Structural Magnetic Resonance Imaging Research Articles

Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application.

Although white matter (WM) accounts for nearly half of adult brain, its wiring diagram is largely unknown. Here, an approach is developed to construct WM networks by estimating interregional morphological similarity based on structural magnetic resonance imaging. It is found that morphological WM networks showed nontrivial topology, presented good-to-excellent test-retest reliability, accounted for phenotypic interindividual differences in cognition, and are under genetic control. Through integration with multimodal and multiscale data, it is further showed that morphological WM networks are able to predict the patterns of hamodynamic coherence, metabolic synchronization, gene co-expression, and chemoarchitectonic covariance, and associated with structural connectivity. Moreover, the prediction followed WM functional connectomic hierarchy for the hamodynamic coherence, is related to genes enriched in the forebrain neuron development and differentiation for the gene co-expression, and is associated with serotonergic system-related receptors and transporters for the chemoarchitectonic covariance. Finally, applying this approach to multiple sclerosis and neuromyelitis optica spectrum disorders, it is found that both diseases exhibited morphological dysconnectivity, which are correlated with clinical variables of patients and are able to diagnose and differentiate the diseases. Altogether, these findings indicate that morphological WM networks provide a reliable and biologically meaningful means to explore WM architecture in health and disease.

Detection of Alzheimer’s disease using Otsu thresholding with tunicate swarm algorithm and deep belief network

Introduction: Alzheimer’s Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce the mortality rate through slowing down its progression. The prevention and detection of AD is the emerging research topic for many researchers. The structural Magnetic Resonance Imaging (sMRI) is an extensively used imaging technique in detection of AD, because it efficiently reflects the brain variations.Methods: Machine learning and deep learning models are widely applied on sMRI images for AD detection to accelerate the diagnosis process and to assist clinicians for timely treatment. In this article, an effective automated framework is implemented for early detection of AD. At first, the Region of Interest (RoI) is segmented from the acquired sMRI images by employing Otsu thresholding method with Tunicate Swarm Algorithm (TSA). The TSA finds the optimal segmentation threshold value for Otsu thresholding method. Then, the vectors are extracted from the RoI by applying Local Binary Pattern (LBP) and Local Directional Pattern variance (LDPv) descriptors. At last, the extracted vectors are passed to Deep Belief Networks (DBN) for image classification.Results and Discussion: The proposed framework achieves supreme classification accuracy of 99.80% and 99.92% on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL) datasets, which is higher than the conventional detection models.

Gait Analysis and Magnetic Resonance Imaging Characteristics in Patients with Isolated Rapid Eye Movement Sleep Behavior Disorder.

Isolated rapid eye movement sleep behavioral disorder (iRBD) can precede neurodegenerative diseases. There is an urgent need for biomarkers to aid early intervention and neuroprotection. The aim is to assess quantitative motor, cognitive, and brain magnetic resonance imaging (MRI) characteristics in iRBD patients. Thirty-eight polysomnography-confirmed iRBD patients and 28 age- and sex-matched healthy controls underwent clinical, cognitive, and motor functional evaluations, along with brain MRI. Motor tasks included nine-hole peg test, five-times-sit-to-stand test, timed-up-and-go test, and 4-meter walking test with and without cognitive dual task. Quantitative spatiotemporal gait parameters were obtained using an optoelectronic system. Brain MRI analysis included functional connectivity (FC) of the main resting-state networks, gray matter (GM) volume using voxel-based morphometry, cortical thickness, and deep GM and brainstem volumes using FMRIB's Integrated Registration and Segmentation Tool and FreeSurfer. iRBD patients relative to healthy subjects exhibited a poorer performance during the nine-hole peg test and five-times-sit-to-stand test, and greater asymmetry of arm-swing amplitude and stride length variability during dual-task gait. Dual task significantly worsened the walking performance of iRBD patients more than healthy controls. iRBD patients exhibited nonmotor symptoms, and memory, abstract reasoning, and visuospatial deficits. iRBD patients exhibited decreased FC of pallidum and putamen within the basal ganglia network and occipital and temporal areas within the visuo-associative network, and a reduced volume of the supramarginal gyrus. Brain functional alterations correlated with gait changes. Subtle motor and nonmotor alterations were identified in iRBD patients, alongside brain structural and functional MRI changes. These findings may represent early signs of neurodegeneration and contribute to the development of predictive models for progression to parkinsonism. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Head motion in the UK Biobank imaging subsample: longitudinal stability, associations with psychological and physical health, and risk of incomplete data

Abstract Participant motion in brain magnetic resonance imaging is associated with processing problems including potentially non-useable/incomplete data. This has implications for representativeness in research. Few large studies have investigated predictors of increased motion in the first instance. We exploratively tested for association between multiple psychological and physical health traits with concurrent motion during T1 structural, diffusion, average resting-state and task functional magnetic resonance imaging in N = 52 951 UK Biobank imaging subsample participants. These traits included history of cardiometabolic, inflammatory, neurological and psychiatric conditions, as well as concurrent cognitive test scores and anthropometric traits. We tested for stability in motion in participants with longitudinal imaging data (n = 5305, average 2.64 years later). All functional and T1 structural motion variables were significantly intercorrelated (Pearson r range 0.3–0.8, all P < 0.001). Diffusion motion variables showed weaker correlations around r = 0.1. Most physical and psychological phenotypes showed significant association with at least one measure of increased motion including specifically in participants with complete useable data (highest β = 0.66 for diabetes versus resting-state functional magnetic resonance imaging motion). Poorer values in most health traits predicted lower odds of complete imaging data, with the largest association for history of traumatic brain injury (odds ratio = 0.720, 95% confidence interval = 0.562 to 0.923, P = 0.009). Worse psychological and physical health are consistent predictors of increased average functional and structural motion during brain imaging and associated with lower odds of complete data. Average motion levels were largely consistent across modalities and longitudinally in participants with repeat data. Together, these findings have implications for representativeness and bias in imaging studies of generally healthy population samples.

Prolonged ventricular repolarization associated with mild cognitive impairment and white matter hyperintensities: a cross-sectional study

Prolonged ventricular repolarization has been associated with cardiovascular disease. We sought to investigate the association of prolonged ventricular repolarization with mild cognitive impairment (MCI) and the potential underlying neuropathological mechanisms in older adults. This cross-sectional study included 4328 dementia-free participants (age ≥ 65 years; 56.8% female) in the baseline examination of the Multidomain INterventions to delay dementia and Disability in rural China; of these, 989 undertook structural brain magnetic resonance imaging (MRI) scans. QT, QTc, JT, JTc, and QRS intervals were derived from 12-lead electrocardiograph. MCI, amnestic MCI (aMCI), and non-amnestic MCI (naMCI) were defined following the Petersen’s criteria. Volumes of gray matter (GM), white matter, cerebrospinal fluid, total white matter hyperintensities (WMH), periventricular WMH (PWMH), and deep WMH (DWMH) were automatically estimated. Data were analyzed using logistic and general linear regression models. Prolonged QT, QTc, JT, and JTc intervals were significantly associated with an increased likelihood of MCI and aMCI, but not naMCI (p < 0.05). In the MRI subsample, QT, QTc, JT, and JTc intervals were significantly associated with larger total WMH and PWMH volumes (p < 0.05), but not with DWMH volume. Statistical interactions were detected, such that prolonged QT and JT intervals were significantly associated with reduced GM volume only among participants with coronary heart disease or without APOE ε4 allele (p < 0.05). Prolonged ventricular repolarization is associated with MCI and cerebral microvascular lesions in a general population of older adults. This underlies the importance of cognitive assessments and brain MRI examination among older adults with prolonged QT interval.

Identification of schizophrenia by applying interpretable radiomics modeling with structural magnetic resonance imaging of the cerebellum.

The cerebellum is involved in higher-order mental processing as well as sensorimotor functions. Although structural abnormalities in the cerebellum have been demonstrated in schizophrenia, neuroimaging techniques are not yet applicable to identify them given the lack of biomarkers. We aimed to develop a robust diagnostic model for schizophrenia using radiomic features from T1-weighted magnetic resonance imaging (T1-MRI) of the cerebellum. A total of 336 participants (174 schizophrenia; 162 healthy controls [HCs]) were allocated to training (122 schizophrenia; 115 HCs) and test (52 schizophrenia; 47 HCs) cohorts. We obtained 2568 radiomic features from T1-MRI of the cerebellar subregions. After feature selection, a light gradient boosting machine classifier was trained. The discrimination and calibration of the model were evaluated. SHapley Additive exPlanations (SHAP) was applied to determine model interpretability. We identified 17 radiomic features to differentiate participants with schizophrenia from HCs. In the test cohort, the radiomics model had an area under the curve, accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.82-0.95), 78.8%, 88.5%, and 75.4%, respectively. The model explanation by SHAP suggested that the second-order size zone non-uniformity feature from the right lobule IX and first-order energy feature from the right lobules V and VI were highly associated with the risk of schizophrenia. The radiomics model focused on the cerebellum demonstrates robustness in diagnosing schizophrenia. Our results suggest that microcircuit disruption in the posterior cerebellum is a disease-defining feature of schizophrenia, and radiomics modeling has potential for supporting biomarker-based decision-making in clinical practice.

An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early diagnosis of AD has not sufficiently focused on tracking the progression of the disease over an extended period in subjects. To address this issue, this paper proposes an ensemble model for assisting early diagnosis of AD that combines structural magnetic resonance imaging (sMRI) data from two time points with clinical information. The model employs a three-dimensional convolutional neural network (3DCNN) and twin neural network modules to extract features from the sMRI data of subjects at two time points, while a multi-layer perceptron (MLP) is used to model the clinical information of the subjects. The objective is to extract AD-related features from the multi-modal data of the subjects as much as possible, thereby enhancing the diagnostic performance of the ensemble model. Experimental results show that based on this model, the classification accuracy rate is 89% for differentiating AD patients from normal controls (NC), 88% for differentiating mild cognitive impairment converting to AD (MCIc) from NC, and 69% for distinguishing non-converting mild cognitive impairment (MCInc) from MCIc, confirming the effectiveness and efficiency of the proposed method for early diagnosis of AD, as well as its potential to play a supportive role in the clinical diagnosis of early Alzheimer's disease.

Identification of male schizophrenia patients using brain morphology based on machine learning algorithms.

Schizophrenia is a severe psychiatric disease, and its prevalence is higher. However, diagnosis of early-stage schizophrenia is still considered a challenging task. To employ brain morphological features and machine learning method to differentiate male individuals with schizophrenia from healthy controls. The least absolute shrinkage and selection operator and t tests were applied to select important features from structural magnetic resonance images as input features for classification. Four commonly used machine learning algorithms, the general linear model, random forest (RF), k-nearest neighbors, and support vector machine algorithms, were used to develop the classification models. The performance of the classification models was evaluated according to the area under the receiver operating characteristic curve (AUC). A total of 8 important features with significant differences between groups were considered as input features for the establishment of classification models based on the four machine learning algorithms. Compared to other machine learning algorithms, RF yielded better performance in the discrimination of male schizophrenic individuals from healthy controls, with an AUC of 0.886. Our research suggests that brain morphological features can be used to improve the early diagnosis of schizophrenia in male patients.

18F]FDG PET integrated with structural MRI for accurate brain age prediction.

Brain aging is a complex and heterogeneous process characterized by both structural and functional decline. This study aimed to establish a novel deep learning (DL) method for predicting brain age by utilizing structural and metabolic imaging data. The dataset comprised participants from both the Universal Medical Imaging Diagnostic Center (UMIDC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The former recruited 395 normal control (NC) subjects, while the latter included 438 NC subjects, 51 mild cognitive impairment (MCI) subjects, and 56 Alzheimer's disease (AD) subjects. We developed a novel dual-pathway, 3D simple fully convolutional network (Dual-SFCNeXt) to estimate brain age using [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET) and structural magnetic resonance imaging (sMRI) images of NC subjects as input. Several prevailing DL models were trained and tested using either MRI or PET data for comparison. Model accuracies were evaluated using mean absolute error (MAE) and Pearson's correlation coefficient (r). Brain age gap (BAG), deviations of brain age from chronologic age, was correlated with cognitive assessments in MCI and AD subjects. Both PET- and MRI-based models achieved high prediction accuracy. The leading model was the SFCNeXt (the single-pathway version) for PET (MAE = 2.92, r = 0.96) and MRI (MAE = 3.23, r = 0.95) on all samples. By integrating both PET and MRI images, the Dual-SFCNeXt demonstrated significantly improved accuracy (MAE = 2.37, r = 0.97) compared to all single-modality models. Significantly higher BAG was observed in both the AD (P < 0.0001) and MCI (P < 0.0001) groups compared to the NC group. BAG correlated significantly with Mini-Mental State Examination (MMSE) scores (r=-0.390 for AD, r=-0.436 for MCI) and the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) scores (r = 0.333 for AD, r = 0.372 for MCI). The integration of [18F]FDG PET with structural MRI enhances the accuracy of brain age prediction, potentially introducing a new avenue for related multimodal brain age prediction studies.

An interpretable deep learning Bayesian optimized random forest framework for the diagnosis of Parkinson's disease in structural magnetic resonance images

AbstractAs one of the fastest‐growing neurological disorders, Parkinson's disease is a neurodegenerative, cardinally motor ailment. Associated with the late onset of symptoms and often misdiagnosed, it inflicts massive life handicaps. To aid in its early detection, the research community is seeking alternative in‐vivo biomarkers, mainly in the field of neuroimaging. This study aimed to build a computer‐aided diagnosis system to distinguish between healthy controls (HC) and early Parkinsonian patients (PD). Our classification paradigm encompasses a Convolutional Neural Network built from scratch to extract discriminatory features, followed by a Bayesian‐optimized Random Forest classifier (CNN_RF). We trained and validated our customized model on the MRI images of 178 PD and 124 HC, taken from the benchmark Parkinson's Progression Markers Initiative (PPMI) dataset, as well as their brain tissue segments and Jacobian determinant maps. The classification was evaluated using seven performance metrics. For visual interpretation, we employed Grad‐CAM to generate saliency maps. Our findings demonstrate the robustness of the proposed method, as it outperformed deep‐tuned ResNet18, VGG16, and SqueezeNet, implemented for comparison. Furthermore, it resulted in 98.9% accuracy, 98.6% specificity, 99.0% precision, 99.5% F1‐score, a 97.7% Matthews correlation coefficient (MCC), and an AUC of 99.6% for the classification of fused gray matter and Jacobian map features. This indicates that the tailored CNN_RF possesses superior adaptability to identify intrinsic characteristics linked to the disease, as highlighted in the salient maps showcasing pertinent regions in the striatum, thalamus, frontal, temporal, and insular cortices for Parkinson's disease classification. Moreover, it offers a single modality and cost‐effective CAD, which could pave the way for a feasible early PD diagnosis. As data availability and AI robustness increase, PD diagnosis will no longer be a challenging task in the future.

The Association Between Chronic Tobacco Smoking and Brain Alterations in Schizophrenia: A Systematic Review of Magnetic Resonance Imaging Studies.

The high co-occurrence of tobacco smoking in patients with schizophrenia spectrum disorders (SSD) poses a serious health concern, linked to increased mortality and worse clinical outcomes. The mechanisms underlying this co-occurrence are not fully understood. Addressing the need for a comprehensive overview of the impact of tobacco use on SSD neurobiology, we conducted a systematic review of neuroimaging studies (including structural, functional, and neurochemical magnetic resonance imaging studies) that investigate the association between chronic tobacco smoking and brain alterations in patients with SSD. Eight structural and fourteen functional studies were included. Structural studies show widespread independent and additive reductions in gray matter in relation to smoking and SSD. The majority of functional studies suggest that smoking might be associated with improvements in connectivity deficits linked to SSD. However, the limited number of and high amount of cross-sectional studies, and high between-studies sample overlap prevent a conclusive determination of the nature and extent of the impact of smoking on brain functioning in patients with SSD. Overall, functional results imply a distinct neurobiological mechanism for tobacco addiction in patients with SSD, possibly attributed to differences at the nicotinic acetylcholine receptor level. Our findings highlight the need for more longitudinal and exposure-dependent studies to differentiate between inherent neurobiological differences and the (long-term) effects of smoking in SSD, and to unravel the complex interaction between smoking and schizophrenia at various disease stages. This could inform more effective strategies addressing smoking susceptibility in SSD, potentially improving clinical outcomes.

Magnetic Resonance Imaging Measures to Track Atrophy Progression in Progressive Supranuclear Palsy in Clinical Trials.

Several magnetic resonance imaging (MRI) measures have been suggested as progression biomarkers in progressive supranuclear palsy (PSP), and some PSP staging systems have been recently proposed. Comparing structural MRI measures and staging systems in tracking atrophy progression in PSP and estimating the sample size to use them as endpoints in clinical trials. Progressive supranuclear palsy-Richardson's syndrome (PSP-RS) patients with one-year-follow-up longitudinal brain MRI were selected from the placebo arms of international trials (NCT03068468, NCT01110720, NCT01049399) and the DescribePSP cohort. The discovery cohort included patients from the NCT03068468 trial; the validation cohort included patients from other sources. Multisite age-matched healthy controls (HC) were included for comparison. Several MRI measures were compared: automated atlas-based volumetry (44 regions), automated planimetric measures of brainstem regions, and four previously described staging systems, applied to volumetric data. Of 508 participants, 226 PSP patients including discovery (n = 121) and validation (n = 105) cohorts, and 251 HC were included. In PSP patients, the annualized percentage change of brainstem and midbrain volume, and a combined index including midbrain, frontal lobe, and third ventricle volume change, were the progression biomarkers with the highest effect size in both cohorts (discovery: >1.6; validation cohort: >1.3). These measures required the lowest sample sizes (n < 100) to detect 30% atrophy progression, compared with other volumetric/planimetric measures and staging systems. This evidence may inform the selection of imaging endpoints to assess the treatment efficacy in reducing brain atrophy rate in PSP clinical trials, with automated atlas-based volumetry requiring smaller sample size than staging systems and planimetry to observe significant treatment effects. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Brain magnetic resonance imaging image classification for Alzheimer's disease and its hardware acceleration

&lt;span lang="EN-US"&gt;Alzheimer's is a progressive neurodegenerative disorder and is considered the sixth leading cause of death after cancer and heart attack. Early detection and diagnosis provide individuals to go through a wider variety of clinical trials and get multiple medical benefits. Research on the application of deep learning and machine learning to the early detection of Alzheimer's disease has recently gained considerable attention. In this paper, we propose a deep learning classification framework to classify the individual with different progression stages of Alzheimer's disease such as mild cognitive impairment (MCI) and cognitive normal (CN). The dataset from Alzheimer’s disease neuroimaging initiative (ADNI) is considered in this paper which is a multisite having collection of Neuroimaging data for researchers. Structural magnetic resonance imaging (MRI) images are considered from the ADNI data set and feature extraction is done using a 2D discrete wavelet transform. 97% of data reduction is achieved during data pre-processing. The algorithm is trained and validated. The algorithm is accelerated in Nvidia Tx2 graphics processing unit (GPU) to get the better throughput. The result shows our algorithm outperforms the other deep learning algorithms with 91.56% accuracy. &lt;/span&gt;

Attention-Like Multimodality Fusion With Data Augmentation for Diagnosis of Mental Disorders Using MRI.

The globally rising prevalence of mental disorders leads to shortfalls in timely diagnosis and therapy to reduce patients' suffering. Facing such an urgent public health problem, professional efforts based on symptom criteria are seriously overstretched. Recently, the successful applications of computer-aided diagnosis approaches have provided timely opportunities to relieve the tension in healthcare services. Particularly, multimodal representation learning gains increasing attention thanks to the high temporal and spatial resolution information extracted from neuroimaging fusion. In this work, we propose an efficient multimodality fusion framework to identify multiple mental disorders based on the combination of functional and structural magnetic resonance imaging. A multioutput conditional generative adversarial network (GAN) is developed to address the scarcity of multimodal data for augmentation. Based on the augmented training data, the multiheaded gating fusion model is proposed for classification by extracting the complementary features across different modalities. The experiments demonstrate that the proposed model can achieve robust accuracies of 75.1 ± 1.5 %, 72.9 ± 1.1 %, and 87.2 ± 1.5 % for autism spectrum disorder (ASD), attention deficit/hyperactivity disorder, and schizophrenia, respectively. In addition, the interpretability of our model is expected to enable the identification of remarkable neuropathology diagnostic biomarkers, leading to well-informed therapeutic decisions.

Convergent Neuroimaging and Molecular Signatures in Mild Cognitive Impairment and Alzheimer's Disease: A Data-Driven Meta-Analysis with N = 3,118.

The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer's disease (AD). We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy patterns. Then we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in AD. Our results showed that the hippocampus and amygdala exhibit the most severe atrophy, followed by the temporal, frontal, and occipital lobes in mild cognitive impairment (MCI) and AD. The extent of atrophy in MCI was less severe than that in AD. A series of biological processes related to the glutamate signaling pathway, cellular stress response, and synapse structure and function were investigated through gene set enrichment analysis. Our study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy, providing new insight for further clinical research on AD.

Discovering the signal subgraph: An iterative screening approach on graphs

Supervised learning on graphs is a challenging task due to the high dimensionality and inherent structural dependencies in the data, where each edge depends on a pair of vertices. Existing conventional methods are designed for standard Euclidean data and do not account for the structural information inherent in graphs. In this paper, we propose an iterative vertex screening method to achieve dimension reduction across multiple graph datasets with matched vertex sets and associated graph attributes. Our method aims to identify a signal subgraph to provide a more concise representation of the full graphs, potentially benefiting subsequent vertex classification tasks. The method screens the rows and columns of the adjacency matrix concurrently and stops when the resulting distance correlation is maximized. We establish the theoretical foundation of our method by proving that it estimates the true signal subgraph with high probability. Additionally, we establish the convergence rate of classification error under the Erdos-Renyi random graph model and prove that the subsequent classification can be asymptotically optimal, outperforming the entire graph under high-dimensional conditions. Our method is evaluated on various simulated datasets and real-world human and murine graphs derived from functional and structural magnetic resonance images. The results demonstrate its excellent performance in estimating the ground-truth signal subgraph and achieving superior classification accuracy.

Posttraumatic survivor guilt is associated with white matter microstructure alterations

BackgroundMilitary veterans with posttraumatic stress disorder (PTSD) commonly experience posttraumatic guilt. Guilt over commission or omission evolves when responsibility is assumed for an unfortunate outcome (e.g., the death of a fellow combatant). Survivor guilt is a state of intense emotional distress experienced by the weight of knowing that one survived while others did not. MethodsThis study of the Translational Research Center for TBI and Stress Disorders (TRACTS) analyzed structural and diffusion-weighted magnetic resonance imaging data from 132 male Iraq/Afghanistan veterans with PTSD. The Clinician-Administered PTSD Scale for DSM-IV (CAPS-IV) was employed to classify guilt. Thirty (22.7 %) veterans experienced guilt over acts of commission or omission, 34 (25.8 %) experienced survivor guilt, and 68 (51.5 %) had no posttraumatic guilt. White matter microstructure (fractional anisotropy, FA), cortical thickness, and cortical volume were compared between veterans with guilt over acts of commission or omission, veterans with survivor guilt, and veterans without guilt. ResultsVeterans with survivor guilt had significantly lower white matter FA compared to veterans who did not experience guilt (p < .001), affecting several regions of major white matter fiber bundles. There were no significant differences in white matter FA, cortical thickness, or volumes between veterans with guilt over acts of commission or omission and veterans without guilt (p > .050). LimitationsThis cross-sectional study with exclusively male veterans precludes inferences of causality between the studied variables and generalizability to the larger veteran population that includes women. ConclusionSurvivor guilt may be a particularly impactful form of posttraumatic guilt that requires specific treatment efforts targeting brain health.

Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification.

Graph neural networks (GNNs) have received increasing interest in the medical imaging field given their powerful graph embedding ability to characterize the non-Euclidean structure of brain networks based on magnetic resonance imaging (MRI) data. However, previous studies are largely node-centralized and ignore edge features for graph classification tasks, resulting in moderate performance of graph classification accuracy. Moreover, the generalizability of GNN model is still far from satisfactory in brain disorder [e.g., autism spectrum disorder (ASD)] identification due to considerable individual differences in symptoms among patients as well as data heterogeneity among different sites. In order to address the above limitations, this study proposes a novel adversarial learning-based node-edge graph attention network (AL-NEGAT) for ASD identification based on multimodal MRI data. First, both node and edge features are modeled based on structural and functional MRI data to leverage complementary brain information and preserved in the constructed weighted adjacent matrix for individuals through the attention mechanism in the proposed NEGAT. Second, two AL methods are employed to improve the generalizability of NEGAT. Finally, a gradient-based saliency map strategy is utilized for model interpretation to identify important brain regions and connections contributing to the classification. Experimental results based on the public Autism Brain Imaging Data Exchange I (ABIDE I) data demonstrate that the proposed framework achieves a classification accuracy of 74.7% between ASD and typical developing (TD) groups based on 1007 subjects across 17 different sites and outperforms the state-of-the-art methods, indicating satisfying classification ability and generalizability of the proposed AL-NEGAT model. Our work provides a powerful tool for brain disorder identification.

Assessing midbrain neuromelanin and its relationship to reward learning in anorexia nervosa: Stage 1 of a registered report.

Anorexia nervosa (AN) is a debilitating and potentially chronic eating disorder, characterized by low hedonic drive toward food, which has been linked with perturbations in both reward processing and dopaminergic activity. Neuromelanin-sensitive magnetic resonance imaging (MRI) is an emerging method to index midbrain neuromelanin-a by-product of dopaminergic synthesis. The assessment of midbrain neuromelanin, and its association with AN psychopathology and reward-related processes, may provide critical insights into reward circuit function in AN. This study will incorporate neuromelanin-sensitive MRI into an existing study of appetitive conditioning in those with AN. Specifically, those with acute and underweight AN (N=30), those with weight-restored AN (N=30), and age-matched healthy controls (N=30) will undergo clinical assessment of current and previous psychopathology, in addition to structural neuromelanin-sensitive MRI, diffusion MRI, and functional MRI (fMRI) during appetitive conditioning. This study will be among the first to interrogate midbrain neuromelanin in AN-a disorder characterized by altered dopaminergic activity. Results will help establish whether abnormalities in the midbrain synthesis of dopamine are evident in those with AN and are associated with symptomatic behavior and reduced ability to experience pleasure and reward.

Lower synaptic density and its association with cognitive dysfunction in patients with obsessive-compulsive disorder

BackgroundUnderstanding synaptic alteration in obsessive-compulsive disorder (OCD) is crucial for elucidating its pathological mechanisms, but in vivo research on this topic remains limited.AimsThis study aimed to identify the synaptic density...