Noise correlations in the human brain and their impact on pattern classification

Multivariate decoding methods, such as multivoxel pattern analysis (MVPA), are highly effective at extracting information from brain imaging data. Yet, the precise nature of the information that MVPA draws upon remains controversial. Most current theories emphasize the enhanced sensitivity imparted by aggregating across voxels that have mixed and weak selectivity. However, beyond the selectivity of individual voxels, neural variability is correlated across voxels, and such noise correlations may contribute importantly to accurate decoding. Indeed, a recent computational theory proposed that noise correlations enhance multivariate decoding from heterogeneous neural populations. Here we extend this theory from the scale of neurons to functional magnetic resonance imaging (fMRI) and show that noise correlations between heterogeneous populations of voxels (i.e., voxels selective for different stimulus variables) contribute to the success of MVPA. Specifically, decoding performance is enhanced when voxels with high vs. low noise correlations (measured during rest or in the background of the task) are selected during classifier training. Conversely, voxels that are strongly selective for one class in a GLM or that receive high classification weights in MVPA tend to exhibit high noise correlations with voxels selective for the other class being discriminated against. Furthermore, we use simulations to show that this is a general property of fMRI data and that selectivity and noise correlations can have distinguishable influences on decoding. Taken together, our findings demonstrate that if there is signal in the data, the resulting above-chance classification accuracy is modulated by the magnitude of noise correlations. fMRI data used to carry out the analyses described in: Vikranth R. Bejjanki, Rava Azeredo da Silveira, Jonathan D. Cohen, & Nicholas B. Turk-Browne, "Noise correlations in the human brain and their impact on pattern classification". Includes data from 17 human participants, acquired with a 3T scanner (Siemens Skyra) using a 16-channel head coil. For each participant, data from two face/scene "localizer" runs, where participants were presented with blocks of face or scene stimuli interleaved with blank periods, and two "rest" runs, where participants passively viewed a central fixation point, is included. Further information is available in the dataset README file.

多变量解码方法，例如多体素模式分析（multivoxel pattern analysis, MVPA），可高效从脑成像数据中提取信息。然而，MVPA所依托的信息的精确本质仍存在争议。当前主流理论多强调，通过整合具有混合且弱选择性的多个体素，可提升解码灵敏度。然而，除单个体素的选择性之外，神经活动的变异性在不同体素间存在相关性，此类噪声相关性（noise correlations）或许对精准解码有着重要贡献。此前已有最新计算理论提出，噪声相关性（noise correlations）可提升异质性神经群体的多变量解码性能。本研究将该理论从神经元尺度拓展至功能磁共振成像（functional magnetic resonance imaging, fMRI）领域，并证实异质性体素群体（即对不同刺激变量具有选择性的体素）间的噪声相关性，是MVPA得以成功的关键因素之一。具体而言，在分类器训练阶段选取噪声相关性较高而非较低的体素（该相关性可在静息状态或任务背景下测得），可提升解码性能。反之，在通用线性模型（general linear model, GLM）中对某一类别具有强选择性，或在MVPA中获得较高分类权重的体素，往往与用于区分的另一类别所对应的选择性体素之间存在较高的噪声相关性。此外，本研究通过模拟实验证实，这一现象是fMRI数据的通用属性，且体素选择性与噪声相关性对解码的影响存在显著差异。综上，本研究结果表明，若数据中存在信号，最终得到的高于随机水平的分类准确率会随噪声相关性的强度发生变化。本研究用于开展上述分析的fMRI数据来自以下文献：Vikranth R. Bejjanki、Rava Azeredo da Silveira、Jonathan D. Cohen 与 Nicholas B. Turk-Browne发表的《人脑噪声相关性及其对模式分类的影响》（"Noise correlations in the human brain and their impact on pattern classification"）。该数据集包含17名人类受试者的数据，扫描使用3T扫描仪（Siemens Skyra）及16通道头部线圈完成。每名受试者均包含两次面孔/场景"localizer"扫描段：任务中受试者会接收交替呈现的面孔、场景刺激块与空白时段；以及两次"rest"扫描段：受试者仅被动注视中央固定点。更多详细信息可参见数据集的README文件。