You had it coming... Precursors of Performance Errors Tom Eichele, MD PhD Department of Biological and Medical Psychology University of Bergen Source: New York Times Tom Eichele, MD PhD, University of Bergen

Pre-Error Speeding e.g. Gehring & Fencsik, JN 2001

ACC/ERN e.g. Debener et al, JN 2005

Stimulus TimingConvolution Matrix Pseudoinverse IC timecourse Estimated HRF = Stimulus Timing Estimated HRF Design Matrix IC timecourse X y β 1..n = = LS Single Trial Weights *:Deconvolution **:Single Trial Analysis Subj.M, s M (v M ) 1 N A1A1 A2A2 AMAM 1 2 K 1 2 K 1 2 K B1B1 B2B2 BMBM 1 2 K 1 2 K 1 2 K T1T1 T2T2 TMTM K 1 2 K 1 L 1 L 1 L 1 N 1 N F 1 -1 F 2 -1 F M -1 G -1  -1 Ĉ -1 x M (j) x(j) ŝ(j) x 2 (j) x 1 (j) Subj. 2, s 2 (v 2 ) Subj. 1, s 1 (v 1 ) u 1 (v 1 ) u 2 (v 2 ) u M (v M ) 1 N 1 N K y 1 (j) y 2 (j) y M (j) y 1 (i 1 ) y 2 (i 2 ) y M (i M ) Events 1. Data Generation2. Acquisition 3. Reduction 4. Decomposition5. Component Selection & Inference 1. Replicability ? 2. Physiology? 3. Population? 4. Event-Related Response?  DeCon* 5. Functional Modulation?  STA** Map-based criteria Timecourse-based criteria Events in a stimulus paradigm evoke neural responses in task- related sources in the presence of background activity in a number of subjects 1..M. Sources s at locations v are spatio- temporally mixed in A and hemodynamically convolved. Mixed signals u are recorded by the MRI scanner in B. The raw data are transformed to T by pre-processing (motion correction, normalization, smoothing, filtering). Preprocessed signals y are compressed to a set number of factors in F with PCA to reduce computational load. Individual PCs are concatenated to an aggregate set G. Spatial ICA estimates the inverse of A, and the aggregate components C. Back-Reconstruction of individual data spatial: (G i -1 Â)F i Y i temporal: F i G i  From the initial set of components, keep those that 1.Replicate across runs, and… 2.Represent grey matter, and… 3.Generalize to the population. 4.For the timecourses of remaining ICs, deconvolve the hemodynamic response 5.If there is a HRF, estimate single trial amplitudes.

RCZ PC oIFGpMFC SMA Cent SMA SFG Ins IC1 IC2 IC3 IC Latency (sec.) Amplitude (a.u.) Trials Error Amplitude (β) Trials Error Trials Error Trials Error Amplitude (β) * * * fdr 0.01: t 12 =4.46, p uncorr. =3.9·10 -4 fdr 0.01: t 12 =4.83, p uncorr. =2.1·10 -4 fdr 0.01: t 12 =4.05, p uncorr. =8.1·10 -4 fdr 0.01: t 12 =3.86, p uncorr. =1.1·10 -3 (a) (d) (g) (j) (k) (l) (h) (i) (e) (f) (b) (c) leftright

Different task, Similar Findings Li et al, NIMG 2007 Contrast: Trial preceding Error vs preceding Correct

X X K X X X K 80%Go; 20% No Go 3 Go stimuli every 6 seconds ISI of 1, 2, or 3 secs No Go stimuli every secs TR = 1.5 secs Another Different Task, same Precursors? Go/NoGo

Preprocessing & Component Selection 100 healthy right-handed participants, 50m/50f, years, 3T SPM2:  realignment  spatial normalization,  resampling to 3x3x3 mm voxels,  8mm FWHM smoothing... GIFT:  group ICA, with 64 estimated components, 50 ICASSO re-runs  map-based selection of ICs  HRF deconvolution from remaining components  HRF-based selection  single trial estimation from event related ICs  second level temporal ICA on concatenated single-trial modulations  precursor estimation ...

IC1 IC8 IC15 IC22 IC2 IC9 IC16 IC23 IC3 IC10 IC17 IC24 IC4 IC11 IC18 IC25 IC5 IC12 IC19 IC26 IC6 IC13 IC20 IC27 IC7 IC14 IC21 IC28 Selected maps from spatial ICA

HRFs from spatial ICA time courses

Trial-by-trial sequences from spatial ICA

Trial-by-trial sequences from 2nd level temporal ICA

Error signal vs. Precursor weights

Axial view Coronal view LR Top 3 Error signals (red ) and Precursors (blue)

Given that there are precursors in the fMRI…...are they in the EEG as well?

Response-locked EEG decomposition tIC1-RtIC2-RtIC3-RtIC4-RtIC5-R RT-sorted single trial images Condition averages R: Error G: Incompatible B: Compatible Time (ms) Potential (µV) Trials Component scalp maps

ms post-stimulus Trial-to-trial EEG dynamics ms post-response ms post-response tIC1-S tIC2-StIC3-StIC4-StIC5-S tIC1-R tIC2-RtIC3-RtIC4-RtIC5-R tIC1-R tIC2-RtIC3-RtIC4-RtIC5-R

Summary Error precursors exist... – In different tasks: Flanker, Go/NoGo, Simon,... – In different modalities: Behavior, fMRI, EEG – with similar trends spanning tens of seconds – with similar spatial patterns involving Increases in default mode regions Decreases in executive/effort regions

What now? Now that we know what the precursors are bad for, we need to figure out what they are good for. Follow up:  EEG and EEG-fMRI follow-up...  computational modelling...  behavior prediction possible ??? Paper: Eichele, et al PNAS 2008 Software: GIFT/EEGIFT icatb.sourceforge.net Data:

Markus Ullsperger Cologne Vince D. Calhoun Albuquerque Stefan Debener Jena Karsten Specht Bergen Thanks!!