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 1.9 billion are overweight, physical exercise is imperative to save lives and to mitigate the burden on the healthcare system. Although the benefits of physical exercise have been explored, the underlying mechanisms to enact these benefits have not been well-characterized. Here we review exercise-induced changes in regional brain activation and modulation. Paradigms differing in intensity, duration, and type of motor movement have been used to assess exercise effects on memory, cognition, and disease mitigation in youth and elderly populations. To evaluate exercise-induced changes in neural activity, the noninvasive imaging technique, functional magnetic resonance imaging (fMRI), is employed. fMRI is recorded either during or after exercise intervention. Post-exercise fMRI is often paired with in-bore tests of cognition to provide insight into the associated brain regions. Whereas, during intervention, fMRI is used to detail muscle-associated neural activation profiles. Characterization of the region and magnitude of brain activation has been used to perform comparative studies and identify specific characteristics from individuals with varying motor and cognitive abilities. Further fMRI and exercise research, with the use of these metrics, could facilitate the development of tools for disease diagnosis or to assess level of dysfunction or progression.

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. Weak extension of the great toe diminished the soleus motoneuron reflex discharge in all but 2 of 16 subjects; the results for weak ankle plantarflexion were less consistent, but overall, there was a reduction in soleus motoneuron output also. The methodology should provide new insights into disorders of movement and tone, especially as it permits estimates of motoneuron depolarization to be made. NEW & NOTEWORTHY Assuming a normal distribution of Ia fiber thresholds to electrical stimulation and using the H-reflex, we determined for the first time an Ia input-α-motoneuron output relationship for the human soleus muscle. The relationship varies greatly among subjects but, overall, is approximately linear. Minimal contraction of a toe muscle alters the relationship dramatically, probably due to presynaptic inhibition of Ia fibers. Drawing on the literature, we can calculate changes in α-motoneuron membrane potential.

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. A leave-2-out (L2O) cross-validation procedure is used to evaluate the prediction performance. This pilot study involves 27 subjects who received either left high-frequency (HF) active rTMS therapy or simultaneous left HF and right low-frequency active rTMS therapy. Our results indicate that it is possible to predict rTMS treatment efficacy of either treatment modality with a specificity of 83% and a sensitivity of 78%, for a combined accuracy of 80%.

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. The classifier was realized as a kernelized partial least squares regression procedure, whose output is the predicted response. A low-dimensional kernelized principal component representation of the feature space was used for the purposes of visualization and clustering analysis. The overall method was evaluated using an 11-fold nested cross-validation procedure for which over 85% average prediction performance is obtained. The results indicate that ML methods hold considerable promise in predicting the efficacy of SSRI antidepressant therapy for major depression.

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. The selected features are then used to evaluate the class likelihoods, through the use of a mixture of factor analysis (MFA) statistical model [7]. In a training set of 207 subjects, including 64 subjects with major depressive disorder (MDD), 40 subjects with chronic schizophrenia, 12 subjects with bipolar depression and 91 normal or healthy subjects, the average correct diagnosis rate attained using the proposed method is over 85%, as determined by various cross-validation experiments. The promise is that, with further development, the proposed methodology could serve as a valuable adjunctive tool for the medical practitioner.

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.