Classification of autism spectrum disorder based on sample entropy of spontaneous functional near infra-red spectroscopy signal

Journal Pre-proofs Classification of autism spectrum disorder based on sample entropy of spontaneous functional near infra-red spectroscopy signal Lingyu Xu, Qianling Hua, Jie Yu, Jun Li PII: DOI: Reference:

S1388-2457(20)30010-9 https://doi.org/10.1016/j.clinph.2019.12.400 CLINPH 2009088

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Clinical Neurophysiology

Received Date: Revised Date: Accepted Date:

16 July 2019 1 December 2019 15 December 2019

Please cite this article as: Xu, L., Hua, Q., Yu, J., Li, J., Classification of autism spectrum disorder based on sample entropy of spontaneous functional near infra-red spectroscopy signal, Clinical Neurophysiology (2020), doi: https://doi.org/10.1016/j.clinph.2019.12.400

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© 2020 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Classification of autism spectrum disorder based on sample entropy of spontaneous functional near infra-red spectroscopy signal Lingyu Xua,b, Qianling Huaa, Jie Yua,*, Jun Lic,d,* aDepartment

of Computer Engineering and Science, Shanghai University, Shanghai, China bShanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China cSouth China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China dKey Lab for Behavioral Economic Science & Technology, South China Normal University, Guangzhou, China

Highlights 1. The functional near-infrared spectroscopy signal time series from autism spectrum disorder (ASD) was unstable, had low fluctuation, and high self-similarity. 2. In terms of sample entropy, the classification between ASD and typically developing (TD) children reached 97.6% in accuracy. 3. The abnormalities occurred more frequently in inferior frontal gyrus than temporal cortex and in left than right hemisphere.

Abstract Objectives: To assess the possibility of distinguishing autism spectrum disorder (ASD) based on the characteristic of spontaneous hemodynamic fluctuations and to explore the location of abnormality in the brain. Methods: Using the sample entropy (SampEn) of functional near-infrared spectroscopy (fNIRS) from bilateral inferior frontal gyrus (IFG) and temporal cortex (TC) on 25 children with ASD and 22 typical development (TD) children, the pattern of mind-wandering was assessed. With the

* Correspondence to both: Jun Li, South China Normal University, South China Academy of Advanced Optoelectronics, 55 Zhongshan Street, Guangzhou, 510006, Phone: 01186-20-34725186, Email: [email protected] Jie Yu, Shanghai University, Department of Computer Engineering and Science, 99 Shangda Road, Shanghai, 201900, Phone: 01186-18621573069, Email: [email protected] 1

SampEn as feature variables, a machine learning classifier was applied to mark ASD and locate the abnormal area in the brain. Results: The SampEn was generally lower for ASD than TD, indicating the fNIRS series from ASD was unstable, had low fluctuation, and high self-similarity. The classification between ASD and TD could reach 97.6% in accuracy. Conclusions: The SampEn of fNIRS could accurately distinguish ASD. The abnormality in terms of the SampEn occurs more frequently in IFG than TC, and more frequently in the left than in the right hemisphere. Significance: The result in this study may help to understand the cortical mechanism of ASD and provide a fNIRS-based diagnosis for ASD.

Keywords Autism spectrum disorder; fNIRS; Time series; classification; Machine learning; Sample entropy.

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1.Introduction Symptoms of autism spectrum disorder (ASD) include social difficulties, verbal and nonverbal communication difficulties, and stereotypical or repetitive behaviors. The prevalence of ASD is rapidly increasing, with a higher incidence in boys than girls, and it may have a significant impact on a person's life. Therefore, early diagnosis and early intervention are of great significance in improving patients' social interaction and communication. Typically, a psychologist or psychiatrist who specializes in autism spectrum disorder will conduct appropriate tests on children with suspected autism spectrum disorder. They often use interviews, observations and assessments to diagnose ASD and to advise on intervention or treatment. Therefore, doctors only provide recommended treatment or re-observation to children, rather than appropriate medicine. In addition, parents need to take their children to the hospital for face to face diagnosis many times, but the appointment may be difficult and have a long-time interval. Finally, the symptoms the child has shown is not scientific enough for the diagnosis, because every child's symptoms may be different and not inevitable. In other words, it's of medical importance to reduce the lag time for autism spectrum disorder diagnosis. Diagnosing autism spectrum disorder based on a single physiological indicator, such as the pattern of brain activity in the cerebral cortex, can be seen as a great breakthrough. Functional magnetic resonance imaging (fMRI) is mostly being used on the research of autism spectrum disorder. The fMRI combines the advantages of positron emission tomography (PET scans) and magnetic resonance imaging (MRI). By examining the magnetic field changes of blood flow in the brain tissue, it can achieve functional brain imaging and give a more accurate relationship between structure and function (Detre and Wang 2002). Most fMRI studies use the BOLD method to detect active regions in the brain (Lowe et al. 2000; Ogawa et al. 1990). The signals obtained by this method are relatively non-quantitative and have a low time resolution (Liu et al. 2011). In addition, fMRI has disadvantages such as large equipment, high cost, high sensitivity to motion artifact such as that induced by head movement, and is not suitable for functional brain imaging studies on children (especially infants), the elderly and special population. Based on the above problems, we decide to take another brain imaging technology, functional near infrared spectroscopy (fNIRS), for the study of ASD. The principle of fNIRS in brain functional imaging is similar to fMRI, that is, cerebral neural activity will lead to local hemodynamic changes. It uses the difference of light absorption between oxygenated hemoglobin and deoxygenated hemoglobin in brain tissue to detect the hemodynamic activity of cerebral cortex in real time. fNIRS has the advantages of non-invasiveness, good portability, small size, low cost, no mandatory state requirements for research objects and it is suitable for monitoring subjects under natural conditions. The commonly used indicators of fNIRS are concentration change in oxygenated hemoglobin, deoxygenated hemoglobin and total hemoglobin, so the measured signal can be absolute and quantitative, and can be repeated for a long time. Due to these advantages, fNIRS has been successfully applied in studies on neurodevelopment (Doi and Shinohara 2017), perception and cognition (Franceschini et al. 2009; Soltanlou et al. 2018), motion control (Perrey 2014), mental illness (Hargie et al. 2016), neurologic (Sonkaya 2018) medicine. Literature shows that there are some inherent characteristics in fNIRS data collected from ASD (Zhu et al. 2015), so the raw time series of fNIRS data may also contain some discriminative information. Since sample entropy can reveal characteristics in time-series data (Alcaraz and Rieta 2010) and has been successfully applied 3

in the medical field (Fabris et al. 2014). Therefore, sample entropy may be also useful in mining the characteristics hidden in the fNIRS signal for classifying individuals with ASD and healthy controls. We hope to explore the differences in cerebral cortex activity between the autism spectrum disorder group and the healthy control group based on sample entropy, and further clarify whether there is a certain clue in the brain activity of ASD, which may suggest the cause of ASD. Another central topic in autism spectrum disorder research is the location of abnormality in the brain. Courchesne and Pierce (2005) believed that abnormal prefrontal cortex is the core of autism spectrum disorder. They found from both macro and micro levels that overgrown gray and white matter in the frontal lobes affected normal development of the prefrontal lobe and caused abnormal frontal-mediated behavior. Regarding the location of abnormality in the brain, some studies found that individuals with ASD have severe left brain dysfunction. For example, Just et al. (2004) used fMRI to measure the brain activity of some high-functioning autistic patients in the comprehension of sentences, and found that the activation of the autism spectrum disorder group in Wernicke’s (left laterosuperior temporal) area was significantly higher than that in the control group, while the activation in Broca’s (left inferior frontal gyrus) area was significantly lower than that in the control group. Gomot et al. (2002) studied the brain mechanism of automatic detection of auditory frequency changes in 15 children with autism spectrum disorder and 15 healthy children by using scalp potential and scalp current density (SCD) mismatching negative (MMN) mapping. They noticed that in children with autism spectrum disorder, the dynamic changes of different MMN generators shown by SCD occurred earlier in the left hemisphere, highlighting the left frontal cortex dysfunction in children with autism spectrum disorder. On the other hand, some studies have demonstrated that the right hemisphere seems to be more active in autism spectrum disorder than the left hemisphere during auditory stimulation such as listening verbal or non-verbal sounds ( Boddaert and Zilbovicius 2002; Müller et al. 2012). Through fMRI experiments, researchers found that the left parietal lobe region of the control group showed more activation than the right parietal lobe region, while the prefrontal lobe and parietal lobe regions of the autism spectrum disorder group showed more activation on the right side (Koshino et al. 2005). Meanwhile, in the control group, the temporal distribution of activity in the prefrontal cortex was more correlated with the left parietal region, while in the autism spectrum disorder group, the prefrontal cortex was more correlated with the right parietal region. Because many of the core symptoms of autism spectrum disorder involve cortical level dysfunction, it is important to study differences in the functional effects of different parts of the brain. Our research work mainly includes two consecutive steps. First, we evaluate the sample entropy for fNIRS signals including the oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (Hb) collected from ASD patients and typically developing (TD) controls, so as to find out whether there are some internal characteristics in the fNIRS signals for the ASD patients. Second, using a bisecting k-means algorithm with the sample entropy as the characteristic feature, we investigate the discriminative ability between ASD and TD for each of 44 optical channels locating on the bilateral inferior frontal and temporal lobes, and test whether the sample entropy of HbO2 and Hb can be used as a reference measure for clinical diagnosis of autistic patients. By comparing the discriminative ability of each channel, we further determine the cortical regions from which the fNIRS signal contains rich discriminative information, which may help to explain the possible mechanisms of autism spectrum disorder.

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2.Methods In order to explore whether there are some internal characteristics in the fNIRS signals in ASD patients, firstly, we segment the fNIRS data by a sliding window method to extract local features and reconstruct derived data sets. Secondly, the sample entropy of sub-series is obtain from derived data set to compose the target data set. Finally, we apply a machine learning algorithm (clustering method) to analyze the target data set, to estimate the discriminative ability for each optical channel. From this, we further determine the regions of the brain where the collected fNIRS signal contain more abundant discriminative information, thus locating the brain dysfunction associated with ASD. It can be seen that the method includes several key steps: data collection, time series segmentation and reconstruction, the sample entropy of the reconstructed data, and classification based on sample entropy.

2.1. fNIRS acquisition The fNIRS data used in this study contains about 8-min spontaneous hemodynamic fluctuation collected from 25 children with ASD (age 9.3 ± 1.4 years) and 22 age-matched TD children. A commercial continuous-wave fNIRS system (FOIRE-3000, Shimadzu Corporation, Kyoto, Japan) was used in this study. FOIRE-3000 is equipped with diode laser sources working at 780 nm, 805 nm and 830 nm. It utilizes optical fibers for delivering light to and collecting emitted light from the scalp. The source-detector distance was fixed to be 3.0 cm. According to the manual of the system, the probing depth is about 1.5 cm below the scalp, reaching the surface region of the cortex. All subjects with ASD in this study came from a rehabilitation center of children with autism who were diagnosed by experienced clinicians in hospitals according to DSM-IV-TR (American Psychiatry Association, 2000). Before data collection, each subject and their parents were informed and agreed to the protocol. A written consent was obtained from the parents of the children. The study protocol was approved by the Ethical Review Board of South China Normal University. During data collection, the experimental room was kept quiet and dim. The subjects closed their eyes and avoided head movement. 22 channels in each hemisphere were used, covering inferior frontal gyrus (IFG) and temporal cortex (TC). To identify locations of the optical probes over the cortical regions, the international 10-20 system for electroencephalography (EEG) was referenced with a EEG cap, in which F7 (F8) and CP5 (CP6) correspond to the inferior frontal gyrus (IFG) and superior temporal gyrus (STG). The optical probes are shown in the front row of Fig. 1, the red circle represents the light emitter, and the blue circle represents the light receiver. The number in the middle white square means the number of measurement channels. Each channel is composed of a pair of emitter and receiver. In the bottom of Fig. 1, the left shows the locations of the channels on the left hemisphere, while the right shows the locations of channels on the right hemisphere. The fNIRS signal from each channel contain HbO2, Hb, and the total hemoglobin (HbT). Since HbT is the sum of HbO2 and Hb, only two of these three blood oxygenation parameters are independent. Therefore, in this study only HbO2 and Hb are analyzed.

2.2 Time series segmentation and reconstruction 5

2.2.1 Time period selection and segmentation Based on the whole data, we choose the sliding window method (Feng and Jian-Hua 2008) for data preprocessing. The reasons are as follows. Firstly, the medical data we collected are time series, in which there is obvious and non-negligible chronological order, so the segmentation method of time series must be applied to preprocess the original data. Secondly, since point-by-point analysis will lose dynamic features, it has to select a period of time for piecemeal analysis. The total time of experimental data is about 8 minutes (some subjects can't keep still for 8 minutes, so the data length is shorter), and it was recorded every 0.07s, which makes the data length of single attribute reach 5,000-7000 units. Prior knowledge tells us that the characteristics of time series in a certain period change according to certain rules, which are often lost if described by each data point. Last but not least, in the process of experimental data collection, the research object may be affected by some factors and show local similarity characteristics. If the whole time series is considered, these local features will be covered up. But the sliding window can extract these local features. Time period is crucial to the calculation and evaluation of time series complexity (Costa et al. 2003). At the same time, we observe when the time reaches about 1 minute, there will be a small range of local regular fluctuation trend. Hence, we select the sliding window with a length of 1000 units and a step length of 100 units to preprocess the original data. The original data was finely cut into equal length subsequences, and the number of sub-sequences and sample size were both expanded to make the results more credible.

2.2.2 Derived data set By the sliding window processing, we get the derived data set. Since some subjects could not remain stationary for 8 minutes, the data length of the original data was inconsistent (but the data length of attribute HbO2 and attribute Hb was consistent), which were all in the range of 5,0007,000 units. As shown in figure 2, single channel data can be divided into 41-61 sub-sequences by sliding window, and 𝑛𝑖𝑐 represents the total sub-sequences of channel c of research object i. So single attribute data of single channel 𝑈𝑖𝑐 = {𝑥1,𝑥2,…,𝑥𝑘,…,𝑥𝑁} is reconstructed to U𝑖𝑐 = {𝑆1,𝑆2,…,𝑆𝑘,…,𝑆𝑛𝑖𝑐}, where 𝑆𝑘 = {𝑥𝑖,𝑥𝑖 + 1,…,𝑥𝑖 + 1000}.

2.3 Sample entropy acquisition of reconstructed data set Sample entropy has been widely used in evaluating the complexity of physiological time series and diagnosing pathological status. The higher the entropy, the more complex the sequence is. Mathematically, the sample entropy is calculated as follows:  U = {𝑥1,𝑥2,…,𝑥𝑘,…,𝑥𝑁} represents a time series of length N. Consider the m-length vector: 𝑋𝑚(𝑖) = {𝑥𝑖,𝑥𝑖 + 1,…,𝑥𝑖 + 𝑚 ― 1}.  𝐵𝑖 is used to represent the number of vectors 𝑋𝑚(𝑗) within the similar tolerance r of 𝑋𝑚 (𝑖).

6

𝐵𝑖

𝐵𝑚 𝑖 (𝑟) = 𝑁 ― 𝑚 is the probability that any vectors 𝑋𝑚(𝑗) within r of 𝑋𝑚(𝑖). 

Compute self-similar probability of sequence (m vector), remembered as 𝐵𝑚(𝑟): 1

𝑁―𝑚+1 𝑚 𝐵𝑖 (𝑟).

𝐵𝑚(𝑟) = 𝑁 ― 𝑚 + 1∑𝑖 = 1 

When the dimension for m+1, let 𝐵𝑚 + 1(𝑟) represent the self-similar probability of sequence (m+1 vector): 1

𝑁―𝑚+1 𝑚+1 (𝑟). 𝐵𝑖

𝐵𝑚 + 1(𝑟) = 𝑁 ― 𝑚 + 1∑𝑖 = 1 

Define, SampEn(N,m,r) = ― ln

[

].

𝐵𝑚 + 1(𝑟) 𝐵𝑚(𝑟)

In general, previous studies of physiologic time series have used m = 2 and r= 0.1*S~0.25*S, where S is the standard deviation of the subsequence. Based on the above algorithm process, it can be seen that the calculation of sample entropy does not depend on the length of data sequence. Otherwise, the sample entropy is consistent among different parameters (Wu et al. 2014), and is not sensitive to data loss. This makes up for the inconsistency of the original data set and can extract the features of the subsequence itself.

2.4 Analysis and mining based on sample entropy 2.4.1 The complete sample entropy set of single source After calculating the sample entropy of each preprocessed sub-sequence, everyone has two entropy values of each subsequence. Correspondingly, the j-th sequence of person i can be denoted by , where 𝐻𝑏𝑂𝑖𝑗_SampEn is the sample entropy of HbO2 in the jth sub-sequence of person i, and 𝐻𝑏𝑖𝑗_SampEn is the sample entropy of Hb in the j-th sub-sequence of person i. Thus, we get a new 47*44*n 2-dimensional data as 𝑆1.𝑆1 = {𝐴1,𝐴2,…,𝐴𝑖,…,𝐴47}, where 𝐴𝑖 is the 44*n*2-dimensional array of the person 𝑖 (𝑖 ∈ ℕ,𝑖 ∈ [1,47]) and n is the number of subsequences after cutting (the number of sub-sequences of each channel may be different. Since our experiment aims to find out the effects of different channels on the classification of autistic patients and normal people, we reconstruct the data set 𝑆1 into set 𝑆2. 𝑆2 = {𝐵1,𝐵2,…,𝐵𝑗,…,𝐵44}), where 𝐵𝑗 is the 47*n*2-dimesional array of the j-th channel (𝑗 ∈ ℕ,𝑗 ∈ [1,44]), and n is the number of subsequences after cutting (the number of sub-sequences of each channel may be different.)

2.4.2 Normal and patient cohesion control analysis The single-channel data in 𝑆2 are randomly shuffled in an independent order. 60% of the data are selected as training set C, and the remaining 40% selected as test set D. The clustering method is bisecting k-means, and the evaluation standard is average accuracy: 1

𝑇𝑃 + 𝑇𝑁

Accuracyavg = 𝑁 ∗ 𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁

(1) 7

where N is the number of crossover experiments. Table 1 gives the meanings of parameters. Table 1. Practical meanings of TP, FP, FN and TN. Cluster label for TD

Cluster label for ASD

Gound truth for TD

TP

FN

Gound truth for ASD

FP

TN

In order to determine the classification accuracy, we carry out 100, 500 and 1000 crossover experiments respectively, and take the average classification accuracy as the evaluation index. We arrange all channels in descending order according to the average classification accuracy. The current best classification accuracy is used as the threshold value, and channels higher than 90% are selected for further traceability reasoning.

3. Results 3.1 Internal feature signal mining In order to verify the existence of characteristics in fNIRS signals from autistic patients, we calculate the sample entropy pairs of all sub-sequences. In Fig. 3(a) and 3(b), the sample entropy of the third channel in the ASD group was mainly in the interval of (0.00, 1.00], and that in the TD group was mainly in [1.25, 2.00]. However, in the interval (1.00, 1.25), subsequences of ASD group and TD group both have a large proportion. The above phenomenon between ASD and TD is more significant in the Hb sample entropy. According to the experimental results, only considering the sample entropy of HbO2 or Hb is not conducive to effectively distinguish the ASD group from the TD group. In fact, the idea of dimensionality raising (mapping the low-dimensional classification problem to the high-dimensional space) is used widely in machine learning to find a hyperplane in the high-dimensional space which may distinguish data sets more effectively. Furthermore, the effect of cerebral cortex activity depends on not a single factor but multiple factors at the same time. It is not a transient change, but a gradual change in a short time. Hence, we take HbO2 sample entropy and Hb sample entropy together as the influencing factors to transform the one-dimensional problem into a two-dimensional space and find a dividing line to identify these two categories through clustering. In figure 3(c), the scatterings of the TD group are mainly concentrated in the upper right corner of the two-dimensional plane, and the data are mainly distributed on the diagonal and above, with a small part of data intersecting with the ASD group. The scatter of ASD group is mainly concentrated in the left part. It is clear that a dividing line separates most of the data. Similarly, we find that in other channels, the sample entropy of ASD group is generally lower than that of TD group, and it is mainly distributed in the left and middle parts of the two-dimensional plane. This indicates that the sample entropy can indeed capture the signals released by the cerebral 8

cortex of autistic patients: compared with normal people, the cerebral cortex activity of autistic patients may be low and the activity level is poor.

3.2 Cluster for classification Since k-means can identify different phenotypic groups of human diseases, which is increasingly recognized by the medical community, we adopt k-means as the clustering algorithm. We built a k-means network for all respondents in each channel. Figure 4(a) is the HbO2 sample entropy and Hb sample entropy of the sub-sequences of all subjects in channel 1 (25 autistic patients and 22 normal people). Obviously, there is a large difference in sample entropy between the autistic group and the healthy control group (both HbO2 sample entropy and Hb sample entropy). The value of the healthy controls (blue cross) are mainly distributed in (1.25, 2.00], and the value of the autism spectrum disorder group (red dot) distribution in [0.00, 1.25). The dichotomous clustering results of 47 respondents in channel 1 is obtained by the bisecting k-means algorithm (Figure 4(b)). The red triangle represents the ASD group and the blue cross represents the TD group. The red triangles are mainly concentrated in the lower left and middle part of the two-dimensional diagram, while the blue crosses are mainly concentrated in the middle and upper right part of the two-dimensional plane. The clustering results with real label coincidence rate is 88.29%. This shows that using sliding window and bisecting k-means method can effectively identify the channel 1 of people with autism spectrum disorder. In order to further confirm the results and determine the accuracy, we input the data of 25 autistic patients and 22 normal people into the model and conduct cross experiments for 100 times, 500 times and 1000 times respectively. The results are shown in table 2. The channels with more than 97% classification rate are channel 3, 11, 32, 6, 8, 13, among which the best is channel 3, with 97.6% accuracy. To intuitively show the difference in the sample entropy between ASD and TD, the sample entropy distribution for the best classification channel (i.e., channel 3) was calculated and shown in figure 5. The differences between the two groups (ASD and TD) in both HbO2 and Hb can be visually identified. As the number of cross experiments increases, the corresponding channel number with the best classification effect tends to be stable. The channels with accuracy higher than 90% were generally concentrated in the frontal lobe (channels 3, 6, 7, 8, 25, 27, 29, 32, accounting for 72.7%) and the left hemisphere (channels 3, 6, 7, 8, 11, 13, accounting for 54.5%).

Table 2. The average accuracy results of the best 13 channels with 1, 100, 500 and 1000 cross-over experiments.

9

Channel Number of experiment

1

100

500

1000

29 3 32 6 25 8 11 7 13 37 16 27 1

0.998 0.991 0.983 0.971 0.966 0.964 0.955 0.95 0.949 0.944 0.913 0.913 0.906

0.965 0.977 0.974 0.973 0.943 0.972 0.976 0.93 0.973 0.924 0.886 0.943 0.884

0.967 0.976 0.973 0.973 0.945 0.972 0.974 0.93 0.97 0.924 0.888 0.942 0.881

0.968 0.976 0.973 0.973 0.945 0.971 0.975 0.93 0.97 0.923 0.888 0.944 0.883

3.3 Analysis We do not have a priori hypotheses regarding a specific hemisphere, and therefore a single analysis is conducted for all the channels in the left and right brain. Figure 6 shows the distribution of cerebral lobes with bisecting k-means clustering accuracy over 90%. It can be seen that the number of channels in the left hemisphere of the brain is more than that in the right hemisphere, and the number of channels in the frontal lobe region is more than that in the temporal lobe region. From figure 7, we know, the channel 1, 3, 6, 8 are on the underside of the left inferior frontal gyrus of the brain, and channel 7 is located in the upper left inferior frontal gyrus of the brain. At the same time, we can see that the channel 11, 13 and 16 are located in the left temporal lobe of the brain area, channel 25, 27, 29, and 32 in the right inferior frontal gyrus, channel 37 in the right temporal lobe area. It is worth noting that the channels with good classification effect are mainly concentrated in the left brain, inferior frontal gyrus, superior temporal gyrus and middle temporal gyrus. Comparison of left and right cerebral hemisphere functions is given in table 3. Comparison of frontal lobe and temporal lobe functions is given in table 4.

Table 3. Functional comparison of left and right cerebral hemispheres. Left hemisphere

Right hemisphere

10

Function

Language, consciousness, mathematical analysis

Non-verbal information processing, such as music, graphics, space-time concepts

Table 4. Comparison of frontal lobe and temporal lobe functions.

Function

The frontal lobe

The temporal lobe

Motility language (speaking) central, eyeball synergy sports center, writing center and sports center (the first Ⅰ body movement area)

Memory function, auditory center (auditory area), auditory language center (listening area), visual center (visual area) and visual language center (reading area)

In building the classifier, we did not consider the individual variance in the data set. To verify the effect of the individual variance on the classification accuracy and the subsequent influence on locating channels for a better discrimination between ASD and TD, we rebuilt the classifier by separating the test set from the training set population, namely, all segments of a single subject belong either to the test set or to the training set. For example, 60% of the subjects were assigned for the training, and the rest 40% for the test. In this case, the top 5 channels with a better classification accuracy were overlaid with those channels obtained without considering the individual variation. But, the highest accuracy rate achieved was 86.6%, slightly smaller than those values without considering the individual variance. This indicates that the individual variance does affect the classification which should be taken into account for building a more robust classifier. This is a limitation of this study. On the other hand, it may also imply that the characteristics of a individual may keep consistent over the measurement time, suggesting that a short-time measurement might be enough for providing the discriminative features on the prediction of ASD, which could be benefit to the clinic application. Despite some difference in the accuracy, the channels (or the locations of cortex) which can be used for the classification were nearly same for the two types of data processing. Locating optical channels for a better classification between ASD and TD is an aim of this study. fNIRS revealed paradigm-specific activation patterns in terms of HbO2 and Hb, which could provide cortical localization of each function, including language area. This could also facilitate the understanding of language development of ASD and help us advance the understanding of language localization and lateralization (Bayazit 2018). Signs of autism spectrum disorder include problems with language and communication, social skills, repetitive and narrow interests and actions, and signs of mental retardation. These defects involve knowledge, judgment, thinking and other functions related to conscious consciousness, which are mainly controlled by the left cerebral hemisphere, consistent with the experimental results (channels 1, 3, 6, 7, 8, 11, 13 and 16 have good classification effect). Moreover, channel 1, channel 3, channel 6, channel 7, channel 25, channel 27, channel 29 and channel 32 are all located in the frontal lobe of the brain, which is mainly the language center (including speaking and writing). More specifically, these channels are located on the lower side of the brain's inferior frontal gyrus (IFG), which is linked with language production. Medically, most children with autism spectrum disorder have delayed or impaired language 11

development, or develop language regression after normal language development. There is a certain degree of obstacle to their language perception and expression ability (Na and Guang xue 2006). Our results provide further evidence of speech deficits in autism spectrum disorder, too. Channel 6 is located in Broca’s area, which is the motor speech center. Damage to this area can lead to repeated use of limited vocabulary, inability to use complex syntax and morphology, and even little speech or response. As a result, the signals from this region of the brain are more characteristic of the group. Some channels with better classification effect are also distributed in the superior temporal gyrus and middle temporal gyrus. Channel 11 is located in the superior temporal lobe region, while channels 13, 16 and 37 are located in the middle temporal lobe region. The temporal lobe is responsible for language processing such as hearing and information, and the superior temporal gyrus is implicated in the intermediate stage of speech processing. The temporal middle gyrus is associated with the processing of semantic vocabulary (Visser et al. 2012). Part of superior temporal gyrus and posterior middle temporal gyrus, superior marginal gyrus and angular gyrus constitute sensory language center (Wernicke’s area). Damage in Wernicke’s area also leads to difficulty in normal speech communication in autistic patients, which may be accompanied by sensory aphasia and loss of auditory memory. In general, these channels are located in the anterior temporal lobe. The anterior temporal lobe is be of relevance to higher neural activities such as memory, association, and comparison. Functional deficits in this area are also characteristic of people with autism spectrum disorder. Therefore, the new channel with good classification effect in temporal lobe region can also provide new thinking for medical judgment.

4. Discussion In this study, the spontaneous activity of fNIRS time-series was segmented by a sliding window and the sample entropy was calculated for HbO2 and Hb. We observed using a pair of entropy of HbO2 and Hb for each channel could lead to a better discrimination than using a single entropy of HbO2 or Hb. We found that different channels have different discriminative ability between individuals with ASD and TD controls. From this we further identified cortical regions from which the fNIRS signals showed more discriminative ability. In the present study, we have observed that children with ASD show smaller sample entropy of the fNIRS signal than TD children, implying that there exists weaker autocorrelation in the fNIRS signal of ASD, especially from the frontal region in the left hemisphere of the brain. With the bisecting k-means clustering method, the channels locating in the left hemisphere and frontal region of the brain show more discriminative ability. This is consistent with previous conclusions that prefrontal cortex abnormalities are the core of autism spectrum disorder (Courchesne and Pierce 2005). In addition, functional defects in the inferior region of the left inferior frontal gyrus of the brain, may be the reason why the sample entropy of ASD patients is lower than that of normal people. Functional magnetic resonance imaging (fMRI) has been widely used to evaluate the characteristics of the pathological mechanism of core symptoms in ASD patients. However, fMRI has disadvantages such as large equipment, high cost and artifact sensitivity, and is not very suitable for functional brain imaging studies on children (especially infants), the elderly and special population. To address these drawbacks, we use fNIRS to distinguish individuals with or without autism spectrum disorder. This study is one of the few existing studies on sample entropy of autistic patients by fNIRS. And it is also among the first to take sample entropy as the key to distinguish autistic patients from normal counterparts and capture characteristics of autistic patients. 12

Spontaneous mental activity might be one of the important indicators of the difference between autistic patients and normal people. We can measure the order of time series according to sample entropy, so as to evaluate the diversity of mind-wandering. Previous experiments have proved that sample entropy can be used as a basis for distinguishing neurological diseases (Begum et al. 2017). Begum et al. used EEG major signal approximate entropy, sample entropy, recursive quantitative analysis (RQA) and wavelet non-linear characteristics to classify different neurological diseases successfully. Our results suggest that the combined sample entropy of oxygenated hemoglobin and deoxygenated hemoglobin may be a promising measure for revealing the pathophysiological characteristics of individuals with autism spectrum disorder. In this study we achieve a considerable accuracy rate of prediction (the highest is 97.6%) of ASD, higher than the previous fNIRS report (the highest classification accuracy was 92.7%) (Cheng et al. 2019). The combined sample entropy of HbO2 and Hb may be a suitable characteristic feature for distinguishing between the two groups of observed objects. In other words, we can use sample entropy to capture the characteristics that distinguish ASD from normal people. Previous researchers have used other features and other techniques to identify people with autism spectrum disorder from normal people. For example, researchers used fNIRS sequence to observe the functional network efficiency of autistic patients and normal people, and used k-means to detect whether the network efficiency could be used as a clinical diagnosis of ASD (Li and Yu 2016). Another group studied the spontaneous hemodynamic activity of the temporal cortex in children with typical developmental(TD) group and autism spectrum disorder (ASD) by using fNIRS (Un et al. 2016). The fluctuation range of oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb) was large. ASD and TD were differentiated based on the support vector machine (SVM) model including bilateral RSFC, HbO2 and Hb as variables of fluctuating power. The optimal values reached were sensitivity of 81.6%, specificity of 94.6% and PPV of 87.5%. Children with both ASD and attention deficit hyperactivity disorder may have partial functional network centrality (Di Martino et al. 2013), which makes it more difficult to use network characteristics for the diagnosis and differential diagnosis of ASD. The sample entropy, which is not a network feature, can avoid this influence factor, and the clustering effect based on sample entropy is very good. Our clustering results track back to the brain regions associated with ASD, and further emphasize the significance of different channels of left and right brain in revealing the pathophysiology of ASD. We believe that there is more research space in the frontal lobe than in the temporal lobe. The fNIRS monitors the changes in oxygenated hemoglobin and deoxygenated hemoglobin in brain tissue that form a two-dimensional time series for direct detection of hemodynamic activity in the cerebral cortex. We extract the sample entropy characteristics of this sequence and find that the average sample entropy of autistic patients is generally lower than that of normal people, and most of values of sample entropy are less than 1. This indicates that both the time series of autistic patients and normal people are complex and unstable, but the time series of autistic people have higher self-similarity and less fluctuation than that of normal people. ASD is characterized by significant deficits in social behavior, such as lack of eye contact, social or emotional interactions, disorders of nonverbal behaviors, and inability to develop peer relationship. These social cognitive deficits are associated with different brain regions. To assess the cognitive flexibility, electroencephalography recordings were collected from twenty-five highfunctioning ASD children and 25 IQ- and age-matched typically developing (TD) children, in addition, a series of neuropsychological assessments were also carried out (Yeung et al. 13

2016). Experiments results indicated that abnormal activations occurred in multiple cortical regions associated with cognitive flexibility deficits in ASD, particularly in the frontal lobe. This is consistent with our findings regarding the frontal lobe as a diagnostic support for autism spectrum disorder. Besides this, we notice that alteration occurs more frequently in the left hemisphere, e.g., 8 channels in the left vs. 5 channels in the right. This finding provide new evidence for the asymmetry and abnormalities of left-right brain in ASD, which may be the basis for ASD impairment in the social domain. There were still some limitations in the current study. First, in our experiment we only selected the data from frontotemporal lobes, the other cerebral cortices were not studied. Therefore the classification effect of those undetected regions could not be explored. For example, changes in hemoglobin in brain regions that contribute to ASD dysfunction, including the anterior insula and anterior cingulate cortex, could not be observed (Navarta et al. 2012). Second, in our subjects we did rule out the possibility that the development of adolescent characteristics, attentional deficit hyperactivity disorder and other complications of autism spectrum disorder may influence the experimental results. It has been demonstrated that behavioral development is also associated with the prefrontal cortex (Passler et al. 2009). Last, combining multiple channels may possibly results in a better classification. For this, we searched a combination of two channels with the highest classification accuracy by an exhaustive search approach from those channels listed in Table 2. We found that the best combination was (channel 6, channel 25), leading to the highest classification accuracy of 87.34%. This value is a bit lower than that achieved by using a single channel, which is probably due to the complexity of the feature space and the limited classification ability of k-means approach when using multiple features (e.g., multiple channels). Therefore searching for a more accurate classification method based on the sample entropy for multiple channels will be our future work.

5. Conclusions In conclusion, this study tracks back to the brain regions associated with ASD symptoms by machine learning, and stresses difference of brain regions in revealing the important value of ASD pathophysiology. The research also indicates the internal characteristic signals in the frontal lobe of autistic patients via sample entropy, and shows that the signals transmitted by the frontal lobe have deeper research space than the temporal lobe. The left hemisphere of the brain is more different from the right than the normal, e.g. 8 channels in the left vs. 5 channels in the right, and there was obvious identification of the inferior frontal gyrus and superior temporal gyrus of ASD. The paper also provides new evidence for developmental abnormalities in the left and right hemispheres of ASD, which may be the foundation for ASD impairment in the social domain. These findings support the development of biomarkers that may serve as markers for the next generation of ASD diagnosis.

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Conflict of interest statement No conflicts of interest, financial or otherwise, are declared by the authors.

Funding source This work was supported by the National Natural Science Foundation of China [grant number 81771876]; the National Program on Key Research Project [grant number 2016YFC1401900].

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Figure captions Fig. 1. Diagram of optical probes and locations of measurement channels on the brain. The upper shows the optical probes. The below shows the locations of the channels on each hemisphere. The red numbers in the below picture indicate the numbers of measurement channels. Red circle: emitter, blue circle: detector, white square: measurement channel. Fig. 2. Data set recomposition. Fig. 3. (a) The curve graph of oxygenated hemoglobin (HbO2) sample entropy results of all research objects in the third channel; (b) the curve graph of deoxygenated hemoglobin (Hb) sample entropy results of all research objects in the third channel. The abscissa represents the sub-sequence number, and each curve represents the sample entropy fluctuation of multiple sub-sequences of a channel. (c) A two-dimensional scatter diagram of sample entropy of all research objects in channel 3. The abscissa represents the sample entropy of HbO2, and the ordinate represents the sample entropy of Hb. The red dot represents the autistic group, and the blue cross represents the healthy control group. Fig. 4. Sample entropy scatter diagram and cluster diagram of autism spectrum disorder (ASD) group and typically developing (TD) group. (a) The sample entropy of oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (Hb) of the sub-sequences of all subjects in channel 1 (25 autistic patients and 22 normal people). (b) The dichotomous clustering results of 47 respondents in channel 1 obtained by the bisecting k-means algorithm. Fig. 5. The sample entropy distribution for channel 3. The red box represents autism spectrum disorder (ASD) and the blue box represents typically developing (TD). The black circles represent outliers. The differences in the sample entropy can be clearly seen between ASD and TD. The means of oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (Hb) are close to 0.73 for ASD and 1.6 for TD. Fig. 6. Histogram of the distribution of lobes in the brain with a bisecting k-means clustering accuracy exceeding 90% (cross-over experiments 1, 100, 500 and 1000 times). Fig. 7. Diagram of the channels with excellent effect corresponding to the region of the cerebral hemisphere.

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