Density Imaging Research Articles

Soma and neurite density abnormalities of paramagnetic rim lesions and core-sign lesions in multiple sclerosis.

In multiple sclerosis (MS), susceptibility-weighted imaging (SWI) may reveal white matter lesions (WML) with a paramagnetic rim ("paramagnetic rim lesions" [PRLs]) or diffuse hypointensity ("core-sign lesions"), reflecting different stages of WML evolution. Using thesoma and neurite density imaging (SANDI) model on diffusion-weighted magnetic resonance imaging (MRI), we characterized microstructural abnormalities of MS PRLs and core-sign lesions and their clinical relevance. Forty MS patients and 20 healthy controls (HC) underwent a 3T brain MRI. Using SANDI, the fractions of neurite (fneurite) and soma (fsoma) and size of soma (rsoma) were quantified in PRLs (including their core and rim separately), and core-sign lesions identified on SWI-phase. Among 1811 WMLs, 122 (6.7%) core-sign lesions and 97 (5.4%) PRLs were identified. Compared to HC and MS normal-appearing white matter, all MS WML showed significantly lower fneurite and fsoma and higher rsoma (FDR-p < 0.001). Compared to SWI-isointense WML, core-sign lesions showed a significantly higher fneurite, and lower fsoma and rsoma (FDR-p ≤ 0.005). Compared to SWI-isointense WML and core-sign lesions, PRLs showed a significantly lower fneurite, higher fsoma, and higher rsoma (FDR-p ≤ 0.001). The PRL-core showed significantly lower fneurite, and higher rsoma than PRL-rim (FDR-p < 0.001). Lower PRL fneurite (β ≤ -0.006, FDR-p ≤ 0.015) and higher rsoma (β ≥ 0.032, FDR-p ≤ 0.024) were significantly associated with a longer disease duration and more severe disability. In PRLs, the significant and clinically relevant neurite loss and increased soma fraction and size possibly reflect increased astrogliosis and activated microglia. Core-sign lesions exhibit milder axonal loss, microglia density and astrogliosis, supporting their less destructive nature.

Shaping the structural dynamics of motor learning through cueing during sleep.

Enhancing the retention of recent memory traces through sleep reactivation is possible via Targeted Memory Reactivation (TMR), involving cueing learned material during post-training sleep. Evidence indicates detectable short-term microstructural changes in the brain within an hour after motor sequence learning, and post-training sleep is believed to contribute to the consolidation of these motor memories, potentially leading to enduring microstructural changes. In this study, we explored how TMR during post-training sleep affects performance gains and delayed microstructural remodeling, using both standard Diffusion Tensor Imaging (DTI) and advanced Neurite Orientation Dispersion & Density Imaging (NODDI). Sixty healthy young adults participated in a five-day protocol, undergoing five Diffusion-Weighted Imaging (DWI) sessions, pre- and post-two motor sequence training sessions, and after a post-training night of either regular sleep (RS) or TMR. Results demonstrated rapid skill acquisition on Day 1, followed by performance stabilization on Day 2, and improvement on Day 5, in both RS and TMR groups. (Re)training induced widespread microstructural changes in motor-related areas, initially involving the hippocampus, followed by a delayed engagement of the caudate nucleus. Mean Diffusivity (MD) changes were accompanied by increased Neurite Density Index (NDI) in the putamen, suggesting increased neurite density, while Free Water Fraction (FWF) reduction indicated glial reorganization. TMR-related structural differences emerged in the dorsolateral prefrontal cortex (DLPFC) on Day 2 and the right cuneus on Day 5, suggesting unique sleep TMR-related neural reorganization patterns. Persistence of practice-related structural changes, although moderated over time, suggest a lasting neural network reorganization, partially mediated by sleep TMR.

The Transmission Muography Technique for Locating Potential Radon Gas Conduits at the Temperino Mine (Tuscany, Italy)

Transmission muography is an imaging technique that allows us to obtain two-dimensional and three-dimensional average-target density images by measuring the transmission of atmospheric muons. Through this technique, it is possible to observe density anomalies inside a target volume and locate them three-dimensionally. In this work, the potential of the technique will be illustrated through the description of the results of two measurements carried out in the tourist path of the Temperino mine (Livorno, Italy) in an area where a higher concentration of Radon gas is measured. This section of the gallery, located at a depth of about 50 m and dating back to the Etruscan period, might contain ancient cavities not yet discovered that could represent preferential conduits into which Radon gas is released into the tourist route. The muographic results are illustrated, focusing on the search for low-density anomalies attributable to cavities. The measurements are part of the MIMA-SITES project aimed at ensuring the safety of specific zones within the Temperino mine.

Distance and Voltage Dependence of Orbital Density Imaging Using a CO-Functionalized Tip in Scanning Tunneling Microscopy.

The appearance of frontier molecular ion resonances measured with scanning tunneling microscopy (STM)─often referred to as orbital density images─of single molecules was investigated using a CO-functionalized tip in dependence on bias voltage and tip-sample distance. As model systems, we studied pentacene and naphthalocyanine on bilayer NaCl on Cu(111). Absolute tip-sample distances were determined by means of atomic force microscopy (AFM). STM imaging revealed a transition from predominant p- to s-wave tip contrast upon increasing the tip-sample distance, but the contrast showed only small changes as a function of voltage. The distance-dependent contrast change is explained with the steeper decay of the tunneling matrix element for tunneling between two p-wave centers, compared to tunneling between two s-wave centers. In simulations with a fixed ratio of s- to p-wave tip states, we can reproduce the experimental data including the distance-dependent transition from predominant p- to s-wave tunneling contribution.

D3-ImgNet: A Framework for Molecular Properties Prediction Based on Data-Driven Electron Density Images.

Artificial intelligence technology has introduced a new research paradigm into the fields of quantum chemistry and materials science, leading to numerous studies that utilize machine learning methods to predict molecular properties. We contend that an exemplary deep learning model should not only achieve high-precision predictions of molecular properties but also incorporate guidance from physical mechanisms. Here, we propose a framework for predicting molecular properties based on data-driven electron density images, referred to as D3-ImgNet. This framework integrates group theory, density functional theory-related mechanisms, deep learning techniques, and multiobjective optimization mechanisms, embodying a methodological fusion of data analytics and system optimization. Initially, we focus on atomization energies as the primary target of our study, using the QM9 data set to demonstrate the framework's ability to predict molecular atomization energies with high accuracy and excellent exploration performance. We then further evaluate its predictive capabilities for dipole moments and forces with the QM9X data set, achieving satisfactory results. Additionally, we tested the D3-ImgNet framework on the SN2 reaction data set to demonstrate its ability to precisely predict the minimum energy paths of SN2 chemical reactions, showcasing its portability and adaptability in chemical reaction modeling. Finally, visualizations of the electronic density generated by the framework faithfully replicate the physical phenomenon of electron density transfer. We believe that this framework has the potential to accelerate property predictions and high-throughput screening of functional materials.

Dental Ultrasonography for Visualizing Osteoimmune Conditions and Assessing Jaw Bone Density: A Narrative Review.

Despite the widespread use of ultrasonography (US) in medical diagnostics, there is no similar US device available for visualizing jawbone density. This study is a narrative review of the possible applications of US in dentistry. This review is divided as follows: (a) Pulse-echo ultrasonography: the applications offer new perspectives for periodontal and peri-implant assessment. (b) Through-transmission alveolar US (TTAU): this technique was a novel imaging modality until 2004, when TTAU devices were last available. Quantitative US scaling made the device useful for diagnosing chronic inflammatory conditions in the jaw. (c) Ultrasound transmission velocity (UTV): in 2008, this technique was introduced in German university dental clinics to analyze the mechanical properties of the jawbone without translating the scientific findings into a practical device. (d) Trans-alveolar US device (TAU): the growing importance of "osteoimmune focal bone marrow defects" has led practitioners to develop a new TAU device. The attenuation of US was used for imaging of jawbone density. (e) Patients who benefit from TAU-guided jawbone surgery: research has shown remarkable results in specific disease cases. This review concludes that US has been undervalued as a diagnostic tool in dentistry. The new TAU-n unit offers the opportunity to change this in the future.

Medical Crimes in Midwifery Cases: Bibliometrics Analysis

Background: Medical crimes in midwifery cases, a term used to describe medical crimes during pregnancy, childbirth, and the postpartum period, is a controversial feminist term in global health policy and midwifery practice and research. This work aimed to discover the trend of medical crime publications regarding obstetric cases, the number of citations, and the direction of further research topics. Main body: The research method used in this study is Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which uses 2,617 scientific articles or publications from the Dimensions database. View articles with VOSviewer. This study conducted a bibliometric analysis of medical crime publications of midwifery case studies from 2013 to 2023 at https://app.dimensions.ai/. The study showed some results. There is an upward trend among the many publications on medical crime in midwifery cases, the number of citations of medical crime in midwifery cases has increased, and the online visualization of medical crime in midwifery cases provides information to find new topics that are not related to each. others, there are 5 clusters in terms of co-occurrence, the overlay imaging of medical crime in obstetric cases provides a trend to future research topics, medical crime in obstetric cases density imaging is still rare. Conclusion: The conclusion of the findings of this study is the development of a medical criminal investigation plan for midwifery cases. Although this study contributed to https://app.dimensions.ai/ to provide the latest insight into medical crime trends in midwifery cases from 2013 to 2023, this study has limitations.

Protective effect of a novel smoke evacuation device during laparoscopic surgery: An experimental proof-of-concept study.

Surgical smoke generated by energy devices poses health risks to medical staff. During laparoscopic surgery, the smoke aggregating around the camera obstructs the visual field, forcing surgeons to interrupt surgery, and may increase surgical risk. We propose a proximal smoke evacuation method to improve surgical quality by effectively eliminating surgical smoke. A smoke evacuation device was designed to attach to a laparoscopic electrode and create a channel for smoke to be suctioned directly from near the tip of the electrode (proximal evacuation). An animal study was conducted to collect videos of electrocautery with proximal (device) evacuation, distal (trocar) evacuation, and no evacuation. We used a computer vision-based model to compare in-screen smoke density and image quality between different evacuation pathways. Compared with distal and no evacuation, proximal evacuation had significantly lower estimated in-screen smoke density and higher image quality (p<0.001). The pneumoperitoneum pressure remained above 8mmHg throughout the procedure with proper suction pressure setting. A total of 62 trials performed by 15 surgeons produced consistent results, supporting the core findings of this study. On average, proximal evacuation can eliminate 85.47% of the smoke around operative fields within 10s. Proximal smoke evacuation is capable of maintaining a clean surgical field and high image quality during laparoscopic surgery. The device can help avoid interrupting surgeries to wait for the smoke to clear or clean the camera lens.

Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies.

Advancements in neuroimaging, particularly diffusion magnetic resonance imaging (MRI) techniques and molecular imaging with positron emission tomography (PET), have significantly enhanced the early detection of biomarkers in neurodegenerative and neuro-ophthalmic disorders. These include Alzheimer's disease, Parkinson's disease, multiple sclerosis, neuromyelitis optica, and myelin oligodendrocyte glycoprotein antibody disease. This review highlights the transformative role of advanced diffusion MRI techniques-Neurite Orientation Dispersion and Density Imaging and Diffusion Kurtosis Imaging-in identifying subtle microstructural changes in the brain and visual pathways that precede clinical symptoms. When integrated with artificial intelligence (AI) algorithms, these techniques achieve unprecedented diagnostic precision, facilitating early detection of neurodegeneration and inflammation. Additionally, next-generation PET tracers targeting misfolded proteins, such as tau and alpha-synuclein, along with inflammatory markers, enhance the visualization and quantification of pathological processes in vivo. Deep learning models, including convolutional neural networks and multimodal transformers, further improve diagnostic accuracy by integrating multimodal imaging data and predicting disease progression. Despite challenges such as technical variability, data privacy concerns, and regulatory barriers, the potential of AI-enhanced neuroimaging to revolutionize early diagnosis and personalized treatment in neurodegenerative and neuro-ophthalmic disorders is immense. This review underscores the importance of ongoing efforts to validate, standardize, and implement these technologies to maximize their clinical impact.

The Spatial Organization of Ascending Auditory Pathway Microstructural Maturation From Infancy Through Adolescence Using a Novel Fiber Tracking Approach.

Auditory perception is established through experience-dependent stimuli exposure during sensitive developmental periods; however, little is known regarding the structural development of the central auditory pathway in humans. The present study characterized the regional developmental trajectories of the ascending auditory pathway from the brainstem to the auditory cortex from infancy through adolescence using a novel diffusion MRI-based tractography approach and along-tract analyses. We used diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) to quantify the magnitude and timing of auditory pathway microstructural maturation. We found spatially varying patterns of white matter maturation along the length of the tract, with inferior brainstem regions developing earlier than thalamocortical projections and left hemisphere tracts developing earlier than the right. These results help to characterize the processes that give rise to functional auditory processing and may provide a baseline for detecting abnormal development.

In vivo evidence for cell body loss in cortical lesions in people with multiple sclerosis.

To quantify alterations in soma and neurite density imaging measures within and surrounding cortical lesions in people with multiple sclerosis using in vivo high-gradient diffusion MRI. In this cross-sectional study, 41 people with multiple sclerosis and 34 age- and sex-matched healthy controls underwent 3 T high-gradient diffusion MRI. Cortical lesions were segmented on artificial intelligence-enabled double inversion recovery images. "Inner" and "outer" perilesional layers were segmented as two expanding shells of 2 mm surrounding a cortical lesion. Intracellular, intra-neurite, and extracellular signal fractions and apparent soma radius were estimated in (peri)lesional and normal-appearing cortex. Cortical lesions were present in all people with multiple sclerosis with a median count of 8 [IQR 5-18] and total volume of 0.16 [0.09-0.46 mL]. People with multiple sclerosis (mean 0.27 ± 0.03) showed lower normalized cortical volumes compared to healthy controls (0.30 ± 0.02). Compared to healthy controls (mean 0.58 ± 0.028), normal-appearing cortex in multiple sclerosis (0.57 ± 0.034) showed lower intra-cellular signal fraction. Cortical lesions (0.49 ± 0.089) exhibited lower intra-cellular signal fractions compared to perilesional ("inner": 0.55 ± 0.049, "outer": 0.55 ± 0.039) and normal-appearing cortex, demonstrating a gradation of change. The soma radius varied significantly across cortices, becoming smaller when moving outward from cortical lesions (cortical lesions: 10.38 ± 0.209 μm, "inner" layer: 10.19 ± 0.140 μm, "outer" layer: 10.07 ± 0.149 μm, normal-appearing cortex: 9.99 ± 0.127 μm). Cortical cell body loss in multiple sclerosis is most pronounced in cortical lesions and also present in normal-appearing cortex. Gradients of diffusion microstructural alterations moving outward from cortical lesions toward normal-appearing cortex highlight the potential of high-gradient diffusion MRI to identify both focal and diffuse cortical pathology.

Accuracy of Iodine and Calcium Concentrations in Dual Energy Computed Tomography (DECT)

Objective: The purpose of this study was to assess accuracies of iodine and calcium concentrations in dual energy computed tomography (DECT). Method: This study was performed using an in-house phantom made from polyester resin. The in-house phantom had a diameter of 16 cm and had 10 holes filled with iodine (with concentrations of 5, 7.5, 10, and 15 mg/ml), calcium (with concentrations 200, 300, 500, and 600 mg/ml), water, and air. The in-house phantom was scanned by an Ultrafast kV Switching DECT (GE Revolution) with a tube voltage of 80/140 kV, rotation time of 0.5 s, and tube current variations of 200, 250, 300, 335, and 370 mA. Images was reconstructed to a material density image (MDI). The iodine and calcium concentrations were measured using GSI Viewer software and compared with set iodine and calcium concentrations. Results: It was found that absolute percentage error (APE) of concentrations on iodine was &lt;12% and on calcium was &lt;25% for all concentration variations. Hence, the APE of iodine is smaller than that of calcium. It was found that increasing the tube current does not necessarily improve the accuracy of iodine and calcium concentration measurements. Conclusion: Measurements of iodine and calcium concentrations have been carried out on DECT using an in-house phantom. Overall, iodine and calcium quantification in DECT using the in-house phantom is accurate.

Assessment of Age-Related Microstructure Changes in Thigh Skeletal Muscle Based on Neurite Orientation Dispersion and Density Imaging.

Neurite orientation dispersion and density imaging (NODDI) could offer information about the morphological properties of tissue. Diffusion microstructure imaging has been widely used, but the applicability of NODDI in skeletal muscle imaging remains to be explored. To evaluate microstructure parameters variations in skeletal muscle as indicators of age-related changes. Prospective, cross-sectional. A total of 108 asymptomatic volunteers, divided into three age groups: 20-39 years (N = 34), 40-59 years (N = 40), and over 60 years (N = 34). 3-T, three-dimensional (3D) gradient echo sequence. T1-weighted imaging, T2-weighted imaging with spectral adiabatic inversion recovery, and NODDI were used to image the thigh skeletal muscles. Four thigh skeletal muscle groups were analyzed, including bilateral thigh quadriceps femoris and hamstrings. The microstructure parameters included orientation dispersion index (ODI), intra-myofibrillar water volume fraction (V-intra), free-water fraction (V-csf), fractional anisotropy (FA), and mean diffusivity (MD). These parameters were quantified using NODDI images and compared among different age, body mass index (BMI), and skeletal muscle index (SMI) subgroups. Segmentation measurement reliability was assessed using a two-way mixed intraclass correlation coefficient (ICC). Shapiro-Wilk tests were used to assess data distribution. Kruskal-Wallis and Mann-Whitney U tests were used to compare ODI, V-intra, V-csf, FA, and MD values among different age, BMI, and SMI subgroups. The Spearman correlation coefficient was utilized to assess the strength of the correlation between the age and microstructure parameters, as well as between age and SMI. Additionally, Bonferroni post hoc tests were conducted on microstructure parameters that exhibited significant differences across various age groups. A P-value <0.05 was considered statistically significant. Significant differences in ODI, V-csf, FA, and MD values were observed among age, BMI, and SMI subgroups. NODDI may be used to reveal information about microstructure integrity and local physiological changes of thigh skeletal muscle fibers in relation to age. 2 TECHNICAL EFFICACY: Stage 2.

Cortical hypometabolism in Parkinson's disease is linked to cholinergic basal forebrain atrophy.

Cortical hypometabolism on FDG-PET is a well-established neuroimaging biomarker of cognitive impairment in Parkinson's disease (PD), but its pathophysiologic origins are incompletely understood. Cholinergic basal forebrain (cBF) degeneration is a prominent pathological feature of PD-related cognitive impairment and may contribute to cortical hypometabolism through cholinergic denervation of cortical projection areas. Here, we investigated in-vivo associations between subregional cBF volumes on 3T-MRI, cortical hypometabolism on [18F]FDG-PET, and cognitive deficits in a cohort of 95 PD participants with varying degrees of cognitive impairment. We further assessed the spatial correspondence of the cortical pattern of cBF-associated hypometabolism with the pattern of cholinergic denervation in PD as assessed by [18F]FEOBV-PET imaging of presynaptic cholinergic terminal density in a second cohort. Lower volume of the cortically-projecting posterior cBF, but not of the anterior cBF, was significantly associated with extensive neocortical hypometabolism [p(FDR) < 0.05], which mediated the association between cBF atrophy and cognitive impairment (mediated proportion:43%, p < 0.001). In combined models, posterior cBF atrophy explained more variance in cortical hypometabolism (R2 = 0.26, p < 0.001) than local atrophy in the cortical areas themselves (R2 = 0.16, p = 0.01). Topographic correspondence analysis with the [18F]FEOBV-PET pattern revealed that cortical areas showing most pronounced cBF-associated hypometabolism correspond to those showing most severe cholinergic denervation in PD (Spearman's ρ = 0.57, p < 0.001). In conclusion, posterior cBF atrophy in PD is selectively associated with hypometabolism in denervated cortical target areas, which mediates the effect of cBF atrophy on cognitive impairment. These data provide first-time in-vivo evidence that cholinergic degeneration represents a principle pathological correlate of cortical hypometabolism underlying cognitive impairment in PD.

Connectivity-based striatal subregion microstructural changes in sporadic amyotrophic lateral sclerosis patients: Relation to motor disability, cognitive deficits, and serum biomarkers.

To date, no previous studies have used multishell diffusion MRI to identify striatal microstructural damage invivo in amyotrophic lateral sclerosis (ALS) patients. Thus, in the present study, we aimed to comprehensively explore connectivity-based selective striatal subregion microstructural damage in sporadic ALS patients and its associations with motor disability, cognitive deficits, and serum biomarkers. In this retrospective study, 79 ALS patients and 53 healthy controls (HCs) who underwent clinical assessment, serum neurofilament light (NfL) measurement, genetic testing, and multishell diffusion MRI scanning were included. Using a probabilistic tractography approach, the striatum was segmented into six subregions based on their corticostriatal connectivity. Three neurite orientation dispersion and density imaging (NODDI) parameters, the neurite density index (NDI), orientation dispersion index (ODI), and isotropic volume fraction (ISO), of the connectivity-based striatal subregions were measured. Compared with HCs, ALS patients had a significantly lower NDI in the bilateral motor and right frontal subregions, a significantly lower ODI in the right motor and frontal subregions, and a significantly higher ISO in the bilateral motor and frontal subregions of the striatum after familywise error (p < 0.05). Moreover, striatal subregion microstructural damage was significantly correlated with motor disabilities, cognitive deficits, and serum NfL levels in ALS patients (p = 0.020-0.002). Our study provides clear evidence demonstrating that connectivity-based selective striatal subregion microstructural damage is a definite feature of sporadic ALS patients and suggesting that striatal damage may play an important role in motor disability and cognitive deficits in ALS patients.

A novel application of neurite orientation dispersion and density imaging to differentiate cognitively recovered versus non-recovered following mild traumatic brain injury

Objective: Cognitive deficits in mild traumatic brain injury (mTBI) can persist over three months, and symptomatic patients may not be readily diagnosed. Although diffusion tensor imaging (DTI) can detect microstructural white matter tract (WMT) changes in mTBI, the underlying recovery process is not fully understood. We aimed to investigate WMT changes at 3 months post-mTBI between cognitively recovered (REC) and non-recovered (NREC) mTBI subjects using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Methods: Fifty-seven mTBI subjects were divided into REC (n=16) and NREC (n=41) groups. Ten healthy controls (HC) were recruited. MRI and Neuropsychological Assessment Battery-Screening Module (S-NAB) performance were assessed at baseline and three months before subjects were classified as REC and NREC. DTI and NODDI parameters of 50 ROIs corresponding to WMTs were compared between REC, NREC and HC. Results: NODDI detected more significant changes (p&lt;0.05) in multiple ROIs than DTI. Lower Neurite Density Index (NDI) was demonstrated in REC versus NREC at multiple ROIs. Increased Orientation Dispersion Index (ODI) and decreased Isotropic Volume Fraction (ISOVF) were detected at several WMTs in both groups. Conclusion: Reduced NDI in the overall mTBI cohort suggests axonal degeneration post-trauma. We postulate that at three months’ timeline, there is a combination of axonal degeneration and astrogliosis, which is more extensive in NREC than REC.

Repetitive Mild Closed-Head Injury Induced Synapse Loss and Increased Local BOLD-fMRI Signal Homogeneity.

Repeated mild head injuries due to sports, or domestic violence and military service are increasingly linked to debilitating symptoms in the long term. Although symptoms may take decades to manifest, potentially treatable neurobiological alterations must begin shortly after injury. Better means to diagnose and treat traumatic brain injuries requires an improved understanding of the mechanisms underlying progression and means through which they can be measured. Here, we employ a repetitive mild traumatic brain injury (rmTBI) and chronic variable stress mouse model to investigate emergent structural and functional brain abnormalities. Brain imaging is achieved with [18F]SynVesT-1 positron emission tomography, with the synaptic vesicle glycoprotein 2A ligand marking synapse density and BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI). Animals were scanned six weeks after concluding rmTBI/Stress procedures. Injured mice showed widespread decreases in synaptic density coupled with an increase in local BOLD-fMRI synchrony detected as regional homogeneity. Injury-affected regions with higher synapse density showed a greater increase in fMRI regional homogeneity. Taken together, these observations may reflect compensatory mechanisms following injury. Multimodal studies are needed to provide deeper insights into these observations.

Elucidating Microstructural Alterations in Neurodevelopmental Disorders: Application of Advanced Diffusion-Weighted Imaging in Children With Rasopathies.

Neurodevelopmental disorders (NDDs) can severely impact functioning yet effective treatments are limited. Greater insight into the neurobiology underlying NDDs is critical to the development of successful treatments. Using a genetics-first approach, we investigated the potential of advanced diffusion-weighted imaging (DWI) techniques to characterize the neural microstructure unique to neurofibromatosis type 1 (NF1) and Noonan syndrome (NS). In this prospective study, children with NF1, NS, and typical developing (TD) were scanned using a multi-shell DWI sequence optimized for neurite orientation density and dispersion imaging (NODDI) and diffusion kurtosis imaging (DKI). Region-of-interest and tract-based analysis were conducted on subcortical regions and white matter tracts. Analysis of covariance, principal components, and linear discriminant analysis compared between three groups. 88 participants (Mage = 9.36, SDage = 2.61; 44 male) were included: 31 NS, 25 NF1, and 32 TD. Subcortical regions differed between NF1 and NS, particularly in the thalamus where the neurite density index (NDI; estimated difference 0.044 [95% CI: -0.034, 0.053], d = 2.36), orientation dispersion index (ODI; estimate 0.018 [95% CI: 0.010, 0.026], d = 1.39), and mean kurtosis (MK; estimate 0.049 [95% CI: 0.025, 0.072], d = 1.39) were lower in NF1 compared with NS (all p < 0.0001). Reduced NDI was found in NF1 and NS compared with TD in all 39 white matter tracts investigated (p < 0.0001). Reduced MK was found in a majority of the tracts in NF1 and NS relative to TD, while fewer differences in ODI were observed. The middle cerebellar peduncle showed lower NDI (estimate 0.038 [95% CI: 0.021, 0.056], p < 0.0001) and MK (estimate 0.057 [95% CI: 0.026, 0.089], p < 0.0001) in NF1 compared to NS. Multivariate analyses distinguished between groups using NDI, ODI, and MK measures. Principal components analysis confirmed that the clinical groups differ most from TD in white matter tract-based NDI and MK, whereas ODI values appear similar across the groups. The subcortical regions showed several differences between NF1 and NS, to the extent that a linear discriminant analysis could classify participants with NF1 with an accuracy rate of 97%. Differences in neural microstructure were detected between NF1 and NS, particularly in subcortical regions and the middle cerebellar peduncle, in line with pre-clinical evidence. Advanced DWI techniques detected subtle alterations not found in prior work using conventional diffusion tensor imaging.

An Improved Pattern Informatics Method for Extracting Ionospheric Disturbances Related to Seismicity Based on CSES Data: A Case Study of the Mw7.3 Maduo Earthquake

AbstractThe exploration of multi‐layer coupling mechanisms between earthquakes and the ionosphere is crucial for utilizing ionospheric precursors in earthquake prediction. A significant research task involves continuously tracking the spatio‐temporal changes in ionospheric parameters, acquiring comprehensive seismic anomaly information, and capturing “deterministic” precursor anomalies. Based on data from the China Seismo‐Electromagnetic Satellite (CSES), we enhance the Pattern Informatics (PI) Method and propose an Improved Pattern Informatics (IPI) Method. The IPI method enables the calculation of the spatio‐temporal dynamics of electron density anomalies detected by the CSES satellite. The seismic signals in the electron density during earthquake on 2021 at Maduo are investigated in this work. The results show that: (a) Compared to original electron density images, the IPI method‐derived models extract distinct electron density anomaly signals, regardless of the data whether are collected during descending (daytime) or ascending (nighttime) orbits, or across different time scales of change window. (b) The electron density anomalies appear about 40 days prior to the Maduo Mw7.3 earthquake. The evolution of these anomalies follows a pattern of appearance, persistence, disappearance, re‐emergence, and final disappearance. Moreover, the evolution trends of the IPI hotspot images at daytime and nighttime are similar. These results suggest that the IPI method can capture the spatio‐temporal trends of ionospheric parameters and effectively extract electronic precursors related to strong earthquakes.

Use of mean apparent propagator (MAP) MRI in patients with acute ischemic stroke: A comparative study with DTI and NODDI

PurposeTo evaluate the Mean Apparent Propagator (MAP) MRI for processing multi-shell diffusion imaging in patients with acute ischemic stroke (AIS) and correlate to diffusion tensor imaging (DTI) and neurite orientation and dispersion density imaging (NODDI). MethodsWe enrolled patients with AIS from 1/2022 to 4/2024 who underwent multi-shell diffusion imaging on a 3.0-Tesla scanner to generate DTI, NODDI and MAP measures. Mean intensity and standard deviation (SD) were calculated for the infarcted regions-of-interest in b0, fractional anisotropy (FA), mean diffusivity (MD), intra-cellular volume fraction (ICVF), free water fraction (FWF), and orientation dispersion index (ODI), return to the origin probability (RTOP), return to the plane probability (RTPP), return to the axis probability (RTAP), propagator anisotropy (PA), q-space Mean Square Displacement (QMSD), and non-Gaussianity (NG). ResultsTwenty-two patients were included with an average age of 69.5 ± 13.5, mean NIHSS of 12.4 ± 7.7, and median infarct of 73.3 ± 10.1 ml. ICVF was correlated with RTPP (ρ = 0.82, p < 0.01), RTAP (ρ = 0.76, p < 0.01) and RTOP (ρ = 0.79, p < 0.01), ODI with PA (ρ = −0.83, p < 0.01), FWF with RTOP (ρ = −0.73, p < 0.01), RTAP (ρ = −0.69, p < 0.01), and RTPP (ρ = −0.73, p < 0.01), MD with RTPP (ρ = −0.80, p < 0.01), RTOP (ρ = −0.79, p < 0.01), and RTAP (ρ = −0.77, p < 0.01), FA with RTAP (ρ = 0.77, p < 0.01), RTOP (ρ = 0.67, p = 0.01), PA (ρ = 0.74, p < 0.01), and SD PA (ρ = 0.85, p < 0.01). Multivariable linear regression identified the SD QMSD (β = 0.406, p = 0.008), thrombectomy (β = 0.481, p = 0.002), and infarct volume (β = 0.292, p = 0.051) as predictive of stroke severity based on NIHSS. ConclusionsGiven its short processing time, MAP MRI is a valuable alternative with potential for clinical use in AIS.