Magnetic Resonance Imaging Analysis Research Articles

A Predictive Model for Cardiomyopathy Progression in a Longitudinal Cohort of DMD Patients

Background: Cardiomyopathy (CM) is currently the leading cause of mortality in patients with Duchenne muscular dystrophy (DMD). DMD CM is asymptomatic during the early stages of disease and age of onset and clinical progression vary. We previously showed that 4D kinematic analysis of cardiovascular magnetic resonance (CMR) imaging is a robust method to visualize strain and function changes in DMD patients. Our objective, using 4D CMR image analysis, is to build a predictive model to quantify disease progression, and predict functional decline as measured by left ventricular ejection fraction (LVEF) in a longitudinal cohort of DMD patients. 
 
 Methods: We obtained 4D CMR images from 40 DMD patients imaged every 12 months for three years. Localized surface area strain (Ea) values were derived from compiled 4D CMR images. Patient demographics, presence or absence (+/-) of late gadolinium enhancement (LGE) and LVEF were obtained by a trained cardiologist. Principal component analysis was used to identify prominent variables contributing most to variability. These variables, along with clinically relevant variables, were selected to build the predictive model. We built a linear mixed effects model using visit, age, basal Ea, and LGE presence. Each patient was considered a random effect to account for variability in initial CM severity. 
 
 Results: Study patients were grouped into stages of heart failure according to the American Heart Association: Stage A, LVEF>55%, LGE(-); Stage B, LVEF>55%, LGE(+); Stage C, 40%<LVEF<55%, LGE(+); Stage D, LVEF<40%, LGE(+). Linear mixed effects model showed a good prediction of LVEF on year 3 with a root mean squared error of 3.6% and an R2 value of 0.75 (p<0.0001.). 
 
 Conclusion: Early detection is crucial for appropriate clinical intervention. This novel predictive model was developed to determine if Ea derived from 4D imaging, LVEF, and (+/-) LGE can be used to predict functional outcomes in DMD CM.

Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.

Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging.

Human Adipose-derived Stem Cells Upregulate IGF-1 and Alleviate Osteoarthritis in a Two-stage Rabbit Osteoarthritis Model.

In recent years, it has been known that mesenchymal stem cells (MSCs) have the potential to treat osteoarthritis (OA). This study aimed to investigate the effects of intraarticular injection of human adipose-derived stem cells (hADSCs) in a new double-damage rabbit osteoarthritis model. The OA model was established surgically first by medial collateral ligament and anterior insertional ligament transection and medical meniscectomy, then by articular cartilage full-thickness defect. At six weeks following surgery, hADSCs were labeled with Enhanced Green Fluorescence Protein expressing lentivirus FG12 and injected into the knee joints. All rabbits were sacrificed at 4- and 8 weeks post-surgery. Assessments were carried out by macroscopic examination, immunohistochemistry staining, magnetic resonance imaging, qRT-PCR and ELISA analysis. At 4- and 8 weeks, hADSCs injection showed less cartilage loss, few fissures and few cracks, decreased volume of joint effusion and cartilage defect measured with MRI. Furthermore, ELISA and qRT-PCR methods showed that hADSCs treatment increased the level of IGF-1. Our data suggest that hADSC transplantation promotes articular cartilage healing in the double-damage rabbit osteoarthritis model, IGF-1 may play an essential role in the hADSC-based cartilage repair process. Transplantation of hADSCs may be suitable for clinical application in the treatment of osteoarthritis.

Post-transfusion severe headache in a patient with thalassemia with superficial siderosis of the central nervous system: a case report and literature review

BackgroundPatients with severe thalassemia may experience adverse effects from transfusion such as fever, rash, and iron overload after long-term transfusion therapy. Severe headaches as a side effect of blood transfusion in patients with thalassemia are not commonly observed, especially when combined with superficial siderosis of the central nervous system, which is easily misdiagnosed and requires excessive examination and treatment.Case PresentationA 31-year-old woman was admitted with severe headache and vomiting over 3 days following blood transfusion. She was diagnosed with intermediate α-thalassemia at 2 years of age and had a history of irregular blood transfusions. Physical examination revealed horizontal nystagmus with no other abnormal neurological signs. Magnetic resonance (MR) imaging, MR venography, MR arteriography, and cerebrospinal fluid analysis were normal. However, susceptibility-weighted imaging showed abnormal signals in the bilateral and fourth ventricles. Initial antibiotics, antivirals, decompression of intracranial pressure, iron chelation, and symptomatic treatments were administered; subsequently, small intermittent blood transfusions were cautiously administered for severe anemia. The patient’s headache was gradually relieved, and she was discharged on day 9. At the 5-month follow-up, the patient’s headache recurred following another transfusion.ConclusionsSevere post-transfusion headache in patients with thalassemia has not been fully recognized and is easily misdiagnosed, leading to excessive examination and treatment. Understanding the clinical features of transfusion-related headaches can help identify this complication, but the exact pathophysiological mechanism requires further research.

Differences in Brain Atrophy Pattern between People with Multiple Sclerosis and Systemic Diseases with Central Nervous System Involvement Based on Two-Dimensional Linear Measures.

Conventional brain magnetic resonance imaging (MRI) in systemic diseases with central nervous system involvement (SDCNS) may imitate MRI findings of multiple sclerosis (MS). In order to better describe the MRI characteristics of these conditions, in our study we assessed brain volume parameters in MS (n = 58) and SDCNS (n = 41) patients using two-dimensional linear measurements (2DLMs): bicaudate ratio (BCR), corpus callosum index (CCI) and width of third ventricle (W3V). In SDCNS patients, all 2DLMs were affected by age (CCI p = 0.005, BCR p < 0.001, W3V p < 0.001, respectively), whereas in MS patients only BCR and W3V were (p = 0.001 and p = 0.015, respectively). Contrary to SDCNS, in the MS cohort BCR and W3V were associated with T1 lesion volume (T1LV) (p = 0.020, p = 0.009, respectively) and T2 lesion volume (T2LV) (p = 0.015, p = 0.009, respectively). CCI was associated with T1LV in the MS cohort only (p = 0.015). Moreover, BCR was significantly higher in the SDCNS group (p = 0.01) and CCI was significantly lower in MS patients (p = 0.01). The best predictive model to distinguish MS and SDCNS encompassed gender, BCR and T2LV as the explanatory variables (sensitivity 0.91; specificity 0.68; AUC 0.86). Implementation of 2DLMs in the brain MRI analysis of MS and SDCNS patients allowed for the identification of diverse patterns of local brain atrophy in these clinical conditions.

RanMerFormer: Randomized vision transformer with token merging for brain tumor classification

Brains are the control center of the nervous system in human bodies, and brain tumor is one of the most deadly diseases. Currently, magnetic resonance imaging (MRI) is the most effective way to brain tumors early detection in clinical diagnoses due to its superior imaging quality for soft tissues. Manual analysis of brain MRI is error-prone which depends on empirical experience and the fatigue state of the radiologists to a large extent. Computer-aided diagnosis (CAD) systems are becoming more and more impactful because they can provide accurate prediction results based on medical images with advanced techniques from computer vision. Therefore, a novel CAD method for brain tumor classification named RanMerFormer is presented in this paper. A pre-trained vision transformer is used as the backbone model. Then, a merging mechanism is proposed to remove the redundant tokens in the vision transformer, which improves computing efficiency substantially. Finally, a randomized vector functional-link serves as the head in the proposed RanMerFormer, which can be trained swiftly. All the simulation results are obtained from two public benchmark datasets, which reveal that the proposed RanMerFormer can achieve state-of-the-art performance for brain tumor classification. The trained RanMerFormer can be applied in real-world scenarios to assist in brain tumor diagnosis.

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

AbstractDetecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study.

Objectives: To develop and validate an MRI radiomics-based decision support tool for the automated grading of cervical disc degeneration. Methods: The retrospective study included 2,610 cervical disc samples of 435 patients from two hospitals. The cervical magnetic resonance imaging (MRI) analysis of patients confirmed cervical disc degeneration grades using the Pfirrmann grading system. A training set (1,830 samples of 305 patients) and an independent test set (780 samples of 130 patients) were divided for the construction and validation of the machine learning model, respectively. We provided a fine-tuned MedSAM model for automated cervical disc segmentation. Then, we extracted 924 radiomic features from each segmented disc in T1 and T2 MRI modalities. All features were processed and selected using minimum redundancy maximum relevance (mRMR) and multiple machine learning algorithms. Meanwhile, the radiomics models of various machine learning algorithms and MRI images were constructed and compared. Finally, the combined radiomics model was constructed in the training set and validated in the test set. Radiomic feature mapping was provided for auxiliary diagnosis. Results: Of the 2,610 cervical disc samples, 794 (30.4%) were classified as low grade and 1,816 (69.6%) were classified as high grade. The fine-tuned MedSAM model achieved good segmentation performance, with the mean Dice coefficient of 0.93. Higher-order texture features contributed to the dominant force in the diagnostic task (80%). Among various machine learning models, random forest performed better than the other algorithms (p < 0.01), and the T2 MRI radiomics model showed better results than T1 MRI in the diagnostic performance (p < 0.05). The final combined radiomics model had an area under the receiver operating characteristic curve (AUC) of 0.95, an accuracy of 89.51%, a precision of 87.07%, a recall of 98.83%, and an F1 score of 0.93 in the test set, which were all better than those of other models (p < 0.05). Conclusion: The radiomics-based decision support tool using T1 and T2 MRI modalities can be used for cervical disc degeneration grading, facilitating individualized management.

Effect of bone marrow aspiration concentrate and platelet-rich plasma combination in anterior cruciate ligament reconstruction: a randomized, prospective, double-blinded study

BackgroundThe effect of bone marrow aspirate concentrate (BMAC) and platelet-rich plasma (PRP) combination in enhancing graft maturation and tendon–bone tunnel interfacial healing after anterior cruciate ligament (ACL) reconstruction remains unclear. We hypothesised that BMAC and PRP combination could lead to better clinical results and better graft maturation/interface healing than PRP alone or conventional ACL reconstruction without any other biologic augmentation.MethodsIn this randomised double-blind prospective study, patients undergoing ACL reconstruction surgery were randomly assigned into three groups: (1) control group (without any biologic augmentation), (2) PRP treatment group, and (3) combined BMAC and PRP (BMAC + PRP) group. Moreover, they were evaluated using the clinical functional score, laxity examination, and magnetic resonance imaging (MRI) analysis.ResultsNo significant difference was observed in the improvement of functional scores among groups. However, laxity improvement at 24 weeks showed a significant difference with the BMAC + PRP group having the lowest laxity. MRI analysis showed no significant change in whole graft maturation among groups. In particular, the BMAC + PRP group showed delayed signal peak and higher graft signal at 24 weeks compared with the other two groups; however, the difference was not significant. With regard to tendon–bone interfacial healing, the BMAC + PRP group showed significantly wider tendon–bone interface in the femoral bone tunnel at 24 weeks compared with the other two groups. Moreover, the BMAC + PRP group showed significantly higher peri-tunnel edema signal in the femoral bone tunnel at 12 weeks compared with the other two groups.ConclusionPRP alone and BMAC and PRP combination showed limited enhancing effect in clinical function, graft maturation and tendon–bone interfacial healing compared with control (no additional treatment). When BMAC is used in ACL reconstruction, the possibility of greater inflammation in the early stage to graft maturation and bone tunnel healing should be considered.

COMPARATIVE ANALYSIS OF MRI, MORPHOLOGICAL AND BIOCHEMICAL FEATURES OF MALIGNANT NEOPLASM OF THE PROSTATE GLAND

Introduction. This article presents a comparative analysis of magnetic resonance imaging (MRI), transrectal ultrasound (TRUS), biopsy and biochemical studies in prostate cancer using histomorphologic grading of the tumor component according to the Gleason score. It has been shown that the level of prostate-specific antigen (PSA) often correlated with the total Gleason score. Materials and methods: In the study, 860 patients with suspected prostate cancer were examined: 530 patients (62 %) were diagnosed with prostate cancer at various stages of development, and 330 patients (38 %) were diagnosed with prostate adenoma. MRI of the prostate was performed in three sections: sagittal, frontal and vertical. The tumor component was assessed using the PI-RADS system. Patients with a PSA level of more than 4.4 ng/ml were selected for the study. The stage of cancer development was determined by the Gleason scale during histological examination. Results of own research: It was found that the MRI diagnostic method was most effective in low and moderately differentiated forms of prostate adenocarcinoma and could also be used to assess the prevalence of the tumor process. The first place among the studied cases (61%) was occupied by stage III prostate cancer, the second place – by stage IV prostate cancer (24%) and the third place – by stage II prostate cancer (15%). Hypoechoic nodular elements were detected in 30.2%, mainly 5–7 by the Gleason score, heterogeneous nodular elements – in 40.1% (8 by the Gleason score), hypointense areas, mainly in the peripheral parts of the gland, were detected in 75.2% (9–10 by the Gleason score). Changes in the vascular pattern depended on the degree of development of the tumor component. Conclusion. Our study revealed a number of features: prostate cancer is mainly characterized by highly differentiated and moderately differentiated forms of adenocarcinoma; in metastasized prostate cancer, a low-grade form of adenocarcinoma is more often detected, which can have an aggressive course and rapid spread of the tumor component.

Early diagnosis of Alzheime`s disease based on VGG19 feature extractor and MRI analysis

Alzheimer’s Disease (AD) is the most prevalent cause of dementia, a neurodegenerative\ndisorder primarily affecting the brain. Mild cases of dementia gradually worsen, resulting in a\ndecline in cognitive abilities, including memory, thinking, reasoning, and behavior. Currently,\nthere is no cure for AD; however, early diagnosis of mild cases can help slow the disease\'s\nprogression, improve its management, and enhance overall quality of life. Existing models for\nearly AD diagnosis encounter challenges, such as low accuracy and precision, poor\ngeneralization and overfitting due to data imbalances in datasets. To address these limitations, we\npropose a novel Deep Learning (DL) based model for early AD diagnosis. Our model comprises\nthree main components: image processing and data augmentation, feature extraction based on\nVGG19 (Visual Geometry Group 19), and a Multi-Layer Perceptron (MLP) based classifier\ncategorizing input Magnetic Resonance Imaging (MRI) scans into four classes representing AD\'s\ndevelopmental stages: Very-Mild-Demented, Mild-Demented, Moderate-Demented, and NonDemented. We optimized our model using the ADAM Algorithm and applied it to images selected\nfrom the Open Access Series of Imaging Studies (OASIS) dataset. Our model yielded impressive\nresults, achieving an accuracy of 92.58%, an AUC of 99%, and precision, recall, and F-measure\nvalues of 0.907, 0.894, and 0.890, respectively. Furthermore, we conducted a comparative\nanalysis with several state-of-the-art models, consistently demonstrating that our proposed model\noutperforms them.

A Multiview Brain Network Transformer Fusing Individualized Information for Autism Spectrum Disorder Diagnosis.

Functional connectivity (FC) networks, built from analyses of resting-state magnetic resonance imaging (rs-fMRI), serve as efficacious biomarkers for identifying Autism Spectrum Disorders (ASD) patients. Given the neurobiological heterogeneity across individuals and the unique presentation of ASD symptoms, the fusion of individualized information into diagnosis becomes essential. However, this aspect is overlooked in most methods. Furthermore, the existing methods typically focus on studying direct pairwise connections between brain ROIs, while disregarding interactions between indirectly connected neighbors. To overcome above challenges, we build common FC and individualized FC by tangent pearson embedding (TP) and common orthogonal basis extraction (COBE) respectively, and present a novel multiview brain transformer (MBT) aimed at effectively fusing common and individualized information of subjects. MBT is mainly constructed by transformer layers with diffusion kernel (DK), fusion quality-inspired weighting module (FQW), similarity loss and orthonormal clustering fusion readout module (OCFRead). DK transformer can incorporate higher-order random walk methods to capture wider interactions among indirectly connected brain regions. FQW promotes adaptive fusion of features between views, and similarity loss and OCFRead are placed on the last layer to accomplish the ultimate integration of information. In our method, TP, DK and FQW modules all help to model wider connectivity in the brain that make up for the shortcomings of traditional methods. We conducted experiments on the public ABIDE dataset based on AAL and CC200 respectively. Our framework has shown promising results, outperforming state-of-the-art methods on both templates. This suggests its potential as a valuable approach for clinical ASD diagnosis.

Analysis of Resting-State Functional Magnetic Resonance Imaging in Alzheimer's Disease.

Alzheimer's disease (AD) has been characterized by widespread network disconnection among brain regions, widely overlapping with the hallmarks of the disease. Functional connectivity has been studied with an upward trend in the last two decades, predominantly in AD among other neuropsychiatric disorders, and presents a potential biomarker with various features that might provide unique contributions to foster our understanding of neural mechanisms of AD. The resting-state functional MRI (rs-fMRI) is usually used to measure the blood-oxygen-level-dependent signals that reflect the brain's functional connectivity. Nevertheless, the rs-fMRI is still underutilized, which might be due to the fairly complex acquisition and analytic methodology. In this chapter, we presented the common methods that have been applied in rs-fMRI literature, focusing on the studies on individuals in the continuum of AD. The key methodological aspects will be addressed that comprise acquiring, processing, and interpreting rs-fMRI data. More, we discussed the current and potential implications of rs-fMRI in AD.

Predictors of Gleason Grading Group upgrading in low-risk prostate cancer patients from transperineal biopsy after radical prostatectomy

Rational and ObjectivesTo investigate the predictors of Gleason Grading Group (GGG) upgrading in low-risk prostate cancer (PC) (Gleason score=3+3) from transperineal biopsy after radical prostatectomy (RP). Materials and MethodsThe clinical data of 160 patients who underwent transperineal biopsy and RP from January 2017 to December 2022 were retrospectively analyzed. First, univariate and multivariate logistic regression analysis were used to obtain independent predictors of postoperative GGG upgrading. Then receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of predictors. Finally, Linear-by-Linear Association test was used to analyze the risk trends of patients in different predictor groups in the postoperative GGG. ResultsIn this study, there were 81 cases (50.6%) in the GGG concordance group and 79 cases (49.4%) in the GGG upgrading group. Univariate analysis showed age, f/tPSA, proportion of positive biopsies, positive target of magnetic-resonance imaging (MRI) and positive target of contrast-enhanced ultrasound had significant effects on GGG upgrading (all P<0.05). In multivariate logistic regression analysis, age (OR=1.066, 95%CI=1.007~1.127, P=0.027), f/tPSA (OR=0.001, 95%CI=0~0.146, P=0.001) and positive target of MRI (OR=3.005, 95%CI=1.353~76.674, P=0.007) were independent predictors. The prediction model (AUC=0.751 P<0.001)) had higher predictive efficacy than all independent predictors. The proportion of patients in exposed group of different GGG increased with the level of GGG, but decreased in non-exposed group, and the linear trend was significantly different (all P<0.001). ConclusionAge, f/tPSA and positive target of MRI were independent predictors of postoperative GGG upgrading. The predictive model constructed had the best diagnostic efficacy.

Neural sensitivity to translational self- and object-motion velocities.

The ability to detect and assess world-relative object-motion is a critical computation performed by the visual system. This computation, however, is greatly complicated by the observer's movements, which generate a global pattern of motion on the observer's retina. How the visual system implements this computation is poorly understood. Since we are potentially able to detect a moving object if its motion differs in velocity (or direction) from the expected optic flow generated by our own motion, here we manipulated the relative motion velocity between the observer and the object within a stationary scene as a strategy to test how the brain accomplishes object-motion detection. Specifically, we tested the neural sensitivity of brain regions that are known to respond to egomotion-compatible visual motion (i.e., egomotion areas: cingulate sulcus visual area, posterior cingulate sulcus area, posterior insular cortex [PIC], V6+, V3A, IPSmot/VIP, and MT+) to a combination of different velocities of visually induced translational self- and object-motion within a virtual scene while participants were instructed to detect object-motion. To this aim, we combined individual surface-based brain mapping, task-evoked activity by functional magnetic resonance imaging, and parametric and representational similarity analyses. We found that all the egomotion regions (except area PIC) responded to all the possible combinations of self- and object-motion and were modulated by the self-motion velocity. Interestingly, we found that, among all the egomotion areas, only MT+, V6+, and V3A were further modulated by object-motion velocities, hence reflecting their possible role in discriminating between distinct velocities of self- and object-motion. We suggest that these egomotion regions may be involved in the complex computation required for detecting scene-relative object-motion during self-motion.

Factors associated with attitudes toward research MRI in older Asian Americans.

South Asian (SA) and East Asian (EA) older adults represent the fastest-growing racial/ethnic groups of Americans at risk for dementia. While recruiting older SA adults into a brain health study, we encountered unexpected hesitancy toward structural brain magnetic resonance imaging (MRI) analysis and stigmatizing attitudes related to internal locus of control (LoC) for future dementia risks. We hypothesized that support for MRI-related research was influenced by these attitudes as well as personal MRI experience, perceived MRI safety, and concerns for personal risk for future dementia/stroke. We developed a brief cross-sectional survey to assess older adults' MRI experiences and perceptions, desire to learn of six incidental findings of increasing impact on health, and attitudes related to dementia (including LoC) and research participation. We recruited a convenience sample of 256 respondents (74% reporting as 50+) from the New Jersey/New York City area to complete the survey (offered in English, Chinese, Korean, and Spanish) and modeled the proportional odds (PO) for favorable attitudes toward research activities. Seventy-seven SA and 84 EA respondents were analyzed alongside 95 White, Black, or Hispanic adults. White (PO = 2.54, p = 0.013) and EA (PO = 2.14, p = 0.019) respondents were both more likely than SA respondents to endorse healthy volunteers' participation in research, and the difference between White and SA respondents was mediated by the latter's greater internal LoC for dementia risks. EA respondents had more worries for future dementia/stroke than SA respondents (p = 0.006) but still shared SA respondents' lower wish (measured by proportion of total) to learn of incidental MRI findings. SA-and EA compared to SA-older adults had low desire to learn of incidental MRI findings but had different attitudes toward future dementia/stroke risks. A culturally appropriate protocol to disclose incidental MRI findings may improve SA and EA participation in brain health research. Older Asian Americans have limited interest in incidental findings on research MRISouth Asians are most likely to attribute dementia to people's own behaviorsSouth Asians' attitudes mediate lower support for healthy volunteers in researchSouth and East Asians differ in dementia worries and research-related attitudes.

Connecting the dots: Motor and default mode network crossroads in post-stroke motor learning deficits

BackgroundStrokes frequently result in long-term motor deficits, imposing significant personal and economic burdens. However, our understanding of the underlying neural mechanisms governing motor learning in stroke survivors remains limited - a fact that poses significant challenges to the development and optimisation of therapeutic strategies. ObjectiveThis study investigates the diversity in motor learning aptitude and its associated neurological mechanisms. We hypothesised that stroke patients exhibit compromised overall motor learning capacity, which is associated with altered activity and connectivity patterns in the motor- and default-mode-network in the brain. MethodsWe assessed a cohort of 40 chronic-stage, mildly impaired stroke survivors and 39 age-matched healthy controls using functional Magnetic Resonance Imaging (fMRI) and connectivity analyses. We focused on neural activity and connectivity patterns during an unilateral motor sequence learning task performed with the unimpaired or non-dominant hand. Primary outcome measures included task-induced changes in neural activity and network connectivity. ResultsCompared to controls, stroke patients showed significantly reduced motor learning capacity, associated with diminished cerebral lateralization. Task induced activity modulation was reduced in the motor network but increased in the default mode network. The modulated activation strength was associated with an opposing trend in task-induced functional connectivity, with increased connectivity in the motor network and decreased connectivity in the DMN. ConclusionsStroke patients demonstrate altered neural activity and connectivity patterns during motor learning with their unaffected hand, potentially contributing to globally impaired motor learning skills. The reduced ability to lateralize cerebral activation, along with the enhanced connectivity between the right and left motor cortices in these patients, may signify maladaptive neural processes that impede motor adaptation, possibly affecting long-term rehabilitation post-stroke. The contrasting pattern of activity modulation and connectivity alteration in the default mode network suggests a nuanced role of this network in post-stroke motor learning. These insights could have significant implications for the development of customised rehabilitation strategies for stroke patients.

Psychometric evaluation of the 5-item Medication Adherence Report Scale questionnaire in persons with multiple sclerosis.

The 5-item Medication Adherence Report Scale (MARS-5) is a reliable and valid questionnaire for evaluating adherence in patients with asthma, hypertension, and diabetes. Validity has not been determined in multiple sclerosis (MS). We aimed to establish criterion validity and reliability of the MARS-5 in persons with MS (PwMS). Our prospective study included PwMS on dimethyl fumarate (DMF). PwMS self-completed the MARS-5 on the same day before baseline and follow-up brain magnetic resonance imaging (MRI) 3 and 9 months after treatment initiation and were graded as highly and medium adherent upon the 24-cut-off score, established by receiver operator curve analysis. Health outcomes were represented by relapse occurrence from the 1st DMF dispense till follow-up brain MRI and radiological progression (new T2 MRI lesions and quantitative analysis) between baseline and follow-up MRI. Criterion validity was established by association with the Proportion of Days Covered (PDC), new T2 MRI lesions, and Beliefs in Medicines questionnaire (BMQ). The reliability evaluation included internal consistency and the test-retest method. We included 40 PwMS (age 37.6 ± 9.9 years, 75% women), 34 were treatment-naive. No relapses were seen during the follow-up period but quantitative MRI analysis showed new T2 lesions in 6 PwMS. The mean (SD) MARS-5 score was 23.1 (2.5), with 24 PwMS graded as highly adherent. The higher MARS-5 score was associated with higher PDC (b = 0.027, P<0.001, 95% CI: (0.0134-0.0403)) and lower medication concerns (b = -1.25, P<0.001, 95% CI: (-1.93-(-0,579)). Lower adherence was associated with increased number (P = 0.00148) and total volume of new T2 MRI lesions (P = 0.00149). The questionnaire showed acceptable internal consistency (Cronbach α = 0.72) and moderate test-retest reliability (r = 0.62, P < 0.0001, 95% CI: 0.33-0.79). The MARS-5 was found to be valid and reliable for estimating medication adherence and predicting medication concerns in persons with MS.

Neuroimaging Methods for MRI Analysis in CSF Biomarkers Studies.

Among others, the existence of pathophysiological biomarkers such as cerebrospinal fluid (CSF) Aβ-42, t-tau, and p-tau preceding the onset of Alzheimer's disease (AD) symptomatology has shifted the conceptualization of AD as a continuum. In addition, magnetic resonance imaging (MRI) enables the study of structural and functional cross-sectional correlates and longitudinal changes in vivo, and therefore, the combination of CSF data and imaging analyses emerges as a synergistic approach to understand the structural correlates related with specific AD-related biomarkers. In this chapter, we describe the methods used in neuroimaging that will allow researchers to combine data on CSF metabolites with imaging analyses.

Bi-Parametric Magnetic Resonance Imaging Analysis of Biochemical Recurrence of Prostate Cancer after Radical Surgery and Its Predictive Value: A Retrospective Study.

This study aimed to analyse the characteristics of biochemical recurrence after radical prostatectomy via bi-parametric magnetic resonance imaging. A total of 200 patients with radical prostatectomy admitted to our hospital from January 2016 to January 2021 were retrospectively enrolled as observation objects. According to whether there was biochemical recurrence after surgery, the patients were divided into the abnormal group (n = 62) and normal group (n = 138). Clinical data, encapsulation infiltration, seminal vesicle infiltration and prostate imaging report and data system (PI-RADS) were collected and compared between the two groups. Propensity score matching (PSM) was used to balance the baseline data of the two groups. Student's t-test and Chi-square test were used to analyse the data. PSM was performed in a 1:1 ratio, and a total of 72 patients were included in the abnormal and normal groups. The baseline data of the patients in each group were not statistically significant. The incidence of extraperitoneal invasion and seminal vesicle invasion was higher in the abnormal group than in the normal group, and we observed a significant difference in PI-RADS scores between the two groups (p < 0.05). Extracapsular invasion, seminal vesicle invasion, PI-RADS score and biochemical recurrence were significantly correlated (p < 0.05). The PI-RADS score has a high value for predicting biochemical recurrence, with an area under the curve value of 0.824, sensitivity of 0.667, specificity of 0.861 and Youden index of 0.528. Bi-parametric magnetic resonance imaging has a high predictive value in biochemical recurrence after radical prostatectomy, which can provide reference for early intervention measures.