Our methods
Our methods
Our methods
Decoding analyses
Optimising MVPA decoding analyses of neuroimaging data involves a trade off between the number of samples provided to a classifier and the quality of those samples. Ongoing work explores this trade off using simulated data.
Key people: Catriona Scrivener
Key paper: Scrivener et al 2023 BioRxiv
Watch: Alex presents simulated data informing how to improve MVPA decoding accuracy
Concurrent TMS-EEG
To understand how the brain responds to TMS stimulation in real time, we have established a concurrent TMS-EEG system at the CBU. We are using this setup to explore the entrainment effects of rhythmic TMS on cognitive processes and to optimize control conditions for TMS protocols.
Key people: Runhao Lu, Elizabeth Michael
Watch: Runhao introduces the concurrent TMS-EEG system at the CBU
Concurrent TMS-fMRI
We have established one of the worlds few concurrent TMS-fMRI systems, which allows us to stimulate the brain and record from it at the same time. Our setup uses an MR compatible TMS system combined with two MR surface coils. These are placed between the TMS coil and the participant’s scalp, improving signal directly beneath the TMS coil.
To increase the flexibility of concurrent TMS-fMRI recording, we recently examined the feasibility of applying TMS during short gaps between fMRI slices. TMS induced MRI artifacts were tested across a range of parameters including TMS amplitude, TMS time in relation to slice onset, and length of slice gap.
We hope to use TMS-fMRI investigate the effects of TMS on the brain during different cognitive tasks and stimulation protocols.
Key publications: Jackson 2021 Nat. Comms Bio, Scrivener 2021BioRxiv
Watch: Jade introduces the promise and challenges of concurrent TMS-fMRI
Watch: Catriona examines the feasibility of applying TMS during short gaps between fMRI slices
Information Flow Analysis
Information flow analysis (IFA) allows us to track the exchange of information between brain regions. Further developments will use the IFA methods to study not only whether and when information is exchanged but also what that information is, and the relationship between information exchange and standard connectivity metrics (e.g., phase synchrony).
Key publications: Goddard et al 2016, Goddard et al 2022 JOCN
Watch: Alex explores analytical approaches and inferences in Information Flow Analysis
Decoding Decision-Making
We explore the extent to which features of multivariate pattern analysis classifiers reflect latent variables of classic decision making models during perceptual decision-making. For example, we study how classifier hyperplanes relate to cognitive decision boundaries, or classification accuracy reflects perceptual certainty. These relationships predict choice behaviour and as such, we are able to identify neural activity that is appropriately formatted for the brain to use in guiding behaviour.
Comparison of EEG systems
Recently, portable, low-cost EEG systems have become available offering exciting possibilities for more convenient testing. We are validating one such system, the Emotiv EPOC+, against traditional research-grade systems to find out whether this fast setup and easy to use headset can be used to examine language processing in children with autism.
Key Publications: Petit et al 2020 JLSHR
Errors analysis
Key insights into how information coding in the brain supports behaviour could come from examining what information is coded when people make mistakes. Typically this is hard to do because participants make few mistaked and decoding analyses require many trials. However, the requirement can be met by training classifiers using the plentiful data from correctly performed trials.
Key Publications: Robinson et al., 2022 JOCN Woolgar et al 2019, BioRxiv
