Replicable brain signatures of emotional bias and memory based on diffusion kurtosis imaging of white matter tracts

Abstract

Diffusion MRI (dMRI) is sensitive to anisotropic diffusion within bundles of nerve axons and can be used to make objective measurements of brain networks. Many brain disorders are now recognised as being caused by network dysfunction or are secondarily associated with changes in networks. There is therefore great potential in using dMRI measures that reflect network integrity as a future clinical tool to help manage these conditions. Here, we used dMRI to identify replicable, robust and objective markers that meaningfully reflect cognitive and emotional performance. Using diffusion kurtosis analysis and a battery of cognitive and emotional tests, we demonstrated strong relationships between white matter structure across networks of anatomically and functionally specific brain regions with both emotional bias and emotional memory performance in a large healthy cohort. When the connectivity of these regions was examined using diffusion tractography, the terminations of the identified tracts overlapped precisely with cortical loci relating to these domains, drawn from an independent spatial meta‐analysis of available functional neuroimaging literature. The association with emotional bias was then replicated using an independently acquired healthy cohort drawn from the Human Connectome Project. These results demonstrate that, even in healthy individuals, white matter dMRI structural features underpin important cognitive and emotional functions. Our robust cross‐correlation and replication supports the potential of structural brain biomarkers from diffusion kurtosis MRI to characterise early neurological changes and risk in individuals with a reduced threshold for cognitive dysfunction, with further testing required to demonstrate clinical utility.

Aberrant levels of cortical myelin distinguish individuals with depressive disorders from healthy controls

“…More recently, studies using very large samples (i.e., the UK Biobank), as well as meta-analyses combining both published and unpublished data (i.e., the ENIGMA consortium), have observed widespread and replicable reductions in FA ( Shen et al, 2017 , van Velzen et al, 2020 ). Notably, white matter integrity in identified regions has been shown to correlate with the cognitive processes disrupted in depression, including processing speed ( Chopra et al, 2018 , Penke et al, 2010 ), emotion regulation ( Eden et al, 2015 , Welton et al, 2020 ), and reward learning ( de Boer et al, 2020 ).…”

Advances in Neuroimaging and Monitoring to Defend Cerebral Perfusion in Noncardiac Surgery

“…7). 77 An assessment of cerebral tissue with diffusion kurtosis imaging revealed that emotional dysregulation was associated with increased diffusivity and reduced cellular complexity in specific regions of the temporal and parietal lobes, as well as the adjoining white matter tracts. Emotional dysregulation is a factor contributing to morbidity in postoperative cognitive dysfunction, 120 and using diffusion kurtosis imaging in the context of perioperative medicine will likely provide novel insights into the structural basis of this disease.…”

“…75,76 Further information regarding the composition of tissue can be acquired using diffusion kurtosis imaging. This technique involves the acquisition of images at multiple magnetic field strengths to determine how diffusion of water in a given region of the brain differs from a normal Gaussian distribution 36,77 (fig. 7).…”

Aberrant levels of cortical myelin distinguish individuals with unipolar depression from healthy controls

“…More recently, studies using very large samples (i.e., the UK Biobank), as well as meta-analyses combining both published and unpublished data (i.e., the ENIGMA consortium), have observed widespread and replicable reductions in FA 15,16 . Notably, white matter integrity in identified regions has been shown to correlate with the cognitive processes disrupted in depression, including processing speed 20,21 , emotion regulation 22,23 , and reward learning 24 .…”