Noise correlations in the human brain and their impact on pattern classification.
Noise correlations in the human brain and their impact on pattern classification.
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DOI：
10.1371/journal.pcbi.1005674
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发表时间：
2017-08
影响因子：
4.3
通讯作者：
Turk-Browne NB
中科院分区：
文献类型：
--
作者：
Bejjanki VR;da Silveira RA;Cohen JD;Turk-Browne NB
多体素模式分析（multivoxel pattern analysis， MVPA）等多变量解码方法在从脑成像数据中提取信息方面非常有效。然而，MVPA利用的信息的确切性质仍然存在争议。目前的大多数理论都强调通过在具有混合和弱选择性的体素之间聚合来增强灵敏度。然而，除了单个体素的选择性之外，神经变异性在体素之间是相关的，这种噪声相关性可能对准确解码有重要贡献。事实上，最近的一项计算理论提出，噪声相关性增强了异质神经群体的多元解码。在这里，我们将这一理论从神经元的尺度扩展到功能磁共振成像（fMRI），并表明异质体素群体（即对不同刺激变量有选择性的体素）之间的噪声相关性有助于MVPA的成功。具体来说，当在分类器训练期间选择具有高噪声相关性和低噪声相关性的体素（在休息期间或在任务背景中测量）时，解码性能得到增强。相反，在GLM中对一个类别具有强选择性的体素或在MVPA中获得高分类权重的体素往往与对另一个类别具有选择性的体素表现出高噪声相关性。此外，我们使用模拟来表明这是fMRI数据的一般特性，并且选择性和噪声相关性可以对解码产生可区分的影响。综上所述，我们的研究结果表明，如果数据中存在信号，则由此产生的高于机会的分类精度由噪声相关性的大小调制。认知神经科学的一个核心挑战是从大脑活动模式中解码心理表征。借助功能磁共振成像（fMRI），多体素模式分析（MVPA）等多变量解码方法已经产生了许多关于大脑的发现。然而，这些方法所利用的信息仍然是争论的主题。通常，每个体素被认为通过其选择性来提供信息（即，它对被解码的类的反应有多不同），具有提高的灵敏度，反映了跨体素的选择性聚合。我们表明，这种解释低估了一个重要因素：MVPA也高度适应具有相反选择性的体素之间的噪声相关性。通过对fMRI数据集的多次分析，我们证明了噪声相关性的大小与多变量解码性能之间的正相关关系。事实上，在一个类别中更具选择性的体素，或者在MVPA中权重较大的体素，往往与相反类别的体素具有更强的相关性。此外，使用一个模型来模拟不同水平的选择性和噪声相关性，我们发现噪声相关性对解码的好处是fMRI数据的一般特性。这些发现有助于阐明认知神经科学中多元解码的计算基础，并提供对神经表征本质的洞察。
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. A central challenge in cognitive neuroscience is decoding mental representations from patterns of brain activity. With functional magnetic resonance imaging (fMRI), multivariate decoding methods like multivoxel pattern analysis (MVPA) have produced numerous discoveries about the brain. However, what information these methods draw upon remains the subject of debate. Typically, each voxel is thought to contribute information through its selectivity (i.e., how differently it responds to the classes being decoded), with improved sensitivity reflecting the aggregation of selectivity across voxels. We show that this interpretation downplays an important factor: MVPA is also highly attuned to noise correlations between voxels with opposite selectivity. Across several analyses of an fMRI dataset, we demonstrate a positive relationship between the magnitude of noise correlations and multivariate decoding performance. Indeed, voxels more selective for one class, or heavily weighted in MVPA, tend to be more strongly correlated with voxels selective for the opposite class. Furthermore, using a model to simulate different levels of selectivity and noise correlations, we find that the benefit of noise correlations for decoding is a general property of fMRI data. These findings help elucidate the computational underpinnings of multivariate decoding in cognitive neuroscience and provide insight into the nature of neural representations.
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影响因子：
64.8
作者：
Gutnisky, Diego A.;Dragoi, Valentin
通讯作者：
Dragoi, Valentin
影响因子：
5.7
作者：
Brants, Marijke;Baeck, Annelies;Op de Beeck, Hans P.
通讯作者：
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DOI：
10.1073/pnas.1008950108
发表时间：
2011-01-04
影响因子：
11.1
作者：
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通讯作者：
Gabrieli, John D. E.
影响因子：
25
作者：
deBettencourt MT;Cohen JD;Lee RF;Norman KA;Turk-Browne NB
通讯作者：
Turk-Browne NB
影响因子：
25
作者：
Kamitani, Y;Tong, F
通讯作者：
Tong, F