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.
Post-traumatic stress disorder (PTSD) is a mental disorder causing great distress to individuals, families and even society, and there is not yet effective way of unified prevention and treatment up till now. Lots of neuroimaging techniques, however, such as the magnetic resonance imaging, are widely used to the study of the pathogenesis of PTSD with the development of medical imaging. Functional magnetic resonance imaging (fMRI) can be applied to detect the abnormalities not only of the brain morphology but also of the function of various cerebral areas and neural circuit, and plays an important role in studying the pathogenesis of psychiatric diseases. In this paper, we mainly review the task-related and resting-state functional magnetic resonance imaging studies of the PTSD, and finally suggest possible directions for future research.
Although a great number of studies have investigated the changes of resting-state functional connectivity (rsFC) in patients with mental disorders, such as depression and schizophrenia etc, little is known how stable the changes are, and whether temporal sad or happy mood can modulate the intrinsic rsFC. In our experiments, happy and sad video clips were used to induce temporally happy and sad mood states in 20 healthy young adults. We collected functional magnetic resonance imaging (fMRI) data while participants were watching happy or sad video clips, which were administrated in two consecutive days. Seed-based functional connectivity analyses were conducted using the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), and amygdala as seeds to investigate neural network related to executive function, attention, and emotion. We also investigated the association of the rsFC changes with emotional arousability level to understand individual differences. There is significantly stronger functional connectivity between the left DLPFC and posterior cingulate cortex (PCC) under sad mood than that under happy mood. The increased connectivity strength was positively correlated with subjects' emotional arousability. The increased positive correlation between the left DLPFC and PCC under sad relative to happy mood might reflect an increased processing of negative emotion-relevant stimuli. The easier one was induced by strong negative emotion (higher emotional arousability), the greater the left DLPFC-PCC connectivity was indicated, the greater the instability of the intrinsic rsFC was shown.
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。
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.
Migraine is the most common primary headache clinically, with high disability rate and heavy burden. Functional MRI (fMRI) plays a significant role in the study of migraine. This article reviews the main advances of migraine without aura (MwoA) based on resting-state fMRI in recent years, including the exploration of the mechanism of fMRI in the occurrence and development of MwoA in terms of regional functional activities and functional network connections, as well as the research progress of the potential clinical application of fMRI in aiding diagnosis and assessing treatment effect for MwoA. At last, this article summarizes the current distresses and prospects of fMRI research on MwoA.
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.