bioRxiv Science⌕ Search

Biology subjects

Haast, R. A. M.

Publications and source records attributed to Haast, R. A. M..

2 recordsLinked to original sources

Focal acetylcholinergic modulation of the human midcingulo-insular network during attention: Meta-analytic neuroimaging and behavioral evidence

The basal forebrain cholinergic neurons provide acetylcholine to the cortex via large projections. Recent molecular imaging work in humans indicates that the cortical cholinergic innervation is not uniformly distributed, but rather may disproportionately innervate cortical areas relevant to supervisory attention. In this study, we therefore reexamined the spatial relationship between acetylcholinergic modulation and attention in the human cortex using meta-analytic strategies targeting both pharmacological and nonpharmacological neuroimaging studies. We found that pharmaco-modulation of acetylcholine evoked both increased activity in the anterior cingulate and decreased activity in the opercular and insular cortex. In large independent meta-analyses of non-pharmacological neuroimaging research, we demonstrate that during attentional engagement these cortical areas exhibit (1) task-related co-activation with the basal forebrain, (2) task-related co-activation with one another, and (3) spatial overlap with dense cholinergic innervations originating from the BF, as estimated by multimodal PET and MR imaging. Finally, we provide meta-analytic evidence that pharmaco-modulation of acetylcholine also induces a speeding of responses to targets with no apparent tradeoff in accuracy. In sum, we demonstrate in humans that acetylcholinergic modulation of midcingulo-insular hubs of the ventral attention/salience network via basal forebrain afferents may coordinate selection of task relevant information, thereby facilitating cognition and behavior.

neuroscience↗

A scalable method to improve gray matter segmentation at ultra high field MRI

High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data.

neuroscience↗