1.

Introduction

Hypoxic-ischemic brain damage (HIBD) is among the leading causes of neonatal death and neurological disorders.¹ Persistent brain injury in the neonatal period has been suggested to disrupt key structural development, which results in serious consequences such as white matter abnormalities, neuronal necrosis, and intracerebral hemorrhage. Nearly 25% of survivors present neurological-related sequelae, including mental retardation, paralysis, epilepsy, and other diseases.^2–4 Typical neurological symptoms of HIBD deteriorate within a few days after birth; therefore, continuous monitoring and effective evaluation of brain function in these children could help determine whether targeted intervention is necessary and allow for decisive disease diagnosis and treatment.⁵

Currently, the clinical HIBD diagnosis mainly relies on two aspects. These include clinical characterization, which specifically refers to abnormal changes in consciousness, original reflection (there are some congenital reflexes in newborns, which reflect whether the body and nervous system function of the newborn is normal), and muscle tension,⁶ as well as detection of HIBD-induced lesions using ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and other medical imaging technologies. These classical technologies have their own advantages and limitations. Ultrasound has gradually optimized resolution in brain structure scanning, but it is insufficient at monitoring capabilities of functional hemodynamics. CT involves a certain radiation degree, with immature brain tissue having an unideal tolerance. MRI has a strong spatial resolution, which can accurately distinguish the perfusion level of regional cerebral blood flow. However, there is an emerging need for convenient and continuous bedside monitoring of neonates who are unable to undergo MRI due to clinical instability and/or the medical equipment required for therapeutic interventions. It would be a positive effort to satisfy the need by functional near-infrared spectroscopy (fNIRS) resting-state brain network analysis.

fNIRS is a relatively new non-invasive brain imaging technology and has attracted great attention from brain researchers due to its friendliness to the participants.^7,8 More importantly, the main advantage of fNIRS in the diagnosis of HIBD is to support portable and continuous bedside monitoring. fNIRS allows us to obtain neonatal high-quality data sets within a few minutes. Notably, the data can be collected with the infants in a quiet or sleep state without the need to perform tasks or other auxiliary reagents (tranquilizers). The short preparation and detection period at the bedside means that pediatricians can record data repeatedly at any critical point. In addition, fNIRS avoids the effects of radiation on newborns compared with CT or positron emission CT.

Brain network analysis has been widely used in the evaluation of brain function. The human brain is a highly complex network system with numerous local or global topological features.^9,10 Some synchronous low-frequency fluctuations are associated with neural activity between some brain regions in the resting state, which indicates that organized activities between different brain regions contribute to maintaining the mechanism of brain activity.^11,12 Different from a random network, the brain functional network is economical, which ensures that the brain can differentiate and integrate information efficiently, providing the physiological basis for information processing and mental representations.¹³ Bullmore and Sporns¹⁴ believe that brain networks can be examined by critical properties of graph theory, such as clustering coefficient, node degree, efficiency, and modularity. These metrics of graph theory provide key information about the network structure and describe the specific organizational style of the network. Over the past decade, resting-state brain networks have had great utility in brain function assessment, especially when assessing neurocognitive development in newborns. Relevant fMRI studies have shown some basic functional networks in healthy newborns;¹⁵ moreover, the precursors of some advanced networks have been identified. Studies have demonstrated the presence of the default mode network of the primary motor cortex and sensory cortex.¹⁶ Smyser et al.¹⁷ used fMRI to explore the resting-state connectivity of premature infants. The results showed that the most obvious decrease of functional connectivity was in the area near the injured site. They also confirmed that abnormal development of periventricular white matter would lead to a decrease of network connectivity. Tusor¹⁸ studied 15 infants with HIBD, and conventional MRI showed that there were varying degrees of damage to white matter and gray matter in the cohort. In these infants, typical resting-state networks, including auditory, somatomotor, visual, and default pattern networks, were identified. In addition, the long-distance connection of the unilateral brain in children with HIBD was weakened.

A series of advances has been made in the study of the neonatal fNIRS connectivity. Homae et al.¹⁹ conducted follow-up fNIRS assessments of healthy newborns for 6 months and observed gradual complication and enhancement of the functional network in each region of the neonatal cerebral cortex. Makiko et al.²⁰ found that infants with Down’s syndrome had lower connectivity and different local hemodynamics, which demonstrated the potential of fNIRS for clinical use in infants. Lin et al.²¹ utilized resting-state fNIRS imaging data to explore topological changes in network organization during development from early childhood and early adolescence to adulthood, and the results showed the developmental maturity of important functional brain organization in early childhood. Kelsey et al.²² explored the link between gut microbiome, brain, and behavior in 63 newborn infants by resting-state fNIRS. They found that the composition of gut microbiota is related to the individual differences of brain network connectivity, which in turn mediates the individual differences of infant behavior temperament. Their findings indicate that the gut microbiome plays an important role in human development.

However, as far as we know, the existing studies on the functional connectivity of the neonatal brain mostly focus on preterm infants (gestational age $<37$ weeks) and do not deeply determine the changes of their network properties. Full-term infants with HIBD often miss the treatment window because of their atypical clinical symptoms, until serious complications such as hydrocephalus are found. The specific network properties have not been extracted as sensitive biological factors for early auxiliary diagnosis of HIBD. Given the aforementioned findings, this study aims to use fNIRS to record the resting-state data of full-term infants with HIBD for constructing a functional network that covers the prefrontal, parietal, and temporal lobes, to observe the local or global topological features of HIBD using network analysis, and to extract sensitive biological factors to achieve effective recognition of HIBD.

2.

Methods

In this experiment, fNIRS was applied to record resting-state signals from neonates with HIBD. We constructed a whole-brain functional network based on the between-channel correlation of the sequences of hemoglobin concentration. Functional connections between six early-developing regions of interest (ROIs) were explored. Using comparisons, sensitive network indicators were extracted as features and used to input support vector machines (SVM) for training and testing. This study was conducted according to the Declaration of Helsinki and approved by the local Ethics Committee of Beihang University.

2.2.

Data Acquisition

The fNIRS signals were acquired using a multichannel fNIRS system (NirSmart-2416, HuiChuang, China) with two wavelengths (760 and 850 nm) at a sampling rate of 10 Hz.

An experimental platform dedicated to newborns was established [see Fig. 1(a)]. To reduce interference from the external environment, the newborns were tested in a room with dim light and sound insulation effects. Before being tested, the newborn was placed in a supine position in the crib and the head was fixed using measuring aids. Subsequently, the special head cap covered the early developing primary functional areas, including the prefrontal, temporal, and parietal lobes. As shown in Fig. 1(b), the cap was formed by 20 sources and 16 detectors, which comprised the channels. To ensure the safety and comfort of the newborns, the probes and head cap were made of soft materials to achieve soft contact with the scalp. Table 2 shows the specific correspondence between the channels and brain regions. Resting-state data were collected for 10 min to subsequently construct brain functional networks, following Wang et al.²³ who found that the functional connectivity remained stable only when the fNIRS data acquisition duration was longer than 7 min. NirSmart-2416 supported the automatic adjustment of the source power and detector gain to optimize signal quality. The average signal-to-noise ratio of channels used was $22.2\pm 12.1\mathrm{dB}$.

Fig. 1

(a) Experimental settings. (b) Schematic illustration of the fNIRS layout (45 channels, 20 sources, and 16 detectors). The green lines represent channels, and the nodes represent optical probes. The arrangement covers the prefrontal, temporal, and parietal lobes.

Table 2

The MNI coordinates and anatomical labels corresponding to the measurement channels.

Channel		Length of the channel (mm)	MNI coordinates			ROI	Anatomic label
			x	y	z
1	TP7-T7	20	$-45$	$-25$	$-2$	LTL	Middle temporal gyrus left
2	TP7-P7	25	$-41$	$-42$	$-1$	LTL	Middle temporal gyrus left
3	FT7-T7	20	$-45$	$-8$	$-2$	LTL	Temporal pole (superior) left
4	FT7-F7	20	$-41$	10	$-3$	LTL	Superior temporal gyrus left
5	FC5-T7	25	$-46$	$-7$	8	LTL	Heschl gyrus left
6	FC5-F7	25	$-42$	11	7	LTL	Inferior frontal gyrus (opercular) left
7	FC5-C3	30	$-44$	$-6$	31	LPL	Postcentralgyrus left
8	FC5-F3	25	$-41$	14	26	LPL	Middle frontal gyrus left
9	CP5-P7	25	$-43$	$-44$	10	LTL	Middle temporal gyrus left
10	CP5-C3	30	$-47$	$-27$	32	LPL	Inferior parietal lobule left
11	CP5-P3	25	$-42$	$-48$	30	LPL	Angular gyrus left
12	AF7-F7	20	$-37$	26	$-2$	LPFC	Inferior frontal gyrus (triangular) left
13	AF7-Fp1	25	$-26$	39	$-2$	LPFC	Middle frontal gyrus left
14	AF3-F3	25	$-29$	34	25	LPFC	Middle frontal gyrus left
15	AF3-Fp1	25	$-17$	44	10	LPFC	Superior frontal gyrus (dorsal) left
16	CP1-C3	30	$-35$	$-27$	55	LPL	Postcentralgyrus left
17	CP1-P3	30	$-31$	$-50$	47	LPL	Superior parietal gyrus left
18	FC1-C3	30	$-33$	$-5$	50	LPL	Precentralgyrus left
19	FC1-F3	30	$-30$	17	42	LPL	Middle frontal gyrus left
20	FC1-Fz	30	$-14$	17	53	LPL	Superior frontal gyrus (dorsal) left
21	FC1-Cz	30	$-15$	$-4$	61	LPL	Supplementary motor area left
22	AFz-Fp1	30	$-10$	45	13	LPFC	Superior frontal gyrus (medial) left
23	AFz-Fz	25	$-2$	36	37	Prefrontal cortex	Superior frontal gyrus (medial)
24	AFz-Fp2	30	7	45	14	RPFC	Superior frontal gyrus (medial) right
25	AF4-Fp2	25	14	43	11	RPFC	Superior frontal gyrus (dorsal) right
26	AF4-F4	25	25	32	28	RPFC	Middle frontal gyrus right
27	FC2-Fz	30	6	18	53	RPL	Superior frontal gyrus (dorsal) right
28	FC2-Cz	30	7	$-3$	61	RPL	Supplementary motor area right
29	FC2-F4	30	24	16	45	RPL	Middle frontal gyrus right
30	FC2-C4	30	27	$-4$	52	RPL	Middle frontal gyrus right
31	AF8-Fp2	25	23	38	0	RPFC	Middle frontal gyrus right
32	AF8-F8	20	32	25	0	RPFC	Inferior frontal gyrus (triangular) right
33	CP2-C4	30	28	$-26$	56	RPL	Postcentralgyrus right
34	CP2-P4	30	26	$-50$	50	RPL	Superior parietal gyrus right
35	FC6-F4	25	35	13	30	RPL	Middle frontal gyrus right
36	FC6-F8	25	37	10	11	RTL	Inferior frontal gyrus (opercular) right
37	FC6-C4	30	39	$-5$	34	RPL	Postcentralgyrus right
38	FC6-T8	25	41	$-6$	11	RTL	Heschl gyrus right
39	FT8-F8	20	36	8	0	RTL	Superior temporal gyrus right
40	FT8-T8	20	40	$-7$	0	RTL	Temporal pole (superior) right
41	CP6-C4	30	41	$-26$	37	RPL	Inferior parietal lobule right
42	CP6-P4	25	37	$-46$	34	RPL	Angular gyrus right
43	CP6-P8	25	40	$-43$	14	RTL	Middle temporal gyrus right
44	TP8-T8	20	41	$-24$	1	RTL	Middle temporal gyrus right
45	TP8-P8	25	38	$-41$	2	RTL	Middle temporal gyrus right

3.

Results

We explored the structural characteristics of the resting brain network in infants with HIBD and assessed for differences in the network phenomenon compared with healthy newborns. In this study, we mainly used HbR signals to characterize the topological development of functional brain networks since they are generally more reliable for most brain network metrics.

3.1.

Channel-Based Functional Connectivity

The grand-average correlation coefficient matrices of infants with HIBD and healthy infants are presented in Figs. 2(a) and 2(b), respectively, and were used to describe the between-channel correlation of the whole brain in each group. Compared with the control group, the HIBD group has significantly weakened functional connections ($\text{mean}\pm \mathrm{SD}$: $0.57\pm 0.10$ and $0.41\pm 0.14$, respectively). We used two-sample, two-sided power analysis to calculate the statistical power. The sample size of each group was 13, and the overall standard deviation was 0.14. The final calculated power was 83.06% at the significance level of 5%. For the results of Fig. 2(c), there were 62 connections with a significant difference (*$p<0.05$) and 9 connections with a significant difference (**$p<0.01$). This indicates that HIBD caused diffuse functional decline throughout the brain.

Fig. 2

Grand-averaged correlation matrix: (a) infants with HIBD and (b) healthy infants. Axes represent the regions. Each channel with its correlation coefficient set at zero (the diagonal line). LPFC, left prefrontal cortex; LTL, left temporal lobe; LPL, left parietal lobe; RPFC, right prefrontal cortex; RTL, right temporal lobe; RPL, right parietal lobe. (c) The inter-group differences in actual channels. The dark blue lines represent connections with significant differences (*$p<0.05$) and the red lines represent connections with extremely significant difference (**$p<0.01$).

3.2.

ROI-Based Functional Connectivity

To further explore the between-ROI connectivity characteristics, the time series of six ROIs’ internal channels were averaged, and two-sample $t$-tests and FDR correction were applied to compare the differences between the HIBD and HC groups. Compared with the control group, the HIBD group had significantly lower cross-interval brain functional connectivity intensity in LTL-RPL [$t(24)=-2.08$, *$p=0.045$], LPFC-RPL [$t(24)=-2.34$, *$p=0.026$], LPL-RTL [$t(24)=-2.11$, *$p=0.042$], LPL-RPFC [$t(24)=-2.21$, *$p=0.035$], and LPL-RPL [$t(24)=-2.38$, *$p=0.023$], as shown in Fig. 3. There was a greatly significant difference in long-distance connectivity associated with the parietal lobe.

Fig. 3

The $p$ values of the inter-group $t$-test of functional connections between all ROIs. The dashed circles represent six ROIs. Red lines indicate significant inter-group differences in the regional connection ($0.01<*p\le 0.05$).

3.3.

Functional Networks

Based on brain functional connectivity, we constructed HIBD brain network models at different scales using threshold sparsity. Given the underlying physiological mechanisms, delayed brain development caused by hypoxia-ischemia probably affected the formation of important cortical hubs and reduced the efficiency of interregional cooperation. Therefore, we explored the brain network attributes of infants with HIBD from the central point, network efficiency, and modularity. Moreover, we attempted to elucidate the internal basis for clinical disease characterization.

Figure 4 demonstrates the functional network metrics of real (solid line) and random (dashed line) networks with an increasing threshold (0.05 to 0.4). It can be seen from Fig. 4(a) that the small-worldness of the HIBD and HC groups were both larger than 1 at all thresholds, which indicates that the brain networks in newborns have small-world attributes. The $C_p$, modularity, and local efficiency of brain networks were large than those of the random networks [see Figs. 4(b)–4(d)], whereas the global efficiency values of the networks were slightly lower than those of the matched random networks [Fig. 4(e)]. Compared with the HC group, the HIBD group showed higher modularity and small-worldness values, whereas the clustering coefficient and network efficiency of the two groups were comparable.

Fig. 4

The functional network metrics in the range of the sparsity thresholds (0.05 to 0.4). (a) The small-worldness, (b) the normalized clustering coefficient, (c) the modularity, (d) the local efficiency, and (e) the global efficiency. Red and blue curves with circles represent the HIBD and healthy control groups, respectively. The curves with an asterisk represent the mean and error bars of the matched random networks.

The group differences in the global network metrics with the sparsity threshold at 0.3 are shown in Fig. 5. The results demonstrated that the small-worldness [HIBD: $1.31\pm 0.23$; HC: $1.14\pm 0.12$; $t(24)=2.27$, *$p=0.037$], normalized $C_p$ [HIBD: $1.42\pm 0.26$; HC: $1.24\pm 0.11$; $t(24)=2.26$, *$p=0.039$], modularity [HIBD: $0.26\pm 0.08$; HC: $0.20\pm 0.04$; $t(24)=2.21$, *$p=0.043$], and normalized local efficiency [HIBD: $1.16\pm 0.09$; HC: $1.10\pm 0.05$; $t(24)=2.41$, *$p=0.024$] of HIBD networks were significantly higher than those of the HC group, while the normalized global efficiency [HIBD: $0.94\pm 0.04$; HC: $0.93\pm 0.08$; $t(24)=0.25$, $p=0.801$] of the HIBD group and HC group had no statistical differences.

Fig. 5

The group differences in the global network metrics with the sparsity threshold at 0.3. (a) The small-worldness, (b) the normalized clustering coefficient, (c) the modularity, (d) the normalized local efficiency, and (e) the normalized global efficiency. Asterisk indicates a significant difference (*$p<0.05$).

In addition, we expect to mine as many effective features as possible in a fixed threshold range for the needs of clinical diagnosis. According to the statistical results, 0.3 to 0.34 is a noticeable threshold range. Table 3 shows the details of the four kinds of effective features used for classification.

Table 3

Details of features with thresholds of 0.3 to 0.34 used for classification.

	Sparsity threshold	t	p	HIBD (Mean±SD)	HC (Mean±SD)
Small-worldness	0.30	2.27	0.037	$1.31\pm 0.23$	$1.14\pm 0.12$
	0.31	2.14	0.048	$1.33\pm 0.25$	$1.16\pm 0.13$
	0.32	2.22	0.042	$1.32\pm 0.24$	$1.15\pm 0.12$
	0.33	2.27	0.037	$1.31\pm 0.23$	$1.14\pm 0.12$
	0.34	2.17	0.046	$1.29\pm 0.23$	$1.14\pm 0.11$
Normalized $Cp$	0.30	2.26	0.039	$1.42\pm 0.26$	$1.24\pm 0.11$
	0.31	2.32	0.035	$1.41\pm 0.25$	$1.23\pm 0.11$
	0.32	2.42	0.029	$1.40\pm 0.24$	$1.22\pm 0.10$
	0.33	2.50	0.025	$1.38\pm 0.23$	$1.21\pm 0.10$
	0.34	2.46	0.027	$1.36\pm 0.22$	$1.20\pm 0.10$
Modularity	0.30	2.21	0.043	$0.26\pm 0.08$	$0.20\pm 0.04$
	0.31	2.42	0.028	$0.25\pm 0.08$	$0.19\pm 0.04$
	0.32	2.34	0.033	$0.25\pm 0.08$	$0.19\pm 0.04$
	0.33	2.46	0.026	$0.25\pm 0.08$	$0.18\pm 0.04$
	0.34	2.40	0.030	$0.24\pm 0.08$	$0.17\pm 0.04$
Normalized local efficiency	0.30	2.41	0.024	$1.16\pm 0.09$	$1.10\pm 0.05$
	0.31	2.39	0.025	$1.15\pm 0.08$	$1.09\pm 0.05$
	0.32	2.39	0.025	$1.14\pm 0.08$	$1.08\pm 0.05$
	0.33	2.68	0.013	$1.14\pm 0.07$	$1.08\pm 0.05$
	0.34	2.64	0.014	$1.13\pm 0.07$	$1.07\pm 0.05$

Note: p<0.05 indicates a significant difference.

The AUC was conducted to quantify the performances of these features in detecting HIBD. The AUC value for small-worldness, normalized $Cp$, modularity, and normalized local efficiency is 0.74, 0.75, 0.76, and 0.75, respectively (see Fig. 6).

Fig. 6

The ROC curves of small-worldness, normalized $Cp$, modularity, and normalized local efficiency, plotted in blue, red, green, and black, respectively. The AUC value for the four types of features is 0.74, 0.75, 0.76, and 0.75, correspondingly.

Figure 7 shows the grand-average central nodes of both groups, which reflected the key node distribution in the newborn brain. At four different sparsity scales, all neonates showed the central development characteristics of lateralization; specifically, there was a higher number of central nodes on the left side than on the right side, which was consistent with previous findings. Furthermore, the difference of distribution of the central nodes between groups was relatively large when the threshold is 0.1 and 0.2, but gradually disappears when the threshold was further increased.

Fig. 7

Grand-averaged central node of the two groups at four thresholds.

4.

Discussion

For the channel-based results, HIBD significantly reduced resting-state brain functional connectivity across the whole brain in infants. The respective correlation matrix of the two data groups revealed that the HIBD group had significantly weaker brain functional network connectivity of infants than the control group. This is consistent with the characteristics of whole brain involvement in perinatal brain injury. In the early postnatal period, the brain of newborns develops rapidly under the joint stimulation of endogenous and environmental factors, and synaptic connections are reshaped under the joint influence of the body itself and the external environment. The findings of Fransson et al.^15,16 suggest that basic functional networks already exist and rapidly develop in healthy newborns. Perinatal brain injury may be related to many factors such as abnormal synaptic development and decreased nerve cell transporters, which is also associated with dysgenesis of functional connectivity.

For the ROI-based results, there were severe losses of long-range functional connectivity between the brain regions of infants with HIBD. This phenomenon was observed between the contralateral parietal-temporal lobe, contralateral parietal-frontal lobe, and contralateral parietal lobe. This is consistent with previous findings regarding brain dysfunction in preterm infants. Tusor¹⁸ observed decreased resting-state single-side long-distance connections compared with healthy newborns. This suggests that when brain injury occurs, especially in the acute phase, bilateral parietal lobes may be most heavily involved. The involvement further weakens the long-distance information transmission, which limits the efficiency of synergistic cooperation between multiple brain regions. This is because, during brain development, HIBD-induced deficiency in blood oxygen supply results in vasoconstriction and white matter deletion; moreover, it may progress to involve neuronal necrosis of the overlying cortex. This hinders the synergistic cooperation between brain regions in newborns, which results in the infants presenting cognitive impairment with clumsiness or spasticity later in life.

The functional networks of infants with HIBD have a stronger ability of local information transmission. Analysis of small-world attributes and operational efficiency showed that the network normalized clustering coefficient, small world, normalized local efficiency, and modularity level were larger in the HIBD group than in the control group in the left auxiliary motor area, RTL area, and right parietal upper gyrus. This phenomenon was reported in an fMRI-based study on extreme preterm neonates compared with normal neonates. Elveda et al.³⁴ found that the small-world degree of extremely premature infants was significantly larger than that of normal newborns at partial thresholds. This could be attributed to extremely premature infants having significantly enhanced intra-regional connectivity between the motor region and the auditory network region, which leads to the increased clustering coefficient in some regions and the increase of the small-world degree. This indicates that the functional connections of the infants also redistribute after acute brain injury, which is consistent with the redistribution of cerebral hemodynamics. The observed increase in local efficiency and the decline in overall ability could be attributed to the compensation effect. The less damaged functional area responsible for basic survival needs demonstrates the phenomenon of excessive compensation, which causes internal network overdevelopment. Contrastingly, severely damaged high-level functional hubs show impeded development. This phenomenon may be involved in the prognosis of patients with mental retardation, cerebral palsy, and other cognitive disorders.

Graph theory analysis based on functional connectivity can derive summary parameters to describe and quantify aberrant communication patterns associated with brain injury. After screening, network indicators based on small-worldness, local efficiency, modularity, and normalized clustering coefficient allowed for efficient HIBD identification. The AUC values demonstrated the ability of all of these features in HIBD detection (*$p<0.05$). The performance of the four types of features was similar, among which modularity was slightly better. These indicators highlight the dysfunction of information transmission and integration in the brain and network efficiency overdevelopment within the region, which can be significantly distinguished from healthy newborns. This provides a new and concise basis for clinical diagnosis, which could increase the general attention of clinicians.

Moreover, central node development in the left side was higher than that in the right side; specifically, there was preferential development of advanced language-related function of the newborn. The left side of the healthy newborn is usually 4.3% larger than the right side,³⁵ which is consistent with our findings. Notably, infants with HIBD had missing regional central nodes responsible for language-related advanced cognitive functions, including channel 8 (left middle frontal gyrus), 15 (left upper frontal gyrus dorsal), and 30 (right central posterior gyrus). The central nodes are responsible for integrating information from each functional region to complete efficient resource allocation and operation; moreover, their absence could cause delayed cognitive function development in newborns. On the other hand, the sample size limits further discussion of distribution of the central nodes. Although the central nodes were not selected as features for classification in this study, their low level of development for HIBD infants makes them still worth collecting for verification, which may build a relationship between HIBD and functional networks.

Taken together, sustained brain damage could disrupt the development of key structural and functional networks, which leads to neurological development disorders in newborns. fNIRS-based analysis of the resting-state brain network could be applied to identify abnormal features regarding brain functional development in infants with HIBD, which contributes to the pathological understanding and clinical diagnosis of the disease. There are also some limitations and suggestions given based on our results. The small sample size restricts the statistical power to a certain extent. We have not made a more in-depth pathological analysis of HIBD. In future studies, fNIRS and clinical manifestations of HIBD will be combined to assess the association between the network of lesions and the core symptoms. Moreover, we expect to mine as many effective features as possible in a fixed threshold range for the needs of clinical diagnosis. The sparsity segment near 0.3 was a recommended threshold range and could be used as a reference for future research on infant brain functional connectivity.

5.

Conclusion

This is the first study to conduct fNIRS analysis of the resting-state brain network for assessing brain function levels in children with HIBD.

By exploring brain network attributes, we observed significant between-group differences in various aspects, including functional connectivity intensity, node center, and information transmission between brain regions. These findings provide a theoretical basis for the clinical characterization of mental retardation, cerebral palsy, convulsion, and cognitive dysfunction in children. Further, according to specific network defects, the extracted sensitivity index based on small-world attributes, efficiency, modularity, and node degree could be effectively applied to identify patients, which indicates that fNIRS-based analysis of the resting-state brain network could be an exciting tool for assisting in the early clinical diagnosis of HIBD. Future studies should assess the utility of this technology for other types of neonatal brain injury.