ObjectiveTo explore performances of functional magnetic resonance imaging (MRI) in evaluation of hepatic warm ischemia-reperfusion injury.MethodThe relative references about the principle of functional MRI and its application in the assessment of hepatic warm ischemia-reperfusion injury were reviewed and summarized.ResultsThe main functional MRI techniques for the assessment of hepatic warm ischemia-reperfusion injury included the diffusion weighted imaging (DWI), intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), blood oxygen level dependent (BOLD), dynamic contrast enhancement MRI (DCE-MRI), and T2 mapping, etc.. These techniques mainly used in the animal model with hepatic warm ischemia-reperfusion injury currently.ConclusionsFrom current results of researches of animal models, functional MRI is a non-invasive tool to accurately and quantitatively evaluate microscopic information changes of liver tissue in vivo. It can provide a useful information on further understanding of mechanism and prognosis of hepatic warm ischemia-reperfusion injury. With development of donation after cardiac death, functional MRI will play a more important role in evaluation of hepatic warm ischemia-reperfusion injury.
White matter lesion (WML) of presumed vascular origin is one of the common imaging manifestations of cerebral small vessel diseases, which is the main reason of cognitive impairment and even vascular dementia in the elderly. However, there is a lack of early and effective diagnostic methods currently. In recent years, studies of diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) have shown that cognitive impairment in patients with WMLs is associated with disrupted white matter microstructural and brain network connectivity. Therefore, it’s speculated that DTI and rs-fMRI can be effective in early imaging diagnosis of WMLs-related cognitive impairment. This article reviews the role and significance of DTI and rs-fMRI in WMLs-related cognitive impairment.
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.
Myopia is a major problem of public health in China, and even in the world, and slowing down the progress of myopia has become a hot issue of concern. However, the effects of the current therapeutic and interventional modalities to myopia, including optical lenses, chemical drugs, and laser surgery, the effect of treatment and intervention is not very satisfactory, and these modalities may incur some side effects. This situation suggests that the pathogenic and regulatory mechanisms of myopia remain elusive, and the myopia treatments lack the accurate and effective targets to the etiology. A complete visual experience depends on the entire visual pathway from the retina to the visual cortex, in which any structural and functional defect can lead to visual abnormalities. In recent years, with the advances in the infrared spectroscopy and the magnetic resonance imaging technology, more and more evidence has shown that the progression of myopia is related to the visual cortex. Improving the functional connectivity and blood prefusion between different regions of the visual cortex may impede myopia profession. In-depth understanding of the interaction between myopia and the visual cortex is helpful to search for accurate and effective myopia treatment targets and novel intervention strategies.
Objective To identify the most consistent and replicable characteristics of altered spontaneous brain activity in mesial temporal lobe epilepsy patients with unilateral hippocampal sclerosis (MTLE-HS). Methods A systematic literature search was performed in PubMed, Embase, The Cochrane Library, China National Knowledge Infrastructure, Wanfang, and CQVIP databases, to identify eligible whole-brain resting state functional magnetic resonance imaging studies that had measured differences in amplitude of low-frequency fluctuations or fractional amplitude of low-frequency fluctuations between patients with MTLE-HS and healthy controls from January 2000 to January 2019. After literature screening and data extraction, Anisotropic Effect-Size Signed Differential Mapping software was used for voxel based pooled meta-analysis. Results Nine datasets from six studies were finally included, which contained 207 MTLE-HS patients and 239 healthy controls. The results demonstrated that, compared with the healthy controls, the MTLE-HS patients showed increased spontaneous brain activity in right hippocampus and parahippocampal gyrus, right superior temporal gyrus, left cingulate gyrus, right fusiform gyrus, and right inferior temporal gyrus; while decreased spontaneous brain activity in left superior frontal gyrus, right angular gyrus, right middle frontal gyrus, left inferior parietal lobule, left precuneus, and right cerebellum (P<0.005, cluster extent≥10). Conclusion The current meta-analysis demonstrates that patients with MTLE-HS show increased spontaneous brain activity in lateral and mesial temporal regions and decreased spontaneous brain activity in default mode network, which preliminarily clarifies the characteristics of altered spontaneous brain activity in patients with MTLE-HS.
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.
Brain functional network changes over time along with the process of brain development, disease, and aging. However, most of the available measurements for evaluation of the difference (or similarity) between the individual brain functional networks are for charactering static networks, which do not work with the dynamic characteristics of the brain networks that typically involve a long-span and large-scale evolution over the time. The current study proposes an index for measuring the similarity of dynamic brain networks, named as dynamic network similarity (DNS). It measures the similarity by combining the “evolutional” and “structural” properties of the dynamic network. Four sets of simulated dynamic networks with different evolutional and structural properties (varying amplitude of changes, trend of changes, distribution of connectivity strength, range of connectivity strength) were generated to validate the performance of DNS. In addition, real world imaging datasets, acquired from 13 stroke patients who were treated by transcranial direct current stimulation (tDCS), were used to further validate the proposed method and compared with the traditional similarity measurements that were developed for static network similarity. The results showed that DNS was significantly correlated with the varying amplitude of changes, trend of changes, distribution of connectivity strength and range of connectivity strength of the dynamic networks. DNS was able to appropriately measure the significant similarity of the dynamics of network changes over the time for the patients before and after the tDCS treatments. However, the traditional methods failed, which showed significantly differences between the data before and after the tDCS treatments. The experiment results demonstrate that DNS may robustly measure the similarity of evolutional and structural properties of dynamic networks. The new method appears to be superior to the traditional methods in that the new one is capable of assessing the temporal similarity of dynamic functional imaging data.
