Assaf Tal

Automated whole-brain N-acetylaspartate proton MRS quantification.

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 (1H)‐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 1H‐MRS from 18 healthy young adults were examined. Non‐localized, whole‐head 1H‐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 latters 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 1H‐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. Copyright

Global gray and white matter metabolic changes after simian immunodeficiency virus infection in CD8-depleted rhesus macaques: proton MRS imaging at 3 T

To test the hypotheses that global decreased neuro‐axonal integrity reflected by decreased N‐acetylaspartate (NAA) and increased glial activation reflected by an elevation in its marker, the myo‐inositol (mI), present in a CD8‐depleted rhesus macaque model of HIV‐associated neurocognitive disorders. To this end, we performed quantitative MRI and 16 × 16 × 4 multivoxel proton MRS imaging (TE/TR = 33/1400 ms) in five macaques pre‐ and 4–6 weeks post‐simian immunodeficiency virus infection. Absolute NAA, creatine, choline (Cho), and mI concentrations, gray and white matter (GM and WM) and cerebrospinal fluid fractions were obtained. Global GM and WM concentrations were estimated from 224 voxels (at 0.125 cm3 spatial resolution over ~35% of the brain) using linear regression. Pre‐ to post‐infection global WM NAA declined 8%: 6.6 ± 0.4 to 6.0 ± 0.5 mM (p = 0.05); GM Cho declined 20%: 1.3 ± 0.2 to 1.0 ± 0.1 mM (p < 0.003); global mI increased 11%: 5.7 ± 0.4 to 6.5 ± 0.5 mM (p < 0.03). Global GM and WM brain volume fraction changes were statistically insignificant. These metabolic changes are consistent with global WM (axonal) injury and glial activation, and suggest a possible GM host immune response. Copyright

When are metabolic ratios superior to absolute quantification? A statistical analysis

Metabolite levels measured using magnetic resonance spectroscopy (MRS) are often expressed as ratios rather than absolute concentrations. However, the inter‐subject variability of the denominator metabolite can introduce uncertainty into a metabolite ratio. In a clinical setting, there are no guidelines on whether ratios or absolute quantification should be used for a more accurate classification of normal versus abnormal results based on their statistical properties. In a research setting, the choice of one over the other can have significant implications on sample size, which must be factored in at the study design stage. Herein, we derive the probability distribution function for the ratio of two normally distributed random variables, and present analytical expressions for the comparison of ratios with absolute quantification in terms of both sample size and area under the receiver operator characteristic curve. The two approaches are compared for typical metabolite values found in the literature, and their respective merits are illustrated using previously acquired clinical MRS data in two pathologies: mild traumatic brain injury and multiple sclerosis. Our analysis shows that the decision between ratios and absolute quantification is non‐trivial: in some cases, ratios might offer a reduction in sample size, whereas, in others, absolute quantification might prove more desirable for individual (i.e. clinical) use. The decision is straightforward and exact guidelines are provided in the text, given that population means and standard deviations of numerator and denominator can be reliably estimated.

Proton MR spectroscopy of lesion evolution in multiple sclerosis: Steady-state metabolism and its relationship to conventional imaging

Although MRI assessment of white matter lesions is essential for the clinical management of multiple sclerosis, the processes leading to the formation of lesions and underlying their subsequent MRI appearance are incompletely understood. We used proton MR spectroscopy to study the evolution of N‐acetyl‐aspartate (NAA), creatine (Cr), choline (Cho), and myo‐inositol (mI) in pre‐lesional tissue, persistent and transient new lesions, as well as in chronic lesions, and related the results to quantitative MRI measures of T1‐hypointensity and T2‐volume. Within 10 patients with relapsing‐remitting course, there were 180 regions‐of‐interest consisting of up to seven semi‐annual follow‐ups of normal‐appearing white matter (NAWM, n = 10), pre‐lesional tissue giving rise to acute lesions which resolved (n = 3) or persisted (n = 3), and of moderately (n = 9) and severely hypointense (n = 6) chronic lesions. Compared with NAWM, pre‐lesional tissue had higher Cr and Cho, while compared with lesions, pre‐lesional tissue had higher NAA. Resolving acute lesions showed similar NAA levels pre‐ and post‐formation, suggesting no long‐term axonal damage. In chronic lesions, there was an increase in mI, suggesting accumulating astrogliosis. Lesion volume was a better predictor of axonal health than T1‐hypointensity, with lesions larger than 1.5 cm3 uniformly exhibiting very low (<4.5 millimolar) NAA concentrations. A positive correlation between longitudinal changes in Cho and in lesion volume in moderately hypointense lesions implied that lesion size is mediated by chronic inflammation. These and other results are integrated in a discussion on the steady‐state metabolism of lesion evolution in multiple sclerosis, viewed in the context of conventional MRI measures. Hum Brain Mapp 38:4047–4063, 2017.

Early glial activation precedes neurodegeneration in the cerebral cortex after SIV infection: a 3D, multivoxel proton magnetic resonance spectroscopy study.

As ∼40% of HIV‐infected individuals experience neurocognitive decline, we investigated whether proton magnetic resonance spectroscopic imaging (1H‐MRSI) detects early metabolic abnormalities in the cerebral cortex of a simian immunodeficiency virus (SIV)‐infected rhesus monkey model of neuroAIDS.

Low rank magnetic resonance fingerprinting

Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI using randomized acquisition. Extraction of physical quantitative tissue values is preformed off-line, based on acquisition with varying parameters and a dictionary generated according to the Bloch equations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore high under-sampling ratio in the sampling domain (k-space) is required. This under-sampling causes spatial artifacts that hamper the ability to accurately estimate the quantitative tissue values. In this work, we introduce a new approach for quantitative MRI using MRF, called Low Rank MRF. We exploit the low rank property of the temporal domain, on top of the well-known sparsity of the MRF signal in the generated dictionary domain. We present an iterative scheme that consists of a gradient step followed by a low rank projection using the singular value decomposition. Experiments on real MRI data demonstrate superior results compared to conventional implementation of compressed sensing for MRF at 15% sampling ratio.

Inhibitory and excitatory mechanisms in the human cingulate-cortex support reinforcement learning: A functional Proton Magnetic Resonance Spectroscopy study

&NA; The dorsal anterior cingulate cortex (dACC) is crucial for motivation, reward‐ and error‐guided decision‐making, yet its excitatory and inhibitory mechanisms remain poorly explored in humans. In particular, the balance between excitation and inhibition (E/I), demonstrated to play a role in animal studies, is difficult to measure in behaving humans. Here, we used functional magnetic‐resonance‐spectroscopy (1H‐fMRS) to measure the brains major inhibitory (GABA) and excitatory (Glutamate) neurotransmitters during reinforcement learning with three different conditions: high cognitive load (uncertainty); probabilistic discrimination learning; and a control null‐condition. Participants learned to prefer the gain option in the discrimination phase and had no preference in the other conditions. We found increased GABA levels during the uncertainty condition, potentially reflecting recruitment of inhibitory systems during high cognitive load when trying to learn. Further, higher GABA levels during the null (baseline) condition correlated with improved discrimination learning. Finally, glutamate and GABA levels were correlated during high cognitive load. These results suggest that availability of dACC inhibitory resources enables successful learning. Our approach helps elucidate the potential contribution of the balance between excitation and inhibition to learning and motivation in behaving humans. HighlightsGABA and Glutamate were measured in the dACC during learning.Learning included: cognitive‐load, discrimination‐learning and control conditions.Increased GABA levels were observed during high cognitive load.GABA levels during the control condition were correlated with better learning.Availability of dACC inhibitory resources enabled successful learning.

Inhibitory and excitatory mechanisms in the human cingulate-cortex support reinforcement learning

The dorsal anterior cingulate cortex (dACC) is crucial for motivation, reward- and error-guided decision-making, yet its excitatory and inhibitory mechanisms remain poorly explored in humans. In particular, the balance between excitation and inhibition (E/I), demonstrated to play a role in animal studies, is difficult to measure in behaving humans. Here, we used magnetic-resonance-spectroscopy (1H-MRS) to examine these mechanisms during reinforcement learning with three different conditions: high cognitive load (uncertainty); probabilistic discrimination learning; and a control null-condition. Subjects learned to prefer the gain option in the discrimination phase and had no preference in the other conditions. We found increased GABA levels during the uncertainty condition, suggesting recruitment of inhibitory systems during high cognitive load when trying to learn. Further, higher GABA levels during the null (baseline) condition correlated with improved discrimination learning. Finally, excitatory and inhibitory levels were correlated during high cognitive load. The result suggests that availability of dACC inhibitory resources enables successful learning. Our approach establishes a novel way to examine the contribution of the balance between excitation and inhibition to learning and motivation in behaving humans.

The application of magnetic resonance fingerprinting to single voxel proton spectroscopy

Magnetic resonance fingerprinting has been proposed as a method for undersampling k‐space while simultaneously yielding multiparametric tissue maps. In the context of single voxel spectroscopy, fingerprinting can provide a unified framework for parameter estimation. We demonstrate the utility of such a magnetic resonance spectroscopic fingerprinting (MRSF) framework for simultaneously quantifying metabolite concentrations, T1 and T2 relaxation times and transmit inhomogeneity for major singlets of N‐acetylaspartate, creatine and choline. This is achieved by varying TR, TE and the flip angle of the first pulse in a PRESS sequence between successive excitations (i.e. successive TR values). The need for multiparametric schemes such as MRSF for accurate medical diagnostics is demonstrated with the aid of realistic in vivo simulations; these show that certain schemes lead to substantial increases to the area under receiver operating characteristics of metabolite concentrations, when viewed as classifiers of pathologies. Numerical simulations and phantom and in vivo experiments using several different schedules of variable length demonstrate superior precision and accuracy for metabolite concentrations and longitudinal relaxation, and similar performance for the quantification of transverse relaxation.

Application of phase rotation to STRESS localization scheme at 3 T

Application of phase rotation to the STRESS (=STEAM+PRESS) localization scheme, to shorten echo time, minimize J‐coupling dephasing and estimate B1+ inhomogeneity. STRESS (=STEAM + PRESS) simultaneously refocuses and acquires the double spin echo (SE123) and stimulated echo (STE‐) pathways, combining PRESS‐like signal with lower chemical shift displacement as in STEAM. Phase rotation effectively separates coherence pathways, allows reduction of spoiling gradients moments leading to reduction in echo time. Implementing it in STRESS allows one to individually phase‐correct SE123 and STE‐ prior to combination. Moreover, B1+ inhomogeneity can be assessed by comparing the measured ratio of resonance intensities of SE123 and STE‐ pathways to the simulated one.