lacunarity analyses using the types of values discussed above have shown that data sets extracted from dense fractals, from patterns that change little when rotated, or from patterns that are homogeneous, have low lacunarity, but as these features increase, so generally does lacunarity. in some instances, it has been demonstrated that fractal dimensions and values of lacunarity were correlated, but more recent research has shown that this relationship does not hold for all types of patterns and measures of lacunarity. indeed, as mandelbrot originally proposed, lacunarity has been shown to be useful in discerning amongst patterns (e.g., fractals, textures, etc.) that share or have similar fractal dimensions in a variety of scientific fields including neuroscience.