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Topologically Convergent and Divergent Morphological Grey Matter Networks in Parkinson’s Disease with and without Mild Cognitive Impairment

Xueling Suo^1,2, Du Lei^1,2,3, Wenbin Li^1,2,3, Jing Yang^1,2, Nannan Li⁴, Lan Cheng⁴, Rong Peng⁴, and Qiyong Gong^1,2
¹Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China, ²Psychoradiology Research Unit of Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China, ³Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, United States, ⁴Department of Neurology, West China Hospital of Sichuan University, Chengdu, China

Synopsis

To use graph theory approaches and high resolution T1-weighted structural MRI to explore the brain grey matter (GM) morphological network in patients with Parkinson's disease (PD) with and without mild cognitive impairment (MCI). The individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional GM volume of 116 brain regions. The lower path length were found in both patients relative to healthy controls. Altered morphological connection primarily in default mode network were common deficits, while different connectivity characteristic in widespread regions involving temporal and subcortical regions, and cerebellum were observed between the patient groups.

Introduction

Mild cognitive impairment in PD (PD-M) is considered a transitional state between normal cognition (PD-N) and dementia with a prevalence of 15-40% at diagnosis, and is a risk factor for the subsequent development of dementia.¹ Cognitive decline poses a significant burden on the patient as well as the caregiver and a better understanding of the underlying pathological processes will aid in directing disease-specific treatment. Recent advances in psychoradiology,² in conjunction with the graph theory analyses, allow the noninvasive characterization of brain functional and white matter network topologic organization in PD-M.^3,4 However, little is known about the individual morphological network in PD-M. Thus, the present study aimed to investigate graph properties of individual morphological brain networks in a sample of early-stage non-demented PD patients.

Methods

MRI scanning were carried out in Trio Tim (3T) MR imaging system (Siemens; Erlangen). High resolution T1-weighted structural MRI brain images were obtained from 39 early stage PD patients either with MCI (PD-M, N = 22) or with normal cognition (PD-N, N =17), and 28 demographically-matched healthy controls (HC). Briefly, individual structural images were first segmented into GM, white matter and cerebrospinal fluid. The GM maps were then divided into 116 numbers of brain regions including 90 cortical and subcortical areas and 16 cerebellar areas according to automated anatomical labeling atlas.⁵ Individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional GM volume in terms of the Kullback–Leibler divergence measure.⁶ Graph theory-based global (clustering coefficient C_p, characteristic path length L_p, normalized C_p γ, normalized L_p λ, local efficiency E_loc, global efficiency E_glob, and small-worldness σ) and nodal (nodal efficiency) network measures ⁷ were calculated. Comparison among the PD-M, PD-N and HC was performed by using analysis of variance followed by pairwise post hoc Fisher’s least significance difference (LSD) tests. To further localize specific pairs of brain regions in which the morphological connection was altered in patients, we used a network-based statistic (NBS) approach. Finally, partial correlations were computed to examine relationships between the network values and cognitive scores (attention and working memory, executive function, language, memory, and visuospatial function) ⁸with age, gender, years of education and Unified Parkinson's Disease Rating Scale part III as covariates.

Results

Significantly poorer performance on executive function, memory, and language abilities were observed in patients with PD-M compared to PD-N and HC. Compared with HC, PD-N patients showed a significant decrease in λ, while PD-M showed lower L_p, and higher γ and E_loc(Figure 1). Higher nodal efficiency of right precentral gyrus and left cerebellum Crus1 with lower nodal efficiency of left lingual gyrus were common deficits in both patient groups, and lower nodal efficiency of right paracentral lobule (PCL) and superior temporal gyrus (STG) observed in PD-M than PD-N patients (Figure 2). Decreased connection were similarly found in the default mode network regions of the patient groups relative to HC, while different connection mainly involved in several temporal regions and cerebellum were observed between the patient groups (Figure 3). The nodal efficiency of right cerebellum was negatively correlated with Brief Visuospatial Memory Test–Revised (BVMT-R) score (r = -0.484, P= 0.030) in PD-M group; the nodal efficiency of right STG were positively correlated with Wechsler Adult Intelligence Scale (WAIS-IV) (r = 0.604, P = 0.010) and Boston Naming Test (BNT) (r = 0.633, P = 0.006) scores in PD-N group (Figure 4).

Discussion

This study applied graph analysis combined with high resolution T1-weighted structural MRI to assess large-scale brain morphological networks in early stage non-demented PD patients. PD-M showed higher network segregation reflected by higher γ and E_loc, consistent with the previous findings of functional networks,³ providing morphological evidence that higher segregation may be more specific of cognitive impairment in PD. However, the lower L_p in the current study was in the opposite direction from that of white matter networks.⁴ A possible explanation may be that decreased integration of white matter network might lead to increased GM similarity as a compensation. Patients with PD-M showed lower nodal efficiency in the PCL and STG relative to PD-N, which might be associated with the frequent impairment of frontal executive and temporal language functions in patients with PD-M. Default mode network connectivity disruption might be a common feature in PD, and exhibits a greater extent with increasing levels of cognitive impairment. Moreover, the involvement of the cerebellum is intriguing as recent evidence indicates that the region acts as a critical hub for the control of a wide range of cognitive processes.⁹ The present study showed a significantly negative correlation of cerebellum with the scores of BVMT-R, indicating that more memory cognitive domain deficit induced more disturbance of the cerebellum. Our findings provided further evidence that disrupted topological organization of large scale complex brain morphological networks contribute to the cognitive impairment in PD patients.

Conclusion

Together, this study provides structural evidence for the association of a minority of convergent and a great majority of divergent patterns of morphological brain networks topology between PD-M and PD-N patients, which provides crucial insights into pathophysiological mechanisms of cognition decline of PD.

Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Nos. 81621003, 81761128023, 81220108013, 81227002, 81030027), the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT, grant IRT16R52) of China, the Changjiang Scholar Professorship Award (Award No. T2014190) of China, and the CMB Distinguished a Professorship Award (Award No. F510000/ G16916411) administered by the Institute of International Education.

References

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3. Baggio HC, Sala-Llonch R, Segura B, et al. Functional brain networks and cognitive deficits in Parkinson's disease. Hum Brain Mapp. 2014;35(9):4620-34.

4. Galantucci S, Agosta F, Stefanova E, et al. Structural Brain Connectome and Cognitive Impairment in Parkinson Disease. Radiology. 2017;283(2):515-25.

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Figures

Figure 1. Different global network properties among the PD-M, PD-N, and HC groups. The bar graph shows the area under the curve value of the global network parameters among the 3 groups. Error bars denote standard deviation. Black asterisks indicate significant differences (P < .05) in the post hoc comparisons. Abbreviations: PD, Parkinson’s disease; PD-M, PD with mild cognitive impairment; PD-N, PD with normal cognition; HC, healthy control; γ, normalized clustering coefficient; L_p, characteristic path length; λ, normalized characteristic path length; E_loc, local efficiency.

Figure 2. Brain regions with significant group effects in the nodal efficiency in morphological brain networks among the three groups. The bar graph shows the post hoc pairwise comparisons with significant differences in nodal efficiency. Abbreviations: PD, Parkinson’s disease; PD-M, PD with mild cognitive impairment; PD-N, PD with normal cognition; HC, healthy control; CRBL, cerebellum; LING, lingual gyrus; PCL, paracentral lobule; PreCG, precentral gyrus; STG, superior temporal gyrus; L, left; R, right.

Figure 3. The connected networks showing altered morphological connections between PD-M, PD-N, and HC group. Every node denotes a brain region and every line denotes a connection, which were mapped onto the cortical surfaces using BrainNet viewer software (www.nitrc.org/projects/bnv/). Blue color represent decreased morphological connections.

Figure 4. The relationships between network measures and clinical variables in PD-M and PD-N patients. Abbreviations: PD, Parkinson’s disease; PD-M, PD with mild cognitive impairment; PD-N, PD with normal cognition; BVMT-R, Brief Visuospatial Memory Test–Revised; BNT, Boston Naming Test; CRBL, cerebellum; STG, superior temporal gyrus; WAIS, Wechsler Adult Intelligence Scale; R, right.

Proc. Intl. Soc. Mag. Reson. Med. 28 (2020)

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