Magnetic Resonance Imaging Datasets Research Articles

Performance analysis of hybrid bio-inspired algorithms for classifying brain tumors in imbalanced magnetic resonance imaging datasets

Performance analysis of hybrid bio-inspired algorithms for classifying brain tumors in imbalanced magnetic resonance imaging datasets

An improved low-rank plus sparse unrolling network method for dynamic magnetic resonance imaging.

Recent advances in deep learning have sparked new research interests in dynamic magnetic resonance imaging (MRI) reconstruction. However, existing deep learning-based approaches suffer from insufficient reconstruction efficiency and accuracy due to the lack of time correlation modeling during the reconstruction procedure. Inappropriate tensor processing steps and deep learning models may lead to not only a lack of modeling in the time dimension but also an increase in the overall size of the network. Therefore, this study aims to find suitable tensor processing methods and deep learning models to achieve better reconstruction results and a smaller network size. We propose a novel unrolling network method that enhances the reconstruction quality and reduces the parameter redundancy by introducing time correlation modeling into MRI reconstruction with low-rank core matrix and convolutional long short-term memory (ConvLSTM) unit. We conduct extensive experiments on AMRG Cardiac MRI dataset to evaluate our proposed approach. The results demonstrate that compared to other state-of-the-art approaches, our approach achieves higher peak signal-to-noise ratios and structural similarity indices at different accelerator factors with significantly fewer parameters. The improved reconstruction performance demonstrates that our proposed time correlation modeling is simple and effective for accelerating MRI reconstruction. We hope our approach can serve as a reference for future research in dynamic MRI reconstruction.

Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics

Background/Objectives: The research addresses algorithmic bias in deep learning models for cardiovascular risk prediction, focusing on fairness across demographic and socioeconomic groups to mitigate health disparities. It integrates fairness-aware algorithms, susceptible carrier-infected-recovered (SCIR) models, and interpretability frameworks to combine fairness with actionable AI insights supported by robust segmentation and classification metrics. Methods: The research utilised quantitative 3D/4D heart magnetic resonance imaging and tabular datasets from the Cardiac Atlas Project’s (CAP) open challenges to explore AI-driven methodologies for mitigating algorithmic bias in cardiac imaging. The SCIR model, known for its robustness, was adapted with the Capuchin algorithm, adversarial debiasing, Fairlearn, and post-processing with equalised odds. The robustness of the SCIR model was further demonstrated in the fairness evaluation metrics, which included demographic parity, equal opportunity difference (0.037), equalised odds difference (0.026), disparate impact (1.081), and Theil Index (0.249). For interpretability, YOLOv5, Mask R-CNN, and ResNet18 were implemented with LIME and SHAP. Bias mitigation improved disparate impact (0.80 to 0.95), reduced equal opportunity difference (0.20 to 0.05), and decreased false favourable rates for males (0.0059 to 0.0033) and females (0.0096 to 0.0064) through balanced probability adjustment. Results: The SCIR model outperformed the SIR model (recovery rate: 1.38 vs 0.83) with a −10% transmission bias impact. Parameters (β=0.5, δ=0.2, γ=0.15) reduced susceptible counts to 2.53×10−12 and increased recovered counts to 9.98 by t=50. YOLOv5 achieved high Intersection over Union (IoU) scores (94.8%, 93.7%, 80.6% for normal, severe, and abnormal cases). Mask R-CNN showed 82.5% peak confidence, while ResNet demonstrated a 10.4% accuracy drop under noise. Performance metrics (IoU: 0.91–0.96, Dice: 0.941–0.980, Kappa: 0.95) highlighted strong predictive accuracy and reliability. Conclusions: The findings validate the effectiveness of fairness-aware algorithms in addressing cardiovascular predictive model biases. The integration of fairness and explainable AI not only promotes equitable diagnostic precision but also significantly reduces diagnostic disparities across vulnerable populations. This reduction in disparities is a key outcome of the research, enhancing clinical trust in AI-driven systems. The promising results of this study pave the way for future work that will explore scalability in real-world clinical settings and address limitations such as computational complexity in large-scale data processing.

NExpR: Neural Explicit Representation for fast arbitrary-scale medical image super-resolution

Medical images often require rescaling to various spatial resolutions to ensure interpretations at different levels. Conventional deep learning-based image super-resolution (SR) enhances the fixed-scale resolution. Implicit neural representation (INR) is a promising way of achieving arbitrary-scale image SR. However, existing INR-based methods require the repeated execution of the neural network (NN), which is slow and inefficient. In this paper, we present Neural Explicit Representation (NExpR) for fast arbitrary-scale medical image SR. Our algorithm represents an image with an explicit analytical function, whose input is the low-resolution image and output is the parameterization of the analytical function. After obtaining the analytical representation through a single NN inference, SR images of arbitrary scales can be derived by evaluating the explicit functions at desired coordinates. Because of the analytical explicit representation, NExpR is significantly faster than INR-based methods. In addition to speed, our method achieves on-par or better image quality than other strong competitors. Extensive experiments on Magnetic Resonance Imaging (MRI) datasets, including ProstateX, fastMRI, and our in-house clinical prostate dataset, as well as the Computerized Tomography (CT) dataset, specifically the Medical Segmentation Decathlon (MSD) liver dataset, demonstrate the superiority of our method. Our method reduces the rescaling time from the order of 1 ms to the order of 0.01 ms, achieving an over 100× speedup without losing the image quality. Code is available at https://github.com/Calvin-Pang/NExpR.

Toward explainable deep learning in healthcare through transition matrix and user-friendly features

Modern artificial intelligence (AI) solutions often face challenges due to the “black box” nature of deep learning (DL) models, which limits their transparency and trustworthiness in critical medical applications. In this study, we propose and evaluate a scalable approach based on a transition matrix to enhance the interpretability of DL models in medical signal and image processing by translating complex model decisions into user-friendly and justifiable features for healthcare professionals. The criteria for choosing interpretable features were clearly defined, incorporating clinical guidelines and expert rules to align model outputs with established medical standards. The proposed approach was tested on two medical datasets: electrocardiography (ECG) for arrhythmia detection and magnetic resonance imaging (MRI) for heart disease classification. The performance of the DL models was compared with expert annotations using Cohen’s Kappa coefficient to assess agreement, achieving coefficients of 0.89 for the ECG dataset and 0.80 for the MRI dataset. These results demonstrate strong agreement, underscoring the reliability of the approach in providing accurate, understandable, and justifiable explanations of DL model decisions. The scalability of the approach suggests its potential applicability across various medical domains, enhancing the generalizability and utility of DL models in healthcare while addressing practical challenges and ethical considerations.

Improving brain MRI denoising using convolutional AutoEncoder and sparse representations

Magnetic Resonance Imaging (MRI) is an essential tool for diagnosing and monitoring diseases under various conditions. However, noise often degrades image quality, leading to inaccurate diagnoses. To address this issue, a Convolutional AutoEncoder-based Orthogonal Matching Pursuit (CAE-OMP) model is proposed for brain MRI image denoising. In this model, the encoder block extracts relevant features from the image while reducing overfitting, and the OMP algorithm creates a sparse representation to enhance denoising. To improve performance and computational efficiency, the traditional greedy search process in OMP is replaced with the Crossover Boosted Elephant Herd Optimization (CBEHO) algorithm. Rather than searching for atoms, CBEHO optimizes parameter selection, thereby reducing search times and enhancing convergence in the OMP process. Using this optimized sparse representation, the model iteratively improves the original image’s approximation by updating residuals and the support set. The decoder block then reconstructs the denoised image features. The proposed method was tested on multiple datasets, including the RSNA MICCAI PNG dataset, the Brain Tumor Detection MRI (BTD-MRI) dataset, the Brain Tumor Classification MRI (BTC-MRI) Images dataset, and the Brain Tumor Segmentation (BraTS2020) dataset. The results show that the CAE-OMP model achieves Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) values of 0.989 and 47.345 on the BTD-MRI dataset, 0.985 and 46.321 on the RSNA MICCAI PNG dataset, 0.978 and 45.453 on the BTC-MRI dataset, and 0.981 and 46.892 on the BraTS2020 dataset, all evaluated at a 15% noise level. These outcomes indicate that the proposed CAE-OMP model outperforms existing methods, demonstrating superior efficiency for denoising brain MRI images.

Multi-task magnetic resonance imaging reconstruction using meta-learning

Using single-task deep learning methods to reconstruct Magnetic Resonance Imaging (MRI) data acquired with different imaging sequences is inherently challenging. The trained deep learning model typically lacks generalizability, and the dissimilarity among image datasets with different types of contrast leads to suboptimal learning performance.This paper proposes a meta-learning approach to efficiently learn image features from multiple MRI datasets. Our algorithm can perform multi-task learning to simultaneously reconstruct MRI images acquired using different imaging sequences with various image contrasts. We have developed a proximal gradient descent-inspired optimization method to learn image features across image and k-space domains, ensuring high-performance learning for every image contrast. Meanwhile, meta-learning, a “learning-to-learn” process, is incorporated into our framework to improve the learning of mutual features embedded in multiple image contrasts.The experimental results reveal that our proposed multi-task meta-learning approach surpasses state-of-the-art single-task learning methods at high acceleration rates. Our meta-learning consistently delivers accurate and detailed reconstructions, achieves the lowest pixel-wise errors, and significantly enhances qualitative performance across all tested acceleration rates.We have demonstrated the ability of our new meta-learning reconstruction method to successfully reconstruct highly-undersampled k-space data from multiple MRI datasets simultaneously, outperforming other compelling reconstruction methods previously developed for single-task learning.

Trajectories of human brain functional connectome maturation across the birth transition.

Understanding the sequence and timing of brain functional network development at the beginning of human life is critically important from both normative and clinical perspectives. Yet, we presently lack rigorous examination of the longitudinal emergence of human brain functional networks over the birth transition. Leveraging a large, longitudinal perinatal functional magnetic resonance imaging (fMRI) data set, this study models developmental trajectories of brain functional networks spanning 25 to 55 weeks of post-conceptual gestational age (GA). The final sample includes 126 fetal scans (GA = 31.36 ± 3.83 weeks) and 58 infant scans (GA = 48.17 ± 3.73 weeks) from 140 unique subjects. In this study, we document the developmental changes of resting-state functional connectivity (RSFC) over the birth transition, evident at both network and graph levels. We observe that growth patterns are regionally specific, with some areas showing minimal RSFC changes, while others exhibit a dramatic increase at birth. Examples with birth-triggered dramatic change include RSFC within the subcortical network, within the superior frontal network, within the occipital-cerebellum joint network, as well as the cross-hemisphere RSFC between the bilateral sensorimotor networks and between the bilateral temporal network. Our graph analysis further emphasized the subcortical network as the only region of the brain exhibiting a significant increase in local efficiency around birth, while a concomitant gradual increase was found in global efficiency in sensorimotor and parietal-frontal regions throughout the fetal to neonatal period. This work unveils fundamental aspects of early brain development and lays the foundation for future work on the influence of environmental factors on this process.

An intelligent magnetic resonance imagining-based multistage Alzheimer's disease classification using swish-convolutional neural networks.

Alzheimer's disease (AD) refers to a neurological disorder that causes damage to brain cells and results in decreasing cognitive abilities and memory. In brain scans, this degeneration can be seen in different ways. The disease can be classified into four stages: Non-demented (ND), moderate demented (MoD), mild demented (MiD), and very mild demented (VMD). To prepare the raw dataset for analysis, the collected magnetic resonance imaging (MRI) images are subjected to several pre-processing techniques in order to improve the performance accuracy of the proposed model. Medical images generally have poor contrast and get affected by noise, which ends up with inaccurate diagnosis. For the different phases of AD to be detected, a clear image is necessary. To address this issue, the influence of the artefacts must be reduced, enhance the contrast, and reduce the loss of information. A novel framework for image enhancement is suggested to increase the accuracy in the detection and identification of AD. In this study, the raw MRI dataset from the Alzheimer's disease neuroimaging initiative (ADNI) database is subjected to skull stripping, contrast enhancement, and image filtering followed by data augmentation to balance the dataset with four types of Alzheimer's classes. The pre-processed data are subjected to five different pre-trained models such as AlexNet, ResNet, VGG 16, EfficientNet, and Inceptionv3 achieving a testing accuracy rate of 91.2%, 88.21%, 92.34%, 93.45%, and 85.12%, respectively. These pre-trained models are compared with the proposed CNN (convolutional neural network) model designed with Adam optimizer and Flatten Swish activation function which reaches the highest accuracy of 96.5% with a learning rate of 0.000001. The five pre-trained CNNmodels along with the proposed swish-based AD-CNN were tested using various performance metrics to evaluate the model efficiency in classifying and identifying the AD classes. From the result analysis, it is evident that the proposed AD-CNN model outperforms all the other models.

Brain tumor diagnosis in MRI scans images using Residual/Shuffle Network optimized by augmented Falcon Finch optimization

Brain tumor diagnosis is an important task in prognosing and treatment planning of the patients with brain cancer. in the meantime, using the Magnetic Resonance Imaging (MRI) as a commonly used non-invasive imaging technique provide the experts a helpful view for detecting the brain tumors. While deep learning methods have shown significant success in analyzing medical images, they often require careful design of architecture and tuning of hyperparameters to achieve optimal results. This study presents a new approach for diagnosing brain tumors in MRI scans using deep learning, focusing on Residual/Shuffle Networks. The designed network structures offer efficient results when compared to traditional deep learning models. To enhance the proposed network for brain tumor classification, a modified metaheuristic algorithm named Augmented Falcon Finch Optimization (AFFO) is introduced. AFFO utilizes bio-inspired principles to effectively search for the best hyperparameter configurations, thereby enhancing the reliability and accuracy of the deep learning model. The performance of the proposed method is evaluated on a standard brain tumor MRI dataset and compared with existing techniques, including ResNet, AlexNet, VGG-16, Inception V3, and U-Net to illustrate the effectiveness of combining Residual/Shuffle Networks with AFFO for brain tumor diagnosis.

Explainable early detection of Alzheimer’s disease using ROIs and an ensemble of 138 3D vision transformers

Early detection and accurate diagnosis of brain morphological abnormalities are essential for the effective management and treatment of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Structural magnetic resonance imaging (MRI) is a powerful support tool to aid in disease diagnosis and prediction. In this research study, we present an innovative approach to predict Alzheimer’s disease (AD) and mild cognitive impairment (MCI) using MRI data, which integrates regional interest (ROI)-based methodology and deep learning within a comprehensible framework. The proposed method involves dividing the brain into 138 predetermined sections based on anatomical information. Next, we apply three-dimensional vision transformers (3D-ViTs) to each ROI individually, harnessing the power of deep learning. To improve prediction accuracy, we employ a deep belief network (DBN) as an ensemble learning model. Evaluating our approach on the baseline structural MRI dataset obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, and comparing it against five other competing models, we demonstrate its performance across four binary classification tasks and a three-class classification test (AD vs MCI vs CN (Cognitively Normal)). The proposed system outperforms existing models and provides interpretable insights into the brain regions that significantly contribute to solving each classification problem. Our findings align with the existing body of literature and hold promise for guiding future research directions in this domain.

Enhanced Magnetic Resonance Imaging-Based Brain Tumor Classification with a Hybrid Swin Transformer and ResNet50V2 Model

Brain tumors can be serious; consequently, rapid and accurate detection is crucial. Nevertheless, a variety of obstacles, such as poor imaging resolution, doubts over the accuracy of data, a lack of diverse tumor classes and stages, and the possibility of misunderstanding, present challenges to achieve an accurate and final diagnosis. Effective brain cancer detection is crucial for patients’ safety and health. Deep learning systems provide the capability to assist radiologists in quickly and accurately detecting diagnoses. This study presents an innovative deep learning approach that utilizes the Swin Transformer. The suggested method entails integrating the Swin Transformer with the pretrained deep learning model Resnet50V2, called (SwT+Resnet50V2). The objective of this modification is to decrease memory utilization, enhance classification accuracy, and reduce training complexity. The self-attention mechanism of the Swin Transformer identifies distant relationships and captures the overall context. Resnet 50V2 improves both accuracy and training speed by extracting adaptive features from the Swin Transformer’s dependencies. We evaluate the proposed framework using two publicly accessible brain magnetic resonance imaging (MRI) datasets, each including two and four distinct classes, respectively. Employing data augmentation and transfer learning techniques enhances model performance, leading to more dependable and cost-effective training. The suggested model achieves an impressive accuracy of 99.9% on the binary-labeled dataset and 96.8% on the four-labeled dataset, outperforming the VGG16, MobileNetV2, Resnet50V2, EfficientNetV2B3, ConvNeXtTiny, and convolutional neural network (CNN) algorithms used for comparison. This demonstrates that the Swin transducer, when combined with Resnet50V2, is capable of accurately diagnosing brain tumors. This method leverages the combination of SwT+Resnet50V2 to create an innovative diagnostic tool. Radiologists have the potential to accelerate and improve the detection of brain tumors, leading to improved patient outcomes and reduced risks.

Intermediary-guided windowed attention Aggregation network for fine-grained characterization of Major Depressive Disorder fMRI

ObjectivesEstablishing objective and quantitative imaging markers at individual level can assist in accurate diagnosis of Major Depressive Disorder (MDD). However, the clinical heterogeneity of MDD leads to a decrease in recognition accuracy, to address this issue, we propose the Windowed Attention Aggregation Network (WAAN) for a medium-sized functional Magnetic Resonance Imaging (fMRI) dataset comprising 111 MDD and 106 Healthy Controls (HC). MethodsThe proposed WAAN model is a dynamic temporal model that contains two important components, Inner-Window Self-Attention (IWSA) and Cross-Window Self-Attention (CWSA), to characterize the MDD-fMRI data at a fine-grained level and fuse global temporal information. In addition, to optimize WAAN, a new Point to Domain Loss (p2d Loss) function is proposed, which intermediate guides the model to learn class centers with smaller class deviations, thus improving the intra-class feature density. ResultsThe proposed WAAN achieved an accuracy of 83.8 % (±1.4 %) in MDD identification task in medium-sized site. The right superior orbitofrontal gyrus and right superior temporal gyrus (pole) were found to be categorically highly attributable brain regions in MDD patients, and the hippocampus had stable categorical attributions. The effect of temporal parameters on classification was also explored and time window parameters for high categorical attributions were obtained. SignificanceThe proposed WAAN is expected to improve the accuracy of personalized identification of MDD. This study helps to find the target brain regions for treatment or intervention of MDD, and provides better scanning time window parameters for MDD-fMRI analysis.

Evaluation of registration-based vs. manual segmentation of rhesus macaque brain MRIs.

With increasing numbers of magnetic resonance imaging (MRI) datasets becoming publicly available, researchers and clinicians alike have turned to automated methods of segmentation to enable population-level analyses of these data. Although prior research has evaluated the extent to which automated methods recapitulate "gold standard" manual segmentation methods in the human brain, such an evaluation has not yet been carried out for segmentation of MRIs of the macaque brain. Macaques offer the important opportunity to bridge gaps between microanatomical studies using invasive methods like tract tracing, neural recordings, and high-resolution histology and non-invasive macroanatomical studies using methods like MRI. As such, it is important to evaluate whether automated tools derive data of sufficient quality from macaque MRIs to bridge these gaps. We tested the relationship between automated registration-based segmentation using an open source and actively maintained NHP imaging analysis pipeline (AFNI) and gold standard manual segmentation of 4 structures (2 cortical: anterior cingulate cortex and insula; 2 subcortical: amygdala and caudate) across 37 rhesus macaques (Macaca mulatta). We identified some variability in the strength of correlation between automated and manual segmentations across neural regions and differences in relationships with demographic variables like age and sex between the two techniques.

A telescopic independent component analysis on functional magnetic resonance imaging dataset

Abstract Brain function can be modeled as dynamic interactions between functional sources at different spatial scales, and each spatial scale can contain its functional sources with unique information, thus using a single scale may provide an incomplete view of brain function. This paper introduces a novel approach, termed “telescopic independent component analysis (TICA),” designed to construct spatial functional hierarchies and estimate functional sources across multiple spatial scales using fMRI data. The method employs a recursive independent component analysis (ICA) strategy, leveraging information from a larger network to guide the extraction of information about smaller networks. We apply our model to the default mode network (DMN), visual network (VN), and right frontoparietal network (RFPN). We investigate further on the DMN by evaluating the difference between healthy people and individuals with schizophrenia. We show that the TICA approach can detect the spatial hierarchy of the DMN, VN, and RFPN. In addition, the TICA revealed DMN-associated group differences between cohorts that may not be captured if we focus on a single-scale ICA. In sum, our proposed approach represents a promising new tool for studying functional sources.

Comparison of Machine Learning Models for Brain Disease Classification Based on CT and MRI Scans

The complexity and severity of brain diseases have led to increased focus on their diagnosis and treatment. Due to the inherent drawbacks of manual medical diagnosis, such as error-prone and costly, and the recent widespread use of Artificial Intelligence (AI) in medicine, it is a worthy topic to explore machine learning in diagnosing brain diseases utilizing Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). In this paper, three brain diseases using CT or MRI datasets from Kaggle were merged and processed, and then three models of decision tree (DT), random forest (RF) and K-Nearest Neighbors (KNN) were used to make classification prediction. Furthermore, their performance was compared through classification reports and confusion matrix to analysis results. The results showed that DT performed worse than the other two models in this task, with an accuracy of 0.91, whereas RF and KNN performed similar overall, each achieving an overall accuracy of 0.96. Notably, RF exhibited less confusion, especially between some similar categories, which indicates the ability of handle complex data more effectively. Additionally, the strengths and weaknesses of the three models are discussed in the paper. These experimental results show that machine learning model, particularly RF and KNN, is a useful tool for diagnosing brain diseases with high accuracy, which could substantially assist clinical practices by providing reliable and efficient diagnostic support.

A multicenter bladder cancer MRI dataset and baseline evaluation of federated learning in clinical application

Bladder cancer (BCa), as the most common malignant tumor of the urinary system, has received significant attention in research on the clinical application of artificial intelligence algorithms. Nevertheless, it has been observed that certain investigations use data from various medical facilities to train models for BCa, which may pose a privacy risk. Given this concern, protecting patient privacy during machine learning algorithm training is a crucial aspect that requires substantial attention. One emerging machine learning paradigm that addresses this concern is federated learning (FL). FL enables multiple entities to collaboratively build machine learning models while preserving data privacy and security. In this study, we present a multicenter BCa magnetic resonance imaging (MRI) dataset. The dataset comprises 275 three-dimensional bladder T2-weighted MRI scans collected from four medical centers, and each scan includes diagnostic pathological labels for muscle invasion and pixel-level annotations of tumor contours. Four FL methods are used to assess the baseline of the dataset for both the task of diagnosing muscle-invasive bladder cancer and automatic bladder tumor lesion segmentation.

MRI Super-Resolution with Partial Diffusion Models.

Diffusion models have achieved impressive performance on various image generation tasks, including image super-resolution. Despite their impressive performance, diffusion models suffer from high computational costs due to the large number of denoising steps. In this paper, we proposed a novel accelerated diffusion model, termed Partial Diffusion Models (PDMs), for magnetic resonance imaging (MRI) super-resolution. We observed that the latents of diffusing a pair of low- and high-resolution images gradually converge and become indistinguishable after a certain noise level. This inspires us to use certain low-resolution latent to approximate corresponding high-resolution latent. With the approximation, we can skip part of the diffusion and denoising steps, reducing the computation in training and inference. To mitigate the approximation error, we further introduced 'latent alignment' that gradually interpolates and approaches the high-resolution latents from the low-resolution latents. Partial diffusion models, in conjunction with latent alignment, essentially establish a new trajectory where the latents, unlike those in original diffusion models, gradually transition from low-resolution to high-resolution images. Experiments on three MRI datasets demonstrate that partial diffusion models achieve competetive super-resolution quality with significantly fewer denoising steps than original diffusion models. In addition, they can be incorporated with recent accelerated diffusion models to further enhance the efficiency.

Patterns of Brain Maturation in Autism and Their Molecular Associations

In the neurotypical brain, regions develop in coordinated patterns, providing a fundamental scaffold for brain function and behavior. Whether altered patterns contribute to clinical profiles in neurodevelopmental conditions, including autism, remains unclear. To examine if, in autism, brain regions develop differently in relation to each other and how these differences are associated with molecular/genomic mechanisms and symptomatology. This study was an analysis of one the largest deep-phenotyped, case-control, longitudinal (2 assessments separated by approximately 12-24 months) structural magnetic resonance imaging and cognitive-behavioral autism datasets (EU-AIMS Longitudinal European Autism Project [LEAP]; study dates, February 2014-November 2017) and an out-of-sample validation in the Brain Development Imaging Study (BrainMapASD) independent cohort. Analyses were performed during the 2022 to 2023 period. This multicenter study included autistic and neurotypical children, adolescents, and adults. Autistic participants were included if they had an existing autism diagnosis (DSM-IV/International Statistical Classification of Diseases and Related Health Problems, Tenth Revision or DSM-5 criteria). Autistic participants with co-occurring psychiatric conditions (except psychosis/bipolar disorder) and those taking regular medications were included. Neuroanatomy of neurotypical and autistic participants. Intraindividual changes in surface area and cortical thickness over time, analyzed via surface-based morphometrics. A total of 386 individuals in the LEAP cohort (6-31 years at first visit; 214 autistic individuals, mean [SD] age, 17.3 [5.4] years; 154 male [72.0%] and 172 neurotypical individuals, mean [SD] age, 16.35 [5.7] years; 108 male [62.8%]) and 146 individuals in the BrainMapASD cohort (11-18 years at first visit; 49 autistic individuals, mean [SD] age, 14.31 [2.4] years; 42 male [85.7%] and 97 neurotypical individuals, mean [SD] age, 14.10 [2.5] years; 58 male [59.8%]). Maturational between-group differences in cortical thickness and surface area were established that were mostly driven by sensorimotor regions (eg, across features, absolute loadings for early visual cortex ranged from 0.07 to 0.11, whereas absolute loadings for dorsolateral prefrontal cortex ranged from 0.005 to 0.06). Neurodevelopmental differences were transcriptomically enriched for genes expressed in several cell types and during various neurodevelopmental stages, and autism candidate genes (eg, downregulated genes in autism, including those regulating synaptic transmission; enrichment odds ratio =3.7; P =2.6 × -10). A more neurotypical, less autismlike maturational profile was associated with fewer social difficulties and more typical sensory processing (false discovery rate P <.05; Pearson r ≥0.17). Results were replicated in the independently collected BrainMapASD cohort. Results of this case-control study suggest that the coordinated development of brain regions was altered in autism, involved a complex interplay of temporally sensitive molecular mechanisms, and may be associated with both lower-order (eg, sensory) and higher-order (eg, social) clinical features of autism. Thus, examining maturational patterns may provide an analytic framework to study the neurobiological origins of clinical profiles in neurodevelopmental/mental health conditions.

An open-access lumbosacral spine MRI dataset with enhanced spinal nerve root structure resolution

Spinal cord injury (SCI) profoundly affects an individual’s ability to move. Fortunately, recent advancements in neuromodulation, particularly the spatio-temporal epidural electrical stimulation (EES) targeting the spinal nerve roots, promoted rapid rehabilitation of SCI patients. Such neuromodulation techniques require precise anatomical modelling of spinal cord. However, the lack of spine imaging datasets, especially high-quality magnetic resonance imaging (MRI) datasets highlighting nerve roots, hinders the translation of EES into medical practice. To address this problem, we introduce an open-access lumbosacral spine MRI dataset acquired in 14 healthy adults, using constructive interference in steady state (CISS) sequence, double echo steady state (DESS) sequence, and T2-weight turbo spin echo (T2-TSE) sequence, with enhanced nerve root resolution. The dataset also includes the corresponding anatomical annotations of nerve roots and the final reconstructed 3D spinal cord models. The quality of our dataset is assessed using image quality metrics implemented in MRI quality control tool (MRIQC). Our dataset provides a valuable platform to promote a wide range of spinal cord neuromodulation research and collaboration among neurorehabilitation engineers.