Changes in Functional Brain Activity in School-Age Children 10 Years Later: A Resting State Functional Magnetic Resonance Imaging Study
Article information
Abstract
Objective
Children’s cognitive function is undergoing great dynamic changes with age. This study aims to explore the characteristics of changes in children’s cognitive function before and after 10 years, and discover brain areas where brain function has undergone significant changes.
Methods
In 2008, functional magnetic resonance imaging (fMRI) data were collected from 30 students aged 7–12 from ordinary primary schools in Changzhou. In 2017, fMRI data of 30 primary school students matched for age and sex were collected again on the same MRI machine using the same parameters. Amplitude of low frequency fluctuation (ALFF) and degree centrality (DC) values were calculated respectively for the two sets of data and analyzed by paired t-test.
Results
The brain areas with ALFF values higher than those of 10 years after 10 years were left posterior cerebellar lobe, right posterior cerebellar lobe, and left middle occipital gyrus; the brain areas with ALFF values lower than those of 10 years after 10 years were left hippocampus, left inferior frontal gyrus, right inferior frontal gyrus, and left medial frontal gyrus. The left superior occipital gyrus was found in the brain area with a higher DC value after 10 years and the left inferior parietal lobe was found in the brain area with a lower DC value after 10 years.
Conclusion
After 10 years, children’s overall attention, memory, and cognition related brain areas are developing. Some brain areas show improvement in brain function, and some brain areas play a role more efficiently. This study provides a basis for predicting the development of brain function in school-age children.
INTRODUCTION
Cognitive development, as a central topic in child development psychology, has attracted more and more attention in recent years. The development of the human brain is a highly complex and carefully planned process. Starting from the embryonic stage, neurons are born, migrate to their final locations, form a network, and are then fine-tuned through pruning and myelination. During regional maturation and regional regulation, sensory, language, and higher-order cognitive functions emerge, setting a cognitive, behavioral, and emotional framework for the rest of your life [1,2]. With the increase of age, the dependence and activation of cognitive activities on certain brain areas increase, while the dependence and activation of cognitive activities on other brain areas decrease, that is, the phenomenon of specialization of cognitive abilities corresponding to brain areas occurs. Much neural maturation and cognitive development occur during childhood and adolescence. The development of higher neural activity is mainly concentrated in the second decade of individual development: adolescence [3,4]. Adolescent years are a period of rapid behav-ioral, cognitive and brain development. The improvements in cognitive abilities such as attention, impulse control, planning, and decision-making generally shown by teenagers are all based on the maturity of the nervous system.
There are many technologies currently used to study cognitive changes, including positron emission imaging, single photon emission tomography, repetitive transcranial magnetic stimulation, transcranial direct current stimulation, low intensity focused ultrasound, electroencephalogram, functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy [5]. fMRI has the advantages of non-invasive and high spatial resolution, and has been widely used in cognitive research on many clinical diseases in recent years [6,7]. This study used amplitude of low frequency fluctuation (ALFF) and degree centrality (DC) for analysis. ALFF can detect the local intensity of blood oxygen level-dependent signals generated by spontaneous brain activities and is an objective indicator reflecting the activity level of local areas of the brain.8 DC is an improved functional connection algorithm based on graph theory. It can identify important nodes in the brain functional network [9]. DC makes the brain network connection more complete and faster, makes the utilization of information resources more effective, and can better reflect brain function. The two methods can comprehensively characterize cognitive-related brain function development.
A study has shown that the brain development of children and adolescents shows the following trends: total brain volume has basically no significant changes with age; cortical gray matter volume shows an inverted “U” trend with age, which is specifically manifested in increasing with age before puberty, and decreasing with age after puberty; while the total white matter volume increases linearly with age, and the absolute volume ratio of gray matter/white matter decreases linearly with age [10]. With age, school-age children’s functions such as executive control, attention activation, three-dimensional spatial cognition, and cognitive processing are gradually improved.
At present, there are no studies on the changes in fMRI of school-age children over a period of 10 years or more. This study followed up normal school-age children, collected fMRI data before and after 10 years, and used ALFF and DC methods to analyze and compare changes in cognitive function, explore the significance of changes in ALFF and DC values in various brain regions of normal school-age children, and analyze the role of relevant brain regions in the mechanism of cognitive development.
METHODS
Study subjects
Between April and August 2008, 30 students from ordinary primary schools in Changzhou City were enrolled, aged between 7 and 12 years (9.5±1.6 years), including 16 males and 14 females. Data from 30 ordinary primary school students were collected again between April and August 2017. The age range was still 7–12 years old (9.5±1.7 years old), including 15 males and 15 females. There was no statistically significant difference in age and sex. The inclusion criteria were: the IQ measured by right-handed and China Children’s Wechsler Intelligence Scale was ≥80. After diagnosis by specialists, a history of mental and neurological diseases such as attention deficit hyperactivity disorder and autism or brain damage was ruled out. All subjects obtained parental consent and signed informed consent forms to voluntarily participate in the experiment. This study was approved by the Ethics Committee of Changzhou Children’s Hospital Affiliated to Nantong University (2006-02).
fMRI method
Imaging data were collected on Siemens Magnetom Avanto 1.5 T machine. Subjects lay flat on the examination table, keep their eyes closed and awake, breathe calmly, and try not to do movements and intentional thinking activities. MRI scan: First, 20-slice transverse T1WI anatomical images were obtained using spin echo pulse sequence. The parameters were: repetition time (TR)=414 ms, echo time (TE)=11 ms, flip angle=90º, matrix=202×256, field of view (FOV)=24 cm×24 cm, slice thickness=5.0 mm, and spacing=1.5 mm. Blood Oxygenation Level Dependent (BOLD) signal acquisition: a single-shot echo planar imaging gradient echo sequence was used to acquire an 18-slice T2*-weighted BOLD sequence, with parameters as follows: TR=2,000 ms, TE=40 ms, flip angle=90º, FOV=24 cm×24 cm, matrix=64×64, slice thickness=6.0 mm, spacing=1.2 mm, and scan time of 360 seconds.
Data preprocessing
Use DPABI (Data Processing & Analysis for Brain Imaging) software (Institute of Psychology) to perform batch processing and related post-analysis on fMRI data [11,12], including converting digital imaging and communications in medicine data to neuroimaging informatics technology initiative format, removing the first 10 time points, eliminating the effects of uneven magnetic field and subject discomfort at the beginning of the scan, and performing the next step of time and space alignment and head movement correction at the remaining 170 time points. Subjects with head movement translation >3 mm and rotation >3° need to be eliminated, and data from all subjects have not been removed. Each avatar was then normalized to the Montreal Neurological Institute (MNI) standard space using 3-mm resampling; smoothing was performed using full width at half maximum as a 6-mm Gaussian kernel.
fMRI processing
ALFF analysis is performed on the preprocessed data using DPABI software. The time series of the whole brain signal intensity is Fourier transformed on a voxel basis, and the time domain is transformed into the frequency domain; the frequency power spectrum is squared and averaged in the 0.01– 0.08 Hz filter band to obtain ALFF; the voxel ALFF is divided by the average value of the whole brain ALFF to obtain mALFF. A higher value indicates stronger local brain activity in that area.
DC was analyzed by DPABI software, and the processing process included filtering the preprocessed images (0.01 Hz<f<0.08 Hz) to eliminate low-frequency drift and high-frequency physiological respiration and heartbeat noise, and removing linear drift at the same time, then DC was calculated respectively.
Statistical processing
Using DPABI software, a paired t-test analysis was performed on normalized ALFF and DC brain maps of schoolage children before and after 10 years. The threshold was set at p<0.001 (before correction). The difference was considered to be statistically significant when found with clusters of no less than 13 continuous voxels, and p<0.05 after AlphaSim correction. The cluster with a voxel size of 13 will be removed. The graph of the results after paired t-test is superimposed on the Ch2 template for viewing.
RESULTS
Comparison of low-frequency amplitude (ALFF) in normal school-age children before and after 10 years
The brain areas with ALFF values higher than those before 10 years were left posterior cerebellar lobe, right posterior cerebellar lobe, and left middle occipital gyrus; the brain areas with ALFF values lower than those after 10 years were left hippocampus, left inferior frontal gyrus, right inferior frontal gyrus, and left medial frontal gyrus (Figure 1 and Table 1).
Brain areas with significant differences in amplitude of low frequency fluctuation (ALFF) before and after 10 years. The red areas showed the ALFF value of the brain regions was higher than 10 years before and the blue areas were the opposite.
Comparison of amplitude of low frequency fluctuation before and after 10 years
Comparison of DC in normal school-age children before and after 10 years
The brain area with DC value higher than 10 years later has the left superior occipital gyrus; the brain area with DC value lower than 10 years later has the left inferior parietal lobe (Figure 2 and Table 2).
Brain areas with significant differences in degree centrality (DC) before and after 10 years. The red area showed the DC value of the brain region was higher than 10 years before and the blue area was the opposite.
Comparison of degree centrality before and after 10 years
DISCUSSION
Histological evidence suggests that brain development is a dynamic process of gradual and progressive change. Throughout the entire life cycle of the human brain, its structural architecture and functional organization will undergo major changes. It is well known that brain development and cognitive maturity occur simultaneously during childhood and adolescence. Advances in neuroimaging technology over the past decade have allowed us to safely track these changes in the human body, and the results suggest that cortical functions are fine-tuned as development develops, with brain areas related to more basic functions such as sensory and motor processes maturing first, and then associated areas involved in top-down behavioral control [13,14]. There is consistency in the order of the development of cognitive abilities of individuals and the development and maturity of their corresponding brain structures. Brain areas related to basic life skills, such as sensation and perception, develop and mature earlier, while brain areas related to advanced cognitive activities, such as decision-making and reasoning, develop and mature later. With age, the dependence and activation of cognitive activities on certain brain areas increases, while the dependence and activation of cognitive activities on other brain areas decreases, that is, the phenomenon of specialization of cognitive abilities corresponding to brain areas occurs [15,16]. Cross-sectional and longitudinal imaging studies in late childhood and early spring suggest that brain areas associated with more basic functions such as motor and sensory processes mature first, followed by related areas involved in top-down control of thought and movement. This development model is in line with the shift from diffuse areas of learning and cognitive development to more concentrated recruitment [17,18]. This is also consistent with the findings of this study.
The cerebellar hemisphere is located in the posterior cerebellar lobe and is connected to the cerebral cortex. It mainly performs cognitive and emotional functions [19]. In addition to being involved in motor balance, the cerebellum is also involved in many areas of advanced cognitive functions, of which the posterior cerebellum lobe is involved in working memory function [20]. The improvement in the myelin component of the left posterior cerebellum may be related to improvements in visuospatial working memory, emotion, and cognitive functions, as well as to children’s executive function [21]. The right posterior lobe is involved in sensory processing and cognitive functional processing, and is related to working memory, executive function, and emotion [22]. The occipital lobes include the left superior occipital gyrus, left middle occipital gyrus, left inferior occipital gyrus, and right inferior occipital gyrus, which are responsible for vision, image recognition, and image perception [23]. The left middle occipital gyrus is associated with cognition, vigilance and attention, and spatial working memory [24]. The ALFF values of the left posterior cerebellar lobe, right posterior cerebellar lobe, and left middle occipital gyrus were higher than 10 years ago, indicating that spontaneous activity in these three brain areas was enhanced, and these brain areas were directly related to children’s memory, indicating that children 10 years later. The brain areas that manage working memory are more active and working memory is developed.
The frontal lobe is an important brain area for emotional processing and cognitive functions. It participates in the experience of reward and punishment and the cognitive evaluation of positive and negative external stimulus [25]. The frontal lobe includes the superior frontal gyrus, the middle frontal gyrus, and the inferior frontal gyrus. The superior frontal gyrus is mainly involved in cognitive and motor control tasks [26]. The left inferior frontal gyrus is related to the production and understanding of language, and semantic representation is related to phonological working memory, episodic memory, emotion and social, and executive function [27]. The right inferior frontal gyrus is associated with propositional speech, whole character pronunciation processing, Chinese character reading, reasoning and social cognitive processing, cognitive control, mathematical processing, attention and reaction speed, working memory, episodic memory, and executive function [28]. The left medial frontal gyrus is associated with emotional and cognitive regulation, working memory, and executive function [29]. The left hippocampus is associated with memory and executive function [30]. This study found that the ALFF values of the left inferior frontal gyrus, right inferior frontal gyrus, left medial frontal gyrus, and left hippocampus 10 years later were significantly lower than those measured 10 years earlier, which may be due to the further development of brain function and the ability to achieve executive control and memory with less energy.
The inferior parietal lobe is a component of the Wer-nicke area. It analyzes and identifies sensory stimuli of language, and jointly maintains sensory, language, and other functions through the connection between the fiber bundles and the frontal lobe [31]. The left inferior parietal lobe has a certain relationship with the activation of visuospatial attention, auditory attention, and executive function [32,33]. The DC value of the left inferior parietal lobe 10 years later is lower than that of 10 years ago, suggesting that executive functions such as attention can be completed with a smaller degree of brain function transmission. The occipital lobe is mainly responsible for the processing of visuospatial information, body language, and emotional regulation [34]. The superior occipital gyrus plays an important role in visual information, visual-motor, and other sensory functions. The left superior occipital gyrus is correlated with cognition and lexical semantics [35]. This study found that the DC value of the left superior occipital gyrus 10 years later was higher than that of 10 years ago, indicating that children’s cognitive, lexical and semantic abilities have improved.
At present, other studies have not observed similar ALFF or DC changes in the brain regions above. Brain cognition is the acquisition, processing, extraction, and output of information input by humans during interaction with the environment. The combination of physiological development and environmental factors (social macro factors, family environmental factors, and school education factors) causes children’s brain development to show a dynamic process. During children and adolescents, individuals experience physical maturity and development. At the same time, with the accumulation of learning and experience, the cognitive levels of children and adolescents continue to differ. As children grow older, children’s cognitive level and brain processing efficiency increase, and key brain areas corresponding to the development of cognitive ability continue to change. And, the current data volume of this research is not very large. In the future, we will further increase the data volume to make this research more convincing.
Notes
Availability of Data and Material
The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.
Conflicts of Interest
The authors have no potential conflicts of interest to disclose.
Author Contributions
Conceptualization: Kaihua Jiang, Wanchao Zhang. Data curation: Lihan Chen. Formal analysis: Fei Liu. Funding acquisition: Kaihua Jiang, Wanchao Zhang. Investigation: Yunzhi Ling. Methodology: Yunzhi Ling. Validation: Kaihua Jiang, Wanchao Zhang. Visualization: Renzhang Lin. Writing—original draft: Lin Li, Yanshu Ding. Writing—review & editing: Kaihua Jiang, Wanchao Zhang.
Funding Statement
This study was funded by the scientific research project of Jiangsu Provincial Health Commission (M2024028); “Sky Mountain Talents” Training Program Project (2024TSYCJC0065); “Pamir Talents” Training Program Project (KZPP2024024); Special Scientific Research Project for Young Medical and Scientific Talents in Health Care of Xinjiang Autonomous Region (WJWY-202456).
Acknowledgments
None