Brain 18FDG-PET pattern in patients with alcohol-related cognitive impairment

Clergue‐Duval, Virgile; Questel, Frank; Azuar, Julien; Paquet, Claire; Cognat, Emmanuel; Amami, Jihed; Queneau, Mathieu; Dereux, Alexandra; Barré, Thomas; Bellivier, Frank; Farid, Karim; Vorspan, Florence

doi:10.1007/s00259-019-04487-1

Cited by 17 publications

(8 citation statements)

References 45 publications

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“…Specifically, we selected the coordinates of two peak voxels (Table 2 and Figure 3), one with the lowest negative weight located in the ipsilateral inferior temporal region and the other with the highest positive weight located in the contralateral middle frontal region. The center of pons was used as a reference (Clergue‐Duval et al., 2020; Marcoux et al., 2018). Subsequently, we built three spherical regions of interest (ROIs) centered at these coordinates with 6‐mm radius.…”

Section: Resultsmentioning

confidence: 99%

Characterizing the hyper‐ and hypometabolism in temporal lobe epilepsy using multivariate machine learning

Yang

Gong

et al. 2021

J of Neuroscience Research

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Mesial temporal lobe epilepsy (MTLE) is the most common type of focal epilepsy, presenting both structural and metabolic abnormalities in the ipsilateral mesial temporal lobe. While it has been demonstrated that the metabolic abnormalities in MTLE actually extend beyond the epileptogenic zone, how such multidimensional information is associated with the diagnosis of MTLE remains to be tested. Here, we explore the whole‐brain metabolic patterns in 23 patients with MTLE and 24 healthy controls using [18F]fluorodeoxyglucose PET imaging. Based on a multivariate machine learning approach, we demonstrate that the brain metabolic patterns can discriminate patients with MTLE from controls with a superior accuracy (>95%). Importantly, voxels showing the most extreme contributing weights to the classification (i.e., the most important regional predictors) distribute across both hemispheres, involving both ipsilateral negative weights over the anterior part of lateral and medial temporal lobe, posterior insula, and lateral orbital frontal gyrus, and contralateral positive weights over the anterior frontal lobe, temporal lobe, and lingual gyrus. Through region‐of‐interest analyses, we verify that in patients with MTLE, the negatively weighted regions are hypometabolic, and the positively weighted regions are hypermetabolic, compared to controls. Interestingly, despite that both hypo‐ and hypermetabolism have mutually contributed to our model, they may reflect different pathological and/or compensative responses. For instance, patients with earlier age at epilepsy onset present greater hypometabolism in the ipsilateral inferior temporal gyrus, while we find no evidence of such association with hypermetabolism. In summary, quantitative models utilizing multidimensional brain metabolic information may provide additional assistance to presurgical workups in TLE.

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Section: Resultsmentioning

confidence: 99%

Characterizing the hyper‐ and hypometabolism in temporal lobe epilepsy using multivariate machine learning

Yang

Gong

et al. 2021

J of Neuroscience Research

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“…In 9 of the 17 (52.9%) patients (19)(20)(21)(22)(23)(24)(25)(26)(27), HA were in the cortical frontotemporal and frontoparietal areas and in 4 of 9 cases also in the limbic regions (19-21, 26) with the highest z score (Table 3). Case 24 is illustrated in Figure 2.…”

Section: Group Bmentioning

confidence: 99%

“…All patients underwent 18 F-FDG PET about 3 months from neurological examination: 12-18 months (group A) or 24-36 months (group B) from symptoms onset. After a 6-h fast (4 h in case of diabetes mellitus type 2), they were intravenously injected with 370 MBq of 18 F-FDG 45 min before images acquisition, while the patients rested with eyes closed and without sensorial stimuli [19].…”

Section: Introductionmentioning

confidence: 99%

“…Table 1 reports demographic, clinical, MMSE, and MRI data of all patients.All patients underwent 18 F-FDG PET about 3 months from neurological examination: 12-18 months (group A) or 24-36 months (group B) from symptoms onset. After a 6-h fast (4 h in case of diabetes mellitus type 2), they were intravenously injected with 370 MBq of 18 F-FDG 45 min before images acquisition, while the patients rested with eyes closed and without sensorial stimuli [19].Images were acquired for 15 min in a single bed position (PET/CT GE Discovery tomograph). CT parameters were 120 kVs, 45 effective mAs, and 1 rotation.…”

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confidence: 99%

“…The regions of interest were prefrontal lateral, prefrontal medial, sensorimotor, anterior cingulate, posterior cingulate, precuneus, parietal superior, parietal inferior, occipital lateral, primary visual, temporal lateral, temporal mesial, cerebellum whole, and pons (referred area). For each patient, the highest negative z score value achieved in each area was considered [19,20].…”

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confidence: 99%

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Qualitative and Quantitative Analyses of Brain <sup>18</sup>Fluoro-Deoxy-Glucose Positron Emission Tomography in Primary Progressive Aphasia

Nuvoli

Tanda

Stazza

et al. 2019

Dement Geriatr Cogn Disord

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KeywordsPrimary progressive aphasia · Dementia · Brain 18 fluoro-deoxy-glucose positron emission neuroimaging Abstract Background: A primary progressive aphasia (PPA) diagnosis is generally based on clinical criteria, but often symptoms and signs may overlap in the different forms. Recent data have evidenced that brain 18 fluoro-deoxy-glucose positron emission tomography ( 18 F-FDG PET) could support the clinical diagnosis, since specific metabolic patterns are described for the different variants. Aims: We further evaluated the usefulness of 18 F-FDG PET, by both visual qualitative (QL) and quantitative (QN) methods in the initial diagnosis of PPA, focusing on the classification of different variants. Moreover, we also analyzed the role of 18 F-FDG PET in clarifying the association of PPA with the early phase of Alzheimer's disease (AD) or frontotemporal (FTD) dementias. Methods: We consecutively enrolled 35 patients with clinical symptoms of aphasia, suspect of or attributable to PPA. Patients were classified into two groups: 18 cases with clinical symptoms of aphasia but normal neuropsychological tests and an unclear classification of a specific PPA variant (group A) and 17 cases with clinical and neuropsychological signs attributable to PPA with an uncertain differential diagnosis between AD and FTD (group B). All patients underwent brain 18 F-FDG PET/CT, and images were evaluated both by QL and QN, the latter applying an automated analysis program that produced brain regional metabolic maps and normal age-matched control group comparative analysis (z score). Results: 18 F-FDG PET showed different patterns of bilateral cortical hypometabo-lism in the two groups. The combined use of QL and QN permitted to achieved a correct PPA variant diagnosis in 8 of 18 (44.4%) cases of group A and in 14 of 17 (82.3%) of group B, while only QN could support the correct classification of PPA variants in 10 of 18 (55.6%) cases of group A and in 3 of 17 (17.7%) cases of group B in whom the procedure better localized the hypometabolic areas. Conclusions: Brain 18 F-FDG PET had an elevated performance in the early diagnosis of PPA variants and in the advanced PPA AD/FTD classification. QL clarified the development of AD or FTD in advanced PPA cases and supported the differential diagnosis of a PPA variant in a few early cases. QN 18 F-FDG PET evaluation better contributed to the early diagnosis of an unclear metabolic pattern. To correctly identify all cases, patients with diffuse cortical hypometabolism were also included. Larger series are necessary to confirm these data.A total of 35 consecutive patients (15 males, 20 females, aged 53-80 years, mean ± SD value: 69 ± 6.0) with clinical symptoms of aphasia, suspect of or attributable to PPA, were enrolled retrospectively. All patients were investigated for neurological familiar diseases, neurological non-PPA affections, and generally related diseases (hypertension and diabetes mellitus). Laboratory analyses excluded secondary cognitive disorders.Neurological examinations, basal...

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Combined 18F‐FDG PET‐CT markers in dementia with Lewy bodies

Mattoli,

Cocciolillo,

Chiacchiaretta

et al. 2023

Alz & Dem Diag Ass & Dis Mo

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INTRODUCTION18F‐Fluoro‐deoxyglucose–positron emission tomography (FDG‐PET) is a supportive biomarker in dementia with Lewy bodies (DLB) diagnosis and its advanced analysis methods, including radiomics and machine learning (ML), were developed recently. The aim of this study was to evaluate the FDG‐PET diagnostic performance in predicting a DLB versus Alzheimer's disease (AD) diagnosis.METHODSFDG‐PET scans were visually and semi‐quantitatively analyzed in 61 patients. Radiomics and ML analyses were performed, building five ML models: (1) clinical features; (2) visual and semi‐quantitative PET features; (3) radiomic features; (4) all PET features; and (5) overall features.RESULTSAt follow‐up, 34 patients had DLB and 27 had AD. At visual analysis, DLB PET signs were significantly more frequent in DLB, having the highest diagnostic accuracy (86.9%). At semi‐quantitative analysis, the right precuneus, superior parietal, lateral occipital, and primary visual cortices showed significantly reduced uptake in DLB. The ML model 2 had the highest diagnostic accuracy (84.3%).DISCUSSIONFDG‐PET is a valuable tool in DLB diagnosis, having visual and semi‐quantitative analyses with the highest diagnostic accuracy at ML analyses.

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Brain 18FDG-PET pattern in patients with alcohol-related cognitive impairment

Cited by 17 publications

References 45 publications

Characterizing the hyper‐ and hypometabolism in temporal lobe epilepsy using multivariate machine learning

Characterizing the hyper‐ and hypometabolism in temporal lobe epilepsy using multivariate machine learning

Qualitative and Quantitative Analyses of Brain <sup>18</sup>Fluoro-Deoxy-Glucose Positron Emission Tomography in Primary Progressive Aphasia

Combined 18F‐FDG PET‐CT markers in dementia with Lewy bodies

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