A defining but elusive feature of the human brain is its astonishing complexity. This complexity arises from the interaction of numerous neuronal circuits that operate over a wide range of temporal and spatial scales, enabling the brain to adapt to the constantly changing environment and to perform various amazing mental functions. In mentally ill patients, such adaptability is often impaired, leading to either ordered or random patterns of behavior. Quantification and classification of these abnormal human behaviors exhibited during mental illness is one of the major challenges of contemporary psychiatric medicine. In the past few decades, attempts have been made to apply concepts adopted from complexity science to better understand complex human behavior. We propose that the complexity of mental illness can be studied under a general framework by quantifying the order and randomness of dynamic macroscopic human behavior and microscopic neuronal activity. Additionally, substantial effort is required to identify the link between macroscopic behaviors and microscopic changes in the neuronal dynamics within the brain.
1. Yang AC , Tsai SJ*. Complexity of mental illness: A new research dimension. Progress in Neuro- Psychopharmacology & Biological Psychiatry 45:251-2 (2013). [ PDF ]
2. Yang AC *, Tsai SJ. Is mental illness complex? From behavior to brain. Progress in Neuro-Psychopharmacology & Biological Psychiatry 45:253 -7 (2013 ). [ Abstract ]
What is complexity?
Generally, complexity refers to a system with multiple components that are intricately entwined together, such as the subway network of the New York City. In the analogy of human physiology, such complexity can be viewed as numerous body components interacting at levels ranging from molecules, cells, to organs. Conventionally, scientists employ a reductionist approach to disassemble the system into constituent pieces, examine each component, and, finally, reassemble them, recreating the original entity. However, this approach is often unrealistic. In most circumstances, we can only observe the macroscopic output of physiological functions, such as an EEG, heart rate, and respiration, and mental functions, such as cognition, mood, and behavior. Even using the most sophisticated imaging techniques that employ functional magnetic resonance imaging (fMRI), a change in the intensity of a single brain voxel still represents compound responses from millions of neurons.
Therefore, a reasonable method for measuring complexity is to observe a system's behavior in temporal time scales. A system may behave in either an ordered or random manner. However, the physical meaning of the randomness does not equal that of the complexity ( Goldberger et al ., 2002). The measurement of complexity should also incorporate the amount of information conveyed in the system. Differences in the physical meaning of randomness and complexity can be illustrated intuitively using texts as an example.
A monkey typing produces random and incomprehensible text (A), whereas the unfortunate writer in the psychological horror movie The Shining (1980) repeatedly typed the sentence “all work and no play makes Jack a dull boy” on reams of paper (C). The monkey typing represents a random process, and the unfortunate writer display compulsive and ordered behavior. Unlike Shakespeare's famous quote (B), both random and ordered conditions barely convey information that is rich enough to be “complex.”
A meaningful measure of complexity has been proposed by quantifying entropy over multiple time scales, also known as multiscale entropy ( MSE) (Costa et al., 2002, 2005). The detail of MSE can be found at http://www.physionet .org/physiotools/mse/ .
Multiscale entropy analysis of EEG signals
The original development of MSE was applied mainly to heart rate time series, with the parameters commonly being set as m = 2 and r = 0.15 (Cheng et al., 2009; Costa et al., 2002; Norris et al., 2008a; Yang et al., 2011). However, studies on EEG signals have examined the use of several other parameters, such as m = 1 and r = 0.25 (Escudero et al., 2006), m = 2 and r = 0.15 (Catarino et al., 2011), m = 2 and r = 0.20 (Mizuno et al., 2010; Takahashi et al., 2010; Takahashi et al., 2009), and m = 2 and r = 0.50 (Protzner et al. , 2011). We evaluated the use of MSE in EEG signals obtained from patients with Alzheimer's disease and found the parameters of m = 2 and r = 0.15 is appropriate in calculating MSE for EEG signals.
Yang AC , Wang SJ, Lai KL, Tsai CF, Yang CH, Hwang JP, Lo MT, Huang NE, Peng CK, Fuh JL*. Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients with Alzheimer's disease. Progress in Psychopharmacology & Biological Psychiatry 47:52-61 (2013).
Multiscale entropy analysis of functional MRI BOLD signals
BOLD time series are usually short (100-200 time points), and the coarse-grained procedure in M SE with a large scale factor (over scale 5 ) may result in short data length and subsequently unreliable sample entropy estimation . To ameliorate this issue, we have estimated the appropriate parameters for MSE calculation from relatively short BOLD signals using parameters of m = 1; r = 0.35; and scale factor up to 5 [Yang et al . 2013 ].
For voxelwise analysis, a simplified approach can be taken to average the entropy value across all scale factors, and used this averaged entropy as the overall MSE value for a single BOLD time series. For individual resting fMRI data, MSE of BOLD signal can be computed at voxelwise levels in all cortical and subcortical gray matter voxels to create the whole-brain MSE parametric map for subsequent group analysis. MSE maps are spatially smoothed (FWHM = 8 mm) to minimize the differences in the functional anatomy of the brain across subjects.
1. Yang AC , Huang CC, Yeh HL, Liu ME, Hong CJ, Tu PC, Chen JF, Huang NE, Peng CK, Lin CP*, Tsai SJ*. Complexity of spontaneous BOLD activity in default mode network is correlated to cognitive Function in normal male elderly: a multiscale entropy analysis. Neurobiology of Aging 34(2):428-38 (2013).
2. Yang AC , Huang CC, Liu ME, Liou YJ, Hong CJ, Lo MT, Huang NE, Peng CK, Lin CP, Tsai SJ*. The APOE ε4 allele affects complexity and functional connectivity of resting brain activity in healthy adults. Human Brain Mapping 35(7):3238-48 (2014).
Matlab code for calculating multiscale entropy
Download Matlab code for calculating multiscale entropy in EEG and neuroimaging time series data