BACKGROUND: Alzheimer\'s disease (AD) and frontotemporal dementia (FTD) are the two most common neurodegenerative dementias, presenting with similar clinical features that challenge accurate diagnosis. Despite extensive research, the underlying pathophysiological mechanisms remain unclear, and effective treatments are limited. This study aims to investigate the alterations in brain network connectivity associated with AD and FTD to enhance our understanding of their pathophysiology and establish a scientific foundation for their diagnosis and treatment.
METHODS: We analyzed preprocessed electroencephalogram (EEG) data from the OpenNeuro public dataset, comprising 36 patients with AD, 23 patients with FTD, and 29 healthy controls (HC). Participants were in a resting state with eyes closed. We estimated the average functional connectivity using the Phase Lag Index (PLI) for lower frequencies (delta and theta) and the Amplitude Envelope Correlation with leakage correction (AEC-c) for higher frequencies (alpha, beta, and gamma). Graph theory was applied to calculate topological parameters, including mean node degree, clustering coefficient, characteristic path length, global and local efficiency. A permutation test was then utilized to assess changes in brain network connectivity in AD and FTD based on these parameters.
RESULTS: Both AD and FTD patients showed increased mean PLI values in the theta frequency band, along with increases in average node degree, clustering coefficient, global efficiency, and local efficiency. Conversely, mean AEC-c values in the alpha frequency band were notably diminished, which was accompanied by decreases average node degree, clustering coefficient, global efficiency, and local efficiency. Furthermore, AD patients in the occipital region showed an increase in theta band node degree and decreased alpha band clustering coefficient and local efficiency, a pattern not observed in FTD.
CONCLUSIONS: Our findings reveal distinct abnormalities in the functional network topology and connectivity in AD and FTD, which may contribute to a better understanding of the pathophysiological mechanisms of these diseases. Specifically, patients with AD demonstrated a more widespread change in functional connectivity, while those with FTD retained connectivity in the occipital lobe. These observations could provide valuable insights for developing electrophysiological markers to differentiate between the two diseases.

UNASSIGNED: Brain-computer interface (BCI) has been widely used for functional recovery after stroke. Understanding the brain mechanisms following BCI intervention to optimize BCI strategies is crucial for the benefit of stroke patients.
UNASSIGNED: Forty-six patients with upper limb motor dysfunction after stroke were recruited and randomly divided into the control group or the BCI group. The primary outcome was measured by the assessment of Fugl-Meyer Assessment of Upper Extremity (FMA-UE). Meanwhile, we performed resting-state functional magnetic resonance imaging (rs-fMRI) in all patients, followed by independent component analysis (ICA) to identify functionally connected brain networks. Finally, we assessed the topological efficiency of both groups using graph-theoretic analysis in these brain subnetworks.
UNASSIGNED: The FMA-UE score of the BCI group was significantly higher than that of the control group after treatment (p = 0.035). From the network topology analysis, we first identified seven subnetworks from the rs-fMRI data. In the following analysis of subnetwork properties, small-world properties including γ (p = 0.035) and σ (p = 0.031) within the visual network (VN) decreased in the BCI group. For the analysis of the dorsal attention network (DAN), significant differences were found in assortativity (p = 0.045) between the groups. Additionally, the improvement in FMA-UE was positively correlated with the assortativity of the dorsal attention network (R = 0.498, p = 0.011).
UNASSIGNED: Brain-computer interface can promote the recovery of upper limbs after stroke by regulating VN and DAN. The correlation trend of weak intensity proves that functional recovery in stroke patients is likely to be related to the brain\'s visuospatial processing ability, which can be used to optimize BCI strategies.
UNASSIGNED: The trial is registered in the Chinese Clinical Trial Registry, number ChiCTR2000034848. Registered 21 July 2020.

UNASSIGNED: Emotional distress frequently occur in cancer patients following diagnosis. Previous neuroimaging studies have demonstrated that depression and anxiety are associated with functional and structural brain abnormalities. However, little is known about the cancer-associated changes of emotional brain network in non-small cell lung cancer (NSCLC) patients. The aim of this study was to assess the topological features of brain structural network and emotions in non-nervous system metastatic NSCLC patients prior to chemotherapy.
UNASSIGNED: Twenty-four treatment-naïve patients with non-nervous system metastatic NSCLC and 25 healthy controls (HC) matched for gender, age and education participated in this study. All subjects underwent diffusion tensor imaging (DTI), and were assessed with the 17 item hamilton depression rating scale (HAMD-17) and hamilton anxiety rating scale (HAMA). Properties of brain network were examined by the method of graph-theoretic analysis. The assessments included small-worldness, clustering coefficient and shortest path length.
UNASSIGNED: NSCLC patients had higher scores of HAMD-17 and HAMA when compared with HC. Additionally, we found a small-world topology of brain white matter network in both NSCLC and HC. NSCLC patients had significantly reduced clustering coefficient compared to healthy controls in the left hippocampus. Moreover, increased shortest path length were identified in NSCLC patients, which included the left middle frontal gyrus (orbital part), superior temporal gyrus and right Rolandic operculum, rectus gyrus, lenticular nucleus (putamen). However, no correlations were found between the impaired brain regions and HAMD-17, HAMA scores of NSCLC patients.
UNASSIGNED: Our results indicated impaired topological characteristics in the brain structural network of non-nervous system metastatic NSCLC patients prior to chemotherapy, which might account for the cancer-related emotional distress. Our findings demonstrated that NSCLC might affect brain regions involved in the process of emotion, which identified the basis of emotional changes associated with cancer.

OBJECTIVE: To investigate the changes in behaviors and brain structural network in patients with idiopathic rapid eye movement sleep behavior disorder (iRBD).
METHODS: Twenty patients with iRBD (iRBD group) and 22 healthy control subjects were evaluated using the Unified Parkinson\'s Disease Rating Scale (UPDRS) and Hoehn-Yahr staging. Diffusion tensor imaging and graph- theoretical analysis were performed for analyzing the topological characteristics of brain structural networks of the patients, and the correlation between the behavioral changes and the changes in the topological characteristics of the brain networks was analyzed.
RESULTS: The UPDRS score was significantly higher in iRBD group than in the healthy control group (P < 0.05). No significant difference was found in small-world attributes between the patients with iRBD and the control subjects (P>0.05). The patients with iRBD exhibited significantly shortened characteristic shortest path length Lp (P < 0.05) and significantly increased global efficiency, local efficiency and assortativity (P < 0.05). Examination of regional topological properties of the brain network revealed abnormal node properties in the frontal, temporal, parietal, occipital, and striatal and limbic lobes in patients with iRBD. The patients also had significantly increased degree centrality of the left pallidum and enhanced nodal efficiency in the left thalamus, superior temporal gyrus, temporal pole and bilateral superior occipital gyrus, bilateral putamens as well as the right gyrus rectus, amygdala, supramarginal gyrus, and middle temporal gyrus. The nodal local efficiency was significantly increased in the left superior frontal gyrus, middle cingulate gyrus, superior parietal gyrus, bilateral fusiform gyrus, right superior motor area, postcentral gyrus and angular gyrus of the patients with iRBD. The nodal shortest path was significantly shortened in the left superior motor area, pallidum, thalamus, superior temporal gyrus, temporal pole, bilateral putamens, bilateral superior occipital gyrus, right rectus gyrus, amygdala, supramarginal gyrus and middle temporal gyrus, and the nodal clustering coefficient was significantly lowered in the left superior occipital gyrus of the patients. In patients with iRBD, the UPDRS I score was positively correlated with the nodal efficiency in the right supramarginal gyrus (r=0.50, P < 0.05) and local nodal efficiency in the right fusiform gyrus (r=0.53, P < 0.05), and negatively correlated with the nodal clustering coefficient in the left superior occipital gyrus (r=-0.552, P < 0.05).
CONCLUSIONS: Patients with iRBD present with abnormal changes in mental condition, behaviors, emotions, activities of daily living and motor functions. The brain structural network of patient with iRBD still has a small-world property with abnormal global topological property and abnormal distribution of local topological property in the cortex, striatum and limbic system.

It is estimated that more than 50% of the individuals affected with Human Immunodeficiency Virus (HIV) present deficits in multiple cognitive domains, collectively known as HIV-associated neurocognitive disorder (HAND). Early stages of brain injury may be clinically silent but potentially measurable via neuroimaging. A total of 40 subjects (20 HIV positive and 20 age-matched controls) volunteered for the study. All subjects underwent a standard battery of neuropsychological tests used for the clinical diagnosis of HAND. Fourteen HIV+ and five healthy subjects showed signs of neurological impairment. Connectivity was computed using mutual connectivity analysis (MCA) with generalized radial basis function neural network, a framework for quantifying non-linear connectivity as well as conventional correlation from 160 regional time-series that were extracted based on the Dosenbach (DOS) atlas. We subsequently applied graph theoretic as well as network analysis approaches for characterizing the connectivity matrices obtained and localizing between-group differences. We focused on trying to detect cognitive impairment using the subset of 29 (14 subjects with HAND and 15 cognitively normal controls) subjects. For the global analysis, significant differences (p < 0.05) were seen in the variance in degree, modularity and Smallworldness. Regional analysis revealed changes occurring mainly in portions of the lateral occipital cortex and the cingulate cortex. Furthermore, using Network Based Statistics (NBS), we uncovered an affected sub-network of 19 nodes comprising predominantly of regions of the default mode network. Similar analysis using the conventional correlation method revealed no significant results at a global scale, while regional analysis shows some differences spread across resting state networks. These results suggest that there is a subtle reorganization occurring in the topology of brain networks in HAND, which can be captured using improved connectivity analysis.

The quantitative analysis of the airway tree is of critical importance in the CT-based diagnosis and treatment of popular pulmonary diseases. The extraction of airway centerline is a precursor to identify airway hierarchical structure, measure geometrical parameters, and guide visualized detection. Traditional methods suffer from extra branches and circles due to incomplete segmentation results, which induce false analysis in applications. This paper proposed an automatic and robust centerline extraction method for airway tree. First, the centerline is located based on the topological thinning method; border voxels are deleted symmetrically to preserve topological and geometrical properties iteratively. Second, the structural information is generated using graph-theoretic analysis. Then inaccurate circles are removed with a distance weighting strategy, and extra branches are pruned according to clinical anatomic knowledge. The centerline region without false appendices is eventually determined after the described phases. Experimental results show that the proposed method identifies more than 96% branches and keep consistency across different cases and achieves superior circle-free structure and centrality.