Objective The ReHo, ALFF, fALFF of resting-state functional magnetic resonance imaging (RS-fMRI) technology were used to study the influencing factors and neural mechanism of cognitive dysfunction in patients with benign epilepsy of childhood with centrotemporal spikes (BECT). Methods Fourteen patients were enrolled (from April 2015 to March 2018) from epilepsy specialist outpatients and Functional Department of Neurosurgery of Tianjin Medical University General Hospital. They underwent the long term VEEG monitoring (one sleep cycle was included at least), the Wechsler Intelligence Scale (China Revised), the head MRI and RS-fMRI examinations. Spike-wave index (SWI), FIQ, VIQ, PIQ scores were calculated. According to full-scale IQ (FIQ), they were divided into two groups: FIQ<90 (scores range from 70 to 89, the average score was 78.3±8.9, 6 cases) and FIQ≥90 (scores range from 90 to 126, the average score was 116.6±12.9, 8 cases). SPSS21.0 statistical software was used to compare the general clinical data and SWI of the two groups, and the correlation between clinical factors and the evaluation results of Wechsler Intelligence Scale was analyzed. The RS-fMRI images were preprocessed and the further data were analysed by two independent samplest-test under the whole brain of regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF) and fractional of ALFF (fALFF) methods. The differences of brain activation regions in RS-fMRI between the two groups were observed, and the results of general clinical data, SWI and cognitive function test were compared and analyzed comprehensively. Results The differences of SWI were statistically significant (P<0.05): FIQ<90 group were greater than FIQ≥90 group. The FIQ, VIQ and PIQ of two groups were negatively correlated with SWI (P<0.05). And the FIQ and PIQ were negatively correlated with the total number of seizures (P<0.05). Compared with FIQ≥90 group by two samplet-test based on whole level ReHo, ALFF, fALFF methods, deactivation of brain regions of FIQ<90 group include bilateral precuneus, posterior cingulate and occipital lobe, and enhanced activation of brain regions include left prefrontal cortex, bilateral superior frontal gyrus medial and right precentral gyrus, supplementary motor area, angular gyrus, supramarginal gyrus, middle temporal gyrus, bilateral insular lobe and subcortical gray matter structures. Conclusions Frequent epileptic discharges during slow wave sleep and recurrent clinical episodes were risk factors for cognitive impairment in BECT patients. Repeated clinical seizures and frequent subclinical discharges could cause dysfunction of local brain areas associated with cognition and the default network, resulting in patients with impaired cognitive function.
The aim of this paper is to reveal the change of the brain function for nicotine addicts after smoking cessation, and explore the basis of neural physiology for the nicotine addicts in the process of smoking cessation. Fourteen subjects, who have a strong dependence on nicotine, have agreed to give up smoking and insist on completing the test, and 11 volunteers were recruited as the controls. The resting state functional magnetic resonance imaging and the regional homogeneity (ReHo) algorithm have been used to study the neural activity before and after smoking cessation. A two factors mixed design was used to investigate within-group effects and between-group effects. After 2 weeks’ smoking cessation, the increased ReHo value were exhibited in the brain area of supplementary motor area, paracentral lobule, calcarine, cuneus and lingual gyrus. It suggested that the synchronization of neural activity was enhanced in these brain areas. And between-group interaction effects were appeared in supplementary motor area, paracentral lobule, precentral gyrus, postcentral gyrus, and superior frontal gyrus. The results indicate that the brain function in supplementary motor area of smoking addicts would be enhanced significantly after 2 weeks’ smoking cessation.
Objective To investigate the differences in the topology of functional brain networks between populations with good spatial navigation ability and those with poor spatial navigation ability. Methods From September 2020 to September 2021, 100 college students from PLA Army Border and Coastal Defense Academy were selected to test the spatial navigation ability. The 25 students with the highest spatial navigation ability were selected as the GN group, and the 25 with the lowest spatial navigation ability were selected as the PN group, and their resting-state functional MRI and 3D T1-weighted structural image data of the brain were collected. Graph theory analysis was applied to study the topology of the brain network, including global and local topological properties. Results The variations in the clustering coefficient, characteristic path length, and local efficiency between the GN and PN groups were not statistically significant within the threshold range (P>0.05). The brain functional connectivity networks of the GN and PN groups met the standardized clustering coefficient (γ)>1, the standardized characteristic path length (λ)≈1, and the small-world property (σ)>1, being consistent with small-world network property. The areas under curve (AUCs) for global efficiency (0.22±0.01 vs. 0.21±0.01), γ value (0.97±0.18 vs. 0.81±0.18) and σ value (0.75±0.13 vs. 0.64±0.13) of the GN group were higher than those of the PN group, and the differences were statistically significant (P<0.05); the between-group difference in AUC for λ value was not statistically significant (P>0.05). The results of the nodal level analysis showed that the AUCs for nodal clustering coefficients in the left superior frontal gyrus of orbital region (0.29±0.05 vs. 0.23±0.07), the right rectus gyrus (0.29±0.05 vs. 0.23±0.09), the middle left cingulate gyrus and its lateral surround (0.22±0.02 vs. 0.25±0.02), the left inferior occipital gyrus (0.32±0.05 vs. 0.35±0.05), the right cerebellar area 3 (0.24±0.04 vs. 0.26±0.03), and the right cerebellar area 9 (0.22±0.09 vs. 0.13±0.13) were statistically different between the two groups (P<0.05). The differences in AUCs for degree centrality and nodal efficiency between the two groups were not statistically significant (P>0.05). Conclusions Compared with people with good spatial navigation ability, the topological properties of the brains of the ones with poor spatial navigation ability still conformed to the small-world network properties, but the connectivity between brain regions reduces compared with the good spatial navigation ability group, with a tendency to convert to random networks and a reduced or increased nodal clustering coefficient in some brain regions. Differences in functional brain network connectivity exist among people with different spatial navigation abilities.
Brain aging can affect the strength of functional connectivity between brain regions. In recent years, studies have shown that functional connectivity is fluctuant over time, and can reflect more physiological and pathological information. Therefore, in the study resting state functional magnetic resonance imaging (fMRI) data of 32 elderly subjects and 36 younger subjects were selected, and the sliding window technique was used to estimate dynamic functional connectivity network. Then, the dependency of fluctuating energy difference on frequency band was studied using wavelet packet analysis, conducting the linear regression with age at the same time. Results showed that the fluctuating energy in older group was significantly higher than that in the young group in low frequency, and it was significantly lower than that in the young people in high frequency. These results suggested that the dynamic functional connectivity between networks in the elderly exist slow wave phenomenon, which may be related to the decreased reaction rate of the elderly. This article provides new ideas and methods for the research about brain aging, and promotes a theoretical basis for further understanding of the physiological significance of brain dynamic functional connectivity.
Nowadays, an increasing number of researches have shown that epilepsy, as a kind of neural network disease, not only affects the brain region of seizure onset, but also remote regions at which the brain network structures are damaged or dysfunctional. These changes are associated with abnormal network of epilepsy. Resting-state network is closely related to human cognitive function and plays an important role in cognitive process. Cognitive dysfunction, a common comorbidity of epilepsy, has adverse impacts on life quality of patients with epilepsy. The mechanism of cognitive dysfunction in epileptic patients is still incomprehensible, but the change of resting-state brain network may be associated with their cognitive impairment. In order to further understand the changes of resting-state network associated with the cognitive function and explore the brain network mechanism of the occurrence of cognitive dysfunction in patients with epilepsy, we review the related researches in recent years.
Early diagnosis and accurate stage of liver fibrosis are important for conducting the clinic therapy and assessing the therapeutic outcome. Functional magnetic resonance imaging (fMRI), as a noninvasive and effective method, plays an important role in diagnosis and stage of liver fibrosis. This review focuses on the advances in fMRI evaluation of liver fibrosis.
目的 利用局部一致性（ReHo）方法探測創傷后應激障礙（PTSD）患者在靜息狀態下是否存在著大腦功能異常。 方法 2010年5月－7月對18例未經治療的地震PTSD患者和19例同樣經歷地震但未患PTSD的對照者進行了靜息態功能磁共振成像（Rs-fMRI） 掃描。應用ReHo方法處理Rs-fMRI數據，得出PTSD患者的異常腦區，并將患者存在組間差異的腦區ReHo值與臨床用PTSD診斷量表（CAPS）、漢密爾頓抑郁量表（HAMD）和漢密爾頓焦慮量表（HAMA）分別進行相關分析。 結果 ① PTSD組ReHo顯著增加的腦區包括右側顳下回、楔前葉、頂下葉、中扣帶回，左側枕中回以及左/右側后扣帶回；ReHo顯著降低的腦區包括左側海馬和左/右側腹側前扣帶回。② 異常腦區中后扣帶回和右側中扣帶回ReHo與HAMD呈負相關（中扣帶回r=?0.575，P=0.012；右側后扣帶回：r=?0.507，P=0.032），其余腦區ReHo與臨床指標無明顯相關性（P＞0.05），左側海馬與CAPS的相關性相對其他腦區較大（r=?0.430，P=0.075）。 結論 PTSD患者在靜息狀態下即存在著局部腦功能活動的降低和增加，ReHo方法可能有助于研究PTSD患者靜息狀態腦活動。