Song’an Shang

OBJECTIVE: Type 2 diabetes is characterized by insulin resistance, which is involved in the development of Alzheimer disease. This study aims to investigate the relationship between abnormal resting-state brain functional connectivity and insulin resistance in type 2 diabetes. RESEARCH DESIGN AND METHODS: A total of 30 patients with type 2 diabetes and 31 healthy well-matched volunteers were prospectively examined. Resting-state brain functional connectivity analysis was used to examine the correlation between the posterior cingulate cortex (PCC) and whole-brain regions. The possible relationships between functional connectivity measures and insulin resistance were evaluated using the homeostasis model assessment of insulin resistance (HOMA-IR). RESULTS: Compared with healthy controls, we observed significantly decreased functional connectivity of the PCC within some selected regions, including the right middle temporal gyrus (MTG), left lingual gyrus, left middle occipital gyrus, and left precentral gyrus; increased functional connectivity of the PCC was detected in the left cerebellum posterior lobe, right superior frontal gyrus, and right middle frontal gyrus. A significant negative correlation was found between the PCC-right MTG connectivity and HOMA-IR in type 2 diabetic patients (P = 0.014; r = -0.446). CONCLUSIONS: Type 2 diabetic patients develop aberrant functional connectivity of the PCC, which is associated with insulin resistance in selected brain regions. Resting-state connectivity disturbance of PCC-MTG may be a central role for evaluating the cognitive dysfunction in type 2 diabetes.

AIMS: This study aimed to detect alterations of brain functional connectivity (FC) in acute mild traumatic brain injury (mTBI) and to estimate the extent to which these FC differences predicted the characteristics of posttraumatic cognitive impairment. METHODS: Resting-state fMRI data were acquired from acute mTBI patients (n = 50) and healthy controls (HCs) (n = 43). Resting-state networks (RSNs) were established based on independent component analysis (ICA), and functional network connectivity (FNC) analysis was performed. Subsequently, we analyzed the correlations between FNC abnormalities and cognitive impairment outcomes. RESULTS: Altered FC within the salience network (SN), sensorimotor network (SMN), default mode network (DMN), executive control network (ECN), visual network (VN), and cerebellum network (CN) was found in the mTBI group relative to the HC group. Moreover, different patterns of altered network interactions were found between the mTBI patients and HCs, including the SN-CN, VN-SMN, and ECN-DMN connections. Correlations between functional disconnection and cognitive impairment measurements in acute mTBI patients were also found. CONCLUSION: This study indicated that widespread FNC impairment and altered integration existed in mTBI patients at acute stage, suggesting that FNC disruption as a biomarker may be applied for the early diagnosis and prediction of cognitive impairment in mTBI.

BACKGROUND: Post-traumatic headache (PTH) is a very common symptom following mild traumatic brain injury (mTBI), yet much remains unknown about the underlying pathophysiological mechanisms of PTH. Neuroimaging studies suggest that aberrant functional network connectivity (FNC) may be an important factor in pain disorders. The present study aimed to investigate the functional characteristics of static FNC (sFNC) and dynamic FNC (dFNC) in mTBI patients with PTH. METHODS: With Institutional Review Board (IRB) approval, we prospectively recruited 50 mTBI patients with PTH, who were diagnosed with ICHD-3 beta diagnostic criteria and 39 mTBI without PTH who were well matched for age, gender and education. Resting-state functional magnetic resonance imaging (fMRI) scanning (3.0 T, Philips Medical Systems, Netherlands), Montreal Cognitive Assessment (MoCA) and headache symptom measurement (headache frequency and headache intensity) were performed. The resting-state fMRI sequence took 8 min and 10 s. Independent component analysis and sliding window method were applied to examine the FNC on the basis of nine resting-state networks, namely, default mode network (DMN), sensorimotor network (SMN), executive control network (ECN), auditory network (AuN), attention network (AN), salience network (SN), visual network (VN), and cerebellum network (CN). The differences in sFNC and dFNC were determined and correlated with clinical variables using Pearson rank correlation. RESULTS: For sFNC, compared with mTBI patients without PTH, mTB with PTH group showed four altered interactions, including decreased interactions in SN-SMN and VN-DMN pairs, increased sFNC in SN-ECN and SMN-DMN pairs. For dFNC, significant group differences were found in State 2, including increased connectivity alteration in the DMN with CN, DMN with SMN, and AuN with CN. Significant reduced connectivity changes in the DMN with VN was found in State 4. Furthermore, the number of transitions (r=0.394, p=0.005) between states was positively associated with headache frequency. Additionally, dwell time (r=-0.320, p=0.025) in State 1 was negatively correlated with MoCA score. CONCLUSIONS: MTBI patients with PTH are characterized with altered sFNC and dFNC, which could provide new perspective to understand the neuropathological mechanism underlying the PTH to determine more appropriate management, and may be a useful imaging biomarker for identifying and predicting mTBI with PTH.