When to Trigger
Activate this skill when the user mentions:
- fMRI, EEG, MEG, PET, MRI brain imaging
- Neural circuits, synaptic transmission, neurotransmitters
- Cognitive experiments, reaction time, psychophysics
- Brain regions, Brodmann areas, connectome
- Neurological disorders (Alzheimer's, Parkinson's, epilepsy)
- Computational neuroscience, spiking neural networks, Hodgkin-Huxley
- Brain-computer interfaces (BCI), neural decoding
Step-by-Step Methodology
- Define the neuroscience question - Specify level of analysis (molecular, cellular, circuit, systems, cognitive, behavioral). Identify target brain regions or networks.
- Experimental design - For imaging studies: specify modality (fMRI for spatial resolution, EEG for temporal resolution, PET for neurochemistry). Design task paradigm with proper controls, counterbalancing, and trial timing (ISI, ITI).
- Data acquisition guidance - Recommend acquisition parameters: fMRI (TR, voxel size, field strength), EEG (sampling rate, electrode montage, impedance thresholds). Specify preprocessing steps.
- Preprocessing - fMRI: slice timing, motion correction, normalization (MNI/Talairach), smoothing. EEG: filtering (bandpass), artifact rejection (ICA for eye blinks/muscle), re-referencing. Always report each step and parameters.
- Analysis - fMRI: GLM for activation, seed-based or ICA for connectivity, MVPA for decoding. EEG: ERP analysis, time-frequency decomposition, source localization. Computational models: implement and fit biophysical or phenomenological models.
- Statistical inference - Apply appropriate correction for multiple comparisons: cluster-level FWE for fMRI, permutation-based corrections for EEG. Report effect sizes. Use Bayesian approaches when frequentist results are ambiguous.
- Interpretation - Map results to known neuroanatomy (use atlases: AAL, Desikan-Killiany, Schaefer). Discuss findings in context of established theoretical frameworks. Avoid reverse inference pitfalls.
Key Databases and Tools
- NeuroSynth / Neuroquery - Meta-analytic functional maps
- Allen Brain Atlas - Gene expression and connectivity
- OpenNeuro - Open neuroimaging datasets
- BrainMap - Functional neuroimaging database
- SPM / FSL / AFNI / FreeSurfer - Neuroimaging analysis software
- MNE-Python / EEGLAB - EEG/MEG analysis tools
- NEURON / Brian2 - Neural simulation environments
Output Format
- Brain activation maps with MNI coordinates (x, y, z), cluster size, peak t/z-value.
- ERP waveforms with component labels (N1, P3, N400), latency, and amplitude.
- Time-frequency plots with frequency bands labeled (delta, theta, alpha, beta, gamma).
- Computational model parameters with biological interpretation.
Quality Checklist
- Brain coordinates in standard space (MNI or Talairach) with atlas labels
- Multiple comparison correction method specified and justified
- Sample size adequate for imaging modality (power analysis cited)
- Preprocessing pipeline fully documented (software version, parameters)
- Task design includes appropriate controls and counterbalancing
- Effect sizes reported alongside statistical significance
- Reverse inference explicitly avoided or qualified
- Raw data sharing or availability discussed (OpenNeuro, BIDS format)