Advancing GABA-edited MRS Research through a Reconstruction Challenge DOI Creative Commons
R. Berto, Hanna Bugler, Gabriel Silva Dias

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 22, 2023

Abstract Purpose To create a benchmark for the comparison of machine learning-based Gamma-Aminobutyric Acid (GABA)-edited Magnetic Resonance Spectroscopy (MRS) reconstruction models using one quarter transients typically acquired during complete scan. Methods The Edited-MRS challenge had three tracks with purpose evaluating learning trained to reconstruct simulated (Track 1), homogeneous in vivo 2), and heterogeneous 3) GABA-edited MRS data. Four quantitative metrics were used evaluate results: mean squared error (MSE), signal-to-noise ratio (SNR), linewidth, shape score metric that we proposed. Challenge participants given months create, train submit their models. organizers provided open access baseline U-NET model initial comparison, as well data, tutorials guides adding synthetic noise simulations. Results most successful approach Track 1 data was covariance matrix convolutional neural network model, while 2 3 vision transformer operating on spectrogram representation achieved success. Deep (DL) based reconstructions reduced equivalent or better SNR, linewidth fit conventional full amount transients. However, some DL also showed ability optimize SNR values without actually improving overall spectral quality, pointing need more robust metrics. Conclusion edited-MRS top performing pipelines can obtain number proposed positively correlated track outcome indicating it is well-suited quality.

Language: Английский

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration DOI Creative Commons
Bin Lü, Xiao Chen, F. Xavier Castellanos

et al.

Science Bulletin, Journal Year: 2024, Volume and Issue: 69(10), P. 1536 - 1555

Published: March 6, 2024

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 subtle abnormalities and robust associations, fostering new research methods. Global collaborations imaging have furthered knowledge neurobiological foundations brain disorders aided imaging-based prediction for more targeted treatment. Large-scale magnetic resonance initiatives driving innovation analytics supporting generalizable psychiatric studies. We also emphasize significant role big understanding neural mechanisms early identification precise treatment However, challenges such as harmonization across different sites, privacy protection, effective sharing must be addressed. With proper governance science practices, we conclude with a projection how large-scale resources could revolutionize diagnosis, selection, outcome prediction, contributing to optimal health.

Language: Английский

Citations

11
An Update on MR Spectroscopy in Cancer Management: Advances in Instrumentation, Acquisition, and Analysis DOI Open Access
Eva Martinez Luque, Zexuan Liu, Dongsuk Sung

et al.

Radiology Imaging Cancer, Journal Year: 2024, Volume and Issue: 6(3)

Published: April 5, 2024

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

Language: Английский

Citations

5
Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time DOI
R. Berto, Hanna Bugler, Gabriel Silva Dias

et al.

Magnetic Resonance Materials in Physics Biology and Medicine, Journal Year: 2024, Volume and Issue: 37(3), P. 449 - 463

Published: April 13, 2024

Language: Английский

Citations

4
Neuroimaging as a Tool for Advancing Pediatric Psychopharmacology DOI
Michael Bartkoski, John Tumberger, Laura E. Martin

et al.

Pediatric Drugs, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

Language: Английский

Citations

0
Rate of abnormalities in quantitative MR neuroimaging of persons with chronic traumatic brain injury DOI Creative Commons
Farzaneh Rahmani,

Richard D. Batson,

Alexandra Zimmerman

et al.

BMC Neurology, Journal Year: 2024, Volume and Issue: 24(1)

Published: July 5, 2024

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

Language: Английский

Citations

2
Understanding Proton Magnetic Resonance Spectroscopy Neurochemical Changes using Alzheimer’s Disease Biofluid, PET, Postmortem Pathology Biomarkers and APOE Genotype DOI Open Access
Firat Kara, Kejal Kantarci

Published: Aug. 26, 2024

In vivo proton (1H) magnetic resonance spectroscopy (MRS) is a powerful noninvasive method which 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 be detected via postmortem analysis, but also in living individuals through positron emission tomography (PET) or biofluid of Aβ tau. This review offers an overview biochemical abnormalities by 1H MRS within biologically defined spectrum. It includes summary earlier studies that explored association metabolites with biofluid, PET, biomarkers, examined how apolipoprotein e4 allele carrier status influences brain biochemistry. Studying these associations crucial for understanding pathology affects homeostasis throughout continuum may eventually facilitate to develop potential novel therapeutic approaches.

Language: Английский

Citations

1
Understanding Proton Magnetic Resonance Spectroscopy Neurochemical Changes Using Alzheimer’s Disease Biofluid, PET, Postmortem Pathology Biomarkers, and APOE Genotype DOI Open Access
Firat Kara,

Kejal Kantarci

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(18), P. 10064 - 10064

Published: Sept. 19, 2024

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 be detected via postmortem analysis but also in living individuals through positron emission tomography (PET) or biofluid of Aβ tau. This review offers an overview biochemical abnormalities by 1H MRS within biologically defined spectrum. It includes summary earlier studies explored association metabolites with biofluid, PET, examined how apolipoprotein e4 allele carrier status influences brain biochemistry. Studying these associations crucial for understanding pathology affects homeostasis throughout continuum may eventually facilitate development potential novel therapeutic approaches.

Language: Английский

Citations

1
Neuroimaging Correlates of Functional Outcome Following Pediatric TBI DOI
Emily L. Dennis, Finian Keleher, Brenda Bartnik‐Olson

et al.

Advances in neurobiology, Journal Year: 2024, Volume and Issue: unknown, P. 33 - 84

Published: Jan. 1, 2024

Language: Английский

Citations

1
Comparison of different approaches to manage multi-site magnetic resonance spectroscopy clinical data analysis DOI Creative Commons
Parker La, Tiffany Bell, William Craig

et al.

Frontiers in Psychology, Journal Year: 2023, Volume and Issue: 14

Published: April 20, 2023

Introduction The effects caused by differences in data acquisition can be substantial and may impact interpretation multi-site/scanner studies using magnetic resonance spectroscopy (MRS). Given the increasing use of multi-site studies, a better understanding how to account for different scanners is needed. Using from concussion population, we compare ComBat harmonization with statistical methods controlling site, vendor, scanner as covariates determine best control data. Methods current study included 545 MRS datasets measure tNAA, tCr, tCho, Glx, mI pediatric acquired across five sites, six scanners, two MRI vendors. For each metabolite, site vendor were accounted seven models general linear (GLM) or mixed-effects while testing group between orthopedic injury. Models 1 2 controlled site. 3 4 scanner. 5 6 applied harmonized ComBat. Model 7 All age sex covariates. Results 2, showed no significant effect any metabolites, but factors GLM. 3, which scanner, tNAA was factor. 4, did not show mixed model. (Models 6) had both GLM models. Lastly, (Model 7) effect. individual suggest there differences. Conclusion large clinical analysis techniques yielded results. findings support data, it removes effects.

Language: Английский

Citations

1
Advancing GABA-edited MRS Research through a Reconstruction Challenge DOI Creative Commons
R. Berto, Hanna Bugler, Gabriel Silva Dias

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 22, 2023

Abstract Purpose To create a benchmark for the comparison of machine learning-based Gamma-Aminobutyric Acid (GABA)-edited Magnetic Resonance Spectroscopy (MRS) reconstruction models using one quarter transients typically acquired during complete scan. Methods The Edited-MRS challenge had three tracks with purpose evaluating learning trained to reconstruct simulated (Track 1), homogeneous in vivo 2), and heterogeneous 3) GABA-edited MRS data. Four quantitative metrics were used evaluate results: mean squared error (MSE), signal-to-noise ratio (SNR), linewidth, shape score metric that we proposed. Challenge participants given months create, train submit their models. organizers provided open access baseline U-NET model initial comparison, as well data, tutorials guides adding synthetic noise simulations. Results most successful approach Track 1 data was covariance matrix convolutional neural network model, while 2 3 vision transformer operating on spectrogram representation achieved success. Deep (DL) based reconstructions reduced equivalent or better SNR, linewidth fit conventional full amount transients. However, some DL also showed ability optimize SNR values without actually improving overall spectral quality, pointing need more robust metrics. Conclusion edited-MRS top performing pipelines can obtain number proposed positively correlated track outcome indicating it is well-suited quality.

Language: Английский

Citations

0