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首页> 外文期刊>IEEE Transactions on Medical Imaging >Nonparametric Extraction of Transient Changes in Neurotransmitter Concentration From Dynamic PET Data
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Nonparametric Extraction of Transient Changes in Neurotransmitter Concentration From Dynamic PET Data

机译：从动态PET数据中非递质提取神经递质浓度的瞬时变化

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摘要

We have developed a nonparametric approach to the analysis of dynamic positron emission tomography (PET) data for extracting temporal characteristics of the change in endogenous neurotransmitter concentration in the brain. An algebraic method based on singular value decomposition (SVD) was applied to simulated data under both rest (neurotransmitter at baseline) and activated (transient neurotransmitter release) conditions. The resulting signals are related to the integral of the change in free neurotransmitter concentration in the tissue. Therefore, a specially designed minimum mean-square error (MMSE) filter must be applied to the signals to recover the desired temporal pattern of neurotransmitter change. To test the method, we simulated sets of realistic time activity curves representing uptake of [11C]raclopride, a dopamine (DA) receptor antagonist, in brain regions, under baseline and dopamine-release conditions. Our tests considered two scenarios: 1) a spatially homogeneous pattern with all voxels in the activated state presenting an identical DA signal; 2) a spatially heterogeneous pattern in which different DA signals were contained in different families of voxels. In the first case, we demonstrated that the timing of a single DA peak can be accurately identified to within 1 min and that two distinct neurotransmitter peaks can be distinguished. In the second case, separate peaks of activation separated by as little as 5 min can be distinguished. A decrease in blood flow during activation could not account for our findings. We applied the method to human PET data acquired with [11C]raclopride in the presence of transiently elevated DA due to intravenous (IV) alcohol. Our results for an area of the nucleus accumbens-a region relevant to alcohol consumption-agreed with a model-based method for estimating the DA response. SVD-based analysis of dynamic PET data promises a completely noninvasive and model-independent technique for determining the dynamics of a neur-otransmitter response to cognitive or pharmacological stimuli. Our results indicate that the method is robust enough for application to voxel-by-voxel data
机译：我们已经开发出一种非参数方法来分析动态正电子发射断层扫描（PET）数据，以提取大脑中内源性神经递质浓度变化的时间特征。将基于奇异值分解（SVD）的代数方法应用于静止（基线神经递质）和激活（瞬态神经递质释放）条件下的模拟数据。产生的信号与组织中游离神经递质浓度变化的积分有关。因此，必须对信号应用专门设计的最小均方误差（MMSE）滤波器，以恢复所需的神经递质变化的时间模式。为了测试该方法，我们模拟了在基线和多巴胺释放条件下代表大脑区域中[11C] raclopride，多巴胺（DA）受体拮抗剂摄取的现实时间活动曲线集。我们的测试考虑了两种情况：1）在空间上均质的模式，所有处于激活状态的体素都呈现相同的DA信号； 2）空间异质性模式，其中不同DA信号包含在不同族的体素中。在第一种情况下，我们证明可以在1分钟内准确识别单个DA峰的时间，并且可以区分两个不同的神经递质峰。在第二种情况下，可以区分仅5分钟的激活峰。激活期间血流量的减少不能解释我们的发现。我们在由于静脉内（IV）酒精导致DA短暂升高的情况下，将该方法应用于通过[11C]雷氯必利获得的人类PET数据。我们针对伏隔核区域（与酒精消耗相关的区域）的结果，通过基于模型的方法估计DA响应得到了认可。基于SVD的动态PET数据分析为确定对认知或药理刺激的神经递质反应的动力学提供了一种完全无创且与模型无关的技术。我们的结果表明，该方法足够鲁棒，可应用于逐个体素数据

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