Optimizing the Hachinski Ischemic Scale.

BACKGROUND: Vascular causes and factors remain the most significant preventable component of cognitive disorders of elderly individuals. The Hachinski Ischemic Score (HIS) is the questionnaire most commonly used for diagnosis of vascular dementia. OBJECTIVE: To consolidate and further validate the HIS. DESIGN: The Canadian Study for Health and Aging was used for this study. It was a cohort study conducted in 3 waves in 1991, 1996-1997, and 2001-2002. The HIS containing 13 items was subjected to correspondence analysis to identify its optimal scaling of item scores and minimal set of items while maximizing the explainable variance. SETTING: A community-based cohort study. PATIENTS: For this analysis, we used 2968 of 3054 well-characterized and well-diagnosed cases with complete HIS data (86 cases had ≥1 item missing) from Canadian Study for Health and Aging phases 2 (1996-1997; n = 2431) and 3 (2001-2002; n = 623). RESULTS: Two optimized HIS versions were identified that classify patients with vascular dementia vs those with nonvascular dementia as well as or more accurately than the original HIS instrument. Assuming the HIS instrument measures only a single dimension, correspondence analysis identified the 7 most discriminative HIS items. Binary scoring (0, 1) of these items led to a 7-item HIS model that classified as well as the original 13-item HIS instrument. By merging highly similar HIS items and applying correspondence analysis, a 5-item composite HIS model was created that measures 2 meaningful dimensions of information and classified vascular vs nonvascular dementia better than the original HIS instrument. Each HIS version developed has specific advantages and disadvantages in terms of simplicity, scoring, generalizability, and accuracy. CONCLUSION: Depending on the specific setting, 2 reduced HIS versions consisting of 5 composite-question items or 7 single-question items classify as well as or better than the original HIS instrument.

Functional computational model for optimal color coding.

This paper presents a computational model for color coding that provides a functional explanation of how humans perceive colors in a homogeneous color space. Beginning with known properties of human cone photoreceptors, the model estimates the locations of the reflectance spectra of Munsell color chips in perceptual color space as represented in the CIE L*a*b* color system. The fit between the two structures is within the limits of expected measurement error. Estimates of the structure of perceptual color space for color anomalous dichromats missing one of the normal cone photoreceptors correspond closely to results from the Farnsworth-Munsell color test. An unanticipated outcome of the model provides a functional explanation of why additive lights are always red, green, and blue and provide maximum gamut for color monitors and color television even though they do not correspond to human cone absorption spectra.

Relating reflectance spectra space to Munsell color appearance space.

The goal is to construct a simple model relating the conceptually defined Munsell color space to a physical representation of the relationship among the reflectance spectra obtained from the color chips comprising the Munsell color atlas. In the model both the Munsell conceptual system and the transformed reflectance spectra are shown to be well represented in Euclidean space, and the two spaces are related by a simple linear transformation. A practical implication is that the method allows one to compare the location of an empirical reflectance spectrum with the aiming point in the conceptual structure.

Transforming reflectance spectra into Munsell color space by using prime colors.

Independent researchers have proved mathematically that, given a set of color-matching functions, there exists a unique set of three monochromatic spectral lights that optimizes luminous efficiency and color gamut. These lights are called prime colors. We present a method for transforming reflectance spectra into Munsell color space by using hypothetical absorbance curves based on Gaussian approximations of the prime colors and a simplified version of opponent process theory. The derived color appearance system is represented as a 3D color system that is qualitatively similar to a conceptual representation of the Munsell color system. We illustrate the application of the model and compare it with existing models by using reflectance spectra obtained from 1,269 Munsell color samples.

Modeling lateral geniculate nucleus cell response spectra and Munsell reflectance spectra with cone sensitivity curves.

We find that the cell response spectra of lateral geniculate nucleus cells, as well as the reflectance spectra of Munsell color chips, may be modeled by using the cone sensitivity functions of the long and medium cones. We propose a simple model for how the neural signals from the photoreceptors might be combined in the retina to closely approximate the reflectance spectra of Munsell color chips without input from the short cone.

The distribution of response spectra in the lateral geniculate nucleus compared with reflectance spectra of Munsell color chips.

This paper compares the spectral response curves of cells in the lateral geniculate nucleus (LGN) with the reflectance spectra of a large sample of Munsell color chips. By examining the color chips with methods used by neural response researchers and the LGN cells with methods used by psychophysical color researchers, we obtain insights that may be useful for advancing knowledge in both fields. For LGN cells, the prevailing view is that they tend to be clustered into distinct types or along discernible lines or planes when data obtained from selected light stimuli are represented in a three-dimensional space derived from cone contributions. In contrast, the Munsell color chips are viewed as rather evenly distributed in a three-dimensional perceptual space based on the psychophysical judgment of surface colors. We demonstrate that, when the Munsell chips are viewed in the space typically applied to LGN cells, the distribution appears similar to that of the cells and vice versa. We show why this result occurs and suggest that it has implications for studies in both fields.

Methods to improve the detection of mild cognitive impairment.

We examined whether the performance of the National Institute of Aging's Consortium to Establish a Registry for Alzheimer's Disease's 10-word list (CWL), part of the consortium's neuropsychological battery, can be improved for detecting Alzheimer's disease and related disorders early. We focused on mild cognitive impairment (MCI) and mild dementia because these stages often go undetected, and their detection is important for treatment. Using standardized diagnostic criteria combined with history, physical examination, and cognitive, laboratory, and neuroimaging studies, we staged 471 community-dwelling subjects for dementia severity by using the Clinical Dementia Rating Scale. We then used correspondence analysis (CA) to derive a weighted score for each subject from their item responses over the three immediate- and one delayed-recall trials of the CWL. These CA-weighted scores were used with logistic regression to predict each subject's probability of impairment, and receiver operating characteristic analysis was used to measure accuracy. For MCI vs. normal, accuracy was 97% [confidence interval (C.I.) 97-98%], sensitivity was 94% (C.I. 93-95%), and specificity was 89% (C.I. 88-91%). For MCI/mild dementia vs. normal, accuracy was 98% (C.I. 98-99%), sensitivity was 96% (C.I. 95-97%), and specificity was 91% (C.I. 89-93%). MCI sensitivity was 12% higher (without lowering specificity) than that obtained with the delayed-recall total score (the standard method for CWL interpretation). Optimal positive and negative predictive values were 100% and at least 96.6%. These results show that CA-weighted scores can significantly improve early detection of Alzheimer's disease and related disorders.

A quantitative model for transforming reflectance spectra into the Munsell color space using cone sensitivity functions and opponent process weights.

This article presents a computational model of the process through which the human visual system transforms reflectance spectra into perceptions of color. Using physical reflectance spectra data and standard human cone sensitivity functions we describe the transformations necessary for predicting the location of colors in the Munsell color space. These transformations include quantitative estimates of the opponent process weights needed to transform cone activations into Munsell color space coordinates. Using these opponent process weights, the Munsell position of specific colors can be predicted from their physical spectra with a mean correlation of 0.989.

Estimating physical reflectance spectra from human color-matching experiments.

This article presents methods for estimating Munsell reflectance spectra, measured physically with a spectrophotometer, from psychophysically derived color-matching functions. The method is general and may also be used to estimate the reflectance spectra from human cone photoreceptor sensitivities. The color-matching functions and the cone sensitivities were found to contain almost identical information and may be considered to give equivalent estimates. The physical description of the Munsell color structure under D65 illumination was compared to the structure estimated from the human perceptual judgments. The results are virtually indistinguishable.

A model for the simultaneous analysis of reflectance spectra and basis factors of Munsell color samples under D65 illumination in three-dimensional Euclidean space.

In this paper we present the results of an analysis of the physically measured surface reflectance spectra of 360 matte Munsell chromatic color chips plus 10 flat achromatic vectors corresponding to Munsell value levels 10 (white) to 1 (near black) for a total sample size of 370. Each of the 370 spectra was multiplied by the spectral radiant power distribution of D65 light so that the final results represent the spectra of reflected light from Munsell color chips under D65 illumination. We simultaneously model the structure of the color chips and the spectra in a common three-dimensional Euclidean space, oriented to yield the most interpretable structure with respect of the Munsell color structure. In this orientation, axis 1 roughly corresponds to the mean power of the spectral reflectance (approximate Munsell value), axis 2 goes from Munsell red to blue-green, and axis 3 goes from Munsell green-yellow to purple. Basis factors for the spectra are also plotted against wavelength and Munsell hue. These plots have implications for theories of opponent processes. By plotting the chips and spectra in the same space we obtain virtually exact correspondences between the various Munsell hues and spectral values in nanometers for comparison to those obtained by previous researchers. Mathematical derivations are provided to validate the common Euclidean model.