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Multimodal Pipeline

The multimodal pipeline runs the ephys and miniscope sub-pipelines, then aligns their time bases for phase-locked and event-aligned analysis. Alignment is implemented through MiniscopeDataManager.sync_timestamps (which calls into EphysDataManager.get_sync_timestamps when TTL-based sync is used), not by hard-coding a single vendor.

Quick Start

Prerequisites

  1. Ensure your project directory has:
  2. experiments.csv with both calcium imaging directory and ephys directory

  3. analysis_parameters.csv with parameters for both modalities

  4. Ensure the correct data managers are selected for those directories (see Creating new data loaders). Typical lab setups:

  5. Neuralynx + UCLA Miniscope V3: TTL pulses on an ephys channel + CSV/JSON miniscope metadata.
  6. ONIX RHS2116 + UCLA Miniscope V4: hardware clock .raw streams; sync may rely on clock alignment rather than TTL extraction — your sync_timestamps / get_sync_timestamps implementations must reflect that.

Command Line

# Run with explicit project path
python -m ace_neuro.pipelines.multimodal --line-num 97 --project-path /my/project

# Run with explicit project and data paths
python -m ace_neuro.pipelines.multimodal --line-num 97 --project-path /my/project --data-path /my/raw_data

# Run in headless mode
python -m ace_neuro.pipelines.multimodal --line-num 97 --project-path /my/project --headless

Python API

from ace_neuro.pipelines.multimodal import MultimodalPipeline

api = MultimodalPipeline()
api.run(
    line_num=97, 
    project_path="/my/project",
    data_path="/my/raw_data",
    headless=True
)

Pipeline Steps

  1. Ephys sub-pipeline: Loads channels via EphysDataManager.create(...) (Neuralynx or RHS2116, depending on files in ephys directory).
  2. Miniscope sub-pipeline: Loads movies and metadata via MiniscopeDataManager.create(...) (UCLA V3 or ONIX V4, depending on files in calcium imaging directory).
  3. Timestamp synchronization: Calls sync_neuralynx_miniscope_timestamps, which delegates to miniscope_dm.sync_timestamps(ephys_dm=...). Neuralynx-specific cleanup of event lists runs only when the ephys manager is NeuralynxDataManager.
  4. TTL / frame mapping: Maps aligned calcium times to ephys sample indices where TTL-based framing applies.
  5. Phase-based event analysis (optional): Computes calcium event phases relative to ephys oscillations when event indices are available.
  6. Phase histograms: Circular histograms of event-phase distributions when phases are computed.

Key Parameters

Parameter Description Default
delete_TTLs Remove problematic TTL events True
fix_TTL_gaps Correct gaps in TTL timing False in MultimodalPipeline.run (CLI defaults may differ — see python -m ace_neuro.pipelines.multimodal defaults)
only_experiment_events Restrict to experimental time window True
all_TTL_events Use all detected TTL events True
ca_events Analyze calcium events False in API default; CLI template sets True
time_range Time window to analyze (seconds) None

All ephys and miniscope parameters from their respective pipelines also apply here.

Data Requirements

The experiments.csv row must include:

  • calcium imaging directory: Path to miniscope recordings (resolved relative to data_path).
  • ephys directory: Path to ephys recordings (resolved relative to data_path).

Those directories should contain one coherent acquisition from the same session. The expected file types depend on which built-in (or custom) loaders match; they are not limited to “Neuralynx folders only” or “UCLA V3 only.” If multimodal alignment fails, verify TTL availability, sync_timestamps behavior for your pair of loaders, and Creating new data loaders.