Multimodal characterization of the human nucleus accumbens.

Accepted

Journal Article

Dysregulation of the nucleus accumbens (NAc) is implicated in numerous neuropsychiatric disorders. Treatments targeting this area directly (e.g. deep brain stimulation) demonstrate variable efficacy, perhaps owing to non-specific targeting of a functionally heterogeneous nucleus. Here we provide support for this notion, first observing disparate behavioral effects in response to direct simulation of different locations within the NAc in a human patient. These observations motivate a segmentation of the NAc into subregions, which we produce from a diffusion-tractography based analysis of 245 young, unrelated healthy subjects. We further explore the mechanism of these stimulation-induced behavioral responses by identifying the most probable subset of axons activated using a patient-specific computational model. We validate our diffusion-based segmentation using evidence from several modalities, including MRI-based measures of function and microstructure, human post-mortem immunohistochemical staining, and cross-species comparison of cortical-NAc projections that are known to be conserved. Finally, we visualize the passage of individual axon bundles through one NAc subregion in a post-mortem human sample using CLARITY 3D histology corroborated by 7T tractography. Collectively, these findings extensively characterize human NAc subregions and provide insight into their structural and functional distinctions with implications for stereotactic treatments targeting this region.

Full Text

Duke Authors

Cited Authors

  • Cartmell, SC; Tian, Q; Thio, BJ; Leuze, C; Ye, L; Williams, NR; Yang, G; Ben-Dor, G; Deisseroth, K; Grill, WM; McNab, JA; Halpern, CH

Published Date

  • September 2019

Published In

Volume / Issue

  • 198 /

Start / End Page

  • 137 - 149

PubMed ID

  • 31077843

Pubmed Central ID

  • 31077843

Electronic International Standard Serial Number (EISSN)

  • 1095-9572

International Standard Serial Number (ISSN)

  • 1053-8119

Digital Object Identifier (DOI)

  • 10.1016/j.neuroimage.2019.05.019

Language

  • eng