Altered brain functional networks in patients with breast cancer after neoadjuvant chemotherapy Running title: Disrupted Network in Breast Cancer After Chemotherapy

Altered brain functional networks in patients with breast cancer after neoadjuvant chemotherapy Running title: Disrupted Network in Breast Cancer After Chemotherapy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Altered brain functional networks in patients with breast cancer after neoadjuvant chemotherapy Running title: Disrupted Network in Breast Cancer After Chemotherapy Jing Yang, Yongchun Deng, Daihong Liu, Yixin Hu, Yu Tang, Xiaoyu Zhou, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4184945/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background In this study, we prospectively investigated changes in the brain connectome at multiple time points in breast cancer (BC) patients treated with neoadjuvant chemotherapy (NAC). Methods Fifty-five participants with a diagnosis of BC underwent clinical assessments and fMRI at three timepoints, including before NAC (tp1), after the first cycle of NAC (tp2), and the end of the NAC regimen (tp3). Two matched healthy controls (HCs) groups received the same assessments at matching time points were also enrolled. Brain functional networks were constructed and analyzed using graph theory approaches to quantify the effect of NAC on brain cognitive dysfunction. We analyzed changes in brain connectome metrics both in HC and patient group and explored the relationship between these changes and clinical scales. Patient-subgroups were created by clinical subtype, chemotherapy regimen and menopausal status, and longitudinal subgroup analysis was performed. Results There were no longitudinal differences within the two HC groups, and no differences between the two HC groups and patient group at tp1. BC patients who underwent NAC showed significantly increased global efficiency ( p = 0.032), decreased characteristic path length ( p = 0.020), and altered nodal centralities mainly in the frontal-limbic system and cerebellar cortex. There were few changes between the two chemotherapy sessions. Changes in the topological parameters were correlated with changes in clinical scales but did not differ between subgroups. Conclusions Our findings demonstrated that NAC might affect brain functional connectivity in BC patients, especially in the early stage. breast cancer (BC) neoadjuvant chemotherapy (NAC) cancer-related cognitive impairment (CRCI) graph theory connectome Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Cancer-related cognitive impairment (CRCI) is a common and important problem in breast cancer (BC) survivors and patients, including problems in memory, attention, executive function and processing speed [ 1 , 2 ]. It damages social, occupational and familial functioning and ultimately reduces individuals’ quality of life [ 3 ]. Studies have found that elderly BC patients with CRCI have a 10–15% probability of developing dementia each year [ 4 , 5 ]. However, the complex mechanism of CRCI is still being explored. Therefore, a deeper theoretical understanding will help improve the early diagnosis and treatment of CRCI. Functional and structural imaging with MRI has been useful not only for understanding illness biology but also for evaluating treatment effects [ 6 , 7 ]. Previous neuroimaging studies have shown that chemotherapy can directly affect the structural integrity [ 8 , 9 ] and functional activity [ 6 , 10 ] in the central nervous system. This central neurotoxicity has a certain time-dependent effect that can be divided into acute, subacute and delayed stages [ 11 ]. Some studies have shown that abnormal changes in brain areas were mainly concentrated in the dorsolateral prefrontal cortex, cingulate and parietal lobe in the postchemotherapy BC cohort [ 12 – 16 ]. A relationship has been identified between these changes and scores on a cognition-related scale (Functional Assessment of Cancer Therapy-Cognitive Function, FACT-Cog), which could indicate that these brain regions with change play important roles in maintaining cognitive balance in BC patients after chemotherapy. However, many longitudinal neuroimaging studies have been focused on a single chemotherapy stage, lacking examination of sustained changes in brain injury throughout the different stages of the whole chemotherapy cycle [ 12 , 17 , 18 ]. Graph-based analysis provides a powerful tool for characterizing topological properties of brain networks (i.e., the connectome) [ 19 , 20 ] and their alteration in neuropsychiatric diseases [ 21 – 24 ]. Watts et al. classified networks into three types according to the range and number of connections, which are regular network, random network and small-world network [ 25 ]. Advances in graph-based theoretical analysis have enabled quantification of the whole brain’s topological properties [ 19 , 26 ], revealing a ‘small-world’ organization (characterized by both high local specialization and high global integration between brain regions) [ 27 , 28 ]. These networks are anatomically and functionally disrupted in neuron-psychiatric disease [ 24 , 29 ], which exhibit more randomized as well as more regular networks. To date, there are still few studies on the connectivity pathways between brain regions after chemotherapy in BC patients [ 30 , 31 ]. The topological organization of the brain connectome in BC patients after chemotherapy is still poorly understood. The results of some studies have indicated that the risk of cognitive dysfunction increases when general anesthesia is used [ 32 , 33 ]. To avoid the confounding effects of anesthesia and surgery, we purposefully recruited BC patients who had undergone neoadjuvant chemotherapy (NAC), which can convert a locally advanced/inoperable breast tumor into an operable tumor or cause downstaging, resulting in a small increase in breast conservation rates [ 34 , 35 ]. Based on the above, in the current study we used a graph-based model to construct an individual functional network to explore the effects of NAC on brain connectivity in BC patients in this current study. Data for the present study were collected in three waves: baseline (time point 1, tp1: before NAC), after the first NAC cycle (time point 2, tp2), and at the end of the NAC regimen (time point 3, tp3). We set out to test two hypotheses. First, given the evidence of abnormal structural and functional neuroimaging reported in BC participants after short-term chemotherapy [ 2 , 13 , 16 ], we hypothesized that BC patients would show acute damage to the brain, reflected in altered topological properties of functional networks after the first NAC cycle. Second, based on reported relationships between brain network characteristics and individual behavioral responses [ 30 ], we hypothesized that there would be a correlation between brain network topological alterations and clinical characteristics. Results Participants A total of 60 BC patients completed the whole cycle of NAC, and data from 5 were excluded because of poor data quality, missing scans, or excessive motion. No patients reported falling asleep during any scans. Close age/duration of education match was achieved across subtype luminal A (14), luminal B (13) and HER2-positive (28); and treatment-divided: group A (33) and group B (22); as well as menopause (19) and nonmenopause (20). In addition, we recruited HC group 1, consisting of 20 women, for whom the average interval between tp1 and tp2 was 30 days, and we recruited HC group 2, consisting of 18 women, for whom the average interval between tp1 and tp3 was 137 days. There were significant differences in educational attainment between the patient group and each HC group. The details were shown in Table 2 and Table 3 . Table 2 Demographic data and clinical characteristics of study participants a . Clinical and Demographic Variables All BC (n = 55) HC 1 (n = 20) HC 2 (n = 18) P value HC 1 Vs. HC 2 c BC Vs. HC 1 c BC Vs. HC 2 c Demographic Age (years) b 49.33 ± 8.55 52.85 ± 11.46 44.94 ± 13.25 0.031 0.113 0.109 Gender (male: female) 0:55 0:20 0:18 NA NA NA Education (years) b 8.74 ± 3.70 12.65 ± 3.54 15.94 ± 4.60 0.002 < 0.001 < 0.001 Neuropsychological Characteristics TP1 TP2 TP3 F value p value d TP1 TP2 TP1 TP3 HC 1 e HC 2 e BC Vs. HC 1 c BC Vs. HC 2 c Working memory DST forwards 7.02 ± 1.21 7.14 ± 1.42 7.02 ± 1.54 0.092 0.912 8.40 ± 1.85 8.50 ± 1.98 10.56 ± 1.76 10.33 ± 1.64 0.872 0.545 0.051 0.078 DST backwards 4.63 ± 1.19 4.33 ± 1.04 4.12 ± 0.99 2.285 0.106 4.55 ± 2.08 4.50 ± 1.93 6.28 ± 2.08 6.00 ± 2.20 0.649 0.566 0.104 0.003 Emotion test SAS 23.79 ± 4.89 22.41 ± 4.07 21.97 ± 2.70 2.390 0.096 27.40 ± 9.18 24.89 ± 6.27 24.28 ± 3.69 24.67 ± 4.59 0.600 0.844 0.130 0.241 SDS 23.07 ± 7.95 23.27 ± 5.58 22.52 ± 4.33 0.240 0.787 28.75 ± 8.33 25.05 ± 5.39 24.11 ± 5.28 24.28 ± 3.82 0.201 0.842 0.020 0.929 FACT VFT 30.03 ± 8.38 36.65 ± 9.21 37.76 ± 8.06 0.375 0.688 39.80 ± 16.45 42.55 ± 14.32 47.56 ± 11.06 49.67 ± 10.93 0.519 0.653 0.038 0.003 PCI 60.44 ± 8.82 59.05 ± 7.80 58.04 ± 8.69 0.858 0.426 52.95 ± 10.16 59.00 ± 10.78 63.67 ± 6.96 61.72 ± 8.77 0.266 0.296 0.399 0.989 OTH 15.38 ± 2.41 15.48 ± 1.39 15.29 ± 1.69 0.113 0.894 13.11 ± 3.46 14.11 ± 2.28 15.33 ± 1.24 15.11 ± 1.94 0.166 0.763 0.098 0.309 PCA 20.08 ± 4.40 18.56 ± 3.40 18.09 ± 3.66 3.097 0.049 19.32 ± 6.76 19.93 ± 5.52 23.78 ± 4.26 22.83 ± 4.85 0.765 0.275 0.107 0.585 QOL 14.90 ± 2.18 14.76 ± 2.31 14.81 ± 2.41 0.042 0.959 12.11 ± 3.95 13.03 ± 2.89 14.50 ± 2.20 13.39 ± 2.93 0.688 0.918 0.074 0.397 Abbreviations: BC, breast cancer; HC, healthy control; DST, the digit span test; SAS, Self-Rating Anxiety Scale; SDS, Self-Rating Depression Scale; FCAT, functional assessment of cancer therapy; VFT, the verbal fluency test; OTH, comments from others; PCA, perceived cognitive abilities; PCI, perceived cognitive impairments; QOL, impact on quality of life; TP1, time point 1; TP2, time point 2; TP3, time point 3. a Data are presented as mean ± standard deviation. b Age defined at the time of MRI scanning. c p value by two-tailed two-sample t test. d p value by One-way ANOVA F test. e p value by paired t test. Table 3 Demographic data and clinical characteristics of patient-subgroups a . Clinical and Demographic Variables Subgroup divided by subtype P value Subgroup divided by treatment P value Subgroup divided by menopause P value Luminal A Luminal B Her2-positive Subgroup A Subgroup B Menopause Non-menopause graphic Sample size 14 13 28 33 22 19 20 Age (years) b 51.62 ± 5.99 53.78 ± 5.07 46.22 ± 8.91 0.063 c 48.14 ± 9.20 51.38 ± 4.98 0.052 c 54.73 ± 4.28 44.70 ± 8.68 < 0.001 c Gender (male: female) 0:14 0:13 0:28 0:33 0:22 0:19 0:20 Education (years) b 9.46 ± 2.90 9.44 ± 4.21 7.68 ± 3.47 0.312 c 8.76 ± 3.77 8.44 ± 2.90 0.419 c 8.47 ± 3.34 8.84 ± 4.14 0.753 c Clinical characteristics Cancer stage Ⅱ 4 5 6 0.518 d 7 8 0.216 d 3 6 < 0.001 d Ⅲ 10 8 22 26 14 16 14 Chemotherapy regimen AC→T 3 4 6 0.479 d 13 0 < 0.001 d 4 4 < 0.001 d EC→T 0 1 0 1 0 0 1 TAC 6 2 9 17 0 8 6 AT 1 1 0 2 0 0 1 TCbHP 2 2 9 0 13 3 7 TCbHB 2 3 4 0 9 4 1 Abbreviations: AC→T, doxorubicin + cyclophosphamide + docetaxel; AT, doxorubicin + docetaxel; EC→T, epirubicin + cyclophosphamide + docetaxel; TAC, paclitaxel + doxorubicin + cyclophosphamide; TCbHB, docetaxel + carboplatin + trastuzumab + pyrotinib; TCbHP, docetaxel + carboplatin + trastuzumab + pertuzumab. a Data are presented as mean ± standard deviation. b Age defined at the time of MRI scanning. c p value by two-tailed two-sample t test. d p value by two-tailed Pearson Chi-square test. Treatment Response In the patient group, BC patients showed significant clinical scale changes compared with their baseline assessments as shown in Table 2 . The PCA scores decreased significantly ( p = 0.049), specifically at tp2 ( p = 0.004) and tp3 ( p = 0.030), the SAS scores showed an increasing trend ( p = 0.096), and there were no other clinical scale changes. In the two HC groups, there were no changes in clinical scales between the corresponding time intervals. The results of the inter-group comparison at TP1 showed that the scores of VFT DST backwards in the patient group were significantly lower than it in HC group 2. And the scores of SDS in the HC group 1 were higher than it in patient group (Table 2 ), however, these scores were in the normal range. Alterations in Brain Network Properties at Baseline In global measurements, each participant had a higher average clustering coefficient (γ > 1) and similar characteristic path length (λ ≈ 1) relative to random reference networks, showing that each network had a small world topology (γ/λ > 1.1). And the nodal level, brain regions with significant between-group differences were defined by at least 1 abnormal nodal property in the specific area (FDR corrected, p < 0.05). At baseline, there were no significant differences in any network properties between the two HC groups and the patient group, as well as the patient-subgroups (group 1: luminal A vs. luminal B vs. HER2-positive; group 2: A vs. B; group 3: menopause vs. nonmenopause). Alterations in Brain Network Properties at Follow-up In patient group, the nodal degree of the left paracentral gyrus showed a significant chemotherapy-by-time interaction ( p = 0.033), and the nodal efficiency of the left temporal gyrus showed a significant chemotherapy-by-responder interaction ( p = 0.029). However, these results were not statistically significant after FDR correction for multiple comparisons. The results showed that there were no significant differences in the effects of brain network properties of different subtypes at the observation time points. Therefore, in further analyses of changes after chemotherapy and their correlations, these subgroups were combined. Following chemotherapy initiation, BC patients showed increased E glob and decreased L p both at tp2 and tp3 (Table 4 , Fig. 1 ). For the significant nodal network alterations observed at tp2, BC patients showed decreased nodal centrality parameters in the right superior frontal gyrus, right inferior temporal gyrus, left frontal gyrus, left amygdala, left paracentral gyrus, left insula and in some areas of the cerebellar cortex (bilateral superior cerebellum gyrus: cerebellum_6; right inferior cerebellum gyrus: cerebellum_7R and vermis_8). Table 4 Longitudinal topological properties differences in patient group a . Topological properties p of ANOVA b p of post hoc b TP1 vs. TP2 TP1 vs. TP3 TP2 vs. TP3 Global level Path length 0.020 0.031 0.011 0.533 Global efficiency 0.032 0.025 < 0.001 0.126 Nodal efficiency Parietal_Sup_R 0.023 0.005 0.111 0.203 Parietal_Inf_R 0.009 0.050 0.002 0.360 Cerebelum_Crus2_L 0.041 0.037 0.031 0.423 Cerebelum_6_L 0.027 0.021 0.043 0.934 Cerebelum_6_R 0.011 0.011 0.015 0.930 Cerebelum_7b_R 0.038 0.026 0.068 0.590 Cerebelum_8_R 0.045 0.074 0.033 0.694 Vermis_7 0.013 0.056 0.005 0.199 Nodal degree Frontal_Inf_Orb_R 0.025 0.221 0.184 0.012 Insula_L 0.016 0.016 0.025 0.912 Parietal_Inf_R 0.012 0.250 0.003 0.085 Paracentral_Lobule_R 0.045 0.808 0.046 0.016 Heschl_L 0.042 0.116 0.025 0.254 Cerebelum_6_L 0.013 0.018 0.018 0.917 Cerebelum_6_R 0.004 0.004 0.006 0.923 Cerebelum_7b_R 0.048 0.046 0.050 0.795 Vermis_7 0.035 0.066 0.009 0.360 Vermis_8 0.048 0.039 0.045 0.881 Nodal betweenness Frontal_Inf_Orb_L 0.016 < 0.001 < 0.001 < 0.001 Supp_Motor_Area_L 0.033 0.005 < 0.001 < 0.001 Amygdala_L 0.014 < 0.001 < 0.001 0.015 Occipital_Mid_L 0.029 0.838 < 0.001 < 0.001 Fusiform_L 0.015 < 0.001 < 0.001 0.015 Paracentral_Lobule_L 0.006 0.015 < 0.001 < 0.001 Heschl_L 0.041 < 0.001 < 0.001 0.001 Temporal_Inf_R 0.026 0.001 < 0.001 < 0.001 Abbreviations: R, right; L, left. TP1, time point 1; TP2, time point 2; TP3, time point 3. a All the brain regions are from AAL (automated anatomical labeling). b The Benjamini-Hochberg false discovery rate correction was applied to each nodal measure, and the p value threshold taken as 0.05. At tp3, BC patients showed decreased nodal centrality parameters in the bilateral inferior parietal gyrus, bilateral paracentral gyrus, right inferior temporal gyrus and in some areas of the cerebellar cortex (bilateral superior cerebellum gyrus: cerebellum_6; right inferior cerebellum gyrus: cerebellum_8 and vermis_7/8). Between the two chemotherapy sessions, BC patients exhibited fewer differences (no differences at the global level), but alterations were still seen at the nodal level, including decreased nodal centrality parameters in the left inferior frontal gyrus and left inferior temporal gyrus (tp3 to tp2). The details are shown in Table 4 and Fig. 2 . In HC group 1, there were no significant differences both in global and nodal parameters between tp1 and tp2. In HC group 2, there were no significant differences both in global and nodal parameters between tp1 and tp3. Alterations of Subnetwork Connectivity The NBS tool was used to identify network alterations in brain regions showing between-group differences in nodal properties. In the patient group, compared with baseline, we identified significantly decreased connectivity alterations within networks comprising 8 nodes and 8 edges and increased connectivity alterations within networks comprising 5 nodes and 3 edges ( p < 0.05, T = 3.0, corrected for multiple comparisons using FWE-corrected network-level) in the BC patients at tp2. The details are shown in Fig. 3 . There were no abnormal connections between tp1 and tp3 or between the two chemotherapy sessions. Correlation Between Network Changes and Clinical Scales In the patient group, at tp2, the change in network efficiency of the left amygdala ( r = 0.358, p = 0.016) was positively correlated with the change in PCA score. Reductions in nodal betweenness of the left inferior frontal gyrus (tp1 to tp2) ( r = 0.299, p = 0.046) were also positively correlated with the PCA score changes (Fig. 4 ). These results were statistically significant even after FDR correction for multiple comparisons. We did not detect other significant correlations between altered network properties and clinical scale changes. Discussion The present study provides an initial window into the neural mechanisms underlying CRCI of BC patients. Using a sample comprised of NAC-treated BC patients and two HC cohorts at the same time, we prospectively investigated changes associated with NAC in vivo in indices of the functional brain connectome that may be related to cognitive dysfunction, excluding population baseline differences and temporal interference. Consistent with our hypothesis, early NAC caused acute damage to the brain functional connectome in BC patients, which manifested as disruption of the brain network after the first cycle of NAC. Importantly, shifts in CRCI-related topological properties were correlated with changes in clinical scales, more directly supporting the idea that brain functional connectome properties can reflect key pieces of the neural mechanisms by which NAC impacts symptoms. At the global level, compared with tp1, BC patients who experienced or were experiencing NAC exhibited a more randomized brain network both at tp2 and tp3, in which the brain network transforms from a small-world pattern to a relatively random network pattern. The occurrence of this pattern in our results is consistent with our first hypothesis and demonstrates that NAC could alter the brain functional connectome in BC patients. Some neuroimaging studies involving application of also applying connectome-based models on fMRI and structural MRI found a disrupted brain network in BC patients after chemotherapy [ 30 , 36 , 37 ]. Such a pattern of brain networks has been also improved in some neuropsychiatric disorders, such as posttraumatic stress disorder [ 21 ] and essential tremor [ 24 ], reflecting a less optimal topological organization in brain networks. In our study, the disrupted network pattern was characterized by an increased E glob (efficiency of exchanging information at the global level [ 38 ]) and decreased L p (the average distance from one node to any other node in the network, expressed as the number of links that must be traveled [ 25 ]), which could lead to information transfer disorder and constrain long-distance functional integration as reflected by the abnormal information processing speed and disrupted executive capacity of BC participants after chemotherapy [ 39 ]. At the nodal level, both tp2 and tp3 showed significant differences compared with tp1 that were related to the frontal-limbic system (right superior frontal gyrus, right inferior temporal gyrus, left frontal gyrus, left amygdala) and cerebellar regions. The frontal-limbic system is believed to support emotion processing and regulation, reward processing, and cognitive control [ 40 , 41 ]. Cerebellar regions are also increasingly recognized as important in higher cognitive functions [ 42 ]. Our observations in the frontal-limbic system suggested that alterations in this circuitry may play an important role in the neurobiology of cognitive dysfunction and mood dysregulation via impact on cognition and emotional reactivity [ 43 ]. As a key brain region in this circuit, the amygdala can not only regulate emotional and cognitive functions through the interaction of the limbic system or other brain regions [ 44 ], but also balance the brain microenvironment, which is dominated by inflammatory response, thus affecting the peripheral clinical symptoms of individuals [ 45 ]. Some studies have shown that BC and its treatment may activate microglia, which remain preactivated in brain regions, mainly the amygdala, even after completion of treatment and resolution of peripheral inflammation [ 46 ]. As has been shown in developmental [ 47 ] and aging models [ 48 ], such priming may increase neural sensitivity to peripheral inflammation with consequences for neural function, cognition, and behavior. This finding was consistent with the positive correlation between the change in nodal parameters of the amygdala and the change in PCA obtained in this study, which indicates that early NAC will bring an acute inflammatory response at the physiological level [ 49 ], and this response may be reflected as cognitive impairment at the individual level. The frontal gyrus in the frontal-limbic system is known to be involved in cognitive processing [ 50 ]. McDonald et al. [ 51 ] reviewed the currently published articles in the changes of brain structure in BC patients after chemotherapy, and the results revealed that the frontal gyrus showed a significant change after chemotherapy that was related to a failure to regulate cognition-related processes. Moreover, functional neuroimaging studies have also shown abnormalities after chemotherapy, manifested as a decrease in gray matter density in the frontal gyrus, which is consistent with our findings (decreased centrality of the frontal gyrus) [ 52 , 53 ]. In addition, it has been suggested that there is a cognitive neurobiological model demonstrating that functional deficits in the frontal gyrus may impair top-down control over the limbic areas [ 54 ]. However, the changes in the frontal gyrus only appeared after the first cycle of NAC. This was consistent with longitudinal studies suggesting that the frontal gyrus gradually recovers over time after early chemotherapy-related acute injury [ 16 , 39 ]. Taken together, alternations in the frontal-limbic system may be associated with acute impairment of cognitive regulation in BC patients after early chemotherapy. Some limitations must also be considered. First, the brain parcellation template we selected for constructing brain networks may affect the network analysis results. As different templates may lead to different estimates of graph theory parameters, this factor needs to be considered [ 55 ]. Second, the sample size of participants in our study was relatively small. Third, our study lacked a BC control group without NAC treatment. In addition, the HC groups in our study consisted of two cohorts that were assessed at two time points (tp1 vs. tp2 and tp1 vs. tp3) due to some people refusing follow-up. Since there was no difference within the HC group and no difference between HC and BC at baseline, we basically ruled out the aging effects on topological properties, and indicated that the tumor itself had no significant effect on topological properties at baseline. In the future, we will try to include and expand healthy controls with data acquisition time intervals identical to those of BC patients who received NAC. Fourth, in this study, the FACT-Cog, the clinical scale we used for the assessment of cognitive function, is not a direct objective measure. We will pay attention to and correct this problem in future clinical-scale collection processes. Conclusions In conclusion, the current study demonstrated that the brain functional network in BC patients after NAC showed a shift toward a more randomized pattern compared with that in BC patients at baseline. There were significant alterations in the clinical BC-related connectome mainly after the first cycle of NAC. The observed pattern of functional network connectivity alterations indicates acute abnormalities in cognitive function and emotional processing in BC patients after NAC. It suggests that the control of emotion and cognitive processing would be mainly affected by acute chemotherapy-related damage, manifested as changes in centrality and connectivity of brain regions within the frontal-limbic system. Material and Methods Participants Participants with BC were recruited at the Chongqing University Cancer Hospital from December 2020 to November 2021. This prospective study was approved by the local ethics committee of Chongqing University Cancer Hospital, and written informed consent was obtained from all BC participants (or their legal guardians) before enrollment. The inclusion criteria were as follows: (1) individuals with a history of primary BC (stage Ⅱ-Ⅲ at diagnosis); (2) individuals aged 30–70 years at the first diagnosis of BC; (3) individuals prepared to receive NAC prior to surgery; (4) right-handed individuals; and (5) female participants with BC. The Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) were used to quantify anxiety and depression symptoms, respectively [ 56 , 57 ]. The verbal fluency test (VFT) was used to distinguish different degrees of cognitive impairment and cognitive impairment of different causes [ 58 ]. The FACT-Cog was used to measure cognitive function [e.g., Perceived Cognitive Impairments (CogPCI) and Perceived Cognitive Abilities (CogPCA)] in cancer patients [ 59 ]. The exclusion criteria were as follows: (1) patients with diabetes mellitus and other metabolic diseases; (2) patients with craniocerebral diseases such as tumor, operation, trauma and stroke; (3) patients taking drugs that have neurological and mental effects; (4) patients with mental diseases (such as depression, anxiety, or alcohol addiction, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria); (5) patients receiving hormonal therapy; (6) the NAC process is interrupted for any reason; and (7) patients with contraindications to MRI examination. Healthy controls (HCs) had no history of cancer and chemotherapy, were recruited from the local region through poster advertisements and excluded using the same criteria as the patient group. Members of patient group underwent MRI scanning and were assessed for clinical characteristics at tp1 (one week before the first NAC), tp2 (one month after the first cycle of NAC), and tp3 (the end of the entire NAC regimen). The time intervals assessed in the HC groups matched the time intervals in the patient group. There were two HC groups in our study: HC group 1 was matched with tp1 and tp2 assessed in the patient group, and HC group 2 was matched with tp1 and tp3 assessed in the patient group. To control the interference of clinical factors, the current study involved defining patient-subgroups for analysis based on the clinical subtype, chemotherapy regimen and menopausal status of BC patients. Specifically, according to the clinical subtype of BC, the included samples were classified into three categories: luminal A, luminal B and HER2-positive; depending on the chemotherapy regimen, the samples were also classified into the anthracycline-containing (A) or the targeted + chemotherapy (B), as well as the menopause and nonmenopause groups. MRI Data Acquisition All subjects underwent scanning in a 3.0 Tesla whole-body MRI system (MAGNETOM Prisma, Siemens, Erlangen, Germany) with a 64-channel phased array head coil. A resting-state fMRI dataset was acquired at tp1, tp2 and tp3. The participants were instructed to keep their eyes closed and to think of nothing in particular during the acquisition. Head motion was minimized by using foam pads. The scanning parameters were as follows: repetition time (TR) 2000 ms, echo time (TE) 30 ms, flip angle 70, slice thickness 3 mm, single excitation, field of view 240 × 240 mm 2 , voxel size 3 × 3 × 3 mm 3 , and matrix dimensions 240 × 240 × 135. A total of 240 volumes were collected for each subject. Image quality was inspected by two experienced neuroradiologists who made decisions about excessive motion artifacts for scan inclusion and evaluated for clinical abnormalities in a double-blinded manner. MRI Data Preprocessing The image data were preprocessed by SPM12 ( http://www.fil.ion.ucl.ac.uk/spm ). The first 10 time points were discarded to avoid instability of the initial MRI signal. After correction for intravolume acquisition time delay and head motion, the images were spatially normalized to a 3 × 3 × 3 mm 3 Montreal Neurological Institute 152 template and then linearly detrended and temporally bandpass filtered (0.01–0.08 Hz) to remove low-frequency drift and high-frequency physiological noise. Finally, the global signal, white matter signal, cerebrospinal fluid signal, and motion parameters (three translational and three rotational parameters) were all regressed out [ 60 , 61 ]. According to the record of head motions within each fMRI run, all participants whose head motion exceeded 1.0 mm of translation or 1.0° of rotation in any direction were excluded. We also calculated the mean framewise displacement (FD) in the groups, and there was no difference in the mean FD between groups [ 62 ]. Network Construction and Topological Properties The network was constructed using GRETNA ( http://www.nitrc.org/projects/gretna/ ) [ 63 , 64 ]. We applied a wide range of sparsity (S) thresholds to all correlation matrices. The value of S was chosen to ensure that thresholded networks were estimable for the small-world index scalar, and the small-world index (σ) was > 1.1. The range of our S thresholds was set to 0.05 < S < 0.40 with an interval of 0.01 [ 25 , 63 ]. For each network metric, the area under the curve (AUC) was calculated, providing a summarized scalar for the topological characterization of brain networks independent of a single threshold selection. The AUC metric has proven to be sensitive in the detection of topological alterations in brain networks. First, the automated anatomical labeling (AAL) atlas [ 65 ] was used to divide the whole brain into 116 cortical and subcortical regions of interest, and each was considered a network node. Next, the mean time series was acquired for each region. The partial correlations of the mean time series between all pairs of nodes (representing their conditional dependencies by excluding the effects of the other 114 regions) were considered the edges of the network [ 63 , 66 ]. This process resulted in a 116 × 116 partial correlation matrix for each subject, which was converted into a binary matrix (i.e., adjacency matrix) according to a predefined threshold (see below for the threshold selection), where the entry aij was = 1 if the absolute partial correlation between regions i and j exceeded the threshold, and where aij was = 0 otherwise [ 63 ]. For the brain networks at each sparsity level, we calculated both global and nodal network metrics. The global metrics examined included small-world parameters (for definitions see [ 25 ]), such as the clustering coefficient C p , characteristic path length L p , normalized clustering coefficient γ, normalized characteristic path length λ, and small-world index σ, as well as network efficiency parameters (for details see [ 38 ]), such as the local efficiency E loc and global efficiency E glob . We calculated L p as the harmonic mean distance between all possible pairs of regions to address the disconnected graphs dilemma [ 67 ]. The nodal metrics examined included the node degree, efficiency, and betweenness centrality [ 68 ]. Finally, global and nodal network topological properties were included to establish a 277-dimensional graphic feature vector, where features 1–7 were global properties (i.e., C p , L p , γ, λ, E loc , E glob ) and features 8-277 were three nodal properties (i.e., degree, betweenness, efficiency) of 116 AAL regions. Detailed information is provided in Table 1 . Table 1 Global Network Properties. Segregation Integration Characteristic path length (L p ) Averaging the minimum number of connections that link all pairs of network nodes [ 1 ]. Clustering coefficient (C p ) Calculated by averaging over all network nodes, is equivalent to the fraction of each node’s neighbors that are also neighbors of each other [ 1 ]. Normalized characteristic path length (λ) λ is L p expressed relative to the mean L p of 100 matched random networks with the same number of nodes and edges as the real network. Normalized clustering coefficient (γ) γ is the normalized C p , expressed relative to the mean C p of 100 matched random networks (as defined above for λ). Global efficiency (E glob ) E glob measures how efficiently information is exchanged at the global level [ 2 ]. Local efficiency (E loc ) E loc measures how efficiently information is exchanged at the local level [ 2 ]: the level of clustering measured by E loc expresses the local connectedness of a network, with high values interpreted as high levels of local organization [ 3 ]. 1. Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks. Nature. 1998; 393(6684): 440-2. 2. Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001; 87(19): 198701. 3. Filippi M, van den Heuvel MP, Fornito A, He Y, Hulshoff Pol HE, Agosta F, et al. Assessment of system dysfunction in the brain through MRI-based connectomics. Lancet Neurol. 2013; 12(12): 1189-99. Statistical Analysis Intra-group analyses included longitudinal changes in the patient group and longitudinal changes in the two HC groups, respectively, which were analyzed by repeated measures analysis of variance (ANOVA) and paired sample t-test, respectively. The AUC values were used to compare each metric (small-world index, network efficiency, and nodal centrality measures) at different timepoints. To assess the trajectory of change in the patient group, pairwise post hoc Fisher's least significance difference tests were performed to determine which of the three timepoints differed significantly. For inter-group analysis, an independent sample t-test was used to compare the patient group and HC groups at baseline (including HC group 1 vs. patient group and HC group 2 vs. patient group; age and education years as covariates). For nodal property analyses, a false discovery rate (FDR)-corrected threshold of p < 0.05 was applied. Regression analysis was performed in R software (version 4.2.1). We investigated relations between changes in network measurements from baseline to endpoint and changes in clinical symptom scores (i.e., SAS). Age and duration of education were treated as covariates in these models. To assess chemotherapy-specific differences between A and B in topological alterations, chemotherapy-by-time interactions were examined using mixed effects models. All global and nodal measurements that differed between chemotherapy groups at baseline were examined across all three time points. The algorithmic models for BC patients according to clinical subtype and menopausal status were consistent with those described above. Abbreviations CRCI cancer-related cognitive impairment BC breast cancer HC healthy control NAC neoadjuvant chemotherapy tp time point SAS Self-Rating Anxiety Scale SDS Self-Rating Depression Scale VFT verbal fluency test FACT-Cog Functional Assessment of Cancer Therapy-Cognitive Function CogPCI Perceived Cognitive Impairments CogPCA Perceived Cognitive Abilities DSM-IV Diagnostic and Statistical Manual of Mental Disorders AUC area under the curve AAL automated anatomical labeling ANOVA analysis of variance FDR false discovery rate L p Characteristic path length Λ Normalized characteristic path length E glob Global efficiency C p Clustering coefficient γ Normalized clustering coefficient E loc Local efficiency. Declarations Ethical approval and consent to participate Approval was obtained from the local ethics committee of Chongqing University Cancer Hospital, and written informed consent was obtained from all BC participants (or their legal guardians). Consent for publication We obtained consent for publication from the participants or their legal guardians. Competing interests All authors report no conflicts of interest. Author details 1 Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China. 2 Department of Breast Cancer Center, Chongqing University Cancer Hospital, School of Medicine, Chongqing, China. 3 Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing University Cancer Hospital, School of Medicine, Chongqing, China. Funding This study was supported by the National Natural Science Foundation of China: 82071883; Chongqing Natural Science Foundation (CSTB2022NSCQ-MSX0823; CSTB2022NSCQ-MSX0951; CSTB2022NSCQ-MSX0396; cstc2021jcyj-msxmX0319; cstc2021jcyj-msxmX0313); the Discipline Construction and Upgrading Project of National Key Clinical Specialty Construction Project, the Talent Program of Chongqing (grant No. CQYC20200303137). Author Contribution YJ, DYC conceived of and designed the study. YJ, DYC, LDH, HYX, TY and ZXY organized the database. YJ, TY and ZJ designed the statistical analysis. YJ, LJ and WCF performed the statistical analysis. YJ, DYC, LDH, HYX and TY interpreted the results. YJ, ZXH and ZJQ finished the draft. Acknowledgement The authors would like to thank the study participants and their families. Availability of data and materials The datasets presented in this article are not readily available due to regulations governing ethical approval. References Park JH, Bae SH, Jung YS, Jung YM. [Prevalence and characteristics of chemotherapy-related cognitive impairment in patients with breast cancer]. J Korean Acad Nurs. 2015;45(1):118–28. Lawrence JA, Griffin L, Balcueva EP, Groteluschen DL, Samuel TA, Lesser GJ, et al. A study of donepezil in female breast cancer survivors with self-reported cognitive dysfunction 1 to 5 years following adjuvant chemotherapy. J Cancer Surviv. 2016;10(1):176–84. Myers JS. Cancer- and chemotherapy-related cognitive changes: the patient experience. Semin Oncol Nurs. 2013;29(4):300–7. Janelsins MC, Kesler SR, Ahles TA, Morrow GR. Prevalence, mechanisms, and management of cancer-related cognitive impairment. Int Rev Psychiatry. 2014;26(1):102–13. Correa DD, Ahles TA. Cognitive adverse effects of chemotherapy in breast cancer patients. Curr Opin Support Palliat Care. 2007;1(1):57–62. Miao H, Li J, Hu S, He X, Partridge SC, Ren J, et al. Long-term cognitive impairment of breast cancer patients after chemotherapy: A functional MRI study. Eur J Radiol. 2016;85(6):1053–7. Li H, Yao L, Jin P, Hu L, Li X, Guo T, et al. MRI and PET/CT for evaluation of the pathological response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis. Breast. 2018;40:106–15. Matsos A, Loomes M, Zhou I, Macmillan E, Sabel I, Rotziokos E, et al. Chemotherapy-induced cognitive impairments: White matter pathologies. Cancer Treat Rev. 2017;61:6–14. Niu R, Du M, Ren J, Qing H, Wang X, Xu G, et al. Chemotherapy-induced grey matter abnormalities in cancer survivors: a voxel-wise neuroimaging meta-analysis. Brain Imaging Behav. 2021;15(4):2215–27. Dumas JA, Makarewicz J, Schaubhut GJ, Devins R, Albert K, Dittus K, et al. Chemotherapy altered brain functional connectivity in women with breast cancer: a pilot study. Brain Imaging Behav. 2013;7(4):524–32. Taillibert S, Le Rhun E, Chamberlain MC. Chemotherapy-Related Neurotoxicity. Curr Neurol Neurosci Rep. 2016;16(9):81. Janelsins MC, Heckler CE, Peppone LJ, Kamen C, Mustian KM, Mohile SG, et al. Cognitive Complaints in Survivors of Breast Cancer After Chemotherapy Compared With Age-Matched Controls: An Analysis From a Nationwide, Multicenter, Prospective Longitudinal Study. J Clin Oncol. 2017;35(5):506–14. Apple AC, Schroeder MP, Ryals AJ, Wagner LI, Cella D, Shih PA, et al. Hippocampal functional connectivity is related to self-reported cognitive concerns in breast cancer patients undergoing adjuvant therapy. Neuroimage Clin. 2018;20:110–8. Deprez S, Vandenbulcke M, Peeters R, Emsell L, Smeets A, Christiaens MR, et al. Longitudinal assessment of chemotherapy-induced alterations in brain activation during multitasking and its relation with cognitive complaints. J Clin Oncol. 2014;32(19):2031–8. Menning S, de Ruiter MB, Veltman DJ, Boogerd W, Oldenburg HS, Reneman L, et al. Changes in brain activation in breast cancer patients depend on cognitive domain and treatment type. PLoS ONE. 2017;12(3):e0171724. McDonald BC, Conroy SK, Ahles TA, West JD, Saykin AJ. Gray matter reduction associated with systemic chemotherapy for breast cancer: a prospective MRI study. Breast Cancer Res Treat. 2010;123(3):819–28. Ho E, Brown A, Barrett P, Morgan RB, King G, Kennedy MJ, et al. Subclinical anthracycline- and trastuzumab-induced cardiotoxicity in the long-term follow-up of asymptomatic breast cancer survivors: a speckle tracking echocardiographic study. Heart. 2010;96(9):701–7. de Ruiter MB, Reneman L, Boogerd W, Veltman DJ, Caan M, Douaud G, et al. Late effects of high-dose adjuvant chemotherapy on white and gray matter in breast cancer survivors: converging results from multimodal magnetic resonance imaging. Hum Brain Mapp. 2012;33(12):2971–83. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10(3):186–98. Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, et al. Toward discovery science of human brain function. Proc Natl Acad Sci U S A. 2010;107(10):4734–9. Suo X, Lei D, Chen F, Wu M, Li L, Sun L, et al. Anatomic Insights into Disrupted Small-World Networks in Pediatric Posttraumatic Stress Disorder. Radiology. 2017;282(3):826–34. Li L, Lei D, Li L, Huang X, Suo X, Xiao F, et al. White Matter Abnormalities in Post-traumatic Stress Disorder Following a Specific Traumatic Event. EBioMedicine. 2016;4:176–83. Lei D, Ma J, Zhang J, Wang M, Zhang K, Chen F et al. Connectome-Scale Assessments of Functional Connectivity in Children with Primary Monosymptomatic Nocturnal Enuresis. Biomed Res Int. 2015; 2015: 463708. Yang J, Lei D, Peng J, Suo X, Pinaya WHL, Li W, et al. Disrupted brain gray matter networks in drug-naïve participants with essential tremor. Neuroradiology. 2021;63(9):1501–10. Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks. Nature. 1998;393(6684):440–2. Salvador R, Suckling J, Coleman MR, Pickard JD, Menon D, Bullmore E. Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb Cortex. 2005;15(9):1332–42. He Y, Chen ZJ, Evans AC. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb Cortex. 2007;17(10):2407–19. Pettersson-Yeo W, Allen P, Benetti S, McGuire P, Mechelli A. Dysconnectivity in schizophrenia: where are we now? Neurosci Biobehav Rev. 2011;35(5):1110–24. Suo X, Lei D, Li K, Chen F, Li F, Li L, et al. Disrupted brain network topology in pediatric posttraumatic stress disorder: A resting-state fMRI study. Hum Brain Mapp. 2015;36(9):3677–86. Phillips NS, Rao V, Kmetz L, Vela R, Medick S, Krull K et al. Changes in Brain Functional and Effective Connectivity After Treatment for Breast Cancer and Implications for Intervention Targets. Brain Connect. 2021. Miao H, Chen X, Yan Y, He X, Hu S, Kong J, et al. Functional connectivity change of brain default mode network in breast cancer patients after chemotherapy. Neuroradiology. 2016;58(9):921–8. Hole A, Terjesen T, Breivik H. Epidural versus general anaesthesia for total hip arthroplasty in elderly patients. Acta Anaesthesiol Scand. 1980;24(4):279–87. Negrini D, Wu A, Oba A, Harnke B, Ciancio N, Krause M, et al. Incidence of Postoperative Cognitive Dysfunction Following Inhalational vs Total Intravenous General Anesthesia: A Systematic Review and Meta-Analysis. Neuropsychiatr Dis Treat. 2022;18:1455–67. Charfare H, Limongelli S, Purushotham AD. Neoadjuvant chemotherapy in breast cancer. Br J Surg. 2005;92(1):14–23. Costa SD, Loibl S, Kaufmann M, Zahm DM, Hilfrich J, Huober J, et al. Neoadjuvant chemotherapy shows similar response in patients with inflammatory or locally advanced breast cancer when compared with operable breast cancer: a secondary analysis of the GeparTrio trial data. J Clin Oncol. 2010;28(1):83–91. Sato C, Sekiguchi A, Kawai M, Kotozaki Y, Nouchi R, Tada H, et al. Postoperative Structural Brain Changes and Cognitive Dysfunction in Patients with Breast Cancer. PLoS ONE. 2015;10(11):e0140655. Tong T, Lu H, Zong J, Lv Q, Chu X. Chemotherapy-related cognitive impairment in patients with breast cancer based on MRS and DTI analysis. Breast Cancer. 2020;27(5):893–902. Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001;87(19):198701. McDonald BC, Conroy SK, Ahles TA, West JD, Saykin AJ. Alterations in brain activation during working memory processing associated with breast cancer and treatment: a prospective functional magnetic resonance imaging study. J Clin Oncol. 2012;30(20):2500–8. Townsend J, Altshuler LL. Emotion processing and regulation in bipolar disorder: a review. Bipolar Disord. 2012;14(4):326–39. Hibar DP, Westlye LT, van Erp TG, Rasmussen J, Leonardo CD, Faskowitz J, et al. Subcortical volumetric abnormalities in bipolar disorder. Mol Psychiatry. 2016;21(12):1710–6. Vardy J, Wefel JS, Ahles T, Tannock IF, Schagen SB. Cancer and cancer-therapy related cognitive dysfunction: an international perspective from the Venice cognitive workshop. Ann Oncol. 2008;19(4):623–9. Hamilton JP, Etkin A, Furman DJ, Lemus MG, Johnson RF, Gotlib IH. Functional neuroimaging of major depressive disorder: a meta-analysis and new integration of base line activation and neural response data. Am J Psychiatry. 2012;169(7):693–703. Gallagher M, Chiba AA. The amygdala and emotion. Curr Opin Neurobiol. 1996;6(2):221–7. Muscatell KA, Eisenberger NI, Dutcher JM, Cole SW, Bower JE. Links between inflammation, amygdala reactivity, and social support in breast cancer survivors. Brain Behav Immun. 2016;53:34–8. Wohleb ES, Hanke ML, Corona AW, Powell ND, Stiner LM, Bailey MT, et al. β-Adrenergic receptor antagonism prevents anxiety-like behavior and microglial reactivity induced by repeated social defeat. J Neurosci. 2011;31(17):6277–88. Schwarz JM, Bilbo SD. Sex, glia, and development: interactions in health and disease. Horm Behav. 2012;62(3):243–53. Henry CJ, Huang Y, Wynne AM, Godbout JP. Peripheral lipopolysaccharide (LPS) challenge promotes microglial hyperactivity in aged mice that is associated with exaggerated induction of both pro-inflammatory IL-1beta and anti-inflammatory IL-10 cytokines. Brain Behav Immun. 2009;23(3):309–17. Denk D, Greten FR. Inflammation: the incubator of the tumor microenvironment. Trends Cancer. 2022;8(11):901–14. Friedman NP, Robbins TW. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology. 2022;47(1):72–89. McDonald BC, Saykin AJ. Alterations in brain structure related to breast cancer and its treatment: chemotherapy and other considerations. Brain Imaging Behav. 2013;7(4):374–87. Ferguson RJ, McDonald BC, Saykin AJ, Ahles TA. Brain structure and function differences in monozygotic twins: possible effects of breast cancer chemotherapy. J Clin Oncol. 2007;25(25):3866–70. Schilder CM, Seynaeve C, Beex LV, Boogerd W, Linn SC, Gundy CM, et al. Effects of tamoxifen and exemestane on cognitive functioning of postmenopausal patients with breast cancer: results from the neuropsychological side study of the tamoxifen and exemestane adjuvant multinational trial. J Clin Oncol. 2010;28(8):1294–300. Disner SG, Beevers CG, Haigh EA, Beck AT. Neural mechanisms of the cognitive model of depression. Nat Rev Neurosci. 2011;12(8):467–77. Wang J, Wang L, Zang Y, Yang H, Tang H, Gong Q, et al. Parcellation-dependent small-world brain functional networks: a resting-state fMRI study. Hum Brain Mapp. 2009;30(5):1511–23. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatry. 1961;4:561–71. Dunstan DA, Scott N, Todd AK. Screening for anxiety and depression: reassessing the utility of the Zung scales. BMC Psychiatry. 2017;17(1):329. Hall J, O'Carroll RE, Frith CD et al. 7 - Neuropsychology , in Companion to Psychiatric Studies (Eighth Edition) , E.C. Johnstone, Editors. 2010, Churchill Livingstone: St. Louis. pp. 121–140. Park JH, Jung YS, Jung YM, Bae SA-OX. The role of depression in the relationship between cognitive decline and quality of life among breast cancer patients. 2019(1433–7339 (Electronic)). Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage. 2010;52(3):1059–69. Fox MD, Zhang D, Snyder AZ, Raichle ME. The global signal and observed anticorrelated resting state brain networks. J Neurophysiol. 2009;101(6):3270–83. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage. 2012;59(3):2142–54. Zhang J, Wang J, Wu Q, Kuang W, Huang X, He Y, et al. Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry. 2011;70(4):334–42. He Y, Chen Z, Evans A. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. J Neurosci. 2008;28(18):4756–66. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage. 2002;15(1):273–89. Jin C, Gao C, Chen C, Ma S, Netra R, Wang Y, et al. A preliminary study of the dysregulation of the resting networks in first-episode medication-naive adolescent depression. Neurosci Lett. 2011;503(2):105–9. Newman MEJ. Mixing patterns in networks. physical review e statistical nonlinear soft matter physics. 2002. Achard S, Bullmore E. Efficiency and cost of economical brain functional networks. PLoS Comput Biol. 2007;3(2):e17. Additional Declarations No competing interests reported. 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01:18:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4184945/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4184945/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54037911,"identity":"03ccbb32-548b-4a9b-b460-73eed56a7d86","added_by":"auto","created_at":"2024-04-03 17:13:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90558,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal topological attribute differences in BC patients among the three chemotherapy sessions. Abbreviations: BC, breast cancer; Ge, global efficiency; L\u003csub\u003ep\u003c/sub\u003e, path length; C\u003csub\u003ep\u003c/sub\u003e, clustering coefficients; γ, normalized clustering coefficient; locE, local efficiency; σ, small world index; λ, normalized path length; tp1, baseline; tp2, after the first cycle of chemotherapy; tp3, the end of the entire chemotherapy regimen.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4184945/v1/740786adc4889006bfc13642.png"},{"id":54037909,"identity":"c2c831b2-cafc-4917-acd1-aec60a4ad20a","added_by":"auto","created_at":"2024-04-03 17:13:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":252480,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in nodal topological attributes in brain functional networks in BC patients between tp1 vs. tp2 and tp1 vs. tp3. Each node denotes a brain region mapped onto the cortical surfaces using BrainNet software (http://www.nitrc.org/projects/bnv). Abbreviations: BC, breast cancer; tp1, baseline; tp2, after the first cycle of chemotherapy; tp3, the end of the entire chemotherapy regimen.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4184945/v1/e66d2126ba5d40a160a15902.png"},{"id":54037908,"identity":"5dfc54a4-cd81-42d3-91dc-f9d61e2c8e8d","added_by":"auto","created_at":"2024-04-03 17:13:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":155010,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in network connectivity in the brain functional network in BC patients between tp1 and tp2. For connections, orange (green) represents increased (decreased) brain connectivity. Abbreviations: PCL, paracentral lobule; FFG, fusiform; AMYG, amygdala; ITG, inferior temporal gyrus; SPG, superior parietal gyrus; HES, Heschl; INS, insula; SMA, supplementary motor area; ITG, inferior temporal gyrus; CRBL, cerebellum. R/L, right/left hemisphere; tp1, baseline; tp2, after the first cycle of chemotherapy; tp3, the end of the entire chemotherapy regimen.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4184945/v1/c4aaed010c51e5058c063e90.png"},{"id":54037910,"identity":"aca3fb6a-eafb-479f-b455-6c760a3e226c","added_by":"auto","created_at":"2024-04-03 17:13:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":172983,"visible":true,"origin":"","legend":"\u003cp\u003eBrain regions that show abnormal nodal centralities in the brain functional networks in BC patients after chemotherapy and their relationships with clinical characteristics. The change in network efficiency of the left amygdala was positively correlated with the change in PCA score (tp1 to tp2). Reductions in nodal betweenness of the left inferior frontal gyrus were also positively correlated with the PCA score changes (tp1 to tp2). Abbreviations: BC, breast cancer; PCA, perceived cognitive ability; tp1, baseline; tp2, after the first cycle of chemotherapy; tp3, the end of the entire chemotherapy regimen.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4184945/v1/34c2cd4fd84c96eb18bc063a.png"},{"id":54040092,"identity":"62bd2ef2-012b-4281-aee6-a465a8897c1f","added_by":"auto","created_at":"2024-04-03 17:29:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1752993,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4184945/v1/1a0f2fca-3437-47d1-a0d3-15ccc55a0082.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Altered brain functional networks in patients with breast cancer after neoadjuvant chemotherapy Running title: Disrupted Network in Breast Cancer After Chemotherapy","fulltext":[{"header":"Background","content":"\u003cp\u003eCancer-related cognitive impairment (CRCI) is a common and important problem in breast cancer (BC) survivors and patients, including problems in memory, attention, executive function and processing speed [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It damages social, occupational and familial functioning and ultimately reduces individuals\u0026rsquo; quality of life [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Studies have found that elderly BC patients with CRCI have a 10\u0026ndash;15% probability of developing dementia each year [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the complex mechanism of CRCI is still being explored. Therefore, a deeper theoretical understanding will help improve the early diagnosis and treatment of CRCI.\u003c/p\u003e \u003cp\u003eFunctional and structural imaging with MRI has been useful not only for understanding illness biology but also for evaluating treatment effects [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Previous neuroimaging studies have shown that chemotherapy can directly affect the structural integrity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and functional activity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] in the central nervous system. This central neurotoxicity has a certain time-dependent effect that can be divided into acute, subacute and delayed stages [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Some studies have shown that abnormal changes in brain areas were mainly concentrated in the dorsolateral prefrontal cortex, cingulate and parietal lobe in the postchemotherapy BC cohort [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A relationship has been identified between these changes and scores on a cognition-related scale (Functional Assessment of Cancer Therapy-Cognitive Function, FACT-Cog), which could indicate that these brain regions with change play important roles in maintaining cognitive balance in BC patients after chemotherapy. However, many longitudinal neuroimaging studies have been focused on a single chemotherapy stage, lacking examination of sustained changes in brain injury throughout the different stages of the whole chemotherapy cycle [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGraph-based analysis provides a powerful tool for characterizing topological properties of brain networks (i.e., the connectome) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and their alteration in neuropsychiatric diseases [\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Watts et al. classified networks into three types according to the range and number of connections, which are regular network, random network and small-world network [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Advances in graph-based theoretical analysis have enabled quantification of the whole brain\u0026rsquo;s topological properties [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], revealing a \u0026lsquo;small-world\u0026rsquo; organization (characterized by both high local specialization and high global integration between brain regions) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These networks are anatomically and functionally disrupted in neuron-psychiatric disease [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], which exhibit more randomized as well as more regular networks. To date, there are still few studies on the connectivity pathways between brain regions after chemotherapy in BC patients [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The topological organization of the brain connectome in BC patients after chemotherapy is still poorly understood.\u003c/p\u003e \u003cp\u003eThe results of some studies have indicated that the risk of cognitive dysfunction increases when general anesthesia is used [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. To avoid the confounding effects of anesthesia and surgery, we purposefully recruited BC patients who had undergone neoadjuvant chemotherapy (NAC), which can convert a locally advanced/inoperable breast tumor into an operable tumor or cause downstaging, resulting in a small increase in breast conservation rates [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on the above, in the current study we used a graph-based model to construct an individual functional network to explore the effects of NAC on brain connectivity in BC patients in this current study. Data for the present study were collected in three waves: baseline (time point 1, tp1: before NAC), after the first NAC cycle (time point 2, tp2), and at the end of the NAC regimen (time point 3, tp3). We set out to test two hypotheses. First, given the evidence of abnormal structural and functional neuroimaging reported in BC participants after short-term chemotherapy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], we hypothesized that BC patients would show acute damage to the brain, reflected in altered topological properties of functional networks after the first NAC cycle. Second, based on reported relationships between brain network characteristics and individual behavioral responses [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], we hypothesized that there would be a correlation between brain network topological alterations and clinical characteristics.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 60 BC patients completed the whole cycle of NAC, and data from 5 were excluded because of poor data quality, missing scans, or excessive motion. No patients reported falling asleep during any scans. Close age/duration of education match was achieved across subtype luminal A (14), luminal B (13) and HER2-positive (28); and treatment-divided: group A (33) and group B (22); as well as menopause (19) and nonmenopause (20). In addition, we recruited HC group 1, consisting of 20 women, for whom the average interval between tp1 and tp2 was 30 days, and we recruited HC group 2, consisting of 18 women, for whom the average interval between tp1 and tp3 was 137 days. There were significant differences in educational attainment between the patient group and each HC group. The details were shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic data and clinical characteristics of study participants \u003csup\u003ea\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClinical and Demographic\u003c/p\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" morerows=\"1\" nameend=\"c6\" namest=\"c2\" rowspan=\"2\"\u003e \u003cp\u003eAll BC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c8\" namest=\"c7\" rowspan=\"2\"\u003e \u003cp\u003eHC 1\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c10\" namest=\"c9\" rowspan=\"2\"\u003e \u003cp\u003eHC 2\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c16\" namest=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003eHC 1 Vs. HC 2\u003c/b\u003e \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u003cb\u003eBC Vs. HC 1\u003c/b\u003e \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e\u003cb\u003eBC Vs. HC 2\u003c/b\u003e \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c16\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e49.33\u0026thinsp;\u0026plusmn;\u0026thinsp;8.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e52.85\u0026thinsp;\u0026plusmn;\u0026thinsp;11.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e44.94\u0026thinsp;\u0026plusmn;\u0026thinsp;13.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male: female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e0:55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0:20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0:18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (years) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e8.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e12.65\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e15.94\u0026thinsp;\u0026plusmn;\u0026thinsp;4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeuropsychological Characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTP1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTP2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTP3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e \u003cb\u003evalue\u003c/b\u003e \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eTP1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eTP2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eTP1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eTP3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eHC 1\u003c/b\u003e \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u003cb\u003eHC 2\u003c/b\u003e \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e\u003cb\u003eBC Vs. HC 1\u003c/b\u003e \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003eBC Vs. HC 2\u003c/b\u003e \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking memory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c16\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDST forwards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDST backwards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmotion test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c16\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.79\u0026thinsp;\u0026plusmn;\u0026thinsp;4.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.97\u0026thinsp;\u0026plusmn;\u0026thinsp;2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.40\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.89\u0026thinsp;\u0026plusmn;\u0026thinsp;6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.07\u0026thinsp;\u0026plusmn;\u0026thinsp;7.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.27\u0026thinsp;\u0026plusmn;\u0026thinsp;5.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.52\u0026thinsp;\u0026plusmn;\u0026thinsp;4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.75\u0026thinsp;\u0026plusmn;\u0026thinsp;8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.05\u0026thinsp;\u0026plusmn;\u0026thinsp;5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.11\u0026thinsp;\u0026plusmn;\u0026thinsp;5.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFACT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"15\" nameend=\"c16\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.03\u0026thinsp;\u0026plusmn;\u0026thinsp;8.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.65\u0026thinsp;\u0026plusmn;\u0026thinsp;9.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.76\u0026thinsp;\u0026plusmn;\u0026thinsp;8.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.80\u0026thinsp;\u0026plusmn;\u0026thinsp;16.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42.55\u0026thinsp;\u0026plusmn;\u0026thinsp;14.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47.56\u0026thinsp;\u0026plusmn;\u0026thinsp;11.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e49.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.44\u0026thinsp;\u0026plusmn;\u0026thinsp;8.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.05\u0026thinsp;\u0026plusmn;\u0026thinsp;7.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.04\u0026thinsp;\u0026plusmn;\u0026thinsp;8.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.95\u0026thinsp;\u0026plusmn;\u0026thinsp;10.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e61.72\u0026thinsp;\u0026plusmn;\u0026thinsp;8.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.08\u0026thinsp;\u0026plusmn;\u0026thinsp;4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.09\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.32\u0026thinsp;\u0026plusmn;\u0026thinsp;6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.93\u0026thinsp;\u0026plusmn;\u0026thinsp;5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.78\u0026thinsp;\u0026plusmn;\u0026thinsp;4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.83\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.76\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"16\"\u003eAbbreviations: BC, breast cancer; HC, healthy control; DST, the digit span test; SAS, Self-Rating Anxiety Scale; SDS, Self-Rating Depression Scale; FCAT, functional assessment of cancer therapy; VFT, the verbal fluency test; OTH, comments from others; PCA, perceived cognitive abilities; PCI, perceived cognitive impairments; QOL, impact on quality of life; TP1, time point 1; TP2, time point 2; TP3, time point 3.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"16\"\u003e\u003csup\u003ea\u003c/sup\u003e Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"16\"\u003e\u003csup\u003eb\u003c/sup\u003e Age defined at the time of MRI scanning.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"16\"\u003e\u003csup\u003ec\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e value by two-tailed two-sample t test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"16\"\u003e\u003csup\u003ed\u003c/sup\u003e p value by One-way ANOVA F test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"16\"\u003e\u003csup\u003ee\u003c/sup\u003e p value by paired t test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic data and clinical characteristics of patient-subgroups \u003csup\u003ea\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClinical and Demographic Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSubgroup divided by subtype\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSubgroup divided by treatment\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSubgroup divided by menopause\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLuminal A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLuminal B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHer2-positive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSubgroup A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSubgroup B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMenopause\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNon-menopause\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003egraphic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.62\u0026thinsp;\u0026plusmn;\u0026thinsp;5.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.78\u0026thinsp;\u0026plusmn;\u0026thinsp;5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.22\u0026thinsp;\u0026plusmn;\u0026thinsp;8.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.063 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.14\u0026thinsp;\u0026plusmn;\u0026thinsp;9.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.052 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e54.73\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e44.70\u0026thinsp;\u0026plusmn;\u0026thinsp;8.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male: female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0:14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0:13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0:28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0:33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0:22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0:19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0:20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (years) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.68\u0026thinsp;\u0026plusmn;\u0026thinsp;3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.312 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.419 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.84\u0026thinsp;\u0026plusmn;\u0026thinsp;4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.753 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.518 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.216 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy regimen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u0026rarr;T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.479 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u0026rarr;T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCbHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCbHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eAbbreviations: AC\u0026rarr;T, doxorubicin\u0026thinsp;+\u0026thinsp;cyclophosphamide\u0026thinsp;+\u0026thinsp;docetaxel; AT, doxorubicin\u0026thinsp;+\u0026thinsp;docetaxel; EC\u0026rarr;T, epirubicin\u0026thinsp;+\u0026thinsp;cyclophosphamide\u0026thinsp;+\u0026thinsp;docetaxel; TAC, paclitaxel\u0026thinsp;+\u0026thinsp;doxorubicin\u0026thinsp;+\u0026thinsp;cyclophosphamide; TCbHB, docetaxel\u0026thinsp;+\u0026thinsp;carboplatin\u0026thinsp;+\u0026thinsp;trastuzumab\u0026thinsp;+\u0026thinsp;pyrotinib; TCbHP, docetaxel\u0026thinsp;+\u0026thinsp;carboplatin\u0026thinsp;+\u0026thinsp;trastuzumab\u0026thinsp;+\u0026thinsp;pertuzumab.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ea\u003c/sup\u003e Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003eb\u003c/sup\u003e Age defined at the time of MRI scanning.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ec\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e value by two-tailed two-sample t test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ed\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e value by two-tailed Pearson Chi-square test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTreatment Response\u003c/h2\u003e \u003cp\u003eIn the patient group, BC patients showed significant clinical scale changes compared with their baseline assessments as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The PCA scores decreased significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049), specifically at tp2 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and tp3 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030), the SAS scores showed an increasing trend (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.096), and there were no other clinical scale changes. In the two HC groups, there were no changes in clinical scales between the corresponding time intervals.\u003c/p\u003e \u003cp\u003eThe results of the inter-group comparison at TP1 showed that the scores of VFT DST backwards in the patient group were significantly lower than it in HC group 2. And the scores of SDS in the HC group 1 were higher than it in patient group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), however, these scores were in the normal range.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAlterations in Brain Network Properties at Baseline\u003c/h2\u003e \u003cp\u003eIn global measurements, each participant had a higher average clustering coefficient (γ\u0026thinsp;\u0026gt;\u0026thinsp;1) and similar characteristic path length (λ\u0026thinsp;\u0026asymp;\u0026thinsp;1) relative to random reference networks, showing that each network had a small world topology (γ/λ\u0026thinsp;\u0026gt;\u0026thinsp;1.1).\u003c/p\u003e \u003cp\u003eAnd the nodal level, brain regions with significant between-group differences were defined by at least 1 abnormal nodal property in the specific area (FDR corrected, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). At baseline, there were no significant differences in any network properties between the two HC groups and the patient group, as well as the patient-subgroups (group 1: luminal A vs. luminal B vs. HER2-positive; group 2: A vs. B; group 3: menopause vs. nonmenopause).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAlterations in Brain Network Properties at Follow-up\u003c/h2\u003e \u003cp\u003eIn patient group, the nodal degree of the left paracentral gyrus showed a significant chemotherapy-by-time interaction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), and the nodal efficiency of the left temporal gyrus showed a significant chemotherapy-by-responder interaction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029). However, these results were not statistically significant after FDR correction for multiple comparisons. The results showed that there were no significant differences in the effects of brain network properties of different subtypes at the observation time points. Therefore, in further analyses of changes after chemotherapy and their correlations, these subgroups were combined.\u003c/p\u003e \u003cp\u003eFollowing chemotherapy initiation, BC patients showed increased E\u003csub\u003eglob\u003c/sub\u003e and decreased L\u003csub\u003ep\u003c/sub\u003e both at tp2 and tp3 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the significant nodal network alterations observed at tp2, BC patients showed decreased nodal centrality parameters in the right superior frontal gyrus, right inferior temporal gyrus, left frontal gyrus, left amygdala, left paracentral gyrus, left insula and in some areas of the cerebellar cortex (bilateral superior cerebellum gyrus: cerebellum_6; right inferior cerebellum gyrus: cerebellum_7R and vermis_8).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLongitudinal topological properties differences in patient group \u003csup\u003ea\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTopological properties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e of ANOVA \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e of post hoc \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTP1 vs. TP2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTP1 vs. TP3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTP2 vs. TP3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNodal efficiency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParietal_Sup_R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParietal_Inf_R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebelum_Crus2_L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebelum_6_L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebelum_6_R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebelum_7b_R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebelum_8_R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVermis_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNodal degree\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrontal_Inf_Orb_R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsula_L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParietal_Inf_R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParacentral_Lobule_R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeschl_L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebelum_6_L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebelum_6_R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebelum_7b_R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVermis_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVermis_8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNodal betweenness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrontal_Inf_Orb_L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupp_Motor_Area_L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmygdala_L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccipital_Mid_L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFusiform_L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParacentral_Lobule_L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeschl_L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal_Inf_R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: R, right; L, left. TP1, time point 1; TP2, time point 2; TP3, time point 3.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e All the brain regions are from AAL (automated anatomical labeling).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e The Benjamini-Hochberg false discovery rate correction was applied to each nodal measure, and the \u003cem\u003ep\u003c/em\u003e value threshold taken as 0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt tp3, BC patients showed decreased nodal centrality parameters in the bilateral inferior parietal gyrus, bilateral paracentral gyrus, right inferior temporal gyrus and in some areas of the cerebellar cortex (bilateral superior cerebellum gyrus: cerebellum_6; right inferior cerebellum gyrus: cerebellum_8 and vermis_7/8).\u003c/p\u003e \u003cp\u003eBetween the two chemotherapy sessions, BC patients exhibited fewer differences (no differences at the global level), but alterations were still seen at the nodal level, including decreased nodal centrality parameters in the left inferior frontal gyrus and left inferior temporal gyrus (tp3 to tp2). The details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn HC group 1, there were no significant differences both in global and nodal parameters between tp1 and tp2. In HC group 2, there were no significant differences both in global and nodal parameters between tp1 and tp3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAlterations of Subnetwork Connectivity\u003c/h2\u003e \u003cp\u003eThe NBS tool was used to identify network alterations in brain regions showing between-group differences in nodal properties. In the patient group, compared with baseline, we identified significantly decreased connectivity alterations within networks comprising 8 nodes and 8 edges and increased connectivity alterations within networks comprising 5 nodes and 3 edges (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, T\u0026thinsp;=\u0026thinsp;3.0, corrected for multiple comparisons using FWE-corrected network-level) in the BC patients at tp2. The details are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. There were no abnormal connections between tp1 and tp3 or between the two chemotherapy sessions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Between Network Changes and Clinical Scales\u003c/h2\u003e \u003cp\u003eIn the patient group, at tp2, the change in network efficiency of the left amygdala (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.358, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) was positively correlated with the change in PCA score. Reductions in nodal betweenness of the left inferior frontal gyrus (tp1 to tp2) (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.299, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) were also positively correlated with the PCA score changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These results were statistically significant even after FDR correction for multiple comparisons. We did not detect other significant correlations between altered network properties and clinical scale changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study provides an initial window into the neural mechanisms underlying CRCI of BC patients. Using a sample comprised of NAC-treated BC patients and two HC cohorts at the same time, we prospectively investigated changes associated with NAC in vivo in indices of the functional brain connectome that may be related to cognitive dysfunction, excluding population baseline differences and temporal interference. Consistent with our hypothesis, early NAC caused acute damage to the brain functional connectome in BC patients, which manifested as disruption of the brain network after the first cycle of NAC. Importantly, shifts in CRCI-related topological properties were correlated with changes in clinical scales, more directly supporting the idea that brain functional connectome properties can reflect key pieces of the neural mechanisms by which NAC impacts symptoms.\u003c/p\u003e \u003cp\u003eAt the global level, compared with tp1, BC patients who experienced or were experiencing NAC exhibited a more randomized brain network both at tp2 and tp3, in which the brain network transforms from a small-world pattern to a relatively random network pattern. The occurrence of this pattern in our results is consistent with our first hypothesis and demonstrates that NAC could alter the brain functional connectome in BC patients. Some neuroimaging studies involving application of also applying connectome-based models on fMRI and structural MRI found a disrupted brain network in BC patients after chemotherapy [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Such a pattern of brain networks has been also improved in some neuropsychiatric disorders, such as posttraumatic stress disorder [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and essential tremor [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], reflecting a less optimal topological organization in brain networks. In our study, the disrupted network pattern was characterized by an increased E\u003csub\u003eglob\u003c/sub\u003e (efficiency of exchanging information at the global level [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]) and decreased L\u003csub\u003ep\u003c/sub\u003e (the average distance from one node to any other node in the network, expressed as the number of links that must be traveled [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]), which could lead to information transfer disorder and constrain long-distance functional integration as reflected by the abnormal information processing speed and disrupted executive capacity of BC participants after chemotherapy [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the nodal level, both tp2 and tp3 showed significant differences compared with tp1 that were related to the frontal-limbic system (right superior frontal gyrus, right inferior temporal gyrus, left frontal gyrus, left amygdala) and cerebellar regions. The frontal-limbic system is believed to support emotion processing and regulation, reward processing, and cognitive control [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Cerebellar regions are also increasingly recognized as important in higher cognitive functions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Our observations in the frontal-limbic system suggested that alterations in this circuitry may play an important role in the neurobiology of cognitive dysfunction and mood dysregulation via impact on cognition and emotional reactivity [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. As a key brain region in this circuit, the amygdala can not only regulate emotional and cognitive functions through the interaction of the limbic system or other brain regions [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], but also balance the brain microenvironment, which is dominated by inflammatory response, thus affecting the peripheral clinical symptoms of individuals [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Some studies have shown that BC and its treatment may activate microglia, which remain preactivated in brain regions, mainly the amygdala, even after completion of treatment and resolution of peripheral inflammation [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. As has been shown in developmental [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and aging models [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], such priming may increase neural sensitivity to peripheral inflammation with consequences for neural function, cognition, and behavior. This finding was consistent with the positive correlation between the change in nodal parameters of the amygdala and the change in PCA obtained in this study, which indicates that early NAC will bring an acute inflammatory response at the physiological level [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], and this response may be reflected as cognitive impairment at the individual level.\u003c/p\u003e \u003cp\u003eThe frontal gyrus in the frontal-limbic system is known to be involved in cognitive processing [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. McDonald et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] reviewed the currently published articles in the changes of brain structure in BC patients after chemotherapy, and the results revealed that the frontal gyrus showed a significant change after chemotherapy that was related to a failure to regulate cognition-related processes. Moreover, functional neuroimaging studies have also shown abnormalities after chemotherapy, manifested as a decrease in gray matter density in the frontal gyrus, which is consistent with our findings (decreased centrality of the frontal gyrus) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In addition, it has been suggested that there is a cognitive neurobiological model demonstrating that functional deficits in the frontal gyrus may impair top-down control over the limbic areas [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. However, the changes in the frontal gyrus only appeared after the first cycle of NAC. This was consistent with longitudinal studies suggesting that the frontal gyrus gradually recovers over time after early chemotherapy-related acute injury [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Taken together, alternations in the frontal-limbic system may be associated with acute impairment of cognitive regulation in BC patients after early chemotherapy.\u003c/p\u003e \u003cp\u003eSome limitations must also be considered. First, the brain parcellation template we selected for constructing brain networks may affect the network analysis results. As different templates may lead to different estimates of graph theory parameters, this factor needs to be considered [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Second, the sample size of participants in our study was relatively small. Third, our study lacked a BC control group without NAC treatment. In addition, the HC groups in our study consisted of two cohorts that were assessed at two time points (tp1 vs. tp2 and tp1 vs. tp3) due to some people refusing follow-up. Since there was no difference within the HC group and no difference between HC and BC at baseline, we basically ruled out the aging effects on topological properties, and indicated that the tumor itself had no significant effect on topological properties at baseline. In the future, we will try to include and expand healthy controls with data acquisition time intervals identical to those of BC patients who received NAC. Fourth, in this study, the FACT-Cog, the clinical scale we used for the assessment of cognitive function, is not a direct objective measure. We will pay attention to and correct this problem in future clinical-scale collection processes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, the current study demonstrated that the brain functional network in BC patients after NAC showed a shift toward a more randomized pattern compared with that in BC patients at baseline. There were significant alterations in the clinical BC-related connectome mainly after the first cycle of NAC. The observed pattern of functional network connectivity alterations indicates acute abnormalities in cognitive function and emotional processing in BC patients after NAC. It suggests that the control of emotion and cognitive processing would be mainly affected by acute chemotherapy-related damage, manifested as changes in centrality and connectivity of brain regions within the frontal-limbic system.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e Participants with BC were recruited at the Chongqing University Cancer Hospital from December 2020 to November 2021. This prospective study was approved by the local ethics committee of Chongqing University Cancer Hospital, and written informed consent was obtained from all BC participants (or their legal guardians) before enrollment. The inclusion criteria were as follows: (1) individuals with a history of primary BC (stage Ⅱ-Ⅲ at diagnosis); (2) individuals aged 30\u0026ndash;70 years at the first diagnosis of BC; (3) individuals prepared to receive NAC prior to surgery; (4) right-handed individuals; and (5) female participants with BC. The Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) were used to quantify anxiety and depression symptoms, respectively [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The verbal fluency test (VFT) was used to distinguish different degrees of cognitive impairment and cognitive impairment of different causes [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The FACT-Cog was used to measure cognitive function [e.g., Perceived Cognitive Impairments (CogPCI) and Perceived Cognitive Abilities (CogPCA)] in cancer patients [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The exclusion criteria were as follows: (1) patients with diabetes mellitus and other metabolic diseases; (2) patients with craniocerebral diseases such as tumor, operation, trauma and stroke; (3) patients taking drugs that have neurological and mental effects; (4) patients with mental diseases (such as depression, anxiety, or alcohol addiction, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria); (5) patients receiving hormonal therapy; (6) the NAC process is interrupted for any reason; and (7) patients with contraindications to MRI examination. Healthy controls (HCs) had no history of cancer and chemotherapy, were recruited from the local region through poster advertisements and excluded using the same criteria as the patient group.\u003c/p\u003e \u003cp\u003eMembers of patient group underwent MRI scanning and were assessed for clinical characteristics at tp1 (one week before the first NAC), tp2 (one month after the first cycle of NAC), and tp3 (the end of the entire NAC regimen). The time intervals assessed in the HC groups matched the time intervals in the patient group. There were two HC groups in our study: HC group 1 was matched with tp1 and tp2 assessed in the patient group, and HC group 2 was matched with tp1 and tp3 assessed in the patient group. To control the interference of clinical factors, the current study involved defining patient-subgroups for analysis based on the clinical subtype, chemotherapy regimen and menopausal status of BC patients. Specifically, according to the clinical subtype of BC, the included samples were classified into three categories: luminal A, luminal B and HER2-positive; depending on the chemotherapy regimen, the samples were also classified into the anthracycline-containing (A) or the targeted\u0026thinsp;+\u0026thinsp;chemotherapy (B), as well as the menopause and nonmenopause groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMRI Data Acquisition\u003c/h2\u003e \u003cp\u003eAll subjects underwent scanning in a 3.0 Tesla whole-body MRI system (MAGNETOM Prisma, Siemens, Erlangen, Germany) with a 64-channel phased array head coil. A resting-state fMRI dataset was acquired at tp1, tp2 and tp3. The participants were instructed to keep their eyes closed and to think of nothing in particular during the acquisition. Head motion was minimized by using foam pads. The scanning parameters were as follows: repetition time (TR) 2000 ms, echo time (TE) 30 ms, flip angle 70, slice thickness 3 mm, single excitation, field of view 240 \u0026times; 240 mm\u003csup\u003e2\u003c/sup\u003e, voxel size 3 \u0026times; 3 \u0026times; 3 mm\u003csup\u003e3\u003c/sup\u003e, and matrix dimensions 240 \u0026times; 240 \u0026times; 135. A total of 240 volumes were collected for each subject. Image quality was inspected by two experienced neuroradiologists who made decisions about excessive motion artifacts for scan inclusion and evaluated for clinical abnormalities in a double-blinded manner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMRI Data Preprocessing\u003c/h2\u003e \u003cp\u003eThe image data were preprocessed by SPM12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fil.ion.ucl.ac.uk/spm\u003c/span\u003e\u003cspan address=\"http://www.fil.ion.ucl.ac.uk/spm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The first 10 time points were discarded to avoid instability of the initial MRI signal. After correction for intravolume acquisition time delay and head motion, the images were spatially normalized to a 3 \u0026times; 3 \u0026times; 3 mm\u003csup\u003e3\u003c/sup\u003e Montreal Neurological Institute 152 template and then linearly detrended and temporally bandpass filtered (0.01\u0026ndash;0.08 Hz) to remove low-frequency drift and high-frequency physiological noise. Finally, the global signal, white matter signal, cerebrospinal fluid signal, and motion parameters (three translational and three rotational parameters) were all regressed out [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. According to the record of head motions within each fMRI run, all participants whose head motion exceeded 1.0 mm of translation or 1.0\u0026deg; of rotation in any direction were excluded. We also calculated the mean framewise displacement (FD) in the groups, and there was no difference in the mean FD between groups [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eNetwork Construction and Topological Properties\u003c/h2\u003e \u003cp\u003eThe network was constructed using GRETNA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nitrc.org/projects/gretna/\u003c/span\u003e\u003cspan address=\"http://www.nitrc.org/projects/gretna/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. We applied a wide range of sparsity (S) thresholds to all correlation matrices. The value of S was chosen to ensure that thresholded networks were estimable for the small-world index scalar, and the small-world index (σ) was \u0026gt;\u0026thinsp;1.1. The range of our S thresholds was set to 0.05\u0026thinsp;\u0026lt;\u0026thinsp;S\u0026thinsp;\u0026lt;\u0026thinsp;0.40 with an interval of 0.01 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. For each network metric, the area under the curve (AUC) was calculated, providing a summarized scalar for the topological characterization of brain networks independent of a single threshold selection. The AUC metric has proven to be sensitive in the detection of topological alterations in brain networks.\u003c/p\u003e \u003cp\u003eFirst, the automated anatomical labeling (AAL) atlas [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] was used to divide the whole brain into 116 cortical and subcortical regions of interest, and each was considered a network node. Next, the mean time series was acquired for each region. The partial correlations of the mean time series between all pairs of nodes (representing their conditional dependencies by excluding the effects of the other 114 regions) were considered the edges of the network [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. This process resulted in a 116 \u0026times; 116 partial correlation matrix for each subject, which was converted into a binary matrix (i.e., adjacency matrix) according to a predefined threshold (see below for the threshold selection), where the entry aij was =\u0026thinsp;1 if the absolute partial correlation between regions i and j exceeded the threshold, and where aij was =\u0026thinsp;0 otherwise [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor the brain networks at each sparsity level, we calculated both global and nodal network metrics. The global metrics examined included small-world parameters (for definitions see [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]), such as the clustering coefficient C\u003csub\u003ep\u003c/sub\u003e, characteristic path length L\u003csub\u003ep\u003c/sub\u003e, normalized clustering coefficient γ, normalized characteristic path length λ, and small-world index σ, as well as network efficiency parameters (for details see [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]), such as the local efficiency E\u003csub\u003eloc\u003c/sub\u003e and global efficiency E\u003csub\u003eglob\u003c/sub\u003e. We calculated L\u003csub\u003ep\u003c/sub\u003e as the harmonic mean distance between all possible pairs of regions to address the disconnected graphs dilemma [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The nodal metrics examined included the node degree, efficiency, and betweenness centrality [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Finally, global and nodal network topological properties were included to establish a 277-dimensional graphic feature vector, where features 1\u0026ndash;7 were global properties (i.e., C\u003csub\u003ep\u003c/sub\u003e, L\u003csub\u003ep\u003c/sub\u003e, γ, λ, E\u003csub\u003eloc\u003c/sub\u003e, E\u003csub\u003eglob\u003c/sub\u003e) and features 8-277 were three nodal properties (i.e., degree, betweenness, efficiency) of 116 AAL regions. Detailed information is provided in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGlobal Network Properties.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSegregation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eIntegration\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristic path length (L\u003c/b\u003e\u003csub\u003e\u003cb\u003ep\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAveraging the minimum number of connections that link all pairs of network nodes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eClustering coefficient (C\u003c/b\u003e\u003csub\u003e\u003cb\u003ep\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCalculated by averaging over all network nodes, is equivalent to the fraction of each node\u0026rsquo;s neighbors that are also neighbors of each other [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormalized characteristic path length (λ)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eλ is L\u003csub\u003ep\u003c/sub\u003e expressed relative to the mean L\u003csub\u003ep\u003c/sub\u003e of 100 matched random networks with the same number of nodes and edges as the real network.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNormalized clustering coefficient (γ)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eγ is the normalized C\u003csub\u003ep\u003c/sub\u003e, expressed relative to the mean C\u003csub\u003ep\u003c/sub\u003e of 100 matched random networks (as defined above for λ).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal efficiency (E\u003c/b\u003e\u003csub\u003e\u003cb\u003eglob\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE\u003csub\u003eglob\u003c/sub\u003e measures how efficiently information is exchanged at the global level [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLocal efficiency (E\u003c/b\u003e\u003csub\u003e\u003cb\u003eloc\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE\u003csub\u003eloc\u003c/sub\u003e measures how efficiently information is exchanged at the local level [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]: the level of clustering measured by E\u003csub\u003eloc\u003c/sub\u003e expresses the local connectedness of a network, with high values interpreted as high levels of local organization [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e1. Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks. Nature. 1998; 393(6684): 440-2.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e2. Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001; 87(19): 198701.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e3. Filippi M, van den Heuvel MP, Fornito A, He Y, Hulshoff Pol HE, Agosta F, et al. Assessment of system dysfunction in the brain through MRI-based connectomics. Lancet Neurol. 2013; 12(12): 1189-99.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIntra-group analyses included longitudinal changes in the patient group and longitudinal changes in the two HC groups, respectively, which were analyzed by repeated measures analysis of variance (ANOVA) and paired sample t-test, respectively. The AUC values were used to compare each metric (small-world index, network efficiency, and nodal centrality measures) at different timepoints. To assess the trajectory of change in the patient group, pairwise post hoc Fisher's least significance difference tests were performed to determine which of the three timepoints differed significantly. For inter-group analysis, an independent sample t-test was used to compare the patient group and HC groups at baseline (including HC group 1 vs. patient group and HC group 2 vs. patient group; age and education years as covariates). For nodal property analyses, a false discovery rate (FDR)-corrected threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied.\u003c/p\u003e \u003cp\u003eRegression analysis was performed in R software (version 4.2.1). We investigated relations between changes in network measurements from baseline to endpoint and changes in clinical symptom scores (i.e., SAS). Age and duration of education were treated as covariates in these models. To assess chemotherapy-specific differences between A and B in topological alterations, chemotherapy-by-time interactions were examined using mixed effects models. All global and nodal measurements that differed between chemotherapy groups at baseline were examined across all three time points. The algorithmic models for BC patients according to clinical subtype and menopausal status were consistent with those described above.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecancer-related cognitive impairment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebreast cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehealthy control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneoadjuvant chemotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003etp\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etime point\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSelf-Rating Anxiety Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSelf-Rating Depression Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVFT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003everbal fluency test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFACT-Cog\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFunctional Assessment of Cancer Therapy-Cognitive Function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCogPCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePerceived Cognitive Impairments\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCogPCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePerceived Cognitive Abilities\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDSM-IV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiagnostic and Statistical Manual of Mental Disorders\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAAL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eautomated anatomical labeling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eanalysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eL\u003csub\u003ep\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCharacteristic path length\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eΛ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNormalized characteristic path length\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eE\u003csub\u003eglob\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal efficiency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC\u003csub\u003ep\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClustering coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eγ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNormalized clustering coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eE\u003csub\u003eloc\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLocal efficiency.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e \u003cp\u003e Approval was obtained from the local ethics committee of Chongqing University Cancer Hospital, and written informed consent was obtained from all BC participants (or their legal guardians).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eWe obtained consent for publication from the participants or their legal guardians.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eAll authors report no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthor details\u003c/h2\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003eDepartment of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China. \u003csup\u003e2\u003c/sup\u003eDepartment of Breast Cancer Center, Chongqing University Cancer Hospital, School of Medicine, Chongqing, China. \u003csup\u003e3\u003c/sup\u003eChongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing University Cancer Hospital, School of Medicine, Chongqing, China.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the National Natural Science Foundation of China: 82071883; Chongqing Natural Science Foundation (CSTB2022NSCQ-MSX0823; CSTB2022NSCQ-MSX0951; CSTB2022NSCQ-MSX0396; cstc2021jcyj-msxmX0319; cstc2021jcyj-msxmX0313); the Discipline Construction and Upgrading Project of National Key Clinical Specialty Construction Project, the Talent Program of Chongqing (grant No. CQYC20200303137).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYJ, DYC conceived of and designed the study. YJ, DYC, LDH, HYX, TY and ZXY organized the database. YJ, TY and ZJ designed the statistical analysis. YJ, LJ and WCF performed the statistical analysis. YJ, DYC, LDH, HYX and TY interpreted the results. YJ, ZXH and ZJQ finished the draft.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank the study participants and their families.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets presented in this article are not readily available due to regulations governing ethical approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePark JH, Bae SH, Jung YS, Jung YM. [Prevalence and characteristics of chemotherapy-related cognitive impairment in patients with breast cancer]. J Korean Acad Nurs. 2015;45(1):118\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawrence JA, Griffin L, Balcueva EP, Groteluschen DL, Samuel TA, Lesser GJ, et al. A study of donepezil in female breast cancer survivors with self-reported cognitive dysfunction 1 to 5 years following adjuvant chemotherapy. J Cancer Surviv. 2016;10(1):176\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMyers JS. Cancer- and chemotherapy-related cognitive changes: the patient experience. Semin Oncol Nurs. 2013;29(4):300\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanelsins MC, Kesler SR, Ahles TA, Morrow GR. Prevalence, mechanisms, and management of cancer-related cognitive impairment. Int Rev Psychiatry. 2014;26(1):102\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorrea DD, Ahles TA. Cognitive adverse effects of chemotherapy in breast cancer patients. Curr Opin Support Palliat Care. 2007;1(1):57\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiao H, Li J, Hu S, He X, Partridge SC, Ren J, et al. Long-term cognitive impairment of breast cancer patients after chemotherapy: A functional MRI study. Eur J Radiol. 2016;85(6):1053\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Yao L, Jin P, Hu L, Li X, Guo T, et al. MRI and PET/CT for evaluation of the pathological response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis. Breast. 2018;40:106\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsos A, Loomes M, Zhou I, Macmillan E, Sabel I, Rotziokos E, et al. Chemotherapy-induced cognitive impairments: White matter pathologies. Cancer Treat Rev. 2017;61:6\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu R, Du M, Ren J, Qing H, Wang X, Xu G, et al. Chemotherapy-induced grey matter abnormalities in cancer survivors: a voxel-wise neuroimaging meta-analysis. Brain Imaging Behav. 2021;15(4):2215\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDumas JA, Makarewicz J, Schaubhut GJ, Devins R, Albert K, Dittus K, et al. Chemotherapy altered brain functional connectivity in women with breast cancer: a pilot study. Brain Imaging Behav. 2013;7(4):524\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaillibert S, Le Rhun E, Chamberlain MC. Chemotherapy-Related Neurotoxicity. Curr Neurol Neurosci Rep. 2016;16(9):81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanelsins MC, Heckler CE, Peppone LJ, Kamen C, Mustian KM, Mohile SG, et al. Cognitive Complaints in Survivors of Breast Cancer After Chemotherapy Compared With Age-Matched Controls: An Analysis From a Nationwide, Multicenter, Prospective Longitudinal Study. J Clin Oncol. 2017;35(5):506\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eApple AC, Schroeder MP, Ryals AJ, Wagner LI, Cella D, Shih PA, et al. Hippocampal functional connectivity is related to self-reported cognitive concerns in breast cancer patients undergoing adjuvant therapy. Neuroimage Clin. 2018;20:110\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeprez S, Vandenbulcke M, Peeters R, Emsell L, Smeets A, Christiaens MR, et al. Longitudinal assessment of chemotherapy-induced alterations in brain activation during multitasking and its relation with cognitive complaints. J Clin Oncol. 2014;32(19):2031\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenning S, de Ruiter MB, Veltman DJ, Boogerd W, Oldenburg HS, Reneman L, et al. Changes in brain activation in breast cancer patients depend on cognitive domain and treatment type. PLoS ONE. 2017;12(3):e0171724.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDonald BC, Conroy SK, Ahles TA, West JD, Saykin AJ. Gray matter reduction associated with systemic chemotherapy for breast cancer: a prospective MRI study. Breast Cancer Res Treat. 2010;123(3):819\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo E, Brown A, Barrett P, Morgan RB, King G, Kennedy MJ, et al. Subclinical anthracycline- and trastuzumab-induced cardiotoxicity in the long-term follow-up of asymptomatic breast cancer survivors: a speckle tracking echocardiographic study. Heart. 2010;96(9):701\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Ruiter MB, Reneman L, Boogerd W, Veltman DJ, Caan M, Douaud G, et al. Late effects of high-dose adjuvant chemotherapy on white and gray matter in breast cancer survivors: converging results from multimodal magnetic resonance imaging. Hum Brain Mapp. 2012;33(12):2971\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10(3):186\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, et al. Toward discovery science of human brain function. Proc Natl Acad Sci U S A. 2010;107(10):4734\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuo X, Lei D, Chen F, Wu M, Li L, Sun L, et al. Anatomic Insights into Disrupted Small-World Networks in Pediatric Posttraumatic Stress Disorder. Radiology. 2017;282(3):826\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Lei D, Li L, Huang X, Suo X, Xiao F, et al. White Matter Abnormalities in Post-traumatic Stress Disorder Following a Specific Traumatic Event. EBioMedicine. 2016;4:176\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei D, Ma J, Zhang J, Wang M, Zhang K, Chen F et al. Connectome-Scale Assessments of Functional Connectivity in Children with Primary Monosymptomatic Nocturnal Enuresis. Biomed Res Int. 2015; 2015: 463708.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Lei D, Peng J, Suo X, Pinaya WHL, Li W, et al. Disrupted brain gray matter networks in drug-na\u0026iuml;ve participants with essential tremor. Neuroradiology. 2021;63(9):1501\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatts DJ, Strogatz SH. Collective dynamics of 'small-world' networks. Nature. 1998;393(6684):440\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalvador R, Suckling J, Coleman MR, Pickard JD, Menon D, Bullmore E. Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb Cortex. 2005;15(9):1332\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Y, Chen ZJ, Evans AC. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb Cortex. 2007;17(10):2407\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePettersson-Yeo W, Allen P, Benetti S, McGuire P, Mechelli A. Dysconnectivity in schizophrenia: where are we now? Neurosci Biobehav Rev. 2011;35(5):1110\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuo X, Lei D, Li K, Chen F, Li F, Li L, et al. Disrupted brain network topology in pediatric posttraumatic stress disorder: A resting-state fMRI study. Hum Brain Mapp. 2015;36(9):3677\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhillips NS, Rao V, Kmetz L, Vela R, Medick S, Krull K et al. Changes in Brain Functional and Effective Connectivity After Treatment for Breast Cancer and Implications for Intervention Targets. Brain Connect. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiao H, Chen X, Yan Y, He X, Hu S, Kong J, et al. Functional connectivity change of brain default mode network in breast cancer patients after chemotherapy. Neuroradiology. 2016;58(9):921\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHole A, Terjesen T, Breivik H. Epidural versus general anaesthesia for total hip arthroplasty in elderly patients. Acta Anaesthesiol Scand. 1980;24(4):279\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNegrini D, Wu A, Oba A, Harnke B, Ciancio N, Krause M, et al. Incidence of Postoperative Cognitive Dysfunction Following Inhalational vs Total Intravenous General Anesthesia: A Systematic Review and Meta-Analysis. Neuropsychiatr Dis Treat. 2022;18:1455\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharfare H, Limongelli S, Purushotham AD. Neoadjuvant chemotherapy in breast cancer. Br J Surg. 2005;92(1):14\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta SD, Loibl S, Kaufmann M, Zahm DM, Hilfrich J, Huober J, et al. Neoadjuvant chemotherapy shows similar response in patients with inflammatory or locally advanced breast cancer when compared with operable breast cancer: a secondary analysis of the GeparTrio trial data. J Clin Oncol. 2010;28(1):83\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSato C, Sekiguchi A, Kawai M, Kotozaki Y, Nouchi R, Tada H, et al. Postoperative Structural Brain Changes and Cognitive Dysfunction in Patients with Breast Cancer. PLoS ONE. 2015;10(11):e0140655.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTong T, Lu H, Zong J, Lv Q, Chu X. Chemotherapy-related cognitive impairment in patients with breast cancer based on MRS and DTI analysis. Breast Cancer. 2020;27(5):893\u0026ndash;902.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLatora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001;87(19):198701.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDonald BC, Conroy SK, Ahles TA, West JD, Saykin AJ. Alterations in brain activation during working memory processing associated with breast cancer and treatment: a prospective functional magnetic resonance imaging study. J Clin Oncol. 2012;30(20):2500\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTownsend J, Altshuler LL. Emotion processing and regulation in bipolar disorder: a review. Bipolar Disord. 2012;14(4):326\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHibar DP, Westlye LT, van Erp TG, Rasmussen J, Leonardo CD, Faskowitz J, et al. Subcortical volumetric abnormalities in bipolar disorder. Mol Psychiatry. 2016;21(12):1710\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVardy J, Wefel JS, Ahles T, Tannock IF, Schagen SB. Cancer and cancer-therapy related cognitive dysfunction: an international perspective from the Venice cognitive workshop. Ann Oncol. 2008;19(4):623\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamilton JP, Etkin A, Furman DJ, Lemus MG, Johnson RF, Gotlib IH. Functional neuroimaging of major depressive disorder: a meta-analysis and new integration of base line activation and neural response data. Am J Psychiatry. 2012;169(7):693\u0026ndash;703.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallagher M, Chiba AA. The amygdala and emotion. Curr Opin Neurobiol. 1996;6(2):221\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuscatell KA, Eisenberger NI, Dutcher JM, Cole SW, Bower JE. Links between inflammation, amygdala reactivity, and social support in breast cancer survivors. Brain Behav Immun. 2016;53:34\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWohleb ES, Hanke ML, Corona AW, Powell ND, Stiner LM, Bailey MT, et al. β-Adrenergic receptor antagonism prevents anxiety-like behavior and microglial reactivity induced by repeated social defeat. J Neurosci. 2011;31(17):6277\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwarz JM, Bilbo SD. Sex, glia, and development: interactions in health and disease. Horm Behav. 2012;62(3):243\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenry CJ, Huang Y, Wynne AM, Godbout JP. Peripheral lipopolysaccharide (LPS) challenge promotes microglial hyperactivity in aged mice that is associated with exaggerated induction of both pro-inflammatory IL-1beta and anti-inflammatory IL-10 cytokines. Brain Behav Immun. 2009;23(3):309\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenk D, Greten FR. Inflammation: the incubator of the tumor microenvironment. Trends Cancer. 2022;8(11):901\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedman NP, Robbins TW. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology. 2022;47(1):72\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDonald BC, Saykin AJ. Alterations in brain structure related to breast cancer and its treatment: chemotherapy and other considerations. Brain Imaging Behav. 2013;7(4):374\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerguson RJ, McDonald BC, Saykin AJ, Ahles TA. Brain structure and function differences in monozygotic twins: possible effects of breast cancer chemotherapy. J Clin Oncol. 2007;25(25):3866\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchilder CM, Seynaeve C, Beex LV, Boogerd W, Linn SC, Gundy CM, et al. Effects of tamoxifen and exemestane on cognitive functioning of postmenopausal patients with breast cancer: results from the neuropsychological side study of the tamoxifen and exemestane adjuvant multinational trial. J Clin Oncol. 2010;28(8):1294\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDisner SG, Beevers CG, Haigh EA, Beck AT. Neural mechanisms of the cognitive model of depression. Nat Rev Neurosci. 2011;12(8):467\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Wang L, Zang Y, Yang H, Tang H, Gong Q, et al. Parcellation-dependent small-world brain functional networks: a resting-state fMRI study. Hum Brain Mapp. 2009;30(5):1511\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatry. 1961;4:561\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunstan DA, Scott N, Todd AK. Screening for anxiety and depression: reassessing the utility of the Zung scales. BMC Psychiatry. 2017;17(1):329.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall J, O'Carroll RE, Frith CD et al. \u003cem\u003e7 - Neuropsychology\u003c/em\u003e, in \u003cem\u003eCompanion to Psychiatric Studies (Eighth Edition)\u003c/em\u003e, E.C. Johnstone, Editors. 2010, Churchill Livingstone: St. Louis. pp. 121\u0026ndash;140.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark JH, Jung YS, Jung YM, Bae SA-OX. The role of depression in the relationship between cognitive decline and quality of life among breast cancer patients. 2019(1433\u0026ndash;7339 (Electronic)).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage. 2010;52(3):1059\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFox MD, Zhang D, Snyder AZ, Raichle ME. The global signal and observed anticorrelated resting state brain networks. J Neurophysiol. 2009;101(6):3270\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePower JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage. 2012;59(3):2142\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Wang J, Wu Q, Kuang W, Huang X, He Y, et al. Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry. 2011;70(4):334\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Y, Chen Z, Evans A. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. J Neurosci. 2008;28(18):4756\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage. 2002;15(1):273\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin C, Gao C, Chen C, Ma S, Netra R, Wang Y, et al. A preliminary study of the dysregulation of the resting networks in first-episode medication-naive adolescent depression. Neurosci Lett. 2011;503(2):105\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewman MEJ. Mixing patterns in networks. physical review e statistical nonlinear soft matter physics. 2002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAchard S, Bullmore E. Efficiency and cost of economical brain functional networks. PLoS Comput Biol. 2007;3(2):e17.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"breast cancer (BC), neoadjuvant chemotherapy (NAC), cancer-related cognitive impairment (CRCI), graph theory, connectome","lastPublishedDoi":"10.21203/rs.3.rs-4184945/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4184945/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e In this study, we prospectively investigated changes in the brain connectome at multiple time points in breast cancer (BC) patients treated with neoadjuvant chemotherapy (NAC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e Fifty-five participants with a diagnosis of BC underwent clinical assessments and fMRI at three timepoints, including before NAC (tp1), after the first cycle of NAC (tp2), and the end of the NAC regimen (tp3). Two matched healthy controls (HCs) groups received the same assessments at matching time points were also enrolled. Brain functional networks were constructed and analyzed using graph theory approaches to quantify the effect of NAC on brain cognitive dysfunction. We analyzed changes in brain connectome metrics both in HC and patient group and explored the relationship between these changes and clinical scales. Patient-subgroups were created by clinical subtype, chemotherapy regimen and menopausal status, and longitudinal subgroup analysis was performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThere were no longitudinal differences within the two HC groups, and no differences between the two HC groups and patient group at tp1. BC patients who underwent NAC showed significantly increased global efficiency (\u003cem\u003ep \u003c/em\u003e= 0.032), decreased characteristic path length (\u003cem\u003ep \u003c/em\u003e= 0.020), and altered nodal centralities mainly in the frontal-limbic system and cerebellar cortex. There were few changes between the two chemotherapy sessions. Changes in the topological parameters were correlated with changes in clinical scales but did not differ between subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eOur findings demonstrated that NAC might affect brain functional connectivity in BC patients, especially in the early stage.\u003c/p\u003e","manuscriptTitle":"Altered brain functional networks in patients with breast cancer after neoadjuvant chemotherapy Running title: Disrupted Network in Breast Cancer After Chemotherapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-03 17:13:48","doi":"10.21203/rs.3.rs-4184945/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"47126d16-28d3-44a5-8634-de940d0ae64c","owner":[],"postedDate":"April 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-03T17:13:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-03 17:13:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4184945","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4184945","identity":"rs-4184945","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}