Are older people prescribed antidepressants on the basis of fewer symptoms of depression, and for longer periods of time? A survey of 1825 New Zealanders

Aims: To determine whether older people are prescribed antidepressants at lower levels of depression and with fewer symptoms, and whether they are more likely to engage in chronic usage. Methods: An online survey about experiences with, and opinions about, depression and antidepressants, was completed by 1,825 New Zealand adults who had been prescribed antidepressants in the preceding five years. Results: Participants over 55 were prescribed antidepressants with significantly fewer symptoms and were significantly less likely to meet DSM criteria for depression. They were also significantly more likely to have used the drugs for three years and still be using them. Conclusions: Prescribing physicians and their older patients might benefit from discussing the pros and cons of antidepressants (including the additional risk factors with this age group) and the alternatives; and, if prescription does occur, careful monitoring to avoid unnecessary, potentially damaging, long-term use is recommended.

Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modelling of neural circuits found in the brain. In recent years, much of the focus in neuron modelling has moved to the study of the connectivity of spiking neural networks. Spiking neural networks provide a vehicle to understand from a computational perspective, aspects of the brain’s neural circuitry. This understanding can then be used to tackle some of the historically intractable issues with artificial neurons, such as scalability and lack of variable binding. Current knowledge of feed-forward, lateral, and recurrent connectivity of spiking neurons, and the interplay between excitatory and inhibitory neurons is beginning to shed light on these issues, by improved understanding of the temporal processing capabilities and synchronous behaviour of biological neurons. This research topic aims to amalgamate current research aimed at tackling these phenomena.

Brain responses to audiovisual speech mismatch in infants are associated with individual differences in looking behaviour

Research on audiovisual speech integration has reported high levels of individual variability, especially among young infants. In the present study we tested the hypothesis that this variability results from individual differences in the maturation of audiovisual speech processing during infancy. A developmental shift in selective attention to audiovisual speech has been demonstrated between 6 and 9 months with an increase in the time spent looking to articulating mouths as compared to eyes (Lewkowicz & Hansen-Tift. (2012) Proc. Natl Acad. Sci. USA, 109, 1431–1436; Tomalski et al. (2012) Eur. J. Dev. Psychol., 1–14). In the present study we tested whether these changes in behavioural maturational level are associated with differences in brain responses to audiovisual speech across this age range. We measured high-density event-related potentials (ERPs) in response to videos of audiovisually matching and mismatched syllables /ba/ and /ga/, and subsequently examined visual scanning of the same stimuli with eye-tracking. There were no clear age-specific changes in ERPs, but the amplitude of audiovisual mismatch response (AVMMR) to the combination of visual /ba/ and auditory /ga/ was strongly negatively associated with looking time to the mouth in the same condition. These results have significant implications for our understanding of individual differences in neural signatures of audiovisual speech processing in infants, suggesting that they are not strictly related to chronological age but instead associated with the maturation of looking behaviour, and develop at individual rates in the second half of the first year of life.

Spiking neural network connectivity and its potential for temporal sensory processing and variable binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well-understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modeling of neural circuits found in the brain.