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Sun W, Liu SH, Wei XJ, Sun H, Ma ZW, Yu XF. Potential of neuroimaging as a biomarker in amyotrophic lateral sclerosis: from structure to metabolism. J Neurol 2024; 271:2238-2257. [PMID: 38367047 DOI: 10.1007/s00415-024-12201-x] [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: 11/18/2023] [Revised: 01/14/2024] [Accepted: 01/16/2024] [Indexed: 02/19/2024]
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by motor neuron degeneration. The development of ALS involves metabolite alterations leading to tissue lesions in the nervous system. Recent advances in neuroimaging have significantly improved our understanding of the underlying pathophysiology of ALS, with findings supporting the corticoefferent axonal disease progression theory. Current studies on neuroimaging in ALS have demonstrated inconsistencies, which may be due to small sample sizes, insufficient statistical power, overinterpretation of findings, and the inherent heterogeneity of ALS. Deriving meaningful conclusions solely from individual imaging metrics in ALS studies remains challenging, and integrating multimodal imaging techniques shows promise for detecting valuable ALS biomarkers. In addition to giving an overview of the principles and techniques of different neuroimaging modalities, this review describes the potential of neuroimaging biomarkers in the diagnosis and prognostication of ALS. We provide an insight into the underlying pathology, highlighting the need for standardized protocols and multicenter collaborations to advance ALS research.
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Affiliation(s)
- Wei Sun
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, 130021, China
| | - Si-Han Liu
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Xiao-Jing Wei
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, 130021, China
| | - Hui Sun
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, 130021, China
| | - Zhen-Wei Ma
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, 130021, China
| | - Xue-Fan Yu
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, 130021, China.
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Li X, Liu Q, Niu T, Liu T, Xin Z, Zhou X, Li R, Li Z, Jia L, Liu Y, Dong H. Sleep disorders and white matter integrity in patients with sporadic amyotrophic lateral sclerosis. Sleep Med 2023; 109:170-180. [PMID: 37459708 DOI: 10.1016/j.sleep.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 06/27/2023] [Accepted: 07/04/2023] [Indexed: 08/12/2023]
Abstract
This study aimed to explore the characteristics of sleep disorders and their relationship with abnormal white-matter integrity in patients with sporadic amyotrophic lateral sclerosis. One hundred and thirty-six patients and 80 healthy controls were screened consecutively, and 56 patients and 43 healthy controls were ultimately analyzed. Sleep disorders were confirmed using the Pittsburgh sleep quality index, the Epworth sleepiness scale, and polysomnography; patients were classified into those with poor and good sleep quality. White-matter integrity was assessed using diffusion tensor imaging and compared between groups to identify the white-matter tracts associated with sleep disorders. The relationship between scores on the Pittsburgh sleep quality index and impaired white-matter tracts was analyzed using multiple regression. Poor sleep quality was more common in patients (adjusted odds ratio, 4.26; p = 0.005). Compared to patients with good sleep quality (n = 30), patients with poor sleep quality (n = 26; 46.4%) showed decreased fractional anisotropy, increased mean diffusivity, and increased radial diffusivity of projection and commissural fibers, and increased radial diffusivity of the right thalamus. The Pittsburgh score showed the best fit with the mean fractional anisotropy of the right anterior limb of the internal capsule (r = - 0.355, p = 0.011) and the mean radial diffusivity of the right thalamus (r = 0.309, p = 0.028). We conclude that sleep disorders are common in patients with sporadic amyotrophic lateral sclerosis and are associated with reduced white-matter integrity. The pathophysiology of amyotrophic lateral sclerosis may contribute directly to sleep disorders.
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Affiliation(s)
- Xin Li
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000, PR China; The Key Laboratory of Neurology (Hebei Medical University), Ministry of Education, Shijiazhuang, Hebei, 050000, PR China; Neurological Laboratory of Hebei Province, Shijiazhuang, Hebei, 050000, PR China
| | - Qi Liu
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000, PR China; The Key Laboratory of Neurology (Hebei Medical University), Ministry of Education, Shijiazhuang, Hebei, 050000, PR China; Neurological Laboratory of Hebei Province, Shijiazhuang, Hebei, 050000, PR China
| | - Tongyang Niu
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000, PR China; The Key Laboratory of Neurology (Hebei Medical University), Ministry of Education, Shijiazhuang, Hebei, 050000, PR China; Neurological Laboratory of Hebei Province, Shijiazhuang, Hebei, 050000, PR China
| | - Tingting Liu
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000, PR China; The Key Laboratory of Neurology (Hebei Medical University), Ministry of Education, Shijiazhuang, Hebei, 050000, PR China; Neurological Laboratory of Hebei Province, Shijiazhuang, Hebei, 050000, PR China
| | - Zikai Xin
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000, PR China; The Key Laboratory of Neurology (Hebei Medical University), Ministry of Education, Shijiazhuang, Hebei, 050000, PR China; Neurological Laboratory of Hebei Province, Shijiazhuang, Hebei, 050000, PR China
| | - Xiaomeng Zhou
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000, PR China; The Key Laboratory of Neurology (Hebei Medical University), Ministry of Education, Shijiazhuang, Hebei, 050000, PR China; Neurological Laboratory of Hebei Province, Shijiazhuang, Hebei, 050000, PR China
| | - Rui Li
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000, PR China; The Key Laboratory of Neurology (Hebei Medical University), Ministry of Education, Shijiazhuang, Hebei, 050000, PR China; Neurological Laboratory of Hebei Province, Shijiazhuang, Hebei, 050000, PR China
| | - Zhenzhong Li
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000, PR China; The Key Laboratory of Neurology (Hebei Medical University), Ministry of Education, Shijiazhuang, Hebei, 050000, PR China; Neurological Laboratory of Hebei Province, Shijiazhuang, Hebei, 050000, PR China
| | - Lijing Jia
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000, PR China; The Key Laboratory of Neurology (Hebei Medical University), Ministry of Education, Shijiazhuang, Hebei, 050000, PR China; Neurological Laboratory of Hebei Province, Shijiazhuang, Hebei, 050000, PR China
| | - Yaling Liu
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000, PR China; The Key Laboratory of Neurology (Hebei Medical University), Ministry of Education, Shijiazhuang, Hebei, 050000, PR China; Neurological Laboratory of Hebei Province, Shijiazhuang, Hebei, 050000, PR China.
| | - Hui Dong
- Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000, PR China; The Key Laboratory of Neurology (Hebei Medical University), Ministry of Education, Shijiazhuang, Hebei, 050000, PR China; Neurological Laboratory of Hebei Province, Shijiazhuang, Hebei, 050000, PR China.
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Jamali AM, Kethamreddy M, Burkett BJ, Port JD, Pandey MK. PET and SPECT Imaging of ALS: An Educational Review. Mol Imaging 2023; 2023:5864391. [PMID: 37636591 PMCID: PMC10460279 DOI: 10.1155/2023/5864391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/11/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a disease leading to progressive motor degeneration and ultimately death. It is a complex disease that can take a significantly long time to be diagnosed, as other similar pathological conditions must be ruled out for a definite diagnosis of ALS. Noninvasive imaging of ALS has shed light on disease pathology and altered biochemistry in the ALS brain. Other than magnetic resonance imaging (MRI), two types of functional imaging, positron emission tomography (PET) and single photon emission computed tomography (SPECT), have provided valuable data about what happens in the brain of ALS patients compared to healthy controls. PET imaging has revealed a specific pattern of brain metabolism through [18F]FDG, while other radiotracers have uncovered neuroinflammation, changes in neuronal density, and protein aggregation. SPECT imaging has shown a general decrease in regional cerebral blood flow (rCBF) in ALS patients. This educational review summarizes the current state of ALS imaging with various PET and SPECT radiopharmaceuticals to better understand the pathophysiology of ALS.
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Affiliation(s)
| | | | | | - John D. Port
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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Rajagopalan V, Chaitanya KG, Pioro EP. Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study. Diagnostics (Basel) 2023; 13:diagnostics13091521. [PMID: 37174914 PMCID: PMC10177762 DOI: 10.3390/diagnostics13091521] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/20/2023] [Accepted: 04/12/2023] [Indexed: 05/15/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease whose diagnosis depends on the presence of combined lower motor neuron (LMN) and upper motor neuron (UMN) degeneration. LMN degeneration assessment is aided by electromyography, whereas no equivalent exists to assess UMN dysfunction. Magnetic resonance imaging (MRI) is primarily used to exclude conditions that mimic ALS. We have identified four different clinical/radiological phenotypes of ALS patients. We hypothesize that these ALS phenotypes arise from distinct pathologic processes that result in unique MRI signatures. To our knowledge, no machine learning (ML)-based data analyses have been performed to stratify different ALS phenotypes using MRI measures. During routine clinical evaluation, we obtained T1-, T2-, PD-weighted, diffusion tensor (DT) brain MRI of 15 neurological controls and 91 ALS patients (UMN-predominant ALS with corticospinal tract CST) hyperintensity, n = 21; UMN-predominant ALS without CST hyperintensity, n = 26; classic ALS, n = 23; and ALS patients with frontotemporal dementia, n = 21). From these images, we obtained 101 white matter (WM) attributes (including DT measures, graph theory measures from DT and fractal dimension (FD) measures using T1-weighted), 10 grey matter (GM) attributes (including FD based measures from T1-weighted), and 10 non-imaging attributes (2 demographic and 8 clinical measures of ALS). We employed classification and regression tree, Random Forest (RF) and also artificial neural network for the classifications. RF algorithm provided the best accuracy (70-94%) in classifying four different phenotypes of ALS patients. WM metrics played a dominant role in classifying different phenotypes when compared to GM or clinical measures. Although WM measures from both right and left hemispheres need to be considered to identify ALS phenotypes, they appear to be differentially affected by the degenerative process. Longitudinal studies can confirm and extend our findings.
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Affiliation(s)
- Venkateswaran Rajagopalan
- Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad 500078, India
| | - Krishna G Chaitanya
- Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad 500078, India
| | - Erik P Pioro
- Neuromuscular Center, Department of Neurology, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
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Wang Y, Shen D, Hou B, Sun X, Yang X, Gao J, Liu M, Feng F, Cui L. Brain structural and perfusion changes in amyotrophic lateral sclerosis-frontotemporal dementia patients with cognitive and motor onset: a preliminary study. Brain Imaging Behav 2022; 16:2164-2174. [PMID: 35838935 DOI: 10.1007/s11682-022-00686-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/06/2022] [Indexed: 11/27/2022]
Abstract
Amyotrophic lateral sclerosis-frontotemporal dementia (ALS-FTD) is rare but exhibits worse prognosis than either ALS or FTD alone. However, cognitive onset ALS-FTD (ALS-FTD-C) confers significantly better patient survival than does motor onset ALS-FTD (ALS-FTD-M), underscoring a meager understanding of pathological group differences. This study aimed to assess disparities in cortical atrophy and perfusion shown by patients with the above disease variants. A total of 38 participants (ALS-FTD-C, 8; ALS-FTD-M, 6; simultaneous-onset ALS-FTD [ALS-FTD-S], 4; healthy controls [HC], 20) qualified for the study and underwent magnetic resonance imaging scan. Three-dimensional T1-weighted structural brain imaging and 3D-pseudocontinuous arterial spin-labeled imaging were routinely collected. Gray matter volume (GMV) and cerebral blood flow (CBF) in ALS-FTD-C and ALS-FTD-M were compared through voxel-based analysis. Correlations between imaging parameters and clinical data were also assessed. Compared with HC, ALS-FTD had significant GMV reduction mainly in bilateral limbic system. GMV reduction in ALS-FTD-C was similar in pattern but less widespread, whereas ALS-FTD-M lacked any significant GMV reduction. In CBF analyses, ALS-FTD displayed hypoperfusion in bilateral motor cortex, frontotemporal lobe, and left basal ganglia. Hypoperfusion involved bilateral temporal lobe, prefrontal cortex, and putamen in ALS-FTD-C but was limited to left parahippocampal gyrus in ALS-FTD-M. Correlations between clinical data and GMV/CBF changes in specific regions were also identified in ALS-FTD. Group-specific patterns of cortical atrophy and perfusion were evident in ALS-FTD-C and ALS-FTD-M. ALS-FTD-C showed pronounced cortical atrophy and hypoperfusion, which were otherwise minimal in ALS-FTD-M. Above findings preliminarily revealed the pathological group differences that may help in classifying patients with ALS-FTD.
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Affiliation(s)
- Yanying Wang
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China
| | - Dongchao Shen
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China
| | - Bo Hou
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Xiaohan Sun
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China
| | - Xunzhe Yang
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China
| | - Jing Gao
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China
| | - Mingsheng Liu
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Liying Cui
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100730, China.
- Neuroscience Centre, Chinese Academy of Medical Sciences, Beijing, China.
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Huang G, Xin M, Hao Y, Bai S, Liu J, Zhang C. Cerebral Metabolic Network in Patients With Anti-N-Methyl-D-Aspartate Receptor Encephalitis on 18F-FDG PET Imaging. Front Neurosci 2022; 16:885425. [PMID: 35573296 PMCID: PMC9098961 DOI: 10.3389/fnins.2022.885425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundAnti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is the most common autoimmune encephalitis (AE), and the prognosis may significantly be improved if identified earlier and immune-related treated more effectively. This study evaluated the brain metabolic network using fluorodeoxyglucose positron emission tomography (FDG PET).Material and methodsFDG PET imaging of patients with NMDAR encephalitis was used to investigate the metabolic connectivity network, which was analyzed using the graph theory. The results in patients were compared to those in age- and sex-matched healthy controls.ResultsThe hub nodes were mainly in the right frontal lobe in patients with NMDAR encephalitis. The global and local efficiencies in most brain regions were significantly reduced, and the shortest characteristic path length was significantly longer, especially in the temporal and occipital lobes. Significant network functions of topology properties were enhanced in the right frontal, caudate nucleus, and cingulate gyrus. In addition, the internal connection integration in the left cerebral hemisphere was poor, and the transmission efficiency of Internet information was low.ConclusionThe present findings indicate that those characteristic and connections of metabolic network were changed in the brain by graph theory analysis quantitatively, which is helpful to better understand neuropathological and physiological mechanisms in patients with anti-NMDAR encephalitis.
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Affiliation(s)
- Gan Huang
- Department of Nuclear Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mei Xin
- Department of Nuclear Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yong Hao
- Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuwei Bai
- Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jianjun Liu
- Department of Nuclear Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Chenpeng Zhang
| | - Chenpeng Zhang
- Department of Nuclear Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Jianjun Liu
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Corticospinal Tract and Related Grey Matter Morphometric Shape Analysis in ALS Phenotypes: A Fractal Dimension Study. Brain Sci 2021; 11:brainsci11030371. [PMID: 33799358 PMCID: PMC8001972 DOI: 10.3390/brainsci11030371] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/10/2021] [Accepted: 03/10/2021] [Indexed: 11/30/2022] Open
Abstract
A pathological hallmark of amyotrophic lateral sclerosis (ALS) is corticospinal tract (CST) degeneration resulting in upper motor neuron (UMN) dysfunction. No quantitative test is available to easily assess UMN pathways. Brain neuroimaging in ALS promises to potentially change this through identifying biomarkers of UMN dysfunction that may accelerate diagnosis and track disease progression. Fractal dimension (FD) has successfully been used to quantify brain grey matter (GM) and white matter (WM) shape complexity in various neurological disorders. Therefore, we investigated CST and whole brain GM and WM morphometric changes using FD analyses in ALS patients with different phenotypes. We hypothesized that FD would detect differences between ALS patients and neurologic controls and even between the ALS subgroups. Neuroimaging was performed in neurologic controls (n = 14), and ALS patients (n = 75). ALS patients were assigned into four groups based on their clinical or radiographic phenotypes. FD values were estimated for brain WM and GM structures. Patients with ALS and frontotemporal dementia (ALS-FTD) showed significantly higher CST FD values and lower primary motor and sensory cortex GM FD values compared to other ALS groups. No other group of ALS patients revealed significant FD value changes when compared to neurologic controls or with other ALS patient groups. These findings support a more severe disease process in ALS-FTD patients compared to other ALS patient groups. FD value measures may be a sensitive index to evaluate GM and WM (including CST) degeneration in ALS patients.
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