Review article
Neural substrates of internally-based and externally-cued timing: An activation likelihood estimation (ALE) meta-analysis of fMRI studies

https://doi.org/10.1016/j.neubiorev.2018.10.003Get rights and content

Highlights

  • Interval timing involves both internally-based and externally-cued timing processes.

  • ALE meta-analysis suggests these processes might be partially dissociated.

  • Internally-based timing involves areas also involved in externally-cued timing.

  • SMA, right IFG, left PCG and INS are more activated during externally-cued timing.

Abstract

A dynamic interplay exists between Internally-Based (IBT) and Externally-Cued (ECT) time processing. While IBT processes support the self-generation of context-independent temporal representations, ECT mechanisms allow constructing temporal representations primarily derived from the structure of the sensory environment. We performed an activation likelihood estimation (ALE) meta-analysis on 177 fMRI experiments, from 79 articles, to identify brain areas involved in timing; two individual ALEs tested the hypothesis of a neural segregation between IBT and ECT. The general ALE highlighted a network involving supplementary motor area (SMA), intraparietal sulcus, inferior frontal gyrus (IFG), insula (INS) and basal ganglia. We found evidence of a partial dissociation between IBT and ECT. IBT relies on a subset of areas also involved in ECT, however ECT tasks activate SMA, right IFG, left precentral gyrus and INS in a significantly stronger way. Present results suggest that ECT involves the detection of environmental temporal regularities and their integration with the output of the IBT processing, to generate a representation of time which reflects the temporal metric of the environment.

Introduction

Time is among the most distinctive dimensions of human experience. Almost all our psychological processes involve time processing, and all of our actions unfold over time and rely on accurate timing. Since there is no physical object corresponding to the concept of “time”, the temporal dimension is built actively by the brain, exploiting the continuous experience of internal and external events (van Wassenhove, 2009). The complex interplay between endogenous and exogenous factors in time processing is evident in circadian timing: indeed, since circadian rhythms maintain their inherent frequency in absence of external temporal cues (Aschoff, 1965), they are thought to reflect the operation of internal timing mechanisms (Reppert and Weaver, 2002). However, circadian processing also entrains to environmental periodic cues, that act as external timekeepers according to which the organism adaptively synchronizes its physiological and behavioural periods (Sharma and Chandrashekaran, 2005).

Interval timing – involving durations ranging from hundreds of milliseconds to hours (Kononowicz and van Wassenhove, 2016; Buhusi and Meck, 2005) – can thus be thought of as characterized by a similar dynamic switching between the involvement of endogenous time-keeping mechanisms, and the exploitation of exogenous temporal cues. Indeed, temporal representations may be derived independently from external inputs. When deciding to call back someone in “ten minutes”, a representation of such a temporal interval can be constructed in completely different environments (from a busy office to a completely dark and silent room), basing entirely on our inner sense of the flowing of time. Conversely, the detection of a violation within a rhythm (for example when a CD skips) depends not only on the representation of single temporal intervals (Vuust and Witek, 2014), but also on the structure of the sensory signal which gives place to the rhythm itself.

Broadly speaking, the experience of time can thus be framed in two main domains: one, “internally-based”, involving an endogenous sense of the flowing of time, and another, “externally-cued”, in which the experience of the temporal dimension mainly depends on the structure of the external environment. Timing a self-initiated movement (e.g. Bortoletto and Cunnington, 2010), deciding to perform an activity after a given amount of time (e.g. Waldum and McDaniel, 2016), or pressing a key when a specifically labelled temporal interval is thought to be elapsed (e.g. Basso et al., 2003), are all examples of internally-based timing, since they involve the self-generation of a temporal representation that is independent from any external cue. The existence of such internally-based timing mechanisms supports the possibility to reliably report the time and accurately produce temporal intervals in sensory deprivation conditions (Zubek et al., 1961), as well as the ability to evaluate the temporal extent of an event with no aid provided by the surrounding context (as it is in laboratory tasks requiring to estimate the duration of discrete stimuli).

However, temporal representations are not always generated independently from exogenous inputs. Movements can be performed responding to external cues (externally-triggered movements), or according to a perceived environmental temporal metric. In these situations, externally-cued timing mechanisms can account for the creation of temporal representations that are primarily derived from the sensory context. This is no surprise considering our brain’s ability to detect different kinds of regularities typically embedded in the stream of the sensory input: A well-known example of this ability is statistical learning, for which the detection of environmental regularities allows to extract probabilistic information about future events, optimizing their prevision (see for example Baker et al., 2014; Di Bernardi Luft et al., 2015; Wang et al., 2017).

Timing can be thus seen as involving a continuous switching between internally-based and externally-cued timing processes, differentially recruited according to task requests and the environmental context. However, though internally-based and externally-cued timing are normally deeply intertwined, both neuropsychological and neuroimaging literature suggest that these two processes might actually be dissociated. Patients with cerebellar degeneration are impaired in interval bisection (Nichelli et al., 1996) and duration discrimination (Grube et al., 2010a), two tasks likely involving internally-based timing mechanisms (see below), but they perform normally when asked to detect deviants within isochronous sequences, or to discriminate rhythm changes – two examples of externally-cued timing tasks – (Grube et al., 2010a). Similarly, transcranial magnetic theta-burst stimulation (cTBS) of the medial cerebellum impairs duration discrimination, but does not affect the ability to discriminate between regular and irregular tone sequences (Grube et al., 2010b). The opposite dissociation characterizes timing in Parkinson’s Disease (PD) patients, that are impaired in forming a representation of time based on the temporal pattern provided by a sensory signal (externally-cued processing of time) (Harrington et al., 1998; Freeman et al., 1993), but do not differ from controls in discriminating temporal sequences devoid of an implied beat (and thus likely tapping internally-based timing mechanisms) (Grahn and Brett, 2009).

The dissociation between internally-based and externally-cued timing mechanisms is also supported by functional mapping in healthy subjects. In the motor domain, self-initiated and externally-cued movements differentially engage a basal ganglia-thalamo-motor loop (Taniwaki et al., 2003). Similarly, in the perceptual timing domain, Teki et al. (2011) showed that a striato-thalamo-cortical network (including caudate nucleus, putamen, supplementary motor area, dorsal premotor and dorsolateral prefrontal cortex) is recruited during duration discrimination when an exogenous regular temporal metric is provided. Conversely, an olivo-cerebellar network seems to be involved in the same task when external-cueing is lacking, and duration discrimination should rely on internally-based timing mechanisms (Teki et al., 2011).

Here, we propose the possibility to identify two complementary, yet distinct, timing mechanisms: an internally-based timing mechanism (IBT), which mediates the generation of temporal representations independently from the sensory environment, and an externally-cued one (ECT), primarily operating when the temporal representations are based on exogenous inputs. Current neuropsychological and neuroimaging findings support the hypothesis that these two timing mechanisms are instantiated in, at least partially, segregated functional networks. With this frame in mind, the current study has the following aims: 1) to identify neural substrates of internally-based and externally-cued timing mechanisms, and 2) to test the possibility of a dissociation between neural correlates of these two forms of temporal processing. To these purposes, we will perform a meta-analytic review using activation likelihood estimation (ALE) (Eickhoff et al., 2009). Even though several ALE meta-analyses on fMRI studies on timing have been performed over recent years (Witt et al., 2008; Wiener et al., 2010; Ortuño et al., 2011; Schwartze et al., 2012; Radua et al., 2014; Chauvigné et al.,2014), this is the first one investigating brain regions supporting two main categories of timing mechanisms, and looking for a dissociation between their neural circuits. Indeed, Witt and colleagues explicitly investigated neural correlates of finger tapping tasks, together with regions differentially involved according to most common task variations. They highlighted a common finger-tapping network, involving primary sensorimotor cortices, SMA, premotor and parietal cortices, basal ganglia and cerebellum. Furthermore, they revealed a set of regions preferentially involved when visual pacing stimuli (rather than auditory or no cues) were used, while less difference was found among brain networks recruited according to movement complexity (Witt et al., 2008). Similarly, Chauvigné and co-authors compared auditory- and self-paced finger tapping studies in order to identify neural substrates of sensorimotor entrainment to auditory cues. They highlighted preferential activation of pallidum in self-paced tasks, and selective involvement of cerebellar vermis in auditory-cued ones, suggesting that this latter region may play a key role in entrainment (Chauvigné et al., 2014). These studies were thus specifically focused on neural substrates of finger-tapping and synchronization, rather than on general temporal processing. Other meta-analytic studies dealt more specifically with timing. Wiener et al. performed a comprehensive meta-analysis on neural substrates of temporal processing, also investigating brain regions selectively involved according to the type of task (perceptual vs. motor) and the duration of the stimuli (subsecond vs. suprasecond). This study revealed the involvement of partially dissociable networks in subsecond and suprasecond timing, with preferential activation of subcortical structures (basal ganglia and cerebellum) in subsecond tasks, and stronger involvement of cortical regions – including right inferior frontal gyrus (IFG) and middle frontal gyrus – in suprasecond timing. Furthermore, common activations in right IFG and SMA were found across all conditions (Wiener et al., 2010). The role of SMA in timing was further explored in a meta-analysis by Schwartze et al., who highlighted a preferential involvement of pre-SMA in suprasecond, sensory and non-sequential tasks, and of SMA-proper in subsecond, sensorimotor and sequential tasks (Schwartze et al., 2012). Finally, Ortuño and colleagues performed an ALE meta-analysis on temporal processing including both implicit and explicit timing tasks, also comparing timing abilities across healthy participants and schizophrenic patients. Results of the meta-analysis on healthy participants partially replicated those of the study by Wiener et al. (2010), highlighting activation in bilateral SMA, frontal and insular regions, right cerebellum and thalamus and left temporal regions (Ortuño et al., 2011). Radua and co-authors later integrated data from the study by Ortuño et al. (2011) with those from a Signed Differential Mapping meta-analysis on cognitive difficulty, investigating the degree of overlap between neural circuits involved in timing and those engaged by cognitive effort increasing. Prefrontal, temporal, parietal, insular regions and putamen were found to be activated by both tasks of time perception and tasks involving cognitive effort, while activation of basal ganglia and temporal regions was specifically found during time perception (Radua et al., 2014).

While these studies have provided important insights into the neural substrates of timing, they have focused on specific aspects of temporal processing, leaving unanswered the question of whether it is possible to identify any superordinate timing mechanism that is common to different categories of tasks and paradigms. Here we suggest the possibility to draw a new subdivision between tasks tapping on internally-based and externally-cued timing processes. To these purposes, we will first present a schematic review of the main experimental paradigms used in fMRI studies on timing. Paradigms will be presented according to the theoretical axis above-described, namely the fact that they involve a self-generated representation of time (internally-based timing tasks) or an environment-driven one (externally-cued timing tasks). Then, data from previous fMRI studies will be integrated performing an ALE meta-analysis, in order to identify the neural substrates of internally-based and of externally-cued timing, and to test the hypothesis of their dissociation. The ALE meta-analysis will allow to test our hypothesis about the possible dissociation between internally-based and externally-cued time processing, overcoming some important limitations of the single study approach, such as small sample size, low reliability and logical subtraction.

As stated above, we propose that IBT and ECT processes are differentially involved depending on whether the surrounding context provides reliable cues on elapsing time (ECT) or not (IBT). The physical and perceptual properties of the time intervals which duration has to be represented are thus critical in triggering the IBT vs. the ECT mechanism. Indeed, different tasks requiring to represent the temporal extent of events during which no structured or predictable pattern is provided should similarly involve IBT processes. In this vein, duration discrimination – one of the most widely used paradigms in timing research – can be considered an example of task likely tapping internally-based timing processes. In this task, participants are required to compare two durations, judging which of the two is longer. Apart from variations in the task structure and stimuli, duration discrimination basically involves five steps: (a) the estimation of the standard interval, (b) the encoding of this duration, (c) the estimation of the probe interval, (d) the retrieval of the standard duration, and (e) its comparison with the probe duration (Coull et al., 2008a,b). In duration discrimination paradigms both the standard and the probe interval have to be estimated in absence of cues which variations can be exploited to help judging the passage of time, either because they correspond to periods in which no stimulation occurs (Harrington et al., 2004, 2010; Rao et al., 2001; Mathiak et al., 2004; Shih et al., 2009a; Harrington et al., 2011; Pouthas et al., 2005; Bueti et al., 2012), or because stimuli are continuous tones (Tregellas et al., 2006), stationary shapes (Hayashi et al., 2013, 2015; Lewis and Miall, 2003; Ferrandez et al., 2003; Shih et al., 2009b; Skagerlund et al., 2016; Gutyrchik et al., 2010), or show rapid fluctuations which are not perceived as sequential and discrete (Livesey et al., 2007; Coull et al., 2004; 2008a,b; Pfeuty et al., 2015).

Another task tapping on internally-based timing is interval production. In this task, participants are asked to produce an interval equivalent to an indicated duration (Macar et al., 2004, 2006; Tracy et al., 2000), thus translating a specifically labelled time interval into a subjective duration (Mioni et al., 2014). Performance in interval production tasks depend on (a) one’s own internal representation of a specific amount of time, and (b) one’s own perception of the elapsing time during the production phase, while no external stimulation is provided.

In another popular task, interval reproduction, participants are asked to reproduce a duration showed by the examiner (Aso et al., 2010; Bueti and Macaluso, 2011; Bueti et al., 2008a; Hinton et al., 2004; Jech et al., 2005; Murai and Yotsumoto, 2016; Teki and Griffiths, 2016; Wittmann et al., 2010b, 2011). Performing this task involves (a) the estimation of the sample duration, and (b) a temporal perceptual judgment or a timed sustained motor response (Bueti et al., 2008a) based on the estimation of the time elapsing during the reproduction phase (Perbal et al., 2005). Both (a) and (b) crucially depend on the individual’s ability to internally estimate and represent a target duration, since typically no reliable environmental input which can be used to provide an exploitable timeframe is provided.

In temporal bisection tasks, a “short” and a “long” durations are repeatedly presented during a training phase. Then, in the test phase, participants are presented with a set of durations, which includes intermediate durations different from those used in training, and judge whether each of them is more similar to the “short” or “long” standard (Mathiak et al., 2004; Tipples et al., 2013). Though the way in which extreme durations are represented and the decision rule on which categorization is based in this task are still a matter of debate (Allan and Gerhardt, 2001; KeiNg et al., 2011), temporal bisection is recognized to require in the first place the construction of a representation of at least one of the two standard durations as an anchor, similarly to what occurs in duration discrimination tasks.

Temporal orienting tasks, instead, require to allocate attention to a specific point in time, exploiting information about the temporal delay before the onset of a target (Coull and Nobre, 1998) in order to respond to its presentation as fast as possible (Coull et al., 2000; Li et al., 2012). Although these tasks are based on the presentation of external cues, notably these cues do not possess any intrinsic temporal meaning, neither provide any structured temporal frame. In order to build endogenous temporal expectations about the onset time of the target, participant have to intentionally associate the cues with self-generated temporal representations of delays of different lengths (Coull and Nobre, 2008). Furthermore, the proper allocation of attention to a given moment in time in these tasks is only possible if the cued time intervals have been discriminated and the elapsing time is correctly estimated (Coull and Nobre, 1998). This involves a fine temporal discrimination ability, that essentially depends on one’s inner sense of the flowing of time.

The formation of endogenous temporal expectations is also crucial in tasks in which participants are required to respond as fast as possible to occurring targets, but no explicit information is given about the pairing between each cue and a specific inter-stimulus interval. In these tasks, targets are nonetheless presented in specific time windows based on probabilities distributions, thus allowing participants to learn to effectively anticipate their onset times. The prediction of temporal probabilities of future events in these tasks thus requires the representation of elapsed time (Bueti et al., 2010; Bueti and Macaluso, 2010), and the continuous discrimination of elapsing time intervals, without any environmental cue to inform this computation.

Other paradigms involving internally-based timing mechanisms include those in which movements are performed according to the subject’s own estimation of the elapsed time and subjective representation of a given time interval (Shergill et al., 2002, 2006; Cunnington et al., 2002; Taniwaki et al., 2003).

Tasks tapping ECT processes are characterized by the presence of an exogenous sensory signal (time-cue) during the interval/event which temporal features have to be represented. Thus, differently from IBT paradigms, ECT tasks allows participants to rely on some kind of predictable sensory pattern in order to develop the temporal representation required by the task. The most straightforward example of externally-cued timing task is, to our advice, sensorimotor synchronization (SMS), which requires the temporal coordination of an action with a periodic external event acting as a referent (Repp, 2005). In its simplest form, SMS requires participants to tap their finger in-phase with an isochronous auditory cue (e.g. Rao et al., 1997; Lewis et al., 2004; Hove et al., 2013), though the pacing cues may also be visual (e.g. Lutz et al., 2000; Cerasa et al., 2005), the type of requested movement may vary (Rubia et al., 1998; Riecker et al., 2003; Indovina and Sanes, 2001; Mayville et al., 2002; Thickbroom et al., 2000; Jantzen et al., 2004, 2005; 2007; 2008; Liu et al., 2011; Gagnon et al., 2002; Shergill et al., 2006), and the tapping may be anti-phase with the stimulus (Stevens et al., 2007). SMS is also commonly performed using metric rhythms as pacing signals (Lewis et al., 2004, 2011; Chen et al., 2006; 2008a; 2008b). Apart from these variations, SMS basically requires to perform a motor sequence which pace mirrors the regular temporal metric of an exogenous cue. It is noteworthy that the motor system tends to entrain to a periodic stimulus also in absence of an explicit intention to synchronize (see Repp, 2005 for a review), suggesting that the external regular context in this kind of task strongly affects the formation of a representation of time. Thus, SMS paradigms should obviously be considered as an instance of timing mechanisms engaged by an exogenous periodic event.

Synchronization paradigms can also contemplate a “continuation phase”, in which the movement rate set during the synchronization phase has to be maintained after the external pacing signal is discontinued (Rao et al., 1997; Lutz et al., 2001; Jäncke et al., 2000; Lewis et al., 2004; Jantzen et al., 2007; Spencer et al., 2007). Although no pacing sensory input is present during the continuation phase, movement timing in this period should reflect externally-cued timing processes as well, given that it had been set during the synchronization and thus depends on the temporal structure of the previously presented external cue. Indeed, it has been suggested that temporal complexity features of movements can simply be maintained during the continuation phase after they have been selected and initiated during the synchronization (Lewis et al., 2004), and that their timing in the continuation phase can be supported by internal generation of the previously presented pacing signal (Rao et al., 1997; Cerasa et al., 2005).

The previous experience with the external stimulus is also critical in tasks in which a rhythm is reproduced after a variable delay from its presentation. Indeed, to perform these tasks, individuals need to encode a rhythmic sequence, keep it in mind for a certain amount of time in which no stimulation or movement occurs, and then perform the sequence according to the remembered temporal metric of the rhythm. Rhythm reproduction (RR) clearly differs from synchronization-continuation paradigms (SC) in that: (1) RR but not SC, involves a working memory component, (2) in SC, but not in RM, a movement is performed while the pacing stimulus is present. Despite these differences, the same kind of information (i.e., the periodic stimulation) is likely encoded in both types of paradigm, making them more similar than they seem at the first glance. In this vein, movement timing in RR tasks depends on the representation of an external stimulation, as it does in the continuation phase of synchronization paradigms.

Rhythm monitoring, requiring the detection of deviations within a rhythm, can be listed among externally-cued timing tasks as well. Though rhythm monitoring tasks do not require to synchronize a motor act with an external stimulation, the decision about the presence of deviant intervals is based on the comparison between the just perceived stimulus and the representation of the regular temporal structure of the stimulation stream. In order to detect deviants, indeed, inter-stimuli intervals have to be compared not on a one-by-one basis (as in duration discrimination paradigms) but rather with the temporal structure of the rhythmic sequence as a whole. In this sense, in this task the intrinsic periodic pattern of an external stimulus is used to make decisions about the expected duration of a single inter-stimulus interval. Other, partially similar, tasks require frequency discriminations or decisions about the speed of rhythms (Hermann et al., 2014; Grahn and McAuley, 2009). Again, the decision in these tasks depends on the perception of the temporal evolution of an external regular input.

Finally, some tasks require the formation of exogenous temporal expectations (Coull and Nobre, 2008), i.e. temporal expectations arising unintentionally as a consequence of the exposition to external stimuli with a regular temporal structure. In paradigms in which sequential stimuli are presented with an isochronous inter-stimulus interval, and participants are asked to predict the onset time of the last stimulus of the series (see for example Tomasi et al., 2015; Kudo et al., 2004), the reliable temporal metric provided by the periodic structure of the stimulation stream acts as a reference frame, which allows accurate predictions. In this kind of tasks, the timing of the motor response is thus entirely determined by the temporal organization of the exogenous signals. The arising of exogenous temporal expectations is also crucially involved in the so-called “time-to-contact” tasks (see for example Coull et al., 2008b; Filip et al., 2016; Li et al., 2015; Limongi et al., 2013). Indeed, these tasks require participants to use the implicit temporal information provided by the speed of moving objects in order to make previsions about their future position. The temporal information to be extracted for performing these tasks is thus entirely embedded in the regular evolution pattern in time of the environmental stimuli which future state has to be predicted.

Section snippets

Inclusion criteria for papers

We adopted a systematic approach to review literature and select relevant peer-reviewed journal articles for our meta-analysis. The search for relevant literature was performed on PubMed, using the following keywords and their possible combinations: fMRI AND “timing”, “time”, “temporal”, “interval”, “internal”, “clock”, “duration”, “rhythmic”, “event timing”, “tempo”, “perception”, “reproduction”, “discrimination”, “processing”, “sequence”, “based”, “expectation”, “orienting”, “prediction”,

General meta-analysis

The general ALE analysis revealed clusters of activation in both hemispheres (Fig. 2 and Table 1). On the medial brain surface we found activation of the bilateral supplementary motor area (SMA), extending also to the middle cingulate cortex (MCC) in the right hemisphere. On the lateral brain surface we found clusters of activation in the bilateral intraparietal sulcus (IPS) and inferior frontal gyrus (IFG), extending to the bilateral insular cortex, specifically to the anterior insula (aINS).

Discussion

The present meta-analysis tested the hypothesis that time representation may be achieved by means of two different types of processing: an internally-based process, independent from contextual cues, and an externally-cued process, which processes time as an intrinsic property of perceived environmental stimuli.

We hypothesised that differences between internally-based and externally-cued timing are not a by-product of experimental conditions and paradigms, but that the two types of processing

Conclusions

We reported the results of a comprehensive ALE meta-analysis on timing literature, and we tested the hypothesis of a dissociation between brain circuits supporting timing according to an endogenous tracking of time, or based on the structure of exogenous inputs. We highlighted a broad network for general time processing, including basal ganglia, parietal and frontal regions, coherently with previous voxel-wise meta-analyses of timing studies and general timing literature. Finally, present

Acknowledgments

The present study was partially supported by a fellowship from the PhD Program in Behavioral Neuroscience of Sapienza University of Rome to AT.

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