Magnetic Resonance Imaging Biomarkers Research Articles

Dorsal root ganglion MRI imaging biomarker correlations with pain in Fabry disease

Abstract Fabry disease is a rare monogenetic, X-linked lysosomal storage disorder with neuropathic pain as one characteristic symptom. Impairment of the enzyme alpha-galactosidase A leads to an accumulation of globotriaosylceramide in the dorsal root ganglia. Here, we investigate novel dorsal root ganglia MR imaging biomarkers and their association with Fabry genotype and pain phenotype. In this prospective study, 89 Fabry patients were examined using a standardised 3T MRI protocol of the dorsal root ganglia. Fabry pain was assessed through a validated Fabry pain questionnaire. The genotype was determined by diagnostic sequencing of the alpha-galactosidase A gene. MR imaging endpoints were dorsal root ganglia volume by voxel-wise morphometric analysis and dorsal root ganglia T2 signal. Reference groups included 55 healthy subjects and Fabry patients of different genotype categories without Fabry pain. In patients with Fabry pain, T2 signal of the dorsal root ganglia was increased by +39.2% compared to healthy controls (P = 0.001) and by +29.4% compared to painless Fabry disease (P = 0.017). This effect was pronounced in hemizygous men (+40.7% compared to healthy; P = 0.008 and +29.1% compared to painless; P = 0.032) and was consistently observed across the genotype spectrum of nonsense (+38.1% compared to healthy, P < 0.001) and missense mutations (+39.2% compared to healthy; P = 0.009). T2 signal of dorsal root ganglia and globotriaosylsphingosine levels were the only independent predictors of Fabry pain (P = 0.047; P = 0.002). Volume of dorsal root ganglia was enlarged by +46.0% in Fabry men in the nonsense compared to missense genotype category (P = 0.005) and by +34.5% compared to healthy controls (P = 0.034). In painful Fabry disease, MRI T2 signal of dorsal root ganglia is increased across different genotypes. Dorsal root ganglion MRI T2 signal as a novel in vivo imaging biomarker may help to better understand whether Fabry pain is modulated or even caused by dorsal root ganglion pathology.

Combined blood Neurofilament light chain and third ventricle width to differentiate Progressive Supranuclear Palsy from Parkinson's Disease: A machine learning study

IntroductionDifferentiating Progressive Supranuclear Palsy (PSP) from Parkinson's Disease (PD) may be clinically challenging. In this study, we explored the performance of machine learning models based on MR imaging and blood molecular biomarkers in distinguishing between these two neurodegenerative diseases. MethodsTwenty-eight PSP patients, 46 PD patients and 60 control subjects (HC) were consecutively enrolled in the study. Serum concentration of neurofilament light chain protein (Nf-L) was assessed by single molecule array (SIMOA), while an automatic segmentation algorithm was employed for T1-weighted measurements of third ventricle width/intracranial diameter ratio (3rdV/ID). Machine learning (ML) models with Logistic Regression (LR), Random Forest (RF), and XGBoost algorithms based on 3rdV/ID and serum Nf-L levels were tested in distinguishing among PSP, PD and HC. ResultsPSP patients showed higher serum Nf-L levels and larger 3rdV/ID ratio in comparison with both PD and HC groups (p < 0.005). All ML algorithms (LR, RF and XGBoost) showed that the combination of MRI and blood biomarkers had excellent classification performances in differentiating PSP from PD (AUC ≥0.92), outperforming each biomarker used alone (AUC: 0.85–0.90). Among the different algorithms, XGBoost was slightly more powerful than LR and RF in distinguishing PSP from PD patients, reaching AUC of 0.94 ± 0.04. ConclusionOur findings highlight the usefulness of combining blood and simple linear MRI biomarkers to accurately distinguish between PSP and PD patients. This multimodal approach may play a pivotal role in patient management and clinical decision-making, paving the way for more effective and timely interventions in these neurodegenerative diseases.

Multiparametric prenatal imaging characterization of fetal brain edema in Chiari II malformation might help to select candidates for fetal surgery.

To identify brain edema in fetuses with Chiari II malformation using a multiparametric approach including structural T2-weighted, diffusion tensor imaging (DTI) metrics, and MRI-based radiomics. A single-center retrospective review of MRI scans obtained in fetuses with Chiari II was performed. Brain edema cases were radiologically identified using the following MR criteria: brain parenchymal T2 prolongation, blurring of lamination, and effacement of external CSF spaces. Fractional anisotropy (FA) values were calculated from regions of interest (ROI), including hemispheric parenchyma, internal capsule, and corticospinal tract, and compared group-wise. After 1:1 age matching and manual single-slice 2D segmentation of the fetal brain parenchyma using ITK-Snap, radiomics features were extracted using pyradiomics. Areas under the curve (AUCs) of the features regarding discriminating subgroups were calculated. Ninety-one fetuses with Chiari II underwent a total of 101 MRI scans at a median gestational age of 24.4 weeks and were included. Fifty scans were visually classified as Chiari II with brain edema group and showed significantly reduced external CSF spaces compared to the nonedema group (9.8 vs. 18.3 mm, p < 0.001). FA values of all used ROIs were elevated in the edema group (p < 0.001 for all ROIs). The 10 most important radiomics features showed an AUC of 0.81 (95%CI: 0.71, 0.91) for discriminating between Chiari II fetuses with and without edema. Brain edema in fetuses with Chiari II is common and radiologically detectable on T2-weighted fetal MRI sequences, and DTI-based FA values and radiomics features provide further evidence of microstructure differences between subgroups with and without edema. A more severe phenotype of fetuses with Chiari II malformation is characterized by prenatal brain edema and more postnatal clinical morbidity and disability. Fetal brain edema is a promising prenatal MR imaging biomarker candidate for optimizing the risk-benefit evaluation of selection for fetal surgery. Brain edema of fetuses prenatally diagnosed with Chiari II malformation is a common, so far unknown, association. DTI metrics and radiomics confirm microstructural differences between the brains of Chiari II fetuses with and without edema. Fetal brain edema may explain worse motor outcomes in this Chiari II subgroup, who may substantially benefit from fetal surgery.

Quantitative MR imaging biomarkers for distinguishing inflammatory pancreatic mass and pancreatic cancer-a systematic review and meta-analysis.

To evaluate the diagnostic performance of quantitative magnetic resonance (MR) imaging biomarkers in distinguishing between inflammatory pancreatic masses (IPM) and pancreatic cancer (PC). A literature search was conducted using PubMed, Embase, the Cochrane Library, and Web of Science through August 2023. Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to evaluate the risk of bias and applicability of the studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated using the DerSimonian-Laird method. Univariate meta-regression analysis was used to identify the potential factors of heterogeneity. Twenty-four studies were included in this meta-analysis. The two main types of IPM, mass-forming pancreatitis (MFP) and autoimmune pancreatitis (AIP), differ in their apparent diffusion coefficient (ADC) values. Compared with PC, the ADC value was higher in MFP but lower in AIP. The pooled sensitivity/specificity of ADC were 0.80/0.85 for distinguishing MFP from PC and 0.82/0.84 for distinguishing AIP from PC. The pooled sensitivity/specificity for the maximal diameter of the upstream main pancreatic duct (dMPD) was 0.86/0.74, with a cutoff of dMPD ≤ 4 mm, and 0.97/0.52, with a cutoff of dMPD ≤ 5 mm. The pooled sensitivity/specificity for perfusion fraction (f) was 0.82/0.68, and 0.82/0.77 for mass stiffness values. Quantitative MR imaging biomarkers are useful in distinguishing between IPM and PC. ADC values differ between MFP and AIP, and they should be separated for consideration in future studies. Quantitative MR parameters could serve as non-invasive imaging biomarkers for differentiating malignant pancreatic neoplasms from inflammatory masses of the pancreas, and hence help to avoid unnecessary surgery. • Several quantitative MR imaging biomarkers performed well in differential diagnosis between inflammatory pancreatic mass and pancreatic cancer. • The ADC value could discern pancreatic cancer from mass-forming pancreatitis or autoimmune pancreatitis, if the two inflammatory mass types are not combined. • The diameter of main pancreatic duct had the highest specificity for differentiating autoimmune pancreatitis from pancreatic cancer.

Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study.

Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach. We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers. The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls. The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%). The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.

Efficacy of pharmacologic interventions on magnetic resonance imaging biomarkers in patients with nonalcoholic fatty liver disease: systematic review and network meta‐analysis

Efficacy of pharmacologic interventions on magnetic resonance imaging biomarkers in patients with nonalcoholic fatty liver disease: systematic review and network meta‐analysis

Pathophysiologic Mapping of Chronic Liver Diseases With Longitudinal Multiparametric MRI in Animal Models.

Chronic liver diseases (CLDs) have diverse etiologies. To better classify CLDs, we explored the ability of longitudinal multiparametric MRI (magnetic resonance imaging) in depicting alterations in liver morphology, inflammation, and hepatocyte and macrophage activity in murine high-fat diet (HFD)- and carbon tetrachloride (CCl4)-induced CLD models. Mice were either untreated, fed an HFD for 24 weeks, or injected with CCl4 for 8 weeks. Longitudinal multiparametric MRI was performed every 4 weeks using a 7 T MRI scanner, including T1/T2 relaxometry, morphological T1/T2-weighted imaging, and fat-selective imaging. Diffusion-weighted imaging was applied to assess fibrotic remodeling and T1-weighted and T2*-weighted dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI using gadoxetic acid and ferucarbotran to target hepatocytes and the mononuclear phagocyte system, respectively. Imaging data were associated with histopathological and serological analyses. Principal component analysis and clustering were used to reveal underlying disease patterns. The MRI parameters significantly correlated with histologically confirmed steatosis, fibrosis, and liver damage, with varying importance. No single MRI parameter exclusively correlated with 1 pathophysiological feature, underscoring the necessity for using parameter patterns. Clustering revealed early-stage, model-specific patterns. Although the HFD model exhibited pronounced liver fat content and fibrosis, the CCl4 model indicated reduced liver fat content and impaired hepatocyte and macrophage function. In both models, MRI biomarkers of inflammation were elevated. Multiparametric MRI patterns can be assigned to pathophysiological processes and used for murine CLD classification and progression tracking. These MRI biomarker patterns can directly be explored clinically to improve early CLD detection and differentiation and to refine treatments.

The genetic basis of early-onset hereditary ataxia in Iran: results of a national registry of a heterogeneous population

BackgroundTo investigate the genetics of early-onset progressive cerebellar ataxia in Iran, we conducted a study at the Children’s Medical Center (CMC), the primary referral center for pediatric disorders in the country, over a three-year period from 2019 to 2022. In this report, we provide the initial findings from the national registry.MethodsWe selected all early-onset patients with an autosomal recessive mode of inheritance to assess their phenotype, paraclinical tests, and genotypes. The clinical data encompassed clinical features, the Scale for the Assessment and Rating of Ataxia (SARA) scores, Magnetic Resonance Imaging (MRI) results, Electrodiagnostic exams (EDX), and biomarker features. Our genetic investigations included single-gene testing, Whole Exome Sequencing (WES), and Whole Genome Sequencing (WGS).ResultsOur study enrolled 162 patients from various geographic regions of our country. Among our subpopulations, we identified known and novel pathogenic variants in 42 genes in 97 families. The overall genetic diagnostic rate was 59.9%. Notably, we observed PLA2G6, ATM, SACS, and SCA variants in 19, 14, 12, and 10 families, respectively. Remarkably, more than 59% of the cases were attributed to pathogenic variants in these genes.ConclusionsIran, being at the crossroad of the Middle East, exhibits a highly diverse genetic etiology for autosomal recessive hereditary ataxia. In light of this heterogeneity, the development of preventive strategies and targeted molecular therapeutics becomes crucial. A national guideline for the diagnosis and management of patients with these conditions could significantly aid in advancing healthcare approaches and improving patient outcomes.

The AUstralian multidomain Approach to Reduce dementia Risk by prOtecting brain health With lifestyle intervention study (AU-ARROW): A study protocol for a single-blind, multi-site, randomized controlled trial.

The Finnish Geriatric Intervention Study (FINGER) led to the global dementia risk reduction initiative: World-Wide FINGERS (WW-FINGERS). As part of WW-FINGERS, the Australian AU-ARROW study mirrors aspects of FINGER, as well as US-POINTER. AU-ARROW is a randomized, single-blind, multisite, 2-year clinical trial (n = 600; aged 55-79). The multimodal lifestyle intervention group will engage in aerobic exercise, resistance training and stretching, dietary advice to encourage MIND diet adherence, BrainHQ cognitive training, and medical monitoring and health education. The Health Education and Coaching group will receive occasional health education sessions. The primary outcome measure is the change in a global composite cognitive score. Extra value will emanate from blood biomarker analysis, positron emission tomography (PET) imaging, brain magnetic resonance imaging (MRI), and retinal biomarker tests. The finalized AU-ARROW protocol is expected to allow development of an evidence-based innovative treatment plan to reduce cognitive decline and dementia risk, and effective transfer of research outcomes into Australian health policy. Study protocol for a single-blind, randomized controlled trial, the AU-ARROW Study.The AU-ARROW Study is a member of the World-Wide FINGERS (WW-FINGERS) initiative.AU-ARROW's primary outcome measure is change in a global composite cognitive score.Extra significance from amyloid PET imaging, brain MRI, and retinal biomarker tests.Leading to development of an innovative treatment plan to reduce cognitive decline.

248 Isolated Traumatic Subarachnoid Hemorrhage on Head Computed Tomography Scan May Not Be Isolated

INTRODUCTION: Isolated traumatic subarachnoid hemorrhage (tSAH) after traumatic brain injury (TBI) on head computed tomography (CT) is often regarded as a “mild” injury, with reduced need for additional workup. However, tSAH is also a predictor of unfavorable outcomes. Magnetic resonance imaging (MRI) and blood-based biomarkers may improve the diagnosis of concurrent intracranial injury. METHODS: The prospective, 18-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury study enrolled patients presenting to ED within 24 hours (h) of TBI who received a clinically-indicated head CT. A subset underwent venipuncture within 24h for plasma glial fibrillary acidic protein (GFAP) analysis, and 2-week research MRI. Participants aged = 17-years with GCS 13-15, isolated tSAH on initial CT, GFAP, and MRI data were analyzed. RESULTS: In 57 patients, median age was 46.0-years [quartile 1 to 3 (Q1-Q3): 34-57] and 53% were male. At ED disposition, 12.3% were discharged home, 61.4% admitted to hospital ward, and 26.3% to intensive care.MRI identified additional traumatic intracranial lesions in 45.6% of patients (1 type: 31.6%; 2 types: 14.0%); of these, 65.4% had axonal injury, 42.3% subdural hematoma, and 23.1% contusion. GFAP levels were higher in patients with additional MRI lesions (median: 630.6-pg/ml, Q1-Q3: [172.4-941.2] vs. 226.4 [105.8-436.1], p = 0.049), and correlated with axonal injury severity (none: median 226.7 [109.6-435.1], traumatic axonal injury: 716.3 [304.0-892.3], diffuse axonal injury: 873.2 [407.2-1321.4], p = 0.034). CONCLUSIONS: Isolated tSAH on head CT is insufficient to rule out additional traumatic injuries in GCS 13-15 TBI. Forty-six percent of patients had additional traumatic lesions on MRI. Plasma GFAP may be an important biomarker for the identification of additional injuries, including axonal injury. Patients with isolated tSAH on CT may require close clinical surveillance and follow-up.

Parkinson's disease and Parkinsonism syndromes: Evaluating iron deposition in the putamen using magnetic susceptibility MRI techniques - A systematic review and literature analysis

Magnetic resonance imaging (MRI) techniques, such as quantitative susceptibility mapping (QSM) and susceptibility-weighted imaging (SWI), can detect iron deposition in the brain. Iron accumulation in the putamen (PUT) can contribute to the pathogenesis of Parkinson's disease (PD) and atypical Parkinsonian disorders. This systematic review aimed to synthesize evidence on iron deposition in the PUT assessed by MRI susceptibility techniques in PD and Parkinsonism syndromes. The PubMed and Scopus databases were searched for relevant studies. Thirty-four studies from January 2007 to October 2023 that used QSM, SWI, or other MRI susceptibility methods to measure putaminal iron in PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and healthy controls (HCs) were included. Most studies have found increased putaminal iron levels in PD patients versus HCs based on higher quantitative susceptibility. Putaminal iron accumulation correlates with worse motor scores and cognitive decline in patients with PD. Evidence regarding differences in susceptibility between PD and atypical Parkinsonism is emerging, with several studies showing greater putaminal iron deposition in PSP and MSA than in PD patients. Alterations in putaminal iron levels help to distinguish these disorders from PD. Increased putaminal iron levels appear to be associated with increased disease severity and progression. Thus, magnetic susceptibility MRI techniques can detect abnormal iron accumulation in the PUT of patients with Parkinsonism. Moreover, quantifying putaminal susceptibility may serve as an MRI biomarker to monitor motor and cognitive changes in PD and aid in the differential diagnosis of Parkinsonian disorders.

Evolution of European prostate cancer screening protocols and summary of ongoing trials.

Population-based organised repeated screening for prostate cancer has been found to reduce disease-specific mortality, but with substantial overdiagnosis leading to overtreatment. Although only very few countries have implemented a screening programme on a national level, individual prostate-specific antigen (PSA) testing is common. This opportunistic testing may have little favourable impact, while stressing the side-effects. The classic early detection protocols as were state-of-the-art in the 1990s applied a PSA and digital rectal examination threshold for sextant systematic prostate biopsy, with a fixed interval for re-testing, and limited indication for expectant management. In the three decades since these trials were started, different important improvements have become available in the cascade of screening, indication for biopsy, and treatment. The main developed aspects include: better identification of individuals at risk (using early/baseline PSA, family history, and/or genetic profile), individualised re-testing interval, optimised and individualised starting and stopping age, with gradual invitation at a fixed age rather than invitation of a wider range of age groups, risk stratification for biopsy (using PSA density, risk calculator, magnetic resonance imaging, serum and urine biomarkers, or combinations/sequences), targeted biopsy, transperineal biopsy approach, active surveillance for low-risk prostate cancer, and improved staging of disease. All these developments are suggested to decrease the side-effects of screening, while at least maintaining the advantages, but Level 1 evidence is lacking. The knowledge gained and new developments on early detection are being tested in different prospective screening trials throughout Europe. In addition, the European Union-funded PRostate cancer Awareness and Initiative for Screening in the European Union (PRAISE-U) project will compare and evaluate different screening pilots throughout Europe. Implementation and sustainability will also be addressed. Modern screening approaches may reduce the burden of the second most frequent cause of cancer-related death in European males, while minimising side-effects. Also, less efficacious opportunistic early detection may be indirectly reduced.

Diffusion MRI relates to plasma Aβ42/40 in PET negative participants without dementia.

Magnetic resonance imaging (MRI) biomarkers are needed for indexing early biological stages of Alzheimer's disease (AD), such as plasma amyloid-β (Aβ42/40) positivity in Aβ positron emission tomography (PET) negative individuals. Diffusion free-water (FW) MRI was acquired in individuals with normal cognition (NC) and mild cognitive impairment (MCI) with Aβ plasma-/PET- (NC=22, MCI=60), plasma+/PET- (NC=5, MCI=20), and plasma+/PET+ (AD dementia=21) biomarker status. Gray and white matter FW and fractional anisotropy (FAt) were compared cross-sectionally and the relationships between imaging, plasma and PET biomarkers were assessed. Plasma+/PET- demonstrated increased FW (24 regions) and decreased FAt (66 regions) compared to plasma-/PET-. FW (16 regions) and FAt (51 regions) were increased in plasma+/PET+ compared to plasma+/PET-. Composite brain FW correlated with plasma Aβ42/40 and p-tau181. FW imaging changes distinguish plasma Aβ42/40 positive and negative groups, independent of group differences in cognitive status, Aβ PET status, and other plasma biomarkers (i.e., t-tau, p-tau181, glial fibrillary acidic protein, neurofilament light). Plasma Aβ42/40 positivity is associated with brain microstructure decline. Plasma+/PET- demonstrated increased FW in 24 total GM and WM regions. Plasma+/PET- demonstrated decreased FAt in 66 total GM and WM regions. Whole-brain FW correlated with plasma Aβ42/40 and p-tau181 measures. Plasma+/PET- demonstrated decreased cortical volume and thickness.

Reliable Prostate Cancer Risk Mapping From MRI Using Targeted and Systematic Core Needle Biopsy Histopathology.

To compute a dense prostate cancer risk map for the individual patient post-biopsy from magnetic resonance imaging (MRI) and to provide a more reliable evaluation of its fitness in prostate regions that were not identified as suspicious for cancer by a human-reader in pre- and intra-biopsy imaging analysis. Low-level pre-biopsy MRI biomarkers from targeted and non-targeted biopsy locations were extracted and statistically tested for representativeness against biomarkers from non-biopsied prostate regions. A probabilistic machine learning classifier was optimized to map biomarkers to their core-level pathology, followed by extrapolation of pathology scores to non-biopsied prostate regions. Goodness-of-fit was assessed at targeted and non-targeted biopsy locations for the post-biopsy individual patient. Our experiments showed high predictability of imaging biomarkers in differentiating histopathology scores in thousands of non-targeted core-biopsy locations (ROC-AUCs: 0.85-0.88), but also high variability between patients (Median ROC-AUC [IQR]: 0.81-0.89 [0.29-0.40]). The sparseness of prostate biopsy data makes the validation of a whole gland risk mapping a non-trivial task. Previous studies i) focused on targeted-biopsy locations although biopsy-specimens drawn from systematically scattered locations across the prostate constitute a more representative sample to non-biopsied regions, and ii) estimated prediction-power across predicted instances (e.g., biopsy specimens) with no patient distinction, which may lead to unreliable estimation of model fitness to the individual patient due to variation between patients in instance count, imaging characteristics, and pathologies. This study proposes a personalized whole-gland prostate cancer risk mapping post-biopsy to allow clinicians to better stage and personalize focal therapy treatment plans.

Histologic tau lesions and magnetic resonance imaging biomarkers differ across two progressive supranuclear palsy variants.

Progressive supranuclear palsy is a neurodegenerative disease characterized by the deposition of four-repeat tau in neuronal and glial lesions in the brainstem, cerebellar, subcortical and cortical brain regions. There are varying clinical presentations of progressive supranuclear palsy with different neuroimaging signatures, presumed to be due to different topographical distributions and burden of tau. The classic Richardson syndrome presentation is considered a subcortical variant, whilst progressive supranuclear palsy with predominant speech and language impairment is considered a cortical variant, although the pathological underpinnings of these variants are unclear. In this case-control study, we aimed to determine whether patterns of regional tau pathology differed between these variants and whether tau burden correlated with neuroimaging. Thirty-three neuropathologically confirmed progressive supranuclear palsy patients with either the Richardson syndrome (n = 17) or speech/language (n = 16) variant and ante-mortem magnetic resonance imaging were included. Tau lesion burden was semi-quantitatively graded in cerebellar, brainstem, subcortical and cortical regions and combined to form neuronal and glial tau scores. Regional magnetic resonance imaging volumes were converted to Z-scores using 33 age- and sex-matched controls. Diffusion tensor imaging metrics, including fractional anisotropy and mean diffusivity, were calculated. Tau burden and neuroimaging metrics were compared between groups and correlated using linear regression models. Neuronal and glial tau burden were higher in motor and superior frontal cortices in the speech/language variant. In the subcortical and brainstem regions, only the glial tau burden differed, with a higher burden in globus pallidus, subthalamic nucleus, substantia nigra and red nucleus in Richardson's syndrome. No differences were observed in the cerebellar dentate and striatum. Greater volume loss was observed in the motor cortex in the speech/language variant and in the subthalamic nucleus, red nucleus and midbrain in Richardson's syndrome. Fractional anisotropy was lower in the midbrain and superior cerebellar peduncle in Richardson's syndrome. Mean diffusivity was greater in the superior frontal cortex in the speech/language variant and midbrain in Richardson's syndrome. Neuronal tau burden showed associations with volume loss, lower fractional anisotropy and higher mean diffusivity in the superior frontal cortex, although these findings did not survive correction for multiple comparisons. Results suggest that a shift in the distribution of tau, particularly neuronal tau, within the progressive supranuclear palsy network of regions is driving different clinical presentations in progressive supranuclear palsy. The possibility of different disease epicentres in these clinical variants has potential implications for the use of imaging biomarkers in progressive supranuclear palsy.

Comparative analysis of multimodal biomarkers for amyloid-beta positivity detection in Alzheimer's disease cohorts.

Efforts to develop cost-effective approaches for detecting amyloid pathology in Alzheimer's disease (AD) have gained significant momentum with a focus on biomarker classification. Recent research has explored non-invasive and readily accessible biomarkers, including magnetic resonance imaging (MRI) biomarkers and some AD risk factors. In this comprehensive study, we leveraged a diverse dataset, encompassing participants with varying cognitive statuses from multiple sources, including cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and our in-house Dementia Disease Initiation (DDI) cohort. As brain amyloid plaques have been proposed as sufficient for AD diagnosis, our primary aim was to assess the effectiveness of multimodal biomarkers in identifying amyloid plaques, using deep machine learning methodologies. Our findings underscore the robustness of the utilized methods in detecting amyloid beta positivity across multiple cohorts. Additionally, we investigated the potential of demographic data to enhance MRI-based amyloid detection. Notably, the inclusion of demographic risk factors significantly improved our models' ability to detect amyloid-beta positivity, particularly in early-stage cases, exemplified by an average area under the ROC curve of 0.836 in the unimpaired DDI cohort. These promising, non-invasive, and cost-effective predictors of MRI biomarkers and demographic variables hold the potential for further refinement through considerations like APOE genotype and plasma markers.

Based on neural network cascade abnormal texture information dissemination of classification of patients with schizophrenia and depression

This study used MRI brain image segmentation to identify novel magnetic resonance imaging (MRI) biomarkers to distinguish patients with schizophrenia (SCZ), major depressive disorder (MD), and healthy control (HC). Brain texture measurements, including entropy and contrast, were calculated to capture variability in adjacent MRI voxel intensity. These measures are then applied to group classification in deep learning techniques and combined with hierarchical correlations to locate results. Texture feature maps were extracted from segmented brain MRI scans of 141 patients with schizophrenia (SCZ), 103 patients with major depressive disorder (MD) and 238 healthy controls (HC). Gray scale coassociation matrix (GLCM) is a monomer matrix calculated in a voxel cube. Deep learning methods were evaluated to determine the application capability of texture feature mapping in binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method is implemented by repeated nesting and cross-validation for feature selection. Regions that show the highest correlation (positive or negative). In this study, the authors successfully classified SCZ, MD and HC. This suggests that texture analysis can be used as an effective feature extraction method to distinguish different disease states. Compared with other methods, texture analysis can capture richer image information and improve classification accuracy in some cases. The classification accuracy of SCZ and HC, MD and HC, SCZ and MD reached 84.6%, 86.4% and 76.21%, respectively. Among them, SCZ and HC are the most significant features with high sensitivity and specificity.

MRI predictors of long-term outcomes of neonatal hypoxic ischaemic encephalopathy: a primer for radiologists.

This review aims to serve as a foundational resource for general radiologists, enhancing their understanding of the role of Magnetic Resonance Imaging (MRI) in early prognostication for newborns diagnosed with hypoxic ischemic encephalopathy (HIE). The article explores the application of MRI as a predictive instrument for determining long-term outcomes in newborns affected by HIE. With HIE constituting a leading cause of neonatal mortality and severe long-term neurodevelopmental impairments, early identification of prognostic indicators is crucial for timely intervention and optimal clinical management. We examine current literature and recent advancements to provide an in-depth overview of MRI predictors, encompassing brain injury patterns, injury scoring systems, spectroscopy, and diffusion imaging. The potential of these MRI biomarkers in predicting long-term neurodevelopmental outcomes and the probability of epilepsy is also discussed.

CLEAR – clozapine in early psychosis: study protocol for a multi-centre, randomised controlled trial of clozapine vs other antipsychotics for young people with treatment resistant schizophrenia in real world settings

BackgroundClozapine is an antipsychotic drug with unique efficacy, and it is the only recommended treatment for treatment-resistant schizophrenia (TRS: failure to respond to at least two different antipsychotics). However, clozapine is also associated with a range of adverse effects which restrict its use, including blood dyscrasias, for which haematological monitoring is required. As treatment resistance is recognised earlier in the illness, the question of whether clozapine should be prescribed in children and young people is increasingly important. However, most research to date has been in older, chronic patients, and evidence regarding the efficacy and safety of clozapine in people under age 25 is lacking. The CLEAR (CLozapine in EARly psychosis) trial will assess whether clozapine is more effective than treatment as usual (TAU), at the level of clinical symptoms, patient rated outcomes, quality of life and cost-effectiveness in people below 25 years of age. Additionally, a nested biomarker study will investigate the mechanisms of action of clozapine compared to TAU.Methods and designThis is the protocol of a multi-centre, open label, blind-rated, randomised controlled effectiveness trial of clozapine vs TAU (any other oral antipsychotic monotherapy licenced in the British National Formulary) for 12 weeks in 260 children and young people with TRS (12–24 years old).Aim and objectivesThe primary outcome is the change in blind-rated Positive and Negative Syndrome Scale scores at 12 weeks from baseline. Secondary outcomes include blind-rated Clinical Global Impression, patient-rated outcomes, quality of life, adverse effects, and treatment adherence. Patients will be followed up for 12 months and will be invited to give consent for longer term follow-up using clinical records and potential re-contact for further research. For mechanism of action, change in brain magnetic resonance imaging (MRI) biomarkers and peripheral inflammatory markers will be measured over 12 weeks.DiscussionThe CLEAR trial will contribute knowledge on clozapine effectiveness, safety and cost-effectiveness compared to standard antipsychotics in young people with TRS, and the results may guide future clinical treatment recommendation for early psychosis.Trial registrationISRCTN Number: 37176025, IRAS Number: 1004947.Trial statusIn set-up. Protocol version 4.0 01/08/23. Current up to date protocol available here: https://fundingawards.nihr.ac.uk/award/NIHR131175#/.

A comprehensive set of ultrashort echo time magnetic resonance imaging biomarkers to assess cortical bone health: A feasibility study at clinical field strength

IntroductionConventional bone imaging methods primarily use X-ray techniques to assess bone mineral density (BMD), focusing exclusively on the mineral phase. This approach lacks information about the organic phase and bone water content, resulting in an incomplete evaluation of bone health. Recent research highlights the potential of ultrashort echo time magnetic resonance imaging (UTE MRI) to measure cortical porosity and estimate BMD based on signal intensity. UTE MRI also provides insights into bone water distribution and matrix organization, enabling a comprehensive bone assessment with a single imaging technique. Our study aimed to establish quantifiable UTE MRI-based biomarkers at clinical field strength to estimate BMD and microarchitecture while quantifying bound water content and matrix organization. MethodsFemoral bones from 11 cadaveric specimens (n = 4 males 67–92 yrs of age, n = 7 females 70–95 yrs of age) underwent dual-echo UTE MRI (3.0 T, 0.45 mm resolution) with different echo times and high resolution peripheral quantitative computed tomography (HR-pQCT) imaging (60.7 μm voxel size). Following registration, a 4.5 mm HR-pQCT region of interest was divided into four quadrants and used across the multi-modal images. Statistical analysis involved Pearson correlation between UTE MRI porosity index and a signal-intensity technique used to estimate BMD with corresponding HR-pQCT measures. UTE MRI was used to calculate T1 relaxation time and a novel bound water index (BWI), compared across subregions using repeated measures ANOVA. ResultsThe UTE MRI-derived porosity index and signal-intensity-based estimated BMD correlated with the HR-pQCT variables (porosity: r = 0.73, p = 0.006; BMD: r = 0.79, p = 0.002). However, these correlations varied in strength when we examined each of the four quadrants (subregions, r = 0.11–0.71). T1 relaxometry and the BWI exhibited variations across the four subregions, though these differences were not statistically significant. Notably, we observed a strong negative correlation between T1 relaxation time and the BWI (r = −0.87, p = 0.0006). ConclusionUTE MRI shows promise for being an innocuous method for estimating cortical porosity and BMD parameters while also giving insight into bone hydration and matrix organization. This method offers the potential to equip clinicians with a more comprehensive array of imaging biomarkers to assess bone health without the need for invasive or ionizing procedures.