1
|
Shahid SS, Grecco GG, Atwood BK, Wu YC. Perturbed neurochemical and microstructural organization in a mouse model of prenatal opioid exposure: A multi-modal magnetic resonance study. PLoS One 2023; 18:e0282756. [PMID: 37471385 PMCID: PMC10358947 DOI: 10.1371/journal.pone.0282756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 07/08/2023] [Indexed: 07/22/2023] Open
Abstract
Methadone-based treatment for pregnant women with opioid use disorder is quite prevalent in the clinical environment. A number of clinical and animal model-based studies have reported cognitive deficits in infants prenatally exposed to methadone-based opioid treatments. However, the long-term impact of prenatal opioid exposure (POE) on pathophysiological mechanisms that govern neurodevelopmental impairment is not well understood. Using a translationally relevant mouse model of prenatal methadone exposure (PME), the aim of this study is to investigate the role of cerebral biochemistry and its possible association with regional microstructural organization in PME offspring. To understand these effects, 8-week-old male offspring with PME (n = 7) and prenatal saline exposure (PSE) (n = 7) were scanned in vivo on 9.4 Tesla small animal scanner. Single voxel proton magnetic resonance spectroscopy (1H-MRS) was performed in the right dorsal striatum (RDS) region using a short echo time (TE) Stimulated Echo Acquisition Method (STEAM) sequence. Neurometabolite spectra from the RDS was first corrected for tissue T1 relaxation and then absolute quantification was performed using the unsuppressed water spectra. High-resolution in vivo diffusion MRI (dMRI) for region of interest (ROI) based microstructural quantification was also performed using a multi-shell dMRI sequence. Cerebral microstructure was characterized using diffusion tensor imaging (DTI) and Bingham-neurite orientation dispersion and density imaging (Bingham-NODDI). MRS results in the RDS showed significant decrease in N-acetyl aspartate (NAA), taurine (tau), glutathione (GSH), total creatine (tCr) and glutamate (Glu) concentration levels in PME, compared to PSE group. In the same RDS region, mean orientation dispersion index (ODI) and intracellular volume fraction (VFIC) demonstrated positive associations with tCr in PME group. ODI also exhibited significant positive association with Glu levels in PME offspring. Significant reduction in major neurotransmitter metabolites and energy metabolism along with strong association between the neurometabolites and perturbed regional microstructural complexity suggest a possible impaired neuroadaptation trajectory in PME offspring which could be persistent even into late adolescence and early adulthood.
Collapse
Affiliation(s)
- Syed Salman Shahid
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States of America
| | - Gregory G. Grecco
- Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, IN, United States of America
- Medical Scientist Training Program, Indiana University School of Medicine, Indianapolis, IN, United States of America
| | - Brady K. Atwood
- Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, IN, United States of America
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, United States of America
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States of America
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, United States of America
| |
Collapse
|
2
|
Shahid SS, Grecco GG, Atwood BK, Wu YC. Perturbed neurochemical and microstructural organization in a mouse model of prenatal opioid exposure: a multi-modal magnetic resonance study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.23.529659. [PMID: 36865153 PMCID: PMC9980104 DOI: 10.1101/2023.02.23.529659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Methadone-based treatment for pregnant women with opioid use disorder is quite prevalent in the clinical environment. A number of clinical and animal model-based studies have reported cognitive deficits in infants prenatally exposed to methadone-based opioid treatments. However, the long-term impact of prenatal opioid exposure (POE) on pathophysiological mechanisms that govern neurodevelopmental impairment is not well understood. Using a translationally relevant mouse model of prenatal methadone exposure (PME), the aim of this study is to investigate the role of cerebral biochemistry and its possible association with regional microstructural organization in PME offspring. To understand these effects, 8- week-old male offspring with PME (n=7) and prenatal saline exposure (PSE) (n=7) were scanned in vivo on 9.4 Tesla small animal scanner. Single voxel proton magnetic resonance spectroscopy ( 1 H-MRS) was performed in the right dorsal striatum (RDS) region using a short echo time (TE) Stimulated Echo Acquisition Method (STEAM) sequence. Neurometabolite spectra from the RDS was first corrected for tissue T1 relaxation and then absolute quantification was performed using the unsuppressed water spectra. High-resolution in vivo diffusion MRI (dMRI) for region of interest (ROI) based microstructural quantification was also performed using a multi-shell dMRI sequence. Cerebral microstructure was characterized using diffusion tensor imaging (DTI) and Bingham-neurite orientation dispersion and density imaging (Bingham-NODDI). MRS results in the RDS showed significant decrease in N-acetyl aspartate (NAA), taurine (tau), glutathione (GSH), total creatine (tCr) and glutamate (Glu) concentration levels in PME, compared to PSE group. In the same RDS region, mean orientation dispersion index (ODI) and intracellular volume fraction (VF IC ) demonstrated positive associations with tCr in PME group. ODI also exhibited significant positive association with Glu levels in PME offspring. Significant reduction in major neurotransmitter metabolites and energy metabolism along with strong association between the neurometabolites and perturbed regional microstructural complexity suggest a possible impaired neuroadaptation trajectory in PME offspring which could be persistent even into late adolescence and early adulthood.
Collapse
|
3
|
Chen S, Lee J, Truong TM, Alhassen S, Baldi P, Alachkar A. Age-Related Neurometabolomic Signature of Mouse Brain. ACS Chem Neurosci 2021; 12:2887-2902. [PMID: 34283556 DOI: 10.1021/acschemneuro.1c00259] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Neurometabolites are the ultimate gene products in the brain and the most precise biomolecular indicators of brain endophenotypes. Metabolomics is the only "omics" that provides a moment-to-moment "snapshot" of brain circuits' biochemical activities in response to external stimuli within the context of specific genetic variations. Although the expression levels of neurometabolites are highly dynamic, the underlying metabolic processes are tightly regulated during brain development, maturation, and aging. Therefore, this study aimed to identify mouse brain metabolic profiles in neonatal and adult stages and reconstruct both the active metabolic network and the metabolic pathway functioning. Using high-throughput metabolomics and bioinformatics analyses, we show that the neonatal mouse brain has its distinct metabolomic signature, which differs from the adult brain. Furthermore, lipid metabolites showed the most profound changes between the neonatal and adult brain, with some lipid species reaching 1000-fold changes. There were trends of age-dependent increases and decreases among lipids and non-lipid metabolites, respectively. A few lipid metabolites such as HexCers and SHexCers were almost absent in neonatal brains, whereas other non-lipid metabolites such as homoarginine were absent in the adult brains. Several molecules that act as neurotransmitters/neuromodulators showed age-dependent levels, with adenosine and GABA exhibiting around 100- and 10-fold increases in the adult compared with the neonatal brain. Of particular interest is the observation that purine and pyrimidines nucleobases exhibited opposite age-dependent changes. Bioinformatics analysis revealed an enrichment of lipid biosynthesis pathways in metabolites, whose levels increased in adult brains. In contrast, pathways involved in the metabolism of amino acids, nucleobases, glucose (glycolysis), tricarboxylic acid cycle (TCA) were enriched in metabolites whose levels were higher in the neonatal brains. Many of these pathways are associated with pathological conditions, which can be predicted as early as the neonatal stage. Our study provides an initial age-related biochemical directory of the mouse brain and warrants further studies to identify temporal brain metabolome across the lifespan, particularly during adolescence and aging. Such neurometabolomic data may provide important insight about the onset and progression of neurological/psychiatric disorders and may ultimately lead to the development of precise diagnostic biomarkers and more effective preventive/therapeutic strategies.
Collapse
Affiliation(s)
- Siwei Chen
- Department of Computer Science, School of Information and Computer Sciences, University of California—Irvine, Irvine, California 92697, United States
- Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California—Irvine, Irvine, California 92697, United States
| | - Justine Lee
- Department of Pharmaceutical Sciences, School of Pharmacy, University of California—Irvine, Irvine, California 92697, United States
| | - Tri Minh Truong
- Department of Pharmaceutical Sciences, School of Pharmacy, University of California—Irvine, Irvine, California 92697, United States
| | - Sammy Alhassen
- Department of Pharmaceutical Sciences, School of Pharmacy, University of California—Irvine, Irvine, California 92697, United States
| | - Pierre Baldi
- Department of Computer Science, School of Information and Computer Sciences, University of California—Irvine, Irvine, California 92697, United States
- Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California—Irvine, Irvine, California 92697, United States
| | - Amal Alachkar
- Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California—Irvine, Irvine, California 92697, United States
- Department of Pharmaceutical Sciences, School of Pharmacy, University of California—Irvine, Irvine, California 92697, United States
| |
Collapse
|
4
|
EkŞİ Z, ÇakiroĞlu M, Öz C, AralaŞmak A, Karadelİ HH, Özcan ME. Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning. ARQUIVOS DE NEURO-PSIQUIATRIA 2020; 78:789-796. [PMID: 33331515 DOI: 10.1590/0004-282x20200094] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 06/04/2020] [Indexed: 11/22/2022]
Abstract
INTRODUCTION Magnetic resonance imaging (MRI) is the most important tool for diagnosis and follow-up in multiple sclerosis (MS). The discrimination of relapsing-remitting MS (RRMS) from secondary progressive MS (SPMS) is clinically difficult, and developing the proposal presented in this study would contribute to the process. OBJECTIVE This study aimed to ensure the automatic classification of healthy controls, RRMS, and SPMS by using MR spectroscopy and machine learning methods. METHODS MR spectroscopy (MRS) was performed on a total of 91 participants, distributed into healthy controls (n=30), RRMS (n=36), and SPMS (n=25). Firstly, MRS metabolites were identified using signal processing techniques. Secondly, feature extraction was performed based on MRS Spectra. N-acetylaspartate (NAA) was the most significant metabolite in differentiating MS types. Lastly, binary classifications (healthy controls-RRMS and RRMS-SPMS) were carried out according to features obtained by the Support Vector Machine algorithm. RESULTS RRMS cases were differentiated from healthy controls with 85% accuracy, 90.91% sensitivity, and 77.78% specificity. RRMS and SPMS were classified with 83.33% accuracy, 81.81% sensitivity, and 85.71% specificity. CONCLUSIONS A combined analysis of MRS and computer-aided diagnosis may be useful as a complementary imaging technique to determine MS types.
Collapse
Affiliation(s)
- Ziya EkŞİ
- Sakarya University, Department of Computer Engineering, Sakarya, Turkey
| | - Murat ÇakiroĞlu
- Sakarya University, Department of Mechatronic Engineering, Sakarya, Turkey
| | - Cemil Öz
- Sakarya University, Department of Computer Engineering, Sakarya, Turkey
| | - Ayse AralaŞmak
- Memorial Bahçelievler Hospital, Department of Radiology, Istanbul, Turkey
| | | | | |
Collapse
|
5
|
Swanberg KM, Landheer K, Pitt D, Juchem C. Quantifying the Metabolic Signature of Multiple Sclerosis by in vivo Proton Magnetic Resonance Spectroscopy: Current Challenges and Future Outlook in the Translation From Proton Signal to Diagnostic Biomarker. Front Neurol 2019; 10:1173. [PMID: 31803127 PMCID: PMC6876616 DOI: 10.3389/fneur.2019.01173] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 10/21/2019] [Indexed: 01/03/2023] Open
Abstract
Proton magnetic resonance spectroscopy (1H-MRS) offers a growing variety of methods for querying potential diagnostic biomarkers of multiple sclerosis in living central nervous system tissue. For the past three decades, 1H-MRS has enabled the acquisition of a rich dataset suggestive of numerous metabolic alterations in lesions, normal-appearing white matter, gray matter, and spinal cord of individuals with multiple sclerosis, but this body of information is not free of seeming internal contradiction. The use of 1H-MRS signals as diagnostic biomarkers depends on reproducible and generalizable sensitivity and specificity to disease state that can be confounded by a multitude of influences, including experiment group classification and demographics; acquisition sequence; spectral quality and quantifiability; the contribution of macromolecules and lipids to the spectroscopic baseline; spectral quantification pipeline; voxel tissue and lesion composition; T1 and T2 relaxation; B1 field characteristics; and other features of study design, spectral acquisition and processing, and metabolite quantification about which the experimenter may possess imperfect or incomplete information. The direct comparison of 1H-MRS data from individuals with and without multiple sclerosis poses a special challenge in this regard, as several lines of evidence suggest that experimental cohorts may differ significantly in some of these parameters. We review the existing findings of in vivo1H-MRS on central nervous system metabolic abnormalities in multiple sclerosis and its subtypes within the context of study design, spectral acquisition and processing, and metabolite quantification and offer an outlook on technical considerations, including the growing use of machine learning, by future investigations into diagnostic biomarkers of multiple sclerosis measurable by 1H-MRS.
Collapse
Affiliation(s)
- Kelley M Swanberg
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, NY, United States
| | - Karl Landheer
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, NY, United States
| | - David Pitt
- Department of Neurology, Yale University School of Medicine, New Haven, CT, United States
| | - Christoph Juchem
- Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, NY, United States.,Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, United States
| |
Collapse
|
6
|
A Comparison of Neuroimaging Abnormalities in Multiple Sclerosis, Major Depression and Chronic Fatigue Syndrome (Myalgic Encephalomyelitis): is There a Common Cause? Mol Neurobiol 2017; 55:3592-3609. [PMID: 28516431 PMCID: PMC5842501 DOI: 10.1007/s12035-017-0598-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 05/03/2017] [Indexed: 01/23/2023]
Abstract
There is copious evidence of abnormalities in resting-state functional network connectivity states, grey and white matter pathology and impaired cerebral perfusion in patients afforded a diagnosis of multiple sclerosis, major depression or chronic fatigue syndrome (CFS) (myalgic encephalomyelitis). Systemic inflammation may well be a major element explaining such findings. Inter-patient and inter-illness variations in neuroimaging findings may arise at least in part from regional genetic, epigenetic and environmental variations in the functions of microglia and astrocytes. Regional differences in neuronal resistance to oxidative and inflammatory insults and in the performance of antioxidant defences in the central nervous system may also play a role. Importantly, replicated experimental findings suggest that the use of high-resolution SPECT imaging may have the capacity to differentiate patients afforded a diagnosis of CFS from those with a diagnosis of depression. Further research involving this form of neuroimaging appears warranted in an attempt to overcome the problem of aetiologically heterogeneous cohorts which probably explain conflicting findings produced by investigative teams active in this field. However, the ionising radiation and relative lack of sensitivity involved probably preclude its use as a routine diagnostic tool.
Collapse
|
7
|
Cawley N, Solanky BS, Muhlert N, Tur C, Edden RAE, Wheeler-Kingshott CAM, Miller DH, Thompson AJ, Ciccarelli O. Reduced gamma-aminobutyric acid concentration is associated with physical disability in progressive multiple sclerosis. Brain 2015; 138:2584-95. [PMID: 26304151 DOI: 10.1093/brain/awv209] [Citation(s) in RCA: 85] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Neurodegeneration is thought to be the major cause of ongoing, irreversible disability in progressive stages of multiple sclerosis. Gamma-aminobutyric acid is the principle inhibitory neurotransmitter in the brain. The aims of this study were to investigate if gamma-aminobutyric acid levels (i) are abnormal in patients with secondary progressive multiple sclerosis compared with healthy controls; and (ii) correlate with physical and cognitive performance in this patient population. Thirty patients with secondary progressive multiple sclerosis and 17 healthy control subjects underwent single-voxel MEGA-PRESS (MEscher-GArwood Point RESolved Spectroscopy) magnetic resonance spectroscopy at 3 T, to quantify gamma-aminobutyric acid levels in the prefrontal cortex, right hippocampus and left sensorimotor cortex. All subjects were assessed clinically and underwent a cognitive assessment. Multiple linear regression models were used to compare differences in gamma-aminobutyric acid concentrations between patients and controls adjusting for age, gender and tissue fractions within each spectroscopic voxel. Regression was used to examine the relationships between the cognitive function and physical disability scores specific for these regions with gamma-aminobuytric acid levels, adjusting for age, gender, and total N-acetyl-aspartate and glutamine-glutamate complex levels. When compared with controls, patients performed significantly worse on all motor and sensory tests, and were cognitively impaired in processing speed and verbal memory. Patients had significantly lower gamma-aminobutyric acid levels in the hippocampus (adjusted difference = -0.403 mM, 95% confidence intervals -0.792, -0.014, P = 0.043) and sensorimotor cortex (adjusted difference = -0.385 mM, 95% confidence intervals -0.667, -0.104, P = 0.009) compared with controls. In patients, reduced motor function in the right upper and lower limb was associated with lower gamma-aminobutyric acid concentration in the sensorimotor cortex. Specifically for each unit decrease in gamma-aminobutyric acid levels (in mM), there was a predicted -10.86 (95% confidence intervals -16.786 to -4.482) decrease in grip strength (kg force) (P < 0.001) and -8.74 (95% confidence intervals -13.943 to -3.015) decrease in muscle strength (P < 0.006). This study suggests that reduced gamma-aminobutyric acid levels reflect pathological abnormalities that may play a role in determining physical disability. These abnormalities may include decreases in the pre- and postsynaptic components of gamma-aminobutyric acid neurotransmission and in the density of inhibitory neurons. Additionally, the reduced gamma-aminobutyric acid concentration may contribute to the neurodegenerative process, resulting in increased firing of axons, with consequent increased energy demands, which may lead to neuroaxonal degeneration and loss of the compensatory mechanisms that maintain motor function. This study supports the idea that modulation of gamma-aminobutyric acid neurotransmission may be an important target for neuroprotection in multiple sclerosis.See De Stefano and Giorgio (doi:10.1093/brain/awv213) for a scientific commentary on this article.
Collapse
Affiliation(s)
- Niamh Cawley
- 1 NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, London, UK
| | - Bhavana S Solanky
- 1 NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, London, UK
| | - Nils Muhlert
- 1 NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, London, UK 2 School of Psychology and Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK 3 School of Psychological Sciences, University of Manchester, Manchester, UK
| | - Carmen Tur
- 1 NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, London, UK
| | - Richard A E Edden
- 4 Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA 5 FM Kirby Centre for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Claudia A M Wheeler-Kingshott
- 1 NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, London, UK 6 Brain Connectivity Centre, C. Mondino National Neurological Institute, Pavia, Italy
| | - David H Miller
- 1 NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, London, UK 7 National Institute of Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK
| | - Alan J Thompson
- 1 NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, London, UK 7 National Institute of Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK
| | - Olga Ciccarelli
- 1 NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, London, UK 7 National Institute of Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK
| |
Collapse
|
8
|
Hellem T, Shi X, Latendresse G, Renshaw PF. The Utility of Magnetic Resonance Spectroscopy for Understanding Substance Use Disorders: A Systematic Review of the Literature. J Am Psychiatr Nurses Assoc 2015; 21:244-75. [PMID: 26282670 PMCID: PMC5495546 DOI: 10.1177/1078390315598606] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The aim of this article is to present a systematic review of magnetic resonance spectroscopy (MRS) studies of substance use disorders. As a noninvasive and nonionizing imaging technique, MRS is being widely used in substance abuse research to evaluate the effects substances of abuse have on brain chemistry. Nearly 40 peer-reviewed research articles that focused on the utility of MRS in alcohol, methamphetamine, 3,4-methylenedioxymethamphetamine, cocaine, opiates, opioids, marijuana, and nicotine use disorders were reviewed. Findings indicate inconsistencies with respect to alterations in brain chemistry within each substance of abuse, and the most consistent finding across substances was decreased N-acetylaspartate and choline levels with chronic alcohol, methamphetamine, and nicotine use. Variation in the brain regions studied, imaging technique, as well as small sample sizes might explain the discrepancies in findings within each substance. Future well-designed MRS studies offer promise in examining novel treatment approaches in substance use disorders.
Collapse
Affiliation(s)
- Tracy Hellem
- Tracy Hellem, PhD, RN, Diagnostic Neuroimaging and College of Nursing, University of Utah, Salt Lake City, UT, USA
| | - Xianfeng Shi
- Xianfeng Shi, PhD, Diagnostic Neuroimaging, University of Utah, Salt Lake City, UT, USA
| | - Gwen Latendresse
- Gwen Latendresse, PhD, CNM, FACNM, College of Nursing, University of Utah, Salt Lake City, UT, USA
| | - Perry F Renshaw
- Perry F. Renshaw, MD, PhD, MBA, Diagnostic Neuroimaging, University of Utah, Salt Lake City, UT, USA and VISN 19 MIRECC, Salt Lake City, UT, USA
| |
Collapse
|
9
|
Soher BJ, Wu WE, Tal A, Storey P, Zhang K, Babb JS, Lui YW, Gonen O. Automated whole-brain N-acetylaspartate proton MRS quantification. NMR IN BIOMEDICINE 2014; 27:1275-84. [PMID: 25196714 PMCID: PMC4212831 DOI: 10.1002/nbm.3185] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2014] [Revised: 07/16/2014] [Accepted: 07/18/2014] [Indexed: 06/03/2023]
Abstract
Concentration of the neuronal marker, N-acetylaspartate (NAA), a quantitative metric for the health and density of neurons, is currently obtained by integration of the manually defined peak in whole-head proton ((1) H)-MRS. Our goal was to develop a full spectral modeling approach for the automatic estimation of the whole-brain NAA concentration (WBNAA) and to compare the performance of this approach with a manual frequency-range peak integration approach previously employed. MRI and whole-head (1) H-MRS from 18 healthy young adults were examined. Non-localized, whole-head (1) H-MRS obtained at 3 T yielded the NAA peak area through both manually defined frequency-range integration and the new, full spectral simulation. The NAA peak area was converted into an absolute amount with phantom replacement and normalized for brain volume (segmented from T1 -weighted MRI) to yield WBNAA. A paired-sample t test was used to compare the means of the WBNAA paradigms and a likelihood ratio test used to compare their coefficients of variation. While the between-subject WBNAA means were nearly identical (12.8 ± 2.5 mm for integration, 12.8 ± 1.4 mm for spectral modeling), the latter's standard deviation was significantly smaller (by ~50%, p = 0.026). The within-subject variability was 11.7% (±1.3 mm) for integration versus 7.0% (±0.8 mm) for spectral modeling, i.e., a 40% improvement. The (quantifiable) quality of the modeling approach was high, as reflected by Cramer-Rao lower bounds below 0.1% and vanishingly small (experimental - fitted) residuals. Modeling of the whole-head (1) H-MRS increases WBNAA quantification reliability by reducing its variability, its susceptibility to operator bias and baseline roll, and by providing quality-control feedback. Together, these enhance the usefulness of the technique for monitoring the diffuse progression and treatment response of neurological disorders.
Collapse
Affiliation(s)
- Brian J. Soher
- Department of Radiology, Duke University Medical Center, Durham NC, 27710, USA
| | - William E. Wu
- Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Assaf Tal
- Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Pippa Storey
- Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Ke Zhang
- Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - James. S. Babb
- Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Yvonne W. Lui
- Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Oded Gonen
- Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
| |
Collapse
|