Magnetic Resonance Imaging Data Research Articles

Exploring Brain Effective Connectivity Networks Through Spatiotemporal Graph Convolutional Models.

Learning brain effective connectivity networks (ECN) from functional magnetic resonance imaging (fMRI) data has gained much attention in recent years. With the successful applications of deep learning in numerous fields, several brain ECN learning methods based on deep learning have been reported in the literature. However, current methods ignore the deep temporal features of fMRI data and fail to fully employ the spatial topological relationship between brain regions. In this article, we propose a novel method for learning brain ECN based on spatiotemporal graph convolutional models (STGCM), named STGCMEC, in which we first adopt the temporal convolutional network to extract the deep temporal features of fMRI data and utilize the graph convolutional network to update the spatial features of each brain region by aggregating information from neighborhoods, which makes the features of brain regions more discriminative. Then, based on such features of brain regions, we design a joint loss function to guide STGCMEC to learn the brain ECN, which includes a task prediction loss and a graph regularization loss. The experimental results on a simulated dataset and a real Alzheimer's disease neuroimaging initiative (ADNI) dataset show that the proposed STGCMEC is able to better learn brain ECN compared with some state-of-the-art methods.

Implementing ABCD studyⓇ MRI sequences for multi-site cohort studies: Practical guide to necessary steps, preprocessing methods, and challenges

Large multi-site studies that combine magnetic resonance imaging (MRI) data across research sites present exceptional opportunities to advance neuroscience research. However, scanner or site variability and non-standardised image acquisition protocols, data processing and analysis pipelines can adversely affect the reliability and repeatability of MRI derived brain measures. We implemented a standardised MRI protocol based on that used in the Adolescent Brain Cognition Development (ABCD)Ⓡ study in two sites, and across four MRI scanners.Twice repeated measurements of a single healthy volunteer were obtained in two sites and in four 3T MRI scanners (vendors: Siemens, Philips, and GE). Imaging data included anatomical scans (T1 weighted, T2 weighted), diffusion weighted imaging (DWI) and resting state functional MRI (rs-fMRI). Standardised containerized pipelines were utilised to pre-process the data and different image quality metrics and test-retest variability of different brain metrics were evaluated.The implementation of the MRI protocols was possible with minor adjustments in acquisition (e.g. repetition time (TR), higher b-values) and exporting (DICOM formats) of images due to different technical performance of the scanners. This study provides practical insights into the implementation of standardised sequences and data processing for multisite studies, showcase the benefits of containerised preprocessing tools, and highlights the need for careful optimisation of multisite image acquisition.

Associations between radiomic phenotypes and genomic mechanisms in HBV-associated cholangiocarcinoma.

e16249 Background: Despite the epidemiological association between cholangiocarcinoma (CCA) and HBV infection, little is known about the relevant oncogenic biomarkers for advancing cancer research and improving future patient therapy. Radiomics-based non-invasive biomarkers are promising to facilitate the translation of therapeutically related molecular subtypes for treatment allocation of patients with HBV-infected CCA. Methods: In this study, we included 55 CCA patients with whole-genome multi-omics data, proteomic data with matched magnetic resonance imaging (MRI) data from institute 1. A total of 431 patients with histopathological confirmed CCA (143 HBV-positive and 288 HBV-negative patients) were retrospectively enrolled from three institutes, and patients were allocated to the training (n = 302) and validation (n = 129) cohorts from different institutes or time period (7:3). Utilizing linear regression and gene set enrichment analysis, we identified statistically significant associations between radiomic imaging and various genomic features in HBV-positive or HBV-negative CCA patients. Additionally, we employed a random forest classifier based on radiomic features to predict the status of HBV infection, HBV-related gene mutations and protein expression profiling, and tested on an internal and an external CCA cohort (n = 53). Results: The analysis revealed significant and widespread associations between genomic features (somatic mutations, transcriptional activities and copy number variations) and radiomic features characterizing HBV infection status. Our results show that quantitative image features are capable of distinguishing several molecular phenotypes. We obtained significant predictive performance for HBV+ (AUC = 0.73), CA-199(AUC = 0.77) and NeuroInvasion (AUC = 0.71), activation of ESRP1(AUC = 0.79) and mutation of HNRNPUL2/MUC4 (AUC = 0.73). We externally validated the HBV prediction model (AUC = 0.76) on the internal and external cohort. The sensitivity, specificity, and accuracy were all > 70%. The radiomic signature succeeded to stratify patients into distinct survival outcomes according to HBV status, and remained an independent prognostic factor in multivariable analysis (DFS, P < 0.0001; OS, P = 0.029). Integrating the radiomic signature with clinicopathological-radiographic factors further improved prediction performance. A nomogram incorporating significant pathological predictors (HBV, CA-125, CA-199, Neuro invasion, peritoneal invasion, pT stage, pN stage, and differentiation, grade) and radiomics signatures displayed good prediction performance in estimating OS with AUC values of 0.895, 0.935, and 0.907 at 1, 3, and 5 years, respectively. Conclusions: This deep learning-driven combined radiomics model may provide as a useful tool to preoperatively predict HBV infection and improve therapeutic management of ICC patients.

Fourier Domain Robust Denoising Decomposition and Adaptive Patch MRI Reconstruction.

The sparsity of the Fourier transform domain has been applied to magnetic resonance imaging (MRI) reconstruction in k -space. Although unsupervised adaptive patch optimization methods have shown promise compared to data-driven-based supervised methods, the following challenges exist in MRI reconstruction: 1) in previous k -space MRI reconstruction tasks, MRI with noise interference in the acquisition process is rarely considered. 2) Differences in transform domains should be resolved to achieve the high-quality reconstruction of low undersampled MRI data. 3) Robust patch dictionary learning problems are usually nonconvex and NP-hard, and alternate minimization methods are often computationally expensive. In this article, we propose a method for Fourier domain robust denoising decomposition and adaptive patch MRI reconstruction (DDAPR). DDAPR is a two-step optimization method for MRI reconstruction in the presence of noise and low undersampled data. It includes the low-rank and sparse denoising reconstruction model (LSDRM) and the robust dictionary learning reconstruction model (RDLRM). In the first step, we propose LSDRM for different domains. For the optimization solution, the proximal gradient method is used to optimize LSDRM by singular value decomposition and soft threshold algorithms. In the second step, we propose RDLRM, which is an effective adaptive patch method by introducing a low-rank and sparse penalty adaptive patch dictionary and using a sparse rank-one matrix to approximate the undersampled data. Then, the block coordinate descent (BCD) method is used to optimize the variables. The BCD optimization process involves valid closed-form solutions. Extensive numerical experiments show that the proposed method has a better performance than previous methods in image reconstruction based on compressed sensing or deep learning.

Correlation of ADC values of adult brain tumors with the diagnosis and pathological grade: A cross-sectional multicenter study.

Brain tumors are common, requiring physicians to have a precise understanding of them for accurate diagnosis and treatment. Considering that various histological tumor types present different cellularity, we conducted this research to examine the role of apparent diffusion coefficient (ADC) values in the differential diagnosis and pathologic grading of brain tumor types. In this cross-sectional study, we gathered pathology reports of histological samples of adult brain tumors. The tissue sample of brain tumors were examined histologically by a pathologist. The magnetic resonance imaging data of these patients were interpreted by a neuroradiologist. The measured ADC values and ADC ratios were calculated. Standard mean ADC values were expressed as 10- 6 mm2/s. The findings were compared according to the histological diagnosis of each tumor. Sixty-eight patients were included in the study: 34 (50%) were male, and 34 (50%) were female. The average age of the patients was 51.69 + 16.40 years. In the examination of tumor type, 16 (23.5%) were astrocytoma, 9 (13.2%) were oligodendroglioma, 20 (29.4%) were glioblastoma, 4 (5.9%) were medulloblastoma, and 19 (27.9%) were metastatic tumors. the average value of ADC was statistically significantly different according to the pathological type of tumor (p < 0.001). The two-by-two comparison of average ADC among tumor types revealed significant differences, except for oligodendroglioma and glioblastoma (p-value = 0.87) and glioblastoma and medulloblastoma (p-value = 0.347). The average value of ADC and ADC ratio was statistically significantly different according to the pathological grade of the tumor (p < 0.001). In the two-by-two comparison of average ADC between all pathological grades of the tumor showed a significance difference except for Grade I and Grade II (p-value = 0.355). The mean value of ADC and ADC ratio for glioblastoma and metastatic tumors showed no significant difference. The assessment of brain tumor grade through ADC examination will help to estimate prognosis and devising suitable therapeutic strategies.

A novel CNN-based image segmentation pipeline for individualized feline spinal cord stimulation modeling

Objective. Spinal cord stimulation (SCS) is a well-established treatment for managing certain chronic pain conditions. More recently, it has also garnered attention as a means of modulating neural activity to restore lost autonomic or sensory-motor function. Personalized modeling and treatment planning are critical aspects of safe and effective SCS (Rowald and Amft 2022 Front. Neurorobotics 16 983072, Wagner et al 2018 Nature 563 65–71). However, the generation of spine models at the required level of detail and accuracy requires time and labor intensive manual image segmentation by human experts. This study aims to develop a maximally automated segmentation routine capable of producing high-quality anatomical models, even with limited data, to facilitate safe and effective personalized SCS treatment planning. Approach. We developed an automated image segmentation and model generation pipeline based on a novel convolutional neural network (CNN) architecture trained on feline spinal cord magnetic resonance imaging data. The pipeline includes steps for image preprocessing, data augmentation, transfer learning, and cleanup. To assess the relative importance of each step in the pipeline and our choice of CNN architecture, we systematically dropped steps or substituted architectures, quantifying the downstream effects in terms of tissue segmentation quality (Jaccard index and Hausdorff distance) and predicted nerve recruitment (estimated axonal depolarization). Main results. The leave-one-out analysis demonstrated that each pipeline step contributed a small but measurable increment to mean segmentation quality. Surprisingly, minor differences in segmentation accuracy translated to significant deviations (ranging between 4% and 13% for each pipeline step) in predicted nerve recruitment, highlighting the importance of careful workflow design. Additionally, transfer learning techniques enhanced segmentation metric consistency and allowed generalization to a completely different spine region with minimal additional training data. Significance. To our knowledge, this work is the first to assess the downstream impacts of segmentation quality differences on neurostimulation predictions. It highlights the role of each step in the pipeline and paves the way towards fully automated, personalized SCS treatment planning in clinical settings.

Age-related changes of node degree in the multiple-demand network predict fluid intelligence

Fluid intelligence is an individual's innate ability to cope with complex situations and is gradually reduced across adults aging. The realization of fluid intelligence requires the simultaneous activity of multiple brain regions and depends on the structural connection of distributed brain regions. Uncovering the structural features of brain connections associated with fluid intelligence decline will provide reference for the development of intervention and treatment programs for cognitive decline. Using structural magnetic resonance imaging data of 454 healthy participants (18-87 years) from the Cam-CAN dataset, we constructed structural similarity network for each participant and calculated the node degree. Spearman correlation analysis showed that age was positively correlated with degree centrality in the cingulate cortex, left insula and subcortical regions, while negatively correlated with that in the orbito-frontal cortex, right middle temporal and precentral regions. Partial least squares (PLS) regression showed that the first PLS components explained 32% (second PLS component: 20%, pperm < 0.001) of the variance in fluid intelligence. Additionally, the degree centralities of anterior insula, supplementary motor area, prefrontal, orbito-frontal and anterior cingulate cortices, which are critical nodes of the multiple-demand network (MDN), were linked to fluid intelligence. Increased degree centrality in anterior cingulate cortex and left insula partially mediated age-related decline in fluid intelligence. Collectively, these findings suggest that the structural stability of MDN might contribute to the maintenance of fluid intelligence.

Distinct attentional and executive profiles in neurofibromatosis type 1: Is there difference with primary attention deficit-hyperactivity disorder?

PurposeAttentional and executive dysfunctions are the most frequent cognitive disorders in neurofibromatosis type 1 (NF1), with a high prevalence of attention deficit-hyperactivity disorder (ADHD). We (i) compared attentional profiles between NF1 children with and without ADHD and children with primary ADHD criteria and (ii) investigated the possible relationship between attentional disorders and “unidentified bright objects” (UBOs) in NF1. MethodsThis retrospective study included 47 NF1 children, 25 with ADHD criteria (NF1+adhd group), matched for age, sex, and cognitive level with 47 children with primary ADHD (ADHD group). We collected computer task (sustained-attention, visuomotor-decision, inhibition, and cognitive-flexibility tasks) scores normalized for age and sex, and brain magnetic resonance imaging data. Results(i) Working memory was impaired in all groups. (ii) Omissions (p < 0.002) and response-time variability (p < 0.05) in sustained-attention and visuomotor-decision tasks and errors (p < 0.02) in the cognitive-flexibility task were lower for the NFI+adhd and ADHD groups than for the NF1-no-adhd group. (iii) The NF1+adhd group had slower response times (p ≤ 0.02) for inhibition and visuomotor-decision tasks than the other groups. (iv) We found no relevant association between cognitive performance and UBOs. ConclusionsNF1 children with ADHD have an attentional and executive functions deficit profile similar to that of children with primary ADHD, but with a slower response-time, increasing learning difficulties. The atypical connectivity of fronto-striatal pathways, poorer dopamine homeostasis, and increased GABA inhibition observed in NF1 renders vulnerable the development of the widely distributed neural networks that support attentional, working-memory, and executive functions.

Detailed mapping of the complex fiber structure and white matter pathways of the chimpanzee brain

Long-standing questions about human brain evolution may only be resolved through comparisons with close living evolutionary relatives, such as chimpanzees. This applies in particular to structural white matter (WM) connectivity, which continuously expanded throughout evolution. However, due to legal restrictions on chimpanzee research, neuroscience research currently relies largely on data with limited detail or on comparisons with evolutionarily distant monkeys. Here, we present a detailed magnetic resonance imaging resource to study structural WM connectivity in the chimpanzee. This open-access resource contains (1) WM reconstructions of a postmortem chimpanzee brain, using the highest-quality diffusion magnetic resonance imaging data yet acquired from great apes; (2) an optimized and validated method for high-quality fiber orientation reconstructions; and (3) major fiber tract segmentations for cross-species morphological comparisons. This dataset enabled us to identify phylogenetically relevant details of the chimpanzee connectome, and we anticipate that it will substantially contribute to understanding human brain evolution.

A study on brain asymmetry in temporal lobe epilepsy

Objective: Temporal lobe epilepsy (TLE) accompanied by hippocampal sclerosis (HS) is the most common type of focal epilepsies. Hemispheric asymmetry is a feature of brain organization in both invertebrates and vertebrates and may be the key to some neurodegenerative diseases. In this context, we aimed to investigate the volumetric asymmetry difference in cerebral structures between TLE patients and the healthy control group, based on magnetic resonance imaging (MRI) data that may be used as a new neuroimaging marker for TLE cases. Patients and Methods: In this retrospective study the cranial MRIs of fourteen clinically manifesting, radiologically HS-identified, and diagnosed TLE patients and fourteen healthy individuals from the Radiology Department of Yeditepe University Hospital were evaluated. Volume measurements and asymmetry index (AI) calculations in the total brain, hippocampus, temporal lobe, amygdala, thalamus, nucleus accumbens (NAc), premotor cortex, primary and somatosensory cortices were performed using the medical NeuroQuant® software. A negative AI value represented asymmetry towards the right due to reduced left hemispheric volume; a positive AI value represented asymmetry towards the left due to reduced right hemispheric volume. Subsequently, differences in volume and asymmetric patterns were investigated among TLE subgroups (right and left-sided TLE) and controls. Results: The left-sided TLE patients showed significant bilateral total brain volume reduction compared to the control group. Significant ipsilateral volumetric declines were also detected in the premotor cortex, the temporal lobe, and NAc with remarkable asymmetry to the right side. No significant changes were detected in right-sided TLE patients compared to the other groups. Conclusion: Overall, findings suggest that TLE patients had volumetric alterations with symmetry changes beyond the mesial temporal structures. With further investigations, the asymmetry measures can provide additional knowledge for TLE diagnosis.

Functional Connectivity-Based Searchlight Multivariate Pattern Analysis for Discriminating Schizophrenia Patients and Predicting Clinical Variables.

Schizophrenia, a multifaceted psychiatric disorder characterized by functional dysconnectivity, poses significant challenges in clinical practice. This study explores the potential of functional connectivity (FC)-based searchlight multivariate pattern analysis (CBS-MVPA) to discriminate between schizophrenia patients and healthy controls while also predicting clinical variables. We enrolled 112 schizophrenia patients and 119 demographically matched healthy controls. Resting-state functional magnetic resonance imaging data were collected, and whole-brain FC subnetworks were constructed. Additionally, clinical assessments and cognitive evaluations yielded a dataset comprising 36 clinical variables. Finally, CBS-MVPA was utilized to identify subnetworks capable of effectively distinguishing between the patient and control groups and predicting clinical scores. The CBS-MVPA approach identified 63 brain subnetworks exhibiting significantly high classification accuracies, ranging from 62.2% to 75.6%, in distinguishing individuals with schizophrenia from healthy controls. Among them, 5 specific subnetworks centered on the dorsolateral superior frontal gyrus, orbital part of inferior frontal gyrus, superior occipital gyrus, hippocampus, and parahippocampal gyrus showed predictive capabilities for clinical variables within the schizophrenia cohort. This study highlights the potential of CBS-MVPA as a valuable tool for localizing the information related to schizophrenia in terms of brain network abnormalities and capturing the relationship between these abnormalities and clinical variables, andthus, deepens our understanding of the neurological mechanisms of schizophrenia.

Performance of LI-RADS category 5 vs combined categories 4 and 5: a systemic review and meta-analysis.

Computed tomography (CT)/magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS, LR) category 5 has high specificity and modest sensitivity for diagnosis of hepatocellular carcinoma (HCC). The purpose of this study was to compare the diagnostic performance of LR-5 vs combined LR-4 and LR-5 (LR-4/5) for HCC diagnosis. MEDLINE and EMBASE databases through January 03, 2023 were searched for studies reporting the performance of LR-5 and combined LR-4/5 for HCC diagnosis, using CT/MRI LI-RADS version 2014, 2017, or 2018. A bivariate random-effects model was used to calculate the pooled per-observation diagnostic performance. Subgroup analysis was performed based on imaging modalities and type of MRI contrast material. Sixty-nine studies (15,108 observations, 9928 (65.7%) HCCs) were included. Compared to LR-5, combined LR-4/5 showed significantly higher pooled sensitivity (83.0% (95% CI [80.3-85.8%]) vs 65.7% (95% CI [62.4-69.1%]); p < 0.001), lower pooled specificity (75.0% (95% CI [70.5-79.6%]) vs 91.7% (95% CI [90.2-93.1%]); p < 0.001), lower pooled positive likelihood ratio (3.60 (95% CI [3.06-4.23]) vs 6.18 (95% CI [5.35-7.14]); p < 0.001), and lower pooled negative likelihood ratio (0.22 (95% CI [0.19-0.25]) vs 0.38 (95% CI [0.35-0.41]) vs; p < 0.001). Similar results were seen in all subgroups. Our meta-analysis showed that combining LR-4 and LR-5 would increase sensitivity but decrease specificity, positive likelihood ratio, and negative likelihood ratio. These findings may inform management guidelines and individualized management. This meta-analysis estimated the magnitude of changes in the sensitivity and specificity of imaging criteria when LI-RADS categories 4 and 5 were combined; these findings can inform management guidelines and individualized management. There is no single worldwide reporting system for liver imaging, partly due to regional needs. Combining LI-RADS categories 4 and 5 increased sensitivity and decreased specificity and positive and negative likelihood ratios. Changes in the sensitivity and specificity of imaging criteria can inform management guidelines and individualized management.

BRAIN TUMOR PERCEPTION USING DEEP LEARNING

The human brain serves as the central component of the human body, orchestrating its functionality. Brain tumors emerge from abnormal cell growth and division within the brain, potentially leading to the development of brain cancer. Computer vision plays a pivotal role in healthcare by alleviating the burden of making precise decisions. Among imaging modalities like magnetic resonance imaging (MRI), computed tomography (CT) scans, and X-rays, MRI scans stand out as one of the most prevalent and safest methods for obtaining detailed images, capable of detecting minute abnormalities. Our research endeavors to explore the diverse applications of brain MRI in detecting brain cancer. In this study, we employed the bilateral filter (BF) to eliminate noise from MR images before processing. Subsequently, the tumor region was delineated using binary thresholding and Convolutional Neural Network (CNN) segmentation techniques. The datasets were divided into training, testing, and validation subsets, enabling our machine to discern the presence of brain tumors in subjects. Various performance metrics including accuracy, sensitivity, and specificity were utilized to evaluate the outcomes. Dense layers within the CNN architecture efficiently extract features from brain MRI images, enhancing diagnostic accuracy. The experiment was conducted primarily on MRI data due to its ability to provide comprehensive insights into cellular

Concurrent response and action effect representations across the somatomotor cortices during novel task preparation

Instructions allow us to fulfill novel and complex tasks on the first try. This skill has been linked to preparatory brain signals that encode upcoming demands in advance, facilitating novel performance. To deepen insight into these processes, we explored whether instructions pre-activated task-relevant motoric and perceptual neural states. Critically, we addressed whether these representations anticipated activity patterns guiding overt sensorimotor processing, which could reflect that internally simulating novel tasks facilitates the preparation. To do so, we collected functional magnetic resonance imaging data while female and male participants encoded and implemented novel stimulus-response associations. Participants also completed localizer tasks designed to isolate the neural representations of the mappings-relevant motor responses, perceptual consequences, and stimulus categories. Using canonical template tracking, we identified whether and where these sensorimotor representations were pre-activated. We found that response-related templates were encoded in advance in regions linked with action control, entailing not only the instructed responses but also their somatosensory consequences. This result was particularly robust in primary motor and somatosensory cortices. While, following our predictions, we found a systematic decrease in the irrelevant stimulus templates’ representational strength compared to the relevant ones, this difference was due to below-zero estimates linked to the irrelevant category activity patterns. Overall, our findings reflect that instruction processing relies on the sensorimotor cortices to anticipate motoric and kinesthetic representations of prospective action plans, suggesting the engagement of motor imagery during novel task preparation. More generally, they stress that the somatomotor system could participate with higher-level frontoparietal regions during anticipatory task control.

A brain subcortical segmentation tool based on anatomy attentional fusion network for developing macaques

Magnetic Resonance Imaging (MRI) plays a pivotal role in the accurate measurement of brain subcortical structures in macaques, which is crucial for unraveling the complexities of brain structure and function, thereby enhancing our understanding of neurodegenerative diseases and brain development. However, due to significant differences in brain size, structure, and imaging characteristics between humans and macaques, computational tools developed for human neuroimaging studies often encounter obstacles when applied to macaques. In this context, we propose an Anatomy Attentional Fusion Network (AAF-Net), which integrates multimodal MRI data with anatomical constraints in a multi-scale framework to address the challenges posed by the dynamic development, regional heterogeneity, and age-related size variations of the juvenile macaque brain, thus achieving precise subcortical segmentation. Specifically, we generate a Signed Distance Map (SDM) based on the initial rough segmentation of the subcortical region by a network as an anatomical constraint, providing comprehensive information on positions, structures, and morphology. Then we construct AAF-Net to fully fuse the SDM anatomical constraints and multimodal images for refined segmentation. To thoroughly evaluate the performance of our proposed tool, over 700 macaque MRIs from 19 datasets were used in this study. Specifically, we employed two manually labeled longitudinal macaque datasets to develop the tool and complete four-fold cross-validations. Furthermore, we incorporated various external datasets to demonstrate the proposed tool’s generalization capabilities and promise in brain development research. We have made this tool available as an open-source resource at https://github.com/TaoZhong11/Macaque_subcortical_segmentation for direct application.

Regional structural abnormalities in thalamus in idiopathic cervical dystonia

BackgroundThe thalamus has a central role in the pathophysiology of idiopathic cervical dystonia (iCD); however, the nature of alterations occurring within this structure remain largely elusive. Using a structural magnetic resonance imaging (MRI) approach, we examined whether abnormalities differ across thalamic subregions/nuclei in patients with iCD.MethodsStructural MRI data were collected from 37 patients with iCD and 37 healthy controls (HCs). Automatic parcellation of 25 thalamic nuclei in each hemisphere was performed based on the FreeSurfer program. Differences in thalamic nuclei volumes between groups and their relationships with clinical information were analysed in patients with iCD.ResultsCompared to HCs, a significant reduction in thalamic nuclei volume primarily in central medial, centromedian, lateral geniculate, medial geniculate, medial ventral, paracentral, parafascicular, paratenial, and ventromedial nuclei was found in patients with iCD (P < 0.05, false discovery rate corrected). However, no statistically significant correlations were observed between altered thalamic nuclei volumes and clinical characteristics in iCD group.ConclusionThis study highlights the neurobiological mechanisms of iCD related to thalamic volume changes.

Performance Enhancement of Alzheimer's Disease Diagnosis Using Generative Adversarial Network

Insufficient medical images have always been a challenge for deep learning-based Alzheimer's disease classification and detection tasks. The availability of Magnetic Resonance Imaging (MRI) data is limited due to patients' privacy issues. Access to individual medical records is strongly protected by the law, and appropriate consent is needed to utilize them for research purposes. Besides that, publicly available databases often experience imbalanced classes, further contributing to the issue of insufficient data. Thus, the performance of deep learning models used to diagnose Alzheimer's disease is often hindered by this issue. Basic data augmentation methods such as geometrical augmentation techniques also had limited applications to medical data. Hence, this study proposes a deep learning technique of Generative Adversarial Network (GAN) to expand the MRI dataset and improve the classification model performance. MRI images from the Open Access Series of Imaging Studies (OASIS) database are used in this study to perform experiments and validate hypotheses. After applying GAN to expand the dataset, a pre-trained Convolutional Neural Network (CNN) model is used to classify the data into multiple classes of Alzheimer's and the model's performance is measured. As a result, an improvement in accuracy for the classification task can be observed, indicating that the GAN is a solution for overcoming the challenge of insufficient data for Alzheimer's diagnosis.

The role of cortical structural variance in deep learning-based prediction of fetal brain age

BackgroundDeep-learning-based brain age estimation using magnetic resonance imaging data has been proposed to identify abnormalities in brain development and the risk of adverse developmental outcomes in the fetal brain. Although saliency and attention activation maps have been used to understand the contribution of different brain regions in determining brain age, there has been no attempt to explain the influence of shape-related cortical structural features on the variance of predicted fetal brain age.MethodsWe examined the association between the predicted brain age difference (PAD: predicted brain age–chronological age) from our convolution neural networks-based model and global and regional cortical structural measures, such as cortical volume, surface area, curvature, gyrification index, and folding depth, using regression analysis.ResultsOur results showed that global brain volume and surface area were positively correlated with PAD. Additionally, higher cortical surface curvature and folding depth led to a significant increase in PAD in specific regions, including the perisylvian areas, where dramatic agerelated changes in folding structures were observed in the late second trimester. Furthermore, PAD decreased with disorganized sulcal area patterns, suggesting that the interrelated arrangement and areal patterning of the sulcal folds also significantly affected the prediction of fetal brain age.ConclusionThese results allow us to better understand the variance in deep learning-based fetal brain age and provide insight into the mechanism of the fetal brain age prediction model.

Age and BMI have different effects on subcutaneous, visceral, liver, bone marrow, and muscle adiposity, as measured by CT and MRI.

We analyzed quantitative computed tomography (CT) and chemical shift-encoded magnetic resonance imaging (MRI) data from a Chinese cohort to investigate the effects of BMI and aging on different adipose tissue (AT) depots. In 400 healthy, community-dwelling individuals aged 22 to 83 years, we used MRI to quantify proton density fat fraction (PDFF) of the lumbar spine (L2-L4) bone marrow AT (BMAT), the psoas major and erector spinae (ES) muscles, and the liver. Abdominal total AT, visceral AT (VAT), and subcutaneous AT (SAT) areas were measured at the L2-L3 level using quantitative CT. Partial correlation analysis was used to evaluate the relationship of each AT variable with age and BMI. Multiple linear regression analysis was performed in which each AT variable was evaluated in turn as a function of age and the other five independent AT measurements. Of the 168 men, 29% had normal BMI (<24.0 kg/m2), 47% had overweight (24.0-27.9 kg/m2), and 24% had obesity (≥ 28.0 kg/m2). In the 232 women, the percentages were 46%, 32%, and 22%, respectively. Strong or very strong correlations with BMI were found for total AT, VAT, and SAT in both sexes. BMAT and ES PDFF was strongly correlated with age in women and moderately correlated in men. In both sexes, BMAT PDFF correlated only with age and not with any of the other AT depots. Psoas PDFF correlated only with ES PDFF and not with age or the other AT depots. Liver PDFF correlated with BMI and VAT and weakly with SAT in men. VAT and SAT correlated with age and each other in both sexes. Age and BMI are both associated with adiposity, but their effects differ depending on the type of AT.

#908 The effect of obesity and fat distribution on estimated glomerular filtration rate (eGFR): A Mendelian randomization and image data analysis study

Abstract Background and Aims Obesity is intimately linked to the initiation and progression of chronic kidney disease, but questions on causality still remain. The diagnosis of chronic kidney disease is often based solely on creatinine- and/or cystatin C-based estimated glomerular filtration rate (eGFR), but both these endogenous biomarkers are also known to be related to body composition. This study aims to further investigate how obesity causally affects eGFR, derived from both biomarkers. Method Methods used were: Traditional cross-sectional analyses and magnetic resonance imaging data analyses performed in the POEM study (502 subjects, all aged 50 years), and two-sample Mendelian randomization using already published summary data. Outcome variables for all analyses were eGFR indexed to body surface area, based on creatinine and cystatin C, respectively. In both the traditional cross-sectional and the Mendelian randomization analyses body mass index and waist circumference were used as exposure variables. In the image data analyses, eGFR was regressed nonparametically on fat content and tissue volume for each 3D voxel. Results were visualized as a correlation “imiomics” map. Results Results from the POEM study showed in an adjusted model a negative correlation between cystatin C calculated eGFR and both body mass index [beta = −0.190 (95% CI: −0.280 to −0.100)] and waist circumference [beta = −0.160 (95% CI: −0.250 to −0.060)]. In contrast, a positive association was found for eGFR when using creatinine levels [beta = 0.120 (95% CI: 0.030 to 0.210) for body mass index; beta = 0.160 (95% CI: 0.070 to 0.260) for waist circumference]. This pattern was repeated in the image data analysis, which was visualized in the imiomics map: both visceral and subcutaneous adipose tissue were negatively correlated to eGFR based on cystatin C but positively correlated to eGFR based on creatinine (Fig. 1). Mendelian randomization implied a negative causal effect of obesity-related measures on cystatin C-based eGFR, with statistically significant estimates for both body mass index [beta = −0.031 (95% CI: −0.037 to −0.026)] and waist circumference [beta = −0.038 (95% CI: −0.045 to −0.031)]. No statistically significant effects was found on creatinine-based eGFR (Fig. 2). Conclusion This study indicates a causal negative effect of obesity on cystatin C-based eGFR, but not on eGFR calculated from creatinine. This discrepancy is further highlighted by analyses of adipose tissue from magnetic resonance imaging. These findings thus warrant further research on the impact of using the endogenous biomarkers creatinine and cystatin C to estimate renal function when assessing the link between obesity and chronic kidney disease.