Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases.

Background: Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification.

Support vector machine-based classification of neuroimages in Alzheimer's disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals.

Objective: To conduct the first support vector machine (SVM)-based study comparing the diagnostic accuracy of T1-weighted magnetic resonance imaging (T1-MRI), F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and regional cerebral blood flow single-photon emission computed tomography (rCBF-SPECT) in Alzheimer's disease (AD).

Method: Brain T1-MRI, FDG-PET and rCBF-SPECT scans were acquired from a sample of mild AD patients (n=20) and healthy elderly controls (n=18). SVM-based diagnostic accuracy indices were calculated using whole-brain information and leave-one-out cross-validation.

[Correlation between thyroid volume determined either by ultrasound or by scintigraphy and its implications in dosimetric radioiodine calculations in Graves disease treatment].

Introduction: Graves disease (GD) is the most common cause of hiperthyroidism, and the most common treatment options are surgery, antithyroid drugs and radioiodine therapy. In radiodosimetric calculations to determine radioiodine dosage it is possible to use thyroid volume estimatives based on ultrasound or scintigraphy.

Objective: The present study aimed to correlate these methodologies emphasizing volume estimatives and dosimetric implications.

Baseline hippocampal theta oscillation speeds correlate with rate of operant task acquisition.

Many lines of evidence indicate that theta rhythm, a prominent neural oscillatory mode found in the mammalian hippocampus, plays a key role in the acquisition, processing, and retrieval of memories. However, a predictive neurophysiological feature of the baseline theta rhythm that correlates with the learning rate across different animals has yet to be identified. Here we show that the mean theta rhythm speed observed during baseline periods of immobility has a strong positive correlation with the rate at which rats learn an operant task.