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Pathophysiological distortions in time perception and timed performance

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Abstract

Distortions in time perception and timed performance are presented by a number of different neurological and psychiatric conditions (e.g. Parkinson’s disease, schizophrenia, attention deficit hyperactivity disorder and autism). As a consequence, the primary focus of this review is on factors that define or produce systematic changes in the attention, clock, memory and decision stages of temporal processing as originally defined by Scalar Expectancy Theory. These findings are used to evaluate the Striatal Beat Frequency Theory, which is a neurobiological model of interval timing based upon the coincidence detection of oscillatory processes in corticostriatal circuits that can be mapped onto the stages of information processing proposed by Scalar Timing Theory.

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Appendix I Striatal beat frequency model simulation parameters based on Matell and Meck (2004). (1) The corticothalamic oscillation period was specified as having a mean of 10 1.6 Hz. This is derived from a normal distribution of neuronal oscillation periods spanning a range of 515 Hz that is typical for corticostriatal neural activity. (2) Oscillation variability within trials was set so that 1 SD was equivalent to 1% of the input’s period, normally distributed. (3) Oscillation variability between trials was defined to be Gaussian with a mean of 0 and a SD of 5% of the oscillation period. (4) The integration period for coincidence detection by striatal spiny neurons of oscillating corticothalamic inputs was set at 25 ms in order to conform to the electrophysiological data reporting this as the average membrane time constant for spiny neurons. (5) The striatal spiny neuron firing threshold was placed slightly below the level at which peaks at the quarter points would induce spiking, i.e. at 50% of the maximal neural activity that occurred at the time of reinforcement. (6) Firing threshold variance for striatal spiny neurons within trials needs to be specified because striatal membrane properties are dynamic, showing slowly inactivating and slowly recovering potassium currents, which effectively lowers and raises the threshold for maintenance of the depolarization state by 30%. Consequently, in order to conform with the electrophysiological data, the simulated within-trial firing threshold decreased by 30% over the first second in a linear manner following the first crossing, and then increasing symmetrically after each subsequent threshold crossing while being reset to the lower threshold following each crossing. (7) In order to conform to the electrophysiological data, the firing threshold was varied between trials in a Gaussian manner with a mean of 0 and a SD of 4% of the selected threshold. (8) Although striatal medium spiny neurons are considered to have up to 30 000 inputs from cortical and thalamic neurons, we used 15 000 inputs in order to simplify the simulations while still maintaining a reasonable degree of physiological accuracy. Each input was weighted at + 1, if it was activated with the previous 25 ms at the time of reinforcement and 0 otherwise. These weights were used for simplicity and are intended to accurately represent the continuous strengthening and weakening of input due to the long-term potentiation or long-term depression expected during conditioning (training). In many models, parameters are free to vary so as to best fit the data. In the reported simulations of the striatal beat frequency model, only the threshold estimate and oscillation variability were allowed to vary in order to fit the experimental data (Matell and Meck, 2004). Estimates of all other parameters were based on neurobiological data or were fixed once a reasonable outcome was achieved for baseline conditions (e.g. oscillation speed) prior to drug treatments. As such, this relatively long list of parameters reflects functional components of the striatal beat frequency model more than it reflects a wide range of free parameters to be adjusted for fitting purposes. It should also be noted that this coincidence-detection model would work equally well for alternative forms of the time base. For example, if clusters of cortical neurons all fired with the same underlying membrane potential/oscillation period, the summed output of these neurons would be sinusoidal. Consequently, this sinusoidal activity would be the primary input to the striatal spiny neurons (as opposed to spike activity only at the peak of every oscillation), and the mechanism for pattern detection would involve a type of fast Fourier transform (see Matell and Meck, 2004 for additional details). Certain aspects of the striatal beat frequency model are similar to the state-dependent network model proposed by Karmarkar and Buonomano (2007). Intrinsic timing models of this sort assume that timing is an inherent property of neural processing and that ‘the duration of a stimulus is coded by the same neural elements that respond to other sensory properties of that stimulus’ (Spencer et al., 2009, p. 1854). In this sense, the same cortical oscillatory mechanisms that code auditory and visual stimuli also converge on striatal medium spiny neurons that can detect patterns or states of neural firing and the sequential transitions among these states. In this sense, the timekeeper of the striatal beat frequency model is not a ‘dedicated’ clock in that it makes use of neural processes that are coding other aspects of the stimulus, including basic working memory processes (Lustig et al., 2005). On the other hand, the timing functions of these distributed circuits can be ‘isolated’ and studied as if they function as the core of a ‘dedicated’ timer because its output demonstrates ‘time scale invariance’ and exhibits consistent properties across a relatively wide range of durations from milliseconds to minutes (Buhusi and Meck, 2005; Meck et al., 2008; Coull et al., 2011)—something that the proposed state-dependent network models (which are intrinsic to the cortex) are currently unable to offer (Buonomano, 2007; Karmarkar and Buonomano, 2007; Spencer et al., 2009).