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SyBil-AA Heidelberg – Sept. 2017
Vasile Vlad Moca
FMRI – features and preprocessing
o FMRI features: how networks are extracted (correlation & scale correlations)
o Preprocessing pipelines:
o Ringing
o Preprocessing
o ROI segmentation
o Data exchange format (what to include and file format)
Contents
Overview
Slow and delayed signal:
o Low sampling: TR = 2s, usually [0.5, 3]s
o Hemodynamic responses last 10s
o Integrates neural responses over time
o Rise/fall multiplicatively – peak 4-6s
o Cross correlation analysis
o Scaled correlation analysis
o To do: nonlinear measures (entropy,
fractal dimension) or causality measures
FMRI
Preprocessing
Filtering (ringing)
Features (correlations)
=> Networks
Network features
Classification - ANN
Features - scaled correlation Two signals composed of slow & fast
Long scale Short scale
Depending on scale, scaled correlation
analysis(SCA) can isolate faster components.
Nikolić D, Muresan RC, Feng W, Singer W (2012) Scaled correlation analysis: a better way to compute a cross-correlogram. European Journal of Neuroscience, pp. 1–21
CCH
Features
Time offset [s]4035302520151050-5-10-15-20-25-30-35-40
Avera
ge P
ears
on
's r
0.5
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0.1
0
-0.1
-0.2
-0.3
-0.4
3210-1
0.6
0.58
0.56
0.54
0.52
0.5
0.48
0.46
0.44
0.42
0.4Value
Akima interpolation: higher temporal precision than the sampling period
Preprocessing - ringing
Ringing: “non-oscillating input yields an oscillating output”
Preprocessing - ringing
o Ringing on AFNI – produced by the sharp low pass filter
o Exacerbated by fixed size trial lengths – ringing locked at trial start Davey CE, Grayden DB, Egan GF, Johnston LA. 2013. Filtering induces correlation in fmri resting state data. NeuroImage. 64:728–740
Preprocessing - pipelines
Pipeline Temporal Filtering Nuisance Regression
TS_pipeline01 - - -
TS_pipeline02 - spikes -
TS_pipeline03 - - GSR
TS_pipeline04 - spikes GSR
TS_pipeline05 FSL-BP - GSR
TS_pipeline06 FSL-BP spikes GSR
TS_pipeline07 FSL-HP - GSR
TS_pipeline08 FSL-HP spikes GSR
TS_pipeline09 AFNI-BP - GSR
TS_pipeline10 AFNI-BP spikes GSR
TS_pipeline11 AFNI-HP - GSR
TS_pipeline12 AFNI-HP spikes GSR
Slice-timing correction
o 32 areas and 29 trials in 14 animals – control condition only (rat) from Santiago’s lab
o 1 trial: 300 samples – 600s
o average reference ( GSR), filtering
Pipelines – GSR & Spikes rem.
Spike nuisance removal reduces slightly the CCH central peak, GSR seems beneficial.
Pipelines – filtering
In house BP filtering (IIR Butterworth 7th order HP 0.01Hz and Butterworth 3rd order LP 0.1Hz)
Discussions – common pipeline
Pipeline Temporal Filtering Nuisance Regression
TS_pipeline02 - spikes -
TS_pipeline04 - spikes GSR
o Slice-timing correction
o What is the best suitable ROI parcellation?
o Compare extracted networks with known anatomical connections.
Data exchange:
o Data format: matlab files (self explanatory to a large degree), easily
manageable
Thank you discussions…
