Journal of neuroscience methods
-
J. Neurosci. Methods · Jun 2016
Directed differentiation of basal forebrain cholinergic neurons from human pluripotent stem cells.
Basal forebrain cholinergic neurons (BFCNs) play critical roles in learning, memory and cognition. Dysfunction or degeneration of BFCNs may connect to neuropathology, such as Alzheimer's disease, Down's syndrome and dementia. Generation of functional BFCNs may contribute to the studies of cell-based therapy and pathogenesis that is related to learning and memory deficits. ⋯ We provide an efficient method to generate BFCNs from multiple hPSC lines, which offers the potential application for disease modeling and pharmacological studies.
-
J. Neurosci. Methods · Jun 2016
CommentMachine learning on Parkinson's disease? Let's translate into clinical practice.
Machine learning techniques represent the third-generation of clinical neuroimaging studies where the principal interest is not related to describe anatomical changes of a neurological disorder, but to evaluate if a multivariate approach may use these abnormalities to predict the correct classification of previously unseen clinical cohort. In the next few years, Machine learning will revolutionize clinical practice of Parkinson's disease, but enthusiasm should be turned down before removing some important barriers.
-
J. Neurosci. Methods · Jun 2016
Optimal digital filters for analyzing the mid-latency auditory P50 event-related potential in patients with Alzheimer's disease.
Filtering is an effective pre-processing technique for improving the signal-to-noise ratio of ERP waveforms. Filters can, however, introduce substantial distortions into the time-domain representations of ERP waveforms. Inappropriate filter parameters may lead to the presence of statistically significant but artificial effects, whereas true effects may appear as insignificant. ⋯ Filtering broadband signals, such as ERP signals, necessary results in time-domain distortions. However, by adjusting the filter parameters carefully according to the components of interest, it is possible to minimize filter artifacts and obtain more easily interpretable ERP waveforms.