Objective The involvement of locus coeruleus (LC) in Parkinson’s disease (PD) motor dysfunction remains unclear. This study aims to investigate LC’s directional influence on the whole-brain in tremor-dominant (TD) and akinetic-rigid (AR) PD. Methods Forty-nine PD patients (20 TD and 29 AR) and 20 healthy controls (HCs) from a single center were retrospectively analyzed. The contrast-to-noise ratio (CNR) of LC was assessed using Neuromelanin-sensitive MRI. Effective connectivity (EC) of the LC was analyzed using seed-based Granger Causality analysis to examine the inflow and outflow of the LC’s directional influence using functional MRI, corrected by Gaussian random field at voxel level (P<0.01) and cluster level (P<0.05). Pearson’s analysis assessed the correlation between EC results and MDS-UPDRS III (P<0.05, Bonferroni corrected). Results The CNRLC was significantly lower in both PD subgroups than in HCs, with TD-PD showing higher CNRLC than AR-PD. Compared to HCs, TD-PD exhibited enhanced EC from the LC to the cerebellum and inferior temporal gyrus; and from the cerebellum to the LC.AR-PD exhibited enhanced EC from the LC to the inferior temporal gyrus and cerebellum. Compared to TD-PD, AR-PD exhibited stronger EC from the LC to the middle frontal, inferior frontal, and middle occipital gyri, and from the cerebellum to the LC. LC-EC correlated significantly with MDS-UPDRS III in TD-PD and AR-PD (R = -0.487 to 0.682, P = 0.001 to 0.007). Conclusion Our results highlight the LC’s directional influence in TD-PD and AR-PD, providing insights into motor dysfunction that could inform LC-noradrenaline-based therapy for personalized intervention.

OBJECTIVE:Functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) results indicate that primary somatosensory cortical (S1) representations vary between individuals. Here we studied how well somatosensory representations determined using TMS and fMRI correspond. METHODS:We used single-pulse TMS and fMRI in 17 healthy subjects to map the S1 representation of the tip of the right index finger stimulated with a Braille device. In the TMS experiment, the S1 site at which tactile sensation was blocked by TMS was considered the S1 representation site (S1HS) of the fingertip. In the fMRI experiment, passive and oddball tactile tasks were employed. RESULTS:The locations of S1HS varied up to 36 mm between subjects. The locations of fMRI peaks varied up to 39 mm between subjects in the passive condition and up to 84 mm in the oddball condition. Within subjects, the mean distance between the S1HS and the peak fMRI activation was 10 mm in the passive condition and 14 mm in the oddball task. CONCLUSIONS:Both TMS and fMRI results showed considerable inter-individual variability in S1 representations, fMRI having larger spatial variability compared to TMS. SIGNIFICANCE:Our findings underscore the importance of multimodal approaches to brain mapping in future studies.

Background and purpose: Diffusion tensor imaging along perivascular spaces (DTI-ALPS) is an index that may provide insights into intracranial waste clearance processes. Glymphatic system dysfunction has been suggested to play a role in the development of major depressive disorder (MDD). Additionally, fatigue-a common precursor of MDD-is also closely connected to the waste clearance function of the central nervous system (CNS), further underscoring the significance of efficient waste removal in MDD. However, evidence linking altered DTI-ALPS index to MDD remains limited. This study aims to investigate the changes in the DTI-ALPS index in patients with MDD and explore the potential interplay between DTI-ALPS index alterations, fatigue, and the presence of MDD. Material and methods: A total of 46 patients with MDD and 55 healthy controls (HC) were included in the study. All participants underwent diffusion tensor imaging using the same 3-T MRI (3-Tesla Magnetic Resonance Imaging) scanner. The DTI-ALPS index was assessed, and the Chalder Fatigue Scale (CFS) was used to evaluate fatigue levels in both groups, and the 17-item Hamilton Depression Rating Scale (HAMD-17) was used to evaluate the severity of depression in the patients. We compared the DTI-ALPS index and clinical characteristics between the MDD and HC group, and explored the relationship among the DTI-ALPS index, CFS scores, and the presence of MDD through mediation analysis. Results: The DTI-ALPS index in the right hemisphere (DTI-ALPS-R) is significantly lower in patients with MDD (t = 2.41, P = 0.02). The MDD patients exhibited significantly higher scores on the CFS scales compared with HCs (t = 13.12, P <.001). Mediation analysis showed that the CFS score plays a significant mediating role between DTI-ALPS-R and the presence of MDD, acting as a full mediator (indirect effect beta = -0.230, 95 % CI: [-0.388, -0.059]). Conclusion: Our study found that patients with MDD have a reduced DTI-ALPS index. This reduction appears to contribute to the development of MDD by facilitating the accumulation of fatigue symptoms. These findings may provide a new perspective on the pathogenesis of MDD, suggest a potential new biomarker for MDD, and offer new insights for its treatment.

Postoperative permanent neurological dysfunction remains a challenging complication in type A acute aortic dissection (TAAAD). Researches evaluating cerebral perfusion and altered blood flow in postoperative patients with TAAAD using non-invasive imaging techniques, such as arterial spin labelling are scarce. This study aims to assess cerebral blood flow (CBF) in postoperative patients with TAAAD, using arterial spin labeling (ASL). This study enrolled 22 postoperative patients and 25 healthy control subjects (HC), they all underwent a three-dimensional pseudo-continuous ASL MRI scanning. Voxel-based comparison of normalized CBF was conducted. The relationship between CBF variation and clinical scale assessment was further analysed. Compared with HC subjects, postoperative patients with TAAAD exhibited lower CBF levels in the right middle frontal gyrus, the right orbit of frontal gyrus, the bilateral fusiform gyrus, the right middle temporal gyrus, the bilateral inferior temporal gyrus, the bilateral lateral occipital cortex and the bilateral cerebellum ( t  > 3.0 and p  < 0.05, FDR corrected at cluster level). These variations were also significantly correlated with multiple clinical rating scales about cognition and emotion. Postoperative patients with TAAAD exhibit abnormalities in several brain regions. The affected areas involve important component of neuro-networks, including psychological cognitive activity, attention and emotion regulation and high-level visual function.

This study aimed to identify cerebral radiomic features related to migraine diagnosis and subtyping into migraine with aura (MwA) and migraine without aura (MwoA) and to develop predictive models based on these markers. We retrospectively analyzed MR imaging from 88 migraine patients (32 MwA and 56 MwoA) and 49 healthy control subjects (HCs). Features representing the gray matter morphometry and diffusion properties were extracted from participants via histogram analysis. These features were put through an all-relevant feature selection procedure within cross-validation loops to identify features with significant discriminative power for migraine diagnosis and subtyping. Based on the selected features, the predictive ability of the random forest models constructed from the previous sample was tested in an independent sample of 30 patients (10 MwA) and 17 HCs. No overall differences in total brain volume or gray matter volume were revealed between patients and HCs, or between MwA and MwoA (all P values > 0.05). Six features significantly differed between patients and HCs for migraine diagnosis, and four features distinguished MwA from MwoA for subtyping (all P values < 0.001). Four features were significantly correlated with headache severity score (all P values < 0.01). Based on these relevant features, the random forest models achieved accuracies of 80.9

Depression treatment responses vary widely among individuals. Identifying objective biomarkers with predictive accuracy for therapeutic outcomes can enhance treatment efficiency and avoid ineffective therapies. This study investigates whether functional near-infrared spectroscopy (fNIRS) and clinical assessment information can predict treatment response in major depressive disorder (MDD) through machine-learning techniques. Seventy patients with MDD were included in this 6-month longitudinal study, with the primary treatment outcome measured by changes in the Hamilton Depression Rating Scale (HAM-D) scores. fNIRS and clinical information were strictly evaluated using nested cross-validation to predict responders and non-responders based on machine-learning models, including support vector machine, random forest, XGBoost, discriminant analysis, Naïve Bayes, and transformers. The task change of total haemoglobin (HbT), defined as the difference between pre-task and post-task average HbT concentrations, in the dorsolateral prefrontal cortex (dlPFC) is significantly correlated with treatment response (p < 0.005). Leveraging a Naïve Bayes model, inner cross-validation performance (bAcc = 70% [SD = 4], AUC = 0.77 [SD = 0.04]) and outer cross-validation results (bAcc = 73% [SD = 3], AUC = 0.77 [SD = 0.02]) were yielded for predicting response using solely fNIRS data. The bimodal model combining fNIRS and clinical data showed inferior performance in outer cross-validation (bAcc = 68%, AUC = 0.70) compared to the fNIRS-only model. Collectively, fNIRS holds potential as a scalable neuroimaging modality for predicting treatment response in MDD.

OBJECTIVES:Cognitive dysfunction is a common neuropsychiatric manifestation in SLE, particularly affecting processing speed (PS) and memory. This study aims to identify behaviourally relevant topological networks of functional connectivity underlying neuropsychological test performances, using connectome-based predictive modelling (CPM). METHODS:Forty-three SLE patients and 34 controls underwent overall cognitive screening, with SLE patients additionally undergoing neurocognitive assessments for PS and memory. Resting-state fMRI was performed to construct functional connectivity matrices. CPM with leave-one-out cross-validation was employed to model the association between functional connectivity and cognitive performances. Linear regression and moderation analyses were employed to identify risk factors for cognitive dysfunction in SLE. RESULTS:CPM models predicted PS and memory scores in SLE in cross-validation, with correlation coefficients (r) between predicted and observed scores ranging from 0.40 to 0.42 (all P < 0.01). Key brain regions contributing to these models commonly included the cingulate cortex, dorsolateral prefrontal cortex, hippocampus, thalamus and cerebellum, which were critical nodes of default mode, frontoparietal and subcortical networks. A combined CPM model incorporating these functional connectomes predicted overall cognitive ability in SLE (r = 0.38, P < 0.05), with no predictive power in controls, confirming specificity. Additionally, higher damage indices were associated with slower PS. Disease activity, daily glucocorticoids dosage and anticardiolipin antibodies levels moderated associations between functional connectivity strength and cognitive performances. CONCLUSION:CPM identified brain networks that underlie critical cognitive functions in individuals and these neural fingerprints could potentially assist the diagnosis of cognitive dysfunction in SLE.

Background MULTIPLEX is a single-scan three-dimensional multi-parametric MRI technique that provides 1 mm isotropic T1-, T2*-, proton density- and susceptibility-weighted images and the corresponding quantitative maps. This study aimed to investigate its feasibility of clinical application in Parkinson’s disease (PD). Methods 27 PD patients and 23 healthy control (HC) were recruited and underwent a MULTIPLEX scanning. All image reconstruction and processing were automatically performed with in-house C + + programs on the Automatic Differentiation using Expression Template platform. According to the HybraPD atlas consisting of 12 human brain subcortical nuclei, the region-of-interest (ROI) based analysis was conducted to extract quantitative parameters, then identify PD-related abnormalities from the T1, T2* and proton density maps and quantitative susceptibility mapping (QSM), by comparing patients and HCs. Results The ROI-based analysis revealed significantly decreased mean T1 values in substantia nigra pars compacta and habenular nuclei, mean T2* value in subthalamic nucleus and increased mean QSM value in subthalamic nucleus in PD patients, compared to HCs (all p values < 0.05 after FDR correction). The receiver operating characteristic analysis showed all these four quantitative parameters significantly contributed to PD diagnosis (all p values < 0.01 after FDR correction). Furthermore, the two quantitative parameters in subthalamic nucleus showed hemicerebral differences in regard to the clinically dominant side among PD patients. Conclusions MULTIPLEX might be feasible for clinical application to assist in PD diagnosis and provide possible pathological information of PD patients’ subcortical nucleus and dopaminergic midbrain regions.

Abstract Purpose: This study aimed to elucidate the impact of brain tumors on cerebral edema and glymphatic drainage by leveraging advanced MRI techniques to explore the relationships among tumor characteristics, glymphatic function, and aquaporin-4 (AQP4) expression levels. Experimental Design: In a prospective cohort from March 2022 to April 2023, patients with glioblastoma, brain metastases, and aggressive meningiomas, alongside age- and sex-matched healthy controls, underwent 3.0T MRI, including diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index and multiparametric MRI for quantitative brain mapping. Tumor and peritumor tissues were analyzed for AQP4 expression levels via immunofluorescence. Correlations among MRI parameters, glymphatic function (DTI-ALPS index), and AQP4 expression levels were statistically assessed. Results: Among 84 patients (mean age: 55 ± 12 years; 38 males) and 59 controls (mean age: 54 ± 8 years; 23 males), patients with brain tumor exhibited significantly reduced glymphatic function (DTI-ALPS index: 2.315 vs. 2.879; P = 0.001) and increased cerebrospinal fluid volume (201.376 cm³ vs. 115.957 cm³; P = 0.001). A negative correlation was observed between tumor volume and the DTI-ALPS index (r: −0.715, P < 0.001), whereas AQP4 expression levels correlated positively with peritumoral brain edema volume (r: 0.989, P < 0.001) and negatively with proton density in peritumoral brain edema areas (ρ: −0.506, P < 0.001). Conclusions: Our findings highlight the interplay among tumor-induced compression, glymphatic dysfunction, and altered fluid dynamics, demonstrating the utility of DTI-ALPS and multiparametric MRI in understanding the pathophysiology of tumor-related cerebral edema. These insights provide a radiological foundation for further neuro-oncological investigations into the glymphatic system. See related commentary by Surov and Borggrefe, p. 4813

The ellipsoid body (EB) is a major structure of the central complex of the Drosophila melanogaster brain. Twenty-two subtypes of EB ring neurons have been identified based on anatomic and morphologic characteristics by light-level microscopy and EM connectomics. A few studies have associated ring neurons with the regulation of sleep homeostasis and structure. However, cell type-specific and population interactions in the regulation of sleep remain unclear. Using an unbiased thermogenetic screen of EB drivers using female flies, we found the following: (1) multiple ring neurons are involved in the modulation of amount of sleep and structure in a synergistic manner; (2) analysis of data for ΔP(doze)/ΔP(wake) using a mixed Gaussian model detected 5 clusters of GAL4 drivers which had similar effects on sleep pressure and/or depth: lines driving arousal contained R4m neurons, whereas lines that increased sleep pressure had R3m cells; (3) a GLM analysis correlating ring cell subtype and activity-dependent changes in sleep parameters across all lines identified several cell types significantly associated with specific sleep effects: R3p was daytime sleep-promoting, and R4m was nighttime wake-promoting; and (4) R3d cells present in 5HT7-GAL4 and in GAL4 lines, which exclusively affect sleep structure, were found to contribute to fragmentation of sleep during both day and night. Thus, multiple subtypes of ring neurons distinctively control sleep amount and/or structure. The unique highly interconnected structure of the EB suggests a local-network model worth future investigation; understanding EB subtype interactions may provide insight how sleep circuits in general are structured.SIGNIFICANCE STATEMENT How multiple brain regions, with many cell types, can coherently regulate sleep remains unclear, but identification of cell type-specific roles can generate opportunities for understanding the principles of integration and cooperation. The ellipsoid body (EB) of the fly brain exhibits a high level of connectivity and functional heterogeneity yet is able to tune multiple behaviors in real-time, including sleep. Leveraging the powerful genetic tools available in Drosophila and recent progress in the characterization of the morphology and connectivity of EB ring neurons, we identify several EB subtypes specifically associated with distinct aspects of sleep. Our findings will aid in revealing the rules of coding and integration in the brain.

Abstract Background Migraine aura is a transient, fully reversible visual, sensory, or other central nervous system symptom that classically precedes migraine headache. This study aimed to investigate cerebral blood flow (CBF) alterations of migraine with aura patients (MwA) and without aura patients (MwoA) during inter-ictal periods, using arterial spin labeling (ASL). Methods We evaluated 88 migraine patients (32 MwA) and 44 healthy control subjects (HC) who underwent a three-dimensional pseudo-continuous ASL MRI scanning. Voxel-based comparison of normalized CBF was conducted between MwA and MwoA. The relationship between CBF variation and clinical scale assessment was further analyzed. The mean CBF values in brain regions showed significant differences were calculated and considered as imaging features. Based on these features, different machine learning–based models were established to differentiate MwA and MwoA under five-fold cross validation. The predictive ability of the optimal model was further tested in an independent sample of 30 migraine patients (10 MwA). Results In comparison to MwoA and HC, MwA exhibited higher CBF levels in the bilateral superior frontal gyrus, bilateral postcentral gyrus and cerebellum, and lower CBF levels in the bilateral middle frontal gyrus, thalamus and medioventral occipital cortex (all p values < 0.05). These variations were also significantly correlated with multiple clinical rating scales about headache severity, quality of life and emotion. On basis of these CBF features, the accuracies and areas under curve of the final model in the training and testing samples were 84.3% and 0.872, 83.3% and 0.860 in discriminating patients with and without aura, respectively. Conclusion In this study, CBF abnormalities of MwA were identified in multiple brain regions, which might help better understand migraine-stroke connection mechanisms and may guide patient-specific decision-making.

Parkinson's disease(PD) is treated effectively by deep brain stimulation(DBS) of the subthalamic nucleus(STN), using an electrode inserted into the head of a PD patient. The electrode has multiple electrical contacts along its length, so the best may be chosen for selectively stimulating the STN. Neurosurgeons usually determine the optimal stimulated contact via the clinical experience of the neurosurgeon and the motor improvement of PD patients. This is a time-consuming and labor-intensive trial-and-error process. The selection of optimal stimulated contact highly depends on the locations of sweet spots, which are manually identified by the characteristic features of microelectrode recordings(MERs). This paper presents an amplitude-frequency-aware deep fusion network for optimal contact selection on STN-DBS electrodes.The method first obtains the amplitude-frequency fusion features by combining the MERs time sequence features and the amplitude sequence features, and then uses the convolutional neural network(CNN) with convolutional block attention module(CBAM) to identify both the border of the STN and the sweet spots to implant the electrode. The optimal stimulated contact can be selected according to the distribution of the sweet spots. Experimental results indicate that, for successful surgeries, neurosurgeons and the proposed AI solution selected the same optimal contacts. Furthermore, the proposed method outperforms the state-of-the-art methods for STN and sweet spot identification. The proposed method shows great potential for optimal contact selection to improve the efficiency of STN-DBS surgery and reduce the dependence on clinicians' experience.

BACKGROUND: Resting tremor is an essential characteristic in patients suffering from Parkinson's disease (PD). OBJECTIVE: Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring. METHODS: Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson's Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis. RESULTS: The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities. CONCLUSION: The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.

Long-term monitoring of resting tremor is key to assess the status of patients suffering from Parkinson’s disease (PD), which is of vital importance for reasonable medication. The detection and quantification of resting tremor in reported works rely heavily on specified movements and are not appropriate for long-term monitoring in real-life condition. The purpose of this study is to develop a detection model for long-term monitoring of resting tremor and explore an effective indicator for tremor quantification. This study included long-term acceleration data from PD patients and proposed a resting tremor detection model based on machine learning classifiers and Synthetic Minority Oversampling Technique (SMOTE). Four machine learning classifiers, K-Nearest Neighbor (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), and Support Vector Machine (SVM), were compared. Furthermore, an indicator called tremor timing ratio (TTR) was defined and calculated for tremor quantification. The detection model with RF classifier achieved the highest overall accuracy of 94.81%. The sample entropy of the acceleration signal was proved most influential in the classification by exploring the feature importance. Through the Kruskal-Wallis test and the Mann-Whitney U test, the TTR had a strong correlation with the subscore of resting tremor in Unified Parkinson Disease Rating Scale (UPDRS). Such two-step evaluation process for resting tremor can detect the tremor effectively and is expected to be applied in long-term monitoring of PD patients in daily life to realize a more comprehensive assessment of PD.

Background Leukoaraiosis (LA) is a phenomenon of the brain that is often observed in elderly people. However, little is known about the role of LA in cognitive impairment in neurodegeneration and disease. This cross-sectional, retrospective Leukoaraiosis And Disability (LADIS) study aimed to characterize the relationship between brain white matter connectivity properties with LA ratings in patients with Alzheimer's disease (AD) as compared with age-matched cognitively normal controls. Methods Patients with AD (n=76) and elderly individuals with normal cognitive (NC) function (n=82) were classified into 3 groups, LA1, LA2, and LA3, according to the rating of their white matter changes (WMCs). Diffusion tensor imaging (DTI) data were analyzed by quantifying and comparing the white matter connectivity properties and gray matter (GM) volume of brain regions of interest (ROIs). Results The rich-club network properties in the AD LA1 and LA2 groups showed significant patterns of disrupted peripheral regions and reduced connectivity compared to those in the NC LA1 and LA2 groups, respectively. However, the rich-club network properties in the AD LA3 group showed similar patterns of disrupted peripheral regions and reduced connectivity compared to those in the NC LA3 group, despite there being significant hippocampal and amygdala atrophic differences between AD patients and NC elders. Compared to the NC LA1 group, the characteristic path length of white matter fiber connectivity in the NC LA3 group was significantly increased, and the brain's global efficiency, clustering coefficient, and network connectivity strength were significantly reduced (P<0.05, respectively). However, no significant differences (P>0.05) were observed in characteristic path length, reduced global efficiency, or the clustering coefficient between the NC LA3 and AD LA1 groups, or between the NC LA3 and AD LA2 groups. Conclusions Our findings offer some insights into a potential role of LA in cognitive impairment that may predict the development of disability in older adults. The occurrence of LA, an intermediate degenerative change, during neurodegeneration and disease may potentially lead to the remodeling of the brain network through brain plasticity. LA, therefore, representing a possible compensatory mechanism to buffer cognitive decline.

The clinical benefit of deep brain stimulation (DBS) for Parkinson's disease (PD) is relevant to the tracts adjacent to the stimulation site, but it remains unclear what connectivity pattern is associated with effective DBS. The aim of this study was to identify clinically effective electrode contacts on the basis of brain connectivity markers derived from diffusion tensor tractography. We reviewed 77 PD patients who underwent bilateral subthalamic nucleus DBS surgery. The patients were assigned into the training (n = 58) and validation (n = 19) groups. According to the therapeutic window size, all contacts were classified into effective and ineffective groups. The whole-brain connectivity of each contact's volume of tissue activated was estimated using tractography with preoperative diffusion tensor data. Extracted connectivity features were put into an all-relevant feature selection procedure within cross-validation loops, to identify features with significant discriminative power for contact classification. A total of 616 contacts on 154 DBS leads were discriminated, with 388 and 228 contacts being classified as effective and ineffective ones, respectively. After the feature selection, the connectivity of contacts with the thalamus, pallidum, hippocampus, primary motor area, supplementary motor area and superior frontal gyrus was identified to significantly contribute to contact classification. Based on these relevant features, the random forest model constructed from the training group achieved an accuracy of 84.9% in the validation group, to discriminate effective contacts from the ineffective. Our findings advanced the understanding of the specific brain connectivity patterns associated with clinical effective electrode contacts, which potentially guided postoperative DBS programming.

Objective: This study was to investigate the relationship of diffusion features with molecule information, and then predict grade and survival in lower-grade gliomas. Methods: 65 patients with primary lower-grade gliomas (WHO Grade II & III) who underwent conventional MRI and diffusion tensor imaging were retrospectively studied. The tumor region was automatically segmented into contrast-enhancing tumor, non-enhancing tumor, edematous and necrotic volumes. Diffusion features, including fractional anisotropy (FA), axial diffusivity, radial diffusivity and apparent diffusion coefficient (ADC), were extracted from each volume using histogram analysis. To estimate molecule biomarkers and predict clinical characteristics of grade and survival, support vector machine, generalized linear model, logistic regression and Cox regression were performed on the related features. Results: The diffusion features in non-enhancing tumor volume showed differences between isocitrate dehydrogenase mutant and wild-type gliomas. And the mean accuracy of support vector machine classifiers was 0.79. Ki-67 labeling index was correlated with these features, which were combined to significantly estimate Ki-67 expression level (r = 0.657, p < 0.001). These features also showed differences between Grade II and III gliomas. A combination of them for grade classification resulted in an area under the curve of 0,914 (0.857-0.971). Mean FA and fifth percentile of ADC were independently associated with overall survival, with lower FA and higher ADC showing better survival outcome. Conclusion: In lower-grade gliomas, multiparametric and multiregional diffusion features could help predict molecule information, histological grade and survival. Advances in knowledge: The multi parametric diffusion features in non-enhancing tumor were associated with molecule information, grade and survival in lower-grade gliomas.

BACKGROUND:To explore the correlation between intracranial pressure (ICP) and cerebrospinal fluid (CSF) parameters assessed by phase-contrast cine MRI (PC-MRI). METHODS:Fifteen normal people and 80 subjects with communicating hydrocephalus who underwent PC-MRI examinations from a single center were included in this cross-sectional study. In addition to recording patient's age, heart rate, blood pressure and body mass index (BMI), ICP and CSF hemodynamic parameters, such as flow velocity and aqueduct diameter, were measured for correlation analysis. RESULTS:The mean ICP and CSF aqueduct diameter in hydrocephalus patients were 151.05 mmH2O and 2.877 mm, respectively, and the maximum (6.938 cm/s) and mean (0.845 cm/s) CSF flow velocities were significantly higher in these patients compared with the controls (P<0.05). After adjusting for age, heart rate, blood pressure, and BMI, there was no significant relationship between peak velocity and ICP (P>0.05). Furthermore, a nonlinear relationship was observed between the ICP and the average velocity of CSF, and the ICP and aqueduct diameter. The ICP increased with the average velocity above 1.628 cm/s (P≤0.01), and the aqueduct diameter increased more than 3.6 mm (P<0.001). CONCLUSIONS:This study found significant correlations between ICP and average velocity and aqueduct diameter. These findings can be useful in assisting clinicians in predicting ICP more effectively, thus improving patient management.

BACKGROUND:Depression is a common comorbid condition in Parkinson's disease and a major contributor to poor quality of life. Despite this, depression in PD is under-diagnosed due to overlapping symptoms and difficulties in the assessment of depression in cognitively impaired old patients.OBJECTIVES:This study is to explore functional connectivity markers of depression in PD patients using resting-state fMRI and help diagnose whether patients have depression or not.METHODS:We reviewed 156 advanced PD patients (duration > 5 years; 59 depressed ones) and 45 healthy control subjects who underwent a resting-state fMRI scanning. Functional connectivity analysis was employed to characterize intrinsic connectivity networks using group independent component analysis and extract connectivity features. Features were put into an all-relevant feature selection procedure within cross-validation loops, to identify features with significant discriminative power for classification. Random forest classifiers were built for depression diagnosis, on the basis of identified features.RESULTS:42 intrinsic connectivity networks were identified and arranged into subcortical, auditory, somatomotor, visual, cognitive control, default-mode and cerebellar networks. Six features were significantly relevant to classification. They were connectivity within posterior cingulate cortex, within insula, between posterior cingulate cortex and insula/hippocampus+amygdala, between insula and precuneus, and between superior parietal lobule and medial prefrontal cortex. The mean accuracy achieved with classifiers to discriminate depressed patients from the non-depressed was 82.4%.CONCLUSIONS:Our findings provide preliminary evidence that resting-state functional connectivity can characterize depressed PD patients and help distinguish them from non-depressed ones.