Multimodal Magnetic Resonance Research Articles

Amygdala structural and functional reorganization as an indicator of affective dysfunction in patients with tinnitus.

The aim of this study was to systematically investigate structural and functional alterations in amygdala subregions using multimodal magnetic resonance imaging (MRI) in patients with tinnitus with or without affective dysfunction. Sixty patients with persistent tinnitus and 40 healthy controls (HCs) were recruited. Based on a questionnaire assessment, 26 and 34 patients were categorized into the tinnitus patients with affective dysfunction (TPAD) and tinnitus patients without affective dysfunction (TPWAD) groups, respectively. MRI-based measurements of gray matter volume, fractional anisotropy (FA), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), degree centrality (DC), and functional connectivity (FC) were conducted within 14 amygdala subregions for intergroup comparisons. Associations between the MRI properties and clinical characteristics were estimated via partial correlation analyses. Compared with that of the HCs, the TPAD and TPWAD groups exhibited significant structural and functional changes, including white matter integrity (WMI), fALFF, ReHo, DC, and FC alterations, with more pronounced WMI changes in the TPAD group, predominantly within the left auxiliary basal or basomedial nucleus (AB/BM), right central nucleus, right lateral nuclei (dorsal portion), and left lateral nuclei (ventral portion containing basolateral portions). Moreover, the TPAD group exhibited decreased FC between the left AB/BM and left middle occipital gyrus and right superior frontal gyrus (SFG), left basal nucleus and right SFG, and right lateral nuclei (intermediate portion) and right SFG. In combination, these amygdalar alterations exhibited a sensitivity of 65.4% and specificity of 96.9% in predicting affective dysfunction in patients with tinnitus. Although similar structural and functional amygdala remodeling were observed in the TPAD and TPWAD groups, the changes were more pronounced in the TPAD group. These changes mainly involved alterations in functionality and white matter microstructure in various amygdala subregions; in combination, these changes could serve as an imaging-based predictor of emotional disorders in patients with tinnitus.

#2484 Diffusion magnetic resonance imaging, a noninvasive tool to assess fibrosis level in both native and allograft kidneys

Abstract Background and Aims Renal cortical interstitial fibrosis is a well-established prognostic indicator for renal health, conventionally estimated by biopsy. Our prior research has enhanced DW-MRI sequencing, facilitating precise distinction between the renal cortex and medulla. This has enabled the measurement of the cortico-medullary apparent diffusion coefficient difference (ΔADC), which inversely correlates with the extent of histologically assessed interstitial fibrosis. The present study is designed to validate these findings, assess their reproducibility in a separate institution, and ascertain their significance in delineated subgroups, particularly between allograft recipients and native kidney patients. Method From 2014 to 2023, we orchestrated a prospective cohort study encompassing 309 patients diagnosed with chronic kidney disease (n = 106) or those with a renal allograft (n = 203). Each patient had undergone a biopsy, subsequent to which a multimodal magnetic resonance imaging (MRI) was systematically conducted in the following weeks. Initially, participants were recruited from Geneva hospitals, with subsequent inclusion of patients from Lausanne in Switzerland. The study's objective was to evaluate the correlation between ΔADC and the degree of renal fibrosis at biopsy. We performed subgroup analyses (per center, native/allograft kidneys) and applied linear regression models to investigate these associations rigorously. Results We included 270 participants in Geneva and 39 patients in Lausanne. A significant negative correlation was observed between ΔADC and the degree of fibrosis across the entire cohort (R = −0.41, p < 0.001). Subgroup analyses yielded consistent findings in both Geneva (R = −0.43, p < 0.001) and Lausanne University Hospitals (R = −0.35, p < 0.001). Furthermore, this association remained significant within subgroups of both native and allograft kidneys (respectively: R = −0.48, p < 0.001 and R = −0.38, p < 0.001). Conclusion Our findings reinforce the link between ΔADC and renal fibrosis, indicating that this association is consistent regardless of the center or kidney type (native or allograft). This supports the potential utility of diffusion MRI as a non-invasive tool for the assessment of renal fibrosis.

Comparison of different group-level templates in gradient-based multimodal connectivity analysis

Abstract The study of large-scale brain connectivity is increasingly adopting unsupervised approaches that derive low-dimensional spatial representations from high-dimensional connectomes, referred to as gradient analysis. When translating this approach to study interindividual variations in connectivity, one technical issue pertains to the selection of an appropriate group-level template to which individual gradients are aligned. Here, we compared different group-level template construction strategies using functional and structural connectome data from neurotypical controls and individuals with autism spectrum disorder (ASD) to identify between-group differences. We studied multimodal magnetic resonance imaging data obtained from the Autism Brain Imaging Data Exchange (ABIDE) Initiative II and the Human Connectome Project (HCP). We designed six template construction strategies that varied in whether: (1) they included typical controls in addition to ASD; or (2) they mapped from one dataset onto another. We found that aligning a combined subject template of the ASD and control subjects from the ABIDE Initiative onto the HCP template exhibited the most pronounced effect size. This strategy showed robust identification of ASD-related brain regions for both functional and structural gradients across different study settings. Replicating the findings on focal epilepsy demonstrated the generalizability of our approach. Our findings will contribute to improving gradient-based connectivity research.

Multi-modal Magnetic Resonance Imaging to Assess Cerebrovascular Function in Atrial Fibrillation Patients

Atrial fibrillation (AF) is associated with an increased risk of cognitive decline, dementia, and stroke. Low cerebral blood flow (CBF) and low cerebrovascular carbon dioxide reactivity (CVRCO2) are associated with poor brain health outcomes. In the current study we employed magnetic resonance imaging (MRI) to quantify resting CBF and CVRCO2 in patients with AF and age-matched normal sinus rhythm (NSR) controls. We hypothesised that both resting CBF and CVRCO2 would be lower in patients with AF. Patients with AF (n=11, 65±14 years, 2 women) and NSR (n=12, 63±9 years, 7 women) underwent arterial spin labelling (ASL) at rest, and blood oxygen level-dependent (BOLD) MRI during a single-step increase in partial pressure of carbon dioxide (measured by end-tidal carbon dioxide (PETCO2)) induced by changing the fraction of inspired CO2 to 5% (21% O2, balance N2). Region of interest time courses were extracted with grey matter, middle cerebral artery, and posterior cerebral artery territory masks. CVRCO2 was defined as the percentage change (%Δ) in BOLD signal vs %Δ in PETCO2. Baseline CBF was not different between NSR and AF in grey matter (62±17 vs. 58±10 ml/min/100g, respectively; p=0.226), middle cerebral artery (59±16 vs. 56±14 ml/min/100g; p=0.361) and posterior cerebral artery (61±17 vs 53±15 ml/min/100g; p=0.138) territories. BOLD-derived CVRC02 was not different between NSR and AF groups in grey matter (0.11±0.04 vs. 0.12±0.03 %ΔBOLD/%ΔmmHg, respectively; p=0.372), middle cerebral artery (0.09±0.04 vs. 0.10±0.03 %ΔBOLD/%ΔmmHg; p=0.405) and posterior cerebral artery (0.13±0.04 vs. 0.14±0.05 %ΔBOLD/%ΔmmHg; p=0.367) territories. Collectively, these pilot data suggest that MRI-derived cerebrovascular CO2 responsiveness is not different in patients with AF and age-matched NSR controls that have similar resting CBF. Further investigations are required to extend these preliminary findings to a larger cohort to fully elucidate the effects of AF on cerebrovascular function. Support provided by the Royal Society of New Zealand Te Apārangi Marsden Fund (19-UOA-170). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Misalignment-Resistant Deep Unfolding Network for multi-modal MRI super-resolution and reconstruction

Multi-modal Magnetic Resonance Imaging (MRI) super-resolution (SR) and reconstruction aims to obtain a high-quality target image from corresponding sparsely sampled signals under the guidance of a reference image. However, existing techniques typically assume that the input multi-modal MR images are well aligned, which is challenging to achieve in clinical practice. This naive assumption has made their algorithms vulnerable to misalignment scenarios. Moreover, they often neglect many non-local common characteristics within and between modalities. In this work, we proposed a MisAlignment-Resistant Deep Unfolding Network (MAR-DUN) embedded in the tailored gradient descent module (GDM) and proximal mapping module (PMM) for multi-modal MRI SR and reconstruction. In the GDM, we employ an adaptive step-size sub-network (ASS-Net) to enhance the texture representation capacity of the proposed MAR-DUN. Furthermore, in the PMM, we propose a cross-modality non-local module (CNLM) featuring the inverse deformation layer (IDL). The IDL aligns features between the target and reference images by adaptively learning their spatial transformations, thus enhancing the robustness of the proposed network and allowing the CNLM to further explore the cross-modality non-local characteristics. On the other hand, the proposed CNLM aims to establish both the intra-modality and inter-modality non-local dependencies for fully exploiting the correlations between the target and reference images. Extensive experimental results show that our proposed method consistently achieves state-of-the-art reconstruction performance in alignment and misalignment scenarios, demonstrating its significant promise for real-world applications.

Multi-teacher cross-modal distillation with cooperative deep supervision fusion learning for unimodal segmentation

Accurate brain tumor segmentation is a labor-intensive and time-consuming task that requires automation to enhance its efficacy. Recent advanced techniques have shown promising results in segmenting brain tumors; however, their dependency on extensive multimodal magnetic resonance imaging (MRI) data limits their practicality in clinical environments where such data may not be readily available. To address this, we propose a novel multi-teacher cross-modal knowledge distillation framework, which utilizes the privileged multimodal data during training while relying solely on unimodal data for inference. Our framework is tailored to the unimodal segmentation of the T1ce MRI sequence, which is prevalently available in clinical practice and structurally akin to the T1 modality, providing ample information for the segmentation task. Our framework introduces two learning strategies for knowledge distillation (KD): (1) performance-aware response-based KD and (2) cooperative deep supervision fusion learning (CDSFL). The first strategy involves dynamically assigning confidence weights to each teacher model based on its performance, ensuring that the KD is performance-driven, and the CDSFL module augments the learning capabilities of the multi-teacher models by fostering mutual learning. Moreover, the fused information is distilled into the student model to improve its deep supervision. Extensive experiments on BraTS datasets demonstrate that our framework achieves promising unimodal segmentation results on the T1ce and T1 modalities and outperforms previous state-of-the-art methods. Code is available at https://github.com/ami-lab-knut/mtcm_kd.

Glioma segmentation based on dense contrastive learning and multimodal features recalibration

Accurate segmentation of different regions of gliomas from multimodal magnetic resonance (MR) images is crucial for glioma grading and precise diagnosis, but many existing segmentation methods are difficult to effectively utilize multimodal MR image information to recognize accurately the lesion regions with small size, low contrast and irregular shape. To address this issue, this work proposes a novel 3D glioma segmentation model DCL-MANet. DCL-MANet has an architecture of multiple encoders and one single decoder. Each encoder is used to extract MR image features of a given modality. To overcome the entangle problems of multimodal semantic features, a dense contrastive learning (DCL) strategy is presented to extract the modality-specific and common features. Following that, feature recalibration block (RFB) based on modality-wise attention is used to recalibrate the semantic features of each modality, enabling the model to focus on the features that are beneficial for glioma segmentation. These recalibrated features are input into the decoder to obtain the segmentation results. To verify the superiority of the proposed method, we compare it with several state-of-the-art (SOTA) methods in terms of Dice, average symmetric surface distance (ASSD), HD95 and volumetric similarity (Vs). The comparison results show that the average Dice, ASSD, HD95 and Vs of DCL-MANet on all tumor regions are improved at least by 0.66%, 3.47%, 8.94% and 1.07% respectively. For small enhance tumor (ET) region, the corresponding improvement can be up to 0.37%, 7.83%, 11.32%, and 1.35%, respectively. In addition, the ablation results demonstrate the effectiveness of the proposed DCL and RFB, and combining them can significantly increase Dice (1.59%) and Vs (1.54%) while decreasing ASSD (40.51%) and HD95 (45.16%) on ET region. The proposed DCL-MANet could disentangle multimodal features and enhance the semantics of modality-dependent features, providing a potential means to accurately segment small lesion regions in gliomas.

Enhancing Robust Liver Cancer Diagnosis: A Contrastive Multi-Modality Learner with Lightweight Fusion and Effective Data Augmentation

This article explores the application of self-supervised contrastive learning in the medical domain, focusing on classification of multi-modality Magnetic Resonance (MR) images. To address the challenges of limited and hard-to-annotate medical data, we introduce multi-modality data augmentation (MDA) and cross-modality group convolution (CGC). In the pre-training phase, we leverage Simple Siamese networks to maximize the similarity between two augmented MR images from a patient, without a handcrafted pretext task. Our approach also combines 3D and 2D group convolution with a channel shuffle operation to efficiently incorporate different modalities of image features. Evaluation on liver MR images from a well-known hospital in Taiwan demonstrates a significant improvement over previous methods. This work contributes to advancing multi-modality contrastive learning, particularly in the context of medical imaging, offering enhanced tools for analyzing complex image data.

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.

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.

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.

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.

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.

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.

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.

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.