neuroscience
Supports neuroscience research including brain imaging analysis (fMRI, EEG), neural circuit modeling, cognitive experiment design, and neurological disorder investigation; trigger when users discuss brain regions, neural signals, cognitive tasks, or neuroimaging data.
Best use case
neuroscience is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Supports neuroscience research including brain imaging analysis (fMRI, EEG), neural circuit modeling, cognitive experiment design, and neurological disorder investigation; trigger when users discuss brain regions, neural signals, cognitive tasks, or neuroimaging data.
Teams using neuroscience should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/neuroscience/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How neuroscience Compares
| Feature / Agent | neuroscience | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Supports neuroscience research including brain imaging analysis (fMRI, EEG), neural circuit modeling, cognitive experiment design, and neurological disorder investigation; trigger when users discuss brain regions, neural signals, cognitive tasks, or neuroimaging data.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
## 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 1. **Define the neuroscience question** - Specify level of analysis (molecular, cellular, circuit, systems, cognitive, behavioral). Identify target brain regions or networks. 2. **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). 3. **Data acquisition guidance** - Recommend acquisition parameters: fMRI (TR, voxel size, field strength), EEG (sampling rate, electrode montage, impedance thresholds). Specify preprocessing steps. 4. **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. 5. **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. 6. **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. 7. **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)
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