Filtering

Filtering Model Doc

Overview

Filtering is a classical non-deep-learning method for neuroimaging signal denoising.

• Model family: non-deep-learning preprocessing and denoising method
• Typical objectives:
  • remove unwanted frequency content from BOLD time series
  • retain frequency bands relevant to resting-state or task analysis
  • prepare cleaner voxel-wise or ROI-wise time series for downstream connectivity, decoding, or statistical analysis
• Primary input: preprocessed fMRI time series, optional confounds, optional mask, TR
• Primary output: denoised BOLD image, cleaned ROI time series, optional QC summaries

In NeuroClaw, this document is model-level guidance for temporal filtering workflows rather than predictive modeling.

Upstream preparation should usually be delegated to:

• fmri-skill for modality-level denoising planning and validated preprocessing sequences
• nilearn-tool for concrete filtering and cleaned image export

Research use only.

Quick Start

1) Prepare denoising inputs

Expected inputs:

• preprocessed BOLD image
• repetition time (TR)
• optional confounds TSV
• optional brain mask
• optional requested frequency band

If images are not preprocessed yet, delegate to fmri-skill first.

2) Filtering route

Representative operations:

• load preprocessed BOLD time series
• apply temporal high-pass / low-pass or band-pass filtering
• optionally combine filtering with standardization or confound regression
• export denoised image and cleaned summaries

Example execution route:

# delegated through claw-shell after preprocessing is confirmed
python skills/nilearn-tool/scripts/preprocess_bold_reference.py \
  --bold path/to/sub-001_rest_preproc_bold.nii.gz \
  --tr 2.0 \
  --high-pass 0.01 \
  --low-pass 0.08 \
  --output run_models_output/filtering/sub-001_rest_filtered_bold.nii.gz

Input / Output Contract

Required inputs

• preprocessed BOLD image or extracted time series
• TR for temporal filtering

Optional inputs

• confounds table
• mask image
• high-pass / low-pass frequency settings
• standardization or smoothing options

Produced outputs

• denoised BOLD image
• cleaned ROI or voxel time series
• optional QC summary of filtering settings

Recommended Delegation

• modality-level denoising plan -> fmri-skill
• concrete implementation of filtering -> nilearn-tool
• shell execution and logging -> claw-shell

No execution before explicit plan confirmation.

When to Use Filtering

• The user wants signal cleaning rather than statistical modeling or prediction.
• The goal is to remove unwanted frequency content before connectivity or decoding.
• The workflow needs standardized temporal preprocessing before ROI extraction.
• A classical transparent denoising baseline is preferred over learned denoising methods.
• The user explicitly asks for band-pass filtering, high-pass filtering, or low-pass filtering.

Limitations and Notes

• Filtering choices depend strongly on TR, study design, and whether the data are resting-state or task-fMRI.
• Over-aggressive filtering can remove meaningful task-related or physiological signals.
• Temporal cleaning parameters should be reported because they directly affect downstream analyses.

Reference

• Lindquist MA. The statistical analysis of fMRI data.
• Nilearn signal cleaning documentation: https://nilearn.github.io/stable/modules/generated/nilearn.image.clean_img.html

Created At: 2026-04-14 00:40 HKT Last Updated At: 2026-04-14 00:45 HKT Author: chengwang96