tDCS induced GABA change is associated with the simulated electric field in M1, an effect mediated by grey matter volume in the MRS voxel

Background and objective Transcranial direct current stimulation (tDCS) has wide ranging applications in neuro-behavioural and physiological research, and in neurological rehabilitation. However, it is currently limited by substantial inter-subject variability in responses, which may be explained, at least in part, by anatomical differences that lead to variability in the electric field (E-field) induced in the cortex. Here, we tested whether the variability in the E-field in the stimulated cortex during anodal tDCS, estimated using computational simulations, explains the variability in tDCS induced changes in GABA, a neurophysiological marker of stimulation effect. Methods Data from five previously conducted MRS studies were combined. The anode was placed over the left primary motor cortex (M1, 3 studies, N = 24) or right temporal cortex (2 studies, N = 32), with the cathode over the contralateral supraorbital ridge. Single voxel spectroscopy was performed in a 2x2x2cm voxel under the anode in all cases. MRS data were acquired before and either during or after 1 mA tDCS using either a sLASER sequence (7T) or a MEGA-PRESS sequence (3T). sLASER MRS data were analysed using LCModel, and MEGA-PRESS using FID-A and Gannet. E-fields were simulated in a finite element model of the head, based on individual structural MR images, using SimNIBS. Separate linear mixed effects models were run for each E-field variable (mean and 95th percentile; magnitude, and components normal and tangential to grey matter surface, within the MRS voxel). The model included effects of time (pre or post tDCS), E-field, grey matter volume in the MRS voxel, and a 3-way interaction between time, E-field and grey matter volume. Additionally, we ran a permutation analysis using PALM to determine whether E-field anywhere in the brain, not just in the MRS voxel, correlated with GABA change. Results In M1, higher mean E-field magnitude was associated with greater anodal tDCS-induced decreases in GABA (t(24) = 3.24, p = 0.003). Further, the association between mean E-field magnitude and GABA change was moderated by the grey matter volume in the MRS voxel (t(24) = −3.55, p = 0.002). These relationships were consistent across all E-field variables except the mean of the normal component. No significant relationship was found between tDCS-induced GABA decrease and E-field in the temporal voxel. No significant clusters were found in the whole brain analysis. Conclusions Our data suggest that the electric field induced by tDCS within the brain is variable, and is significantly related to anodal tDCS-induced decrease in GABA, a key neurophysiological marker of stimulation. These findings strongly support individualised dosing of tDCS, at least in M1. Further studies examining E-fields in relation to other outcome measures, including behaviour, will help determine the optimal E-fields required for any desired effects

Association between tDCS induced GABA change and estimated electric field in the cortex

Transcranial direct current stimulation (tDCS) has wide ranging applications in neuro- behavioural and physiological research, and in neurological rehabilitation. However, it is currently limited by inter-subject variability in responses, which may be explained, at least in part, by anatomical differences that lead to variability in the actual electric field in the cortex. Our aim was to examine whether the variability in electric fields, estimated using computational simulations, explains the variability in tDCS induced GABA changes measured using magnetic resonance spectroscopy (MRS). Data from five studies (total N = 56 complete cases) were combined. The anode and cathode were placed over the left M1 (3 studies, N = 24) or right temporal cortex (2 studies, N = 32), and contralateral supraorbital ridge respectively. GABA to total Creatine ratios were measured and estimated, before and after tDCS application. sLASER MRS data were analysed using LCModel, and MEGA-PRESS using FID-A and Gannet. The electric fields were simulated in a finite element model of the head, based on individual MPRAGE images, using SimNIBS. Twelve linear mixed effects models were run, one for each E-field variable (mean and 95th percentile of magnitude, normal and tangential components), and separately for the M1 and temporal data. We found that in M1, E-field in the MRS voxel is related to the GABA drop, adding to the accumulating evidence that supports individualised dosing of tDCS. We also found an interaction with grey matter volume within the MRS voxel, emphasising the need to appropriately choose and evaluate any outcome measures which we expect to be related to E-field. While we did not find a similar association in the temporal region, given the challenges of modelling the E-field in this region and possible homeostatic metaplastic effects, such an association cannot be ruled out

tDCS induced GABA change is associated with the simulated electric field in M1, an effect mediated by grey matter volume in the MRS voxel

Background and objectiveTranscranial direct current stimulation (tDCS) has wide ranging applications in neuro-behavioural and physiological research, and in neurological rehabilitation. However, it is currently limited by substantial inter-subject variability in responses, which may be explained, at least in part, by anatomical differences that lead to variability in the electric field (E-field) induced in the cortex. Here, we tested whether the variability in the E-field in the stimulated cortex during anodal tDCS, estimated using computational simulations, explains the variability in tDCS induced changes in GABA, a neurophysiological marker of stimulation effect. MethdsData from five previously conducted MRS studies were combined. The anode was placed over the left primary motor cortex (M1, 3 studies, N = 24) or right temporal cortex (2 studies, N = 32), with the cathode over the contralateral supraorbital ridge. Single voxel spectroscopy was performed in a 2x2x2cm voxel under the anode in all cases. MRS data were acquired before and either during or after 1 mA tDCS using either a sLASER sequence (7T) or a MEGA-PRESS sequence (3T). sLASER MRS data were analysed using LCModel, and MEGA-PRESS using FID-A and Gannet. E-fields were simulated in a finite element model of the head, based on individual structural MR images, using SimNIBS. Separate linear mixed effects models were run for each E-field variable (mean and 95th percentile; magnitude, and components normal and tangential to grey matter surface, within the MRS voxel). The model included effects of time (pre or post tDCS), E-field, grey matter volume in the MRS voxel, and a 3-way interaction between time, E-field and grey matter volume. Additionally, we ran a permutation analysis using PALM to determine whether E-field anywhere in the brain, not just in the MRS voxel, correlated with GABA change. ResultsIn M1, higher mean E-field magnitude was associated with greater anodal tDCS-induced decreases in GABA (t(24) = 3.24, p = 0.003). Further, the association between mean E-field magnitude and GABA change was moderated by the grey matter volume in the MRS voxel (t(24) = -3.55, p = 0.002). These relationships were consistent across all E-field variables except the mean of the normal component. No significant relationship was found between tDCS-induced GABA decrease and E-field in the temporal voxel. No significant clusters were found in the whole brain analysis. ConclusionsOur data suggest that the electric field induced by tDCS within the brain is variable, and is significantly related to anodal tDCS-induced decrease in GABA, a key neurophysiological marker of stimulation. These findings strongly support individualised dosing of tDCS, at least in M1. Further studies examining E-fields in relation to other outcome measures, including behaviour, will help determine the optimal E-fields required for any desired effects

Towards a cancer mission in Horizon Europe: recommendations.

A comprehensive translational cancer research approach focused on personalized and precision medicine, and covering the entire cancer research-care-prevention continuum has the potential to achieve in 2030 a 10-year cancer-specific survival for 75% of patients diagnosed in European Union (EU) member states with a well-developed healthcare system. Concerted actions across this continuum that spans from basic and preclinical research through clinical and prevention research to outcomes research, along with the establishment of interconnected high-quality infrastructures for translational research, clinical and prevention trials and outcomes research, will ensure that science-driven and social innovations benefit patients and individuals at risk across the EU. European infrastructures involving comprehensive cancer centres (CCCs) and CCC-like entities will provide researchers with access to the required critical mass of patients, biological materials and technological resources and can bridge research with healthcare systems. Here, we prioritize research areas to ensure a balanced research portfolio and provide recommendations for achieving key targets. Meeting these targets will require harmonization of EU and national priorities and policies, improved research coordination at the national, regional and EU level and increasingly efficient and flexible funding mechanisms. Long-term support by the EU and commitment of Member States to specialized schemes are also needed for the establishment and sustainability of trans-border infrastructures and networks. In addition to effectively engaging policymakers, all relevant stakeholders within the entire continuum should consensually inform policy through evidence-based advice

The role of cerebellar GABA in visuomotor adaptation: The role of GABA in adaptation

In daily life we must constantly adapt our movements to changes in the environment or changes in our body. Adaptation is a fundamental property of the central nervous system and involves plastic changes across a network of brain regions, in particular in the cerebellum. These changes likely involve the major inhibitory neurotransmitter GABA. How cerebellar GABA changes during adaptation and how these changes are linked to behaviour is currently not well understood. By combining brain imaging (Magnetic Resonance Spectroscopy and Magnetic Resonance Imaging) and brain stimulation (transcranial direct current stimulation; tDCS) with computational modelling of behaviour, this thesis investigates the relationship between GABA, plasticity and motor adaptation. First, the effects of stimulating the cerebellum with anodal tDCS were investigated. We found no effect of tDCS on adaptation, but an effect of session. We then studied the relationship between M1 GABA concentration and adaptation. We found that GABA concentration at baseline measured from left M1 hand area predicted retention of an adapted movement of the right hand. Next we investigated changes in functional connectivity elicited by adaptation. After performing an adaptation task, cerebellar resting-state functional connectivity was increased. Further, the change in a cerebellar network correlated with adaptation performance. Finally, we explored changes in cerebellar GABA during adaptation. During adaptation, cerebellar GABA decreased in the right compared to the left cerebellar hemisphere, controlling for GABA changes during movement execution. This work provides first evidence that GABA decreases during adaptation in the right compared to the left cerebellar hemisphere. It strengthens the theory of cerebellar involvement in adaptation and suggests that cerebellar GABA plays an important role during adaptation. It also highlights the importance of M1 GABA in retaining the adaptive movement. Taken together, these results demonstrate a potential mechanism by which adaptation may be achieved in the cerebellum. In order to develop interventions that successfully and reliably enhance adaptation, it is crucial to understand the factors that influence these changes in the cerebellum and across the motor network

Data supporting "Learning to optimize perceptual decisions through suppressive interactions in the human brain"

Behavioural data. Resting state fMRI data. GABA measurements. For further information, see the associated Data Description file. Raw data can be provided upon request to the first and/or corresponding author

Cerebellar GABA change during visuomotor adaptation relates to adaptation performance and cerebellar network connectivity: a magnetic resonance spectroscopic imaging study

Motor adaptation is crucial for performing accurate movements in a changing environment and relies on the cerebellum. Although cerebellar involvement has been well characterized, the neurochemical changes in the cerebellum underpinning human motor adaptation remain unknown. We used a novel magnetic resonance spectroscopic imaging (MRSI) technique to measure changes in the inhibitory neurotransmitter GABA in the human cerebellum during visuomotor adaptation. Participants (n = 17, six female) used their right hand to adapt to a rotated cursor in the scanner, compared with a control task requiring no adaptation. We spatially resolved adaptation-driven GABA changes at the cerebellar nuclei and cerebellar cortex in the left and the right cerebellar hemisphere independently and found that simple right-hand movements increase GABA in the right cerebellar nuclei and decreases GABA in the left. When isolating adaptation-driven GABA changes, we found that GABA in the left cerebellar nuclei and the right cerebellar nuclei diverged, although GABA change from baseline at the right cerebellar nuclei was not different from zero at the group level. Early adaptation-driven GABA fluctuations in the right cerebellar nuclei correlated with adaptation performance. Participants showing greater GABA decrease adapted better, suggesting early GABA change is behaviorally relevant. Early GABA change also correlated with functional connectivity change in a cerebellar network. Participants showing greater decreases in GABA showed greater strength increases in cerebellar network connectivity. Results were specific to GABA, to adaptation, and to the cerebellar network. This study provides first evidence for plastic changes in cerebellar neurochemistry during motor adaptation. Characterizing these naturally occurring neurochemical changes may provide a basis for developing therapeutic interventions to facilitate human motor adaptation.SIGNIFICANCE STATEMENT Despite motor adaptation being fundamental to maintaining accurate movements, its neurochemical basis remains poorly understood, perhaps because measuring neurochemicals in the human cerebellum is technically challenging. Using a novel magnetic resonance spectroscopic imaging method, this study provides evidence for GABA changes in the left compared with the right cerebellar nuclei driven by both simple movement and motor adaptation. Although right cerebellar GABA changes were not significantly different from zero at the group level, the adaptation-driven GABA fluctuations in the right cerebellar nuclei correlated with adaptation performance and with functional connectivity change in a cerebellar network. These results show the first evidence for plastic changes in cerebellar neurochemistry during a cerebellar learning task. This provides the basis for developing therapeutic interventions that facilitate these naturally occurring changes to amplify cerebellar-dependent learning

Learning to optimize perceptual decisions through suppressive interactions in the human brain.

Translating noisy sensory signals to perceptual decisions is critical for successful interactions in complex environments. Learning is known to improve perceptual judgments by filtering external noise and task-irrelevant information. Yet, little is known about the brain mechanisms that mediate learning-dependent suppression. Here, we employ ultra-high field magnetic resonance spectroscopy of GABA to test whether suppressive processing in decision-related and visual areas facilitates perceptual judgments during training. We demonstrate that parietal GABA relates to suppression of task-irrelevant information, while learning-dependent changes in visual GABA relate to enhanced performance in target detection and feature discrimination tasks. Combining GABA measurements with functional brain connectivity demonstrates that training on a target detection task involves local connectivity and disinhibition of visual cortex, while training on a feature discrimination task involves inter-cortical interactions that relate to suppressive visual processing. Our findings provide evidence that learning optimizes perceptual decisions through suppressive interactions in decision-related networks.This work was supported by 717 funding to ZK from the Alan Turing Institute, the Biotechnology and Biological Sciences 718 Research Council (Grants: H012508, P021255), the European Community’s Seventh 719 Framework Programme (Grant FP7/ 2007–2013 under agreement PITN-GA 2011-290011), 720 and the Wellcome Trust (Grant 205067). CJS holds a Sir Henry Dale Fellowship, funded by 721 the Wellcome Trust and the Royal Society (102584/Z/13/Z). ELH is supported by the NIHR 722 Oxford Health Biomedical Research Centre. The Wellcome Centre for Integrative 723 Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z)