Altered intra- and inter-network functional connectivity in pituitary adenomas with chiasmal compression
Original Article

Altered intra- and inter-network functional connectivity in pituitary adenomas with chiasmal compression

Jing Tang1, Wencheng Shen2, Pingyi Zhu3, Yuchen Liu4, Pinfa Zou5, Jurong Luo1, Yuting Wang1

1Department of Radiology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China; 2Second Department of Oncology, Chengdu Third People’s Hospital, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, China; 3Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; 4GE HealthCare MR Research, Beijing, China; 5Department of Radiology, the Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China

Contributions: (I) Conception and design: J Tang, W Shen, Y Wang; (II) Administrative support: Y Liu, Y Wang; (III) Provision of study materials or patients: P Zou; (IV) Collection and assembly of data: J Tang, J Luo; (V) Data analysis and interpretation: P Zhu; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Correspondence to: Yuting Wang, MD. Department of Radiology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, No. 32 West Second Section, Yihuan Road, Qingyang District, Chengdu 610072, China. Email: wangyuting_330@163.com.

Background: Pituitary adenomas (PA) frequently compress the optic chiasm, leading to visual field defects (VFDs) and potentially affecting the function of brain networks. This cross-sectional study aimed to investigate alterations in brain networks in PA patients with chiasmal compression using resting-state functional magnetic resonance imaging (rs-fMRI).

Methods: In this study, 35 PA patients with chiasmal compression and 33 healthy controls (HCs) were enrolled and underwent rs-fMRI scanning. Network-Based Statistic (NBS) and large-scale network analyses were performed. Additionally, correlations were analyzed between altered functional connectivity (FC) and suprasellar extension distance, duration of VFDs, as well as mean deviation (MD), reflecting the degree of VFDs.

Results: Combining NBS and large-scale network analyses, we found that PA patients with chiasmal compression mainly showed significantly decreased intra- and inter-network connectivity, including the visual network (VN), dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN), frontoparietal network (FPN), somatosensory-motor network (SMN), and subcortical network (SCN). Moreover, the decreased mean FC values within VN and between VN-VAN were negatively correlated with suprasellar extension distance, and the decreased mean FC within VN was positively correlated with MD.

Conclusions: This study highlights the widespread dysfunction of brain networks in PA patients with chiasmal compression. These findings offer new insights into the brain dysfunction in PA patients with chiasmal compression and could also aid in the evaluation of therapeutic efficacy for the disease.

Keywords: Pituitary adenomas (PA); resting-state functional magnetic resonance imaging (rs-fMRI); functional connectivity (FC); brain network

Submitted May 06, 2025. Accepted for publication Oct 22, 2025. Published online Nov 21, 2025.

doi: 10.21037/qims-2025-1062

Introduction

Pituitary adenomas (PA) are the most common neoplasms of the sellar region, with a reported prevalence ranging from 1 in 865 to 1 in 2,688 adults (1,2). As these tumors enlarge, they can compress adjacent structures, leading to a range of clinical symptoms. Among these manifestations, visual field defects (VFDs) are particularly prominent, primarily resulting from compression of the optic chiasm (2). This compression may result in reduced neuronal responsiveness in brain regions involved in visual processing. Several studies have confirmed abnormal function in the visual cortex in PA patients with chiasmal compression (3-5). However, visual processing involves a great range of neural networks inside and beyond the visual cortex. Information transmission and collaboration between brain networks produce more complex neurological functions, such as visual cognition, visual attention, and visual working memory (6,7). Although previous studies have demonstrated dysfunction within the visual cortex of PA patients with chiasmal compression, it remains unclear whether and how brain networks are affected in PA patients with chiasmal compression.

In recent decades, functional connectivity (FC) has provided high spatial resolution for studying brain networks based on resting-state functional magnetic resonance imaging (rs-fMRI) (8,9). Previous studies have demonstrated its effectiveness in identifying network-specific alterations in neurological conditions, including those affecting vision-related processing (10,11).

Our current study aimed to investigate alterations of brain networks in PA patients with chiasmal compression based on rs-fMRI data. Additionally, we evaluated the relationships between altered mean FC and suprasellar extension distance of the optic chiasm, duration of VFDs, as well as degree of VFDs. We hypothesized that PA patients with chiasmal compression would show extensive changes in both intra- and inter-network FC, and that these alterations would be associated with the aforementioned clinical indicators. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1062/rc).

Methods

Participants

The cross-sectional study was approved by the ethics committee of Sichuan Provincial People’s Hospital (No. 2025149), and all procedures complied with the Declaration of Helsinki and its subsequent amendments. Written informed consent was obtained from all participants. A total of 41 patients with PA and 37 healthy controls (HCs), matched for age, gender, education, and handedness, were initially recruited from the hospital or local community. Exclusion criteria included: (I) ophthalmologic diseases or other intracranial lesions; (II) history of eye surgery, radiotherapy, psychiatric or neurological disorders; (III) MRI contraindications; (IV) poor image quality; (V) severe chiasmal compression making the optic chiasm unidentifiable; and (VI) cortical distortion. Patients were included if they had: (I) pathologically confirmed PA; (II) high-quality 3.0 T MRI within 2 weeks pre-surgery; and (III) presence of preoperative chiasmal compression and VFDs.

Following the application of these criteria, five participants (four HCs and one patient) were excluded due to excessive head motion (>2 mm translation or >2° rotation). Additionally, two patients with prior eye surgery, two with cortical deformities, and one with incomplete imaging data were excluded. Ultimately, 35 PA patients with chiasmal compression (20 males, 15 females; mean age 55.63±15.76 years) and 33 HCs (17 males, 16 females; mean age 58.27±9.77 years) were included in the final analysis.

Ophthalmological assessment

Preoperative visual field assessments for all patients were conducted using standard automated perimetry (Humphrey Field Analyzer, Carl Zeiss Meditec Inc., Dublin, CA, USA). VFDs severity was quantified by mean deviation (MD), with MD <−3.0 dB indicating VFDs (12). Duration was defined as the interval between symptom onset and rs-fMRI.

MRI data acquisition and preprocessing

MRI was performed using a 3.0 T SIGNA Architect scanner (GE Medical Systems, Milwaukee, WI, USA) with a 40-channel head coil. Earplugs and foam pads minimized scanner noise and head motion. Participants lay supine with eyes closed, relaxed but awake. Coronal T2-weighted images of the pituitary were obtained with the following parameters: repetition time (TR)/echo time (TE) =3,000/120 ms, field of view (FOV) =210×210 mm2, voxel size =0.4×0.4×1 mm3, 30 slices. Structural T1-weighted images were obtained using a three-dimensional brain volume imaging (3D-BRAVO) sequence: TR/TE =6.0/2.3 ms, flip angle (FA) =8°, FOV =240×240 mm2, voxel size =1×1×1 mm3, 160 slices. Functional images were acquired using an axial gradient-echo echo-planar imaging sequence (see Figure S1): TR/TE =2,000/30 ms, FA =90°, FOV =240×240 mm2, voxel size =3×3×4 mm3, 40 slices without gap, and 260 volumes. The total scan duration was 8 minutes and 40 seconds.

Preprocessing of the rs-fMRI data was conducted using the DPABI toolbox (http://rfmri.org/dpabi) (13). The first 10 volumes were discarded to allow for signal stabilization. The remaining volumes were corrected for slice timing and head motion. Subject movement was determined to ensure translations of less than 2 mm and rotations of less than 2°. Nuisance signals, including white matter, cerebrospinal fluid, linear trends, and the 24 Friston head-motion parameters, were regressed out. Derived functional images were coregistered to corresponding structural images, normalized to Montreal Neurological Institute (MNI) space via DARTEL, and resampled to 3×3×3 mm3. Data were then bandpass filtered (0.01–0.08 Hz). Notably, the two groups did not show significant differences in the mean framewise displacement (FD) (14).

Morphometric assessment

The suprasellar extension distance was used to render the condition of chiasmal compression based on a previous study (15), which was measured using the open-source software ITK-SNAP (version 3.8.0; http://www.itk-snap.org) (16) on coronal T2-weighted images. The interested reader can find them in a supplementary appendix online (Figure S2). It was defined as the maximal perpendicular distance from the inferior optic chiasm boundary to a reference line at the superior surface of the cavernous internal carotid arteries (17-19). Two experienced neuroradiologists independently measured the suprasellar extension distance, blinded to clinical data. To assess intraobserver reliability, one neuroradiologist re-measured the suprasellar extension distance after a 1-month interval. Both intraobserver and interobserver agreements were excellent, with ICCs of 0.964 (P<0.001) and 0.942 (P<0.001), respectively.

Edge-based FC comparison

We used the Dosenbach functional atlas, which defines 160 regions of interest (ROIs) based on meta-analyses of fMRI studies (20), to define brain nodes for our analysis. Each node was represented by a 5 mm radius sphere, with the cerebellum excluded due to incomplete coverage, leaving 142 ROIs for further analysis. For each ROI, blood oxygen level-dependent (BOLD) signals were extracted and averaged across all voxels in the ROI (Figure S3). Edge-based FC for any pair of two ROIs was calculated using Pearson’s correlation coefficient of the BOLD signals, which was then transformed to z-scores using Fisher’s r-to-z formula.

We used the Network-Based Statistics (NBS) toolbox (https://www.nitrc.org/projects/nbs) to assess group differences across 10,011 functional connections (142×141/2), controlling for age, sex, education, and head motion as covariates. NBS offers enhanced statistical power compared to traditional mass-univariate correction methods like false discovery rate (FDR) by identifying significant connection differences at the network level while controlling for family-wise error (FWE) (21).

In our analysis, a cluster was defined as a set of suprathreshold edges interconnected in topological space. We applied a primary threshold of P<0.0005 (one-tailed) in two-sample t-tests to identify suprathreshold edges while decreasing FWE. A permutation test with 10,000 iterations was then performed to establish the null distribution of the largest observed cluster size (22). In each permutation, participants were randomly reassigned to two groups, and t-tests were computed for each edge using the same primary threshold. The largest connected cluster size was recorded for each iteration. Finally, the significance of the actual network clusters was determined by comparing their edge counts to the null distribution (22). Two-tailed tests were conducted separately, with P<0.025 considered statistically significant.

We categorized suprathreshold edges based on their membership in the seven networks defined by Yeo et al. (23), including the visual network (VN), ventral attention network (VAN), dorsal attention network (DAN), frontoparietal network (FPN), subcortical network (SCN), somatosensory-motor network (SMN), and default mode network (DMN). The SCN was used instead of the limbic network due to its small ROI count. We counted the number of edges falling into each of the seven within-network classes and 21 between-network classes.

Large-scale network FC comparison

We calculated large-scale within- and between-network FC by averaging FC z-scores across relevant edges. This produced seven within-network and 21 between-network averaged FC values. Group comparisons were conducted using two-sample t-tests, adjusted for age, sex, education, and head motion, with FDR correction for multiple comparisons. The relationships between clinical characteristics and altered large-scale network FC were assessed using Pearson’s or Spearman’s correlations, with statistical significance set at P<0.05 for all correlations.

Statistical analyses

Demographic and clinical variables between the PA and HC groups were analyzed using SPSS version 25.0 (SPSS Inc., Chicago, IL, USA). The Kolmogorov-Smirnov test was employed to assess the normality of data. For normally distributed continuous variables, group differences were evaluated using Student’s t-test. For non-normally distributed continuous variables, Mann-Whitney U tests were applied. Categorical variables were assessed using χ2 tests. Statistical significance was determined at P<0.05, two-tailed.

Results

Demographic and clinical characteristics

The demographic and clinical characteristics of the PA and HC groups are summarized in Table 1. No significant inter-group differences were observed in gender, age, mean FD, years of education, or handedness (all P>0.05).

Table 1

Demographic and clinical features of PA and HC groups

Subject characteristics PA (n=35) HC (n=33) P value
Age (years) 55.63±15.76 58.27±9.77 0.412†
Gender (male/female) 20/15 17/16 0.641‡
Mean FD (mm) 0.04 [0.03–0.07] 0.05 [0.03–0.08] 0.429§
Handedness (right/left) 35/0 33/0 >0.99‡
Education (years) 10 [9–11] 9 [9–11] 0.442§
Suprasellar extension distance (mm) 14.41±4.20 NA NA
MD (dB) −8±2.79 NA NA
Duration of VFD (months) 1 [1–3] NA NA

Data are presented as mean ± SD, number or median [IQR]. †, two-sample t-test; ‡, Chi-squared test; §, Mann-Whitney U test. FD, framewise displacement; HC, healthy control; IQR, interquartile range; MD, mean deviation; NA, not available; PA, pituitary adenomas; SD, standard deviation; VFD, visual field defect.

Edge-based FC

NBS analysis revealed a significant cluster (P<0.001) consisting of 52 ROIs and 66 edges with decreased FC, as well as four ROIs and three edges with increased FC in the PA group (Figure 1). The suprathreshold edges with abnormal FC involved several networks (Figure 2). The highest decrease in edges was in the interconnection between VN and VAN (Figure 2).

Figure 1 Between-group differences in edge-based FC. The brain maps show the affected edges (lines) and their connecting nodes (spheres) from several perspectives. The size of a node indicates how many affected edges are connected to this sphere. Bigger nodes have more affected edges than smaller ones. The color of a node indicates which network it belongs to. The color of edges (blue) indicates that the FC is decreased in PA group. The color of edges (red) indicates that the FC is increased in PA group. A, anterior; DAN, dorsal attention network; DMN, default mode network; FC, functional connectivity; FPN, frontoparietal network; L, left; P, posterior; PA, pituitary adenomas; R, right; SCN, subcortical network; SMN, somatosensory-motor network; VAN, ventral attention network; VN, visual network.
Figure 2 Between-group differences in edge-based FC. Heatmaps summarize the absolute number and percentage of FC within and between brain networks showing significant differences between patients and controls. (A) Hypoconnectivity (PA group < HC group), (B) hyperconnectivity (PA group > HC group). DAN, dorsal attention network; DMN, default mode network; FC, functional connectivity; FPN, frontoparietal network; HC, healthy control; PA, pituitary adenomas; SCN, subcortical network; SMN, somatosensory-motor network; VAN, ventral attention network; VN, visual network.

Large-scale network FC

In the large-scale network analysis, we found that the PA group showed decreased within-network FC of the VN and DAN compared with the HC group (Figure 3, Table 2). In addition, the PA group also exhibited decreased between-network FC for seven pairs of networks, including VN-SMN, VN-DAN, VN-VAN, SMN-DAN, SMN-VAN, SCN-FPN, and DAN-VAN (Figure 3, Table 2).

Figure 3 Between-group differences in large-scale network FC. The schematic diagram shows the network connections with significant FC decrease for the seven networks in the PA group compared to HC group. DAN, dorsal attention network; DMN, default mode network; FC, functional connectivity; FPN, frontoparietal network; HC, healthy control; PA, pituitary adenomas; SCN, subcortical network; SMN, somatosensory-motor network; VAN, ventral attention network; VN, visual network.

Table 2

Decreased large-scale network FC in PA group

Brain network VN SMN DAN VAN SCN FPN DMN
VN t=−3.07*, P=0.023
SMN t=−2.91*, P=0.023 t=−2.19, P=0.076
DAN t=−2.87*, P=0.023 t=−3.61*, P=0.0067 t=−3.57*, P=0.0067
VAN t=−3.71*, P=0.007 t=−2.86*, P=0.024 t=−2.77*, P=0.026 t=−1.48, P=0.19
SCN t=−1.18, P=0.28 t=−1.83, P=0.12 t=−1.7, P=0.14 t=−2.36, P=0.058 t=−1.10, P=0.31
FPN t=−1.90, P=0.12 t=−2.03, P=0.099 t=−1.87, P=0.12 t=−1.28, P=0.26 t=−2.67*, P=0.03 t=−1.73, P=0.14
DMN t=0.49, P=0.64 t=−1.04, P=0.32 t=−1.62, P=0.16 t=−1.19, P=0.28 t=−1.47, P=0.19 t=−2.31, P=0.059 t=−0.44, P=0.66

t values and FDR-corrected P values from two-sample t-tests are shown. *, significant after FDR correction to P<0.05 (two-tailed) among seven within-network and 21 between-network connections. DAN, dorsal attention network; DMN, default mode network; FC, functional connectivity; FDR, false discovery rate; FPN, frontoparietal network; PA, pituitary adenomas; SCN, subcortical network; SMN, somatosensory-motor network; VAN, ventral attention network; VN, visual network.

Correlation between clinical characteristics and altered large-scale network FC

The altered mean FC within VN was negatively correlated with suprasellar extension distance in PA patients with chiasmal compression (r=−0.37, P=0.029; Figure 4A). The altered mean FC between VN-VAN was negatively correlated with suprasellar extension distance in PA patients with chiasmal compression (r=−0.39, P=0.018; Figure 4B). Additionally, the MD was positively correlated with mean FC within VN in PA patients with chiasmal compression (r=0.38, P=0.023; Figure 4C). The duration of VFDs showed no significant correlation with altered large-scale network FC.

Figure 4 Correlations between suprasellar extension distance and FC in PA patients with chiasmal compression. (A) Mean FC within VN was negatively correlated with suprasellar extension distance (r=−0.37, P=0.029), namely, the greater suprasellar extension distance corresponds to the lower mean FC within VN. (B) Mean FC between VN and VAN was negatively correlated with suprasellar extension distance (r=−0.39, P=0.018), namely, the greater suprasellar extension distance corresponds to the lower mean FC between VN and VAN. (C) MD was positively correlated with mean FC within VN (r=0.38, P=0.023), namely, the greater mean FC within VN corresponds to the greater MD. Pearson’s correlation analysis was used to assess the correlations. FC, functional connectivity; MD, mean deviation; PA, pituitary adenomas; VAN, ventral attention network; VN, visual network.

Discussion

This study analyzed functional brain networks in PA patients with chiasmal compression. NBS and large-scale network analyses exhibited decreased intrinsic FC extensively in PA patients with chiasmal compression, affecting several brain networks. Moreover, our exploratory analysis showed that the decreased mean FC values within VN and between VN-VAN were negatively correlated with suprasellar extension distance, and the MD was positively correlated with mean FC within VN. These findings further excavated and complemented the understanding of brain dysfunction in PA patients with chiasmal compression from a more comprehensive perspective.

In our study, the NBS method captured nearly all findings from large-scale network analysis and identified additional FC abnormalities, likely because large-scale analyses summarize network-level trends while NBS is more sensitive to local connectivity changes (11,24). Combining both approaches, therefore, provides a complementary and more comprehensive characterization of functional network alterations in PA patients with chiasmal compression.

Both NBS and large-scale network analyses demonstrated significantly reduced intrinsic FC within the VN in PA patients with chiasmal compression, consistent with prior studies (4,5). This disruption likely reflects impaired afferent visual input caused by chiasmal compression, leading to cortical desynchronization and deficits in visual information integration (3-5,25,26). Exploratory correlation analysis revealed that reduced intra-VN FC was associated with more severe chiasmal compression and VFDs, thereby directly linking structural compression to VN dysfunction.

Visual processing is complex, involving communication and integration among multiple functional networks (3,27,28). Dysfunction within the VN could disrupt its coordination with other vision-related brain networks, as confirmed by our study. We observed that PA patients with chiasmal compression exhibited reduced FC between the VN and other brain networks, including the VAN, DAN, DMN, SMN, and FPN. These FCs are essential for transmission of visual information and higher-order visual processing achieved through network collaboration (6,29-32). Specifically, VN-DMN coupling supports memory retrieval during visual encoding (6); VN-SMN interaction is essential for spatial visual processing (30); VN-FPN coordination influences visual processing speed (29); and integration between VN and attention networks (VAN/DAN) facilitates visuospatial cognition (31,32). Disrupted FC mentioned above indicates impaired visual information transmission and weakened inter-network collaboration, contributing to higher-order visual dysfunction.

Our study found that the highest decrease in edges was in the interconnection between VN and VAN. In contrast to the DAN, which is involved in top-down attention (33,34), the VAN is responsible for bottom-up attention, namely stimulus-driven attention (33). The VN participates in the perception and processing of the visual stimulus (35). The VAN is found to be activated if an unexpected stimulus appears (36,37), suggesting its close relationship with VN. Therefore, it is reasonable that the interconnection between VN and VAN showed the highest decrease in edges in the case of VN dysfunction. Moreover, we also found that the mean FC between VN-VAN was negatively correlated with suprasellar extension distance in PA patients with chiasmal compression. This finding indicates that the more severe compression of optic chiasm in PA patients, the lower FC between VN-VAN. Reduced VAN-VN connectivity may indicate a less alerting state in preparation for upcoming stimuli.

Additionally, we found increased FC between the VN and DMN using the NBS method. The DMN is a high-level cognitive network, whereas the VN is a low-level perceptual network (38,39). Stronger FC between them may indicate enhanced synergy in visual processing (40). Previous studies have shown that the increased FC between the visual cortex and the DMN in visual-related disorders reflects the recruitment of additional cognitive resources to compensate for impaired visual function by enhancing the interaction between high-level cognitive and low-level perceptual networks (27,39). In line with these observations, our findings may reflect compensatory reorganization in response to optic chiasm compression.

We also observed decreased intra-network FC in DMN, VAN, and DAN. The DMN is mainly involved in self-referential processing, emotional regulation, and episodic memory (27,41-43), while DAN and VAN are critical for attention control (34,44-46). These FC alterations may partly underlie the emotional, memory, and attentional deficits commonly reported in PA patients (47-49). Notably, our exploratory correlation analysis revealed no association between the duration of VFDs and altered large-scale network FC, possibly because of poor subjective symptoms caused by PA due to their slow growth.

Visual disturbance is a common clinical manifestation in PA patients and the main reason for surgery. However, postoperative visual outcomes vary widely, ranging from permanent loss to complete recovery (4). Accurate prediction of postoperative recovery is therefore crucial for clinical decision-making, yet reliable prognostic markers remain limited. Previous studies have shown that FC are important predictive markers of treatment efficacy in CNS-related disorders (50-52). In this study, results suggest that preoperative FC alterations may help identify patients at higher risk of persistent network dysfunction and poor postoperative recovery. Such information could guide individualized surgical decisions, particularly with respect to the timing and necessity of intervention, by distinguishing patients who might benefit from earlier surgery before irreversible network damage occurs. Moreover, rs-fMRI may hold promise as a non-invasive biomarker for disease monitoring. Longitudinal FC assessment could potentially be used to track disease progression, detect subclinical brain network changes, and evaluate postoperative recovery dynamics. This approach may complement conventional structural imaging and visual field testing, offering a more comprehensive evaluation of neural integrity in PA patients. Taken together, our results highlight the potential clinical utility of rs-fMRI both for optimizing surgical decision-making and for developing network-based biomarkers to monitor disease course and recovery.

The current study has several limitations. First, as a cross-sectional study, our research limits the assessment of changes in the brain networks before and after decompression of optic chiasm in PA patients. Longitudinal studies incorporating pre- and post-surgical rs-fMRI assessments are crucial to determining whether the observed alterations in intra- and inter-network connectivity recover following surgery. We recognize this as an important direction for future research. The second limitation of our study is the relatively small sample size, and the results need to be validated further in a large sample size. The third limitation is the lack of detailed neuropsychological or cognitive assessments. Future studies should incorporate standardized neuropsychological or cognitive assessments to establish direct correlations with connectivity changes and offer a more comprehensive understanding of FC alterations in PA patients. Fourth, endocrine status in patients may have an impact on our results. Future prospective studies with comprehensive endocrine profiling will be essential to formally control for endocrine effects and clarify their interaction with compression-related mechanisms.

Conclusions

This study showed widespread abnormalities in intra- and inter-network connectivity related to neurobehavioral functions, offering comprehensive insights into the brain dysfunction of PA patients with chiasmal compression. These findings could also aid in the evaluation of therapeutic efficacy for the disease.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1062/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1062/dss

Funding: This work was supported by the National Key R&D Program of China (Nos. 2022YFC2009900 and 2022YFC2009906) and the National Natural Science Foundation (Youth Project) (No. 82202123).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1062/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments, and was approved by the Ethics Committee of Sichuan Provincial People’s Hospital (No. 2025149). Written informed consent was obtained from all participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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Cite this article as: Tang J, Shen W, Zhu P, Liu Y, Zou P, Luo J, Wang Y. Altered intra- and inter-network functional connectivity in pituitary adenomas with chiasmal compression. Quant Imaging Med Surg 2025;15(12):12361-12371. doi: 10.21037/qims-2025-1062

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