Neuroanatomical and Functional Correlates of Cognitive and Affective Empathy in Young Healthy Adults

Neural substrates of empathy are mainly investigated through task-related functional MRI. However, the functional neural mechanisms at rest underlying the empathic response have been poorly studied. We aimed to investigate neuroanatomical and functional substrates of cognitive and affective empathy. The self-reported empathy questionnaire Cognitive and Affective Empathy Test (TECA), T1 and T2∗-weighted 3-Tesla MRI were obtained from 22 healthy young females (mean age: 19.6 ± 2.4) and 20 males (mean age: 22.5 ± 4.4). Groups of low and high empathy were established for each scale. FreeSurfer v6.0 was used to estimate cortical thickness and to automatically segment the subcortical structures. FSL v5.0.10 was used to compare resting-state connectivity differences between empathy groups in six defined regions: the orbitofrontal, cingulate, and insular cortices, and the amygdala, hippocampus, and thalamus using a non-parametric permutation approach. The high empathy group in the Perspective Taking subscale (cognitive empathy) had greater thickness in the left orbitofrontal and ventrolateral frontal cortices, bilateral anterior cingulate, superior frontal, and occipital regions. Within the affective empathy scales, subjects with high Empathic Distress had higher thalamic volumes than the low-empathy group. Regarding resting-state connectivity analyses, low-empathy individuals in the Empathic Happiness scale had increased connectivity between the orbitofrontal cortex and the anterior cingulate when compared with the high-empathy group. In conclusion, from a structural point of view, there is a clear dissociation between the brain correlates of affective and cognitive factors of empathy. Neocortical correlates were found for the cognitive empathy dimension, whereas affective empathy is related to lower volumes in subcortical structures. Functionally, affective empathy is linked to connectivity between the orbital and cingulate cortices

Impaired Structural Connectivity In Parkinson's Disease Patients With Mild Cognitive Impairment: A Study Based On Probabilistic Tractography

Background: Probabilistic tractography, in combination with graph theory, has been used to reconstruct the structural whole-brain connectome. Threshold-free network-based statistics (TFNBS) is a useful technique to study structural connectivity in neurodegenerative disorders; however, there are no previous studies using TFNBS in Parkinson's disease (PD) with and without mild cognitive impairment (MCI). Methods: Sixty-two PD patients, 27 of whom classified as PD-MCI, and 51 healthy controls (HC) underwent diffusion-weighted 3T MRI. Probabilistic tractography, using FSL, was used to compute the number of streamlines (NOS) between regions. NOS matrices were used to find group differences with TFNBS, and to calculate global and local measures of network integrity using graph theory. A binominal logistic regression was then used to assess the discrimination between PD with and without MCI using non-overlapping significant tracts. Tract-based spatial statistics (TBSS) were also performed with FSL to study changes in fractional anisotropy (FA) and mean diffusivity (MD). Results: PD-MCI showed 37 white matter (WM) connections with reduced connectivity strength compared to HC, mainly involving temporo-occipital regions. These were able to differentiate PD-MCI from PD without MCI with an area under the curve of 83-85%. PD without MCI showed disrupted connectivity in 18 connections involving fronto-temporal regions. No significant differences were found in graph measures. Only PD-MCI showed reduced FA compared with HC. Discussion: TFNBS based on whole-brain probabilistic tractography can detect structural connectivity alterations in PD with and without MCI. Reduced structural connectivity in fronto-striatal and posterior corticocortical connections is associated with PD-MCI

Proceedings of the 13th annual conference of INEBRIA

CITATION: Watson, R., et al. 2016. Proceedings of the 13th annual conference of INEBRIA. Addiction Science & Clinical Practice, 11:13, doi:10.1186/s13722-016-0062-9.The original publication is available at https://ascpjournal.biomedcentral.comENGLISH SUMMARY : Meeting abstracts.https://ascpjournal.biomedcentral.com/articles/10.1186/s13722-016-0062-9Publisher's versio

Threshold-free network-based statistics

<i>TFNBS algorithm. </i>Initially, the raw F statistics matrix <i>Mstat</i> (1) is thresholded at a series of steps <i>h</i> (2). The step interval <i>dh</i> was defined as a hundredth of the maximum value in <i>Mstat</i>. At each thresholding step, possible connected components are identified (3). The value of each matrix element belonging to a connected component is replaced by the component’s topological size (number of connections) raised to the power <i>E</i>, multiplied by the component’s height (equal to the current threshold) raised to the power <i>H</i> (3). The matrices obtained at each step are subsequently summed, giving the final TFNBS score for every network edge (4). Statistical significance is established through permutation testing (5). At each permutation, group membership is shuffled across subjects, and the steps above are repeated. Raw statistics are obtained from the whole connectivity matrix at each permutation, thus preserving topological dependencies among connections. Whole-connectome FWE-corrected p-values are obtained by comparing each connection’s TFNBS score with the null distribution of maximal connectome-wise scores at each permutation

Permutation testing for non-imaging data using FSL randomise

The <i>randomise_non_imaging</i> script is designed to take advantage of the functionalities of FSL randomise (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomise/) to perform GLM-based non-parametric permutation testing using non-imaging data. This can be done fairly easily with other programs, but using randomise could be convenient to FSL users, who are accustomed to creating the necessary input files. <div><br></div><div><div><u>How to make it work</u></div><div><br></div><div><b>System requirements:</b></div><div>This scripts requires FSL (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSL) as well as python (including the numpy (http://www.numpy.org/) and nipy (http://nipy.org/) packages), and is meant to be used in Linux systems. The necessary python packages can be easily obtained by installing Anaconda (https://www.continuum.io/downloads).<br></div></div><div><br></div><div><div><b>Add alias and route to .bashrc:</b></div><div>After unzipping <i>randomise_non_imaging.zip</i>, we recommend adding an alias to the user's .bashrc as an easy way to call the script from any terminal. The path to the folder containing the <i>parameter2nifti.py</i> script should also be specified as <i>route_NIR </i>in the .bashrc:</div><div><br></div><div><i>alias randomise_non_imaging='bash <b>/full/path/to/your/folder/</b>randomise_non_imaging.sh'<br></i></div><div><i>export route_NIR=<b>/full/path/to/your/folder/</b></i></div></div><div><br></div><div><b>Input files:</b></div><div><div>Three basic input files are required:</div><div>1. Dependent variable matrix: text file containing the variables to be tested, consisting of one column per variable and one row for each observation. This is equivalent to the image input in randomise – and will in fact be converted to image format so it can be fed into the program</div><div>2. Design matrix (<i>.mat</i>) </div><div>3. Contrast matrix (<i>.con</i>)</div><div><br></div><div><div>Some options require additional input files:</div><div>1. F tests: requires <i>.fts </i>files (with the same root name as the design and contrast files)</div><div>2. Block permutation: requires exchangeability block labels <i>.grp</i> file (with the same root name as the design and contrast files)</div></div><div><br></div><div><b>Output (text) files:</b><br></div><div><div>1. P value file: named <i>(output)_p_all_contrasts</i></div><div>2. Stats file: named <i>(output)_stat_all_contrasts</i></div><div>3. F test p value file: <i>(output)_p_F_test</i></div><div>4. F test stats file: <i>(output)_Fstat</i></div><div>5. Corrected p value file: <i>(output)_corrp_all_contrasts</i></div><div>6. Corrected F test p value file: <i>(output)_corrp_F_test</i></div></div><div><br></div><div><b>Usage instructions are given here:</b> <i>https://cjneurolab.org/2017/07/21/permutation-testing-for-non-imaging-data-using-fsl-randomise/</i><br></div></div

Rich club organization and cognitive performance in healthy older participants

The human brain is a complex network that has been noted to contain a group of densely interconnected hub regions. With a putative “rich club” of hubs hypothesized to play a central role in global integrative brain functioning, we assessed whether hub and rich club organizations are associated with cognitive performance in healthy participants and whether the rich club might be differentially involved in cognitive functions with a heavier dependence on global integration. A group of 30 relatively older participants (range = 39-79 years of age) underwent extensive neuropsychological testing, combined with diffusion-weighted magnetic resonance imaging to reconstruct individual structural brain networks. Rich club connectivity was found to be associated with general cognitive performance. More specifically, assessing the relationship between the rich club and performance in two specific cognitive domains, we found rich club connectivity to be differentially associated with attention/executive functions—known to rely on the integration of distributed brain areas—rather than with visuospatial/visuoperceptual functions, which have a more constrained neuroanatomical substrate. Our findings thus provide first empirical evidence of a relevant role played by the rich club in cognitive processes

Gray/White Matter Contrast in Parkinson’s Disease

Gray/white matter contrast (GWC) decreases with aging and has been found to be a useful MRI biomarker in Alzheimer’s disease (AD), but its utility in Parkinson’s disease (PD) patients has not been investigated. The aims of the study were to test whether GWC is sensitive to aging changes in PD patients, if PD patients differ from healthy controls (HCs) in GWC, and whether the use of GWC data would improve the sensitivity of cortical thickness analyses to differentiate PD patients from controls. Using T1-weighted structural images, we obtained individual cortical thickness and GWC values from a sample of 90 PD patients and 27 controls. Images were processed with the automated FreeSurfer stream. GWC was computed by dividing the white matter (WM) by the gray matter (GM) values and projecting the ratios onto a common surface. The sample characteristics were: 52 patients and 14 controls were males; mean age of 64.4 ± 10.6 years in PD and 64.7 ± 8.6 years in controls; 8.0 ± 5.6 years of disease evolution; 15.6 ± 9.8 UPDRS; and a range of 1.5–3 in Hoehn and Yahr (H&amp;Y) stage. In both PD and controls we observed significant correlations between GWC and age involving almost the entire cortex. When applying a stringent cluster-forming threshold of p &lt; 0.0001, the correlation between GWC and age also involved the entire cortex in the PD group; in the control group, the correlation was found in the parahippocampal gyrus and widespread frontal and parietal areas. The GWC of PD patients did not differ from controls’, whereas cortical thickness analyses showed thinning in temporal and parietal cortices in the PD group. Cortical thinning remained unchanged after adjusting for GWC. GWC is a very sensitive measure for detecting aging effects, but did not provide additional information over other parameters of atrophy in PD