Explore the vast range of titles available.

Dynamic brain networks (DBNs) can capture the intricate connections and temporal evolution among brain regions, becoming increasingly crucial in the diagnosis of neurological disorders. However, most existing researches tend to focus on isolated brain network sequence segmented by sliding windows, and they are difficult to effectively uncover the higher-order spatio-temporal topological pattern in DBNs. Meantime, it remains a challenge to utilize the structure connectivity prior in the DBNs analysis. To address these problems, we propose a multi-channel spatio-temporal graph attention contrastive network for DBNs analysis. Specifically, we first construct dynamic brain functional networks from fMRI data with sliding windows, and embed the structural connectivity derived from diffusion tensor imaging (DTI) to the dynamic functional connectivity graph representation to construct multi-modal brain network. Second, we develop a multi-channel spatial attention contrastive network to extract topological features from the brain network within each time window. This network incorporates an intra-window graph contrastive constraint to enhance the discriminative ability of the extracted features. Moreover, temporal dependencies across windows are captured by integrating feature embeddings through a self-attention mechanism, and the inter-window recurrent contrastive constraint is devised to extract higher-order spatio-temporal topological features. Finally, a multi-layer perceptron (MLP) is used to classify the brain networks. Experiments on epilepsy and ADNI datasets show that our method outperforms several state-of-the-art approaches in diagnosing performance, and it provides discriminative graph features for related brain diseases. •A deep graph model is introduced for DBNs analysis, enabling higher-order spatio-temporal information propagation.•A multi-modal method integrates structural and functional brain connectivity into a unified graph representation.•A graph attention network with contrastive loss improves spatio-temporal feature discrimination.•Our method outperforms state-of-the-art approaches on epilepsy and ADNI datasets, and demonstrates potential in identifying neurological biomarkers.

ObjectiveTo investigate atrophy patterns in hypothalamic subunits at different stages of ALS and examine correlations between hypothalamic subunit volume and clinical information.MethodsWe used the King’s clinical staging system to divide 91 consecutive ALS patients into the different disease stages. We investigated patterns of hypothalamic atrophy using a recently published automated segmentation method in ALS patients and in 97 healthy controls. We recorded all subjects’ demographic and clinical information.ResultsCompared with healthy controls, we found significant atrophy in the bilateral anterior–superior subunit and the superior tubular subunit, as well as a reduction in global hypothalamic volume in ALS patients. When we used the King’s clinical staging system to divide patients into the different disease stages, we found neither global nor specific subunit atrophy until King’s stage 3 in the hypothalamus. Moreover, specific subunit volumes were significantly associated with body mass index.ConclusionsIn a relatively large sample of Chinese patients with ALS, using a recently published automated segmentation method for the hypothalamus, we found the pattern of hypothalamic atrophy in ALS patients differed greatly across King’s clinical disease stages. Moreover, specific hypothalamic subunit atrophy may play an important role in energy metabolism in ALS patients. Thus, our findings suggest that hypothalamic atrophy may have potential phenotypic associations, and improved energy metabolism may become an important component of individualised therapy for ALS.

To explore the relationship between glymphatic dysfunction and cognitive impairment in unilateral temporal lobe epilepsy (TLE). This study retrospectively included 38 patients with unilateral TLE and 26 age- and gender-matched healthy controls (HCs). The diffusion tensor image analysis along the perivascular space (DTI-ALPS) index, choroid plexus volume (CPV), and cognitive assessment were obtained for each participant. Neuropsychological test batteries included Montreal Cognitive Assessment (MoCA), Minimum Mental State Examination, Arithmetic Test (AT), Digit Symbol Substitution Test (DSST), Digit Span Test (DST), Boston Naming Test, Block design, Phonological Fluency Test (PFT), and Semantic Verbal Fluency (SVF). Compared to HCs, TLE patients had lower scores of MoCA, AT, DSST, DST, Block design, PFT and SVF (all  < 0.05) and lower values of mean DTI-ALPS index (1.491 ± 0.142 vs. 1.642 ± 0.123,  < 0.001). Significantly lower DTI-ALPS index values were observed in the ipsilateral hemisphere than in the contralateral hemisphere (1.466 ± 0.129 vs. 1.517 ± 0.175,  = 0.013) for patients with unilateral TLE. Correlation analyses found that SVF performance was significantly or borderline significantly associated with glymphatic function (  < 0.05 for all DTI-ALPS index and  = 0.057 for CPV) in TLE patients. Linear regression analyses showed that increased CPV and decreased DTI-ALPS index were independent risk factors for semantic fluency impairment (all  < 0.05). Furthermore, mediation analyses found the mediator role of the mean DTI-ALPS index in the relationship between choroid plexus enlargement and semantic fluency impairment (indirect effect:  = -0.182,  = -0.486 to -0.037). These findings reveal the important role of the DTI-ALPS index and CPV in SVF performance in unilateral TLE. Decreased DTI-ALPS index and increased CPV are the independent risk factors for semantic fluency impairment. The DTI-ALPS index may fully mediate the relationship between CP enlargement and SVF performance. These insights provide a radiological foundation for further investigations into the mechanism of the glymphatic system in TLE pathophysiology.

Background and purpose: We previously established a radiological protocol to discriminate Parkinsonism-predominant multiple system atrophy (MSA-P) from Parkinson’s disease (PD). However, we do not know if it can differentiate early-stage disease. This study aimed to investigate whether the morphological and intensity changes in susceptibility weighted imaging (SWI) of the lentiform nucleus (LN) could discriminate MSA-P from PD at early stages. Methods: We retrospectively enrolled patients with MSA-P, PD and sex- and age-matched controls whose brain MRI included SWI, between January 2015 and January 2022 at the Movement Disorder Center. Two specialists at the center reviewed the medical records and made the final diagnosis, and two experienced neuroradiologists performed MRI analysis, based on a defined and revised protocol for conducting morphological measurements of the LN and signal intensity. Results: Nineteen patients with MSA-P and 19 patients with PD, with less than two years of disease duration, and 19 control individuals were enrolled in this study. We found that patients with MSA-P presented significantly decreased size in the short line (SL) and corrected short line (cSL), ratio of the SL to the long line (SLLr) and corrected SLLr (cSLLr) of the LN, increased standard deviation of signal intensity (SIsd_LN, cSIsd_LN) compared to patients with PD and controls (P < 0.05). With ROC analysis, this finding had a sensitivity of 89.5% and a specificity of 73.7% to distinguish MSA-P from PD. Conclusions: Compared to PD and controls, patients with MSA-P are characterized by a narrowing morphology of the posterior region of the LN. Quantitative morphological changes provide a potential marker for differentiating MSA-P from PD at early stages.

Background Using in vivo neuroimaging techniques, growing evidence has demonstrated that the choroid plexus (CP) volume is enlarged in patients with several neurodegenerative diseases, including Alzheimer’s disease and Parkinson’s disease. However, although animal and postmortem findings suggest that CP abnormalities are likely important pathological mechanisms underlying amyotrophic lateral sclerosis (ALS), the third most common neurodegenerative disease, no available study has been conducted to thoroughly assess CP abnormalities and their clinical relevance in vivo in ALS patients to date. Thus, we aimed to determine whether in vivo CP enlargement may occur in ALS patients. We also aimed to identify the relationships of CP volume with clinical disabilities and blood-CSF barrier (BCSFB) permeability in ALS patients. Methods In this retrospective study, based on structural MRI data, CP volume was assessed using a Gaussian mixture model and underwent further manual correction in 155 ALS patients and 105 age- and sex-matched HCs from October 2021 to April 2023. The ALS Functional Rating Scale-Revised (ALSFRS-R) was used to assess clinical disability. The CSF/serum albumin quotient (Qalb) was used to assess BCSFB permeability. Moreover, all the ALS patients completed genetic testing, and according to genetic testing, the ALS patients were further divided into genetic ALS subgroup and sporadic ALS subgroup. Results We found that compared with HCs, ALS patients had a significantly higher CP volume ( p  < 0.001). Moreover, compared with HCs, CP volume was significantly increased in both ALS patients with and without known genetic mutations after family-wise error correction ( p  = 0.006 and p  < 0.001, respectively), while there were no significant differences between the two ALS groups. Furthermore, the CP volume was significantly correlated with the ALSFRS-r score ( r = -0.226; p  = 0.005) and the Qalb ( r  = 0.479; p  < 0.001) in ALS patients. Conclusion Our study first demonstrates CP enlargement in vivo in ALS patients, and continues to suggest an important pathogenetic role for CP abnormalities in ALS. Moreover, assessing CP volume is likely a noninvasive and easy-to-implement approach for screening BCSFB dysfunction in ALS patients.

To propose a multimodal functional brain network (FBN) and structural brain network (SBN) topological feature fusion technique based on resting-state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), 3D-T1-weighted imaging (3D-T1WI), and demographic characteristics to diagnose mild cognitive impairment (MCI) in patients with unilateral middle cerebral artery (MCA) steno-occlusive disease. The performances of different algorithms on the MCI dataset were evaluated using 5-fold cross-validation. The diagnostic results of the multimodal performance were evaluated using t-distributed stochastic neighbor embedding (t-SNE) analysis. The four-modal analysis method proposed in this study was applied to identify brain regions and connections associated with MCI, thus confirming its validity. Based on the fusion of the topological features of the multimodal FBN and SBN, the accuracy for the diagnosis of MCI in patients with unilateral MCA steno-occlusive disease reached 90.00%. The accuracy, recall, sensitivity, and F1-score were higher than those of the other methods, as was the diagnostic efficacy (AUC = 0.9149). The multimodal FBN and SBN topological feature fusion technique, which incorporates rs-fMRI, DTI, 3D-T1WI, and demographic characteristics, obtains the most discriminative features of MCI in patients with unilateral MCA steno-occlusive disease and can effectively identify disease-related brain areas and connections. Efficient automated diagnosis facilitates the early and accurate detection of MCI and timely intervention and treatment to delay or prevent disease progression.

Background Using neuroimaging techniques, growing evidence has suggested that the choroid plexus (CP) volume is enlarged in multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). Notably, the CP has been suggested to play an important role in inflammation-induced CNS damage under disease conditions. However, to our knowledge, no study has investigated the relationships between peripheral inflammation and CP volume in sporadic ALS patients. Thus, in this study, we aimed to verify CP enlargement and explore its association with peripheral inflammation in vivo in independent ALS cohorts. Methods Based on structural MRI data, CP volume was measured using Gaussian mixture models and further manually corrected in two independent cohorts of sporadic ALS patients and healthy controls (HCs). Serum inflammatory protein levels were measured using a novel high-sensitivity Olink proximity extension assay (PEA) technique. Xtreme gradient boosting (XGBoost) was used to explore the contribution of peripheral inflammatory factors to CP enlargement. Then, partial correlation analyses were performed. Results CP volumes were significantly higher in ALS patients than in HCs in the independent cohorts. Compared with HCs, serum levels of CRP, IL-6, CXCL10, and 35 other inflammatory factors were significantly increased in ALS patients. Using the XGBoost approach, we established a model-based importance of features, and the top three predictors of CP volume in ALS patients were CRP, IL-6, and CXCL10 (with gains of 0.24, 0.18, and 0.15, respectively). Correlation analyses revealed that CRP, IL-6, and CXCL10 were significantly associated with CP volume in ALS patients ( r  = 0.462 ∼ 0.636, p <  0.001). Conclusion Our study is the first to reveal a consistent and replicable contribution of peripheral inflammation to CP enlargement in vivo in sporadic ALS patients. Given that CP enlargement has been recently detected in other brain diseases, these findings should consider extending to other disease conditions with a peripheral inflammatory component.

Limbic structures have recently garnered increased attention in Parkinson's disease (PD) research. This study aims to explore changes at the whole-brain level in the structural network, specifically the white matter fibres connecting the thalamus and limbic system, and their correlation with the clinical characteristics of patients with PD. Between December 2020 and November 2021, we prospectively enrolled 42 patients with PD and healthy controls at the movement disorder centre. All participants underwent diffusion tensor imaging (DTI), 3D T1-weighted imaging (3D-T1WI), and routine brain magnetic resonance imaging on a 3.0 T MR scanner. We employed the tract-based spatial statistical (TBSS) analytic approach, examined structural network properties, and conducted probabilistic fibre tractography to identify alterations in white matter pathways and the topological organisation associated with PD. In patients with PD, significant changes were observed in the fibrous tracts of the prefrontal lobe, corpus callosum, and thalamus. Notably, the fibrous tracts in the prefrontal lobe and corpus callosum showed a moderate negative correlation with the Freezing of Gait Questionnaire (FOG-Q) scores (  = -0.423,  = 0.011). The hippocampus and orbitofrontal gyrus exhibited more fibre bundle parameter changes than other limbic structures. The mean streamline length between the thalamus and the orbitofrontal gyrus demonstrated a moderate negative correlation with Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III (  = -0.435,  = 0.006). Topological parameters, including characteristic path length ( ), global efficiency ( ), normalised shortest path length ( ) and nodal local efficiency ( ), correlated moderately with the MDS-UPDRS, HAMA, MoCA, PDQ-39, and FOG-Q, respectively. DTI is a valuable tool for detecting changes in water molecule dispersion and the topological structure of the brain in patients with PD. The thalamus may play a significant role in the gait abnormalities observed in PD.

This study aimed to investigate the diagnostic value of combined glutamate chemical exchange saturation transfer (GluCEST) imaging and γ-aminobutyric acid (GABA)-edited proton magnetic resonance spectroscopy ( 1 H-MRS) in localizing epileptogenic foci and differentiating drug-resistant epilepsy (DR) from drug-responsive epilepsy (DRES) in temporal lobe epilepsy (TLE) using 5T MRI. Twenty-four TLE patients (13 left, 11 right) and 25 age-/gender-matched healthy controls (HCs) underwent GluCEST and MEGA-PRESS MRS at 5T MRI. Directional asymmetry indices (DAIglu_H for hippocampus, DAIglu_A for amygdala) and a novel composite biomarker (DAIglu_GABA) integrating GluCEST asymmetry and GABA/Cr ratios were analyzed. Another asymmetry metric was employed to discriminate the left and right TLE groups [DAIglu_H(epi) for hippocampus, DAIglu_A(epi) for amygdala]. Subgroup comparisons (HC vs. DR vs. DRES) and receiver-operating characteristic (ROC) analyses were performed.

It is reported that radiomic features extracted from quantitative susceptibility mapping (QSM) had promising clinical value for the diagnosis of Parkinson's disease (PD). We aimed to explore the usefulness of radiomics features based on magnitude images to distinguish PD from non-PD controls. We retrospectively recruited PD patients and controls who underwent brain 3.0T MR including susceptibility-weighted imaging (SWI). A total of 396 radiomics features were extracted from the SN of 95 PD patients and 95 non-PD controls based on SWI. Intra-/inter-observer correlation coefficients (ICCs) were applied to measure the observer agreement for the radiomic feature extraction. Then the patients were randomly grouped into training and validation sets in a ratio of 7:3. In the training set, the maximum correlation minimum redundancy algorithm (mRMR) and the least absolute shrinkage and selection operator (LASSO) were conducted to filter and choose the optimized subset of features, and a radiomics signature was constructed. Moreover, radiomics signatures were constructed by different machine learning models. Area under the ROC curves (AUCs) were applied to evaluate the predictive performance of the models. Then correlation analysis was performed to evaluate the correlation between the optimized features and clinical factors. The intro-observer CC ranged from 0.82 to 1.0, and the inter-observer CC ranged from 0.77 to 0.99. The LASSO logistic regression model showed good prediction efficacy in the training set [AUC = 0.82, 95% confidence interval (CI, 0.74-0.88)] and the validation set [AUC = 0.81, 95% CI (0.68-0.91)]. One radiomic feature showed a moderate negative correlation with Hoehn-Yahr stage ( = -0.49, = 0.012). Radiomic predictive features based on SWI magnitude images could reflect the Hoehn-Yahr stage of PD to some extent.