophy of the cervical enlargement in ALS participants, but no evidence of atrophy of the thoracolumbar enlargement. These results suggest that MR-based analyses of the cervical cord may be sufficient for in vivo investigations of spinal cord atrophy in ALS, and that atrophy of the cervical enlargement (C4-C7) is a potential imaging marker for quantifying lower motor neuron degradation.
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Affiliation(s)
- Robert L. Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA,Department of Radiology, Harvard Medical School, Boston, MA, USA,Harvard–Massachusetts Institute of Technology Health Sciences & Technology, Cambridge, MA, USA,Corresponding authors.
| | - Angel Torrado-Carvajal
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA,Medical Image Analysis and Biometry Laboratory, Universidad Rey Juan Carlos, Madrid, Spain
| | - John E. Kirsch
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Grae E. Arabasz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Daniel S. Albrecht
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Zeynab Alshelh
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Olivia Pijanowski
- Sean M. Healey & AMG Center for ALS at Massachusetts General Hospital, Department of Neurology, Neurological Clinical Research Institute, Boston, MA, USA
| | - Austin J. Lewis
- Sean M. Healey & AMG Center for ALS at Massachusetts General Hospital, Department of Neurology, Neurological Clinical Research Institute, Boston, MA, USA
| | - Mackenzie Keegan
- Sean M. Healey & AMG Center for ALS at Massachusetts General Hospital, Department of Neurology, Neurological Clinical Research Institute, Boston, MA, USA
| | - Beverly Reynolds
- Sean M. Healey & AMG Center for ALS at Massachusetts General Hospital, Department of Neurology, Neurological Clinical Research Institute, Boston, MA, USA
| | - Paulina C. Knight
- Sean M. Healey & AMG Center for ALS at Massachusetts General Hospital, Department of Neurology, Neurological Clinical Research Institute, Boston, MA, USA
| | - Erin J. Morrissey
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Marco L. Loggia
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA,Department of Radiology, Harvard Medical School, Boston, MA, USA,Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Nazem Atassi
- Sean M. Healey & AMG Center for ALS at Massachusetts General Hospital, Department of Neurology, Neurological Clinical Research Institute, Boston, MA, USA,Department of Neurology, Harvard Medical School, Boston, MA, USA,Sanofi Genzyme, Cambridge, MA, USA
| | - Jacob M. Hooker
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Suma Babu
- Sean M. Healey & AMG Center for ALS at Massachusetts General Hospital, Department of Neurology, Neurological Clinical Research Institute, Boston, MA, USA,Department of Neurology, Harvard Medical School, Boston, MA, USA,Corresponding authors.
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Juengling FD, Wuest F, Kalra S, Agosta F, Schirrmacher R, Thiel A, Thaiss W, Müller HP, Kassubek J. Simultaneous PET/MRI: The future gold standard for characterizing motor neuron disease-A clinico-radiological and neuroscientific perspective. Front Neurol 2022; 13:890425. [PMID: 36061999 PMCID: PMC9428135 DOI: 10.3389/fneur.2022.890425] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 07/20/2022] [Indexed: 01/18/2023] Open
Abstract
Neuroimaging assessment of motor neuron disease has turned into a cornerstone of its clinical workup. Amyotrophic lateral sclerosis (ALS), as a paradigmatic motor neuron disease, has been extensively studied by advanced neuroimaging methods, including molecular imaging by MRI and PET, furthering finer and more specific details of the cascade of ALS neurodegeneration and symptoms, facilitated by multicentric studies implementing novel methodologies. With an increase in multimodal neuroimaging data on ALS and an exponential improvement in neuroimaging technology, the need for harmonization of protocols and integration of their respective findings into a consistent model becomes mandatory. Integration of multimodal data into a model of a continuing cascade of functional loss also calls for the best attempt to correlate the different molecular imaging measurements as performed at the shortest inter-modality time intervals possible. As outlined in this perspective article, simultaneous PET/MRI, nowadays available at many neuroimaging research sites, offers the perspective of a one-stop shop for reproducible imaging biomarkers on neuronal damage and has the potential to become the new gold standard for characterizing motor neuron disease from the clinico-radiological and neuroscientific perspectives.
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Affiliation(s)
- Freimut D. Juengling
- Division of Oncologic Imaging, University of Alberta, Edmonton, AB, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
- Faculty of Medicine, University Bern, Bern, Switzerland
| | - Frank Wuest
- Division of Oncologic Imaging, University of Alberta, Edmonton, AB, Canada
| | - Sanjay Kalra
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
- Department of Neurology, University of Alberta, Edmonton, AB, Canada
| | - Federica Agosta
- Division of Neuroscience, San Raffaele Scientific Institute, University Vita Salute San Raffaele, Milan, Italy
| | - Ralf Schirrmacher
- Division of Oncologic Imaging, University of Alberta, Edmonton, AB, Canada
- Medical Isotope and Cyclotron Facility, University of Alberta, Edmonton, AB, Canada
| | - Alexander Thiel
- Lady Davis Institute for Medical Research, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Wolfgang Thaiss
- Department of Nuclear Medicine, University of Ulm Medical Center, Ulm, Germany
- Department of Diagnostic and Interventional Radiology, University of Ulm Medical Center, Ulm, Germany
| | - Hans-Peter Müller
- Department of Neurology, Ulm University Medical Center, Ulm, Germany
| | - Jan Kassubek
- Department of Neurology, Ulm University Medical Center, Ulm, Germany
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McKenna MC, Tahedl M, Lope J, Chipika RH, Li Hi Shing S, Doherty MA, Hengeveld JC, Vajda A, McLaughlin RL, Hardiman O, Hutchinson S, Bede P. Mapping cortical disease-burden at individual-level in frontotemporal dementia: implications for clinical care and pharmacological trials. Brain Imaging Behav 2022; 16:1196-1207. [PMID: 34882275 PMCID: PMC9107414 DOI: 10.1007/s11682-021-00523-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/20/2021] [Indexed: 01/25/2023]
Abstract
Imaging studies of FTD typically present group-level statistics between large cohorts of genetically, molecularly or clinically stratified patients. Group-level statistics are indispensable to appraise unifying radiological traits and describe genotype-associated signatures in academic studies. However, in a clinical setting, the primary objective is the meaningful interpretation of imaging data from individual patients to assist diagnostic classification, inform prognosis, and enable the assessment of progressive changes compared to baseline scans. In an attempt to address the pragmatic demands of clinical imaging, a prospective computational neuroimaging study was undertaken in a cohort of patients across the spectrum of FTD phenotypes. Cortical changes were evaluated in a dual pipeline, using standard cortical thickness analyses and an individualised, z-score based approach to characterise subject-level disease burden. Phenotype-specific patterns of cortical atrophy were readily detected with both methodological approaches. Consistent with their clinical profiles, patients with bvFTD exhibited orbitofrontal, cingulate and dorsolateral prefrontal atrophy. Patients with ALS-FTD displayed precentral gyrus involvement, nfvPPA patients showed widespread cortical degeneration including insular and opercular regions and patients with svPPA exhibited relatively focal anterior temporal lobe atrophy. Cortical atrophy patterns were reliably detected in single individuals, and these maps were consistent with the clinical categorisation. Our preliminary data indicate that standard T1-weighted structural data from single patients may be utilised to generate maps of cortical atrophy. While the computational interpretation of single scans is challenging, it offers unrivalled insights compared to visual inspection. The quantitative evaluation of individual MRI data may aid diagnostic classification, clinical decision making, and assessing longitudinal changes.
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Affiliation(s)
- Mary Clare McKenna
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Marlene Tahedl
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
- Institute for Psychology, University of Regensburg, Regensburg, Germany
| | - Jasmin Lope
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Mark A Doherty
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | - Jennifer C Hengeveld
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | - Alice Vajda
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | - Russell L McLaughlin
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | | | - Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.
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7
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McKenna MC, Tahedl M, Murad A, Lope J, Hardiman O, Hutchinson S, Bede P. White matter microstructure alterations in frontotemporal dementia: Phenotype-associated signatures and single-subject interpretation. Brain Behav 2022; 12:e2500. [PMID: 35072974 PMCID: PMC8865163 DOI: 10.1002/brb3.2500] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/22/2021] [Accepted: 01/01/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Frontotemporal dementias (FTD) include a genetically heterogeneous group of conditions with distinctive molecular, radiological and clinical features. The majority of radiology studies in FTD compare FTD subgroups to healthy controls to describe phenotype- or genotype-associated imaging signatures. While the characterization of group-specific imaging traits is academically important, the priority of clinical imaging is the meaningful interpretation of individual datasets. METHODS To demonstrate the feasibility of single-subject magnetic resonance imaging (MRI) interpretation, we have evaluated the white matter profile of 60 patients across the clinical spectrum of FTD. A z-score-based approach was implemented, where the diffusivity metrics of individual patients were appraised with reference to demographically matched healthy controls. Fifty white matter tracts were systematically evaluated in each subject with reference to normative data. RESULTS The z-score-based approach successfully detected white matter pathology in single subjects, and group-level inferences were analogous to the outputs of standard track-based spatial statistics. CONCLUSIONS Our findings suggest that it is possible to meaningfully evaluate the diffusion profile of single FTD patients if large normative datasets are available. In contrast to the visual review of FLAIR and T2-weighted images, computational imaging offers objective, quantitative insights into white matter integrity changes even at single-subject level.
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Affiliation(s)
- Mary Clare McKenna
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Marlene Tahedl
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Aizuri Murad
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Jasmin Lope
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | | | - Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland.,Department of Neurology, St James's Hospital, Dublin, Ireland
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8
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Bede P, Murad A, Lope J, Li Hi Shing S, Finegan E, Chipika RH, Hardiman O, Chang KM. Phenotypic categorisation of individual subjects with motor neuron disease based on radiological disease burden patterns: A machine-learning approach. J Neurol Sci 2022; 432:120079. [PMID: 34875472 DOI: 10.1016/j.jns.2021.120079] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 11/25/2021] [Accepted: 11/29/2021] [Indexed: 12/20/2022]
Abstract
Motor neuron disease is an umbrella term encompassing a multitude of clinically heterogeneous phenotypes. The early and accurate categorisation of patients is hugely important, as MND phenotypes are associated with markedly different prognoses, progression rates, care needs and benefit from divergent management strategies. The categorisation of patients shortly after symptom onset is challenging, and often lengthy clinical monitoring is needed to assign patients to the appropriate phenotypic subgroup. In this study, a multi-class machine-learning strategy was implemented to classify 300 patients based on their radiological profile into diagnostic labels along the UMN-LMN spectrum. A comprehensive panel of cortical thickness measures, subcortical grey matter variables, and white matter integrity metrics were evaluated in a multilayer perceptron (MLP) model. Additional exploratory analyses were also carried out using discriminant function analyses (DFA). Excellent classification accuracy was achieved for amyotrophic lateral sclerosis in the testing cohort (93.7%) using the MLP model, but poor diagnostic accuracy was detected for primary lateral sclerosis (43.8%) and poliomyelitis survivors (60%). Feature importance analyses highlighted the relevance of white matter diffusivity metrics and the evaluation of cerebellar indices, cingulate measures and thalamic radiation variables to discriminate MND phenotypes. Our data suggest that radiological data from single patients may be meaningfully interpreted if large training data sets are available and the provision of diagnostic probability outcomes may be clinically useful in patients with short symptom duration. The computational interpretation of multimodal radiology datasets herald viable diagnostic, prognostic and clinical trial applications.
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Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland; Pitié-Salpêtrière University Hospital, Sorbonne University, Paris, France.
| | - Aizuri Murad
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Jasmin Lope
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Eoin Finegan
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Kai Ming Chang
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland; Department of Electronics and Computer Science, University of Southampton, UK
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9
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Tahedl M, Li Hi Shing S, Finegan E, Chipika RH, Lope J, Murad A, Hardiman O, Bede P. Imaging data reveal divergent longitudinal trajectories in PLS, ALS and poliomyelitis survivors: Group-level and single-subject traits. Data Brief 2021; 39:107484. [PMID: 34901337 PMCID: PMC8640870 DOI: 10.1016/j.dib.2021.107484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/28/2021] [Accepted: 10/11/2021] [Indexed: 01/02/2023] Open
Abstract
Imaging profiles from a longitudinal single-centre motor neuron disease study are presented. A standardized T1-weighted MRI protocol was implemented to characterise cortical disease burden trajectories across the UMN (upper motor neuron) - LMN (lower motor neuron) spectrum of motor neuron diseases (MNDs) (Tahedl et al., 2021). Patients with amyotrophic lateral sclerosis (ALS n = 61), patients with primary lateral sclerosis (PLS n = 23) and poliomyelitis survivors (PMS n = 45) were included. Up to four longitudinal scans were available for each patient, separated by an inter-scan-interval of four months. Individual and group-level cortical thickness profiles were appraised using a normalisation procedure with reference to subject-specific control groups. A z-scoring approach was utilised, where each patients' cortex was first segmented into 1000 cortical regions, and then rated as 'thin', 'thick', or 'comparable' to the corresponding region of a demographically-matched control cohort. Fractions of significantly 'thin' and 'thick' patches were calculated across the entire cerebral vertex as well as in specific brain regions, such as the motor cortex, parietal, frontal and temporal cortices. This approach allows the characterisation of disease burden in individual subjects as well as at a group-level, both cross-sectionally and longitudinally. The presented framework may aid the interpretation of individual cortical disease burden in other patient cohorts.
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Affiliation(s)
- Marlene Tahedl
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Pearse Street Room 5.43, Dublin, Ireland.,Department of Psychiatry and Psychotherapy and Institute for Psychology, University of Regensburg, Germany
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Pearse Street Room 5.43, Dublin, Ireland
| | - Eoin Finegan
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Pearse Street Room 5.43, Dublin, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Pearse Street Room 5.43, Dublin, Ireland
| | - Jasmin Lope
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Pearse Street Room 5.43, Dublin, Ireland
| | - Aizuri Murad
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Pearse Street Room 5.43, Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Pearse Street Room 5.43, Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Pearse Street Room 5.43, Dublin, Ireland.,Pitié-Salpêtrière University Hospital, Sorbonne University, Paris, France
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10
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Li Hi Shing S, Bede P. The neuroradiology of upper motor neuron degeneration: PLS, HSP, ALS. Amyotroph Lateral Scler Frontotemporal Degener 2021; 23:1-3. [PMID: 34894929 DOI: 10.1080/21678421.2021.1951293] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Stacey Li Hi Shing
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
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11
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Eisen A, Bede P. The strength of corticomotoneuronal drive underlies ALS split phenotypes and reflects early upper motor neuron dysfunction. Brain Behav 2021; 11:e2403. [PMID: 34710283 PMCID: PMC8671797 DOI: 10.1002/brb3.2403] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/02/2021] [Accepted: 10/05/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Split phenotypes, (split hand, elbow, leg, and foot), are probably unique to ALS, and are characterized by having a shared peripheral input of both affected and unaffected muscles. This implies an anatomical origin rostral to the spinal cord, primarily within the cerebral cortex. Therefore, split phenotypes are a potential marker of ALS upper motor neuron pathology. However, to date, reports documenting upper motor neuron dysfunction in split phenotypes have been limited to using transcranial magnetic stimulation and cortical threshold tracking techniques. Here, we consider several other potential methodologies that could confirm a primary upper motor neuron pathology in split phenotypes. METHODS We review the potential of: 1. measuring the compound excitatory post-synaptic potential recorded from a single activated motor unit, 2. cortical-muscular coherence, and 3. new advanced modalities of neuroimaging (high-resolution imaging protocols, ultra-high field MRI platforms [7T], and novel Non-Gaussian diffusion models). CONCLUSIONS We propose that muscles involved in split phenotypes are those functionally involved in the human motor repertoire used particularly in complex activities. Their anterior horn cells receive the strongest corticomotoneuronal input. This is also true of the weakest muscles that are the earliest to be affected in ALS. Descriptions of split hand in non-ALS cases and proposals that peripheral nerve or muscle dysfunction may be causative are contentious. Only a few carefully controlled cases of each form of split phenotype, using upper motor neuron directed methodologies, are necessary to prove our postulate.
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Affiliation(s)
- Andrew Eisen
- Division of Neurology, Department of Medicine, University of British Columbia, British Columbia, Canada
| | - Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Pitié-Salpêtrière University Hospital, Sorbonne University, Paris, France
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12
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Li Hi Shing S, Lope J, Chipika RH, Hardiman O, Bede P. Extra-motor manifestations in post-polio syndrome (PPS): fatigue, cognitive symptoms and radiological features. Neurol Sci 2021; 42:4569-4581. [PMID: 33635429 DOI: 10.1007/s10072-021-05130-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 02/20/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND There is a paucity of cerebral neuroimaging studies in post-polio syndrome (PPS), despite the severity of neurological and neuropsychological sequelae associated with the condition. Fatigue, poor concentration, limited exercise tolerance, paraesthesia and progressive weakness are frequently reported, but the radiological underpinnings of these symptoms are poorly characterised. OBJECTIVE The aim of this study is to evaluate cortical and subcortical alterations in a cohort of adult polio survivors to explore the anatomical substrate of extra-motor manifestations. METHODS Thirty-six patients with post-polio syndrome, a disease-control group with amyotrophic lateral sclerosis patients and a cohort of healthy individuals were included in a prospective neuroimaging study with a standardised clinical and radiological protocol. Validated clinical instruments were utilised to assess mood, cognitive and behavioural domains and specific aspects of fatigue. Cortical thickness analyses, subcortical volumetry, brainstem segmentation and region-of-interest (ROI) white matter analyses were undertaken to assess regional grey and white matter alterations. RESULTS A high proportion of PPS patients exhibited apathy, verbal fluency deficits and reported self-perceived fatigue. On ROI analyses, cortical atrophy was limited to the cingulate gyrus, and the temporal pole and subcortical atrophy were only detected in the left nucleus accumbens. No FA reductions were noted to indicate white matter degeneration in any of the lobes. CONCLUSIONS Despite the high incidence of extra-motor manifestations in PPS, only limited cortical, subcortical and white matter degeneration was identified. Our findings suggest that non-structural causes, such as polypharmacy and poor sleep, may contribute to the complex symptomatology of post-polio syndrome.
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Affiliation(s)
- Stacey Li Hi Shing
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Ireland
| | - Jasmin Lope
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Ireland.
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13
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Ratti E, Domoto-Reilly K, Caso C, Murphy A, Brickhouse M, Hochberg D, Makris N, Cudkowicz ME, Dickerson BC. Regional prefrontal cortical atrophy predicts specific cognitive-behavioral symptoms in ALS-FTD. Brain Imaging Behav 2021; 15:2540-2551. [PMID: 33587281 PMCID: PMC8862734 DOI: 10.1007/s11682-021-00456-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/14/2021] [Indexed: 01/01/2023]
Abstract
Amyotrophic Lateral Sclerosis-Frontotemporal Dementia (ALS-FTD) may present typical behavioral variant FTD symptoms. This study aims to determine whether profile and severity of cognitive-behavioral symptoms in ALS/ALS-FTD are predicted by regional cortical atrophy. The hypothesis is that executive dysfunction can be predicted by dorsolateral prefrontal cortical (dlPFC) atrophy, apathy by dorsomedial PFC (dmPFC) and anterior cingulate cortical (ACC) atrophy, disinhibition by orbitofrontal cortical (OFC) atrophy. 3.0 Tesla MRI scans were acquired from 22 people with ALS or ALS-FTD. Quantitative cortical thickness analysis was performed with FreeSurfer. A priori-defined regions of interest (ROI) were used to measure cortical thickness in each participant and calculate magnitude of atrophy in comparison to 115 healthy controls. Spearman correlations were used to evaluate associations between frontal ROI cortical thickness and cognitive-behavioral symptoms, measured by Neuropsychiatric Inventory Questionnaire (NPI-Q) and Clinical Dementia Rating (CDR) scale. ALS-FTD participants exhibited variable degrees of apathy (NPI-Q/apathy: 1.6 ± 1.2), disinhibition (NPI-Q/disinhibition: 1.2 ± 1.2), executive dysfunction (CDR/judgment-problem solving: 1.7 ± 0.8). Within the ALS-FTD group, executive dysfunction correlated with dlPFC atrophy (ρ:-0.65;p < 0.05); similar trends were seen for apathy with ACC (ρ:-0.53;p < 0.10) and dmPFC (ρ:-0.47;p < 0.10) atrophy, for disinhibition with OFC atrophy (ρ:-0.51;p < 0.10). Compared to people with ALS, those with ALS-FTD showed more diffuse atrophy involving precentral gyrus, prefrontal, temporal regions. Profile and severity of cognitive-behavioral symptoms in ALS-FTD are predicted by regional prefrontal atrophy. These findings are consistent with established brain-behavior models and support the role of quantitative MRI in diagnosis, management, counseling, monitoring and prognostication for a neurodegenerative disorder with diverse phenotypes.
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Affiliation(s)
- Elena Ratti
- Neurological Clinical Research Institute (NCRI), Massachusetts General Hospital, 165 Cambridge Street, Suite 600, Boston, MA, 02114, USA.
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
- Amyotrophic Lateral Sclerosis Multidisciplinary Clinic, Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA.
- Massachusetts Alzheimer's Disease Research Center (ADRC), 149 13th Street, Charlestown, MA, 02129, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, 149 13th Street, Charlestown, MA, 02129, USA.
- Biogen, 300 Binney Street, Cambridge, MA, 02142, USA.
| | - Kimiko Domoto-Reilly
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
- Massachusetts Alzheimer's Disease Research Center (ADRC), 149 13th Street, Charlestown, MA, 02129, USA
- Athinoula A. Martinos Center for Biomedical Imaging, 149 13th Street, Charlestown, MA, 02129, USA
- Department of Neurology, University of Washington Medical Center, 1959 NE Pacific Street, Seattle, WA, 98195, USA
| | - Christina Caso
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
- Department of Neurology, University of Washington Medical Center, 1959 NE Pacific Street, Seattle, WA, 98195, USA
| | - Alyssa Murphy
- Neurological Clinical Research Institute (NCRI), Massachusetts General Hospital, 165 Cambridge Street, Suite 600, Boston, MA, 02114, USA
- Amyotrophic Lateral Sclerosis Multidisciplinary Clinic, Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Michael Brickhouse
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Daisy Hochberg
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
- Speech and Language Pathology, Massachusetts General Hospital, 275 Cambridge Street, Boston, MA, 02114, USA
| | - Nikos Makris
- Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Merit E Cudkowicz
- Neurological Clinical Research Institute (NCRI), Massachusetts General Hospital, 165 Cambridge Street, Suite 600, Boston, MA, 02114, USA
- Amyotrophic Lateral Sclerosis Multidisciplinary Clinic, Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Bradford C Dickerson
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
- Massachusetts Alzheimer's Disease Research Center (ADRC), 149 13th Street, Charlestown, MA, 02129, USA
- Athinoula A. Martinos Center for Biomedical Imaging, 149 13th Street, Charlestown, MA, 02129, USA
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Pathological neural networks and artificial neural networks in ALS: diagnostic classification based on pathognomonic neuroimaging features. J Neurol 2021; 269:2440-2452. [PMID: 34585269 PMCID: PMC9021106 DOI: 10.1007/s00415-021-10801-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/07/2021] [Accepted: 09/09/2021] [Indexed: 12/26/2022]
Abstract
The description of group-level, genotype- and phenotype-associated imaging traits is academically important, but the practical demands of clinical neurology centre on the accurate classification of individual patients into clinically relevant diagnostic, prognostic and phenotypic categories. Similarly, pharmaceutical trials require the precision stratification of participants based on quantitative measures. A single-centre study was conducted with a uniform imaging protocol to test the accuracy of an artificial neural network classification scheme on a cohort of 378 participants composed of patients with ALS, healthy subjects and disease controls. A comprehensive panel of cerebral volumetric measures, cortical indices and white matter integrity values were systematically retrieved from each participant and fed into a multilayer perceptron model. Data were partitioned into training and testing and receiver-operating characteristic curves were generated for the three study-groups. Area under the curve values were 0.930 for patients with ALS, 0.958 for disease controls, and 0.931 for healthy controls relying on all input imaging variables. The ranking of variables by classification importance revealed that white matter metrics were far more relevant than grey matter indices to classify single subjects. The model was further tested in a subset of patients scanned within 6 weeks of their diagnosis and an AUC of 0.915 was achieved. Our study indicates that individual subjects may be accurately categorised into diagnostic groups in an observer-independent classification framework based on multiparametric, spatially registered radiology data. The development and validation of viable computational models to interpret single imaging datasets are urgently required for a variety of clinical and clinical trial applications.
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15
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Bede P, Pradat PF, Lope J, Vourc'h P, Blasco H, Corcia P. Primary Lateral Sclerosis: Clinical, radiological and molecular features. Rev Neurol (Paris) 2021; 178:196-205. [PMID: 34243936 DOI: 10.1016/j.neurol.2021.04.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/23/2021] [Accepted: 04/29/2021] [Indexed: 10/20/2022]
Abstract
Primary Lateral Sclerosis (PLS) is an uncommon motor neuron disorder. Despite the well-recognisable constellation of clinical manifestations, the initial diagnosis can be challenging and therapeutic options are currently limited. There have been no recent clinical trials of disease-modifying therapies dedicated to this patient cohort and awareness of recent research developments is limited. The recent consensus diagnostic criteria introduced the category 'probable' PLS which is likely to curtail the diagnostic journey of patients. Extra-motor clinical manifestations are increasingly recognised, challenging the view of PLS as a 'pure' upper motor neuron condition. The post mortem literature of PLS has been expanded by seminal TDP-43 reports and recent PLS studies increasingly avail of meticulous genetic profiling. Research in PLS has gained unprecedented momentum in recent years generating novel academic insights, which may have important clinical ramifications.
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Affiliation(s)
- P Bede
- Pitié-Salpêtrière University Hospital, Sorbonne University, Paris, France; Computational Neuroimaging Group, Trinity College Dublin, Ireland.
| | - P-F Pradat
- Pitié-Salpêtrière University Hospital, Sorbonne University, Paris, France
| | - J Lope
- Computational Neuroimaging Group, Trinity College Dublin, Ireland
| | - P Vourc'h
- Department of Biochemistry and Molecular Biology, CHRU Bretonneau, Tours, France; UMR 1253 iBrain, Université de Tours, Inserm, France
| | - H Blasco
- Department of Biochemistry and Molecular Biology, CHRU Bretonneau, Tours, France; UMR 1253 iBrain, Université de Tours, Inserm, France
| | - P Corcia
- UMR 1253 iBrain, Université de Tours, Inserm, France; ALS and MND centre (FILSLAN), University of Tours, "iBrain", inserm, France
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16
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Li Hi Shing S, Lope J, McKenna MC, Chipika RH, Hardiman O, Bede P. Increased cerebral integrity metrics in poliomyelitis survivors: putative adaptation to longstanding lower motor neuron degeneration. J Neurol Sci 2021; 424:117361. [PMID: 33773768 DOI: 10.1016/j.jns.2021.117361] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 02/14/2021] [Accepted: 02/17/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Post-polio syndrome (PPS) has been traditionally considered a slowly progressive condition that affects poliomyelitis survivors decades after their initial infection. Cerebral changes in poliomyelitis survivors are poorly characterised and the few existing studies are strikingly conflicting. OBJECTIVE The overarching aim of this study is the comprehensive characterisation of cerebral grey and white matter alterations in poliomyelitis survivors with reference to healthy- and disease-controls using quantitative imaging metrics. METHODS Thirty-six poliomyelitis survivors, 88 patients with ALS and 117 healthy individuals were recruited in a prospective, single-centre neuroimaging study using uniform MRI acquisition parameters. All participants underwent standardised clinical assessments, T1-weighted structural and diffusion tensor imaging. Whole-brain and region-of-interest morphometric analyses were undertaken to evaluate patterns of grey matter changes. Tract-based spatial statistics were performed to evaluate diffusivity alterations in a study-specific whiter matter skeleton. RESULTS In contrast to healthy controls, poliomyelitis survivors exhibited increased grey matter partial volumes in the brainstem, cerebellum and occipital lobe, accompanied by increased FA in the corticospinal tracts, cerebellum, bilateral mesial temporal lobes and inferior frontal tracts. Polio survivors exhibited increased integrity metrics in the same anatomical regions where ALS patients showed degenerative changes. CONCLUSIONS Our findings indicate considerable cortical and white matter reorganisation in poliomyelitis survivors which may be interpreted as compensatory, adaptive change in response to severe lower motor neuron injury in infancy.
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Affiliation(s)
- Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Jasmin Lope
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Mary Clare McKenna
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland.
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17
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Extra-motor cerebral changes and manifestations in primary lateral sclerosis. Brain Imaging Behav 2021; 15:2283-2296. [PMID: 33409820 DOI: 10.1007/s11682-020-00421-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/12/2020] [Indexed: 12/22/2022]
Abstract
Primary lateral sclerosis (PLS) is classically considered a 'pure' upper motor neuron disorder. Motor cortex atrophy and pyramidal tract degeneration are thought to be pathognomonic of PLS, but extra-motor cerebral changes are poorly characterized. In a prospective neuroimaging study, forty PLS patients were systematically evaluated with a standardised imaging, genetic and clinical protocol. Patients were screened for ALS and HSP associated mutations, as well as C9orf72 hexanucleotide repeats. Clinical assessment included composite reflex scores, spasticity scales, functional rating scales, and screening for cognitive and behavioural deficits. The neuroimaging protocol evaluated cortical atrophy patterns, subcortical grey matter changes and white matter alterations in whole-brain and region-of-interest analyses. PLS patients tested negative for known ALS- and HSP-associated mutations and C9orf72 repeat expansions. Voxel-wise analyses revealed anterior cingulate, dorsolateral prefrontal, insular, opercular, orbitofrontal and bilateral mesial temporal grey matter changes and white matter alterations in the fornix, brainstem, temporal lobes, and cerebellum. Significant thalamus, caudate, hippocampus, putamen and accumbens nucleus volume reductions were also identified. Extra-motor clinical manifestations were dominated by verbal fluency deficits, language deficits, apathy and pseudobulbar affect. Our clinical and radiological evaluation confirms considerable extra-motor changes in a population-based cohort of PLS patients. Our data suggest that PLS should no longer be considered a neurodegenerative disorder selectively affecting the pyramidal system. PLS is associated with widespread extra-motor changes and manifestations which should be carefully considered in the multidisciplinary management of this low-incidence condition.
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18
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Cortical progression patterns in individual ALS patients across multiple timepoints: a mosaic-based approach for clinical use. J Neurol 2021; 268:1913-1926. [PMID: 33399966 DOI: 10.1007/s00415-020-10368-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 12/09/2020] [Accepted: 12/10/2020] [Indexed: 12/11/2022]
Abstract
INTRODUCTION The majority of imaging studies in ALS infer group-level imaging signatures from group comparisons, as opposed to estimating disease burden in individual patients. In a condition with considerable clinical heterogeneity, the characterisation of individual patterns of pathology is hugely relevant. In this study, we evaluate a strategy to track progressive cortical involvement in single patients by using subject-specific reference cohorts. METHODS We have interrogated a multi-timepoint longitudinal dataset of 61 ALS patients to demonstrate the utility of estimating cortical disease burden and the expansion of cerebral atrophy over time. We contrast our strategy to the gold-standard approach to gauge the advantages and drawbacks of our method. We modelled the evolution of cortical integrity in a conditional growth model, in which we accounted for age, gender, disability, symptom duration, education and handedness. We hypothesised that the variance associated with demographic variables will be successfully eliminated in our approach. RESULTS In our model, the only covariate which modulated the expansion of atrophy was motor disability as measured by the ALSFRS-r (t(153) = - 2.533, p = 0.0123). Using the standard approach, age also significantly influenced progression of CT change (t(153) = - 2.151, p = 0.033) demonstrating the validity and potential clinical utility of our approach. CONCLUSION Our strategy of estimating the extent of cortical atrophy in individual patients with ALS successfully corrects for demographic factors and captures relevant cortical changes associated with clinical disability. Our approach provides a framework to interpret single T1-weighted images in ALS and offers an opportunity to track cortical propagation patterns both at individual subject level and at cohort level.
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Li Hi Shing S, McKenna MC, Siah WF, Chipika RH, Hardiman O, Bede P. The imaging signature of C9orf72 hexanucleotide repeat expansions: implications for clinical trials and therapy development. Brain Imaging Behav 2021; 15:2693-2719. [PMID: 33398779 DOI: 10.1007/s11682-020-00429-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/08/2020] [Indexed: 01/14/2023]
Abstract
While C9orf72-specific imaging signatures have been proposed by both ALS and FTD research groups and considerable presymptomatic alterations have also been confirmed in young mutation carriers, considerable inconsistencies exist in the literature. Accordingly, a systematic review of C9orf72-imaging studies has been performed to identify consensus findings, stereotyped shortcomings, and unique contributions to outline future directions. A formal literature review was conducted according to the STROBE guidelines. All identified papers were individually reviewed for sample size, choice of controls, study design, imaging modalities, statistical models, clinical profiling, and identified genotype-associated pathological patterns. A total of 74 imaging papers were systematically reviewed. ALS patients with GGGGCC repeat expansions exhibit relatively limited motor cortex involvement and widespread extra-motor pathology. C9orf72 positive FTD patients often show preferential posterior involvement. Reports of thalamic involvement are relatively consistent across the various phenotypes. Asymptomatic hexanucleotide repeat carriers often exhibit structural and functional changes decades prior to symptom onset. Common shortcomings included sample size limitations, lack of disease-controls, limited clinical profiling, lack of genetic testing in healthy controls, and absence of post mortem validation. There is a striking paucity of longitudinal studies and existing presymptomatic studies have not evaluated the predictive value of radiological changes with regard to age of onset and phenoconversion. With the advent of antisense oligonucleotide therapies, the meticulous characterisation of C9orf72-associated changes has gained practical relevance. Neuroimaging offers non-invasive biomarkers for future clinical trials, presymptomatic ascertainment, diagnostic and prognostic applications.
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Affiliation(s)
- Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Mary Clare McKenna
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - We Fong Siah
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.
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Bede P, Bogdahn U, Lope J, Chang KM, Xirou S, Christidi F. Degenerative and regenerative processes in amyotrophic lateral sclerosis: motor reserve, adaptation and putative compensatory changes. Neural Regen Res 2021; 16:1208-1209. [PMID: 33269779 PMCID: PMC8224145 DOI: 10.4103/1673-5374.300440] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland; Biomedical Imaging Laboratory, Sorbonne University, Paris, France
| | - Ulrich Bogdahn
- Department of Neurology, University of Regensburg, Regensburg, Germany
| | - Jasmin Lope
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Kai Ming Chang
- Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Sophia Xirou
- First Department of Neurology, Aeginition Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Foteini Christidi
- First Department of Neurology, Aeginition Hospital, National and Kapodistrian University of Athens, Athens, Greece
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Pioro EP, Turner MR, Bede P. Neuroimaging in primary lateral sclerosis. Amyotroph Lateral Scler Frontotemporal Degener 2020; 21:18-27. [PMID: 33602015 DOI: 10.1080/21678421.2020.1837176] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 10/08/2020] [Accepted: 10/09/2020] [Indexed: 12/15/2022]
Abstract
Increased interest in the underlying pathogenesis of primary lateral sclerosis (PLS) and its relationship to amyotrophic lateral sclerosis (ALS) has corresponded to a growing number of CNS imaging studies, especially in the past decade. Both its rarity and uncertainty of definite diagnosis prior to 4 years from symptom onset have resulted in PLS being less studied than ALS. In this review, we highlight most relevant papers applying magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and positron emission tomography (PET) to analyzing CNS changes in PLS, often in relation to ALS. In patients with PLS, mostly brain, but also spinal cord has been evaluated since significant neurodegeneration is essentially restricted to upper motor neuron (UMN) structures and related pathways. Abnormalities of cortex and subcortical white matter tracts have been identified by structural and functional MRI and MRS studies, while metabolic and cell-specific changes in PLS brain have been revealed using various PET radiotracers. Future neuroimaging studies will continue to explore the interface between the PLS-ALS continuum, identify more changes unique to PLS, apply novel MRI and MRS sequences showing greater structural and neurochemical detail, as well as expand the repertoire of PET radiotracers that reveal various cellular pathologies. Neuroimaging has the potential to play an important role in the evaluation of novel therapies for patients with PLS.
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Affiliation(s)
- Erik P Pioro
- Section of ALS & Related Disorders, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Martin R Turner
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
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22
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Chipika RH, Siah WF, McKenna MC, Li Hi Shing S, Hardiman O, Bede P. The presymptomatic phase of amyotrophic lateral sclerosis: are we merely scratching the surface? J Neurol 2020; 268:4607-4629. [PMID: 33130950 DOI: 10.1007/s00415-020-10289-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 10/18/2020] [Accepted: 10/20/2020] [Indexed: 02/06/2023]
Abstract
Presymptomatic studies in ALS have consistently captured considerable disease burden long before symptom manifestation and contributed important academic insights. With the emergence of genotype-specific therapies, however, there is a pressing need to address practical objectives such as the estimation of age of symptom onset, phenotypic prediction, informing the optimal timing of pharmacological intervention, and identifying a core panel of biomarkers which may detect response to therapy. Existing presymptomatic studies in ALS have adopted striking different study designs, relied on a variety of control groups, used divergent imaging and electrophysiology methods, and focused on different genotypes and demographic groups. We have performed a systematic review of existing presymptomatic studies in ALS to identify common themes, stereotyped shortcomings, and key learning points for future studies. Existing presymptomatic studies in ALS often suffer from sample size limitations, lack of disease controls and rarely follow their cohort until symptom manifestation. As the characterisation of presymptomatic processes in ALS serves a multitude of academic and clinical purposes, the careful review of existing studies offers important lessons for future initiatives.
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Affiliation(s)
- Rangariroyashe H Chipika
- Computational Neuroimaging Group (CNG), Biomedical Sciences Institute, Trinity College Dublin, Pearse Street, Dublin, Ireland
| | - We Fong Siah
- Computational Neuroimaging Group (CNG), Biomedical Sciences Institute, Trinity College Dublin, Pearse Street, Dublin, Ireland
| | - Mary Clare McKenna
- Computational Neuroimaging Group (CNG), Biomedical Sciences Institute, Trinity College Dublin, Pearse Street, Dublin, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group (CNG), Biomedical Sciences Institute, Trinity College Dublin, Pearse Street, Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group (CNG), Biomedical Sciences Institute, Trinity College Dublin, Pearse Street, Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group (CNG), Biomedical Sciences Institute, Trinity College Dublin, Pearse Street, Dublin, Ireland.
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Manifold learning for amyotrophic lateral sclerosis functional loss assessment : Development and validation of a prognosis model. J Neurol 2020; 268:825-850. [PMID: 32886252 DOI: 10.1007/s00415-020-10181-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 08/05/2020] [Accepted: 08/06/2020] [Indexed: 12/11/2022]
Abstract
Amyotrophic lateral sclerosis (ALS) is an inexorably progressive neurodegenerative condition with no effective disease-modifying therapy at present. Given the striking clinical heterogeneity of the condition, the development and validation of reliable prognostic models is a recognised research priority. We present a prognostic model for functional decline in ALS where outcome uncertainty is taken into account. Patient data were reduced and projected onto a 2D space using Uniform Manifold Approximation and Projection (UMAP), a novel non-linear dimension reduction technique. Information from 3756 patients was included. Development data were sourced from past clinical trials. Real-world population data were used as validation data. Predictors included age, gender, region of onset, symptom duration, weight at baseline, functional impairment, and estimated rate of functional loss. UMAP projection of patients showed an informative 2D data distribution. As limited data availability precluded complex model designs, the projection was divided into three zones defined by a functional impairment range probability. Zone membership allowed individual patient prediction. Patients belonging to the first zone had a probability of [Formula: see text] (± [Formula: see text]) to have an ALSFRS score over 20 at 1-year follow-up. Patients within the second zone had a probability of [Formula: see text] (± [Formula: see text]) to have an ALSFRS score between 10 and 30 at 1 year follow-up. Finally, patients within the third zone had a probability of [Formula: see text] (± [Formula: see text]) to have an ALSFRS score lower than 20 at 1 year follow-up. This approach requires a limited set of features, is easily updated, improves with additional patient data, and accounts for results uncertainty. This method could therefore be used in a clinical setting for patient stratification and outcome projection.
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MRI data confirm the selective involvement of thalamic and amygdalar nuclei in amyotrophic lateral sclerosis and primary lateral sclerosis. Data Brief 2020; 32:106246. [PMID: 32944601 PMCID: PMC7481815 DOI: 10.1016/j.dib.2020.106246] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 08/12/2020] [Accepted: 08/25/2020] [Indexed: 12/20/2022] Open
Abstract
A standardised imaging protocol was implemented to evaluate disease burden in specific thalamic and amygdalar nuclei in 133 carefully phenotyped and genotyped motor neuron disease patients. “Switchboard malfunction in motor neuron diseases: selective pathology of thalamic nuclei in amyotrophic lateral sclerosis and primary lateral sclerosis” [1] “Amygdala pathology in amyotrophic lateral sclerosis and primary lateral sclerosis” [2] Raw volumetric data, group comparisons, effect sizes and percentage change are presented. Both ALS and PLS patients exhibited focal thalamus atrophy in ventral lateral and ventral anterior regions revealing extrapyramidal motor degeneration. Reduced accessory basal nucleus and cortical nucleus volumes were noted in the amygdala of C9orf72 negative ALS patients compared to healthy controls. ALS patients carrying the GGGGCC hexanucleotide repeats in C9orf72 exhibited preferential pathology in the mediodorsal-paratenial-reuniens thalamic nuclei and in the lateral nucleus and cortico-amygdaloid transition area of the amygdala. Considerable thalamic atrophy was observed in the sensory nuclei and lateral geniculate region of PLS patients. Our data demonstrate genotype-specific patterns of thalamus and amygdala involvement in ALS and a distinct disease-burden pattern in PLS. The dataset may be utilised for validation purposes, meta-analyses and the interpretation of thalamic and amygdalar profiles from other ALS genotypes.
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Finegan E, Siah WF, Shing SLH, Chipika RH, Chang KM, McKenna MC, Doherty MA, Hengeveld JC, Vajda A, Donaghy C, Hutchinson S, McLaughlin RL, Hardiman O, Bede P. Imaging and clinical data indicate considerable disease burden in 'probable' PLS: Patients with UMN symptoms for 2-4 years. Data Brief 2020; 32:106247. [PMID: 32944602 PMCID: PMC7481824 DOI: 10.1016/j.dib.2020.106247] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 08/12/2020] [Accepted: 08/25/2020] [Indexed: 11/19/2022] Open
Abstract
Primary lateral sclerosis (PLS) is an adult-onset upper motor neuron disease manifesting in progressive spasticity and gradually resulting in considerably motor disability. In the absence of early disease-specific diagnostic indicators, the majority of patients with PLS face a circuitous diagnostic journey. Until the recent publication of consensus diagnostic criteria, 4-year symptom duration was required to establish the diagnosis. The new diagnostic criteria introduced the category of ‘probable PLS’ for patients with a symptom duration of 2–4 years. “Evolving diagnostic criteria in primary lateral sclerosis: The clinical and radiological basis of "probable PLS" [1]. This dataset provides radiological metrics in a cohort of ‘probable PLS’ patients, ‘definite PLS’ patients and age-matched healthy controls. Region-of-interest radiological data include diffusivity metrics in the corticospinal tracts and corpus callosum as well as mean cortical thickness values in the pre- and para-central gyri in each hemisphere. Our data indicate considerable grey matter and relatively limited white matter involvement in ‘probable PLS’ which supports the rationale for this diagnostic category as a clinically useful entity. The introduction of this diagnostic category will likely facilitate the timely recruitment of PLS patients into research studies and pharmacological trials before widespread neurodegenerative change ensues.
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Affiliation(s)
- Eoin Finegan
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - We Fong Siah
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | | | - Kai Ming Chang
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
- Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
| | - Mary Clare McKenna
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Mark A. Doherty
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Ireland
| | - Jennifer C. Hengeveld
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Ireland
| | - Alice Vajda
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Ireland
| | - Colette Donaghy
- Department of Neurology, Western Health & Social Care Trust, Belfast, United Kingdom
| | | | - Russel L. McLaughlin
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
- Corresponding author.
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26
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Grollemund V, Chat GL, Secchi-Buhour MS, Delbot F, Pradat-Peyre JF, Bede P, Pradat PF. Development and validation of a 1-year survival prognosis estimation model for Amyotrophic Lateral Sclerosis using manifold learning algorithm UMAP. Sci Rep 2020; 10:13378. [PMID: 32770027 PMCID: PMC7414917 DOI: 10.1038/s41598-020-70125-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 07/23/2020] [Indexed: 02/08/2023] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS) is an inexorably progressive neurodegenerative condition with no effective disease modifying therapies. The development and validation of reliable prognostic models is a recognised research priority. We present a prognostic model for survival in ALS where result uncertainty is taken into account. Patient data were reduced and projected onto a 2D space using Uniform Manifold Approximation and Projection (UMAP), a novel non-linear dimension reduction technique. Information from 5,220 patients was included as development data originating from past clinical trials, and real-world population data as validation data. Predictors included age, gender, region of onset, symptom duration, weight at baseline, functional impairment, and estimated rate of functional loss. UMAP projection of patients shows an informative 2D data distribution. As limited data availability precluded complex model designs, the projection was divided into three zones with relevant survival rates. These rates were defined using confidence bounds: high, intermediate, and low 1-year survival rates at respectively [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]). Predicted 1-year survival was estimated using zone membership. This approach requires a limited set of features, is easily updated, improves with additional patient data, and accounts for results uncertainty.
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Affiliation(s)
- Vincent Grollemund
- Laboratoire d'Informatique de Paris 6, Sorbonne Université, Paris, 75005, France.
- FRS Consulting, Paris, 75009, France.
| | | | | | - François Delbot
- Laboratoire d'Informatique de Paris 6, Sorbonne Université, Paris, 75005, France
- Nanterre Université, Modal'X, Nanterre, 92014, France
| | - Jean-François Pradat-Peyre
- Laboratoire d'Informatique de Paris 6, Sorbonne Université, Paris, 75005, France
- Nanterre Université, Modal'X, Nanterre, 92014, France
| | - Peter Bede
- Laboratoire d'Imagerie Biomédicale, Sorbonne Université, Paris, 75005, France
- Département de Neurologie, Pitié-Salpêtrière University Hospital, APHP, Paris, 75013, France
- Computational Neuroimaging Group, Trinity College, Dublin, D02 PN40, Ireland
| | - Pierre-François Pradat
- Laboratoire d'Imagerie Biomédicale, Sorbonne Université, Paris, 75005, France
- Département de Neurologie, Pitié-Salpêtrière University Hospital, APHP, Paris, 75013, France
- Antnagelvin Hospital, Northern Ireland Center for Stratified Medecine, Biomedical Sciences Research Institute Ulster University, C-TRIC, Londonderry, BT47 6SB, United Kingdom
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27
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Alruwaili AR, Pannek K, Henderson RD, Gray M, Kurniawan ND, McCombe PA. Serial MRI studies over 12 months using manual and atlas-based region of interest in patients with amyotrophic lateral sclerosis. BMC Med Imaging 2020; 20:90. [PMID: 32746800 PMCID: PMC7397614 DOI: 10.1186/s12880-020-00489-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 07/23/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by loss of upper and lower motor neurons. There is a need for an imaging biomarker to track disease progression. Previously, magnetic resonance imaging (MRI) has shown loss of grey and white matter in the brain of patients with ALS compared to controls. We performed serial diffusion tractography imaging (DTI) study of patients with ALS looking for changes over time. METHODS On all subjects (n = 15), we performed three MRI studies at 6 month intervals. DTI changes were assessed with tract-based spatial statistics (TBSS) and region of interest (ROI) studies. Cortic-spinal tract (CST) was selected for our ROI at the upper level; the posterior limb of internal capsule (PLIC), and a lower level in the pons. RESULTS There was no significant change in DTI measures over 12 months of observation. Better correlation of manual and atlas-based ROI methods was found in the posterior limb of the internal capsule than the pons. CONCLUSION While previous DTI studies showed significant differences between ALS subjects and controls, within individual subjects there is little evidence of progression over 12 months. This suggests that DTI is not a suitable biomarker to assess disease progression in ALS.
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Affiliation(s)
- Ashwag R Alruwaili
- The University of Queensland, Centre for Clinical Research, Brisbane, Australia. .,King Saud University, Riyadh, Saudi Arabia. .,School of Medicine, The University of Queensland, Brisbane, QLD, Australia.
| | - Kerstin Pannek
- Australian E-Health Research Centre, CSIRO, Brisbane, Australia
| | - Robert D Henderson
- School of Medicine, The University of Queensland, Brisbane, QLD, Australia.,The Department of Neurology, Royal Brisbane and Women's Hospital, Herston, Brisbane, Australia
| | - Marcus Gray
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia.,Gehrmann Laboratory, University of Queensland, Brisbane, QLD, Australia
| | - Nyoman D Kurniawan
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Pamela A McCombe
- The University of Queensland, Centre for Clinical Research, Brisbane, Australia.,School of Medicine, The University of Queensland, Brisbane, QLD, Australia.,The Department of Neurology, Royal Brisbane and Women's Hospital, Herston, Brisbane, Australia
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Finegan E, Li Hi Shing S, Siah WF, Chipika RH, Chang KM, McKenna MC, Doherty MA, Hengeveld JC, Vajda A, Donaghy C, Hutchinson S, McLaughlin RL, Hardiman O, Bede P. Evolving diagnostic criteria in primary lateral sclerosis: The clinical and radiological basis of "probable PLS". J Neurol Sci 2020; 417:117052. [PMID: 32731060 DOI: 10.1016/j.jns.2020.117052] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 07/15/2020] [Accepted: 07/17/2020] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Primary lateral sclerosis is a rare neurodegenerative disorder of the upper motor neurons. Diagnostic criteria have changed considerably over the years, and the recent consensus criteria introduced 'probable PLS' for patients with a symptom duration of 2-4 years. The objective of this study is the systematic evaluation of clinical and neuroimaging characteristics in early PLS by studying a group of 'probable PLS patients' in comparison to a cohort of established PLS patients. METHODS In a prospective neuroimaging study, thirty-nine patients were stratified by the new consensus criteria into 'probable' (symptom duration 2-4 years) or 'definite' PLS (symptom duration >4 years). Patients were evaluated with a standardised battery of clinical instruments (ALSFRS-r, Penn upper motor neuron score, the modified Ashworth spasticity scale), whole genome sequencing, and underwent structural and diffusion MRI. The imaging profile of the two PLS cohorts were contrasted to a dataset of 100 healthy controls. All 'probable PLS' patients subsequently fulfilled criteria for 'definite' PLS on longitudinal follow-up and none transitioned to develop ALS. RESULTS PLS patients tested negative for known ALS- or HSP-associated mutations on whole genome sequencing. Despite their shorter symptom duration, 'probable PLS' patients already exhibited considerable functional disability, upper motor neuron disease burden and the majority of them required walking aids for safe ambulation. Their ALSFRS-r, UMN and modified Ashworth score means were 83%, 98% and 85% of the 'definite' group respectively. Motor cortex thickness was significantly reduced in both PLS groups in comparison to controls, but cortical changes were less widespread in 'probable' PLS on morphometric analyses. Corticospinal tract and corpus callosum metrics were relatively well preserved in the 'probable' group in contrast to the widespread white matter degeneration observed in the 'definite' group. CONCLUSIONS Our clinical and radiological analyses support the recent introduction of the 'probable' PLS category, as this cohort already exhibits considerable disability and cerebral changes consistent with established PLS. Before the publication of the new consensus criteria, these patients would have not been diagnosed with PLS on the basis of their symptom duration despite their significant functional impairment and motor cortex atrophy. The introduction of this new category will facilitate earlier recruitment into clinical trials, and shorten the protracted diagnostic uncertainty the majority of PLS patients face.
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Affiliation(s)
- Eoin Finegan
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - We Fong Siah
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Kai Ming Chang
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland; Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
| | - Mary Clare McKenna
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Mark A Doherty
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Ireland
| | - Jennifer C Hengeveld
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Ireland
| | - Alice Vajda
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Ireland
| | - Colette Donaghy
- Department of Neurology, Belfast, Western Health & Social Care Trust, UK
| | | | - Russell L McLaughlin
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland.
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Chipika RH, Christidi F, Finegan E, Li Hi Shing S, McKenna MC, Chang KM, Karavasilis E, Doherty MA, Hengeveld JC, Vajda A, Pender N, Hutchinson S, Donaghy C, McLaughlin RL, Hardiman O, Bede P. Amygdala pathology in amyotrophic lateral sclerosis and primary lateral sclerosis. J Neurol Sci 2020; 417:117039. [PMID: 32713609 DOI: 10.1016/j.jns.2020.117039] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 06/19/2020] [Accepted: 07/13/2020] [Indexed: 12/26/2022]
Abstract
Temporal lobe studies in motor neuron disease overwhelmingly focus on white matter alterations and cortical grey matter atrophy. Reports on amygdala involvement are conflicting and the amygdala is typically evaluated as single structure despite consisting of several functionally and cytologically distinct nuclei. A prospective, single-centre, neuroimaging study was undertaken to comprehensively characterise amygdala pathology in 100 genetically-stratified ALS patients, 33 patients with PLS and 117 healthy controls. The amygdala was segmented into groups of nuclei using a Bayesian parcellation algorithm based on a probabilistic atlas and shape deformations were additionally assessed by vertex analyses. The accessory basal nucleus (p = .021) and the cortical nucleus (p = .022) showed significant volume reductions in C9orf72 negative ALS patients compared to controls. The lateral nucleus (p = .043) and the cortico-amygdaloid transition (p = .024) were preferentially affected in C9orf72 hexanucleotide carriers. A trend of total volume reduction was identified in C9orf72 positive ALS patients (p = .055) which was also captured in inferior-medial shape deformations on vertex analyses. Our findings highlight that the amygdala is affected in ALS and our study demonstrates the selective involvement of specific nuclei as opposed to global atrophy. The genotype-specific patterns of amygdala involvement identified by this study are consistent with the growing literature of extra-motor clinical features. Mesial temporal lobe pathology in ALS is not limited to hippocampal pathology but, as a key hub of the limbic system, the amygdala is also affected in ALS.
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Affiliation(s)
- Rangariroyashe H Chipika
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Foteini Christidi
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland; Department of Neurology, Aeginition Hospital, University of Athens, Greece
| | - Eoin Finegan
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Mary Clare McKenna
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Kai Ming Chang
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland; Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Efstratios Karavasilis
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland; 2nd Department of Radiology, Attikon University Hospital, University of Athens, Athens, Greece
| | - Mark A Doherty
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Jennifer C Hengeveld
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Alice Vajda
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Niall Pender
- Department of psychology, Beaumont Hospital Dublin, Ireland
| | - Siobhan Hutchinson
- Department of Neurology, St James's Hospital, James's St, Ushers, Dublin 8 D08 NHY1, Ireland
| | - Colette Donaghy
- Department of Neurology, Belfast, Western Health & Social Care Trust, UK
| | - Russell L McLaughlin
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland.
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30
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Bede P, Chipika RH. Commissural fiber degeneration in motor neuron diseases. Amyotroph Lateral Scler Frontotemporal Degener 2020; 21:321-323. [PMID: 32290711 DOI: 10.1080/21678421.2020.1752253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group (CNG), Trinity Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group (CNG), Trinity Biomedical Sciences Institute, Trinity College Dublin, Ireland
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31
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The French national protocol for Kennedy's disease (SBMA): consensus diagnostic and management recommendations. Orphanet J Rare Dis 2020; 15:90. [PMID: 32276665 PMCID: PMC7149864 DOI: 10.1186/s13023-020-01366-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 03/19/2020] [Indexed: 02/07/2023] Open
Abstract
Background Kennedy’s disease (KD), also known as spinal and bulbar muscular atrophy (SBMA), is a rare, adult-onset, X-linked recessive neuromuscular disease caused by CAG expansions in exon 1 of the androgen receptor gene (AR). The objective of the French national diagnostic and management protocol is to provide evidence-based best practice recommendations and outline an optimised care pathway for patients with KD, based on a systematic literature review and consensus multidisciplinary observations. Results The initial evaluation, confirmation of the diagnosis, and management should ideally take place in a tertiary referral centre for motor neuron diseases, and involve an experienced multidisciplinary team of neurologists, endocrinologists, cardiologists and allied healthcare professionals. The diagnosis should be suspected in an adult male presenting with slowly progressive lower motor neuron symptoms, typically affecting the lower limbs at onset. Bulbar involvement (dysarthria and dysphagia) is often a later manifestation of the disease. Gynecomastia is not a constant feature, but is suggestive of a suspected diagnosis, which is further supported by electromyography showing diffuse motor neuron involvement often with asymptomatic sensory changes. A suspected diagnosis is confirmed by genetic testing. The multidisciplinary assessment should ascertain extra-neurological involvement such as cardiac repolarisation abnormalities (Brugada syndrome), signs of androgen resistance, genitourinary abnormalities, endocrine and metabolic changes (glucose intolerance, hyperlipidemia). In the absence of effective disease modifying therapies, the mainstay of management is symptomatic support using rehabilitation strategies (physiotherapy and speech therapy). Nutritional evaluation by an expert dietician is essential, and enteral nutrition (gastrostomy) may be required. Respiratory management centres on the detection and treatment of bronchial obstructions, as well as screening for aspiration pneumonia (chest physiotherapy, drainage, positioning, breath stacking, mechanical insufflation-exsufflation, cough assist machnie, antibiotics). Non-invasive mechanical ventilation is seldom needed. Symptomatic pharmaceutical therapy includes pain management, endocrine and metabolic interventions. There is no evidence for androgen substitution therapy. Conclusion The French national Kennedy’s disease protocol provides management recommendations for patients with KD. In a low-incidence condition, sharing and integrating regional expertise, multidisciplinary experience and defining consensus best-practice recommendations is particularly important. Well-coordinated collaborative efforts will ultimately pave the way to the development of evidence-based international guidelines.
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Bede P, Chipika RH, Finegan E, Li Hi Shing S, Chang KM, Doherty MA, Hengeveld JC, Vajda A, Hutchinson S, Donaghy C, McLaughlin RL, Hardiman O. Progressive brainstem pathology in motor neuron diseases: Imaging data from amyotrophic lateral sclerosis and primary lateral sclerosis. Data Brief 2020; 29:105229. [PMID: 32083157 PMCID: PMC7016370 DOI: 10.1016/j.dib.2020.105229] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 01/24/2020] [Accepted: 01/27/2020] [Indexed: 12/22/2022] Open
Abstract
A standardised, single-centre, longitudinal imaging protocol was used to evaluate longitudinal brainstem alterations in 100 patients with amyotrophic lateral sclerosis (ALS) with reference to 33 patients with primary lateral sclerosis (PLS), 30 patients with frontotemporal dementia (FTD) and 100 healthy controls. “Brainstem pathology in amyotrophic lateral sclerosis and primary lateral sclerosis: A longitudinal neuroimaging study” [1] ALS patients were scanned twice; 4 months apart. T1-weighted imaging data were acquired on a 3 T Philips Achieva MRI system, using a 3D Inversion Recovery prepared Spoiled Gradient Recalled echo (IR-SPGR) sequence. Raw MRI data underwent meticulous quality control before pre-processing. A Bayesian segmentation algorithm was utilised to parcellate the brainstem into the medulla oblongata, pons and mesencephalon before estimating the volume of each segment. Vertex-based shape analyses were carried out to characterise anatomical patterns of atrophy. Brainstem volume loss in ALS was dominated by medulla oblongata atrophy, but significant pontine pathology was also detected. Brainstem volume reductions were more significant in PLS than in ALS after correcting for demographic variables and total intracranial volume. Shape analyses revealed bilateral ‘flattening’ of the medullary pyramids in ALS compared to healthy controls. Our data demonstrate that computational neuroimaging readily detects brainstem pathology in vivo in both amyotrophic lateral sclerosis and primary lateral sclerosis.
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Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
- Corresponding author.
| | - Rangariroyashe H. Chipika
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Eoin Finegan
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Kai Ming Chang
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
- Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, United Kingdom
| | - Mark A. Doherty
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Jennifer C. Hengeveld
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Alice Vajda
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Siobhan Hutchinson
- Department of Neurology, St James's Hospital, James's St, Ushers, Dublin 8, D08 NHY1, Ireland
| | - Colette Donaghy
- Department of Neurology, Belfast, Western Health & Social Care Trust, UK
| | - Russell L. McLaughlin
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
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33
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Bede P, Pradat PF. Editorial: Biomarkers and Clinical Indicators in Motor Neuron Disease. Front Neurol 2020; 10:1318. [PMID: 31920939 PMCID: PMC6920250 DOI: 10.3389/fneur.2019.01318] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 11/28/2019] [Indexed: 12/18/2022] Open
Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland.,Department of Neurology, Pitié-Salpêtrière University Hospital, Paris, France.,Sorbonne University, CNRS, INSERM, Biomedical Imaging Laboratory, Paris, France
| | - Pierre-Francois Pradat
- Department of Neurology, Pitié-Salpêtrière University Hospital, Paris, France.,Sorbonne University, CNRS, INSERM, Biomedical Imaging Laboratory, Paris, France
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Finegan E, Hi Shing SL, Chipika RH, McKenna MC, Doherty MA, Hengeveld JC, Vajda A, Donaghy C, McLaughlin RL, Hutchinson S, Hardiman O, Bede P. Thalamic, hippocampal and basal ganglia pathology in primary lateral sclerosis and amyotrophic lateral sclerosis: Evidence from quantitative imaging data. Data Brief 2020; 29:105115. [PMID: 32055654 PMCID: PMC7005372 DOI: 10.1016/j.dib.2020.105115] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Revised: 12/26/2019] [Accepted: 01/03/2020] [Indexed: 01/12/2023] Open
Abstract
Primary lateral sclerosis and amyotrophic lateral sclerosis are primarily associated with motor cortex and corticospinal tract pathology. A standardised, prospective, single-centre neuroimaging protocol was used to characterise thalamic, hippocampal and basal ganglia involvement in 33 patients with primary lateral sclerosis (PLS), 100 patients with amyotrophic lateral sclerosis (ALS), and 117 healthy controls. “Widespread subcortical grey matter degeneration in primary lateral sclerosis: a multimodal imaging study with genetic profiling” [1] Imaging data were acquired on a 3 T MRI system using a 3D Inversion Recovery prepared Spoiled Gradient Recalled echo sequence. Model based segmentation was used to estimate the volumes of the thalamus, hippocampus, amygdala, caudate, pallidum, putamen and accumbens nucleus in each hemisphere. The hippocampus was further parcellated into cytologically-defined subfields. Total intracranial volume (TIV) was estimated for each participant to aid the interpretation of subcortical volume alterations. Group comparisons were corrected for age, gender, TIV, education and symptom duration. Considerable thalamic, hippocampal and accumbens nucleus atrophy was detected in PLS compared to healthy controls and selective dentate, molecular layer, CA1, CA3, and CA4 hippocampal pathology was also identified. In ALS, additional volume reductions were noted in the amygdala, left caudate and the hippocampal-amygdala transition area of the hippocampus. Our imaging data provide evidence of extensive and phenotype-specific patterns of subcortical degeneration in PLS.
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Affiliation(s)
- Eoin Finegan
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Mary C McKenna
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Mark A Doherty
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, 1-5 College Green, Dublin 2, Ireland
| | - Jennifer C Hengeveld
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, 1-5 College Green, Dublin 2, Ireland
| | - Alice Vajda
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, 1-5 College Green, Dublin 2, Ireland
| | | | - Russell L McLaughlin
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, 1-5 College Green, Dublin 2, Ireland
| | - Siobhan Hutchinson
- Department of Neurology, St James's Hospital, James's St, Ushers, Dublin 8, D08 NHY1, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
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Christidi F, Karavasilis E, Rentzos M, Velonakis G, Zouvelou V, Xirou S, Argyropoulos G, Papatriantafyllou I, Pantolewn V, Ferentinos P, Kelekis N, Seimenis I, Evdokimidis I, Bede P. Neuroimaging data indicate divergent mesial temporal lobe profiles in amyotrophic lateral sclerosis, Alzheimer's disease and healthy aging. Data Brief 2019; 28:104991. [PMID: 31921944 PMCID: PMC6948121 DOI: 10.1016/j.dib.2019.104991] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 12/05/2019] [Indexed: 12/12/2022] Open
Abstract
A prospective, standardised neuroimaging protocol was implemented to characterise mesial temporal lobe pathology in amyotrophic lateral sclerosis, Alzheimer's disease and healthy controls focusing on the evaluation of interconnected white and grey matter structures. “Hippocampal pathology in Amyotrophic Lateral Sclerosis: selective vulnerability of subfields and their associated projections” [1]. High-resolution diffusion tensor and structural imaging data were acquired on a 3 T MRI platform using standardised sequence parameters. The integrity of the fornix and the perforant pathway was assessed by tractography, to provide fractional anisotropy, axial diffusivity and radial diffusivity measures. Quantitative structural imaging was used to estimate the total intracranial volume, total hippocampal volumes and hippocampal subfield volumes for each participant. Raw white- and grey-matter measures, demographic and clinical data are available online at ‘Mendeley Data’. Amyotrophic lateral sclerosis and Alzheimer's disease exhibit divergent hippocampal profiles.
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Affiliation(s)
- Foteini Christidi
- First Department of Neurology, Aeginition Hospital, National and Kapodistrian University of Athens, Greece
| | - Efstratios Karavasilis
- Second Department of Radiology, General University Hospital "Attikon", National and Kapodistrian University of Athens, Greece
| | - Michail Rentzos
- First Department of Neurology, Aeginition Hospital, National and Kapodistrian University of Athens, Greece
| | - Georgios Velonakis
- Second Department of Radiology, General University Hospital "Attikon", National and Kapodistrian University of Athens, Greece
| | - Vasiliki Zouvelou
- First Department of Neurology, Aeginition Hospital, National and Kapodistrian University of Athens, Greece
| | - Sofia Xirou
- First Department of Neurology, Aeginition Hospital, National and Kapodistrian University of Athens, Greece
| | - Georgios Argyropoulos
- Second Department of Radiology, General University Hospital "Attikon", National and Kapodistrian University of Athens, Greece
| | | | - Varvara Pantolewn
- Second Department of Radiology, General University Hospital "Attikon", National and Kapodistrian University of Athens, Greece
| | - Panagiotis Ferentinos
- Second Department of Psychiatry, General University Hospital "Attikon", National and Kapodistrian University of Athens, Greece
| | - Nikolaos Kelekis
- Second Department of Radiology, General University Hospital "Attikon", National and Kapodistrian University of Athens, Greece
| | - Ioannis Seimenis
- Department of Medical Physics, Medical School, Democritus University of Thrace, Alexandroupolis, Greece
| | - Ioannis Evdokimidis
- First Department of Neurology, Aeginition Hospital, National and Kapodistrian University of Athens, Greece
| | - Peter Bede
- Department of Neurology, Pitié-Salpêtrière University Hospital, Paris, France.,Biomedical Imaging Laboratory, Sorbonne University, INSERM, Paris, France.,Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, Trinity College Dublin, Ireland
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36
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Christidi F, Karavasilis E, Rentzos M, Velonakis G, Zouvelou V, Xirou S, Argyropoulos G, Papatriantafyllou I, Pantolewn V, Ferentinos P, Kelekis N, Seimenis I, Evdokimidis I, Bede P. Hippocampal pathology in amyotrophic lateral sclerosis: selective vulnerability of subfields and their associated projections. Neurobiol Aging 2019; 84:178-188. [DOI: 10.1016/j.neurobiolaging.2019.07.019] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 07/10/2019] [Accepted: 07/10/2019] [Indexed: 12/29/2022]
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37
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Finegan E, Li Hi Shing S, Chipika RH, Doherty MA, Hengeveld JC, Vajda A, Donaghy C, Pender N, McLaughlin RL, Hardiman O, Bede P. Widespread subcortical grey matter degeneration in primary lateral sclerosis: a multimodal imaging study with genetic profiling. NEUROIMAGE-CLINICAL 2019; 24:102089. [PMID: 31795059 PMCID: PMC6978214 DOI: 10.1016/j.nicl.2019.102089] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 10/02/2019] [Accepted: 11/09/2019] [Indexed: 01/21/2023]
Abstract
BACKGROUND Primary lateral sclerosis (PLS) is a low incidence motor neuron disease which carries a markedly better prognosis than amyotrophic lateral sclerosis (ALS). Despite sporadic reports of extra-motor symptoms, PLS is widely regarded as a pure upper motor neuron disorder. The post mortem literature of PLS is strikingly sparse and very little is known of subcortical grey matter pathology in this condition. METHODS A prospective imaging study was undertaken with 33 PLS patients, 117 healthy controls and 100 ALS patients to specifically assess the integrity of subcortical grey matter structures and determine whether PLS and ALS have divergent thalamic, hippocampal and basal ganglia signatures. Volumetric, morphometric, segmentation and vertex-wise analyses were carried out in the three study groups to evaluate the integrity of thalamus, hippocampus, caudate, amygdala, pallidum, putamen and accumbens nucleus in each hemisphere. The hippocampus was further parcellated to characterise the involvement of specific subfields. RESULTS Considerable thalamic, caudate, and hippocampal atrophy was detected in PLS based on both volumetric and vertex analyses. Significant volume reductions were also detected in the accumbens nuclei. Hippocampal atrophy in PLS was dominated by dentate gyrus, hippocampal tail and CA4 subfield volume reductions. The morphometric comparison of ALS and PLS cohorts revealed preferential medial bi-thalamic pathology in PLS compared to the predominant putaminal degeneration detected in ALS. Another distinguishing feature between ALS and PLS was the preferential atrophy of the amygdala in ALS. CONCLUSIONS PLS is associated with considerable subcortical grey matter degeneration and due to the extensive extra-motor involvement, it should no longer be regarded a pure upper motor neuron disorder. Given its unique pathological features and a clinical course which differs considerably from ALS, dedicated research studies and disease-specific therapeutic strategies are urgently required in PLS.
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Affiliation(s)
- Eoin Finegan
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Mark A Doherty
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Jennifer C Hengeveld
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Alice Vajda
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | | | - Niall Pender
- Department of Psychology, Beaumont Hospital Dublin, Ireland
| | - Russell L McLaughlin
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland.
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38
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Bede P, Chipika RH, Finegan E, Li Hi Shing S, Doherty MA, Hengeveld JC, Vajda A, Hutchinson S, Donaghy C, McLaughlin RL, Hardiman O. Brainstem pathology in amyotrophic lateral sclerosis and primary lateral sclerosis: A longitudinal neuroimaging study. NEUROIMAGE-CLINICAL 2019; 24:102054. [PMID: 31711033 PMCID: PMC6849418 DOI: 10.1016/j.nicl.2019.102054] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 10/10/2019] [Accepted: 10/21/2019] [Indexed: 01/06/2023]
Abstract
Computational neuroimaging captures focal brainstem pathology in motor neuron diseases in contrast to both healthy- and disease controls. ALS patients exhibit progressive medulla oblongata, pontine and mesencephalic volume loss over time. Brainstem atrophy in ALS and PLS is dominated by medulla oblongata volume reductions. Vertex analyses of ALS patients reveal flattening of the medullary pyramids bilaterally. Morphometric analyses in ALS detect density reductions in the mesencephalic crura consistent with corticospinal tract degeneration.
Background Brainstem pathology is a hallmark feature of ALS, yet most imaging studies focus on cortical grey matter alterations and internal capsule white matter pathology. Brainstem imaging in ALS provides a unique opportunity to appraise descending motor tract degeneration and bulbar lower motor neuron involvement. Methods A prospective longitudinal imaging study has been undertaken with 100 patients with ALS, 33 patients with PLS, 30 patients with FTD and 100 healthy controls. Volumetric, vertex and morphometric analyses were conducted correcting for demographic factors to characterise disease-specific patterns of brainstem pathology. Using a Bayesian segmentation algorithm, the brainstem was segmented into the medulla, pons and mesencephalon to measure regional volume reductions, shape analyses were performed to ascertain the atrophy profile of each study group and region-of-interest morphometry was used to evaluate focal density alterations. Results ALS and PLS patients exhibit considerable brainstem atrophy compared to both disease- and healthy controls. Volume reductions in ALS and PLS are dominated by medulla oblongata pathology, but pontine atrophy can also be detected. In ALS, vertex analyses confirm the flattening of the medullary pyramids bilaterally in comparison to healthy controls and widespread pontine shape deformations in contrast to PLS. The ALS cohort exhibit bilateral density reductions in the mesencephalic crura in contrast to healthy controls, central pontine atrophy compared to disease controls, peri-aqueduct mesencephalic and posterior pontine changes in comparison to PLS patients. Conclus ions: Computational brainstem imaging captures the degeneration of both white and grey matter components in ALS. Our longitudinal data indicate progressive brainstem atrophy over time, underlining the biomarker potential of quantitative brainstem measures in ALS. At a time when a multitude of clinical trials are underway worldwide, there is an unprecedented need for accurate biomarkers to monitor disease progression and detect response to therapy. Brainstem imaging is a promising addition to candidate biomarkers of ALS and PLS.
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Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland.
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Eoin Finegan
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Mark A Doherty
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Jennifer C Hengeveld
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Alice Vajda
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Siobhan Hutchinson
- Department of Neurology, St James's Hospital, James's St, Ushers, Dublin 8 D08 NHY1, Ireland
| | - Colette Donaghy
- Department of Neurology, Western Health & Social Care Trust, Belfast, UK
| | - Russell L McLaughlin
- Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, Ireland
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Abidi M, de Marco G, Couillandre A, Feron M, Mseddi E, Termoz N, Querin G, Pradat PF, Bede P. Adaptive functional reorganization in amyotrophic lateral sclerosis: coexisting degenerative and compensatory changes. Eur J Neurol 2019; 27:121-128. [PMID: 31310452 DOI: 10.1111/ene.14042] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 07/10/2019] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND PURPOSE Considerable functional reorganization takes place in amyotrophic lateral sclerosis (ALS) in face of relentless structural degeneration. This study evaluates functional adaptation in ALS patients with lower motor neuron predominant (LMNp) and upper motor neuron predominant (UMNp) dysfunction. METHODS Seventeen LMNp ALS patients, 14 UMNp ALS patients and 14 controls participated in a functional magnetic resonance imaging study. Study-group-specific activation patterns were evaluated during preparation for a motor task. Connectivity analyses were carried out using the supplementary motor area (SMA), cerebellum and striatum as seed regions and correlations were explored with clinical measures. RESULTS Increased cerebellar, decreased dorsolateral prefrontal cortex and decreased SMA activation were detected in UMNp patients compared to controls. Increased cerebellar activation was also detected in UMNp patients compared to LMNp patients. UMNp patients exhibit increased effective connectivity between the cerebellum and caudate, and decreased connectivity between the SMA and caudate and between the SMA and cerebellum when performing self-initiated movement. In UMNp patients, a positive correlation was detected between clinical variables and striato-cerebellar connectivity. CONCLUSIONS Our findings indicate that, despite the dysfunction of SMA-striatal and SMA-cerebellar networks, cerebello-striatal connectivity increases in ALS indicative of compensatory processes. The coexistence of circuits with decreased and increased connectivity suggests concomitant neurodegenerative and adaptive changes in ALS.
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Affiliation(s)
- M Abidi
- CeRSM Laboratory, Nanterre University, UPL, Paris, France
| | - G de Marco
- CeRSM Laboratory, Nanterre University, UPL, Paris, France.,COMUE Paris Lumières University, Paris, France
| | - A Couillandre
- CeRSM Laboratory, Nanterre University, UPL, Paris, France.,COMUE Paris Lumières University, Paris, France
| | - M Feron
- CeRSM Laboratory, Nanterre University, UPL, Paris, France
| | - E Mseddi
- CeRSM Laboratory, Nanterre University, UPL, Paris, France
| | - N Termoz
- CeRSM Laboratory, Nanterre University, UPL, Paris, France.,COMUE Paris Lumières University, Paris, France
| | - G Querin
- Department of Neurology, Pitié-Salpêtrière Hospital, Paris, France.,Biomedical Imaging Laboratory, Sorbonne University, Paris, France
| | - P-F Pradat
- Department of Neurology, Pitié-Salpêtrière Hospital, Paris, France.,Biomedical Imaging Laboratory, Sorbonne University, Paris, France
| | - P Bede
- Department of Neurology, Pitié-Salpêtrière Hospital, Paris, France.,Biomedical Imaging Laboratory, Sorbonne University, Paris, France.,Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
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The clinical and radiological profile of primary lateral sclerosis: a population-based study. J Neurol 2019; 266:2718-2733. [PMID: 31325016 DOI: 10.1007/s00415-019-09473-z] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 07/09/2019] [Accepted: 07/11/2019] [Indexed: 12/17/2022]
Abstract
BACKGROUND Primary lateral sclerosis is a progressive upper-motor-neuron disorder associated with markedly longer survival than ALS. In contrast to ALS, the genetic susceptibility, histopathological profile and imaging signature of PLS are poorly characterised. Suspected PLS patients often face considerable diagnostic delay and prognostic uncertainty. OBJECTIVE To characterise the distinguishing clinical, genetic and imaging features of PLS in contrast to ALS and healthy controls. METHODS A prospective population-based study was conducted with 49 PLS patients, 100 ALS patients and 100 healthy controls using genetic profiling, standardised clinical assessments and neuroimaging. Whole-brain and region-of-interest analyses were undertaken to evaluate patterns of grey and white matter degeneration. RESULTS In PLS, disease burden in the motor cortex is more medial than in ALS consistent with its lower limb symptom-predominance. PLS is associated with considerable cerebellar white and grey matter degeneration and the extra-motor profile of PLS includes marked insular, inferior frontal and left pars opercularis pathology. Contrary to ALS, PLS spares the postcentral gyrus. The body and splenium of the corpus callosum are preferentially affected in PLS, in contrast to the genu involvement observed in ALS. Clinical measures show anatomically meaningful correlations with imaging metrics in a somatotopic distribution. PLS patients tested negative for C9orf72 repeat expansions, known ALS and HSP-associated genes. CONCLUSIONS Multiparametric imaging in PLS highlights disease-specific motor and extra-motor involvement distinct from ALS. In a condition where limited post-mortem data are available, imaging offers invaluable pathological insights. Anatomical correlations with clinical metrics confirm the biomarker potential of quantitative neuroimaging in PLS.
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Li Hi Shing S, Chipika RH, Finegan E, Murray D, Hardiman O, Bede P. Post-polio Syndrome: More Than Just a Lower Motor Neuron Disease. Front Neurol 2019; 10:773. [PMID: 31379723 PMCID: PMC6646725 DOI: 10.3389/fneur.2019.00773] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Accepted: 07/02/2019] [Indexed: 12/13/2022] Open
Abstract
Post-polio syndrome (PPS) is a neurological condition that affects polio survivors decades after their initial infection. Despite its high prevalence, the etiology of PPS remains elusive, mechanisms of progression are poorly understood, and the condition is notoriously under-researched. While motor dysfunction is a hallmark feature of the condition, generalized fatigue, sleep disturbance, decreased endurance, neuropsychological deficits, sensory symptoms, and chronic pain are also often reported and have considerable quality of life implications in PPS. The non-motor aspects of PPS are particularly challenging to evaluate, quantify, and treat. Generalized fatigue is one of the most distressing symptoms of PPS and is likely to be multifactorial due to weight-gain, respiratory compromise, poor sleep, and polypharmacy. No validated diagnostic, monitoring, or prognostic markers have been developed in PPS to date and the mainstay of therapy centers on symptomatic relief and individualized rehabilitation strategies such as energy conservation and muscle strengthening exercise regimes. Despite a number of large clinical trials in PPS, no effective disease-modifying pharmacological treatments are currently available.
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Affiliation(s)
- Stacey Li Hi Shing
- Computational Neuroimaging Group, Academic Unit of Neurology, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group, Academic Unit of Neurology, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Eoin Finegan
- Computational Neuroimaging Group, Academic Unit of Neurology, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Deirdre Murray
- Computational Neuroimaging Group, Academic Unit of Neurology, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Academic Unit of Neurology, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Academic Unit of Neurology, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
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Bede P. The histological correlates of imaging metrics: postmortem validation of in vivo findings. Amyotroph Lateral Scler Frontotemporal Degener 2019; 20:457-460. [PMID: 31293187 DOI: 10.1080/21678421.2019.1639195] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin , Dublin , Ireland
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El Mendili MM, Querin G, Bede P, Pradat PF. Spinal Cord Imaging in Amyotrophic Lateral Sclerosis: Historical Concepts-Novel Techniques. Front Neurol 2019; 10:350. [PMID: 31031688 PMCID: PMC6474186 DOI: 10.3389/fneur.2019.00350] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Accepted: 03/21/2019] [Indexed: 01/13/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is the most common adult onset motor neuron disease with no effective disease modifying therapies at present. Spinal cord degeneration is a hallmark feature of ALS, highlighted in the earliest descriptions of the disease by Lockhart Clarke and Jean-Martin Charcot. The anterior horns and corticospinal tracts are invariably affected in ALS, but up to recently it has been notoriously challenging to detect and characterize spinal pathology in vivo. With recent technological advances, spinal imaging now offers unique opportunities to appraise lower motor neuron degeneration, sensory involvement, metabolic alterations, and interneuron pathology in ALS. Quantitative spinal imaging in ALS has now been used in cross-sectional and longitudinal study designs, applied to presymptomatic mutation carriers, and utilized in machine learning applications. Despite its enormous clinical and academic potential, a number of physiological, technological, and methodological challenges limit the routine use of computational spinal imaging in ALS. In this review, we provide a comprehensive overview of emerging spinal cord imaging methods and discuss their advantages, drawbacks, and biomarker potential in clinical applications, clinical trial settings, monitoring, and prognostic roles.
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Affiliation(s)
- Mohamed Mounir El Mendili
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Biomedical Imaging Laboratory (LIB), Sorbonne University, CNRS, INSERM, Paris, France
| | - Giorgia Querin
- Biomedical Imaging Laboratory (LIB), Sorbonne University, CNRS, INSERM, Paris, France.,Department of Neurology, Pitié-Salpêtrière University Hospital (APHP), Paris, France
| | - Peter Bede
- Biomedical Imaging Laboratory (LIB), Sorbonne University, CNRS, INSERM, Paris, France.,Department of Neurology, Pitié-Salpêtrière University Hospital (APHP), Paris, France.,Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
| | - Pierre-François Pradat
- Biomedical Imaging Laboratory (LIB), Sorbonne University, CNRS, INSERM, Paris, France.,Department of Neurology, Pitié-Salpêtrière University Hospital (APHP), Paris, France
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44
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Finegan E, Chipika RH, Li Hi Shing S, Hardiman O, Bede P. Pathological Crying and Laughing in Motor Neuron Disease: Pathobiology, Screening, Intervention. Front Neurol 2019; 10:260. [PMID: 30949121 PMCID: PMC6438102 DOI: 10.3389/fneur.2019.00260] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 02/26/2019] [Indexed: 12/11/2022] Open
Abstract
Pathological crying and laughing (PCL) has significant quality-of-life implications in amyotrophic lateral sclerosis (ALS); it can provoke restrictive life-style modifications and lead to social isolation. Despite its high prevalence and quality of life implications, it remains surprisingly understudied. Divergent pathophysiological models have been proposed, centered on corticobulbar tract degeneration, prefrontal cortex pathology, sensory deafferentation, and impaired cerebellar gate-control mechanisms. Quantitative MRI techniques and symptom-specific clinical instruments offer unprecedented opportunities to elucidate the anatomical underpinnings of PCL pathogenesis. Emerging neuroimaging studies of ALS support the role of cortico-pontine-cerebellar network dysfunction in context-inappropriate emotional responses. The characterization of PCL-associated pathophysiological processes is indispensable for the development of effective pharmacological therapies.
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Affiliation(s)
| | | | | | | | - Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
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Chipika RH, Finegan E, Li Hi Shing S, Hardiman O, Bede P. Tracking a Fast-Moving Disease: Longitudinal Markers, Monitoring, and Clinical Trial Endpoints in ALS. Front Neurol 2019; 10:229. [PMID: 30941088 PMCID: PMC6433752 DOI: 10.3389/fneur.2019.00229] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Accepted: 02/22/2019] [Indexed: 12/13/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) encompasses a heterogeneous group of phenotypes with different progression rates, varying degree of extra-motor involvement and divergent progression patterns. The natural history of ALS is increasingly evaluated by large, multi-time point longitudinal studies, many of which now incorporate presymptomatic and post-mortem assessments. These studies not only have the potential to characterize patterns of anatomical propagation, molecular mechanisms of disease spread, but also to identify pragmatic monitoring markers. Sensitive markers of progressive neurodegenerative change are indispensable for clinical trials and individualized patient care. Biofluid markers, neuroimaging indices, electrophysiological markers, rating scales, questionnaires, and other disease-specific instruments have divergent sensitivity profiles. The discussion of candidate monitoring markers in ALS has a dual academic and clinical relevance, and is particularly timely given the increasing number of pharmacological trials. The objective of this paper is to provide a comprehensive and critical review of longitudinal studies in ALS, focusing on the sensitivity profile of established and emerging monitoring markers.
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Affiliation(s)
| | - Eoin Finegan
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
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Grollemund V, Pradat PF, Querin G, Delbot F, Le Chat G, Pradat-Peyre JF, Bede P. Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions. Front Neurosci 2019; 13:135. [PMID: 30872992 PMCID: PMC6403867 DOI: 10.3389/fnins.2019.00135] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 02/06/2019] [Indexed: 12/23/2022] Open
Abstract
Background: Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressive neurodegenerative condition with limited therapeutic options at present. Survival from symptom onset ranges from 3 to 5 years depending on genetic, demographic, and phenotypic factors. Despite tireless research efforts, the core etiology of the disease remains elusive and drug development efforts are confounded by the lack of accurate monitoring markers. Disease heterogeneity, late-stage recruitment into pharmaceutical trials, and inclusion of phenotypically admixed patient cohorts are some of the key barriers to successful clinical trials. Machine Learning (ML) models and large international data sets offer unprecedented opportunities to appraise candidate diagnostic, monitoring, and prognostic markers. Accurate patient stratification into well-defined prognostic categories is another aspiration of emerging classification and staging systems. Methods: The objective of this paper is the comprehensive, systematic, and critical review of ML initiatives in ALS to date and their potential in research, clinical, and pharmacological applications. The focus of this review is to provide a dual, clinical-mathematical perspective on recent advances and future directions of the field. Another objective of the paper is the frank discussion of the pitfalls and drawbacks of specific models, highlighting the shortcomings of existing studies and to provide methodological recommendations for future study designs. Results: Despite considerable sample size limitations, ML techniques have already been successfully applied to ALS data sets and a number of promising diagnosis models have been proposed. Prognostic models have been tested using core clinical variables, biological, and neuroimaging data. These models also offer patient stratification opportunities for future clinical trials. Despite the enormous potential of ML in ALS research, statistical assumptions are often violated, the choice of specific statistical models is seldom justified, and the constraints of ML models are rarely enunciated. Conclusions: From a mathematical perspective, the main barrier to the development of validated diagnostic, prognostic, and monitoring indicators stem from limited sample sizes. The combination of multiple clinical, biofluid, and imaging biomarkers is likely to increase the accuracy of mathematical modeling and contribute to optimized clinical trial designs.
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Affiliation(s)
- Vincent Grollemund
- Laboratoire d'Informatique de Paris 6, Sorbonne University, Paris, France
- FRS Consulting, Paris, France
| | - Pierre-François Pradat
- Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Sorbonne Université, Paris, France
- APHP, Département de Neurologie, Hôpital Pitié-Salpêtrière, Centre Référent SLA, Paris, France
- Northern Ireland Center for Stratified Medecine, Biomedical Sciences Research Institute Ulster University, C-TRIC, Altnagelvin Hospital, Londonderry, United Kingdom
| | - Giorgia Querin
- Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Sorbonne Université, Paris, France
- APHP, Département de Neurologie, Hôpital Pitié-Salpêtrière, Centre Référent SLA, Paris, France
| | - François Delbot
- Laboratoire d'Informatique de Paris 6, Sorbonne University, Paris, France
- Département de Mathématiques et Informatique, Paris Nanterre University, Nanterre, France
| | | | - Jean-François Pradat-Peyre
- Laboratoire d'Informatique de Paris 6, Sorbonne University, Paris, France
- Département de Mathématiques et Informatique, Paris Nanterre University, Nanterre, France
- Modal'X, Paris Nanterre University, Nanterre, France
| | - Peter Bede
- Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Sorbonne Université, Paris, France
- APHP, Département de Neurologie, Hôpital Pitié-Salpêtrière, Centre Référent SLA, Paris, France
- Computational Neuroimaging Group, Trinity College, Dublin, Ireland
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Yunusova Y, Plowman EK, Green JR, Barnett C, Bede P. Clinical Measures of Bulbar Dysfunction in ALS. Front Neurol 2019; 10:106. [PMID: 30837936 PMCID: PMC6389633 DOI: 10.3389/fneur.2019.00106] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 01/28/2019] [Indexed: 12/31/2022] Open
Abstract
Bulbar impairment represents a hallmark feature of Amyotrophic Lateral Sclerosis (ALS) that significantly impacts survival and quality of life. Speech and swallowing dysfunction are key contributors to the clinical heterogeneity of ALS and require well-timed and carefully coordinated interventions. The accurate clinical, radiological and electrophysiological assessment of bulbar dysfunction in ALS is one of the most multidisciplinary aspects of ALS care, requiring expert input from speech-language pathologists (SLPs), neurologists, otolaryngologists, augmentative alternative communication (AAC) specialists, dieticians, and electrophysiologists—each with their own evaluation strategies and assessment tools. The need to systematically evaluate the comparative advantages and drawbacks of various bulbar assessment instruments and to develop integrated assessment protocols is increasingly recognized. In this review, we provide a comprehensive appraisal of the most commonly utilized clinical tools for assessing and monitoring bulbar dysfunction in ALS based on the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) evaluation framework. Despite a plethora of assessment tools, considerable geographical differences exist in bulbar assessment practices and individual instruments exhibit considerable limitations. The gaps identified in the literature offer unique opportunities for the optimization of existing and development of new tools both for clinical and research applications. The multicenter validation and standardization of these instruments will be essential for guideline development and best practice recommendations.
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Affiliation(s)
- Yana Yunusova
- Department of Speech Language Pathology, University of Toronto, Toronto, ON, Canada.,Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada.,Volcal Tract Visualization Lab, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
| | - Emily K Plowman
- Swallowing Systems Core, Department of Speech, Language, and Hearing Sciences, University of Florida, Gainesville, FL, United States
| | - Jordan R Green
- Department of Communication Sciences and Disorders, MGH Institute of Health Professions, Boston, MA, United States.,Speech and Hearing Biosciences and Technology Program, Harvard University, Cambridge, MA, United States
| | - Carolina Barnett
- Division of Neurology, Department of Medicine, University of Toronto and University Health Network, Toronto, ON, Canada.,Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Peter Bede
- Computational Neuroimaging Group, Academic Unit of Neurology, Trinity College Dublin, Dublin, Ireland
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Finegan E, Chipika RH, Shing SLH, Hardiman O, Bede P. Primary lateral sclerosis: a distinct entity or part of the ALS spectrum? Amyotroph Lateral Scler Frontotemporal Degener 2019; 20:133-145. [PMID: 30654671 DOI: 10.1080/21678421.2018.1550518] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Primary lateral sclerosis (PLS) has been traditionally viewed as a distinct upper motor neuron condition (UMN) but is increasingly regarded as a sub-phenotype within the amyotrophic lateral sclerosis (ALS) spectrum. Despite established diagnostic criteria, formal diagnosis can be challenging and the protracted diagnostic journey and uncertainty about longer-term prognosis cause considerable distress to patients and caregivers. PLS patients are invariably excluded from ALS clinical trials, while PLS pharmacological trials are lacking. There remains an unmet need for diagnostic biomarkers for upper motor neuron predominant conditions and prognostic indicators regarding prognosis, survival, and risk of conversion to ALS. Validated biomarkers will not only have implications for individualized patient care but also serve as outcome measures in pharmaceutical trials. Given the paucity of post-mortem studies in PLS, novel pathological insights are generally inferred from state-of-the-art imaging studies. Computational neuroimaging has already contributed significantly to the characterization of PLS-associated pathology in vivo and has underscored the role of neuro-inflammation, the presence of extra-motor changes, and confirmed pathological patterns similar to ALS. This systematic review assesses the current state of PLS research across clinical, neuroimaging and neuropathological domains from a combined clinical and academic perspective. We discuss patterns of pathological overlap with other ALS phenotypes, examine if the biological processes of PLS warrant therapeutic strategies distinct from ALS, and evaluate the evidence that classes PLS as a distinct clinico-pathological entity.
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Affiliation(s)
- Eoin Finegan
- a Computational Neuroimaging Group, Academic Unit of Neurology , Biomedical Sciences Institute, Trinity College , Dublin , Ireland
| | - Rangariroyashe H Chipika
- a Computational Neuroimaging Group, Academic Unit of Neurology , Biomedical Sciences Institute, Trinity College , Dublin , Ireland
| | - Stacey Li Hi Shing
- a Computational Neuroimaging Group, Academic Unit of Neurology , Biomedical Sciences Institute, Trinity College , Dublin , Ireland
| | - Orla Hardiman
- a Computational Neuroimaging Group, Academic Unit of Neurology , Biomedical Sciences Institute, Trinity College , Dublin , Ireland
| | - Peter Bede
- a Computational Neuroimaging Group, Academic Unit of Neurology , Biomedical Sciences Institute, Trinity College , Dublin , Ireland
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49
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Proudfoot M, Bede P, Turner MR. Imaging Cerebral Activity in Amyotrophic Lateral Sclerosis. Front Neurol 2019; 9:1148. [PMID: 30671016 PMCID: PMC6332509 DOI: 10.3389/fneur.2018.01148] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 12/11/2018] [Indexed: 01/30/2023] Open
Abstract
Advances in neuroimaging, complementing histopathological insights, have established a multi-system involvement of cerebral networks beyond the traditional neuromuscular pathological view of amyotrophic lateral sclerosis (ALS). The development of effective disease-modifying therapy remains a priority and this will be facilitated by improved biomarkers of motor system integrity against which to assess the efficacy of candidate drugs. Functional MRI (FMRI) is an established measure of both cerebral activity and connectivity, but there is an increasing recognition of neuronal oscillations in facilitating long-distance communication across the cortical surface. Such dynamic synchronization vastly expands the connectivity foundations defined by traditional neuronal architecture. This review considers the unique pathogenic insights afforded by the capture of cerebral disease activity in ALS using FMRI and encephalography.
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Affiliation(s)
- Malcolm Proudfoot
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Peter Bede
- Computational Neuroimaging Group, Academic Unit of Neurology, Trinity College Dublin, Dublin, Ireland
| | - Martin R Turner
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.,Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
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50
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Wirth AM, Johannesen S, Khomenko A, Baldaranov D, Bruun TH, Wendl C, Schuierer G, Greenlee MW, Bogdahn U. Value of fluid-attenuated inversion recovery MRI data analyzed by the lesion segmentation toolbox in amyotrophic lateral sclerosis. J Magn Reson Imaging 2018; 50:552-559. [PMID: 30569457 PMCID: PMC6767504 DOI: 10.1002/jmri.26577] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 10/28/2018] [Accepted: 10/29/2018] [Indexed: 12/11/2022] Open
Abstract
Background MRI fluid‐attenuated inversion recovery (FLAIR) studies reported hyperintensity in the corticospinal tract and corpus callosum of patients with amyotrophic lateral sclerosis (ALS). Purpose To evaluate the lesion segmentation toolbox (LST) for the objective quantification of FLAIR lesions in ALS patients. Study Type Retrospective. Population Twenty‐eight ALS patients (eight females, mean age: 50 range: 24–73, mean ALSFRS‐R sum score: 36) were compared with 31 age‐matched healthy controls (12 females, mean age: 45, range: 25–67). ALS patients were treated with riluzole and additional G‐CSF (granulocyte‐colony stimulating factor) on a named patient basis. Field Strength/Sequence 1.5 T, FLAIR, T1‐weighted MRI. Assessment The lesion prediction algorithm (LPA) of the LST enabled the extraction of individual binary lesion maps, total lesion volume (TLV), and number (TLN). Location and overlap of FLAIR lesions across patients were investigated by registration to FLAIR average space and an atlas. ALS‐specific functional rating scale revised (ALSFRS‐R), disease progression, and survival since diagnosis served as clinical correlates. Statistical Tests Univariate analysis of variance (ANOVA), repeated‐measures ANOVA, t‐test, Bravais‐Pearson correlation, Chi‐square test of independence, Kaplan–Meier analysis, Cox‐regression analysis. Results Both ALS patients and healthy controls exhibited FLAIR alterations. TLN significantly depended on age (F(1,54) = 24.659, P < 0.001) and sex (F(1,54) = 5.720, P = 0.020). ALS patients showed higher TLN than healthy controls depending on sex (F(1, 54) = 5.076, P = 0.028). FLAIR lesions were small and most pronounced in male ALS patients. FLAIR alterations were predominantly detected in the superior and posterior corona radiata, anterior capsula interna, and posterior thalamic radiation. Patients with pyramidal tract (PT) lesions exhibited significantly inferior survival than patients without PT lesions (P = 0.013). Covariate age exhibited strong prognostic value for survival (P = 0.015). Data Conclusion LST enables the objective quantification of FLAIR alterations and is a potential prognostic biomarker for ALS. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:552–559.
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Affiliation(s)
- Anna M Wirth
- Department of Neurology, University Hospital of Regensburg, Germany.,Department of Experimental Psychology, University of Regensburg, Germany
| | - Siw Johannesen
- Department of Neurology, University Hospital of Regensburg, Germany
| | - Andrei Khomenko
- Department of Neurology, University Hospital of Regensburg, Germany
| | - Dobri Baldaranov
- Department of Neurology, University Hospital of Regensburg, Germany
| | - Tim-Henrik Bruun
- Department of Neurology, University Hospital of Regensburg, Germany
| | - Christina Wendl
- Center of Neuroradiology, University Hospital and District Medical Hospital of Regensburg, Germany
| | - Gerhard Schuierer
- Center of Neuroradiology, University Hospital and District Medical Hospital of Regensburg, Germany
| | - Mark W Greenlee
- Department of Experimental Psychology, University of Regensburg, Germany
| | - Ulrich Bogdahn
- Department of Neurology, University Hospital of Regensburg, Germany
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