Multimodal data fusion reveals functional and neurochemical correlates of Parkinson's disease.

BACKGROUND: Neurotransmitter deficits and spatial associations among neurotransmitter distribution, brain activity, and clinical features in Parkinson's disease (PD) remain unclear. Better understanding of neurotransmitter impairments in PD may provide potential therapeutic targets. Therefore, we aimed to investigate the spatial relationship between PD-related patterns and neurotransmitter deficits. METHODS: We included 59 patients with PD and 41 age- and sex-matched healthy controls (HCs). The voxel-wise mean amplitude of the low-frequency fluctuation (mALFF) was calculated and compared between the two groups. The JuSpace toolbox was used to test whether spatial patterns of mALFF alterations in patients with PD were associated with specific neurotransmitter receptor/transporter densities. RESULTS: Compared to HCs, patients with PD showed reduced mALFF in the sensorimotor- and visual-related regions. In addition, mALFF alteration patterns were significantly associated with the spatial distribution of the serotonergic, dopaminergic, noradrenergic, glutamatergic, cannabinoid, and acetylcholinergic neurotransmitter systems (p < 0.05, false discovery rate-corrected). CONCLUSIONS: Our results revealed abnormal brain activity patterns and specific neurotransmitter deficits in patients with PD, which may provide new insights into the mechanisms and potential targets for pharmacotherapy.

Updated incidence rates and risk factors of esophageal cancer in Nan'ao Island, a coastal high-risk area in southern China.

Esophageal cancer (EC) is one of the most common cancers in China. The purpose of this study was to investigate the updated incidence rates and risk factors of EC in Nan'ao Island, where the EC incidence rate was chronically the highest in southern China. To calculate the annual incidence rate, data on 338 EC cases from Nan'ao Cancer Registry system diagnosed during 2005-2011 were collected. A case-control study was conducted to explore the EC risk factors. One hundred twenty-five alive EC patients diagnosed during 2005-2011 and 250 controls were enrolled into the case-control study. A pre-test questionnaire on demography, dietary factors, drinking water treatment, and behavioral factors was applied to collect information of all participants. The average EC incidence rates during 2005-2011 were 66.09/105, 94.62/105, 36.83/105 for both genders, males and females, respectively, in Nan'ao Island. The EC incidence rate in males was 2.40- to 4.55-fold higher than that in females in the period from 2006 to 2011 (P < 0.05). Considering the onset age, males tend to be much younger than females and reached peak incidence rate at a younger age (P < 0.05). Drinking water treatment by filter (odds ratio [OR] = 0.28, 95% confidence interval [95% CI] = 0.13-0.58) and fruit consumption (OR = 0.55, 95% CI = 0.32-0.94) reduced the risk for EC. On the contrary, the pickled vegetables consumption (OR = 2.64, 95% CI = 1.46-4.76) and liquor drinking (OR = 2.32, 95% CI = 1.21-4.44) increased the risk for EC. These results may be of importance for future research on EC etiology and prevention strategies.

Quantitative separation of CEST effect by Rex-line-fit analysis of Z-spectra.

The process of chemical exchange saturation transfer (CEST) is quantified by evaluating a Z-spectra, where CEST signal quantification and Z-spectra fitting have been widely used to distinguish the contributions from multiple origins. Based on the exchange-dependent relaxation rate in the rotating frame (Rex), this paper introduces an additional pathway to quantitative separation of CEST effect. The proposed Rex-line-fit method is solved by a multi-pool model and presents the advantage of only being dependent of the specific parameters (solute concentration, solute-water exchange rate, solute transverse relaxation, and irradiation power). Herein we show that both solute-water exchange rate and solute concentration monotonously vary with Rex for Amide, Guanidino, NOE and MT, which has the potential to assist in solving quantitative separation of CEST effect. Furthermore, we achieve Rex imaging of Amide, Guanidino, NOE and MT, which may provide direct insight into the dependency of measurable CEST effects on underlying parameters such as the exchange rate and solute concentration, as well as the solute transverse relaxation.

Deep learning for dense Z-spectra reconstruction from CEST images at sparse frequency offsets.

A direct way to reduce scan time for chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) is to reduce the number of CEST images acquired in experiments. In some scenarios, a sufficient number of CEST images acquired in experiments was needed to estimate parameters for quantitative analysis, and this prolonged the scan time. For that, we aim to develop a general deep-learning framework to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets so as to reduce the number of experimentally acquired CEST images and achieve scan time reduction. The main innovation works are outlined as follows: (1) a general sequence-to-sequence (seq2seq) framework is proposed to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets; (2) we create a training set from wide-ranging simulated Z-spectra instead of experimentally acquired CEST data, overcoming the limitation of the time and labor consumption in manual annotation; (3) a new seq2seq network that is capable of utilizing information from both short-range and long-range is developed to improve reconstruction ability. One of our intentions is to establish a simple and efficient framework, i.e., traditional seq2seq can solve the reconstruction task and obtain satisfactory results. In addition, we propose a new seq2seq network that includes the short- and long-range ability to boost dense CEST Z-spectra reconstruction. The experimental results demonstrate that the considered seq2seq models can accurately reconstruct dense CEST images from experimentally acquired images at 11 frequency offsets so as to reduce the scan time by at least 2/3, and our new seq2seq network contributes to competitive advantage.

Repurposing of the PET Probe Prototype PiB for Label and Radiation-Free CEST MRI Molecular Imaging of Amyloid.

Positron emission tomography (PET) probes are specific and sensitive while suffering from radiation risk. It is worthwhile to explore the chemical emission saturation transfer (CEST) effects of the probe prototypes and repurpose them for CEST imaging to avoid radiation. In this study, we used 11C-PiB as an example of a PET probe for detecting amyloid and tested the feasibility of repurposing this PET probe prototype, PiB, for CEST imaging. After optimizing the parameters through preliminary phantom experiments, we used APP/PS1 transgenic mice and age-matched C57 mice for in vivo CEST magnetic resonance imaging (MRI) of amyloid. Furthermore, the pathological assessment was conducted on the same brain slices to evaluate the correlation between the CEST MRI signal abnormality and ß-amyloid deposition detected by immunohistochemical staining. In our results, the Z-spectra revealed an apparent CEST effect that peaked at approximately 6 ppm. APP/PS1 mice as young as 9 months injected with PiB showed a significantly higher CEST effect compared to the control groups. The hyperintense region was correlated with the Aß deposition shown by pathological staining. In conclusion, repurposing the PET probe prototype for CEST MRI imaging is feasible and enables label- and radiation-free detection of the amyloid while maintaining the sensitivity and specificity of the ligand. This study opens the door to developing CEST probes based on PET probe prototypes.

Automated detection of intracranial artery stenosis and occlusion in magnetic resonance angiography: A preliminary study based on deep learning.

BACKGROUND AND OBJECTIVES: Intracranial atherosclerotic stenosis of a major intracranial artery is the common cause of ischemic stroke. We evaluate the feasibility of using deep learning to automatically detect intracranial arterial steno-occlusive lesions from time-of-flight magnetic resonance angiography. METHODS: In a retrospective study, magnetic resonance images with radiological reports of intracranial arterial stenosis and occlusion were extracted. The images were randomly divided into a training set and a test set. The manual annotation of lesions with a bounding box labeled "moderate stenosis," "severe stenosis," "occlusion," and "absence of signal" was considered as ground truth. A deep learning algorithm based on you only look once version 5 (YOLOv5) detection model was developed with the training set, and its sensitivity and positive predictive values to detect lesions were evaluated in the test set. RESULTS: A dataset of 200 examinations consisted of a total of 411 lesions-242 moderate stenoses, 84 severe stenoses, 70 occlusions, and 15 absence of signal. The magnetic resonance images contained 291 lesions in the training set and 120 lesions in the test set. The sensitivity and positive predictive values were 64.2 and 83.7%, respectively. The detection sensitivity in relation to the location was greatest in the internal carotid artery (86.2%). CONCLUSIONS: Applying deep learning algorithms in the automated detection of intracranial arterial steno-occlusive lesions from time-of-flight magnetic resonance angiography is feasible and has great potential.

The value of a shorter-delay arterial spin labeling protocol for detecting cerebrovascular impairment.

BACKGROUND: The aim of this study was to determine the relationship between blood oxygen level dependent (BOLD) cerebrovascular reactivity (CVR) and cerebral blood flow (CBF) obtained from arterial spin labeling (ASL) using different post labeling delays (PLD). METHODS: Forty-two patients with steno-occlusive diseases and impaired CVR were divided into two groups, one scanned with a 1.5-second (1.5-s) and the other with a 2.5-second (2.5-s) PLD ASL protocol. For all patients, a region of interest (ROI) was drawn around the CVR impairment. This affected ROI was then left-right flipped across the brain midline to obtain the control ROI. For both groups, the difference in grey matter CVR between affected and control ROI was first tested to confirm significance. The average grey matter CBF of affected and control ROIs were then compared. The same analysis method was used to compare affected and control hemispheres. RESULTS: In both groups of 1.5-s and 2.5-s PLD, CVR values in the affected ROI (-0.049±0.055 and -0.042±0.074%/mmHg, respectively) were significantly lower compared to that in the control ROI (0.152±0.054 and 0.152±0.053%/mmHg, respectively, P<0.0001). In the group with the 1.5-s PLD, CBF in the affected ROI (37.62±11.37 mL/100 g/min) was significantly lower compared to CBF in the control ROI (44.13±11.58 mL/100 g/min, P<0.05). However, in the group with the 2.5-s PLD, no significant differences could be seen between CBF in the affected ROI (40.50±14.82 mL/100 g/min) and CBF in the control ROI (39.68±12.49 mL/100 g/min, P=0.73). In the hemisphere-based analysis, CBF was significantly lower in the affected side than in the control side for the group with the 1.5-s PLD (P<0.05) when CVR was impaired (P<0.0001), but not for the group with the 2.5-s PLD (P=0.49). CONCLUSIONS: In conclusion, our study reveals and highlights the value of a shorter-PLD ASL protocol, which is able to reflect CVR impairment. At the same time, we offer a better understanding of the relationship between BOLD CVR and CBF obtained from ASL.

Noninvasive Detection of Extracellular pH in Human Benign and Malignant Liver Tumors Using CEST MRI.

PURPOSE: In this study, we aimed to use 3T magnetic resonance imaging (MRI), which is clinically available, to determine the extracellular pH (pHe) of liver tumors and prospectively evaluate the ability of chemical exchange saturation transfer (CEST) MRI to distinguish between benign and malignant liver tumors. METHODS: Different radiofrequency irradiation schemes were assessed for ioversol-based pH measurements at 3T. CEST effects were quantified in vitro using the asymmetric magnetization transfer ratio (MTRasym) at 4.3 ppm from the corrected Z spectrum. Generalized ratiometric analysis was conducted by rationing resolved ioversol CEST effects at 4.3 ppm at a flip angle of 60 and 350°. Fifteen patients recently diagnosed with hepatic carcinoma and five patients diagnosed with hepatic hemangioma [1 male; mean age, 48.6 (range, 37-59) years] were assessed. RESULTS: By conducting dual-power CEST MRI, the pH of solutions was determined to be 6.0-7.2 at 3T in vitro. In vivo, ioversol signal intensities in the tumor region showed that the extracellular pH in hepatic carcinoma was acidic(mean ± standard deviation, 6.66 ± 0.19), whereas the extracellular pH was more physiologically neutral in hemangioma (mean ± standard deviation, 7.34 ± 0.09).The lesion size was similar between CEST pH MRI and T2-weighted imaging. CONCLUSION: dual-power CEST MRI can detect extracellular pH in human liver tumors and can provide molecular-level diagnostic tools for differentiating benign and malignant liver tumors at 3T.

APT Weighted MRI as an Effective Imaging Protocol to Predict Clinical Outcome After Acute Ischemic Stroke.

To explore the capability of the amide-proton-transfer weighted (APTW) magnetic resonance imaging (MRI) in the evaluation of clinical neurological deficit at the time of hospitalization and assessment of long-term daily functional outcome for patients with acute ischemic stroke (AIS). We recruited 55 AIS patients with brain MRI acquired within 24-48 h of symptom onset and followed up with their 90-day modified Rankin Scale (mRS) score. APT weighted MRI was performed for all the study subjects to measure APTW signal quantitatively in the acute ischemic area (APTWipsi) and the contralateral side (APTWcont). Change of the APT signal between the acute ischemic region and the contralateral side (ΔAPTW) was calculated. Maximum APTW signal (APTWmax) and minimal APTW signal (APTWmin) were also acquired to demonstrate APTW signals heterogeneity (APTWmax-min). In addition, all the patients were divided into 2 groups according to their 90-day mRS score (good prognosis group with mRS score <2 and poor prognosis group with mRS score ≥2). In the meantime, ΔAPTW of these groups was compared. We found that ΔAPTW was in good correlation with National Institutes of Health Stroke Scale (NIHSS) score (R 2 = 0.578, p < 0.001) and 90-day mRS score (R 2 = 0.55, p < 0.001). There was significant difference of ΔAPTW between patients with good prognosis and patients with poor prognosis. Plus, APTWmax-min was significantly different between two groups. These results suggested that APT weighted MRI could be used as an effective tool to assess the stroke severity and prognosis for patients with AIS, with APTW signal heterogeneity as a possible biomarker.

Extracellular pH is a biomarker enabling detection of breast cancer and liver cancer using CEST MRI.

Extracellular pH (pHe) decrease is associated with tumor growth, invasion, metastasis, and chemoresistance, which can be detected by chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI). Here, we demonstrated that ioversol CEST MRI can be exploited to achieve pHe mapping of the liver cancer microenvironment. In in vitro studies, we firstly explored whether ioversol signal is pH-dependent, and calculated the function equation between the CEST effects of ioversol and pH values, in the range of 6.0 to 7.8, by a ratiometric method. Then we verified the feasibility of this technique and the equation in vivo by applying pHe imaging in an MMTV-Erbb2 transgenic mouse breast cancer model, which is often used in CEST pHe studies. Furthermore, in vivo ioversol CEST MRI, we were able to map relative pHe and differentiate between tumor and normal tissue in a McA-RH7777 rat hepatoma model. This suggests pHe may be a useful biomarker for human liver cancer.