Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations

Neuroimaging as a Tool for Advancing Pediatric Psychopharmacology

Neuroimaging, specifically magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and positron emission tomography (PET), plays an important role in improving the therapeutic landscape of pediatric neuropsychopharmacology by detecting target engagement, pathway modulation, and disease-related changes in the brain. This review provides a comprehensive update on the application of neuroimaging to detect neural effects of psychotropic medication in pediatrics. Additionally, we discuss opportunities and challenges for expanding the use of neuroimaging to advance pediatric neuropsychopharmacology. PubMed and Embase were searched for studies published between 2012 and 2024 reporting neural effects of attention deficit hyperactivity disorder (ADHD) medications (e.g., methylphenidate, amphetamine, atomoxetine, guanfacine), selective serotonin reuptake inhibitors (e.g., fluoxetine, escitalopram, sertraline), serotonin/norepinephrine reuptake inhibitors (e.g., duloxetine, venlafaxine), second-generation antipsychotics (e.g., aripiprazole, olanzapine, risperidone, quetiapine, ziprasidone), and others (e.g., lithium, carbamazepine, lamotrigine, ketamine, naltrexone) used to treat pediatric psychiatric conditions. Of the studies identified (N = 57 in 3314 pediatric participants), most (86%, total participants n = 3045) used MRI to detect functional pathway modulation or anatomical changes. Fewer studies (14%, total participants n = 269) used MRS to understand neurochemical modulation. No studies used PET. Studies that included healthy controls detected normalization of disease-altered pathways following treatment. Studies that focused on affected youth detected neuromodulation following single-dose and ongoing treatment. Neuroimaging is positioned to serve as a biomarker capable of demonstrating acute brain modulation, predicting clinical response, and monitoring disease, yet biomarker validation requires further work. Neuroimaging is also well suited to fill the notable knowledge gap of long-term neuromodulatory effects of psychotropic medications in the context of ongoing brain development in children and adolescents. Future studies can leverage advancements in neuroimaging technology, acquisition, and analysis to fill these gaps and accelerate the discovery of novel therapeutics, leading to more effective prescribing and ensuring faster recovery.

Understanding Proton Magnetic Resonance Spectroscopy Neurochemical Changes Using Alzheimer's Disease Biofluid, PET, Postmortem Pathology Biomarkers, and APOE Genotype

In vivo proton (1H) magnetic resonance spectroscopy (MRS) is a powerful non-invasive method that can measure Alzheimer's disease (AD)-related neuropathological alterations at the molecular level. AD biomarkers include amyloid-beta (Aβ) plaques and hyperphosphorylated tau neurofibrillary tangles. These biomarkers can be detected via postmortem analysis but also in living individuals through positron emission tomography (PET) or biofluid biomarkers of Aβ and tau. This review offers an overview of biochemical abnormalities detected by 1H MRS within the biologically defined AD spectrum. It includes a summary of earlier studies that explored the association of 1H MRS metabolites with biofluid, PET, and postmortem AD biomarkers and examined how apolipoprotein e4 allele carrier status influences brain biochemistry. Studying these associations is crucial for understanding how AD pathology affects brain homeostasis throughout the AD continuum and may eventually facilitate the development of potential novel therapeutic approaches.

Rate of abnormalities in quantitative MR neuroimaging of persons with chronic traumatic brain injury

BACKGROUND

Mild traumatic brain injury (mTBI) can result in lasting brain damage that is often too subtle to detect by qualitative visual inspection on conventional MR imaging. Although a number of FDA-cleared MR neuroimaging tools have demonstrated changes associated with mTBI, they are still under-utilized in clinical practice.

METHODS

We investigated a group of 65 individuals with predominantly mTBI (60 mTBI, 48 due to motor-vehicle collision, mean age 47 ± 13 years, 27 men and 38 women) with MR neuroimaging performed in a median of 37 months post-injury. We evaluated abnormalities in brain volumetry including analysis of left-right asymmetry by quantitative volumetric analysis, cerebral perfusion by pseudo-continuous arterial spin labeling (PCASL), white matter microstructure by diffusion tensor imaging (DTI), and neurometabolites via magnetic resonance spectroscopy (MRS).

RESULTS

All participants demonstrated atrophy in at least one lobar structure or increased lateral ventricular volume. The globus pallidi and cerebellar grey matter were most likely to demonstrate atrophy and asymmetry. Perfusion imaging revealed significant reductions of cerebral blood flow in both occipital and right frontoparietal regions. Diffusion abnormalities were relatively less common though a subset analysis of participants with higher resolution DTI demonstrated additional abnormalities. All participants showed abnormal levels on at least one brain metabolite, most commonly in choline and N-acetylaspartate.

CONCLUSION

We demonstrate the presence of coup-contrecoup perfusion injury patterns, widespread atrophy, regional brain volume asymmetry, and metabolic aberrations as sensitive markers of chronic mTBI sequelae. Our findings expand the historic focus on quantitative imaging of mTBI with DTI by highlighting the complementary importance of volumetry, arterial spin labeling perfusion and magnetic resonance spectroscopy neurometabolite analyses in the evaluation of chronic mTBI.

Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time

PURPOSE

Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan.

METHODS

There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data.

RESULTS

Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics.

CONCLUSION

DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.

An Update on MR Spectroscopy in Cancer Management: Advances in Instrumentation, Acquisition, and Analysis

MR spectroscopy (MRS) is a noninvasive imaging method enabling chemical and molecular profiling of tissues in a localized, multiplexed, and nonionizing manner. As metabolic reprogramming is a hallmark of cancer, MRS provides valuable metabolic and molecular information for cancer diagnosis, prognosis, treatment monitoring, and patient management. This review provides an update on the use of MRS for clinical cancer management. The first section includes an overview of the principles of MRS, current methods, and conventional metabolites of interest. The remainder of the review is focused on three key areas: advances in instrumentation, specifically ultrahigh-field-strength MRI scanners and hybrid systems; emerging methods for acquisition, including deuterium imaging, hyperpolarized carbon 13 MRI and MRS, chemical exchange saturation transfer, diffusion-weighted MRS, MR fingerprinting, and fast acquisition; and analysis aided by artificial intelligence. The review concludes with future recommendations to facilitate routine use of MRS in cancer management. Keywords: MR Spectroscopy, Spectroscopic Imaging, Molecular Imaging in Oncology, Metabolic Reprogramming, Clinical Cancer Management © RSNA, 2024.

Neuroimaging Correlates of Functional Outcome Following Pediatric TBI

Neuroimaging plays an important role in assessing the consequences of TBI across the postinjury period. While identifying alterations to the brain is important, associating those changes to functional, cognitive, and behavioral outcomes is an essential step to establishing the value of advanced neuroimaging for pediatric TBI. Here we highlight research that has revealed links between advanced neuroimaging and outcome after TBI and point to opportunities where neuroimaging could expand our ability to prognosticate and potentially uncover opportunities to intervene.

Comparison of different approaches to manage multi-site magnetic resonance spectroscopy clinical data analysis

Introduction

The effects caused by differences in data acquisition can be substantial and may impact data interpretation in multi-site/scanner studies using magnetic resonance spectroscopy (MRS). Given the increasing use of multi-site studies, a better understanding of how to account for different scanners is needed. Using data from a concussion population, we compare ComBat harmonization with different statistical methods in controlling for site, vendor, and scanner as covariates to determine how to best control for multi-site data.

Methods

The data for the current study included 545 MRS datasets to measure tNAA, tCr, tCho, Glx, and mI to study the pediatric concussion acquired across five sites, six scanners, and two different MRI vendors. For each metabolite, the site and vendor were accounted for in seven different models of general linear models (GLM) or mixed-effects models while testing for group differences between the concussion and orthopedic injury. Models 1 and 2 controlled for vendor and site. Models 3 and 4 controlled for scanner. Models 5 and 6 controlled for site applied to data harmonized by vendor using ComBat. Model 7 controlled for scanner applied to data harmonized by scanner using ComBat. All the models controlled for age and sex as covariates.

Results

Models 1 and 2, controlling for site and vendor, showed no significant group effect in any metabolites, but the vendor and site were significant factors in the GLM. Model 3, which included a scanner, showed a significant group effect for tNAA and tCho, and the scanner was a significant factor. Model 4, controlling for the scanner, did not show a group effect in the mixed model. The data harmonized by the vendor using ComBat (Models 5 and 6) had no significant group effect in both the GLM and mixed models. Lastly, the data harmonized by the scanner using ComBat (Model 7) showed no significant group effect. The individual site data suggest there were no group differences.

Conclusion

Using data from a large clinical concussion population, different analysis techniques to control for site, vendor, and scanner in MRS data yielded different results. The findings support the use of ComBat harmonization for clinical MRS data, as it removes the site and vendor effects.