Huiyou Chen

The phantom sound of tinnitus is believed to be triggered by aberrant neural activity in the central auditory pathway, but since this debilitating condition is often associated with emotional distress and anxiety, these comorbidities likely arise from maladaptive functional connections to limbic structures such as the amygdala and hippocampus. To test this hypothesis, resting-state functional magnetic resonance imaging (fMRI) was used to identify aberrant effective connectivity of the amygdala and hippocampus in tinnitus patients and to determine the relationship with tinnitus characteristics. Chronic tinnitus patients (n = 26) and age-, sex-, and education-matched healthy controls (n = 23) were included. Both groups were comparable for hearing level. Granger causality analysis utilizing the amygdala and hippocampus as seed regions were used to investigate the directional connectivity and the relationship with tinnitus duration or distress. Relative to healthy controls, tinnitus patients demonstrated abnormal directional connectivity of the amygdala and hippocampus, including primary and association auditory cortex, and other non-auditory areas. Importantly, scores on the Tinnitus Handicap Questionnaires were positively correlated with increased connectivity from the left amygdala to left superior temporal gyrus (r = 0.570, P = 0.005), and from the right amygdala to right superior temporal gyrus (r = 0.487, P = 0.018). Moreover, enhanced effective connectivity from the right hippocampus to left transverse temporal gyrus was correlated with tinnitus duration (r = 0.452, P = 0.030). The results showed that tinnitus distress strongly correlates with enhanced effective connectivity that is directed from the amygdala to the auditory cortex. The longer the phantom sensation, the more likely acute tinnitus becomes permanently encoded by memory traces in the hippocampus. Hum Brain Mapp 38:2384-2397, 2017. © 2017 Wiley Periodicals, Inc.

Clinical pathogen diagnostics detect targets by qPCR (but with low sensitivity) or blood culturing (but time-consuming). Here we leverage a dual-stem-loop DNA amplifier to enhance non-specific collateral enzymatic cleavage of an oligonucleotide linker between a fluophore and its quencher by CRISPR-CasΦ, achieving ultrasensitive target detection. Specifically, the target pathogens are lysed to release DNA, which binds its complementary gRNA in CRISPR-CasΦ to activate the collateral DNA-cleavage capability of CasΦ, enabling CasΦ to cleave the stem-loops in the amplifier. The cleavage product binds its complementary gRNA in another CRISPR-CasΦ to activate more CasΦ. The activated CasΦ collaterally cleaves the linker, releasing the fluophore to recover its fluorescent signal. The cycle of stem-loop-cleavage/CasΦ-activation/fluorescence-recovery amplifies the detection signal. Our target amplification-free collateral-cleavage-enhancing CRISPR-CasΦ method (TCC), with a detection limit of 0.11 copies/μL, demonstrates enhanced sensitivity compared to qPCR. It can detect pathogenic bacteria as low as 1.2 CFU/mL in serum within 40 min.

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.

Purpose: Recently, high-resolution magnetic resonance imaging (HR-MRI) has been used to depict the wall characteristics of the intracranial arteries. The aim of this study was to explain the relationship between the remodeling patterns and acute ischemic stroke in patients with atherosclerotic middle cerebral artery (MCA) stenosis using HR-MRI. Material and methods: From August 2015 to May 2016, we prospectively screened 33 consecutive patients with unilateral MCA stenosis using time-to-flight (TOF) MR angiography, including 15 patients with symptomatic MCA stenosis and 18 patients with asymptomatic MCA stenosis. Among them, 14 patients were diagnosed as positive remodeling (PR) and 19 as negative remodeling or non-remodeling. The cross-sectional images of the stenotic MCA wall on HR-MRI including T1WI, T2WI, and PDWI were compared between the symptomatic group and the asymptomatic group as well as the PR group and the non-PR group, based on the vessel area, lumen area, wall area, plaque area, degree of stenosis, remodeling index, and NIHSS score. Results: The symptomatic group had larger wall area (P=0.040), plaque area (P<0.001), degree of stenosis (P=0.038), remodeling index (P<0.001), and NIHSS score (P=0.003) as well as smaller lumen area (P=0.001) than the asymptomatic group. In addition, more PR patients were observed in symptomatic group. The PR group had larger plaque area (P=0.014) and NIHSS score (P=0.037) than the non-PR group. Demographic and clinical characteristics between the symptomatic group and the asymptomatic group, the PR group and the non-PR group showed no statistical difference. Conclusions: The current study suggests that the HR-MRI has emerged as a promising tool to detect the characteristics of intracranial arteries wall and reveal the relationship between remodeling patterns and ischemic stroke. The PR is an unsafe remodeling way and is prone to cause acute ischemic stroke.

Hemorrhagic transformation (HT) is one of the most serious complications after endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) patients. The purpose of this study is to develop and validate deep-learning (DL) models based on multiparametric magnetic resonance imaging (MRI) to automatically predict HT in AIS patients. Multiparametric MRI and clinical data of AIS patients with EVT from two centers (data set 1 for training and testing: n = 338; data set 2 for validating: n = 54) were used in the DL models. The acute infarction area of diffusion-weighted imaging (DWI) and hypoperfusion of perfusion-weighted imaging (PWI) was labeled manually. Two forms of data sets (volume of interest [VOI] data sets and slice data sets) were analyzed, respectively. The models based on single parameter and multiparameter models were developed and validated to predict HT in AIS patients after EVT. Performance was evaluated by area under the receiver-operating characteristic curve (AUC), accuracy (ACC), sensitivity, specificity, negative predictive value, and positive predictive value. The results showed that the performance of single parameter model based on MTT (VOI data set: AUC = 0.933, ACC = 0.843; slice data set: AUC = 0.945, ACC = 0.833) and TTP (VOI data set: AUC = 0.916, ACC = 0.873; slice data set: AUC = 0.889, ACC = 0.818) were better than the other single parameter model. The multiparameter model based on DWI & MTT & TTP & Clinical (DMTC) had the best performance for predicting HT (VOI data set: AUC = 0.948, ACC = 0.892; slice data set: AUC = 0.932, ACC = 0.873). The DMTC model in the external validation set achieved similar performance with the testing set (VOI data set: AUC = 0.939, ACC = 0.884; slice data set: AUC = 0.927, ACC = 0.871) (p > 0.05). The proposed clinical, DWI, and PWI multiparameter DL model has great potential for assisting the periprocedural management in the early prediction HT of the AIS patients with EVT.