This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Previous research on Physical Activity (PA) has been highly valuable in elucidating how PA affects the structure and function of the hippocampus in elderly populations that take part in structured interventions. However, how PA affects the hippocampus in younger populations that perform PA during daily-life activities remains poorly understood. In addition, this research has not examined the impact of PA on the internal structure of the hippocampus. Here, we performed a cross-sectional exploration of the way structural and functional aspects of the hippocampus are associated with habitual PA performed during work, leisure time, and sports in the daily lives of healthy young adults (n = 30; 14 female; mean age = 23.9 y.o.; SD = 7.8 y.o.). We assessed PA in these three different contexts through a validated questionnaire. The results show that PA performed during work time correlated with higher subicular volumes. In addition, we found that PA changed functional connectivity (FC) between a location in the middle/posterior hippocampus and regions of the default mode network, and between a location in the anterior hippocampus and regions of the somatomotor network. No statistical effects of PA performed during leisure time and sports were found. The results generalize the impact of PA on younger populations and show how PA performed in daily-life situations correlates with the precise internal structure and functional connectivity of the hippocampus.

A major drawback of functional Magnetic Resonance Imaging (fMRI) concerns the lack of detection accuracy of the measured signal. Although this limitation stems in part from the neuro-vascular nature of the fMRI signal, it also reflects particular methodological decisions in the fMRI data analysis pathway. Here we show that the signal detection accuracy of fMRI is affected by the specific way in which whole-brain volumes are created from individually acquired brain slices, and by the method of statistically extracting signals from the sampled data. To address these limitations, we propose a new framework for fMRI data analysis. The new framework creates whole-brain volumes from individual brain slices that are all acquired at the same point in time relative to a presented stimulus. These whole-brain volumes contain minimal temporal distortions, and are available at a high temporal resolution. In addition, statistical signal extraction occurred on the basis of a non-standard time point-by-time point approach. We evaluated the detection accuracy of the extracted signal in the standard and new framework with simulated and real-world fMRI data. The new slice-based data-analytic framework yields greatly improved signal detection accuracy of fMRI signals.

A major drawback of functional Magnetic Resonance Imaging (fMRI) concerns the lack of temporal accuracy of the measured signal. Although this limitation stems in part from the neuro-vascular nature of the fMRI signal, it also reflects particular methodological decisions in the fMRI data analysis pathway. Here we show that the temporal accuracy of fMRI is affected by the specific way in which whole-brain volumes are created from individually acquired brain slices. Specifically, we show how the current volume creation method leads to whole-brain volumes that contain within-volume temporal distortions and that are available at a low temporal resolution. To address these limitations, we propose a new framework for fMRI data analysis. The new framework creates whole-brain volumes from individual brain slices that are all acquired at the same point in time relative to a presented stimulus. These whole-brain volumes contain no temporal distortions, and are available at a high temporal resolution. Statistical signal extraction occurs on the basis of a novel time point-by-time point approach. We evaluated the temporal characteristics of the extracted signal in the standard and new framework with simulated and real-world fMRI data. The new slice-based data-analytic framework yields greatly improved temporal accuracy of fMRI signals.

As a complex three-dimensional organ, the inside of a human brain is difficult to properly visualize. Magnetic Resonance Imaging provides an accurate model of the brain of a patient, but its medical or educational analysis as a set of flat slices is not enough to fully grasp its internal structure. A virtual reality application has been developed to generate a complete three-dimensional model based on MRI data, which users can explore internally through random planar cuts and color cluster isolation. An indexed vertex triangulation algorithm has been designed to efficiently display large amounts of complex three-dimensional vertex clusters in simple mobile devices. Feedback from students suggests that the resulting application satisfactorily complements theoretical lectures, as virtual reality allows them to better observe different structures within the human brain.