Association between Amygdala Subregions and Non-Motor Symptoms in Parkinson’s Disease: A Fixel-based Analysis

Association between Amygdala Subregions and Non-Motor Symptoms in Parkinson’s Disease: A Fixel-based Analysis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 15 December 2025 V1 Latest version Share on Association between Amygdala Subregions and Non-Motor Symptoms in Parkinson’s Disease: A Fixel-based Analysis Authors : Yuchen She , Jiahao Wei , Junyi Wang , Ying Liu , Sichen Chen , Man Zhang , Qi Yang , Lijuan Mo , Changhong Tan , Xi Liu 0000-0003-3142-7843 , and Lifen Chen 0000-0003-3355-1338 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176580736.60333549/v1 169 views 87 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Objective: Non-motor symptoms in Parkinson’s disease impair quality of life, yet their neural basis is unclear, the link between amygdala subregion fiber properties and these symptoms remains unexplored. Methods: We analyzed 114 Parkinson’s disease patients from the PPMI group using diffusion tensor imaging and FreeSurfer to segment nine amygdala subregions. By using fixel-based analysis, each subregion’s volume and diffusion metrics (FD, FDC, log FC) were extracted.Finally, correlation and regression analyses were assessed associations with Non-motor symptoms scores. Results: Diffusion metrics of white matter tracts from amygdala subregions correlated with Non-motor symptoms，like emotion, visuospatial performance, and REM sleep behavior disorder. Multiple regression analysis showed that FDC/log FC in bilateral amygdala subregions, especially the right cortico-amygdaloid transition, predicted visuospatial function. Right-sided nuclei volume was also correlated with performance. Anxiety severity was linked to bilateral amygdala tract changes, more notably on the left, with reduced white matter integrity (log FC) and decreased volume in the left basal and paralaminar nuclei. Additionally, FD from the left cortical amygdala was negatively correlated with REM Sleep Behavior Disorder Screening Questionnaire. Conclusions: Our study comprehensively assesses amygdala subregional changes in Parkinson’s disease, integrating white matter microstructure and volume. This highlights the need for a multi-modal approach to fully understand the neural basis of Parkinson’s disease symptoms. Association between Amygdala Subregions and Non-Motor Symptoms in Parkinson’s Disease: A Fixel-based Analysis Yuchen She 1 , Jiahao We 1 , Junyi Wang 1 , Ying Liu 1 , Sichen Chen 1 , Man Zhang 1 , Qi Yang 1 , Lijuan Mo 1 , Changhong Tan 1 , Xi Liu 1 , Lifen Chen 1* 1 Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University *Correspondence: Lifen Chen 74 Linjiang Road, Yuzhong District, Chongqing, 400010, China Telephone: 86-23-62887737 Email: [email protected] & [email protected] (Lifen Chen). Keywords: Parkinson’s disease; amygdala subnuclei; white matter; volume; Fixel-based analysis Words count:3720 words Figures count: 4 Acknowledgments Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative database (www.ppmi-info.org/data). For up-to-date information on the study, visit http://www.ppmi-info.org. PPMI, a public-private partnership, is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including 4D Pharma; AbbVie Inc.; AcureX Therapeutics; Allergan; Amathus Therapeutics; Aligning Science Across Parkinson’s (ASAP); Avid Radiopharmaceuticals; Bial Biotech; Biogen; BioLegend; Bristol Myers Squibb; Calico Life Sciences LLC; Celgene Corporation; DaCapo Brainscience; Denali Therapeutics; the Edmond J. Safra Foundation; Eli Lilly and Company; GE Healthcare; GlaxoSmithKline; Golub Capital; Handl Therapeutics; Insitro; Janssen Pharmaceuticals; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; Neurocrine Biosciences; Pfzer Inc.; Piramal Imaging; Prevail Therapeutics; F. Hofmann-La Roche Ltd and its affiliated company Genentech Inc.; Sanof Genzyme; Servier; Takeda Pharmaceutical Company; Teva Neuroscience, Inc.; UCB, Vanqua Bio; Verily Life Sciences; Voyager Therapeutics, Inc.; and Yumanity Therapeutics, Inc (http://www.ppmi-info.org/fundingpartners). Association between Amygdala Subregions and Non-Motor Symptoms in Parkinson’s Disease: A Fixel-based Analysis Abstract Non-motor symptoms in Parkinson’s disease significantly impair quality of life, yet their neural basis remains unclear. The relationship between amygdala subregion fiber properties and these non-motor symptoms has not been fully explored. In this study, we analyzed diffusion tensor imaging data from 114 Parkinson’s disease patients in the PPMI cohort and used FreeSurfer to segment nine amygdala subregions. Fixel-based analysis was employed to extract white matter volume and diffusion metrics (FD, FDC, log FC) for each subregion. Correlation and regression analyses were then conducted to assess associations between these metrics and non-motor symptom scores. The results showed that diffusion metrics of white matter tracts from amygdala subregions were significantly correlated with non-motor symptoms, such as emotional disturbances, visuospatial function, and REM sleep behavior disorder. Multiple regression analysis revealed that FDC/log FC in bilateral amygdala subregions, particularly in the right cortico-amygdaloid transition, predicted visuospatial function. Right-sided amygdala volume was also correlated with cognitive performance. Anxiety severity was associated with changes in bilateral amygdala tracts, more prominently on the left, with reduced white matter integrity (log FC) and decreased volume in the left basal and paralaminar nuclei. Additionally, FD from the left cortical amygdala was negatively correlated with REM Sleep Behavior Disorder Screening Questionnaire scores. Overall, this study provides a comprehensive assessment of amygdala subregional changes in Parkinson’s disease, integrating white matter microstructure and volume, and underscores the importance of a multi-modal approach to fully understand the neural basis of Parkinson’s disease symptoms. List of Abbreviations DTI – Diffusion Tensor Imaging ESS – Epworth Sleepiness Scale FBA – Fixel-Based Analysis FDC – Fiber Density & Cross-section Composite FC – Fiber Cross-section FD – Fiber Density FDR – False Discovery Rate FOD – Fiber Orientation Distribution GDS-15 – 15-item Geriatric Depression Scale HVLT – Hopkins Verbal Learning Test JLO – Benton Judgement of Line Orientation LA – Left Amygdala LNS – Letter-Number Sequencing MoCA – Montreal Cognitive Assessment MSF – Modified Semantic Fluency NMSs – Non-Motor Symptoms OLS – Ordinary Least Squares PD – Parkinson’s Disease PPMI – Parkinson’s Progression Markers Initiative RA – Right Amygdala RBDSQ – REM Sleep Behavior Disorder Questionnaire SDMT – Symbol Digit Modalities Test STAI – State/Trait Anxiety Inventory Scale 1.Background Parkinson’s disease (PD) is a degenerative disorder of the central nervous system characterized by both motor and non-motor symptoms (NMSs) (Bloem et al., 2021). NMSs, such as depression, anxiety, rapid eye movement sleep behavior disorder (RBD) and cognitive dysfunction, often present at any stage of PD (Armstrong & Okun, 2020). Recently, increasing evidences suggest that the pathological processes of PD extend well beyond the substantia nigra and involve other brain regions, particularly the amygdala, which plays a crucial role in regulating emotion, cognition, and behavior (Harding et al., 2002). Amygdala, a group of almond-shaped nuclei located deep within the medial temporal lobe, constitutes key zone for emotional processing, memory, and social behavior regulation (Sah et al., 2003). Amygdala is anatomically heterogeneous, with distinct subnuclei demonstrating different function that align with their unique connectivity profiles (LeDoux & Pine, 2016). The abnormality of the amygdala and its neural connections represents a core neurobiological basis for the symptoms of PD, particularly emotional disorders (Diederich et al., 2016). At the same time, we have observed that many NMSs, such as anosmia, constipation, and depression, can occur years before the onset of typical motor symptoms (Durcan et al., 2019). NMSs in PD are thought to arise from dysfunction in distributed brain networks, with emerging evidence pointing to structural connectivity abnormalities in specific circuits, such as the amygdalar-prefrontal pathway (Wu et al., 2019). Specifically, connectivity between the amygdala and the medial prefrontal cortex, anterior cingulate cortex, and para-hippocampal gyrus is significantly reduced, representing a core mechanism underlying the early NMSs (Nigro et al., 2016). Notably, in PD patients with mild behavioral impairment, the white matter microstructural damage of the amygdala has been shown to be significantly correlated with various emotional problems, including anxiety, and lack of motivation (Monchi et al., 2024). This suggests that early changes in specific subregions of the amygdala may serve as sensitive neuroimaging biomarkers for the prodromal stage of PD. However, previous structural MRI studies in PD have primarily focused on gray matter atrophy, with a lack of conclusive evidence supporting disease-specific alterations in white matter fiber structures (Atkinson-Clement et al., 2017). Nevertheless, the intricate structural connectivity within the amygdala, a small, densely packed, and cytoarchitectonically complex subcortical structure, poses significant methodological challenges for detailed investigation (Jayakar et al., 2020). While diffusion tensor imaging (DTI) has proven valuable in characterizing PD-related white matter degeneration, its limitations in resolving crossing fibers necessitate advanced fixel-based analysis (FBA) to quantify microstructure specific pathology, facilitating its potential advantage in analyzing small and complex nuclei such as amygdala (Rau et al., 2019; Y. Zhang & Burock, 2020). Despite considerable progress in PD-related amygdala changes, numerous unresolved questions remain regarding on alterations of the amygdala subnuclei and related clinical manifestations. To bridge this gap, the use of new atlases and segmentation tools can provide a better understanding of the individual nuclei of the amygdala. Combined with the FBA method, this approach offers neuroimaging researchers the ability to explore the connectivity of human amygdala nuclei, enabling a comprehensive explanation of the interactions between the microstructural integrity of connecting fibers and subnuclear morphological measurements, while also revealing their collective association with clinical symptoms of PD. 2.1 Participants Participants data were sourced from the Parkinson’s Progression Markers Initiative (PPMI) (https://www.ppmi-info.org/about-ppmi/who-we-are/study-sponsors, accessed on June 18, 2022) (https://doi.org/10.1016/j.pneurobio.2011.09.005). The study included PD patients from the PPMI database who had available DTI data and did not meet the following criteria: 1) excluded from PD cohort during follow-up; 2) undergone neurosurgical operations such as deep brain stimulation; and 3) Have a current or active clinical diagnosis of any neurological or psychiatric disease, or are currently using medications (e.g., anxiolytics or antidepressants) that could affect outcome measures and scale scores; The PPMI study has been approved by the respective institutional review boards of all participating sites, and written informed consents were obtained from each participant. 2.2 Clinical assessments For PPMI group, all clinical assessment data were obtained directly from the PPMI database, and the assessment procedures followed the PPMI standard operating procedures. Demographic information, including age, sex, onset side, education and duration of PD were obtained from both PPMI and validation group. Autonomic symptoms were evaluated using Scale for Outcomes in Parkinson’s Disease-Autonomic Dysfunction (SCOPA-AUT). Sub-scores were calculated for specific domains: gastrointestinal (items 1-7), urinary (items 8-13), cardiovascular (items 14-16), and thermoregulatory (items 17–18, 20–21) dysfunction. Depression and anxiety symptoms were assessed using 15-item Geriatric Depression Scale (GDS-15), State/Trait Anxiety Inventory (STAI) scale (including state anxiety and trait anxiety), respectively. Current impulsive and compulsive behaviors were identified using the QUIP-Current-Short questionnaire. Sleep disturbances were measured with the REM Sleep Behavior Disorder Questionnaire (RBDSQ) and Epworth Sleepiness Scale (ESS). Global cognition was assessed using the Montreal Cognitive Assessment (MoCA). In addition, five cognitive domains were assessed through various standardized tests: Letter-Number Sequencing (LNS) and Symbol Digit Modalities Test (SDMT), assessing the attention/working memory; Modified Semantic Fluency (MSF), assessing the executive function and the language function; the revised Hopkins Verbal Learning Test (HVLT, including immediate recall test and delayed recall test), assessing the memory function; the Benton Judgement of Line Orientation (JLO), assessing the visual spatial function. 2.3 Image acquisition DTI data for PPMI participants were acquired using 3 T Siemens scanners (Siemens Healthcare, Malvern, USA) with a DTI_gated imaging protocol (Supplementary Information for details). DTI data for participants from the Second Affiliated Hospital of Chongqing Medical University were also obtained on 3 T Siemens scanners (Siemens Healthcare, Malvern, USA), with detailed scanning parameters in the Supplementary Information. 2.4 Calculation of FBA metrics The fixel-based analysis was conducted in accordance with standardized procedures outlined in the MRtrix3 (version 3.0.4, https://www.mrtrix.org/) technical documentation (Raffelt et al., 2017). Initial processing included diffusion data denoising, motion/distortion correction, and preliminary brain mask generation. Subsequent stages encompassed: bias field correction, global signal intensity normalization, white matter response function derivation, image resolution enhancement, refined brain mask calculation, and fiber orientation distribution (FOD) modeling (Raffelt et al., 2015, 2017; Tournier et al., 2013). A population-specific FOD template was created through multi-stage spatial normalization, involving FOD image co-registration, template mask generation, and construction of a fixel-based analysis mask. Spatial transformation of individual FOD data into the standardized template space preceded quantitative analysis. Three primary fixel-based metrics were derived for analysis: 1) Fiber Density (FD) - intra-axonal volume index reflecting microstructural integrity (Dhollander et al., 2021; Raffelt et al., 2017); 2) Fiber Cross-section (FC) - macrostructural measure of cross-sectional area changes (Dhollander et al., 2021; Raffelt et al., 2017); 3) Fiber Density & Cross-section Composite (FDC) - combined index sensitive to both micro- and macrostructural alterations (Dhollander et al., 2021; Raffelt et al., 2017). 2.5 Automated segmentation of the amygdala using FreeSurfer and registration All participants data and collin27 single subject template was processed using the FreeSurfer “recon-all” pipeline v7.4.2 (http://surfer.nmr.mgh.harvard.edu) (Holmes et al., 1998). For amygdala subnuclei segmentation, the pipeline implemented a hippocampal-amygdalar co-segmentation strategy combining boundary exclusion algorithms to prevent structural overlaps and multi-contrast registration techniques integrating T1-weighted intensity gradients with histology-derived boundary features (Saygin et al., 2017). This allowed automated bilateral parcellation into nine distinct subnuclei: basal (corresponding to label 7003), lateral (7001), accessory basal (7008), paralaminar (7015), cortical (7007), central (7005), medial nuclei (7006), along with cortico-amygdaloid transition (7009) and anterior amygdaloid areas (7015), with anatomical localization validated against reference templates. Additionally, we obtained the volumes for each amygdala subnucleus, which were used for further structural analysis and comparisons. Building on the amygdala mask obtained in the preceding step, spatial normalization was achieved by directly registering the study-specific FOD template to the Collin27/MNI-152 standard space using nonlinear diffeomorphic transformation in FSL (v6.0.3; Oxford, UK). Registration accuracy was rigorously verified through: (1) MRview (MRtrix3 v3.0.4; Melbourne, Australia) visualization quantifying alignment of white matter boundaries and amygdala positioning, and (2) blinded anatomical validation by two board-certified neurologists (> 10 years’ experience) confirming precise mask placement. 2.6 Statistical analyses Descriptive statistics summarized demographic and clinical characteristics in the PPMI group, reporting means ± SD for continuous variables (age, education, disease duration, UPDRS I-III, MoCA, SCOPA-AUT, SDMT, MSF, HVLT, JLO, QUIP-Current-short, ESS, RBDSQ, GDS-15, STAI) and proportions for categorical variables (sex, onset side). Analyses were performed using IBM SPSS 26.0 (IBM, Armonk, NY) and Python 3.10 (Python Software Foundation, Beaverton, OR). This study used Pearson correlation to assess the linear relationship of FBA metrics and volumes with non-motor scores. Given that the sample size exceeds 30, and based on the Central Limit Theorem, the assumption of normality for the variables was considered reasonable, allowing the use of Pearson correlation. We calculated the Pearson correlation coefficient (r) and p-values for each pair of variables. A p-value of less than 0.05 was considered indicative of a statistically significant correlation. To further examine the independence of these associations, multiple linear regression models were applied, adjusting for age, sex, disease duration, side of onset and education. Each regression model included one FBA metric/volume and one NMSs, with the regression results estimated using ordinary least squares (OLS). Model outputs included the fiber coefficient (Beta), p-value, coefficient of determination (R²), and Benjamini–Hochberg–adjusted q-value. Considering the large number of statistical tests conducted in this exploratory analysis, the p-values obtained from the pearson correlations and regression models were corrected using the Benjamini-Hochberg False Discovery Rate (FDR) method to control for the false discovery rate. 3.Results 3.1 Demographics and clinical characteristics The demographic and clinical characteristics of the PPMI group (n = 114) are summarized in Table 1. In the PPMI group, motor symptoms (UPDRS I–III), autonomic function scores, and a comprehensive set of non-motor assessments were available. The state anxiety scores and trait anxiety scores were 47.07 ± 5.11 and 46.18 ± 4.30, respectively. Detailed neuropsychological test results are presented in Table 1. 3.2 Analysis of the Relationship Between the Amygdala Subregions’ Volume and NMSs. To further explore the potential connection between amygdala subregions and NMSs, we conducted Pearson correlation analysis and multiple linear regression analysis on the volumes of amygdala subregions. The results showed a significant positive correlation between performance on the Benton Line Orientation Test and the volume of the right cortical nucleus (FDR-q = 0.046) as well as the right cortical-amygdaloid transition (FDR-q = 0.046). Subsequent multiple regression analysis further supported the independence of these associations. After controlling for covariates, the volume of the right cortical nucleus remained significantly associated with better cognitive performance (ß = 0.80, R² = 0.16, FDR-q = 0.015), and the association with the right cortical-amygdaloid transition was in the same direction and similarly significant (ß = 0.78, R² = 0.14, FDR-q = 0.028). In terms of anxiety, the correlation analysis revealed a significant negative correlation between state anxiety scores and the volume of the left basal nucleus (FDR-q = 0.046) as well as the left central nucleus (FDR-q = 0.046). Multiple regression analysis confirmed these findings, indicating that smaller volumes of the left basal nucleus (ß = -2.01, R² = 0.14, FDR-q = 0.004) and the left central nucleus (ß = -1.71, R² = 0.13, FDR-q = 0.004) were associated with higher anxiety scores (Supplementary Information for details). 3.3 Amygdala Microstructure Links to Diverse Non-Motor Symptoms in PD Altered amygdala microstructure in PD is linked to visuospatial function, with significant positive correlations between diffusion metrics of bilateral amygdala white matter tracts and JLO performance (e.g., right cortico-amygdaloid transition log FC, FDR-q = 0.004; left lateral nucleus FDC, FDR-q = 0.004). For emotion regulation, diffusion characteristics of white matter tracts originating from left amygdala subnuclei were significantly negatively associated with state anxiety scores (e.g., left cortico-amygdaloid transition log FC, FDR-q = 0.014). In addition, diffusion metrics of white matter tracts originating from left cortical nucleus showed significant correlation with REM sleep behavior disorder (RBDSQ; FDR-q = 0.022). Notably, other non-motor symptom scales assessing domains (MoCA, HVLT, GDS-15, MSF, SDMT, LNS, QUIP-Current Short SCOPA-AUT and ESS) did not show significant associations with amygdala diffusion metrics in pearson correlation (p >0.05) (details in Figure 1). 3.4 Multivariate Regression Linking Amygdala Connectivity to Non-Motor Symptoms To further evaluate associations between amygdala subnuclear diffusion metrics and non-motor symptoms, multiple linear regression analyses were performed and the detailed results are as follows. 3.4.1 Bilateral Amygdala Fiber Integrity Predicts Visuospatial Performance Regression models revealed significant positive associations between the FDC/log FC of white matter fiber bundles originating from bilateral amygdala subnuclei and enhanced JLO task performance. The strongest association was for log FC in fibers from the right corticoamygdaloid transition (ß = 1.23, R² = 0.22, FDR-q = 0.00002), followed by log FC across from right medial nucleus (ß = 1.123, R² = 0.21, FDR-q = 0.00001). For the left amygdala, FDC in fibers from the lateral nucleus showed the highest predictive validity (ß = 1.13, R² = 0.21, FDR-q = 0.00003). However, certain fiber bundles extracted from the right basal nucleus (FDC, FDR-q = 0.05556) and the right anterior amygdaloid area(FDC, FDR-q = 0.09216) did not show statistical significance (details in Figure 2; Figure 3). 3.4.2 The FD originating from the left cortical amygdala was negatively correlated with RBDSQ ( ß = -0.67, R² = 0.15, FDR-q = 0.001 ) (Supplementary Information for details). 3.4.3 Bilateral Amygdala Tract Alterations Associated with Anxiety Severity, with Stronger Associations on the Left Side Structural correlations were predominantly localized in subregions of the left amygdala, with weaker, though notable, associations observed in the right amygdala. Specifically, State Anxiety scores were negatively correlated with log FC values across left amygdala subneclei. Multiple regression analyses identified log FC of fibers from the left cortico-amygdaloid transition area as the most robust predictor of reduced state anxiety (ß = -2.29, R² = 0.15, FDR-q = 0.005), followed by the left lateral nucleus (ß = -2.11, R² = 0.14, FDR-q = 0.006). Similar effects were observed in fibers from the left basal nucleus (ß = -2.07, R² = 0.13, FDR-q = 0.006), left cortical nuclues (ß = -1.92, R² = 0.12, FDR-q = 0.006) and the paralaminar nucleus (ß = -1.99, R² = 0.13, FDR-q = 0.006). log FC values from right paralaminar nucleus also showed negative relationship with anxiety score (ß = -1.90, R² = 0.12, FDR-q = 0.007) (details in Figure 4). 4.Discussion This study, by comprehensively evaluating the amygdala subnuclei and white matter fiber connections, reveals that the neurobiological basis of PD is multi-layered. It involves not only local atrophy in the amygdala subnuclei (the left basal nucleus/central nucleus and the right cortical nucleus/cortical-amygdaloid transition) but also more widespread effects on their associated white matter connectivity networks. Specifically, we found that: 1) The integrity of white matter fibers and the volume of the bilateral amygdala subregions (especially the right cortical-amygdaloid transition area) independently predict visuospatial function; 2) Anxiety symptoms are significantly related to reduced white matter fiber metrics (log FC) in the bilateral amygdala subregions (with a stronger effect on the left) and smaller volumes of the left basal nucleus and central nucleus; 3) The fiber metrics (FD) in the left cortical-amygdaloid transition negatively correlates with the severity of RBD, suggesting a new role of this subregion in sleep regulation. These studies indicate a close interplay between the integrity of white matter fibers and the structural volume of subregions of the amygdala, both of which contribute to the development of non-motor symptoms in Parkinson’s disease. While the volume of structures such as the basal ganglia and the cortical-amygdala transition zone is closely associated with anxiety and cognition, the integrity of white matter tracts exhibits more widespread and significant changes. Damage to the fiber tracts and alterations in volume are interwoven, revealing that in the pathological progression of Parkinson’s disease, structural and functional impairments in subregions of the amygdala may exacerbate the manifestation of non-motor symptoms by affecting the integrity of white matter pathways. This highlights the heterogeneity and regional contributions of different amygdala subregions in non-motor symptoms of Parkinson’s disease. These diverse findings lead us to rethink some classic theories of functional localization in PD, especially regarding visuospatial functions. Although visuospatial memory has been classically attributed to right amygdala function, our evidence for bilateral engagement necessitates functional refinement (Pegna et al., 2002). Increasing evidence suggests that the bilateral amygdala may be involved in visuospatial tasks through emotional arousal mechanisms (Ay et al., 2023). Notably, the right amygdala excels at rapidly processing emotional information (Noesselt et al., 2005). It functions like an emotional filter, amplifying the significance of key visual stimuli, thereby prioritizing the allocation of attentional resources, while the left amygdala participates synchronously, responsible for binding emotional details (Domínguez-Borràs & Vuilleumier, 2022; Markowitsch, 1999). Subsequent findings indicate that the basolateral amygdalar nucleus, functioning as a central integrative hub, facilitates the encoding of object location–reward contingencies within the ventral striatum via γ-aminobutyric acid–mediated projections, thereby expediting spatial decision-making processes (Esber et al., 2015). At the same time, the amygdala modulates motivational signals to the cortical network through the basal forebrain, enhancing attention processing (Peck & Salzman, 2014). The amygdala’s ability to rapidly process emotional stimuli contributes to more efficient visual-spatial responses (Domínguez-Borràs & Vuilleumier, 2022; Easterbrook, 1959). Therefore, the amygdala, via its basolateral nucleus, may regulate spatial attention by supporting visuospatial functions, with dopaminergic modulation of the posterior parietal cortex (PPC) as a core mechanism requiring coordinated amygdala engagement (Tomasi et al., 2009). In PD, diminished dopaminergic projections from the substantia nigra impair both the amygdala-striatal circuit’s ability to assign motivational valence to spatial locations and the PPC’s capacity to translate emotion-spatial signals into directed behavioral responses (Leek et al., 2014; Parnetti & Calabresi, 2006). These disruptions culminate in systemic degradation of visuospatial capacity. The observed associations between white matter microstructure and visuospatial function in this study suggest a potential compensatory mechanism. Specifically, microstructural remodeling within amygdala subnuclei may preserve neural pathways connecting the amygdala to the parietal cortex. This could partially maintain visuospatial abilities while offsetting deficits in emotion-driven attentional regulation, potentially delaying broader cognitive decline in PD. Our findings provide preliminary evidence that structural abnormalities in the amygdala, present bilaterally but more prominent on the left, may constitute a core mechanism underlying anxiety in PD. Specifically, the left cortico-amygdaloid transition area appears to be critically involved. It is noteworthy that this imbalance does not involve the entire amygdala, but rather specific subregions of the left amygdala, which may reflect the ’pathological dominance’ of the left amygdala in emotion regulation. Our analysis revealed that variations in the microstructure of the left amygdala white matter fiber tracts were associated with anxiety symptoms in PD patients. Consistent with our findings, previous studies have pointed out that a significant reduction in the volume of the left amygdala is positively correlated with anxiety levels in PD patients, which suggest that structural degeneration of the left amygdala may be an important basis for the development of anxiety in PD (Vriend et al., 2016). Further evidence supporting this view is that this structural change, characterized by lesions in the left amygdala, closely corresponds to the functional disconnection observed in specific neural circuits. Studies show that weaker connectivity between the left amygdala and left prefrontal cortex is linked to poor emotion regulation, suggesting reduced top-down control and greater emotional reactivity and anxiety (Carey et al., 2020; Criaud et al., 2021; H. Zhang et al., 2019). It is worth noting that the occurrence and maintenance of this spatially specific amygdala damage essentially depend on the imbalance of extensive dopaminergic network regulation. Research has found that the loss of dopaminergic neurons in the basal ganglia is significantly associated with anxiety in PD (Thobois et al., 2017). Reduced dopaminergic activity in the striatum may disrupt the cortico-striatal-thalamic-cortical circuit, which is essential for prefrontal regulation of emotion and behavior. This dysfunction can impair the prefrontal cortex’s inhibitory control over the amygdala, leading to emotional hyperreactivity and increased anxiety (Carey et al., 2021; Erro et al., 2012; Hartley & Phelps, 2010). Further investigation revealed that in patients with higher anxiety levels, administration of dopaminergic medications significantly enhanced functional coupling between the amygdala and prefrontal cortex (H. Zhang et al., 2019). This suggests that dopaminergic therapy may restore normal connectivity within this pathway, thereby alleviating anxiety symptoms. These research findings all highlight the critical role of the dopaminergic system in the regulation of anxiety through the amygdala. At the molecular pathology level, pathological α-synuclein aggregation within the amygdala is proposed to damage inhibitory interneurons, leading to aberrant amygdala activation and subsequent induction of anxiety symptoms (Pan et al., 2023). Studies in animals offer a more detailed understanding of molecular changes in distinct circuits. In PD models with substantia nigra lesions, reduced serotonergic receptor expression in the anterior basolateral amygdala–ventral hippocampus circuit, a key anxiety-regulating pathway, weakens responses to targeted pharmacotherapy (Yang et al., 2025). This compromises amygdala-mediated emotional regulation and contributes to the progression of anxiety symptoms in PD. This study has several limitations that should be considered when interpreting the findings. Firstly, the cross-sectional design establishes associations but cannot prove causation; it remains uncertain whether the white matter alterations lead to the symptoms or are a consequence of the broader disease process. Secondly, the absence of a healthy control group makes it difficult to determine if the observed microstructural changes are specific to Parkinson’s disease or represent a general neural correlate of the symptoms. Lastly, while fixel-based analysis offers distinct metrics like FD and FC, their precise neurobiological interpretations are still evolving. The observed differences could reflect variations in axon density, myelination, or other processes, underscoring the need for future studies to correlate these metrics with histopathological data. Conclusions This study, by combining fiber-based analysis and morphological measurements, reveals that the neuropathological basis of NMSs in PD involves a multi-level disruption of the amygdala structure. We identified distinct and lateralized structural-behavioral relationships: anxiety was associated with volume loss in the left subnucleus and bilateral white matter damage; visuospatial function was linked to changes in the volume on the right side and bilateral fiber integrity; while the severity of RBD was particularly associated with changes in the left-sided fibers. Overall, while specific subnuclear atrophy was discretely associated with symptoms, the broader correlates of symptoms were the microstructural degeneration of white matter pathways.These findings provide a critical structural framework for future research, shifting the focus from the amygdala as a homogeneous entity to investigating the specific contributions of its distinct subnuclei and their interconnected networks. Statements and Declarations Data availability Publicly datasets were used in this study. The data from the Parkinson’s Progression Markers Initiative database (PPMI) is available at: www.ppmi-info.org/data. All code used in this study will be shared upon reasonable request to the corresponding author. Competing interests The authors have no relevant financial or non-financial interests to disclose. Ethical approval The PPMI study has been approved by the respective institutional review boards of all participating sites, and written informed consents were obtained from each participant. The validation group was also approved by the Ethics Committee of The Second Affiliated Hospital of Chongqing Medical University (2022-759), and written informed consent was also obtained from each participant. Funding This study was supported by National Natural Science Foundation of China (grant number: 82371262, receiver: Lifen Chen), Natural Science Foundation of Chongqing (grant number: CSTB2023NSCQ-MSX0104, receiver: Changhong Tan), Kuanren Talent Program of The Second Affiliated Hospital of Chongqing Medical University (receiver: Xi Liu), and Graduate Students Science Innovation Project of Chongqing (grant number: CYS22349, receiver: Yuke Zhong). Author Contribution Conception and design: Lifen Chen, Xi Liu, Changhong Tan; data screening: Sichen Chen, Man Zhang, Qi Yang, Lijuan Mo; image processing and statistical analysis: Yuchen She, Jiahao Wei, Junyi Wang, Ying Liu; first draft: Yuchen She; review and editing: Yuchen She, Lifen Chen, Xi Liu, Changhong Tan. All authors reviewed and approved the manuscript. References Armstrong, M. J., & Okun, M. S. (2020). Diagnosis and Treatment of Parkinson Disease: A Review. JAMA , 323 (6), 548. https://doi.org/10.1001/jama.2019.22360Atkinson-Clement, C., Pinto, S., Eusebio, A., & Coulon, O. (2017). Diffusion tensor imaging in Parkinson’s disease: Review and meta-analysis. NeuroImage: Clinical , 16 , 98–110. https://doi.org/10.1016/j.nicl.2017.07.011Ay, U., Yıldırım, Z., Erdogdu, E., Kiçik, A., Ozturk-Isik, E., Demiralp, T., & Gurvit, H. (2023). Shrinkage of olfactory amygdala connotes cognitive impairment in patients with Parkinson’s disease. 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Keywords amygdala neuroimaging parkinson's disease Authors Affiliations Yuchen She The Second Affiliated Hospital of Chongqing Medical University View all articles by this author Jiahao Wei The Second Affiliated Hospital of Chongqing Medical University View all articles by this author Junyi Wang The Second Affiliated Hospital of Chongqing Medical University View all articles by this author Ying Liu The Second Affiliated Hospital of Chongqing Medical University View all articles by this author Sichen Chen The Second Affiliated Hospital of Chongqing Medical University View all articles by this author Man Zhang The Second Affiliated Hospital of Chongqing Medical University View all articles by this author Qi Yang The Second Affiliated Hospital of Chongqing Medical University View all articles by this author Lijuan Mo The Second Affiliated Hospital of Chongqing Medical University View all articles by this author Changhong Tan The Second Affiliated Hospital of Chongqing Medical University View all articles by this author Xi Liu 0000-0003-3142-7843 The Second Affiliated Hospital of Chongqing Medical University View all articles by this author Lifen Chen 0000-0003-3355-1338 [email protected] The Second Affiliated Hospital of Chongqing Medical University View all articles by this author Metrics & Citations Metrics Article Usage 169 views 87 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yuchen She, Jiahao Wei, Junyi Wang, et al. 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