Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, New York, USA.
PLoS One. 2013 May 21;8(5):e63448. doi: 10.1371/journal.pone.0063448. Print 2013.
Complexity in the brain has been well-documented at both neuronal and hemodynamic scales, with increasing evidence supporting its use in sensitively differentiating between mental states and disorders. However, application of complexity measures to fMRI time-series, which are short, sparse, and have low signal/noise, requires careful modality-specific optimization.
HERE WE USE BOTH SIMULATED AND REAL DATA TO ADDRESS TWO FUNDAMENTAL ISSUES: choice of algorithm and degree/type of signal processing. Methods were evaluated with regard to resilience to acquisition artifacts common to fMRI as well as detection sensitivity. Detection sensitivity was quantified in terms of grey-white matter contrast and overlap with activation. We additionally investigated the variation of complexity with activation and emotional content, optimal task length, and the degree to which results scaled with scanner using the same paradigm with two 3T magnets made by different manufacturers. Methods for evaluating complexity were: power spectrum, structure function, wavelet decomposition, second derivative, rescaled range, Higuchi's estimate of fractal dimension, aggregated variance, and detrended fluctuation analysis. To permit direct comparison across methods, all results were normalized to Hurst exponents.
Power-spectrum, Higuchi's fractal dimension, and generalized Hurst exponent based estimates were most successful by all criteria; the poorest-performing measures were wavelet, detrended fluctuation analysis, aggregated variance, and rescaled range.
Functional MRI data have artifacts that interact with complexity calculations in nontrivially distinct ways compared to other physiological data (such as EKG, EEG) for which these measures are typically used. Our results clearly demonstrate that decisions regarding choice of algorithm, signal processing, time-series length, and scanner have a significant impact on the reliability and sensitivity of complexity estimates.
大脑的复杂性在神经元和血液动力学层面都得到了充分的证明，越来越多的证据支持将其用于敏感地区分心理状态和障碍。然而，将复杂性测量应用于 fMRI 时间序列，这些时间序列短、稀疏且信号/噪声比低，需要对特定模式进行仔细的优化。
在这里，我们使用模拟和真实数据来解决两个基本问题：算法的选择和信号处理的程度/类型。评估方法是针对与 fMRI 常见的采集伪影的弹性以及检测灵敏度。检测灵敏度是根据灰质-白质对比度和与激活的重叠来量化的。我们还研究了复杂性与激活和情绪内容的变化、最佳任务长度以及使用相同范式和两个由不同制造商制造的 3T 磁铁的结果与扫描仪的比例。评估复杂性的方法有：功率谱、结构函数、小波分解、二阶导数、重标极差、Higuchi 分形维数估计、聚合方差和去趋势波动分析。为了允许跨方法直接比较，所有结果都归一化为 Hurst 指数。
功率谱、Higuchi 分形维数和广义 Hurst 指数的估计是所有标准中最成功的；表现最差的方法是小波、去趋势波动分析、聚合方差和重标极差。
与通常用于心电图、脑电图等其他生理数据的方法相比，功能磁共振成像数据具有与复杂性计算以非平凡方式相互作用的伪影。我们的结果清楚地表明，关于算法选择、信号处理、时间序列长度和扫描仪的决策对复杂性估计的可靠性和灵敏度有重大影响。
