Magnetic Resonance Imaging Analysis Research Articles

Whole-tumor histogram analysis of synthetic magnetic resonance imaging predicts isocitrate dehydrogenase mutation status in gliomas.

An accurate assessment of isocitrate dehydrogenase (IDH) status in patients with glioma is crucial for treatment planning and is a key factor in predicting patient outcomes. In this study, we investigated the potential value of whole-tumor histogram metrics derived from synthetic magnetic resonance imaging (MRI) in distinguishing IDH mutation status between astrocytoma and glioblastoma. In this prospective study, 80 glioma patients were enrolled from September 2019 to June 2022. All patients underwent pre- and post-contrast synthetic MRI scan protocol. Immunohistochemistry (IHC) staining or gene sequencing were used to assess IDH mutation status in tumor tissue samples. Whole-tumor histogram metrics, including T1, T2, proton density (PD), etc., were extracted from the quantitative maps, while radiological features were assessed by synthetic contrast-weighted maps. Basic clinical features of the patients were also evaluated. Differences in clinical, radiological, and histogram metrics between IDH-mutant astrocytoma and IDH-wildtype glioblastoma were analyzed using univariate analyses. Variables with statistical significance in univariate analysis were included in multivariate logistic regression analysis to develop the combined model. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to assess the diagnostic performance of metrics and models. The histopathologic analysis revealed that of the 80 cases, 41 were classified as IDH-mutant astrocytoma and 39 as IDH-wildtype glioblastoma. Compared to IDH-wildtype glioblastoma, IDH-mutant astrocytoma showed significantly lower T1 [10th percentile (10th), mean, and median] and post-contrast PD (10th, 90th percentile, mean, median, and maximum) values as well as higher post-contrast T1 (cT1) (10th, mean, median, and minimum) values (all P<0.05). The combined model (T1-10th + cT1-10th + age) was developed by integrating the independent influencing factors of IDH-mutant astrocytoma using the multivariate logistic regression. The diagnostic performance of this model [AUC =0.872 (0.778-0.936), sensitivity =75.61%, and specificity =89.74%] was superior to the clinicoradiological model, which was constructed using age and enhancement degree (AUC =0.822 (0.870-0.898), P=0.035). The combined model constructed using histogram metrics derived from synthetic MRI could be a valuable preoperative tool to distinguish IDH mutation status between astrocytoma and glioblastoma, and subsequently, could assist in the decision-making process of pretreatment.

Comparative evaluation of multiparametric lumbar MRI radiomic models for detecting osteoporosis

BackgroundOsteoporosis is a serious global public health issue. Currently, there are few studies that explore the use of multiparametric MRI radiomics for osteoporosis detection. The purpose of this study was to compare the performance of radiomics features from multiple MRI sequences (T1WI, T2WI and T1WI combined with T2WI) for detecting osteoporosis in patients.MethodsA retrospective analysis was performed on 160 patients who had undergone dual-energy X-ray absorptiometry(DXA) and lumbar magnetic resonance imaging (MRI) at our hospital. Among them, 86 patients were diagnosed with abnormal bone mass (osteoporosis or low bone mass), and 74 patients were diagnosed with normal bone mass based on the DXA results. Sagittal T1-and T2-weighted images of all patients were imported into the uAI Research Portal (United Imaging Intelligence) for image delineation and radiomics analysis, where a series of radiomic features were obtained. A radiomic model that included T1WI, T2WI, and T1WI+T2WI was established using features selected by LASSO regression. We used ROC curve analysis to evaluate the predictive efficacy of each model for identifying bone abnormalities and conducted decision curve analysis (DCA) to evaluate the net benefit of each model. Finally, we validated the model in a sample of 35 patients from different health care institution.ResultsThe T1WI + T2WI radiomics model showed better screening performance for patients with abnormal bone mass. In the training group, the sensitivity was 0.758, the specificity was 0.78, and the accuracy was 0.768 (AUC =0.839, 95% CI=0.757-0.901). In the validation group, the sensitivity was 0.792, the specificity was 0.875, and the accuracy was 0.833 (AUC =0.86, 95% CI=0.73-0.943).The DCA also showed that the combined model had better net benefits. In the external validation group, the sensitivity was 0.764, the specificity was 0.833, and the accuracy was 0.8 (AUC =0.824, 95% CI 0.678-0.969).ConclusionsRadiomics-based multiparametric MRI can be used for the quantitative analysis of lumbar MRI and for accurately screening patients with abnormal bone mass.

GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances.

The approach of using more than one processor to compute in order to overcome the complexity of different medical imaging methods that make up an overall job is known as GPU (graphic processing unit)-based parallel processing. It is extremely important for several medical imaging techniques such as image classification, object detection, image segmentation, registration, and content-based image retrieval, since the GPU-based parallel processing approach allows for time-efficient computation by a software, allowing multiple computations to be completed at once. On the other hand, a non-invasive imaging technology that may depict the shape of an anatomy and the biological advancements of the human body is known as magnetic resonance imaging (MRI). Implementing GPU-based parallel processing approaches in brain MRI analysis with medical imaging techniques might be helpful in achieving immediate and timely image capture. Therefore, this extended review (the extension of the IWBBIO2023 conference paper) offers a thorough overview of the literature with an emphasis on the expanding use of GPU-based parallel processing methods for the medical analysis of brain MRIs with the imaging techniques mentioned above, given the need for quicker computation to acquire early and real-time feedback in medicine. Between 2019 and 2023, we examined the articles in the literature matrix that include the tasks, techniques, MRI sequences, and processing results. As a result, the methods discussed in this review demonstrate the advancements achieved until now in minimizing computing runtime as well as the obstacles and problems still to be solved in the future.

In vivo microstructural heterogeneity of white matter and cognitive correlates in aging using tissue compositional analysis of diffusion magnetic resonance imaging.

Age-related cognitive decline is linked to changes in the brain, particularly the deterioration of white matter (WM) microstructure that accelerates after the age of 60. WM deterioration is associated with mild cognitive impairment and dementia, but the origin and role of white matter signal abnormalities (WMSA) seen in standard MRI remain debated due to their heterogeneity. This study explores the potential of single-shell 3-tissue constrained spherical deconvolution (SS3T-CSD), a novel technique that models diffusion data in terms of gray matter (TG ), white matter (Tw ), and cerebrospinal fluid (TC ), to differentiate WMSA from normal-appearing white matter and better understand the interplay between changes in WM microstructure and decline in cognition. A total of 189 individuals from the GENIC cohort were included. MRI data, including T1-weighted and diffusion images, were obtained. Preprocessing steps were performed on the diffusion MRI data, followed by the SS3T-CSD. WMSA were segmented using FreeSurfer. Statistical analyses were conducted to assess the association between age, WMSA volume, 3-tissue signal fractions (Tw , TG , and TC ), and neuropsychological variables. Participants above 60 years old showed worse cognitive performance and processing speed compared to those below 60 (p < .001). Age was negatively associated with Tw in normal-appearing white matter (p < .001) and positively associated with TG in both WMSA (p < .01) and normal-appearing white matter (p < .001). Age was also significantly associated with WMSA volume (p < .001). Higher processing speed was associated with lower Tw and higher TG , in normal-appearing white matter (p < .01 and p < .001, respectively), as well as increased WMSA volume (p < .001). Similarly, lower MMSE scores correlated with lower Tw and higher TG in normal-appearing white matter (p < .05). High cholesterol and hypertension were associated with higher WMSA volume (p < .05). The microstructural heterogeneity within normal-appearing white matter and WMSA is associated with increasing age and cognitive variation, in cognitively unimpaired individuals. Furthermore, the 3-tissue signal fractions are more specific to potential white matter alterations than conventional MRI measures such as WMSA volume. These findings also support the view that the WMSA volumes may be more influenced by vascular risk factors than the 3-tissue metrics. Finally, the 3-tissue metrics were able to capture associations with cognitive tests and therefore capable of capturing subtle pathological changes in the brain in individuals who are still within the normal range of cognitive performance.

Bi-allelic NIT1 variants cause a brain small vessel disease characterized by movement disorders, massively dilated perivascular spaces, and intracerebral hemorrhage

PurposeTo describe a recessively inherited cerebral small vessel disease, caused by loss-of-function variants in Nitrilase1 (NIT1). MethodsWe performed exome sequencing, brain magnetic resonance imaging, neuropathology, electron microscopy, western blotting, and transcriptomic and metabolic analyses in 7 NIT1-small vessel disease patients from 5 unrelated pedigrees. ResultsThe first identified patients were 3 siblings, compound heterozygous for the NIT1 c.727C>T; (p.Arg243Trp) variant and the NIT1 c.198_199del; p.(Ala68∗) variant. The 4 additional patients were single cases from 4 unrelated pedigrees and were all homozygous for the NIT1 c.727C>T; p.(Arg243Trp) variant. Patients presented in mid-adulthood with movement disorders. All patients had striking abnormalities on brain magnetic resonance imaging, with numerous and massively dilated basal ganglia perivascular spaces. Three patients had non-lobar intracerebral hemorrhage between age 45 and 60, which was fatal in 2 cases. Western blotting on patient fibroblasts showed absence of NIT1 protein, and metabolic analysis in urine confirmed loss of NIT1 enzymatic function. Brain autopsy revealed large electron-dense deposits in the vessel walls of small and medium sized cerebral arteries. ConclusionNIT1-small vessel disease is a novel, autosomal recessively inherited cerebral small vessel disease characterized by a triad of movement disorders, massively dilated basal ganglia perivascular spaces, and intracerebral hemorrhage.

A multi-cohort study of the hippocampal radiomics model and its associated biological changes in Alzheimer’s Disease

There have been no previous reports of hippocampal radiomics features associated with biological functions in Alzheimer’s Disease (AD). This study aims to develop and validate a hippocampal radiomics model from structural magnetic resonance imaging (MRI) data for identifying patients with AD, and to explore the mechanism underlying the developed radiomics model using peripheral blood gene expression. In this retrospective multi-study, a radiomics model was developed based on the radiomics discovery group (n = 420) and validated in other cohorts. The biological functions underlying the model were identified in the radiogenomic analysis group using paired MRI and peripheral blood transcriptome analyses (n = 266). Mediation analysis and external validation were applied to further validate the key module and hub genes. A 12 radiomics features-based prediction model was constructed and this model showed highly robust predictive power for identifying AD patients in the validation and other three cohorts. Using radiogenomics mapping, myeloid leukocyte and neutrophil activation were enriched, and six hub genes were identified from the key module, which showed the highest correlation with the radiomics model. The correlation between hub genes and cognitive ability was confirmed using the external validation set of the AddneuroMed dataset. Mediation analysis revealed that the hippocampal radiomics model mediated the association between blood gene expression and cognitive ability. The hippocampal radiomics model can accurately identify patients with AD, while the predictive radiomics model may be driven by neutrophil-related biological pathways.

A Phase 2 Trial of Primary Tumor Stereotactic Body Radiation Therapy Boost Before Concurrent Chemoradiation for Locally Advanced Non-Small Cell Lung Cancer

Primary tumor failure is common in patients treated with chemoradiation (CRT) for locally advanced NSCLC (LA-NSCLC). Stereotactic body radiation therapy (SBRT) yields high rates of primary tumor control (PTC) in early-stage NSCLC. This trial tested an SBRT boost to the primary tumor before the start of CRT to improve PTC. Patients with LA-NSCLC received an SBRT boost in 2 fractions (central location 12 Gy, peripheral location 16 Gy) to the primary tumor, followed by standard CRT (60 Gy in 30 fractions). The primary objective was PTC rate at 1 year, and the hypothesis was that the 1-year PTC rate would be ≥90%. Secondary objectives included objective response rate, regional and distant control, disease-free survival (DFS), and overall survival (OS). Correlative studies included functional magnetic resonance imaging and blood-based miRNA analysis. The study enrolled 21 patients (10 men and 11 women); the median age was 62 years (range, 52-78). The median pretreatment primary tumor size was 5.0 cm (range, 1.0-8.3). The most common nonhematologic toxicities were pneumonitis, fatigue, esophagitis/dysphagia, dyspnea, and cough. Only 1 treatment-related grade 4 nonhematologic toxicity occurred (respiratory failure/radiation pneumonitis), and no grade 5 toxicities occurred. The objective response rate at 3 and 6 months was 72.7% and 80.0%, respectively, and PTC at 1 and 2 years was 100% and 92.3%, respectively. The 2-year regional and distant control rates were 81.6% and 70.3%, respectively. Disease-free survival and overall survival at 2 years were 46.1% and 50.3%, respectively, and median survival was 37.8 months. Functional magnetic resonance imaging detected a mean relative decrease in blood oxygenation level-dependent signal of -87.1% (P = .05), and miR.142.3p was correlated with increased risk of grade ≥3 pulmonary toxicity (P = .01). Dose escalation to the primary tumor using upfront SBRT appears feasible and safe. PTC was high and other oncologic endpoints compared favorably to standard treatment. Functional magnetic resonance imaging suggested changes in oxygenation with the first SBRT boost dose, and miR.142.3p was correlated with pulmonary toxicity.

Asymmetric Deficit of Knee Flexion: An Occasional Symptom of Discoid Lateral Meniscus in Children.

A knee flexion deficit can be the unique symptom and sign of discoid lateral meniscus (DLM) in children. The aim of this study was to investigate the accuracy of this clinical sign. The hypothesis was that deficit of knee flexion was associated with anterior rim disinsertion and posteriorly fixed DLM. A retrospective, single-center study was conducted in a larger series of 114 operated knees for symptomatic DLM from 2007 to 2018. A focus was done on 9 knees with a deficit of knee flexion without snapping. History and clinical examination with grading of instability, preoperative magnetic resonance imaging, and arthroscopic findings were studied. Among 35 knees with DLM grade 3 of instability, 26 had an isolated extension deficit. Nine knees in 5 boys and 4 girls with a mean age of 9 years (range 6 to 12) had a flexion deficit. It was the only symptom of DLM in 6, and it was associated to extension deficit in 3. In the history, 8 knees were grade 2 (snapping knee) before evolving toward a flexion deficit. All had a complete DLM with posterocentral (n=7) or central (n=2) shift at magnetic resonance imaging analysis. All had DLM with posterior shift fixed during arthroscopic evaluation. Moreover, looking at the entire series, 1 knee was a false negative and had a full flexion despite a posterior and fixed DLM during arthroscopic evaluation. The sensitivity of asymmetrical knee flexion to predict posterior fixed DLM was 90%. An asymmetric deficit in knee flexion is highly suggestive of DLM with anterior detachment and fixed posterior meniscal shift (specificity and positive predictive value of 100%). Given this could be the only clinical sign of DLM, specific attention therefore must be paid in the assessment of knee flexion by measuring the distance between heel and bottom on both sides, especially in a knee without snapping currently but with a history of snapping (grade 3). Level III.

MRI-based prostate cancer classification using 3D efficient capsule network.

Prostate cancer (PCa) is the most common cancer in men and the second leading cause of male cancer-related death. Gleason score (GS) is the primary driver of PCa risk-stratification and medical decision-making, but can only be assessed at present via biopsy under anesthesia. Magnetic resonance imaging (MRI) is a promising non-invasive method to further characterize PCa, providing additional anatomical and functional information. Meanwhile, the diagnostic power of MRI is limited by qualitative or, at best, semi-quantitative interpretation criteria, leading to inter-reader variability. Computer-aided diagnosis employing quantitative MRI analysis has yielded promising results in non-invasive prediction of GS. However, convolutional neural networks (CNNs) do not implicitly impose a frame of reference to the objects. Thus, CNNs do not encode the positional information properly, limiting method robustness against simple image variations such as flipping, scaling, or rotation. Capsule network (CapsNet) has been proposed to address this limitation and achieves promising results in this domain. In this study, we develop a 3D Efficient CapsNet to stratify GS-derived PCa risk using T2-weighted (T2W) MRI images. In our method, we used 3D CNN modules to extract spatial features and primary capsule layers to encode vector features. We then propose to integrate fully-connected capsule layers (FC Caps) to create a deeper hierarchy for PCa grading prediction. FC Caps comprises a secondary capsule layer which routes active primary capsules and a final capsule layer which outputs PCa risk. To account for data imbalance, we propose a novel dynamic weighted margin loss. We evaluate our method on a public PCa T2W MRI dataset from the Cancer Imaging Archive containing data from 976 patients. Two groups of experiments were performed: (1) we first identified high-risk disease by classifying low + medium risk versus high risk; (2) we then stratified disease in one-versus-one fashion: low versus high risk, medium versus high risk, and low versus medium risk. Five-fold cross validation was performed. Our model achieved an area under receiver operating characteristic curve (AUC) of 0.83 and 0.64 F1-score for low versus high grade, 0.79 AUC and 0.75 F1-score for low + medium versus high grade, 0.75 AUC and 0.69 F1-score for medium versus high grade and 0.59 AUC and 0.57 F1-score for low versus medium grade. Our method outperformed state-of-the-art radiomics-based classification and deep learning methods with the highest metrics for each experiment. Our divide-and-conquer strategy achieved weighted Cohen's Kappa score of 0.41, suggesting moderate agreement with ground truth PCa risks. In this study, we proposed a novel 3D Efficient CapsNet for PCa risk stratification and demonstrated its feasibility. This developed tool provided a non-invasive approach to assess PCa risk from T2W MR images, which might have potential to personalize the treatment of PCa and reduce the number of unnecessary biopsies.

Effects of high-intensity training on fatty infiltration in paraspinal muscles in elderly males with osteosarcopenia – the randomized controlled FrOST study

BackgroundOsteosarcopenia is a common geriatric syndrome with an increasing prevalence with age, leading to secondary diseases and complex consequences such as falls and fractures, as well as higher mortality and frailty rates. There is a great need for prevention and treatment strategies.MethodsIn this analysis, we used magnetic resonance imaging (MRI) data from the randomised controlled FrOST trial, which enrolled community-dwelling osteosarcopenic men aged > 72 years randomly allocated to 16 months of twice-weekly high-intensity resistance training (HIRT) or a non-training control group. MR Dixon imaging was used to quantify the effects of HIRT on muscle fat infiltration in the paraspinal muscles, determined as changes in muscle tissue, fat faction and intermuscular adipose tissue (IMAT) in the erector spinae and psoas major muscles. Intention-to-treat analysis with multiple imputation was used to analyse the data set.ResultsAfter 16 months of intervention, 15 men from the HIRT and 16 men from the CG were included in the MRI analysis. In summary, no positive effects on the fat infiltration of the erector spinae and psoas major muscles were observed.ConclusionsThe previously reported positive effects on lumbar spine bone mineral density (BMD) suggest that mechanotransduction induces tropic effects on bone, but that fat infiltration of the erector spinae and psoas major muscles are either irreversible or, for some unknown reason, resistant to exercise. Because of the beneficial effects on spinal BMD, HIRT is still recommended in osteosarcopenic older men, but further research is needed to confirm appropriate age-specific training exercises for the paraspinal muscles. The potential of different MRI sequences to quantify degenerative and metabolic changes in various muscle groups must be better characterized.Trial registrationsFrOST was approved by the University Ethics Committee of the Friedrich-Alexander University of Erlangen-Nürnberg (number 67_15b and 4464b) and the Federal Office for Radiation Projection (BfS, number Z 5–2,246,212 – 2017–002). Furthermore, it fully complies with the Declaration of Helsinki and is registered at ClinicalTrials.gov: NCT03453463 (05/03/2018). JAMA 310:2191–2194, 2013.

The role of preoperative MRI in endoscopic transnasal transsphenoidal hypophysectomy of pituitary adenoma.

The trans-sphenoidal approach, commonly used for removing pituitary adenomas, has become a widely accepted and successful method. In recent years, the endoscopic trans-sphenoidal technique has emerged as a minimally invasive surgical approach for pituitary adenoma removal. The majority of pituitary adenomas exhibit a soft consistency and can be successfully extracted with aspiration and curettage using the trans-sphenoidal approach. However, a subset of around 5-15% of these adenomas possess a solid and fibrous texture. The occurrence of firm and fibrous adenomas is relatively common; unfortunately, there are no reliable predictors to identify them preoperatively. The ability to forecast the reliability of magnetic resonance imaging (MRI) holds promise for improving prior preparation and impacts the extent of resection. A cross-sectional analysis of the investigation of magnetic resonance imaging (MRI) in relation to cancer histology was performed on 68 patients who had endoscopic trans-nasal excision for nonfunctional adenomas. The determination of an intensity ratio was performed by employing quantitative estimates of MRI signal intensity obtained from both the adenoma and pons. During the surgical procedure, a series of sequential-graded procedures were used for the removal of tumours with varying consistencies. Softer tumours were addressed using the Suction technique (R1), while tumours of intermediate consistency were treated using curettes (R2). In order to evaluate the fibrotic content of firmer tumours, the utilization of Cavitron Ultrasound Surgical Aspirator(CUSA), and/or other micro-instruments (R3) was employed, with the histologic collagen fraction being quantified. In order to investigate and analyse the data, a statistical analysis was conducted. A predictive relationship between resection category and both intensity ratio, and collagen percentage was noted. The primary objective of this study was to determine the appropriate cutoff criteria for clinical utilization, as well as to investigate the association between intensity ratios and collagen percentage. Tumors with ratios ≤ 1.6 on the T2-weighted image and collagen content > 5.3% required more meticulous and sharp dissection for resection. The utilization of MRI analysis may offer some assistance, but not conclusive, in the prediction of tumour consistency.

Evaluation of glial tumors: correlation between magnetic resonance imaging and histopathological analysis.

To determine the correlation of conventional and diffusion-weighted imaging findings on magnetic resonance imaging (MRI) of the brain, based on Visually AcceSAble Rembrandt Images (VASARI) criteria, with the histopathological grading of gliomas: low-grade or high-grade. Preoperative MRI scans of 178 patients with brain gliomas and pathological confirmation were rated by two neuroradiologists for tumor size, location, and tumor morphology, using a standardized imaging feature set based on the VASARI criteria. In the univariate analysis, more than half of the MRI characteristics evaluated showed a significant association with the tumor grade. The characteristics most significantly associated with the tumor grade were hemorrhage; restricted diffusion; pial invasion; enhancement; and a non-contrast-enhancing tumor crossing the midline. In a multivariable regression model, the presence of enhancement and hemorrhage maintained a significant association with high tumor grade. The absence of contrast enhancement and restricted diffusion were associated with the presence of an isocitrate dehydrogenase gene mutation. Our data illustrate that VASARI MRI features, especially intratumoral hemorrhage, contrast enhancement, and multicentricity, correlate strongly with glial tumor grade.

Functional and morphological evaluation of the trapezius muscle after spinal accessory nerve transfer to brachial plexus nerves.

The main innervation of the trapezius muscle is provided by the spinal accessory nerve. Several studies describe the contributions of cervical plexus roots to the trapezius muscle innervation, either directly or through connections with the spinal accessory nerve. There is no adequate understanding of how the trapezius muscle is affected after using the spinal accessory nerve in nerve transfer procedures with the usual technique, preserving at least 1 branch for the upper trapezius. We evaluated 20 patients with sequelae of traumatic brachial plexus injury who underwent surgical procedures for brachial plexus repair or free muscle transfer, which included the spinal accessory nerve transfer technique and were followed for a minimum of 1 year. The three portions trapezius muscle were evaluated by physical examination, magnetic resonance imaging (analysis of fatty degeneration) and electromyography. In all evaluation methods, the middle and lower portions of the trapezius muscle showed more significant morphological and/or functional impairment than the upper portion, in most cases. There was a statistically significant difference in all the complementary exams results, between the affected side (with sacrifice of the nerve) versus the normal side, in the middle and lower portions of the trapezius muscle. Physical examination alone is not sufficient to determine the residual functionality of the trapezius muscle. Magnetic resonance imaging and electromyography are useful tools to assess both morphological involvement of the trapezius muscle and nerve conduction impairment of the trapezius muscle, respectively. The results suggest that the middle and lower portions of the trapezius muscle are affected by previous SAN transfer and should be considered with caution for further muscle transfer procedures.

Cerebral Cortical Surface Structure and Neural Activation Pattern Among Adolescent Football Players

Recurring exposure to head impacts in American football has garnered public and scientific attention, yet neurobiological associations in adolescent football players remain unclear. To examine cortical structure and neurophysiological characteristics in adolescent football players. This cohort study included adolescent football players and control athletes (swimming, cross country, and tennis) from 5 high school athletic programs, who were matched with age, sex (male), and school. Neuroimaging assessments were conducted May to July of the 2021 and 2022 seasons. Data were analyzed from February to November 2023. Playing tackle football or noncontact sports. Structural magnetic resonance imaging (MRI) data were analyzed for cortical thickness, sulcal depth, and gyrification, and cortical surface-based resting state (RS)-functional MRI analyses examined the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and RS-functional connectivity (RS-FC). Two-hundred seventy-five male participants (205 football players; mean [SD] age, 15.8 [1.2] years; 5 Asian [2.4%], 8 Black or African American [3.9%], and 189 White [92.2%]; 70 control participants; mean [SD] age 15.8 [1.2] years, 4 Asian [5.7], 1 Black or African American [1.4%], and 64 White [91.5%]) were included in this study. Relative to the control group, the football group showed significant cortical thinning, especially in fronto-occipital regions (eg, right precentral gyrus: t = -2.24; P = .01; left superior frontal gyrus: -2.42; P = .002). Elevated cortical thickness in football players was observed in the anterior and posterior cingulate cortex (eg, left posterior cingulate cortex: t = 2.28; P = .01; right caudal anterior cingulate cortex 3.01; P = .001). The football group had greater and deeper sulcal depth than the control groups in the cingulate cortex, precuneus, and precentral gyrus (eg, right inferior parietal lobule: t = 2.20; P = .004; right caudal anterior cingulate cortex: 4.30; P < .001). Significantly lower ALFF was detected in the frontal lobe and cingulate cortex of the football group (t = -3.66 to -4.92; P < .01), whereas elevated ALFF was observed in the occipital regions (calcarine and lingual gyrus, t = 3.20; P < .01). Similar to ALFF, football players exhibited lower ReHo in the precentral gyrus and medial aspects of the brain, such as precuneus, insula, and cingulum, whereas elevated ReHo was clustered in the occipitotemporal regions (t = 3.17; P < .001; to 4.32; P < .01). There was no group difference in RS-FC measures. In this study of adolescent athletes, there was evidence of discernible structural and physiological differences in the brains of adolescent football players compared with their noncontact controls. Many of the affected brain regions were associated with mental health well-being.

Joint diffusional kurtosis magnetic resonance imaging analysis of white matter and the thalamus to identify subcortical ischemic vascular disease

Identifying subcortical ischemic vascular disease (SIVD) in older adults is important but challenging. Growing evidence suggests that diffusional kurtosis imaging (DKI) can detect SIVD-relevant microstructural pathology, and a systematic assessment of the discriminant power of DKI metrics in various brain tissue microstructures is urgently needed. Therefore, the current study aimed to explore the value of DKI and diffusion tensor imaging (DTI) metrics in detecting early-stage SIVD by combining multiple diffusion metrics, analysis strategies, and clinical-radiological constraints. This prospective study compared DKI with diffusivity and macroscopic imaging evaluations across the aging spectrum including SIVD, Alzheimer's disease (AD), and cognitively normal (NC) groups. Using a white matter atlas and segregated thalamus analysis with considerations of the pre-identified macroscopic pathology, the most effective diffusion metrics were selected and then examined using multiple clinical-radiological constraints in a two-group or three-group paradigm. A total of 122 participants (mean age, 74.6 ± 7.38 years, 72 women) including 42 with SIVD, 50 with AD, and 30 NC were evaluated. Fractional anisotropy, mean kurtosis, and radial kurtosis were critical metrics in detecting early-stage SIVD. The optimal selection of diffusion metrics showed 84.4–100% correct classification of the results in a three-group paradigm, with an area under the curve of .909–.987 in a two-group paradigm related to SIVD detection (all P < .001). We therefore concluded that greatly resilient to the effect of pre-identified macroscopic pathology, the combination of DKI/DTI metrics showed preferable performance in identifying early-stage SIVD among adults across the aging spectrum.

Noninvasive Assessment of Kidney Injury by Combining Structure and Function Using Artificial Intelligence-Based Manganese-Enhanced Magnetic Resonance Imaging.

Contrast-enhanced magnetic resonance imaging (MRI) is seriously limited in kidney injury detection due to the nephrotoxicity of clinically used gadolinium-based contrast agents. Herein, we propose a noninvasive method for the assessment of kidney injury by combining structure and function information based on manganese (Mn)-enhanced MRI for the first time. As a proof of concept, the Mn-melanin nanoprobe with good biocompatibility and excellent T1 relaxivity is applied in MRI of a unilateral ureteral obstruction mice model. The abundant renal structure and function information is obtained through qualitative and quantitative analysis of MR images, and a brand new comprehensive assessment framework is proposed to precisely identify the degree of kidney injury successfully. Our study demonstrates that Mn-enhanced MRI is a promising approach for the highly sensitive and biosafe assessment of kidney injury in vivo.

Ridge-Type Pretest and Shrinkage Estimation Strategies in Spatial Error Models with an Application to a Real Data Example

Spatial regression models are widely available across several disciplines, such as functional magnetic resonance imaging analysis, econometrics, and house price analysis. In nature, sparsity occurs when a limited number of factors strongly impact overall variation. Sparse covariance structures are common in spatial regression models. The spatial error model is a significant spatial regression model that focuses on the geographical dependence present in the error terms rather than the response variable. This study proposes an effective approach using the pretest and shrinkage ridge estimators for estimating the vector of regression coefficients in the spatial error mode, considering insignificant coefficients and multicollinearity among regressors. The study compares the performance of the proposed estimators with the maximum likelihood estimator and assesses their efficacy using real-world data and bootstrapping techniques for comparison purposes.

Reward Learning as a Potential Mechanism for Improvement in Schizophrenia Spectrum Disorders Following Cognitive Remediation: Protocol for a Clinical, Nonrandomized, Pre-Post Pilot Study.

Cognitive impairment is common with schizophrenia spectrum disorders. Cognitive remediation (CR) is effective in improving global cognition, but not all individuals benefit from this type of intervention. A better understanding of the potential mechanism of action of CR is needed. One proposed mechanism is reward learning (RL), the cognitive processes responsible for adapting behavior following positive or negative feedback. It is proposed that the structure of CR enhances RL and motivation to engage in increasingly challenging tasks, and this is a potential mechanism by which CR improves cognitive functioning in schizophrenia. Our primary objective is to examine reward processing in individuals with schizophrenia before and after completing CR and to compare this with a group of matched clinical controls. We will assess whether RL mediates the relationship between CR and improved cognitive function and reduced negative symptoms. Potential differences in social RL and nonsocial RL in individuals with schizophrenia will also be investigated and compared with a healthy matched control group. We propose a clinical, nonrandomized, pre-post pilot study comparing the impact of CR on RL and neurocognitive outcomes. The study will use a combination of objective and subjective measures to assess neurocognitive, psychiatric symptoms, and neurophysiological domains. A total of 40 individuals with schizophrenia spectrum disorders (aged 18-35 years) will receive 12 weeks of CR therapy (n=20) or treatment as usual (n=20). Reward processing will be evaluated using a reinforcement learning task with 2 conditions (social reward vs nonsocial reward) at baseline and the 12-week follow-up. Functional magnetic resonance imaging responses will be measured during this task. To validate the reinforcement learning task, RL will also be assessed in 20 healthy controls, matched for age, sex, and premorbid functioning. Mixed-factorial ANOVAs will be conducted to evaluate treatment group differences. For the functional magnetic resonance imaging analysis, computational modeling will allow the estimation of learning parameters at each point in time, during each task condition, for each participant. We will use a variational Bayesian framework to measure how learning occurred during the experimental task and the subprocesses that underlie this learning. Second-level group analyses will examine how learning in patients differs from that observed in control participants and how CR alters learning efficiency and the underlying neural activity. As of September 2023, this study has enrolled 15 participants in the CR group, 1 participant in the treatment-as-usual group, and 11 participants in the healthy control group. Recruitment is expected to be completed by September 2024. Data analysis is expected to be completed and published in early 2025. The results of this study will contribute to the knowledge of CR and RL processes in severe mental illness and the understanding of the systems that impact negative symptoms and cognitive impairments within this population. DERR1-10.2196/52505.

Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity.

Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e., T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images.

Effects of different isochoric freeze-thaw cycles on the physicochemical quality attributes of chicken breast meat

This study investigated the impact of isochoric freezing-thawing (ISF-T) cycles (C0–C5) on the quality of chicken breast. The results indicated a significant increase (P < 0.05) in pH from 5.41 to 5.96 after C1 to C4 before slightly decreasing to 5.72 (C5). Lightness (L*) varied between 50.13 and 44.55 across C1 to C4, with redness notably decreasing from −1.15 after C0 to −2.09 after C5 while yellowness (b) sharply increased from 2.65 (C0) to 4.65 (C5). Moisture content decreased from 74.01% to 68.13% after C5. WHC slightly reduced from 89.25% after C0 to 85.66% after C4, with an 83.78% reduction after C5. Cooking loss increased linearly across the ISF-T cycles from 18.08% reaching 24.14% after C5. Conversely, shear force decreased to 21.83 N after C5 from 25.21 N after C1. Protein solubility slowly increased to 88.79% after C3, then decreased to 72.84 after C5. The myofibrillar index remained stable until C3, then increased from 35.51 after C5. Lipid oxidation slowly increased to 0.27 mg MDA/kg after C3, then sharply increased to 0.44 mg MDA/kg after C5. In addition, Ca2+-ATPase activity showed that the samples obtained after C1 to C3 fluctuated between 23.59% and 24.71%, followed by a substantial reduction to 12.35% after C5. Furthermore, validation of the results by magnetic resonance imaging and nuclear magnetic resonance analysis confirmed the findings, suggesting that isochoric freezing could offer a viable solution to control temperature fluctuations, a challenging problem during meat storage and distribution in the cold chain food industry.