
==== Front
Neuroimage Clin
Neuroimage Clin
NeuroImage : Clinical
2213-1582
Elsevier

S2213-1582(24)00100-1
10.1016/j.nicl.2024.103661
103661
Regular Article
Unveiling MRI markers for Parkinson’s Disease: GABAergic dysfunction and cortical changes
Tian Yuan 6318@hrbmu.edu.cn
a
Geng Sijia 18346544139@163.com
a
Liu Tianyi liutyi92@163.com
b
Wang Qi wangqi981024@163.com
a
Lian Jianxiu Natalie.lian@philips.com
c
Lin Liangjie liangjie.lin@philips.com
c
Li Jiayu 1205221080@qq.com
d
Gong Tao aitao880801@126.com
e
Duan Junhong 188302054@csu.edu.cn
f
Wang Dan dywang46@yeah.net
g
Liu Pengfei liupengfei@hrbmu.edu.cn
a⁎
a Department of Magnetic Resonance, the First Affiliated Hospital of Harbin Medical University, Heilongjiang, Harbin 150001, PR China
b The First Department of Neurology, the First Affiliated Hospital of Harbin Medical University, Heilongjiang, Harbin 150001, PR China
c Clinical and Technical Support, Philips Healthcare, Beijing, PR China
d Department of Radiology, the third People’s Hospital of Chengdu, Sichuan, PR China
e Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, PR China
f Department of Radiology, Third Xiangya Hospital, Central South University, No. 138 Tongzipo Road, Changsha 410013, Hunan, PR China
g Department of Medical Imaging, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, PR China
⁎ Corresponding author:, 1st Floor, 3#Building, No.23, Youzheng Street, Nangang District, Harbin 150001, PR China. liupengfei@hrbmu.edu.cn
30 8 2024
2024
30 8 2024
43 10366119 5 2024
21 8 2024
24 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Graphical abstract

Highlights

• GABA level and local gyrification indices negatively correlated with PD likelihood.

• Neurotransmitter and morphological changes are potential markers for early PD diagnosis.

• Focus on early-onset Parkinson’s disease (EOPD) with changes in GABA and cortical gyrification.

• Significant negative correlations between GABA/Cr levels and LGI in specific cortical regions.

Objective

The study aimed to investigate changes in basal levels of the inhibitory γ-aminobutyric acid (GABA) neurotransmitter in the sensorimotor cortex (SMC) and cortical gyrification in patients with Parkinson’s disease (PD), which could further identify potential imaging biomarkers for PD, particularly in patients with early-onset Parkinson’s disease (EOPD).

Method

Fifty patients with PD (EOPD: 10, late-onset Parkinson’s disease [LOPD]: 40) and fifty-two age- and gender-matched healthy controls (HC) underwent GABA-edited 1H MRS of the SMC and high-resolution 3D T1-weighted brain imaging. GABA levels and local gyrification index (LGI) were calculated to assess GABAergic and cortical gyrification deficits in PD.

Result

The Pearson correlation coefficients revealed significant negative associations between eight indicators, including GABA/Cr level and local gyrification index (LGI) of specific cortical regions (precentral, postcentral, entorhinal, superiortemporal, posteriorcingulate, cuneus, and transversetemporal cortex), and the likelihood of Parkinson’s disease (r < -0.4, p < 0.001). Additionally, GABA levels were significantly lower in the SMC region of both EOPD and LOPD patients compared to healthy controls (mean ± SD [u.i.]: EOPD=0.081 ± 0.022 vs. Young-HC=0.112 ± 0.021, p = 0.003; LOPD=0.054 ± 0.024 vs. Old-HC=0.099 ± 0.021, p < 0.001). The logistic regression model was established by using multivariate analysis, identifying two statistically significant indicators: GABA/Cr and LGI of the transversetemporal. The combined model exhibited the highest AUC values in both younger and older populations.

Conclusion

GABAergic dysfunction may play an important role in the pathogenesis of PD patients. Changes in neurotransmitter and morphological may serve as potential markers for the preclinical diagnosis and progression of PD, including EOPD.

Keywords

Parkinson’s Disease
GABA
LGI
MEGA-PRESS
Sensoriotor cortex
==== Body
pmc1 Introduction

Parkinson's disease (PD) is a neurodegenerative disorder. As the population ages, the number of PD patients is increasing continuously. It has been found that the incidence rate of this disease is rising in China (Qi et al., 2021). Previous studies have shown that although it is not significant, the frequency of the reported cases of bradykinesia or rigidity at any time in PD was around 90 % (Chang et al., 2024). Specific movements require a series of sensory-motor transformations. The sensorimotor cortex (SMC), as a major source of descending motor commands for voluntary movement, has been shown to be essential for various motor functions and movements (Cassady et al., 2019, Tang et al., 2021).

γ-aminobutyric acid (GABA) is the predominant inhibitory neurotransmitter and GABA abnormalities have been implicated in specific neuropsychiatric disorders (Al-kuraishy et al., 2024, Barcomb and Ford, 2023). Cortical GABA plays a vital role in PD, and GABA depletion may contribute to increased motor symptom expression (Li, 2022, Van Nuland et al., 2020). Some neuropathologic studies have found that reduced GABAergic activity in the SMC is associated with poorer sensorimotor performance (Van Nuland et al., 2020, Stagg, 2014).

The diagnosis of PD primarily relies on clinical symptoms, making early-stage diagnosis challenging. Existing diagnostic methods, such as Transcranial Sonography (TCS), have the disadvantage of high false positive rate. Compared to imaging techniques such as Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET), magnetic resonance imaging (MRI) / spectroscopy (MRS) can non-invasively detect neurotransmitters without the need for exogenous contrast agents or radioactive tracers. Mescher-Garwood point-resolved spectroscopy (MEGA-PRESS) would be used in this study, which has been proved to reliably and stably detect the concentration of GABA (Stagg, 2014).

The local gyrification index (LGI) quantitatively reflects the degree of local brain cortical folding, reflecting the morphology of the brain cortex. Utilizing longitudinal data from the Parkinson Progression Markers Initiative (PPMI) database, it has also been confirmed that PD is associated with experiences a progressive reduction in cortical folding degree (Tang et al., 2021). Studies showed that abnormalities affecting cortical gyrification may emerge early in neurodevelopmental disorders (Palaniyappan and Liddle, 2012, Park et al., 2023). Additionally, LGI measurements can be valuable in understanding the relationship between brain structure and function, as well as in for characterizing individual differences in cortical morphology. Therefore, investigating whether changes in the cortical folding degree in PD is of significant importance. If such markers are significantly associated with PD, then combining these markers to construct a diagnostic method for Parkinson's disease may be feasible. Since the early symptoms of PD are typically nonspecific, traditional neuroimaging methods are not sensitive for diagnosing early-stage PD. Thus, further investigates on whether the indicators could predict early-stage Parkinson's disease is highly meaningful.

Thus, the aim of this study is 1) measure the levels of the inhibitory neurotransmitter GABA in the SMC of PD; 2) evaluate the changes in cortical folding of brain structures; 3) further access indicators and their predictive value in identifying significant correlates with PD; 4) due to the potential for underdiagnosis in the early stages of the disease course, this study aim to subdivide PD into finer groups to further calculate the indicators between the EOPD (early-onset PD) group and the control group. The hypothesis is that the observed imaging biomarker can indicate the probability of PD, even in EOPD, which would be meaningful for guiding clinicians in targeted and personalized treatment.

2 Material and methods

2.1 Study sample

This study was approved by the local institutional ethics committee and all participants provided written informed consent prior to the commencement of the study. This multi-center study initially recruited 322 participants (Fig. 1). Inclusion Criteria: 1. All enrolled patients were diagnosed with Parkinson's disease (UK Brain Bank Criteria) for the first time. 2. Primary Parkinson's disease. 3. EOPD: PD onset at ≤ 50 years with H-Y stage 1–2, mainly resting tremor; LOPD: onset at > 50 years with H-Y stage 1–4, mainly manifesting with both motor and non-motor symptoms. 4. All subjects were right-handed. Patients were selected following strict exclusion criteria: 1. Secondary Parkinsonism (n = 45), 2. Undergone deep brain stimulation (DBS) therapy (n = 11), 3. MRI contraindication or claustrophobia (n = 11), 4. Personality disorder or any present mental disorder (n = 21), 5. Not age or sex matched (n = 13), 6. Any potential brain abnormalities and vascular lesions apparent on conventional T2- and FLAIR-scans (n = 52), 7. Being hospitalized for treatment (n = 6), and 8. Poor image quality (n = 27). PD medications were withdrawn on the day of MRI acquisition and reintroduced after MRI acquisition. Finally, the study cohort included 10 early-onset PD (EOPD) patients, 40 late-onset PD (LOPD) patients, 12 age- and sex-matched young healthy controls, and 40 age- and sex-matched old healthy controls.Fig. 1 Flowchart of patient.

2.2 MRI protocols

MRI data were collected with a 3 T scanner (Ingenia Elition, Philips Healthcare, Best, the Netherlands) using a 32-channel head coil. Structural images were acquired using a 3-dimensional T1-weighted turbo field-echo sequence with sagittal acquisition, resolution = 1.0 × 1.0 × 1.0 mm3; TR/TE=8.2/3.7 ms; flip angle = 8°, matrix = 256 × 256; field of view = 24 × 24 cm2. The acquisition of MEGA-PRESS on SMC was scanned with parameters as follows: voxel size = 3.0 × 3.0 × 3.0 cm3 (AP×LR×HF); TR/ TE=2000/68 ms; number of signal averages = 320; spectral width = 2 kHz; acquisition data points = 2048; ON/OFF frequencies = 1.9/7.5 ppm; bandwidth of editing (Gaussian type) pulses = 100 Hz; and scan time = 10 min 50 s. The water signal suppression was achieved using the VAPOR scheme with a bandwidth of 120 Hz (Tkác et al., 1999).

2.3 MRI processing

2.3.1 MRS

MEGA-PRESS data were processed using the MATLAB based toolbox Gannet (version 3.1.3). (Fig. 2B). First, the frequency and phase corrections for each ON/OFF average spectrum were conducted using the spectral registration module developed by Near et al (Near et al., 2015). The final GABA-edited spectrum was generated by substracting the OFF-averaged from the ON-averaged spectrum. The line broadening of 3 Hz was used for each edited spectrum, and the spectum was zero-filled by a factor of 16. GABA and Glx levels were quantified by fitting of the edited signals around 3.0 (a single Gaussian model) and 3.8 (a two-Gaussian model) ppm respectively with reference to the total creatine (tCr) signal at 3.0 ppm from the OFF-averaged spectum (GABA/Cr and Glx/Cr). The tCr peak from the “OFF” spectrum and the water signal from an extra water-unsuppressed spectum (32-averages) were modeled with the Lorentzian and Lorentz-Gaussian models, respectively. The full width at half maximum (FWHM) of the tCr signal and fitting errors were recorded for each subject. Only spectra with the FWHM less than 25 Hz and the fitting errors of GABA and Glx below 15 % were included in the final statistical analysis. SPM12 (https://www.fil. ion.ucl.ac.uk/spm/software/download/) is used to register the MRS voxel to the 3D T1WI and segment the 3D T1WI image into partitions of GM, white matter (WM), and cerebrospinal fluid (CSF). The volume fractions of GM, WM and CSF within the voxel were recorded. Examples of the acquired spectra from each groups and the fitting results were shown in Fig. 2.Fig. 2 Proton magnetic resonance spectroscopy (1H-MRS). Panel A depicts a voxel of 3.0 × 3.0 × 3.0 cm3 centered on the sensorimotor cortex. Panel B shows the GABA+and Glx peaks of four examples from the participant groups: EOPD, LOPD, younger HC, and older HC. GABA: inhibitory γ-aminobutyric acid; Glx: excitatory glutamate + glutamine.

2.3.2 Cortical gyrification analysis

The local gyrification index (LGI) was calculated in this study to measure cortical gyrification. The LGI is a surface-based vertex-wise variant of the 2D gyrification index, which is computed as the ratio of the perimeter of the inner folded contour to that of the outer contour of a 2D brain section. The LGI map of each subject can be readily computed from T1-weighted images using FreeSurfer (surfer.nmr.mgh.harvard.edu/) with its standard MRI processing pipeline. Briefly, the LGI map could be derived using the following 4 main steps. First, a 3D mesh representation obtained for the pial surface of each hemisphere. Second, a smooth outer surface mesh was created to tightly wrap the individual pial surface. Third, the LGI of a given vertex on the newly created artificial outer surface computed as the ratio between the areas of corresponding surface patches extracted respectively from the pial surface and the outer surface11. Finally, the LGI map of the pial surface could be derived by redistributing the LGI values of the artificial outer surface nearby vertices of the pial surface mash, based on their prior involvement in the calculation of correlative outer surface LGI values. The individual LGI map (of the pial surface) was resampled onto the built-in average template of FreeSurfer and further smoothed using a 10-mm-full-width-at-half-maximum Gaussian kernel prior to statistical analysis.

2.4 Statistical analysis

To assess the magnitude of morphological differences between PD and HC groups, Cliff's Delta effect size was calculated. According to Romano et al., Cliff's Delta values less than 0.147 can be considered negligible; values between 0.147 and 0.33 represent small effect sizes; values between 0.33 and 0.474 represent moderate effect sizes; and values greater than 0.474 represent large effect sizes.

According to equation (1), Pearson correlation analysis was conducted to determine the key indicators impacting the prevalence of Parkinson's Disease (PD) among 36 variables (GABA/Cr, GLX/Cr, LGI of Bankssts, Caudalanteriorcingulate, Caudalmiddlefrontal, Cuneus, Entorhinal, Fusiform, Inferiorparietal, Inferiortemporal, Isthmmuscingulate, Lateraloccipital, Lateralorbitofrontal, Lingual, Medialorbitofrontal, Middletemporal, Parahippocampal, Paracentral, Parsopercularis, Parsorbitalis, Parstriangularis, Pericalcarine, Postcentral, Posteriorcingulate, Precentral, Precuneus, Rostralanteriorcingulate, Rostralmiddlefrontal, Superiorfrontal, Superiorparietal, Superiortemporal, Supramarginal, Frontalpole, Temporalpole, Transversetemporal, Insula).

In this study, we defined the probability of disease occurrence as 0 for healthy individuals and as 1 for Parkinson's patients within the sample. In the equation, r(i) represents the Pearson correlation coefficient between the probability of disease occurrence and the i-th indicator among the 36 indicators; Xj represents the probability of disease occurrence for the j-th sample; X¯ represents the average probability of disease occurrence across all samples; Yi,j represents the value of the i-th indicator for the j-th sample; and Yi¯ represents the average value of the i-th indicator across all samples.(1) r(i)=∑j=1102(Xj-X¯)(Yi,j-Yi¯)∑j=1102(Xj-X¯)2∑j=1102(Yi,j-Yi¯)2

The dendrogram was generated through cluster analysis of judgments significantly associated with the morbidity of PD.

Patients classified into young and old groups based on whether they were above 50 years old. Quantitative data is described using mean ± standard deviation, while qualitative data is described using frequency (percentage, %). Statistical analysis was used to detect differences in age, GABA/Cr level, Glx/Cr level, GM fraction, WM fraction, CSF fraction, and LGI of brain regions identified through Pearson correlation analysis between young PD, old PD, young control, and old control using analysis of variance (ANOVA). Before conducting the ANOVA, we conducted normality tests and homogeneity of variance tests using the Shapiro-Wilk test and the Levene test. The results all meet the conditions to use ANOVA. Following the ANOVA, all tests were adjusted for multiple comparison using a Bonferroni correction. The sex differences between the four groups were calculated using Chi-square Test. The sex indicator was compared pairwise and adjusted for p-value using the Bonferroni method. Using logistic regression models to estimate the probability of Parkinson's disease in both young and elderly groups and producing ROC curves.

3 Results

3.1 Differences in cortical morphology and neuro metabolite measurements for PD prevalence

This study compared the differences in cortical morphological characteristics between the PD and HC groups in the 34 brain regions of the left hemisphere, and set the significance threshold at P<0.05/34 = 0.00147 to eliminate the errors caused by multiple comparisons. Compared with HC, the LGI values in several brain regions of PD decreased (Fig. 3), including the left precentral gyrus (P<0.001, Cliff's delta = -0.563) and the left postcentral gyrus (P<0.001, Cliff's delta = -0.559). Then Pearson correlation analysis were used to determine the key indicators impacting the prevalence of PD among 36 variables (Fig. 4). When the absolute value of the Pearson correlation coefficient is greater than 0.4, it can be considered to have a correlation (Schober et al., 2018). The intergroup Pearson correlation analysis indicated that the eight indicators are negatively correlated with the disease. Specifically, the correlations were as follows: GABA/Cr: r = -0.679, P<0.001; Cuneus: r = -0.413, P<0.001; Entorhinal: r = -0.423, P<0.001; Postcentral: r = -0.475, P<0.001; Posteriorcingulate: r = -0.413, P<0.001; Precentral: r = -0.496, P<0.001; Superiortemporal: r = -0.435, P<0.001; Transversetemporal: r = -0.432, P<0.001.Fig. 3 These images showed brain regions with reduced cortical folding in PD patients. The size of the effect for the comparison between PD (N=50) and HC (N=52) groups were showed in the figure, Cliff's delta: effect size.

Fig. 4 The radar chart illustrated the intergroup correlation analysis between the PD (N=50) group and the HC (N=52) group.

3.2 Heatmap and dendrogram for cluster analysis

Eight indicators were identified by computing the differences between the PD and HC groups. The heatmap was drawn to demonstrate the correlations among these eight indicators and to conduct cluster analysis. Results showed that these indicators exhibited positive correlations. Fig. 5A presented a heatmap of the complex observed correlation patterns between the GABA/Cr level and LGIs, ranging from near 0 (0.34) to near perfect (0.94). These correlations may stem from both motor and non-motor dysfunction in PD patients. The correlation between brain regions Precentral and Postcentral could reach 0.94. Cluster analysis is a way to reduce a large number of variables into a smaller number of dimensions (factors, clusters) that are comprehensible. Differences among clusters on the dendrogram were quantified by relative distance, an interval scale (Fig. 5B). Descriptors within a cluster were separated by smaller distances. Within a relative distance range of 1–3, all indicators could be categorized into four groups, which are: a) GABA/Cr, b) LGI of Postcentral, Precentral, Cuneus, c) LGI of Entorhinal, Posteriorcingulate, d) LGI of Superiortemporal, Transversetemporal. Then, one indicator could be selected from each category as the reference diagnostic indicator.Fig. 5 Heatmap of Pearson correlation estimates between the eight indicators in HC (N=52) and PD(N=50). B. Within a relative distance range of 1–3, all indicators can be categorized into four groups, which are: a) GABA/Cr, b) LGI of Postcentral, Precentral, Cuneus, c) LGI of Entorhinal, Posterircingulate, d) LGI of Superiortemporal, Transversetemporal.

3.3 Differences of demographic features and imaging biomarkers among the PD and HC subgroups

The demographic, clinical, and structural imaging features of the study participants were shown in Table 1. GABA levels were significantly lower in SMC regions of EOPD and LOPD patients compared with healthy controls (mean ± standard deviation: EOPD=0.081 ± 0.021 vs. Young-HC=0.112 ± 0.021; LOPD=0.054 ± 0.024 vs. Old-HC=0.099 ± 0.021, all P<0.001).Table 1 Demographic, clinical, and structural imaging features of the study participants.

Variables	Young	Old	F	P	
PD (n = 10)	HC (n = 12)	Adjust P	PD (n = 40)	HC (n = 40)	Adjust P	
Age	44.7 ± 5.5	45.7 ± 3.1	0.745	66.3 ± 6.1	65.6. ± 6.6	0.679	54.200	<0.001	
Sex (Male)	7 (70 %)	8 (66.7 %)	1.000	10 (52.5 %)	10 (56.1 %)	0.919	1.49	0.685	
GABA/Cr	0.081 ± 0.022	0.112 ± 0.021	0.003	0.054 ± 0.024	0.099 ± 0.021	<0.001	25.556	<0.001	
Glx/Cr	0.113 ± 0.064	0.114 ± 0.035	0.991	0.107 ± 0.03	0.109 ± 0.012	0.894	0.219	0.883	
WM	0.585 ± 0.053	0.604 ± 0.041	0.864	0.573 ± 0.107	0.564 ± 0.110	0.954	0.458	0.713	
GM	0.325 ± 0.042	0.313 ± 0.026	0.694	0.293 ± 0.056	0.287 ± 0.031	0.851	2.301	0.086	
CSF	0.128 ± 0.065	0.082 ± 0.026	0.452	0.146 ± 0.069	0.171 ± 0.156	0.568	1.676	0.181	
Cuneus	2.894 ± 0.231	3.065 ± 0.116	0.088	2.798 ± 0.183	2.965 ± 0.188	0.003	6.67	<0.001	
Entorhinal	2.429 ± 0.146	2.557 ± 0.092	0.051	2.382 ± 0.145	2.501 ± 0.125	0.004	6.14	<0.001	
Postcentral	3.250 ± 0.170	3.394 ± 0.138	0.073	3.166 ± 0.2	3.358 ± 0.133	<0.001	7.94	<0.001	
Posterior Cingulate	2.093 ± 0.139	2.168 ± 0.121	0.283	2.055 ± 0.104	2.157 ± 0.111	0.004	4.36	<0.001	
Precentral	3.180 ± 0.159	3.313 ± 0.125	0.072	3.113 ± 0.185	3.304 ± 0.13	0.001	8.22	<0.001	
Superiortemporal	3.708 ± 0.314	3.946 ± 0.254	0.107	3.605 ± 0.242	3.842 ± 0.253	0.002	6	<0.001	
Transversetemporal	4.038 ± 0.343	4.487 ± 0.361	0.014	4.098 ± 0.33	4.397 ± 0.325	0.003	6.51	<0.001	
Values are expressed as mean standard deviation. EOPD, early-onset Parkinson's disease; LOPD, late-onset Parkinson's disease; Young-HC, young healthy control; Old-HC, old healthy control; GABA, γ-aminobutyric acid; Cr, creatine; Glx, contributes of glutamate and glutamine; GM, gray matter; WM, white matter; CSF, cerebrospinal fluid; Data represent mean ± SD.

3.4 The diagnostic accuracy of the predictive model in younger and older age groups

The multivariate logistic regression analysis was performed, and the results indicated that two variables were statistically significant. The selected variables are: GABA/Cr and LGI of transversetemporal.

ROC curves (Fig. 6) showed that in the predictive model for the younger group (under 50 years old): AUC=0.967, p = 0.001, and in the older group (over 50 years old): AUC=0.933, p < 0.001. Upon comparison, the combined diagnostic model had the highest AUC values in both subgroups, with the AUC being higher in the younger group. Young-group: Model 1: GABA/Cr, transversetemporal, auc = 0.967, p < 0.001; Model 2: GABA/Cr, auc = 0.872, p < 0.001; Model 3: Transversetemporal, auc = 0.722, p = 0.076. Old-group: Model 1: GABA/Cr, transversetemporal, auc = 0.933, p < 0.001; Model 2: GABA/Cr, auc = 0.928, p < 0.001; Model 3: Transversetemporal, auc = 0.730, p = 0.002.Fig. 6 A. ROC Curve of the Predictive Model for the Younger Group B. ROC Curve of the Predictive Model for the Older Group.

4 Discussion

In this study, we complied a dataset comprising values of neurotransmitters of sensorimotor cortex and cortical gyrification degrees in 34 brain areas for both healthy controls and Parkinson's patients. We then analyzed the characteristics of PD across 36 indicators. The main findings were summarized as follows: 1. We identified 8 indicators significantly associated with Parkinson's disease. These included GABA/Cr，LGI of Precentral, Postcentral, Entorhinal, Superiortemporal, Posterircingulate, Cuneus, and Transversetemporal cortex. 2. Utilizing the inherent relationships among the 8 indicators, we conducted cluster analysis to categorize them into 4 clusters of indicators: a) GABA/Cr, b) LGIs of Postcentral, Precentral, Cuneus, c) LGIs of Entorhinal, Posterircingulate, d) LGIs of Superiortemporal, Transversetemporal cortex. 3. Through multivariate logistic regression analysis, two statistically significant variables were identified: GABA/Cr and the LGI of transversetemporal. In the ROC curves, the combined model of these two indices achieved the highest AUC values in both younger and older groups.

Overall, these findings illuminated on the distinctive characteristics of Parkinson's disease and highlighted the potential utility of these indicators, particularly in differentiating between EOPD and healthy individuals. The reason we selected 36 indicators is that multiple studies have shown that cortical GABA played the beneficial role in Parkinson's disease (Van Nuland et al., 2020), and that GABA depletion may contribute to increased motor symptom expression (Demartini et al., 2019, Gong et al., 2018, Pesch et al., 2019). Simultaneously, Parkinson's disease patients exhibited a progressive reduction in cortical gyrification. (Tang et al., 2021, Sterling et al., 2016, Zhang et al., 2014). Therefore, all important indicators were selected for predicting Parkinson's disease.

Studies (Barcomb and Ford, 2023, Delli Pizzi et al., 2022, Maes et al., 2021) have indicated that GABA/Cr was associated with the occurrence and development of motor and non-motor symptoms in Parkinson's patients, which aligned with the findings of this study. In the prodromal phase of PD, the function of dopamine neurons begins to diminish before significant degeneration or overt motor symptoms are observed. Previous studies have suggested that overall GABA levels are particularly related to bimanually coordinated movements, and lower GABA levels have been obtained in SM region (Maes et al., 2021). Moreover; previous work have established the association between GABA and motor performance commonly employed tasks requiring paced and/or selective motor output, such as response selection tasks, discrimination tasks and coordination tasks (Dharmadhikari et al., 2015). In Parkinson's Disease (PD), there is an increased occurrence of dystonia during the onset stage, accompanied by the emergence of motor symptoms. Therefore, the levels of GABA of SMC decrease (Li, 2022, Kolasinski et al., 2017) indicating that individuals with lower baseline SMC GABA levels were less sensitive to the tactile vibration frequency differences and selective cortical tunning. This is consistent with the symptoms of poor adaptability and bradykinesia commonly observed in young patients.

Our finding of cortical gyrification reductions in precentral and postcentral is partially supported by previous studies (Tang et al., 2021, Sterling et al., 2016). The precentral gyrus, also known as the primary motor cortex, serves as a major source of descending motor commands for voluntary movement. It is essential for various motor functions, such as laryngeal swallowing movement, contralateral mouth/face movement, contralateral limb movement and others. Stimulation of the primary motor cortex was found to significantly reduce akinesia and bradykinesia in PD, highlighting a key role of motor cortex abnormalities in the pathogenesis of akinesia and bradykinesia (Dileone et al., 2022). The postcentral gyrus, also known as the primary somatosensory cortex, plays a critical role in processing proprioceptive and somatosensory input, while its output can guide movement through connections to motor cortices. Our findings regarding LGI partially coincide with those proposed by Xie Tang et al., indicating decreased LGI in the precentral, postcentral, posterior cingulate, precuneus4. These indexes exhibit a significant correlation with PD. In PD, cognitive decline is generally limited, with bradykinesia and movement impairments being the primary sumptoms (Souza et al., 2024). Sterling et al. (Sterling et al., 2016) investigated the cortical gyrification index in PD at different stages of the disease and found that the overall cortical gyrification,as well as gyrification in bilateral inferior parietal, precentral, postcentral, superiorfrontal, and lateral occipital regions, decreased over the course of the disease.These results partially aligns with our findings.

Decreased LGI has been observed in the Entorhinal of PD compared with healthy controls. Allocentric movement, crucial for navigating between landmarks, depends on grid cells (GCs) located in the entorhinal cortex (Reinshagen, 2024). GCs integrate spatial information in the form of copies of multimodal sensory input from external landmarks through self-motion data. This data originate from “vestibular, proprioceptive, visual and motor (motor-efference copy) systems” (Nadasdy et al., 2017). Therefore the atrophy of entorhinal may contribute to bradykinesia, impaired movement scaling, and the strong reliance on visual feedback” (Contreras-Vidal and Gold, 2004) This is consistent with Zhang's findings (Zhang et al., 2014). Moreover, the tissue structure of the olfactory cortex is interconnected with various regions of the brain. Thus, the LGI of may impact in EOPD dysfunctional symptoms.

Satyajit Mohite (Mohite et al., 2020) proposed that alterations in immune factors in the temporal gyri and reductions in temporal lobe volume are associated with the progression of psychiatric disorders. Studies have demonstrated that depression is one the most common non-motor symptoms in PD. A study from Iran corroborated these findings, showing significantly worse depression and the “emotional” domain score of QoL in the PD cohort (Yoon et al., 2024). Therefore, we believe that the decrease in LGI of the superiortemporal cuneus is related to its psychiatric symptoms. Additionally, the reasons for the decrease in LGI of the gyrus may be attributed to its involvement in the “insula” network, where facial communication and internal sensations are primary functions. Research on the structural connections of this area support these functions (Fathy et al., 2019); PD can present with a mask-like face, so the LGI value in this area may decrease. Impaired or disrupted connectivity of the precuneus has been identified as a key characteristic in the development of many symptoms. Disruption of connectivity in the precuneus is associated with symptoms of depression, bipolar disorder, and psychiatric disorders (Liu et al., 2024). Therefore, the decrease in LGI of the superiortemporal and cuneus may indicate a correlation with psychiatric disorders in PD.

The posteriorcingulate cortex serves as the central node of default network activity. These results are supported by previous reports of aberrant DMN regional homogeneity and connectivity in PD and that some of these DMN alterations were correlated with cognitive scores of these patients (Delli Pizzi et al., 2022). Taken together, our findings of widespread LGI decreases may indicate the anatomical substrates underlying the motor and nonmotor impairments in patients with PD.

Postcentral, Precentral, and Cuneus regions may exhibit correlations in sensory information processing, motor control, and visual information processing. Hence, they can be grouped together as one single category of indicators in cluster analysis (Cai et al., 2024, Krigolson et al., 2015). While the functions of the entorhinal and posteriorcingulate areas differ, both are related to memory and cognitive functions. Studies suggest that the entorhinal area may be linked to connections between hippocampus and the posterior cingulate area, which may involve interactions related to emotional memory and emotion regulation (Georgiopoulos et al., 2024); thus serving as a type of benchmark.

From the indicators correlated with PD, further multivariate analysis revealed two statistically significant indicators: GABA/Cr and LGI of the transversetemporal. The combined model exhibited the highest AUC values in both younger and older populations. The reduced GABA levels in our study could indicate that the pathogenesis of EOPD may be related to GABAergic neuronal loss or dysfunction (Stagg, 2014, Pesch et al., 2019), which in line with the GABA-collapse hypothesis. Transversetemporal region not only participates in the perception and understanding of auditory information but also plays a role in emotional processing and response, making them another type of benchmark (Ethofer et al., 2006). EOPD is a progressive neurodegenerative disorder of the nervous system, with clinical symptoms reflecting advanced GABA pathology. Meanwhile, differences in LGI could arise because due to early abnormalities affecting cortical gyrification that emerge during the neurodegenerative disorders in PD, the neurodegeneration process begins and lead to mild cortical gyrification changes (Sterling et al., 2016).

There are several limitations in this study. The main limitation is our relatively small sample size of patients. Due to our multicenter study's effort to maintain consistent inclusion criteria, many ineligible patients were rigorously excluded. However, our research is ongoing, and the further study is expected to significantly increase the number of patients and analyze the correlation of non-motor symptoms or other functional impairments in patients. The value of our article lies in 1) conducting feature selection to aid in identifying relevant indicators for further development of diagnostic methods and criteria for PD; 2) helping to elucidate the pathogenic mechanisms of PD; 3) conducting detailed grouping and separately discussing several indicators that are meaningful for the diagnosis of the EOPD subgroup.

In conclusion, 1) GABAergic dysfunction may play an important role in the pathogenesis of PD. 2) we identified significant reductions in cortical gyrification and GABA concentrations of sensorimotor cortex in patients with PD. These morphological changes and may serve as potential markers for the preclinical diagnosis and progression of PD, especially for EOPD.

CRediT authorship contribution statement

Yuan Tian: Writing – original draft, Software. Sijia Geng: Review and comment. Tianyi Liu: Formal analysis. Qi Wang: Visualization. Jianxiu Lian: Validation. Liangjie Lin: Data curation. Jiayu Li: Data curation. Tao Gong: Resources. Junhong Duan: Resources. Dan Wang: Resources. Pengfei Liu: Project administration.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

Acknowledgement

This work was supported by the National Nature Science Foundation of China (82272053) and Health Commision of Heilongjiang (20230909010239).
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