Brain Imaging And Modeling

我们的实验室研究了功能性神经影像学研究中观察到的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，执行视觉或听觉对象匹配任务，类似于PET/fMRI/MEG研究设计的任务。Horwitz & Husain（2007）对这两种模型进行了综述。 我们还开发了fMRI和MEG数据的计算方法，使我们能够研究正常人类受试者和感觉和认知加工障碍患者的功能性脑网络。 人们已经认识到，网络分析可以是相当复杂的，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各个方面的复杂性。 在Banerjee和Horwitz（2013）中，我们证明了网络分析与量子物理中使用的概率论有很多共同之处。 在Horwitz et al.（2013）和Horwitz（in press）中，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，我们进行神经影像学实验，以了解听觉和语言处理的神经基础。在Smith et al.（2013）中，我们提出了一种新的范式，使用纵向fMRI实验来识别非语义抽象视听（AV）记忆与命名的共享和独特的大脑区域。参与者接受培训，以关联新的AV刺激对包含隐藏的语言内容。其中一半是动物的扭曲图像和它们名字的正弦波语音版本。 图像和声音被扭曲，使其语言内容只有在意识到其存在后才容易识别。通过呈现AV对并要求受试者验证这两种刺激是否形成了习得的配对来测试配对的记忆。在记忆测试后，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命名图片来完成任务。我们发现参与识别非语言感官记忆的区域有大量重叠。 会议之间的对比确定左角回和颞中回的关键额外的球员在命名网络。左下额叶区域参与命名和非语言AV记忆，这表明该地区是负责AV记忆独立的语音内容，与以前的建议。当执行命名的AV任务时，角回与左额下回和左颞中回之间的功能连接增加。 我们的研究结果是一致的假设，在这个水平的空间分辨率区域，促进非语言AV协会的一个子集，促进命名，虽然重组成不同的网络。 我们的实验室还进行了研究，以阐明言语产生及其障碍的神经基础。 去年，我们发表了一篇论文（Simonyan等人，2013），其使用正电子发射断层扫描（PET）与多巴胺D2/D3受体放射性配体（11- C）雷氯必利和fMRI功能和结构连接性分析的组合，研究了纹状体中内源性多巴胺释放的程度及其对产生有意义的英语句子期间功能性纹状体言语网络组织的影响（使用扩散张量成像（DTI）纤维束成像测量纹状体结构连接性）。我们的论文首次证明了健康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的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，特别是谐波。 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。在这项研究中（菊池等人，2014）我们检查了两只恒河猴在听纯音和音高变化的同种发声（coos）时听觉皮层核心（A1）和侧带（LB）区域记录的单单位活动。后者由复音段组成，其中F0与相应的纯音刺激相匹配。在这两种动物中，神经元的潜伏期，以最佳频率（BF）的纯音刺激LB比A1长10至15毫秒。这是可以预期的，因为LB被认为是在比A1更高的层级上。另一方面，LB响应的潜伏期为10至20毫秒短于相应的纯音BF，这表明在LB的谐波的促进。在A1中没有观察到这种由coos引起的潜伏期缩短，导致A1和LB中类似的coos潜伏期。多峰神经元存在于A1和LB中，并且它们在两个区域中的间隔分布在完美的五分之一处达到峰值（间隔比为3：2）。然而，在A1中，这些峰值仅以适度的数量出现，并且仅出现在后期响应期间，而在LB中，谐波相关的间隔（完美的五度和八度）通常出现在早期和后期响应期间。我们的研究结果表明，谐波的功能，如通信呼叫的特定频率间隔之间的关系，处理在相对早期的听觉皮层通路，但优先在LB。

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.