The "limbic network," comprising orbitofrontal and anterior temporal cortex, is part of an extended default network: Evidence from multi-echo fMRI.

Resting-state functional magnetic resonance imaging (fMRI) investigations have provided a view of the default network (DN) as composed of a specific set of frontal, parietal, and temporal cortical regions. This spatial topography is typically defined with reference to an influential network parcellation scheme that designated the DN as one of seven large-scale networks (Yeo et al., 2011). However, the precise functional organization of the DN is still under debate, with studies arguing for varying subnetwork configurations and the inclusion of subcortical regions. In this vein, the so-called limbic network-defined as a distinct large-scale network comprising the bilateral temporal poles, ventral anterior temporal lobes, and orbitofrontal cortex-is of particular interest. A large multi-modal and multi-species literature on the anatomical, functional, and cognitive properties of these regions suggests a close relationship to the DN. Notably, these regions have poor signal quality with conventional fMRI acquisition, likely obscuring their network affiliation in most studies. Here, we leverage a multi-echo fMRI dataset with high temporal signal-to-noise and whole-brain coverage, including orbitofrontal and anterior temporal regions, to examine the large-scale network resting-state functional connectivity of these regions and assess their associations with the DN. Consistent with our hypotheses, our results support the inclusion of the majority of the orbitofrontal and anterior temporal cortex as part of the DN and reveal significant heterogeneity in their functional connectivity. We observed that left-lateralized regions within the temporal poles and ventral anterior temporal lobes, as well as medial orbitofrontal regions, exhibited the greatest resting-state functional connectivity with the DN, with heterogeneity across DN subnetworks. Overall, our findings suggest that, rather than being a functionally distinct network, the orbitofrontal and anterior temporal regions comprise part of a larger, extended default network.

The precise functional organization of the default network is still under debate. Limitations in temporal signal-to-noise of functional MRI BOLD signal data may have restricted estimations of the topography of the default network. The "limbic network," defined as a distinct large-scale network comprising bilateral anterior temporal and orbitofrontal cortex, has been affiliated with the default network in nonhuman animal tractography and task-based fMRI studies in humans. We leverage a multi-echo fMRI dataset with high temporal signal-to-noise and whole-brain coverage to examine the large-scale network resting-state functional connectivity of these regions and assess their associations with the default network. Our results support the inclusion of anterior temporal and orbitofrontal cortex as part of the default network. Overall, our findings suggest that, rather than being a functionally distinct limbic network, the anterior temporal and orbitofrontal regions comprise part of an extended default network.

Acquisition and reconstruction with motion suppression DWI enhance image quality in nasopharyngeal carcinoma patients: Non-echo-planar DWI comparison with single-shot echo-planar DWI.

PURPOSE: To evaluate the impact of application acquisition and reconstruction with motion suppression (ARMS) technology on improving the image quality of diffusion-weighted Imaging (DWI) for nasopharyngeal carcinoma (NPC), compared to single-shot echo-planar imaging (SS-EPI). METHODS: A total of 90 patients with NPC underwent MR examination, including ARMS DWI and SS-EPI DWI sequences. Both DWI sequences were acquired with b-values 0 and 800 s/mm2. Two radiologists evaluated the visibility of the lesion, geometric distortion, and overall image quality of the two DWI sequences. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), geometric distortion degree, and apparent diffusion coefficient (ADC) values of the nasopharyngeal lesions were assessed and compared for two sequences. The Wilcoxon signed-rank test was used to compare the quantitative and qualitative parameters of the two sequences. RESULTS: The lesion visibility, geometric distortion, and overall image quality scores were significantly higher in ARMS DWI (all P<0.001). Four small-sized lesions were not visible and four lesions were partially visible in the SS-EPI DWI sequence. Lesion detection rate of ARMS DWI is 100 %, while that of SS-EPI is 95.56 %, P<0.043. The mismatch distance between the fusion images of ARMS DWI and T2WI was smaller than that of SS-EPI DWI and T2WI (all P<0.001). The SNR and CNR of ARMS DWI were lower than that of SS-EPI DWI (114.48 ± 37.89 vs. 202.61 ± 78.84, P<0.001 and 1.81 ± 1.84 vs. 3.29 ± 3.71, P<0.003) while the ADC value was higher (839.19 ± 138.44 × 10-6 mm2/s vs. 788.82 ± 110.96 × 10-6 mm2/s, P<0.002). CONCLUSION: ARMS DWI improves the image quality by reducing geometric distortion and magnetic susceptibility artifacts. ARMS DWI is superior to SS-EPI DWI for diagnosing small-sized nasopharyngeal lesions, although it has lower SNR and CNR.

Silent severe aortic regurgitation due to blunt chest trauma: ignore it at your peril-a case report.

Background: Blunt chest trauma (BCT) presenting to the emergency department is common and may cause life-threatening cardiac complications. Whilst complications causing haemodynamic instability are generally detected promptly, others may present late with long-term consequences. We describe a rare, serious complication of BCT presenting five years after a road traffic accident (RTA). Case summary: A 23-year-old man was incidentally found to have a murmur. Past history was notable only for BCT with rib fracture sustained in a RTA 5 years prior. Examination revealed a hyperdynamic pulse, loud decrescendo diastolic murmur, and Duroziez's sign over the femoral arteries. Echocardiography showed severe valvular aortic regurgitation (AR) from a hole in the left coronary cusp and holodiastolic flow reversal in the descending aorta. The left ventricle (LV) showed marked dilatation in diastole, mild dilatation in systole, and preserved systolic function. The aorta was normal. Severe AR was attributed to his previous BCT, with AR causing subsequent LV dilatation. He underwent aortic valve replacement (AVR) with rapid recovery. He remains well, and his echo shows a well-functioning AVR with normalization of LV dimensions. Discussion: Aortic regurgitation following BCT is rare but well-recognized, most often resulting from RTAs. Only a third of cases are diagnosed acutely. In others, lack of haemodynamic instability means that emergency echocardiography is not routinely performed, such that this may go unrecognized with long-term consequences. Clinicians should be aware of possible valve damage following BCT. Prompt echocardiography should be routinely performed for all BCT at initial presentation, even without haemodynamic instability.

Accelerated model-based T1, T2* and proton density mapping using a Bayesian approach with automatic hyperparameter estimation.

PURPOSE: To achieve automatic hyperparameter estimation for the model-based recovery of quantitative MR maps from undersampled data, we propose a Bayesian formulation that incorporates the signal model and sparse priors among multiple image contrasts. THEORY: We introduce a novel approximate message passing framework "AMP-PE" that enables the automatic and simultaneous recovery of hyperparameters and quantitative maps. METHODS: We employed the variable-flip-angle method to acquire multi-echo measurements using gradient echo sequence. We explored undersampling schemes to incorporate complementary sampling patterns across different flip angles and echo times. We further compared AMP-PE with conventional compressed sensing approaches such as the l 1 $$ {l}_1 $$ -norm minimization, PICS and other model-based approaches such as GraSP, MOBA. RESULTS: Compared to conventional compressed sensing approaches such as the l 1 $$ {l}_1 $$ -norm minimization and PICS, AMP-PE achieved superior reconstruction performance with lower errors in T 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ mapping and comparable performance in T 1 $$ {\mathrm{T}}_1 $$ and proton density mappings. When compared to other model-based approaches including GraSP and MOBA, AMP-PE exhibited greater robustness and outperformed GraSP in reconstruction error. AMP-PE offers faster speed than MOBA. AMP-PE performed better than MOBA at higher sampling rates and worse than MOBA at a lower sampling rate. Notably, AMP-PE eliminates the need for hyperparameter tuning, which is a requisite for all the other approaches. CONCLUSION: AMP-PE offers the benefits of model-based recovery with the additional key advantage of automatic hyperparameter estimation. It works adeptly in situations where ground-truth is difficult to obtain and in clinical environments where it is desirable to automatically adapt hyperparameters to individual protocol, scanner and patient.

Clinical Application of Skeletal Muscle Quantity and Quality Assessment Using Bioelectrical Impedance and Ultrasound Images.

A decline in muscle strength is a key factor responsible for physical dysfunction in older individuals. Both loss of muscle quantity and quality are associated with muscle strength decline. While the gold standard method for evaluating muscle mass and quality is magnetic resonance imaging, it is not suitable for clinical settings because of the measurement and analysis costs. Bioelectrical impedance analysis (BIA) and B-mode ultrasonography are clinically useful alternatives for skeletal muscle assessment owing to their feasibility and noninvasiveness. The recent advancements in the techniques for BIA and ultrasonography have improved their accuracy in assessing skeletal muscle quantity and quality, making them useful in detecting age-related and disease-specific alterations. This review comprehensively analyzes the advantages of using BIA and ultrasound imaging for assessing skeletal muscle quantity and quality and detecting muscle degeneration. We summarize the recent findings regarding age-related changes in muscle characteristics and the associations of muscle degeneration with physical dysfunction in patients with knee osteoarthritis. Furthermore, we discuss the clinical application of skeletal muscle assessment using BIA and ultrasound for evaluating training effects and exercise prescription.

Multimodal Imaging Approach to Atrial Myxoma to Optimize Surgical Management.

Cardiac masses are rare and of the neoplastic group, myxomas are the most common. An elderly male with a background of multiple myeloma and previous autologous stem cell transplant presented with dyspnea and chest heaviness with a subsequent cardiac mass found. Multimodal imaging, including three-dimensional (3D) echocardiography and cardiac magnetic resonance (CMR) imaging, was utilized to guide the diagnostic process, particularly given the differential of a cardiac plasmacytoma in the setting of multiple myeloma. CMR was used to identify characteristic features of the mass and 3D echocardiography highlighted the anatomical relationships of the mass in relation to surrounding structures to complement surgical planning. The different imaging approaches and clinical decision-making were implemented to aid in definitive surgical management.

The presence of spontaneous echo contrast didn't increase the risk for left atrial appendage closure: A propensity score matching analysis based on the CLACBAC study.

Background: Left atrial appendage closure (LAAC) was effective in preventing thromboembolic events and stroke in patients with atrial fibrillation (AF). However, whether left atrial spontaneous echo contrast (LA-SEC) poses a higher risk for thromboembolism is contradictory. We aimed to investigate whether LA-SEC is a risk factor for thromboembolic events in patients who underwent LAAC. Methods: 258 consecutive patients who underwent successful LAAC were enrolled and divided according to the presence or absence of LA-SEC detected by transesophageal echocardiography (TEE). Propensity score matching (PSM) was used to eliminate covariate imbalances. Baseline characteristics, periprocedural details, and clinical outcomes were compared between LA-SEC and non-LA-SEC groups and PSM-matched groups. Results: Of the 258 patients enrolled, mean age was 71.8 ± 8.3 years and 59.3 % were male. LA-SEC group had a higher percentage of persistent AF and worse cardiac function. No significant difference in peri-procedure parameters was found. Through follow-up of 38.1 ± 10.7 months, the total incidence of thromboembolic events and stroke was 7.8 % and 6.6 %, respectively. Though the event-free survival rate of thromboembolic events (Log-Rank P = 0.042) and stroke (Log-Rank P = 0.010) was significantly lower in the LA-SEC group, multivariable COX regression analysis showed LA-SEC was not an independent predictor of thromboembolic events (Hazard ratio 2.073, 95 % Confidence interval 0.845-5.082, P = 0.111). Further survival analysis between PSM-matched groups with comparable baseline characteristics presented no significant difference in survival free from thromboembolic events (Log-Rank P = 0.616) and stroke (Log-Rank P = 0.312). Conclusion: Patients with LA-SEC had worse condition, while LA-SEC per se did not increase the incidence of thromboembolic events and stroke for patients who underwent LAAC.

trajPredRNN+: A new approach for precipitation nowcasting with weather radar echo images based on deep learning.

：Short-term rainfall prediction is a crucial and practical research area, with the accuracy of rainfall prediction, particularly for heavy rainfall, significantly impacting people's lives, property, and even their safety. Existing models, such as ConvLSTM, TrajGRU, and PredRNN, exhibit limitations in capturing fine-grained appearances due to insufficient memory units or addressing positional misalignment issues, thereby compromising the accuracy of model predictions. In this study, we propose trajPredRNN+, an innovative approach that integrates the trajectory segmentation model and the PredRNN deep learning model to address both limitations in nowcasting precipitation using weather radar echo images. By incorporating attention mechanisms, the model demonstrates an enhanced focus on short-term and imminent heavy rainfall events. To ensure improved stability during training, a residual network is introduced. Lastly, a more rational and effective training loss function is proposed, encompassing weight mechanism, SSIM index, and GAN loss. To validate the proposed model, we conducted a comparative experiment and an ablation experiment using the radar echo map dataset obtained from the Shenzhen Meteorological Bureau. The results of these experiments demonstrate that our model has achieved significant improvements across multiple key performance indicators.

Single-shot multi-b-value (SSMb) diffusion-weighted MRI using spin echo and stimulated echoes with variable flip angles.

Conventional diffusion-weighted imaging (DWI) sequences employing a spin echo or stimulated echo sensitize diffusion with a specific b-value at a fixed diffusion direction and diffusion time (Δ). To compute apparent diffusion coefficient (ADC) and other diffusion parameters, the sequence needs to be repeated multiple times by varying the b-value and/or gradient direction. In this study, we developed a single-shot multi-b-value (SSMb) diffusion MRI technique, which combines a spin echo and a train of stimulated echoes produced with variable flip angles. The method involves a pair of 90° radio frequency (RF) pulses that straddle a diffusion gradient lobe (GD), to rephase the magnetization in the transverse plane, producing a diffusion-weighted spin echo acquired by the first echo-planar imaging (EPI) readout train. The magnetization stored along the longitudinal axis is successively re-excited by a series of n variable-flip-angle pulses, each followed by a diffusion gradient lobe GD and a subsequent EPI readout train to sample n stimulated-echo signals. As such, (n + 1) diffusion-weighted images, each with a distinct b-value, are acquired in a single shot. The SSMb sequence was demonstrated on a diffusion phantom and healthy human brain to produce diffusion-weighted images, which were quantitative analyzed using a mono-exponential model. In the phantom experiment, SSMb provided similar ADC values to those from a commercial spin-echo EPI (SE-EPI) sequence (r = 0.999). In the human brain experiment, SSMb enabled a fourfold scan time reduction and yielded slightly lower ADC values (0.83 ± 0.26 µm2/ms) than SE-EPI (0.88 ± 0.29 µm2/ms) in all voxels excluding cerebrospinal fluid, likely due to the influence of varying diffusion times. The feasibility of using SSMb to acquire multiple images in a single shot for intravoxel incoherent motion (IVIM) analysis was also demonstrated. In conclusion, despite a relatively low signal-to-noise ratio, the proposed SSMb technique can substantially increase the data acquisition efficiency in DWI studies.

The genome sequence of the Mauritius parakeet, Alexandrinus eques (formerly Psittacula eques) (A.Newton & E. Newton, 1876).

We present a genome assembly from an individual male Alexandrinus eques, formerly Psittacula eques (the Mauritius Parakeet; Chordata; Aves; Psittaciformes; Psittacidae). The genome sequence is 1203.8 megabases in span. Most of the assembly is scaffolded into 35 chromosomal pseudomolecules, including the Z sex chromosome. The mitochondrial genome has also been assembled and is 18.86 kilobases in length.

Ultra-high-resolution mapping of myelin and g-ratio in a panel of Mbp enhancer-edited mouse strains using microstructural MRI.

Non-invasive myelin water fraction (MWF) and g-ratio mapping using microstructural MRI have the potential to offer critical insights into brain microstructure and our understanding of neuroplasticity and neuroinflammation. By leveraging a unique panel of variably hypomyelinating mouse strains, we validated a high-resolution, model-free image reconstruction method for whole-brain MWF mapping. Further, by employing a bipolar gradient echo MRI sequence, we achieved high spatial resolution and robust mapping of MWF and g-ratio across the whole mouse brain. Our regional white matter-tract specific analyses demonstrated a graded decrease in MWF in white matter tracts which correlated strongly with myelin basic protein gene (Mbp) mRNA levels. Using these measures, we derived the first sensitive calibrations between MWF and Mbp mRNA in the mouse. Minimal changes in axonal density supported our hypothesis that observed MWF alterations stem from hypomyelination. Overall, our work strongly emphasizes the potential of non-invasive, MRI-derived MWF and g-ratio modeling for both preclinical model validation and ultimately translation to humans.

Cortical encoding of hierarchical linguistic information when syllabic rhythms are obscured by echoes.

In speech perception, low-frequency cortical activity tracks hierarchical linguistic units (e.g., syllables, phrases, and sentences) on top of acoustic features (e.g., speech envelope). Since the fluctuation of speech envelope typically corresponds to the syllabic boundaries, one common interpretation is that the acoustic envelope underlies the extraction of discrete syllables from continuous speech for subsequent linguistic processing. However, it remains unclear whether and how cortical activity encodes linguistic information when the speech envelope does not provide acoustic correlates of syllables. To address the issue, we introduced a frequency-tagging speech stream where the syllabic rhythm was obscured by echoic envelopes and investigated neural encoding of hierarchical linguistic information using electroencephalography (EEG). When listeners attended to the echoic speech, cortical activity showed reliable tracking of syllable, phrase, and sentence levels, among which the higher-level linguistic units elicited more robust neural responses. When attention was diverted from the echoic speech, reliable neural tracking of the syllable level was also observed in contrast to deteriorated neural tracking of the phrase and sentence levels. Further analyses revealed that the envelope aligned with the syllabic rhythm could be recovered from the echoic speech through a neural adaptation model, and the reconstructed envelope yielded higher predictive power for the neural tracking responses than either the original echoic envelope or anechoic envelope. Taken together, these results suggest that neural adaptation and attentional modulation jointly contribute to neural encoding of linguistic information in distorted speech where the syllabic rhythm is obscured by echoes.

Comparison of 3D UTE free-breathing lung MRI with hyperpolarized 129Xe MRI in pediatric cystic fibrosis.

PURPOSE: To compare phase-resolved functional lung (PREFUL) regional ventilation derived from a free breathing 3D UTE radial MRI acquisition to hyperpolarized 129Xe-MRI (Xe-MRI), conventional 2D multi-slice PREFUL MRI, and pulmonary function tests in pediatric cystic fibrosis (CF) lung disease. METHODS: Free-breathing 3D UTE and 2D multi-slice 1H MRI as well as Xe-MRI were acquired in 12 stable pediatric CF patients. Using PREFUL, regional ventilation (RVent) maps were calculated from the free-breathing data. Ventilation defect percentage (VDP) was determined from 3D and 2D RVent maps (2D VDPRVent and 3D VDPRVent, respectively) and Xe-MRI ventilation (VDPXe). VDP was calculated for the whole lung and for eight regions based on left/right, anterior/posterior, and superior/inferior divisions of the lung. Global and regional VDP was compared between the three methods using Bland-Altman analysis, linear mixed model-based correlation, and one-way analysis of variance and multiple comparisons tests. RESULTS: Global 3D VDPRVent, VDPXe, and 2D VDPRVent were all strongly correlated (all R2 > 0.62, p < 0.0001) and showed minimal, non-significant bias (all <2%, p > 0.05). Three dimensional and 2D VDPRVent significantly correlated to VDPXe in most of the separate lung regions (R2 = 0.18-0.74, p < 0.04), but showed lower inter-agreement. The superior/anterior lung regions showed the least agreement between all three methods (all p > 0.12). CONCLUSION: Absolute VDP assessed by 3D UTE PREFUL MRI showed good global agreement with Xe-MRI and 2D multi-slice PREFUL MRI in pediatric CF lung disease. Therefore, 3D UTE PREFUL MRI offers a sensitive and potentially more accessible alternative to Xe-MRI for regional volumetric evaluation of ventilation.

Enhancing Ultrasonic Echo Response of AlN Thin Film Transducer Deposited by RF Magnetron Sputtering.

Accurate measurement of the pretightening stress for bolts has great significance for improving the assembly quality and safety, especially in severe environments. In this study, AlN thin film transducers were deposited on GH4169 nickel base alloy bolts using the RF magnetron sputtering, enabling a systematic investigation into the correlation between structures and the intensity of ultrasonic echo signals. Employing the finite element method resulted in consistency with the experimental data, enabling further exploration of the enhancement mechanism. With the increasing thickness of both the piezoelectric layer and the electrode layer, the intensity of the ultrasonic echo signals saw a great enhancement. The maximum-intensity observed increase is 14.7 times greater than that of the thinnest layers. Specifically, the thicker piezoelectric layer improves its mechanical displacement, while the increased thickness of the electrode layer contributes to better densification. An electrode diameter of nearly 4 mm is optimal for an AlN thin film transducer of M8 bolts. For pretightening the stress measurement, the sample with a strong and stable echo signal shows a low measurement error of pretightening below ±2.50%.

Ultrashort-Echo-Time MRI of the Disco-Vertebral Junction: Modulation of Image Contrast via Echo Subtraction and Echo Times.

INTRODUCTION: The disco-vertebral junction (DVJ) of the lumbar spine contains thin structures with short T2 values, including the cartilaginous endplate (CEP) sandwiched between the bony vertebral endplate (VEP) and the nucleus pulposus (NP). We previously demonstrated that ultrashort-echo-time (UTE) MRI, compared to conventional MRI, is able to depict the tissues at the DVJ with improved contrast. In this study, we sought to further optimize UTE MRI by characterizing the contrast-to-noise ratio (CNR) of these tissues when either single echo or echo subtraction images are used and with varying echo times (TEs). METHODS: In four cadaveric lumbar spines, we acquired 3D Cones (a UTE sequence) images at varying TEs from 0.032 ms to 16 ms. Additionally, spin echo T1- and T2-weighted images were acquired. The CNRs of CEP-NP and CEP-VEP were measured in all source images and 3D Cones echo subtraction images. RESULTS: In the spin echo images, it was challenging to distinguish the CEP from the VEP, as both had low signal intensity. However, the 3D Cones source images at the shortest TE of 0.032 ms provided an excellent contrast between the CEP and the VEP. As the TE increased, the contrast decreased in the source images. In contrast, the 3D Cones echo subtraction images showed increasing CNR values as the second TE increased, reaching statistical significance when the second TE was above 10 ms (p < 0.05). CONCLUSIONS: Our study highlights the feasibility of incorporating UTE MRI for the evaluation of the DVJ and its advantages over conventional spin echo sequences for improving the contrast between the CEP and adjacent tissues. Additionally, modulation of the contrast for the target tissues can be achieved using either source images or subtraction images, as well as by varying the echo times.

Intraoperative Tumor Migration During Renal Tumor Excision, Veno-Arterial Extracorporeal Membrane Oxygenation (V-A ECMO), and Mechanical Thrombectomy with FlowTriever.

We describe a case of a 76-year-old male with stage 3 renal cell carcinoma and known thrombus burden in his inferior vena cava (IVC) who presented for a scheduled radical right open nephrectomy with regional lymph node dissection and IVC thrombectomy. During this procedure, the patient went into pulseless-electrical activity. A trans-esophageal echocardiogram showed thrombus transit into the right atria. Emergent initiation of veno-arterial extracorporeal membrane oxygenation and mechanical embolectomy using a FlowTriever retrieval catheter was required. The patient remained intubated in critical but stable condition. Shortly afterward, he expired due to subsequent complications of massive hemorrhage and disseminated intravascular coagulopathy.

Sex differences in quantitative ultrasonographic measurements of the rectus femoris in children.

The distribution and amount of intramuscular fat and fibrous tissue can be influenced by biological sex and impact muscle quality in both the functional (force-generating capacity) and morphological (muscle composition) domains. While ultrasonography (US) has proven effective in assessing age- or sex-related differences in muscle quality, limited information is available on sex differences in children. Quantitative ultrasonographic measurements, such as echo intensity (EI), EI bands (number of pixels across 50-unit intervals) and texture, may offer a comprehensive framework for identifying sex differences in muscle composition. The aim of our study was to examine the effect of sex on the rectus femoris (RF) muscle quality in children. We used EI (mean and bands) and texture as muscle quality estimates derived from B-mode US. We hypothesised that RF muscle quality differs significantly between girls and boys. Additionally, we also hypothesised that there is a significant correlation between EI bands and texture. Forty-four non-active healthy children were recruited (n = 22 girls, 12.8 ± 1.5 years; and n = 22 boys, 13.5 ± 1.2 years). RF was assessed using EI mean, EI bands, and texture analysis (homogeneity and correlation) using the Gray-Level Co-Occurrence Matrix. The results revealed significant (p < 0.05) sex differences in RF EI bands and texture. Boys displayed higher values in the 0-50 EI band and had more homogeneous muscle texture than girls. Conversely, girls displayed greater values in the 51-100 EI band and had less homogenous texture compared to boys (p < 0.05). A positive correlation was observed between the 0-50 EI band and muscle homogeneity. However, the 51-100 EI band correlated negatively with homogeneity (p < 0.05), particularly for girls. In conclusion, our study revealed sex-specific differences in mean EI, EI bands, and texture of the RF muscle in children. The variations in the correlations between the first and second EI bands and texture reveal different levels of homogeneity in each band. This indicates that distinct muscle tissue constituents, such as intramuscular fat and/or connective tissue, may be reflected in EI bands. Overall, the methods used in this study may be useful for examining muscle quality in healthy children and those with medical conditions.

LLMDiff: Diffusion Model Using Frozen LLM Transformers for Precipitation Nowcasting.

Precipitation nowcasting, which involves the short-term, high-resolution prediction of rainfall, plays a crucial role in various real-world applications. In recent years, researchers have increasingly utilized deep learning-based methods in precipitation nowcasting. The exponential growth of spatiotemporal observation data has heightened interest in recent advancements such as denoising diffusion models, which offer appealing prospects due to their inherent probabilistic nature that aligns well with the complexities of weather forecasting. Successful application of diffusion models in rainfall prediction tasks requires relevant conditions and effective utilization to direct the forecasting process of the diffusion model. In this paper, we propose a probabilistic spatiotemporal model for precipitation nowcasting, named LLMDiff. The architecture of LLMDiff includes two networks: a conditional encoder-decoder network and a denoising network. The conditional network provides conditional information to guide the denoising network for high-quality predictions related to real-world earth systems. Additionally, we utilize a frozen transformer block from pre-trained large language models (LLMs) in the denoising network as a universal visual encoder layer, which enables the accurate estimation of motion trend by considering long-term temporal context information and capturing temporal dependencies within the frame sequence. Our experimental results demonstrate that LLMDiff outperforms state-of-the-art models on the SEVIR dataset.

Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA.

Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired blood oxygenation level dependent (BOLD) signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example, in a chronic stroke cohort with varying stroke location and degree of tissue damage.