Distinct brain susceptibility alteration pattern in Parkinson’s disease with drooling: evidence from quantitative susceptibility mapping

Introduction

Parkinson’s disease (PD) is a common progressive neurodegenerative disorder, pathologically characterized by the progressive loss of dopaminergic neurons in the nigrostriatal pathway. Clinically, it manifests with a variety of motor and non-motor symptoms. Drooling (sialorrhea), defined as the involuntary leakage of saliva due to its excessive accumulation, is one of the relatively frequent non-motor symptoms in PD patients (1). Drooling not only leads to inconveniences such as frequent wiping of saliva, damp clothing, and oral odor, significantly impairing personal image and life comfort (1), but may also cause embarrassment, diminished self-esteem, and social avoidance behaviors, contributing to social isolation and mood disorders (2). In severe cases, it may also increase the risk of aspiration pneumonia and perioral skin infections, thereby further adding to the healthcare burden (3). The pathophysiology of drooling in PD is not yet completely clarified. Thus, elucidating its underlying mechanisms is crucial for developing effective interventions, bearing significant clinical and societal implications.

Drooling in PD involves multi-system, multi-level neural dysfunction, and its mechanisms can be parsed from peripheral to central levels. At the local level, salivary gland secretion function may be affected by dysregulation of autonomic control (4). For instance, some studies have reported accelerated parotid excretion rates in PD patients (5), suggesting alterations in saliva production and clearance processes. However, the core issue of drooling is not an absolute increase in saliva secretion, but rather impaired swallowing function. The latter stems from degenerative changes in the nigrostriatal dopaminergic pathway, leading to bradykinesia and incoordination of the oropharyngeal muscles, thereby substantially reducing the efficiency of saliva clearance and causing saliva retention in the oral cavity (4,6).

Beyond peripheral mechanisms, the dysregulation of central regulatory networks plays a more critical role in PD-related drooling. Neuroimaging studies suggest that drooling is closely associated with abnormal brain network connectivity, involving functional integration disturbances in cortico-limbic-striatal-cerebellar circuits and cortico-cortical networks (7), indicating the presence of neural compensatory or decompensatory mechanisms at the systems level. Furthermore, the degree of nigral dopaminergic neuron degeneration (8) and co-morbid cognitive dysfunction (9,10) have also been demonstrated to be important factors exacerbating drooling.

The origin of these disruptions in brain functional networks lies in the characteristic pathological damage of PD. The defining neuropathology of the disease comprises the degeneration and loss of dopaminergic neurons in the midbrain substantia nigra, coupled with the formation of Lewy bodies—intracellular inclusions of aggregated α-synuclein—in surviving neurons (11). Although a clear correlation between Lewy body pathological burden and clinical symptom severity has not been consistently demonstrated (12), recent research has gradually revealed that disordered brain iron metabolism might play a more critical driving role in the neurodegenerative process of PD. Disrupted iron homeostasis could promote the production of neurotoxic substances and reactive oxygen species, triggering iron-induced oxidative stress (13,14), which in turn promotes the pathological aggregation of α-synuclein and accelerates dopaminergic neuron loss in the substantia nigra (15). Notably, abnormal iron deposition is particularly prominent in the nigral region and has become a hallmark pathological change in PD (16), with its dynamic accumulation pattern potentially closely related to different stages of PD development (17). Earlier studies by Riederer et al. (18,19), Hirsch et al. (20), and Dexter et al. (21,22) first demonstrated altered iron metabolism in PD, including increased iron accumulation in the substantia nigra and changes in ferritin and other trace metals, providing foundational evidence for the involvement of iron dysregulation in PD pathology. However, it remains unclear whether there is a characteristic spatial pattern of cerebral iron deposition in PD patients with drooling, and this has not been systematically investigated.

Although conventional structural magnetic resonance imaging (MRI) often lacks specific findings in PD, quantitative MRI can detect subtle neurodegeneration-related tissue alterations (23). In addition to susceptibility-based imaging, relaxometry-based methods, including T1, T2, T2*, R2, and R2* mapping, are sensitive to iron-related and microstructural changes (24,25). T2*/R2* measurements are particularly sensitive to local magnetic field inhomogeneities induced by paramagnetic substances such as iron, whereas T1 and T2 values may also reflect water content, myelin integrity, perfusion, gliosis, and other tissue properties (23,25). Recent multiparametric MRI techniques, including magnetic resonance fingerprinting, magnetic resonance spin tomography in time-domain (MR-STAT), and multi-parametric magnetic resonance imaging with flexible design (MULTIPLEX), allow rapid acquisition of complementary quantitative maps for evaluating PD-related brain changes (23-25).

Quantitative susceptibility mapping (QSM) enables non-invasive quantification of tissue magnetic susceptibility and provides an iron-sensitive imaging measure. Recently, QSM has found broad application in cerebral iron quantification in PD (26-29), proving valuable for auxiliary and differential diagnosis (30,31), assessment of disease progression and severity (32-34), and exploration of underlying pathology (35,36). Emerging evidence suggests that QSM, particularly when combined with other quantitative MRI metrics, may aid in the diagnosis and severity assessment of PD (37). Moreover, QSM has been applied to investigate brain iron deposition associated with specific non-motor manifestations of PD. A study (38) reported that regional brain iron deposition was associated with pure apathy in PD, suggesting that QSM-derived susceptibility alterations may be linked to distinct non-motor phenotypes. Zucca et al. reported that neuromelanin and iron independently contribute to neuromelanin-sensitive MRI signals in midbrain regions, and found increased substantia nigra iron in PD, supporting MRI-based assessment of brain alterations in neurodegenerative disorders (39). Nevertheless, QSM is not fully iron-specific, as susceptibility values may also be influenced by myelin, calcium, and other susceptibility sources; therefore, QSM and relaxometry-based quantitative MRI should be regarded as complementary approaches.

Based on the above research background, we propose the following scientific hypothesis: drooling symptoms in PD may be associated with abnormal QSM-derived susceptibility alterations in specific brain regions; this pattern of susceptibility alterations can be non-invasively quantified using QSM technology and may show a significant correlation with the severity of drooling. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-0746/rc).

Methods

Participants

PD patients in this study were recruited from the movement disorders specialty clinic, neurology outpatient department and inpatients of The Affiliated Wuxi People’s Hospital of Nanjing Medical University. Healthy controls (HC) were recruited from multiple channels: the hospital’s physical examination center and through referrals from patient spouses. Some participants were excluded due to reasons such as poor image quality or failure to complete all MRI sequences. Ultimately, this study included 38 PD patients with drooling (PD-DR), 23 PD patients without drooling (PD-NDR), and 42 HC. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of The Affiliated Wuxi People’s Hospital of Nanjing Medical University (No. KY21133). All participants provided informed consent after fully understanding the study’s purpose and procedures.

The inclusion criteria for the PD-DR group were as follows: (I) fulfilled the clinical diagnostic criteria for idiopathic PD as defined by the Movement Disorder Society (MDS) (40); presynaptic dopaminergic imaging, such as dopamine transporter single-photon emission computed tomography (DaT SPECT), was not performed in this study; (II) aged between 40 and 80 years; (III) scored ≥1 on Item 6 of Part II (activities of daily living) of the Unified Parkinson’s Disease Rating Scale (UPDRS) (41); (IV) right-handed. The inclusion criteria for the PD-NDR group were as follows: (I) fulfilled the clinical diagnostic criteria for idiopathic PD as defined by the MDS (40); (II) aged between 40 and 80 years; (III) scored 0 on Item 6 of Part II of the UPDRS (41); (IV) right-handed. The inclusion criteria for the HC group were as follows: (I) matched to the patient groups in terms of sex, age, and education level; (II) no history of major cardiac, pulmonary, cerebral, renal or stomatological diseases, and no history of alcohol or substance abuse; (III) confirmed absence of cognitive impairment or psychiatric abnormalities through clinical interview and neuropsychological scale assessment; (IV) no significant structural abnormalities on MRI examination; (V) aged between 40 and 80 years; (VI) right-handed. The exclusion criteria (all participants) were as follows: (I) presence of cognitive impairment, Mini-Mental State Examination (MMSE) score ≤26 (42); (II) presence of psychiatric manifestations such as hallucinations; (III) history of other neurological disorders, such as striatal infarction or calcification, hydrocephalus, white matter lesions, head trauma, encephalitis, epilepsy, and so on; (IV) significant language or hearing impairment affecting cooperation during examination; (V) comorbid severe organic diseases of the heart, liver, kidneys, and so on; (VI) presence of metal implants, hearing aids, or other MRI contraindications; (VII) presence of severe dyskinesia, limb tremor, or uncontrolled head tremor; (VIII) claustrophobia preventing completion of MRI examination; (IX) comorbid iron metabolism-related disorders such as iron deficiency anemia, and restless legs syndrome.

Demographic and clinical assessment

Age, sex, and years of education were recorded for all participants. For the PD patients, disease duration, types and dosages of anti-Parkinson’s medications, and the average levodopa dose per administration were additionally collected. The levodopa equivalent daily dose (LEDD) was calculated accordingly (43). All clinical assessments were performed by two senior neurologists, Dr. Weiguo Cheng and Dr. Guofeng Shi, each of whom had received standardized training and had 20 years of clinical experience. Any discrepancies were resolved through discussion and consensus. Screening for sialorrhea was conducted using Item 6 of Part II of the UPDRS. Patients with a score of ≥1 were assigned to the PD-DR group, whereas those with a score of 0 were assigned to the PD-NDR group (41). The severity of drooling was further evaluated using Item 19 of the Non-Motor Symptoms Scale (NMSS) (44) and the Sialorrhea Clinical Scale for Parkinson’s Disease (SCS-PD) (45). The Unified Parkinson’s Disease Rating Scale Motor Section (UPDRS-III) score and the Hoehn-Yahr (H-Y) stage were used to assess motor symptom severity (46). The MMSE, Montreal Cognitive Assessment (MoCA) (47), and Frontal Assessment Battery (FAB) (48) were used to comprehensively assess participants’ cognitive function.

MRI data acquisition

All enrolled participants underwent both three-dimensional (3D) T1-weighted (T1W) structural imaging and QSM on a Siemens 3.0 T Prisma MRI scanner (Siemens, Erlangen, Germany) at The Affiliated Wuxi People’s Hospital of Nanjing Medical University. All patients with PD were instructed to withhold their anti-parkinsonian medications for at least 12 hours (overnight) prior to MRI scanning to minimize the potential acute effects of dopaminergic treatment on neuroimaging measures and clinical assessments.

Prior to scanning, participants were instructed to remain relaxed and keep their heads still throughout the procedure. All metallic objects were removed with assistance before entering the scanner room. A multi-modal head immobilization strategy, including foam padding, a forehead strap, and a chin strap, was employed to minimize head motion. The scanning sequences and parameters were as follows: 3D T1-weighted imaging (T1WI) using magnetization-prepared rapid gradient-echo sequence: repetition time (TR) =2,300 ms; echo time (TE) =2.98 ms; inversion time =900 ms; flip angle (FA) =9°; slice thickness 1 mm; number of slices 192; field of view (FOV) =256×256 mm²; acquisition matrix =256×256; voxel size =1×1×1 mm³ (scan time: 5 minutes 30 seconds). QSM using a 3D multi-echo fast low-angle shot sequence: TR =35 ms; TEs: TE 1 =7.5 ms, TE 2 =14.42 ms, TE 3 =21.34 ms, TE 4 =28.26 ms; FA =20°; slice thickness 1 mm; FOV =220×220×128 mm³; acquisition matrix size =220×220; resolution 1×1×1 mm³ (scan time: 7 minutes 21 seconds).

Additionally, conventional MRI sequences, including T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps, were acquired for all participants to rule out significant structural brain abnormalities. The ADC maps were used for image quality inspection and anatomical reference. ADC metrics were not included in further statistical analyses because diffusion-derived measures were not predefined outcome variables in this study, which primarily focused on susceptibility alterations assessed by susceptibility-based imaging.

QSM reconstruction pipeline

The QSM computation in this study was based on multi-echo gradient echo data, and the processing pipeline was as follows: first, the total field map was computed using a nonlinear fitting algorithm (49), followed by phase unwrapping via the Laplacian-based phase-unwrapping method. Subsequently, based on the echo-averaged magnitude image, a brain mask was generated using the Brain Extraction Tool algorithm integrated into the FMRIB Software Library (FSL, v6.0.1) (50). Then, the projection onto dipole fields method (51) was employed to remove the background field contributions originating from outside the brain. Finally, the final QSM maps were reconstructed using the morphology-enabled dipole inversion method, combined with automatic uniform cerebrospinal fluid zero-reference correction (morphology-enabled dipole inversion + 0) (52). Key algorithm parameters were set as follows: weight for the L1 regularization term for spatial smoothing =1,000, weight for the cerebrospinal fluid zero reference term =100.

QSM preprocessing

This study used ANTs software (http://stnava.github.io/ANTs) for image spatial preprocessing and normalization. The pipeline was as follows: (I) bias field correction: first, N4 bias field correction was applied to the native space T1W image to correct spatial intensity inhomogeneity, resulting in a corrected image (T1W-corrected). (II) Intermediate modality selection and native space co-registration: given that the magnitude gradient echo image resides in the same space as QSM and has similar tissue contrast to the T1W image, the mag-echo image was chosen as the intermediary modality linking QSM and T1W. A rigid registration was used to register the individual mag-echo image to the corresponding T1W-corrected image, and the same transformation matrix was applied to register the QSM to the T1W-corrected space, generating a co-registered QSM. (III) Spatial normalization: the Montreal Neurological Institute (MNI) space T1W template including the skull was registered to the individual T1W-corrected image using a hierarchical transformation (successively performing rigid, affine, and nonlinear registration). The inverse of this hierarchical transformation was then used to map the native space co-registered QSM maps to the MNI standard space. All normalized QSM-MNI images were resampled to 1 mm isotropic resolution. (IV) Spatial smoothing: to reduce the impact of registration errors and other potential inaccuracies, the normalized QSM-MNI images were finally spatially smoothed using a 3D Gaussian kernel with a full width at half maximum of 3 mm, a parameter choice consistent with previous studies (32,33).

Voxel-wise QSM analysis

To assess differences in brain susceptibility alterations between groups, this study first performed a voxel-wise whole-brain one-way analysis of covariance (ANCOVA) comparing QSM among the PD-DR, PD-NDR, and HC groups, with age and sex as covariates. The statistical significance threshold was set at a voxel-level P<0.001, combined with a cluster-level family-wise error (FWE) correction of P<0.05. Based on this, a binary mask was created by combining the significant clusters identified by the ANCOVA, and post-hoc two-sample t-tests were conducted within this mask. For the comparisons between the PD-DR and HC groups, as well as the PD-NDR and HC groups, age and sex were used as covariates. The comparison between the PD-DR and PD-NDR groups controlled for age, sex, H-Y stage, and the UPDRS-III score. All post-hoc tests employed the same significance threshold (voxel-level P<0.001, cluster-level FWE-corrected P<0.05).

In PD-DR patients, the mean magnetic susceptibility values were extracted from the brain regions that showed significant differences in the previous comparison between the PD-DR and PD-NDR groups. Partial correlation analysis was then used to explore the correlation between magnetic susceptibility levels in these regions and SCS-PD scores, controlling for age, sex, H-Y stage, and UPDRS-III score as covariates. The statistical significance threshold was set at P<0.05.

Region of interest (ROI)-based analysis of subcortical nuclei susceptibility alterations

Given that ROI-based local analysis allows for more precise comparison of smaller subcortical nuclei, this study specifically selected the bilateral nucleus accumbens (NAc), bilateral extended amygdala (EAc), bilateral ventral pallidum (VP), and bilateral parabrachial pigmented nucleus (PBP)—iron-rich deep small nuclei—as ROIs. Analysis was based on the spatially normalized, unsmoothed MNI space QSM maps. The mean QSM value was extracted from each ROI for comparing differences among the PD-DR, PD-NDR, and HC groups. Depending on the results of normality and homogeneity of variance tests, intergroup comparisons were performed using ANCOVA or the Kruskal-Wallis test. ANCOVA controlled for age and sex as covariates, whereas the Kruskal-Wallis test was adjusted for age and sex. The statistical significance threshold was set at P<0.05, with Bonferroni correction applied for multiple comparisons.

In PD-DR patients, for the ROIs that showed significant differences in the previous intergroup comparisons, partial correlation analysis was further used to explore the correlation between their mean magnetic susceptibility values and the SCS-PD scores, controlling for age, sex, H-Y stage, and UPDRS-III score as covariates. The statistical significance threshold was set at P<0.05.

Statistical analysis

Data processing and statistical analysis in this study employed the following methods: continuous variables conforming to a normal distribution are presented as mean ± standard deviation, otherwise as median (interquartile range); categorical variables are presented as frequency or median (interquartile range). For comparisons among three groups, one-way analysis of variance (ANOVA) was used for normally distributed continuous variables with homogeneous variance, the Kruskal-Wallis test was used for non-normally distributed variables or those with heterogeneous variance, and the Chi-squared test was used for categorical variables. For comparisons between two independent groups, the independent-samples t-test was used for normally distributed continuous variables, Mann-Whitney U test was used for non-normally distributed continuous variables, and the Chi-squared test was used for categorical variables, as appropriate. PD-related clinical indicators not applicable to the HC group are marked with “NA”. Statistical significance was set at P<0.05. All analyses were performed using the software SPSS 20.0 (IBM Corp., Armonk, NY, USA).

Results

Demographic and clinical characteristics of participants

This study ultimately included 103 participants: 23 in the PD-NDR group, 38 in the PD-DR group, and 42 in the HC group. The three groups showed no statistically significant differences in sex, age, years of education, MMSE score, MoCA score, or FAB score (all P>0.05). Between the PD subgroups, there were no significant differences in disease duration or LEDD (all P>0.05), whereas the intergroup comparisons for H-Y stage and UPDRS-III score showed statistically significant differences (all P<0.05). Compared to the PD-NDR group, the PD-DR group had significantly higher scores on UPDRS Part II item 6, NMSS item 19, and the SCS-PD (all P<0.001). Detailed data are shown in Table 1.

Voxel-wise QSM analysis

Whole-brain voxel-wise comparison among the three groups

In the voxel-wise analysis of whole-brain QSM data among the three groups, one-way ANCOVA revealed statistically significant differences in multiple brain regions. These regions included: bilateral medial superior frontal gyrus (SFGmed), right fusiform gyrus, left superior temporal gyrus (STG), bilateral middle temporal gyrus (MTG), left gyrus rectus, and left olfactory cortex (voxel-level P<0.001, cluster-level FWE-corrected P<0.05). Detailed results are shown in Table 2.

Post-hoc comparison analysis showed that compared to the PD-NDR group, the PD-DR group exhibited significantly higher magnetic susceptibility values only in the left STG (voxel-level P<0.001, cluster-level FWE-corrected P<0.05). Compared to the HC group, the PD-DR group showed significantly higher susceptibility values in multiple brain regions, including the right fusiform gyrus, bilateral SFGmed, left STG, and left MTG (voxel-level P<0.001, cluster-level FWE-corrected P<0.05). In contrast, the PD-NDR group showed significantly higher susceptibility values than the HC group in bilateral SFGmed (voxel-level P<0.001, cluster-level FWE-corrected P<0.05). Detailed results are shown in Table 3 and Figure 1.

Figure 1 Voxel-wise whole-brain QSM differences among the three groups. Voxel-level P<0.001; cluster-level FWE-corrected P<0.05. FWE, family-wise error; HC, healthy controls; L, left; MTG, middle temporal gyrus; PD-DR, Parkinson’s disease with drooling; PD-NDR, Parkinson’s disease without drooling; QSM, quantitative susceptibility mapping; R, right; SFGmed, medial superior frontal gyrus; STG, superior temporal gyrus.

Correlation analysis between left STG susceptibility alterations and SCS-PD scores

To further explore the correlation between susceptibility alterations in the intergroup difference brain regions and PD drooling symptoms, this study extracted the magnetic susceptibility values from the brain region that showed differences between the PD-DR and PD-NDR groups, namely the left STG. In PD-DR patients, partial correlation analysis with the SCS-PD score was performed, controlling for age, sex, H-Y stage, and UPDRS-III score as covariates. The results showed a significant positive correlation between the magnetic susceptibility values in the left STG and the SCS-PD scores in PD-DR patients (partial correlation coefficient r=0.361, P=0.036). Detailed results are shown in Figure 2.

Figure 2 Positive correlation between left STG magnetic susceptibility values and SCS-PD scores in patients with PD-DR. In patients with PD-DR, partial correlation analysis (controlling for age, sex, H-Y stage, and UPDRS-III) revealed a significant positive correlation between left STG magnetic susceptibility values and SCS-PD scores (r=0.361, P=0.036). H-Y, Hoehn-Yahr; L, left; PD-DR, Parkinson’s disease with drooling; QSM, quantitative susceptibility mapping; SCS-PD, Sialorrhea Clinical Scale for Parkinson’s Disease; STG, superior temporal gyrus; UPDRS-III, Unified Parkinson’s Disease Rating Scale Motor Section.

ROI-based QSM analysis

For subcortical nuclei with small gray matter volumes, this study further conducted ROI analysis. The ANCOVA revealed statistically significant differences in magnetic susceptibility values among the three groups for the left EAc (P=0.041), left VP (P=0.047), and left PBP (P=0.049), whereas no statistically significant differences were found for the bilateral NAc, right EAc, right VP and right PBP (all P>0.05).

Post-hoc pairwise comparisons with Bonferroni correction indicated that compared to the HC group, the PD-DR group exhibited significantly higher susceptibility values in the left VP (corrected P=0.042) and left PBP (corrected P=0.043); the PD-NDR group exhibited higher susceptibility values in the left EAc (corrected P=0.049). The comparison between the PD-DR and PD-NDR groups did not reveal any statistically significant differences (all P>0.05). Detailed results are presented in Table 4 and Figure 3.

Figure 3 Comparison of regional magnetic susceptibility values with significant group differences. *, P<0.05 with Bonferroni correction. EAc, extended amygdala; HC, healthy controls; L, left; PBP, parabrachial pigmented nucleus; PD-DR, Parkinson’s disease with drooling; PD-NDR, Parkinson’s disease without drooling; QSM, quantitative susceptibility mapping; R, right; VP, ventral pallidum.

Based on the previously identified difference brain regions (including left VP and left PBP) in the comparison between PD-DR and HC groups, this study further analyzed the correlation between the magnetic susceptibility values in these regions and PD drooling symptoms. In PD-DR patients (n=38), after controlling for age, sex, H-Y stage, and UPDRS-III score, partial correlation analysis showed that only the susceptibility values in the left PBP were significantly positively correlated with the SCS-PD score (partial correlation coefficient r=0.506, P=0.002). Detailed results are shown in Figure 4.

Figure 4 Positive correlation between left PBP magnetic susceptibility values and SCS-PD scores in patients with PD-DR. In patients with PD-DR, partial correlation analysis (controlling for age, sex, H-Y stage, and UPDRS-III) revealed a significant positive correlation between left PBP susceptibility values and SCS-PD scores (r=0.506, P=0.002). H-Y, Hoehn-Yahr; L, left; PBP, parabrachial pigmented nucleus; PD-DR, Parkinson’s disease with drooling; QSM, quantitative susceptibility mapping; SCS-PD, Sialorrhea Clinical Scale for Parkinson’s Disease; UPDRS-III, Unified Parkinson’s Disease Rating Scale Motor Section.

Discussion

This study employed QSM technology to systematically evaluate the different patterns of susceptibility alterations across multiple brain regions among PD-DR patients, PD-NDR patients, and HC, and further explored associations between regional susceptibility levels and drooling severity. The main findings include the following: (I) PD-DR patients exhibited significant abnormal susceptibility alterations in the left STG, left VP, and left PBP; (II) susceptibility levels in the left STG and left PBP showed significant positive correlations with drooling severity (assessed by SCS-PD). These findings suggest that drooling in PD may be associated with a distributed pattern of regional susceptibility alterations, rather than being attributable to a single focal brain abnormality.

Potential brain susceptibility pattern of drooling in PD: a cortical network centered on the left STG

The whole-brain voxel-wise analysis revealed increased susceptibility values in the left STG of PD-DR group compared to PD-NDR group, which correlated positively with drooling severity. These findings raise the possibility that susceptibility alterations in the left STG are associated with PD-related drooling and may reflect the involvement of this region in a broader neural network related to this symptom.

From a functional anatomy perspective, the pathophysiology of drooling involves impaired autonomic regulation of salivation and swallowing execution. PD patients commonly display reduced swallowing frequency, poor coordination, and orofacial motor deficits (1), promoting oral saliva accumulation. Although the STG does not directly control salivary secretion, its role in multimodal sensory and higher-order cognitive processing suggests that its dysfunction might contribute to drooling indirectly by disrupting networks involved in swallowing preparation and orofacial sensorimotor integration (53,54).

The STG is widely involved in higher functions such as emotional processing and social cognition. Previous studies have found that abnormal functional connectivity of the STG is significantly correlated with anhedonia in patients with major depressive disorder (55); cortical thinning in the STG and other regions has also been observed in PD patients with mild cognitive impairment (56). Such impairments in the affective and cognitive domains may plausibly influence a patient’s perceptual sensitivity to oral saliva accumulation and their motivation to swallow. In this context, susceptibility alterations in the STG may be associated with changes in multimodal sensory integration or swallowing-related motivational processes, which could be relevant to drooling severity.

Moreover, the STG has close functional connections with higher autonomic control centers, such as the insula (57), which plays a key role in processing interoceptive information and regulating autonomic activity, directly involved in modulating saliva secretion. Therefore, susceptibility alterations in the STG might be related to altered interactions with regions such as the insula, which could reflect network-level changes relevant to salivary regulation and swallowing behavior. This hypothesis is also consistent with previous evidence suggesting altered functional connectivity in multiple networks, including cortico-limbic-striatal-cerebellar circuits, in PD patients with drooling (7). In summary, susceptibility alterations in the left STG might reflect cortical network dysfunction or decompensation related to drooling, rather than being merely a local effect.

Widespread cortical system susceptibility differences between PD-DR and HC groups

In direct comparison with HC, this study found that PD-DR patients exhibited abnormal susceptibility alterations in multiple cortical and limbic brain regions, including bilateral SFGmed, left STG, left MTG, and right fusiform gyrus. These regions collectively form a network system involved in sensory integration, interoception, motivation, and executive control, for which abnormalities likely contribute to the manifestation of drooling from multiple dimensions.

The bilateral SFGmed has been implicated in higher-order cognitive control, motivation generation, initiation of behavioral intentions, and decision-making (58,59). From a behavioral mechanism perspective, drooling may involve not only an abnormality of the swallowing reflex arc but also reduced proactive swallowing behavior. Dysfunction of the SFGmed may be associated with a lack of swallowing motivation and reduced cognitive control in patients, making it difficult for them to autonomously and appropriately initiate swallowing actions. Such alterations may also be relevant to reduced attentional engagement with internal cues signaling the need to swallow. This “motivation-execution deficit” model aligns with clinical observations that cognitive impairment, especially in the advanced stages of the disease, is considered an important factor exacerbating drooling (9).

The fusiform gyrus serves not only as the visual face recognition area (60) but also as a critical hub for multimodal sensory integration, particularly involving visual and somatosensory information (61). Saliva accumulation in the oral cavity constitutes a somatosensory signal (e.g., wetness, fullness) that may serve as an internal cue for swallowing. Susceptibility alterations in the right fusiform gyrus may be related to altered sensory integration or reduced perceptual sensitivity to oral saliva accumulation, which could be relevant to impaired swallowing initiation. Furthermore, the right-sided predominance of this abnormality found in this study is noteworthy, given the established dominance of the right hemisphere in interoceptive awareness and somatosensory representation.

Both the STG and MTG are involved in auditory, language processing, as well as multimodal information integration (62,63), with the MTG also being an important component of the default mode network (64). Susceptibility alterations in these regions might weaken their ability to monitor interoceptive signals during resting states. Although speculative, such changes could be relevant to altered perception of saliva accumulation and reduced efficiency of swallowing initiation.

Increased susceptibility alterations in these cortical regions might reflect broader tissue changes occurring during PD progression, potentially including altered iron homeostasis. Collectively, these regions might form part of a distributed network involved in sensory integration, interoception, motivation, and motor preparation that could be relevant to drooling symptoms.

Subcortical small nuclei ROI findings: susceptibility alterations in the left PBP and drooling severity

In addition to cortical systems, this study also examined subcortical structures via ROI-based analysis, finding that the PD-DR group exhibited higher susceptibility values in the left VP and left PBP compared to the HC group. Notably, the susceptibility levels in the left PBP demonstrated a significant positive correlation with drooling severity, suggesting a possible association between left PBP susceptibility alterations and drooling severity.

The VP, as an important component of the basal ganglia circuit, is traditionally recognized for its roles in reward, motivation processing (65), and motor regulation (66). Although direct research on the role of VP in swallowing remains limited, its position as a hub within motor circuits supports its potential regulatory function in the initiation and coordination of automatic swallowing actions (66). Susceptibility alterations in this region may reflect changes in motor or motivational processes relevant to swallowing control. However, this interpretation remains speculative and requires further validation.

The finding regarding the left PBP is especially critical. The parabrachial nuclei (including the pigmented subregion) are pivotal relay stations and integration centers in the central autonomic network. Their main functions include the following: first, relaying various viscerosensory information [such as oropharyngeal signals from the nucleus of the solitary tract (NTS)] to the thalamus and cortical regions (e.g., the insula) (67,68); second, engaging in dense reciprocal connections with brainstem nuclei controlling saliva secretion and swallowing movements (e.g., nucleus ambiguous, salivary nuclei) (69). Recent animal studies suggest that the motor cortex can regulate NTS activity via the parabrachial nuclei, thereby modulating swallowing behavior (70). Therefore, susceptibility alterations in the left PBP could reflect altered relay or integrative processing within this region. Although speculative, such alterations could be relevant to less efficient transmission of saliva-related sensory signals to higher brain regions, altered autonomic regulation, and impaired coordination of swallowing-related responses.

Potential brain susceptibility alteration pattern of drooling in PD: multi-level neural network dysfunction

Integrating the results from whole-brain voxel-wise and ROI analyses, this study suggests that drooling in PD is closely associated with a multi-level, network-like pattern of brain susceptibility alterations. The left STG was identified as a key region, for which susceptibility alterations may be specifically associated with dysfunction of cortical networks involved in swallowing. More widespread abnormalities were distributed across a cortical network encompassing sensory integration, interoception, motivation, and executive functions, including: bilateral SFGmed, which may be relevant to lack of proactive swallowing motivation and insufficient cognitive control; right fusiform gyrus and temporal regions, which may be relevant to decreased efficiency of oral somatosensory signal integration, interoceptive and multimodal perceptual impairment.

Notably, at the subcortical level, susceptibility alterations in the left PBP showed a significant positive correlation with drooling severity, highlighting this nucleus as a potentially important region in PD-related drooling. Given its role in relaying viscerosensory and autonomic-related information between brainstem and higher-order regions, alterations in this area may be relevant to abnormal sensory relay and swallowing-related regulation. Furthermore, susceptibility alterations in the VP may reflect changes in basal ganglia-related motor or motivational processes relevant to the semi-automatic execution of swallowing.

Significant differences in H-Y stage and UPDRS-III score were also observed between groups, indicating differences in motor symptom severity. As H-Y stage reflects overall disease progression and UPDRS-III quantifies motor impairment, these differences may be clinically relevant to the QSM-derived susceptibility alterations observed in this study. To reduce the potential confounding effect of motor severity, H-Y stage and UPDRS-III score were included as covariates in both the between-group comparisons and partial correlation analyses, allowing us to assess the imaging findings while accounting for differences in motor impairment. However, due to the cross-sectional design, further longitudinal studies are needed to clarify the relationship between QSM-derived susceptibility changes and clinical progression.

Although increased QSM values are commonly interpreted as reflecting iron deposition, brain iron is present in multiple molecular forms, including ferritin, hemosiderin, and neuromelanin-bound iron. Aging, the major risk factor for PD, is also associated with changes in iron metabolism in PD-vulnerable regions. Zecca et al. reported age-related alterations in iron, ferritin, and neuromelanin-bound iron in the substantia nigra and locus coeruleus, indicating that iron dysregulation in PD should be interpreted in the context of age-related changes in iron-containing molecules (71). Therefore, the increased QSM values observed in the present study may reflect complex alterations in iron-related molecular species rather than accumulation of a single iron form.

In summary, drooling in PD likely represents a manifestation of susceptibility-related alterations across multiple brain regions and systems within the context of neurodegeneration. These findings support a working model in which PD-related drooling may involve dysfunction of a distributed “sensory integration-interoception-motivation-execution” network, with disturbances in information processing across cortical and brainstem-related systems.

Limitations

Although this study reveals a specific spatial pattern of brain susceptibility alterations in PD patients with drooling, several limitations need to be acknowledged. First, the cross-sectional design only allows identification of associations between QSM-derived susceptibility alterations and drooling, and cannot determine causality or temporal sequence. Moreover, the current design cannot fully disentangle drooling-related alterations from changes associated with PD progression or motor severity. Longitudinal studies with well-matched clinical subgroups are warranted to clarify these relationships. Second, the sample size, despite meeting statistical requirements, is relatively limited, potentially restricting the reproducibility and statistical power of the results, especially in subgroup analyses and when controlling for multiple confounding factors. Future validation in larger, independent cohorts is needed. Third, QSM provides an indirect measure of magnetic susceptibility and cannot precisely determine tissue iron concentration or distinguish among different iron-containing compounds. Although increased QSM values are commonly interpreted as reflecting iron-related changes, they may also be influenced by other tissue components or microstructural factors, such as calcium deposition, myelin content or degradation, deoxyhemoglobin, and local tissue architecture. Future studies should combine QSM with neuropathological and neurochemical analyses of brain regions associated with drooling in PD. In addition, measuring tissue concentrations of iron-related molecules, such as ferritin, hemosiderin, transferrin-related proteins, and neuromelanin-bound iron, would be important for validating imaging findings and clarifying the biological mechanisms underlying drooling in PD. Fourth, this study primarily focused on abnormalities in the central nervous system and did not objectively measure peripheral nervous system function (e.g., autonomic innervation of salivary glands) or salivary flow rate. Fifth, the study consisted exclusively of patients with idiopathic PD. Therefore, it remains unclear whether the identified patterns of brain susceptibility alterations are specific to idiopathic PD or might also be present in other parkinsonian syndromes that frequently manifest drooling (e.g., progressive supranuclear palsy, vascular parkinsonism). Sixth, hemispheric analyses were based on anatomical left-right classification rather than the more affected versus less affected hemisphere. Given that PD typically shows asymmetric onset and progression, this analytical framework may not fully capture disease-related laterality. Functional dopaminergic imaging, such as DaT SPECT, would be valuable for defining hemispheric disease burden more precisely. However, because DaT SPECT is not available at our center, such data were not obtained in the current cohort. Therefore, the relationship between QSM changes and disease-dominant hemispheric involvement could not be evaluated in the present study. Future investigations combining QSM with DaT SPECT or other laterality-sensitive biomarkers are needed to address this issue. Besides, according to the MDS Clinical Diagnostic Criteria for Parkinson’s Disease, normal presynaptic dopaminergic functional imaging serves as an absolute exclusion criterion for PD. Without DaT SPECT data, we could not apply this imaging-based exclusion criterion to further enhance the diagnostic certainty of the included patients. Seventh, although test-retest MRI data were not available in the present cohort, previous studies have demonstrated good reproducibility of QSM measurements across different field strengths, MRI vendors, and standardized acquisition protocols (72,73). These findings support the reliability of QSM for assessing susceptibility changes in iron-rich brain regions (74). Future studies incorporating dedicated test-retest scans, inter-rater reliability analyses, and multimodal validation approaches are needed to further establish the robustness and biological specificity of these findings. Finally, although internationally recognized scales were used for clinical assessment, they inherently carry a degree of subjectivity. Future studies incorporating objective instrumental evaluations (e.g., videofluoroscopic swallowing studies, quantitative saliva collection) would facilitate more objective and quantitative symptom characterization.

Conclusions

This study suggests that drooling in PD is associated with a specific multi-level pattern of brain susceptibility alterations. The identified pattern highlights the left STG as a key cortical region within a cortical network for sensory integration and motivation-execution, together with increased susceptibility in the left PBP, which may reflect altered brainstem sensory relay related to drooling severity. These findings support the view that PD-related drooling may involve distributed neural network dysfunction associated with susceptibility alterations, thereby providing imaging evidence for understanding its pathophysiology and exploring potential therapeutic targets.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-0746/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-0746/dss

Funding: This work was supported by the General Program of Wuxi Medical Center, Nanjing Medical University (No. WMCG202525), the Natural Science Foundation of Jiangsu Province (No. BK2019114), the National Natural Science Foundation of China (No. 81271629), the Medical Expert Team Program of the Wuxi Taihu Talent Plan (No. THRC-TD-YXYXK-2021), the Specialized Disease Queue Project of Wuxi Medical Center, Nanjing Medical University (No. WMCC202402), and the National Science and Technology Innovation 2030-Major Program of “Brain Science and Brain-Inspired Intelligence Research” (No. 2021ZD0201807).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-0746/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. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of The Affiliated Wuxi People’s Hospital of Nanjing Medical University (No. KY21133). All participants provided informed consent after fully understanding the study’s purpose and procedures.

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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