Quantification of Optical Images of Cortical Responses for Inferring Functional Maps

Abstract

Purushothaman G, Khaytin I, Casagrande VA. Quantification of optical images of cortical responses for inferring functional maps. J Neurophysiol 101: 2708 -2724, 2009. First published February 18, 2009 doi:10.1152/jn.90696.2008. Optical imaging of cortical signals enables the mapping of functional organization across large patches of cortex with good spatial resolution. But techniques for the quantitative analysis and interpretation of these images are limited. Frequently the functional architecture of the cortex is inferred from the visible topography of cortical reflectance images averaged or differenced across stimulus conditions and scaled or color-coded for presentation. Such qualitative assessments have sometimes led to divergent conclusions particularly about the organization of spatial and temporal frequency preferences in the primary visual cortex. We applied quantitative methods derived from signal detection theory to objectively interpret optical images. The differential response to any two arbitrary stimuli was represented at each pixel as the probability of discriminating between the two stimuli given the reflectance values at that pixel. These probability maps reduced false alarms and provided better signal-to-noise ratio in fewer trials than difference maps. We applied these methods to optical images of primate primary visual area (V1) obtained in response to sinusoidal gratings of different orientations and spatiotemporal frequencies. Clustering by orientation preference was stronger than that for spatial frequency, whereas clustering by temporal frequency preference was the weakest, largely in agreement with a previous electrophysiological study that quantified the degree of clustering of neurons for various response properties using uniform, quantitative criterion. We suggest that probability maps can extend the applicability of optical imaging to investigations of finer aspects of cortical functional organization through better signal-to-noise ratio and uniform, quantitative criteria for interpretation. I N T R O D U C T I O NSince the 1980s, optical imaging has revealed the functional organization of sensory cortical areas in rich detail (rev. Bonhoeffer and Grinvald 1996;Zepeda et al. 2004). Building on past successes, recent studies have used optical imaging to investigate finer and more complex aspects of cortical feature maps. This has led to an increased recognition that the low signal-to-noise ratio (SNR) of optical images can be a major limiting factor (Everson et al. 1998;Issa et al. 2000;Khaytin et al. 2008;Sirovich and Uglesich 2004). We applied methods based on signal-detection theory to analyze and interpret the weak signals captured by optical imaging. These methods offer an objective scale for representing processed optical-imaging data in a physiologically and behaviorally meaningful manner. These methods can also facilitate the objective interpretation of optical images by prescribing the statistical significance of the inferences drawn and have the potential to mitigate probl...

Pinwheel-dipole configuration in cat early visual cortex

“…In contrast, more recent techniques with single cell resolution (Ohki et al, 2006) only allow mapping a surface on the order of two to three PCs simultaneously (Nauhaus et al, 2013). Second, previous studies based on electrophysiology have concluded that preferred SF is only weakly clustered among neighboring neurons in the visual cortex of felines (DeAngelis et al, 1999) and bush babies (Purushothaman 2009). Our own electrophysiological data (see Fig.…”

Differential tuning of excitation and inhibition shapes direction selectivity in ferret visual cortex

“…Single-condition maps were computed by comparing whether reflectance changes evoked by a single-stimulus condition could be discriminated from reflectance changes evoked across all presented stimuli 42 . To discriminate a single-condition stimulus at each pixel, reflectance changes across all stimuli were combined into a normalized histogram, and then a pixel’s single condition response was computed non-parametrically as the probability of the area under a ROC curve (using the trapezoidal rule).…”

A Generalized ideal observer model for decoding sensory neural responses

“…For the population average choice probability of 0.55, at least 100 trials were required for the odd and even estimates to differ by less than 0.05 (i.e., 0.55–0.5). A different empirical approach was required to estimate the number of trials required to significantly reduce estimation errors in the ROC analysis of optically imaged intrinsic signals (Purushothaman et al, 2009). …”

“…Parker and Newsome, 1998). Ideal observer analysis can also be applied to optically imaged cortical signals to assess neural population sensitivity for detection or discrimination (Chen et al, 2006, 2008; Purushothaman et al, 2009; see also rev: Cohen et al, 2011). …”

“…Statistically valid methods of computing confidence intervals are known for some applications (e.g., Agarwal et al, 2005; Sarma et al, 2011) but this is not true in general. Therefore, heuristic or Monte-Carlo simulations are used to compute confidence intervals of ideal observer performance where necessary (e.g., Purushothaman et al, 2009). The main goal of this paper is to investigate the statistical properties and limitations of ideal observer models commonly used in the analyses of neurophysiological data.…”