Processing Time Reduction: an Application in Living Human High-Resolution Diffusion Magnetic Resonance Imaging Data.

High Angular Resolution Diffusion Imaging (HARDI) is a type of brain imaging that collects a very large amount of data, and if many subjects are considered then it amounts to a big data framework (e.g., the human connectome project has 20 Terabytes of data). HARDI is also becoming increasingly relevant for clinical settings (e.g., detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter-WM anatomy using tractography). Thus, this method is becoming a routine assessment in clinical settings. In such settings, the computation time is critical, and finding forms of reducing the processing time in high computation processes such as Diffusion Spectrum Imaging (DSI), a form of HARDI data, is very relevant to increase data-processing speed. Here we analyze a method for reducing the computation time of the dMRI-based axonal orientation distribution function h by using Monte Carlo sampling-based methods for voxel selection. Results evidenced a robust reduction in required data sampling of about 50 % without losing signal's quality. Moreover, we show that the convergence to the correct value in this type of Monte Carlo HARDI/DSI data-processing has a linear improvement in data-processing speed of the ODF determination. Although further improvements are needed, our results represent a promissory step for future processing time reduction in big data.

Locating Temporal Functional Dynamics of Visual Short-Term Memory Binding using Graph Modular Dirichlet Energy.

Visual short-term memory binding tasks are a promising early marker for Alzheimer's disease (AD). To uncover functional deficits of AD in these tasks it is meaningful to first study unimpaired brain function. Electroencephalogram recordings were obtained from encoding and maintenance periods of tasks performed by healthy young volunteers. We probe the task's transient physiological underpinnings by contrasting shape only (Shape) and shape-colour binding (Bind) conditions, displayed in the left and right sides of the screen, separately. Particularly, we introduce and implement a novel technique named Modular Dirichlet Energy (MDE) which allows robust and flexible analysis of the functional network with unprecedented temporal precision. We find that connectivity in the Bind condition is less integrated with the global network than in the Shape condition in occipital and frontal modules during the encoding period of the right screen condition. Using MDE we are able to discern driving effects in the occipital module between 100-140 ms, coinciding with the P100 visually evoked potential, followed by a driving effect in the frontal module between 140-180 ms, suggesting that the differences found constitute an information processing difference between these modules. This provides temporally precise information over a heterogeneous population in promising tasks for the detection of AD.