Functional-structural decoupling in visual network is associated with cognitive decline in patients with type 2 diabetes mellitus: evidence from a multimodal MRI analysis.

Type 2 diabetes mellitus (T2DM) and cognitive dysfunction are highly prevalent disorders worldwide. Although visual network (VN) alteration and functional-structural coupling are potential warning factors for mild cognitive impairment (MCI) in T2DM patients, the relationship between the three in T2DM without MCI is unclear. Thirty T2DM patients without MCI and twenty-nine healthy controls (HC) were prospectively enrolled. Visual components (VC) were estimated by independent component analysis (ICA). Degree centrality (DC), amplitude of low frequency fluctuation (ALFF) and fractional anisotropy (FA) were established to reflect functional and structural characteristics in these VCs respectively. Functional-structural coupling coefficients were further evaluated using combined FA and DC or ALFF. Partial correlations were performed among neuroimaging indicators and neuropsychological scores and clinical variables. Three VCs were selected using group ICA. Deteriorated DC, ALFF and DC-FA coefficients in the VC1 were observed in the T2DM group compared with the HC group, while FA and ALFF-FA coefficients in these three VCs showed no significant differences. In the T2DM group, DC in the VC1 positively correlated with 2 dimensions in the California Verbal Learning Test, including Trial 4 and Total trial 1-5. The impaired DC-FA coefficients in the VC1 markedly affected the Total perseverative responses % of the Wisconsin Card Sorting Test. These findings indicate that DC and DC-FA coefficients in VN may be potential imaging biomarkers revealing early cognitive deficits in T2DM.

Disruption of functional and structural networks in first-episode, drug-naïve adolescents with generalized anxiety disorder.

BACKGROUND: Individuals with generalized anxiety disorder (GAD) tend to worry exaggeratedly and uncontrollably about various daily routines. Previous studies have demonstrated that the GAD patients exhibited widespread alternations in both functional networks (FN) and structural networks (SN). However, the simultaneous alternations of the topological organization of FN, SN, as well as their couplings in GAD still remain unknown. METHODS: Using multimodal approach, we constructed FN from resting-state functional magnetic imaging (R-fMRI) data and SN from diffusion magnetic resonance imaging (dMRI) data of 32 adolescent GAD patients and 25 healthy controls (HC). Graph theory analysis was employed to investigate the topological properties of FN, SN, and FN-SN coupling. RESULTS: Compared to HC, the GAD patients showed disruptions in global (i.e., decreased clustering coefficient, global, and local efficiency) and subnetwork (i.e., reduced intermodular connections, rich club, and feeder connections) levels in FN. Abnormal global level properties (i.e., increased characteristic path length and reduced global efficiency) were also observed in SN. Altered FN-SN couplings in normalized characteristic path length and feeder connections were identified in the GAD patients. The identified network measures were correlated with anxiety severity in the GAD patients. LIMITATIONS: The sample size of the current study is small and the cross-sectional nature can not infer causal relationship. CONCLUSIONS: Our findings identified GAD-related topological alternations in both FN and SN, together with the couplings between FN and SN, providing us with a novel perspective for understanding the pathophysiological mechanisms of GAD.

Selective Aberrant Functional-Structural Coupling of Multiscale Brain Networks in Subcortical Vascular Mild Cognitive Impairment.

Subcortical vascular mild cognitive impairment (svMCI) is a common prodromal stage of vascular dementia. Although mounting evidence has suggested abnormalities in several single brain network metrics, few studies have explored the consistency between functional and structural connectivity networks in svMCI. Here, we constructed such networks using resting-state fMRI for functional connectivity and diffusion tensor imaging for structural connectivity in 30 patients with svMCI and 30 normal controls. The functional networks were then parcellated into topological modules, corresponding to several well-defined functional domains. The coupling between the functional and structural networks was finally estimated and compared at the multiscale network level (whole brain and modular level). We found no significant intergroup differences in the functional-structural coupling within the whole brain; however, there was significantly increased functional-structural coupling within the dorsal attention module and decreased functional-structural coupling within the ventral attention module in the svMCI group. In addition, the svMCI patients demonstrated decreased intramodular connectivity strength in the visual, somatomotor, and dorsal attention modules as well as decreased intermodular connectivity strength between several modules in the functional network, mainly linking the visual, somatomotor, dorsal attention, ventral attention, and frontoparietal control modules. There was no significant correlation between the altered module-level functional-structural coupling and cognitive performance in patients with svMCI. These findings demonstrate for the first time that svMCI is reflected in a selective aberrant topological organization in multiscale brain networks and may improve our understanding of the pathophysiological mechanisms underlying svMCI.

Different Scales of Cortical Organization are Selectively Targeted in the Progression to Alzheimer's Disease.

Previous studies have shown that the topological organization of the cerebral cortex is altered in Alzheimer's disease (AD). However, it remains unknown whether different levels of the cortical hierarchy are homogeneously affected during disease progression, and which of these levels are mostly involved in the breakdown of metabolic (functional) connectivity. To fulfill these goals, we acquired structural magnetic resonance images (MRI) and positron emission tomography (PET) with the radiotracer 18F-fludeoxyglucose (FDG) in 29 healthy old (HO) adults, 29 amnestic mild cognitive impairment (aMCI) and 29 mild AD patients. Structural and metabolic connections were obtained from inter-regional correlations of cortical thickness and glucose consumption, respectively. Results showed that AD and HO groups differed at all levels of cortical organization (i.e. whole cortex, hemisphere, lobe and node), whereas differences among the three groups were only evident at the lobe and node levels. The correlation between structural and metabolic connectivity (F-S coupling) was also disturbed during AD progression, affecting to different connectivity scales: it decreased at the local level, revealing a progressive increase of metabolic connections in those local communities with fewer structural connections; whereas it increased at the global level, likely due to a parallel reduction of cortical thickness and glucose consumption between long-distance cortical regions. Collectively, these results reveal that different levels of cortical organization are selectively affected during the transition from normal aging to dementia, which could be helpful to track cortical dysfunctions in the progression to AD.

Dynamic functional-structural coupling within acute functional state change phases: Evidence from a depression recognition study.

BACKGROUND: Dynamic functional-structural connectivity (FC-SC) coupling might reflect the flexibility by which SC relates to functional connectivity (FC). However, during the dynamic acute state change phases of FC, the relationship between FC and SC may be distinctive and embody the abnormality inherent in depression. This study investigated the depression-related inter-network FC-SC coupling within particular dynamic acute state change phases of FC. METHODS: Magnetoencephalography (MEG) and diffusion tensor imaging (DTI) data were collected from 26 depressive patients (13 women) and 26 age-matched controls (13 women). We constructed functional brain networks based on MEG data and structural networks from DTI data. The dynamic connectivity regression algorithm was used to identify the state change points of a time series of inter-network FC. The time period of FC that contained change points were partitioned into types of dynamic phases (acute rising phase, acute falling phase,acute rising and falling phase and abrupt FC variation phase) to explore the inter-network FC-SC coupling. The selected FC-SC couplings were then fed into the support vector machine (SVM) for depression recognition. RESULTS: The best discrimination accuracy was 82.7% (P=0.0069) with FC-SC couplings, particularly in the acute rising phase of FC. Within the FC phases of interest, the significant discriminative network pair was related to the salience network vs ventral attention network (SN-VAN) (P=0.0126) during the early rising phase (70-170ms). LIMITATIONS: This study suffers from a small sample size, and the individual acute length of the state change phases was not considered. CONCLUSIONS: The increased values of significant discriminative vectors of FC-SC coupling in depression suggested that the capacity to process negative emotion might be more directly related to the SC abnormally and be indicative of more stringent and less dynamic brain function in SN-VAN, especially in the acute rising phase of FC. We demonstrated that depressive brain dysfunctions could be better characterized by reduced FC-SC coupling flexibility in this particular phase.

Functional-structural degeneration in dorsal and ventral attention systems for Alzheimer's disease, amnestic mild cognitive impairment.

Growing evidence of attention related failures in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) has already been proposed by previous studies. However, previous studies lacked of systematic investigation on the functional and structural substrates for attention function for patients with AD and aMCI. In this work, we investigated the functional connectivity and gray matter density in dorsal and ventral attention networks (DAN, VAN) of normal participants (n = 15) and patients with aMCI (n = 12) and AD (n = 16) by applying group independent component analysis (ICA) and voxel-based morphometry (VBM) analysis. Using ICA, we extracted the functional patterns of DAN and VAN which are respectively responsible for the "top-down" attention process and "bottom-up" process. One-way analysis of variance (ANOVA) revealed significant group-differed functional connectivity in bilateral frontal eye fields (FEF) area and intraparietal sulcus (IPS) area, as well as posterior cingulate cortex and precuneus in the dorsal system. With regard to the ventral system, group-effects were significantly focused in right orbital superior/middle frontal gyrus, right inferior parietal lobule, angular gyrus, and supramarginal gyrus around the temporal-parietal junction area. Post hoc cluster-level comparisons revealed totally impaired functional substrates for both attentional networks for patients with AD, whereas selectively impaired attention systems for patients with aMCI with impaired functional patent of DAN but preserved functional pattern of VAN. Correspondingly, VBM analysis revealed gray matter loss in right ventral and dorsal frontal cortex was in the AD group, whereas preserved gray matter density was in aMCI, even a little extent of expansion of gray matter density in several participants. Using multivariate regression analysis we found discrepant couplings of functional-structural degenerations between both patient groups. Specifically, positive coupling of structural-functional degeneration was found in right dorsal and ventral frontal cortex in the AD group, whereas inverse coupling in dorsal frontal cortex was found in the aMCI group. These findings suggested discrepant functional-structural degenerations in both attention systems between both patient groups, widening avenues to better understanding the attentional deficits in patients with aMCI and AD.

Predictors of coupling between structural and functional cortical networks in normal aging.

Understanding how the mammalian neocortex creates cognition largely depends on knowledge about large-scale cortical organization. Accumulated evidence has illuminated cortical substrates of cognition across the lifespan, but how topological properties of cortical networks support structure-function relationships in normal aging remains an open question. Here we investigate the role of connections (i.e., short/long and direct/indirect) and node properties (i.e., centrality and modularity) in predicting functional-structural connectivity coupling in healthy elderly subjects. Connectivity networks were derived from correlations of cortical thickness and cortical glucose consumption in resting state. Local-direct connections (i.e., nodes separated by less than 30 mm) and node modularity (i.e., a set of nodes highly interconnected within a topological community and sparsely interconnected with nodes from other modules) in the functional network were identified as the main determinants of coupling between cortical networks, suggesting that the structural network in aging is mainly constrained by functional topological properties involved in the segregation of information, likely due to aging-related deficits in functional integration. This hypothesis is supported by an enhanced connectivity between cortical regions of different resting-state networks involved in sensorimotor and memory functions in detrimental to associations between fronto-parietal regions supporting executive processes. Taken collectively, these findings open new avenues to identify aging-related failures in the anatomo-functional organization of the neocortical mantle, and might contribute to early detection of prevalent neurodegenerative conditions occurring in the late life.