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Updated: Sep 22, 2025
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
Published on: August 5, 2014
Abnormal default mode network could be a potential prognostic marker in patients with disorders of consciousness
Jun Zhang1, Enpeng Zhang2, Cong Yuan3
1Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China; Neurosurgical Institute of Fudan University, Shanghai 200040, China; Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China; Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai 200040, China; Department of Neurosurgery, Clinical Medical College,Yangzhou University, Yangzhou 225000, China.
Objectives:
The study aimed to investigate disorders of consciousness (DOC) mechanisms of patients with severe traumatic brain injury (sTBI) related to default mode network (DMN) and to introduce a machine learning model that predicts the prognosis of these patients for 6 months.
Methods:
The sTBI patients suffering from DOC and healthy controls underwent functional magnetic resonance imaging. We defined patients with Extended Glasgow Outcome Score ≥ 5 as good outcome group, otherwise they were poor outcome group. The differences of DMN between sTBI and healthy controls and between good and poor outcome groups were compared. Based on the brain regions with altered functional connectivity between good and poor outcome groups, they were divided into 8 regions of interests according to side. The Z values of the regions of interests were extracted by Rest 1.8. Based on Z values, the Subspace K-Nearest Neighbor (Subspace KNN) was conducted to classify prognosis of sTBI patients suffering from DOC.
Results:
A total of 84 DMNs derived from patients and 45 DMNs from healthy controls were finally analyzed. The connectivity of the DMN was significantly decreased in sTBI patients suffering from DOC (Alphasim corrected, P < 0.05). In addition, compared with the poor outcome group (DMN samples = 60), the brain regions of DMN with decreased functional connectivity in the good outcome group (DMN samples = 24) the following bilateral areas: brodman Area 11, anterior cingulate and paracingulate gyri, brodman Area 25, olfactory cortex (Alphasim corrected, P < 0.05). The ability of Subspace KNN machine learning to distinguish the prognosis of patients (area under curve) was 0.97.
Conclusions:
The interruption of DMN may be one of the reasons for DOC in patients with sTBI. Furthermore, based on early DMN (1-4 weeks), Subspace KNN machine learning has the potential value to distinguish the prognosis (6 months after brain trauma) of sTBI patients suffering from DOC.
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