Proton magnetic resonance spectroscopy in frontotemporal lobar degeneration-related syndromes

Murley, Alexander G; Tsvetanov, Kamen A.; Rouse, Matthew A; Jones, P. Simon; Sværke, Katrine; Li, Win; Carpenter, Adrian; Rowe, James B.

doi:10.1016/j.neurobiolaging.2021.10.012

Cited by 13 publications

(16 citation statements)

References 51 publications

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“…We focus on the relationship between neurochemical concentrations and synaptic physiology in one cortical region. The choice of the right inferior frontal gyrus was motivated by our interest in frontal lobe function and its impairment in frontotemporal lobar degeneration (Eliasova et al, 2014, Hughes et al, 2015, Murley et al, 2020, Murley et al, 2022) and other neuropsychiatric disorders. However, the method can be applied to multiple regions.…”

Section: Discussionmentioning

confidence: 99%

“…A 2cm isotropic cubic voxel was located over the right inferior frontal gyrus (IFG). The details of the MRS processing have been reported previously in (Murley et al, 2020, Murley et al, 2022). In brief, the spectra were pre-processed to remove effects of eddy currents and frequency/phase shifts using MRspa (Dinesh Deelchand, University of Minnesota, www.cmrr.umn.edu/downloads/mrspa).…”

Section: Methodsmentioning

confidence: 99%

“…Magnetic resonance spectroscopy (MRS) can be used to estimate individual differences in key neurochemical concentrations in local brain regions (Blüml and Panigrahy, 2012). It has been used to quantify neurochemical changes in neurological and neuropsychiatric disorders, such as Alzheimer’s disease (Wang et al, 2015), frontotemporal lobar degeneration-related syndromes (Murley et al, 2020, Murley et al, 2022), and schizophrenia (Jelen et al, 2018, Cohen et al, 2015, Duarte and Xin, 2019), as well as normal aging (Boumezbeur et al, 2010). Such spectroscopy can be predictive of task performance and the response to pharmacological interventions (Chowdhury et al, 2012, Stagg et al, 2011, McColgan et al, 2020, Gawne et al, 2020, Schmitz et al, 2017, Adams et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

Jafarian

Hughes

Adams

et al. 2022

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We present a hierarchical (i.e., empirical) Bayesian framework for testing hypotheses about synaptic neurotransmission, based on the integration of ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography. A first level dynamic causal modelling of cortical microcircuits is used to infer the connectivity parameters of a generative model of neurophysiological observations. At the second level, 7T- magnetic resonance spectroscopy (MRS) estimates of regional neurotransmitter concentration supply empirical priors on synaptic connectivity. We compare the evidence for alternative empirical priors, defined by monotonic functions of spectroscopic estimates, on subsets of synaptic connections. For efficiency and reproducibility, we used Bayesian model reduction, parametric empirical Bayes and variational Bayesian inversion. In particular, we used Bayesian model reduction to compare models of how spectroscopic neurotransmitter measures inform estimates of synaptic connectivity. This identifies the synaptic connections that are influenced by neurotransmitter levels, as measured by 7T-MRS . We demonstrate the method using resting-state magnetoencephalography (i.e., task-free recording) and 7T-MRS data from healthy adults. We validate the analysis by split-sampling of the magnetoencephalography dataset. Our results confirm the hypotheses that GABA concentration influences local recurrent inhibitory intrinsic connectivity in deep and superficial cortical layers, while glutamate influences the excitatory connections between superficial and deep layers and connections from superficial to inhibitory interneurons. The method is suitable for applications with magnetoencephalography or electroencephalography, and is well-suited to evince the mechanisms of neurological and psychiatric disorders, including responses to psychopharmacological interventions.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

Jafarian

Hughes

Adams

et al. 2022

Preprint

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“…There are other multivariate approaches that might be more robust and sensitive in discovering covariance, such as CCA and PLS (Murley et al, 2020; Murley et al, 2022; Passamonti et al, 2019; Tibon et al, 2021; K. A. Tsvetanov, Gazzina, et al, 2021; K.…”

Section: Discussionmentioning

confidence: 99%

Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy ageing subjects

Liu

Tyler

Rowe

et al. 2021

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Cognitive ageing is a complex process which requires multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization and vascular dynamics. However, most neuroimaging studies of ageing have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and the interpretation of neural correlates of cognitive decline in ageing. Here, we used linked independent component analysis as a data-driven multimodal approach to jointly analyze magnetic resonance imaging of grey matter density, cerebrovascular, and functional network topographies. Neuroimaging and behavioural data (n = 215) from the Cambridge Centre for Ageing and Neuroscience study were used, containing healthy subjects aged 18 to 88. In the output components, fusion was found between structural and cerebrovascular topographies in multiple components with cognitive-relevance across the lifespan. A component reflecting global atrophy with regional cerebrovascular changes and a component reflecting right frontoparietal network activity were correlated with fluid intelligence over and above age and gender. No meaningful fusion between functional network topography and structural or cerebrovascular signals was observed. We propose that integrating multiple neuroimaging modalities allows to better characterize brain pattern variability and to differentiate brain changes in healthy ageing.

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“…Both tCho and tCr either increase or show no change with normal aging (18,22,24), and are either elevated (31,32) or not different (29,32) in MCI and AD compared with normal aging. Functionally, lower MRS-measured tNAA, Glx (or glutamate), and GABA, and higher tCho, have each been associated with poorer cognitive performance in normal aging (33–37), MCI (25,38–40), AD (39), and frontotemporal lobar degeneration patients (41). Taken together, it is plausible that these metabolites might differ between the three PPA subtypes and associate with metrics of disease severity.…”

Section: Introductionmentioning

confidence: 99%

Brain Total Creatine Differs Between Primary Progressive Aphasia (PPA) Subtypes and Correlates with Disease Severity

Hupfeld

Zöllner

Oeltzschner

et al. 2022

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Background: Primary progressive aphasia (PPA) is a debilitating neurodegenerative disease that primarily impairs language function. It is comprised of three subtypes with varied clinical presentation and cortical atrophy patterns: logopenic variant (lvPPA), non-fluent variant (nfvPPA), and semantic variant (svPPA). No prior work has examined differences in brain chemistry between the PPA subtypes, or associations of brain chemistry with symptom severity. Methods: We collected magnetic resonance spectroscopy (MRS) data in the left inferior frontal gyrus (IFG, a main language production region) and the right sensorimotor cortex (SMC) from 61 PPA patients. We analyzed tissue-corrected metabolite levels to account for the effects of cortical atrophy and other tissue-specific properties. We aimed to: 1) characterize differences between PPA subtypes for 5 brain metabolites: total N-acetylaspartate (tNAA), total choline (tCho), total creatine (tCr), glutamate+glutamine (Glx), and gamma-aminobutyric acid (GABA); and 2) test for associations between these neurometabolites and PPA symptom severity. Results: tCr levels differed by PPA subtype across both the left IFG and right SMC. In both regions, tCr levels were lowest among lvPPA patients and highest among svPPA patients. Moreover, across the whole cohort, higher tCr and lower Glx levels in the left IFG correlated with greater disease severity. Conclusions: Given that tCr is involved in brain energy metabolism and homeostasis, our results indicate that svPPA pathology might involve changes in specific cellular energy processes. Moreover, our finding that higher tCr was associated with worse PPA symptom severity suggests that perturbations to cellular energy homeostasis in cortical language areas may contribute to PPA symptoms. In addition, reduced cortical excitatory capacity (i.e., lower Glx) in brain areas related to language processing may also contribute to PPA symptoms. Together, these findings suggest that tCr could serve as a biomarker to differentiate between PPA subtypes, and that both tCr and Glx might have utility for better understanding PPA disease mechanisms and tracking disease progression. Trial registration: ClinicalTrials.gov (NCT02606422). Registered 11 November 2015 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02606422?term=NCT02606422&draw=2&rank=1

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Proton magnetic resonance spectroscopy in frontotemporal lobar degeneration-related syndromes

Cited by 13 publications

References 51 publications

Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy ageing subjects

Brain Total Creatine Differs Between Primary Progressive Aphasia (PPA) Subtypes and Correlates with Disease Severity

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