THE SPECTRAL SIGNATURE METHOD FOR BRAIN PATTERN ANALYSIS

该研究项目的总体目标是开发和 验证一种基于计算机的方法，有助于分析 产生的三维人脑图像中出现的功能模式 通过PET或SPECT摄像机。这种方法与健康问题有关， (1)脑功能障碍的模式发生在精神 疾病;（2）与药物引起的代谢活动模式有关， 药物滥用或对大脑解剖区域的其他刺激;（3）检测 两组之间的模式差异;（4）检测一个 异质性组有助于单个 个脑袋因此，大多数方法都依赖于先验的定义， 感兴趣的解剖区域（ROI），以减少功能性 信息进行分析，但在此过程中失去了功能模式 跨越整个大脑或落在所选ROI之外。在整个 多年来，科学家们一直在稳步增加预定义的投资回报率的数量 直到覆盖整个大脑然而，由于人数不多， PET研究中的受试者和较大的受试者间变异性， 对调查结果的重要性的统计限制开始发挥作用。 我们所寻求的客观方法与现有方法相比有几个优点。 (1)它将被设计为从每个图像中提取突出的几何信息， 整个三维PET图像，例如大脑的几何质心， 大脑的主要几何轴，提供了一个参考框架， 对PET摄像机内受试者头部的定位不敏感 不需要额外的X射线或MRI图像;（2）它将使用这个 参考框架来分析整体所包含的信息 功能图像，而不仅仅是包含在 先验选择的感兴趣的解剖区域;（3）它将使用描述性的 - 增强其信噪比的功能图像的特征， 对于确定性、随机噪声和光子散射，即，部分 容积效应，从PET或SPECT获得的典型功能图像 相机;因此，可以用较小的 （4）能够映射检测到的功能 模式到大脑图谱或MRI扫描，从而选择解剖模式 感兴趣的区域后验。该方法将通过以下两项进行验证： 幻象大脑和PET图像组，包括休息， 运动激活和视觉激活的正常人。它将被测试， 包括正常人和精神分裂症患者的PET图像组。

The overall objective of this research project is the development and validation of a computer-based method that helps in the analysis of functional patterns occurring in 3-dimensional human brain images produced by PET or SPECT cameras. This method is related to health problems since it (1) characterizes patterns of brain dysfunction occurring in mental illness; (2) relates patterns of metabolic activity caused by medication, drug abuse or other stimuli to anatomical areas of the brain; (3) detects pattern differences between two groups; (4) detects subgroups within a heterogeneous group helping in the clinical classification of a single brain. Heretofore, most methods have relied on a priori definitions of anatomical Regions of Interest (ROI) to reduce the amount of functional information to be analyzed, but in the process lose the functional patterns that span the whole brain or fall outside the selected ROI. Throughout the years scientists have steadily increased the number of predefined ROI's until they cover the whole brain. However, due to the small number of subjects in a PET study and the large inter- subject variability, statistical limitations on the significance of the findings come into play. The objective method we seek has several advantages over existing methods. (1) It will be designed to extract salient geometric information from each whole 3-dimensional PET image, such as the brain's geometric centroid and the brain's principal geometric axis, providing a reference frame which is insensitive to the positioning of the subject's head within the PET camera without requiring additional X-ray or MRI images; (2) it will use this reference frame to analyze the information contained in the whole functional image rather than just the functional information contained in a priori selected anatomical regions of interest; (3) it will use descriptive features of the functional image which enhance its signal to noise ratio, both for deterministic, random noise and photon scatter, i.e., partial volume effect, typical of functional images obtained from PET or SPECT cameras; thus, a good intersubject averaging can be obtained with a smaller number of subjects; (4) it will be able to map the detected functional pattern to a Brain Atlas or MRI scan thus selecting a pattern of anatomical regions of interest a posteriori. The method will be validated both with phantom brains and with groups of PET images including resting, motor-activated and optically activated normals. It will be tested with groups of PET images including normals and schizophrenics.