Multimodal Magnetic Resonance Imaging Research Articles

Wallerian Degeneration Assessed by Multi-Modal Magnetic Resonance Imaging of Cervical Spinal Cord Is Associated With Neurological Impairment After Spinal Cord Injury.

While Wallerian degeneration (WD) is a crucial pathological process induced with spinal cord injury (SCI), its underlying mechanisms is still understudied. In this study, we aim to assess structural alterations and clinical significance of WD in the cervical cord following SCI using multi-modal magnetic resonance imaging (MRI), which combines T2*-weighted imaging and diffusion tensor imaging (DTI). T2*-weighted images allow segmentation of anatomical structures and the detection of WD on macrostructural level. DTI, on the other hand, can identify the reduction in neuroaxonal integrity by measuring the diffusion of water molecules on the microstructural level. In this prospective study, 35 SCI patients (19 paraplegic and 16 tetraplegic patients) and 12 healthy controls were recruited between July 2020 and May 2022. The hyperintensity voxels in the dorsal column was manually labeled as WD on T2*-weighted images. The mean cross-sectional area (CSA) and mean DTI indexes of WD at the C2 level were calculated and compared between groups. Correlation analysis was used to determine the associations of the magnitude of WD with lesion characteristics and clinical outcomes. Compared with controls, SCI patients showed evident hyperintensity (35/35) and decreased neuroaxonal integrity (p < 0.05) within the dorsal column at the C2 level. A higher neurological level of injury was associated with a larger mean CSA and reduction in neuroaxonal integrity within WD (p < 0.05). Smaller total and dorsal tissue bridges were related to greater mean CSA and lower fractional anisotropy values in WD (p < 0.05), respectively. Moreover, SCI participants with significantly larger CSAs and significantly lower microstructural integrity had worse sensory outcomes (p < 0.05). This comprehensive evaluation of WD can help us better understand the mechanisms of WD, monitor progression, and assess the effectiveness of therapeutic interventions after SCI.

Correlation between rCBV Delineation Similarity and Overall Survival in a Prospective Cohort of High-Grade Gliomas Patients: The Hidden Value of Multimodal MRI?

The accuracy of target delineation in radiation treatment planning of high-grade gliomas (HGGs) is crucial to achieve high tumor control, while minimizing treatment-related toxicity. Magnetic resonance imaging (MRI) represents the standard imaging modality for delineation of gliomas with inherent limitations in accurately determining the microscopic extent of tumors. The purpose of this study was to assess the survival impact of multi-observer delineation variability of multiparametric MRI (mpMRI) and [18F]-FET PET/CT. Thirty prospectively included patients with histologically confirmed HGGs underwent a PET/CT and mpMRI including diffusion-weighted imaging (DWI: b0, b1000, ADC), contrast-enhanced T1-weighted imaging (T1-Gado), T2-weighted fluid-attenuated inversion recovery (T2Flair), and perfusion-weighted imaging with computation of relative cerebral blood volume (rCBV) and K2 maps. Nine radiation oncologists delineated the PET/CT and MRI sequences. Spatial similarity (Dice similarity coefficient: DSC) was calculated between the readers for each sequence. Impact of the DSC on progression-free survival (PFS) and overall survival (OS) was assessed using Kaplan-Meier curves and the log-rank test. The highest DSC mean values were reached for morphological sequences, ranging from 0.71 +/- 0.18 to 0.84 +/- 0.09 for T2Flair and T1Gado, respectively, while metabolic volumes defined by PET/CT achieved a mean DSC of 0.75 +/- 0.11. rCBV variability (mean DSC0.32 +/- 0.20) significantly impacted PFS (p = 0.02) and OS (p = 0.002). Our data suggest that the T1-Gado and T2Flair sequences were the most reproducible sequences, followed by PET/CT. Reproducibility for functional sequences was low, but rCBV inter-reader similarity significantly impacted PFS and OS.

Cortical-cerebellar circuits changes in preschool ASD children by multimodal MRI.

To investigate the alterations in cortical-cerebellar circuits and assess their diagnostic potential in preschool children with autism spectrum disorder using multimodal magnetic resonance imaging. We utilized diffusion basis spectrum imaging approaches, namely DBSI_20 and DBSI_combine, alongside 3D structural imaging to examine 31 autism spectrum disorder diagnosed patients and 30 healthy controls. The participants' brains were segmented into 120 anatomical regions for this analysis, and a multimodal strategy was adopted to assess the brain networks using a multi-kernel support vector machine for classification. The results revealed consensus connections in the cortical-cerebellar and subcortical-cerebellar circuits, notably in the thalamus and basal ganglia. These connections were predominantly positive in the frontoparietal and subcortical pathways, whereas negative consensus connections were mainly observed in frontotemporal and subcortical pathways. Among the models tested, DBSI_20 showed the highest accuracy rate of 86.88%. In addition, further analysis indicated that combining the 3 models resulted in the most effective performance. The connectivity network analysis of the multimodal brain data identified significant abnormalities in the cortical-cerebellar circuits in autism spectrum disorder patients. The DBSI_20 model not only provided the highest accuracy but also demonstrated efficiency, suggesting its potential for clinical application in autism spectrum disorder diagnosis.

101 Glymphatic System Impairment Associated With Neurocognitive Dysfunction and Alzheimers Biomarkers in Moyamoya Disease

INTRODUCTION: Glymphatic system alterations have been proved to be associated with cognitive dysfunction in Alzheimer’s Disease (AD). The glymphatic pathway has not been elucidated in moyamoya disease (MMD), which was recognized as a chronic hypoperfusion model for AD. METHODS: We prospectively recruited 30 MMD patients and 30 matched healthy controls (HC) from October 2022 to March 2023. Participants underwent neurocognition evaluation, and multimodal magnetic resonance imaging (MRI). AD-related serum biomarkers, as well as correlations among these variables were examined. The glymphatic function was assessed by diffusion tensor image analysis along perivascular space (DTI-ALPS). Whole-brain (white matter and grey matter) volumes were calculated by using 3D T1-weighted MRI. Microstructural changes of white matter (intracellular volume fraction, Vic; orientation dispersion index, OD) were evaluated by neurite orientation dispersion and density imaging (NODDI). RESULTS: Serum Alzheimer’s biomarkers (ApoE, Aβ40, Aβ42, and tau) were significantly elevated and associated with neurocognitive dysfunction in MMD. Lower periventricular ALPS-index was identified in patients with MMD (P &lt; 0.001). The decreased ALPS-index correlated with declined neurocognitive performance of intelligence quotient, attention, and memory, increased levels of serum ApoE, Aβ40, Aβ42, as well as higher periventricular white matter volume and lower periventricular grey matter volume (P &lt; 0.001). Moreover, MMD patients had significantly lower Vic in the white matter and a lower OD in the cortex than HC. CONCLUSIONS: We firstly demonstrated the impaired glymphatic system as assessed by lower ALPS-index in patients with MMD. Glymphatic impairment was associated with AD-related biomarkers and cognitive dysfunction in MMD. These findings suggested that glymphatic disorder may interact with MMD-related pathophysiological processes.

GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox

Multimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with inattentive disorders. In this study, we proposed a comprehensive framework to provide multiple imaging features related to the brain microstructure using only T1-weighted MRI. Our toolbox consists of (i) synthesizing T2-weighted MRI from T1-weighted MRI using a conditional generative adversarial network; (ii) estimating microstructural features, including intracortical covariance and moment features of cortical layer-wise microstructural profiles; and (iii) generating a microstructural gradient, which is a low-dimensional representation of the intracortical microstructure profile. We trained and tested our toolbox using T1- and T2-weighted MRI scans of 1,104 healthy young adults obtained from the Human Connectome Project database. We found that the synthesized T2-weighted MRI was very similar to the actual image and that the synthesized data successfully reproduced the microstructural features. The toolbox was validated using an independent dataset containing healthy controls and patients with episodic migraine as well as the atypical developmental condition of autism spectrum disorder. Our toolbox may provide a new paradigm for analyzing multimodal structural MRI in the neuroscience community and is openly accessible at https://github.com/CAMIN-neuro/GAN-MAT.

NaMa: Neighbor-Aware Multi-Modal Adaptive Learning for Prostate Tumor Segmentation on Anisotropic MR Images

Accurate segmentation of prostate tumors from multi-modal magnetic resonance (MR) images is crucial for diagnosis and treatment of prostate cancer. However, the robustness of existing segmentation methods is limited, mainly because these methods 1) fail to adaptively assess subject-specific information of each MR modality for accurate tumor delineation, and 2) lack effective utilization of inter-slice information across thick slices in MR images to segment tumor as a whole 3D volume. In this work, we propose a two-stage neighbor-aware multi-modal adaptive learning network (NaMa) for accurate prostate tumor segmentation from multi-modal anisotropic MR images. In particular, in the first stage, we apply subject-specific multi-modal fusion in each slice by developing a novel modality-informativeness adaptive learning (MIAL) module for selecting and adaptively fusing informative representation of each modality based on inter-modality correlations. In the second stage, we exploit inter-slice feature correlations to derive volumetric tumor segmentation. Specifically, we first use a Unet variant with sequence layers to coarsely capture slice relationship at a global scale, and further generate an activation map for each slice. Then, we introduce an activation mapping guidance (AMG) module to refine slice-wise representation (via information from adjacent slices) for consistent tumor segmentation across neighboring slices. Besides, during the network training, we further apply a random mask strategy to each MR modality to improve feature representation efficiency. Experiments on both in-house and public (PICAI) multi-modal prostate tumor datasets show that our proposed NaMa performs better than state-of-the-art methods.

TMFormer: Token Merging Transformer for Brain Tumor Segmentation with Missing Modalities

Numerous techniques excel in brain tumor segmentation using multi-modal magnetic resonance imaging (MRI) sequences, delivering exceptional results. However, the prevalent absence of modalities in clinical scenarios hampers performance. Current approaches frequently resort to zero maps as substitutes for missing modalities, inadvertently introducing feature bias and redundant computations. To address these issues, we present the Token Merging transFormer (TMFormer) for robust brain tumor segmentation with missing modalities. TMFormer tackles these challenges by extracting and merging accessible modalities into more compact token sequences. The architecture comprises two core components: the Uni-modal Token Merging Block (UMB) and the Multi-modal Token Merging Block (MMB). The UMB enhances individual modality representation by adaptively consolidating spatially redundant tokens within and outside tumor-related regions, thereby refining token sequences for augmented representational capacity. Meanwhile, the MMB mitigates multi-modal feature fusion bias, exclusively leveraging tokens from present modalities and merging them into a unified multi-modal representation to accommodate varying modality combinations. Extensive experimental results on the BraTS 2018 and 2020 datasets demonstrate the superiority and efficacy of TMFormer compared to state-of-the-art methods when dealing with missing modalities.

Nomogram based on multimodal magnetic resonance combined with B7-H3mRNA for preoperative lymph node prediction in esophagus cancer.

Accurate preoperative prediction of lymph node metastasis (LNM) in esophageal cancer (EC) patients is of crucial clinical significance for treatment planning and prognosis. To develop a clinical radiomics nomogram that can predict the preoperative lymph node (LN) status in EC patients. A total of 32 EC patients confirmed by clinical pathology (who underwent surgical treatment) were included. Real-time fluorescent quantitative reverse transcription-polymerase chain reaction was used to detect the expression of B7-H3 mRNA in EC tissue obtained during preoperative gastroscopy, and its correlation with LNM was analyzed. Radiomics features were extracted from multi-modal magnetic resonance imaging of EC using Pyradiomics in Python. Feature extraction, data dimensionality reduction, and feature selection were performed using XGBoost model and leave-one-out cross-validation. Multivariable logistic regression analysis was used to establish the prediction model, which included radiomics features, LN status from computed tomography (CT) reports, and B7-H3 mRNA expression, represented by a radiomics nomogram. Receiver operating characteristic area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical application value of the model. The relative expression of B7-H3 mRNA in EC patients with LNM was higher than in those without metastasis, and the difference was statistically significant (P < 0.05). The AUC value in the receiver operating characteristic (ROC) curve was 0.718 (95%CI: 0.528-0.907), with a sensitivity of 0.733 and specificity of 0.706, indicating good diagnostic performance. The individualized clinical prediction nomogram included radiomics features, LN status from CT reports, and B7-H3 mRNA expression. The ROC curve demonstrated good diagnostic value, with an AUC value of 0.765 (95%CI: 0.598-0.931), sensitivity of 0.800, and specificity of 0.706. DCA indicated the practical value of the radiomics nomogram in clinical practice. This study developed a radiomics nomogram that includes radiomics features, LN status from CT reports, and B7-H3 mRNA expression, enabling convenient preoperative individualized prediction of LNM in EC patients.

Macrophage Migration Inhibitory Factor as a Potential Plasma Biomarker of Cognitive Impairment in Cerebral Small Vessel Disease.

As the pathogenesis of cerebral small vessel disease with cognitive impairment (CSVD-CI) remains unclear, identifying effective biomarkers can contribute to the clinical management of CSVD-CI. This study recruited 54 healthy controls (HCs), 60 CSVD-CI patients, and 57 CSVD cognitively normal (CSVD-CN) patients. All participants underwent neuropsychological assessments and multimodal magnetic resonance imaging. Macrophage migration inhibitory factors (MIFs) were assessed in plasma. The least absolute shrinkage and selection operator model was used to determine a composite marker. Compared with HCs or CSVD-CN patients, CSVD-CI patients had significantly increased plasma MIF levels. In CSVD-CI patients, plasma MIF levels were significantly correlated with multiple cognitive assessment scores, plasma levels of blood-brain barrier (BBB)-related indices, white matter hyperintensity Fazekas scores, and the mean amplitude of low-frequency fluctuation in the right superior temporal gyrus. Higher plasma MIF levels were significantly associated with worse global cognition and information processing speed in CSVD-CI patients. The composite marker (including plasma MIF) distinguished CSVD-CI patients from CSVD-CN and HCs with >80% accuracy. Meta-analysis indicated that blood MIF levels were significantly increased in CSVD-CI patients. In conclusion, plasma MIF is a potential biomarker for early identification of CSVD-CI. Plasma MIF may play a role in cognitive decline in CSVD through BBB dysfunction and changes in white matter hyperintensity and brain activity.

Quantifying blood-brain barrier permeability in patients with ischemic stroke using non-contrast MRI

Increased blood-brain barrier permeability (BBBP) after ischemic stroke predisposes patients to hemorrhagic conversion. While altered BBBP can impact patient recovery, it is not routinely assessed during the workup of acute ischemic stroke (AIS). We study the effectiveness of the non-contrast MRI sequences diffusion-prepared pseudocontinuous arterial spin labeling (DP-pCASL) and Neurite Orientation Dispersion and Density Imaging (NODDI) in assessing BBBP and correlating to tissue microstructure after ischemic insult. Twelve patients with AIS were prospectively enrolled to undergo our multimodal MR imaging, which generated the DP-pCASL-derived cerebral blood flow (CBF), arterial transit time (ATT), and water exchange rate (kw) and the NODDI-derived b0, mean diffusivity (MD), orientation dispersion index (ODI), intracellular volume fraction (ICVF), and isotropic volume fraction (ISO) parametric maps. The mean age of the patients was 70.2 ± 14.8 with an average NIHSS of 13.0 (7.3–19.8). MR imaging was performed on average at 53.7 (27.8–93.3) hours from stroke symptom onset. The water exchange rate (kw) of the infarcted area and its contralateral territory were 89.7 min−1 (66.7–121.9) and 89.9 min−1 (65.9–106.0) respectively (p = 0.887). Multivariable linear regression analysis showed that b0, ODI, ISO and mechanical thrombectomy were significant predictors of kw. DP-pCASL and NODDI are promising non-contrast sequences for the routine assessment of BBBP.

A clinico-anatomical dissection of the magnocellular and parvocellular pathways in a patient with the Riddoch syndrome

Key messageThe Riddoch syndrome is thought to be caused by damage to the primary visual cortex (V1), usually following a vascular event. This study shows that damage to the anatomical input to V1, i.e., the optic radiations, can result in selective visual deficits that mimic the Riddoch syndrome. The results also highlight the differential susceptibility of the magnocellular and parvocellular visual systems to injury. Overall, this study offers new insights that will improve our understanding of the impact of brain injury and neurosurgery on the visual pathways.The Riddoch syndrome, characterised by the ability to perceive, consciously, moving visual stimuli but not static ones, has been associated with lesions of primary visual cortex (V1). We present here the case of patient YL who, after a tumour resection surgery that spared his V1, nevertheless showed symptoms of the Riddoch syndrome. Based on our testing, we postulated that the magnocellular (M) and parvocellular (P) inputs to his V1 may be differentially affected. In a first experiment, YL was presented with static and moving checkerboards in his blind field while undergoing multimodal magnetic resonance imaging (MRI), including structural, functional, and diffusion, acquired at 3 T. In a second experiment, we assessed YL’s neural responses to M and P visual stimuli using psychophysics and high-resolution fMRI acquired at 7 T. YL’s optic radiations were partially damaged but not severed. We found extensive activity in his visual cortex for moving, but not static, visual stimuli, while our psychophysical tests revealed that only low-spatial frequency moving checkerboards were perceived. High-resolution fMRI revealed strong responses in YL's V1 to M stimuli and very weak ones to P stimuli, indicating a functional P lesion affecting V1. In addition, YL frequently reported seeing moving stimuli and discriminating their direction of motion in the absence of visual stimulation, suggesting that he was experiencing visual hallucinations. Overall, this study highlights the possibility of a selective loss of P inputs to V1 resulting in the Riddoch syndrome and in hallucinations of visual motion.

Contralateral acupuncture for migraine without aura: a randomized trial protocol with multimodal MRI.

Migraine is a common clinical disorder, ranks as the second most disabling disease worldwide, and often manifests with unilateral onset. Contralateral acupuncture (CAT), as a classical acupuncture method, has been proven to be effective in the treatment of migraine without aura (MWoA). However, its neural mechanisms have not been investigated using multimodal magnetic resonance imaging (MRI). In this multimodal neuroimaging randomized trial, a total of 96 female MWoA participants and 30 female healthy controls (HCs) will be recruited. The 96 female MWoA participants will be randomized into three groups: Group A (CAT group), Group B [ipsilateral acupuncture (IAT) group], and Group C (sham CAT group) in a 1:1:1 allocation ratio. Each group will receive 30 min of treatment every other day, three times a week, for 8 weeks, followed by an 8-week follow-up period. The primary outcome is the intensity of the migraine attack. Data will be collected at baseline (week 0), at the end of the 8-week treatment period (weeks 1-8), and during the 8-week follow-up (weeks 9-16). Adverse events will be recorded. Multimodal MRI scans will be conducted at baseline and after 8-week treatment. This study hypothesized that CAT may treat MWoA by restoring pathological alterations in brain neural activity, particularly by restoring cross-integrated functional connectivity with periaqueductal gray (PAG) as the core pathological brain region. The findings will provide scientific evidence for CAT in the treatment of MWoA. The Medical Ethics Committee of the Second Affiliated Hospital of Yunnan University of Chinese Medicine has given study approval (approval no. 2022-006). This trial has been registered with the Chinese Clinical Trials Registry (registration no. ChiCTR2300069456). Peer-reviewed papers will be used to publicize the trial's findings. https://clinicaltrials.gov/, identifier ChiCTR2300069456.

Enhancing Acute Bilirubin Encephalopathy Diagnosis with Multi-Modal MRI: A Deep Learning Approach

Background: Acute Bilirubin Encephalopathy (ABE) is a major cause of infant mortality and disability, making early detection and treatment essential to prevent further progression and complications. Methods: To enhance the diagnostic capabilities of multi-modal Magnetic Resonance Imaging (MRI) for ABE, we proposed a deep learning model integrating an attention module (AM) with a central network (CentralNet). This model was tested on MRI data from 145 newborns diagnosed with ABE and 140 non-ABE newborns, utilizing both T1-weighted and T2-weighted images. Results: The findings indicated the following: (1) In single-modality experiments, the inclusion of AM significantly improved all the performance metrics compared to the models without AM. Specifically, for T1-weighted MRI, the accuracy was 0.639 ± 0.04, AUC was 0.682 ± 0.037, and sensitivity was 0.688 ± 0.09. For the T2-weighted images, the accuracy was 0.738 ± 0.039 and the AUC was 0.796 ± 0.025. (2) In multi-modal experiments, using T1 + T2 images, our model achieved the best accuracy of 0.845 ± 0.018, AUC of 0.913 ± 0.02, and sensitivity of 0.954 ± 0.069, compared to models without an AM and CentralNet. The specificity remained relatively stable, while the precision and F1 scores significantly increased, reaching 0.792 ± 0.048 and 0.862 ± 0.017, respectively. Conclusions: This study emphasizes the effectiveness of combining attention modules with CentralNet, significantly enhancing the accuracy of multi-modal MRI in classifying ABE. It presents a new perspective and possibility for the clinical application of multi-modal MRI imaging in the diagnosis of ABE.

Application of 3.0 T multimodal MRI in FIGO staging of cervical cancer.

In malignant tumours of the female reproductive system, cervical cancer is second only to breast cancer, seriously threatening the health and safety of most women. To evaluate the clinical value of 3.0 T multimodal nuclear magnetic resonance imaging (MRI) in the International Federation of Gynecology and Obstetrics' (FIGO) staging of cervical cancer. The clinical data of 30 patients with pathologically diagnosed cervical cancer admitted to our hospital from January 2018 to August 2022 were analysed retrospectively. Before treatment, all patients were examined with conventional MRI, diffusion-weighted imaging and multi-directional contrast-enhanced imaging. The accuracy of multimodal MRI in the FIGO staging of cervical cancer (29/30, 96.7%) was significantly higher than the accuracy obtained in a control group (21/30, 70.0%), with a statistically significant difference (p= 0.013). In addition, there was good agreement between two observers applying multimodal imaging (kappa= 0.881) and moderate agreement between two observers in the control group (kappa= 0.538). Multimodal MRI can evaluate cervical cancer comprehensively and accurately to enable accurate FIGO staging, providing significant evidence for clinical operation planning and subsequent combined therapy.

The value of multimodality MR in T staging evaluation after neoadjuvant therapy for rectal cancer.

Surgery is the preferred treatment for rectal cancer, but surgical treatment alone sometimes does not achieve satisfactory results. To explore the value of multimodal Magnetic Resonance (MR) images in evaluating T staging of rectal cancer after neoadjuvant therapy and to compare and analyze with pathological results. This study retrospectively analyzed 232 patients with stage T3, T4 rectal cancer between January 1, 2017 and October 31, 2022. MR examination was performed within 3 days before surgery. Different MR sequences were used for mrT staging of rectal cancer after neoadjuvant therapy and compared with pathological pT staging. The accuracy of different MR sequences in evaluating T staging of rectal cancer was calculated, and the consistency between the two was analyzed by kappa test. The sensitivity, specificity, negative predictive value and positive predictive value of different MR sequences in evaluating rectal cancer invading mesorectal fascia after neoadjuvant therapy were calculated. A total of 232 patients with rectal cancer were included in the study. The accuracy of high-resolution T2 WI in evaluating T staging of rectal cancer after neoadjuvant therapy was 49.57%, and the Kappa value was 0.261. The accuracy of high-resolution T2WI combined with diffusion weighted imaging (DWI) in evaluating T staging of rectal cancer after neoadjuvant therapy was 61.64%, and the Kappa value was 0.411. The accuracy of high-resolution combined with DCE-MR images in evaluating T staging of rectal cancer after neoadjuvant therapy was 80.60%, and the Kappa value was 0.706. The sensitivity and specificity of high-resolution t2-weighted imaging (HR-T2WI) combined with dynamic contrast-enhancement magnetic resonance (DCE-MR) in evaluating the invasion of mesorectal fascia were 83.46% and 95.33%, respectively. Compared with HR-T2WI combined with DWI images for mrT staging of rectal cancer after neoadjuvant chemoradiotherapy (N-CRT), HR-T2WI combined with DCE-M has the highest accuracy in evaluating mrT staging of rectal cancer after neoadjuvant therapy (80.60%), and has a high consistency with pathological pT staging. It is the best sequence for T staging of rectal cancer after neoadjuvant therapy. At the same time, the sequence has high sensitivity and specificity in evaluating mesorectal fascia invasion, which can provide accurate perioperative information for the formulation of surgical plan.

Prefrontal-Limbic Circuitry Is Associated With Reward Sensitivity in Nonhuman Primates

BackgroundAbnormal reward sensitivity is a risk factor for psychiatric disorders, including eating disorders such as overeating and binge-eating disorder, but the brain structural mechanisms that underlie it are not completely understood. Here, we sought to investigate the relationship between multimodal whole-brain structural features and reward sensitivity in nonhuman primates. MethodsReward sensitivity was evaluated through behavioral economic analysis in which monkeys (adult rhesus macaques; 7 female, 5 male) responded for sweetened condensed milk (10%, 30%, 56%), Gatorade, or water using an operant procedure in which the response requirement increased incrementally across sessions (i.e., fixed ratio 1, 3, 10). Animals were divided into high (n = 6) or low (n = 6) reward sensitivity groups based on essential value for 30% milk. Multimodal magnetic resonance imaging was used to measure gray matter volume and white matter microstructure. Brain structural features were compared between groups, and their correlations with reward sensitivity for various stimuli was investigated. ResultsAnimals in the high sensitivity group had greater dorsolateral prefrontal cortex, centromedial amygdaloid complex, and middle cingulate cortex volumes than animals in the low sensitivity group. Furthermore, compared with monkeys in the low sensitivity group, high sensitivity monkeys had lower fractional anisotropy in the left dorsal cingulate bundle connecting the centromedial amygdaloid complex and middle cingulate cortex to the dorsolateral prefrontal cortex, and in the left superior longitudinal fasciculus 1 connecting the middle cingulate cortex to the dorsolateral prefrontal cortex. ConclusionsThese results suggest that neuroanatomical variation in prefrontal-limbic circuitry is associated with reward sensitivity. These brain structural features may serve as predictive biomarkers for vulnerability to food-based and other reward-related disorders.

Multi-modality MRI fusion with patch complementary pre-training for internet of medical things-based smart healthcare

Magnetic Resonance Imaging (MRI) is a pivotal neuroimaging technique capable of generating images with various contrasts, known as multi-modal images. The integration of these diverse modalities is essential for improving model performance across various tasks. However, in real clinical scenarios, acquiring MR images for all modalities is frequently hindered by factors such as patient comfort and scanning costs. Therefore, effectively fusing different modalities to synthesize missing modalities has become a research hot-spot in the field of smart healthcare, particularly in the context of the Internet of Medical Things (IoMT). In this study, we introduce a multi-modal coordinated fusion network (MCF-Net) with Patch Complementarity Pre-training. This network leverages the complementarity and correlation between different modalities to make the fusion of multi-modal MR images, addressing challenges in the IoMT. Specifically, we first employ a Patch Complementarity Mask Autoencoder (PC-MAE) for self-supervised pre-training. The complementarity learning mechanism is introduced to align masks and visual annotations between two modalities. Subsequently, a dual-branch MAE architecture and a shared encoder–decoder are adopted to facilitate cross-modal interactions within mask tokens. Furthermore, during the fine-tuning phase, we incorporate an Attention-Driven Fusion (ADF) module into the MCF-Net. This module synthesizes missing modal images by fusion of multi-modal features from the pre-trained PC-MAE encoder. Additionally, we leverage the pre-trained encoder to extract high-level features from both synthetic and corresponding real images, ensuring consistency throughout the training process. Our experimental findings showcase a notable enhancement in performance across various modalities with our fusion method, outperforming state-of-the-art techniques.

Edema progression in proximity to traumatic microbleeds: Evolution of cytotoxic and vasogenic edema on serial MRI

IntroductionAlthough cerebral edema is common following traumatic brain injury (TBI), its formation and progression are poorly understood. This is especially true for the mild TBI population, who rarely undergo magnetic resonance imaging (MRI) studies, which can pick up subtle structural details not visualized on computed tomography, in the first few days after injury. This study aimed to visually classify and quantitatively measure edema progression in relation to traumatic microbleeds (TMBs) in a cohort of primarily mild TBI patients up to 30 days after injury. Researchers hypothesized that hypointense lesions on Apparent Diffusion Coefficient (ADC) detected acutely after injury would evolve into hyperintense Fluid Attenuated Inversion Recover (FLAIR) lesions. MethodsThis study analyzed the progression of cerebral edema after acute injury using multimodal MRI to classify TMBs as potential edema-related biomarkers. ADC and FLAIR MRI were utilized for edema classification at three different timepoints: ≤48 h, ∼1 week, and 30 days after injury. Hypointense lesions on ADC (ADC+) suggested the presence of cytotoxic edema while hyperintense lesions on FLAIR (FLAIR+) suggested vasogenic edema. Signal intensity Ratio (SIR) calculations were made using ADC and FLAIR to quantitatively confirm edema progression. ResultsOur results indicated the presence of ADC+ lesions ≤48 h and ∼1 week were associated with FLAIR + lesions at ∼1 week and 30 days, respectively, suggesting some progression of cytotoxic edema to vasogenic edema over time. Ten out of 15 FLAIR + lesions at 30 days (67%) were ADC+ ≤48 h. However, ADC + lesions ≤48 h were not associated with FLAIR + lesions at 30 days; 10 out of 25 (40%) ADC + lesions ≤48 h were FLAIR + at 30 days, which could indicate that some lesions resolved or were not visualized due to associated atrophy or tissue necrosis. Quantitative analysis confirmed the visual progression of some TMB lesions from ADC + to FLAIR+. FLAIR SIRs at ∼1 week were significantly higher when lesions were ADC+ ≤48 h (1.22 [1.08–1.32] vs 1.03 [0.97–1.11], p = 0.002). ConclusionAwareness of how cerebral edema can evolve in proximity to TMBs acutely after injury may facilitate identification and monitoring of patients with traumatic cerebrovascular injury and assist in development of novel therapeutic strategies.

Sliding transformer with uncertainty estimation for vestibular schwannoma automatic segmentation.

Objective.Automated segmentation of vestibular schwannoma (VS) using magnetic resonance imaging (MRI) can enhance clinical efficiency. Though many advanced methods exist for automated VS segmentation, the accuracy is hindered by ambivalent tumor borders and cystic regions in some patients. In addition, these methods provide results that do not indicate segmentation uncertainty, making their translation into clinical workflows difficult due to potential errors. Providing a definitive segmentation result along with segmentation uncertainty or self-confidence is crucial for the conversion of automated segmentation programs to clinical aid diagnostic tools.Approach.To address these issues, we propose a U-shaped cascade transformer structure with a sliding window that utilizes multiple sliding samples, a segmentation head, and an uncertainty head to obtain both the segmentation mask and uncertainty map. We collected multimodal MRI data from 60 clinical patients with VS from Xuanwu Hospital. Each patient case includes T1-weighted images, contrast-enhanced T1-weighted images, T2-weighted images, and a tumor mask. The images exhibit an in-plane resolution ranging from 0.70 × 0.70 to 0.76 × 0.76 mm, an in-plane matrix spanning from 216 × 256 to 284 × 256, a slice thickness varying between 0.50 and 0.80 mm, and a range of slice numbers from 72 to 120.Main results.Extensive experimental results show that our method achieves comparable or higher results than previous state-of-the-art brain tumor segmentation methods. On our collected multimodal MRI dataset of clinical VS, our method achieved the dice similarity coefficient (DSC) of 96.08% ± 1.30. On a publicly available VS dataset, our method achieved the mean DSC of 94.23% ± 2.53.Significance.The method efficiently solves the VS segmentation task while providing an uncertainty map of the segmentation results, which helps clinical experts review the segmentation results more efficiently and helps to transform the automated segmentation program into a clinical aid diagnostic tool.

Characteristics of acute ischemic stroke and unusual aura in patients with migraine with aura.

Sometimes migraine aura changes from attack to attack, raising the question of whether the change is heralding an ischemic stroke or an unusual aura. Differentiating unusual migraine aura from the onset of an acute ischemic stroke in patients with migraine with aura (MwA) can be challenging. The aim of this cohort study was to assess clinical characteristics that help distinguish between MwA and minor stroke in patients with a previous history of MwA who presented with suspicion of stroke. We interviewed patients with MwA and ischemic stroke (MwA + IS) and patients with MwA and unusual aura, but without ischemic stroke (MwA - IS) from a tertiary hospital using a structured questionnaire. We assessed how symptoms of ischemic stroke or unusual aura differed from usual, that is, the typical aura in each patient. Stroke or exclusion of stroke was verified by multimodal magnetic resonance imaging. Seventeen patients with MwA + IS and twelve patients with MwA - IS were included. New focal neurological symptoms (13/17 [76%] vs. 3/12 [25%]), change of the first symptom (10/17 [59%] vs. 1/12 [8%]), and absence of headache (6/15 [40%] vs. 2/10 [20%]) were more often reported during ischemic stroke. The physical examination was normal in 8/17 (47%) MwA + IS and in 6/12 (50%) MwA - IS patients. In 5/17 (29%) patients with MwA + IS, there were unequivocal physical signs suggestive of stroke such as persistent visual loss, ataxia, or paresis. There are clues from the history that might help identify stroke in patients with MwA with changed aura symptoms. These might be particularly useful in patients presenting without physical findings suggestive of stroke.