我们感谢吉姆·哈克斯比和他的同事对提供作进一步分析他们的数据。马克北Coutanche是由来自霍华德休斯医学研究所的奖学金。这项工作是由美国国立卫生研究院资助R0I-DC009209和R01-EY02171701授予沙龙L.汤普森 - 希尔的支持。

1，准备功能磁共振成像数据注：进行的功能磁共振成像扫描后，前处理使用功能磁共振成像大多数软件包开始本协议之前，可用的工具（但应避免或减少维护多体素模式空间平滑）收集的数据。一个合适的数据集的例子中的方法7的先前申请中描述。 <ol><li>除去运动和平均脑白质信号从时间序列预处理的fMRI数据的创建与预测回归模型运动参数（横滚，俯仰，偏航，X，Y，Z）和平均脑白质信号。进行下面对所得的残差（ 即剩余的方差）的分析。 </li><li>导入生成的残差成分析软件包（ 如 MATLAB，Python的）。开源信息化连通性工具箱（http://www.informationalconnectivity.org）可以导入fMRI数据到MATLAB中。 </li><li> Z评分​​EACħ体素的时间序列。 </li><li>独立数据集的时间点到独立集（&#39;折叠&#39;），如不同的扫描仪运行。注意：使用扫描仪的运行，确保褶皱，可以以其他方式难以保证（例如，依赖关系可以运行的时间点之间的前处理过程中产生）之间的独立性。运行可被组合在一起，以减少褶皱的数量（ 例如 ，偶数和奇数奔跑2），虽然采用单次运行将给予更多的训练数据。 </li><li>创建与时间点通过生成的条件标签的向量是N久时间点相关联的条件标签的记录。 </li><li>向前移动的状态标签中的每个通过数次到重复（TRS）相当于5秒运行，为了解释事件和记录的fMRI信号之间的血流动力学滞后。 </li></ol> （2）选择和分析一个地区种子<ol><li>通过隔离解剖选择种子区域人区，在功能上局部区域或表现最出色的“信息脑地形图&#39;射灯10。 注：步骤2.2至4.2以下可以由开源信息化连通MATLAB工具箱进行（ <a href="http://www.informationalconnectivity.org">http://www.informationalconnectivity.org</a> ）。 </li><li>比较每个时间点的最有价值球员为原型的MVP每个条件（注：这是因为在流行的相关性为基础的最近邻分类2用同样的方法）。 图1（上）给出了一个例子从收集真实的数据与会者认为，四种类型的人造物体块。 <ol><li>通过平均所有，但一折的每个条件的时间点计算出一个原型（平均值）最有价值球员为每个条件。这是&#39;训练&#39;数据为每个倍（ 例如 ，5倍2，均值的MVP是由褶皱1，3，4个时间点计算，和5）。 </li><li>每一个相关时间点“;最有价值球员从训练数据中每个条件的均MVP。这将使每一个时间点的每个条件一个相关值（注：这里有最高相关性的条件将是受欢迎的相关基础MVPA分类2的预测） </li><li>费雪变换的r值z分数。 </li></ol></li><li>量化&#39;MVP的辨别力&#39;为每个时间点：首先确定从2.2.3，表示该时间点的MVP和该时间点的状态的均值-MVP之间的关系的相关性，然后减去最高的剩余相关性（ 即 &#39;相关的正确条件&#39;减&#39;有一个不正确的条件“最大相关）。其结果是，时间点的MVP鉴别力。一种替代（和有效的）的方法是减去的不正确的条件下的平均相关性。 注：建议的方法具有直观的优势的GE与正面的鉴别力值时间点的相关​​性为基础的分类是正确的分类，而时间点与负的值不正确预测。所得到的值的一个例子示于图1（下部）。步骤至此为止被捕获在下面的公式。 <img alt="2.3方程" fo:content-width="4.5in" src="/files/ftp_upload/51226/51226_2.3_equations.jpg" /> x是归一化的1×M个行向量，在时间点n×m个像素的激活值，Y是正确的（c）或不正确的（ⅰ）的条件下的平均训练模式的归一化的1×M个行向量时间点 n。该artanh功能应用费希尔Z变换。 </li></ol> 3，计算时间序列的MVP区分度为每个探照灯<ol><li>进行分析探照灯10：将三维C周围依次在每个体素（A&#39;探照灯&#39;）光泽。 </li><li>重复步骤2.2和2.3为每个探照灯，让每一个探照灯具有时间序列的MVP鉴别力值（每个时间点一个）。 </li></ol> 4，计算信息化连接的种子和探照灯之间<ol><li>相关种子的MVP鉴别力的时间序列（2.3）使用Spearman秩相关各探照灯的鉴别力的时间序列（3.2）。由此产生的R S值是种子和探照灯之间的IC。 </li><li>每个探照灯的IC值分配给探照灯的中心像素，并写出所产生的个体的大脑地图。 </li></ol> 5，计算集团统计地图<ol><li>如果数据是不是已经在标准化的空间（ 例如 ，塔莱拉什或MNI），变换参与者的集成电路映射到相同的空间中。 </li><li>可选的平滑和个人＃39;探照灯地图。 </li><li>创建使用单程t-检验对每个探照灯的集成电路值是否显著大于零的一组统计图。 </li></ol> 6，测试的意义注：许多方法来确定的功能磁共振成像组地图统计学意义存在。作为置换检验可以用最少的假设确定的意义，而占平滑（因为每个置换组映射经过相同的处理）的数据集的水平，这个选项概述如下。 <ol><li>对于每1,000排列，随机整个时间序列洗牌种子的MVP，鉴别力值。保持相邻的时间点与时间自相关性（如相同的块内的时间点）在一起（例如，通过混洗块，而不是相邻的TR）。 </li><li>计算个体的集成电路地图的每个置换（上面的步骤4）。 </li><li>产生1,000置换群图：RandomlŸ选择从每个参与者一个置换IC图，并在此随机设置（上面的步骤5）进行一组测试。 </li><li>阈值（ 例如 ，P &lt;0.001），每置换组映射在所需的阈值，并从地图中提取的最大簇大小。 </li><li>排序产生的1,000最大类卷和识别该集群的大小在95 百分位（ 如 50 个最大的1000排列）。 </li><li>为了使群集校正为p &lt;0.05适用于6.4中使用的阈值（ 例如 ，P &lt;0.001）和最小群集大小从6.5到真正的（非重排的）集成电路组的映射。因为每一个置换图绘制在同一MVP的鉴别力值（以不同的顺序），这个地图的意义与突出偶然比预期的更同步鉴别力值区域。 </li></ol>

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The authors declare that they have no competing financial interests.

信息连接有MVPA的灵敏度分布模式信息，并给出了通过连接的方式来研究区域之间的相互作用的能力。 MVPA和标准的单因素分析可以揭示每个不同区域的参与，有时他们的结果1 3之间几乎没有重叠。正如预期的那样进行，吸引了这些分析方法的方法，集成电路和FC也给相辅相成的结果7。是否采用集成电路的决定最终将取决于调查的条件和所提出的理论问题。设计考虑这种影响是否MVPA是对数据集进行的，也会影响芯片是否被使用。研究在设计时考虑到IC明确将要遵循建议的MVPA 1 4，同时确保审判级别的数据可以从整个扫描的时间过程来提取。 当检查和报告集成电路的效果，重要的是，探照灯与种子重叠被去除，以避免圆形度。此外，如果直接比较集成电路和FC的效果，建议也比较基于均值活化探照灯，而不是仅仅一个体素的FC分析。这个附加的分析，可以确保结果之间的差异是因为在信号与噪声在探照灯与体素的水平差异不。 这里所描述的步骤主要侧重于探索性分析采用探照灯。值得注意的是，通过与区域的感兴趣更换探照灯，IC还可以比较选择一个我之前的区域。目前的辨别力指标 - 一个比较MVP的相关性为“真”状态的相关性为最大的交替条件 - 也是可以修改的。许多机器学习分类有预测的权重差异erent类，它可以很容易地更换这里进行（ 例如 ，跟踪一个分类器在一段时间的&#39;信心&#39;）的相关性比较。集成电路具有多种潜在的用途。以及作为一个主要的分析，调查信息网络，集成电路可以进行二次随访分析到MVPA探照灯。 MVPA探照灯地图是为了解哪些区域可以区分不同条件有价值的，但通常不会分解成不同的网络。该IC的方法可以帮助在这里，通过揭示这台探照灯有同步的鉴别力。最后，从不同的任务IC图可以为了了解任务的网络进行比较，患者可与对照组相比，以更好地理解多体素差额1 5是如何体现在网络层面。

这里所描述的分析方法的目的是测量的基础上在其多体素信息的波动的大脑区域之间的连接。在功能磁共振成像（fMRI）分析技术的进展已经表明，大量的信息可以被包含在被分布在多个个体素1-3的血液氧合水平依赖（BOLD）的活动模式。一组技术，是多元信息敏感-被称为多体素模式分析（MVPA） -已被用来证明条件可以有区别的最有价值球员，尽管有区分单变量的反应1,2,4。标准的分析，该比较单变量的响应，可以是不敏感的这种多体素信息。 多个脑区当人类处理刺激和进行认知操作订婚了。功能连接（FC）是常用来调查个方法门这样的功能网络5,6。在其最基本的形式，FC量化共激活，或同步性，不同的体素或区域之间。 FC已经被用于鉴定功能性连接大脑网络取得很大的成功。对于许多地区和条件，但是，单变量的反应并不反映BOLD活动中的所有可用信息。跟踪动态变化的单变量响应级别的FC技术可能缺乏敏感性，在多体素信息，共同波动。这里的分析方法介绍，信息连通性（IC，在最近的一篇论文7首次描述），桥梁MVPA和FC之间的差距，通过测量连接性是跨时多体素敏感信息的度量。虽然FC跟踪动态变化的单因素激活，IC音轨动态变化的MVP鉴别力 - 如何以及一个MVP的真实状况可以从（不正确）的替代品加以区别的措施。重要的是，在T他同样的方式，不同的区域可以显示单变量的反应相似水平的条件，尽管执行不同的计算（ 例如 ，视觉处理或行动计划，当一个人观看人造物体），不同的区域也可以有类似的（和同步）的MVP级别而他们加工条件不同鉴别力。最近的一项调查表明，IC可以揭示区域间的连接是无法检测用标准的FC方法7。因此，研究人员可以使用的IC来探测大脑区域之间的互动为参与者有特征的分布式模式的条件或刺激作出反应。 IC是从最近的几个连接的应用程序，在有关分类结果8,9检验单因素激活的波动显着。不同于这些方法，IC检测区域之间同步多体素模式鉴别力。

<table border="0" cellpadding="0" cellspacing="0" width="585">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">MATLAB software package</td>
			<td style="width:83px;">MathWorks</td>
			<td style="width:119px;"></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">AFNI software package</td>
			<td style="width:83px;">NIMH</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
	</tbody>
</table>

using informational connectivity to measure the synchronous emergence of fmri multi-voxel information across time

人们现在认识到，条件相关的信息可以是分布式功能性磁共振成像（fMRI）大脑活动模式中存在，即使是单变量的激活水平相近的条件。多体素模式（MVP）的分析已被用于这些信息与巨大的成功解码。 FMRI研究人员还经常设法了解大脑区域在互联网络的交互方式，并使用功能连接（FC），以确定有相关的反应随着时间的推移地区。正如单变量分析中可以不敏感的信息的MVP，FC可能无法完全表征大脑网络，处理与特征的MVP签名的条件。这里介绍的方法，信息连通性（IC），可识别区域与MVP-辨别力与时间相关的变化，揭示连接是无法访问的FC。该方法可以是探索性的，用探照灯来确定种子CONNected区，或计划，预先选择的区域 - 的利益之间。该结果可以阐明处理的MVP相关条件的地区网络，可以击穿MVPA探照灯地图为独立的网络，或者可以在任务和患者群体进行比较。

该IC结果现在可以使用研究者的首选功能磁共振成像分析软件包装上显示。 图2显示芯片的结果，从视觉上呈现男人的块计算的人造物体（在相关的出版物7全部细节）。 该IC分析是已知有关联的MVP情况尤为可贵：条件与特点的MVP，但没有在单因素反应的差异，更可能有集成电路和FC之间的区别（说明与被记录为参与者的数据观察不同类型的男人在图3中制造的物品）， 图4示出了与显著多体素信息可具有高的集成电路，但不能很好地在FC结果表示射灯。 <img alt="图1" fo:content-width="5in" src="/files/ftp_upload/51226/51226fig1highres.jpg" wi嗞嗞=“500”/&gt; 。图1图案辨别力随时间的实例上： 从在着Haxby受试者等计算MVP识别性的基片（2001）2，如在Coutanche与汤普森-希尔（2013）分析了7。蓝线表示的时间点“最有价值球员，平均（&#39;培训&#39;）正确的类的模式之间的z得分的相关性。绿线代表了最有价值球员“的相关性有三个不正确的类。底部：模式鉴别力是正确的类和最高不正确的类的相关性之间的差异。时间点与正面图案辨别力的值将通过基于相关的分类器被正确地分类。图最初发表于Coutanche＆汤普森-希尔（2013年）7。 <a href="https://www.jove.com/files/ftp_upload/51226/51226fig1highres.jpg" target="_blank">请点击此处查看该图的放大版本</a>。 <img alt="图2" fo:content-width="5in" src="/files/ftp_upload/51226/51226fig2highres.jpg" width="500" /> 图2实施例的连接图。 每行显示显著连接到一个种子（显示为蓝色）的区域。意义是由一组的t检验（P &lt;0.001）与由置换检测最小的簇大小来确定。该IC结果显示使用AFNI 1 1与FreeSurfer 1 2生产表面地图。图是从Coutanche＆汤普森-希尔（2013年）修改7。 <a href="https://www.jove.com/files/ftp_upload/51226/51226fig2highres.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图3" fo:content-width="5in" src="/files/ftp_upload/51226/51226fig3highres.jpg" width="500" /> 数字3，同步MVP鉴别力相比，意味着激活MVP的鉴别力在两个地区与同步MVP的鉴别力（ 即信息连接）不同步的平均激活（ 即功能连接）的例子;数据来自从哈克斯比等人的主题。（2001）2，如Coutanche＆汤普森希尔（2013年）分析7。这些数据点采集，而受观察的人造物体，它是可区分由多体素模式，但并不意味着响应的可视化演示。 <a href="https://www.jove.com/files/ftp_upload/51226/51226fig3highres.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图4" fo:content-width="5in" src="/files/ftp_upload/51226/51226fig4highres.jpg" width="500" /> 图4例的IC和LEF种子之间的FC值吨梭状回和整个大脑探照灯，信息性 ​​和功能性的连接强度（Z轴）显示种子和探照灯，对于每个探照灯的平均响应（x轴）和MVPA分类精度（y轴）到四之间类型的人造物体（几率= 25％）。探照灯与种子区域共享的体素被拆除。该IC图包括具有较高的分类性能，但较低的平均响应级别，这是不拿起一个典型的FC方式（通过在右图的左上角八分的差距可见）具有较强的连接探照灯的例子。图最初发表于Coutanche＆汤普森-希尔（2013年）7。 <a href="https://www.jove.com/files/ftp_upload/51226/51226fig4highres.jpg" target="_blank">请点击此处查看该图的放大版本。</a>

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Informational connectivity measures the correspondence between time courses of multi-voxel information across different brain regions. Multi-voxel pattern discriminability time series are extracted from regions and compared, revealing networks that are not identified in a typical functional connectivity approach.

利用信息化连通性来衡量的功能磁共振成像多体素穿越时空信息的同步崛起

It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain ...

using-informational-connectivity-to-measure-synchronous-emergence

Research

JoVE Journal

Neuroscience

12.2K 次观看.  University of Pennsylvania. Informational connectivity measures the correspondence between time courses of multi-voxel information across different brain regions. Multi-voxel pattern discriminability time series are extracted from regions and compared, revealing networks that are not identified in a typical functional connectivity approach.

视频: Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Combining Transcranial Magnetic Stimulation and fMRI to Examine the Default Mode Network

The default mode network is a group of brain regions that are active when an individual is not focused on the outside world and the brain is at "wakeful rest."1,2,3 It is thought the default mode network corresponds to self-referential or "internal mentation".2,3
It has been hypothesized that, in humans, activity within the default mode network is correlated with certain pathologies (for instance, hyper-activation has been linked to schizophrenia 4,5,6 and autism spectrum disorders 7 whilst hypo-activation of the network has been linked to Alzheimer's and other neurodegenerative diseases 8). As such, noninvasive modulation of this network may represent a potential therapeutic intervention for a number of neurological and psychiatric pathologies linked to abnormal network activation. One possible tool to effect this modulation is Transcranial Magnetic Stimulation: a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate cortical excitability (either increasing or decreasing it) via the application of localized magnetic field pulses.9
In order to explore the default mode network's propensity towards and tolerance of modulation, we will be combining TMS (to the left inferior parietal lobe) with functional magnetic resonance imaging (fMRI). Through this article, we will examine the protocol and considerations necessary to successfully combine these two neuroscientific tools.

In this article, we examine the methodology and considerations relevant to the combination of TMS and fMRI to examine the effects of brain stimulation on the default network.

结合经颅磁刺激和功能磁共振成像检查默认模式网络

The default mode network is a group of brain regions that are active when an individual is not focused on the outside world and the brain is at "wakeful ...

科研

神经科学

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

The aim of this methods paper is to describe how to implement a neuroimaging technique to examine complementary brain processes engaged by two similar tasks. Participants' behavior during task performance in an fMRI scanner can then be correlated to the brain activity using the blood-oxygen-level-dependent signal. We measure behavior to be able to sort correct trials, where the subject performed the task correctly and then be able to examine the brain signals related to correct performance. Conversely, if subjects do not perform the task correctly, and these trials are included in the same analysis with the correct trials we would introduce trials that were not only for correct performance. Thus, in many cases these errors can be used themselves to then correlate brain activity to them. We describe two complementary tasks that are used in our lab to examine the brain during suppression of an automatic responses: the stroop1 and anti-saccade tasks. The emotional stroop paradigm instructs participants to either report the superimposed emotional 'word' across the affective faces or the facial 'expressions' of the face stimuli1,2. When the word and the facial expression refer to different emotions, a conflict between what must be said and what is automatically read occurs. The participant has to resolve the conflict between two simultaneously competing processes of word reading and facial expression. Our urge to read out a word leads to strong 'stimulus-response (SR)' associations; hence inhibiting these strong SR's is difficult and participants are prone to making errors. Overcoming this conflict and directing attention away from the face or the word requires the subject to inhibit bottom up processes which typically directs attention to the more salient stimulus. Similarly, in the anti-saccade task3,4,5,6, where an instruction cue is used to direct only attention to a peripheral stimulus location but then the eye movement is made to the mirror opposite position. Yet again we measure behavior by recording the eye movements of participants which allows for the sorting of the behavioral responses into correct and error trials7 which then can be correlated to brain activity. Neuroimaging now allows researchers to measure different behaviors of correct and error trials that are indicative of different cognitive processes and pinpoint the different neural networks involved.

The goal of our research is to correlate behavior to brain activity. Accurate behavioral measures and imaging techniques allow us to elucidate brain-behavior relationships.

关联从人类前额叶皮层的fMRI信号的行为反应：检查的认知过程，使用任务分析

The aim of this methods paper is to describe how to implement a neuroimaging technique to examine complementary brain processes engaged by two similar ...

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

The study of complex computational systems is facilitated by network maps, such as circuit diagrams. Such mapping is particularly informative when studying the brain, as the functional role that a brain area fulfills may be largely defined by its connections to other brain areas. In this report, we describe a novel, non-invasive approach for relating brain structure and function using magnetic resonance imaging (MRI). This approach, a combination of structural imaging of long-range fiber connections and functional imaging data, is illustrated in two distinct cognitive domains, visual attention and face perception. Structural imaging is performed with diffusion-weighted imaging (DWI) and fiber tractography, which track the diffusion of water molecules along white-matter fiber tracts in the brain (Figure 1). By visualizing these fiber tracts, we are able to investigate the long-range connective architecture of the brain. The results compare favorably with one of the most widely-used techniques in DWI, diffusion tensor imaging (DTI). DTI is unable to resolve complex configurations of fiber tracts, limiting its utility for constructing detailed, anatomically-informed models of brain function. In contrast, our analyses reproduce known neuroanatomy with precision and accuracy. This advantage is partly due to data acquisition procedures: while many DTI protocols measure diffusion in a small number of directions (e.g., 6 or 12), we employ a diffusion spectrum imaging (DSI)1, 2 protocol which assesses diffusion in 257 directions and at a range of magnetic gradient strengths. Moreover, DSI data allow us to use more sophisticated methods for reconstructing acquired data. In two experiments (visual attention and face perception), tractography reveals that co-active areas of the human brain are anatomically connected, supporting extant hypotheses that they form functional networks. DWI allows us to create a "circuit diagram" and reproduce it on an individual-subject basis, for the purpose of monitoring task-relevant brain activity in networks of interest.

We describe a novel approach for simultaneous analysis of brain function and structure using magnetic resonance imaging (MRI). We assess brain structure with high-resolution diffusion-weighted imaging and white-matter fiber tractography. Unlike standard structural MRI, these techniques allow us to directly relate anatomical connectivity to functional properties of brain networks.

使用功能磁共振成像和弥散加权成像脑结构与功能分析

The study of complex computational systems is facilitated by network maps, such as circuit diagrams. Such mapping is particularly informative when ...

A Simple Way to Measure Alterations in Reward-seeking Behavior Using Drosophila melanogaster

We describe a protocol for measuring ethanol self-administration in fruit flies (Drosophila melanogaster) as a proxy for changes in reward states. We demonstrate a simple way to tap into the fly reward system, modify experiences related to natural reward, and use voluntary ethanol consumption as a measure for changes in reward states. The approach serves as a relevant tool to study the neurons and genes that play a role in experience-mediated changes of internal state. The protocol is composed of two discrete parts: exposing the flies to rewarding and nonrewarding experiences, and assaying voluntary ethanol consumption as a measure of the motivation to obtain a drug reward. The two parts can be used independently to induce the modulation of experience as an initial step for further downstream assays or as an independent two-choice feeding assay, respectively. The protocol does not require a complicated setup and can therefore be applied in any laboratory with basic fly culture tools.

A Simple Way to Measure Alterations in Reward-seeking Behavior Using Drosophila melanogaster

We describe a protocol for inducing rewarding and nonrewarding experiences in fruit flies (Drosophila melanogaster) using voluntary ethanol consumption as a measure for changes in reward states.

一个简单的方法来衡量在改建悬赏寻求行为使用<em&gt;果蝇</em

We describe a protocol for measuring ethanol self-administration in fruit flies (Drosophila melanogaster) as a proxy for changes in reward states. We ...

Analyzing Dendritic Morphology in Columns and Layers

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and columns to facilitate brain wiring during development and information processing in developed animals. Postsynaptic neurons elaborate dendrites in type-specific patterns in specific layers to synapse with appropriate presynaptic terminals. The fly medulla neuropil is composed of 10 layers and about 750 columns; each column is innervated by dendrites of over 38 types of medulla neurons, which match with the axonal terminals of some 7 types of afferents in a type-specific fashion. This report details the procedures to image and analyze dendrites of medulla neurons. The workflow includes three sections: (i) the dual-view imaging section combines two confocal image stacks collected at orthogonal orientations into a high-resolution 3D image of dendrites; (ii) the dendrite tracing and registration section traces dendritic arbors in 3D and registers dendritic traces to the reference column array; (iii) the dendritic analysis section analyzes dendritic patterns with respect to columns and layers, including layer-specific termination and planar projection direction of dendritic arbors, and derives estimates of dendritic branching and termination frequencies. The protocols utilize custom plugins built on the open-source MIPAV (Medical Imaging Processing, Analysis, and Visualization) platform and custom toolboxes in the matrix laboratory language. Together, these protocols provide a complete workflow to analyze the dendritic routing of Drosophila medulla neurons in layers and columns, to identify cell types, and to determine defects in mutants.

Here, we show how to analyze dendritic routing of Drosophila medulla neurons in columns and layers. The workflow includes a dual-view imaging technique to improve the image quality and computational tools for tracing, registering dendritic arbors to the reference column array and for analyzing the dendritic structures in 3D space.

分析树突状形态的列和图层

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and ...

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Neurologic disorders are characterized by abnormal cellular-, molecular-, and circuit-level functions in the brain. New methods to induce and control neuroplastic processes and correct abnormal function, or even shift functions from damaged tissue to physiologically healthy brain regions, hold the potential to dramatically improve overall health. Of the current neuroplastic interventions in development, neurofeedback training (NFT) from functional Magnetic Resonance Imaging (fMRI) has the advantages of being completely non-invasive, non-pharmacologic, and spatially localized to target brain regions, as well as having no known side effects. Furthermore, NFT techniques, initially developed using fMRI, can often be translated to exercises that can be performed outside of the scanner without the aid of medical professionals or sophisticated medical equipment. In fMRI NFT, the fMRI signal is measured from specific regions of the brain, processed, and presented to the participant in real-time. Through training, self-directed mental processing techniques, that regulate this signal and its underlying neurophysiologic correlates, are developed. FMRI NFT has been used to train volitional control over a wide range of brain regions with implications for several different cognitive, behavioral, and motor systems. Additionally, fMRI NFT has shown promise in a broad range of applications such as the treatment of neurologic disorders and the augmentation of baseline human performance. In this article, we present an fMRI NFT protocol developed at our institution for modulation of both healthy and abnormal brain function, as well as examples of using the method to target both cognitive and auditory regions of the brain.

The ability to induce and/or control neural plasticity may be critical in future treatments for neurologic disorders and the recovery from brain injury. In this paper, we present a protocol on the use of neurofeedback training with functional magnetic resonance imaging to modulate human brain function.

实时 fMRI 神经训练管理协议

Neurologic disorders are characterized by abnormal cellular-, molecular-, and circuit-level functions in the brain. New methods to induce and control ...

Assay to Measure Nucleocytoplasmic Transport in Real Time within Motor Neuron-like NSC-34 Cells

Nucleocytoplasmic transport refers to the import and export of large molecules from the cell nucleus. Recently, a number of studies have shown a connection between amyotrophic lateral sclerosis (ALS) and impairments in the nucleocytoplasmic pathway. ALS is a neurodegenerative disease affecting the motor neurons and resulting in paralysis and ultimately in death, within 2-5 years on average. Most cases of ALS are sporadic, lacking any apparent genetic linkage, but 10% are inherited in a dominant manner. Recently, hexanucleotide repeat expansions (HREs) in the chromosome 9 open reading frame 72 (C9orf72) gene were identified as a genetic cause of ALS and frontotemporal dementia (FTD). Importantly, different groups have recently proposed that these mutants affect nucleocytoplasmic transport. These studies have mostly shown the final outcome and manifestations caused by HREs on nucleocytoplasmic transport, but they do not demonstrate nuclear transport dysfunction in real time. As a result, only severe nucleocytoplasmic transport deficiency can be determined, mostly due to high overexpression or exogenous protein insertion. 
This protocol describes a new and very sensitive assay to evaluate and quantify nucleocytoplasmic transport dysfunction in real time. The rate of import of a NLS-NES-GFP protein (shuttle-GFP) can be quantified in real time using fluorescent microscopy. This is performed by using an exportin inhibitor, thus allowing the shuttle GFP only to enter the nucleus. To validate the assay, the C9orf72 HRE translated dipeptide repeats, poly(GR) and poly(PR), which have been previously shown to disrupt nucleocytoplasmic transport, were used. Using the described assay, a 50% decrease in the nuclear import rate was observed compared to the control. Using this system, minute changes in nucleocytoplasmic transport can be examined and the ability of different factors to rescue (even partially) a nucleocytoplasmic transport defect can be determined.

This protocol describes a method to sensitively measure the nucleocytoplasmic transport rate within motor neuron-like NSC-34 cells by quantifying the real-time change in the nuclear import of a NLS-NES-GFP protein.

测定在运动神经元样NSC-34细胞中实时测量核细胞质运输

Nucleocytoplasmic transport refers to the import and export of large molecules from the cell nucleus. Recently, a number of studies have shown a ...

Quadruple Immunostaining of the Olfactory Bulb for Visualization of Olfactory Sensory Axon Molecular Identity Codes

The mouse olfactory system is often used to study mechanisms of neural circuit formation&#12288;because of its simple anatomical structure. An Olfactory Sensory Neuron (OSN) is a bipolar cell with a single dendrite and a single unbranched axon. An OSN expresses only one Olfactory Receptor (OR) gene, OSNs expressing a given type of OR converge their axons to a few sets of invariant glomeruli in the Olfactory Bulb (OB). A remarkable feature of OSN projection is that the expressed ORs play instructive roles in axonal projection. ORs regulate the expression of multiple axon-sorting molecules and generate the combinatorial molecular code of axon-sorting molecules at the OSN axon termini. Thus, to understand the molecular mechanisms of OR-specific axon guidance mechanisms, it is vital to characterize their expression profiles at the OSN axon termini within the same glomerulus. The aim of this article was to introduce methods for collecting as many glomeruli as possible on a single OB section and for performing immunostaining using multiple antibodies. This would allow the comparison and analysis of the expression patterns of axon-sorting molecules without staining variation between OB sections.

Olfactory sensory neurons express a wide variety of axon-sorting molecules to establish proper neural circuitry. This protocol describes an immunohistochemical staining method to visualize combinatorial expressions of axon-sorting molecules at the axon termini of olfactory sensory neurons.

嗅球的四次免疫染色用于嗅觉感觉轴突分子识别码的可视化

The mouse olfactory system is often used to study mechanisms of neural circuit formation&#12288;because of its simple anatomical structure. An Olfactory ...

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive neuroscience. Intracranial electroencephalography (EEG) can directly record electrical neural activity and has the unique potential to accomplish this goal. Traditionally, averaging analysis has been applied to analyze intracranial EEG data; however, several new techniques are available for depicting neural activity and intra- and inter-regional connectivity. Here, we introduce two analytical protocols we recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software: time-frequency SPM analysis for neural activity and dynamic causal modeling of induced responses for intra- and inter-regional connectivity. We report our analysis of intracranial EEG data during the observation of faces as representative results. The results revealed that the inferior occipital gyrus (IOG) showed gamma-band activity at very early stages (110 ms) in response to faces, and both the IOG and amygdala showed rapid intra- and inter-regional connectivity using various types of oscillations. These analytical protocols have the potential to identify the neural mechanisms underlying cognitive functions with high spatial and temporal profiles.

We present two analytical protocols that can be used to analyze intracranial electroencephalography data using the Statistical Parametric Mapping (SPM) software: time-frequency statistical parametric mapping analysis for neural activity, and dynamic causal modeling of induced responses for intra- and inter-regional connectivity.

利用 SPM 软件分析颅内脑电图数据的神经活动和连接性

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive ...

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Non-invasive electrophysiological recordings are useful for the evaluation of nervous system function. These techniques are inexpensive, fast, replicable, and less resource-intensive than imaging. Further, the functional data produced have excellent temporal resolution, which is not achievable with structural imaging.
Current applications of electroencephalograms (EEG) are limited by data processing methods. Standard analysis techniques using raw time series data at individual channels are very limited methods of interrogating nervous system activity. More detailed information about cortical function can be achieved by examining relationships between channels and deriving statistical models of how areas are interacting, allowing visualization of connectivity between networks.
This manuscript describes a method for deriving statistical models of cortical network activity by recording EEG in a standard manner, then examining the interelectrode coherence measures to assess relationships between the recorded areas. Higher order interactions can be further examined by assessing the covariance between the coherence pairs, producing high-dimensional &#34;maps&#34; of network interactions. These data constructs can be examined to assess cortical network function and its relationship to pathology in ways not achievable with traditional techniques.
This approach offers greater sensitivity to network level interactions than is achievable with raw time series analysis. It is, however, limited by the complexity of drawing specific mechanistic conclusions about the underlying neural populations and the high volumes of data generated, requiring more advanced statistical techniques for evaluation, including dimensionality reduction and classifier-based approaches.

Standard EEG analysis techniques offer limited insight into nervous system function. Deriving statistical models of cortical connectivity offers far greater ability to investigate underlying network dynamics. Improved functional assessment opens new possibilities for diagnosis, prognostication, and outcome prediction in nervous system diseases.

使用非侵入性脑电图的皮质连通性统计建模

Non-invasive electrophysiological recordings are useful for the evaluation of nervous system function. These techniques are inexpensive, fast, replicable, ...