Journal: Human brain mapping

Article Title: An investigation of positive and inverted hemodynamic response functions across multiple visual areas

doi: 10.1002/hbm.22569

Figure Lengend Snippet: (A1, B1) Group average positive and inverted HRF estimates across visual areas. Solid lines: sum of two gammas model fits, error bars: +/− SEM, dashed lines: “Cox special” canonical model (arbitrarily scaled to half the maximum response measured). (A2, B2) Dendrograms of HRF Mahalanobis distances among visual areas. (A3, B3) Pair-wise HRF Mahalanobis distance matrices. Color bar scaled to the maximum Mahalanobis distance between areas, which for positive HRFs was between VO and V3AB and for inverted HRFs was between V4 and V3AB.

Article Snippet: The group average positive and inverted HRFs for each visual area ROI were fit by a sum of two gamma functions model using a constrained nonlinear optimization (a bounded version of MATLAB’s fminsearch), that is, each HRF was modeled by: y ( t ) = g a m 1 ( t ) + g a m 2 ( t ) + C where, gam 1 ( t ) = { A 1 ( x ( t ) − ∂ 1 τ 1 ) 2 e − ( x ( t ) − δ 1 τ 1 ) 2 τ 1 , if x ( t ) ≥ δ 1 0 , if x ( t ) < δ 1 gam 2 ( t ) = { A 2 ( x ( t ) − ∂ 2 τ 1 ) 2 e − ( x ( t ) − δ 2 τ 2 ) 2 τ 2 , if x ( t ) ≥ δ 2 0 , if x ( t ) < δ 2 Using the parameters estimated by this optimization the time-to-onset ( δ 1 ), time-to-peak, peak amplitude, and peak full-width-at-half-maximum (FWHM) for each ROI’s group average HRF was determined.

Techniques:

Journal: Human brain mapping

Article Title: An investigation of positive and inverted hemodynamic response functions across multiple visual areas

doi: 10.1002/hbm.22569

Figure Lengend Snippet: Group average HRF characteristics across visual area ROIs for both positive and inverted HRFs

Article Snippet: The group average positive and inverted HRFs for each visual area ROI were fit by a sum of two gamma functions model using a constrained nonlinear optimization (a bounded version of MATLAB’s fminsearch), that is, each HRF was modeled by: y ( t ) = g a m 1 ( t ) + g a m 2 ( t ) + C where, gam 1 ( t ) = { A 1 ( x ( t ) − ∂ 1 τ 1 ) 2 e − ( x ( t ) − δ 1 τ 1 ) 2 τ 1 , if x ( t ) ≥ δ 1 0 , if x ( t ) < δ 1 gam 2 ( t ) = { A 2 ( x ( t ) − ∂ 2 τ 1 ) 2 e − ( x ( t ) − δ 2 τ 2 ) 2 τ 2 , if x ( t ) ≥ δ 2 0 , if x ( t ) < δ 2 Using the parameters estimated by this optimization the time-to-onset ( δ 1 ), time-to-peak, peak amplitude, and peak full-width-at-half-maximum (FWHM) for each ROI’s group average HRF was determined.

Techniques:

Journal: Human brain mapping

Article Title: An investigation of positive and inverted hemodynamic response functions across multiple visual areas

doi: 10.1002/hbm.22569

Figure Lengend Snippet: Proportion of voxels characterized by an inverted HRF

Article Snippet: The group average positive and inverted HRFs for each visual area ROI were fit by a sum of two gamma functions model using a constrained nonlinear optimization (a bounded version of MATLAB’s fminsearch), that is, each HRF was modeled by: y ( t ) = g a m 1 ( t ) + g a m 2 ( t ) + C where, gam 1 ( t ) = { A 1 ( x ( t ) − ∂ 1 τ 1 ) 2 e − ( x ( t ) − δ 1 τ 1 ) 2 τ 1 , if x ( t ) ≥ δ 1 0 , if x ( t ) < δ 1 gam 2 ( t ) = { A 2 ( x ( t ) − ∂ 2 τ 1 ) 2 e − ( x ( t ) − δ 2 τ 2 ) 2 τ 2 , if x ( t ) ≥ δ 2 0 , if x ( t ) < δ 2 Using the parameters estimated by this optimization the time-to-onset ( δ 1 ), time-to-peak, peak amplitude, and peak full-width-at-half-maximum (FWHM) for each ROI’s group average HRF was determined.

Techniques:

Journal: Human brain mapping

Article Title: An investigation of positive and inverted hemodynamic response functions across multiple visual areas

doi: 10.1002/hbm.22569

Figure Lengend Snippet: Spatial distribution of HRFs. Left and right hemispheres are shown for a single subject. Above are cortical flat maps and below are medial, posterior views of an inflated surface. Inverted HRFs are shown in blue. Arrows (a-d) denote large clusters of inverted HRFs likely to be associated with the transverse sinus and/or communicating veins. Maps were FDR corrected and thresholded with q < 0.01 using the correlation data. Visual area identification as in Figure 2.

Article Snippet: The group average positive and inverted HRFs for each visual area ROI were fit by a sum of two gamma functions model using a constrained nonlinear optimization (a bounded version of MATLAB’s fminsearch), that is, each HRF was modeled by: y ( t ) = g a m 1 ( t ) + g a m 2 ( t ) + C where, gam 1 ( t ) = { A 1 ( x ( t ) − ∂ 1 τ 1 ) 2 e − ( x ( t ) − δ 1 τ 1 ) 2 τ 1 , if x ( t ) ≥ δ 1 0 , if x ( t ) < δ 1 gam 2 ( t ) = { A 2 ( x ( t ) − ∂ 2 τ 1 ) 2 e − ( x ( t ) − δ 2 τ 2 ) 2 τ 2 , if x ( t ) ≥ δ 2 0 , if x ( t ) < δ 2 Using the parameters estimated by this optimization the time-to-onset ( δ 1 ), time-to-peak, peak amplitude, and peak full-width-at-half-maximum (FWHM) for each ROI’s group average HRF was determined.

Techniques:

Journal: Human brain mapping

Article Title: An investigation of positive and inverted hemodynamic response functions across multiple visual areas

doi: 10.1002/hbm.22569

Figure Lengend Snippet: Cortical parametric maps for HRF, eccentricity, and polar angle mapping data for primary visual cortex, V1. The HRF maps are colored by correlation and the retinotopic maps are colored by position in the visual field (phase delay). (A) Unsmoothed data. (B) Smoothed data (3.5 mm spherical kernel). (C) “Inversion corrected” data. Arrows a, b, c, and d point to regions exhibiting inverted HRFs. Arrow e points to a region that is likely characterized by an inverted HRF, but its response to the HRF stimulus does not reach statistical significance. All maps were FDR corrected and thresholded with q < 0.01 using the correlation data.

Article Snippet: The group average positive and inverted HRFs for each visual area ROI were fit by a sum of two gamma functions model using a constrained nonlinear optimization (a bounded version of MATLAB’s fminsearch), that is, each HRF was modeled by: y ( t ) = g a m 1 ( t ) + g a m 2 ( t ) + C where, gam 1 ( t ) = { A 1 ( x ( t ) − ∂ 1 τ 1 ) 2 e − ( x ( t ) − δ 1 τ 1 ) 2 τ 1 , if x ( t ) ≥ δ 1 0 , if x ( t ) < δ 1 gam 2 ( t ) = { A 2 ( x ( t ) − ∂ 2 τ 1 ) 2 e − ( x ( t ) − δ 2 τ 2 ) 2 τ 2 , if x ( t ) ≥ δ 2 0 , if x ( t ) < δ 2 Using the parameters estimated by this optimization the time-to-onset ( δ 1 ), time-to-peak, peak amplitude, and peak full-width-at-half-maximum (FWHM) for each ROI’s group average HRF was determined.

Techniques:

Journal: Human brain mapping

Article Title: An investigation of positive and inverted hemodynamic response functions across multiple visual areas

doi: 10.1002/hbm.22569

Figure Lengend Snippet: Conceptual relationships among positive and inverted HRFs, neural activity, and the BOLD response. (A) Positive HRF. (B) Inverted HRF. Note: both HRFs were taken from the sum of two gammas model fit for visual area V2.

Article Snippet: The group average positive and inverted HRFs for each visual area ROI were fit by a sum of two gamma functions model using a constrained nonlinear optimization (a bounded version of MATLAB’s fminsearch), that is, each HRF was modeled by: y ( t ) = g a m 1 ( t ) + g a m 2 ( t ) + C where, gam 1 ( t ) = { A 1 ( x ( t ) − ∂ 1 τ 1 ) 2 e − ( x ( t ) − δ 1 τ 1 ) 2 τ 1 , if x ( t ) ≥ δ 1 0 , if x ( t ) < δ 1 gam 2 ( t ) = { A 2 ( x ( t ) − ∂ 2 τ 1 ) 2 e − ( x ( t ) − δ 2 τ 2 ) 2 τ 2 , if x ( t ) ≥ δ 2 0 , if x ( t ) < δ 2 Using the parameters estimated by this optimization the time-to-onset ( δ 1 ), time-to-peak, peak amplitude, and peak full-width-at-half-maximum (FWHM) for each ROI’s group average HRF was determined.

Techniques: Activity Assay

Journal: Human brain mapping

Article Title: An investigation of positive and inverted hemodynamic response functions across multiple visual areas

doi: 10.1002/hbm.22569

Figure Lengend Snippet: An example of the effect of inverted HRFs on fMRI analysis. (A) Empirical fMRI time-courses from polar angle mapping in voxels with (A1) positive and (A2) inverted HRFs. (B,C) Simulation illustrating error in temporal phase mapping analysis caused by an inverted HRF. Ideal BOLD responses were predicted by convolving HRFs (B – positive, C – inverted) with identical stimulus timing waveforms. Stimulus timing was for a quarter-field checkerboard wedge rotating about the center of gaze 5 times. Apparent phase delay is shifted by edelay.

Article Snippet: The group average positive and inverted HRFs for each visual area ROI were fit by a sum of two gamma functions model using a constrained nonlinear optimization (a bounded version of MATLAB’s fminsearch), that is, each HRF was modeled by: y ( t ) = g a m 1 ( t ) + g a m 2 ( t ) + C where, gam 1 ( t ) = { A 1 ( x ( t ) − ∂ 1 τ 1 ) 2 e − ( x ( t ) − δ 1 τ 1 ) 2 τ 1 , if x ( t ) ≥ δ 1 0 , if x ( t ) < δ 1 gam 2 ( t ) = { A 2 ( x ( t ) − ∂ 2 τ 1 ) 2 e − ( x ( t ) − δ 2 τ 2 ) 2 τ 2 , if x ( t ) ≥ δ 2 0 , if x ( t ) < δ 2 Using the parameters estimated by this optimization the time-to-onset ( δ 1 ), time-to-peak, peak amplitude, and peak full-width-at-half-maximum (FWHM) for each ROI’s group average HRF was determined.

Techniques: