Advanced MRI, LFMI, NINDS, National Institutes of Health, Bethesda, MD, USA.
Advanced MRI, LFMI, NINDS, National Institutes of Health, Bethesda, MD, USA.
Neuroimage. 2017 Nov 15;162:45-55. doi: 10.1016/j.neuroimage.2017.08.053. Epub 2017 Aug 24.
Studies involving multivariate pattern analysis (MVPA) of BOLD fMRI data generally attribute the success of the information-theoretic approach to BOLD signal contrast on the fine spatial scale of millimeters facilitating the classification or decoding of perceptual stimuli. However, to date MVPA studies that have actually explored fMRI resolutions at less than 2 mm voxel size are rare and limited to small sets of unnatural stimuli (like visual gratings) as well as specific sub-regions of the brain, notably the primary somatosensory cortices. To investigate what spatial scale best supports high information extraction under more general conditions this study combined naturalistic movie stimuli with high-resolution fMRI at 7 T and linear discriminant analysis (LDA) of global and local BOLD signal patterns. Contrary to predictions, LDA and similar classifiers reached a maximum in classification accuracy (CA) at a smoothed resolution close to 3 mm, well above the 1.2 mm voxel size of the fMRI acquisition. Maximal CAs around 90% were contingent upon global fMRI signal patterns comprising 4 k-16 k of the most reactive voxels distributed sparsely throughout the occipital and ventro-temporal cortices. A Searchlight analysis of local fMRI patterns largely confirmed the global results, but also revealed a small subset of brain regions in early visual cortex showing limited increases in CA with higher resolution. Principal component analysis of the global and local fMRI signal patterns suggested that reproducible neuronal contributions were spatially auto-correlated and smooth, while other components of higher spatial frequency were likely related to physiological noise and responsible for the reduced CA at higher resolution. Systematic differences between experiments and subjects suggested that higher CA was significantly correlated with more consistent behavior revealed by eye tracking. Thus, the optimal resolution of fMRI data for MVPA was mainly limited by physiological noise of high spatial frequency as well as behavioral (in-)consistency.
涉及 BOLD fMRI 数据的多元模式分析 (MVPA) 的研究通常将信息论方法在毫米级精细空间尺度上对 BOLD 信号对比的成功归因于促进感知刺激分类或解码的能力。然而，迄今为止，实际探索小于 2 毫米体素大小 fMRI 分辨率的 MVPA 研究很少，并且仅限于小的非自然刺激集（如视觉光栅）以及大脑的特定亚区，特别是初级体感皮质。为了研究在更一般的条件下最佳的空间尺度支持高信息提取，本研究结合了自然电影刺激和 7T 高分辨率 fMRI 以及全局和局部 BOLD 信号模式的线性判别分析（LDA）。与预测相反，LDA 和类似的分类器在接近 3 毫米的平滑分辨率处达到分类准确性（CA）的最大值，远高于 fMRI 采集的 1.2 毫米体素大小。最大 CA 约为 90%，取决于包含 4k-16k 最反应性体素的全局 fMRI 信号模式，这些体素稀疏地分布在枕叶和腹侧颞叶皮质中。局部 fMRI 模式的搜索光分析在很大程度上证实了全局结果，但也揭示了早期视觉皮层中一小部分脑区的 CA 随着分辨率的提高而略有增加。全局和局部 fMRI 信号模式的主成分分析表明，可复制的神经元贡献在空间上是自相关且平滑的，而更高空间频率的其他成分可能与生理噪声有关，并且是导致更高分辨率下 CA 降低的原因。实验和个体之间的系统差异表明，更高的 CA 与眼动追踪揭示的更一致的行为显著相关。因此，MVPA 的 fMRI 数据的最佳分辨率主要受到高空间频率生理噪声以及行为（不一致）的限制。
