The structural-functional-connectivity coupling of the aging brain.

Aging primarily affects memory and executive functions, a relationship that may be underpinned by the fact that almost all adults over 60 years old develop small vessel disease (SVD). The fact that a wide range of neuropathologies could only explain up to 43% of the variation in age-related cognitive impairment suggests that other factors, such as cognitive reserve, may play a role in the brain's resilience against aging-related cognitive decline. This study aims to examine the relationship between structural-functional-connectivity coupling (SFC), and aging, cognitive abilities and reserve, and SVD-related neuropathologies using a cohort of n = 176 healthy elders from the Harvard Aging Brain Study. The SFC is a recently proposed biomarker that reflects the extent to which anatomical brain connections can predict coordinated neural activity. After controlling for the effect of age, sex, and years of education, global SFC, as well as the intra-network SFC of the dorsolateral somatomotor and dorsal attention networks, and the inter-network SFC between dorsolateral somatomotor and frontoparietal networks decreased with age. The global SFC decreased with total cognitive score. There were significant interaction effects between years of education versus white matter hyperintensities and between years of education versus cerebral microbleeds on inter-network SFC. Enlarged perivascular space in basal ganglia was associated with higher inter-network SFC. Our results suggest that cognitive ability is associated with brain coupling at the global level and cognitive reserve with brain coupling at the inter-functional-brain-cluster level with interaction effect from white matter hyperintensities and cerebral microbleed in a cohort of healthy elderlies.

Behavioral disinhibition in stroke.

Background: Post-stroke behavioral disinhibition (PSBD) is common in stroke survivors and often presents as impulsive, tactless or vulgar behavior. However, it often remains undiagnosed and thus untreated, even though it can lead to a longer length of stay in a rehabilitation facility. The proposed study will aim to evaluate the clinical, neuropsychological and magnetic resonance imaging (MRI) correlates of PSBD in a cohort of stroke survivors and describe its 12-month course. Methods: This prospective cohort study will recruit 237 patients and will be conducted at the Neurology Unit of the Prince of Wales Hospital. The project duration will be 24 months. The patients will be examined by multiple MRI methods, including diffusion-weighted imaging, within 1 week after stroke onset. The patients and their caregivers will receive a detailed assessment at a research clinic at 3, 9 and 15 months after stroke onset (T1, T2 and T3, respectively). The disinhibition subscale of the Frontal Systems Behavior Scale (FrSBe) will be completed by each subject and caregiver, and scores ≥65 will be considered to indicate PSBD.A stepwise logistic regression will be performed to assess the importance of lesions in the regions of interest (ROIs), together with other significant variables identified in the univariate analyses. For patients with PSBD at T1, the FrSBe disinhibition scores will be compared between the groups of patients with and without ROI infarcts, using covariance analysis. The demographic, clinical and MRI variables of remitters and non-remitters will be examined again at T2 and T3 by logistic regression. Discussion: This project will be the first MRI study on PSBD in stroke survivors. The results will shed light on the associations of lesions in the orbitofrontal cortex, anterior temporal lobe and subcortical brain structures with the risk of PSBD. The obtained data will advance our understanding of the pathogenesis and clinical course of PSBD in stroke, as well as other neurological conditions. The findings are thus likely to be applicable to the large population of patients with neurological disorders at risk of PSBD and are expected to stimulate further research in this field.

Reconstruction of multi-phase parametric maps in 4D-magnetic resonance fingerprinting (4D-MRF) by optimization of local T1 and T2 sensitivities.

BACKGROUND: Time-resolved magnetic resonance fingerprinting (MRF), or 4D-MRF, has been demonstrated its feasibility in motion management in radiotherapy (RT). However, the prohibitive long acquisition time is one of challenges of the clinical implementation of 4D-MRF. The shortening of acquisition time causes data insufficiency in each respiratory phase, leading to poor accuracies and consistencies of the predicted tissues' properties of each phase. PURPOSE: To develop a technique for the reconstruction of multi-phase parametric maps in four-dimensional magnetic resonance fingerprinting (4D-MRF) through the optimization of local T1 and T2 sensitivities. METHODS: The proposed technique employed an iterative optimization to tailor the data arrangement of each phase by manipulation of inter-phase frames, such that the T1 and T2 sensitivities, which were quantified by the modified Minkowski distance, of the truncated signal evolution curve was maximized. The multi-phase signal evolution curves were modified by sliding window reconstruction and inter-phase frame sharing (SWIFS). Motion correction (MC) and dot product matching were sequentially performed on the modified signal evolution and dictionary to reconstruct the multi-parametric maps. The proposed technique was evaluated by numerical simulations using the extended cardiac-torso (XCAT) phantom with regular and irregular breathing patterns, and by in vivo MRF data of three health volunteers and six liver cancer patients acquired at a 3.0 T scanner. RESULTS: In simulation study, the proposed SWIFS approach achieved the overall mean absolute percentage error (MAPE) of 8.62% ± 1.59% and 16.2% ± 3.88% for the eight-phases T1 and T2 maps, respectively, in the sagittal view with irregular breathing patterns. In contrast, the overall MAPE of T1 and T2 maps generated by the conventional approach with multiple MRF repetitions were 22.1% ± 11.0% and 30.8% ± 14.9%, respectively. For in-vivo study, the predicted mean T1 and T2 of liver by the proposed SWIFS approach were 795 ms ± 38.9 ms and 58.3 ms ± 11.7 ms, respectively. CONCLUSIONS: Both simulation and in vivo results showed that the approach empowered by T1 and T2 sensitivities optimization and sliding window under the shortened acquisition of MRF had superior performance in the estimation of multi-phase T1 and T2 maps as compared to the conventional approach with oversampling of MRF data.

Deep-Learning-Based MRI Microbleeds Detection for Cerebral Small Vessel Disease on Quantitative Susceptibility Mapping.

BACKGROUND: Cerebral microbleeds (CMB) are indicators of severe cerebral small vessel disease (CSVD) that can be identified through hemosiderin-sensitive sequences in MRI. Specifically, quantitative susceptibility mapping (QSM) and deep learning were applied to detect CMBs in MRI. PURPOSE: To automatically detect CMB on QSM, we proposed a two-stage deep learning pipeline. STUDY TYPE: Retrospective. SUBJECTS: A total number of 1843 CMBs from 393 patients (69 ± 12) with cerebral small vessel disease were included in this study. Seventy-eight subjects (70 ± 13) were used as external testing. FIELD STRENGTH/SEQUENCE: 3 T/QSM. ASSESSMENT: The proposed pipeline consisted of two stages. In stage I, 2.5D fast radial symmetry transform (FRST) algorithm along with a one-layer convolutional network was used to identify CMB candidate regions in QSM images. In stage II, the V-Net was utilized to reduce false positives. The V-Net was trained using CMB and non CMB labels, which allowed for high-level feature extraction and differentiation between CMBs and CMB mimics like vessels. The location of CMB was assessed according to the microbleeds anatomical rating scale (MARS) system. STATISTICAL TESTS: The sensitivity and positive predicative value (PPV) were reported to evaluate the performance of the model. The number of false positive per subject was presented. RESULTS: Our pipeline demonstrated high sensitivities of up to 94.9% at stage I and 93.5% at stage II. The overall sensitivity was 88.9%, and the false positive rate per subject was 2.87. With respect to MARS, sensitivities of above 85% were observed for nine different brain regions. DATA CONCLUSION: We have presented a deep learning pipeline for detecting CMB in the CSVD cohort, along with a semi-automated MARS scoring system using the proposed method. Our results demonstrated the successful application of deep learning for CMB detection on QSM and outperformed previous handcrafted methods. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Randomized Controlled Trial of Acupressure for Perception of Stress and Health-Related Quality of Life Among Health Care Providers During the COVID-19 Pandemic: The Self-Acupressure for Stress (SAS) Trial.

Background: The efficacy of providing self-acupressure educational materials in reducing stress and improving health-related quality of life (HRQOL) is uncertain. Evidence-based data to recommend for or against self-acupressure as an intervention for reducing stress and improving HRQOL is needed. Objective: The Self-Acupressure for Stress (SAS) trial evaluates whether providing self-acupressure educational materials would reduce stress and improve HRQOL among health care providers (HCPs). Design: Randomized behavioral clinical trial. Setting: The entire study took place remotely. Participants: One hundred fifty-nine adult HCPs with no prior experience or training in acupressure. Intervention: The intervention group received self-acupressure educational materials. Measurements: Primary outcomes were perception of stress measured by the Perceived Stress Scale (PSS), as well as scores on the physical and mental components of the 12-item Short Form Health Survey version 2 (SF-12v2). Results: From the baseline to midpoint evaluations, the intervention group significantly reduced their PSS score (P ≤ .001) and increased their SF-12v2 Mental score (P = .002) but not their SF-12v2 Physical score (P = .55). These findings persisted at the final follow-up (both PSS and SF-12v2 Mental changes from baseline P < .001). However the control group also significantly improved their SF-12v2 Mental from baseline to midpoint (P = .01) which was maintained at final follow-up (P = .02), whereas PSS and SF-12v2 Physical did not significantly change from baseline at either mid or final. Finally, the intervention group improved by significantly more than the control group from baseline to final follow-up for both PSS (P = .007) and SF-12v2 Mental (P = .02) HRQOL measures. Limitation: The trial was not blinded. Conclusion: Among HCPs during the coronavirus disease 2019 (COVID-19) pandemic, the provision of self-acupressure educational materials safely improved self-reported assessments of perception of stress and mental health. Self-acupressure represents a promising intervention for other populations. The study findings support the use of self-acupressure to reduce stress and improve HRQOL. Trial Registration: ClinicalTrials.gov: NCT04472559.

Respiratory-Correlated 4-Dimensional Magnetic Resonance Fingerprinting for Liver Cancer Radiation Therapy Motion Management.

PURPOSE: The objective of this study was to develop a respiratory-correlated (RC) 4-dimensional (4D) imaging technique based on magnetic resonance fingerprinting (MRF) (RC-4DMRF) for liver tumor motion management in radiation therapy. METHODS AND MATERIALS: Thirteen patients with liver cancer were prospectively enrolled in this study. k-space MRF signals of the liver were acquired during free-breathing using the fast acquisition with steady-state precession sequence on a 3T scanner. The signals were binned into 8 respiratory phases based on respiratory surrogates, and interphase displacement vector fields were estimated using a phase-specific low-rank optimization method. Hereafter, the tissue property maps, including T1 and T2 relaxation times, and proton density, were reconstructed using a pyramid motion-compensated method that alternatively optimized interphase displacement vector fields and subspace images. To evaluate the efficacy of RC-4DMRF, amplitude motion differences and Pearson correlation coefficients were determined to assess measurement agreement in tumor motion between RC-4DMRF and cine magnetic resonance imaging (MRI); mean absolute percentage errors of the RC-4DMRF-derived tissue maps were calculated to reveal tissue quantification accuracy using digital human phantom; and tumor-to-liver contrast-to-noise ratio of RC-4DMRF images was compared with that of planning CT and contrast-enhanced MRI (CE-MRI) images. A paired Student t test was used for statistical significance analysis with a P value threshold of .05. RESULTS: RC-4DMRF achieved excellent agreement in motion measurement with cine MRI, yielding the mean (± standard deviation) Pearson correlation coefficients of 0.95 ± 0.05 and 0.93 ± 0.09 and amplitude motion differences of 1.48 ± 1.06 mm and 0.81 ± 0.64 mm in the superior-inferior and anterior-posterior directions, respectively. Moreover, RC-4DMRF achieved high accuracy in tissue property quantification, with mean absolute percentage errors of 8.8%, 9.6%, and 5.0% for T1, T2, and proton density, respectively. Notably, the tumor contrast-to-noise ratio in RC-4DMRI-derived T1 maps (6.41 ± 3.37) was found to be the highest among all tissue property maps, approximately equal to that of CE-MRI (6.96 ± 1.01, P = .862), and substantially higher than that of planning CT (2.91 ± 1.97, P = .048). CONCLUSIONS: RC-4DMRF demonstrated high accuracy in respiratory motion measurement and tissue properties quantification, potentially facilitating tumor motion management in liver radiation therapy.

Advanced Diffusion MRI for prediction of Stroke Recovery.

There is an urgent need for ways to improve our understanding of poststroke recovery to inform the development of novel rehabilitative interventions, and improve the clinical management of stroke patients. Supported by the notion that predictive information on poststroke recovery is embedded not only in the individual brain regions, but also the connections throughout the brain, majority of previous investigations have focused on the relationship between brain functional connections and post-stroke deficit and recovery. However, considering the fact that it is the static anatomical brain connections that constrain and facilitate the dynamic functional brain connections, the microstructures and structural connections of the brain may potentially be better alternatives to the functional MRI-based biomarkers of stroke recovery. This review, therefore, seeks to provide an overview of the basic concept and applications of two recently proposed advanced diffusion MRI techniques, namely lesion network mapping and fixel-based morphometry, that may be useful for the investigation of stroke recovery at the local and global levels of the brain. This review will also highlight the application of some of other emerging advanced diffusion MRI techniques that warrant further investigation in the context of stroke recovery research.

Potential mechanisms underlying the accelerated cognitive decline in people with chronic low back pain: A scoping review.

A growing body of evidence has shown that people with chronic low back pain (CLBP) demonstrate significantly greater declines in multiple cognitive domains than people who do not have CLBP. Given the high prevalence of CLBP in the ever-growing aging population that may be more vulnerable to cognitive decline, it is important to understand the mechanisms underlying the accelerated cognitive decline observed in this population, so that proper preventive or treatment approaches can be developed and implemented. The current scoping review summarizes what is known regarding the potential mechanisms underlying suboptimal cognitive performance and cognitive decline in people with CLBP and discusses future research directions. Five potential mechanisms were identified based on the findings from 34 included studies: (1) altered activity in the cortex and neural networks; (2) grey matter atrophy; (3) microglial activation and neuroinflammation; (4) comorbidities associated with CLBP; and (5) gut microbiota dysbiosis. Future studies should deepen the understanding of mechanisms underlying this association so that proper prevention and treatment strategies can be developed.

Early stage NSCLS patients' prognostic prediction with multi-information using transformer and graph neural network model.

Background: We proposed a population graph with Transformer-generated and clinical features for the purpose of predicting overall survival (OS) and recurrence-free survival (RFS) for patients with early stage non-small cell lung carcinomas and to compare this model with traditional models. Methods: The study included 1705 patients with lung cancer (stages I and II), and a public data set for external validation (n=127). We proposed a graph with edges representing non-imaging patient characteristics and nodes representing imaging tumour region characteristics generated by a pretrained Vision Transformer. The model was compared with a TNM model and a ResNet-Graph model. To evaluate the models' performance, the area under the receiver operator characteristic curve (ROC-AUC) was calculated for both OS and RFS prediction. The Kaplan-Meier method was used to generate prognostic and survival estimates for low- and high-risk groups, along with net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis. An additional subanalysis was conducted to examine the relationship between clinical data and imaging features associated with risk prediction. Results: Our model achieved AUC values of 0.785 (95% confidence interval [CI]: 0.716-0.855) and 0.695 (95% CI: 0.603-0.787) on the testing and external data sets for OS prediction, and 0.726 (95% CI: 0.653-0.800) and 0.700 (95% CI: 0.615-0.785) for RFS prediction. Additional survival analyses indicated that our model outperformed the present TNM and ResNet-Graph models in terms of net benefit for survival prediction. Conclusions: Our Transformer-Graph model was effective at predicting survival in patients with early stage lung cancer, which was constructed using both imaging and non-imaging clinical features. Some high-risk patients were distinguishable by using a similarity score function defined by non-imaging characteristics such as age, gender, histology type, and tumour location, while Transformer-generated features demonstrated additional benefits for patients whose non-imaging characteristics were non-discriminatory for survival outcomes. Funding: The study was supported by the National Natural Science Foundation of China (91959126, 8210071009), and Science and Technology Commission of Shanghai Municipality (20XD1403000, 21YF1438200).

Review of dermatologic medications and impact on male fertility, sexual dysfunction and teratogenicity.

BACKGROUND: Dermatologic medications have been linked to issues with safety during pregnancy and lactation. Despite this, limited research, often with conflicting findings, has been published on the association between dermatologic medications, male infertility, sexual dysfunction and teratogenicity following paternal exposure. OBJECTIVE: This review seeks to provide evidence-based guidance for physicians who are prescribing dermatologic medications to male patients who are trying to conceive. METHODS: Common medications used in the largest outpatient specialist dermatologic centre in Singapore were the focus of this review. A PubMed search using MeSH terms from inception to April 22, 2021, was conducted. A secondary search was conducted to include common non-dermatologic medications. Drug information from various online clinical resources and the Tenth Edition of Drugs in Pregnancy and Lactation was also used as a reference. RESULTS: In this review of 234 studies, 131 medications were covered. A total of 34 medications were associated with male infertility and sexual dysfunction, while 16 medications were implicated with concerns of teratogenicity. DISCUSSION AND CONCLUSION: Physicians are advised to discuss the potential impact on male fertility and teratogenicity with males who are trying to conceive while taking into consideration the clinical efficacy and tolerability of these medications and alternative treatments.

Motion-resolved and free-breathing liver MRF.

PURPOSE: To develop a motion-resolved and free-breathing liver magnetic resonance fingerprinting (MRF) protocol. METHODS: The deformation maps were obtained from the first singular image of MRF data. The reconstruction method enforced the consistency of the MRF data with the deformation maps by adding the deformation maps to the encoding matrix. A sliding window reconstruction was inherently assumed, with a window size of 60 repetition times (TRs) and a step size of 30 TRs. L1 wavelet regularization was applied to reduce the undersampling artifact. MRF was tested on four healthy volunteers with parameters: 13 s/slice, 0.39 s/frame, and 33 time frames/slice. RESULTS: For measuring the accuracy of the deformation map, the typical normalized root mean square error (NRMSE) of the first singular image after motion correction was 0.19. In the sagittal scan, the liver T1 and T2 were 808.7±96.8 ms and 52.7±11.6 ms, respectively. They agreed with our previously reported values, i.e., T1 = 759 ms and T2 = 51 ms at 3 T, using free-breathing liver MRF. Compared to breath-hold MRF, the NRMSEs for T1 and T2 maps (without considering vessel pixels) from the proposed method were 0.13 and 0.18, respectively. CONCLUSION: We demonstrated a motion-resolved MRF with a nominal frame rate of 2.5 Hz for free-breathing liver imaging.

Association between High Diffusion-Weighted Imaging-Derived Functional Tumor Burden of Peritoneal Carcinomatosis and Overall Survival in Patients with Advanced Ovarian Carcinoma.

OBJECTIVE: To investigate the association between functional tumor burden of peritoneal carcinomatosis (PC) derived from diffusion-weighted imaging (DWI) and overall survival in patients with advanced ovarian carcinoma (OC). MATERIALS AND METHODS: This prospective study was approved by the local research ethics committee, and informed consent was obtained. Fifty patients (mean age ± standard deviation, 57 ± 12 years) with stage III-IV OC scheduled for primary or interval debulking surgery (IDS) were recruited between June 2016 and December 2021. DWI (b values: 0, 400, and 800 s/mm²) was acquired with a 16-channel phased-array torso coil. The functional PC burden on DWI was derived based on K-means clustering to discard fat, air, and normal tissue. A score similar to the surgical peritoneal cancer index was assigned to each abdominopelvic region, with additional scores assigned to the involvement of critical sites, denoted as the functional peritoneal cancer index (fPCI). The apparent diffusion coefficient (ADC) of the largest lesion was calculated. Patients were dichotomized by immediate surgical outcome into high- and low-risk groups (with and without residual disease, respectively) with subsequent survival analysis using the Kaplan-Meier curve and log-rank test. Multivariable Cox proportional hazards regression was used to evaluate the association between DWI-derived results and overall survival. RESULTS: Fifteen (30.0%) patients underwent primary debulking surgery, and 35 (70.0%) patients received neoadjuvant chemotherapy followed by IDS. Complete tumor debulking was achieved in 32 patients. Patients with residual disease after debulking surgery had reduced overall survival (p = 0.043). The fPCI/ADC was negatively associated with overall survival when accounted for clinicopathological information with a hazard ratio of 1.254 for high fPCI/ADC (95% confidence interval, 1.007-1.560; p = 0.043). CONCLUSION: A high DWI-derived functional tumor burden was associated with decreased overall survival in patients with advanced OC.

A data-driven deep learning pipeline for quantitative susceptibility mapping (QSM).

PURPOSE: This study developed a data-driven optimization to improve the accuracy of deep learning QSM quantification. METHODS: The proposed deep learning QSM pipeline consisted of two projections onto convex set (POCS) models designed to decouple trainable network components with the spherical mean value (SMV) filters and dipole kernel in the data-driven optimization. They were a background field removal network (named POCSnet1) and a dipole inversion network (named POCSnet2). Both POCSnet1 and POCSnet2 were the unrolled V-Net with iterative data-driven optimization to enforce the data fidelity. For training POCSnet1, we simulated phantom data with random geometric shapes as the background susceptibility sources. For training POCSnet2, we used geometric shapes to mimic the QSM. The evaluation was performed on synthetic data, a public COSMOS (N = 1), and clinical data from a Parkinson's disease cohort (N = 71) and small-vessel disease cohort (N = 26). For comparison, DLL2, FINE, and autoQSM, were implemented and tested under the same experimental setting. RESULTS: On COSMOS, results from POCSnet1 were more similar to that of the V-SHARP method with NRMSE = 23.7% and SSIM = 0.995, compared with the NRMSE = 62.7% and SSIM = 0.975 for SHARQnet, a naïve V-Net model. On COSMOS, the NRMSE and HFEN for POCSnet2 were 58.1% and 56.7%; while for DLL2, FINE, and autoQSM, they were 62.0% and 61.2%, 69.8% and 67.5%, and 87.5% and 85.3%, respectively. On the Parkinson's disease cohort, our results were consistent with those obtained from VSHARP+STAR-QSM with biases <3% and outperformed the SHARQnet+DeepQSM that had biases of 7% to 10%. The sensitivity of cerebral microbleed detection using our pipeline was 100%, compared with 92% by SHARQnet+DeepQSM. CONCLUSION: Data-driven optimization improved the accuracy of QSM quantification compared with that of naïve V-Net models.

Biomarkers for prognostic functional recovery poststroke: A narrative review.

Background and objective: Prediction of poststroke recovery can be expressed by prognostic biomarkers that are related to the pathophysiology of stroke at the cellular and molecular level as well as to the brain structural and functional reserve after stroke at the systems neuroscience level. This study aimed to review potential biomarkers that can predict poststroke functional recovery. Methods: A narrative review was conducted to qualitatively summarize the current evidence on biomarkers used to predict poststroke functional recovery. Results: Neurophysiological measurements and neuroimaging of the brain and a wide diversity of molecules had been used as prognostic biomarkers to predict stroke recovery. Neurophysiological studies using resting-state electroencephalography (EEG) revealed an interhemispheric asymmetry, driven by an increase in low-frequency oscillation and a decrease in high-frequency oscillation in the ipsilesional hemisphere relative to the contralesional side, which was indicative of individual recovery potential. The magnitude of somatosensory evoked potentials and event-related desynchronization elicited by movement in task-related EEG was positively associated with the quantity of recovery. Besides, transcranial magnetic stimulation (TMS) studies revealed the potential values of using motor-evoked potentials (MEP) and TMS-evoked EEG potentials from the ipsilesional motor cortex as prognostic biomarkers. Brain structures measured using magnetic resonance imaging (MRI) have been implicated in stroke outcome prediction. Specifically, the damage to the corticospinal tract (CST) and anatomical motor connections disrupted by stroke lesion predicted motor recovery. In addition, a wide variety of molecular, genetic, and epigenetic biomarkers, including hemostasis, inflammation, tissue remodeling, apoptosis, oxidative stress, infection, metabolism, brain-derived, neuroendocrine, and cardiac biomarkers, etc., were associated with poor functional outcomes after stroke. However, challenges such as mixed evidence and analytical concerns such as specificity and sensitivity have to be addressed before including molecular biomarkers in routine clinical practice. Conclusion: Potential biomarkers with prognostic values for the prediction of functional recovery after stroke have been identified; however, a multimodal approach of biomarkers for prognostic prediction has rarely been studied in the literature. Future studies may incorporate a combination of multiple biomarkers from big data and develop algorithms using data mining methods to predict the recovery potential of patients after stroke in a more precise way.

Relationship between Amyloid-ß Deposition and the Coupling between Structural and Functional Brain Networks in Patients with Mild Cognitive Impairment and Alzheimer's Disease.

Previous studies have demonstrated that the accumulation of amyloid-ß (Aß) pathologies has distinctive stage-specific effects on the structural and functional brain networks along the Alzheimer's disease (AD) continuum. A more comprehensive account of both types of brain network may provide a better characterization of the stage-specific effects of Aß pathologies. A potential candidate for this joint characterization is the coupling between the structural and functional brain networks (SC-FC coupling). We therefore investigated the effect of Aß accumulation on global SC-FC coupling in patients with mild cognitive impairment (MCI), AD, and healthy controls. Patients with MCI were dichotomized according to their level of Aß pathology seen in 18F-flutemetamol PET-CT scans-namely, Aß-negative and Aß-positive. Our results show that there was no difference in global SC-FC coupling between different cohorts. During the prodromal AD stage, there was a significant negative correlation between the level of Aß pathology and the global SC-FC coupling of MCI patients with positive Aß, but no significant correlation for MCI patients with negative Aß. During the AD dementia stage, the correlation between Aß pathology and global SC-FC coupling in patients with AD was positive. Our results suggest that Aß pathology has distinctive stage-specific effects on global coupling between the structural and functional brain networks along the AD continuum.

Repeatability of MR fingerprinting in normal cervix and utility in cervical carcinoma.

BACKGROUND: Magnetic resonance fingerprinting (MRF) is a fast-imaging acquisition technique that generates quantitative and co-registered parametric maps. The aim of this feasibility study was to evaluate the agreement between MRF and phantom reference values, scan-rescan repeatability of MRF in normal cervix, and its ability to distinguish cervical carcinoma (CC) from normal cervical tissues. METHODS: An International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) phantom was scanned using MRF 15 times over 65 days. Agreement between MRF and phantom reference T1 and T2 values was assessed by linear regression. Healthy volunteers and patients with suspected CC were prospectively recruited. MRF was repeated twice for healthy volunteers (MRF1 and MRF2). Volumes of interest of normal cervical tissues and CC were delineated on T1 and T2 maps. MRF scan-rescan repeatability was evaluated by Bland-Altman plots, within-subject coefficients of variation (wCV), and intraclass correlation coefficients (ICC). T1 and T2 values were compared between CC and normal cervical tissues using Mann-Whitney U test. Receiver operating characteristic (ROC) analysis was performed to evaluate diagnostic efficiency. RESULTS: Strong correlations were observed between MRF and phantom (R2=0.999 for T1, 0.981 for T2). Twelve healthy volunteers (28.7±5.1 years) and 28 patients with CC (54.6±15.2 years) were recruited for the in-vivo experiments. Repeatability of MRF parameters were wCV <3% for T1, <5% for T2 and ICC ≥0.92 for T1, ≥0.94 for T2. T1 value of CC (1,529±112 ms) was higher than normal mucosa [MRF1: 1,430±129 ms, MRF2: 1,440±130 ms; P=0.031, area under the curve (AUC) ≥0.717] and normal stroma (MRF1: 1,258±101 ms, MRF2: 1,276±105 ms; P<0.001, AUC ≥0.946). T2 value of CC (69±9 ms) was lower than normal mucosa (MRF1: 88±16 ms, MRF2: 87±13 ms; P<0.001, AUC ≥0.854), but was not different from normal stroma (P=0.919). CONCLUSIONS: Excellent agreement was observed between MRF and phantom reference values. MRF exhibited excellent scan-rescan repeatability in normal cervix with potential value in differentiating CC from normal cervical tissues.

Brain Microstructural Changes Associated With Neurocognitive Outcome in Intracranial Germ Cell Tumor Survivors.

BACKGROUND: Childhood intracranial germ cell tumor (GCT) survivors are prone to radiotherapy-related neurotoxicity, which can lead to neurocognitive dysfunctions. Diffusion kurtosis imaging (DKI) is a diffusion MRI technique that is sensitive to brain microstructural changes. This study aimed to investigate the association between DKI metrics versus cognitive and functional outcomes of childhood intracranial GCT survivors. METHODS: DKI was performed on childhood intracranial GCT survivors (n = 20) who had received cranial radiotherapy, and age and gender-matched healthy control subjects (n = 14). Neurocognitive assessment was performed using the Hong Kong Wechsler Intelligence Scales, and functional assessment was performed using the Lansky/Karnofsky performance scales (KPS). Survivors and healthy controls were compared using mixed effects model. Multiple regression analyses were performed to determine the effects of microstructural brain changes of the whole brain as well as the association between IQ and Karnofsky scores and the thereof. RESULTS: The mean Intelligence Quotient (IQ) of GCT survivors was 91.7 (95% CI 84.5 - 98.8), which was below the age-specific normative expected mean IQ (P = 0.013). The mean KPS score of GCT survivors was 85.5, which was significantly lower than that of controls (P < 0.001). Cognitive impairments were significantly associated with the presence of microstructural changes in white and grey matter, whereas functional impairments were mostly associated with microstructural changes in white matter. There were significant correlations between IQ versus the mean diffusivity (MD) and mean kurtosis (MK) of specific white matter regions. The IQ scores were negatively correlated with the MD of extensive grey matter regions. CONCLUSION: Our study identified vulnerable brain regions whose microstructural changes in white and grey matter were significantly associated with impaired cognitive and physical functioning in survivors of pediatric intracranial GCT.

A multi-inversion-recovery magnetic resonance fingerprinting for multi-compartment water mapping.

PURPOSE: This study aimed at introducing short-T1/T2 compartment to MR fingerprinting (MRF) at 3 T. Water that is bound to myelin macromolecules have significantly shorter T1 and T2 than free water and can be distinguished from free water by multi-compartment analysis. METHODS: We developed a new multi-inversion-recovery (mIR) water mapping-MRF based on an unbalanced steady-state coherent sequence (FISP). mIR pulses with an interval of 400 or 500 repetition times (TRs) were inserted into the conventional FISP MRF sequence. Data from our proposed mIR MRF was used to quantify different compartments, including myelin water, gray matter free water, and white matter free water, of brain water by virtue of the iterative non-negative least square (NNLS) with reweighting. Three healthy volunteers were scanned with mIR MRF on a clinical 3 T MRI. RESULTS: Using an extended phase graph simulation, we found that our proposed mIR scheme with four IR pulses allowed differentiation between short and long T1/T2 components. For in vivo experiments, we achieved the quantification of myelin water, gray matter water, and white matter water at an image resolution of 1.17 × 1.17 × 5 mm3/pixel. As compared to the conventional MRF technique with single IR, our proposed mIR improved the detection of myelin water content. In addition, mIR MRF using spiral-in/out trajectory provided a higher signal level compared with that with spiral-out trajectory. Myelin water quantification using mIR MRF with 4 IR and 5 IR pulses were qualitatively similar. Meanwhile, 5 IR MRF showed fewer artifacts in myelin water detection. CONCLUSION: We developed a new mIR MRF sequence for the rapid quantification of brain water compartments.

A Patient-Centered Integrative Approach Improves Visual Field Defect: A Case Report.

We present a case involving a patient with a complicated visual field defect preventing her from renewing her driver license. It highlights the underappreciated role of chronic stress in the genesis and perpetuation of ill health and the potential of Chinese medicine (CM) to complement biomedicine in the treatment of an intractable visual disorder. The patient experienced impaired vision from age 15, and ophthalmologists considered various diagnoses including optic neuritis and acute zonal occult outer retinopathy (AZOOR)-complex disorder with acute macular neuroretinopathy. She was treated with an integrative East-West medical approach incorporating acupuncture, cupping, trigger point injections, guidance on self-care and lifestyle modification. Although the eye disorder was not cured, there was visual improvement as demonstrated by various objective ophthalmologic tests, and the patient was able to renew her driver license. Visual improvement remained stable upon follow-up examination three years after the treatment intervention. Other concomitant health issues reported by the patient also improved including amelioration of neck pain, a more regular menstrual cycle, and decreased anxiety. This case demonstrates how a patient with an intractable complex eye disorder can have objective visual improvement when treated with an integrative patient-centered approach.

Investigation of the effect of acquisition schemes on time-resolved magnetic resonance fingerprinting.

Purpose.This study aims to investigate the feasibility of different acquisition methods for time-resolved magnetic resonance fingerprinting (TR-MRF) in computer simulation.Methods.An extended cardiac-torso (XCAT) phantom is used to generate abdominal T1, T2, and proton density maps for MRF simulation. The simulated MRF technique consists of an IR-FISP MRF sequence with spiral trajectory acquisition. MRF maps were simulated with different numbers of repetitions from 1 to 15. Three different methods were used to generate TR-MRF maps: (1) continuous acquisition without delay between MRF repetitions; (2) continuous acquisition with 5 s delay between MRF repetitions; (3) triggered acquisition with variable delay between MRF repetitions to allow the next acquisition to start at different respiration phase. After the generation of TR-MRF maps, the image quality indexes including the absolute T1 and T2 values, signal-to-noise-ratio (SNR), tumor-to-liver contrast-to-noise ratio, error in the amplitude of diaphragm motion and tumor volume error were used to evaluate the reconstructed parameter maps. Three volunteers were recruited to test the feasibility of the selected acquisition method.Results.Dynamic MR parametric maps using three different acquisition methods were estimated. The overall and liver T1 value error, liver SNR in T1 and T2 maps, and tumor SNR from T1 maps from triggered method is statistically significantly better than the other two methods (p-value < 0.05). The other image quality indexes have no significant difference between the triggered method and the other two continuous acquisition methods. All image quality indexes exhibit no significant difference between the acquisition methods with 0 s and 5 s delay. The triggered method was successfully performed in three healthy volunteers.Conclusion.TR-MRF technique was investigated using three different acquisition methods in computer simulation where the triggered method showed better performance than the other two methods. The triggered method has been tested successfully in healthy volunteers.