Protein-based amide proton transfer-weighted MR imaging of amnestic mild cognitive impairment

A Denoising Convolutional Autoencoder for SNR Enhancement in Chemical Exchange Saturation Transfer imaging: (DCAE-CEST)

PURPOSE

To develop a SNR enhancement method for chemical exchange saturation transfer (CEST) imaging using a denoising convolutional autoencoder (DCAE), and compare its performance with state-of-the-art denoising methods.

METHOD

The DCAECEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z spectrum via a series of 1D convolutions, nonlinearity applications and pooling. Subsequently, the decoder reconstructs an output denoised Z-spectrum using a series of up-sampling and convolution layers. The DCAE-CEST model underwent multistage training in an environment constrained by Kullback Leibler divergence, while ensuring data adaptability through context learning using Principal Component Analysis processed Z-spectrum as a reference. The model was trained using simulated Z-spectra, and its performance was evaluated using both simulated data and in-vivo data from an animal tumor model. Maps of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) effects were quantified using the multiple pool Lorentzian fit, along with an apparent exchange-dependent relaxation metric.

RESULTS

In digital phantom experiments, the DCAECEST method exhibited superior performance, surpassing existing denoising techniques, as indicated by the peak SNR and Structural Similarity Index. Additionally, in vivo data further confirms the effectiveness of the DCAECEST in denoising the APT and NOE maps when compared to other methods. While no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings.

CONCLUSION

The DCAECEST can learn the most important features of the CEST Z spectrum and provide the most effective denoising solution compared to other methods.

Machine learning-based amide proton transfer imaging using partially synthetic training data

PURPOSE

Machine learning (ML) has been increasingly used to quantify CEST effect. ML models are typically trained using either measured data or fully simulated data. However, training with measured data often lacks sufficient training data, whereas training with fully simulated data may introduce bias because of limited simulations pools. This study introduces a new platform that combines simulated and measured components to generate partially synthetic CEST data, and to evaluate its feasibility for training ML models to predict amide proton transfer (APT) effect.

METHODS

Partially synthetic CEST signals were created using an inverse summation of APT effects from simulations and the other components from measurements. Training data were generated by varying APT simulation parameters and applying scaling factors to adjust the measured components, achieving a balance between simulation flexibility and fidelity. First, tissue-mimicking CEST signals along with ground truth information were created using multiple-pool model simulations to validate this method. Second, an ML model was trained individually on partially synthetic data, in vivo data, and fully simulated data, to predict APT effect in rat brains bearing 9 L tumors.

RESULTS

Experiments on tissue-mimicking data suggest that the ML method using the partially synthetic data is accurate in predicting APT. In vivo experiments suggest that our method provides more accurate and robust prediction than the training using in vivo data and fully synthetic data.

CONCLUSION

Partially synthetic CEST data can address the challenges in conventional ML methods.

Amide Proton Transfer (APT) imaging in tumor with a machine learning approach using partially synthetic data

Purpose

Machine learning (ML) has been increasingly used to quantify chemical exchange saturation transfer (CEST) effect. ML models are typically trained using either measured data or fully simulated data. However, training with measured data often lacks sufficient training data, while training with fully simulated data may introduce bias due to limited simulations pools. This study introduces a new platform that combines simulated and measured components to generate partially synthetic CEST data, and to evaluate its feasibility for training ML models to predict amide proton transfer (APT) effect.

Methods

Partially synthetic CEST signals were created using an inverse summation of APT effects from simulations and the other components from measurements. Training data were generated by varying APT simulation parameters and applying scaling factors to adjust the measured components, achieving a balance between simulation flexibility and fidelity. First, tissue-mimicking CEST signals along with ground truth information were created using multiple-pool model simulations to validate this method. Second, an ML model was trained individually on partially synthetic data, in vivo data, and fully simulated data, to predict APT effect in rat brains bearing 9L tumors.

Results

Experiments on tissue-mimicking data suggest that the ML method using the partially synthetic data is accurate in predicting APT. In vivo experiments suggest that our method provides more accurate and robust prediction than the training using in vivo data and fully synthetic data.

Conclusion

Partially synthetic CEST data can address the challenges in conventional ML methods.

Isolation of amide proton transfer effect and relayed nuclear Overhauser enhancement effect at -3.5ppm using CEST with double saturation powers

PURPOSE

Quantifications of amide proton transfer (APT) and nuclear Overhauser enhancement (rNOE(-3.5)) mediated saturation transfer with high specificity are challenging because their signals measured in a Z-spectrum are overlapped with confounding signals from direct water saturation (DS), semi-solid magnetization transfer (MT), and CEST of fast-exchange pools. In this study, based on two canonical CEST acquisitions with double saturation powers (DSP), a new data-postprocessing method is proposed to specifically quantify the effects of APT and rNOE.

METHODS

For CEST imaging with relatively low saturation powers (ω 1 2 $$ {\upomega}_1^2 $$ ), both the fast-exchange CEST effect and the semi-solid MT effect roughly depend onω 1 2 $$ {\upomega}_1^2 $$ , whereas the slow-exchange APT/rNOE(-3.5) effect do not, which is exploited to isolate a part of the APT and rNOE effects from the confounding signals in this study. After a mathematical derivation for the establishment of the proposed method, numerical simulations based on Bloch equations are then performed to demonstrate its specificity to detections of the APT and rNOE effects. Finally, an in vivo validation of the proposed method is conducted using an animal tumor model at a 4.7 T MRI scanner.

RESULTS

The simulations show that DSP-CEST can quantify the effects of APT and rNOE and substantially eliminate the confounding signals. The in vivo experiments demonstrate that the proposed DSP-CEST method is feasible for the imaging of tumors.

CONCLUSION

The data-postprocessing method proposed in this study can quantify the APT and rNOE effects with considerably increased specificities and a reduced cost of imaging time.

GBA1 Gene Mutations in α-Synucleinopathies-Molecular Mechanisms Underlying Pathology and Their Clinical Significance

α-Synucleinopathies comprise a group of neurodegenerative diseases characterized by altered accumulation of a protein called α-synuclein inside neurons and glial cells. This aggregation leads to the formation of intraneuronal inclusions, Lewy bodies, that constitute the hallmark of α-synuclein pathology. The most prevalent α-synucleinopathies are Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). To date, only symptomatic treatment is available for these disorders, hence new approaches to their therapy are needed. It has been observed that GBA1 mutations are one of the most impactful risk factors for developing α-synucleinopathies such as PD and DLB. Mutations in the GBA1 gene, which encodes a lysosomal hydrolase β-glucocerebrosidase (GCase), cause a reduction in GCase activity and impaired α-synuclein metabolism. The most abundant GBA1 gene mutations are N370S or N409S, L444P/L483P and E326K/E365K. The mechanisms by which GCase impacts α-synuclein aggregation are poorly understood and need to be further investigated. Here, we discuss some of the potential interactions between α-synuclein and GCase and show how GBA1 mutations may impact the course of the most prevalent α-synucleinopathies.

Amide Proton Transfer-weighted MRI combined with serum prostate-specific antigen levels for differentiating malignant prostate lesions from benign prostate lesions: a retrospective cohort study

BACKGROUND

Early diagnosis of prostate cancer improves its prognosis, while it is essential to upgrade screening tools. This study aimed to explore the value of a novel functional magnetic resonance imaging (MRI) technique, namely amide proton transfer (APT)-weighted MRI, combined with serum prostate-specific antigen (PSA) levels to differentiate malignant prostate lesions from benign prostate lesions.

METHODS

Data of patients who underwent prostate examinations at Chongqing University Cancer Hospital between July 2019 and March 2022 were retrospectively analyzed. All patients underwent T2-weighted imaging (T2WI), APT, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI. Two radiologists analyzed the images independently. The ability of the quantitative parameters alone or in different combinations in differentiating malignant prostate lesions from benign prostate lesions were compared by using receiver operating characteristic (ROC) curves. According to the DeLong test, the combined parameters were significantly different from the corresponding single parameter (P < 0.05).

RESULTS

A total of 79 patients were finally enrolled, including 52 patients in the malignant group and 27 patients in the benign group. The separate assessment of indexes revealed that APTmax, APTmean, mean apparent diffusion coefficient (ADCmean), ADCmax, ADCmin, tPAD, free prostate-specific antigen (FPSA), FPSA/total prostate-specific antigen (tPSA), and PSA density (PSAD) were significantly different between the two groups (P < 0.05), while APTmin was not significantly different between the two groups (P > 0.05). APTmax and APTmean had the high values of area under the ROC curve (AUC), which were 0.780 and 0.710, respectively. APTmax had a high sensitivity, and APTmean had a high specificity. The combination of APTmax, APTmean, ADCmean, and PSAD had the highest AUC value (AUC: 0.880, sensitivity: 86.540, specificity: 78.260).

CONCLUSION

APTmax, APTmean, ADCmean, ADCmin, tPAD, FPSA, and PSAD showed to have a high value in differentiating malignant prostate lesions from benign prostate lesions in the separate assessment of indexes. The combination of APTmax, APTmean, ADCmean, and PSAD had the highest diagnostic value.

Altered glutamate-glutamine and amide proton transfer-weighted values in the hippocampus of patients with amnestic mild cognitive impairment: A novel combined imaging diagnostic marker

Background and purpose

Early diagnosis of amnestic mild cognitive impairment (aMCI) and timely management to delay the onset of Alzheimer's disease (AD) would benefit patients. Pathological metabolic changes of excitatory/inhibitory neurotransmitters and abnormal protein deposition in the hippocampus of aMCI may provide a new clue to imaging diagnosis. However, the diagnostic performance using these hippocampal metabolite measurements is still unclear. We aimed to quantify right hippocampal glutamate-glutamine (Glx) and gamma-aminobutyric acid (GABA) levels as well as protein-based amide proton transfer-weighted (APTw) signals of patients with aMCI and investigate the diagnostic performance of these metabolites.

Methods

In this cross-sectional study, 20 patients with aMCI and 20 age- and gender-matched healthy controls (HCs) underwent MEGA Point Resolved Spectroscopy (MEGA-PRESS) and APTw MR imaging at 3 T. GABA+, Glx, and APTw signals were measured in the right hippocampus. The GABA+ levels, Glx levels, Glx/GABA+ ratios, and APTw values were compared between the HCs and aMCI groups using the Mann-Whitney U test. Binary logistic regression and receiver operating characteristic (ROC) curve analyses were used to evaluate MEGA-PRESS and APTw parameters' diagnostic performance.

Results

Compared with HCs, patients with aMCI had significantly lower Glx levels in the right hippocampus (7.02 ± 1.41 i.u. vs. 5.81 ± 1.33 i.u., P = 0.018). No significant changes in the GABA+ levels were observed in patients with aMCI (HCs vs. aMCI: 2.54 ± 0.28 i.u. vs. 2.47 ± 0.36 i.u., P = 0.620). In addition, Glx/GABA+ ratios between the two groups (HCs vs. aMCI: 2.79 ± 0.60 vs. 2.37 ± 0.55, P = 0.035) were significantly different. Compared with HCs, patients with aMCI showed higher APTw values in the right hippocampus (0.99 ± 0.26% vs. 1.26% ± 0.28, P = 0.006). The ROC curve analysis showed that Glx, GABA+, Glx/GABA+, and APTw values had an area under the curve (AUC) of 0.72, 0.55, 0.70, and 0.75, respectively, for diagnosing aMCI. In the ROC curve analysis, the AUC of the combination of the parameters increased to 0.88, which is much higher than that observed in the univariate analysis (P < 0.05).

Conclusion

The combination of right hippocampal Glx levels and APTw values improved the diagnostic performance for aMCI, indicating it as a promising combined imaging diagnostic marker. Our study provided a potential imaging diagnostic strategy of aMCI, which may promote early detection of aMCI and facilitate timely intervention to delay the pathological progress toward AD.

Relaxation-Compensated Chemical Exchange Saturation Transfer MRI in the Brain at 7T: Application in Relapsing-Remitting Multiple Sclerosis

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) can probe tissue biochemistry in vivo with high resolution and sensitivity without requiring exogenous contrast agents. Applying CEST MRI at ultrahigh field provides advantages of increasing spectral resolution and improving sensitivity to metabolites with faster proton exchange rates such as glutamate, a critical neurotransmitter in the brain. Prior magnetic resonance spectroscopy and CEST MRI studies have revealed altered regulation of glutamate in patients with multiple sclerosis (MS). While CEST imaging facilitates new strategies for investigating the pathology underlying this complex and heterogeneous neurological disease, CEST signals are contaminated or diluted by concurrent effects (e.g., semi-solid magnetization transfer (MT) and direct water saturation) and are scaled by the T1 relaxation time of the free water pool which may also be altered in the context of disease. In this study of 20 relapsing-remitting MS patients and age- and sex-matched healthy volunteers, glutamate-weighted CEST data were acquired at 7.0 T. A Lorentzian fitting procedure was used to remove the asymmetric MT contribution from CEST z-spectra, and the apparent exchange-dependent relaxation (AREX) correction was applied using an R1 map derived from an inversion recovery sequence to further isolate glutamate-weighted CEST signals from concurrent effects. Associations between AREX and cognitive function were examined using the Minimal Assessment of Cognitive Function in MS battery. After isolating CEST effects from MT, direct water saturation, and T1 effects, glutamate-weighted AREX contrast remained higher in gray matter than in white matter, though the difference between these tissues decreased. Glutamate-weighted AREX in normal-appearing gray and white matter in MS patients did not differ from healthy gray and white matter but was significantly elevated in white matter lesions. AREX in some cortical regions and in white matter lesions correlated with disability and measures of cognitive function in MS patients. However, further studies with larger sample sizes are needed to confirm these relationships due to potential confounding effects. The application of MT and AREX corrections in this study demonstrates the importance of isolating CEST signals for more specific characterization of the contribution of metabolic changes to tissue pathology and symptoms in MS.

Magnetic Resonance Imaging in Tauopathy Animal Models

The microtubule-associated protein tau plays an important role in tauopathic diseases such as Alzheimer’s disease and primary tauopathies such as progressive supranuclear palsy and corticobasal degeneration. Tauopathy animal models, such as transgenic, knock-in mouse and rat models, recapitulating tauopathy have facilitated the understanding of disease mechanisms. Aberrant accumulation of hyperphosphorylated tau contributes to synaptic deficits, neuroinflammation, and neurodegeneration, leading to cognitive impairment in animal models. Recent advances in molecular imaging using positron emission tomography (PET) and magnetic resonance imaging (MRI) have provided valuable insights into the time course of disease pathophysiology in tauopathy animal models. High-field MRI has been applied for in vivo imaging in animal models of tauopathy, including diffusion tensor imaging for white matter integrity, arterial spin labeling for cerebral blood flow, resting-state functional MRI for functional connectivity, volumetric MRI for neurodegeneration, and MR spectroscopy. In addition, MR contrast agents for non-invasive imaging of tau have been developed recently. Many preclinical MRI indicators offer excellent translational value and provide a blueprint for clinical MRI in the brains of patients with tauopathies. In this review, we summarized the recent advances in using MRI to visualize the pathophysiology of tauopathy in small animals. We discussed the outstanding challenges in brain imaging using MRI in small animals and propose a future outlook for visualizing tau-related alterations in the brains of animal models.

Saturation Transfer MRI for Detection of Metabolic and Microstructural Impairments Underlying Neurodegeneration in Alzheimer's Disease

Alzheimer's disease (AD) is one of the most common causes of dementia and difficult to study as the pool of subjects is highly heterogeneous. Saturation transfer (ST) magnetic resonance imaging (MRI) methods are quantitative modalities with potential for non-invasive identification and tracking of various aspects of AD pathology. In this review we cover ST-MRI studies in both humans and animal models of AD over the past 20 years. A number of magnetization transfer (MT) studies have shown promising results in human brain. Increased computing power enables more quantitative MT studies, while access to higher magnetic fields improves the specificity of chemical exchange saturation transfer (CEST) techniques. While much work remains to be done, results so far are very encouraging. MT is sensitive to patterns of AD-related pathological changes, improving differential diagnosis, and CEST is sensitive to particular pathological processes which could greatly assist in the development and monitoring of therapeutic treatments of this currently incurable disease.

Amide Proton Transfer MRI Could Be Used to Evaluate the Pathophysiological Status of White Matter Hyperintensities

BACKGROUND

The pathophysiology of white matter hyperintensities (WMH) remains unclear, investigations of amide proton transfer (APT) signals in WMH disease may provide relevant pathophysiological information.

PURPOSE

To evaluate the APT signals differences and heterogeneity of WMH and adjacent normal-appearing white matter (NAWM) at different Fazekas grades and different locations.

STUDY TYPE

Prospective.

POPULATION

In all, 180 WMH patients (age, 40-76; male/female, 77/103) and 59 healthy controls (age, 42-70; male/female, 23/36).

FIELD STRENGTH/SEQUENCE

A 3 T; 3D fluid-attenuated inversion recovery (FLAIR), 3D APT-weighted (APTw).

ASSESSMENT

The mean APTw values (APTw_mean ) and the APTw signals heterogeneity (APTw_max-min ) among different grades WMH and NAWM and the APTw_mean of the same grade deep WMH (DWMH) and paraventricular WMH (PWMH) were calculated and compared. Regions of interests were delineated on WMH lesions, NAWM and healthy white matter.

STATISTICAL TESTS

One-way analysis of variance (ANOVA); independent sample t test; Chi-square test. Significance level: P < 0.05.

RESULTS

APTw_mean among different grade WMH (from grade 0 to 3, 0.58 ± 0.14% vs. 0.29 ± 0.23% vs. 0.37 ± 0.24% vs. 0.61 ± 0.22%, respectively) were significantly different except between grade 1 and 2 (P = 0.27) and between grade 0 and 3 (P = 0.97). The differences in APTw_mean between WMH and NAWM were significant (WMH vs. NAWM from grade 1 to 3, 0.29% ± 0.23% vs. 0.55% ± 0.27%; 0.37% ± 0.24% vs. 0.59% ± 0.22%; 0.61% ± 0.22% vs. 0.42% ± 0.24%, respectively). Lower APTw_mean values were found only in grade 3 NAWM than other grades NAWM and controls. The APTw_max-min values of grade 1-3 WMH (0.38% ± 0.27% vs. 0.51% ± 0.31% vs. 0.67% ± 0.34%, respectively) were significantly different. Higher APT_mean values were found only in grade 2 PWMH (0.47% ± 0.22% vs. 0.32% ± 0.24%).

DATA CONCLUSION

Significant differences of APT signals were found in WMH of different Fazekas grades and different locations.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 3.

Amide Proton Transfer-Weighted MRI Might Help Distinguish Amnestic Mild Cognitive Impairment From a Normal Elderly Population

Objectives: To evaluate whether 3D amide proton transfer weighted (APTw) imaging based on magnetization transfer analysis can be used as a novel imaging marker to distinguish amnestic mild cognitive impairment (aMCI) patients from the normal elderly population by measuring changes in APTw signal intensity in the hippocampus and amygdala. Materials and Methods: Seventy patients with aMCI and 74 age- and sex-matched healthy volunteers were recruited for routine MRI and APT imaging examinations. Magnetic transfer ratio asymmetry (MTRasym) of the amide protons (at 3.5 ppm), or APTw values, were measured in the bilateral hippocampus and amygdala on three consecutive cross-sectional APT images and were compared between the aMCI and control groups. The independent sample t-test was used to evaluate the difference in APTw values of the bilateral hippocampus and amygdala between the aMCI and control groups. Receiver operator characteristic analysis was used to assess the diagnostic performance of the APTw. The paired t-test was used to assess the difference in APTw values between the left and right hippocampus and amygdala, in both the aMCI and control groups. Results: The APTw values of the bilateral hippocampus and amygdala in the aMCI group were significantly higher than those in the control group (left hippocampus 1.01 vs. 0.77% p < 0.001; right hippocampus 1.02 vs. 0.74%, p < 0.001; left amygdala 0.98 vs. 0.70% p < 0.001; right amygdala 0.94 vs. 0.71%, p < 0.001). The APTw values of the left amygdala had the largest AUC (0.875) at diagnosis of aMCI. There was no significant difference in APTw values between the left and right hippocampus and amygdala, in either group. (aMCI group left hippocampus 1.01 vs. right hippocampus 1.02%, p = 0.652; healthy control group left hippocampus 0.77 vs. right hippocampus 0.74%, p = 0.314; aMCI group left amygdala 0.98 vs. right amygdala 0.94%, p = 0.171; healthy control group left amygdala 0.70 vs. right amygdala 0.71%, p = 0.726). Conclusion: APTw can be used as a new imaging marker to distinguish aMCI patients from the normal elderly population by indirectly reflecting the changes in protein content in the hippocampus and amygdala.

Deep neural network based CEST and AREX processing: Application in imaging a model of Alzheimer's disease at 3 T

PURPOSE

To optimize and apply deep neural network based CEST (deepCEST) and apparent exchange dependent-relaxation (deepAREX) for imaging the mouse brain with Alzheimer's disease (AD) at 3T MRI.

METHODS

CEST and T₁ data of central and anterior brain slices of 10 AD mice and 10 age-matched wild type (WT) mice were acquired at a 3T animal MRI scanner. The networks of deepCEST/deepAREX were optimized and trained on the WT data. The CEST/AREX contrasts of AD and WT mice predicted by the networks were analyzed and further validated by immunohistochemistry.

RESULTS

After optimization and training on CEST data of WT mice, deepCEST/deepAREX could rapidly (~1 s) generate precise CEST and AREX results for unseen CEST data of AD mice, indicating the accuracy and generalization of the networks. Significant lower amide weighted (3.5 ppm) signal related to amyloid β-peptide (Aβ) plaque depositions, which was validated by immunohistochemistry results, was detected in both central and anterior brain slices of AD mice compared to WT mice. Decreased magnetization transfer (MT) signal was also found in AD mice especially in the anterior slice.

CONCLUSION

DeepCEST/deepAREX could rapidly generate accurate CEST/AREX contrasts in animal study. The well-optimized deepCEST/deepAREX have potential for AD differentiation at 3T MRI.

Early detection of Alzheimer's disease using creatine chemical exchange saturation transfer magnetic resonance imaging

Detecting Alzheimer's disease (AD) at an early stage brings a lot of benefits including disease management and actions to slow the progression of the disease. Here, we demonstrate that reduced creatine chemical exchange saturation transfer (CrCEST) contrast has the potential to serve as a new biomarker for early detection of AD. The results on wild type (WT) mice and two age-matched AD models, namely tauopathy (Tau) and Aβ amyloidosis (APP), indicated that CrCEST contrasts of the cortex and corpus callosum in the APP and Tau mice were significantly reduced compared to WT counterpart at an early stage (6-7 months) (p < 0.011). Two main causes of the reduced CrCEST contrast, i.e. cerebral pH and creatine concentration, were investigated. From phantom and hypercapnia experiments, CrCEST showed excellent sensitivity to pH variations. From MRS results, the creatine concentration in WT and AD mouse brain was equivalent, which suggests that the reduced CrCEST contrast was dominated by cerebral pH change involved in the progression of AD. Immunohistochemical analysis revealed that the abnormal cerebral pH in AD mice may relate to neuroinflammation, a known factor that can cause pH reduction.

Clinical routine acquisition protocol for 3D relaxation-compensated APT and rNOE CEST-MRI of the human brain at 3T

PURPOSE

The value of relaxation-compensated amide proton transfer (APT) and relayed nuclear Overhauser effect (rNOE) chemical exchange saturation transfer (CEST)-MRI has already been demonstrated in various neuro-oncological clinical applications. Recently, we translated the approach from 7T to a clinically relevant magnetic field strength of 3T. However, the overall acquisition time was still too long for a broad application in the clinical setting. The aim of this study was to establish a shorter acquisition protocol whilst maintaining the contrast behavior and reproducibility.

METHODS

Ten patients with glioblastoma were examined using the previous state-of-the-art acquisition protocol at 3T. The acquired spectral data were retrospectively reduced to find the minimal amount of required information that allows obtaining the same contrast behavior. To further reduce the acquisition time, also the image readout was accelerated and the pre-saturation parameters were further optimized.

RESULTS

In total, the overall acquisition time could be reduced from 19 min to under 7 min. One key finding was that, when evaluated by the relaxation-compensated inverse metric, a contrast correction for B₁ -field inhomogeneities at 3T can also be achieved reliably with CEST data at only one B₁ value. In contrast, a 1-point B₁ -correction was not sufficient for the common linear difference evaluation. The reproducibility of the new clinical routine acquisition protocol was similar to the previous state-of-the-art protocol with limits of agreement below 20%.

CONCLUSIONS

The substantial reduction in acquisition time by about 64% now allows the application of 3D relaxation-compensated APT and rNOE CEST-MRI for examinations of the human brain at 3T in clinical routine.

Characterization of a pathogenic variant in GBA for Parkinson's disease with mild cognitive impairment patients

Parkinson's disease (PD) is the second most common neurodegenerative disease, and mild cognitive impairment (MCI) is a well-established risk factor for the development of dementia in PD. A growing body of evidence suggests that low expression of glucocerebrosidase (GBA) promotes the transmission of α-synuclein (α-Syn) interpolymers and the progression of PD. However, how GBA mutations affect the pathogenesis of PD via abnormal aggregation of α-Syn is unclear, and no clinically valid PD-MCI genetic markers have been identified. Here, we first located a GBA eQTL, rs12411216, by analysing DHS, eQTL SNP, and transcription factor binding site data using the UCSC database. Subsequently, we found that rs12411216 was significantly associated with PD-MCI (P < 0.05) in 306 PD patients by genotyping. In exploring the relationship between rs12411216 and GBA expression, the SNP was found to be associated with GBA expression in 50 PD patients through qPCR verification. In a further CRISPR/Cas9-mediated genome editing module, the SNP was identified to cause a decrease in GBA expression, weaken enzymatic activity and enhance the abnormal aggregation of α-Syn in SH-SY5Y cells. Additionally, using an electrophoretic mobility shift assay, we confirmed that the binding efficiency of transcription factor E2F4 was affected by the rs12411216 SNP. In conclusion, our results showed that rs12411216 regulated GBA expression, supporting its potential role as a PD-MCI genetic biomarker and highlighting novel mechanisms underlying Parkinson's disease.

Amide proton transfer-weighted magnetic resonance imaging of human brain aging at 3 Tesla

Background

Amide proton transfer-weighted (APTw) imaging has been revealed to hold great potential in the diagnosis of several brain diseases. The purpose of this proof-of-concept study was to evaluate the feasibility and value of APTw magnetic resonance imaging (MRI) in characterizing normal brain aging.

Methods

A total of 106 healthy subjects were recruited and scanned at 3.0 Tesla, with APTw and conventional magnetization transfer (MT) sequences. Quantitative image analyses were performed in 12 regions of interest (ROIs) for each subject. The APTw or MT ratio (MTR) signal differences among five age groups (young, mature, middle-aged, young-old, and middle-old) were assessed using the one-way analysis of variance, with the Benjamini-Hochberg correction for multiple comparisons. The relationship between APTw and MTR signals and the age dependencies of APTw and MTR signals were assessed using the Pearson correlation and non-linear regression.

Results

There were no significant differences between the APTw or MTR values for males and females in any of the 12 ROIs analyzed. Among the five age groups, there were significant differences in the three white matter regions in the temporal, occipital, and frontal lobes. Overall, the mean APTw values in the older group were higher than those in the younger group. Positive correlations were observed in relation to age in most brain regions, including four with significant positive correlations (r=0.2065-0.4182) and five with increasing trends. As a comparison, the mean MTR values did not appear to be significantly different among the five age groups. In addition, the mean APTw and MTR values revealed significant positive correlations in 10 ROIs (r=0.2214-0.7269) and a significant negative correlation in one ROI (entorhinal cortex, r=-0.2141).

Conclusions

Our early results show that the APTw signal can be used as a promising and complementary imaging biomarker with which normal brain aging can be evaluated at the molecular level.