Algorithm for simulation of craniotomies assisted by peripheral for 3D virtual navigation.

Neurosurgical procedures require high precision and an accurate localization of the structures. For that reason and due to the advances in 3D visualization, the software for planning and training neurosurgeries has become an important tool for neurosurgeons and students, but the manipulation of the 3D structures is not always easy for the staff that usually works with 2D images. This paper describes a system developed in open source software that allows performing a virtual craniotomy (a common procedure in neurosurgery that enables the access to intracranial lesions) in 3D slicer; the system includes a peripheral input in order to permit the manipulation of the 3D structures according to camera movements and to guide the movement of the craniotomy tool.

Neurophysiological correlates in Mild Cognitive Impairment detected using group Independent Component Analysis.

Alzheimer's disease is the most prevalent cause of dementia. Mild Cognitive Impairment (MCI) is defined as a grey area between intact cognitive functioning and clinical dementia. Electroencephalography (EEG) has been used to identify biomarkers in dementia. Currently, there is a great interest in translating the study from raw signals to signal generators, trying to keep the relationship with neurophysiology. In the current study, EEG recordings during an encoding task were acquired in MCI subjects and healthy controls. Data was decomposed using group Independent Component Analysis (gICA) and the most neuronal components were analyzed using Phase Intertrial Coherence (PIC) and Phase shift Intertrial Coherence (PsIC). MCI subjects exhibited an increase of PIC in the theta band, while controls showed increase in PsIC in the alpha band. Correlation between PIC and PsIC and clinical scales were also found. Those findings indicate that the methodology proposed based in gICA can help to extract information from EEG recordings with neurophysiological meaning.