Brain Structure and Function: There are abnormalities in the gray matter network of early Parkinson's patients

Parkinson's disease (PD) is a progressive neurological disease with a variety of motor and non-motor characteristics
.

The main pathological process involves the destruction of the dopamine system in the substantia nigra striatum, but neuroimaging has shown abnormalities in the function and structure of multiple brain regions

The Gong Qiyong team of West China Hospital studied the topology of gray matter (GM) networks, their relationship with disease severity, and their potential imaging diagnostic value in Parkinson's disease
.

The Gong Qiyong team of West China Hospital studied the topology of gray matter (GM) networks, their relationship with disease severity, and their potential imaging diagnostic value in Parkinson's disease
.

diagnosis

This study uses similarity based on Kullback-Leibler divergence (KLS) to study the topological organization of single-agent GM networks in early PD
.

54 early PD patients and 54 healthy controls (HC) were recruited

The difference in the overall topological properties of the gray matter network between PD and HC
.

There are significant differences in Cp and Eloc between the two groups

The difference in the overall topological properties of the gray matter network between PD and HC

The central area of ​​each group and the node centrality of PD and HC are different
.

In each group, the brain area with the highest node centrality (top 10%) is defined as the center and displayed in purple

The central area of ​​each group and the node centrality of PD and HC are different

PD-related changes in the network connection
.

Each node represents a brain area, and each line represents a connection

PD-related changes in the network connection

The 20 brain regions that contribute the most to the single-person classification of PD and HC

The 20 brain regions that contribute the most to the single-person classification of PD and HC

Compared with HC, PD's GM network has higher clustering coefficient (P = 0.
014) and local efficiency (P = 0.
014)
.

The center of local Parkinson's disease nodules is lower in the posterior central gyrus and temporo-occipital area, and higher in the right superior frontal gyrus and left putamen

The study showed that compared with HC, the topological properties of the single-subject whole brain GM network of early PD patients have undergone significant changes
.
They show higher network separation, which is reflected in higher Cp and Eloc, and changes the nodal centrality of the putamen and temporo-occipital areas
.
The analysis of a single patient network showed that the centrality of the node in the right central posterior gyrus was negatively correlated with UPDRS-III score, Hoehn and Yahr staging
.
PD showed changes in sub-networks, the morphological connection between DMN and sensorimotor network decreased, and the connection between frontal top and prominent network increased
.
Finally, the GM network matrix and graph-based metrics show the potential to allow individual subject classification of PD patients and HC patients, with significant accuracy rates of 73.
1% and 72.
7%, respectively; while graph-based metrics allow tremor to dominate and exercise stiffness The subtype of individual subject classification has a significant accuracy rate of 67.
0%
.

The characteristic of PD is the suboptimal topology of the GM network, which is reflected in the higher network separation in the early stage of the disease, suggesting that the GM network may contribute to the imaging evidence for the classification of PD patients .
These results demonstrate the potential of graph theory's brain network measurement as an imaging biomarker for understanding and characterizing Parkinson's disease .
Specifically, this research adds the field of psychoradiology, a developing radiology sub-specialty that has important clinical significance in guiding the diagnosis and treatment decisions of patients with neuropsychiatric diseases
.

The characteristic of PD is the sub-optimal topological structure of the GM network, which is reflected in the higher network separation in the early stage of the disease, suggesting that the GM network may contribute to the imaging evidence for the classification of PD patients .
These results demonstrate the potential of graph theory's brain network measurement as an imaging biomarker for understanding and characterizing Parkinson's disease .
The characteristic of PD is the suboptimal topology of the GM network, which is reflected in the higher network separation in the early stage of the disease, suggesting that the GM network may contribute to the imaging evidence for the classification of PD patients
.
These results demonstrate the potential of graph theory's brain network measurement as an imaging biomarker for understanding and characterizing Parkinson's disease
.

Original source

Suo, X.
, Lei, D.
, Li, N.
  et al.
 Disrupted morphological grey matter networks in early-stage Parkinson's disease.
  Brain Struct Funct  226,  1389–1403 (2021).
https://doi.
org/10.
1007 /s00429-020-02200-9

Suo, X.
, Lei, D.
, Li, N.
  et al.
 Disrupted morphological grey matter networks in early-stage Parkinson's disease.
  Brain Struct Funct  226,  1389–1403 (2021).
https://doi.
org/10.
1007 / s00429-020-02200-9 et Al.
Brain Funct.
StructEntryTable 226,  https://doi.
org/10.
1007/s00429-020-02200-9 in this message