Tsukumo Ryusuke is a quirky, yet brilliant neuroscientist working for the National Research Institute of Police Science. Wielding a unique perspective and psychology, Tsukumo tackles the nation's most baffling crimes and scandals, going head-to-head with the most brilliant and twisted criminal minds. But his eccentricities and poor social timing can also aggravate people and circumstances, further complicating matters.
Runtime: 58 minutes
Mr. Brain - Brain morphometry - Netflix
Brain morphometry is a subfield of both morphometry and the brain sciences, concerned with the measurement of brain structures and changes thereof during development, aging, learning, disease and evolution. Since autopsy-like dissection is generally impossible on living brains, brain morphometry starts with noninvasive neuroimaging data, typically obtained from magnetic resonance imaging (or MRI for short). These data are born digital, which allows researchers to analyze the brain images further by using advanced mathematical and statistical methods such as shape quantification or multivariate analysis. This allows researchers to quantify anatomical features of the brain in terms of shape, mass, volume (e.g. of the hippocampus, or of the primary versus secondary visual cortex), and to derive more specific information, such as the encephalization quotient, grey matter density and white matter connectivity, gyrification, cortical thickness, or the amount of cerebrospinal fluid. These variables can then be mapped within the brain volume or on the brain surface, providing a convenient way to assess their pattern and extent over time, across individuals or even between different biological species. The field is rapidly evolving along with neuroimaging techniques — which deliver the underlying data — but also develops in part independently from them, as part of the emerging field of neuroinformatics, which is concerned with developing and adapting algorithms to analyze those data.
Mr. Brain - Deformation-based morphometry - Netflix
In DBM, highly non-linear registration algorithms are used, and the statistical analyses are not performed on the registered voxels but on the deformation fields used to register them (which requires multivariate approaches) or derived scalar properties thereof, which allows for univariate approaches. One common variant—sometimes referred to as Tensor-based morphometry (TBM)—is based on the Jacobian determinant of the deformation matrix. Of course, multiple solutions exist for such non-linear warping procedures, and to balance appropriately between the potentially opposing requirements for global and local shape fit, ever more sophisticated registration algorithms are being developed. Most of these, however, are computationally expensive if applied with a high-resolution grid. The biggest advantage of DBM with respect to VBM is its ability to detect subtle changes in longitudinal studies. However, due to the vast variety of registration algorithms, no widely accepted standard for DBM exists, which also prevented its incorporation into major neuroimaging software packages.
Mr. Brain - References - Netflix