Finding significantly connected voxels based on histograms of connection strengths
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Finding significantly connected voxels based on histograms of connection strengths. / Kasenburg, Niklas; Pedersen, Morten Vester; Darkner, Sune.
Medical Imaging 2016: Image Processing. ed. / Martin A. Styner; Elsa D. Angelini. SPIE - International Society for Optical Engineering, 2016. 978431 (Progress in Biomedical Optics and Imaging; No. 39, Vol. 17).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Finding significantly connected voxels based on histograms of connection strengths
AU - Kasenburg, Niklas
AU - Pedersen, Morten Vester
AU - Darkner, Sune
PY - 2016
Y1 - 2016
N2 - We explore a new approach for structural connectivity based segmentations of subcortical brain regions. Connectivity based segmentations are usually based on fibre connections from a seed region to predefined target regions. We present a method for finding significantly connected voxels based on the distribution of connection strengths. Paths from seed voxels to all voxels in a target region are obtained from a shortest-path tractography. For each seed voxel we approximate the distribution with a histogram of path scores. We hypothesise that the majority of estimated connections are false-positives and that their connection strength is distributed differently from true-positive connections. Therefore, an empirical null-distribution is defined for each target region as the average normalized histogram over all voxels in the seed region. Single histograms are then tested against the corresponding null-distribution and significance is determined using the false discovery rate (FDR). Segmentations are based on significantly connected voxels and their FDR. In this work we focus on the thalamus and the target regions were chosen by dividing the cortex into a prefrontal/temporal zone, motor zone, somatosensory zone and a parieto-occipital zone. The obtained segmentations consistently show a sparse number of significantly connected voxels that are located near the surface of the anterior thalamus over a population of 38 subjects. textcopyright (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
AB - We explore a new approach for structural connectivity based segmentations of subcortical brain regions. Connectivity based segmentations are usually based on fibre connections from a seed region to predefined target regions. We present a method for finding significantly connected voxels based on the distribution of connection strengths. Paths from seed voxels to all voxels in a target region are obtained from a shortest-path tractography. For each seed voxel we approximate the distribution with a histogram of path scores. We hypothesise that the majority of estimated connections are false-positives and that their connection strength is distributed differently from true-positive connections. Therefore, an empirical null-distribution is defined for each target region as the average normalized histogram over all voxels in the seed region. Single histograms are then tested against the corresponding null-distribution and significance is determined using the false discovery rate (FDR). Segmentations are based on significantly connected voxels and their FDR. In this work we focus on the thalamus and the target regions were chosen by dividing the cortex into a prefrontal/temporal zone, motor zone, somatosensory zone and a parieto-occipital zone. The obtained segmentations consistently show a sparse number of significantly connected voxels that are located near the surface of the anterior thalamus over a population of 38 subjects. textcopyright (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
U2 - 10.1117/12.2216184
DO - 10.1117/12.2216184
M3 - Article in proceedings
SN - 9781510600195
T3 - Progress in Biomedical Optics and Imaging
BT - Medical Imaging 2016
A2 - Styner, Martin A.
A2 - Angelini, Elsa D.
PB - SPIE - International Society for Optical Engineering
Y2 - 27 February 2016 through 3 March 2016
ER -
ID: 160636654