Neural adaptations to resistance training - Implications for movement control

It has long been believed that resistance training is accompanied by changes within the nervous system that play an important role in the development of strength. Many elements of the nervous system exhibit the potential for adaptation in response to resistance training, including supraspinal centres, descending neural tracts, spinal circuitry and the motor end plate connections between motoneurons and muscle fibres. Yet the specific sites of adaptation along the neuraxis have seldom been identified experimentally, and much of the evidence for neural adaptations following resistance training remains indirect. As a consequence of this current lack of knowledge, there exists uncertainty regarding the manner in which resistance training impacts upon the control and execution of functional movements. We aim to demonstrate that resistance training is likely to cause adaptations to many neural elements that are involved in the control of movement, and is therefore likely to affect movement execution during a wide range of tasks. We review a small number of experiments that provide evidence that resistance training affects the way in which muscles that have been engaged during training are recruited during related movement tasks. The concepts addressed in this article represent an important new approach to research on the effects of resistance training. They are also of considerable practical importance, since most individuals perform resistance training in the expectation that it will enhance their performance in-related functional tasks.

Invertebrate D2 type dopamine receptor exhibits age-based plasticity of expression in the mushroom bodies of the honeybee brain

We have isolated a cDNA clone from the honeybee brain encoding a dopamine receptor, AmDop2, which is positively coupled to adenylyl cyclase. The transmembrane domains of this receptor are 88% identical to the orthologous Drosophila D2 dopamine receptor, DmDop2, though phylogenetic analysis and sequence homology both indicate that invertebrate and vertebrate D2 receptors are quite distinct. In situ hybridization to mRNA in whole-mount preparations of honeybee brains reveals gene expression in the mushroom bodies, a primary site of associative learning. Furthermore, two anatomically distinct cell types in the mushroom bodies exhibit differential regulation of AmDop2 expression. In all nonreproductive females (worker caste) and reproductive males (drones) the receptor gene is strongly and constitutively expressed in all mushroom body interneurons with small cell bodies. In contrast, the large cell-bodied interneurons exhibit dramatic plasticity of AmDop2 gene expression. In newly emerged worker bees (cell-cleaning specialists) and newly emerged drones, no AmDop2 transcript is observed in the large interneurons whereas this transcript is abundant in these cells in the oldest worker bees (resource foragers) and older drones. Differentiation of the mushroom body interneurons into two distinct classes (i.e., plastic or nonplastic with respect to AmDop2 gene expression) indicates that this receptor contributes to the differential regulation of distinct neural circuits. Moreover, the plasticity of expression observed in the large cells implicates this receptor in the behavioral maturation of the bee.

Governing coordination: Behavioural principles and neural correlates

The coordination of movement is governed by a coalition of constraints. The expression of these constraints ranges from the concrete—the restricted range of motion offered by the mechanical configuration of our muscles and joints; to the abstract—the difficulty that we experience in combining simple movements into complex rhythms. We seek to illustrate that the various constraints on coordination are complementary and inclusive, and the means by which their expression and interaction are mediated systematically by the integrative action of the central nervous system (CNS). Beyond identifying the general principles at the behavioural level that govern the mutual interplay of constraints, we attempt to demonstrate that these principles have as their foundation specific functional properties of the cortical motor systems. We propose that regions of the brain upstream of the motor cortex may play a significant role in mediating interactions between the functional representations of muscles engaged in sensorimotor coordination tasks. We also argue that activity in these ldquosupramotorrdquo regions may mediate the stabilising role of augmented sensory feedback.

The sites of neural adaptation induced by resistance training in humans

Although it has long been supposed that resistance training causes adaptive changes in the CNS, the sites and nature of these adaptations have not previously been identified. In order to determine whether the neural adaptations to resistance training occur to a greater extent at cortical or subcortical sites in the CNS, we compared the effects of resistance training on the electromyographic (EMG) responses to transcranial magnetic (TMS) and electrical (TES) stimulation. Motor evoked potentials (MEPs) were recorded from the first dorsal interosseous muscle of 16 individuals before and after 4 weeks of resistance training for the index finger abductors (n = 8), or training involving finger abduction-adduction without external resistance (n = 8). TMS was delivered at rest at intensities from 5 % below the passive threshold to the maximal output of the stimulator. TMS and TES were also delivered at the active threshold intensity while the participants exerted torques ranging from 5 to 60 % of their maximum voluntary contraction (MVC) torque. The average latency of MEPs elicited by TES was significantly shorter than that of TMS MEPs (TES latency = 21.5 ± 1.4 ms; TMS latency = 23.4 ± 1.4 ms; P < 0.05), which indicates that the site of activation differed between the two forms of stimulation. Training resulted in a significant increase in MVC torque for the resistance-training group, but not the control group. There were no statistically significant changes in the corticospinal properties measured at rest for either group. For the active trials involving both TMS and TES, however, the slope of the relationship between MEP size and the torque exerted was significantly lower after training for the resistance-training group (P < 0.05). Thus, for a specific level of muscle activity, the magnitude of the EMG responses to both forms of transcranial stimulation were smaller following resistance training. These results suggest that resistance training changes the functional properties of spinal cord circuitry in humans, but does not substantially affect the organisation of the motor cortex.

Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbourne's freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the model's fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways. (C) 1997 Elsevier Science Ltd. All rights reserved.

Neuroplasticity and psychiatry

Objective: There is increasing concern that the course of psychiatric disorders may be affected by parameters such as the duration and intensity of symptoms of initial episodes of illness. As this indicates that abnormal function produces long-term changes within the brain, a review of the neuroscience literature regarding neuroplasticity is warranted. Method: This article is a selective review, focusing in particular on results obtained from physiological experiments assessing plasticity within the mammalian neocortex. The possible relevance of results to psychiatry is discussed. Results: While the most dramatic examples of neuroplasticity occur during a critical period of neural development, neuroplasticity can also occur in adult neocortex. Neuroplasticity appears to be activity-dependent: synaptic pathways that are intensively used may become strengthened, and conversely, there may be depression of transmission in infrequently used pathways. Conclusions: Results from neurophysiological experiments fend support to the clinical observation that the intensity and duration of a psychiatric disorder may adversely alter its long-term course. Rapid aggressive treatment may prevent this from occurring. While pharmacotherapy may reduce the duration and severity of symptoms, it may also have an independent, as yet unknown, effect on neuroplasticity.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Designs for an asymmetric gradient set and a compact superconducting magnet for neural magnetic resonance imaging

Imaging of the head and neck is the most commonly performed clinical magnetic resonance imaging (MRI) examination [R. G. Evans and J. R. G. Evans, AJR 157, 603 (1991)]. This is usually undertaken in a generalist MRI instrument containing superconducting magnet system capable of imaging all organs. These generalist instruments are large, typically having a bore of 0.9-1.0 m and a length of 1.7-2.5 m and therefore are expensive to site, somewhat claustrophobic to the patient, and offer little access by attending physicians. In this article, we present the design of a compact, superconducting MRI magnet for head and neck imaging that is less than 0.8 m in length and discuss in detail the design of an asymmetric gradient coil set, tailored to the magnet profile. In particular, the introduction of a radio-frequency FM modulation scheme in concert with a gradient sequence allows the epoch of the linear region of the gradient set to be much closer to the end of the gradient structure than was previously possible. Images from a prototype gradient set demonstrate the effectiveness of the designs. (C) 1999 American Institute of Physics. [S0034-6748(99)04910-2].

Altered adhesion, proliferation and death in neural cultures from adults with schizophrenia

The causes of schizophrenia are unknown, but there is evidence linking subtle deviations in neural development with schizophrenia. Embryonic brain development cannot be studied in an adult with schizophrenia, but neurogenesis and early events in neuronal differentiation can be investigated throughout adult life in the human olfactory epithelium. Our past research has demonstrated that neuronal cultures can be derived from biopsy of the human adult olfactory epithelium. In the present study, we examined mechanisms related to neurogenesis and neuronal differentiation in adults with schizophrenia versus well controls. Forty biopsies were collected under local anaesthesia from ten individuals with DSM III-R schizophrenia and ten age- and sex-matched well controls. All patients, except one, were receiving antipsychotic medication at the time of the biopsy, Immunostaining for neuronal markers indicated that neurogenesis occurred in the biopsies from both patients and controls since all contained cells expressing tubulin and/or olfactory marker protein. The major findings of this study are: 1. biopsies from patients with schizophrenia showed a significantly reduced ability to attach to the culture slide: 29.9% of patient biopsies attached compared to 73.5% of control biopsies; 2. biopsies from patients with schizophrenia had a significantly greater proportion of cells undergoing mitosis: 0.69% in the patients compared to 0.29% in the controls; and 3. dopamine (10 mu M) significantly increased the proportion of apoptotic cells in the control cultures but significantly decreased the proportion in patients' cultures. (C) 1999 Elsevier Science B.V. All rights reserved.

Context-free and context-sensitive dynamics in recurrent neural networks

Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languages. The dynamics of such networks is usually based on damped oscillation around fixed points in state space and requires that the dynamical components are arranged in certain ways. It is shown that qualitatively similar dynamics with similar constraints hold for a(n)b(n)c(n), a context-sensitive language. The additional difficulty with a(n)b(n)c(n), compared with the context-free language a(n)b(n), consists of 'counting up' and 'counting down' letters simultaneously. The network solution is to oscillate in two principal dimensions, one for counting up and one for counting down. This study focuses on the dynamics employed by the sequential cascaded network, in contrast to the simple recurrent network, and the use of backpropagation through time. Found solutions generalize well beyond training data, however, learning is not reliable. The contribution of this study lies in demonstrating how the dynamics in recurrent neural networks that process context-free languages can also be employed in processing some context-sensitive languages (traditionally thought of as requiring additional computation resources). This continuity of mechanism between language classes contributes to our understanding of neural networks in modelling language learning and processing.

An object-oriented neural network approach to short-term traffic forecasting

This paper discusses an object-oriented neural network model that was developed for predicting short-term traffic conditions on a section of the Pacific Highway between Brisbane and the Gold Coast in Queensland, Australia. The feasibility of this approach is demonstrated through a time-lag recurrent network (TLRN) which was developed for predicting speed data up to 15 minutes into the future. The results obtained indicate that the TLRN is capable of predicting speed up to 5 minutes into the future with a high degree of accuracy (90-94%). Similar models, which were developed for predicting freeway travel times on the same facility, were successful in predicting travel times up to 15 minutes into the future with a similar degree of accuracy (93-95%). These results represent substantial improvements on conventional model performance and clearly demonstrate the feasibility of using the object-oriented approach for short-term traffic prediction. (C) 2001 Elsevier Science B.V. All rights reserved.

Inter-subband redundancy prediction using neural network for video coding

High performance video codec is mandatory for multimedia applications such as video-on-demand and video conferencing. Recent research has proposed numerous video coding techniques to meet the requirement in bandwidth, delay, loss and Quality-of-Service (QoS). In this paper, we present our investigations on inter-subband self-similarity within the wavelet-decomposed video frames using neural networks, and study the performance of applying the spatial network model to all video frames over time. The goal of our proposed method is to restore the highest perceptual quality for video transmitted over a highly congested network. Our contributions in this paper are: (1) A new coding model with neural network based, inter-subband redundancy (ISR) prediction for video coding using wavelet (2) The performance of 1D and 2D ISR prediction, including multiple levels of wavelet decompositions. Our result shows a short-term quality enhancement may be obtained using both 1D and 2D ISR prediction.

Neural correlates of the emergence of consciousness of thirst

Thirst was induced by rapid i.v. infusion of hypertonic saline (0.51 M at 13.4 ml/min). Ten humans were neuroimaged by positron-emission tomography (PET) and four by functional MRI (fMRI). PET images were made 25 min after beginning infusion, when the sensation of thirst began to enter the stream of consciousness. The fMRI images were made when the maximum rate of increase of thirst occurred. The PET results showed regional cerebral blood flow changes similar to those delineated when thirst was maximal. These loci involved the phylogenetically ancient areas of the brain. fMRI showed activation in the anterior wall of the third ventricle, an area that is key in the genesis of thirst but is not an area revealed by PET imaging. Thus, this region plays as major a role in thirst for humans as for animals. Strong activations in the brain with fMRI included the anterior cingulate, parahippocampal gyrus, inferior and middle frontal gyri, insula, and cerebellum. When the subjects drank water to satiation, thirst declined immediately to baseline. A precipitate decline in intensity of activation signal occurred in the anterior cingulate area (Brodmann area 32) putatively related to consciousness of thirst. The intensity of activation in the anterior wall of the third ventricle was essentially unchanged, which is consistent with the fact that a significant time (15-20 min) would be needed before plasma Na concentration changed as a result of water absorption from the gut.

Mutations in the DLG3 gene cause nonsyndromic X-linked mental retardation

We have identified truncating mutations in the human DLG3 ( neuroendocrine dlg) gene in 4 of 329 families with moderate to severe X-linked mental retardation. DLG3 encodes synapse-associated protein 102 (SAP102), a member of the membrane-associated guanylate kinase protein family. Neuronal SAP102 is expressed during early brain development and is localized to the postsynaptic density of excitatory synapses. It is composed of three amino-terminal PDZ domains, an src homology domain, and a carboxyl-terminal guanylate kinase domain. The PDZ domains interact directly with the NR2 subunits of the NMDA glutamate receptor and with other proteins responsible for NMDA receptor localization, immobilization, and signaling. The mutations identified in this study all introduce premature stop codons within or before the third PDZ domain, and it is likely that this impairs the ability of SAP102 to interact with the NMDA receptor and/or other proteins involved in downstream NMDA receptor signaling pathways. NMDA receptors have been implicated in the induction of certain forms of synaptic plasticity, such as long-term potentiation and long-term depression, and these changes in synaptic efficacy have been proposed as neural mechanisms underlying memory and learning. The disruption of NMDA receptor targeting or signaling, as a result of the loss of SAP102, may lead to altered synaptic plasticity and may explain the intellectual impairment observed in individuals with DLG3 mutations.