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Lehman, J. F.

Publications and source records attributed to Lehman, J. F..

2 recordsLinked to original sources

Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data

Anatomic tracing is the gold standard tool for delineating brain connections and for validating more recently developed imaging approaches such as diffusion MRI tractography. A key step in the analysis of data from tracer experiments is the careful, manual charting of fiber trajectories on histological sections. This is a very time-consuming process, which limits the amount of annotated tracer data that are available for validation studies. Thus, there is a need to accelerate this process by developing a method for computer-assisted segmentation. Such a method must be robust to the common artifacts in tracer data, including variations in the intensity of stained axons and background, as well as spatial distortions introduced by sectioning and mounting the tissue. The method should also achieve satisfactory performance using limited manually charted data for training. Here we propose the first deep-learning method, with a self-supervised loss function, for segmentation of fiber bundles on histological sections from macaque brains that have received tracer injections. We address the limited availability of manual labels with a semi-supervised training technique that takes advantage of unlabeled data to improve performance. We also introduce anatomic and across-section continuity constraints to improve accuracy. We show that our method can be trained on manually charted sections from a single case and segment unseen sections from different cases, with a true positive rate of ~0.80. We further demonstrate the utility of our method by quantifying the density of fiber bundles as they travel through different white-matter pathways. We show that fiber bundles originating in the same injection site have different levels of density when they travel through different pathways, a finding that can have implications for microstructure-informed tractography methods. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing.

neuroscience↗

Anatomical and functional connectivity support the existence of a salience network node within the caudal ventrolateral prefrontal cortex

Three large-scale brain networks are considered essential to cognitive flexibility: the ventral and dorsal attention (VAN and DAN) and salience (SN) networks. The ventrolateral prefrontal cortex (vlPFC) is a known component of the VAN and DAN, but there is an important gap in the current knowledge regarding its involvement in the SN. In this study, we used a translational and multimodal approach to fulfill this gap and demonstrate the existence of a SN node within the vlPFC. First, we used tract-tracing methods in non-human primates (NHP) to quantify the anatomic connectivity strength between the different vlPFC areas and the frontal and insular cortices. The strongest connections with the dorsal anterior cingulate cortex (dACC) and anterior insula (AI) locations comprising the two main cortical SN nodes were derived from the caudal area 47/12. This location also has strong axonal projections to subcortical structures of the salience network, including the dorsomedial thalamus, hypothalamus, sublenticular extended amygdala, and periaqueductal gray. Second, we used a seed-based functional connectivity analysis in NHP resting-state functional MRI (rsfMRI) data to validate the caudal area 47/12 as an SN node. Third, we used the same approach in human rsfMRI data to identify a homologous structure in caudal area 47/12, also showing strong connections with the SN cortical nodes, thus confirming the caudal area 47/12 as the SN node in the vlPFC. Taken together, the vlPFC contains nodes for all three cognitive networks, the VAN, DAN, and SN. Thus, the vlPFC is in a position to switch between these three cognitive networks, pointing to a key role as an attentional hub. Its tight additional connections to the orbitofrontal, dorsolateral, and ventral premotor cortices, places the vlPFC at the center for switching behaviors based on environmental stimuli, computing value and cognitive control.

neuroscience↗