Brain Imaging And Modeling

我们的实验室研究了在功能神经成像研究中观察到的现象与潜在的神经动力学之间的关系。为了做到这一点，我们使用了大规模的神经元动力学计算机模型，这些模型执行视觉或听觉对象匹配任务，类似于为PET/fMRI/MEG研究设计的任务。对这两种模型的评论可以在Horwitz&Husain(2007)中找到。 我们还开发了功能磁共振成像和脑磁图数据的计算方法，使我们能够研究正常人以及感觉和认知加工障碍患者的大脑功能网络。人们已经认识到，网络分析可能相当复杂，因此，解释这种分析的结果需要非常小心。在过去的一年里，我们发表了几篇文章，试图阐明大脑网络分析的各个方面的复杂性。在Banerjee和Horwitz(2013)中，我们表明网络分析与量子物理中使用的那种概率理论有许多相似之处。在Horwitz等人中。(2013)和Horwitz(正在出版)，我们指出，大规模神经建模可以用来帮助解释正常受试者和大脑疾病患者的大脑网络分析。 此外，我们还进行了神经成像实验，以了解听觉和语言处理的神经基础。在Smith等人中。(2013)，我们提出了一种新的范式，通过纵向fMRI实验来识别非语义抽象视听(AV)记忆与命名之间的共享和独特的脑区。参与者被训练将包含隐藏的语言内容的新的视听刺激对联系起来。其中一半是扭曲的动物图像和它们名字的正弦波语音版本。图像和声音被扭曲，以至于只有在意识到它们的存在后，才能容易地识别它们的语言内容。对配对的记忆测试是通过展示一对AV并要求受试者验证这两个刺激是否形成了习得的配对来进行的。记忆测试后，隐藏的语言内容被揭示出来，参与者再次接受测试，但这一次他们可以通过说出图片的名字来完成任务。我们发现，参与识别非语言感觉记忆的区域有很大的重叠。会话之间的对比发现，左侧角回和颞中回是命名网络中的关键额外参与者。左下额叶区域同时参与了命名和非语言AV记忆，这表明该区域负责独立于语音内容的AV记忆，这与以前的观点相反。命名时，角回与左侧额下回和左额中回之间的功能连接性增强。我们的结果与这样的假设是一致的，即在这个空间分辨率水平上，促进非语言AV联系的区域是那些促进命名的区域的子集，尽管被重组为不同的网络。 我们的实验室还进行了研究，以阐明语音产生的神经基础及其障碍。去年，我们发表了一篇论文(Simonyan et al.，2013)，通过结合正电子发射断层扫描(PET)和多巴胺D2/D3受体放射性配基(11-C)雷氯普利以及功能和结构连接性分析(纹状体结构连接性是通过扩散张量成像(DTI)轨迹描述术测量的)，研究了在产生有意义的英语句子过程中纹状体内源性多巴胺释放的程度及其对功能纹状体语言网络组织的影响。本文首次证实了正常人正常言语产生过程中纹状体多巴胺能传递，并为人类言语和语言控制大脑半球优势的神经化学基础提供了第一个证据。在本年度，我们发表了一篇评论，强烈支持继续使用PET来研究正常的大脑功能(Horwitz和Simonyan，2014)。 我们还研究了大脑如何处理复杂的声音，特别是和声。许多语音和动物发声包含由基频(F0)和高次谐波组成的分量。在这项研究(Kikuchi等人，2014)中，我们研究了两只恒河猴在听纯音和音调变化的同种发声(COO)时，在听觉皮质的核心(A1)和侧带(Lb)区域记录的单单位活动。后者由复音片段组成，其中F0与相应的纯音刺激相匹配。在这两种动物中，在最佳频率(BF)下对纯音刺激的神经元潜伏期，在Lb比在A1长10到15ms。这可能是意料之中的，因为Lb被认为处于比A1更高的层次级别。另一方面，LB对COOS的反应潜伏期比对应的纯音BF反应的潜伏期短10~20ms，这表明谐波对LB有促进作用。在A1中没有观察到COO的这种延迟缩短，导致A1和LB中的COO延迟相似。A1区和Lb区均存在多峰神经元，其间隔分布在理想的五分之一(间隔比为3：2)时达到峰值。然而，在A1中，这些峰值只出现在较少的数量和仅在较晚的反应期，而在Lb中，与和声相关的间隔(完美的五度和八度)通常出现在较早和较晚的反应期。我们的结果表明，谐波特征，如通信呼叫的特定频率间隔之间的关系，在听觉皮质通路的相对早期阶段被处理，但在Lb优先处理。

Our laboratory studies the relationship between w hat is observed in functional neuroimaging studies and the underlying neural dynamics. To do this, w e use large-scale computer models of neuronal dynamics that perform either a visual or auditory object- matching task similar to those designed for PET/fMRI/MEG studies. A review of both models can be found in Horwitz & Husain (2007). W e also develop computational methods for fMRI and MEG data that allow us to investigate functional brain networks in normal human subjects and in patients with sensory and cognitive processing disorders. It has been recognized that network analysis can be quite complicated, and as a result, interpreting the results of such analyses requires great care. In the last year we have published several articles that have attempted to elucidate the intricacies of various aspects of brain network analysis. In Banerjee and Horwitz (2013), we showed that network analysis has much in common with the kind of probability theory employed in quantum physics. In Horwitz et al. (2013) and Horwitz (in press), we pointed out that large-scale neural modeling can be utilized to help interpret brain network analysis in normal subjects and in patients with brain disorders. Furthermore, w e perform neuroimaging experiments to understand the neural basis of auditory and language processing. In Smith et al. (2013), we presented a novel paradigm to identify shared and unique brain regions underlying non-semantic abstract audio-visual (AV) memory vs. naming using a longitudinal fMRI experiment. Participants were trained to associate novel AV stimulus pairs containing hidden linguistic content. Half of the pairs were distorted images of animals and sine-wave speech versions of their names. Images and sounds were distorted in such a way as to make their linguistic content easily recognizable only after being made aware of its existence. Memory for the pairings was tested by presenting an AV pair and asking subjects to verify if the two stimuli formed a learned pairing. After memory testing, the hidden linguistic content was revealed, and participants were again tested, but this time they could perform the task by naming the picture. We found substantial overlap of the regions involved in recognition of non-linguistic sensory memory. Contrasts between sessions identified left angular gyrus and middle temporal gyrus as key additional players in the naming network. Left inferior frontal regions participated in both naming and non-linguistic AV memory, suggesting the region is responsible for AV memory independent of phonological content contrary to previous proposals. Functional connectivity between angular gyrus and left inferior frontal gyrus and left middle temporal gyrus increased when performing the AV task as naming. Our results are consistent with the hypothesis that at this level of spatial resolution regions that facilitate non-linguistic AV associations are a subset of those that facilitate naming, although reorganized into distinct networks. Our laboratory also has performed studies to elucidate the neural basis of speech production and its disorders. Last year, we published a paper (Simonyan et al., 2013) that investigated the extent of endogenous dopamine release in the striatum and its influences on the organization of functional striatal speech networks during production of meaningful English sentences using a combination of positron emission tomography (PET) with the dopamine D2/D3 receptor radioligand (11- C)raclopride and fMRI functional and structural connectivity analyses (striatal structural connectivity w as measured using diffusion tensor imaging (DTI) tractography). Our paper presented the first demonstration of striatal dopaminergic transmission during normal speech production in healthy humans and provided the first evidence for the neurochemical underpinnings of hemispheric dominance of human speech and language control. During the current year, we published a commentary that strongly supported the continued use of PET to investigate normal brain functioning (Horwitz and Simonyan, 2014). We also have examined how the brain processes complex sounds, specifically harmonics. Many speech sounds and animal vocalizations contain components consisting of a fundamental frequency (F0) and higher harmonics. In this study (Kikuchi et al., 2014) we examined single-unit activity recorded in the core (A1) and lateral belt (LB) areas of auditory cortex in two rhesus monkeys as they listened to pure tones and pitch-shifted conspecific vocalizations (coos). The latter consisted of complex-tone segments in which F0 was matched to a corresponding pure-tone stimulus. In both animals, neuronal latencies to pure-tone stimuli at the best frequency (BF) were 10 to 15 ms longer in LB than in A1. This might be expected, since LB is considered to be at a hierarchically higher level than A1. On the other hand, the latency of LB responses to coos was 10 to 20 ms shorter than to the corresponding pure-tone BF, suggesting facilitation in LB by the harmonics. This latency reduction by coos was not observed in A1, resulting in similar coo latencies in A1 and LB. Multi-peaked neurons were present in both A1 and LB, and their interval distributions in both areas peaked at the perfect fifth (interval ratio of 3:2). In A1, however, these peaks appeared only in modest numbers and only during a late response period, whereas in LB harmonically-related intervals (perfect fifth and octave) were commonly present during both early and late response periods. Our results suggest that harmonic features, such as relationships between specific frequency intervals of communication calls, are processed at relatively early stages of the auditory cortical pathway, but preferentially in LB.