Neural Basis Of Interval Timing

A rich tradition of normative psychophysics has identified two ubiquitous properties of interval timing: the scalar property, a strong form of Weber's law, and ratio comparison mechanisms (Gallistel and Gibbon, 2001; Gibbon, 1977). Temperature and reinforcement density effects on the speed of the internal clock have also been studied in order to determine mechanisms of compensation and regulation (see Hills, this volume). Isolating the neural substrate of these properties is a major challenge for neurobiology (Gibbon et al., 1997). On the basis of the accumulation of evidence from drug and lesion studies, a potential mapping between the information-processing elements of SET and structures in the brain has been proposed (see MacDonald and Meck, this volume; Malapani and Rakitin, this volume; Matell and Meck, 2000; Matell et al., this volume; Meck, 1996; Meck and Benson, 2002). Specifically, the output from dopaminergic neurons in the substantia nigra pars compacta is proposed to play a central role in initiating and maintaining the temporal integration process involving cortico-striatal circuits. This hypothesis is supported by the observation that methamphetamine, a stimulant drug that acts by facilitating the synaptic release of dopamine, speeds up the clock, whereas haloperidol, which acts by blocking dopamine receptors, slows down the clock (e.g., Abner et al., 2001; Maricq et al., 1981; Meck, 1983). Dopamine D2 receptors are specifically implicated in the function of the internal clock by a study showing that the in vitro binding affinity of different neuroleptic drugs for the D2 receptor predicts the size of the rightward shift in timing functions they produce (Meck, 1986).

Much progress has been made in the last 25 years in terms of our understanding of the psychological processes involved in timing and time perception. Nevertheless, our understanding of the neural basis of interval timing is far from complete. The ability of the brain to process time in the seconds-to-minutes range remains a fascinating problem given that the basic electrophysiological properties of neurons operate on a millisecond time scale. One physiologically realistic model of interval timing integrates a multitude of cortical and thalamic oscillations with a "perceptron" processing system in the basal ganglia to arrive at the detection of intervals much larger than the oscillation periods (see Matell and Meck, 2000; Matell et al., this volume). This model is based on the observation that striatal spiny neurons receive input from 10,000 to 30,000 separate inputs from a wide variety of cortical and thalamic areas. These cortical and thalamic neurons oscillate with a mean periodicity of 10 Hz. The striatal spiny neurons have been hypothesized to be capable of detecting and responding to select patterns of cortical input. The particular pattern of excitatory input is selected by long-term potentiation and long-term depression, which are believed to result from dopaminergic activity from the midbrain (e.g., substantia nigra pars compacta and the ventral tegmental area) following the delivery of feedback (e.g., Houk, 1995). Additionally, these dopamine neurons have been shown to transfer their activation onset to the signals that predict subsequent feedback (e.g., Schultz et al., 1993). These neurobiological properties of the cortico-striatal circuitry can be combined with a "beat frequency" model of timing (Miall, 1989, 1996) that suggests that after resetting a range of oscillatory inputs, a specific time can be encoded by selectively weighting which inputs are currently active at the criterion time. This model's time coding is similar to the idea that one can code the number 15 by asking for the lowest common multiple of 3 and 5, thereby coding large numbers with much smaller numbers. Thus, the model provides a manner to encode a long interval with very short neuronal mechanisms using the concept of coincidence detection, which has been hypothesized as a function of basal ganglia information processing (see Houk, 1995; Matell and Meck, 2000; Matell et al., this volume).

Specifically, upon onset of a meaningful signal (e.g., a cue that predicts important outcomes), dopamine neurons fire in a burst pattern that transiently synchronizes the cortical and thalamic oscillations, as well as hyperpolarizes the striatal membrane, thereby resetting the integrating mechanism. The cortical and thalamic neurons begin to oscillate at their inherent periods, thus eliminating their synchronization and allowing particular patterns of activity to become meaningful. Upon detection of a previously reinforced pattern of input, via the crossing of a coherent activity threshold (set by baseline levels of dopamine input and striatal interneurons), an ensemble of striatal spiny neurons fire, thereby engendering a response that the encoded time has been reached. This striatal activity passes out of the basal ganglia to the thalamus, and from there back to the cortex and striatum, thereby impinging on the current oscillatory inputs, allowing alterations of timing and time perception. Such information flow through cortico-striato-thalamo-cortical loops has been observed in functional magnetic resonance imaging data during psychophysical timing tasks with human participants (see Hinton, this volume; Hinton et al., 1996; Meck et al., 1998). Alternative views concerning the roles of the dorsolateral prefrontal cortex (dlPFC) and striatum in timing have been proposed that rely on decay processes within the dlPFC to form the central clock process (e.g., Lewis, 2002). Such "decay models" do not attribute a significant role in interval timing to the striatum, although they may do so once they are fully specified. It is clear, however, that the types of inhibitory delay circuits described by Constantinidis et al. (2002) are an important feature of temporal processing in the dlPFC and may provide the basis for an internal clock, as suggested by Lewis and Miall (this volume).

Additional studies of central nervous system electrophysiology have suggested an important role for oscillatory neuronal activity in sensory perception, sensorim-otor integration, and movement timing. These studies have demonstrated significant structure in basal ganglia neuron spiking activity at relatively long time scales. The modulation of multisecond periodicities in the firing rate of basal ganglia neurons by dopaminergic agonists and their correlation with theta bursts in transcortical and hippocampal electroencephalographs (EEGs) suggest the involvement of these and other brain structures (e.g., frontal cortex, hippocampus, and cerebellum) in the coordination of cognitive processes (e.g., Allers et al., 2002; Ruskin et al., 1999; Sakata and Onoda, this volume).

In addition to the basal ganglia, the cerebellum has been proposed to constitute an important component of a neural circuit including the cerebral cortex that is involved in timing and time perception (see Diedrichsen et al., this volume). Cerebellar damage alters the cerebral metabolism in the prefrontal cortex (e.g., Botez et al., 1991; Junck et al., 1988). In humans, frontal, temporal, occipital, and also subcortical lesions affect temporal processing, which is supported by the observation that during tasks where time is a crucial parameter, the contingent negative variation, a slow variation of the cerebral potentials correlated with the estimation of the time separating two stimuli, has a maximum amplitude over the prefrontal areas (see Macar et al., 1990; Monfort et al., 2000; Pouthas, this volume; Vidal et al., 1992).

On the basis of their analysis of patients with cerebellar lesions of various etiology, Ivry and Keele (1989) and Keele and Ivry (1990) proposed that the cerebellum serves as a biological clock. Patients with cerebellar damage had deficits in a tapping task performed in the absence of an external cue, their interresponse variance times being much higher than those of normal subjects. The deficits of cerebellar patients were not limited to tasks requiring movement, as these patients also had difficulty in tasks requiring the estimation of the duration of an auditory stimulus and of the velocity of a visual stimulus. The latter results are especially convincing in demonstrating a role for the cerebellum in time estimation, as the required responses were emitted in the absence of external cues and in the absence of movement.

Just as in the case of the basal ganglia, however, there are some uncertainties about the precise role of the cerebellum in interval timing. Surgical intervention or diseases of the cerebellum generally result in increased variability in temporal processing, whereas both clock and memory effects are seen for pharmacological interventions, lesions, and diseases of the basal ganglia. Some theorists have argued that cerebellar dysfunction may induce deregulation of tonic thalamic tuning, which disrupts gating of the mnemonic temporal information generated in the basal ganglia through striato-thalamo-cortical loops (see Casini and Ivry, 1999; Diedrichsen et al., this volume; Ivry and Richardson, 2002). Other researchers have claimed somewhat different roles for the cerebellum and basal ganglia in timing and time perception (see Gibbon et al., 1997; Malapani et al., 1998a; Malapani and Rakitin, this volume; Miall and Reckless, 2001). Furthermore, some studies have shown that damage to the cerebellum caused by developmental stunting (Ferguson et al., 2001) or by lesions to the cerebellar vermis and hemispheres (Breukelaar and Dalrymple-Alford, 1999) has little or no effect on time estimation within the range of seconds, but may play a greater role in millisecond timing and counting processes where constant variability is a prominent source of error (e.g., Clarke et al., 1996; Ivry, 1996).

To the extent that there is disagreement in the field as to the nature of the contribution of the basal ganglia and cerebellum to interval timing, it should be kept in mind that different investigators may be optimizing their behavioral tasks to detect the timing contributions of specific brain structures. Consequently, we will conclude this discussion of the neural basis of interval timing by describing the results of a provocative experiment reported by Woodruff-Pak and Papka (1996). In this study Huntington's disease (HD) patients were evaluated for eye blink classical conditioning (EBCC) — a millisecond timing task that shows a high degree of sensitivity to damage to the cerebellum (e.g., Bao et al., 2002; Perrett, 1998). Because HD causes severe atrophy of the basal ganglia and thinning of the cortex, but no disruption or damage to the brain circuits engaged in EBCC, Woodruff-Pak and Papka (1996) predicted that HD patients would perform like normal age-matched controls in a 400-msec delay EBCC paradigm. The findings indicated that there were no differences in the production of conditioned responses between the patients and the controls, but the timing of the conditioned responses was abnormal in HD. Comparison of HD patients to patients with other neurodegenerative diseases (e.g., probable Alzheimer's disease, Down's syndrome) and patients with cerebellar lesions demonstrated significantly better EBCC performance in HD patients, indicating a normal ability to acquire the conditioned response, but an impaired ability to time the conditioned response. Taken together, these data provide support for the striatum having a role in optimizing the timing of the conditioned response in the EBCC paradigm — a task specifically designed to assess the integrity of the cerebellum.

As the interested reader will soon see, the following chapters synthesize the latest information on both human and animal timing behavior as related to both technical and theoretical approaches. Chapters written by the foremost experts in the field provide the necessary background to understanding the psychophysics and neurobiology of interval timing. Such a synthesis sets the stage for an interdisciplinary dialogue among investigators on either side of the behavior-biology divide and leads us in new directions with advances made in molecular biology and neuroge-nomics. This type of integrated approach has proven invaluable in studies of perceptual systems employed across a wide range of species and will likely do the same for the study of timing and time perception. It is the expectation of all the contributors of this book that understanding temporal integration by the brain will be among the premier topics to unite systems, cellular, computational, and cognitive neuroscience over the next decade.

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