The Effect of Exercise on Neural Activation and Cognition: A Review of Task-Based fMRI Studies.

Numerous studies have stressed the importance of exercise in promoting physical and mental health and for aiding in cognition. Encouragingly, physical exercise has been shown to reduce the risk of developing Alzheimer's disease and to mitigate hemiparesis experienced by stroke patients. Additionally, today where over 1.

Quantitative input-output relationships between human soleus muscle spindle afferents and motoneurons.

A method is described that, for the first time, allows instantaneous estimation of the Ia fiber input to human soleus motoneurons following electrical stimulation of the tibial nerve. The basis of the method is to determine the thresholds of the most and least excitable 1a fibers to electrical stimulation, and to treat the intervening thresholds as having a normal distribution about the mean; the validity of this approach is discussed. It was found that, for the same Ia fiber input, the percentage of soleus motoneurons contributing to the H (Hoffmann)-reflex differed considerably among subjects; when the results were pooled, however, there was an approximately linear relationship between Ia input and motoneuron output.

Using pre-treatment electroencephalography data to predict response to transcranial magnetic stimulation therapy for major depression.

We investigate the use of machine learning methods based on the pre-treatment electroencephalograph (EEG) to predict response to repetitive transcranial magnetic stimulation (rTMS), which is a non-pharmacological form of therapy for treating major depressive disorder (MDD). The learning procedure involves the extraction of a large number of candidate features from EEG data, from which a very small subset of most statistically relevant features is selected for further processing. A statistical prediction model based on mixture of factor analysis (MFA) model is constructed from a training set that classifies the respective subject into responder and non-responder classes.

Diagnosis of psychiatric disorders using EEG data and employing a statistical decision model.

An automated diagnosis procedure based on a statistical machine learning methodology using electroencephalograph (EEG) data is proposed for diagnosis of psychiatric illness. First, a large collection of candidate features, mostly consisting of various statistical quantities, are calculated from the subject's EEG. This large set of candidate features is then reduced into a much smaller set of most relevant features using a feature selection procedure.

Using pre-treatment EEG data to predict response to SSRI treatment for MDD.

The problem of identifying in advance the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we propose a machine learning (ML) methodology to predict the response to a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD), using pre-treatment electroencephalograph (EEG) measurements. The proposed feature selection technique is a modification of the method of Peng et al [10] that is based on a Kullback-Leibler (KL) distance measure.

Speculations surrounding a spinal reflex.

A method has been developed for measuring the Ia fibre input/motoneurone output relationship for the soleus H-reflex in healthy human volunteers. The shift in the relationship during weak toe extension, and in some subjects during weak plantar flexion, indicates the imposition of an inhibitory mechanism, presumably presynaptic. From these observations, and others previously made on long-loop reflexes, it is argued that the inhibitory mechanism may have evolved to suppress unwanted information from the periphery, not only during movement but in the resting state, and that this development was a necessary accompaniment of encephalisation.