Project 4 - aimsGOALAdvance the development of DOI to improve image accuracy, repeatability, CNR, and depth sensitivity.

AIM 1 InstrumentationContinuous-wave and Time-Domain

AIM 2 Signal Processing Algorithms to filter signal clutter

AIM 3 Experiments to quantify improvements

Parallel Efforts of Hardware development (year 1) and Signal Processing of physiological fluctuations (year 2)To come together in future years

instrument development - CWTechen Inc

Framework for Multimodal AnalysisNeurovascular physiology is modeled with a cascade of functionsLead fields model the biophysics of each imaging modalityThe functions evolve with neuroscience while the framework remains the same Key: u=observable inputs x=state variables w=process noise =model parameters z=physiological signals F=lead fields v=measurement noise y=measurements

Why fuse DOT and fMRI?Combine best of both SPECTRAL, TEMPORAL, and SPATIAL better quantification of physiology !

Improve statistical power in spatial map by improving rejection / filtering of systemic signals !

Caution: a bit of math

Caution: a bit of mathSimulations confirm improvement in quantitative accuracy in HbR !

Experimental Results

FusionImproved Quantitative HbRReconstructed [HbR]Subject ACortical ConstraintDOTBOLD

Windkessel Model Flow/Volume changes are driven by changes in arteriole resistanceAnalogous to a circuit model with oxygen diffusion

Windkessel Analysis of Human DataHbO2HbTHbRTime (sec)Molar0246810-202468x 10-6Percent ChangeASL - CBFTime (sec)BOLD x 100246810-0.0500.050.10.150.20.250.3Baseline CMRO25.2 ml O2 / 100 g / min

Framework for Multimodal AnalysisNeurovascular physiology is modeled with a cascade of functionsLead fields model the biophysics of each imaging modalityThe functions evolve with neuroscience while the framework remains the same Key: u=observable inputs x=state variables w=process noise =model parameters z=physiological signals F=lead fields v=measurement noise y=measurements

signal processing to filter physiological clutter from brain activation signalsour raw optical data show ~1Hz cardiac pulsation, ~0.1-0.3 Hz blood pressure and respiration fluctuations Need to filter this interference Interference is physiology of interest (autoregulation)

Baseline physiology with simulated functional response added

Recovering the hemodynamic response function

Cerebral autoregulation response to a step increase in blood pressure

Combine DOI and fMRIto get more complete understandingof physiology

Coupling of the hemoglobin response with the N1 somatosensory evoked potential component and uncoupling with the other SEP components

123401Hz3Hz5Hz7Hz1Hz3Hz5Hz7Hz51015Time (s)Time (s)0hemoglobin conc. changes (au)SEP (au)frequency

MTHbO2 changes (au)12345678sdurationamplitudefrequency0.5.7511.2512345678HzTime (s)HbO2 changes (au)Time (s)SP1N1P2HbO2

STSP1SN1SP2(a)(b)(c)Normalized SHbO2Normalized SSEPSHbO2

measuring brain development in healthy infants

frontal cortexCBV (ml/100g)age (wks)StO2 (%)HbT (mM)2040608012344050607001020304050P 0.02P 0.004P 0.001P 0.001P 0.005P 0.1P 0.00008P 0.000000001P 0. 8changes with age

CBV (ml/100g)StO2 (%)HbT (mM)2040608012344050607001020304050P 0.02P 0.004P 0.1effect of the transition from fetal to adult hemoglobinage (wks)

cerebral metabolic rate of oxygen (CMRO2)StO2 = aSaO2 + bSvO2 with a+b=1 (we assume b constant with age and SaO2=1)

frontal cortexCBV (ml/100g)age (wks)StO2 (%)HbT (mM)2040608012344050607001020304050P 0.02P 0.004P 0.1P 0.001P 0.001P 0.005changes in CMRO2

frontal cortexDCMRO2=0.91-6 wksDCMRO2=1.3, b=2DCMRO2=1.7, b=312-52 wkschanges in CMRO2CMRO2 constant & initial drop of StO2 due to the hematocrit dropelectrophysiological studies first two months synaptic activity very lowNMR studies in newborn very low rates of CMRO2 energy requirements possibly met by nonoxidative metabolism 40% increase in the sensorimotor cortex

The TeamOptical Imaging GroupAnna CustoSol DiamondMaria Angela FranceschiniTed HuppertDanny JosephIlkka NissilaJuliette Selb

MGHGiorgio BonmassarBruce FischlEllen GrantMatti HamalainenBruce RosenLarry Wald

USCDAnders Dale

Northeastern UniversityDana BrooksEric Miller

Texas A&MHeather BortfeldTeresa Wilcox

Univ College LondonSimon Arridge

Univ of Kuopio, FinlandJari KaipioVille Kolehmainen

A little bit of history of the field. I worked in Nirs for ~10 years. First in Illinois @ UIUC on the group of Enrico Gratton, ~1993-94 we developed the first prototype and ISS marketed the following generations of F-D oximeters.At MGH David Boas developed various CW instruments which are now assembled by Techen....

If anyone asks, the BPV input and HbT outputs were high pass filtered with a 0.05 Hz cutoff. The sampling rate is 1 Hz. I fit a 5th order ARX model to the data with one of Matlab's system id tools. The step response amplitudes were normalized so that the final value of HbT in the scalp was unity.HbT changes region to region and more marked changes with ageStO2 shows much less regional and temporal changes despite marked changes in HbTThis suggests that capillary development is keeping pace with demand such that StO2 remains relatively stableHbT changes region to region and more marked changes with ageStO2 shows much less regional and temporal changes despite marked changes in HbTThis suggests that capillary development is keeping pace with demand such that StO2 remains relatively stableHbT changes region to region and more marked changes with ageStO2 shows much less regional and temporal changes despite marked changes in HbTThis suggests that capillary development is keeping pace with demand such that StO2 remains relatively stableHbT changes region to region and more marked changes with ageStO2 shows much less regional and temporal changes despite marked changes in HbTThis suggests that capillary development is keeping pace with demand such that StO2 remains relatively stable