Reduced efficiency of functional brain network underlying intellectual decline in patients with low-grade glioma
Introduction
Although the median survival time for LGG patients may extend to more than one decade, many survivors of LGG suffer from devastating mental dysfunctions such as intellectual decline that negatively affects their health-related quality of life (QOL) [18]. The intellectual ability of tumor patients was caused by many factors, including tumor itself, tumor-related epilepsy, antiepileptic drugs and oncological treatments. Traditionally, neuroimaging investigations of the physiological basis of intelligence have focused on activated brain regions based on cognitive tasks [12]. Neural efficiency hypothesis of intelligence demonstrates that poorly intelligent subjects tend to activate more cortexes to solve cognitive tasks than brighter subjects [16]. In this study, we adopt the method of network analysis to study fMRI data collected during resting-state without a task, which is different from the conventional research.
The brain is organized as a complex network composed of spatially distributed but functionally linked brain regions [17]. The concept of functional connectivity refers to statistical correlations between physiological time series of brain activity recorded in different brain regions [25]. Many studies have demonstrated that the connection patterns of the whole-brain functional networks exhibit effective small-world properties [21], allowing both local segregation and global integration of information [26]. Such networks of human brains are thought to provide the physiological basis for the high efficiency of information processing and mental performance [8]. The functional brain imaging research shows that brain activities are still present even during task-free conditions [3]. Previous studies have confirmed the existence of default mode network, which consists of specific brain regions and shows high level of activity during rest [20]. Recently, fMRI, recording the brain spontaneous activity as low-frequency fluctuations in blood oxygen level dependent (BOLD) signals, has been widely used to investigate the functional networks in psychiatric and neurological disorders such as schizophrenia [15], Alzheimer's disease [19], epilepsy [32]. Magnetoencephalography (MEG) studies showed that brain tumor patients displayed disturbed functional network architecture compared with healthy controls [4], [6]. Although MEG directly reflects the neural activities by recording related magnetic fields, it may not provide accurate information about specific subcortical regions.
So far, there are only few studies investigating the relationship between functional brain network and intelligence. One study using resting-state fMRI reported negative association between the normalized characteristic path length and IQ in healthy subjects [27]. In another study of healthy controls, clustering coefficient and path length of electroencephalography (EEG) network are associated with IQ [13].
This study investigates the correlation between resting-state fMRI network properties of the brain and intellectual performance in LGG patients. We hypothesize that reduced small-world network efficiency is related to intellectual decline in LGG patients. Furthermore, network hubs play a crucial role in the whole-network efficiency and brain functioning [23]. We also hypothesize that the network hubs in LGG group are altered compared to that of control group.
Section snippets
Subject
This study included 21 LGG patients (age 43.4 ± 12.0; 11 female; education 9.8 ± 3.0 y) and 20 healthy controls (age 44.5 ± 11.2; 10 female; education 10.1 ± 2.6 y), matched in age, gender and educational level. All subjects were right-handed. The patients were recruited from the department of neurosurgery at Nanjing Brain Hospital. Inclusion criteria were: (1) LGG was confirmed by pathological diagnosis; (2) The patients had the basic ability of motor and verbal communication to complete the
Intelligence test
The 21 patients and 20 controls were administered the Chinese revised Wechsler adult intelligence scale (WAIS-RC) test to measure the individual intelligence after fMRI scanning. The WAIS-RC test includes eleven subtests as follows information, similarities, arithmetic, vocabulary, comprehension, digit span, picture completion, block design, picture arrangement, object assembly and coding. Intelligence test scores comprise full-scale intelligence quotient (FSIQ), verbal IQ (VIQ) and performance
Data acquisition
MR imaging was performed with a 3.0 T Siemens Verio scanner. During resting-state fMRI scanning, the participants were instructed to keep their bodies still with eyes closed. For each subject, functional image was acquired axially by using an echo-planar imaging (EPI) sequence (TR = 2000 ms, TE = 30 ms, FA = 90°, matrix = 64 × 64, FOV = 220 × 220 cm). fMRI scanning lasted 280 s and 140 volumes of EPI images were obtained. Additionally, structural image was collected using T1 FLAIR sequence (TR = 1900 ms, TE = 2.48 ms,
Data preprocessing
Data preprocessing was carried out using statistical parametric mapping (SPM8, http://www.fil.ion.ucl.ac.uk/spm). After discarding the first 10 volumes, the remaining 130 volumes were first corrected for slice timing and then realigned to correct for head motion. The functional scans were further spatially normalized to a standard template (MNI) and resampled to 3 mm × 3 mm × 3 mm cubic voxels. Finally, the resting-state fMRI data were band-pass filtered (0.01–0.08 Hz).
Identification of hubs
To identify the hub regions of LGG and control groups, we calculated the centrality of each node by averaging across all subjects in each group. A node with high centrality was considered a hub in the network and was crucial to efficient communication of network [29]. The centrality of a node measures the number of the shortest paths between any other node pairs in the network that pass through the node.
Intelligence test results
The results were expressed as mean ± standard deviation (SD). FSIQ, PIQ and VIQ scores of LGG group were 85.62 ± 13.87, 88.90 ± 16.53, and 83.95 ± 12.01; IQ scores of control groups were 101.15 ± 12.09, 101.60 ± 11.22 and 100.40 ± 12.46 respectively. LGG group had significantly lower FSIQ, PIQ and VIQ than control group (P < 0.05).
Different network parameters between LGG and control groups
Since all thresholds were similar in their trends with respect to the differences in their network properties between the two groups, we have chosen to report only one typical
Discussion
In the present study, we used resting-state fMRI data of LGG patients and healthy controls to construct functional brain network and compared the network topological properties between the two groups, respectively. The functional brain networks of healthy controls exhibited efficient small-world organization patterns, which is consistent with many previous studies of functional networks in healthy subjects [21], [22]. However, compared with those of controls, LGG group showed disturbed network
Acknowledgement
This study was supported by the special fund for Clinical Science and Technology of Jiangsu Province (No. BL2012041).
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