Metabolic patterns in brain 18F-fluorodeoxyglucose PET relate to aetiology in paediatric dystonia.
Metabolic patterns in brain 18F-fluorodeoxyglucose PET relate to aetiology in paediatric dystonia.
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DOI：
10.1093/brain/awac439
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发表时间：
2023-06-01
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缺乏揭示小儿肌张力障碍不同脑区功能特征的影像学标志物。在这项观察性研究中，我们通过揭示不同儿童肌张力障碍亚组中特定的静息清醒脑葡萄糖代谢模式，评估了 [18F]2-氟-2-脱氧-D-葡萄糖 (FDG)-PET 在理解肌张力障碍病理生理学方面的效用。研究人员对 2007 年 9 月至 2018 年 2 月期间在英国埃维琳娜伦敦儿童医院 (ELCH) 进行的 267 名肌张力障碍儿童的 PET 扫描进行了评估，以确定是否需要接受深部脑刺激手术。使用统计参数映射 (SPM12) 分析无总体解剖异常（例如大囊肿、显着脑室扩大；n = 240）的扫描结果。对 144/240 (60%) 10 种最常见的儿童期肌张力障碍病例的葡萄糖代谢模式进行了检查，重点关注 9 个解剖区域。使用一组 39 名成人对照进行比较。遗传性肌张力障碍与以下基因相关：TOR1A、THAP1、SGCE、KMT2B、HPRT1（Lesch Nyhan 病）、PANK2 和 GCDH（1 型戊二酸尿症）。获得性脑瘫（CP）病例分为早产相关病例（CP-Preterm）、新生儿黄疸/核黄疸（CP-Kernicterus）和缺氧缺血性脑病（CP-Term）。每个肌张力障碍亚组都有不同的 FDG-PET 摄取改变模式。苍白球、壳核或两者的局灶性葡萄糖代谢低下是最常见的发现，但 PANK2 除外，其中基底神经节代谢似乎正常。 HPRT1 独特地显示出所有九个大脑区域的葡萄糖代谢低下。 KMT2B、HPRT1 和 CP-Kernicterus 中发现颞叶葡萄糖代谢低下。 SGCE、HPRT1 和 PANK2 中发现额叶代谢减退。丘脑和脑干代谢低下仅见于 HPRT1、CP 早产和 CP 足月肌张力障碍病例。额叶和顶叶代谢亢进的结合在 CP 足月病例中是独一无二的。 PANK2病例表现出壁叶代谢亢进和小脑代谢低下的独特组合，但壳核-苍白球葡萄糖代谢完整。 HPRT1、PANK2、CP-核黄疸和CP-早产病例有小脑和岛叶葡萄糖代谢低下以及壁叶葡萄糖代谢亢进。该研究结果提供了对肌张力障碍病理生理学的见解，并支持肌张力障碍发病机制的网络理论。每个肌张力障碍亚组的“特征”模式可能是有用的生物标志物，可指导鉴别诊断并为个性化管理策略提供信息。萨格卡里斯等人。报告称，具有不同病因的小儿肌张力障碍患者的亚组表现出不同的 18F-FDG-PET 代谢模式，这可能与每组的临床症状相关。这些特征模式可能是有用的成像生物标志物，可指导鉴别诊断和个性化管理。
There is a lack of imaging markers revealing the functional characteristics of different brain regions in paediatric dystonia. In this observational study, we assessed the utility of [18F]2-fluoro-2-deoxy-D-glucose (FDG)-PET in understanding dystonia pathophysiology by revealing specific resting awake brain glucose metabolism patterns in different childhood dystonia subgroups. PET scans from 267 children with dystonia being evaluated for possible deep brain stimulation surgery between September 2007 and February 2018 at Evelina London Children’s Hospital (ELCH), UK, were examined. Scans without gross anatomical abnormality (e.g. large cysts, significant ventriculomegaly; n = 240) were analysed with Statistical Parametric Mapping (SPM12). Glucose metabolism patterns were examined in the 144/240 (60%) cases with the 10 commonest childhood-onset dystonias, focusing on nine anatomical regions. A group of 39 adult controls was used for comparisons. The genetic dystonias were associated with the following genes: TOR1A, THAP1, SGCE, KMT2B, HPRT1 (Lesch Nyhan disease), PANK2 and GCDH (Glutaric Aciduria type 1). The acquired cerebral palsy (CP) cases were divided into those related to prematurity (CP-Preterm), neonatal jaundice/kernicterus (CP-Kernicterus) and hypoxic-ischaemic encephalopathy (CP-Term). Each dystonia subgroup had distinct patterns of altered FDG-PET uptake. Focal glucose hypometabolism of the pallidi, putamina or both, was the commonest finding, except in PANK2, where basal ganglia metabolism appeared normal. HPRT1 uniquely showed glucose hypometabolism across all nine cerebral regions. Temporal lobe glucose hypometabolism was found in KMT2B, HPRT1 and CP-Kernicterus. Frontal lobe hypometabolism was found in SGCE, HPRT1 and PANK2. Thalamic and brainstem hypometabolism were seen only in HPRT1, CP-Preterm and CP-term dystonia cases. The combination of frontal and parietal lobe hypermetabolism was uniquely found in CP-term cases. PANK2 cases showed a distinct combination of parietal hypermetabolism with cerebellar hypometabolism but intact putaminal-pallidal glucose metabolism. HPRT1, PANK2, CP-kernicterus and CP-preterm cases had cerebellar and insula glucose hypometabolism as well as parietal glucose hypermetabolism. The study findings offer insights into the pathophysiology of dystonia and support the network theory for dystonia pathogenesis. ‘Signature’ patterns for each dystonia subgroup could be a useful biomarker to guide differential diagnosis and inform personalized management strategies. Tsagkaris et al. report that subgroups of patients with paediatric dystonia with different aetiologies show distinct patterns of 18F-FDG-PET metabolism that can be linked to the clinical signs in each group. These signature patterns could be useful imaging biomarkers to guide differential diagnosis and personalised management.
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影响因子：
8.6
作者：
Albanese, Alberto;Bhatia, Kailash;Bressman, Susan B.;DeLong, Mahlon R.;Fahn, Stanley;Fung, Victor S. C.;Hallett, Mark;Jankovic, Joseph;Jinnah, Hyder A.;Klein, Christine;Lang, Anthony E.;Mink, Jonathan W.;Teller, Jan K.
通讯作者：
Teller, Jan K.
影响因子：
11
作者：
Dresel, Christian;Li, Yong;Haslinger, Bernhard
通讯作者：
Haslinger, Bernhard
DOI：
10.1007/s00259-021-05603-w
发表时间：
2022-01
影响因子：
9.1
作者：
Guedj E;Varrone A;Boellaard R;Albert NL;Barthel H;van Berckel B;Brendel M;Cecchin D;Ekmekcioglu O;Garibotto V;Lammertsma AA;Law I;Peñuelas I;Semah F;Traub-Weidinger T;van de Giessen E;Van Weehaeghe D;Morbelli S
通讯作者：
Morbelli S
影响因子：
9.9
作者：
BURKE, RE;FAHN, S;FRIEDMAN, J
通讯作者：
FRIEDMAN, J
影响因子：
14.5
作者：
Carbon, Maren;Argyelan, Miklos;Eidelberg, David
通讯作者：
Eidelberg, David