T1-weighted MRI Images Research Articles

Identification of Depression Subtypes in Parkinson's Disease Patients via Structural MRI Whole-Brain Radiomics: An Unsupervised Machine Learning Study.

Current clinical evaluation may tend to lack precision in detecting depression in Parkinson's disease (DPD). Radiomics features have gradually shown potential as auxiliary diagnostic tools in identifying and distinguishing different subtypes of Parkinson's disease (PD), and a radiomic approach that combines unsupervised machine learning has the potential to identify DPD. Analyze the clinical and imaging data of 272 Parkinson's disease (PD) patients from the PPMI dataset, along with 45 PD patients from the NACC dataset. Extract radiomic features from T1-weighted MRI images and employ principal component analysis (PCA) for dimensionality reduction. Subsequently, apply four unsupervised clustering methods including Gaussian mixture model (GMM), hierarchical clustering, K-means, and partitioning around medoids (PAM) to classify cases in the PPMI dataset into distinct subtypes. Identify high-risk subtypes of DPD on the basis of the time and number of depression progression, and validate these findings using the NACC dataset. The data from the high-risk subtype were divided into a training subtype and a testing subtype in a 7:3 ratio. Multiple logistic regression analysis was conducted on the training subtype data to develop a traditional logistic regression model for the high-risk subtype, which was subsequently compared with a supervised logistic regression model constructed for the entire PPMI cohort. Finally, the performance of both models was evaluated using receiver operating characteristic (ROC) curves. In addition, a decision tree (DT) model was constructed based on independent risk factors of high-risk subtypes and validated using low-risk subtype data. ROC curves were employed to validate this model across training subtype, testing subtype, and low-risk subtype datasets. The PAM clustering method demonstrates superior performance compared to the other three clustering methods when the number of clusters is 2. High-risk subtypes of DPD can be effectively distinguished in both the PPMI and NACC datasets. A traditional logistic regression model was developed based on rapid-eye-movement behavior disorder, UPDRS I score, UPDRS II score, and ptau in high-risk subgroups. This model exhibits a diagnostic efficacy (AUC = 0.731) that surpasses that of the traditional regression model constructed using the entire PPMI cohort (AUC = 0.674). The prediction model based on high-risk subtypes had AUC values of 0.853 and 0.81 in the training and testing subtypes, sensitivities of 0.765 and 0.786, and specificities of 0.771 and 0.815, respectively. The AUC, sensitivity, and specificity in the nonhigh-risk subtype were 0.859, 0.654, and 0.852, respectively. By identifying MRI structural radiomics and clinical features as potential biomarkers, the radiomic approach and UCA provide new insights into the pathophysiology of DPD to support the clinical diagnosis with high prediction accuracy.

Risk factors for progression of nucleus pulposus degeneration in the lumbar intervertebral disc: A retrospective analysis using the disc signal intensity index.

Degenerative disc changes are associated with low back pain and negatively impact quality of life. Disc degeneration process usually starts with nucleus pulposus change. There is uncertainty about the risk factors associated with the progression of disc nucleus degeneration due to the lack of an objective evaluation method. Pfirrmann grade, which assesses the morphological characteristics of a disc on T2-weighted MRI images on a scale of 1 to 5, is one of the most frequently used assessment systems. This method inherently has a degree of subjectivity that may lead to inaccurate and inconsistent grading. A recent study introduced the disc signal intensity index (DSI2) for quantitative assessment of nucleus pulposus degeneration with promising results in identifying of risk factors for progression of disc degeneration. The aim of this study was to investigate the risk factors for the progression of nucleus pulpous degeneration in the lumbar vertebral disc on longitudinal MRI data using DSI2. Retrospective longitudinal study PATIENT SAMPLE: Patients with lumbar MRIs at least three years apart and did not undergo the lumbar spine surgery between both time points were included. Potential contributing factors were collected that included age, biological sex, race, body mass index (BMI), current smoking status, alcohol consumption, history of previous lumbar decompression surgery, and comorbidities such as congestive heart failure (CHF), myocardial infarction, diabetes mellitus, chronic obstructive pulmonary disease, hypertension, and rheumatoid arthritis. Disc nucleus degeneration was assessed using the DSI2 and Pfirrmann grade on T2- weighted MRI. The DSI2 values are based on the mean signal of disc spaces within a circular region of interest (ROI), which was adjusted by the signal intensity of cerebrospinal fluid (CSF) on the midsagittal plane. Three ROIs were set per disc (anterior 1/3, middle, posterior 1/3), and the mean values of all three measurements from L1/2 to L5/S1 disc space were utilized. Discs with complete collapse and no space for ROI selection were excluded. The difference in DSI2 scores between these two time points were compared. Multivariate linear mixed regression analysis was conducted to determine the factors associated with disc degenerative changes. A total of 325 patients and 1439 discs were included in the final analysis. 173 patients (53.2%) were female and the median age of all patients was 60.1 years. The mean (SD) DSI2 score was 0.177 (0.074) for thefirst MRI and 0.184 (0.081) for the second MRI. The Pfirrmann grading for the first MRI timepoint were as follows: 23 discs (1.42%) were Grade 1, 377 discs (23.2%) were Grade 2, 583 discs (35.9%) were Grade 3, 456 discs (28.1%) were Grade 4, and 186 discs (11.5%) were Grade 5. Multivariable linear mixed regression analysis demonstrated that older age (p=0.030) and the presence of congestive heart failure (p<0.001) were significant risk factors for disc nucleus degenerative progression. The present study demonstrated that age and congestive heart failure were risk factors for progression of disc nucleus degeneration. These insights may aid in identifying patients at risk for degenerative lumbar conditions and guide further studies about preventive measures. The DSI2 method offers a promising alternative evaluation method for future disc research.

The value of hippocampal sub-region imaging features for the diagnosis and severity grading of ASD in children

Hippocampal structural changes in Autism Spectrum Disorder (ASD) are inconsistent. This study investigates hippocampal subregion changes in ASD patients to reveal intrinsic hippocampal anomalies. A retrospective study from Hainan Children's Hospital database (2020-2023) included ASD patients and matched controls. We classified ASD participants based on severity, dividing all subjects into four groups: normal, mild, moderate, and severe. High-resolution T1-weighted MRI images were analyzed for hippocampal subregion segmentation and volume calculations using Freesurfer. Texture features were extracted via the Gray-Level Co-occurrence Matrix. The Receiver Operating Characteristic curve was used to evaluate seven random forest predictive models constructed from volume, subregion, and texture features, as well as their combinations following feature selection. The study included 114 ASD patients (98 boys, 2-8years; 16 girls, 2-6years; 17 mild, 57 moderate, 40 severe) and 111 healthy controls (HCs). No significant differences in volumes were found between ASD patients and HCs (adjusted P-value>0.05). The seven random forest models showed that single volume and texture features performed poorly for ASD classification; however, integrating various feature types improved AUC values. Further selection of texture, subregion, and volume features enhanced AUC performance across normal and varying severity categories, demonstrating the potential value of specific subregions and integrated features in ASD diagnosis. Random forest models revealed that hippocampal volume, texture features, and subregion characteristics are crucial for diagnosing and assessing the severity of ASD. Integrating selected texture and subregion features optimized diagnostic efficacy, while combining texture, subregion, and volume features further improved severity grading effectiveness.

Deep learning MRI models for the differential diagnosis of tumefactive demyelination versus IDH-wildtype glioblastoma.

Diagnosis of tumefactive demyelination can be challenging. The diagnosis of indeterminate brain lesions on MRI often requires tissue confirmation via brain biopsy. Noninvasive methods for accurate diagnosis of tumor and non-tumor etiologies allows for tailored therapy, optimal tumor control, and a reduced risk of iatrogenic morbidity and mortality. Tumefactive demyelination has imaging features that mimic isocitrate dehydrogenase-wildtype glioblastoma (IDHwt GBM). We hypothesized that deep learning applied to postcontrast T1-weighted (T1C) and T2-weighted (T2) MRI images can discriminate tumefactive demyelination from IDHwt GBM. Patients with tumefactive demyelination (n=144) and IDHwt GBM (n=455) were identified by clinical registries. A 3D DenseNet121 architecture was used to develop models to differentiate tumefactive demyelination and IDHwt GBM using both T1C and T2 MRI images, as well as only T1C and only T2 images. A three-stage design was used: (i) model development and internal validation via five-fold cross validation using a sex-, age-, and MRI technology-matched set of tumefactive demyelination and IDHwt GBM, (ii) validation of model specificity on independent IDHwt GBM, and (iii) prospective validation on tumefactive demyelination and IDHwt GBM. Stratified AUCs were used to evaluate model performance stratified by sex, age at diagnosis, MRI scanner strength, and MRI acquisition. The deep learning model developed using both T1C and T2 images had a prospective validation area under the receiver operator characteristic curve (AUC) of 88% (95% CI: 0.82 - 0.95). In the prospective validation stage, a model score threshold of 0.28 resulted in 91% sensitivity of correctly classifying tumefactive demyelination and 80% specificity (correctly classifying IDHwt GBM). Stratified AUCs demonstrated that model performance may be improved if thresholds were chosen stratified by age and MRI acquisition. MRI images can provide the basis for applying deep learning models to aid in the differential diagnosis of brain lesions. Further validation is needed to evaluate how well the model generalizes across institutions, patient populations, and technology, and to evaluate optimal thresholds for classification. Next steps also should incorporate additional tumor etiologies such as CNS lymphoma and brain metastases. AUC = area under the receiver operator characteristic curve; CNS = central nervous system; CNSIDD = central nervous system inflammatory demyelinating disease; FeTS = federated tumor segmentation; GBM = glioblastoma; IDHwt = isocitrate dehydrogenase wildtype; IHC = immunohistochemistry; MOGAD = myelin oligodendrocyte glycoprotein antibody associated disorder; MS = multiple sclerosis; NMOSD = neuromyelitis optica spectrum disorder; wt = wildtype.

Haralick Texture Analysis for Differentiating Suspicious Prostate Lesions from Normal Tissue in Low-Field MRI.

This study evaluates the feasibility of using Haralick texture analysis on low-field, T2-weighted MRI images for detecting prostate cancer, extending current research from high-field MRI to the more accessible and cost-effective low-field MRI. A total of twenty-one patients with biopsy-proven prostate cancer (Gleason score 4+3 or higher) were included. Before transperineal biopsy guided by low-field (58-74mT) MRI, a radiologist annotated suspicious regions of interest (ROIs) on high-field (3T) MRI. Rigid image registration was performed to align corresponding regions on both high- and low-field images, ensuring an accurate propagation of annotations to the co-registered low-field images for texture feature calculations. For each cancerous ROI, a matching ROI of identical size was drawn in a non-suspicious region presumed to be normal tissue. Four Haralick texture features (Energy, Correlation, Contrast, and Homogeneity) were extracted and compared between cancerous and non-suspicious ROIs. Two extraction methods were used: the direct computation of texture measures within the ROIs and a sliding window technique generating texture maps across the prostate from which average values were derived. The results demonstrated statistically significant differences in texture features between cancerous and non-suspicious regions. Specifically, Energy and Homogeneity were elevated (p-values: <0.00001-0.004), while Contrast and Correlation were reduced (p-values: <0.00001-0.03) in cancerous ROIs. These findings suggest that Haralick texture features are both feasible and informative for differentiating abnormalities, offering promise in assisting prostate cancer detection on low-field MRI.

SELECTIVE GREY MATTER PATTERNS OF MOBILITY IN OLDER ADULTS WITH AND WITHOUT MULTIPLE SCLEROSIS

Abstract Background: Both healthy aging and multiple sclerosis (MS) are associated with changes in cortical grey matter (GM), notably frontal grey matter atrophy consistent with the last in-first out hypothesis, but its relationship to mobility impairments is unknown. We sought to identify brain patterns of cortical GM associated with walking performance, as measured by single- and dual-task walking speed, in older adults with and without MS. Methods: In a cohort of 97 MS and 105 healthy older adults, we measured cortical volume from T1-weighted MRI images and Freesurfer software, along with quantitative gait assessment during natural walking (single-task) and walking while reciting alternate letters of the alphabet (dual-task) conducted within a few weeks of the MRI. Principal components (PC) analysis identified significant latent patterns of GM volume, from the bilateral 68 Freesurfer cortical regions, and pattern scores were entered into regression analyses of gait velocity vs pattern scores, unadjusted and adjusted for demographics variables. Results: The first two PCs showed a significant association with single-task (p = 0.027/0.0025), but only PC2 was associated with dual-task (p=0.082/0.027). In group-stratified analyses, only PC1 was associated with single-task in healthy controls but just below the significance level (p=0.057), and only PC2 was associated with single-task in MS participants (p=0.0052). PC1 consisted of mostly posterior regions (medial occipital, inferior parietal), while PC2 consisted of mostly anterior regions (frontal, cingulate). Conclusion: Grey matter pattern analysis may distinguish between older adults with and without MS, corresponding to anterior, cognitive-control vs posterior, visual/automated control of mobility.

BRAIN TUMOR CLASSIFICATION USING A HYBRID DEEP LEARNING MODEL: LEVERAGING DENSENET121 AND INCEPTIONV2 ARCHITECTURES

Brain tumors represent one of the most severe forms of cancer, posing significant challenges due to their complex nature and critical location. Accurate and early diagnosis is crucial for effective treatment and improved patient outcomes. In this study, we propose a novel hybrid deep learning model that combines the strengths of DenseNet121 and InceptionV2 architectures to enhance brain tumor classification accuracy. The Figshare Brain Tumor Dataset, comprising 3,064 T1-weighted contrast-enhanced MRI images from 233 patients, is utilized to train and evaluate the proposed model. The dataset includes three primary tumor classes: glioma, meningioma, and pituitary tumors. Preprocessing steps such as normalization, resizing, and data augmentation are applied to ensure data consistency and enhance the model’s robustness. The DenseNet121 component of the hybrid model facilitates efficient feature reuse through densely connected layers, while the InceptionV2 component captures multi-scale contextual information via parallel convolutional layers. This combination allows the model to leverage detailed and high-level features, improving classification performance. The proposed hybrid model is evaluated using standard metrics, demonstrating significant improvements in accuracy, robustness, and generalization compared to single architecture models. This study highlights the potential of hybrid deep learning models in advancing brain tumor classification, offering a promising direction for future research and clinical applications.

Robust Autism Spectrum Disorder-Related Spatial Covariance Gray Matter Pattern Revealed With a Large-Scale Multi-Center Dataset.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and its underlying neuroanatomical mechanisms still remain unclear. The scaled subprofile model of principal component analysis (SSM-PCA) is a data-driven multivariate technique for capturing stable disease-related spatial covariance pattern. Here, SSM-PCA is innovatively applied to obtain robust ASD-related gray matter volume pattern associated with clinical symptoms. We utilized T1-weighted structural MRI images (sMRI) of 576 subjects (288 ASDs and 288 typically developing (TD) controls) aged 7-29 years from the Autism Brain Imaging Data Exchange II (ABIDE II) dataset. These images were analyzed with SSM-PCA to identify the ASD-related spatial covariance pattern. Subsequently, we investigated the relationship between the pattern and clinical symptoms and verified its robustness. Then, the applicability of the pattern under different age stages were further explored. The results revealed that the ASD-related pattern primarily involves the thalamus, putamen, parahippocampus, orbitofrontal cortex, and cerebellum. The expression of this pattern correlated with Social Response Scale and Social Communication Questionnaire scores. Moreover, the ASD-related pattern was robust for the ABIDE I dataset. Regarding the applicability of the pattern for different age stages, the effect sizes of its expression in ASD were medium in the children and adults, while small in adolescents. This study identified a robust ASD-related pattern based on gray matter volume that is associated with social deficits. Our findings provide new insights into the neuroanatomical mechanisms of ASD and may facilitate its future intervention.

Imaging observation of intervertebral disc degeneration in patients with old thoracolumbar fracture-related kyphotic deformity

Old thoracolumbar fracture with kyphosis (OTLFK) often results in low back pain, with intervertebral disc degeneration being a significant contributor. We hypothesized that patients with OTLFK exhibit distinct patterns of disc degeneration compared to those with chronic low back pain without kyphotic deformity. This study aimed to investigate the characteristics of disc degeneration in OTLFK patients and explore its association with sagittal spinal parameters and endplate injury. A retrospective analysis was conducted on 52 patients with OTLFK (observation group, OG) and 104 age-, gender-, and BMI-matched patients with chronic low back pain (control group, CG) treated at our hospital between February 2017 and March 2023. Intervertebral disc degeneration from T11/12 to L5/S1 was assessed using MRI T2-weighted images and the Pfirrmann grading system. Sagittal spinal parameters—including lumbar lordosis (LL), thoracic kyphosis (TK), local kyphosis Cobb angle (LKCA), thoracolumbar kyphosis (TLK), pelvic tilt(PT), sacral slope(SS), and sagittal vertical axis (SVA)—and endplate injury grades were measured in the OG. Differences in disc degeneration between the two groups were compared, and correlations between disc degeneration, sagittal parameters, and endplate injury were analyzed. The OG exhibited significantly higher overall disc degeneration grades compared to the CG (p < 0.05), particularly at levels T11/12, T12/L1, L1/2, and L2/3. In the OG, grade IV and V degenerations were predominantly found from T11/12 to L2/3, whereas in the CG, they were mainly at L4/5 and L5/S1. Disc degeneration in the OG was significantly correlated with sagittal parameters and endplate injury grades (p < 0.05). Patients with OTLFK have higher grades of disc degeneration in the thoracolumbar region compared to those with chronic low back pain without kyphosis. Disc degeneration in OTLFK is associated with abnormal sagittal alignment and endplate injury, suggesting that kyphotic deformity and altered spinal biomechanics contribute to accelerated disc degeneration.

Motor predictors of cortical brain development and full-IQ in children born extremely preterm with and without discrete white matter abnormalities.

To describe the cortical brain development and full-IQ performance in middle school age children after extremely preterm (EPT) birth considering discrete white matter abnormalities (WMA). In addition, to assess possible early motor predictors of cortical brain development and full-IQ in children born EPT with and without discrete WMA diagnosed at 10 years. T1-weighted MRI images from fifty-one children born before 27 weeks' gestation and 40 full-term born controls (Mage=10.09 years; SDage=0.77) were scored for discrete WMA and analyzed with Freesurfer (v7.2.0). The assessments included motor assessments (i.e., fine- and gross motor function) of Bayley Scales of Infant and Toddler Development - Third Edition (BSID-III) at a mean age of 2½ years. Full-IQ was also assessed with Wechsler Intelligence Scale for Children - Fifth Edition (WISC-V) at 12 years. No differences were displayed in motor function or full-IQ score between children born EPT with and without discrete WMA at 10 years. Moreover, no global differences were found in cortex volume. However, bilateral mean cortical thicknesses (CTh) were exhibited to be thicker in children born EPT with discrete WMA. Children born EPT with discrete WMA exhibited regional increases mainly in the frontal and temporal lobes apart from left caudal anterior cingulate gyrus (mean difference = -0.11 (-0.22, -0.01), p = 0.026). Full-IQ was predicted by impairments in fine motor skills in children born EPT with discrete WMA, explaining 42.9% of the variance. Bilateral mean and regional CTh were found to be greater in children born EPT with discrete WMA at 10 years compared to those without. Fine motor function at 2½ years was a strong predictor of full-IQ dependent in children with discrete WMA.

Cognitive and Clinical Dimensions of Structural and Functional Insula Alterations in Patients with Depression: A Resting-State fMRI Study

Depression is characterized by pervasive cognitive and emotional disturbances, yet the neural mechanisms underlying these deficits remain incompletely understood. This study utilized multimodal neuroimaging, including resting-state functional MRI and structural T1-weighted imaging, alongside the MATRICS Consensus Cognitive Battery (MCCB) and the Hamilton Depression Rating Scale (HAMD), to delineate the structural and functional alterations in the insula in first-episode, medication-naïve patients with depression. Compared to matched healthy controls, patients with depression exhibited significant reductions in gray matter density in the left insula, which were robustly associated with impairments in reasoning and problem-solving abilities. Mediation analyses revealed that insular gray matter density mediated the relationship between depressive symptom severity and cognitive deficits, emphasizing the insula's critical role in linking emotional and cognitive dysfunctions. Furthermore, functional connectivity analyses identified disrupted insula-medial prefrontal cortex circuits, highlighting their contribution to the pathophysiology of depression. These findings underscore the insula’s dual role as a structural and functional hub in depression, advancing our understanding of the neural substrates of cognitive dysfunction and informing potential targets for intervention.

Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques

IntroductionGadolinium-based T1-weighted MRI sequence is the gold standard for the detection of active multiple sclerosis (MS) lesions. The performance of machine learning (ML) and deep learning (DL) models in the classification of active and non-active MS lesions from the T2-weighted MRI images has been investigated in this study.Methods107 Features of 75 active and 100 non-active MS lesions were extracted by using SegmentEditor and Radiomics modules of 3D slicer software. Sixteen ML and one sequential DL models were created using the 5-fold cross-validation method and each model with its special optimized parameters trained using the training-validation datasets. Models’ performances in test data set were evaluated by metric parameters of accuracy, precision, sensitivity, specificity, AUC, and F1 score.ResultsThe sequential DL model achieved the highest AUC of 95.60% on the test dataset, demonstrating its superior ability to distinguish between active and non-active plaques. Among traditional ML models, the Hybrid Gradient Boosting Classifier (HGBC) demonstrated a commendable test AUC of 86.75%, while the Gradient Boosting Classifier (GBC) excelled in cross-validation with an AUC of 87.92%.ConclusionThe performance of sixteen ML and one sequential DL models in the classification of active and non-active MS lesions was evaluated. The results of the study highlight the effectiveness of sequential DL approach and ensemble methods in achieving robust predictive performance, underscoring their potential applications in classifying MS plaques.

Quantitative assessment of cervical disc degeneration using disc signal intensity index.

The assessment of disc degeneration remains a significant challenge in clinical research. Pfirrmann grade is a frequently used classification for lumbar disc degeneration on MRI. However, there has been no gold standard for cervical spine disc degeneration. Recently, we introduced the Disc Signal Intensity Index (DSI2) as a quantitative disc assessment for the lumbar spine, which is easily measurable in the cervical spine. The aim of this study was to apply DSI2 in the cervical intervertebral disc and investigate the factors associated with the cervical disc degeneration. Cross-sectional study using retrospectively collected data. Cervical MRIs from a database of patients undergoing ACDF between 2015 and 2018 were retrospectively reviewed. Demographic variables included age, sex, body mass index (BMI), race, smoking status, and comorbidities such as diabetes, chronic kidney disease, and coronary artery disease. DSI2 measurements were performed on midsagittal T2-weighted MRI images by determining the intensity within regions of interest (ROI). One ROI was set in the cerebrospinal fluid (CSF) and three ROIs were set per disc at the anterior, middle, and posterior third. The mean of the three measurements per disc was then divided by that of the CSF to calculate the DSI2 score. Multivariable linear regression analyses with mixed model were conducted to determine the potential contributing factors for disc degeneration. A total of 149 patients and 770 discs were included in the final analysis. Ninety-three patients (37.6%) were female and the mean (SD) age was 55.6 (11.7) years. The distribution of DSI2 scores among the different Pfirrmann grades was as follows: Grade 1: 0.259±NA; Grade 2: 0.226±0.090; Grade 3: 0.175±0.070; Grade 4: 0.136±0.060; Grade 5: 0.131±0.050. Multivariable linear mixed-effect regression analysis, setting with DSI2 as the objective variable, demonstrated that age (β= -0.130, p< 0.05), BMI (β= -2.06, p< 0.05), Modic changes (Type1 β= -2.70, p<0.01) were independent contributors to disc degeneration. The segments C4/5 and C7/T1 were less prone to disc degeneration (C4/5: β=1.37, p<0.001; C7/T1: β= 2.63, p<0.001) and the history of diabetes (β=5.31, p<0.01) was associated with high DSI2.(p<0.01) CONCLUSIONS: The present study provides valuable insights for identifying risk factors in degenerative cervical conditions utilizing the DSI2. The DSI2 method emerges as a promising alternative for future disc research, excelling in the detection of subtle progressions of degeneration and distinguishing itself from the subjective Pfirrmann grading system.

The Degree of Cervical Intervertebral Disc Degeneration Is Associated With Denser Bone Quality of the Cervical Sub-endplate and Vertebral Body.

The relationship between cervical disc degeneration and bone quality of adjacent vertebral body remains controversial. This study aims to investigate the relationship between cervical disc degeneration and bone quality of the adjacent vertebral body and sub-endplate bone with a new MRI-based bone quality score in patients over 50 years with cervical spondylosis. We retrospectively reviewed 479 cervical disc segments from 131 patients. Disc degeneration at levels C3/C4-C6/C7 was graded using T2-weighted MRI. Vertebral body quality (VBQ) score and sub-endplate bone quality (EBQ) score from C3 to C7 were computed from T1-weighted MRI images. Additionally, bone mineral density (BMD) of the cervical vertebrae was measured in 52 patients using a novel phantom-less quantitative computed tomography (PL-QCT) system. The correlation between bone quality score and Pfirrmann grade was analyzed and risk factors for VBQ and EBQ were further evaluated. Significant differences were found in cranial VBQ among different Pfirrmann grades, with a score of 2.55 ± 0.54 for Grade 5 discs, which was lower compared to Grades 4 (2.70 ± 0.56) (p < 0.05) and 3 (2.81 ± 0.58) (p < 0.01). Caudal VBQ for Grade 5 discs (2.43 ± 0.52) was also significantly lower than for Grade 3 discs (2.66 ± 0.54) (p < 0.01). EBQ scores decreased with increasing Pfirrmann grades. Negative correlations were observed between both cranial and caudal VBQ and EBQ scores and Pfirrmann grades. Grades 4 and 5 discs were identified as independent risk factors for decreased caudal VBQ and EBQ, whereas only Grade 5 was a significant risk factor for decreased cranial EBQ. Additionally, a moderate correlation (0.4 < R < 0.6, p < 0.05) was noted between vertebral body BMD and VBQ at each cervical level. In individuals over 50 years with cervical spondylosis, the severity of disc degeneration was closely correlated with denser bone quality in both the caudal vertebral body and sub-endplate, as measured by VBQ and EBQ scores. These findings suggest that worsening disc degeneration is associated with increased bone density in specific areas of the cervical spine.

MRI Investigation of Kidneys, Ureters and Urinary Bladder in Rabbits.

Twelve clinically healthy and sexually mature New Zealand White rabbits were studied. The non-contrast imaging included T1-weighted and T2-weighted spin-echo and gradient-echo sequences in the transverse, sagittal and dorsal planes. Transverse MRI (T2-weighted image) through L1 demonstrated only the right kidney. The transverse T2-weighted image through L2 showed both kidneys. The cranial part of the urinary bladder on T1-weighted transverse scans through L4 was flexed to the left. The T2-weighted sagittal image 30 mm to the right of the median plane showed the right kidney, the right ureter and the urinary bladder. The T2-weighted sagittal image 30 mm to the left of the median plane showed part of the left kidney, the left ureter and the urinary bladder. The T2-weighted sagittal image 45 mm to the left of the median plane presented the lateral part of the left kidney. The dorsal MRI image (T2-weighted sequence) through the horizontal plane 30 mm ventral to the spine demonstrated the whole organs. These data will be useful in imaging anatomy and diagnostic studies of various pathologies of the excretory system in rabbits and other mammalian species.

The Association between prior arthroplasty and Paraspinal Muscle Degeneration in patients undergoing elective lumbar surgery.

Spinal and lower extremity degeneration often causes pain and disability. Lower extremity osteoarthritis, eventually leading to total knee- (TKA) and -hip arthroplasty (THA), can alter posture through compensatory mechanisms, potentially causing spinal misalignment and paraspinal muscle (PM) atrophy. This study aims to evaluate the association between prior THA or TKA and PM-degeneration in patients undergoing elective lumbar surgery for degenerative conditions. A retrospective analysis of patients undergoing lumbar surgery for degenerative conditions was conducted. Patients were categorized based on prior THA, TKA, or both. Quantitative analysis of functional cross-sectional area (fCSA) and fat infiltration (FI) of psoas, multifidus (MF), and erector spinae (ES) muscles at L4-level was performed using T2-weighted MRI images. The association between the FI and fCSA of the PM and prior arthroplasty was investigated. Differences were assessed using ANOVA and multivariable linear regression. Overall, 584 patients (60% female, 64 ± 12 years) were included. 66 patients (11%) had prior TKA, 36 patients (6%) THA, and 15 patients (3%) both TKA and THA. Patients with arthroplasty were mostly female (57%) and notably older (p < 0.001). The FI of the MF and the ES was significantly higher in the arthroplasty-group (both p < 0.001). Patients with prior TKA showed significantly higher FI (Est = 4.3%, p = 0.013) and lower fCSA (Est=-0.9 cm2, p = 0.012) in the MF compared to the non-arthroplasty-group. This study demonstrates a significant lower fCSA and higher FI in the MF among individuals with prior TKA. This highlights the complex knee-spine relationship and how these structures interact with each other.

Estimating lumbar bone mineral density from conventional MRI and radiographs with deep learning in spine patients.

This study aimed to develop machine learning methods to estimate bone mineral density and detect osteopenia/osteoporosis from conventional lumbar MRI (T1-weighted and T2-weighted images) and planar radiography in combination with clinical data and imaging parameters of the acquisition protocol. A database of 429 patients subjected to lumbar MRI, radiographs and dual-energy x-ray absorptiometry within 6 months was created from an institutional database. Several machine learning models were trained and tested (373 patients for training, 86 for testing) with the following objectives: (1) direct estimation of the vertebral bone mineral density; (2) classification of T-score lower than - 1 or (3) lower than - 2.5. The models took as inputs either the images or radiomics features derived from them, alone or in combination with metadata (age, sex, body size, vertebral level, parameters of the imaging protocol). The best-performing models achieved mean absolute errors of 0.15-0.16g/cm2 for the direct estimation of bone mineral density, and areas under the receiver operating characteristic curve of 0.82 (MRIs) - 0.80 (radiographs) for the classification of T-scores lower than - 1, and 0.80 (MRIs) - 0.65 (radiographs) for T-scores lower than - 2.5. The models showed good discriminative performances in detecting cases of low bone mineral density, and more limited capabilities for the direct estimation of its value. Being based on routine imaging and readily available data, such models are promising tools to retrospectively analyse existing datasets as well as for the opportunistic investigation of bone disorders.

Evaluation of Basal Ganglia in Paediatric Patients With Primary Nephrotic Syndrome by Brain Magnetic Resonance Histogram Analysis.

Primary nephrotic syndrome is an important cause of chronic renal failure in childhood. Important neuronal complications may develop during the disease. This study aims to demonstrate basal ganglia involvement in children with nephrotic syndrome by texture analysis. Brain MRI images of 22 paediatric patients with primary nephrotic syndrome and 40 healthy children of similar age groups were analysed. Brain MRI T2-weighted images were extracted from the thalamus, lentiform nucleus and nucleus caudatus and texture analysis was performed. The images of 22 children with primary nephrotic syndrome and 40 children in the control group were evaluated. There were no notable distinctions identified in terms of age and gender between the patient and control groups (P value 0,410; 0,516, respectively). Accordingly, a significant difference was found between mean, 1.P, 10.P, 50.P, 90.P, 99.P values of histogram parameters obtained from thalamus (P values were 0.001; 0.000; 0.001; 0.002; 0.004; 0.009, respectively). A significant difference was found between mean, 1.P, 10.P, 50.P, 90.P, 99.P values of histogram parameters obtained from lentiform nuclei (P values were 0.031; 0.019; 0.006; 0.006; 0.003; 0.003; 0.001; 0.002, respectively). A significant difference was found between the mean, 1.P, 10.P, 50.P, 90.P, 99.P values of the histogram parameters obtained from the nucleus caudatus (P values 0,002; 0,005; 0,002; 0,002; 0,002; 0,003; 0,003, respectively). Texture analysis may be helpful in demonstrating brain parenchymal involvement in paediatric patients with primary nephrotic syndrome by showing changes that are not recognised on conventional images.

The Impact of Paraspinal Muscle Degeneration on Oswestry Disability Index Subsections Two Years After Spinal Surgery for Degenerative Lumbar Spondylolisthesis.

Secondary analysis of prospective study. To investigate the impact of fatty infiltration (FI) in the paraspinal muscles (PM) on postoperative Oswestry Disability Index (ODI) subsection-scores in patients undergoing elective lumbar surgery for degenerative lumbar spondylolisthesis (DLS). DLS can increase FI in the PM like the multifidus (MF), erector spinae (ES), and psoas (PS), leading to greater spinal disability and higher ODI-scores. It is unclear if increased PM FI affects all ODI subsections equally. This study reviewed data from a single-center cohort of DLS-patients who underwent elective lumbar surgery. Overall ODI and the subsection scores were prospectively assessed before and 2 years post-surgery. FI of the PS, MF, and ES was measured using T2-weighted MRI images. The relationship between PM FI and postoperative ODI subsections were analyzed, using multivariable linear regression analysis. Overall, 229 patients (59% female, mean age of 67±8y) were included with an overall baseline ODI of 50[40-62] and a postoperative ODI of 16.[2-34] The highest preoperative subsection-scores were seen in pain intensity (4[2-4]), changing degree of pain (4[3-4]), lifting (4[1-5]), and standing (4[3-4]). PM-measurements showed a mean FI of 41±10% for ES, 58±15% for MF, and 6±5% for PS. Patients with increased ES FI were more likely to show higher postoperative scores in all ODI subsections and in the overall ODI (Est=0.45, 95%CI 0.20-0.71, P=0.004). Increased MF FI was significantly associated with higher postoperative ODI subsection scores in standing (Est=0.02, 95%CI 0.01-0.03, P=0.033) and walking (Est=0.02, 95%CI 0.01-0.03, P=0.017). Increased erector spinae fatty infiltration is significantly associated with higher ODI scores across all subsections 2 years after lumbar surgery, while higher multifidus fatty infiltration is linked to greater disability in standing and walking. These findings underscore the need to maintain paraspinal muscle health to improve surgical planning, improve rehabilitation outcomes, and reduce postoperative disability.

Intra-individual structural covariance network in schizophrenia patients with persistent auditory hallucinations

Neuroimaging studies have revealed that the mechanisms of auditory hallucinations are related to morphological changes in multiple cortical regions, but studies on brain network properties are lacking. This study aims to construct intra-individual structural covariance networks and reveal network changes related to auditory hallucinations. T1-weighted MRI images were acquired from 90 schizophrenia patients with persistent auditory hallucinations (pAH group), 55 schizophrenia patients without auditory hallucinations (non-pAH group), and 83 healthy controls (HC group). Networks were constructed using the voxel-based gray matter volume and the intra-individual structural covariance was based on the similarity between the morphological variations of any two regions. One-way ANCOVA was employed to compare global and local network metrics among the three groups, and edge analysis was conducted via network-based statistics. In the pAH group, Pearson correlation analysis between network metrics and clinical symptoms was conducted. Compared with the HC group, both the pAH group (p = 0.01) and the non-pAH group (p = 3.56 × 10−4) had lower nodal efficiency of the left medial superior frontal gyrus. Compared to the non-pAH group and HC group, the pAH group presented lower nodal efficiency of the temporal pole of the left superior temporal gyrus (p = 1.09 × 10−3; p = 7.67 × 10−4) and right insula (p = 0.02; p = 8.99 × 10−6), and lower degree centrality of the right insula (p = 0.04; p = 1.65 × 10−5). The pAH group had a subnetwork with reduced structural covariance centered by the left temporal pole of the superior temporal gyrus. In the pAH group, the normalized clustering coefficient (r = −0.36, p = 8.45 × 10−3) and small-worldness (r = −0.35, p = 9.89 × 10−3) were negatively correlated with the PANSS positive scale score. This study revealed network changes in schizophrenia patients with persistent auditory hallucinations, and provided new insights into the structural architecture related to auditory hallucinations at the network level.