UNITY: A low-field magnetic resonance neuroimaging initiative to characterize neurodevelopment in low and middle-income settings

Characterization of Portable Ultra-Low Field MRI Scanners for Multi-Center Structural Neuroimaging

The lower infrastructure requirements of portable ultra-low field MRI (ULF-MRI) systems have enabled their use in diverse settings such as intensive care units and remote medical facilities. The UNITY Project is an international neuroimaging network harnessing this technology, deploying portable ULF-MRI systems globally to expand access to MRI for studies into brain development. Given the wide range of environments where ULF-MRI systems may operate, there are external factors that might influence image quality. This work aims to introduce the quality control (QC) framework used by the UNITY Project to investigate how robust the systems are and how QC metrics compare between sites and over time. We present a QC framework using a commercially available phantom, scanned with 64 mT portable MRI systems at 17 sites across 12 countries on four continents. Using automated, open-source analysis tools, we quantify signal-to-noise, image contrast, and geometric distortions. Our results demonstrated that the image quality is robust to the varying operational environment, for example, electromagnetic noise interference and temperature. The Larmor frequency was significantly correlated to room temperature, as was image noise and contrast. Image distortions were less than 2.5 mm, with high robustness over time. Similar to studies at higher field, we found that changes in pulse sequence parameters from software updates had an impact on QC metrics. This study demonstrates that portable ULF-MRI systems can be deployed in a variety of environments for multi-center neuroimaging studies and produce robust results.

Application and Acceptance of Bedside MRI in the NICU Setting

PURPOSE

This study aims to assess the applicability of ultralow-field (ULF) magnetic resonance imaging (MRI) in the neonatal intensive care unit (NICU), its impact on both the neonate being scanned and neighboring patients, and its effects on medical procedures and early parent-child interaction.

BACKGROUND

Neonatal MRI is crucial for diagnosis and treatment in the NICU, but access is limited, both in high-income countries and low- and middle-income countries. Portable ULF MRI presents an opportunity to expand access, but its applicability and potential impacts on neonates and nearby patients have not been studied, including its effects on medical care and early parent-child interaction.

METHODS

We assessed applicability, safety and stress levels of neonates during ULF MRI at a NICU by measuring heart rate, oxygen saturation and blood pressure of the neonate scanned and neighboring patients and by measuring subjective stress levels assessed by attending physicians, nurses and parents. Using questionnaires, we assessed whether medical care and early parent-child interaction was affected.

RESULTS

No significant differences were found in the physiological measures of the scanned and neighboring neonates. Medical care and parent-child interaction were not affected by ULF MRI.

CONCLUSIONS

ULF MRI can be safely performed in the NICU without causing stress to neonates or affecting medical care or parent-child interaction. It can be performed at the bedside during natural sleep, requiring fewer resources compared to high-field MRI, making it a viable point-of-care option in both the NICU and low-resource settings. This could significantly increase MRI accessibility.

Bahir Dar Child Development Cross-Sectional Study, Ethiopia: study protocol

INTRODUCTION

Foundational preacademic skills are crucial for academic success and serve as predictors of socioeconomic status, income and access to healthcare. However, there is a gap in our understanding of neurodevelopmental patterns underlying preacademic skills in children across low-income and middle-income countries (LMICs). It is essential to identify primary global and regional factors that drive children's neurodevelopment in LMICs. This study aims to characterise the typical development of healthy children and factors that influence child development in Bahir Dar, Ethiopia.

METHODS AND ANALYSIS

The Bahir Dar Child Development Study is a cross-sectional study implemented in two health centres, Shimbit and Abaymado and in Felege Hiwot Comprehensive Specialized Hospital (FHCSH) in Bahir Dar, Amhara, Ethiopia. Healthy children between 6 and 60 months of age will be recruited from the health centres during vaccination visits or via community outreach. Young children aged 6-36 months will complete the Global Scale for Early Development. A battery of paper and tablet-based assessments of neurocognitive outcomes including visual and verbal reasoning, executive functions and school readiness will be completed for children aged 48-60 months. Caregivers will respond to surveys covering sociodemographic information, the child's medical history and nutrition, and psychosocial experiences including parental stress and mental health. During a second visit, participants will undergo a low-field MRI scan using the ultra-low-field point-of-care Hyperfine MRI machine at FHCSH. Analyses will examine relationships between risk and protective factors, brain volumes and neurocognitive/developmental outcomes.

ETHICS AND DISSEMINATION

The study is approved by the Institutional Review Boards of Addis Continental Institute of Public Health (ACIPH/lRERC/004/2023/Al/05-2024), Mass General Brigham Hospital (2022P002539) and Brown University (STUDY00000474). Findings will be disseminated via local dissemination events, international conferences and publications.

TRIAL REGISTERATION NUMBER

NCT06648863.

Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging

Introduction

Combining many types of imaging data-especially structural MRI (sMRI) and functional MRI (fMRI)-may greatly assist in the diagnosis and treatment of brain disorders like Alzheimer's. Current approaches are less helpful for forecasting, however, as they do not always blend spatial and temporal patterns from different sources properly. This work presents a novel mixed deep learning (DL) method combining data from many sources using CNN, GRU, and attention techniques. This work introduces a novel hybrid deep learning method combining CNN, GRU, and a Dynamic Cross-Modality Attention Module to help more efficiently blend spatial and temporal brain data. Through working around issues with current multimodal fusion techniques, our approach increases the accuracy and readability of diagnoses.

Methods

Utilizing CNNs and models of temporal dynamics from fMRI connection measures utilizing GRUs, the proposed approach extracts spatial characteristics from sMRI. Strong multimodal integration is made possible by including an attention mechanism to give diagnostically important features top priority. Training and evaluation of the model took place using the Human Connectome Project (HCP) dataset including behavioral data, fMRI, and sMRI. Measures include accuracy, recall, precision and F1-score used to evaluate performance.

Results

It was correct 96.79% of the time using the combined structure. Regarding the identification of brain disorders, the proposed model was more successful than existing ones.

Discussion

These findings indicate that the hybrid strategy makes sense for using complimentary information from several kinds of photos. Attention to detail helped one choose which aspects to concentrate on, thereby enhancing the readability and diagnostic accuracy.

Conclusion

The proposed method offers a fresh benchmark for multimodal neuroimaging analysis and has great potential for use in real-world brain assessment and prediction. Researchers will investigate future applications of this technique to new picture kinds and clinical data.

Magnetization transfer imaging using non-balanced SSFP at ultra-low field

PURPOSE

Ultra-low field MRI scanners have the potential to improve health care delivery, both through improved access in areas where there are few MRI scanners and allowing more frequent monitoring of disease progression and treatment response. This may be particularly true in white matter disorders, including leukodystrophies and multiple sclerosis, in which frequent myelin-sensitive imaging, such as magnetization transfer (MT) imaging, might improve clinical care and patient outcomes.

METHODS

We implemented an on-resonance approach to MT imaging on a commercial point-of-care 64 mT scanner using a non-balanced steady-state free precession sequence. Phantom and in vivo experiments were used to evaluate and optimize the sequence sensitivity and reproducibility, and to demonstrate in vivo performance and inter-site reproducibility.

RESULTS

From phantom experiments, T1 and T2 effects were determined to have a negligible effect on the differential MT weighting. MT ratio (MTR) values in white matter were 23.1 ± 1.0% from 10 healthy volunteers, with an average reproducibility coefficient of variation of 1.04%. Normal-appearing white matter MTR values in a multiple sclerosis participant (21.5 ± 6.2%) were lower, but with a similar spread of values, compared to an age-matched healthy volunteer (23.3 ± 6.2%).

CONCLUSION

An on-resonance MT imaging approach was developed at 64 mT that can be performed in as little as 4 min. A semi-quantitative myelin-sensitive imaging biomarker at this field strength is available for assessing both myelination and demyelination.

Feasibility and Usability of Low-Field Magnetic Resonance Imaging for Pediatric Neuroimaging in Low- and Middle-Income Countries: A Qualitative Study

Background

The burden of neurological disorders in low- and middle-income countries (LMICs) may be underestimated due to the limited number of diagnostic imaging devices and trained specialists to operate and interpret scans. Recent advancements in low-field (<100 milliteslas) magnetic resonance imaging (LFMRI) hold significant promise for improving access to pediatric neuroimaging due to the technology's lower costs, portability, and reduced infrastructure and training requirements.

Purpose

Explore user needs and experiences on the training and use of a portable LFMRI for pediatric neuroimaging in LMICs.

Methods

We conducted qualitative interviews with end users of the LFMRI systems across 11 sites in Bangladesh, Ethiopia, Ghana, Malawi, Pakistan, South Africa, Uganda, and Zambia. A semi-structured questionnaire with open-ended questions on usability and feasibility was used to encourage participants to share their experiences and opinions on ease of use, user satisfaction, and integration into local health systems.

Results

Among 46 participants, key challenges were reported in infant positioning, power stability, and internet connectivity. Suggestions included developing reference materials with content and format tailored to local needs and contexts, conducting refresher trainings, and providing education that includes technical and maintenance support crucial for appropriate utilization and implementation sustainability.

Conclusion

This study underscores the importance of incorporating human-centered design principles and user feedback into identifying and resolving usability issues, sharing insights for successful integration of LFMRI within existing health care infrastructures in LMICs, and optimizing LFMRI use for pediatric populations.

Structural MRI of brain similarity networks

Recent advances in structural MRI analytics now allow the network organization of individual brains to be comprehensively mapped through the use of the biologically principled metric of anatomical similarity. In this Review, we offer an overview of the measurement and meaning of structural MRI similarity, especially in relation to two key assumptions that often underlie its interpretation: (i) that MRI similarity can be representative of architectonic similarity between cortical areas and (ii) that similar areas are more likely to be axonally connected, as predicted by the homophily principle. We first introduce the historical roots and technical foundations of MRI similarity analysis and compare it with the distinct MRI techniques of structural covariance and tractography analysis. We contextualize this empirical work with two generative models of homophilic networks: an economic model of cost-constrained connectional homophily and a heterochronic model of ontogenetically phased cortical maturation. We then review (i) studies of the genetic and transcriptional architecture of MRI similarity in population-averaged and disorder-specific contexts and (ii) developmental studies of normative cohorts and clinical studies of neurodevelopmental and neurodegenerative disorders. Finally, we prioritize knowledge gaps that must be addressed to consolidate structural MRI similarity as an accessible, valid marker of the architecture and connectivity of an individual brain network.