An Overview of a MEG Study

Transcription

1 An Overview of a MEG Study

2 The Research Cycle Formulating a research Question Planning an investigation of the research question Devising the experimental and technical resources needed Selecting an experimental participant sample Analysis of data and interpretation of results Communicating and preserving research outputs

3 Developing a research question Many scientists owe their greatness not to their skill in solving problems but to their wisdom in choosing them An uninterested worker is unlikely to produce the new ideas necessary for progress. Opportunities: Novelty of technology, methods,.. Interests and experience of the researcher and the team around her. Hypothesis generation: the role of theory and literature Experiments for experiment s sake are [un]likely to lead anywhere.

4 Pure vs Applied Research Pure research planning by a master board of strategists The utter folly of this idea is apparent to anyone with the slightest knowledge of the history of science. Growing importance of directed, applied research Successes such as LHC / HGP Concentration of funding around core research themes identified by funding bodies. Applied researchers still need to apply the same critical insight to problem development.

5 Research Planning Understanding the capabilities of the instrumentation: will it answer your research question? Try and focus on the question, not the technology MEG Has High temporal precision Is closely linked to neural processes cf fmri/pet Has modest spatial resolution cf. fmri, but better than EEG Allows us to observe an apparently fundamental brain function: its spontaneous electrical oscillations For your research question to be suitable for investigation using MEG the changes in brain state induced by your experimental procedure must be optimally observable using MEG.

6 Three main measurement approaches Magnetic evoked field averaging: Pros: simple, reliable, quick, very sensitive, big effects Cons: averages out non-phased locked activity; not suitable for spontaneous or non-phase locked MEFs Evoked / Induced oscillatory power Pros: handles phase- and non-phase locked signals equally well; sensitive; relatively simple and quick, big effects. Cons: trade-off between frequency and time resolution Connectivity measurements Pros: gets closer to ideal of observing communication between brain regions during cognition; Cons: technically tricky; plethora of competing methods; interpretation is difficult; very small effect sizes, so might be all bollocks.

7 MEG technique Training and experience: MEG is not plug-n-play Refer to Gross et al. ( 2012) for comprehensive guidance Adopt recommended practices explicitly Specify and record protocols carefully MEG instrumentation MEG system manufacturer calibration: validation? Head registration system calibration and validation. Screening and external artefact monitoring. Stimulus protocol (if used) Audio / visual equipment calibration this is needed otherwise stimuli are not as advertised. Identifying and dealing with stimulus artefacts Physiological recording and monitoring EOG / ECG Participant Reponses

8 Meg Technique II Participant preparation Screening for sources of artefact Head positioning Monitoring position during recording co-registration scalp surface recording Co-registration with MRI Combining data over sessions / groups

9 How much data? This is a difficult question. The answer depends on: Whether you already have good information about the measurements you will take. If you are using an established protocol then earlier and /or published work provides estimates to help with power planning If you are working with a large effect fewer samples are needed e.g. detecting visual gamma If you are working with a small effect many more samples are needed e.g. detecting changes in visual gamma between two conditions. Gross (2012) again and also Brookes et al (2008) Range in literature ~40 - >60,000 trials per condition

10 Participant / patient protocols Ethical permission: always needed for research participants (even your PG mates!); clinical research needs NHS as well as local ethics. Pre-screen and consent (24hr) with in-house screen an consent immediately before MEG scan. Take into account ability and needs of research participant Balance your need for data with the participant s need for rest and comfort Many older patients will struggle to get into and out of typical system patient supports (MEG chairs) Carefully think through experiments with children to make them engaging and easy to perform Adapt your experimental protocol carefully for people with special needs

11 Post-processing Minimize the need to exclude data by good artefact control in the design stage. The less you fiddle with your data the better Main data cleaning steps Exclude data with environmental or system-generated artefact pop-corn noise, channel drop-out Exclude data with gross participant artefact Eye blinks, gross movements Baseline correction Remove low frequency drift by filtering Advanced data cleaning Use physiological recordings to identify and remove artefact with ICA

12 Post-processing II Decide a priori on processing pipeline. Or if exploratory analysis, replicate the study Commit to a platform Support, shared experience and expertise Source or sensor space analysis Sensor data are incontrovertible But reflect mixtures of source outputs Source analysis (eg spatial beamformer) Interpretation closer to neural origins - more assumptions Necessary for comparison with e.g. fmri results All methods involve inversion of the signals later talks

13 Statistical Issues Large effects with simple methods are unproblematic E.g. detecting visual gamma Small effects with simple methods can be very problematic Modulations of induced responses with attention Small effects with complex measures typically need expert support and development of custom software E.g. using non-linear measures of cross-frequency coupling between sources in different brain regions Always there are worries such as Non-stationarity of responses within and between participants and runs Type I errors - often correction e.g Bonferroni is very conservative Type II errors underpowered studies Problem of combining data over groups, time etc Replication is the gold standard Especially over labs and equipment Infrequently attempted Much recent progress on statistical methods - see talks later today

14 Interpretation and Reporting Ideally: One-pass analysis referenced to apriori defined outcomes Genuine confidence in results Good chance of null results Report nulls! And effect sizes of null results. Typically: multiple analysis pathways with loosely defined hypotheses Always produces positive results if enough options are explored Low confidence Meaningful in context of exploratory research Either way Replication is your friend

15 Data Stewardship Increasingly important Infrequently considered Here s what NASA say Science data stewardship is the protection of science data records, their integrity, long term utility, and other actions that maximize the return on investment. Research council requirement for data sharing. What s your plan? More than local archiving data sharing is needed Meta-data Provenence Licencing data Victoria Stodden If we re not sharing code and data, we re limiting avenues of inquiry, she says. But this has to happen at the grassroots level. There has to be cooperation from researchers or it won t work. MEG partnership now underway across 8 UK universities. Damning govt. report on clinical data sharing out this week Don t let the next one be about MEG