Skip to content

/neuroflow:data-analyze

Run an analysis pipeline on your preprocessed data.

/data-analyze covers the full range of neuroscience analysis methods โ€” ERPs, time-frequency, connectivity, decoding, and GLM โ€” with built-in statistical auditing to ensure your results are rigorous.

When to use it

  • After /data-preprocess โ€” you have cleaned, epoched data
  • You want to compute ERPs, time-frequency representations, or connectivity
  • You need to run statistical comparisons or multivariate decoding
  • You want an analysis plan before running anything (pre-registration support)

What it does

Claude asks:

  1. Analysis goal? (ERP, time-frequency, connectivity, decoding, GLM, other)
  2. Where is the preprocessed data?
  3. Is there a pre-registered analysis plan to follow?

Analysis approaches

Claude selects the appropriate tooling based on your goal:

Event-related potential analysis using MNE-Python.

# Example: P300 ERP comparison
evoked_standard = epochs['standard'].average()
evoked_target = epochs['target'].average()

# Grand average across subjects
grand_avg = mne.grand_average([evoked_target, ...])

# Statistical test: cluster permutation
T_obs, clusters, p_values, _ = mne.stats.permutation_cluster_1samp_test(
    X, n_permutations=1000
)

Time-frequency analysis using Morlet wavelets or multitaper.

# Morlet wavelet TFR
freqs = np.arange(4, 40, 1)
n_cycles = freqs / 2.0

power = mne.time_frequency.tfr_morlet(
    epochs, freqs=freqs, n_cycles=n_cycles,
    return_itc=True
)

Functional connectivity using MNE spectral connectivity tools.

from mne_connectivity import spectral_connectivity_epochs

con = spectral_connectivity_epochs(
    epochs, method='coh',
    fmin=8, fmax=12,  # alpha band
    faverage=True
)

Multivariate pattern analysis (MVPA) using scikit-learn.

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import cross_val_score

clf = LinearDiscriminantAnalysis()
scores = cross_val_score(clf, X, y, cv=5, scoring='roc_auc')

First- and second-level GLM using nilearn.

from nilearn.glm.first_level import FirstLevelModel

fmri_glm = FirstLevelModel(t_r=2.0, hrf_model='spm')
fmri_glm.fit(fmri_imgs, events_df)
z_map = fmri_glm.compute_contrast('target - standard')

Statistical auditing

After running analysis, Claude audits the statistical approach:

  • Test assumptions โ€” normality, sphericity, independence
  • Multiple comparison correction โ€” cluster permutation, FDR, Bonferroni
  • Effect size reporting โ€” Cohen's d, partial ฮทยฒ, AUC
  • Pre-registration compliance โ€” if a plan exists, flags any deviations

Example session

/neuroflow:data-analyze

Claude: Research question: "Does noise background reduce P300 amplitude?"

        Analysis goal?

You: P300 ERP comparison between noise and silent conditions, 
     then cluster permutation test.

Claude: Analysis plan:
        1. Average epochs by condition (noise, silent) per subject
        2. Compute difference wave: noise - silent
        3. Cluster permutation test across 300-600ms window, Pz

        Writing analysis-plan.md...
        Writing scripts/analysis/erp_analysis.py...

        [runs analysis, generates figures/erp_comparison.png]

        Results: Significant cluster at 350-520ms over central-parietal 
        electrodes (p = 0.012). P300 amplitude reduced by 2.3 ยตV in noise 
        condition (Cohen's d = 0.71).

Output

File Where What it contains
analysis-plan.md .neuroflow/data-analyze/ What will be computed, comparisons, statistical tests, expected output
analysis.py scripts/analysis/ Analysis code
analysis-summary.md .neuroflow/data-analyze/ Key findings, figures produced, open questions
Figures figures/ All generated plots
Results results/ Statistical output tables

Files read and written

Direction Files
Reads .neuroflow/project_config.md, .neuroflow/flow.md, .neuroflow/ideation/flow.md, .neuroflow/data-preprocess/flow.md, .neuroflow/data-analyze/flow.md
Writes .neuroflow/data-analyze/, .neuroflow/data-analyze/flow.md, .neuroflow/sessions/YYYY-MM-DD.md, scripts/analysis/, results/, figures/