scholarly journals Distinguishing Hemodynamics from Function in the Human LGN Using a Temporal Response Model

Vision ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 27 ◽  
Author(s):  
Kevin DeSimone ◽  
Keith A. Schneider

We developed a temporal population receptive field model to differentiate the neural and hemodynamic response functions (HRF) in the human lateral geniculate nucleus (LGN). The HRF in the human LGN is dominated by the richly vascularized hilum, a structure that serves as a point of entry for blood vessels entering the LGN and supplying the substrates of central vision. The location of the hilum along the ventral surface of the LGN and the resulting gradient in the amplitude of the HRF across the extent of the LGN have made it difficult to segment the human LGN into its more interesting magnocellular and parvocellular regions that represent two distinct visual processing streams. Here, we show that an intrinsic clustering of the LGN responses to a variety of visual inputs reveals the hilum, and further, that this clustering is dominated by the amplitude of the HRF. We introduced a temporal population receptive field model that includes separate sustained and transient temporal impulse response functions that vary on a much short timescale than the HRF. When we account for the HRF amplitude, we demonstrate that this temporal response model is able to functionally segregate the residual responses according to their temporal properties.

Author(s):  
Kevin DeSimone ◽  
Keith A. Schneider

We developed a temporal population receptive field model to differentiate the functional and hemodynamic responses in the human LGN. The hemodynamic response of the human LGN is dominated by the richly vascularized hilum, a structure that serves as a point of entry for blood vessels entering the LGN and supplying the substrates of central vision.  The location of the hilum along the ventral surface of the LGN and the resulting gradient in the amplitude of the hemodynamic response across the extent of the LGN has made it difficult to segment the human LGN into its more interesting magnocellular and parvocellular regions that represent two distinct visual processing streams.  Here, we show that an intrinsic clustering of the LGN responses to a variety of visual input reveals the hilum, and further that this clustering is dominated by the amplitude of the hemodynamic response. We introduce a temporal population receptive field model that includes both a sustained and transient temporal impulse response.  When we account for the hemodynamic amplitude, we demonstrate that this temporal response model is able to functionally segregate the residual responses according to their temporal properties.


1995 ◽  
Vol 22 (4) ◽  
pp. 413-416 ◽  
Author(s):  
Francesco N. Tubiello ◽  
Michael Oppenheimer

NeuroImage ◽  
2021 ◽  
Vol 244 ◽  
pp. 118554
Author(s):  
Eline R. Kupers ◽  
Akhil Edadan ◽  
Noah C. Benson ◽  
Wietske Zuiderbaan ◽  
Maartje C. de Jong ◽  
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2010 ◽  
Vol 09 (04) ◽  
pp. 387-394 ◽  
Author(s):  
YANG CHEN ◽  
YIWEN SUN ◽  
EMMA PICKWELL-MACPHERSON

In terahertz imaging, deconvolution is often performed to extract the impulse response function of the sample of interest. The inverse filtering process amplifies the noise and in this paper we investigate how we can suppress the noise without over-smoothing and losing useful information. We propose a robust deconvolution process utilizing stationary wavelet shrinkage theory which shows significant improvement over other popular methods such as double Gaussian filtering. We demonstrate the success of our approach on experimental data of water and isopropanol.


Author(s):  
Jan Prüser ◽  
Christoph Hanck

Abstract Vector autoregressions (VARs) are richly parameterized time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, in small samples the rich parametrization of VAR models may come at the cost of overfitting the data, possibly leading to imprecise inference for key quantities of interest such as impulse response functions (IRFs). Bayesian VARs (BVARs) can use prior information to shrink the model parameters, potentially avoiding such overfitting. We provide a simulation study to compare, in terms of the frequentist properties of the estimates of the IRFs, useful strategies to select the informativeness of the prior. The study reveals that prior information may help to obtain more precise estimates of impulse response functions than classical OLS-estimated VARs and more accurate coverage rates of error bands in small samples. Strategies based on selecting the prior hyperparameters of the BVAR building on empirical or hierarchical modeling perform particularly well.


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