Neurite orientation dispersion and density imaging reveals abnormal white matter and glymphatic function in active young boxers.

The neurological effects and underlying pathophysiological mechanisms of sports-related concussion (SRC) in active young boxers remain poorly understood. This study aims to investigate the impairment of white matter microstructure and assess changes in glymphatic function following SRC by utilizing neurite orientation dispersion and density imaging (NODDI) on young boxers who have sustained SRC. A total of 60 young participants were recruited, including 30 boxers diagnosed with SRC and 30 healthy individuals engaging in regular exercise. The assessment of whole-brain white matter damage was conducted using diffusion metrics, while the evaluation of glymphatic function was performed through diffusion tensor imaging (DTI) analysis along the perivascular space (DTI-ALPS) index. A two-sample t-test was utilized to examine group differences in DTI and NODDI metrics. Spearman correlation and generalized linear mixed models were employed to investigate the relationship between clinical assessments of SRC and NODDI measurements. Significant alterations were observed in DTI and NODDI metrics among young boxers with SRC. Additionally, the DTI-ALPS index in the SRC group exhibited a significantly higher value than that of the control group (left side: 1.58 vs. 1.48, PFDR = 0.009; right side: 1.61 vs. 1.51, PFDR = 0.02). Moreover, it was observed that the DTI-ALPS index correlated with poorer cognitive test results among boxers in this study population. Repetitive SRC in active young boxers is associated with diffuse white matter injury and glymphatic dysfunction, highlighting the detrimental impact on brain health. These findings highlight the importance of long-term monitoring of the neurological health of boxers.

A clinical-radiomics combined model based on carotid atherosclerotic plaque for prediction of ischemic stroke.

Objectives: To accurately predict the risk of ischemic stroke, we established a radiomics model of carotid atherosclerotic plaque-based high-resolution vessel wall magnetic resonance imaging (HR-VWMRI) and combined it with clinical indicators. Materials and methods: In total, 127 patients were finally enrolled and randomly divided into training and test cohorts. HR-VWMRI three-dimensional T1-weighted imaging (T1WI) and contrast-enhanced T1WI (T1CE) were collected. A traditional model was built by recording and calculating radiographic features of the carotid plaques and patients' clinical indicators. After extracting radiomics features from T1WI and T1CE images, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select the optimal features and construct the radiomics_T1WI model and the radiomics_T1CE model. The traditional and radiomics features were used to build combined models. The performance of all the models predicting ischemic stroke was evaluated in the training and test cohorts, respectively. Results: Body mass index (BMI) and intraplaque hemorrhage (IPH) were independently related to ischemic stroke and were used to build the traditional model, which achieved an area under the curve (AUC) of 0.79 versus 0.78 in the training and test cohorts, respectively. The AUC value of the radiomics_T1WI model is the lowest in the training and test cohorts, but the prediction performance is significantly improved when the model combines IPH and BMI. The AUC value of the combined_T1WI model was 0.78 and 0.81 in the training and test cohorts, respectively. In addition, in the training and test cohorts, the radiomics_T1CE model based on HR-VWMRI combined clinical characteristics, which is the combined_T1CE model, had the highest AUC value of 0.84 and 0.82, respectively. Conclusion: Compared with other models, the radiomics_T1CE model based on HR-VWMRI combined clinical characteristics, which is a combined_T1CE model, can accurately predict the risk of ischemic stroke.

Application of multi-echo Dixon and MRS in quantifying hepatic fat content and staging liver fibrosis.

This study associated the liver proton density fat fraction (PDFF), measured by multi-echo Dixon (ME-Dixon) and breath-hold single-voxel high-speed T2-corrected multi-echo 1H magnetic resonance spectroscopy (HISTO) at 1.5 T, with serum biomarkers and liver fibrosis stages. This prospective study enrolled 75 patients suspected of liver fibrosis and scheduled for liver biopsy and 23 healthy participants with normal liver function. The participant underwent ME-Dixon and HISTO scanning. The agreement of PDFF measured by ME-Dixon (PDFF-D) and HISTO (PDFF-H) were compared. Correlations between PDFF and serum fat biomarkers (total cholesterol, triglyceride, and high- and low-density lipoproteins) and the liver fibrosis stages were assessed. PDFF were compared among the liver fibrosis stages (F0-F4) based on clinical liver biopsies. The Bland-Altman plot showed agreement between PDFF-D and PDFF-H(LoA, - 4.44 to 6.75), which have high consistency (ICC 0.752, P < 0.001). The correlations with the blood serum markers were mild to moderate (PDFF-H: r = 0.261-0.410, P < 0.01; PDFF-D: r = 0.265-0.367, P < 0.01). PDFF-D, PDFF-H, and steatosis were distributed similarly among the liver fibrosis stages. PDFF-H showed a slight negative correlation with the liver fibrosis stages (r = - 0.220, P = 0.04). Both ME-Dixon and HISTO sequences measured liver fat content noninvasively. Liver fat content was not directly associated with liver fibrosis stages.

Imaging and Hemodynamic Characteristics of Vulnerable Carotid Plaques and Artificial Intelligence Applications in Plaque Classification and Segmentation.

Stroke is a massive public health problem. The rupture of vulnerable carotid atherosclerotic plaques is the most common cause of acute ischemic stroke (AIS) across the world. Currently, vessel wall high-resolution magnetic resonance imaging (VW-HRMRI) is the most appropriate and cost-effective imaging technique to characterize carotid plaque vulnerability and plays an important role in promoting early diagnosis and guiding aggressive clinical therapy to reduce the risk of plaque rupture and AIS. In recent years, great progress has been made in imaging research on vulnerable carotid plaques. This review summarizes developments in the imaging and hemodynamic characteristics of vulnerable carotid plaques on the basis of VW-HRMRI and four-dimensional (4D) flow MRI, and it discusses the relationship between these characteristics and ischemic stroke. In addition, the applications of artificial intelligence in plaque classification and segmentation are reviewed.

Altered Cerebro-Cerebellar Effective Connectivity in New-Onset Juvenile Myoclonic Epilepsy.

(1) Objective: Resting-state fMRI studies have indicated that juvenile myoclonic epilepsy (JME) could cause widespread functional connectivity disruptions between the cerebrum and cerebellum. However, the directed influences or effective connectivities (ECs) between these brain regions are poorly understood. In the current study, we aimed to evaluate the ECs between the cerebrum and cerebellum in patients with new-onset JME. (2) Methods: Thirty-four new-onset JME patients and thirty-four age-, sex-, and education-matched healthy controls (HCs) were included in this study. We compared the degree centrality (DC) between the two groups to identify intergroup differences in whole-brain functional connectivity. Then, we used a Granger causality analysis (GCA) to explore JME-caused changes in EC between cerebrum regions and cerebellum regions. Furthermore, we applied a correlation analysis to identify associations between aberrant EC and disease severity in patients with JME. (3) Results: Compared to HCs, patients with JME showed significantly increased DC in the left cerebellum posterior lobe (CePL.L), the right inferior temporal gyrus (ITG.R) and the right superior frontal gyrus (SFG.R), and decreased DC in the left inferior frontal gyrus (IFG.L) and the left superior temporal gyrus (STG.L). The patients also showed unidirectionally increased ECs from cerebellum regions to the cerebrum regions, including from the CePL.L to the right precuneus (PreCU.R), from the left cerebellum anterior lobe (CeAL.L) to the ITG.R, from the right cerebellum posterior lobe (CePL.R) to the IFG.L, and from the left inferior semi-lunar lobule of the cerebellum (CeISL.L) to the SFG.R. Additionally, the EC from the CeISL.L to the SFG.R was negatively correlated with the disease severity. (4) Conclusions: JME patients showed unidirectional EC disruptions from the cerebellum to the cerebrum, and the negative correlation between EC and disease severity provides a new perspective for understanding the cerebro-cerebellar neural circuit mechanisms in JME.

Noninvasive Classification of Glioma Subtypes Using Multiparametric MRI to Improve Deep Learning.

Background: Deep learning (DL) methods can noninvasively predict glioma subtypes; however, there is no set paradigm for the selection of network structures and input data, including the image combination method, image processing strategy, type of numeric data, and others. Purpose: To compare different combinations of DL frameworks (ResNet, ConvNext, and vision transformer (VIT)), image preprocessing strategies, magnetic resonance imaging (MRI) sequences, and numerical data for increasing the accuracy of DL models for differentiating glioma subtypes prior to surgery. Methods: Our dataset consisted of 211 patients with newly diagnosed gliomas who underwent preoperative MRI with standard and diffusion-weighted imaging methods. Different data combinations were used as input for the three different DL classifiers. Results: The accuracy of the image preprocessing strategies, including skull stripping, segment addition, and individual treatment of slices, was 5%, 10%, and 12.5% higher, respectively, than that of the other strategies. The accuracy increased by 7.5% and 10% following the addition of ADC and numeric data, respectively. ResNet34 exhibited the best performance, which was 5% and 17.5% higher than that of ConvNext tiny and VIT-base, respectively. Data Conclusions: The findings demonstrated that the addition of quantitatively numeric data, ADC images, and effective image preprocessing strategies improved model accuracy for datasets of similar size. The performance of ResNet was superior for small or medium datasets.

Early Changes in the White Matter Microstructure and Connectome Underlie Cognitive Deficit and Depression Symptoms After Mild Traumatic Brain Injury.

Cognitive and emotional impairments are frequent among patients with mild traumatic brain injury (mTBI) and may reflect alterations in the brain structural properties. The relationship between microstructural changes and cognitive and emotional deficits remains unclear in patients with mTBI at the acute stage. The purpose of this study was to analyze the alterations in white matter microstructure and connectome of patients with mTBI within 7 days after injury and investigate whether they are related to the clinical questionnaires. A total of 79 subjects (42 mTBI and 37 healthy controls) underwent neuropsychological assessment and diffusion-tensor MRI scan. The microstructure and connectome of white matter were characterized by tract-based spatial statistics (TBSSs) and graph theory approaches, respectively. Mini-mental state examination (MMSE) and self-rating depression scale (SDS) were used to evaluate the cognitive function and depressive symptoms of all the subjects. Patients with mTBI revealed early increases of fractional anisotropy in most areas compared with the healthy controls. Graph theory analyses showed that patients with mTBI had increased nodal shortest path length, along with decreased nodal degree centrality and nodal efficiency, mainly located in the bilateral temporal lobe and right middle occipital gyrus. Moreover, lower nodal shortest path length and higher nodal efficiency of the right middle occipital gyrus were associated with higher SDS scores. Significantly, the strength of the rich club connection in the mTBI group decreased and was associated with the MMSE. Our study demonstrated that the neuroanatomical alterations of mTBI in the acute stage might be an initial step of damage leading to cognitive deficits and depression symptoms, and arguably, these occur due to distinct mechanisms.

Aberrant dynamic structure-function relationship of rich-club organization in treatment-naïve newly diagnosed juvenile myoclonic epilepsy.

Neuroimaging studies have shown that juvenile myoclonic epilepsy (JME) is characterized by impaired brain networks. However, few studies have investigated the potential disruptions in rich-club organization-a core feature of the brain networks. Moreover, it is unclear how structure-function relationships dynamically change over time in JME. Here, we quantify the anatomical rich-club organization and dynamic structural and functional connectivity (SC-FC) coupling in 47 treatment-naïve newly diagnosed patients with JME and 40 matched healthy controls. Dynamic functional network efficiency and its association with SC-FC coupling were also calculated to examine the supporting of structure-function relationship to brain information transfer. The results showed that the anatomical rich-club organization was disrupted in the patient group, along with decreased connectivity strength among rich-club hub nodes. Furthermore, reduced SC-FC coupling in rich-club organization of the patients was found in two functionally independent dynamic states, that is the functional segregation state (State 1) and the strong somatomotor-cognitive control interaction state (State 5); and the latter was significantly associated with disease severity. In addition, the relationships between SC-FC coupling of hub nodes connections and functional network efficiency in State 1 were found to be absent in patients. The aberrant dynamic SC-FC coupling of rich-club organization suggests a selective influence of densely interconnected network core in patients with JME at the early phase of the disease, offering new insights and potential biomarkers into the underlying neurodevelopmental basis of behavioral and cognitive impairments observed in JME.

Altered brain functional network dynamics in classic trigeminal neuralgia: a resting-state functional magnetic resonance imaging study.

BACKGROUND: Accumulating studies have indicated a wide range of brain alterations with respect to the structure and function of classic trigeminal neuralgia (CTN). Given the dynamic nature of pain experience, the exploration of temporal fluctuations in interregional activity covariance may enhance the understanding of pain processes in the brain. The present study aimed to characterize the temporal features of functional connectivity (FC) states as well as topological alteration in CTN. METHODS: Resting-state functional magnetic resonance imaging and three-dimensional T1-weighted images were obtained from 41 CTN patients and 43 matched healthy controls (HCs). After group independent component analysis, sliding window based dynamic functional network connectivity (dFNC) analysis was applied to investigate specific FC states and related temporal properties. Then, the dynamics of the whole brain topological organization were estimated by calculating the coefficient of variation of graph-theoretical properties. Further correlation analyses were performed between all these measurements and clinical data. RESULTS: Two distinct states were identified. Of these, the state 2, characterized by complicated coupling between default mode network (DMN) and cognitive control network (CC) and tight connections within DMN, was expressed more in CTN patients and presented as increased fractional windows and dwell time. Moreover, patients switched less frequently between states than HCs. Regarding the dynamic topological analysis, disruptions in global graph-theoretical properties (including network efficiency and small-worldness) were observed in patients, coupled with decreased variability in nodal efficiency of anterior cingulate cortex (ACC) in the salience network (SN) and the thalamus and caudate nucleus in the subcortical network (SC). The variation of topological properties showed negative correlation with disease duration and attack frequency. CONCLUSIONS: The present study indicated disrupted flexibility of brain topological organization under persistent noxious stimulation and further highlighted the important role of "dynamic pain connectome" regions (including DMN/CC/SN) in the pathophysiology of CTN from the temporal fluctuation aspect. Additionally, the findings provided supplementary evidence for current knowledge about the aberrant cortical-subcortical interaction in pain development.

Tractography in Type 2 Diabetes Mellitus With Subjective Memory Complaints: A Diffusion Tensor Imaging Study.

The brain white matter (WM) structural injury caused by type 2 diabetes mellitus (T2DM) has been linked to cognitive impairment. However, the focus was mainly on the mild cognitive impairment (MCI) stage in most previous studies, with little attention made to subjective memory complaints (SMC). The main purpose of the current study was to investigate the characteristics of WM injury in T2DM patients and its correlation with SMC symptoms. In a group of 66 participants (33 HC and 33 T2DM-S), pointwise differences along WM tracts were identified using the automated fiber quantification (AFQ) approach. Then we investigated the utility of DTI properties along major WM tracts as features to distinguish patients with T2DM-S from HC via the support vector machine (SVM). Based on AFQ analysis, 10 primary fiber tracts that represent the subtle alterations of WM in T2DM-S were identified. Lower fractional anisotropy (FA) in the right SLF tract (r = -0.538, p = 0.0013), higher radial diffusivity (RD) in the thalamic radiation (TR) tract (r = 0.433, p = 0.012), and higher mean diffusivity (MD) in the right inferior fronto-occipital fasciculus (IFOF) tract (r = 0.385, p = 0.0029) were significantly associated with a long period of disease. Decreased axial diffusivity (AD) in the left arcuate was associated with HbA1c (r = -0.368, p = 0.049). In addition, we found a significant negative correlation between delayed recall and abnormal MD in the left corticospinal tract (r = -0.546, p = 0.001). The FA of the right SLF tracts and bilateral arcuate can be used to differentiate the T2DM-S and the HC at a high accuracy up to 88.45 and 87.8%, respectively. In conclusion, WM microstructure injury in T2DM may be associated with SMC, and these abnormalities identified by DTI can be used as an effective biomarker.