44 research outputs found
Data_Sheet_1_Complexity changes in functional state dynamics suggest focal connectivity reductions.PDF
The past two decades have seen an explosion in the methods and directions of neuroscience research. Along with many others, complexity research has rapidly gained traction as both an independent research field and a valuable subdiscipline in computational neuroscience. In the past decade alone, several studies have suggested that psychiatric disorders affect the spatiotemporal complexity of both global and region-specific brain activity (Liu et al., 2013; Adhikari et al., 2017; Li et al., 2018). However, many of these studies have not accounted for the distributed nature of cognition in either the global or regional complexity estimates, which may lead to erroneous interpretations of both global and region-specific entropy estimates. To alleviate this concern, we propose a novel method for estimating complexity. This method relies upon projecting dynamic functional connectivity into a low-dimensional space which captures the distributed nature of brain activity. Dimension-specific entropy may be estimated within this space, which in turn allows for a rapid estimate of global signal complexity. Testing this method on a recently acquired obsessive-compulsive disorder dataset reveals substantial increases in the complexity of both global and dimension-specific activity versus healthy controls, suggesting that obsessive-compulsive patients may experience increased disorder in cognition. To probe the potential causes of this alteration, we estimate subject-level effective connectivity via a Hopf oscillator-based model dynamic model, the results of which suggest that obsessive-compulsive patients may experience abnormally high connectivity across a broad network in the cortex. These findings are broadly in line with results from previous studies, suggesting that this method is both robust and sensitive to group-level complexity alterations.</p
Clusters of significant gray matter volume increase in excess weight compared with normal weight subjects.
<p>Peak coordinates were located in the right hippocampus (x, y, <i>z</i>_ 38, −13, −18; t = 4.21; pFWE-SVC<0.05). <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049185#s4" target="_blank">Results</a> are overlaid on coronal and sagittal sections of a normalized brain, and the numbers correspond to the ‘y’ and ‘x’ coordinates in MNI space. Color bar represents t value. For demonstration purposes the images are displayed at p<0.001 (uncorrected, k>50).</p
Sociodemographic and biometric characteristics of study subjects.
a<p>The excess weight group is composed of participants originally classified as having overweight (n = 16) or obesity (n = 20) according to the International Obesity Task Force criteria;</p>b<p>SES: Socioeconomic status. Quintiles for SES are defined according to data from the Financial Survey for Spanish Families, <a href="http://www.bde.es/webbde/es/estadis/eff/eff.html" target="_blank">http://www.bde.es/webbde/es/estadis/eff/eff.html</a>;</p>c<p>BMI: Body mass index.</p
Between-group interaction between regional gray matter volume and reward sensitivity.
<p>A. Voxel-wise correlations between regional gray matter volume and reward sensitivity score specifically observed in normal weight subjects. Peak coordinate was located in the left secondary somatosensory cortex (SII, Brodmann area 43) (x, y, z = −60, −7, 11; t = 4.51; pFWE-SVC<0.05). <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049185#s4" target="_blank">Results</a> are overlaid on coronal (left) and axial (right) sections of a normalized brain, and the numbers correspond to the ‘y’ and ‘z’ coordinates in MNI space, respectively. Color bar represents t value. For demonstration purposes the images are displayed at p<0.001 (uncorrected, k>100). B. Plot of the correlation between gray matter volume at the peak coordinate and the reward sensitivity score. Normal weight group (filled circles, solid line) showed a significant correlation between these two measures (r = −0.750; p<0.005), while in the excess weight group the correlation was not significant (r = 0.284; p>0.05).</p
Between-group comparison of impulsivity and SPSRQ scores.
a<p>SPSRQ: Sensitivity to Punishment and Sensitivity to Reward Questionnaire.</p
Correlations of SPSRQ, impulsivity and inhibitory control scores with brain anatomy in normal weight subjects.
a<p>SII L, left secondary somatosensory cortex;</p>b<p>DLPFC L, left dorsolateral prefrontal cortex. Significant peaks are given in MNI coordinates. The corresponding anatomical names were obtained using the aal toolbox for SPM8.</p
Between-group interaction between regional gray matter volume and positive urgency.
<p>A. Voxel-wise correlations between regional gray matter volume and positive urgency (UPPS-P) score specifically observed in normal weight subjects. Peak coordinate was located in the left secondary somatosensory cortex (SII, Brodmann area 43) (x, y, z = −63, −7, 15; t = 4.89; pFWE-SVC<0.05). <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049185#s4" target="_blank">Results</a> are overlaid on coronal (left) and axial (right) sections of a normalized brain, and the numbers correspond to the ‘y’ and ‘z’ coordinates in MNI space, respectively. Color bar represents t value. For demonstration purposes the images are displayed at p<0.001 (uncorrected, k>100). B. Plot of the correlation between gray matter volume at the peak coordinate and the positive urgency score. Normal weight group (filled circles, solid line) showed a significant correlation between these two measures (r = −0.856; p<0.0005), while in the excess weight group the correlation was not significant (r = 0.058; p>0.05).</p
Correlation map between subjective pain scores and brain activations.
<p>(Adjusted for response duration -data-driven analysis- including all individuals). Display threshold, <i>P</i><0.01, 10 voxels. R indicates right hemisphere. The plot illustrates the correlation at peak activation in anterior cingulate cortex (ACC) (<i>r</i> = 0.82, <i>P</i><0.0001 and adjusted <i>r<sup>2</sup></i> = 0.66). A.u., arbitrary units. Red and blue dots correspond to patients and control subjects, respectively. The names of the regions are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005224#pone-0005224-t003" target="_blank">Table 3</a>.</p
Temporal dynamics of the brain response to painful stimulation.
<p>(A) shows time courses and representative brain slices for the somatosensory component in fibromyalgia patients (top) and healthy subjects (bottom) derived from activation temporal analysis. (B) shows the corresponding data for the insular component in patients (top) and healthy subjects (bottom). (C–F) show block-average time courses for the somatosensory component in patients (C) and healthy subjects (D), and the insular component in patients (E) and healthy subjects (F). Yellow bars identify stimulation scans. R indicates right hemisphere.</p
Brain Activations Adjusted for Response Duration (Data-Driven Analysis).
<p>Group activations show <i>P</i><0.05 False Discovery Rate (FDR) whole brain corrected. The contrast patients>controls shows <i>P</i><0.05 FDR corrected for the volume of activated regions (pain network). Coordinates (mm) are in the standard Talairach space. SII, second somatosensory cortex, SMA, supplementary motor area.</p