1.
van Es, MA, Hardiman, O, Chio, A, et al. Amyotrophic lateral sclerosis. Lancet 2017; 390: 2084–2098.
Google Scholar |
Crossref |
Medline2.
Kassubek, J, Müller, HP. Computer-based magnetic resonance imaging as a tool in clinical diagnosis in neurodegenerative diseases. Expert Rev Neurother 2016; 16: 295–306.
Google Scholar |
Crossref |
Medline3.
Kassubek, J, Müller, HP. Advanced neuroimaging approaches in amyotrophic lateral sclerosis: refining the clinical diagnosis. Expert Rev Neurother 2020; 20: 237–249.
Google Scholar |
Crossref |
Medline4.
Arbabshirani, MR, Plis, S, Sui, J, et al. Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. NeuroImage 2017; 145: 137–165.
Google Scholar |
Crossref |
Medline5.
Wilkinson, J, Arnold, KF, Murray, EJ, et al. Time to reality check the promises of machine learning-powered precision medicine. Lancet Digit Health 2020; 2: e677–e680.
Google Scholar |
Medline6.
Grollemund, V, Pradat, PF, Querin, G, et al. Machine learning in amyotrophic lateral sclerosis: achievements, pitfalls, and future directions. Front Neurosci 2019; 13: 135.
Google Scholar |
Crossref |
Medline7.
Welsh, RC, Jelsone-Swain, LM, Foerster, BR. The utility of independent component analysis and machine learning in the identification of the amyotrophic lateral sclerosis diseased brain. Front Hum Neurosci 2013; 7: 251.
Google Scholar |
Crossref |
Medline8.
Fekete, T, Zach, N, Mujica-Parodi, LR, et al. Multiple kernel learning captures a systems-level functional connectivity biomarker signature in amyotrophic lateral sclerosis. PLoS One 2013; 8: e85190.
Google Scholar |
Crossref9.
Schuster, C, Hardiman, O, Bede, P. Development of an automated MRI-based diagnostic protocol for amyotrophic lateral sclerosis using disease-specific pathognomonic features: a quantitative disease-state classification study. PLoS One 2016; 11: e0167331.
Google Scholar |
Crossref10.
Bede, P, Iyer, PM, Finegan, E, et al. Virtual brain biopsies in amyotrophic lateral sclerosis: diagnostic classification based on in vivo pathological patterns. Neuroimage Clin 2017; 15: 653–658.
Google Scholar |
Crossref |
Medline11.
Ferraro, PM, Agosta, F, Riva, N, et al. Multimodal structural MRI in the diagnosis of motor neuron diseases. Neuroimage Clin 2017; 16: 240–247.
Google Scholar |
Crossref |
Medline12.
Elahi, GMME, Kalra, S, Zinman, L, et al. Texture classification of MR images of the brain in ALS using M-CoHOG: a multi-center study. Comput Med Imaging Graph 2020; 79: 101659.
Google Scholar |
Crossref |
Medline13.
He, K, Zhang, X, Ren, S, et al. Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition, Las Vegas, NV, , pp. 770–778. New York: IEEE.
Google Scholar14.
Simonyan, K, Zisserman, A. Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations, San Diego, CA, . LaJolla: ICRL.
Google Scholar15.
Raudys, S. Statistical and neural classifiers: An integrated approach to design. London: Springer-Verlag, 2001.
Google Scholar |
Crossref16.
Grolez, G, Moreau, C, Danel-Brunaud, V, et al. The value of magnetic resonance imaging as a biomarker for amyotrophic lateral sclerosis: a systematic review. BMC Neurol 2016; 16: 155.
Google Scholar |
Crossref |
Medline17.
Page, MJ, McKenzie, JE, Bossuyt, PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372: n71.
Google Scholar18.
Shen, D, Cui, L, Fang, J, et al. Voxel-wise meta-analysis of gray matter changes in amyotrophic lateral sclerosis. Front Aging Neurosci 2016; 8: 64.
Google Scholar |
Crossref |
Medline19.
Gorges, M, Del Tredici, K, Dreyhaupt, J, et al. Corticoefferent pathology distribution in amyotrophic lateral sclerosis: in vivo evidence from a meta-analysis of diffusion tensor imaging data. Sci Rep 2018; 8: 15389.
Google Scholar |
Crossref |
Medline20.
Finegan, E, Chipika, RH, Shing, SLH, et al. Primary lateral sclerosis: a distinct entity or part of the ALS spectrum? Amyotroph Lateral Scler Frontotemporal Degener 2019; 20: 133–145.
Google Scholar |
Crossref |
Medline21.
Rosenbohm, A, Müller, HP, Hübers, A, et al. Corticoefferent pathways in pure lower motor neuron disease: a diffusion tensor imaging study. J Neurol 2016; 263: 2430–2437.
Google Scholar |
Crossref |
Medline22.
Buhour, MS, Doidy, F, Mondou, A, et al. Voxel-based mapping of grey matter volume and glucose metabolism profiles in amyotrophic lateral sclerosis. EJNMMI Res 2017; 7: 21.
Google Scholar |
Crossref |
Medline23.
Illán-Gala, I, Montal, V, Pegueroles, J, et al. Cortical microstructure in the amyotrophic lateral sclerosis-frontotemporal dementia continuum. Neurology 2020; 95: e2565–e2576.
Google Scholar |
Crossref24.
Cheng, L, Tang, X, Luo, C, et al. Fiber-specific white matter reductions in amyotrophic lateral sclerosis. Neuroimage Clin 2020; 28: 102516.
Google Scholar |
Crossref |
Medline25.
de Albuquerque, M, Branco, LM, Rezende, TJ, et al. Longitudinal evaluation of cerebral and spinal cord damage in amyotrophic lateral sclerosis. Neuroimage Clin 2017; 14: 269–276.
Google Scholar |
Crossref |
Medline26.
Schuster, C, Hardiman, O, Bede, P. Survival prediction in amyotrophic lateral sclerosis based on MRI measures and clinical characteristics. BMC Neurol 2017; 17: 73.
Google Scholar |
Crossref |
Medline27.
Shen, D, Hou, B, Xu, Y, et al. Brain structural and perfusion signature of amyotrophic lateral sclerosis with varying levels of cognitive deficit. Front Neurol 2018; 9: 364.
Google Scholar |
Crossref |
Medline28.
Hu, T, Hou, Y, Wei, Q, et al. Patterns of brain regional functional coherence in cognitive impaired ALS. Int J Neurosci 2020; 130: 751–758.
Google Scholar |
Crossref |
Medline29.
Dadar, M, Manera, AL, Zinman, L, et al. Cerebral atrophy in amyotrophic lateral sclerosis parallels the pathological distribution of TDP43. Brain Commun 2020; 2: fcaa061.
Google Scholar |
Crossref |
Medline30.
Bede, P, Omer, T, Finegan, E, et al. Connectivity-based characterisation of subcortical grey matter pathology in frontotemporal dementia and ALS: a multimodal neuroimaging study. Brain Imaging Behav 2018; 12: 1696–1707.
Google Scholar |
Crossref |
Medline31.
Agosta, F, Ferraro, PM, Riva, N, et al. Structural and functional brain signatures of C9orf72 in motor neuron disease. Neurobiol Aging 2017; 57: 206–219.
Google Scholar |
Crossref |
Medline32.
Branco, LMT, de Rezende, TJR, Roversi, CO, et al. Brain signature of mild stages of cognitive and behavioral impairment in amyotrophic lateral sclerosis Psychiatry. Res Neuroimaging 2018; 272: 58–64.
Google Scholar |
Crossref |
Medline33.
Kim, HJ, de Leon, M, Wang, X, et al. Relationship between clinical parameters and brain structure in sporadic amyotrophic lateral sclerosis patients according to onset type: a voxel-based morphometric study. PLoS One 2017; 12: e0168424.
Google Scholar |
Medline34.
Christidi, F, Karavasilis, E, Ferentinos, P, et al. Investigating the neuroanatomical substrate of pathological laughing and crying in amyotrophic lateral sclerosis with multimodal neuroimaging techniques. Amyotroph Lateral Scler Frontotemporal Degener 2018; 19: 12–20.
Google Scholar |
Crossref |
Medline35.
Acosta-Cabronero, J, Machts, J, Schreiber, S, et al. Quantitative susceptibility MRI to detect brain iron in amyotrophic lateral sclerosis. Radiology 2018; 289: 195–203.
Google Scholar |
Crossref |
Medline36.
Ogura, A, Watanabe, H, Kawabata, K, et al. Semantic deficits in ALS related to right lingual/fusiform gyrus network involvement. Ebiomedicine 2019; 47: 506–517.
Google Scholar |
Crossref |
Medline37.
Trojsi, F, Di Nardo, F, Siciliano, M, et al. Resting state functional MRI brain signatures of fast disease progression in amyotrophic lateral sclerosis: a retrospective study. Amyotroph Lateral Scler Frontotemporal Degener 2020; 4: 1–10.
Google Scholar38.
Trojsi, F, Di Nardo, F, Caiazzo, G, et al. Hippocampal connectivity in amyotrophic lateral sclerosis (ALS): more than Papez circuit impairment. Brain Imaging Behav 2021; 15: 2126–2138.
Google Scholar |
Crossref |
Medline39.
Qiu, T, Zhang, Y, Tang, X, et al. Precentral degeneration and cerebellar compensation in amyotrophic lateral sclerosis: a multimodal MRI analysis. Hum Brain Mapp 2019; 40: 3464–3474.
Google Scholar |
Medline40.
Gellersen, HM, O’Guo, CC, O’Callaghan, C, et al. Cerebellar atrophy in neurodegeneration—a meta-analysis. J Neurol Neurosurg Psychiatry 2017; 88: 780–788.
Google Scholar |
Crossref |
Medline41.
Qin, Y, Zhang, S, Jiang, R, et al. Region-specific atrophy of precentral gyrus in patients with amyotrophic lateral sclerosis. J Magn Reson Imaging 2018; 47: 115–122.
Google Scholar |
Crossref |
Medline42.
Consonni, M, Contarino, VE, Catricalà, E, et al. Cortical markers of cognitive syndromes in amyotrophic lateral sclerosis. Neuroimage Clin 2018; 19: 675–682.
Google Scholar |
Crossref |
Medline43.
Contarino, VE, Conte, G, Morelli, C, et al. Toward a marker of upper motor neuron impairment in amyotrophic lateral sclerosis: a fully automatic investigation of the magnetic susceptibility in the precentral cortex. Eur J Radiol 2020; 124: 108815.
Google Scholar |
Crossref |
Medline44.
Bharti, K, Khan, M, Beaulieu, C, et al. Involvement of the dentate nucleus in the pathophysiology of amyotrophic lateral sclerosis: a multi-center and multi-modal neuroimaging study. Neuroimage Clin 2020; 28: 102385.
Google Scholar |
Crossref |
Medline45.
Chipika, RH, Finegan, E, Li, Hi, Shing, S, et al. “Switchboard” malfunction in motor neuron diseases: selective pathology of thalamic nuclei in amyotrophic lateral sclerosis and primary lateral sclerosis. Neuroimage Clin 2020; 27: 102300.
Google Scholar |
Crossref |
Medline46.
Machts, J, Keute, M, Kaufmann, J, et al. Longitudinal clinical and neuroanatomical correlates of memory impairment in motor neuron disease. Neuroimage Clin 2021; 29: 102545.
Google Scholar |
Crossref |
Medline47.
Tu, S, Menke, RAL, Talbot, K, et al. Regional thalamic MRI as a marker of widespread cortical pathology and progressive frontotemporal involvement in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry 2018; 89: 1250–1258.
Google Scholar |
Crossref |
Medline48.
Consonni, M, Dalla Bella, E, Contarino, VE, et al. Cortical thinning trajectories across disease stages and cognitive impairment in amyotrophic lateral sclerosis. Cortex 2020; 131: 284–294.
Google Scholar |
Crossref |
Medline49.
Wirth, AM, Khomenko, A, Baldaranov, D, et al. Combinatory biomarker use of cortical thickness, MUNIX, and ALSFRS-R at baseline and in longitudinal courses of individual patients with amyotrophic lateral sclerosis. Front Neurol 2018; 9: 614.
Google Scholar |
Crossref |
Medline50.
Chipika, RH, Christidi, F, Finegan, E, et al. Amygdala pathology in amyotrophic lateral sclerosis and primary lateral sclerosis. J Neurol Sci 2020; 417: 117039.
Google Scholar |
Crossref |
Medline51.
Finegan, E, Li, Hi, Shing, S, Chipika, RH, et al. Widespread subcortical grey matter degeneration in primary lateral sclerosis: a multimodal imaging study with genetic profiling. Neuroimage Clin 2019; 24: 102089.
Google Scholar |
Crossref |
Medline52.
Jin, J, Hu, F, Zhang, Q, et al. Dominant heterogeneity of upper and lower motor neuron degeneration to motor manifestation of involved region in amyotrophic lateral sclerosis. Sci Rep 2019; 9: 20059.
留言 (0)