claude-scientific-skills/scientific-skills/neuropixels-analysis/ANALYSIS.md

Post-Processing & Analysis Reference

Comprehensive guide to quality metrics, visualization, and analysis of sorted Neuropixels data.

Sorting Analyzer

The SortingAnalyzer is the central object for post-processing.

Create Analyzer

import spikeinterface.full as si

# Create analyzer
analyzer = si.create_sorting_analyzer(
    sorting,
    recording,
    sparse=True,                    # Use sparse representation
    format='binary_folder',         # Storage format
    folder='analyzer_output'        # Save location
)

Compute Extensions

# Compute all standard extensions
analyzer.compute('random_spikes')       # Random spike selection
analyzer.compute('waveforms')           # Extract waveforms
analyzer.compute('templates')           # Compute templates
analyzer.compute('noise_levels')        # Noise estimation
analyzer.compute('principal_components')  # PCA
analyzer.compute('spike_amplitudes')    # Amplitude per spike
analyzer.compute('correlograms')        # Auto/cross correlograms
analyzer.compute('unit_locations')      # Unit locations
analyzer.compute('spike_locations')     # Per-spike locations
analyzer.compute('template_similarity') # Template similarity matrix
analyzer.compute('quality_metrics')     # Quality metrics

# Or compute multiple at once
analyzer.compute([
    'random_spikes', 'waveforms', 'templates', 'noise_levels',
    'principal_components', 'spike_amplitudes', 'correlograms',
    'unit_locations', 'quality_metrics'
])

Save and Load

# Save
analyzer.save_as(folder='analyzer_saved', format='binary_folder')

# Load
analyzer = si.load_sorting_analyzer('analyzer_saved')

Quality Metrics

Compute Metrics

analyzer.compute('quality_metrics')
qm = analyzer.get_extension('quality_metrics').get_data()
print(qm)

Available Metrics

Metric	Description	Good Values
snr	Signal-to-noise ratio	> 5
isi_violations_ratio	ISI violation ratio	< 0.01 (1%)
isi_violations_count	ISI violation count	Low
presence_ratio	Fraction of recording with spikes	> 0.9
firing_rate	Spikes per second	0.1-50 Hz
amplitude_cutoff	Estimated missed spikes	< 0.1
amplitude_median	Median spike amplitude	-
amplitude_cv	Coefficient of variation	< 0.5
drift_ptp	Peak-to-peak drift (um)	< 40
drift_std	Standard deviation of drift	< 10
drift_mad	Median absolute deviation	< 10
sliding_rp_violation	Sliding refractory period	< 0.05
sync_spike_2	Synchrony with other units	< 0.5
isolation_distance	Mahalanobis distance	> 20
l_ratio	L-ratio (isolation)	< 0.1
d_prime	Discriminability	> 5
nn_hit_rate	Nearest neighbor hit rate	> 0.9
nn_miss_rate	Nearest neighbor miss rate	< 0.1
silhouette_score	Cluster silhouette	> 0.5

Compute Specific Metrics

analyzer.compute(
    'quality_metrics',
    metric_names=['snr', 'isi_violations_ratio', 'presence_ratio', 'firing_rate']
)

Custom Quality Thresholds

qm = analyzer.get_extension('quality_metrics').get_data()

# Define quality criteria
quality_criteria = {
    'snr': ('>', 5),
    'isi_violations_ratio': ('<', 0.01),
    'presence_ratio': ('>', 0.9),
    'firing_rate': ('>', 0.1),
    'amplitude_cutoff': ('<', 0.1),
}

# Filter good units
good_units = qm.query(
    "(snr > 5) & (isi_violations_ratio < 0.01) & (presence_ratio > 0.9)"
).index.tolist()

print(f"Good units: {len(good_units)}/{len(qm)}")

Waveforms & Templates

Extract Waveforms

analyzer.compute('waveforms', ms_before=1.5, ms_after=2.5, max_spikes_per_unit=500)

# Get waveforms for a unit
waveforms = analyzer.get_extension('waveforms').get_waveforms(unit_id=0)
print(f"Shape: {waveforms.shape}")  # (n_spikes, n_samples, n_channels)

Compute Templates

analyzer.compute('templates', operators=['average', 'std', 'median'])

# Get template
templates_ext = analyzer.get_extension('templates')
template = templates_ext.get_unit_template(unit_id=0, operator='average')

Template Similarity

analyzer.compute('template_similarity')
sim = analyzer.get_extension('template_similarity').get_data()
# Matrix of cosine similarities between templates

Unit Locations

Compute Locations

analyzer.compute('unit_locations', method='monopolar_triangulation')
locations = analyzer.get_extension('unit_locations').get_data()
print(locations)  # x, y coordinates per unit

Spike Locations

analyzer.compute('spike_locations', method='center_of_mass')
spike_locs = analyzer.get_extension('spike_locations').get_data()

Location Methods

• 'center_of_mass' - Fast, less accurate
• 'monopolar_triangulation' - More accurate, slower
• 'grid_convolution' - Good balance

Correlograms

Auto-correlograms

analyzer.compute('correlograms', window_ms=50, bin_ms=1)
correlograms, bins = analyzer.get_extension('correlograms').get_data()

# correlograms shape: (n_units, n_units, n_bins)
# Auto-correlogram for unit i: correlograms[i, i, :]
# Cross-correlogram units i,j: correlograms[i, j, :]

Visualization

Probe Map

si.plot_probe_map(recording, with_channel_ids=True)

Unit Templates

# All units
si.plot_unit_templates(analyzer)

# Specific units
si.plot_unit_templates(analyzer, unit_ids=[0, 1, 2])

Waveforms

# Plot waveforms with template
si.plot_unit_waveforms(analyzer, unit_ids=[0])

# Waveform density
si.plot_unit_waveforms_density_map(analyzer, unit_id=0)

Raster Plot

si.plot_rasters(sorting, time_range=(0, 10))  # First 10 seconds

Amplitudes

analyzer.compute('spike_amplitudes')
si.plot_amplitudes(analyzer)

# Distribution
si.plot_all_amplitudes_distributions(analyzer)

Correlograms

# Auto-correlograms
si.plot_autocorrelograms(analyzer, unit_ids=[0, 1, 2])

# Cross-correlograms
si.plot_crosscorrelograms(analyzer, unit_ids=[0, 1])

Quality Metrics

# Summary plot
si.plot_quality_metrics(analyzer)

# Specific metric distribution
import matplotlib.pyplot as plt
qm = analyzer.get_extension('quality_metrics').get_data()
plt.hist(qm['snr'], bins=50)
plt.xlabel('SNR')
plt.ylabel('Count')

Unit Locations on Probe

si.plot_unit_locations(analyzer)

Drift Map

si.plot_drift_raster(sorting, recording)

Summary Plot

# Comprehensive unit summary
si.plot_unit_summary(analyzer, unit_id=0)

LFP Analysis

Load LFP Data

lfp = si.read_spikeglx('/path/to/data', stream_id='imec0.lf')
print(f"LFP: {lfp.get_sampling_frequency()} Hz")

Basic LFP Processing

# Downsample if needed
lfp_ds = si.resample(lfp, resample_rate=1000)

# Common average reference
lfp_car = si.common_reference(lfp_ds, reference='global', operator='median')

Extract LFP Traces

import numpy as np

# Get traces (channels x samples)
traces = lfp.get_traces(start_frame=0, end_frame=30000)

# Specific channels
traces = lfp.get_traces(channel_ids=[0, 1, 2])

Spectral Analysis

from scipy import signal
import matplotlib.pyplot as plt

# Get single channel
trace = lfp.get_traces(channel_ids=[0]).flatten()
fs = lfp.get_sampling_frequency()

# Power spectrum
freqs, psd = signal.welch(trace, fs, nperseg=4096)
plt.semilogy(freqs, psd)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Power')
plt.xlim(0, 100)

Spectrogram

f, t, Sxx = signal.spectrogram(trace, fs, nperseg=2048, noverlap=1024)
plt.pcolormesh(t, f, 10*np.log10(Sxx), shading='gouraud')
plt.ylabel('Frequency (Hz)')
plt.xlabel('Time (s)')
plt.ylim(0, 100)
plt.colorbar(label='Power (dB)')

Export Formats

Export to Phy

si.export_to_phy(
    analyzer,
    output_folder='phy_export',
    compute_pc_features=True,
    compute_amplitudes=True,
    copy_binary=True
)
# Then: phy template-gui phy_export/params.py

Export to NWB

from spikeinterface.exporters import export_to_nwb

export_to_nwb(
    recording,
    sorting,
    'output.nwb',
    metadata=dict(
        session_description='Neuropixels recording',
        experimenter='Name',
        lab='Lab name',
        institution='Institution'
    )
)

Export Report

si.export_report(
    analyzer,
    output_folder='report',
    remove_if_exists=True,
    format='html'
)

Complete Analysis Pipeline

import spikeinterface.full as si

def analyze_sorting(recording, sorting, output_dir):
    """Complete post-processing pipeline."""

    # Create analyzer
    analyzer = si.create_sorting_analyzer(
        sorting, recording,
        sparse=True,
        folder=f'{output_dir}/analyzer'
    )

    # Compute all extensions
    print("Computing extensions...")
    analyzer.compute(['random_spikes', 'waveforms', 'templates', 'noise_levels'])
    analyzer.compute(['principal_components', 'spike_amplitudes'])
    analyzer.compute(['correlograms', 'unit_locations', 'template_similarity'])
    analyzer.compute('quality_metrics')

    # Get quality metrics
    qm = analyzer.get_extension('quality_metrics').get_data()

    # Filter good units
    good_units = qm.query(
        "(snr > 5) & (isi_violations_ratio < 0.01) & (presence_ratio > 0.9)"
    ).index.tolist()

    print(f"Quality filtering: {len(good_units)}/{len(qm)} units passed")

    # Export
    si.export_to_phy(analyzer, f'{output_dir}/phy')
    si.export_report(analyzer, f'{output_dir}/report')

    # Save metrics
    qm.to_csv(f'{output_dir}/quality_metrics.csv')

    return analyzer, qm, good_units

# Usage
analyzer, qm, good_units = analyze_sorting(recording, sorting, 'output/')