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Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network

Resting-state functional connectivity predicts individual la...

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第二十二届全国心理学学术会议

作者： Binke Yuan Nan Zhang Jing Yan Junfeng Lu Jinsong Wu Center for Language and Brain Shenzhen Institute of Neuroscience Department of Neurosurgery The First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of China glioma Surgery Division Department of NeurosurgeryHuashan HospitalFudan University Department of MRI The First Affiliated Hospital of Zhengzhou University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

Aim: Language deficits following brain tumors should consider the dynamic interactions between different tumor growth kinetics and functional network reorganization. However, no study has attempted to investigate the language network basis at the whole brain level to determine the language network reorganization differences between low-grade glioma(LGG) and high-grade glioma(HGG) patients. Methods: Inclusion Criteria:(1) Pathologically confirmed glioma in left cerebral hemisphere and overlapped or was within the language network areas proposed by Fedorenko et al.(2010);(2) patients’ age was 18–75 years old;(3) Right-handedness;(4) No symptoms of motor impairment;(5) No history of brain surgery;(6) No midline shift in structural images;(7) No history of other major neurological or psychiatric disorders. In total, 126 patients,including 77 patients with LGG and 49 patients with HGG met all inclusion criteria and completed the entire preoperative clinical, language, and imaging data acquisition. All processes strictly followed the requirements of the Declaration of Helsinki. This study was approved and supervised by the Ethics Committee of Huashan Hospital and the Ethics Committee of the First Affiliated Hospital of Zhengzhou University, respectively. Written informed consents were obtained from the human subjects and their legal guardians. Language function: ABC(Aphasia Battery for Chinese speakers) was used and Aphasia Quotient(AQ) score(range, 0–100) was calculated for each patient. Rs-f MRI data: TR = 2,000 ms;TE = 35 ms;FA =90°;240 volumes;eyes closed. T1 and T2 images were also acquired for tumor mapping and registration. Rs-f MRI data preprocessing: slice timing, motion correction, normalization by DARTEL(MNI), smoothness(6 mm), detrend, nuisance regression, filter(0.01-0.1 Hz) and scrubbing(FD > 0.05). No patients exhibited a head motion > 3 mm maximum translation or 3° rotation. Nuisance signals included 36 head motion parameters, global signals, cerebrospinal fluid signals and white matter signals. Network

关键词： low-grade glioma high-grade glioma resting-state fMRI language machine learning

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