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SCIENTIFIC PROGRAMS AND ACTIVITIES |
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| April 19, 2024 |
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Lakshminarayan V.
Chinta METHODS: Experiments were conducted on male adult Sprague–Dawley rats (140±45g). The animals were anesthetized with isoflurane during surgical preparation and alpha-choralose during imaging. The right femoral artery, femoral vein, and tail vein were cannulated for blood gas analysis, intravenous administration of anesthesia, and fluorescent agents respectively. Stereotaxic surgery was performed to prepare a small cranial window (4mm x 3mm) over the forelimb representation in the primary somatosensory cortex. Two-photon microscopy imaging was performed following three 5 mg/kg bolus of Texas Red dextran (70 kDa). To measure the vascular transit time, the bolus passage was tracked by acquiring a time series of a single ~300 µm2 imaging plane, ~50 µm below the cortical surface, at 0.31 ± 0.07 fps and with a spatial resolution of 1.59 µm/pixel. To estimate vessel-wise volume, we obtained a stack of high-resolution images, 300 slices at 1µm lateral and 3µm axial resolution. ANALYSIS: The signal intensity curves from bolus passage experiments were integrated over time for all labeled vessels in the bolus plane. A second order plus dead time model [1] was used to estimate dead time (?), damping ratio (?), and natural frequency (?). Laplace domain transfer functions were next calculated and the corresponding impulse response was used to compute onset and peak time. Imaris (Bitplane Scientific Software) was used for semi-automatic segmentation of the 3D vascular network. Next, the 2D bolus passage time series was registered to the segmented 3D network and a standard method applied for estimating the transit time between transected vessels [2]. We identified closed paths between any two vessels of the bolus tracking plane by tracing through the 3D network. For the multiple connecting paths, we devised a methodology to estimate the transit times of the individual segments. We solved for the unknown transit times based on equations of transit times and CBF, where CBF at each segment was calculated from the central volume principle as CBF=CBV/TT. Perfusion was then computed by normalizing CBF by the volume of the irrigated tissue. Based on the 2PFM literature [3], the diffusion distance for oxygen was assumed to be ~55-70 µm in the rat’s somatosensory cortex and the tissue volume irrigated was thus estimated as a convolution of ~65 µm sphere and our vascular subtree centrelines. RESULTS AND CONCLUSION: The application of our methodology to a sample subject shows a skewed distribution of perfusion in the vascular paths: most (~65%) of the paths exhibit perfusion in the 0.56 ± 0.25 mL/g/min range, ~25% exhibit a mean perfusion of 1.53 ± 0.36 mL/g/min while ~10% show high perfusion of 2.56 ± 0.5 mL/g/min under alpha-chloralose anesthesia in the somatosensory cortex of rats. Recent work using iodo[14C]antipyrine autoradiographic estimated perfusion at ~0.6 mL/g/min [4] and optical coherence tomography measurements suggested 0.51-0.68 mL/g/min [5] in the somatosensory cortex of rats under the same anesthesia protocol. Our results show evidence of heterogeneity in perfusion, we expect this heterogeneity to relate to local vascular density. This work describes a novel methodology to estimate perfusion at the micron level from the 2PFM imaging of cerebral microvasculature, its application to a cohort of subjects should allow detailed investigation of the relationship between cortical microvascular topology and blood flow.
Nataliya Portman , Montreal
Neurological Institute, McGill University (slides available here) More precisely, I will focus on a pattern theoretic model for a biological growth called GRID (Growth as Random Iterated Diffeomorphisms). It was first introduced by Dr. Ulf Grenander in 2005 in response to the need to study growth and development of human anatomy based on a sequence of images. The GRID model represents a modification of growth models employed in the field of computational anatomy, acknowledging that diffeomorphic transformations induced by growth are dependent on genetic controls within an organism. The genetic control is expressed by a probability law which governs spatial-temporal patterns of cell decisions (cell division/death, enlargement) in such a way that the image of an initial organism becomes continuously transformed into the image of a grown organism. I will then demonstrate the GRID-based inference algorithm developed in my doctoral thesis that automatically estimates growth characteristics of an organism directly from image data. An example of larval growth of the Drosophila wind disc as seen in confocal micrographs of Wingless gene expression patterns will be considered. Overall, with implementation of the GRID
model one can gain a new insight into the growth-induced shape generation
process as controlled by the genes. Namely, one can reveal spatial-temporal
patterns of intensity of cell divisions hidden deeper in given observations
of growth. Michael Smith University of Calgary Moving the best ideas from 1985’s constrained reconstruction techniques into 2011’s compressed sensing reconstruction
Yogesh Chinta
Venkateswarao Qiong Wu McMaster University
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