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Kleinerova J, Tahedl M, Tan EL, Delaney S, Hengeveld JC, Doherty MA, McLaughlin RL, Hardiman O, Chang KM, Finegan E, Bede P. Supra- and infra-tentorial degeneration patterns in primary lateral sclerosis: a multimodal longitudinal neuroradiology study. J Neurol 2024; 271:3239-3255. [PMID: 38438819 PMCID: PMC11136747 DOI: 10.1007/s00415-024-12261-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 03/06/2024]
Abstract
BACKGROUND Primary lateral sclerosis (PLS) is traditionally solely associated with progressive upper motor neuron dysfunction manifesting in limb spasticity, gait impairment, bulbar symptoms and pseudobulbar affect. Recent studies have described frontotemporal dysfunction in some patients resulting in cognitive manifestations. Cerebellar pathology is much less well characterised despite sporadic reports of cerebellar disease. METHODS A multi-timepoint, longitudinal neuroimaging study was conducted to characterise the evolution of both intra-cerebellar disease burden and cerebro-cerebellar connectivity. The volumes of deep cerebellar nuclei, cerebellar cortical volumes, cerebro-cerebellar structural and functional connectivity were assessed longitudinally in a cohort of 43 individuals with PLS. RESULTS Cerebello-frontal, -temporal, -parietal, -occipital and cerebello-thalamic structural disconnection was detected at baseline based on radial diffusivity (RD) and cerebello-frontal decoupling was also evident based on fractional anisotropy (FA) alterations. Functional connectivity changes were also detected in cerebello-frontal, parietal and occipital projections. Volume reductions were identified in the vermis, anterior lobe, posterior lobe, and crura. Among the deep cerebellar nuclei, the dorsal dentate was atrophic. Longitudinal follow-up did not capture statistically significant progressive changes. Significant primary motor cortex atrophy and inter-hemispheric transcallosal degeneration were also captured. CONCLUSIONS PLS is not only associated with upper motor neuron dysfunction, but cerebellar cortical volume loss and deep cerebellar nuclear atrophy can also be readily detected. In addition to intra-cerebellar disease burden, cerebro-cerebellar connectivity alterations also take place. Our data add to the evolving evidence of widespread neurodegeneration in PLS beyond the primary motor regions. Cerebellar dysfunction in PLS is likely to exacerbate bulbar, gait and dexterity impairment and contribute to pseudobulbar affect.
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Affiliation(s)
- Jana Kleinerova
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Dublin 2, Ireland
| | - Marlene Tahedl
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Dublin 2, Ireland
| | - Ee Ling Tan
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Dublin 2, Ireland
| | - Siobhan Delaney
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Dublin 2, Ireland
- Department of Neurology, St James's Hospital, 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, Dublin 2, Ireland
| | - Kai Ming Chang
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Dublin 2, Ireland
| | - Eoin Finegan
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Dublin 2, Ireland
| | - Peter Bede
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Dublin 2, Ireland.
- Department of Neurology, St James's Hospital, Dublin, Ireland.
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Umar TP, Jain N, Papageorgakopoulou M, Shaheen RS, Alsamhori JF, Muzzamil M, Kostiks A. Artificial intelligence for screening and diagnosis of amyotrophic lateral sclerosis: a systematic review and meta-analysis. Amyotroph Lateral Scler Frontotemporal Degener 2024:1-12. [PMID: 38563056 DOI: 10.1080/21678421.2024.2334836] [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: 09/07/2023] [Accepted: 03/18/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurological disease that leads to progressive motor function degeneration. Diagnosing ALS is challenging due to the absence of a specific detection test. The use of artificial intelligence (AI) can assist in the investigation and treatment of ALS. METHODS We searched seven databases for literature on the application of AI in the early diagnosis and screening of ALS in humans. The findings were summarized using random-effects summary receiver operating characteristic curve. The risk of bias (RoB) analysis was carried out using QUADAS-2 or QUADAS-C tools. RESULTS In the 34 analyzed studies, a meta-prevalence of 47% for ALS was noted. For ALS detection, the pooled sensitivity of AI models was 94.3% (95% CI - 63.2% to 99.4%) with a pooled specificity of 98.9% (95% CI - 92.4% to 99.9%). For ALS classification, the pooled sensitivity of AI models was 90.9% (95% CI - 86.5% to 93.9%) with a pooled specificity of 92.3% (95% CI - 84.8% to 96.3%). Based on type of input for classification, the pooled sensitivity of AI models for gait, electromyography, and magnetic resonance signals was 91.2%, 92.6%, and 82.2%, respectively. The pooled specificity for gait, electromyography, and magnetic resonance signals was 94.1%, 96.5%, and 77.3%, respectively. CONCLUSIONS Although AI can play a significant role in the screening and diagnosis of ALS due to its high sensitivities and specificities, concerns remain regarding quality of evidence reported in the literature.
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Affiliation(s)
- Tungki Pratama Umar
- Department of Medical Profession, Faculty of Medicine, Universitas Sriwijaya, Palembang, Indonesia
| | - Nityanand Jain
- Faculty of Medicine, Riga Stradinš University, Riga, Latvia
| | | | | | | | - Muhammad Muzzamil
- Department of Public Health, Health Services Academy, Islamabad, Pakistan, and
| | - Andrejs Kostiks
- Department of Neurology, Riga East University Clinical Hospital, Riga, Latvia
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Simmatis LER, Robin J, Spilka MJ, Yunusova Y. Detecting bulbar amyotrophic lateral sclerosis (ALS) using automatic acoustic analysis. Biomed Eng Online 2024; 23:15. [PMID: 38311731 PMCID: PMC10838438 DOI: 10.1186/s12938-023-01174-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 11/19/2023] [Indexed: 02/06/2024] Open
Abstract
Automatic speech assessments have the potential to dramatically improve ALS clinical practice and facilitate patient stratification for ALS clinical trials. Acoustic speech analysis has demonstrated the ability to capture a variety of relevant speech motor impairments, but implementation has been hindered by both the nature of lab-based assessments (requiring travel and time for patients) and also by the opacity of some acoustic feature analysis methods. These challenges and others have obscured the ability to distinguish different ALS disease stages/severities. Validation of automated acoustic analysis tools could enable detection of early signs of ALS, and these tools could be deployed to screen and monitor patients without requiring clinic visits. Here, we sought to determine whether acoustic features gathered using an automated assessment app could detect ALS as well as different levels of speech impairment severity resulting from ALS. Speech samples (readings of a standardized, 99-word passage) from 119 ALS patients with varying degrees of disease severity as well as 22 neurologically healthy participants were analyzed, and 53 acoustic features were extracted. Patients were stratified into early and late stages of disease (ALS-early/ALS-E and ALS-late/ALS-L) based on the ALS Functional Ratings Scale-Revised bulbar score (FRS-bulb) (median [interquartile range] of FRS-bulbar scores: 11[3]). The data were analyzed using a sparse Bayesian logistic regression classifier. It was determined that the current relatively small set of acoustic features could distinguish between ALS and controls well (area under receiver-operating characteristic curve/AUROC = 0.85), that the ALS-E patients could be separated well from control participants (AUROC = 0.78), and that ALS-E and ALS-L patients could be reasonably separated (AUROC = 0.70). These results highlight the potential for automated acoustic analyses to detect and stratify ALS.
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Affiliation(s)
- Leif E R Simmatis
- KITE-Toronto Rehabilitation Institute, UHN, Toronto, ON, Canada.
- Department of Speech-Language Pathology, University of Toronto, Toronto, ON, Canada.
- Sunnybrook Research Institute, Toronto, ON, Canada.
| | | | | | - Yana Yunusova
- KITE-Toronto Rehabilitation Institute, UHN, Toronto, ON, Canada
- Department of Speech-Language Pathology, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Toronto, ON, Canada
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McMackin R, Bede P, Ingre C, Malaspina A, Hardiman O. Biomarkers in amyotrophic lateral sclerosis: current status and future prospects. Nat Rev Neurol 2023; 19:754-768. [PMID: 37949994 DOI: 10.1038/s41582-023-00891-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/09/2023] [Indexed: 11/12/2023]
Abstract
Disease heterogeneity in amyotrophic lateral sclerosis poses a substantial challenge in drug development. Categorization based on clinical features alone can help us predict the disease course and survival, but quantitative measures are also needed that can enhance the sensitivity of the clinical categorization. In this Review, we describe the emerging landscape of diagnostic, categorical and pharmacodynamic biomarkers in amyotrophic lateral sclerosis and their place in the rapidly evolving landscape of new therapeutics. Fluid-based markers from cerebrospinal fluid, blood and urine are emerging as useful diagnostic, pharmacodynamic and predictive biomarkers. Combinations of imaging measures have the potential to provide important diagnostic and prognostic information, and neurophysiological methods, including various electromyography-based measures and quantitative EEG-magnetoencephalography-evoked responses and corticomuscular coherence, are generating useful diagnostic, categorical and prognostic markers. Although none of these biomarker technologies has been fully incorporated into clinical practice or clinical trials as a primary outcome measure, strong evidence is accumulating to support their clinical utility.
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Affiliation(s)
- Roisin McMackin
- Discipline of Physiology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, The University of Dublin, Dublin, Ireland
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, The University of Dublin, Dublin, Ireland
| | - Peter Bede
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, The University of Dublin, Dublin, Ireland
- Computational Neuroimaging Group, School of Medicine, Trinity College Dublin, The University of Dublin, Dublin, Ireland
- Department of Neurology, St James's Hospital, Dublin, Ireland
| | - Caroline Ingre
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Andrea Malaspina
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Orla Hardiman
- Academic Unit of Neurology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, The University of Dublin, Dublin, Ireland.
- Department of Neurology, Beaumont Hospital, Dublin, Ireland.
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Fernandes F, Barbalho I, Bispo Júnior A, Alves L, Nagem D, Lins H, Arrais Júnior E, Coutinho KD, Morais AHF, Santos JPQ, Machado GM, Henriques J, Teixeira C, Dourado Júnior MET, Lindquist ARR, Valentim RAM. Digital Alternative Communication for Individuals with Amyotrophic Lateral Sclerosis: What We Have. J Clin Med 2023; 12:5235. [PMID: 37629277 PMCID: PMC10455505 DOI: 10.3390/jcm12165235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/05/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
Amyotrophic Lateral Sclerosis is a disease that compromises the motor system and the functional abilities of the person in an irreversible way, causing the progressive loss of the ability to communicate. Tools based on Augmentative and Alternative Communication are essential for promoting autonomy and improving communication, life quality, and survival. This Systematic Literature Review aimed to provide evidence on eye-image-based Human-Computer Interaction approaches for the Augmentative and Alternative Communication of people with Amyotrophic Lateral Sclerosis. The Systematic Literature Review was conducted and guided following a protocol consisting of search questions, inclusion and exclusion criteria, and quality assessment, to select primary studies published between 2010 and 2021 in six repositories: Science Direct, Web of Science, Springer, IEEE Xplore, ACM Digital Library, and PubMed. After the screening, 25 primary studies were evaluated. These studies showcased four low-cost, non-invasive Human-Computer Interaction strategies employed for Augmentative and Alternative Communication in people with Amyotrophic Lateral Sclerosis. The strategies included Eye-Gaze, which featured in 36% of the studies; Eye-Blink and Eye-Tracking, each accounting for 28% of the approaches; and the Hybrid strategy, employed in 8% of the studies. For these approaches, several computational techniques were identified. For a better understanding, a workflow containing the development phases and the respective methods used by each strategy was generated. The results indicate the possibility and feasibility of developing Human-Computer Interaction resources based on eye images for Augmentative and Alternative Communication in a control group. The absence of experimental testing in people with Amyotrophic Lateral Sclerosis reiterates the challenges related to the scalability, efficiency, and usability of these technologies for people with the disease. Although challenges still exist, the findings represent important advances in the fields of health sciences and technology, promoting a promising future with possibilities for better life quality.
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Affiliation(s)
- Felipe Fernandes
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Ingridy Barbalho
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Arnaldo Bispo Júnior
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Luca Alves
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Danilo Nagem
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Hertz Lins
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Ernano Arrais Júnior
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Karilany D. Coutinho
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Antônio H. F. Morais
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal 59015-000, Brazil; (A.H.F.M.); (J.P.Q.S.)
| | - João Paulo Q. Santos
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal 59015-000, Brazil; (A.H.F.M.); (J.P.Q.S.)
| | | | - Jorge Henriques
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, 3030-788 Coimbra, Portugal; (J.H.); (C.T.)
| | - César Teixeira
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, 3030-788 Coimbra, Portugal; (J.H.); (C.T.)
| | - Mário E. T. Dourado Júnior
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
- Department of Integrated Medicine, Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil
| | - Ana R. R. Lindquist
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Ricardo A. M. Valentim
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
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Bede P, Pradat PF. Editorial: The gap between academic advances and therapy development in motor neuron disease. Curr Opin Neurol 2023; 36:335-337. [PMID: 37462047 DOI: 10.1097/wco.0000000000001179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group, School of Medicine, Trinity College
- Department of Neurology, St James's Hospital, Dublin, Ireland
- Department of Neurology, Pitié-Salpêtrière University Hospital
| | - Pierre-Francois Pradat
- Department of Neurology, Pitié-Salpêtrière University Hospital
- Laboratoire d'Imagerie Biomédicale, Sorbonne University, CNRS, INSERM, Paris, France
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Gondim FDAA, Pinto WBVDR, Chieia MAT, Correia CDC, Cunha FMB, Dourado MET, França Júnior MC, Marques Júnior W, Oliveira ASB, Rodrigues CL, Silva DJD, Dias-Tosta E. Definitions, phenomenology, diagnosis, and management of the disorders of laughter and crying in amyotrophic lateral sclerosis (ALS): Consensus from ALS and Motor Neuron Disease Scientific Department of the Brazilian Academy of Neurology. ARQUIVOS DE NEURO-PSIQUIATRIA 2023; 81:764-775. [PMID: 37647907 PMCID: PMC10468253 DOI: 10.1055/s-0043-1771176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
The spectrum of neuropsychiatric phenomena observed in amyotrophic lateral sclerosis (ALS) is wide and not fully understood. Disorders of laughter and crying stand among the most common manifestations. The aim of this study is to report the results of an educational consensus organized by the Brazilian Academy of Neurology to evaluate the definitions, phenomenology, diagnosis, and management of the disorders of laughter and crying in ALS patients. Twelve members of the Brazilian Academy of Neurology - considered to be experts in the field - were recruited to answer 12 questions about the subject. After exchanging revisions, a first draft was prepared. A face-to-face meeting was held in Fortaleza, Brazil on 9.23.22 to discuss it. The revised version was subsequently emailed to all members of the ALS Scientific Department from the Brazilian Academy of Neurology and the final revised version submitted for publication. The prevalence of pseudobulbar affect/pathological laughter and crying (PBA/PLC) in ALS patients from 15 combined studies and 3906 patients was 27.4% (N = 1070), ranging from 11.4% to 71%. Bulbar onset is a risk factor but there are limited studies evaluating the differences in prevalence among the different motor neuron diseases subtypes, including patients with and without frontotemporal dementia. Antidepressants and a combination of dextromethorphan and quinidine (not available in Brazil) are possible therapeutic options. This group of panelists acknowledge the multiple gaps in the current literature and reinforces the need for further studies.
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Affiliation(s)
- Francisco de Assis Aquino Gondim
- Universidade Federal do Ceará, Departamento de Clínica Médica, Núcleo de Desenvolvimento e Pesquisa de Medicamentos, Fortaleza CE, Brazil
| | - Wladimir Bocca Vieira de Rezende Pinto
- Universidade Federal de São Paulo, Departamento de Neurologia e Neurocirurgia/Ebserh, Setor de Investigações nas Doenças Neuromusculares, São Paulo SP, Brazil
| | - Marco Antônio Troccoli Chieia
- Universidade Federal de São Paulo, Departamento de Neurologia e Neurocirurgia/Ebserh, Setor de Investigações nas Doenças Neuromusculares, São Paulo SP, Brazil
| | | | | | | | | | - Wilson Marques Júnior
- Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto, Departamento de Neurociências, Ribeirão Preto SP, Brazil
| | - Acary Souza Bulle Oliveira
- Universidade Federal de São Paulo, Departamento de Neurologia e Neurocirurgia/Ebserh, Setor de Investigações nas Doenças Neuromusculares, São Paulo SP, Brazil
| | | | - Delson José da Silva
- Universidade Federal de Goiás, Hospital das Clínicas, Unidade de Neurologia e Neurocirurgia/Ebserh, Goiânia GO, Brazil
| | - Elza Dias-Tosta
- Comissão de Ética da Academia Brasileira de Neurologia, São Paulo SP, Brazil
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Tahedl M, Tan EL, Chipika RH, Hengeveld JC, Vajda A, Doherty MA, McLaughlin RL, Siah WF, Hardiman O, Bede P. Brainstem-cortex disconnection in amyotrophic lateral sclerosis: bulbar impairment, genotype associations, asymptomatic changes and biomarker opportunities. J Neurol 2023:10.1007/s00415-023-11682-6. [PMID: 37022479 DOI: 10.1007/s00415-023-11682-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/19/2023] [Accepted: 03/21/2023] [Indexed: 04/07/2023]
Abstract
BACKGROUND Bulbar dysfunction is a cardinal feature of ALS with important quality of life and management implications. The objective of this study is the longitudinal evaluation of a large panel imaging metrics pertaining to bulbar dysfunction, encompassing cortical measures, structural and functional cortico-medullary connectivity indices and brainstem metrics. METHODS A standardised, multimodal imaging protocol was implemented with clinical and genetic profiling to systematically appraise the biomarker potential of specific metrics. A total of 198 patients with ALS and 108 healthy controls were included. RESULTS Longitudinal analyses revealed progressive structural and functional disconnection between the motor cortex and the brainstem over time. Cortical thickness reduction was an early feature on cross-sectional analyses with limited further progression on longitudinal follow-up. Receiver operating characteristic analyses of the panel of MR metrics confirmed the discriminatory potential of bulbar imaging measures between patients and controls and area-under-the-curve values increased significantly on longitudinal follow-up. C9orf72 carriers exhibited lower brainstem volumes, lower cortico-medullary structural connectivity and faster cortical thinning. Sporadic patients without bulbar symptoms, already exhibit significant brainstem and cortico-medullary connectivity alterations. DISCUSSION Our results indicate that ALS is associated with multi-level integrity change from cortex to brainstem. The demonstration of significant corticobulbar alterations in patients without bulbar symptoms confirms considerable presymptomatic disease burden in sporadic ALS. The systematic assessment of radiological measures in a single-centre academic study helps to appraise the diagnostic and monitoring utility of specific measures for future clinical and clinical trial applications.
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Affiliation(s)
- Marlene Tahedl
- Computational Neuroimaging Group (CNG), Trinity Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Dublin, Ireland
| | - Ee Ling Tan
- Computational Neuroimaging Group (CNG), Trinity Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Dublin, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group (CNG), Trinity Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Dublin, Ireland
| | | | - Alice Vajda
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | - Mark A Doherty
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | | | - We Fong Siah
- Computational Neuroimaging Group (CNG), Trinity Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Dublin, Ireland
| | - Orla Hardiman
- Computational Neuroimaging Group (CNG), Trinity Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group (CNG), Trinity Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, 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: 0] [Impact Index Per Article: 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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Tahedl M, Tan EL, Shing SLH, Chipika RH, Siah WF, Hengeveld JC, Doherty MA, McLaughlin RL, Hardiman O, Finegan E, Bede P. Not a benign motor neuron disease: longitudinal imaging captures relentless motor connectome disintegration in primary lateral sclerosis. Eur J Neurol 2023; 30:1232-1245. [PMID: 36739888 DOI: 10.1111/ene.15725] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/20/2023] [Accepted: 01/26/2023] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND PURPOSE Primary lateral sclerosis (PLS) is a progressive upper motor neuron disorder associated with considerable clinical disability. Symptoms are typically exclusively linked to primary motor cortex degeneration and the contribution of pre-motor, supplementary motor, cortico-medullary and inter-hemispheric connectivity alterations are less well characterized. METHODS In a single-centre, prospective, longitudinal neuroimaging study 41 patients with PLS were investigated. Patients underwent standardized neuroimaging, genetic profiling with whole exome sequencing, and comprehensive clinical assessments including upper motor neuron scores, tapping rates, mirror movements, spasticity assessment, cognitive screening and evaluation for pseudobulbar affect. Longitudinal neuroimaging data from 108 healthy controls were used for image interpretation. A standardized imaging protocol was implemented including 3D T1-weighted structural, diffusion tensor imaging and resting-state functional magnetic resonance imaging. Following somatotopic segmentation, cortical thickness analyses, probabilistic tractography, blood oxygenation level dependent signal analyses and brainstem volumetry were conducted to evaluate cortical, brainstem, cortico-medullary and inter-hemispheric connectivity alterations both cross-sectionally and longitudinally. RESULTS Our data confirm progressive primary motor cortex degeneration, considerable supplementary motor and pre-motor area involvement, progressive brainstem atrophy, cortico-medullary and inter-hemispheric disconnection, and close associations between clinical upper motor neuron scores and somatotopic connectivity indices in PLS. DISCUSSION Primary lateral sclerosis is associated with relentlessly progressive motor connectome degeneration. Clinical disability in PLS is likely to stem from a combination of intra- and inter-hemispheric connectivity decline and primary, pre- and supplementary motor cortex degeneration. Simple 'bedside' clinical tools, such as tapping rates, are excellent proxies of the integrity of the relevant fibres of the contralateral corticospinal tract.
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Affiliation(s)
- Marlene Tahedl
- Computational Neuroimaging Group (CNG), Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Ee Ling Tan
- Computational Neuroimaging Group (CNG), Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Stacey Li Hi Shing
- Computational Neuroimaging Group (CNG), Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Rangariroyashe H Chipika
- Computational Neuroimaging Group (CNG), Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - We Fong Siah
- Computational Neuroimaging Group (CNG), Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | | | - Mark A Doherty
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | | | - Orla Hardiman
- Computational Neuroimaging Group (CNG), Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Eoin Finegan
- Computational Neuroimaging Group (CNG), Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group (CNG), Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Department of Neurology, St James's Hospital, Dublin, Ireland
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11
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Diffusion Tensor Imaging in Amyotrophic Lateral Sclerosis: Machine Learning for Biomarker Development. Int J Mol Sci 2023; 24:ijms24031911. [PMID: 36768231 PMCID: PMC9915541 DOI: 10.3390/ijms24031911] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
Diffusion tensor imaging (DTI) allows the in vivo imaging of pathological white matter alterations, either with unbiased voxel-wise or hypothesis-guided tract-based analysis. Alterations of diffusion metrics are indicative of the cerebral status of patients with amyotrophic lateral sclerosis (ALS) at the individual level. Using machine learning (ML) models to analyze complex and high-dimensional neuroimaging data sets, new opportunities for DTI-based biomarkers in ALS arise. This review aims to summarize how different ML models based on DTI parameters can be used for supervised diagnostic classifications and to provide individualized patient stratification with unsupervised approaches in ALS. To capture the whole spectrum of neuropathological signatures, DTI might be combined with additional modalities, such as structural T1w 3-D MRI in ML models. To further improve the power of ML in ALS and enable the application of deep learning models, standardized DTI protocols and multi-center collaborations are needed to validate multimodal DTI biomarkers. The application of ML models to multiparametric MRI/multimodal DTI-based data sets will enable a detailed assessment of neuropathological signatures in patients with ALS and the development of novel neuroimaging biomarkers that could be used in the clinical workup.
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12
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Das T, Kaur H, Gour P, Prasad K, Lynn AM, Prakash A, Kumar V. Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review. Brief Bioinform 2022; 23:6780269. [PMID: 36411673 DOI: 10.1093/bib/bbac442] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/12/2022] [Accepted: 09/13/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques. OBJECTIVE This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways. METHODS The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria. RESULTS We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.
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Affiliation(s)
- Trishala Das
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Harbinder Kaur
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Pratibha Gour
- Dept. of Plant Molecular Biology, University of Delhi, South Campus, New Delhi-110021, India
| | - Kartikay Prasad
- Amity Institute of Neuropsychology & Neurosciences (AINN), Amity University, Noida, UP-201303, India
| | - Andrew M Lynn
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Amresh Prakash
- Amity Institute of Integrative Sciences and Health, Amity University Haryana, Gurgaon-122413, India
| | - Vijay Kumar
- Amity Institute of Neuropsychology & Neurosciences (AINN), Amity University, Noida, UP-201303, India
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13
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Ashhurst JF, Tu S, Timmins HC, Kiernan MC. Progress, development, and challenges in amyotrophic lateral sclerosis clinical trials. Expert Rev Neurother 2022; 22:905-913. [PMID: 36543326 DOI: 10.1080/14737175.2022.2161893] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Amyotrophic Lateral Sclerosis (ALS) brings unique challenges to a clinical trial setting, due in part to relatively low disease prevalence coupled with a poor prognosis, in addition to the complexities linked to disease heterogeneity. As critical understanding of the disease develops, particularly in relation to clinical phenotype and the mechanisms of disease progression, so too new concepts evolve in relation to clinical trials, including the advent of precision therapy, targeted to subgroups of ALS patients. AREAS COVERED Individualized, or precision medicine in ALS recognizes the heterogeneous nature of the disease and utilizes information such as the clinical phenotype of the disease, clinical biomarkers, and genotyping to promote a tailored approach to treatment. Separate to these considerations, the present review will discuss clinical trial design and how this can be improved to better match patient and investigator needs in ALS clinical trials. EXPERT OPINION Precision therapy will promote a more focused treatment approach, with the goal of improving clinical outcomes for ALS patients. An increased community awareness of ALS, coupled with significant industry and philanthropic funding for ALS research, is accelerating this process.
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Affiliation(s)
| | - Sicong Tu
- Brain and Mind Centre, University of Sydney, Camperdown, Australia
| | - Hannah C Timmins
- Brain and Mind Centre, University of Sydney, Camperdown, Australia
| | - Matthew C Kiernan
- Brain and Mind Centre, University of Sydney, Camperdown, Australia.,Department of Neurology, Royal Prince Alfred Hospital, Sydney, Australia
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14
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Mulkerrin G, França MC, Lope J, Tan EL, Bede P. Neuroimaging in hereditary spastic paraplegias: from qualitative cues to precision biomarkers. Expert Rev Mol Diagn 2022; 22:745-760. [PMID: 36042576 DOI: 10.1080/14737159.2022.2118048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
INTRODUCTION : Hereditary spastic paraplegias (HSP) include a clinically and genetically heterogeneous group of conditions. Novel imaging modalities have been increasingly applied to HSP cohorts which helps to quantitatively evaluate the integrity of specific anatomical structures and develop monitoring markers for both clinical care and future clinical trials. AREAS COVERED : Advances in HSP imaging are systematically reviewed with a focus on cohort sizes, imaging modalities, study design, clinical correlates, methodological approaches, and key findings. EXPERT OPINION : A wide range of imaging techniques have been recently applied to HSP cohorts. Common shortcomings of existing studies include the evaluation of genetically unconfirmed or admixed cohorts, limited sample sizes, unimodal imaging approaches, lack of postmortem validation, and a limited clinical battery, often exclusively focusing on motor aspects of the condition. A number of innovative methodological approaches have also be identified, such as robust longitudinal study designs, the implementation of multimodal imaging protocols, complementary cognitive assessments, and the comparison of HSP cohorts to MND cohorts. Collaborative multicentre initiatives may overcome sample limitations, and comprehensive clinical profiling with motor, extrapyramidal, cerebellar, and neuropsychological assessments would permit systematic clinico-radiological correlations. Academic achievements in HSP imaging have the potential to be developed into viable clinical applications to expedite the diagnosis and monitor disease progression.
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Affiliation(s)
| | - Marcondes C França
- Department of Neurology, The State University of Campinas, São Paulo, Brazil
| | - Jasmin Lope
- Computational Neuroimaging Group, Trinity College Dublin, Ireland
| | - Ee Ling Tan
- Computational Neuroimaging Group, Trinity College Dublin, Ireland
| | - Peter Bede
- Department of Neurology, St James's Hospital, Dublin, Ireland.,Computational Neuroimaging Group, Trinity College Dublin, Ireland
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15
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McKenna MC, Lope J, Tan EL, Bede P. Pre-symptomatic radiological changes in frontotemporal dementia: propagation characteristics, predictive value and implications for clinical trials. Brain Imaging Behav 2022; 16:2755-2767. [PMID: 35920960 DOI: 10.1007/s11682-022-00711-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2022] [Indexed: 11/25/2022]
Abstract
Computational imaging and quantitative biomarkers offer invaluable insights in the pre-symptomatic phase of neurodegenerative conditions several years before clinical manifestation. In recent years, there has been a focused effort to characterize pre-symptomatic cerebral changes in familial frontotemporal dementias using computational imaging. Accordingly, a systematic literature review was conducted of original articles investigating pre-symptomatic imaging changes in frontotemporal dementia focusing on study design, imaging modalities, data interpretation, control cohorts and key findings. The review is limited to the most common genotypes: chromosome 9 open reading frame 72 (C9orf72), progranulin (GRN), or microtubule-associated protein tau (MAPT) genotypes. Sixty-eight studies were identified with a median sample size of 15 (3-141) per genotype. Only a minority of studies were longitudinal (28%; 19/68) with a median follow-up of 2 (1-8) years. MRI (97%; 66/68) was the most common imaging modality, and primarily grey matter analyses were conducted (75%; 19/68). Some studies used multimodal analyses 44% (30/68). Genotype-associated imaging signatures are presented, innovative study designs are highlighted, common methodological shortcomings are discussed and lessons for future studies are outlined. Emerging academic observations have potential clinical implications for expediting the diagnosis, tracking disease progression and optimising the timing of pharmaceutical trials.
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Affiliation(s)
- Mary Clare McKenna
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Ireland.,Department of Neurology, St James's Hospital, Dublin, Ireland
| | - Jasmin Lope
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Ireland
| | - Ee Ling Tan
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Ireland. .,Department of Neurology, St James's Hospital, Dublin, Ireland.
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16
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Müller HP, Nagel AM, Keidel F, Wunderlich A, Hübers A, Gast LV, Ludolph AC, Beer M, Kassubek J. Relaxation-weighted 23Na magnetic resonance imaging maps regional patterns of abnormal sodium concentrations in amyotrophic lateral sclerosis. Ther Adv Chronic Dis 2022; 13:20406223221109480. [PMID: 35837670 PMCID: PMC9274400 DOI: 10.1177/20406223221109480] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/01/2022] [Indexed: 11/30/2022] Open
Abstract
Objectives: Multiparametric magnetic resonance imaging (MRI) is established as a
technical instrument for the characterisation of patients with amyotrophic
lateral sclerosis (ALS). The contribution of relaxation-weighted sodium
(23NaR) MRI remains to be defined. The aim of this study is
to apply 23NaR MRI to investigate brain sodium homeostasis and
map potential alterations in patients with ALS as compared with healthy
controls. Materials and Methods: Seventeen patients with ALS (mean age 61.1 ± 11.4 years, m/f = 9/8) and 10
healthy control subjects (mean age 60.3 ± 15.3 years, m/f = 6/4) were
examined by 23NaR MRI at 3 T. Regional sodium maps were obtained
by the calculation of the weighted difference from two image data sets with
different echo times (TE1 = 0.3 ms, TE2 = 25 ms).
Voxel-based analysis of the relaxation-weighted maps, together with
23Na concentration maps for comparison, was performed. Results: ROI-based analyses of relaxation-weighted brain sodium concentration maps
demonstrated increased sodium concentrations in the upper corticospinal
tracts and in the frontal lobes in patients with ALS; no differences between
ALS patients and controls were found in reference ROIs, where no involvement
in ALS-associated neurodegeneration could be anticipated. Conclusion: 23NaR MRI mapped regional alterations within disease-relevant
areas in ALS which correspond to the stages of the central nervous system
(CNS) pathology, providing evidence that the technique is a potential
biological marker of the cerebral neurodegenerative process in ALS.
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Affiliation(s)
| | - Armin M Nagel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Franziska Keidel
- Department of Diagnostic and Interventional Radiology, University of Ulm, Ulm, Germany
| | - Arthur Wunderlich
- Department of Diagnostic and Interventional Radiology, University of Ulm, Ulm, Germany
| | | | - Lena V Gast
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Albert C Ludolph
- Department of Neurology, University of Ulm, Ulm, Germany German Center for Neurodegenerative Diseases (DZNE), Ulm, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, University of Ulm, Ulm, Germany
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Oberer Eselsberg 45, Ulm 89081, Germany
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17
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Bede P, Chang KM, Tan EL. Machine-learning in motor neuron diseases: Prospects and pitfalls. Eur J Neurol 2022; 29:2555-2556. [PMID: 35699315 PMCID: PMC9546434 DOI: 10.1111/ene.15443] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 05/26/2022] [Accepted: 06/09/2022] [Indexed: 12/22/2022]
Affiliation(s)
- Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland.,Department of Neurology, St James's Hospital, Dublin, Ireland
| | - Kai Ming Chang
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland.,Department of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Ee Ling Tan
- Computational Neuroimaging Group, Trinity College Dublin, Dublin, Ireland
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18
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Münch M, Müller HP, Behler A, Ludolph AC, Kassubek J. Segmental alterations of the corpus callosum in motor neuron disease: A DTI and texture analysis in 575 patients. Neuroimage Clin 2022; 35:103061. [PMID: 35653913 PMCID: PMC9163839 DOI: 10.1016/j.nicl.2022.103061] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/15/2022] [Accepted: 05/26/2022] [Indexed: 10/29/2022]
Abstract
INTRODUCTION Within the core neuroimaging signature of amyotrophic lateral sclerosis (ALS), the corpus callosum (CC) is increasingly recognized as a consistent feature. The aim of this study was to investigate the sensitivity and specificity of the microstructural segmental CC morphology, assessed by diffusion tensor imaging (DTI) and high-resolution T1-weighted (T1w) imaging, in a large cohort of ALS patients including different clinical phenotypes. METHODS In a single-centre study, 575 patients with ALS (classical phenotype, N = 432; restricted phenotypes primary lateral sclerosis (PLS) N = 55, flail arm syndrome (FAS) N = 45, progressive bulbar palsy (PBP) N = 22, lower motor neuron disease (LMND) N = 21) and 112 healthy controls underwent multiparametric MRI, i.e. volume-rendering T1w scans and DTI. Tract-based fractional anisotropy statistics (TFAS) was applied to callosal tracts of CC areas I-V, identified from DTI data (tract-of-interest (TOI) analysis), and texture analysis was applied to T1w data. In order to further specify the callosal alterations, a support vector machine (SVM) algorithm was used to discriminate between motor neuron disease patients and controls. RESULTS The analysis of white matter integrity revealed predominantly FA reductions for tracts of the callosal areas I, II, and III (with highest reductions in callosal area III) for all ALS patients and separately for each phenotype when compared to controls; texture analysis demonstrated significant alterations of the parameters entropy and homogeneity for ALS patients and all phenotypes for the CC areas I, II, and III (with again highest reductions in callosal area III) compared to controls. With SVM applied on multiparametric callosal parameters of area III, a separation of all ALS patients including phenotypes from controls with 72% sensitivity and 73% specificity was achieved. These results for callosal area III parameters could be improved by an SVM of six multiparametric callosal parameters of areas I, II, and III, achieving a separation of all ALS patients including phenotypes from controls with 84% sensitivity and 85% specificity. DISCUSSION The multiparametric MRI texture and DTI analysis demonstrated substantial alterations of the frontal and central CC with most significant alterations in callosal area III (motor segment) in ALS and separately in all investigated phenotypes (PLS, FAS, PBP, LMND) in comparison to controls, while no significant differences were observed between ALS and its phenotypes. The combination of the texture and the DTI parameters in an unbiased SVM-based approach might contribute as a neuroimaging marker for the assessment of the CC in ALS, including subtypes.
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Affiliation(s)
| | | | - Anna Behler
- Department of Neurology, University of Ulm, Germany
| | - Albert C Ludolph
- Department of Neurology, University of Ulm, Germany; German Center for Neurodegenerative Diseases (DZNE), Ulm, Germany
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Germany; German Center for Neurodegenerative Diseases (DZNE), Ulm, Germany.
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19
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McKenna MC, Li Hi Shing S, Murad A, Lope J, Hardiman O, Hutchinson S, Bede P. Focal thalamus pathology in frontotemporal dementia: Phenotype-associated thalamic profiles. J Neurol Sci 2022; 436:120221. [DOI: 10.1016/j.jns.2022.120221] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/21/2022] [Accepted: 03/03/2022] [Indexed: 11/25/2022]
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20
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Clusters of anatomical disease-burden patterns in ALS: a data-driven approach confirms radiological subtypes. J Neurol 2022; 269:4404-4413. [PMID: 35333981 PMCID: PMC9294023 DOI: 10.1007/s00415-022-11081-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 12/28/2022]
Abstract
Amyotrophic lateral sclerosis (ALS) is associated with considerable clinical heterogeneity spanning from diverse disability profiles, differences in UMN/LMN involvement, divergent progression rates, to variability in frontotemporal dysfunction. A multitude of classification frameworks and staging systems have been proposed based on clinical and neuropsychological characteristics, but disease subtypes are seldom defined based on anatomical patterns of disease burden without a prior clinical stratification. A prospective research study was conducted with a uniform imaging protocol to ascertain disease subtypes based on preferential cerebral involvement. Fifteen brain regions were systematically evaluated in each participant based on a comprehensive panel of cortical, subcortical and white matter integrity metrics. Using min–max scaled composite regional integrity scores, a two-step cluster analysis was conducted. Two radiological clusters were identified; 35.5% of patients belonging to ‘Cluster 1’ and 64.5% of patients segregating to ‘Cluster 2’. Subjects in Cluster 1 exhibited marked frontotemporal change. Predictor ranking revealed the following hierarchy of anatomical regions in decreasing importance: superior lateral temporal, inferior frontal, superior frontal, parietal, limbic, mesial inferior temporal, peri-Sylvian, subcortical, long association fibres, commissural, occipital, ‘sensory’, ‘motor’, cerebellum, and brainstem. While the majority of imaging studies first stratify patients based on clinical criteria or genetic profiles to describe phenotype- and genotype-associated imaging signatures, a data-driven approach may identify distinct disease subtypes without a priori patient categorisation. Our study illustrates that large radiology datasets may be potentially utilised to uncover disease subtypes associated with unique genetic, clinical or prognostic profiles.
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21
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McKenna MC, Murad A, Huynh W, Lope J, Bede P. The changing landscape of neuroimaging in frontotemporal lobar degeneration: from group-level observations to single-subject data interpretation. Expert Rev Neurother 2022; 22:179-207. [PMID: 35227146 DOI: 10.1080/14737175.2022.2048648] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION While the imaging signatures of frontotemporal lobar degeneration (FTLD) phenotypes and genotypes are well-characterised based on group-level descriptive analyses, the meaningful interpretation of single MRI scans remains challenging. Single-subject MRI classification frameworks rely on complex computational models and large training datasets to categorise individual patients into diagnostic subgroups based on distinguishing imaging features. Reliable individual subject data interpretation is hugely important in the clinical setting to expedite the diagnosis and classify individuals into relevant prognostic categories. AREAS COVERED This article reviews (1) the neuroimaging studies that propose single-subject MRI classification strategies in symptomatic and pre-symptomatic FTLD, (2) potential practical implications and (3) the limitations of current single-subject data interpretation models. EXPERT OPINION Classification studies in FTLD have demonstrated the feasibility of categorising individual subjects into diagnostic groups based on multiparametric imaging data. Preliminary data indicate that pre-symptomatic FTLD mutation carriers may also be reliably distinguished from controls. Despite momentous advances in the field, significant further improvements are needed before these models can be developed into viable clinical applications.
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Affiliation(s)
| | - Aizuri Murad
- Computational Neuroimaging Group, Trinity College Dublin, Ireland
| | - William Huynh
- Brain and Mind Centre, University of Sydney, Australia
| | - Jasmin Lope
- Computational Neuroimaging Group, Trinity College Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group, Trinity College Dublin, Ireland.,Pitié-Salpêtrière University Hospital, Sorbonne University, France
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22
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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: 1] [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: 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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23
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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: 21] [Impact Index Per Article: 10.5] [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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Tahedl M, Li Hi Shing S, Finegan E, Chipika RH, Lope J, Hardiman O, Bede P. Propagation patterns in motor neuron diseases: Individual and phenotype-associated disease-burden trajectories across the UMN-LMN spectrum of MNDs. Neurobiol Aging 2021; 109:78-87. [PMID: 34656922 DOI: 10.1016/j.neurobiolaging.2021.04.031] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 03/29/2021] [Accepted: 04/13/2021] [Indexed: 01/18/2023]
Abstract
Motor neuron diseases encompass a divergent group of conditions with considerable differences in clinical manifestations, survival, and genetic vulnerability. One of the key aspects of clinical heterogeneity is the preferential involvement of upper (UMN) and lower motor neurons (LMN). While longitudinal imaging patters are relatively well characterized in ALS, progressive cortical changes in UMN,- and LMN-predominant conditions are seldom evaluated. Accordingly, the objective of this study is the juxtaposition of longitudinal trajectories in 3 motor neuron phenotypes; a UMN-predominant syndrome (PLS), a mixed UMN-LMN condition (ALS), and a lower motor neuron condition (poliomyelitis survivors). A standardized imaging protocol was implemented in a prospective, multi-timepoint longitudinal study with a uniform follow-up interval of 4 months. Forty-five poliomyelitis survivors, 61 patients with amyotrophic lateral sclerosis (ALS), and 23 patients with primary lateral sclerosis (PLS) were included. Cortical thickness alterations were evaluated in a dual analysis pipeline, using standard cortical thickness analyses, and a z-score-based individualized approach. Our results indicate that PLS patients exhibit rapidly progressive cortical thinning primarily in motor regions; ALS patients show cortical atrophy in both motor and extra-motor regions, while poliomyelitis survivors exhibit cortical thickness gains in a number of cerebral regions. Our findings suggest that dynamic cortical changes in motor neuron diseases may depend on relative UMN and/or LMN involvement, and increased cortical thickness in LMN-predominant conditions may represent compensatory, adaptive processes.
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Affiliation(s)
- Marlene Tahedl
- Computational Neuroimaging Group, Biomedical Sciences Institute, Trinity College Dublin, Ireland; Department of Psychiatry and Psychotherapy and Institute for Psychology, University of Regensburg, 93053 Regensburg, Germany
| | - 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
| | - Jasmin Lope
- 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; Pitié-Salpêtrière University Hospital, Sorbonne University, Paris, France.
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