3.1. Background characteristics of the participants
The differences were not significant in age, sex, years of education, eTIV, MoCA scores, ALVT-immediate recall and AVLT-recognition scores between the NC and SCD group. In particular, the SCD group performed worse than the NC group in delayed recall of the AVLT (p < 0.05). The results of this section were summarized in Table 1.
3.2 Between-group differences of network metrics
3.2.1 Global network properties
The small-world networks characterized with higher clustering coefficients and similar characteristic path length compared with random networks. In this study, all individual morphometric networks exhibited small-world properties, where the Lp were similar with the matched random networks (lambda ≈ 1) and the Cp were higher than the matched random networks (gamma > 1) in all sparsity thresholds. In addition, that the sigma higher than 1 in all networks demonstrates a small-world organization in each network. Compared with NC group, the sigma was slightly lower in SCD group(p < 0.05), which the sparsity located in 17–33%. Besides, another small world parameter gamma (at the sparsity of 15–32% with exception of 16%) was lower in individuals with SCD than NC (p < 0.05). Compared with NC group, the changes of Eglob (lower in SCD) and Lp (higher in SCD) were significant in SCD group at the sparsity from 22–45%. Significant decreases of Cp (except the sparsity of 17% and 37%) and Eloc (except the sparsity of 21%) had been detected in SCD compared with NC group (Fig. 2). In addition, all the network properties at the sparsity of 30% in individuals with SCD were deterioration compared with NC.
Figure 2 Between-group differences in global network metrics as a function of sparsity. (1) The middle column is the schematic representation and calculation formula of network metrics. In the order from top to bottom, they are clustering coefficient, shortest path length or characteristic path length, local efficiency, global efficiency and the schematic representation of small-world network and random network. (2) The left and right column are the between-group differences of the network metrics. The arrows point from the representation of the network parameters to the results of the between-group differences. One asterisk means p < 0.05, two asterisk means p < 0.01, three asterisk means p < 0.001.
3.2.2 Nodal network properties and Rich-club organization
Compared with NC group, the nodal local and global efficiency were significant decreased only in left paracentral lobule (p < 0.05, Bonferroni corrected). Based on the combined measure, similar hub distributions were observed in the two group, mainly located in left thalamus, Pre-frontal lobe, occipital lobe and parietal lobe, which 17 regions in NC and 16 regions in SCD (Fig. 3A and Table S2). In addition, 13 hub regions in the two groups were overlapped. For different categories of connections classified by hub regions (Fig. 3B), the feeder connections showed lower strength in SCD subjects compared with NC (F = 11.515, p = 0.001), which consistent with the previous WM network studies(Shu et al. 2018; Yan et al. 2018). All comparisons in this section were performed at the sparsity of 30%.
3.2.4 Functional organization and Anatomical distance
For intra-connections within the five functional organizations, the lower connectivity strength of paralimbic system was observed in individuals with SCD (F = 5.216, p = 0.025) (Fig. 3E). The inter-module connections between paralimbic and association area (F = 4.375, p = 0.04) as well as the inter-connections between paralimbic and subcortical (F = 4.291, p = 0.042) were significantly lower in SCD subjects compared with NC. In addition, the strength of long connectivity (anatomical distance larger than 80mm) was significantly decreased in individuals with SCD compared with NC (F = 4.22, p = 0.044) (Fig. 3B).
Analysis of the network-based statistic resulted with one subnetwork with 24 nodes and 24 connections (edge-p < 0.01 and component-p = 0.002) (Fig. 3C). The NBS connectivity strength was the total strength within the disconnected network. The receiver operating characteristic curve (ROC) analysis revealed the connectivity strength of the subnetwork identified by NBS, showing high area under curve (AUC) value of 0.959 for classifying the two groups (Fig. 3D). In addition, the network connectivity strength in SCD group was lower than in NC group (F = 4.538, p = 0.037) (Fig. 3B). The results in this section were based on the networks with sparsity of 30%.
Figure 3 Between-group differences of rich-club organization and network connections as well as age-related differences in SCD and NC. (A) The hub distributions of the GM networks in the NC group and SCD group. An illustration of the connections divided by hub regions. (B) The between-group differences of the strength of network, feeder connectivity and long connectivity. (C) The disrupted subnetwork in individuals with SCD calculated by network-based statistic (NBS) approach. (D) The receiver operating characteristic (ROC) curve of the NBS connection strength. (E) Between-group differences of the intra-connectivity of the five functional organizations. The intra-connectivity strength of the five modules in NC group (bule line) were normalized to 0 and the relative difference differences of intra-connectivity strength of the five modules in SCD group were showed by the orange line. (F) The group-specific age-related differences of NBS connection strength in individuals with SCD and normal controls.
3.3 Age-related effects on topological properties of network
All the network metrics which showed significant relationships with age\(\times\)group interaction in the stepwise regression model and the partial correlation analyses for the selected network metrics were summarized in this section.
For the global network metrics, the age\(\times\)group interaction effects exhibited significance in characteristic path length (t = 3.063, p = 0.003, beta = 0.333) (Fig. 4A), global efficiency (t=-3.069, p = 0.003, beta=-0.334) (Fig. 4A), clustering coefficient (t=-3.603, p = 0.001, beta=-0.384) (Fig. 5A) and local efficiency (t=-3.991, p < 0.001, beta=-0.419) (Fig. 5A) (Table 2). The followed partial correlation analyses showed that significant relationships between characteristic path length (r = 0.416, p = 0.008), global efficiency (r=-0.421, p = 0.007), clustering coefficient (r=-0.291, p = 0.0499), local efficiency (r=-0.402, p = 0.01) and age within SCD group, while those network metrics exhibited nonsignificant correlation within NC group.
Table 2
Global and nodal network metrics with significant age× group interaction effects through regression model and partial correlation analysis.
Network metrics
|
P value (T value)
of interaction
|
Partial correlation within each group
|
NC
|
SCD
|
Lp
|
0.003(3.063)
|
r = 0.162; p = 0.159
|
r = 0.416; p = 0.008
|
Cp
|
0.001(-3.603)
|
r=-0.01; p = 0.477
|
r=-0.291; p = 0.0499
|
Eglob
|
0.003(-3.069)
|
r=-0.172; p = 0.144
|
r=-0.421; p = 0.007
|
Eloc
|
< 0.001(-3.991)
|
r = 0.026; p = 0.437
|
r=-0.402; p = 0.010
|
Regions for nodal Eglob
|
|
|
|
Left superior frontal dorsolateral gyrus
|
0.049(-2.002)
|
r = 0.029; p = 0.430
|
r=-0.068; p = 0.354
|
Left inferior frontal opercular gyrus
|
0.024(-2.300)
|
r=-0.057; p = 0.364
|
r=-0.331; p = 0.030
|
Right inferior frontal opercular gyrus
|
0.024(-2.305)
|
r=-0.177; p = 0.138
|
r=-0.289; p = 0.052
|
Right superior frontal medial orbital gyrus
|
0.017(-2.438)
|
r=-0.087; p = 0.298
|
r=-0.396; p = 0.011
|
Left anterior cingulate
|
0.039(-2.100)
|
r=-0.187; p = 0.124
|
r=-0.300; p = 0.045
|
Right parahippocampal gyrus
|
0.031(-2.198)
|
r=-0.347; p = 0.014
|
r=-0.174; p = 0.166
|
Left temporal pole: middle temporal gyrus
|
0.008(-2.705)
|
r=-0.146; p = 0.184
|
r=-0.232; p = 0.097
|
Regions for nodal Eloc
|
|
|
|
Right middle frontal orbital gyrus
|
0.018(t=-2.416)
|
r=-0.062; p = 0.353
|
r=-0.068; p = 0.354
|
Right superior frontal medial orbital gyrus
|
0.007(t=-2.774)
|
r = 0.157; p = 0.167
|
r=-0.331; p = 0.030
|
Right gyrus rectus
|
0.021(t=-2.363)
|
r=-0.185; p = 0.126
|
r=-0.289; p = 0.052
|
Right insula
|
0.014(t=-2.505)
|
r = 0.02; p = 0.452
|
r=-0.396; p = 0.011
|
Right inferior occipital gyrus
|
0.006(t=-2.808)
|
r=-0.041; p = 0.400
|
r=-0.300; p = 0.045
|
Right paracentral lobule
|
< 0.001(t=-4.199)
|
r = -0.302; p = 0.029
|
r=-0.174; p = 0.166
|
Left putamen
|
0.008(t=-2.747)
|
r= -0.081; p = 0.310
|
r=-0.232; p = 0.097
|
Key: NC, normal controls; SCD, subjective cognitive decline; Eglob, global efficiency; Eloc, local efficiency; Lp, Characteristic Path Length; Cp, Clustering coefficient |
Figure 4 Age-related differences of global efficiency and characteristic path length (A) and the distribution of regions with significant age-related differences of nodal global efficiency(B). The anatomical structures were visualized by BrainNet Viewer toolbox.
For nodal global efficiency, the regression model revealed significant age\(\times\)group interaction effects in 7 regions (Table 2) (all p < 0.05), including left superior frontal dorsolateral gyrus, bilateral inferior frontal opercular gyrus, right superior frontal medial orbital gyrus, left anterior cingulate and paracingulate gyri, right parahippocampal gyrus and temporal pole of middle temporal gyrus (Fig. 4B). Partial correlation analyses demonstrated that nodal global efficiency of bilateral inferior frontal opercular gyrus (left: r=-0.331, p = 0.03; right: r=-0.289, p = 0.05), right superior frontal medial orbital gyrus(r=-0.396, p = 0.011), left anterior cingulate and paracingulate gyri(r=-0.300, p = 0.045) showed significant negative correlations with age with in SCD group, while nonsignificant correlations were found in NC group (Fig. 4B). In addition, the nodal global efficiency of right parahippocampal gyrus (r=-0.347, p = 0.014) showed significant negative correlation with age in NC group, otherwise in SCD group (Fig. 4B). For nodal local efficiency, 7 regions demonstrated significant age\(\times\)group interaction effects in the stepwise regression model (Table 2) (all p < 0.05), including right middle frontal orbital gyrus, right superior frontal medial orbital gyrus, right insula, right inferior occipital gyrus, right paracentral lobule and left putamen (Fig. 5B). Partial correlation analyses revealed that nodal local efficiency of right insula (r=-0.486, p = 0.002), right inferior occipital gyrus (r=-0.292, p = 0.05) and left putamen (r=-0.379, p = 0.014) showed significant negative age-related within SCD group, while nonsignificant correlations were found in NC group (Fig. 5B). Besides, the right paracentral lobule (r=-0.302, p = 0.029) showed significant decrease with age in nodal local efficiency in NC group, but not within SCD group (Fig. 5B).
Figure 5 Age-related differences in local efficiency and clustering coefficient (A) and the distribution of regions with significant age-related differences of nodal local efficiency(B). The anatomical structures were visualized by BrainNet Viewer toolbox.
For the connectivity at divisional level (association area, limbic, paralimbic, primary sensory, and subcortical), the stepwise regression model revealed that intra-connectivity of paralimbic system, inter-connectivity between association area and paralimbic system, as well as inter-connectivity between paralimbic system and subcortical showed significant age\(\times\)group interaction effects across all participants(p < 0.05). For partial correlation analyses, the inter-connectivity between association area and paralimbic system (r=-0.321, p = 0.034) showed significant correlation with age within SCD group, while nonsignificant correlation was found in NC group. Additionally, the intra-connectivity of paralimbic system in NC group showed significant correlation with age but not in SCD group. The connectivity strength of the subnetwork calculated by NBS exhibited significant age\(\times\)group interaction effects in the regression model (t=-11.514, p < 0.001, beta=-0.799), and the followed partial correlation analyses revealed significant correlation between age and the calculated connectivity strength within SCD group (r=-0.412, p = 0.009), while nonsignificant correlation was found in NC group (Fig. 3F). All comparisons in this section were performed at the sparsity of 30%.
3.4 Associations between altered network metrics and clinical performance
For small-world properties, the sigma in SCD group showed significant correlation with delayed recall score of AVLT (r=-0.329, p = 0.033) and recognition score of AVLT (r=-0.297, p = 0.049). Besides, the gamma showed significant correlation with recognition score of AVLT (r=-0.308 p = 0.043) in individuals with SCD. Within SCD group, the nodal global efficiency of left superior frontal dorsolateral gyrus (r = 0.473, p = 0.003) and right inferior frontal opercular gyrus (r=-0.305, p = 0.045) showed significant correlation with delayed recall score of AVLT. Then, lower nodal efficiency of bilateral inferior frontal opercular gyrus (left: r=-0.331, p = 0.032; right: r=-0.362, p = 0.021) were correlated with higher recognition scores of AVLT in SCD group. The nodal local efficiency of right insula exhibited significant correlation with delayed recall score of AVLT (r = 0.321, p = 0.036). In addition, the nodal local efficiency in right superior frontal medial orbital gyrus (r = 0.321, p = 0.037) showed significant correlation with MoCA scores. For connectivity strength, the strength of feeder (-0.302, p = 0.046) connectivity showed significant correlation with MoCA scores.
3.5 Reproducibility findings
The effects of the sparsity threshold for global network metrics were validated, and the results were summarized above. All 12 subcortical regions were excluded from the AAL atlas and the cortical network were constructed by using the same method. All cortical networks we have constructed were characterized with small world properties, which the Lp were similar with the matched random networks (lambda ≈ 1) and the Cp were higher than the matched random networks (gamma > 1) in all sparsity thresholds (sigma > 1) (Fig. S1). The clustering coefficients in individuals with SCD showed higher significance compared with NC group, at sparsity range of 15%-44%, except 19%-20% (all p < 0.05). Then, the local efficiency exhibited decrease in SCD group compared with NC at all sparsity thresholds excepted 21% (all p < 0.05). Thereafter, the characterized path length was longer in SCD group compared with NC at the sparsity of 23%-45% (all p < 0.05). In addition, the global efficiency was higher in NC group compared with SCD at the sparsity of 23%-45% (all p < 0.05). For the small world properties, sigma (at the sparsity of 23%-39% and 41%-45%) and gamma (at the sparsity of 15%-16%, 21%-34% and 43%-45%) were higher in NC group compared with SCD group (all p < 0.05). The global network metrics in this section were consistent with the findings before (Fig. S1). For rich-club organization, 15 hub regions were found in each group (Table S2) and the strength of feeder connections were lower in SCD group compared with NC. Also, the subnetwork based on NBS can divide the two groups accurately (edge-p < 0.01 and component-p = 0.005).
For age-related difference, the local/global efficiency (Eloc: t=-3.737, p < 0.001, beta=-0.396; Eglob: t=-2.916, p = 0.005, beta=-0.319), clustering coefficients (t=-3.381, p = 0.001, beta=-0.364), shortest path length (t = 2.907, p = 0.005, beta = 0.318) and sigma (t=-2.456, p = 0.016, beta=-0.273) showed significant age\(\times\)group interaction effects in the regression model. Partial correlation analyses revealed the global efficiency (r=-0.462, p = 0.003) and shortest path length (r=-0.460 p = 0.004) showed significant correlation with age within SCD group but not in NC (Table S3). For regional global efficiency, similar distributions of region were found significantly correlated with age, which mainly located in bilateral inferior frontal opercular gyrus, right superior frontal medial orbital gyrus left anterior cingulate and paracingulate gyri and right parahippocampal gyrus (Table S3). In general, the findings showed slight effects of subcortical structures for global metrics, but changed local network properties.
DISSCUSSION
In this study, we used individual morphometric networks and graph theory analysis to assess the altered topological properties in individuals with SCD and age-related differences compared with NC. Main findings were as follows: (1) the global network metrics such as global/local efficiency, clustering coefficients and small world properties decreased in SCD compared with NC; (2) the altered nodal network metrics in SCD mainly located in prefrontal lobe, parietal lobe and subcortical system; (3) compared with NC, significant decreases of global/local efficiency with increasing age were found in SCD subjects; (4) the significant age-related differences of nodal network metrics between two groups mainly located in prefrontal lobe; (5) disrupted strength of paralimbic system and feeder connectivity were found in SCD group. Finally, the robustness of the results was validated by using different sparsity thresholds as well as the effects of subcortical structures.
4.1 Aberrant topological organization in individuals with SCD
Compared with NC, we have observed lower global/local efficiency, clustering coefficients, sigma and gamma, as well as higher shortest path length in SCD group. The results of global/local efficiency, clustering coefficients, shortest path length and gamma were consistent with previous WM structural network studies of SCD(Shu et al. 2018; Yan et al. 2018). In addition, the strength of network connectivity in SCD group was lower than in NC group. Several previous morphometric networks analyses of MCI and AD have revealed that significant increased shortest path length and decreased global efficiency were exhibited in AD-related patients(Li et al. 2018; He et al. 2008; Yao et al. 2010; Li et al. 2016). In contrast with our results, the clustering coefficients in AD-related patients based on GM networks exhibited significant increase(He et al. 2008; Li et al. 2016; Yao et al. 2010). In our opinion, the main factor is that those studies were based on group networks, while our study was based on individual networks. Specifically, the group network means “one group, one network”, while the individual network means the network are constructed for all participants separately. Thus, the group network may reduce individual differences and need large number of participants. With the studies based on functional MRI, decreased strength of functional connectivity in individuals with SCD, AD and MCI patients were found(Wang et al. 2013; Bai et al. 2011; Li et al. 2019; Viviano et al. 2019). With regard to the WM structural network studies based on diffusion MRI, increased shortest path length and decreased efficiency exhibited in AD and MCI patients(Shu et al. 2012; Zhao et al. 2017; Lo et al. 2010; Daianu et al. 2015; Cao et al. 2020). In addition, the direction of the topological organization alterations in individuals with SCD were similar with the AD-related patients, which indicate higher risk to cognitive decline in the future.
The functional network based on fMRI coordinated brain activity correlations of the fluctuate magnetic properties of oxygenated blood between regions, which can reflect the synchronized activity between brain regions(Alexander-Bloch et al. 2013). Disrupted topological organization of functional network in AD-related patients meant that functional integration and segregation of the brain activity was deterioration. While, WM fiber bundles across the entire brain traced in diffusion MRI was labelled ‘WM anatomical network’, which can reflect the WM fiber connections between brain regions. Altered topological organization of WM structure network in AD-related patients meant that the WM fiber connections were impaired. Then, the GM network coordinated the morphological features between GM regions of brain, which can reflect the synchronized anatomical changes of GM in brain(Alexander-Bloch et al. 2013). Altered topological organization of GM network in AD-related patients may be suggestive of GM loss in correlated regions or localized degeneration in one region. The similarities in results across imaging modalities meant that the synchronized anatomical change indeed results from brain connectivity of some kind, such as synchronized brain activity change and/or WM fiber connections.
Our results partial consistent with previous studies across imaging modalities above, which meant that the individuals GM networks can accurately explore the structural alterations of brain at network level in individuals with SCD independently.
For rich-club organization, the distributions of hub regions were similar in two groups, and the decreased strength of feeder connectivity was showed in SCD group, which our results are consistent with previous network SCD studies based on diffusion MRI(Shu et al. 2018; Yan et al. 2018). Previous studies demonstrated that in AD and MCI patients the rich-club organization was persevered, and the connections including peripheral regions were attacked(Cao et al. 2020; Daianu et al. 2015). Although the hub regions characterized with high activity and metabolism may accelerate pathology of AD(Buckner et al. 2009), whether the attacks beginning within hub regions or nonhub regions remains unclear. Our results showed that the connections including peripheral regions were vulnerability in individuals with SCD compared with NC.
For functional organization, the intra- and inter-connections including paralimbic system decreased in SCD group compared with NC in this study. The paralimbic system is one of the transmodal areas with highest synaptic levels of sensory-fugal processing(Mesulam 1998), which plays a causal role in activating attentional and memory systems within association areas to facilitate controlled processing of stimuli during cognitively demanding tasks(Supekar et al. 2009; Sridharan et al. 2008). In our results, the decreased integration of paralimbic system and association areas in individuals with SCD may induce the decline of the cognitive ability. In addition, the altered intra-connections in paralimbic system and inter-connections between paralimbic system and subcortical in SCD group may enhance that individuals with SCD are at higher risk to cognitive decline in the future compared with NC.
The GM degeneration in individuals with SCD has been detected in previous studies(Zhao et al. 2019; Rabin et al. 2017). However, the underlying neuropathological mechanism of GM degeneration in SCD still remains barely unknown. We have revealed the GM degeneration at a large system level, and studies combined with multimodal imaging techniques should be considerate by researchers in the future.
4.2 Age-related differences of network metrics
For global network metrics, the global efficiency and local efficiency in SCD showed significant age\(\times\)group interaction effects, which was consistent with previous results from MCI studies(Zhao et al. 2017). Then, the partial correlation analyses revealed that global efficiency and local efficiency were significant correlated with age in individuals with SCD, while nonsignificant correlations were found in NC subjects. Moreover, similar with global/local efficiency, the shortest path length and clustering coefficients showed significant age\(\times\)group interaction effects, and there were significant relationships between those properties and age in individuals with SCD. Age is the main risk factor of AD, and accelerated decrease of global network metrics with age in SCD indicates that SCD subjects with the future risk of cognitive decline.
For nodal efficiency metrics, some brain regions showed decrease with age in individuals with SCD, including bilateral inferior frontal opercular gyrus, right superior frontal medial orbital gyrus, left anterior cingulate and paracingulate gyri, right insula, right inferior occipital gyrus and left putamen. These regions mainly located in prefrontal lobe and subcortical system. While, the lateral prefrontal cortex plays a critical role in working memory-executive function network(Mesulam 1998), and the anterior medial prefrontal cortex belong to midline core of default mode network (DMN)(Montembeault et al. 2016). In addition, the prefrontal regions and DMN are vulnerable to AD pathology(Zhou et al. 2015; McKenna et al. 2016; Simic et al. 2014).
Moreover, the nodal efficiency of parahippocampal gyrus showed significant correlation with age in NC group, while not in SCD group. The parahippocampal gyrus is an important region for memory function, which has been shown to preferentially targeted in AD and MCI patients(Yin et al. 2015). To the best of our knowledge, majority of individuals with SCD will not show progressive cognitive decline(Jessen et al. 2020) and it is a stage independent with AD and MCI, indicating that the parahippocampal gyrus connections in individuals with SCD may be similar to NC compared with AD-related patients. A longitudinal study revealed that MCI patients showed accelerated GM decrease compared with normal controls in whole brain volume, temporal gray matter, and orbitofrontal and temporal association cortices, including the hippocampus(Driscoll et al. 2009). Our study revealed that individuals with SCD showed accelerated GM degeneration with age at a macroscopic scale. However, the underlying mechanism of the age-related changes, AD-related pathology changes and their co-pathologies were still need to explore.
4.3 Correlation between network metrics and clinical scores
Clinical neuropsychological testing is a conventional method for memory examinations and disease-assisted diagnoses. Then, two test scores were used for correlation analysis, including AVLT and MoCA. We have observed that the delayed recall score of AVLT exhibited significant correlation with sigma and gamma in individuals with SCD. Moreover, significant correlation between sigma and recognition score of AVLT was found in individuals with SCD but not in NC subjects. Sigma and gamma were classical small world indices, and if the indices were higher, the property of small world was stronger. In the sense, stronger the small world organization, the higher and more efficient information segregation and integration. A longitudinal study based on morphometric networks for individuals with SCD revealed that lower gamma and lambda values were significantly associated with steeper decline in global cognition including memory decline(Verfaillie et al. 2018). However, previous WM structural network analyses(Shu et al. 2018; Zhao et al. 2017) revealed that the connectivity of rich-club, feeder and local were correlated with the delayed recall score of AVLT, but not showed in our study. In this study, the strength of feeder connectivity showed significant correlation with MoCA scores. In our opinion, the difference between our study and previous studies is mainly due to the different neurobiological mechanisms behind GM networks and WM networks. The correlative variation in GM network means the changing of regional morphology, and the correlations in WM network represents the strength of WM fiber connectivity between two regions. The GM network and WM network characterize the network in human brain in two ways. In addition, majority of correlations between the network measures and cognitive test scores were negative, a more randomly organized GM network would be a reason(Verfaillie et al. 2018). In the end, the coordination of GM network, WM network and functional network to investigate the alterations in individuals with SCD should be considered in future studies.
4.4 Limitations
Methodological issues in our research should be addressed. First, the sample size is small. Although we have constructed the individual network in this study, a large sample size will be better. Second, cross-sectional samples were used in this study, so a longitudinal MRI data will be collected for future study. Third, only distribution of GM density was used to construct the networks, and thus more morphological indices will be used to define the network connections. Fourth, very limited neuropsychological battery adopted was always a limitation. As the modified research framework for SCD was published(Jessen et al. 2020), more comprehensive neuropsychological tests should be addressed.