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Public Defense Public Defense Presentation Transcript

  • Automated Subject-Specific Peak Identification and Ballistocardiographic Artifact Correction in EEG-fMRI Sara Assecondi Dep. Electronics and Information Systems MEDISIP, Ghent University Promotors: Prof. Dr. Ir. S. Staelens, Prof. Dr. P. Boon
  • The secret dream of a neuroscientist is to map the human brain: WHERE and WHEN personality sensory emotions problem solving reasoning hearing language speech vision bla…. bla…. bla…. Introduction: general background 2
  • Brain mapping has useful applications Pathologies • Dyslexia • Epilepsy (5% school aged children) (0.5-1 % of the population) EL DYS XIA Cognition • mind, reasoning, … • perception, … • intelligence, learning… Introduction: general background 3
  • Nowadays we have reliable means to localize brain functions EEG exam fMRI exam Take an image of the brain- fMRI Introduction: general background 4
  • What an ElectroEncephaloGram (EEG) is 100 billion (100000000000) Neurons communicate by means of electrical impulses: the brain is characterized by an electrical activity that can be measured in μV Signal amplitude (μV) Time (ms) Introduction: general background 5
  • EEG exam EEG electrode Signal amplitude (μV) Time (ms) Introduction: general background 6
  • EEG exam: Event Related Potentials (ERP) Signal amplitude μV ERP (μV) STIMULUS - electric, - auditory - visual Time ms (ms) - cognitive... Introduction: general background 7
  • Information in EEG and ERP The latency of peaks , i.e. the time interval between the stimulus and the occurrence of a peak, gives temporal information about the sequence of events in the brain μV perception decoding comprehension μV . . . ms stimulus ms stimulus latency Each peak is related to a specific stage Scalp of the information process, through its map latency Introduction: general background 8
  • Basics of Magnetic Resonance Imaging (MRI) Up to 70 % of body weight is made up by water 70% O N H H H 1H nucleus ≈ S Water has a spin molecule and a magnetic moment By making use of the magnetic properties in brain tissues, an image of the brain can be reconstructed Introduction: general background 9
  • MRI exam MR-scanner strong magnet Sagittal view Radio-frequency waves and time-varying magnetic fields Axial view Introduction: general background 10
  • MRI exam: MR-scanner functional MRI Activity: on on on off off off off time Activity: • Endogenous (epileptic spikes) • Exogenous (external stimuli) Introduction: general background 11
  • Do we have everything we need? personality We have the means: sensory emotions • EEG μV problem solving reasoning hearing language ms speech vision • fMRI Two new important concepts: • Temporal resolution • Spatial resolution Introduction: general background 12
  • Temporal resolution The smaller the interval the higher the temporal resolution μV μV μV ms ms ms EEG-ERP (ms) fMRI (s) μV ms Introduction: general background 13
  • Spatial resolution The finer the grid the higher the spatial resolution EEG-ERP (cm) fMRI (mm) Scalp map Introduction: general background 14
  • EEG-fMRI achieves high temporal and spatial resolution Invasive 0 Brain PET -1 EEG+ERP Map -2 fMRI NIRS Log size (m) -3 Column Layer -4 Adapted from Cohen and Bookheimer, 1994 Neuron -5 Non-invasive -3 -2 -1 0 1 2 3 4 5 Log time (s) Take the advantage of both EEG and fMRI to have, at the same time, high temporal and high spatial resolution Introduction: general background 15
  • What do we need for EEG-fMRI? • MR-compatible EEG equipment Amplifier syncbox Power pack Galvanic isolation • MR scanner Introduction: general background 16
  • EEG-fMRI exam Scanner room Control room EEG electrode EEG wires PC amplifier Activity: on on on off off off off Adapted from Bénar, PhD thesis Introduction: general background 17
  • Some examples 18
  • How we approach the problem of cerebral localization of brain functions Integration of EEG and fMRI Time domain Time and spatial domain Improving the temporal Data quality of simultaneous localization of brain functions EEG-fMRI recordings Quantitative and automated identification of ERP peaks Data quality of simultaneous ERP-fMRI recordings Data interpretation Effect of different recording environments on the process under investigation Overview 19
  • How we approach the problem of cerebral localization of brain functions Integration of EEG and fMRI Time domain Time and spatial domain Improving the temporal Data quality of simultaneous localization of brain functions EEG-fMRI recordings Quantitative and automated identification of ERP peaks Data quality of simultaneous ERP-fMRI recordings Data interpretation Effect of different recording environments on the process under investigation Overview 20
  • The quantification of amplitudes and latencies in ERPs may be challenging PRE-LEXICAL LEXICAL POST-LEXICAL N3 N4 N0 N1 N2 P0 P1 P4 P600 P2b P2a 0ms 160ms 420ms 800ms • Challenges • Information to retrieve – Number of peaks – Latency of each peak – Labelling – Amplitude of each peak – Inter-subject variability – Label of each peak Time domain: Automatic identification of ERP peaks 21
  • Features of peak quantification approaches  Reliability  Reproducibility, over subjects and over time  Objectivity Gold standard: expert clinician Automated methods: Peak-picking, • Time consuming Dynamic Time Warping • subjective • Fast • Objective N1 N1 μV  μV   P2  P2 stimulus ms stimulus ms Time domain: Automatic identification of ERP peaks 22
  • Method 1: Peak-picking is a search for extrema in predefined time intervals N3 N4 N0 N1 N2 P0 P1 P4 P600 P2b P2a Predifined time interval Example: Looking for the N3 peak Drawbacks • Define the time interval • Highly dependent on the • N3 is a negative peak: defined intervals search for a minimum • Does not take into account • Assign the N3 label the inter-subject variability to the minimum found Time domain: Automatic identification of ERP peaks 23
  • Method 2: Dynamic time warping (DTW) is a nonlinear mapping of two signals The ERP is compared with a reference signal containing the peaks of interest Linear alignment Dynamic Time Warping reference reference subject subject Time (ms) Time (ms)     Drawback A peak is always found: it may not be a peak Time domain: Automatic identification of ERP peaks 24
  • We proposed a method (ppDTW) that integrates peack-picking and Dynamic Time Warping Compared by means of DTW REFERENCE SUBJECT Sequence of candidate peaks Peak-picking in a time interval centered at the candidate peak Measured latencies and amplitudes Time domain: Automatic identification of ERP peaks 25
  • The reference signal must contain all the peaks of interest We computed the reference by interpolating mean amplitudes and latencies derived from a normal population Peak variability derived Reference from a normal population Time domain: Automatic identification of ERP peaks 26
  • Case study (Assecondi et al., Clinical Neurophysiology 2009) • Normal children and children diagnosed with developmental dyslexia • Reading related ERP • Latencies automatically determined: – Peak picking – ppDTW • Validation – Comparison with the visual scoring of an expert clinician Time domain: Automatic identification of ERP peaks 27
  • Performance of the methods on normal and dyslexic subjects normal dyslexic N0 N2 N3 N4 N1N2 N3 N4 P1 P2b P4 P2b P4 P600 ppdtw ppdtw visual scoring visual scoring peak picking peak picking Time domain: Automatic identification of ERP peaks 28
  • We evaluate the methods by comparing the automated scoring with the visual scoring C = correct identifications S = substitutions C S D I D = deletions expert     I = insertions method     N1 precision recall F-score Latency N1 1 C C 1 1 C S I C S D 2P 2R Peak-picking 93% 80% 85% PP-DTW 93% 86% 89% Time domain: Automatic identification of ERP peaks 29
  • Conclusion We developed an automated peak detection method that takes into account the inter-subject variability and a-priori knowledge about the ERP (in the reference) A-priori knowledge may be derived from literature, hypothesis about the ERP experiment, expertise of the clinician The method is valuable with different ERPs, when huge databases have to be measured or when the same subject must be examined during a therapy or rehabilitation Time domain: Automatic identification of ERP peaks 30
  • How we approach the problem of cerebral localization of brain functions Integration of EEG and fMRI Time domain Time and spatial domain Improving the temporal Data quality of simultaneous localization of brain functions EEG-fMRI recordings Quantitative and automated identification of ERP peaks Data quality of simultaneous ERP-fMRI recordings Data interpretation Effect of different recording environments on the process under investigation Overview 31
  • EEG-fMRI achieves high temporal and spatial resolution Invasive 0 Brain PET -1 EEG+ERP Map -2 fMRI NIRS Log size (m) -3 Column Layer -4 Adapted from Cohen and Bookheimer, 1994 Neuron -5 Non-invasive -3 -2 -1 0 1 2 3 4 5 Log time (s) EEG - fMRI implies the interaction of different physical and physiological systems ARTIFACTS (human being, EEG system, MR scanner) Time and spatial domain: Data quality of simultaneous EEG-fMRI 32
  • An artifact is a contamination of the data of interest Suppose you want to listen to the radio while a bell is ringing din don Source of noise o Data of interest l i d e ? i a i s s n i n d s i i Time and spatial domain: Data quality of simultaneous EEG-fMRI 33
  • Two main artifacts affect EEG-fMRI recordings • Image acquisition artifact caused by the RF-waves and time-varying gradients ~0.5 s Very deterministic BCG-peak • Ballistocardiographic artifact is a blood related effect due to the high static magnetic field R-peak Only QUASI-deterministic ~1 s Time and spatial domain: Data quality of simultaneous EEG-fMRI 34
  • The BallistoCardioGraphic artifact (BCGa) R R R R R R R R 100 µV 10 seconds • It depends on the proximity of the EEG electrodes to blood vessels • It is synchronous to the heart beat and spread throughout the heart beat and all over the scalp Time and spatial domain: Data quality of simultaneous EEG-fMRI 35
  • Distribution of scalp arteries Adapted from Gray, 1918 Time and spatial domain: Data quality of simultaneous EEG-fMRI 36
  • The BCG artifact can be removed if an additive model is assumed Noise and Recorded other non-MR signal = EEG + BCGa + related artifacts Estimate of the BCGa that depends on the algorithm used subtraction BCGa model Time and spatial domain: Data quality of simultaneous EEG-fMRI 37
  • Intra-subject variability of the BCGa Heart beat -0.2 0 0.2 0.4 0.6 seconds Time and spatial domain: Data quality of simultaneous EEG-fMRI 38
  • Inter-subject variability of the BCGa S1 S2 S3 S4 S5 S6 channels seconds Time and spatial domain: Data quality of simultaneous EEG-fMRI 39
  • The BCG artifact removal algorithms must take the inter- and intra-subject variability into account Group 1: methods based on Average Artifact Subtraction  Moving Average  Weighted Average  Selective Averaging based on clustering Group 2: methods based on Blind Source Separation  Optimal Basis Set (OBS)  Independent Component Analysis (ICA)  Canonical Correlation Analysis (CCA) Time and spatial domain: Data quality of simultaneous EEG-fMRI 40
  • Blind Source Separation identifies the sources generating the recorded signal s1 Blind Cerebral Source s2 sources Separation s3 (BSS) Non-cerebral x2 x3 X s4 sources x1 x4 constraints s1 s2 s3 s4 BSS as an artifact removal technique is twofold: • Step 1: Identification of sources • Step 2: Selection of sources (brain signal or artifact) Time and spatial domain: Data quality of simultaneous EEG-fMRI 41
  • We proposed a BSS method that uses Canonical Correlation Analysis to identify the sources Step 1: Identification of sources Step 2: Selection of artifact sources EEG epoch 1 EEG epoch 2 Criteria have to be met simultaneously: - correlation of sources common to two consecutive EEG epochs - Sources must not be sinusoidal EEG rhythms Sources or Canonical BSS-CCA variates time . . . . . . Time and spatial domain: Data quality of simultaneous EEG-fMRI 42
  • Case study (Assecondi et al., Physics in Medicine and Biology 2009) • Patients affected by epilepsy • Recording of 20 minutes of simultaneous EEG-fMRI • Artifact removal – AAS – CCA • Validation – Frequency content at the harmonics of the ECG – Global Field Power – Signal Envelope Time and spatial domain: Data quality of simultaneous EEG-fMRI 43
  • Comparison of cleaned and raw EEG BSS-CCA AAS RAW Time and spatial domain: Data quality of simultaneous EEG-fMRI 44
  • Comparison of cleaned and raw EEG BSS-CCA AAS RAW Time and spatial domain: Data quality of simultaneous EEG-fMRI 45
  • Conclusion The BCG artifact has an intrinsic inter- and intra-subject variability that make the performance of artifact removal algorithms subject-dependent We proposed a method to remove the BCG artifact that deals with the intra- and inter-subject variability The proposed method is an added value especially in those cases where the AAS fails, because of the excessive intra- subject variability of the artifact Time and spatial domain: Data quality of simultaneous EEG-fMRI 46
  • How we approach the problem of cerebral localization of brain functions Integration of EEG and fMRI Time domain Time and spatial domain Improving the temporal Data quality of simultaneous localization of brain functions EEG-fMRI recordings Quantitative and automated identification of ERP peaks Data quality of simultaneous ERP-fMRI recordings Data interpretation Effect of different recording environments on the process under investigation Overview 47
  • The methodologies of analysis of ERPs and EEG are intrinsically different s s s s s • Smaller amplitude • Involve additional averaging • Different signal-to-noise ratio The method to remove the BCG artifact needs modifications to select the non-cerebral sources Time and spatial domain: Data quality of simultaneous ERP-fMRI 48
  • The proposed method is adapted to ERPs Step 1: Identification of sources Step 2: Selection of artifact sources Average BCG EEG epoch Criteria have to be met simultaneously: - correlation of sources extracted from the average BCG and the EEG epoch - Sources that maximally contribute at the same time to the artifact and to the EEG Sources or Canonical BSS-CCA Sources important for the EEG but not variates for the artifact are not removed . . . . . . Time and spatial domain: Data quality of simultaneous ERP-fMRI 49
  • Case study (Assecondi et al., submitted to Clinical Neurophysiology) • Healthy volunteers • Three tasks: – Visual: Visual detection task (5 subjects) – Cognitive: Go-nogo task (5 subjects) – Motor: Motor task (7 subjects) • In two situations: – Outside the MR-scanner room (0T) – Inside the MR-scanner, without scanning (3T) • BCG artifact removal – AAS (Average Artifact Subtraction) – CCA (Canonical Correlation Analysis) Time and spatial domain: Data quality of simultaneous ERP-fMRI 50
  • Three different types of stimuli were used Detection GoNogo Motor Left visual field No Go Left keypress Central visual field Go Withhold Right visual field Right keypress Time and spatial domain: Data quality of simultaneous ERP-fMRI 51
  • In most cases both methods are able to recover ERP time series Detection task 0T 3T Time and spatial domain: Data quality of simultaneous ERP-fMRI 52
  • BSS-CCA seems more effective to recover small amplitude low frequency components Motor task AAS BSS-CCA 0T 3T Time and spatial domain: Data quality of simultaneous ERP-fMRI 53
  • How we approach the problem of cerebral localization of brain functions Integration of EEG and fMRI Time domain Time and spatial domain Improving the temporal Data quality of simultaneous localization of brain functions EEG-fMRI recordings Quantitative and automated identification of ERP peaks Data quality of simultaneous ERP-fMRI recordings Data interpretation Effect of different recording environments on the process under investigation Overview 54
  • Do the very different environments in which the data are recorded have an effect on the physiological process under investigation? ERP lab MR scanner position sitting laying Light dimmed dimmed Noise no yes Screen orientation In front mirrors Magnetic fields no yes Time and spatial domain: Effect of different environments 55
  • To disentangle effects of factors affecting EEG-fMRI data, different situations must be compared Effect of the static field +position Static 0T field No MR-related artifacts BCG artifact Time and spatial domain: Effect of different environments 56
  • Case study (Assecondi et al., submitted to Clinical Neurophysiology) • Healthy volunteers • Three tasks: – Visual: Visual detection task (5 subjects) – Cognitive: Go-nogo task (5 subjects) – Motor: Motor task (7 subjects) • In two situations: – Outside the MR-scanner room (0T) – Inside the MR-scanner, without scanning (3T) • BCG artifact removal – AAS (Average Artifact Subtraction) – CCA (Canonical Correlation Analysis) Time and spatial domain: Effect of different environments 57
  • We found differences between 0T, 3T and cleaned data • Amplitude decrease in 3T • Latency increase in 3T • Strong contamination of the data due to the artifact • Difference in the scalp distribution 0T 3T After BCG removal goP3 GoNogo task Ipsi N1 Detection task Time and spatial domain: Effect of different environments 58
  • Reaction time: rt@3T-rt@0T *** *** *** 50 *** *** *** *** ** * * ** * 0 - - ms - subject - ** *** -50 - p>0.05 * p<0.05 *** *** ** p<0.01 *** p<0.001 -100 Leuven -150 *** Detection Go-Nogo Motor Time and spatial domain: Effect of different environments 59
  • Conclusion The quality of average ERP is less sensitive to the method used to remove the BCG. However, BSS-CCA seems to be more effective with low frequency low amplitude components and when less trials are available Differences are found between different environment. More subjects are needed to obtain robust results Different situations must also be taken into account (dummy scanner, actual fMRI acquisition) Time and spatial domain: Effect of different environments 60
  • How we approach the problem of cerebral localization of brain functions Integration of EEG and fMRI Time domain Time and spatial domain Improving the temporal Data quality of simultaneous localization of brain functions EEG-fMRI recordings Quantitative and automated identification of ERP peaks Data quality of simultaneous ERP-fMRI recordings Data interpretation Effect of different recording environments on the process under investigation Overview 61
  • Overall considerations and future prospects • The localization of brain functions can be improved by taking into account a-priori knowledge of the process and the inter- and intra- subject variability • New multimodal approaches, e.g. EEG-fMRI, offer a deeper insight into how the brain works (great help by the availability of commercial MR-compatible EEG systems) Fundamental research Development of methods • to understand the relation between • to improve the integration of EEG and fMRI the two modalities and to • to comprehend the effect of the actually benefit by the different very different environments on EEG information extracted by data different modalities Overall considerations and future prospects 62
  • Automated Subject-Specific Peak Identification and Ballistocardiographic Artifact Correction in EEG-fMRI Sara Assecondi Dep. Electronics and Information Systems MEDISIP, Ghent University Promotors: Prof. Dr. Ir. S. Staelens, Prof. Dr. P. Boon Thank you!
  • Related publications • S. Assecondi, et al. Effect of the static magnetic field of the MR- scanner on ERPs: evaluation of visual, cognitive and motor potentials Submitted. • S. Assecondi, et al. Automated identification of ERP peaks through Dynamic Time Warping: an application to developmental dyslexia Clinical Neurophysiology, DOI: 10.1016/j.clinph.2009.06.023. • S. Assecondi, et al. Removal of the ballistocardiographic artifact from EEG-fMRI data: a canonical correlation approach Physics in Medicine and Biology. Vol. 54 (2). 2009. pp. 1673-1689 64