Brain/diagnostic imaging* ; Head

Humans ; Brain/diagnostic imaging* ; Head

Brain ; diagnostic imaging ; Diagnostic Imaging ; methods ; Humans ; Radiography ; Schizophrenia ; pathology

Asthma is a serious health problem that involves not only the respiratory system but also the central nervous system. Previous studies identified either regional or network alterations in patients with asthma, but inconsistent results were obtained. A key question remains unclear: are the regional and neural network deficits related or are they two independent characteristics in asthma? Answering this question is the aim of this study. By collecting resting-state functional magnetic resonance imaging from 39 patients with asthma and 40 matched health controls, brain functional measures including regional activity (amplitude of low-frequency fluctuations) and neural network function (degree centrality (DC) and functional connectivity) were calculated to systematically characterize the functional alterations. Patients exhibited regional abnormities in the left angular gyrus, right precuneus, and inferior temporal gyrus within the default mode network. Network abnormalities involved both the sensorimotor network and visual network with key regions including the superior frontal gyrus and occipital lobes. Altered DC in the lingual gyrus was correlated with the degree of airway obstruction. This study elucidated different patterns of regional and network changes, thereby suggesting that the two parameters reflect different brain characteristics of asthma. These findings provide evidence for further understanding the potential cerebral alterations in the pathophysiology of asthma.
Asthma/diagnostic imaging* ; Brain/diagnostic imaging* ; Brain Mapping ; Humans ; Magnetic Resonance Imaging

Obstructive sleep apnea (OSA) is a common clinic sleep disorder, and characterized by obstruction of upper airway during sleep, resulting in sleep fragmentation and intermittent hypoxemia. We reviewed the brain imaging studies in OSA patients compared with healthy subjects, including studies of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). The resting-state EEG studies showed increased power of δ and θ in the front and central regions of the cerebral cortex in OSA patients. While resting-state fMRI studies demonstrated altered large-scale networks in default-mode network (DMN), central executive network (CEN) and salience network (SN). Evidence from resting-state studies of both fMRI and EEG focused on the abnormal activity in prefrontal cortex (PFC), which is correlated with OSA severity. These findings suggested that the PFC may play a key role in the abnormal function of OSA patients. Finally, based on the perspectives of treatment effect, multimodal data acquisition, and comorbidities, we discussed the future research direction of the neuroimaging study of OSA.
Brain ; diagnostic imaging ; Brain Mapping ; Electroencephalography ; Humans ; Magnetic Resonance Imaging ; Sleep Apnea, Obstructive ; diagnostic imaging

Humans ; Huntington Disease/diagnostic imaging* ; Brain/diagnostic imaging* ; Brain Mapping ; Magnetic Resonance Imaging

Entropy model is widely used in epileptic electroencephalogram (EEG) analysis, but there are few reports on how to objectively select the parameters to compute the entropy model in the analysis of resting-state functional magnetic resonance imaging (rfMRI). Therefore, an optimization algorithm to confirm the parameters in multi-scale entropy (MSE) model was proposed, and the location of epileptogenic hemisphere was taken as an example to test the optimization effect by supervised machine learning. The rfMRI data of 20 temporal lobe epilepsy (TLE) patients with hippocampal sclerosis, positive on structural magnetic resonance imaging, were divided into left and right groups. Then, the parameters in MSE model were optimized by the receiver operating characteristic curves (ROC) and area under ROC curve (AUC) values in sensitivity analysis, and the entropy value of the brain regions with statistically significant difference between the groups were taken as sensitive features to epileptogenic hemisphere lateral. The optimized entropy values of these bio-marker brain areas were considered as feature vectors input into the support vector machine (SVM). Finally, combining optimized MSE model with SVM could accurately distinguish epileptogenic hemisphere in TLE at an average accuracy rate of 95%, which was higher than the current level. The results show that the MSE model parameter optimization algorithm can accurately extract the functional imaging markers sensitive to the epileptogenic hemisphere, and achieve the purpose of objectively selecting the parameters for MSE in rfMRI, which provides the basis for the application of entropy in advanced technology detection.
Brain/diagnostic imaging* ; Brain Mapping ; Entropy ; Epilepsy, Temporal Lobe/diagnostic imaging* ; Humans ; Magnetic Resonance Imaging

Biomarkers ; Brain/diagnostic imaging* ; Brain Diseases/diagnostic imaging* ; Humans ; Machine Learning ; Magnetic Resonance Imaging ; Neuroimaging

The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.
Aging ; Brain ; diagnostic imaging ; physiology ; Humans ; Neuroimaging

The disorder of brain-gut interaction is an important cause of irritable bowel syndrome (IBS), but the dynamic characteristics of the brain remain unclear. Since there are many shortcomings for evaluating brain dynamic nature in the previous studies, we proposed a new method based on slope calculation by point-by-point analysis of the data from functional magnetic resonance imaging, and detected the abnormalities of brain dynamic changes in IBS patients. The results showed that compared with healthy subjects, there were dynamic changes in the brain for the IBS patients. After correction by false discovery rate (FDR), significant abnormalities were only found in two functional connections of the right posterior cingulate gyrus linked to left middle frontal gyrus, and the right posterior cingulate gyrus linked to left pallidus. The above results of the brain dynamic analysis were totally different from those of the brain static analysis of IBS patients. Our findings provide novel complementary information for illustrating the central nervous mechanism of IBS and may offer a new direction to explore central target for patients with IBS.
Brain/diagnostic imaging* ; Brain Mapping ; Gyrus Cinguli/diagnostic imaging* ; Humans ; Irritable Bowel Syndrome/diagnostic imaging* ; Magnetic Resonance Imaging