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{1489}  
{1214}  
PMID7972766 Brain and cerebrospinal fluid motion: realtime quantification with Mmode MR imaging.
 
{1082}  
Just fer kicks, I tested what happens to loworder butterworth filters when you maladjust one of the feedback coefficients. [B, A] = butter(2, 0.1); [h, w] = freqz(B,A); A(2) = A(2) * 0.9; [h2, ~] = freqz(B,A); hold off subplot(1,2,1) plot(w,abs(h)) hold on; plot(w,abs(h2), 'r') title('10% change in one FB filter coef 2nd order butterworth') xlabel('freq, rads / sec'); ylabel('filter response'); % do the same for a higher order filter. [B, A] = butter(3, 0.1); [h, w] = freqz(B,A); A(2) = A(2) * 0.9; [h2, ~] = freqz(B,A); subplot(1,2,2) hold on plot(w,abs(h), 'b') plot(w,abs(h2), 'r') title('10% change in one FB filter coef 3rd order butterworth') xlabel('freq, rads / sec'); ylabel('filter response'); The filters show a resonant peak, even though feedback was reduced. Not surprising, really; a lot of systems will show reduced phase margin and will begin to oscillate when poles are moved. Does this mean that a given coefficient (anatomical area) is responsible for resonance? By itself, of course not; one can not extrapolate one effect from one manipulation in a feedback system, especially a higherorder feedback system. This, of course hold in the mapping of digital (or analog) filters to pathology or anatomy. Pathology is likely reflective of how the loop is structured, not how one element functions (well, maybe). For a paper, see {1083}  
{814}  
PMID19199762[0] Optical Detection of Brain Cell Activity Using Plasmonic Gold Nanoparticles
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