Cerebral small vessel disease is common in most individuals aged 60 years or older, and it is associated with cognitive dysfunction, depression, anxiety disorder, and mobility problems. Currently, many cerebral small vessel disease patients have both cognitive impairment and depressive symptoms, but the relationship between the 2 is unclear. The present research combined static and dynamic functional network connectivity methods to explore the patterns of functional networks in cerebral small vessel disease individuals with cognitive impairment and depression (cerebral small vessel disease-mild cognitive impairment with depression) and their relationship. We found specific functional network patterns in the cerebral small vessel disease-mild cognitive impairment with depression individuals (P < 0.05). The cerebral small vessel disease individuals with depression exhibited unstable dynamic functional network connectivity states (transitions likelihood: P = 0.040). In addition, we found that the connections within the lateral visual network between the sensorimotor network and ventral attention network could mediate white matter hyperintensity-related cognitive impairment (indirect effect: 0.064; 95% CI: 0.003, 0.170) and depression (indirect effect: -0.415; 95% CI: -1.080, -0.011). Cognitive function can negatively regulate white matter hyperintensity-related depression. These findings elucidate the association between cognitive impairment and depression and provide new insights into the underlying mechanism of cerebral small vessel disease-related cognitive dysfunction and depression.

Purpose: The aim of this study was to explore the correlation between brain functional network alterations and cerebrospinal fluid (CSF) pathological biomarkers in Alzheimer’s disease (AD)-spectrum patients. Method: A total of 39 individuals were recruited, including 23 AD patients and 16 control subjects. All subjects underwent a battery of neuropsychological examinations, CSF measurement and multimodal magnetic resonance imaging scans. Independent component analysis was used to investigate the differences of functional connectivity（FC）among the two groups based on the resting-sate functional MRI (fMRI) data. Then correlation analyses were used to estimate the potential relationship between functional network alterations and cerebral amyloid and tau burden. Result: Multiple inter- and intra-network functional connections were altered in AD patients compared to the Non ADCI group. Alterations in FC between VN and PCC, as well as the functional connections of SMN were found to be associated with the dynamic changes of CSF Tau. Compared the AD group with the non-AD CI group, the aforementioned altered functional brain connectivity, with the exception of FC in the PCC and VN, was significantly associated with a decrease in CSF Aβ Conclusion: This study provides provisional evidence that the brain functional network alterations was closely associated with CSF pathological characteristics, and these exploratory results support new research ideas for the early diagnosis of AD.

Excessive iron accumulation in the brain cortex increases the risk of cognitive deterioration. However, interregional relationships (defined as susceptibility connectivity) of local brain iron have not been explored, which could provide new insights into the underlying mechanisms of cognitive decline. Seventy-six healthy controls (HC), 58 participants with mild cognitive impairment due to probable Alzheimer’s disease (MCI-AD) and 66 participants with white matter hyperintensity (WMH) were included. We proposed a novel approach to construct a brain susceptibility network by using Kullback‒Leibler divergence similarity estimation from quantitative susceptibility mapping and further evaluated its topological organization. Moreover, sparse logistic regression (SLR) was applied to classify MCI-AD from HC and WMH with normal cognition (WMH-NC) from WMH with MCI (WMH-MCI).The altered susceptibility connectivity in the MCI-AD patients indicated that relatively more connectivity was involved in the default mode network (DMN)-related and visual network (VN)-related connectivity, while more altered DMN-related and subcortical network (SN)-related connectivity was found in the WMH-MCI patients. For the HC vs. MCI-AD classification, the features selected by the SLR were primarily distributed throughout the DMN-related and VN-related connectivity (accuracy = 76.12%). For the WMH-NC vs. WMH-MCI classification, the features with high appearance frequency were involved in SN-related and DMN-related connectivity (accuracy = 84.85%). The shared and specific patterns of the susceptibility network identified in both MCI-AD and WMH-MCI may provide a potential diagnostic biomarker for cognitive impairment, which could enhance the understanding of the relationships between brain iron burden and cognitive decline from a network perspective.

Stroke is the leading cause of disability and the second leading cause of death worldwide. Diabetes mellitus is a critical independent cardiovascular risk factor in patients, irrespective of age, smoking, and hypertension. Approximately one-third of first-time ischemic stroke patients have diabetes. Inflammation is among the most important pathological mechanisms in atheroma formation, the damage cascades of the acute phase, as well as during the subacute and chronic phases after stroke. Diabetes, as a common risk factor for stroke, is often present for a long time before a stroke occurs, causing low-grade inflammation, and disrupting the proper functioning of the neurovascular units. These proinflammatory processes and maladaptive immune mechanisms are further accelerated after cerebral ischemia and worsen the stroke outcome in diabetic patients. Clinical treatments for ischemic stroke are currently focused on restoring cerebral blood flow (reperfusion) in the acute phase, including thrombolysis and mechanical thrombectomy, which are not applicable to patients that fall outside of the treatment window and/or without large-vessel occlusion. There are few approved treatments targeting cellular injury caused by inflammation. There are even fewer data on effective treatment for diabetic stroke targeting inflammation. This paper presents the first part of a review focusing on the temporospatial aspects of inflammation in ischemic stroke pathophysiology in stroke patients with type 2 diabetes.

Stroke is the leading cause of disability and the second leading cause of death worldwide. Diabetes mellitus is an important independent cardiovascular risk factor in patients, regardless of age, smoking habit, and hypertension. Approximately one-third of first-time ischemic stroke patients have diabetes. Inflammation is among the most important pathological mechanisms in atheroma formation, the damage cascades of the acute phase, as well as during the subacute and chronic phases after stroke. Diabetes, as a common risk factor for stroke, is often present for a long time before a stroke occurs, causing low-grade inflammation, and disrupting the proper functioning of the neurovascular units. These proinflammatory processes and maladaptive immune mechanisms are further accelerated after cerebral ischemia and worsen the stroke outcome in diabetic patients. Clinical treatments for ischemic stroke are currently focused on restoring cerebral blood flow (reperfusion) in the acute phase, including thrombolysis and mechanical thrombectomy, which are not applicable to patients that fall outside of the treatment window and/or without large-vessel occlusion. There are few approved treatments targeting cellular injury caused by inflammation. There are even fewer data on effective treatment for diabetic stroke targeting inflammation. This paper presents the second part of a review focusing on the potential therapeutic targets in stroke patients with type 2 diabetes.

White matter hyperintensities (WMH) of assumed vascular origin are common in elderly individuals and are closely associated with cognitive decline. However, the underlying neural mechanisms of WMH-related cognitive impairment remain unclear. After strict screening, 59 healthy controls (HC, n = 59), 51 patients with WMH and normal cognition (WMH-NC, n = 51) and 68 patients with WMH and mild cognitive impairment (WMH-MCI, n = 68) were included in the final analyses. All individuals underwent multimodal magnetic resonance imaging (MRI) and cognitive evaluations. We investigated the neural mechanism underlying WMH-related cognitive impairment based on static and dynamic functional network connectivity (sFNC and dFNC) approaches. Finally, the support vector machine (SVM) method was performed to identify WMH-MCI individuals. The sFNC analysis indicated that functional connectivity within the visual network (VN) could mediate the impairment of information processing speed related to WMH (indirect effect: 0.24; 95% CI: 0.03, 0.88 and indirect effect: 0.05; 95% CI: 0.001, 0.14). WMH may regulate the dFNC between the higher-order cognitive network and other networks and enhance the dynamic variability between the left frontoparietal network (lFPN) and the VN to compensate for the decline in high-level cognitive functions. The SVM model achieved good prediction ability for WMH-MCI patients based on the above characteristic connectivity patterns. Our findings shed light on the dynamic regulation of brain network resources to maintain cognitive processing in individuals with WMH. Crucially, dynamic reorganization of brain networks could be regarded as a potential neuroimaging biomarker for identifying WMHrelated cognitive impairment.

White matter hyperintensities (WMH) are widely observed in older adults and are closely associated with cognitive impairment. However, the underlying neuroimaging mechanisms of WMH-related cognitive dysfunction remain unknown. This study recruited 61 WMH individuals with mild cognitive impairment (WMH-MCI, n = 61), 48 WMH individuals with normal cognition (WMH-NC, n = 48) and 57 healthy control (HC, n = 57) in the final analyses. We constructed morphological networks by applying the Kullback-Leibler divergence to estimate interregional similarity in the distributions of regional gray matter volume. Based on morphological networks, graph theory was applied to explore topological properties, and their relationship to WMH-related cognitive impairment was assessed. There were no differences in small-worldness, global efficiency and local efficiency. The nodal local efficiency, degree centrality and betweenness centrality were altered mainly in the limbic network (LN) and default mode network (DMN). The rich-club analysis revealed that WMH-MCI subjects showed lower average strength of the feeder and local connections than HC (feeder connections: P = 0.034; local connections: P = 0.042). Altered morphological connectivity mediated the relationship between WMH and cognition, including language (total indirect effect: -0.010; 95 % CI: -0.024, -0.002) and executive (total indirect effect: -0.010; 95 % CI: -0.028, -0.002) function. The altered topological organization of morphological networks was mainly located in the DMN and LN and was associated with WMH-related cognitive impairment. The rich-club connection was relatively preserved, while the feeder and local connections declined. The results suggest that single-subject morphological networks may capture neurological dysfunction due to WMH and could be applied to the early imaging diagnostic protocol for WMH-related cognitive impairment.