Combating Concussions with "Virtual Recordings"

1. Combating Concussions with "Virtual Recordings" Don Krieger, Malcolm McNeil, Walter Schneider, David O. Okonkwo Catalin Trenchea, Bedda Rosario-Rivera

2. Support XSEDE: Extreme Computing Consortium Army Research Laboratory Pittsburgh Foundation Clinical and Translational Science Institute (CTSI)

3. Our clinical problem is to understand and treat concussion. • We use magnetoencephalography (MEG) during task performance to look for disordered brain activity. • We extract neuroelectric information from the MEG recordings using a novel nonlinear optimization method, referee consensus. • The OSG was and is absolutely vital to our effort. • Our results to date demonstrate the power and promise of this approach. Summary

4. Concussion alters thinking: processing speed, cognition, memory, attention. • Neuropsychological measures are sensitive to behavioral correlates of these changes. • MEG measures are sensitive to neuroelectric correlates of these changes. • Clinical objectives: • Identify sites and mechanisms of disordered neurophysiologic function. • Promote development of targeted therapies. Clinical Neuroscience

5. Continuously record the extracranial magnetic field while the volunteer performs a cognitive task … The Measurement

6. A detectable MEG source: electrical current flow within localized neural populations (105 – 108 cells). • The field strength measured at each sensor is a linear combination of fields due to … • an unknown number of sources • at unknown locations • Each source is defined by 3 location values (xyz coordinates) and 2 amplitude values. • The field strength at the sensors is linear in the amplitudes and nonlinear in the xyz coordinates. Neuroelectricity

7. Identify magnetic field sources one at a time, i.e. active neural populations one at a time. • Use single trial data. • Use short data epochs (40 msec). Neuroelectric Information from MEG

8. We use a novel nonlinear optimization method: referee consensus ... • The measurements must be linear in at least one variable for each source. • A sequence of measurements in time must be used. A single “snapshot” is not sufficient. • Referee consensusenables identification of one source at a time, regardless of the number and location of other active sources. • This independence … • enables solution of the general problem and • insures suitability to grid computing. Methodology

9. The Task • Activate many areas of the brain: • Language • Graphic • Decision. • Many trials • Many timing marks

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18. Two trials with event markers … - Normal time scale: about 4 seconds/trial - Stimuli (words and test figures): black arrows - Responses (button presses): magenta arrows

19. MEG Recordings, Each of 26 Volunteers … • 80 – 320 trials, i.e. 80 – 320 sentences • 240 – 960 test figures, i.e. 3 test figures/trial • 320 – 1280 data blocks, i.e. 1 presentation of the “place” word and 3 test figures • Each data block: 560 msec long • 27 data epochs (40 msec) for each data block • 8,640 – 34,560 data epochs for each volunteer IRB PRO09040294

20. Brain volume divided into 3000+ cubes (8 mm3). • 1 mvrXS instance for each 8 mm3 cube x 20 trials x 27 data epochs (45 – 120 min clock time). • For each data epoch: • Abridged referee consensus used to identify best of 35 fixed locations for startup. • Gradient search on 1 mm grid from best startup; limited to 6 steps. • Total work requirement is 1/40th of that required for an exhaustive search on the 1 mm grid. The search would be improved by step scaling, e.g. Marquardt-Levenberg. Search for MEG Sources,Virtual Recordings

21. OSG Usage: 3.2 million hours/8 weeks • tcsh scripts: job spawning, control, and monitoring, housekeeping, data aggregation and transport. • mvrXS executable: written in Fortran 77 and compiled using gfortran, static linked. • 230 Mbytes core per mvrXS instance • 10-15 Mbytes file transport per mvrXS instance

22. Acceptance threshold referee consensus: p < 10-12. • More than 500 sources found for each 40 msec data epoch. • 8,640 – 34,560 data epochs for each volunteer. • More than 4,000,000 sources found for each volunteer. This represents 1000+ times the information extracted from brain recordings by any other method. • For each source: • Spatial location within the brain. • Time course of the amplitude of the electric current Referee Consensus Yield

23. Resolving power: 1-2 mm Correlation between pairs of 40 msec waveforms for sources 1 to 100 mm apart. Single trial data. This is better than what is achievable with electrocorticography, i.e. direct surface recordings.

24. Is task specific information preserved in the virtual recordings? ANOVA … 9 Dependent variables: Total power in 9 bands: 0, 25, 50, 100, 125, 150, 175, 200 Hz. 5 Brain volumes, each 24 mm thick: Each result (dot): • p < 0.000001 • Area scaled to F statistic • df = 13,100000

25. Is task specific information preserved in the virtual recordings? ANOVA … Typical results: Last word in the sentence Linguistic

26. Is task specific information preserved in the virtual recordings? ANOVA … Typical results: 1st test figure Graphic/spatial Localization change

27. Is task specific information preserved in the virtual recordings? ANOVA … Typical results: 2nd test figure Graphic/spatial Frequency band change

28. Is task specific information preserved in the virtual recordings? ANOVA … Typical results: 3rd test figure Graphic/spatial Localization change Frequency band change

29. Activation of the Retina and Optic Nerve These sources are from a single subject from 120 single trial time segments. Each spanned 40 msec beginning 30 msec after visual stimulation. Note the apparently tonic activation of the cerebellum.

30. Referee consensus enables extraction of neurophysiologic information from single trial MEG with 1 mm resolving power. • Task specific information is retained and is detected with very high confidence from the data for each individual. This is the hallmark of clinical utility. • Referee consensus is applicable to an important class of problems which includes the MEG problem, e.g. the functional connectome, diffused images, active and passive SONAR. • The MEG problem is formalized over the real numbers. Preliminary analysis suggests that the method will work for systems formalized over the complex numbers, the quaternion, and possibly the octonions. • This and future work relies entirely on grid and grid-like supercomputing resources. Concluding Remarks