The intracortical myelin content of impulsive choices: results from T1- and T2-weighted MRI myelin mapping
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Abstract Delay discounting (DD) refers to a phenomenon that humans tend to choose small-sooner over large-later rewards during intertemporal choices. Steep discounting of delayed outcome is related to a variety of maladaptive behaviors and is considered as a transdiagnostic process across psychiatric disorders. Previous studies have investigated the association between brain structure (e.g. gray matter volume) and DD; however, it is unclear whether the intracortical myelin (ICM) influences DD. Here, based on a sample of 951 healthy young adults drawn from the Human Connectome Project, we examined the relationship between ICM, which was measured by the contrast of T1w and T2w images, and DD and further tested whether the identified associations were mediated by the regional homogeneity (ReHo) of brain spontaneous activity. Vertex-wise regression analyses revealed that steeper DD was significantly associated with lower ICM in the left temporoparietal junction (TPJ) and right middle-posterior cingulate cortex. Region-of-interest analysis revealed that the ReHo values in the left TPJ partially mediated the association of its myelin content with DD. Our findings provide the first evidence that cortical myelination is linked with individual differences in decision impulsivity and suggest that the myelin content affects cognitive performances partially through altered local brain synchrony.Keywords:
Temporoparietal junction
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Posterior cingulate
Abstract Genetic variants may confer risks for depression by modulating brain structure and function. Prior evidence has underscored a key role of the subgenual anterior cingulate cortex (sgACC) in depression. Here, we built on the literature and examined how the resting state functional connectivity (rsFC) of the sgACC was associated with polygenic risks for depression. We followed published routines and computed seed-based whole-brain sgACC rsFC and polygenic risk scores (PRS) of 717 young adults curated from the Human Connectome Project. We performed whole-brain regression against PRS and severity of depression symptoms in a single model for all subjects and for men and women alone, controlling for age, sex (for all), race, severity of alcohol use, and household income, and evaluated the results at a corrected threshold. We found lower sgACC rsFC with the default mode network and frontal regions in association with PRS and lower sgACC-cerebellar rsFC in association with depression severity. We also noted sex differences in the connectivity correlates of PRS and depression severity. In an additional set of analyses, we observed a significant correlation between PRS and somatic complaints score and altered sgACC-somatosensory cortical connectivity in link with the severity of somatic complaints. Our findings collectively highlighted the pivotal role of distinct sgACC-based networks in the genetic predisposition to depression and the clinical manifestation of depression. Distinguishing the risk from severity markers of depression may have implications in developing early and effective treatments for individuals at risk for depression.
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This database contains the connectivity matrices of the resting-state functional MRI scans that were collected in two databases of the Human Connectome Project, Young Adult and Aging. These matrices contain the functional connectivity between brain regions (here, several different brain atlases were used, leading to several different connectivity matrices for each subject). The connectivity matrices are symmetrical n x n matrices. Here, n indicates the number of regions present in the atlas, and any number ni,j in the matrix is generated by calculating a simple Pearson correlation coefficient between the functional time series that describe the functional activation of regions i and j throughout the resting-state functional scan. The matrices presented in this database are present as .pconn.nii files (which can be handled using software like wb_command) or as .txt file. A full explanation of the database and the brain atlases used here, as well as all the scripts used to generate these connectivity matrices can be found on the GitHub page of this project: floristijhuis/HCP-rfMRI-repository (github.com).
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FRONTIERS COMMENTARY article Front. Neurosci., 27 June 2016 Volume 10 - 2016 | https://doi.org/10.3389/fnins.2016.00302
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Human brain
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Functional magnetic resonance imaging (fMRI) is a non-invasive and in-vivo imaging technique essential for measuring brain activity. Functional connectivity is used to study associations between brain regions, either while study subjects perform tasks or during periods of rest. In this paper, we propose a rigorous definition of task-evoked functional connectivity at the population level (ptFC). Importantly, our proposed ptFC is interpretable in the context of task-fMRI studies. An algorithm for estimating the ptFC is provided. We present the performance of the proposed algorithm compared to existing functional connectivity frameworks using simulations. Lastly, we apply the proposed algorithm to estimate the ptFC in a motor-task study from the Human Connectome Project.
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The origin of the "resting-state" brain activity recorded with functional magnetic resonance imaging (fMRI) is still uncertain. Here we provide evidence for the neurovascular origins of the amplitude of the low-frequency fluctuations (ALFF) and the local functional connectivity density (lFCD) by comparing them with task-induced blood-oxygen level dependent (BOLD) responses, which are considered a proxy for neuronal activation. Using fMRI data for 2 different tasks (Relational and Social) collected by the Human Connectome Project in 426 healthy adults, we show that ALFF and lFCD have linear associations with the BOLD response. This association was significantly attenuated by a novel task signal regression (TSR) procedure, indicating that task performance enhances lFCD and ALFF in activated regions. We also show that lFCD predicts BOLD activation patterns, as was recently shown for other functional connectivity metrics, which corroborates that resting functional connectivity architecture impacts brain activation responses. Thus, our findings indicate a common source for BOLD responses, ALFF and lFCD, which is consistent with the neurovascular origin of local hemodynamic synchrony presumably reflecting coordinated fluctuations in neuronal activity. This study also supports the development of task-evoked functional connectivity density mapping.
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Abstract The search for an ‘ideal’ approach to investigate the functional connections in the human brain is an ongoing challenge for the neuroscience community. While resting-state functional magnetic resonance imaging (fMRI) has been widely used to study individual functional connectivity patterns, recent work has highlighted the benefits of collecting functional connectivity data while participants are exposed to naturalistic stimuli, such as watching a movie or listening to a story. For example, functional connectivity data collected during movie-watching were shown to predict cognitive and emotional scores more accurately than resting-state-derived functional connectivity. We have previously reported a tight link between resting-state functional connectivity and task-derived neural activity, such that the former successfully predicts the latter. In the current work we use data from the Human Connectome Project to demonstrate that naturalistic-stimulus-derived functional connectivity predicts task-induced brain activation maps more accurately than resting-state-derived functional connectivity. We then show that activation maps predicted using naturalistic stimuli are better predictors of individual intelligence scores than activation maps predicted using resting-state. We additionally examine the influence of naturalistic-stimulus type on prediction accuracy. Our findings emphasize the potential of naturalistic stimuli as a promising alternative to resting-state fMRI for connectome-based predictive modelling of individual brain activity and cognitive traits.
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Intraparietal sulcus
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