Neuroimaging Qc

Neuroimaging QC Decision-Making

Evidence-based guidance for interpreting QC metrics and making principled inclusion/exclusion decisions.

Core Principles

1. No Universal Thresholds

QC thresholds are study-specific. Factors affecting appropriate cutoffs:

• Population: Infants tolerate higher motion than adults
• Paradigm: Task fMRI has different constraints than resting-state
• Analysis: Connectivity analyses are more motion-sensitive than activation
• Sample size: Stricter thresholds with larger N; lenient with small N

2. Distribution-Based Decisions

Always examine your sample's QC distribution before applying thresholds:

1. Plot histograms of key metrics
2. Identify natural breakpoints/outliers (>2-3 SD from mean)
3. Apply literature-based thresholds as starting points, adjust based on distribution
4. Report both threshold AND resulting exclusion rate

3. Multi-Metric Assessment

Never exclude based on single metric. Combine:

• Motion metrics (FD, DVARS)
• Signal quality metrics (tSNR, SNR)
• Artifact indicators (outlier volumes, registration quality)
• Visual inspection for edge cases

Decision Workflow

1. IDENTIFY your QC source
   ├── Known pipeline (fMRIPrep, MRIQC, etc.) → See modality references
   └── Custom/unknown output → Parse available metrics, map to known categories

2. CHARACTERIZE your study
   ├── Population: adult / pediatric / infant / clinical
   ├── Paradigm: rest / task / naturalistic / sleep
   └── Analysis: activation / connectivity / other

3. ESTABLISH thresholds
   ├── Start with literature recommendations (see references)
   ├── Examine your sample distribution
   └── Adjust based on trade-off: data quality vs. statistical power

4. APPLY and DOCUMENT
   ├── Generate exclusion summary
   ├── Report thresholds with citations
   └── Conduct sensitivity analysis with stricter/lenient thresholds

Quick Reference: Common Thresholds

fMRI Motion (FD)

Additional motion criteria:

• fd_perc (% volumes > threshold): typically exclude if >20-50%
• Maximum FD spike: consider >3-5 mm as problematic
• Minimum usable data: ≥5 min for resting-state, task-dependent for task fMRI

EEG Amplitude (Peak-to-Peak)

Additional EEG criteria:

• Channel rejection: >20-30% bad epochs → mark as bad channel
• Epoch rejection: typically accept 10-30% epoch loss; >50% problematic
• Interpolation limit: ≤10% of channels can be interpolated

Structural MRI

Modality-Specific References

For detailed metrics, thresholds, and Python code:

• fMRI (fMRIPrep/MRIQC): See references/fmri_qc.md
• EEG/MEG (MNE-Python): See references/eeg_qc.md
• fNIRS (Homer3/MNE-NIRS): See references/fnirs_qc.md
• Structural MRI: See references/structural_qc.md

Python Utilities

Scripts for parsing QC outputs and applying thresholds:

• scripts/parse_mriqc.py: Parse MRIQC group TSV, flag subjects
• scripts/parse_fmriprep_confounds.py: Summarize fMRIPrep confounds
• scripts/qc_report.py: Generate QC summary reports

Methods Section Templates

fMRI QC Methods

Quality control was performed using [MRIQC/fMRIPrep] outputs. Subjects were 
excluded based on the following criteria: (1) mean framewise displacement 
(FD) > X mm [cite Power et al., 2012], (2) >Y% of volumes exceeding FD 
threshold of Z mm, or (3) visual inspection revealing [registration 
failures/artifacts]. This resulted in N subjects excluded (X% of sample), 
yielding a final sample of M participants.

EEG QC Methods

Continuous EEG data underwent artifact rejection using MNE-Python. Epochs 
containing peak-to-peak amplitudes exceeding X µV were rejected. Channels 
with >Y% rejected epochs were marked as bad and interpolated using spherical 
spline interpolation. Participants with >Z% rejected epochs or >N bad 
channels were excluded from analysis.

Handling Unknown QC Outputs

When encountering unfamiliar QC metrics:

1. Identify metric category:

   • Motion/movement: Look for displacement, rotation, translation terms
   • Signal quality: SNR, tSNR, CNR, variance-related
   • Artifacts: Outlier counts, spike detection, artifact indices

2. Determine directionality:

   • Higher-is-better: SNR, tSNR, CNR
   • Lower-is-better: FD, DVARS, artifact indices, outlier counts

3. Establish thresholds:

   • Plot distribution, identify outliers
   • If metric has known analog, use those thresholds
   • Otherwise: use ±2-3 SD from mean as starting point

4. Validate:

   • Cross-reference with visual inspection
   • Check correlation with known metrics
   • Verify excluded subjects are actually problematic

Population-Specific Considerations

Infants (0-24 months)

• Higher baseline motion expected; adjust FD thresholds upward
• Shorter usable data segments acceptable
• Age-appropriate templates critical for registration QC
• Sleep state affects data quality (deep sleep preferred)

Pediatric (3-12 years)

• Motion decreases with age; consider age as covariate
• Task compliance affects data quality
• Mock scanner training reduces motion
• Consider breaks during long protocols

Adolescents

• Motion intermediate between children and adults
• Developmental stage affects hemodynamics
• Consider puberty stage as potential confound

Clinical Populations

• Disease-specific considerations (lesions, atrophy)
• Medication effects on signal
• May need population-specific templates
• Balance data quality vs. already-reduced sample sizes

Paradigm-Specific Considerations

Resting-State

• Scrubbing viable (can remove timepoints)
• Need minimum continuous/total duration (≥5 min recommended)
• Strict motion thresholds (FD < 0.2-0.3 mm)

Task fMRI

• Cannot arbitrarily remove timepoints
• Consider motion relative to task timing
• More lenient thresholds acceptable (FD < 0.5-0.9 mm)
• Ensure sufficient trials survive exclusion

Naturalistic (movies, stories)

• Long durations increase motion likelihood
• Consider segment-wise QC
• Drift artifacts more relevant

Sleep Studies

• State-dependent QC (arousal events)
• EEG quality for sleep staging
• Movement during state transitions

Converted and distributed by TomeVault — claim your Tome and manage your conversions.