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Foucher J, Öijerstedt L, Lovik A, Sun J, Ismail MAM, Sennfält S, Savitcheva I, Estenberg U, Pagani M, Fang F, Pereira JB, Ingre C. ECAS correlation with metabolic alterations on FDG-PET imaging in ALS. Amyotroph Lateral Scler Frontotemporal Degener 2024:1-9. [PMID: 38836336 DOI: 10.1080/21678421.2024.2361695] [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: 11/17/2023] [Accepted: 05/17/2024] [Indexed: 06/06/2024]
Abstract
Background: Cognitive impairment is observed in up to 50% of patients with amyotrophic lateral sclerosis (ALS). The Edinburgh Cognitive and Behavioral ALS Screen (ECAS) is an ALS-specific multi-domain screening tool. Few studies have examined the relationship between ECAS scores and [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) findings. Objective: To assess the relationship between ECAS scores and glucose metabolism patterns on [18F]FDG -PET images in ALS. Methods: We collected [18F]FDG-PET images from 65 patients with ALS and 39 healthy controls. ECAS scores were collected on all patients and we calculated the correlation to [18F]FDG-PET in order to investigate the potential links between cognition and glucose metabolism. Results: We observed hypometabolism in the frontal cortex, insula, and limbic system, together with hypermetabolism in the cerebellum in patients with ALS compared to controls. A lower ECAS total score was associated with lower glucose metabolism in the right orbitofrontal gyrus and higher glucose metabolism in lateral occipital, medial occipital, and cerebellar regions, among patients with ALS. Similar results, although less widespread, were observed in the analyses of ECAS ALS-specific scores. Conclusions: The metabolic patterns in [18F]FDG -PET show that changes in the glucose metabolism of corresponding areas are related to cognitive dysfunction in ALS, and can be detected using the ECAS.
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Affiliation(s)
- Juliette Foucher
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, ME Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Linn Öijerstedt
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, ME Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Anikó Lovik
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Methodology and Statistics Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Jiawei Sun
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Muhammad-Al-Mustafa Ismail
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, ME Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Stefan Sennfält
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, ME Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Irina Savitcheva
- Medical Radiation Physics and Nuclear Medicine Imaging, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden, and
| | - Ulrika Estenberg
- Medical Radiation Physics and Nuclear Medicine Imaging, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden, and
| | - Marco Pagani
- Medical Radiation Physics and Nuclear Medicine Imaging, Section for Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden, and
- Institute of Cognitive Sciences and Technologies, Italian National Research Council, Rome, Italy
| | - Fang Fang
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Joana B Pereira
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Caroline Ingre
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, ME Neurology, Karolinska University Hospital, Stockholm, Sweden
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Sennfält S, Pagani M, Fang F, Savitcheva I, Estenberg U, Ingre C. FDG-PET shows weak correlation between focal motor weakness and brain metabolic alterations in ALS. Amyotroph Lateral Scler Frontotemporal Degener 2023:1-10. [PMID: 36755485 DOI: 10.1080/21678421.2023.2174881] [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: 02/10/2023]
Abstract
Objective: Amyotrophic lateral sclerosis (ALS) is a clinically heterogenous disease, typically presenting with focal motor weakness that eventually generalizes. Weather there is a correlation between focal motor weakness and metabolic alterations in specific areas of the brain has not been thoroughly explored. This study aims to systematically investigate this by using fluorodeoxyglucose-positron emission tomography (FDG-PET), including longitudinal imaging. Methods: This observational imaging study included 131 ALS patients diagnosed and examined with FDG-PET at the ALS Clinical Research Center at the Karolinska University Hospital in Stockholm, Sweden. Thirteen ALS patients had a second scan and were analyzed longitudinally. The findings were compared to 39 healthy controls examined at the University Medical Center of Gröningen, the Netherlands. Results: There was a general pattern of brain metabolic alterations consistent with previously reported findings in ALS, namely hypometabolism in frontal regions and hypermetabolism in posterior regions. A higher symptom burden was associated with increased hypometabolism and decreased hypermetabolism. However, there was no clear correlation between focal motor weakness and specific metabolic alterations, neither when analyzing focal motor weakness with concomitant upper motor neuron signs or when including all focal motor weakness. Longitudinal FDG-PET imaging showed inconsistent results with little correlation between progression of motor weakness and metabolic alterations. Conclusion: Our results support the disease model of ALS as a diffuse process since no clear correlation was seen between focal motor weakness and specific metabolic alterations. However, there is need for further research on a larger number of patients, particularly including longitudinal imaging.
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Affiliation(s)
- Stefan Sennfält
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Marco Pagani
- Institute of Cognitive Sciences and Technologies, C.N.R, Rome, Italy.,Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden, and
| | - Fang Fang
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Irina Savitcheva
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden, and
| | - Ulrika Estenberg
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden, and
| | - Caroline Ingre
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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Perovnik M, Rus T, Schindlbeck KA, Eidelberg D. Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat Rev Neurol 2023; 19:73-90. [PMID: 36539533 DOI: 10.1038/s41582-022-00753-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2022] [Indexed: 12/24/2022]
Abstract
Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Tomaž Rus
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | | | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA.
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van Veen R, Meles SK, Renken RJ, Reesink FE, Oertel WH, Janzen A, de Vries GJ, Leenders KL, Biehl M. FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107042. [PMID: 35970056 DOI: 10.1016/j.cmpb.2022.107042] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 07/11/2022] [Accepted: 07/27/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVES 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with principal component analysis (PCA) has been applied to identify disease-related brain patterns in neurodegenerative disorders such as Parkinson's disease (PD), Dementia with Lewy Bodies (DLB) and Alzheimer's disease (AD). These patterns are used to quantify functional brain changes at the single subject level. This is especially relevant in determining disease progression in idiopathic REM sleep behavior disorder (iRBD), a prodromal stage of PD and DLB. However, the PCA method is limited in discriminating between neurodegenerative conditions. More advanced machine learning algorithms may provide a solution. In this study, we apply Generalized Matrix Learning Vector Quantization (GMLVQ) to FDG-PET scans of healthy controls, and patients with AD, PD and DLB. Scans of iRBD patients, scanned twice with an approximate 4 year interval, were projected into GMLVQ space to visualize their trajectory. METHODS We applied a combination of SSM/PCA and GMLVQ as a classifier on FDG-PET data of healthy controls, AD, DLB, and PD patients. We determined the diagnostic performance by performing a ten times repeated ten fold cross validation. We analyzed the validity of the classification system by inspecting the GMLVQ space. First by the projection of the patients into this space. Second by representing the axis, that span this decision space, into a voxel map. Furthermore, we projected a cohort of RBD patients, whom have been scanned twice (approximately 4 years apart), into the same decision space and visualized their trajectories. RESULTS The GMLVQ prototypes, relevance diagonal, and decision space voxel maps showed metabolic patterns that agree with previously identified disease-related brain patterns. The GMLVQ decision space showed a plausible quantification of FDG-PET data. Distance traveled by iRBD subjects through GMLVQ space per year (i.e. velocity) was correlated with the change in motor symptoms per year (Spearman's rho =0.62, P=0.004). CONCLUSION In this proof-of-concept study, we show that GMLVQ provides a classification of patients with neurodegenerative disorders, and may be useful in future studies investigating speed of progression in prodromal disease stages.
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Affiliation(s)
- Rick van Veen
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, the Netherlands; Data Science Department, Software Competence Center Hagenberg, Hagenberg, Austria.
| | - Sanne K Meles
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Remco J Renken
- Department of Biomedical Sciences of Cells & Systems, University of Groningen, University Medical Center Groningen, Cognitive Neuroscience Center, Groningen, the Netherlands
| | - Fransje E Reesink
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Wolfgang H Oertel
- Department of Neurology, Philipps-Universität Marburg, Marburg, Germany; Institute for Neurogenomics, Helmholtz Center for Health and Environment, Munich, Germany
| | - Annette Janzen
- Department of Neurology, Philipps-Universität Marburg, Marburg, Germany
| | | | - Klaus L Leenders
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Michael Biehl
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, the Netherlands; SMQB, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, Birmingham, United Kingdom
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Peretti DE, Vállez García D, Renken RJ, Reesink FE, Doorduin J, de Jong BM, De Deyn PP, Dierckx RAJO, Boellaard R. Alzheimer's disease pattern derived from relative cerebral flow as an alternative for the metabolic pattern using SSM/PCA. EJNMMI Res 2022; 12:37. [PMID: 35737201 PMCID: PMC9226207 DOI: 10.1186/s13550-022-00909-8] [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] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 06/15/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND 2-Deoxy-2-[18F]fluoroglucose (FDG) PET is an important tool for the identification of Alzheimer's disease (AD) patients through the characteristic neurodegeneration pattern that these patients present. Regional cerebral blood flow (rCBF) images derived from dynamic 11C-labelled Pittsburgh Compound B (PIB) have been shown to present a similar pattern as FDG. Moreover, multivariate analysis techniques, such as scaled subprofile modelling using principal component analysis (SSM/PCA), can be used to generate disease-specific patterns (DP) that may aid in the classification of subjects. Therefore, the aim of this study was to compare rCBF AD-DPs with FDG AD-DP and their respective performances. Therefore, 52 subjects were included in this study. Fifteen AD and 16 healthy control subjects were used to generate four AD-DP: one based on relative cerebral trace blood (R1), two based on time-weighted average of initial frame intervals (ePIB), and one based on FDG images. Furthermore, 21 subjects diagnosed with mild cognitive impairment were tested against these AD-DPs. RESULTS In general, the rCBF and FDG AD-DPs were characterized by a reduction in cortical frontal, temporal, and parietal lobes. FDG and rCBF methods presented similar score distribution. CONCLUSION rCBF images may provide an alternative for FDG PET scans for the identification of AD patients through SSM/PCA.
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Affiliation(s)
- Débora E Peretti
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - David Vállez García
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Remco J Renken
- Department of Biomedical Sciences of Cells and Systems, Cognitive Neuroscience Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Fransje E Reesink
- Department of Neurology, Alzheimer Centre, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Janine Doorduin
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Bauke M de Jong
- Department of Neurology, Alzheimer Centre, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Peter P De Deyn
- Department of Neurology, Alzheimer Centre, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Laboratory of Neurochemistry and Behaviour, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Rudi A J O Dierckx
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. .,Department of Radiology and Nuclear Medicine, Location VU Medical Center, Amsterdam University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
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