Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback (rt-fMRI NF) system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine (SVM) was trained from the neural information of happy autobiographical imagery and motor imagery blocks received from a healthy female participant during an MRI session.
View Article and Find Full Text PDFBackground: The assessment of Attentional Deficit Hyperactivity Disorder (ADHD) among ethnic groups may reveal environmental or cultural variables that influence the appearance of this disorder.
Aim: To assess the presence and characteristics of ADHD in two communities of the inland Arica valleys (Azapa and Lluta), where the Aymara population predominates.
Material And Methods: Starting from a screening based on the Conner's test, we evaluated 79 children aged 8 to 13 years.
We have generated immortal neuronal cell lines from normal and trisomy 16 (Ts16) mice, a model for Down syndrome (DS). Ts16 lines overexpress DS-related genes (App, amyloid precursor protein; Sod1, Cu/Zn superoxide dismutase) and show altered cholinergic function (reduced choline uptake, ChAT expression and fractional choline release after stimulation). As previous evidence has related amyloid to cholinergic dysfunction, we reduced APP expression using specific mRNA antisense sequences in our neuronal cell line named CTb, derived from Ts16 cerebral cortex, compared to a cell line derived from a normal animal, named CNh.
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