Simulated phantoms used in algorithm validation, with theoretical fractal dimensions ranging between 1 and 3.

(a) Circle: radius = 8, image size: 120 x 120, line width is 1 pixel (theoretical FD = 1). (b) Fourth-iteration Koch, image size: 283 x 84, line width is 1 pixel (theoretical FD = 1.2619). (c) 3D random Cantor set with p = 0.7, image size: 128 x 128 x 128, Voxels set to 1 (theoretical FD = 2.485).</p

Differences in fractal dimension for left hippocampus, right hippocampus and left thalamus between schizophrenia patients and healthy controls.

<p>Each data point represents <i>D</i>₁ information dimension value for each participant for (a) Left hippocampus, (b) Right hippocampus, and (c) Left thalamus. The black dash-dot line and the magenta dash-dash line denote median fractal dimension values, for schizophrenia patients and healthy control groups, respectively. Significantly lower FD values were found for schizophrenia patients relative to healthy controls (Mann-Whitney <i>U</i> test, <i>p</i>< 0.05; FDR correction). <i>Note</i>. SCZ: patients with schizophrenia; HC: healthy controls.</p

Fractal dimension values for subcortical structures.

<p>Fractal dimension values for subcortical structures.</p

Computing fractal dimension using 3D information measure.

<p>Information dimension, <i>D</i>₁, measure. In the scatterplot of log(1/<i>r</i>) versus log(<i>I</i>(<i>r</i>)), <i>r</i> is box size and <i>I</i>(<i>r</i>) is the information theoretic entropy for the box size <i>r</i>. <i>Note</i>. For information measure, the initial, pre-determined range of box sizes is <i>r</i> = 2… 30 voxels (in increments of 1 voxel). Data shown are for left hippocampus from one healthy control participant. Linear regression analysis is performed iteratively. Blue line indicates the linear fit over the entire range of <i>r</i>. Red dotted line indicates the final fit (<i>R</i>²); the slope of this line corresponds to the fractal dimension, <i>D</i>₁. Breakpoint separates non-linear data points from the data used in the final regression analysis. ln denotes natural log. <i>Min r</i> is the new smallest box size and <i>Max r</i> is the new largest box size.</p

Illustration of fractal self-similarity.

<p>(a) A Sierpinski triangle is an example of a pure fractal. A small portion of the triangle looks exactly like the whole triangle. (b) Self-similarity holds across a limited range of spatial scales for a natural object such as this Romanesco Broccoli (Photos courtesy of Live Earth Farm).</p

Fractal dimension values and box size range of phantoms.

<p>Fractal dimension values and box size range of phantoms.</p

Illustration of subtle surface non-linearities in schizophrenia as captured by the FD measure, using individual participants’ data for left and right hippocampus.

<p>(a) Representative healthy control, and (b) Patient with schizophrenia. Left panel shows left hippocampus, right panel shows right hippocampus. In (b), shadow highlight indicates data points used in the final fit. Subtle deviations from linearity are seen in (b), which shows data points whose <i>I</i>(r) counts deviate relative to the line of best fit. In the left panel, an example of this can be observed at a point with x and y coordinates [-1.946, 2.652] (fourth from the bottom of shaded area) and at [-1.386, 3.608] (third from the top), and in the right panel, at [-2.079, 2.445] (third from the bottom) (please see main text for detailed explanation). Insets show reconstructions of left and right hippocampi for these participants; 1 cube represents 1 voxel (1.5 x 1.5 x 1.5 mm).</p

Table_1_Disease-Specific Contribution of Pulvinar Dysfunction to Impaired Emotion Recognition in Schizophrenia.docx

One important aspect for managing social interactions is the ability to perceive and respond to facial expressions rapidly and accurately. This ability is highly dependent upon intact processing within both cortical and subcortical components of the early visual pathways. Social cognitive deficits, including face emotion recognition (FER) deficits, are characteristic of several neuropsychiatric disorders including schizophrenia (Sz) and autism spectrum disorders (ASD). Here, we investigated potential visual sensory contributions to FER deficits in Sz (n = 28, 8/20 female/male; age 21–54 years) and adult ASD (n = 20, 4/16 female/male; age 19–43 years) participants compared to neurotypical (n = 30, 8/22 female/male; age 19–54 years) controls using task-based fMRI during an implicit static/dynamic FER task. Compared to neurotypical controls, both Sz (d = 1.97) and ASD (d = 1.13) participants had significantly lower FER scores which interrelated with diminished activation of the superior temporal sulcus (STS). In Sz, STS deficits were predicted by reduced activation of early visual regions (d = 0.85, p = 0.002) and of the pulvinar nucleus of the thalamus (d = 0.44, p = 0.042), along with impaired cortico-pulvinar interaction. By contrast, ASD participants showed patterns of increased early visual cortical (d = 1.03, p = 0.001) and pulvinar (d = 0.71, p = 0.015) activation. Large effect-size structural and histological abnormalities of pulvinar have previously been documented in Sz. Moreover, we have recently demonstrated impaired pulvinar activation to simple visual stimuli in Sz. Here, we provide the first demonstration of a disease-specific contribution of impaired pulvinar activation to social cognitive impairment in Sz.</p