scholarly journals Cortical thickness of superior frontal cortex predicts impulsiveness and perceptual reasoning in adolescence

2012 ◽  
Vol 18 (5) ◽  
pp. 624-630 ◽  
Author(s):  
C Schilling ◽  
◽  
S Kühn ◽  
T Paus ◽  
A Romanowski ◽  
...  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meng Li ◽  
Jianhao Yan ◽  
Hua Wen ◽  
Jinzhi Lin ◽  
Lianbao Liang ◽  
...  

AbstractNeuroimaging studies have documented brain structural alterations induced by chronic pain, particularly in gray matter volume. However, the effects of trigeminal neuralgia (TN), a severe paroxysmal pain disorder, on cortical morphology are not yet known. In this study, we recruited 30 TN patients and 30 age-, and gender-matched healthy controls (HCs). Using Computational Anatomy Toolbox (CAT12), we calculated and compared group differences in cortical thickness, gyrification, and sulcal depth with two-sample t tests (p < 0.05, multiple comparison corrected). Relationships between altered cortical characteristics and pain intensity were investigated with correlation analysis. Compared to HCs, TN patients exhibited significantly decreased cortical thickness in the left inferior frontal, and left medial orbitofrontal cortex; decreased gyrification in the left superior frontal cortex; and decreased sulcal depth in the bilateral superior frontal (extending to anterior cingulate) cortex. In addition, we found significantly negative correlations between the mean cortical thickness in left medial orbitofrontal cortex and pain intensity, and between the mean gyrification in left superior frontal cortex and pain intensity. Chronic pain may be associated with abnormal cortical thickness, gyrification and sulcal depth in trigeminal neuralgia. These morphological changes might contribute to understand the underlying neurobiological mechanism of trigeminal neuralgia.


2020 ◽  
Author(s):  
Nevena Kraljević ◽  
H. Lina Schaare ◽  
Simon B. Eickhoff ◽  
Peter Kochunov ◽  
B.T.Thomas Yeo ◽  
...  

AbstractAffective experience and cognition are key human traits that are proposed to be inherently coupled in the human brain. Here we studied shared genetic basis of cognitive and affective traits in behavior and brain structure in the twin-based Human Connectome Project sample (n=1087). Both affective and cognitive trait scores were highly heritable and showed significant phenotypic correlation on the behavioral level. We further evaluated the correlation of affect and cognition, respectively, with local brain structure (cortical thickness, subcortical volumes, and surface area). Cortical thickness in the left superior frontal cortex showed a phenotypic association with both affect and cognition, which was driven by shared genetic effects. Quantitative functional decoding of this region yielded associations with cognitive and emotional functioning. This study provides a multi-level approach to study the association between affect and cognition and suggests a convergence of both in superior frontal anatomy.


2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S194-S194
Author(s):  
Emily Hedges ◽  
Jenny Zinser ◽  
Mihail Dimitrov ◽  
Mathilde Antoniades ◽  
Lilla Porffy ◽  
...  

Abstract Background High-resolution structural MRI has been widely used in clinical research to detect and quantify subtle brain changes in patient populations. Findings from prospective, longitudinal studies show structural brain abnormalities as well as progressive gray matter changes over time in individuals at clinical high risk for psychosis compared to healthy subjects. In recent years, research in this field has seen an increase in multicentre neuroimaging projects, such as EU-GEI, PSYSCAN, PRONIA and NAPLS. Additional sources of variance, alongside known technological and biological factors, may be introduced when MRI images are acquired and combined from different sites. It is imperative for longitudinal multicentre studies to determine the accuracy of quantitative MRI measurements and account for systematic differences both between scanners and across scanning sessions. This is particularly true within psychosis research where morphometric changes as small as 3% or less are expected. Methods Six healthy participants were scanned on four separate occasions over a two-month period at King’s College London; twice on a GE SIGNA HDx 3T scanner used locally in the EU-GEI High Risk Study and twice on a GE MR750 3T scanner used locally in the PSYSCAN study. Both scanners implemented the ADNI-2 T1 protocol which is used globally across the EU-GEI and PSYSCAN consortia. Structural imaging data was segmented using the FreeSurfer 6.0 longitudinal pipeline. Intraclass correlation coefficients (ICCs) with a two-way mixed effects model of absolute agreement were calculated to assess intra- and inter-scanner reliability of brain morphometry. For volumetric studies, ICC values greater than 0.9 indicate ‘excellent’ reliability. Reliability analyses of key regions implicated in psychosis included gray matter volume estimates of the hippocampus, insula, lateral ventricle, orbitofrontal cortex and anterior cingulate cortex, and average cortical thickness measurements of the whole brain, parahippocampus and superior frontal cortex. Results Gray matter volume estimates of all structures yielded ‘excellent’ reliability for both intra-scanner (ICCs of 0.979 – 0.998) and inter-scanner analyses (ICCs of 0.976 – 0.999). Intra-scanner reliability for mean cortical thickness measurements was ‘excellent’ for right total cortex, resulting in an ICC of 0.901, but otherwise ‘good’ for left and total cortex, parahippocampus, superior frontal cortex (ICCs of 0.754 – 0.875). Inter-scanner reliability for mean cortical thickness estimates were most variable across the brain structures. Here, results demonstrated ‘excellent’ reliability for the parahippocampus and left total cortex (ICCs of 0.907 – 0.965), ‘good’ for total cortex (ICC of 0.835), ‘moderate’ for right total cortex, right and total superior frontal cortex (ICCs of 0.520 – 0.676), and ‘poor’ for the left superior frontal cortex which produced an ICC of 0.470. Overall, mean cortical thickness estimates of the superior frontal cortex from two different MR scanners showed the least reliability. Discussion Results confirmed highly reliable estimates for gray matter volumes in all brain structures, both from images acquired within the same scanner and across two different scanners. However, the findings indicated increased variability of mean cortical thickness estimates, particularly between scanners, which should be considered when interpreting study findings. Multicentre structural neuroimaging within the field of psychosis is becoming more common and it must be acknowledged that combining MRI data in multicentre studies will contribute additional sources of variance and potential bias with certain brain regions affected more than others.


2008 ◽  
Vol 29 (2) ◽  
pp. 222-236 ◽  
Author(s):  
Alex Fornito ◽  
Stephen J. Wood ◽  
Sarah Whittle ◽  
Jack Fuller ◽  
Chris Adamson ◽  
...  

2020 ◽  
pp. 089198872096425
Author(s):  
Rakshathi Basavaraju ◽  
Xinyang Feng ◽  
Jeanelle France ◽  
Edward D. Huey ◽  
Frank A. Provenzano

Objectives: To understand the differential neuroanatomical substrates underlying apathy and depression in Frontotemporal dementia (FTD). Methods: T1-MRIs and clinical data of patients with behavioral and aphasic variants of FTD were obtained from an open database. Cortical thickness was derived, its association with apathy severity and difference between the depressed and not depressed were examined with appropriate covariates. Results: Apathy severity was significantly associated with cortical thinning of the lateral parts of the right sided frontal, temporal and parietal lobes. The right sided orbitofrontal, parsorbitalis and rostral anterior cingulate cortex were thicker in depressed compared to patients not depressed. Conclusions: Greater thickness of right sided ventromedial and inferior frontal cortex in depression compared to patients without depression suggests a possible requisite of gray matter in this particular area for the manifestation of depression in FTD. This study demonstrates a method for deriving neuroanatomical patterns across non-harmonized neuroimaging data in a neurodegenerative disease.


2016 ◽  
Vol 12 ◽  
pp. P742-P743
Author(s):  
Eric E. Abrahamson ◽  
Gillian I. Kruszka ◽  
Zhiping Mi ◽  
William R. Paljug ◽  
Manik L. Debnath ◽  
...  

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