Alternation of brain intrinsic activity in patients with hypertensive retinopathy: a resting-state fMRI study

Altered spontaneous brain activity patterns in hypertensive retinopathy using fractional amplitude of low-frequency fluctuations: a functional magnetic resonance imaging study

AIM

To study functional brain abnormalities in patients with hypertensive retinopathy (HR) and to discuss the pathophysiological mechanisms of HR by fractional amplitude of low-frequency fluctuations (fALFFs) method.

METHODS

Twenty HR patients and 20 healthy controls (HCs) were respectively recruited. The age, gender, and educational background characteristics of the two groups were similar. After functional magnetic resonance imaging (fMRI) scanning, the subjects' spontaneous brain activity was evaluated with the fALFF method. Receiver operating characteristic (ROC) curve analysis was used to classify the data. Further, we used Pearson's correlation analysis to explore the relationship between fALFF values in specific brain regions and clinical behaviors in patients with HR.

RESULTS

The brain areas of the HR group with lower fALFF values than HCs were the right orbital part of the middle frontal gyrus (RO-MFG) and right lingual gyrus. In contrast, the values of fALFFs in the left middle temporal gyrus (MTG), left superior temporal pole (STP), left middle frontal gyrus (MFG), left superior marginal gyrus (SMG), left superior parietal lobule (SPL), and right supplementary motor area (SMA) were higher in the HR group. The results of a t-test showed that the average values of fALFFs were statistically significantly different in the HR group and HC group (P<0.001). The fALFF values of the left middle frontal gyrus in HR patients were positively correlated with anxiety scores (r=0.9232; P<0.0001) and depression scores (r=0.9682; P<0.0001).

CONCLUSION

fALFF values in multiple brain regions of HR patients are abnormal, suggesting that these brain regions in HR patients may be dysfunctional, which may help to reveal the pathophysiological mechanisms of HR.

Spontaneous brain activity in patients with central retinal artery occlusion: a resting-state functional MRI study using machine learning

Central retinal artery occlusion (CRAO) is a serious eye condition that poses a risk to vision, resulting from the blockage of the central retinal artery. Because of the anatomical connection between the ocular artery, which derives from the internal carotid artery, and the anterior middle cerebral artery, hemodynamic alterations and sudden vision loss associated with CRAO may impact brain functionality. This study aimed to examine alterations in spontaneous neural activity among patients with CRAO by resting-state functional MRI. In addition, we selected the amplitude of low-frequency fluctuation (ALFF) and fractional amplitude of low-frequency fluctuation (fALFF) values as classification features for distinguishing CRAO from healthy controls (HCs) using a support vector machine classifier. A total of 18 patients diagnosed with CRAO and 18 HCs participated in the study. Resting-state brain function images and structural images were acquired from both groups. Aberrant changes in spontaneous brain functional activity among CRAO patients were investigated utilizing ALFF and fALFF analysis methods. Group differences in ALFF/fALFF values were assessed through a two-sample t -test. Subsequently, a machine learning classifier was developed to evaluate the clinical diagnostic potential of ALFF and fALFF values. In comparison to HCs, individuals with CRAO exhibited significantly higher ALFF values in the left cerebellum_6, vermis_7, left superior frontal gyrus, and left inferior frontal gyrus, triangular part. Conversely, the CRAO group displayed notably lower ALFF values in the left precuneus and left median cingulum gyri. Furthermore, higher fALFF values were observed in the left inferior frontal gyrus, triangular part, whereas lower fALFF values were noted in the right cerebellum_Crus2, left precuneus, right angular gyrus, left angular gyrus, right supramarginal gyrus, right superior parietal gyrus, and left precuneus. Utilizing the ALFF/fALFF values, the receiver operating characteristic curves (area under the curve) yielded 0.99 and 0.94 through machine learning analysis techniques. CRAO patients exhibit atypical neural activity in the brain, characterized by ALFF and fALFF values predominantly localized in the frontal, parietal, and cerebellar regions, which are closely linked to visual cognition and motor control impairments. Furthermore, ALFF and fALFF could serve as potential neuroimaging markers beyond the orbit among CRAO.

Frequency-specific alterations in intrinsic low-frequency oscillations in newly diagnosed male patients with obstructive sleep apnea

Purpose

Previous studies found abnormal low-frequency spontaneous brain activity related to cognitive impairment in patients with obstructive sleep apnea (OSA). However, it is unclear if low-frequency spontaneous brain activity is related to specific frequency bands in OSA patients. In this study, we used the amplitude of low-frequency fluctuation (ALFF) method in patients with OSA to explore characteristics of spontaneous brain activity in the classical (0.01–0.1 Hz) and five sub-frequency bands (slow-2 to slow-6) and analyzed the relationship between spontaneous brain activity and clinical evaluation was analyzed.

Patients and methods

Resting-state magnetic resonance imaging data and clinical assessments were collected from 52 newly-diagnosed OSA patients and 62 healthy controls (HCs). We calculated the individual group ALFF values in the classical and five different sub-frequency bands. A two-sample t-test compared ALFF differences, and one-way analysis of variance explored interactions in frequency bands between the two groups.

Results

ALFF values in the OSA group were lower than those in the HC group in the bilateral precuneus/posterior cingulate cortex, bilateral angular gyrus, left inferior parietal lobule, brainstem, and right fusiform gyrus. In contrast, ALFF values in the OSA group were higher than those in the HC group in the bilateral cerebellum posterior lobe, bilateral superior frontal gyrus, bilateral middle frontal gyrus, left inferior frontal gyrus, left inferior temporal gyrus, and left fusiform gyrus. Some ALFF values in altered brain regions were associated with body mass index, apnea-hypopnea index, neck circumference, snoring history, minimum SaO₂, average SaO₂, arousal index, oxygen reduction index, deep sleep period naming, abstraction, and delayed recall in specific frequency bands.

Conclusion

Our results indicated the existence of frequency-specific differences in spontaneous brain activity in OSA patients, which were related to cognitive and other clinical symptoms. This study identified frequency-band characteristics related to brain damage, expanded the cognitive neuroimaging mechanism, and provided additional OSA neuroimaging markers.

The alterations of brain functional connectivity networks in major depressive disorder detected by machine learning through multisite rs-fMRI data

BACKGROUND

The current diagnosis of major depressive disorder (MDD) is mainly based on the patient's self-report and clinical symptoms. Machine learning methods are used to identify MDD using resting-state functional magnetic resonance imaging (rs-fMRI) data. However, due to large site differences in multisite rs-fMRI data and the difficulty of sample collection, most of the current machine learning studies use small sample sizes of rs-fMRI datasets to detect the alterations of functional connectivity (FC) or network attribute (NA), which may affect the reliability of the experimental results.

METHODS

Multisite rs-fMRI data were used to increase the size of the sample, and then we extracted the functional connectivity (FC) and network attribute (NA) features from 1611 rs-fMRI data (832 patients with MDD (MDDs) and 779 healthy controls (HCs)). ComBat algorithm was used to harmonize the data variances caused by the multisite effect, and multivariate linear regression was used to remove age and sex covariates. Two-sample t-test and wrapper-based feature selection methods (support vector machine recursive feature elimination with cross-validation (SVM-RFECV) and LightGBM's "feature_importances_" function) were used to select important features. The Shapley additive explanations (SHAP) method was used to assign the contribution of features to the best classification effect model.

RESULTS

The best result was obtained from the LinearSVM model trained with the 136 important features selected by SVMRFE-CV. In the nested five-fold cross-validation (consisting of an outer and an inner loop of five-fold cross-validation) of 1611 data, the model achieved the accuracy, sensitivity, and specificity of 68.90 %, 71.75 %, and 65.84 %, respectively. The 136 important features were tested in a small dataset and obtained excellent classification results after balancing the ratio between patients with depression and HCs.

CONCLUSIONS

The combined use of FC and NA features is effective for classifying MDDs and HCs. The important FC and NA features extracted from the large sample dataset have some generalization performance and may be used as a reference for the altered brain functional connectivity networks in MDD.

Altered regional brain white matter in dry eye patients: a brain imaging study

This study aimed to investigate the regional changes of brain white matter (WM) in DE patients using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). A total of 25 dry eye patients (PAT) and 25 healthy controls (HC) were recruited. All subjects underwent DTI and NODDI, fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), isotropic volume fraction (FISO), intra-cellular volume fraction (FICVF), and orientation dispersion index (ODI) were obtained respectively. Then complete Hospital Anxiety and Depression Scale (HADS), anxiety score (AS) or depression scores (DS) were obtained. Receiver operating characteristic (ROC) curve analysis was used to evaluate the reliability of DTI and NODDI in distinguishing the two groups. DTI revealed that PAT had lower FA in both the left superior longitudinal fasciculus (LSLF) and the corpus callosum (CC), and higher MD in the LSLF, the right posterior limb of the internal capsule and the right posterior thalamic radiation. PAT had significant AD changes in regions including the genu of the CC, the right posterior limb of internal capsule, and the right splenium of the CC. From NODDI, PAT showed increased ODI in the LSLF and increased FISO in the right splenium of the CC. FICVF showed a significant decrease in the LSLF while increased in the left anterior corona radiata and the CC. Furthermore, the average values of MD and FICVF were significantly correlated with DS and AS. Hence the results of this study suggest that there are regional changes in WM in DE patients which may contribute to further understanding of the pathological mechanism of DE.