Biology subjects

Ersland, L.

Publications and source records attributed to Ersland, L..

9 recordsLinked to original sources

bioRxiv2024

GABA, Glutamate dynamics and BOLD observed during cognitive processing in psychosis patients with hallucinatory traits

The perception of a voice in the absence of an external auditory source - an auditory verbal hallucination - is a characteristic symptom of schizophrenia. To better understand this phenomenon requires integration of findings across behavioural, functional, and neurochemical levels. We address this with a locally adapted MEGA-PRESS sequence incorporating interleaved unsuppressed water acquisitions, allowing concurrent assessment of behaviour, blood-oxygenation-level-dependent (BOLD) functional changes, Glutamate+Glutamine (Glx), and GABA, synchronised with a cognitive (flanker) task. We acquired data from the anterior cingulate cortex (ACC) of 51 patients with psychosis (predominantly schizophrenia spectrum disorder) and hallucinations, matched to healthy controls. Consistent with the notion of an excitatory/inhibitory imbalance, we hypothesized differential effects for Glx and GABA between groups, and aberrant dynamics in response to task. Results showed impaired task performance, lower baseline Glx and positive association between Glx and BOLD in patients, contrasting a negative correlation in healthy controls. Task-related increases in Glx were observed in both groups, with no significant difference between groups. No significant effects were observed for GABA. These findings suggest that a putative excitatory/inhibitory imbalance affecting inhibitory control in the ACC is primarily observed as tonic, baseline glutamate differences, rather than GABAergic effects or aberrant dynamics in relation to a task. HighlightsO_LIIn-vivo, GABA-edited functional 1H-MRS data were collected from 51 patients with hallucinations and a similar number of matched healthy controls C_LIO_LIReduced Glutamate+Glutamine (Glx) levels were observed in the patient group. C_LIO_LIBOLD association to baseline Glutamate+Glutamine (Glx) differed between patients and controls C_LIO_LIRobust task-related increases in measured Glx were observed in the Anterior Cingulate Cortex (ACC) C_LIO_LITask-related changes in measured Glx did not differ between patients and controls C_LI

neuroscience↗

bioRxiv2024

Modelling inter-shot variability for robust temporal sub-sampling of dynamic, GABA-edited MR spectroscopy data

Variability between individual transients in an MRS acquisition presents a challenge for reliable quantification, particularly in functional scenarios where discrete subsets of the available transients may be compared. The current study aims to develop and validate a model for removing unwanted variance from GABA-edited MRS data, whilst preserving variance of potential interest - such as metabolic response to a functional task. A linear model is used to describe sources of variance in the system: intrinsic, periodic variance associated with phase cycling and spectral editing, and abrupt changes associated with subject movement. We broadly hypothesize that modelling these factors appropriately will improve spectral quality and reduce variance in quantification outcomes, without introducing bias to the estimates. We additionally anticipate that the models will improve (or at least maintain) sensitivity to functional changes, outperforming established methods in this regard. In vivo GABA-edited MRS data (203 subjects from the publicly available Big GABA collection) were sub-sampled strategically to assess individual components of the model, benchmarked against the uncorrected case and against established approaches such as spectral improvement by Fourier thresholding (SIFT). Changes in metabolite concentration and lineshape simulating response to a functional task were synthesized, and sensitivity to such changes was assessed. Composite models yielded improved SNR and reduced variability of GABA+ estimates compared to the uncorrected case in all scenarios, with performance for individual model components varying. Similarly, while some model components in isolation led to increased variability in estimates, no bias was observed in these or in the composite models. While SIFT yielded the greatest reductions in unwanted variance, the resultant data were substantially less sensitive to synthetic functional changes. We conclude that the modelling presented is effective at reducing unwanted variance, whilst retaining temporal dynamics of interest for functional MRS applications, and recommend its inclusion in fMRS processing pipelines. HighlightsO_LIA novel technique for modelling unwanted variance between transients is investigated. C_LIO_LISuitable covariate models yield improved SNR and reduced variability in GABA+ estimates from the resultant spectra. C_LIO_LIExtracted spectra remain sensitive to temporal dynamics of interest for functional MRS applications. C_LI Graphical AbstractIn dynamic MRS analysis, unwanted variability between transients may confound findings when sub-sampling within a single acquisition. We investigate covariate models and lineshape matching strategies to address this. We present composite models yielding improved quality metrics and within-scan repeatability while maintaining sensitivity to (synthetic) functional changes. O_FIG O_LINKSMALLFIG WIDTH=171 HEIGHT=200 SRC="FIGDIR/small/627018v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@174334forg.highwire.dtl.DTLVardef@1d4a500org.highwire.dtl.DTLVardef@19ce283org.highwire.dtl.DTLVardef@db2a29_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

Linewidth-related bias in modelled concentration estimates from GABA-edited 1H-MRS

J-difference-edited MRS is widely used to study GABA in the human brain. Editing for low-concentration target molecules (such as GABA) typically exhibits lower signal-to-noise ratio (SNR) than conventional non-edited MRS, varying with acquisition region, volume and duration. Moreover, spectral lineshape may be influenced by age-, pathology-, or brain-region-specific effects of metabolite T2, or by task-related blood-oxygen level dependent (BOLD) changes in functional MRS contexts. Differences in both SNR and lineshape may have systematic effects on concentration estimates derived from spectral modelling. The present study characterises the impact of lineshape and SNR on GABA+ estimates from different modelling algorithms: FSL-MRS, Gannet, LCModel, Osprey, spant and Tarquin. Publicly available multi-site GABA-edited data (222 healthy subjects from 20 sites; conventional MEGA-PRESS editing; TE = 68 ms) were pre-processed with a standardised pipeline, then filtered to apply controlled levels of Lorentzian and Gaussian linebroadening and SNR reduction. Increased Lorentzian linewidth was associated with a 2-5% decrease in GABA+ estimates per Hz, observed consistently (albeit to varying degrees) across datasets and most algorithms. Weaker, often opposing effects were observed for Gaussian linebroadening. Variations are likely caused by differing baseline parametrization and lineshape constraints between models. Effects of linewidth on other metabolites (e.g., Glx and tCr) varied, suggesting that a linewidth confound may persist after scaling to an internal reference. These findings indicate a potentially significant confound for studies where linewidth may differ systematically between groups or experimental conditions, e.g. due to T2 differences between brain regions, age, or pathology, or varying T2* due to BOLD-related changes. We conclude that linewidth effects need to be rigorously considered during experimental design and data processing, for example by incorporating linewidth into statistical analysis of modelling outcomes or development of appropriate lineshape matching algorithms. HighlightsO_LIIn-vivo GABA-edited 1H-MRS data from 222 subjects were filtered to simulate varying linewidth and SNR conditions C_LIO_LIFiltered datasets were quantified with six different modelling algorithms to assess the impact of linewidth and SNR on the metabolite level estimates. C_LIO_LISynthetic spectra with controlled GABA+ levels and in-vivo-like background signals (applied incrementally) were also assessed. C_LIO_LIFor both in-vivo and synthetic datasets, GABA+ estimates showed a significant association with Lorentzian linewidth across most algorithms, even for small changes in linewidth. C_LIO_LIWeaker, often opposing associations were observed for Gaussian linebroadening. C_LIO_LIThis indicates a potentially significant confound for studies where linewidth or lineshape may be expected to differ, even slightly, between groups. C_LIO_LIThe need for appropriate strategies to account for lineshape differences is highlighted. C_LI O_FIG O_LINKSMALLFIG WIDTH=173 HEIGHT=200 SRC="FIGDIR/small/582249v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@612fbaorg.highwire.dtl.DTLVardef@8462aforg.highwire.dtl.DTLVardef@16af040org.highwire.dtl.DTLVardef@1c3141f_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG To assess the degree to which aspects of linewidth, lineshape and SNR may confound GABA+ estimates, a collection of in-vivo datasets were quantified with six modelling algorithms, with linebroadening and SNR varied experimentally. Most algorithms showed a strong association between GABA+ estimate and Lorentzian linebroadening (2-5% decrease per Hz), with weaker effects for Gaussian broadening. This indicates a potentially significant confound in cases of differing relaxation parameters between groups or experimental conditions.

neuroscience↗

bioRxiv2023

GABA, glutamatergic dynamics and BOLD contrast concurrently assessed using functional MR spectroscopy during a cognitive task

A recurring issue in functional neuroimaging is how to link task-driven hemodynamic BOLD-fMRI responses to underlying neurochemistry at the synaptic level. Glutamate and GABA, the major excitatory and inhibitory neurotransmitters respectively, are typically measured with MR spectroscopy (MRS) sequences in a resting state, in the absence of a task. We propose to resolve this disconnect by applying a novel method to concurrently acquire BOLD, Glx and GABA measurements from a single voxel, using a locally adapted MEGA-PRESS sequence implementation which incorporates unsuppressed water reference signals at a regular interval, allowing continuous assessment of BOLD-related linewidth variation for fMRS applications. Healthy subjects (N = 81) performed a cognitive task (Eriksen Flanker) which was presented visually in a task-OFF, task-ON block design, with individual event stimulus timing varied with respect to the MRS readout. BOLD data acquired with the adapted MEGA-PRESS sequence were correlated with data acquired using a standard fMRI EPI sequence as a means of validating the concurrent approach: a significant (although moderate) correlation was observed, specific to the fMRS-targeted region of interest. We additionally present a novel linear model for extracting modelled spectra associated with discrete functional stimuli, building on well-established processing and quantification tools. Behavioural outcomes from the Flanker task, and activation patterns from the BOLD-fMRI sequence, were as expected from the literature. fMRS-assessed BOLD response correlated strongly with slowing of response time in the incongruent Flanker condition. Moreover, there was a significant increase in measured Glx levels (~8.8%), between task-OFF and task-ON periods. These findings verify the efficacy of our functional task and analysis pipelines for the simultaneous assessment of BOLD and metabolite fluctuations in a single voxel. As well as providing a robust basis for further work using these techniques, we also identify a number of clear directions for further refinement in future studies. HighlightsO_LIConcurrent measurement of temporally resolved metabolite estimates and local BOLD-related signal changes is demonstrated C_LIO_LIIn-vivo, GABA-edited functional 1H-MRS data were collected from 81 healthy subjects whilst they performed a cognitive task C_LIO_LIRobust task-related increases in measured Glutamate+Glutamine were observed in the Anterior Cingulate Cortex C_LIO_LIModerate correlation was observed between BOLD contrast strength as assessed by fMRS and fMRI techniques, specific to the prescribed region C_LIO_LIA novel technique for extraction of block- or event-related sub-spectra is demonstrated C_LI Graphical AbstractA novel sequence adaption for concurrent measurement of GABA-edited spectroscopy and functional BOLD changes is demonstrated on N=81 healthy subjects. Significant changes in measured Glutamate+Glutamine concentration are found, along with the expected behavioural and BOLD effects in response to a Flanker task. O_FIG O_LINKSMALLFIG WIDTH=170 HEIGHT=200 SRC="FIGDIR/small/539017v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@e06295org.highwire.dtl.DTLVardef@1683a30org.highwire.dtl.DTLVardef@1c45f7aorg.highwire.dtl.DTLVardef@1587cf0_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2021

Comparison of seven modelling algorithms for GABA-edited 1H-MRS

Edited MRS sequences are widely used for studying GABA in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multi-site study. GABA-edited (GABA+, TE = 68 ms MEGA-PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL-MRS, Gannet, AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor-specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An inter-class correlation across all algorithms showed single-rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single-rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies. HighlightsO_LIGABA-edited MRS data from 222 healthy adults across 20 research sites were analyzed C_LIO_LIData were modelled using seven different algorithms, yielding GABA+ and Glx estimates C_LIO_LIModerate agreement was seen across all the tested algorithms C_LIO_LIAdding a component to represent co-edited macromolecule signals improved concordance C_LIO_LIBaseline modelling emerged as major factor differentiating outcomes C_LI

neuroscience↗

bioRxiv2020

Neural activity precedes conscious awareness of being in or out of a transient hallucinatory state

Auditory verbal hallucinations, or "hearing voices", is a remarkable state of the mind, occurring in psychiatric and neurological patients, and in a significant minority of the general population. An unexplained characteristic of this phenomenon is that it transiently fluctuates, with coming and going of episodes with time. We monitored neural activity with BOLD-fMRI second-by-second before and after participants indicated the start and end of a transient hallucinatory episode during the scanning session by pressing a response-button. We show that a region in the ventro-medial frontal cortex is activated in advance of conscious awareness of going in or out of a transient hallucinatory state. There was an increase in activity initiated a few seconds before the button-press for onsets, and a corresponding decrease in activity initiated a few seconds before the button-press for offsets. We identified the time between onset and offset button-presses, extracted the corresponding BOLD time-courses from nominated regions-of-interest, and analyzed changes in the signal from 10 seconds before to 15 seconds after the response-button was pressed, which identified onset and offset events. We suggest that this brain region act as a switch to turn on and off a hallucinatory episode. The results may have implications for new interventions for intractable hallucinations.

neuroscience↗

bioRxiv2020

Dynamic switching between intrinsic and extrinsic mode networks as demands change from passive to active processing

We here report on the relationship between default and extrinsic mode networks across alternating brief periods of rest and active task processing. We used three different visual tasks: mental rotation, working memory and mental arithmetic in a classic fMRI ON-OFF block design where task (ON) blocks alternated with equal periods of rest (OFF) blocks. By analysing data in two ways, using an ON-OFF contrast, we showed the existence of a generalized task-positive network, labelled the extrinsic mode network (EMN) which was anti-correlated with the default mode network (DMN) as processing demands shifted from rest to active processing. We then identified two key regions of interest (ROIs) in the SMA and Precuneus/PCC regions as hubs for the extrinsic and intrinsic networks, and extracted the time-course from these ROIs. The results showed a close to perfect correlations for the SMA and Precuneus/PCC time-courses for ON-respective OFF-blocks. We suggest the existence of two large-scale networks, an extrinsic mode network and an intrinsic mode network, respectively, which are up- and down-regulated as environmental demands change from active to passive processing.

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bioRxiv2019

Positive relation between arcuate fasciculus white matter fiber structure and severity of auditory hallucinations: A DTI tractography study

The arcuate fasciculus (AF) has been implicated in the pathology behind schizophrenia and auditory verbal hallucinations (AVHs). White matter tracts forming the arcuate fasciculus can be quantified and visualized using diffusion tensor imaging (DTI) tractography. Although there have been a number of studies on this topic, the results have been conflicting. Studying the underlying white matter structure of the AF could shed light on functional connectivity between temporal and frontal language areas in AVHs. The participants were 66 patients with a schizophrenia diagnosis, where AVHs were defined from the Positive and Negative Syndrome Scale (PANSS), and compared with a healthy control group. DTI was performed on a 3T MR scanner, and tensor estimation was done using deterministic streamline tractography. Statistical analysis of the data showed significantly longer tracts along the AF in patients with severe and frequent AVHs, as well as an overall significant asymmetry with longer fibers on the left side. In addition, there were significant positive correlations between PANSS scores and tract length, tract volume, and number of streamlines for the posterior AF segment on the left side. It is concluded that the present structural results complement previous functional findings of fronto-temporal connectivity in AVH patients.

neuroscience↗

bioRxiv2019

Dynamic up- and down-regulation of the default (DMN) and extrinsic (EMN) mode networks during alternating task-on and task-off periods

Using fMRI, Hugdahl et al. (2015) reported the existence of a general-domain cortical network during active task-processing which was non-specific to the cognitive task being processed. They labelled this network the extrinsic mode network (EMN). The EMN would be predicted to be negatively, or anti-correlated with the classic default mode network (DMN), typically observed during periods of rest, such that while the EMN should be down-regulated and the DMN up-regulated in the absence of demands for task-processing, the reverse should occur when demands change from resting to task-processing. This would require alternating periods of task-processing and resting, and analyzing data continuously when demands change from active to passive periods and vice versa. We were particularly interested in how the networks interact in the critical transition points between conditions. For this purpose we used an auditory task with multiple cognitive demands in a standard fMRI block-design. Task-present (ON) blocks were alternated with an equal number of task-absent, or rest (OFF) blocks to capture network dynamics across time and changing environmental demands. To achieve this, we specified the onset of each block, and used a finite-impulse response function (FIR) as basis function for estimation of the fMRI-BOLD response. During active (ON) blocks, the results showed an initial rapid onset of activity in the EMN network, which remained throughout the period, and faded away during the first scan of the OFF-block. During OFF blocks, activity in the DMN network showed an initial time-lag where neither the EMN nor the DMN was active, after which the DMN was up-regulated. Studying network dynamics in alternating passive and active periods may provide new insights into brain network interaction and regulation.

neuroscience↗