Xie Jianwen, Douglas Pamela K, Wu Ying Nian, Brody Arthur L, Anderson Ariana E
Department of Statistics, University of California, Los Angeles, United States.
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, United States.
J Neurosci Methods. 2017 Apr 15;282:81-94. doi: 10.1016/j.jneumeth.2017.03.008. Epub 2017 Mar 18.
Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks.
The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects.
The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (p<0.001) for predicting the task being performed within each scan using artifact-cleaned components. The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy compared to the ICA and sparse coding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p<0.001). Lower classification accuracy occurred when the extracted spatial maps contained more CSF regions (p<0.001).
The success of sparse coding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations.
功能磁共振成像（fMRI）中的脑网络通常使用空间独立成分分析（ICA）来识别，但其他数学约束为生成脑网络提供了替代的生物学上合理的框架。非负矩阵分解（NMF）通过强制正值来抑制负性BOLD信号。空间稀疏编码算法（L1正则化学习和K-SVD）将施加局部专业化并抑制多任务处理，其中单个体素中观察到的总活动源自有限数量的可能脑网络。
比较了编码任务相关脑网络的独立性、正值性和稀疏性假设；将扫描内不同约束下得到的脑网络用作基函数来编码观察到的功能活动。然后使用机器学习对这些编码进行解码，通过时间序列权重预测在51名受试者的304次fMRI扫描中，受试者在扫描内是正在观看视频、听音频提示还是处于休息状态。
使用经过伪影清理的成分，L1正则化学习的稀疏编码算法在预测每次扫描内执行的任务方面优于ICA的4种变体（p<0.001）。与ICA和稀疏编码算法相比，抑制负性BOLD信号的NMF算法准确性最差。在提取算法的效果保持不变的情况下，使用更稀疏的空间网络（包含更多零值体素）进行编码具有更高的分类准确率（p<0.001）。当提取的空间图包含更多脑脊液区域时，分类准确率较低（p<0.001）。
稀疏编码算法的成功表明，与允许如ICA那样无穷无尽的局部过程的算法相比，强制稀疏性、抑制多任务处理并促进局部专业化的算法可能更好地捕捉潜在的源过程。负性BOLD信号可能捕捉到与任务相关的激活。
