Association between brain iron deposition and pure apathy in Parkinson’s disease: a cross-sectional quantitative susceptibility mapping imaging study

Introduction

Parkinson’s disease (PD) is a chronic progressive neurodegenerative disease typically characterized by degeneration or loss of nigrostriatal dopaminergic neurons and pathologic deposition of Lewy bodies. PD has been widely recognized as a movement disorder due to the presence of characteristic motor impairments including tremor, bradykinesia, rigidity, and impaired gait and posture. However, PD is also commonly comorbid with a number of non-motor symptoms (NMSs) (1). Apathy, defined by the International Society for Central Nervous System Clinical Trials Methodology (ISCTM) as a quantitative and persistent reduction in goal-directed behaviors (GDBs), is one of the most common and debilitating neuropsychiatric disorders in PD (2-4). The reduction in GDBs is not attributable to emotional distress, cognitive impairment, or diminished level of consciousness. Apathy frequently occurs in PD, with a prevalence of 23–70% (5). Apathy is distinct from depression and cognitive impairment, although they do commonly co-occur (4,6-9). Recently, it has been posited that apathy is an independent neuropsychiatric syndrome in PD, known as pure apathy (i.e., without depression or dementia) (10,11). Patients with PD with pure apathy (PD-PA) may lose motivation for their surroundings, show indifference to objective things and their own conditions, lack the corresponding inner experiences, become oblivious to things around them, and even exhibit indifference to matters closely related to their own interests. Clinically, patients with PD-PA may exhibit a reduction or even inability to complete previously enjoyed hobbies (such as reading and sports), daily activities, or work due to a lack of motivation. Apathy substantially impairs patients’ daily activities (2,3), affecting their quality of life and that of their families (5,12). Furthermore, previous studies have confirmed that apathy is a sign of the disease progression of PD (4,5), and PD patients with apathy have a high risk of dementia (13). However, apathy in PD currently lacks an established effective treatment. Thus, it is urgent to investigate the mechanism of apathy in PD.

The characteristic pathological changes of PD are the degenerative death of dopaminergic neurons in the substantia nigra of the midbrain and the presence of Lewy bodies composed of α-synuclein (α-syn) in the remaining neurons (14). However, α-syn aggregation is reported not be associated with the severity of symptoms in PD (15). Instead, brain iron deposition and the resulting oxidative stress are important driving factors in neurodegenerative diseases (16-18). Excessive iron can interact directly or indirectly with α-syn via free radicals, promoting the pathological α-syn aggregation (19). Whether brain iron deposition is a surrogate marker or a cause of neuronal damage remains unknown, but it offers a new way to explore the regional effects of neurodegeneration. Notably, previous studies have found that PD patients with apathy exhibit abnormalities in peripheral and central iron metabolism (20,21). Wang et al. found that iron levels were increased in the cerebrospinal fluid (CSF) of PD patients and positively correlated with the severity of apathy (21). This evidence indicated that apathy in PD may be related with brain iron accumulation and subsequent neurodegeneration. However, until now, the brain iron deposition of patients with PD-PA in vivo has remained unclear. As normal GDB involves a broad network of mesocorticolimbic areas (22-24), we hypothesized that PD patients with apathy, characterized by reduced GDBs, may show abnormal iron deposition in the mesocorticolimbic circuit.

Quantitative susceptibility mapping (QSM), an emerging brain imaging technique, can sensitively detect changes in magnetic susceptibility in brain tissue, especially changes in brain iron content (25,26). QSM provides a novel approach to explore the degenerative process of neurodegenerative disorders (27). Recently, many studies have used QSM to investigate the characteristics of iron deposition in PD (27-29) and explore the correlation between iron deposition and clinical symptoms in PD (27-32). However, no study to date has used QSM to explore brain iron deposition in patients with PD-PA.

This study used QSM to detect brain iron deposition in patients with PD-PA and examined apathy symptoms related regional brain iron deposition in order to explore the underlying mechanism of neurodegeneration in PD-PA. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2626/rc).

Methods

Participants

PD patients in this study were recruited from the Neurology Department and Functional Neurology Department of The Affiliated Wuxi People’s Hospital of Nanjing Medical University. Healthy controls (HCs) were recruited via patients spouses and advertisements posted on the bulletin boards of neurology outpatient clinic and medical examination center. A total of 105 participants were recruited, including 35 patients with PD-PA, 35 PD without pure apathy (PD-NPA), and 35 HCs. Of these, 11 cases were excluded due to image quality issues or inability to complete all sequence scans. Eventually, 29 patients with PD-PA, 33 patients with PD-NPA, and 32 HCs were included in this study. The purpose and significance of this study were explained to all participants and their families. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The Affiliated Wuxi People’s Hospital of Nanjing Medical University (No. KY21133) and informed consent was provided by all participants.

All patients were diagnosed with idiopathic PD in accordance with Movement Disorder Society (MDS) criteria (33). Apathy was diagnosed based on the ISCTM criteria (2,3) and via a 14-item self-report Apathy Scale (AS) (a score greater than 14 indicated apathy) (9). To be diagnosed with PD-PA, patients were required to meet the ISCTM diagnostic criteria for apathy and have an AS score greater than 14. The AS score of patients with PD-PA in present study ranged from 15 to 37. Patients with PD-NPA did not meet the ISCTM diagnostic criteria for apathy and had an AS score less than or equal to 14. The AS score of patients with PD-NPA in present study ranged from 2 to 8. The exclusion criteria were as follows: (I) dementia [the Mini-Mental State Examination (MMSE) score ≤24] (34); (II) depression [the 17-item Hamilton Depression Rating Scale (HAMD) score >7] (35); (III) anxiety [the Hamilton Anxiety Rating Scale (HAMA) score >7] (36); (IV) hallucinations, obsessive-compulsive symptoms, or other psychiatric disorders; (V) epilepsy, or a history of epilepsy or epileptic seizures; (VI) inability to cooperate with the examination or questionnaire due to severe language or hearing impairments; (VII) combination of severe organic diseases such as those of the heart, liver, and kidneys; (VIII) history of head trauma; (IX) imaging suggestive of striatal calcification, striatal infarction, hydrocephalus, and cerebral white matter abnormalities; (X) having metallic implants or cochlear implants in the body; (XI) severe dyskinesia, severe limb tremors, or uncontrollable head tremors; (XII) inability to complete the magnetic resonance imaging (MRI) examination because of claustrophobia; (XIII) abnormalities on brain MRI, such as tumor, infarction, or white matter lesions; and (XIV) combination of abnormal iron metabolism-related diseases such as iron deficiency anemia or restless leg syndrome. Additionally, 32 HCs, without neurological and psychological disorders or imaging abnormalities, were also enrolled. All participants were matched for gender, age, education, and cognitive and affective performance.

Demographic and clinical assessment

We collected demographic data including age, gender, and years of education of all participants. Moreover, information on the disease duration, dosages of anti-PD medications, and levodopa equivalent daily dose (LEDD) was collected in PD patients. Assessment of apathy symptoms was performed by two senior specialists. The AS was used to assess the severity of apathy. During the off-medication period, the Unified Parkinson’s Disease Rating Scale Motor section (UPDRS-III) and the Hoehn and Yahr stage (H-Y) (37) were used to assess the severity of PD. The HAMD, HAMA, MMSE, Frontal Assessment Battery (FAB) (38), Baylor Hallucination Questionnaire (BHQ) (39), and Questionnaire for Impulsive Compulsive Disorders in Parkinson’s Disease (QUIP) (40) were used to assess depressive and anxiety symptoms, global cognitive status, executive function, hallucination, and impulse control disorders, respectively.

MRI data acquisition

All participants underwent three-dimensional T1-weighted imaging (3DT1) and QSM scans. To minimize head movement, many methods were used to immobilize participants’ heads such as placing foam pads in the airspace, securing with forehead straps, and taping around the chin area. MRI scans were performed by a Siemens 3.0-T Prisma scanner (Siemens, Erlangen, Germany) at the Affiliated Wuxi People’s Hospital of Nanjing Medical University. A three-dimensional (3D) magnetization-prepared rapid acquisition gradient echo (MP-RAGE) sequence was used to obtain T1-weighted (T1w) anatomical images. The parameters of this sequence were as follows: repetition time (TR) =2,300 ms, echo time (TE) =2.98 ms, inversion time (TI) =900 ms, flip angle (FA) =9°, slice thickness =1 mm, slices =192, field of view (FOV) =256×256 mm², matrix size =256×256, voxel size =1×1×1 mm³, and acquisition time (TA) =5 minutes 30 seconds. The scan of QSM used a 3D fast low-angle shot sequence with four echoes to obtain magnitude and phase images. The parameters of this sequence were as follows: TR =35 ms, TE: TE1 =7.5 ms, TE2 =14.42 ms, TE3 =21.34 ms, TE4 =28.26 ms, FA =20°, slice thickness =1 mm, FOV =220×220×128 mm³, matrix size =220×220, voxel size =1×1×1 mm³, and TA =7 minutes 21 seconds. Before scanning, all participants were instructed to ensure that they remained relaxed, closed their eyes naturally, and kept their head still during the scan. Before entering the scanning room, they were reminded and assisted to check for any metal or other carries on their bodies. Moreover, all participants were routinely scanned with T2-weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) to rule out any brain abnormalities.

QSM data reconstruction

The QSM images in this study were computed using multi-echo gradient-recalled echo (GRE) data and generated by the following steps. Firstly, the total field map was computed using a nonlinear fitting method (41) and unwrapped using a Laplacian-based phase-unwrapping method. Then, a brain mask based on averaged amplitude images across echoes was extracted using the Brain Extraction Tool (BET) algorithm integrated in the Functional Magnetic Resonance Imaging of the Brain (FMRIB) software library (https://www.oxcin.ox.ac.uk/; v6.0.1) (42). The projection on dipole fields (PDF) method (43) was used to eliminate the background field inside the brain. Finally, the morphology-enabled dipole inversion method with automatic uniform CSF zero reference (MEDI+0) (44) was used to reconstruct the QSM spectrum. In this algorithm, the weight of the L1-regularization term, which enforces spatial smoothness in the reconstructed susceptibility map, was set to 1,000, and the weight of the CSF zero-referencing term was set to 100.

QSM preprocessing and analysis based on voxel level

Image preprocessing was performed using Advanced Normalization Tools (ANTs) (http://stnava.github.io/ANTs/). The spatial inhomogeneity of individual space T1w images was corrected using the N4 bias field correction. Since the magnitude gradient echo (mag-echo) images shared the same space with the QSM images and had similar tissue contrast to T1w, the mag-echo images were used as an intermediate modality to bridge QSM and T1w when normalizing individual space QSM images to Montreal Neurological Institute (MNI) space. Specifically, individual space mag-echo images were firstly aligned to the corresponding bias field-corrected T1w (T1w-corrected) images by rigid transformation. Then, the same transformation matrix was used to align the QSM to T1w-corrected to obtain the co-aligned QSM (co-QSM). Thereafter, the T1w templates in MNI space were aligned to individual T1w-corrected images using rigid, affine, and nonlinear transformations. The co-QSM images were normalized to MNI space by the inverse transformation of the above transformations (rigid, affine, and nonlinear transformations), and then all QSM-MNI images were resampled to 1 mm isotropic resolution. Finally, the final images were spatially smoothed using the 3D Gaussian smoothing kernel with the full width at half maximum (FWHM) of 3 mm to minimize the effects of alignment errors and other inaccuracies, which was chosen in line with previous studies (29,45,46). When performing the one-way analysis of covariance (ANCOVA) for QSM among the three groups, we used age and gender as covariates to determine the inter-group differences (28). The multiple comparison correction was set at the cluster-level family-wise error (FWE) corrected P<0.05 (voxel-level P<0.001, cluster-level FWE-corrected P<0.05). Then, statistically significant brain regions were used as masks. Subsequently, post hoc two-sample t-tests were performed within masks with the same covariates as above (voxel-level P<0.001, cluster-level FWE-corrected P<0.05).

In addition, to assess the relationship between the severity of apathy and iron deposition, we explored brain regions in patients with PD-PA where susceptibility values were positively correlated with AS scores at the whole-brain voxel level. Previous studies have stated that PD disease severity (mainly reflected by UPDRS) (45-47) and age (47-49) are associated with brain iron accumulation. To account for potential confounding effects, we included both age and UPDRS scores as covariates in the correlation analyses. Age and UPDRS-III scores were used as covariates, with multiple comparison correction set at the cluster-level FWE P<0.05 (voxel-level P<0.001, cluster-level FWE-corrected P<0.05).

QSM analysis based on the region of interest (ROI) level

To avoid the spatial uncertainty of QSM images after smoothing, QSM analysis based on the ROI level can be used to more accurately compare the subcortical gray nuclei with smaller volumes that were closely related to iron deposition. In this section, unsmoothed QSM images in MNI space were used for group comparison analysis at the ROI level regarding subcortical nuclei. Previous literature has reported that subcortical nuclei with more obvious iron deposition in PD patients include substantia nigra pars compacta (SNc), globus pallidus (GP), red nucleus (RN), caudate, and putamen. In addition, recent studies have found increased iron deposition in the ventral tegmental area (VTA) in patients with PD (50). Therefore, we also selected the VTA as an ROI for analysis. The above ROI brain regions were derived from the probabilistic atlas of subcortical nuclei proposed by Pauli et al., with a threshold of 50% (Figure 1) (51). The mean susceptibility values of each ROI in the three groups were compared using ANCOVA with age and gender as covariates. Multiple comparison correction was performed using the false discovery rate (FDR) method, and the level of significance was defined as P<0.05. The flowchart of preprocessing and statistical analysis of QSM is shown in Figure 2.

Figure 1 Subcortical ROI brain regions. ROI, region of interest.

Figure 2 Summary steps of preprocessing and statistical analysis of QSM. QSM data were constructed, preprocessed, and analyzed. 3DT1, three-dimensional T1-weighted imaging; ANTs, Advanced Normalization Tools; MNI, Montreal Neurological Institute; MP-RAGE, magnetization prepared rapid acquisition gradient echo; QSM, quantitative susceptibility mapping; ROI, region of interest.

Statistical analysis

Comparisons of demographic and clinical characteristics were performed using the two-sample t-test, Chi-squared test, Kruskal-Wallis test, Mann-Whitney test, or one-way analysis of variance (ANOVA), as appropriate. The Shapiro-Wilk method was used to test the normality of all variables. Bonferroni correction was used for multiple comparisons. The demographic and clinical data of all participants were analyzed using the software SPSS 20.0 (IBM Corp., Armonk, NY, USA). A P value <0.05 was considered statistically different, and Bonferroni correction was used for multiple comparisons among the three groups. For brain regions with differences in QSM groupwise comparisons, the mean susceptibility values were extracted, and further partial correlation analyses were performed between the mean susceptibility values and AS scores with age and UPDRS-III as covariates in patients with PD-PA. Again, a P value <0.05 was considered statistically different.

Results

Sociodemographic and clinical characteristics

A total of 94 participants were finally enrolled in this study, including 29 in the PD-PA group, 33 in the PD-NPA group, and 32 in the HC group. There were no statistically significant differences in age, gender, years of education, HAMA, HAMD, MMSE, and FAB among the three groups. AS scores were significantly higher in the PD-PA group compared with the HC group and the PD-NPA group (P<0.001). The detailed results are shown in Table 1.

QSM analysis based on voxel level

Group comparisons among the three groups based on voxel level

Whole-brain QSM analysis based on voxel level suggested significant differences in certain brain regions between the PD-PA group and the HC group or between the PD-NPA group and the HC group, but no significant differences between the PD-PA group and the PD-NPA group (voxel-level P<0.001, cluster-level FWE-corrected P<0.05). Compared to the HC group, the susceptibility values in the PD-PA group were significantly increased in bilateral medial superior frontal gyrus (SFGmed), whereas brain regions with significantly increased susceptibility values in the PD-NPA group included the right SFGmed, the right superior frontal gyrus-orbital part, and the left putamen. Detailed information is presented in Table 2 and Figure 3.

Figure 3 Differences in susceptibility values between the groups based on voxel-wise analysis. Voxel-wise whole-brain QSM analysis suggested significant differences in certain brain regions between the PD-PA group and the HC group or between the PD-NPA group and the HC group, but no significant differences between the PD-PA group and the PD-NPA group (voxel-level P<0.001, cluster-level FWE-corrected P<0.05). The color bar represents the t-values of the group comparisons, with warm colors indicating higher susceptibility in the patient group (PD-PA or PD-NPA) relative to HC, and cool colors indicating lower susceptibility. FWE, family-wise error; HC, healthy control; L, left; PD-NPA, Parkinson’s disease without pure apathy; PD-PA, Parkinson’s disease with pure apathy; PUT, putamen; QSM, quantitative susceptibility mapping; R, right; SFGmed, medial superior frontal gyrus; SFGorb, orbital superior frontal gyrus.

Correlation analysis between susceptibility values and AS scores in the PD-PA group based on voxel level

To assess the relationship between the severity of apathy and iron deposition, we explored brain regions where susceptibility values and AS scores were positively correlated based on whole-brain voxel level in the PD-PA group. The results suggested that AS scores were positively correlated with susceptibility values in the left SFGmed, the right superior temporal gyrus (STG), the left anterior cingulate cortex (ACC), and the right thalamus (Table 3 and Figure 4). To confirm the robustness of the results, we also presented correlation analysis across the entire PD cohort (including both apathetic and non-apathetic patients). Notably, the correlation between AS scores and susceptibility values remained significant in the left SFGmed and extended to the left middle temporal gyrus (MTG) (Table S1 and Figure S1).

Figure 4 Positive correlation between susceptibility values with AS scores in the PD-PA group based on voxel-wise analysis. We explored brain regions where susceptibility values and AS scores were positively correlated based on whole-brain voxel-wise analysis in patients with PD-PA. The color bar represents the strength of partial correlation coefficients between susceptibility values and AS scores, with warmer colors indicating stronger positive correlations. ACC, anterior cingulate cortex; AS, Apathy Scale; L, left; PD-PA, Parkinson’s disease with pure apathy; R, right; SFGmed, medial superior frontal gyrus; STG, superior temporal gyrus; THA, thalamus.

QSM analysis based on the ROI level

We further used ROI analysis for subcortical nuclei with smaller gray matter volumes. The result suggested that the susceptibility values of SNc (FDR-corrected P=0.010) and VTA (FDR-corrected P=0.009) were significantly higher in PD groups compared to the HC group, but there was no statistically significant difference between the two PD subgroups. The susceptibility values of caudate, GP, putamen, and RN showed no statistically significant difference among the three groups. Relative to PD-NPA, PD-PA showed nonsignificant susceptibility increases in the SNc (P=0.619), VTA (P=0.296), RN (P=0.912), and putamen (P=0.816), but decreases in the caudate (P=0.941) and GP (P=0.620). These results are displayed in Table 4 and Figure 5.

Figure 5 Differences in susceptibility values between the groups based on ROI-wise analysis. *, P_FDR<0.05; **, P_FDR<0.01. FDR, false discovery rate; GP, globus pallidus; HC, healthy control; PD-NPA, Parkinson’s disease without pure apathy; PD-PA, Parkinson’s disease with pure apathy; ppm, parts per million; QSM, quantitative susceptibility mapping; RN, red nucleus; ROI, region of interest; SNc, substantia nigra pars compacta; VTA, ventral tegmental area.

Correlation analysis of brain regions with differences in susceptibility values among groups

To further analyze whether the differences of susceptibility values among the three groups were related to PD apathy symptoms, the susceptibility values of the above brain regions and ROIs showing different iron deposition among groups were extracted for further partial correlation analyses with AS scores in patients with PD-PA, with age and UPDRS-III as covariates. These analyses indicated that the susceptibility values of the left SFGmed were positively correlated with AS scores in patients with PD-PA (r=0.651, P=0.001, Figure 6).

Figure 6 Positive correlation between left SFGmed susceptibility values and AS scores in patients with PD-PA. In patients with PD-PA, left SFGmed susceptibility values were positively correlated with AS scores (r=0.651, P=0.001). AS, Apathy Scale; L, left; PD-PA, Parkinson’s disease with pure apathy; QSM, quantitative susceptibility mapping; SFGmed, medial superior frontal gyrus.

Discussion

In this study, we investigated brain tissue iron deposition in patients with PD-PA using QSM. The results suggested that patients with PD had significant iron deposition in the substantia nigra and VTA compared to HCs. Further analysis indicated that compared with HCs, patients with PD-NPA had significant iron deposition in the right SFGmed, whereas those with PD-PA had a more extensive range of brain iron deposition, extending to the bilateral SFGmed. The correlation analysis found that increased regional iron deposition in the left SFGmed was associated with more severe apathy symptoms. In patients with PD-PA, voxel-based whole-brain correlation analysis indicated that the severity of iron deposition in the left SFGmed, left ACC, right thalamus, and right STG was positively correlated with the severity of apathy.

This study demonstrated that patients with PD-PA had increased iron deposition in the SFGmed, and the severity of iron deposition was positively correlated with the severity of apathy. It implied that severe neurodegeneration in the SFGmed was related with severe apathy in PD. Previous clinical studies have found that if the infarction lesion includes the SFGmed, apathy is significantly more likely to occur (52), and patients with bilateral SFGmed lesions show more severe apathy symptoms than those with unilateral lesions (53). In addition, previous multimodal neuroimaging studies have found gray matter atrophy and abnormal brain glucose metabolism in the SFGmed in patients with other neurological disorders with apathy (23,54). Sheelakumari et al. reported that the severity of apathy symptoms in patients with frontotemporal dementia is related to the extent of iron deposition in the frontal superior gyrus (55). Together with our results, we speculated that neurodegeneration in the SFGmed played an important role in the pathogenesis of PD apathy.

The SFGmed is a central node in the mesocorticolimbic circuit and is thought to be involved in a variety of functions such as activity selection, inhibitory control, and attentional shifting. The SFGmed is related to a series of action selections and guides the selection of more valuable activities with the ACC (56). These functions are important for the initiation of motivated behavior. In addition to the role of action selection at the onset of behavior, inhibitory control is also frequently associated with the SFGmed (57-61). Once behavior is generated, continuous persistence and inhibition of irrelevant things in the surrounding environment are needed to achieve the goal. Importantly, inhibitory control is an important component of executive functions, which exerts top-down influence to shape behavior and persistence in the expected GDBs, regardless of changes in the external environment (53,62). Moreover, the SFGmed has been reported to be involved in attention shifting (63,64), which is a core component of cognitive flexibility. To maintain reward-based GDBs, the flexible shift of attention in cognitive processes is also important as the environment changes. Consistent with this theory, a recent study has found that PD patients with apathy exhibit impaired cognitive flexibility and inhibitory control (65). Therefore, we speculated that the SFGmed, as a core node in the mesocorticolimic circuit, may be involved in the “top-down” regulation of high-level brain functions such as action selection, inhibitory control, and attention shifting, thereby participating in the pathogenesis of apathy in PD.

The thalamus, as a sensory relay station, plays a role in information regulation of cortical and subcortical structures (66,67). Previous studies have confirmed that cortical atrophy of the thalamus correlates with the severity of apathy symptoms in neurological disorders (68). The correlation analysis in our study demonstrated that the severity of apathy in patients with PD-PA was positively correlated with iron deposition in the thalamus. It indicated that the dysfunction of the thalamus may be involved in the pathogenesis of apathy in PD. A recent study published in Nature revealed that the thalamus and the medial prefrontal cortex play an interactive role in regulating GDBs (69). Our study found that the severity of apathy symptoms in patients with PD-PA was positively correlated with brain iron deposition in the thalamus and medial prefrontal cortex, which suggested that the interaction between the thalamus and the medial prefrontal cortex in regulating GDBs in patients with PD-PA may be abnormal. We speculated that the thalamus may be involved in the pathogenesis of PD apathy by regulating GDBs through a “bottom-up” mechanism.

The ACC is another core node in the mesocorticolimbic circuit. Previous studies have suggested that cortical atrophy of the ACC in patients with cognitive disorders is associated with apathy symptoms (70). It has been found that neural activity and local coherence of the ACC are decreased in PD patients with apathy (71-73). Furthermore, compensatory serotonergic innervation of the ACC in PD patients with apathy can reverse the symptoms of apathy in PD (74). The present study found that the severity of apathy in PD patients was positively correlated with iron deposition in the ACC. Therefore, we speculated that the ACC may also be involved in the pathogenesis of PD apathy.

The ACC, as a limbic structure, is connected with the prefrontal cortex and is involved in reward processing (75). Specifically, the ACC is involved in learning behaviors to obtain rewards, working with the orbital frontal cortex and the ventromedial prefrontal cortex to provide “navigation” for goals, and partially mediating reward-related effects on memory consolidation through the cholinergic system (76). Previous research has found that PD patients with apathy have reduced sensitivity to rewards (77), which is relevant to the initiation of GDBs (78). Moreover, this reduced reward sensitivity in PD with apathy is dopamine resistant. Therefore, we speculated that the iron deposition in the ACC may be related to the reward processing process, thereby participating in the pathogenesis of apathy in PD.

The present study also found a positive correlation between the susceptibility values of the STG and the AS scores of patients with PD-PA, which suggested that greater iron accumulation in the STG correlated with more severe apathy symptoms. It has been reported that there is abnormal cerebral blood flow in the STG of patients with apathy (79). Previous research has suggested that the STG is involved in the execution and the observation of activities (80). Moreover, the STG plays a role in recognizing displayed objects during GDBs (81). Ilg proposed that the STG is closely associated with the generation of GDBs, and specifically, plays a very important role in motor processing (82). Combining these findings, we hypothesized that iron deposition in the STG may be associated with the dysfunction of the observation and the execution of GDBs, thereby participating in the pathogenesis of apathy in PD.

Regional brain iron deposition may lead to cellular dysfunction and corresponding neurodegeneration, leading to the development of clinical symptoms (29). However, why iron deposition exhibits selective brain tissue deposition is still unclear. Recently, some studies have attempted to use the Allen Atlas to explore the transcriptomics of brain regions related to PD iron deposition. The results of their studies have suggested that there is high expression of genes related to heavy metal detoxification and synaptic function, as well as an increased presence of astrocytes and glutamatergic neurons in the brain areas of iron deposition in PD (27). In the future, it may be possible to further explore and analyze whether there are transcriptomic abnormalities in the brain iron deposition regions related to apathy in PD based on the Allen Atlas, which would provide important insights into the specific molecular mechanisms of neurodegeneration in PD with apathy.

Although present study found an association between brain iron deposition and apathy in PD, there were still some limitations. Firstly, the sample size of the present study was relatively small; brain regions where iron deposits were not evident may not have been detected. Secondly, this study was a cross-sectional study and could not evaluate the dynamic changes of neurodegeneration reflected by iron deposition over the time. Iron deposition can be used as a monitoring indicator for disease progression, and PD apathy is often prone to conversion to dementia, requiring further follow-up studies to observe whether iron deposition extends to cognitive-related brain regions as the disease progresses. Although previous literature has suggested that PD apathy involves abnormalities in iron metabolism in peripheral blood and central CSF, iron metabolism-related indicators were not tested in our study. In future research, we will include indicators related to iron metabolism and ferroptosis to further explore the molecular mechanism related to brain iron deposition in PD with apathy. It should also be mentioned that distinguishing the subthalamic nucleus from the SNc may be challenging due to indistinct boundaries. For this reason, our team is now conducting 5-T MRI research on PD, hoping to provide better resolution and segmentation of small deep nuclei (including brainstem nuclei) for QSM research in the future. Lastly, although QSM-derived susceptibility values are frequently interpreted as markers of iron content in neurodegenerative diseases, iron is the most important factor affecting susceptibility differences. It is critical to acknowledge that susceptibility reflects a composite of paramagnetic (e.g., iron) and diamagnetic (e.g., myelin, proteins) contributions (83). For example, the loss of diamagnetic myelin or cellular proteins may synergistically elevate susceptibility alongside iron accumulation. Future advanced multi-parametric MRI (e.g., quantitative magnetization transfer imaging for myelin) and hybrid positron emission tomography (PET)-MRI studies {e.g., using [11C]PBB3 for tau or [11C]PiB for amyloid} may further elucidate the molecular underpinnings of susceptibility alterations in neurodegeneration.

Conclusions

This study used the QSM to detect the characteristics of brain iron deposition in patients with PD-PA. The results suggested that brain regions showing iron deposition in patients with PD-PA were mainly located in the SFGmed and the ACC of the mesocorticolimbic circuit, as well as in the temporal STG and thalamus. Our findings suggest that abnormal iron deposition in these core brain regions may disrupt the mesocorticolimbic circuit, potentially contributing to the mechanism of apathy in PD. This study provides new insights into the pathophysiologic mechanisms of PD-PA.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2626/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2626/dss

Funding: This work was sponsored by the National Natural Science Foundation of China (Nos. 81271629 and 82101339), the Jiangsu Province Natural Science Foundation (Nos. BK20191143 and BK20221205), the Nanjing Medical University Science and Technology Development Fund (No. NMUB2020304), and the Wuxi Taihu Lake Talent Plan, Supports for Leading Talents in Medical and Health Profession (No. 2020THRC-DJ-SNW).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2626/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. The study was approved by the Ethics Committee of The Affiliated Wuxi People’s Hospital of Nanjing Medical University (No. KY21133) and informed consent was provided by 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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