Structural network communication differences in drug-naive depressed adolescents with non-suicidal self-injury and suicide attempts

Abstract

BACKGROUND

Depression, non-suicidal self-injury (NSSI), and suicide attempts (SA) often co-occur during adolescence and are associated with long-term adverse health outcomes. Unfortunately, neural mechanisms underlying self-injury and SA are poorly understood in depressed adolescents but likely relate to the structural abnormalities in brain regions.

AIM

To investigate structural network communication within large-scale brain networks in adolescents with depression.

METHODS

We constructed five distinct network communication models to evaluate structural network efficiency at the whole-brain level in adolescents with depression. Diffusion magnetic resonance imaging data were acquired from 32 healthy controls and 85 depressed adolescents, including 17 depressed adolescents without SA or NSSI (major depressive disorder group), 27 depressed adolescents with NSSI but no SA (NSSI group), and 41 depressed adolescents with SA and NSSI (NSSI + SA group).

RESULTS

Significant differences in structural network communication were observed across the four groups, involving spatially widespread brain regions, particularly encompassing cortico-cortical connections (e.g., dorsal posterior cingulate gyrus and the right ventral posterior cingulate gyrus; connections based on precentral gyrus) and cortico-subcortical circuits (e.g., the nucleus accumbens-frontal circuit). In addition, we examined whether compromised communication efficiency was linked to clinical symptoms in the depressed adolescents. We observed significant correlations between network communication efficiencies and clinical scale scores derived from depressed adolescents with NSSI and SA.

CONCLUSION

This study provides evidence of structural network communication differences in depressed adolescents with NSSI and SA, highlighting impaired neuroanatomical communication efficiency as a potential contributor to their symptoms. These findings offer new insights into the pathophysiological mechanisms underlying the comorbidity of NSSI and SA in adolescent depression.

Key Words: Depression; Non-suicidal self-injury; Suicide attempts; Adolescents; Communication models; Structural network efficiency

Core Tip: This study investigates structural network communication differences in depressed adolescents, highlighting impaired brain network efficiency in those with non-suicidal self-injury and suicide attempts. Using diffusion magnetic resonance imaging, the research identifies disrupted cortico-cortical and cortico-subcortical circuits, particularly in regions such as the posterior cingulate gyrus and nucleus accumbens. The findings suggest that compromised neural communication is linked to clinical symptoms, offering new insights into the neurobiological mechanisms behind the comorbidity of non-suicidal self-injury and suicide attempts in depression.

INTRODUCTION

Depression, non-suicidal self-injury (NSSI), and suicide represent significant public health challenges in adolescents[1-3], often co-occurring during this developmental stage and contributing to long-term adverse health outcomes[4,5]. Depression is widely recognized as one of the primary risk factors for suicidal thoughts and behaviors[6]. Frequent NSSI incidents are associated with an elevated risk of suicide attempts (SA)[7], a critical predictor of completed suicide[8]. Suicidal behavior often originates from NSSI. However, the etiologies of suicidal behavior and NSSI are multifaceted and only partially overlap, suggesting the involvement of both shared and distinct vulnerability factors in the development of SA and NSSI[9].

Depression is conceptualized as a disconnection syndrome[10]. Consistent with this perspective, diffusion magnetic resonance imaging (dMRI) studies have identified widespread abnormalities in white matter structures in depression, encompassing both local micro-structure integrity[11] and system-level connectome disruptions[12]. Structural connectivity constrains and facilitates neural information transfer, which in turn gives rise to synchronization between neural elements[13]. Hence, decreases in structural connectivity might influence anatomically disconnected pairs of regions’ communication, which could have an effect on the brain’s overall signal traffic[14]. There is evidence to support the idea that examining models of indirect connectivity can offer a deeper understanding of psychosis symptomatology than what can be determined solely by direct anatomical connections[15]. Thus, examining not only direct connections but also indirect communication pathways can offer a more thorough understanding of disruptions in connectivity in depression. The neural mechanisms underlying self-injury and SA in depressed adolescents remain poorly understood but are likely associated with structural abnormalities in specific brain regions. To date, limited evidence from a handful of dMRI studies has suggested an association between white matter abnormalities and NSSI or SA in depression[16,17]. For example, by using tract-based spatial statistics with a region of interest analysis, Hu et al[16] found that reduced cingulum integrity in the left dorsal cingulum was negatively associated with the severity of NSSI in depressed adolescents with NSSI.

The architecture of structural brain networks influences patterns of interaction and signaling between neurons and brain areas, and the ensuing communication dynamics are critical for brain function[18]. To capture information flow between nodes that are directly linked and those that are not, a variety of network communication models have been proposed to investigate structural network communication in large-scale brain networks[18]. These models cover a wide range of neural signaling conceptualizations. For instance, shortest paths[19] and navigation[20] deterministically route information using centralized and decentralized strategies, respectively. In contrast, diffusion[21] and search information model communication[22] from the stochastic perspective of random walk processes. Finally, communicability[23] implements a broadcasting model of signaling, in which signals are simultaneously propagated along multiple network fronts. Network communication offers a theoretical framework that can contribute to understanding behavior and brain dynamics in complex mental disorders[24]. Nevertheless, no previous study has examined information transfer between both directly connected and unconnected nodes using communication models mentioned above in depressed adolescents. Therefore, investigating how large-scale neural signaling differs in depressed adolescents using different communication models, and whether these possible differences are related to symptom profiles in depressed adolescents might help to improve our understanding of the pathophysiology of comorbidity between NSSI and SA in depression.

By establishing network communication models including shortest path efficiency, navigation efficiency, diffusion efficiency, search information and communicability, we intended to explore structural network communication in large-scale brain networks in depressed adolescents. We hypothesized that depressed adolescents with NSSI and SA would show different patterns of impaired whole-brain communication. Drawing on previous studies[16,17], we further hypothesized that symptom profiles (i.e., clinical scale scores) would correlate with abnormal communication metrics in depressed adolescents.

MATERIALS AND METHODS

Participants

We recruited 105 adolescents with depression from The Affiliated Mental Health Center of Jiangnan University, China. Depressed adolescents met the criteria for major depressive disorder in the fifth edition of Diagnostic and Statistical Manual of Mental Disorders[25] and were classified into three groups: 20 depressed adolescents without SA or NSSI (major depressive disorder group), 35 depressed adolescents with NSSI but no SA (NSSI group), and 50 depressed adolescents with SA and NSSI (NSSI + SA group). At the time of their magnetic resonance imaging (MRI) scans, all depressed adolescents were drug-naive. Two independent Ottawa Self-Injury Inventory sub-scales were used to assess the frequency and functions of NSSI behaviors[26]. The evaluation of SA was assessed with the Beck Scale for Suicide Ideation (BSS)[27]. Additionally, depressed adolescents’ current levels of depression and anxiety were assessed using the 17-item Hamilton Depression Scale (HAMD-17) and the 14-item Hamilton Anxiety Scale (HAMA-14)[28,29]. Healthy controls (HC) were recruited from the local community via advertisements and were free of a history or current diagnosis of any psychiatric disorder. All participants were assessed to be right-handed using the Edinburgh Handedness Inventory[30]. A brief structured clinical interview tool, the Mini International Neuropsychiatric Interview[31], was used to screen for several psychiatric disorders. Exclusion criteria included intracranial pathology, brain injury, neurological illness, alcohol or substance abuse, contraindications to MRI, and excessive head motion in the subsequent data analysis. Finally, 85 depressed adolescents and 32 HC were included in the imaging analysis (Table 1). All participants were given detailed information regarding the purpose and procedures of the study. Formal written consents were obtained from participants and their parents or legal guardians before participation. The study was approved by the Medical Ethics Committee of The Affiliated Mental Health Center of Jiangnan University and performed in accordance with the Declaration of Helsinki.

Table 1 Demographic and clinical characteristics of depressed adolescents, mean ± SD.

Variables	NSSI + SA	NSSI	MDD	HC	P value
Gender (male/female)	6/35	4/23	7/10	12/20	0.03a
Age (years)	15.59 ± 2.16	15.70 ± 2.64	16.41 ± 2.35	20.18 ± 2.67	< 0.001b
Education (years)	8.66 ± 1.77	9.44 ± 2.72	10.05 ± 1.91	14.34 ± 2.42	< 0.001b
HAMD-17	22.83 ± 4.75	18.37 ± 5.04	16.71 ± 5.11	-	< 0.001b
HAMA-14	21.51 ± 7.57	20.44 ± 8.20	17.82 ± 8.40		0.002b

^aP < 0.05, χ² test.

^bP < 0.01, One way analysis of variance.

NSSI: Non-suicidal self-injury; SA: Suicide attempts; MDD: Major depressive disorder; HC: Healthy control; HAMD-17: The 17-item Hamilton Depression Scale; HAMA-14: The 14-item Hamilton Anxiety Scale.

MRI acquisition

MRI scans were acquired with a Magnetom Skyra 3.0T MRI (Siemens Medical System, Erlangen, Germany) at the Department of Medical Imaging, Huadong Sanatorium, Wuxi, China. All participants obtained dMRI data and high-resolution three-dimensional T1-weighted images. Foam pads were used to reduce head motion and noise from the scanner. T1-weighted images were acquired using a 3D-MPRAGE sequence with the following parameters: Repetition time/echo time = 2530/2.98 milliseconds, 192 sagittal slices, thickness = 1 mm, flip angle = 7°, matrix = 256 × 256, voxel size = 1 mm × 1 mm × 1 mm, and field of view (FOV) 256 mm. Whole brain, high angular resolution diffusion imaging was acquired using a spin-echo echo-planar imaging pulse sequence with the following parameters: Echo time = 78 milliseconds, repetition time = 11600 milliseconds, voxel size = 2 mm × 2 mm × 2 mm, 75 axial slices, FOV 224 mm, b-value = 1000 second/mm² in 64 non-collinear gradient directions. A single non-diffusion-weighted b0 image was also obtained.

Data preprocessing

We visually inspected the T1-weighted (T1W) and dMRI images of all subjects to detect any signal dropouts or artifacts. Next, we pre-processed the images via the well-established pipeline, described as follows: For both T1W and dMRI data, the procedure began with axial alignment, centering, Gibbs ringing removal based on Local Subvoxel-Shifts[32], and intensity inhomogeneity correction via N4ITK[33]. For dMRI data, we also included the following steps: (1) Marchenko-Pastur principal-component analysis denoising[34] to improve the signal-to-noise ratio without reducing spatial resolution; (2) FSL’s eddy correct tool was used for eddy current correction[35]; (3) Brain mask generation using a convolutional neural network based segmentation tool in pnlNipype (github.com/pnlbwh/pnlNipype); and (4) Distortion correction via registration of individual T1W and dMRI data. Finally, the transformation was applied to each diffusion-weighted volume, and the gradient vectors were rotated using the rotation matrix estimated from the affine transformation. Moreover, each individual’s T1W images were transformed from structural space into diffusion space through a rigid registration using FSL[35]. This transformation information was saved for later use. The diffusion images remained in native space.

We conducted whole-brain tractography using a probability fiber tracking algorithm, which fits a mixture of fiber orientation distribution function to the diffusion signal. The fiber orientation distribution function was estimated using the multi-shell multi-tissue constrained spherical deconvolution model[36]. Whole-brain streamlines were generated using the iFOD2 algorithm[37] with the following parameters: 50 million seeds, random placement in gray and white matter volume, Runge-Kutta 4^th order integration, 15° and 30° angle, respectively, constrained by anatomical tissues and cropping at gray-white matter interface. The resulting tractograms were filtered with the spherical-deconvolution informed filtering of the tractograms algorithm[38] to reduce the false positive connections to 5 million streamlines[39].

White matter network construction

Figure 1 depicts the flowchart of this work. Specifically, network nodes were based on the 246 cortical and subcortical regions comprising the brainnetome atlas[40]. Using the inverse of the transform information, this atlas in the Montreal Neurologic Institute space was registered into each subject’s native space. Edges were defined as interregional fibers between each pair of nodes and met the conditions: (1) At least two double-ended fibers passed through pairwise nodes; and (2) The length of the passing fibers was greater than 10 mm. Each element of the weighted connectivity matrix W was populated with streamline counts between the corresponding pair of regions.

Figure 1 Methodology overview. Follow the direction indicated by the arrow, first white matter tractography applied to diffusion magnetic resonance imaging data was used to map undirected weighted adjacency matrices respecting the structural connectivity between n = 246 cortical regions. Shortest path efficiency, navigation efficiency, diffusion efficiency, search information, and communicability were then computed between every pair of regions to generate communication matrices. In the analysis phase, a whole-brain communication analysis was conducted and subsequently the between-group significantly different connections were explored in association with clinical variables.

Communication models calculation

We computed five popular network communication models, namely, shortest-path efficiency, navigation efficiency, diffusion efficiency, search information, and communicability. Their formal mathematical definitions and meanings have been described in detail elsewhere[13,41]. In brief, the connectivity measures used in this study capture different aspects of brain network function. Shortest paths represent the most efficient routes for information transfer, highlighting rapid and energy-efficient communication. Navigation simulates decentralized routing based on local information, reflecting adaptive signal transmission in dynamic networks. Diffusion models random signal propagation, is relevant for exploring neural plasticity and distributed processing. Search information quantifies the complexity of identifying specific pathways, linked to tasks involving uncertainty or exploration. Communicability accounts for simultaneous signal propagation across multiple paths, emphasizing network resilience and robustness. All computations were carried out using Brain Connectivity Toolbox (https: //sites.google.com/site/bctnet/)[42]. This yielded five communication matrices of the same dimension as the W for each individual. Briefly, each individual weighted connectivity matrix was normalized by its maximal value. To calculate the shortest-path efficiency, navigation efficiency and search information metrics, this normalized matrix was further remapped to connection lengths logarithmically[41], so that strong weights corresponded to shorter lengths and vice versa between nodes. The shortest-path efficiency was computed as the inverse of the total connection lengths along the topological shortest paths between all region pairs[43]. However, the navigation efficiency assumes a geometrical distance rather than a topological distance between nodes to guide the neural communication[20]. Diffusion efficiency quantifies the expected number of steps it takes a random walker starting at source node to arrive for the first time at target node[21]. Search information relates to the probability that a random walker will serendipitously travel between two nodes via their shortest path, quantifying the extent to which efficient routes are hidden in the network topology[22]. The communicability[44] is assigned to survey communicability between two nodes by counting the total number of walks between them, so that longer walks have less influence than shorter walks. Besides, communication matrices derived from the navigation efficiency, diffusion efficiency and search information metrics are asymmetric. We further symmetrized these matrices by averaging the communication measures across both directions of information flow[41].

Statistical analysis

To detect statistical significance of group differences in demographic variables between the patients and HC group, the χ² analysis and one-way analysis of variance were used as appropriate by SPSS 25.0 software (SPSS, Chicago, IL, United States). The Mann-Whitney U test was used to compare the difference of scale scores between NSSI + SA and SA for each of the measures. Using the F-test in conjunction with a false discovery rate (FDR) method within the network-based statistic framework, we conducted a comparative analysis across four groups for five distinct network communication models[45]. Notably, our analysis employed a significance threshold of P < 0.05 and incorporated 5000 permutations to ensure a comprehensive whole-brain comparison. The FDR can be more sensitive to focal effects involving single, isolated connections. The FDR enables rejection of the null hypothesis at the level of individual connections, while controlling the FDR; that is, the proportion of false positive connections among all positive connections. After the above comparison, we used the Wilcoxon signed-rank test to conduct post-hoc for the five communication models. It should be noted that age, gender and education year were treated as covariables, which were regressed out of all the communication models. The observed connections with significant between-group differences across the groups were subsequently subjected to a correlation analysis with the clinical variables of each patient cohort. This approach aimed to elucidate potential associations between network communication models and clinical manifestations. Regressing the aforementioned covariates from both the connections and clinical variables separately, we subsequently conducted correlation analysis using the Spearman method.

RESULTS

Demographics and clinical characteristics

Demographic and clinical characteristics of the adolescents are presented in Table 1. There were significant differences in the gender, age, education level, HAMD-17 and HAMA-14 scores among the groups (P < 0.05). The results showed that there was no significant difference in the Ottawa self-injury inventory-frequency between the NSSI + SA and NSSI groups (P > 0.05) (Table 2). Regarding Ottawa self-injury inventory-functions, except for anti-suicide, interpersonal influence and sensation seeking, the remaining indicators were significantly different between the NSSI + SA and NSSI groups (P < 0.05). BSS scores in the NSSI + SA group are summarized in Table 3.

Table 2 Ottawa self-injury inventory sub-scales scores in depressed adolescents, median (lower quartile-upper quartile).

Variables	NSSI + SA	NSSI	Z value (P value)
OSI-frequency
In the past one month	2 (1-3)	2 (1-2)	-0.32 (0.74)a
In the past six months	3 (1-3)	2 (1-3)	-0.41 (0.68)a
In the past one year	2 (1-3)	2 (1-3)	-5.11 (0.61)a
One year ago	2 (0-3)	1 (0-3)	-0.52 (0.61)a
OSI-functions
Affect regulation	19 (14-23)	16 (9-18)	-2.37 (0.018)a
Anti-dissociation	9 (7-11)	7 (4-8)	-3.23 (0.001)a
Anti-suicide	0 (0-5)	1 (1-6)	-3.36 (0.71)a
Interpersonal boundaries	1 (0-3)	0 (0-2)	-2.33 (0.02)a
Interpersonal influence	6 (2-12)	5 (3-9)	-0.45 (0.66)a
Self-punishment	3 (2-4)	2 (0-3)	-2.37 (0.018)a
Sensation seeking	2 (0-4)	0 (0-1)	-1.58 (0.114)a
Addictive features	2 (0-3)	1 (0-2)	-2.35 (0.019)a

^aP < 0.05.

NSSI: Non-suicidal self-injury; SA: Suicide attempts; MDD: Major depressive disorder; OSI: Ottawa self-injury inventory.

Table 3 Beck Scale for Suicide Ideation scores in depressed adolescents with non-suicidal self-injury and suicide attempts.

Variables	Median
BSS-screen (in the past one week)	12.0 (9.5-13.5)
BSS-screen (previous most depressed)	15.0 (13.5-15.0)
Total BSS score (in the past one week)	51.5 (40.9-66.7)
Total BSS score (previous most depressed)	75.8 (68.2-84.9)

BSS: Beck Scale for Suicide Ideation.

Whole brain communication analysis

Table 4 presents the significantly different connections identified by the F-test statistical analysis. In the analysis of communicability, four distinct connections exhibited significant variance across the HC, NSSI + SA, NSSI, and major depressive disorder groups, particularly involving the frontal regions such as the orbital and precentral gyrus (PrG), the cingulum, and temporal regions including the inferior temporal gyrus (ITG) and fusiform gyrus, as well as the thalamus. Regarding diffusion efficiency, three connections demonstrated significant differences primarily associated with the left PrG. In the domain of navigation efficiency, five edges were identified with significant disparities, encompassing the cingulum, frontal regions (middle frontal gyrus, and PrG), the right nucleus accumbens (NAc) (BG_R_6_3), and the right insula (INS_R_6_4). Furthermore, in the measure of search information, three connections showed significant differences, which included the cingulum, temporal regions (middle temporal gyrus and superior temporal gyrus), and the superior frontal gyrus. Lastly, only one connection in shortest path efficiency revealed a significant difference, located within the cingulum. Figure 2A-E illustrate the spatial distribution of these results, presented from axial perspectives of the brain. Additionally, Figure 2F-U, presents the post-hoc results of these connections. Among them, a consistent pattern emerges across the three significant group-difference edges of diffusion efficiency, with the NSSI group exhibiting a notably lower value compared to the HC and the other groups. Conversely, the specific edges (i.e., PrG_R_6_5-ITG_R_7_3, and middle frontal gyrus_R_7_1-BG_R_6_3) of navigation efficiency reveal a significant increase in values for the NSSI when contrasted with the HC and the remaining groups.

Figure 2 The results of five whole-brain communication models analysis across the healthy controls, non-suicidal self-injury + suicide attempts, non-suicidal self-injury, and major depressive disorder groups. ^aP < 0.05; ^bP <0.001. A-E: Illustrate the F-test results presented from the dorsal view of the brain; F-U: Depict the post-hoc results of these connection results. HC: Healthy controls; NSSI: Non-suicidal self-injury; SA: Suicide attempts; MDD: Major depressive disorder; CG: Cingulate gyrus; PrG: Precentral gyrus; STG: Superior temporal gyrus; SFG: Superior frontal gyrus; OrG: Orbital gyrus; FuG: Fusiform gyrus; PCun: Precuneus; SPL: Superior parietal lobule; MFG: Middle frontal gyrus.

Table 4 Connection differences of the five network communication models across the groups using F-test comparisons.

Network communication models	Node name	Anatomical descriptions1	Node name	Anatomical descriptions	F value (P value)	Eta-squared (η²)
Communicability	OrG_R_6_2	Orbital area	OrG_L_6_3	Lateral orbital area	8.620 (3.35e-05)	0.185
	PrG_R_6_1	Head and face region of PrG	ITG_R_7_4	Intermediate lateral area of ITG	6.903 (2.61e-04)	0.154
	CG_L_7_1	Dorsal area of CG	CG_R_7_4	Ventral area of CG	6.871 (2.71e-04)	0.153
	FuG_L_3_3	Lateroventral area of FuG	Tha_L_8_7	Caudal temporal Tha	7.581 (1.15e-04)	0.166
Diffusion efficiency	PrG_L_6_2	Caudal dorsolateral area of PrG	PhG_R_6_1	Rostral area of PhG	7.017 (2.27e-04)	0.156
	PrG_L_6_2	Caudal dorsolateral area of PrG	SPL_L_5_3	Lateral area of SPL	6.298 (5.45e-04)	0.142
	PrG_L_6_2	Caudal dorsolateral area of PrG	CG_L_7_2	Rostroventral area of CG	8.450 (4.10e-05)	0.182
Navigation efficiency	PrG_R_6_5	Tongue and larynx region of PrG	ITG_R_7_3	Rostral area of ITG	7.899 (7.88e-05)	0.172
	PCun_R_4_4	PCun	INS_R_6_4	Ventral dysgranular and granular insula	7.436 (1.37e-04)	0.164
	SPL_R_5_2	Caudal area of SPL	CG_L_7_2	Rostroventral area of CG	7.440 (1.36e-04)	0.164
	CG_L_7_1	Dorsal area of CG	CG_R_7_4	Ventral area of CG	7.362 (1.50e-04)	0.162
	MFG_R_7_1	Dorsal area of MFG	BG_R_6_3	Nucleus accumbens	6.486 (4.33e-04)	0.146
Search information	STG_L_6_5	Lateral area of STG	MTG_L_4_4	Anterior superior temporal sulcus	6.995 (2.33e-04)	0.155
	CG_L_7_1	Dorsal area of CG	CG_R_7_4	Ventral area of CG	6.419 (4.70e-04)	0.145
	SFG_R_7_3	Lateral area of SFG	CG_R_7_5	Caudodorsal area of CG	6.270 (5.64e-04)	0.142
Shortest path efficiency	CG_L_7_1	Dorsal area of CG	CG_R_7_4	Ventral area of CG	8.579 (3.52e-05)	0.184

¹Brainnetome atlas.

OrG: Orbital gyrus; PrG: Precentral gyrus; ITG: Inferior temporal gyrus; CG: Cingulate gyrus; FuG: Fusiform gyrus; Tha: Thalamus; PhG: Parahippocampal gyrus; SPL: Superior parietal lobule; PCun: Precuneus; INS: Insular gyrus; MFG: Middle frontal gyrus; BG: Basal ganglia; STG: Superior temporal gyrus; MTG: Middle temporal gyrus; SFG: Superior frontal gyrus; L: Left; R: Right.

Relationship between network measures and clinical variables

We examined the relationship between the mean communication efficiency values of significant connections that we found to be altered in depressed adolescents and symptom profiles within corresponding groups. With regard to the NSSI + SA group, Spearman correlation analyses revealed significant negative relationships between communicability scores (edge PrG_R_6_1-ITG_R_7_4) and suicidality derived from BSS-screen (previous most depressed) (r = -0.41, P = 0.023; Figure 3A), between navigation efficiency [edge superior parietal lobule (SPL)_R_5_2-cingulate gyrus (CG)_R_7_4] and Self-Punishment scores (r = -0.42, P = 0.018; Figure 3B), between the search information (edge STG_L_6_5-middle temporal gyrus_L_4_4) and HAMD-17 total scores (r = -0.52, P = 0.001; Figure 3C). With regard to the NSSI group, Spearman correlation analyses revealed a significant negative relationship between diffusion efficiency (edge PrG_L_6_2-parahippocampal gyrus_R_6_1) and anti-dissociation scores (r = -0.47, P = 0.045; Figure 3D), and a significant positive relationship between diffusion efficiency (edge PrG_L_6_2-SPL_L_5_3) and HAMA-14 total scores (r = 0.44, P = 0.049; Figure 3E).

Figure 3 The correlation results between network communication models and clinical variables. A: Suicidality; B: Self-punishment factor scores; C: The 17-item Hamilton Depression Scale total scores; D: Anti-dissociation factor scores; E: The 14-item Hamilton Anxiety Scale total scores. The contour lines indicate the boot-strapped Mahalanobis distance from the bivariate mean, and filled circles indicate data included in the correlation, while open circles indicate outliers. The solid black line denoted a linear regression over the data after outlier removal. SA: Suicide attempts; NSSI: Non-suicidal self-injury; PrG: Precentral gyrus; ITG: Inferior temporal gyrus; CG: Cingulate gyrus; SPL: Superior parietal lobule; STG: Superior temporal gyrus; MTG: Middle temporal gyrus; PhG: Parahippocampal gyrus; HAMD-17: The 17-item Hamilton Depression Scale; HAMA-14: The 14-item Hamilton Anxiety Scale.

DISCUSSION

To the best of our knowledge, this study is the first to investigate structural network communication in large-scale brain networks in depressed adolescents. Using dMRI-based probabilistic tractography, we constructed five distinct network communication models that incorporate both direct and indirect connections to evaluate the structural network efficiency across the whole brain level in depressed adolescents with SA and NSSI. We identified structural network communication differences across spatially widespread brain regions across the four groups, particularly involving cortico-cortical connections and cortico-subcortical circuits (Figure 2). Subsequently, we investigated whether compromised communication efficiency was associated with clinical symptoms in the depressed adolescents. We observed significant correlations between network communication efficiencies and clinical scale scores derived from depressed adolescents with SA and NSSI. Specifically, we observed that network communication efficiencies were negatively correlated with suicidality, self-punishment scores and HAMD-17 total scores in the NSSI + SA group. Additionally, we found that network communication efficiencies were associated with anti-dissociation scores and HAMA-14 total scores in the NSSI group. One of the key consistent findings in this study was observed differences in communication efficiencies between the left dorsal posterior CG and the right ventral posterior CG (edge CG_L_7_1-CG_R_7_4) across the groups in four network communication models (Figure 2). It is noteworthy that the NSSI + SA group showed a consistent pattern of significant decreasing communication efficiencies in those models when compared with the NSSI group. This pattern provides an important clue for carving out differences in structural network communication characteristics between SA and NSSI, and suggests that the alterations of the posterior cingulate cortex (PCC) in SA are highly reproducible across network models. Despite recent neuroimaging developments, structural network abnormalities of the PCC in depressed adolescents are still poorly understood. Broadly speaking, the PCC has extremely high metabolic expenditure, and network studies of functional and anatomical data indicate that it is a key hub in the human connectome[46]. Consistent with the cytoarchitecture, recent work leveraging complex analytical approaches reveals that the connections the PCC forms with other regions are heterogeneous, extending beyond a single network, while recent studies of its function reveal a role in a wide range of complex forms of cognition, including memory, navigation, and narrative comprehension[47]. Anatomically, decomposition of the PCC into subregions is broadly consistent with the cytoarchitectural division of the PCC along a dorsal-ventral axis, suggesting how different dorsal and ventral PCC regions differentially communicate with large-scale brain networks[47]. Clinically, PCC subregion-based functional network abnormalities have been reported in depression with suicidal ideation[48]. Nevertheless, structural network dysfunction of the PCC at the subregional level has rarely been reported in depressed adolescents. Hence, to understand how network connectivity between PCC subregions strengthen or weaken could help provide novel insight into the structural connectome in adolescents with SA and NSSI from the view of structural network communication. As far as we know, the present study is the first to report aberrant communication efficiencies within PCC subregions in depressed adolescents with SA and NSSI, which may contribute to advance our understanding of the potential neural mechanisms in adolescent depression and search for a neuromarker for diagnostic and predictive models in the study of depressed adolescents. Another key finding was a consistent pattern observed across three significant group-difference edges of diffusion efficiency, with the NSSI group exhibiting notably lower values compared to the HC and the other groups (Figure 2). The implication of PrG and its structural connections to the parahippocampal gyrus, SPL and rostroventral CG composed a structural network underpinning communication difference in depressed adolescents with NSSI. This special network consists of several distinct brain regions implicated in cognition, action observation and execution, pain monitoring, and emotion processing[40]. We were curious about whether differences within the network could be contributing to clinical manifestations in the NSSI group. Consequently, in our data, we further observed the compromised structural connections (i.e., edge PrG_L_6_2-PhG_R_6_1 and edge PrG_L_6_2-SPL_L_5_3) within the network were correlated with anti-dissociation scores and HAMA-14 total scores in the NSSI group (Figure 3). Such information emphasizes the pivotal role of PrG in structural network communication in depressed adolescents with NSSI. Our findings were in line with recent studies, which consistently reported that people with NSSI had spontaneous neural activity changes in the PrG when compared with non-NSSI people[49,50]. The converging evidence shows that PrG and its functional and structural connectivities provide insight into the neural underpinnings of NSSI but also help bridge the gap between the pathophysiological basis and the observed abnormal brain changes in adolescents with NSSI. Interestingly, in this study, we found that navigation efficiency between the right NAc and right medial frontal gyrus was increased in the NSSI group when compared with the other groups (Figure 2). When considered in the context of previous findings derived from functional MRI data, in which a reduction in NSSI frequency was associated with a decrease in resting-state functional connectivity between the NAc and medial frontal cortex[51], the current results suggested a possible coexistence of functional and anatomical organization of the NAc-frontal circuit in NSSI. In fact, the NAc receives convergent information from subcortical and cortical brain regions and projects to structures mainly responsible for motor control, memory and emotion, making it ideally situated to guide learning, motivation and reward behaviors[52-54]. On the one hand, research evidence shows age-related variations in functional connectivity between the NAc and frontal medial cortex during pubertal development[55]. This gives reason to presume that these variations in functional connectivity may maximize the neural efficiency of interregional communication and set the stage for further inquiry of biological factors driving adolescent connectivity changes. On the other hand, dysfunctions of the NAc-frontal circuit have been linked to the neurobiology of adolescent depression[56] and NSSI behaviors[51]. In line with the previous studies, the present study further demonstrated that NSSI could be mapped to specific structural brain networks changes in the NAc-frontal circuit. Given that acute and chronic antidepressant effects have also been reported with deep brain stimulation to the NAc[57], the NAc-frontal circuit might offer potential treatment targets to depressed adolescents with NSSI, and set the stage for future research designed to confirm clinical effects and mechanisms. The specific properties of brain network organization are functionally significant[58], and their alterations may serve as clinically valuable diagnostic markers for neuropsychiatric disorders. In the present study, we observed significant correlations between network communication efficiencies and clinical scale scores derived from depressed adolescents with SA and NSSI. Our findings were consistent with the results from previous structural and functional network connectome studies on depression with suicidality, suggesting a strong correlation between the brain network properties and clinical symptom severity[59,60]. There is evidence of slowed information processing speed in adolescents with depression[61], likely contributing to deficits in domains which rely on rapid and efficient assimilation of information[62]. For instance, Wang et al[60] demonstrated that the disruptions in the whole-brain connectome accounted for a portion of the neurological foundation of the suicidality gradient in patients with depression, and the changed brain connections may serve as potent indicators for diagnosis, enabling the separation of depression from suicide ideation and conduct. Thus, the significant correlations between network communication efficiencies and clinical scale scores in our study indicates that compromised communication efficiency in neuroanatomical networks appears to be one of the underlying neural mechanisms responsible for NSSI and suicidality in depressed adolescents. Undoubtedly, more studies with increased numbers of subjects using specific neuropsychological testing or cognitive tasks are needed to confirm this finding. There were some limitations in our study. First, this is a cross-sectional study, and thus potential changes in the structural network efficiency over the course of adolescent depression remain to be established in longitudinal studies. Second, demographic differences in participants may be a possible confounding factor in our findings. For example, epidemiological research on the prevalence of NSSI and SA has shown significant gender and age differences[63-65]. Although we included age, gender and years of education as nuisance covariates which were regressed out of all the communication models, their underlying effects on brain structure cannot be removed thoroughly. Finally, the small sample size may restrict the generalizability of our findings. Future research should aim to include a larger and more diverse sample to enhance the robustness of the results and to investigate additional variables that may influence these behaviors.

CONCLUSION

In conclusion, the present study provided evidence that structural network communication differences in depressed adolescents with NSSI and SA, and the compromised communication efficiency in neuroanatomical networks were associated with the symptoms of depressed adolescents. In particular, our data highlight aberrant structural network communication efficiencies within PCC subregions, PrG-based connections and the NAc-frontal circuit, which offers new insight into the pathophysiological mechanisms underlying the comorbidity between NSSI and SA in depression.

ACKNOWLEDGEMENTS

Sincere appreciation is extended to all adolescents for their valuable participation.