The moderating role of information processing speed in the relationship between brain remodeling and episodic memory in amnestic mild cognitive impairment.

INTRODUCTION: The role of information processing speed (IPS) on relationships between episodic memory (EM) and central remodeling features in amnestic mild cognitive impairment (aMCI) was investigated. METHODS: Neuropsychological evaluations and multimodal magnetic resonance imaging were performed on 48 patients diagnosed with aMCI and 50 healthy controls (HC). Moderation models explored the moderating effect of IPS on associations between EM and imaging features at single-region, connectivity, and network levels. RESULTS: IPS significantly enhanced the positive correlations between recall and cortical thickness of left inferior temporal gyrus. IPS also notably amplified negative correlations between recognition and functional connectivity (FC) of left inferior parietal lobe and right occipital, as well as between recall/recognition and nodal clustering coefficient of left anterior cingulate cortex. DISCUSSION: IPS functioned as a moderator of associations between recall and neuroimaging metrics at the "single region-connectivity-network" level, providing new insights for cognitive rehabilitation in aMCI patients. HIGHLIGHTS: aMCI patients exhibited brain functional and structural remodeling alterations. IPS moderated relations between episodic memory and brain remodeling metrics. Therapy targeted at IPS can be considered for improving episodic memory in aMCI.

Multi-modal multi-resolution atlas of the human neonatal cerebral cortex based on microstructural similarity.

The neonatal period is a critical window for the development of the human brain and may hold implications for the long-term development of cognition and disorders. Multi-modal connectome studies have revealed many important findings underlying the adult brain but related studies were rare in the early human brain. One potential challenge is the lack of an appropriate and unbiased parcellation that combines structural and functional information in this population. Using 348 multi-modal MRI datasets from the developing human connectome project, we found that the information fused from the structural, diffusion, and functional MRI was relatively stable across MRI features and showed high reproducibility at the group level. Therefore, we generated automated multi-resolution parcellations (300 - 500 parcels) based on the similarity across multi-modal features using a gradient-based parcellation algorithm. In addition, to acquire a parcellation with high interpretability, we provided a manually delineated parcellation (210 parcels), which was approximately symmetric, and the adjacent areas around each boundary were statistically different in terms of the integrated similarity metric and at least one kind of original features. Overall, the present study provided multi-resolution and neonate-specific parcellations of the cerebral cortex based on multi-modal MRI properties, which may facilitate future studies of the human connectome in the early development period.

Combined Features in Region of Interest for Brain Tumor Segmentation.

Diagnosis of brain tumor gliomas is a challenging task in medical image analysis due to its complexity, the less regularity of tumor structures, and the diversity of tissue textures and shapes. Semantic segmentation approaches using deep learning have consistently outperformed the previous methods in this challenging task. However, deep learning is insufficient to provide the required local features related to tissue texture changes due to tumor growth. This paper designs a hybrid method arising from this need, which incorporates machine-learned and hand-crafted features. A semantic segmentation network (SegNet) is used to generate the machine-learned features, while the grey-level co-occurrence matrix (GLCM)-based texture features construct the hand-crafted features. In addition, the proposed approach only takes the region of interest (ROI), which represents the extension of the complete tumor structure, as input, and suppresses the intensity of other irrelevant area. A decision tree (DT) is used to classify the pixels of ROI MRI images into different parts of tumors, i.e. edema, necrosis and enhanced tumor. The method was evaluated on BRATS 2017 dataset. The results demonstrate that the proposed model provides promising segmentation in brain tumor structure. The F-measures for automatic brain tumor segmentation against ground truth are 0.98, 0.75 and 0.69 for whole tumor, core and enhanced tumor, respectively.

Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks.

BACKGROUND: The identification of early mild cognitive impairment (EMCI), which is an early stage of Alzheimer's disease (AD) and is associated with brain structural and functional changes, is still a challenging task. Recent studies show great promises for improving the performance of EMCI identification by combining multiple structural and functional features, such as grey matter volume and shortest path length. However, extracting which features and how to combine multiple features to improve the performance of EMCI identification have always been a challenging problem. To address this problem, in this study we propose a new EMCI identification framework using multi-modal data and graph convolutional networks (GCNs). Firstly, we extract grey matter volume and shortest path length of each brain region based on automated anatomical labeling (AAL) atlas as feature representation from T1w MRI and rs-fMRI data of each subject, respectively. Then, in order to obtain features that are more helpful in identifying EMCI, a common multi-task feature selection method is applied. Afterwards, we construct a non-fully labelled subject graph using imaging and non-imaging phenotypic measures of each subject. Finally, a GCN model is adopted to perform the EMCI identification task. RESULTS: Our proposed EMCI identification method is evaluated on 210 subjects, including 105 subjects with EMCI and 105 normal controls (NCs), with both T1w MRI and rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our proposed framework achieves an accuracy of 84.1% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.856 for EMCI/NC classification. In addition, by comparison, the accuracy and AUC values of our proposed framework are better than those of some existing methods in EMCI identification. CONCLUSION: Our proposed EMCI identification framework is effective and promising for automatic diagnosis of EMCI in clinical practice.

DRRNet: Dense Residual Refine Networks for Automatic Brain Tumor Segmentation.

Glioma is one of the most common and aggressive brain tumors. Segmentation and subsequent quantitative analysis of brain tumor MRI are routine and crucial for treatment. Due to the time-consuming and tedious manual segmentation, automatic segmentation methods are required for accurate and timely treatment. Recently, segmentation methods based on deep learning are popular because of their self-learning and generalization ability. Therefore, we propose a novel automatic 3D CNN-based method for brain tumor segmentation. In order to better capture the contextual information, we design the network architecture based on u-net and replace the simple skip connection with encoder adaptation blocks. To further improve the performance and reduce computational burden at the same time, we also use dense connected fusion blocks in decoder. We train our model with generalised dice loss function to alleviate the problem of class imbalance. The proposed model is evaluated on the BRATS 2015 testing dataset and obtains dice scores of 0.84, 0.72 and 0.62 for whole tumor, tumor core and enhancing tumor, respectively. Our model is accurate and efficient, achieving results that comparable to the reported state-of-the-art results.

Single-subject independent component analysis-based intensity normalization in non-quantitative multi-modal structural MRI.

Non-quantitative MRI is prone to intersubject intensity variation rendering signal intensity level based analyses limited. Here, we propose a method that fuses non-quantitative routine T1-weighted (T1w), T2w, and T2w fluid-saturated inversion recovery sequences using independent component analysis and validate it on age and sex matched healthy controls. The proposed method leads to consistent and independent components with a significantly reduced coefficient-of-variation across subjects, suggesting potential to serve as automatic intensity normalization and thus to enhance the power of intensity based statistical analyses. To exemplify this, we show that voxelwise statistical testing on single-subject independent components reveals in particular a widespread sex difference in white matter, which was previously shown using, for example, diffusion tensor imaging but unobservable in the native MRI contrasts. In conclusion, our study shows that single-subject independent component analysis can be applied to routine sequences, thereby enhancing comparability in-between subjects. Unlike quantitative MRI, which requires specific sequences during acquisition, our method is applicable to existing MRI data. Hum Brain Mapp 38:3615-3622, 2017. © 2017 Wiley Periodicals, Inc.

Multi-modal MRI classifiers identify excessive alcohol consumption and treatment effects in the brain.

Robust neuroimaging markers of neuropsychiatric disorders have proven difficult to obtain. In alcohol use disorders, profound brain structural deficits can be found in severe alcoholic patients, but the heterogeneity of unimodal MRI measurements has so far precluded the identification of selective biomarkers, especially for early diagnosis. In the present work we used a combination of multiple MRI modalities to provide comprehensive and insightful descriptions of brain tissue microstructure. We performed a longitudinal experiment using Marchigian-Sardinian (msP) rats, an established model of chronic excessive alcohol consumption, and acquired multi-modal images before and after 1 month of alcohol consumption (6.8 ± 1.4 g/kg/day, mean ± SD), as well as after 1 week of abstinence with or without concomitant treatment with the antirelapse opioid antagonist naltrexone (2.5 mg/kg/day). We found remarkable sensitivity and selectivity to accurately classify brains affected by alcohol even after the relative short exposure period. One month drinking was enough to imprint a highly specific signature of alcohol consumption. Brain alterations were regionally specific and affected both gray and white matter and persisted into the early abstinence state without any detectable recovery. Interestingly, naltrexone treatment during early abstinence resulted in subtle brain changes that could be distinguished from non-treated abstinent brains, suggesting the existence of an intermediate state associated with brain recovery from alcohol exposure induced by medication. The presented framework is a promising tool for the development of biomarkers for clinical diagnosis of alcohol use disorders, with capacity to further inform about its progression and response to treatment.

Advanced MRI techniques to improve our understanding of experience-induced neuroplasticity.

Over the last two decades, numerous human MRI studies of neuroplasticity have shown compelling evidence for extensive and rapid experience-induced brain plasticity in vivo. To date, most of these studies have consisted of simply detecting a difference in structural or functional images with little concern for their lack of biological specificity. Recent reviews and public debates have stressed the need for advanced imaging techniques to gain a better understanding of the nature of these differences - characterizing their extent in time and space, their underlying biological and network dynamics. The purpose of this article is to give an overview of advanced imaging techniques for an audience of cognitive neuroscientists that can assist them in the design and interpretation of future MRI studies of neuroplasticity. The review encompasses MRI methods that probe the morphology, microstructure, function, and connectivity of the brain with improved specificity. We underline the possible physiological underpinnings of these techniques and their recent applications within the framework of learning- and experience-induced plasticity in healthy adults. Finally, we discuss the advantages of a multi-modal approach to gain a more nuanced and comprehensive description of the process of learning.

Transformer based multi-modal MRI fusion for prediction of post-menstrual age and neonatal brain development analysis.

The brain development during the perinatal period is characterized by rapid changes in both structure and function, which have significant impact on the cognitive and behavioral abilities later in life. Accurate assessment of brain age is a crucial indicator for brain development maturity and can help predict the risk of neonatal pathology. However, evaluating neonatal brains using magnetic resonance imaging (MRI) is challenging due to its complexity, multi-dimension, and noise with subtle alterations. In this paper, we propose a multi-modal deep learning framework based on transformers for precise post-menstrual age (PMA) estimation and brain development analysis using T2-weighted structural MRI (T2-sMRI) and diffusion MRI (dMRI) data. First, we build a two-stream dense network to learn modality-specific features from T2-sMRI and dMRI of brain individually. Then, a transformer module based on self-attention mechanism integrates these features for PMA prediction and preterm/term classification. Finally, saliency maps on brain templates are used to enhance the interpretability of results. Our method is evaluated on the multi-modal MRI dataset of the developing Human Connectome Project (dHCP), which contains 592 neonates, including 478 term-born and 114 preterm-born subjects. The results demonstrate that our method achieves a 0.5-week mean absolute error (MAE) in PMA estimation for term-born subjects. Notably, preterm-born subjects exhibit delayed brain development, worsening with increasing prematurity. Our method also achieves 95% accuracy in classification of term-born and preterm-born subjects, revealing significant group differences.

Mixture-of-experts and semantic-guided network for brain tumor segmentation with missing MRI modalities.

Accurate brain tumor segmentation with multi-modal MRI images is crucial, but missing modalities in clinical practice often reduce accuracy. The aim of this study is to propose a mixture-of-experts and semantic-guided network to tackle the issue of missing modalities in brain tumor segmentation. We introduce a transformer-based encoder with novel mixture-of-experts blocks. In each block, four modality experts aim for modality-specific feature learning. Learnable modality embeddings are employed to alleviate the negative effect of missing modalities. We also introduce a decoder guided by semantic information, designed to pay higher attention to various tumor regions. Finally, we conduct extensive comparison experiments with other models as well as ablation experiments to validate the performance of the proposed model on the BraTS2018 dataset. The proposed model can accurately segment brain tumor sub-regions even with missing modalities. It achieves an average Dice score of 0.81 for the whole tumor, 0.66 for the tumor core, and 0.52 for the enhanced tumor across the 15 modality combinations, achieving top or near-top results in most cases, while also exhibiting a lower computational cost. Our mixture-of-experts and sematic-guided network achieves accurate and reliable brain tumor segmentation results with missing modalities, indicating its significant potential for clinical applications. Our source code is already available at https://github.com/MaggieLSY/MESG-Net .

Predicting depression risk in early adolescence via multimodal brain imaging.

Depression is an incapacitating psychiatric disorder with increased risk through adolescence. Among other factors, children with family history of depression have significantly higher risk of developing depression. Early identification of pre-adolescent children who are at risk of depression is crucial for early intervention and prevention. In this study, we used a large longitudinal sample from the Adolescent Brain Cognitive Development (ABCD) Study (2658 participants after imaging quality control, between 9-10 years at baseline), we applied advanced machine learning methods to predict depression risk at the two-year follow-up from the baseline assessment, using a set of comprehensive multimodal neuroimaging features derived from structural MRI, diffusion tensor imaging, and task and rest functional MRI. Prediction performance underwent a rigorous cross-validation method of leave-one-site-out. Our results demonstrate that all brain features had prediction scores significantly better than expected by chance, with brain features from rest-fMRI showing the best classification performance in the high-risk group of participants with parental history of depression (N = 625). Specifically, rest-fMRI features, which came from functional connectomes, showed significantly better classification performance than other brain features. This finding highlights the key role of the interacting elements of the connectome in capturing more individual variability in psychopathology compared to measures of single brain regions. Our study contributes to the effort of identifying biological risks of depression in early adolescence in population-based samples.

BrainSegFounder: Towards 3D foundation models for neuroimage segmentation.

The burgeoning field of brain health research increasingly leverages artificial intelligence (AI) to analyze and interpret neuroimaging data. Medical foundation models have shown promise of superior performance with better sample efficiency. This work introduces a novel approach towards creating 3-dimensional (3D) medical foundation models for multimodal neuroimage segmentation through self-supervised training. Our approach involves a novel two-stage pretraining approach using vision transformers. The first stage encodes anatomical structures in generally healthy brains from the large-scale unlabeled neuroimage dataset of multimodal brain magnetic resonance imaging (MRI) images from 41,400 participants. This stage of pertaining focuses on identifying key features such as shapes and sizes of different brain structures. The second pretraining stage identifies disease-specific attributes, such as geometric shapes of tumors and lesions and spatial placements within the brain. This dual-phase methodology significantly reduces the extensive data requirements usually necessary for AI model training in neuroimage segmentation with the flexibility to adapt to various imaging modalities. We rigorously evaluate our model, BrainSegFounder, using the Brain Tumor Segmentation (BraTS) challenge and Anatomical Tracings of Lesions After Stroke v2.0 (ATLAS v2.0) datasets. BrainSegFounder demonstrates a significant performance gain, surpassing the achievements of the previous winning solutions using fully supervised learning. Our findings underscore the impact of scaling up both the model complexity and the volume of unlabeled training data derived from generally healthy brains. Both of these factors enhance the accuracy and predictive capabilities of the model in neuroimage segmentation tasks. Our pretrained models and code are at https://github.com/lab-smile/BrainSegFounder.

Multi-grained contrastive representation learning for label-efficient lesion segmentation and onset time classification of acute ischemic stroke.

Ischemic lesion segmentation and the time since stroke (TSS) onset classification from paired multi-modal MRI imaging of unwitnessed acute ischemic stroke (AIS) patients is crucial, which supports tissue plasminogen activator (tPA) thrombolysis decision-making. Deep learning methods demonstrate superiority in TSS classification. However, they often overfit task-irrelevant features due to insufficient paired labeled data, resulting in poor generalization. We observed that unpaired data are readily available and inherently carry task-relevant cues, but are less often considered and explored. Based on this, in this paper, we propose to fully excavate the potential of unpaired unlabeled data and use them to facilitate the downstream AIS analysis task. We first analyze the utility of features at the varied grain and propose a multi-grained contrastive learning (MGCL) framework to learn task-related prior representations from both coarse-grained and fine-grained levels. The former can learn global prior representations to enhance the location ability for the ischemic lesions and perceive the healthy surroundings, while the latter can learn local prior representations to enhance the perception ability for semantic relation between the ischemic lesion and other health regions. To better transfer and utilize the learned task-related representation, we designed a novel multi-task framework to simultaneously achieve ischemic lesion segmentation and TSS classification with limited labeled data. In addition, a multi-modal region-related feature fusion module is proposed to enable the feature correlation and synergy between multi-modal deep image features for more accurate TSS decision-making. Extensive experiments on the large-scale multi-center MRI dataset demonstrate the superiority of the proposed framework. Therefore, it is promising that it helps better stroke evaluation and treatment decision-making.

Tongue coating-dependent superior temporal sulcus remodeling in amnestic mild cognitive impairment.

Tongue coating affects cognition, and cognitive decline at early stage also showed relations to functional and structural remodeling of superior temporal sulcus (STS) in amnestic mild cognitive impairment (aMCI). The potential correlation between disparate cognitive manifestations in aMCI patients with different tongue coatings, and corresponding mechanisms of STS remodeling remains uncharted. In this case-control study, aMCI patients were divided into thin coating (n = 18) and thick coating (n = 21) groups. All participants underwent neuropsychological evaluations and multimodal magnetic resonance imaging. Group comparisons were conducted in clinical assessments and neuroimaging measures of banks of the STS (bankssts). Generalized linear models were constructed to explore relationships between neuroimaging measures and cognition. aMCI patients in the thick coating group exhibited significantly poorer immediate and delayed recall and slower information processing speed (IPS) (P < 0.05), and decreased functional connectivity (FC) of bilateral bankssts with frontoparietal cortices (P < 0.05, AlphaSim corrected) compared to the thin coating group. It was found notable correlations between cognition encompassing recall and IPS, and FC of bilateral bankssts with frontoparietal cortices (P < 0.05, Bonferroni's correction), as well as interaction effects of group × regional homogeneity (ReHo) of right bankssts on the first immediate recall (P < 0.05, Bonferroni's correction). aMCI patients with thick coating exhibited poor cognitive performance, which might be attributed to decreased FC seeding from bankssts. Our findings strengthen the understanding of brain reorganization of STS via which tongue coating status impacts cognition in patients with aMCI.

BTMF-GAN: A multi-modal MRI fusion generative adversarial network for brain tumors.

Image fusion techniques have been widely used for multi-modal medical image fusion tasks. Most existing methods aim to improve the overall quality of the fused image and do not focus on the more important textural details and contrast between the tissues of the lesion in the regions of interest (ROIs). This can lead to the distortion of important tumor ROIs information and thus limits the applicability of the fused images in clinical practice. To improve the fusion quality of ROIs relevant to medical implications, we propose a multi-modal MRI fusion generative adversarial network (BTMF-GAN) for the task of multi-modal MRI fusion of brain tumors. Unlike existing deep learning approaches which focus on improving the global quality of the fused image, the proposed BTMF-GAN aims to achieve a balance between tissue details and structural contrasts in brain tumor, which is the region of interest crucial to many medical applications. Specifically, we employ a generator with a U-shaped nested structure and residual U-blocks (RSU) to enhance multi-scale feature extraction. To enhance and recalibrate features of the encoder, the multi-perceptual field adaptive transformer feature enhancement module (MRF-ATFE) is used between the encoder and the decoder instead of a skip connection. To increase contrast between tumor tissues of the fused image, a mask-part block is introduced to fragment the source image and the fused image, based on which, we propose a novel salient loss function. Qualitative and quantitative analysis of the results on the public and clinical datasets demonstrate the superiority of the proposed approach to many other commonly used fusion methods.

Differential Trajectory of Diffusion and Perfusion Magnetic Resonance Imaging of Rat Spinal Cord Injury.

Traumatic spinal cord injury causes rapid neuronal and vascular injury, and predictive biomarkers are needed to facilitate acute patient management. This study examined the progression of magnetic resonance imaging (MRI) biomarkers after spinal cord injury and their ability to predict long-term neurological outcomes in a rodent model, with an emphasis on diffusion-weighted imaging (DWI) markers of axonal injury and perfusion-weighted imaging of spinal cord blood flow (SCBF). Adult Sprague-Dawley rats received a cervical contusion injury of varying severity (injured = 30, sham = 9). MRI at 4 h, 48-h, and 12-weeks post-injury included T1, T2, perfusion, and DWI. Locomotor outcome was assessed up to 12 weeks post-injury. At 4 h, the deficit in SCBF was larger than the DWI lesion, and although SCBF partially recovered by 48 h, the DWI lesion expanded. At 4 h, the volume of the SCBF deficit (R2 = 0.56, padj < 0.01) was significantly correlated with 12-week locomotor outcome, whereas DWI (R2 = 0.30, padj < 0.01) was less predictive of outcome. At 48 h, SCBF (R2 = 0.41, padj < 0.01) became less associated with outcome, and DWI (R2 = 0.38, padj < 0.01) lesion volume became more closely related to outcome. Spinal cord perfusion has unique spatiotemporal dynamics compared with diffusion measures of axonal damage and highlights the importance of acute perfusion abnormalities. Perfusion and diffusion offer complementary and clinically relevant insight into physiological and structural abnormalities following spinal cord injury beyond those afforded by T1 or T2 contrasts.

Multi-modal MRI measures reveal sensory abnormalities in major depressive disorder patients: A surface-based study.

BACKGROUND: Multi-modal magnetic resonance imaging (MRI) measures are supposed to be able to capture different brain neurobiological aspects of major depressive disorder (MDD). A fusion analysis of structural and functional modalities may better reveal the disease biomarker specific to the MDD disease. METHODS: We recruited 30 MDD patients and 30 matched healthy controls (HC). For each subject, we acquired high-resolution brain structural images and resting-state fMRI (rs-fMRI) data using a 3 T MRI scanner. We first extracted the brain morphometric measures, including the cortical volume (CV), cortical thickness (CT), and surface area (SA), for each subject from the structural images, and then detected the structural clusters showing significant between-group differences in each measure using the surface-based morphology (SBM) analysis. By taking the identified structural clusters as seeds, we performed seed-based functional connectivity (FC) analyses to determine the regions with abnormal FC in the patients. Based on a logistic regression model, we performed a classification analysis by selecting these structural and functional cluster-wise measures as features to distinguish the MDD patients from the HC. RESULTS: The MDD patients showed significantly lower CV in a cluster involving the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), and lower SA in three clusters involving the bilateral STG, temporal pole gyrus, and entorhinal cortex, and the left inferior temporal gyrus, and fusiform gyrus, than the controls. No significant difference in CT was detected between the two groups. By taking the above-detected clusters as seeds to perform the seed-based FC analysis, we found that the MDD patients showed significantly lower FC between STG/MTG (CV's cluster) and two clusters located in the bilateral visual cortices than the controls. The logistic regression model based on the structural and functional features reached a classification accuracy of 86.7% (p < 0.001) between MDD and controls. CONCLUSION: The present study showed sensory abnormalities in MDD patients using the multi-modal MRI analysis. This finding may act as a disease biomarker distinguishing MDD patients from healthy individuals.

Mapping the long-term delayed recall-based cortex-hippocampus network constrained by the structural and functional connectome: a case-control multimodal MRI study.

BACKGROUND: Connectome mapping may reveal new treatment targets for patients with neurological and psychiatric diseases. However, the long-term delayed recall based-network with structural and functional connectome is still largely unknown. Our objectives were to (1) identify the long-term delayed recall-based cortex-hippocampus network with structural and functional connectome and (2) investigate its relationships with various cognitive functions, age, and activities of daily living. METHODS: This case-control study enrolled 131 subjects (73 amnestic mild cognitive impairment [aMCI] patients and 58 age- and education-matched healthy controls [HCs]). All subjects completed a neuropsychological battery, activities of daily living assessment, and multimodal magnetic resonance imaging. Nodes of the cortical-hippocampal network related to long-term delayed recall were identified by probabilistic fiber tracking and functional connectivity (FC) analysis. Then, the main and interaction effects of the network on cognitive functions were assessed by a generalized linear model. Finally, the moderating effects of the network on the relationships between long-term delayed recall and clinical features were analyzed by multiple regression and Hayes' bootstrap method. All the effects of cortex-hippocampus network were analyzed at the connectivity and network levels. RESULTS: The result of a generalized linear model showed that the bilateral hippocampus, left dorsolateral superior frontal gyrus, right supplementary motor area, left lingual gyrus, left superior occipital gyrus, left superior parietal gyrus, left precuneus, and right temporal pole (superior temporal gyrus) are the left and right cortex-hippocampus network nodes related to long-term delayed recall (P < 0.05). Significant interaction effects were found between the Auditory Verbal Learning Test Part 5 (AVLT 5) scores and global properties of the left cortex-hippocampus network [hierarchy, clustering coefficient, characteristic path length, global efficiency, local efficiency, Sigma and synchronization (P < 0.05 Bonferroni corrected)]. Significant interaction effects were found between the general cognitive function/executive function/language and global properties of the left cortex-hippocampus network [Sigma and synchronization (P < 0.05 Bonferroni corrected)]. CONCLUSION: This study introduces a novel symptom-based network and describes relationships among cognitive functions, brain function, and age. The cortex-hippocampus network constrained by the structural and functional connectome is closely related to long-term delayed recall.

The impact of HCN4 channels on CNS brain networks as a new target in pain development.

While it is well established that the isoform 2 of the hyperpolarization-activated cyclic nucleotide-gated cation channel (HCN2) plays an important role in the development and maintenance of pain, the role of the closely related HCN4 isoform in the processing of nociceptive signals is not known. HCN4 channels are highly expressed in the thalamus, a region important for stimulus transmission and information processing. We used a brain-specific HCN4-knockout mouse line (HCN4-KO) to explore the role of HCN4 channels in acute nociceptive processing using several behavioral tests as well as a multimodal magnetic resonance imaging (MRI) approach. Functional MRI (fMRI) brain responses were measured during acute peripheral thermal stimulation complemented by resting state (RS) before and after stimulation. The data were analyzed by conventional and graph-theoretical approaches. Finally, high-resolution anatomical brain data were acquired. HCN4-KO animals showed a central thermal, but not a mechanical hypersensitivity in behavioral experiments. The open field analysis showed no significant differences in motor readouts between HCN4-KO and controls but uncovered increased anxiety in the HCN4-KO mice. Thermal stimulus-driven fMRI (s-fMRI) data revealed increased response volumes and response amplitudes for HCN4-KO, most pronounced at lower stimulation temperatures in the subcortical input, the amygdala as well as in limbic/hippocampal regions, and in the cerebellum. These findings could be cross-validated by graph-theoretical analyses. Assessment of short-term RS before and after thermal stimulation revealed that stimulation-related modulations of the functional connectivity only occurred in control animals. This was consistent with the finding that the hippocampus was found to be smaller in HCN4-KO. In summary, the deletion of HCN4 channels impacts on processing of acute nociception, which is remarkably manifested as a thermal hypersensitive phenotype. This was mediated by the key regions hypothalamus, somatosensory cortex, cerebellum and the amygdala. As consequence, HCN4-KO mice were more anxious, and their brain-wide RS functional connectivity could not be modulated by thermal nociceptive stimulation.

Bi-directional Synthesis of Pre- and Post-contrast MRI via Guided Feature Disentanglement.

Magnetic resonance imaging (MRI) with gadolinium contrast is widely used for tissue enhancement and better identification of active lesions and tumors. Recent studies have shown that gadolinium deposition can accumulate in tissues including the brain, which raises safety concerns. Prior works have tried to synthesize post-contrast T1-weighted MRIs from pre-contrast MRIs to avoid the use of gadolinium. However, contrast and image representations are often entangled during the synthesis process, resulting in synthetic post-contrast MRIs with undesirable contrast enhancements. Moreover, the synthesis of pre-contrast MRIs from post-contrast MRIs which can be useful for volumetric analysis is rarely investigated in the literature. To tackle pre- and post- contrast MRI synthesis, we propose a BI-directional Contrast Enhancement Prediction and Synthesis (BICEPS) network that enables disentanglement of contrast and image representations via a bi-directional image-to-image translation(I2I)model. Our proposed model can perform both pre-to-post and post-to-pre contrast synthesis, and provides an interpretable synthesis process by predicting contrast enhancement maps from the learned contrast embedding. Extensive experiments on a multiple sclerosis dataset demonstrate the feasibility of applying our bidirectional synthesis and show that BICEPS outperforms current methods.