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Published on: August 11, 2016
Structural network performance for early diagnosis of spastic cerebral palsy in periventricular white matter injury
Haoxiang Jiang1,2,3, Heng Liu1,2, Tingting Huang1
1Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
Insights
Periventricular white matter injury (PWMI) can cause spastic cerebral palsy (SCP). Diffusion tensor imaging (DTI) brain network analysis, particularly node efficiency, accurately identifies SCP in infants with PWMI.
Area of Science:
- Neuroimaging
- Developmental Neuroscience
- Neurology
Background:
- Periventricular white matter injury (PWMI) is a primary cause of spastic cerebral palsy (SCP) in infants.
- Diffusion tensor imaging (DTI) offers high sensitivity but limited specificity in predicting SCP due to varied brain injuries.
- Accurate prediction of SCP in infants with PWMI is crucial for timely intervention.
Purpose of the Study:
- To compare DTI-based brain network properties in infants with PWMI and SCP, PWMI without CP, and controls.
- To evaluate the diagnostic performance of brain network properties for identifying SCP in infants with PWMI.
- To assess the correlation between brain network properties and motor function severity.
Main Methods:
- Enrolled 72 infants (6-18 months corrected age) into three groups: PWMI with SCP (n=20), non-CP PWMI (n=19), and controls (n=33).
- Utilized diffusion tensor imaging (DTI) to analyze brain network properties, including global and local efficiency and shortest path length.
- Employed logistic regression to determine the diagnostic accuracy of specific network parameters for differentiating SCP in PWMI.
Main Results:
- Infants with PWMI and SCP exhibited abnormal global network parameters (reduced efficiency, increased path length) and local parameters (reduced node efficiency).
- Combined node efficiency of the bilateral precentral gyrus and right middle frontal gyrus demonstrated high diagnostic performance (90% sensitivity, 95% specificity) for differentiating PWMI with SCP from non-CP PWMI.
- Node efficiency significantly correlated with Gross Motor Function Classification System scores, indicating a link to motor impairment severity.
Conclusions:
- DTI-based brain network analysis provides a highly effective tool for diagnosing SCP in infants with PWMI.
- Specific brain network properties, particularly combined node efficiency in key motor regions, significantly enhance diagnostic accuracy for SCP.
- These findings support the use of advanced neuroimaging network analysis for early and precise identification of cerebral palsy in at-risk infants.
Abstract:
Periventricular white matter injury (PWMI) is a common cause of spastic cerebral palsy (SCP). Diffusion tensor imaging (DTI) shows high sensitivity but moderate specificity for predicting SCP. The limited specificity may be due to the diverse and extensive brain injuries seen in infants with PWMI. We enrolled 72 infants with corrected age from 6 to 18 months in 3 groups: PWMI with SCP (n = 20), non-CP PWMI (n = 19), and control (n = 33) groups. We compared DTI-based brain network properties among the three groups and evaluated the diagnostic performance of brain network properties for SCP in PWMI infants. Our results show abnormal global parameters (reduced global and local efficiency, and increased shortest path length), and local parameters (reduced node efficiency) in the PWMI with SCP group. On logistic regression, the combined node efficiency of the bilateral precentral gyrus and right middle frontal gyrus had a high sensitivity (90%) and specificity (95%) for differentiating PWMI with SCP from non-CP PWMI, and significantly correlated with the Gross Motor Function Classification System scores. This study confirms that DTI-based brain network has great diagnostic performance for SCP in PWMI infants, and the combined node efficiency improves the diagnostic accuracy.
