Glucose metabolic brain patterns to discriminate amyotrophic lateral sclerosis from Parkinson plus syndromes

Abstract Background 18F-FDG brain PET measures metabolic changes in neurodegenerative disorders and may discriminate between different diseases even at an early stage. The objective of this study was to classify patients with amyotrophic lateral sclerosis (ALS) and Parkinson plus syndromes (PP). To this end, different approaches were evaluated using generalized linear models and corresponding glucose metabolic brain patterns. Besides direct classification, healthy controls were also included to generate disease-specific metabolic brain patterns and to perform a classification using disease expression scores. Methods ALS patients (n = 70) and PP patients (n = 33: 20 PSP, 3 CBD, and 10 MSA) were available from an existing database of patients with neuromuscular and movement disorders while age-matched healthy controls (n = 29) were selected from a prospective study. To generate both disease-discriminative (direct classification) and disease-specific (classification versus controls) metabolic brain patterns, data were spatially normalized and a principal component analysis (PCA) was performed prior to classification using either logistic regression (PCA-LR) or a support vector machine (PCA-SVM). Furthermore, a direct SVM approach was considered. To compare the three different approaches, Pearson correlations (r) between pattern expression scores and metabolic brain patterns were evaluated, while pairs of ALS- and PP-specific pattern expression scores were compared using the RV coefficient. Results Classification between ALS and PP resulted in a sensitivity and specificity ≥ 0.82 for both direct classification and classification according to disease-specific pattern expression scores. PCA-LR, PCA-SVM, and SVM generated very similar metabolic brain patterns with voxelwise correlations ≥ 0.66, while all patterns allowed straightforward identification of ALS- and PP-specific brain regions of hyper- and hypometabolism. Moreover, pattern expression scores were highly correlated among different classifiers with a mean r of 0.94 while a RV coefficient ≥ 0.91 was found between pairs of ALS- and PP-specific pattern expression scores. Conclusion We demonstrated that a classification between ALS and PP using expression scores of an ALS and PP metabolic brain pattern leads to a similar and high prediction accuracy as direct classification between ALS and PP. Classification performance and disease-specific metabolic patterns, which could support visual reading and improve insight in brain pathology, were very related for different classifiers

Is there a glucose metabolic signature of spreading TDP-43 pathology in Amyotrophic Lateral Sclerosis ?

BACKGROUND: Recently, four neuropathological stages of amyotrophic lateral sclerosis (ALS) with spreading of transactive response DNA-Binding Protein-43 pathology were described. Although 18F-FDG PET has been useful in diagnosis and prognosis of ALS patients, in vivo disease staging using glucose metabolic patterns across the different ALS stages have not been attempted so far. In this study, we investigated whether the discriminant brain regions of the neuropathological stage model can be translated to metabolic patterns for in vivo staging of ALS. Furthermore, we examined the correlation of these metabolic patterns with disease duration, the Revised ALS Functional Rating Scale (ALSFRS-R) and the Forced Vital Capacity (FVC). METHODS: 146 ALS patients (age 66.0 ± 11.0 y; 86M/60F) were divided into four metabolic stages depending on glucose metabolism in discriminant regions of neuropathological stages. 18F-FDG data were analysed voxel-based to compare local metabolic patterns between different stages. Additionally, correlation analyses were performed between pathologic stage and clinical parameters. RESULTS: Relative hypometabolism was present in regions known to be affected from the post- mortem pathological spread model, but relative hypermetabolism was also observed across the different ALS stages. In particular, stage 4 reflected a different frontotemporal pattern discordant with mere progression of stage 1-3, which may point to a potential different subgroup in ALS. Furthermore, metabolic stage correlated with disease duration (Spearman ρ = -0.21, p = 0.01) and FVC (Spearman ρ = -0.24, p = 0.04). CONCLUSIONS: The neuropathological ALS stages correspond to discriminative regional brain glucose metabolism patterns correlating with disease duration and forced vital capacity. Furthermore, metabolic stage 4 may represents a separate group of ALS progression towards frontotemporal dementia.status: accepte

Glucose metabolic brain patterns to discriminate amyotrophic lateral sclerosis from Parkinson plus syndromes

BACKGROUND: 18F-FDG brain PET measures metabolic changes in neurodegenerative disorders and may discriminate between different diseases even at an early stage. The objective of this study was to classify patients with amyotrophic lateral sclerosis (ALS) and Parkinson plus syndromes (PP). To this end, different approaches were evaluated using generalized linear models and corresponding glucose metabolic brain patterns. Besides direct classification, healthy controls were also included to generate disease-specific metabolic brain patterns and to perform a classification using disease expression scores. METHODS: ALS patients (n = 70) and PP patients (n = 33: 20 PSP, 3 CBD, and 10 MSA) were available from an existing database of patients with neuromuscular and movement disorders while age-matched healthy controls (n = 29) were selected from a prospective study. To generate both disease-discriminative (direct classification) and disease-specific (classification versus controls) metabolic brain patterns, data were spatially normalized and a principal component analysis (PCA) was performed prior to classification using either logistic regression (PCA-LR) or a support vector machine (PCA-SVM). Furthermore, a direct SVM approach was considered. To compare the three different approaches, Pearson correlations (r) between pattern expression scores and metabolic brain patterns were evaluated, while pairs of ALS- and PP-specific pattern expression scores were compared using the RV coefficient. RESULTS: Classification between ALS and PP resulted in a sensitivity and specificity ≥ 0.82 for both direct classification and classification according to disease-specific pattern expression scores. PCA-LR, PCA-SVM, and SVM generated very similar metabolic brain patterns with voxelwise correlations ≥ 0.66, while all patterns allowed straightforward identification of ALS- and PP-specific brain regions of hyper- and hypometabolism. Moreover, pattern expression scores were highly correlated among different classifiers with a mean r of 0.94 while a RV coefficient ≥ 0.91 was found between pairs of ALS- and PP-specific pattern expression scores. CONCLUSION: We demonstrated that a classification between ALS and PP using expression scores of an ALS and PP metabolic brain pattern leads to a similar and high prediction accuracy as direct classification between ALS and PP. Classification performance and disease-specific metabolic patterns, which could support visual reading and improve insight in brain pathology, were very related for different classifiers.status: publishe

Relationship between brain metabolism and cognitive/behavioral functioning in ALS-FTD

status: publishe

Combined brain and spinal FDG PET allows differentiation between ALS and ALS mimics

PURPOSE: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder with on average a 1-year delay between symptom onset and diagnosis. Studies have demonstrated the value of [18F]-FDG PET as a sensitive diagnostic biomarker, but the discriminatory potential to differentiate ALS from patients with symptoms mimicking ALS has not been investigated. We investigated the combination of brain and spine [18F]-FDG PET-CT for differential diagnosis between ALS and ALS mimics in a real-life clinical diagnostic setting. METHODS: Patients with a suspected diagnosis of ALS (n = 98; 64.8 ± 11 years; 61 M) underwent brain and spine [18F]-FDG PET-CT scans. In 62 patients, ALS diagnosis was confirmed (67.8 ± 10 years; 35 M) after longitudinal follow-up (average 18.1 ± 8.4 months). In 23 patients, another disease was diagnosed (ALS mimics, 60.9 ± 12.9 years; 17 M) and 13 had a variant motor neuron disease, primary lateral sclerosis (PLS; n = 4; 53.6 ± 2.5 years; 2 M) and progressive muscular atrophy (PMA; n = 9; 58.4 ± 7.3 years; 7 M). Spine metabolism was determined after manual and automated segmentation. VOI- and voxel-based comparisons were performed. Moreover, a support vector machine (SVM) approach was applied to investigate the discriminative power of regional brain metabolism, spine metabolism and the combination of both. RESULTS: Brain metabolism was very similar between ALS mimics and ALS, whereas cervical and thoracic spine metabolism was significantly different (in standardised uptake values; cervical: ALS 2.1 ± 0.5, ALS mimics 1.9 ± 0.4; thoracic: ALS 1.8 ± 0.3, ALS mimics 1.5 ± 0.3). As both brain and spine metabolisms were very similar between ALS mimics and PLS/PMA, groups were pooled for accuracy analyses. Mean discrimination accuracy was 65.4%, 80.0% and 81.5%, using only brain metabolism, using spine metabolism and using both, respectively. CONCLUSION: The combination of brain and spine FDG PET-CT with SVM classification is useful as discriminative biomarker between ALS and ALS mimics in a real-life clinical setting.status: publishe

TSPO Versus P2X7 as a Target for Neuroinflammation: An In Vitro and In Vivo Study

Neuroinflammation is important in amyotrophic lateral sclerosis (ALS). The P2X7 receptor (P2X7R) is a promising target for neuroinflammation. The objective of this study was to compare 18F-DPA714, a second-generation translocator protein tracer, with 11C-JNJ717, a novel P2X7R tracer, in vitro and in vivo in ALS. Methods: For the in vitro portion of the study, autoradiography with 18F-DPA714 and 11C-JNJ717 was performed on human ALS brain sections in comparison to immunofluorescence with Iba1 and GFAP. For the in vivo portion, 3 male patients with early-stage ALS (59.3 ± 7.2 y old) and 6 healthy volunteers (48.2 ± 16.5 y old, 2 men and 4 women) underwent dynamic PET/MR scanning with 18F-DPA714 and 11C-JNJ717. Volume-of-distribution images were calculated using Logan plots and analyzed on a volume-of-interest basis. Results: Autoradiography showed no difference in 11C-JNJ717 binding but did show increased 18F-DPA714 binding in the motor cortex correlating with Iba1 expression (glial cells). Similar findings were observed in vivo, with a 13% increase in 18F-DPA714 binding in the motor cortex. Conclusion: In symptomatic ALS patients, 18F-DPA714 showed increased signal whereas 11C-JNJ717 was not elevated

Multicenter validation of [18F]-FDG PET and support-vector machine discriminant analysis in automatically classifying patients with amyotrophic lateral sclerosis versus controls.

OBJECTIVE: 18F-Fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) single-center studies using support vector machine (SVM) approach to differentiate amyotrophic lateral sclerosis (ALS) from controls have shown high overall accuracy on an individual patient basis using local a priori defined classifiers. The aim of the study was to validate the SVM accuracy on a multicentric level. METHODS: A previously defined Belgian (BE) group of 175 ALS patients (61.9 ± 12.2 years, 120M/55F) and 20 screened healthy controls (62.4 ± 6.4 years, 12M/8F) was used to classify another large dataset from Italy (IT), consisting of 195 patients (63.2 ± 11.6 years, 117M/78F) and 40 controls (62 ± 14.4 years; 29M/11F) free of any neurological and psychiatric disorder who underwent whole-body 18F-FDG PET-CT for lung cancer without any evidence of paraneoplastic symptoms. 18F-FDG within-center group comparisons based on statistical parametric mapping (SPM) were performed and SVM classifiers based on the local training sets were applied to differentiate ALS from controls from the other centers. RESULTS: SPM group analysis showed only minor differences between both ALS groups, indicating pattern consistency. SVM using BE data set as training, classified 183/193 ALS-IT correctly (accuracy of 94.8%). However, 35/40 CON-IT were misclassified as ALS (accuracy 12.5%). Furthermore, using IT data as training, ALS-BE could not be distinguished from CON-BE. Within-center SPM group analysis confirmed prefrontal hypometabolism in CON-IT versus CON-BE, indicating subclinical brain changes in patients undergoing oncological scanning. CONCLUSION: This multicenter study confirms that the 18F-FDG ALS pattern is stable across centers. Furthermore, it highlights the importance of carefully selected controls, as subclinical frontal changes might be present in patients in an oncological setting.status: publishe

TSPO Versus P2X7 as a Target for Neuroinflammation: An In Vitro and In Vivo Study

Neuroinflammation is important in amyotrophic lateral sclerosis (ALS). The P2X7 receptor (P2X7R) is a promising target for neuroinflammation. The objective of this study was to compare 18F-DPA714, a second-generation translocator protein tracer, with 11C-JNJ717, a novel P2X7R tracer, in vitro and in vivo in ALS. Methods: For the in vitro portion of the study, autoradiography with 18F-DPA714 and 11C-JNJ717 was performed on human ALS brain sections in comparison to immunofluorescence with Iba1 and GFAP. For the in vivo portion, 3 male patients with early-stage ALS (59.3 ± 7.2 y old) and 6 healthy volunteers (48.2 ± 16.5 y old, 2 men and 4 women) underwent dynamic PET/MR scanning with 18F-DPA714 and 11C-JNJ717. Volume-of-distribution images were calculated using Logan plots and analyzed on a volume-of-interest basis. Results: Autoradiography showed no difference in 11C-JNJ717 binding but did show increased 18F-DPA714 binding in the motor cortex correlating with Iba1 expression (glial cells). Similar findings were observed in vivo, with a 13% increase in 18F-DPA714 binding in the motor cortex. Conclusion: In symptomatic ALS patients, 18F-DPA714 showed increased signal whereas 11C-JNJ717 was not elevated.status: publishe

Derivation of norms for the Dutch version of the Edinburgh cognitive and behavioral ALS screen

Background: The Edinburgh cognitive and behavioral ALS screen (ECAS) was developed specifically to detect cognitive and behavioral changes in patients with amyotrophic lateral sclerosis (ALS). Differences with regard to normative data of different (language) versions of neuropsychological tests such as the ECAS exist. Objective: To derive norms for the Dutch version of the ECAS. Methods: Normative data were derived from a large sample of 690 control subjects and cognitive profiles were compared between a matched sample of 428 patients with ALS and 428 control subjects. Results: Age, level of education, and sex were significantly associated with performance on the ECAS in the normative sample. ECAS data were not normally distributed and therefore normative data were expressed as percentile ranks. The comparison of ECAS scores between patients and control subjects demonstrated that patients obtained significantly lower scores for language, executive function, verbal fluency, and memory, which is in line with the established cognitive profile of ALS. Conclusion: For an accurate interpretation of ECAS results, it is important to derive normative data in large samples with nonparametric methods. The present normative data provide healthcare professionals with an accurate estimate of how common or uncommon patients’ ECAS scores are and provide a useful supplement to existing cut-off scores