# TIME PERCEPTION AND DYSFUNCTION: CLINICAL AND PRACTICAL IMPLICATIONS

EDITED BY : Deana Davalos, Giovanna Mioni, Simon Grondin and Felipe Ortuño PUBLISHED IN : Frontiers in Human Neuroscience

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# TIME PERCEPTION AND DYSFUNCTION: CLINICAL AND PRACTICAL IMPLICATIONS

Topic Editors:

Deana Davalos, Colorado State University, United States Giovanna Mioni, Università degli Studi di Padova, Italy Simon Grondin, Université Laval Québec, Canada Felipe Ortuño, Universidad de Navarra, Spain

"Mi tiempo, tu tiempo, su tiempo" by Carlos Iraburu.

Processing time requires a complex set of abilities that dictates how one adapts to the environment. Timing is key in how we communicate, organize our space and understand the world. When timing is intact, it often exists below one's conscious awareness, but when one's ability to process time is disrupted, the effects are noticeable and widespread. To better understand the construct of temporal dysfunction, one has to examine the concept of timing from multiple angles. An integrative approach is required to understand the possible biological, cognitive and psychological etiologies of temporal dysfunction. In addition, expertise in language, measurement and psychopathology are necessary to comprehend how timing affects one's representation of the world. This Research Topic is dedicated to examining timing and temporal dysfunction across a variety of tasks and disorders. Specifically, we seek to understand the most basic types of timing dysfunction and how they may affect a wide range of behaviors and symptoms as well as higher levels of temporal deficits involved in how one perceives the time spent. This Research Topic represents the intersection of medicine, psychology, cognition and perception in a unified attempt to shed light on temporal processing. The collection of original research articles and case studies, highlighting novel methodologies and interventions, illustrates the complexity of timing dysfunction and how understanding these deficits helps us to get a fresh look at a wide range of clinical psychopathologies and to develop better interventions.

Citation: Davalos, D., Mioni, G., Grondin, S., Ortuño, F., eds. (2019). Time Perception and Dysfunction: Clinical and Practical Implications. Lausanne: Frontiers Media. doi: 10.3389/978-2-88945-704-5

# Table of Contents

*06 Editorial: Time Perception and Dysfunction: Clinical and Practical Implications*

Deana Davalos, Giovanna Mioni, Simon Grondin and Felipe Ortuño

*09 Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing*

Valentina Ciullo, Daniela Vecchio, Tommaso Gili, Gianfranco Spalletta and Federica Piras

Caroline Gagnon, Catherine Bégin, Vincent Laflamme and Simon Grondin

*61 Time Perception and the Experience of Time When Immersed in an Altered Sensory Environment*

Joseph Glicksohn, Aviva Berkovich-Ohana, Federica Mauro and Tal D. Ben-Soussan

*72 Multiple Looks of Auditory Empty Durations Both Improve and Impair Temporal Sensitivity*

Tsuyoshi Kuroda, Daiki Yoshioka, Tomoya Ueda and Makoto Miyazaki

*85 Overlapping but Divergent Neural Correlates Underpinning Audiovisual Synchrony and Temporal Order Judgments*

Scott A. Love, Karin Petrini, Cyril R. Pernet, Marianne Latinus and Frank E. Pollick

*96 Minimal Self and Timing Disorders in Schizophrenia: A Case Report* Brice Martin, Nicolas Franck, Michel Cermolacce, Jennifer T. Coull and Anne Giersch

### *104 Dissociating Explicit and Implicit Timing in Parkinson's Disease Patients: Evidence From Bisection and Foreperiod Tasks* Giovanna Mioni, Mariagrazia Capizzi, Antonino Vallesi, Ángel Correa, Raffaella Di Giacopo and Franca Stablum

*116 Temporal Resolution Needed for Auditory Communication: Measurement With Mosaic Speech*

Yoshitaka Nakajima, Mizuki Matsuda, Kazuo Ueda and Gerard B. Remijn

*124 Training in Temporal Information Processing Ameliorates Phonetic Identification*

Aneta Szymaszek, Anna Dacewicz, Paulina Urban and Elzbieta Szelag

*135 Disturbed Experience of Time in Depression—Evidence From Content Analysis*

David H. V. Vogel, Katharina Krämer, Theresa Schoofs, Christian Kupke and Kai Vogeley

Adrienne Wise and Michael Barnett-Cowan

# Editorial: Time Perception and Dysfunction: Clinical and Practical Implications

Deana Davalos 1 \*, Giovanna Mioni 2 , Simon Grondin3 and Felipe Ortuño4

1 Department of Psychology, Colorado State University, Fort Collins, CO, United States, 2 Department of General Psychology, Università degli Studi di Padova, Padova, Italy, 3 École de Psychologie, Université Laval, Quebec, QC, Canada, 4 Department of Psychiatry and Medical Psychology, Universidad de Navarra, Pamplona, Spain

Keywords: timing, time processing, temporal processing, timing dysfunction, temporal dysfunction

#### **Editorial on the Research Topic**

#### **Time Perception and Dysfunction: Clinical and Practical Implications**

The relationship between one's ability to process time and psychopathology has been documented for nearly a100 years. Schilder (1936) made detailed observations of temporal dysfunction across various types of psychopathology. More recent research has permitted to reach a point where different types of time processing could be parsed apart and eventually be regarded as "a precise marker" of certain diagnoses (e.g., schizophrenia) (Ciullo et al., 2018). As the research in temporal processing evolves, there is greater interest in understanding not only the different ways to measure time processing, but also how certain disorders may be affected by temporal dysfunction. The 13 articles that examine timing in this research topic allow us to better understand the temporal dysfunction encountered across a variety of disorders. In most domains of daily life, language plays a critical role in one's ability to communicate effectively and convey thoughts and emotions effectively. Language represents a domain, which has an intimate connection with time processing. Nakajima et al. tackle the basics of timing in language by exploring the temporal resolution needed for successful speech communication. The authors find that linguistic information is conveyed almost perfectly by auditory blocks around 40 ms. They note that the process resembles that of the visual system stringing together static movie frames of ∼40 ms into vivid motion, supporting the argument that while the auditory and the visual modalities are different in their peripheries, their cortical organizations may employ similar temporal grids. Dacewicz et al. extend the language and timing exploration by assessing children with specific language impairment (SLI) who are characterized by deficiencies in temporal information processing (TIP). Their results suggest that temporal training (exercises and games involving sequencing abilities and duration judgment), compared to non-temporal training, may play a unique role in improving both language and cognition of children with SLI. Szymaszek et al. extend on these findings, noting that TIP appears to be omnipresent in language communication and is reflected not only in phonemic hearing but also in phonetic identification. The authors explore the etiology of developmental language disorders, specifically the neural basis of SLI. Again, only those interventions focused on temporal training result in performance similar to normal control children when compared to non-temporal based interventions.

While language appears to be a characteristic that is associated with time processing across development and across disorders, it is schizophrenia that has received possibly the greatest attention over the years as "the" disorder most characterized by timing dysfunction. About schizophrenia, Schilder (1936) quoted a patient who reported, "There is no hour, no noon, no

#### Edited by:

Ryouhei Ishii, Osaka University, Japan

> Reviewed by: Hisato Sugata, Oita University, Japan

\*Correspondence: Deana Davalos<EMAIL_ADDRESS>
Received: 16 September 2018 Accepted: 03 October 2018 Published: 23 October 2018

#### Citation:

Davalos D, Mioni G, Grondin S and Ortuño F (2018) Editorial: Time Perception and Dysfunction: Clinical and Practical Implications. Front. Hum. Neurosci. 12:435. doi: 10.3389/fnhum.2018.00435 night. . . . Time does not move. I am wavering between past and future." Martin et al. report similar dysfunction in a case report involving a young man with schizophrenia, "AF." AF describes, "I do not feel the time," "The word 'time' has no meaning for me. . . " The authors explore the possibility that distortions of the temporal structure of consciousness contribute to the emergence of "self-disorders," including schizophrenia. The ability to process time is described as an implicit aspect of our relationship with the world and disturbances in the temporal structure of consciousness appear to be a key issue in the psychopathology of schizophrenia. Wilquin et al. also tested patients with schizophrenia to distinguish between interval timing and synchronization difficulties and, more generally, the spatial-temporal organization disturbances for voluntary actions. The authors used a new sensorimotor synchronization task to test these abilities. The patterns of results suggested that patients with schizophrenia were able to perceive and produce both simple and complex sequences of time intervals but are impaired in the ability to synchronize their actions with external events. The authors suggested that a specific deficit in predictive timing may be at the core of early symptoms previously described in schizophrenia. Glicksohn et al. continue the examination of one's subjective experience of time and how it relates to psychopathology by manipulating sensory environments. Participants experienced a whole-body altered sensory environment, including periods of white-colored vs. colored illumination of the chamber. Subjective experience of elapsed time was assessed, as were traditional measures of time production. The authors found that for those participants who reported a marked change in time experience, such as "the sensation of time disappeared," their time production data could not be linearized using a log–log plot, suggesting a "break" in the psychophysical function. The authors report descriptions of time experience induced by the changes in sensory environment that are strikingly similar to descriptions of time made in previous studies focused on depression and timing. Vogel et al. used qualitative research approaches and content analysis to specifically investigate disturbances of time experience in major depressive disorder. Their findings suggest that better understanding of the disturbance of lived time in MDD may help specify subgroups of MDD, degrees of symptom severity or distinct depressive syndromes. Similarly, Wise and Barnett-Cowan study timing in those with a history of concussions to see if timing can provide a cue to recovery. The authors find that people with concussion history are much less precise in an audiovisual TOJ task than control participants. The authors also note that the precision at the TOJ task increases as time elapses since concussion diagnosis, and suggest using this task, following concussion, to determine whether or not a return to work, or to play in the case of sport, is a correct decision. Gagnon et al. propose to use timing tasks like bisection for studying the emotions elicited by food pictures in people with an eating disorder, anorexia (AN) or bulimia nervosa (BN), instead of relying on traditional self-reported procedures or on invasive psychophysiological techniques. They showed that the duration of food pictures, in comparison to neutral ones, is overestimated by AN women and that, compared to participants with BN, AN participants perceived the duration of joyful food pictures as longer, and tended to overestimate the duration of the disgusting ones.

Across all of these studies of temporal processing in clinical populations, there is also a general basic question regarding how one measures timing. Are measures of time processing thought to be interchangeable, does each timing task relate to a different difficulty in everyday living, and most importantly, how should we be measuring timing? Love et al. attempt to disentangle temporal processing by examining the neural basis of synchrony and temporal order judgments (TOJ). As the authors point out, synchrony and TOJ are commonly used to investigate synchrony perception between sensory cues and to measure multisensory perception in general despite behavioral evidence suggesting that these tasks do not measure identical perceptual processes. Using fMRI, the clear differences in BOLD responses observed between the two tasks suggest the presence of overlapping, but divergent, neural mechanisms. The authors point out that studies requiring multisensory integration mechanisms may be task dependent, and may have implications for the study of atypical temporal processing in clinical populations and how we interpret temporal dysfunction. Kuroda et al. further examine how temporal information is presented to better understand how one perceives time-based changes in stimuli. Specifically, the authors examined predictive timing and how one perceives the regular rhythm of information to detect when a sound stimulus is displaced from the predicted sequence. An additional area of interest is the distinction between explicit and implicit timing. The crucial difference is whether or not the task instructions require subjects to provide an overt estimate of duration. Explicit timing is engaged whenever subjects make a deliberate estimate of discrete duration in order to compare it with previously memorized standard. Conversely, implicit timing is engaged, even without a specific instruction to time, whenever sensorimotor information is temporally structured and can be used to predict the duration of future events. Mioni et al. showed preserved temporal ability in patients with Parkinson's disease in the implicit timing task (tested with foreperiod paradigm) but PD patients failed in the explicit timing task (time bisection task) as they displayed shorter perceived durations and higher variability compared to controls. Implicit timing was further investigated by Ciullo et al. by testing the ability to generate probabilistic expectancies regarding when and where sensory stimuli will occur. Using graph theoretical analysis applied to structural connectivity data, they tested the extent of brain connectivity properties associated with spatio-temporal predictive performance. When the interaction between explicit and implicit temporal orienting processes was considered at the long interval, the authors found that explicit processes were related to centrality measures of the bilateral inferior parietal lobule, confirming a dissociation between explicit and implicit timing.

Overall, findings across these studies add to the literature in research assessing the role of timing in clinical disorders, how best to study timing, and how timing abilities may be associated with everyday tasks.

## AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

## REFERENCES

Ciullo, V., Piras, F., Vecchio, D., Banaj, N., Coull, J. T., and Spalletta, G. (2018). Predictive timing disturbance is a precise marker of schizophrenia. Schizophr. Res. Cogn. 12, 42–49. doi: 10.1016/j.scog.2018.04.001

Schilder, P. (1936). Psychopathology of time. J. Nerv. Ment. Disord. 83, 530–456.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

### ACKNOWLEDGMENTS

We thank all of the authors and reviewers who made substantial contributions to this research topic.

Copyright © 2018 Davalos, Mioni, Grondin and Ortuño. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing

Valentina Ciullo1,2, Daniela Vecchio1,3, Tommaso Gili1,4, Gianfranco Spalletta1,5 \* and Federica Piras1

1 Neuropsychiatry Laboratory, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy,

2 Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA), University of Florence, Florence, Italy,

3 Department of Psychology, Sapienza University of Rome, Rome, Italy, 4 IMT School for Advanced Studies, Lucca, Italy,

5 Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States

The ability to generate probabilistic expectancies regarding when and where sensory stimuli will occur, is critical to derive timely and accurate inferences about updating contexts. However, the existence of specialized neural networks for inferring predictive relationships between events is still debated. Using graph theoretical analysis applied to structural connectivity data, we tested the extent of brain connectivity properties associated with spatio-temporal predictive performance across 29 healthy subjects. Participants detected visual targets appearing at one out of three locations after one out of three intervals; expectations about stimulus location (spatial condition) or onset (temporal condition) were induced by valid or invalid symbolic cues. Connectivity matrices and centrality/segregation measures, expressing the relative importance of, and the local interactions among specific cerebral areas respect to the behavior under investigation, were calculated from whole-brain tractography and cortico-subcortical parcellation.

Results: Response preparedness to cued stimuli relied on different structural connectivity networks for the temporal and spatial domains. Significant covariance was observed between centrality measures of regions within a subcortical-fronto-parietaloccipital network -comprising the left putamen, the right caudate nucleus, the left frontal operculum, the right inferior parietal cortex, the right paracentral lobule and the right superior occipital cortex-, and the ability to respond after a short cue-target delay suggesting that the local connectedness of such nodes plays a central role when the source of temporal expectation is explicit. When the potential for functional segregation was tested, we found highly clustered structural connectivity across the right superior, the left middle inferior frontal gyrus and the left caudate nucleus as related to explicit temporal orienting. Conversely, when the interaction between explicit and implicit temporal orienting processes was considered at the long interval, we found that explicit processes were related to centrality measures of the bilateral inferior parietal lobule. Degree centrality of the same region in the left hemisphere covaried with behavioral measures indexing the process of attentional re-orienting. These results

#### Edited by:

Giovanna Mioni, Università degli Studi di Padova, Italy

#### Reviewed by:

Anne Giersch, Institut National de la Santé et de la Recherche Médicale (INSERM), France Mariagrazia Capizzi, Università degli Studi di Padova, Italy

> \*Correspondence: Gianfranco Spalletta<EMAIL_ADDRESS>
Received: 30 November 2017 Accepted: 08 May 2018 Published: 24 May 2018

#### Citation:

Ciullo V, Vecchio D, Gili T, Spalletta G and Piras F (2018) Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing. Front. Hum. Neurosci. 12:212. doi: 10.3389/fnhum.2018.00212

**9**

represent a crucial step forward the ordinary predictive processing description, as we identified the patterns of connectivity characterizing the brain organization associated with the ability to generate and update temporal expectancies in case of contextual violations.

Keywords: predictive timing, spatio-temporal predictive performance, structural connectivity, diffusion tensor imaging, complex network theory, insula, schizophrenia

### INTRODUCTION

fnhum-12-00212 May 23, 2018 Time: 17:11 # 2

The brain is capable of allocating attention dynamically on the basis of expectations concerning events occurrence, and this ability is critical for goal-directed behavior in a changeable environment. Indeed, real world information is often dynamic and imprecise, and the ability to derive timely and accurate inferences about changing contexts is crucial, given that prospective action control bears the potential for a successful interaction with the environment.

Behavioral findings (Coull and Nobre, 1998; Coull et al., 2000, 2013, 2016; Davranche et al., 2011; Carvalho et al., 2016) evidenced faster reaction times (RTs) to target stimuli preceded by informative pre-cues signaling event location and onset, indicating that attention can be deliberately and strategically oriented in space and time when overt (explicit) information is provided. Such behavioral optimization has been described as an endogenous process based on learned cue-target association that enhances action preparation (Coull and Nobre, 2008). Temporal expectations can also be derived more implicitly from the intrinsic predictive power of the flow of time, which is vector-like in nature, implying that probability of target occurrence increases with passing time (i.e., hazard function, Luce, 1986). Importantly, when prior expectations are violated, sensory (exogenous) information is integrated into the existing prediction model, and attention re-allocated to the novel context (Courville et al., 2006). For example, invalid trials with delayed target onset are detected faster than premature ones (Coull and Nobre, 1998), due to the increasing conditional probability over time that the event will occur given that it has not already occurred. Thus, both the explicit and the implicit nature of the source of temporal predictions modulate attentional orienting in order to optimize our interaction with unfolding sensory stimulation (Correa et al., 2005; Nobre et al., 2007; Correa, 2012).

Accordingly, computational approaches (Friston, 2005; Clark, 2013) have conceptualized the brain as an inference machine that integrates acquired experience about the world (prior believes) and incoming sensory information to generate probabilistic predictive models (posterior probabilities). Prior believes are updated in the light of new sensory input, allowing the system to arrive at posterior estimates. In other words, the posterior estimate depends on what is already known, and how much is learnt through the evidence, summarized in the probability of events occurrence (Hohwy, 2017). Any imbalance between expected and actual sensory data generates prediction error signals, computed as the difference between the prediction and the new evidence (Mathys et al., 2014), that adjust subjective expectations accordingly (Phillips et al., 2015; Hohwy, 2017). This theoretical framework is of particular clinical relevance, given that positive symptoms of schizophrenia (SZ) have been hypothesized as consequent to failures of predictive coding (Frith, 2005; Adams et al., 2013; Giersch et al., 2016; Sterzer et al., 2016; Martin et al., 2017), such that predictions are determined more by the new evidence and less by prior belief, while irrelevant stimuli are considered salient (e.g., delusion of reference) (Fletcher and Frith, 2009; Adams et al., 2013).

That's why defining the neural basis of predictive coding is a major challenge in psychiatry and neuroscience. Conversely, functional Magnetic Resonance Imaging (fMRI) investigations on predictive timing in healthy subjects (Coull and Nobre, 1998; Coull et al., 2000, 2013, 2016; Davranche et al., 2011; Carvalho et al., 2016) have implicated complex and distributed networks -including the cerebellum, frontal cortex and inferior parietal cortex-, in forming temporal expectations, but the relevance of these structures in optimizing prospective motor behavior as a function of informative cues is still debated due to heterogeneity in experimental methods and the intrinsic complexity of the studied process (see Correa et al., 2006).

Pharmacological and fMRI evidence suggest that explicit and implicit processes of temporal expectations are functionally dissociable. Indeed, ketamine selectively impaired the ability to use internal estimates of time to make predictions (Coull et al., 2011), while partially distinct neuronal networks are preferentially activated both by the fixed temporal predictability of temporal cues and by the dynamic updating of temporal probabilities in the neutral cue condition (Coull et al., 2016).

However, as far as we know, the neural substrates of the attentional re-orienting in case of invalid trials and of the explicit forms of temporal expectations have been directly compared in one investigation only (Coull et al., 2000).

In recent years, advances in network neuroscience and graph theory proved to be effective tools in mapping neural connectomics (i.e., the pattern of neural elements and structural interactions of the neural system, Sporns et al., 2005), providing a deeper insight of the brain structural connectivity organization and its relation to behavior and cognition (Bullmore and Sporns, 2009; Rubinov and Sporns, 2010). Indeed, such network-based approach to MRI data analysis permits to quantify key structural properties and dynamics, which shape the anatomical substrate for functional specialized processing and information binding (Sporns et al., 2000, 2005).

Here we aimed at investigating the relationships between behavioral indices of explicit spatial and explicit-implicit

temporal orienting effects and the brain structural network in a spatio-temporal predictive task in healthy subjects. To our knowledge, this is the first study that examined brain wiring in the context of explicit (i.e., based on learned cuetarget association established through valid cueing) and implicit (i.e., derived from the sensory evidence in case of invalid cueing) predictive timing using centrality and segregation metrics derived from complex network analysis of diffusion tensor imaging (DTI) data. Specifically, we aimed at identifying the connectivity patterns underlying predictive behavior in terms of local connectedness (i.e., individual elements within the whole structural network having the highest number of connections) (Bullmore and Sporns, 2009) and local segregation (i.e., the number of connections that exist between the nearest neighbors of an element within the whole structural network) (Sporns et al., 2000) as to characterize the relative importance of, and the local interactions among specific cerebral areas respect to the behavior under investigation.

## MATERIALS AND METHODS

### Participants

Twenty-nine healthy volunteers (age: 40.1 ± 12.3, 13F + 16M) were enrolled in the present study. Participants were recruited within the local community through advertising, and consecutively assessed at IRCCS Santa Lucia Foundation in Rome. All participants were screened for current or past diagnosis of any DSM-5 Axis I or II disorder using the SCID-5 Research Version edition (First et al., 2014) and SCID-5- PD (First et al., 2016a). Inclusion criteria were: (1) age between 18 and 65 years, (2) at least 8 years of education and (3) suitability for MRI scanning. Exclusion criteria included: (1) the presence of any psychiatric disorders according to DSM-5 criteria, (2) history of psychoactive substance dependence or abuse as investigated by the structured interview SCID-5-CV (First et al., 2016b), (3) history of neurologic illness or traumatic brain injury, (4) major medical illnesses, such as non-stabilized diabetes, obstructive pulmonary disease or asthma, hematological/oncological disorders, B12 or folate deficiency as indicated by blood concentrations below the lower normal limit, pernicious anemia, clinically significant and unstable active gastrointestinal, renal, hepatic, endocrine or cardiovascular system disease, newly treated hypothyroidism, (5) presence of any brain abnormality and microvascular lesion apparent on conventional FLAIR-scans (Iorio et al., 2013), (6) global cognitive dysfunction according to a Mini-Mental State Examination (MMSE) (Folstein et al., 1975) score lower than 24, consistent with normative data of the Italian population (Measso et al., 1993), (7) diagnosis of major neurocognitive disorder according to DSM-5 criteria and (8) non-Italian language native speaker.

All participants were right-handed, with normal or correctedto-normal vision. They gave written informed consent to participate after the procedures had been fully explained. The study was approved and carried out in accordance with the guidelines of the IRCCS Santa Lucia Foundation Ethics Committee.

## Temporal and Spatial Orienting of Attention Task

Subjects were seated comfortably in a quiet room facing a Toshiba computer screen (1600∗ 1200 resolution 1280∗ 768 pixels, frame rate 60 Hz).

Predictive timing was investigated with an adaptation of the temporal and spatial orienting of attention task (Coull and Nobre, 1998), which measured RT to a briefly presented target (100 ms) appearing after one of three foreperiod durations (FP: 540 ms/1080 ms/1620 ms) in one of three boxes (left/up/right) depicted on the computer screen (7◦ eccentricity). Subjects were asked to press a response button to detect the target, as quickly as possible, using information provided by one of three types of cue. The cue was presented prior to the target and was either informative (spatial and temporal conditions) or uninformative (neutral condition). During the neutral condition, no spatial or temporal information was provided, as the whole image brightened. Spatial locations and temporal onsets were balanced across trials for all the three experimental conditions. In the majority of trials (80%) informative cues validly predicted where (spatial cue) or when (temporal cue) the target would appear ("valid" trials). In the remaining 20% of trials, the cue incorrectly predicted the spatial location or temporal onset of the target ("invalid" trials). The three experimental conditions (spatial/temporal/neutral) were presented separately in three blocks of trials and blocks' order counterbalanced across subjects. The inter-trial interval (ITI) varied randomly from 600 to 1000 ms. A practice session (15 trials for the neutral and spatial condition each, 30 trials for the temporal condition) preceded the main task, which consisted of 90 trials for each experimental condition. In the temporal and spatial conditions, a total of 72 valid trials and 18 invalid trials were presented (**Figure 1**).

Stimuli presentation and collection of behavioral responses were controlled using e-Prime 2.0 software and a Serial Response Box Model 200a (Psychology Software Tools, Inc., Schneider et al., 2002), which allows for millisecond accuracy recording.

### MRI Acquisition

MRI data were collected at 3T (Philips Achieva) using a thirty-two channel receive-only head RF coil. A diffusionweighted spin-echo echo-planar imaging sequence was used to acquire high angular resolution diffusion weighted images (HARDI) (Jones et al., 1999). One hundred twenty-eight gradient orientations and six unweighted (b = 0 s/mm2) images were acquired with the following parameters: TR/TE = 10000/76 ms, b = 1,000 s/mm2 , 60 slices, slice thickness = 2 mm, FOV = 224 × 224, acquisition matrix = 112 × 112, resulting in data acquired with a 2 mm3 isotropic resolution. A highresolution T1-weighted whole-brain structural scan was also acquired (1 mm3 isotropic).

### MRI Pre-processing

Data were preprocessed and analyzed in ExploreDTI v4.8.6 (Leemans et al., 2009). Data were corrected for motion and eddy currents. Motion artifacts and eddy current distortions were corrected with B-matrix rotation using the approach of Leemans

FIGURE 1 | Spatio-temporal predictive task. Participants detected, as quickly as possible, visual targets ('+' or an '×') appearing at one of three locations (left/up/right) after one of three intervals (short/medium/long). Expectations about stimulus location (spatial condition) or onset (temporal condition) were conveyed by symbolic cues with 80% validity. The cue consisted of a central image (1◦ eccentricity) composed of a diamond and two rings. Part of the cue briefly brightened (100 ms) to inform participants on the possible spatial location or temporal onset of the upcoming target. During the spatial condition, the left, up or right side of the diamond was highlighted to inform subjects that the target was likely to appear in the left, up or right peripheral box. In the temporal condition, brightening of the inner circle, diamond or the outer circle indicated that the target would occur after a short (540 ms), medium (1080 ms) or long (1620 ms) delay, respectively. During the neutral condition, no spatial or temporal information was provided. The inter-trial interval (ITI) varied randomly from 600 to 1000 ms. The three experimental conditions (spatial/temporal/neutral) were presented separately in three blocks of trials (90 each).

and Jones (2009). During this processing procedure, all brain scans were rigidly normalized to Montreal Neurological Institute (MNI) space during the motion-distortion correction step.

### Tractography

Tensor estimation was performed using a non-linear least square method (Jones and Basser, 2004). Whole-brain tractography was performed using the DTI fiber tract-reconstructing algorithm implemented in ExploreDTI. The following tracking parameters were applied: step-size of 1 mm, minimum FA thresholds of 0.2 to initiate and continue tracking, an angle threshold of 30◦ and fiber length range 10–500 mm (Mori and van Zijl, 2002; Langen et al., 2012).

### Network Measures

The automated atlas labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002) was registered to the HARDI data. A weighted connectivity matrix for each subject was generated in ExploreDTI using the AAL atlas regions as nodes. The atlas used to parcellate the gray matter consisted of 116 ROIs in total. However, the 26 cerebellar ROI were excluded for two main reasons: firstly, the cerebellum was not homogeneously covered by MRI across subjects, giving rise to a large inter-subject variability in the number of streamlines counted between cerebellar ROIS and the rest of the atlas; secondly, the streamline count from each cerebellar region resulted unavoidably altered by magnetic susceptibility artifacts due to the inhomogeneity of the local magnetic field induced by the magnetic field gradient used for diffusion tracking. Thus, a total of 88 nodes, 76 cortical and 12 sub-cortical, were used. The matrices used were weighted by number of streamlines connecting node i to node j. This matrix was thresholded to five streamlines and two nodes (i.e., left/right Heschl) were excluded from statistical analyses, in order to avoid singular matrices across subjects. Degree centrality and clustering coefficient (commonly used complex network measurements) were used to characterize network topology in terms of connectedness and segregation at the local level. All metrics were computed using the Brain Connectivity Toolbox (Rubinov and Sporns, 2010).

### Statistical Analysis Behavioral Data

Reaction times to target stimuli faster than 100 ms were considered anticipatory and removed from the analysis. The benefit of temporal and spatial cueing on performance (i.e., individual mean RTs) was analyzed in one repeated measures ANOVA, with cue type (neutral, temporal valid, spatial valid) as the within-subjects factor. In order to examine the costs of invalid cueing and the potential interaction with the foreperiod length, another repeated measures ANOVA with cue type (time, space), validity (valid, invalid) and foreperiod (540, 1080, 1620 ms) as within-subjects factors was performed. A Bonferroni correction for multiple comparisons was applied. The benefits and costs of spatial cuing were explored by calculating 2 indices: (1) Validityspatial as the difference between RTs at spatial valid trials and RTs at neutral trials; (2) Invalidityspatial as the difference between RTs at spatial invalid trials and RTs at spatial valid trials. In order to further explore the temporal validity/invalidity effects unmasked by the foreperiod effect, 4 indices were calculated as follows: (1) Temporal Validityshort as the difference between RTs at the short interval for early cue (V540) and RTs at the short interval when no cue was provided (N540); (2) Temporal Validitylong as the difference between RTs at the long delay for late cue (V1620) and RTs at the long delay when no cue was provided (N1620); (3) Temporal Invalidityshort as the difference in RTs at the short interval for late cue (I540) and RTs at the short interval for early cue (V540); (4) Temporal Invaliditylong as the difference between RTs at the long interval for early cue (I1620) and RTs at the long interval for late cue (V1620).

### Neuroimaging Data

Behavioral indices (Validityspatial, Invalidityspatial, Temporal Validityshort, Temporal Validitylong , Temporal Invalidityshort, Temporal Invaliditylong ) and network topology measures from the eighty-eight selected brain regions were included in the analyses, by investigating reciprocal dependencies. In order to look at relationships without inflating the risk of a type I error (of erroneously concluding that a significant correlation is present) (Draper and Smith, 1998), and to increase the number of descriptors in the regression equation as to improve predictors' fit, a forward stepwise multiple regression model (F > 4 to enter) was chosen. Indeed, the forward stepwise procedure starts with no variables in the model and it tries out

the variables one by one, including them if they are statistically significant, thus identifying the best set of predictors that gives the biggest improvement to the model. Simple linear regressions were preliminarily performed to include in subsequent multiple regressions analyses only variables significantly (p < 0.05) related to the behavioral measures considered.

All statistics were performed on StatView statistical software.

### RESULTS

### Behavioral Data

A main effect of cue type (neutral, temporal valid, spatial valid) [F(2,28) = 5.597; p = 0.0061] confirmed that targets appearing in a predictable location or moment in time are detected faster than uncued ones. Post hoc analyses with paired t-test evidenced a stronger effect for the spatial [t(28) = 3.011; p = 0.0055] as opposed to temporal [t(28) = 1.61; p = 0.1186] cueing, indicating that the latter did not significantly affect performance. The second analysis concerning temporal and spatial invalidity costs showed a significant effect of cue type, validity, and foreperiod [F(1,28) = 23.71; p < 0.001; F(1,28) = 89.3; p < 0.001; F(2,56) = 75.83; p < 0.001 respectively], a significant cue × validity interaction [F(1,28) = 32.23; p < 0.001], a significant validity × foreperiod interaction [F(2,56) = 10.42; p < 0.001] and a significant three way interaction between cue type, validity and foreperiod [F(2,56) = 4.10; p = 0.02]. Post hoc analyses (Bonferroni test) confirmed that for the temporal domain, RTs at the short interval after an early cue were not different from RTs for the medium interval at the medium cue, but significantly slower than RTs for the long interval at the late cue (due to the interaction with the foreperiod effect) [p = 0.0002]. Concurrently, RTs at the short interval for the late cue were slower from RTs at the medium interval after an invalid cue (either early or late) [p < 0.0001] and from RTs at the long interval after an early cue [p < 0.0001], while no difference was observed between the latter two variables. Behavioral performance at the medium interval was therefore discharged from the further indices computation and only RTs at the short and long delay considered (see **Table 1**).

TABLE 1 | Mean reaction times (RTs) to target stimuli split by (a) cue type; (b) validity, and (c) the interaction between validity and foreperiod.

### Local Connectedness

### Temporal Validity and Invalidity Effects

The covariance between the behavioral benefit of knowing in advance when the target will occur and the centrality of nodes within the whole structural connectivity network was investigated. Statistical analyses were separately performed for both Temporal Validityshort and Temporal Validitylong effects, considered as measures of explicit temporal orienting processes. Results from simple linear regressions showed significant negative correlations between the Temporal Validityshort effect and degree centrality of a set of cortical regions. Moreover, a significant covariance between the degree centrality of a widespread pattern of cortical and subcortical nodes and the Temporal Validitylong effect was found both as negative and positive correlations (see **Table 2**). Results from subsequent stepwise regressions showed that 51% of total variance observed in the Temporal Validityshort effect was explained by right inferior parietal (r = −0.41), right superior occipital (r = −0.31) and left rolandic operculum (r = 0.48) nodes' degree centrality variability [F(3,25) = 10.854; r = 0.75; adjusted R 2 = 0.51; p < 0.0001]. Further, for the Temporal Validitylong effect the same analysis revealed that 67% of total variance was explained by right supramarginal (r = −0.29), left inferior parietal (r = −0.39), left fusiform (r = 0.51) and right insula (r = 0.24) nodes' degree centrality variability [F(4,24) = 15.455; r = 0.85; adjusted R 2 = 0.67; p < 0.0001].

The covariance between the behavioral cost of receiving incoherent information about the target temporal occurrence and the centrality of nodes within the whole structural connectivity network was also investigated. Again, in the temporal invalid condition, two different variables were analyzed as to explore implicit temporal orienting processes (Temporal Invalidityshort, Temporal Invaliditylong ). Positive and negative correlations between the two indices and nodes centrality of a corticalsubcortical network (see **Table 2**) were found. Results from stepwise regressions showed that 40% of total variance observed in the Temporal Invalidityshort effect was explained by left putamen (r = 0.44), right paracentral lobule (r = 0.4) and right caudate nucleus (r = 0.32) nodes' degree centrality variability [F(3,25) = 7.434; r = 0.7; adjusted R 2 = 0.4; p = 0.001]. Conversely, in the case of the Temporal Invaliditylong effect, results from the stepwise regression evidenced a negative relationship between the ability to re-orient attention to a later time point and degree centrality of the left inferior parietal lobe [F(1,27) = 6.608; r = 0.44; adjusted R 2 = 0.2; p = 0.02].

#### Spatial Validity and Invalidity Effects

The analyses applied to subjects' ability to orient attention in space evidenced that centrality of two regions was positively and negatively correlated with RTs to stimuli appearing in a predictable location. Results from the stepwise regression evidenced that the observed positive covariance between the Validityspatial effect and degree centrality of the para-hippocampal gyrus (r = 0.5) and the negative one with centrality of the post-central gyrus (r = −0.4) [F(2,26) = 9.947; r = 0.7; adjusted R 2 = 0.4; p = 0.0006] could explain 40% of the total covariance.

TABLE 2 | Observed covariance between degree centrality and spatio-temporal predictive performance.

(Continued)

#### TABLE 2 | Continued

fnhum-12-00212 May 23, 2018 Time: 17:11 # 7

The cost of spatial invalidity on performance (Invalidityspatial) was found to positively covary with degree centrality of one region. However, the stepwise regression model produced no significant result (**Table 2** and **Figure 2**).

### Local Segregation

#### Temporal Validity and Invalidity Effects

The covariance between the four temporal indices (Temporal Validityshort, Temporal Validitylong, Temporal Invalidityshort, Temporal Invaliditylong ) and the prevalence of clustered connectivity around individual nodes was investigated. Simple linear regressions results showed significant negative correlations between the Temporal Validityshort effect and clustering coefficient of nodes within a cortical network (see **Table 3**). Conversely, significant positive correlations were found between the Temporal Validitylong effect and clustering coefficient of nodes within a cortical-subcortical network (see **Table 3**). The stepwise regression model showed a specific negative relationship between the Temporal Validityshort effect and the clustering coefficient of the right superior frontal cortex, indicating that its' connectedness with topological neighbors nodes explained 18% of the total variance [F(1,27) = 7.001; r = −0.45; adjusted R 2 = 0.18; p = 0.013]. Moreover, significant results from stepwise regression analyses indicates that 75% of total variance observed in the Temporal Validitylong effect was explained by right putamen (r = 0.5), left paracentral lobule (r = 0.6) and right amygdala nodes' clustering coefficient (r = 0.6) [F(3,25) = 28.908; r = 0.9; adjusted R 2 = 0.75; p < 0.0001]. The same analysis applied to the two temporal invalidity indices showed significant correlations between the latters and the clustering coefficient of a set of cortical nodes and in a single subcortical region (see **Table 3**). In the subsequent stepwise regressions, we found that the 50% of total covariance between the whole sub-network's clustering coefficient and RTs indexing

the Temporal Invalidityshort effect [F(2,26) = 12.31; r = 0.7; adjusted R 2 = 0.5; p = 0.0002] was explained by the left middle frontal gyrus (r = 0.6) and the left caudate nucleus (r = 0.4). Conversely, when temporal expectations had to be updated to a later time point (as expressed by the Temporal Invaliditylong effect), the clustering coefficient of the right posterior cingulate gyrus (r = −0.4), the right middle temporal pole (r = −0.4) and the left superior temporal pole (r = 0.4) explained the 40% of the observed covariance [F(3,25) = 8.339; r = 0.7; adjusted R 2 = 0.4; p = 0.0005].

#### Spatial Validity and Invalidity Effects

In case of predictable targets, the clustering coefficient of a single area was found to covary with the ability to orient attention in space (Validspatial effect). However, such correlation was no longer significant in the stepwise regression model.

Conversely, results evidenced positive correlations between the Invalidspatial effect and the clustering coefficient of cortical and subcortical areas (see **Table 3**). Results from the stepwise regression evidenced that 30% of the total covariance between the whole sub-network's clustering coefficient and the cueing cost for spatial invalid trials [F(2,26) = 7.124; r = 0.6; adjusted R 2 = 0.3; p = 0.0034] was explained by the left paracentral lobule's (r = 0.4) and right putamen's (r = 0.4) clustering coefficient (**Table 3**).

### DISCUSSION

In the present study, we investigated the covariance between performance on a spatio-temporal predictive task and topological measures of complex brain networks. Degree centrality (i.e., a measure of local connectedness identifying critical network elements as those having the highest number of connections or degree) and clustering coefficient (i.e., a measure of local segregation indicating the presence of clustered connectivity around individual nodes) were computed (Barabaìsi, 2016) and correlated to behavioral indices reflecting temporal and spatial expectations. Specifically, we aimed at investigating critical network elements for explicit spatial and temporal orienting, and to examine potential variations for implicit temporal expectations. Indeed, given that temporal predictions can rely on different sources of relevant temporal information (Correa, 2012), such as explicit predictive cues and probabilistic information associated with the passage of time (hazard function and foreperiod effects), we disentangled explicit processes from more implicit measures reflecting the interaction between these two different types of temporal orienting effects.

To date, although relevant behavioral (MacKay and Juola, 2007; Weinbach et al., 2015; Laidlaw and Kingstone, 2017) and fMRI (Coull and Nobre, 1998; Coull et al., 2015; Peer et al., 2015) investigations examined the interrelation between predictive behavior and processing of spatial and temporal stimuli features, this is the first study that applied complex network analysis to structural connectivity data for studying the brain topological organization underlying spatial and temporal predictive processing.

Here we found that response preparedness to cued stimuli relies on different structural connectivity networks for the temporal and spatial domains. Specifically, while the use of temporal informative cues was found to covary with centrality and segregation measures within a distributed corticalsubcortical network, its spatial counterpart was related to clustered connectivity around few brain regions. Particularly, the behavioral advantage of being validly cued to a certain spatial location was found to covary with local connectedness (increased when RTs were faster after valid as opposed to neutral cues) of a region in the right parietal lobe (the postcentral gyrus) more anterior relative to previous fMRI reports (see Chica et al., 2013 for a review; Coull and Nobre, 1998; Corbetta and Shulman, 2002). Although this region is traditionally linked to somatosensory perception (including proprioception), some functional and lesion studies (Corbetta, 1998; Balslev et al., 2013) suggested that the postcentral gyrus participates in movement organization and anticipation processes by contributing to code the locus of attention. Since spatial attention can be defined as the selection of locations for perception and/or for action, the postcentral gyrus may modulate the visual processing stream toward a motor response by using gaze-direction signals that have a proprioceptive component (Balslev et al., 2013). Alternatively, the observed correlation may be related to the postcentral gyrus participation to one of the three distinct key subsystems of attention, namely the orienting network (Fan et al., 2005; Posner, 2008). Indeed, clustered connectivity was found around the left paracentral lobule when the behavioral cost of being invalidly cued to a target location was considered. Since both the postcentral and the paracentral gyri participate to the network responsible for directing attention to target stimuli, triggered by specific spatial cues (Petersen and Posner, 2012), the patterns of relations among these brain areas and their topological neighbors may sustain cued orienting of attention to spatial locations. The fact that increased connectivity was found in a local network community comprising the right putamen when spatial invalidly cued trials were considered, seems to further confirm the hypothesis that this region is one of the hubs of the intrinsic connectivity networks in resting brain involved in attentional processes (Xiao et al., 2016).

As for temporal orienting, significant covariance was observed between centrality measures (calculated according to the number of axonal bundles incident upon a node) of regions within a subcortical-fronto-parieto-occipital network -comprising the left putamen, the right caudate nucleus, the left frontal operculum, the right inferior parietal cortex, the right paracentral lobule and the right superior occipital cortex-, and the ability to respond after a short cue-target delay suggesting that the local connectedness of such nodes plays a central role when the source of temporal expectation is explicit (Correa, 2012). When local segregation was taken into account, we found highly clustered structural connectivity across the right superior frontal gyrus, the left middle inferior frontal gyrus and the left caudate nucleus as related to explicit temporal orienting.

Interestingly, different connectivity clusters were observed when the automatic shifting of attention (related to unexpected premature targets) was separated from the voluntary top–down

TABLE 3 | Observed covariance between clustering coefficient and spatio-temporal predictive performance.

TABLE 3 | Continued

control of attentional orienting (in case of valid cuing) since clustering coefficient of the left middle frontal gyrus and caudate nucleus covaried with the former process, while the latter was related to the right superior frontal gyrus clustering coefficient.

 They also indicate the presence in the whole brain structural network, of a group of regions whose reciprocal connectivity scaled with the increased preparedness in responding to fast upcoming pre-cued stimuli and was specific for the process under investigation (automatic attentional shift vs. voluntary attentional orienting). Indeed, the functionality of a node is defined by the pattern of its connections with other nodes in the network (Bullmore and Sporns, 2009), since centrality increases as the potential for communication from a region increases, while the segregation measure here used is indicative of lasting patterns of relations among brain areas for supporting specific cognitive processes (Rubinov and Sporns, 2010).

Although a large corpus of research has implicated complex and distributed networks -including prefrontal, premotor, parietal and insula cortices and comprising also the striatum (Triviño et al., 2010)-, in temporal orienting of attention, the relevance of these structures in optimizing prospective motor behavior as a function of informative cues is still debated. Our results suggest that the behavioral benefit of being validly cued to a short interval is subtended by the reciprocal connectivity within a fronto-parieto-occipital complex. Activation of the inferior parietal cortex has been widely observed in fMRI studies on temporal orienting (Coull and Nobre, 1998; Coull et al., 2000, 2013, 2016; Cotti et al., 2011; Davranche et al., 2011). Although a frontoparietal network has been implicated in temporal as well as spatial orienting, the inferior parietal cortex seems specifically related to predictive timing (Wiener et al., 2010), as it has been found active not only when participants had to use temporal cues to optimize motor responses, but also for enhancing perceptual discrimination (Davranche et al., 2011), irrespectively of the effector and the type of requested action (button press/ocular saccades) (Cotti et al., 2011), both in the visual and auditory modalities (Merchant et al., 2013; Bolger et al., 2014). In terms of the structural network here observed, results confirm a pivotal role of this area in implementing the response benefits of temporal prediction, which is consistent with the thesis that temporal expectations tune action planning by optimizing prospective motor behavior (Coull and Nobre, 1998). The fact that densely connected local clusters were structurally centered around the right superior frontal gyrus in explicit temporal orienting supports the assumption that the latter is a voluntary process that requires more evolved structures such as the frontal cortex, involved in the strategic and voluntary (top– down) regulation of behavior (Konishi et al., 2008; Triviño et al., 2010).

In case of premature targets -target appears at the short interval after a late cue- a bottom–up, automatic grabbing of attention has been previously described and linked to the activity of the posterior extrastriate visual cortex (Coull et al., 2000). Here we found that premature targets are related to the local connectedness of the left putamen, the right caudate and the right paracentral lobule. The involvement of the striatum in timing is well known, being described as a 'core timer' of a more distributed neural network underlying temporal processing in the subsecond and multisecond range (Meck et al., 2008). Putaminal activity has been traditionally associated with motor preparation and execution, and specifically with the internal generation of precisely timed movements (Rao et al., 1997). Indeed, previous studies (Filip et al., 2016) suggested that the putamen is engaged in the evaluation of success and precision of the undergoing prospective temporal analysis, and our result would underscore its function as a neural node in a network engaged in cases of breaches of temporal expectations.

When the interaction between explicit and implicit temporal orienting processes was considered at the long interval, we found that explicit processes (indexed by the net advantage of being validly cued when the foreperiod effect was partialled out) were related to centrality measures of the bilateral inferior parietal lobule. Degree centrality of the same region in the left hemisphere covaried with behavioral measures (RTs at the long interval for early cue minus RTs at the long interval for late cue) indexing the process of attentional re-orienting. These results confirm the key role of the left parietal cortex in instantiating the behavioral benefits of temporal predictability, whether temporal information is conveyed by explicit predictive cues or by the probabilistic information associated with the passage of time (Coull et al., 2016). Densely connected local clusters were structurally centered on the right middle temporal pole and the right posterior cingulum when the re-orienting effect was specifically considered. The supramodal involvement of the temporal lobe in time processing has been already demonstrated (Kanai et al., 2011; Filip et al., 2016), while the posterior cingulate cortex seems necessary for organizing flexible behavior in response to an ever-changing environment. Indeed,

this region contributes to signaling environmental changes and, when necessary, to recombining variables into strategy-specific measures of Bayesian evidence that the environment has changed (Pearson et al., 2011). The fact that lasting patterns of relations among the posterior cingulate and its topological neighbors were observable when the process of attentional re-orienting to a later point in time occurred, suggests that this area is specifically involved in the dynamic updating of current expectancies as a function of the foreperiod.

The selectivity of our results, indicating segregated structural connectivity networks for temporal and non-temporal stimuli dimensions, but also for explicit and implicit temporal orienting provides empirical support to the notion that complex networks theory enhances behavioral driven neuroimaging data analysis of predictive timing. Indeed, actual evidence on the neural underpinnings of temporal orienting is currently restricted to task-based fMRI experiments in which the core of the process circuitry is blurred by the recruitment of additional areas related to ancillary cognitive/sensory processes. Conversely, a task-independent structural neuroimaging approach within a network-based modular framework provides quantitative information on anatomical brain connectivity, at both global and regional levels, thus unraveling the lasting patterns of relations among brain areas for supporting the process under investigation (Sporns, 2013; Mišic and Sporns, 2016 ´ ). We also think that our findings have a translational impact since alterations in predictive timing have been evidenced in psychiatric disorders such as schizophrenia. As such, we believe that our results have the potential to better define the neural circuits involved in the pathophysiology of the illness, and to provide an explanatory framework for symptoms and clinical manifestations. Given that graph theory allows a quantitative analysis of the pattern of interconnections each area has with other areas belonging to the same or different systems (Mišic´ and Sporns, 2016), its application may provide new insight on the dysfunctional interplay between timing deficits, clinical symptoms and connectopathy in SZ. Indeed, neuroimaging investigations evidenced connectivity deficits in patients with SZ, with positron emission tomography (Andreasen et al., 1996; Mallet et al., 1998), fMRI (Fornito and Bullmore, 2015; Crossley et al., 2016; Nelson et al., 2017) and diffusion-tensor imaging techniques (see Kelly et al., 2017 for a meta-analysis), suggesting that SZ pathophysiology may be explained in terms of abnormal or disrupted integration of spatially distributed brain regions (disconnection hypothesis, Friston and Frith, 1995; Friston et al., 2016). Here we observed that centrality and segregation properties of the parietal and frontal cortices modulate explicit and implicit temporal orienting in healthy subjects. Intriguingly, these areas have been identified as putative hubs across the whole brain network (Iturria-Medina et al., 2008; Gong et al., 2009) [i.e., highly connected nodes that allow segregated functional systems to share information by means of neural interaction (Collin et al., 2016)]. Such pivotal nodes infrastructure has been found disproportional in SZ patients, with abnormal high clustering, anomalous modularity structure (Zalesky et al., 2011; van den Heuvel et al., 2013), and reductions of global efficiency in the overall network structure (Zhang et al., 2012). Moreover, this altered connectivity profile was observed in specific areas (Bassett et al., 2008; van den Heuvel et al., 2010; Wang et al., 2012; Kaplan et al., 2016) that partially overlap with the predictive timing network here described, and are responsible for the computation of prediction error signals to guide learning and updating of expectancies (Kaplan et al., 2016).

Before concluding, few limitations have to be acknowledged. First, the relatively small sample size might reduce the impact of our findings and rescale the assertions here discussed. However, we intended to provide preliminary evidence of the neural architecture subtending the ability to optimize prospective motor behavior as a function of informative cues. In fact, complex and distributed networks have been thus far implicated in predictive timing, while aberrant connectivity has been suggested to underlie failures in predictive coding (Kraepelin, 1904; Friston et al., 2016). In addition, it might be argued that the network measures chosen are restricted to a small-sized pool, however, we intended to exploit the straightforward characterization given by the used ones, in order to elicit the cerebral interconnectivity correlates of such a complex process as predictive timing. Moreover, although the small number of temporal invalid trials here used could be a potential limitation, we nevertheless observed the traditional behavioral costs of being invalidly cued in time, further confirming the strength of the temporal orienting effects here investigated, even with a relatively reduced data pool. Lastly, recent studies on attentional orienting suggested that cues in the temporal and spatial conditions from the seminal study by Coull and Nobre (1998) are hardly comparable as spatial arrow cues elicit both voluntary and involuntary attention shifts (Hommel et al., 2001; Olk, 2014), being over-learned symbols of direction (Ristic and Kingstone, 2012) as opposed to temporal ones. Future behavioral and neuroimaging studies should employ cues that differ strictly in terms of temporal and non-temporal properties, in order to minimize potential confounds on the type of attentional orienting engaged.

### AUTHOR CONTRIBUTIONS

FP conceived the experimental design, supervised the data analysis, contributed to results interpretation, and wrote the paper. VC conceived the experimental design, collected the data, performed the behavioral data analysis, contributed to results interpretation, and wrote the paper. GS supervised the data analysis, contributed to results interpretation, and revised the paper. TG conceived the experimental design, performed the MRI data analysis, contributed to results interpretation, and wrote the paper. DV conceived the experimental design, collected the data, performed the MRI data analysis, contributed to results interpretation, and revised the paper.

### FUNDING

This study was conducted within the project "Multidimensional study of timing abilities and sense of agency in schizophrenia and bipolar patients" funded through 5Xmille 2016 from the Italian Ministry of Health.

### REFERENCES

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Barabaìsi, A.-L. (2016). Network Science. Cambridge: Cambridge University Press.

Neuropsychologia 51, 309–319. doi: 10.1016/j.neuropsychologia.2012.0 8.017

in schizophrenia. JAMA Psychiatry 70, 783–792. doi: 10.1001/jamapsychiatry. 2013.1328

Zhang, Y., Lin, L., Lin, C. P., Zhou, Y., Chou, K. H., Lo, C. Y., et al. (2012). Abnormal topological organization of structural brain networks in schizophrenia. Schizophr. Res. 141, 109–118. doi: 10.1016/j.schres.2012.0 8.021

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer MC and handling Editor declared their shared affiliation.

Copyright © 2018 Ciullo, Vecchio, Gili, Spalletta and Piras. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Training-Induced Changes in Rapid Auditory Processing in Children With Specific Language Impairment: Electrophysiological Indicators

#### Anna Dacewicz 1† , Aneta Szymaszek 1 \* † , Kamila Nowak 2 and Elzbieta Szelag1

1Laboratory of Neuropsychology, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland, 2Laboratory of Social Psychology, Department of Ergonomics, Central Institute for Labour Protection–National Research Institute, Warsaw, Poland

The brain's ability to recognize acoustic changes occurring in rapid temporal succession is important for speech and successful language development. Children with specific language impairment (SLI) are characterized by deficient dynamics of temporal information processing (TIP) in the millisecond time range accompanied by disordered language development. Furthermore, previous studies have found that intervention based on amelioration of TIP resulted in improvement of both language and other cognitive functions. This study aimed to explain the changes associated with TIP training from the perspective of event-related potentials (ERPs). Thirty-six children aged 5–8 years (26 boys, 10 girls) diagnosed with SLI underwent two types of intense audio-visual computer intervention: experimental TIP training targeted at the millisecond time range (n = 18) or control non-TIP training (n = 18). Paired 50 ms tones of 1000 Hz and 1200 Hz were presented with inter-stimulus intervals (ISIs) of either 50 ms (Short ISI Condition) or 200 ms (Long ISI Condition). Auditory ERPs were measured in a passive oddball paradigm before and after each type of training. The mismatch negativity (MMN) paradigm was applied as an electrophysiological indicator of the brain's ability to automatically detect violations of regularity in paired tones presented in rapid succession. Moreover, the P3a component was also analyzed. After 24 sessions of temporal training (in the experimental group) MMN amplitude enhancement was observed in both ISI conditions, reflecting increased efficiency in perceiving changes in rapid auditory sequences. In both experimental and control groups, P3a amplitude was enhanced in both ISIs. This may be due to the improvement of involuntary attention shifting to the auditory events involved in each training type. To conclude, temporal training, compared to non-temporal control training, improved the ability to detect changes in a rapid auditory stream in children with SLI.

Keywords: specific language impairment (SLI), event related potentials (ERPs), temporal information processing, temporal windows, cognitive training

#### Edited by:

Deana Davalos, Colorado State University, United States

#### Reviewed by:

Mireille Besson, Institut de Neurosciences Cognitives de la Méditerranée (INCM), France Annalisa Setti, University College Cork, Ireland

> \*Correspondence: Aneta Szymaszek<EMAIL_ADDRESS>
†These authors have contributed equally to this work.

> Received: 20 October 2017 Accepted: 16 July 2018 Published: 07 August 2018

#### Citation:

Dacewicz A, Szymaszek A, Nowak K and Szelag E (2018) Training-Induced Changes in Rapid Auditory Processing in Children With Specific Language Impairment: Electrophysiological Indicators. Front. Hum. Neurosci. 12:310. doi: 10.3389/fnhum.2018.00310

## INTRODUCTION

### Temporal Dynamics of Cognitive Function

In recent years, temporal information processing (TIP) has been emphasized as the neural basis of several mental functions, such as learning, memory, attention, decision making, motor control and speech processing (Szelag et al., 2010). All these functions may be characterized by their specific temporal dynamics at different ranges of TIP. Two main ranges crucial to cognitive processes may be distinguished: millisecond and multisecond ranges (Pöppel, 1997, 2004, 2009; Buhusi et al., 2018). The temporal dynamics of these processes provide a framework for understanding the neural mechanisms underlying human mental activity, including a structure for speech and any perceptual or motor activity.

Furthermore, specific distortions of TIP have been found in various clinical populations (e.g., ADHD, autism spectrum disorder, depression, schizophrenia), co-existing with disordered psychological functioning; which may elucidate the consequences of deficient timing (e.g., Davalos et al., 2003; Teixeira et al., 2013; Vatakis and Allman, 2016). This line of research is important as it may foster our understanding of the ''timing—behavior'' relationship and potentially contribute to the remediation of psychological conditions. On this basis, one cannot claim that certain disorders are due to time distortions per se, but deviations from the ''normal'' template seem critical to everyday functioning and, hence, are a crucial factor for research and rehabilitation.

The number of experimental studies on the co-existence of ''deficient timing—deficient language'' in children and adults has grown rapidly. In this article, we concentrate on neurodevelopmental disorders, specifically on children with specific language impairment (SLI). Children suffering from SLI display problems with language acquisition (both comprehension and expression), however, their general cognitive functions and nonverbal intelligence remain in the normal range. Furthermore, these problems cannot be explained in terms of hearing deficits, neurological and speech mechanism abnormalities, or environmental factors. It is estimated that SLI affects approximately 7% of the 5-year-old population (Tomblin et al., 1997). In children with SLI, the coexistence of language difficulties and deficient TIP was first shown in an early article by Tallal and Piercy (1973) and subsequently confirmed in more recent studies (Grondin et al., 2007; Szelag et al., 2015). These findings raised the question of whether the improvement of TIP of nonverbal information may induce subsequent gains in language skills. Hence, the funding of training programs based on improvement of TIP to aid language competency has been given a high priority.

The widely used computer-based remediation Fast ForWordr (FFW) has received a lot of attention in studies on improving language development in children. The program is based on the hypothesis that language impairments result from difficulties in rapid auditory processing (compare above). In the early studies of Tallal et al. (1996), as well as of Merzenich et al. (1996), it was found that after performing FFW exercises, there was a significant improvement in the language skills of children with expressive and/or receptive language difficulties and reading problems. However in some studies, the beneficial effects of FFW were comparable to those of other computerbased interventions (e.g., Cohen et al., 2005; Gillam et al., 2008; Given et al., 2008). Previous research on intervention-related effects indicated a transfer of improvement from the trained time domain to untrained behavioral domains, i.e., speech, auditory processing and other cognitive functions. Moreover, changes in brain function associated with auditory discrimination of both verbal (Lovio et al., 2012) and nonverbal stimuli presented in rapid sequences were reported (McArthur et al., 2010; Heim et al., 2013, 2016).

This research approach was applied in the Dr. Neuronowskir computer program developed at our Institute (Szelag and Szymaszek, 2016). This program offers targeted training in millisecond TIP, sequencing abilities and duration judgment and has been developed on the basis of our many years of research (for an overview, see Szelag et al., 2009). Using this intervention program in our previous study on children with SLI, we found significant improvements in behavioral measures of timing, language, attention working memory and executive functions (Szelag et al., 2015).

### Time Perception Windows

As mentioned above, time perception can be considered on various time scales or processing units which are often called ''temporal windows'' (Pöppel, 1997). Experimental support for the existence of such time windows comes from a large number of different paradigms (Pöppel, 1997; Szelag et al., 2004). At this point, the close association between TIP and speech is well established and has been the topic of long discussions in modern neuroscience. This association may be rooted in the temporal dynamics of our verbal utterances which indicate the temporal constraints on different time ranges. These ranges correspond to tens of milliseconds (single phonemes), hundreds of milliseconds (syllables or prosodic elements), or a few seconds (phrases or sentences). Speech processing requires temporal decoding of the signal because the speech stream is a wave of rapidly changing complex sounds (millisecond processing level). Furthermore, it is necessary to parse the perceived signal into manageable chunks (multisecond level).

The concept of temporal characteristics of the speech signal was proposed in **?** (**?**) Asymmetrical Sampling in Time hypothesis. This assumes that speech perception is associated with processing within two different temporal windows—shorter and longer windows—corresponding to two levels of processing: namely, phonological decoding and semantic parsing of the chunks of the incoming speech stream. Accordingly, the short window (∼20–50 ms) is crucial for phoneme reception (e.g., rapid formant transitions, voice-onset-time), whereas the long window (∼150–300 ms) contributes to syllable processing, intonation contour and prosody. Speech perception thus requires the integration of auditory information within these two ranges, which are the topic of the present study.

Existing data reveals that the length of such temporal integration windows (TIWs) of sensory information may be related to developmental fluctuations (Fox et al., 2010). For example, such TIWs are expected to be wider in children than in adults (Horváth et al., 2007; Fox et al., 2010) because the latter age-group can integrate information within shorter temporal segments than the former group. According to EEG studies, the length of the TIW in adults is around 200 ms, while in children of 5–8 years it is about 350 ms (Wang et al., 2005). It is also postulated that the length of such TIWs may change in subjects suffering from speech disorders.

### Mismatch Negativity (MMN) as an Indicator of "Genuine" Timing Efficiency

Different methods for measurement of millisecond timing efficiency have been developed (Szelag et al., 2004). One can distinguish here the classical behavioral methods, such as the detection of the temporal order of incoming events (Szymaszek et al., 2009). In these tasks, two consecutive stimuli are presented in rapid succession with a short gap in-between and the subject must report the order of occurrence (left-right, low-high, shortlong, etc.). It is commonly believed that these tasks measure the effectiveness of sequencing ability and temporal ordering of incoming events on the basis of perceptual thresholds (auditory or visual) for order detection. Such threshold values are usually of tens of milliseconds in young healthy subjects (Wittmann and Fink, 2004; Szymaszek et al., 2009). However, the efficiency of TIP involves strong contributions from resources other than TIP, e.g., attention, working and short-term memory and moreover, inhibitory processes and decision making. Hence, behavioral measures of temporal acuity are heavily influenced by these cognitive functions.

On the other hand, the mismatch negativity (MMN) paradigm may be used as an electrophysiological indicator of timing efficiency in the processing of rapid auditory stimuli. The power of such event-related potentials (ERPs) lies in that they provide information about the sequencing, timing and in some cases, location of neural activity elicited by particular stimuli long before subjects produce an overt response. ERPs are increasingly used in developmental research because they are non-invasive and, in many cases, do not necessitate active subject participation, which is a huge advantage when dealing with children or patients.

MMN is a fronto-central negative potential elicited by any discriminable change in a sequence of auditory stimuli (Winkler, 2007; Näätänen et al., 2011). MMN can be induced by unattended stimuli, unlike behavioral methods which require attention and cooperation from a participant (Campbell and Davalos, 2015). Therefore, MMN can be a useful tool for measuring ''genuine'' timing deficits in subjects whose behavioral measures may be affected by attentional or other cognitive deficits.

MMN obtained in response to auditory stimuli has been proposed as a reliable objective method to measure sensory memory traces, as well as the effectiveness of rapid auditory discrimination processes (Davalos et al., 2003; Ervast et al., 2015). Therefore, MMN has been recognized as a sensitive indicator of auditory processing impairments in children with language difficulties (Davids et al., 2011), as well as in infants of parents with a history of language impairment (Benasich et al., 2002, 2006).

### Experimental Aim

This study aims to verify whether in children with SLI the application of the experimental temporal training compared to the control non-temporal training may result in enhanced electrophysiological responses in detection of changes in a rapid auditory stimuli stream. Another goal was to gain an understanding of the electrophysiological underpinnings of rapid nonverbal auditory processing in both the shorter and longer temporal windows. The application of the MMN paradigm allowed to concentrate on pure TIP, minimizing the influence of cognitive functions (attention, executive functions, etc.) involved in the behavioral timing measures.

### MATERIALS AND METHODS

### Subjects

Participants were 36 children aged between 5 years and 8 years (26 boys, 10 girls) diagnosed with SLI according to ICD-10 (World Health Organization, 1992). They were recruited from either the Early Intervention Centre or the Children's Memorial Health Institute in Warsaw. The core inclusion criterion was a language development disorder, defined as an overall standard score or score on at least two standard subtests below or equal to the 4th sten according to the Test for Assessment of Global Language Skills (TAGLS; Tarkowski, 2001), which constitutes the screening assessment for language development in Polish children. The recruited children scored below the mean score for their age. Moreover, all participants had a normal level of nonverbal intelligence (IQ of 85 or higher, measured with the Polish version of Raven's Colored Progressive Matrices, CPM; Szustrowa and Jaworowska, 2003) and normal hearing level as verified by pure-tone audiometry screening at 500 Hz, 1000 Hz, 2000 Hz, 4000 Hz (using an AS208 audiometer), which covers the sound frequency spectrum used in this study. All children were monolingual Polish native speakers and were right-handed, based on the modified Edinburgh Handedness Questionnaire (Hill and Khanem, 2009). Participants had no neurological or psychiatric diagnosis, autism spectrum disorder, attention deficits, or socio-emotional disorders as determined by a parent questionnaire and clinician reports. Moreover, children did not attend any other speech or cognitive therapy while participating in this study.

### Ethical Approval

The study protocol was approved by the Bioethics Committee of the Medical University of Warsaw (permission no. KB/162/2010). Written informed consent was obtained from the parents of all children before the study. The children provided verbal approval before each session.

### Procedure

This was a blind study in which children were randomly assigned to two training groups using the RITAr software (Pahlke et al., 2004) according to their age, gender, non-verbal IQ and level of language development: the Experimental Group (EG; n = 18; 13 boys, 5 girls) and the Control Group (CG; n = 18; 13 boys, 5 girls). These two groups did not differ significantly in terms of age, non-verbal intelligence, or level of language development. Descriptive data for EG and CG are shown in **Table 1**. EG underwent the computerized Dr. Neuronowskir intervention program (Szelag and Szymaszek, 2016), focused on the enhancement of TIP in the millisecond time range. CG used a computer program which implemented classical speech therapy extended by 16 simple computer games. Thus, the intervention in EG and CG was matched in terms of the mental load, visual appearance, motivational aspects and training protocol with the exception of TIP exercises which were only included in the experimental training.

### Study Protocol

Each participant underwent a pre-test electrophysiological assessment. Afterwards, the children underwent either experimental (EG) or control training sessions (CG; **Figure 1**). The duration of the whole intervention comprised 24 1-h sessions performed over 6 weeks (four sessions weekly). After completing the training program, a post-test was conducted using the same electrophysiological assessment as applied in the pre-test.

### Audiovisual Training Programs

Both trainings were computer-based intervention programs provided in several visually attractive exercises. To motivate and increase the children's commitment to the training, exercises were conducted with the use of tablets because of some indications that this has a positive impact on young students (Dhir et al., 2013). Each child completed the training individually and each therapy session was guided by a trained consultant (speech-therapist, psychologist, special educator, or psychology student). The training took place in a separate room at the Nencki Institute or at the Early Intervention Centre.

In experimental and control training, both correct and incorrect answers were followed by appropriate visual and auditory feedbacks. The exercises in both interventions were conducted using a predefined specific agenda which allocated a comparable amount of time to training particular cognitive functions.

### Experimental Training

The experimental intervention procedure was provided with the Dr. Neuronowskir computer program developed at our Institute (Szelag and Szymaszek, 2016). The software consists of 46 games grouped into nine modules for improving particular cognitive functions (e.g., attention, non-verbal auditory processing, verbal short-term memory, executive functions, receptive language and phonemic hearing). In addition, the majority of these exercises involved millisecond TIP, sequencing abilities and duration judgment. The difficulty of particular exercises was adjusted individually on the basis of the actual level of a child's performance.

The games mostly targeted the discrimination and identification of sounds, tones, syllables and words presented at a rapid rate, as well as recognizing the sequence or duration of two sounds, reproducing sequences of sounds or word strings and matching sounds and words.

CPM, Raven's Colored Progressive Matrices; TAGLS, Test for Assessment of Global Language Skills. aTwo-tailed t-tests for independent samples.

### Control Training

In contrast to the experimental training, none of the control intervention exercises involved TIP. The control training included three computer programs employing classical speech therapy and 16 simple computer games involving attention, short-term and working memory and executive functions.

The games employing speech therapy targeted the identification and discrimination of syllables and words presented at a regular rate of exposition as well as reproducing sequences of words or matching sounds and words. In the computer games, the tasks were to react as fast as possible to particular objects, to remember the pairs of sounds or pictures and to complete some logic puzzles.

### Electrophysiological Assessment

### Procedure

In the electrophysiological assessment, children were exposed to paired sinusoidal tones in a passive oddball paradigm. This means that they were asked to ignore sounds while watching silent videos. The standard stimulus was a pair of identical tones at 1000 Hz, while the deviant stimulus was a pair of tones differing in pitch, at 1000 Hz and 1200 Hz. The duration of each pair of tones was 50 ms (with 5 ms rising and falling) with an intensity of 80 dB SPL, measured with a Brüel & Kjær sound level meter. The time between consecutive tone pairs was 800 ms. The schema of the passive oddball paradigm is presented in **Figure 2**.

Stimuli were delivered binaurally using Presentation Software version 14.9 (Neurobehavioral Systems Inc., Berkeley, CA, USA) via E·A·RToner 5A Insert Earphone headphones, inserted into the right and left ear canal. We implemented two conditions corresponding to the two inter-stimulus-intervals (ISIs) within paired-tones in standard and deviant stimuli. The ISIs were set at 50 or 200 ms creating the Short ISI Condition (Short ISI) and the Long ISI Condition (Long ISI), respectively (**Figure 2**).

In each condition, 600 standard and 150 deviant stimuli were presented (standard to deviant ratio was 75% to 25%) within six blocks (three blocks with Long ISI and three blocks with Short ISI) in two different orders randomized between subjects. In each block, 15 standards were presented at the beginning and the distribution of deviants was quasi-randomized: a minimum of 5 and maximum of 15 consecutive standards could be presented in a row. At the end of data collection, 300 deviant stimuli (150 for each condition) in four control blocks were also presented. This gave us an opportunity to use the same stimulus method (see below for description).

#### Data Acquisition

EEG data were recorded from 32 scalp electrodes (EasyCap, Germany) with Ag/AgCl active electrodes (ActiCAP, Brain Products) placed according to the 10-20 system of the BrainVision Recorder© v.1.10 software (Brain Products, Germany). The electrodes' contact impedances were kept below 10 k. Data were referenced to the FCz electrode and a bandpass filter of 0.1–100 Hz was applied.

#### Data Analysis

Offline analysis was performed using BrainVision Analyzerr v.2.0 software (Brain Products, Germany). First, the data were down-sampled to 256 Hz and re-referenced to TP9 and TP10 electrodes. Butterworth zero-phase filters were implemented with high-pass—1 Hz, low-pass—30 Hz (both 8 order), and notch filter—50 Hz. Next, artifacts were

removed using Independent Component Analysis. The data were segmented into the epochs extending from 200 ms before to 1000 ms after the stimulus (standard or deviant) onset. After baseline correction (from −200 ms to 0 ms), trials exceeding ±120 µV were excluded from the analysis. Epochs were averaged for three stimulus types separately in the two conditions: standards, regular deviants and control deviants (deviants presented in control blocks).

To obtain difference waves dissociated from physical stimuli properties, the same stimulus method was used. In this method, the control deviants were subtracted from the deviants. The control deviants were physically identical to deviants presented in the oddball paradigm (regular), whereas control deviants were presented alone in a row in separate control blocks (**Figure 3**). As suggested in Jacobsen and Schröger (2003), a proper control block should control for both the physical properties of the stimuli and the adaptation effect. Nevertheless, in this study the numerous deviant repetitions in the control block did not fulfil the equal probability control block criteria, which might bias the reported effects. We refer to this issue in the ''Discussion'' section.

The main aim of this study was the analysis of MMN, which usually has frontocentral topography; hence the following analyses were performed only on FCz, Fz, F3, F4, FC1 and FC2 electrodes.

#### Waveform Analysis: Amplitude and Latency

The main analysis of data concerns ERPs identified on a difference wave, i.e., MMN and P3a, both before and after the interventions (experimental vs. control). Additionally, we performed explanatory analyses comparing N2 elicited by deviant and control deviant stimuli to the training-induced effects indicated by the MMN.

MMN and P3a amplitudes (or latencies) were obtained for each child. MMN was the most negative value in the time window between 195 ms and 395 ms after the first stimulus onset (i.e., 95–295 ms after the onset of deviation) for Short ISI or between 360 ms and 530 ms (110–280 ms after the onset of deviation) for Long ISI (e.g., Näätänen, 2000). The P3a component was the most positive value between 335–560 ms and 480–720 ms after the first stimulus onset for Short and Long ISI, respectively (e.g.,Escera and Corral, 2007). The maximum peak amplitudes for each participant were calculated in these time windows (**Table 2**).

For deviants (presented in oddball) and control deviants (presented in separate blocks), P1-N2 and P1'-N2' components were identified on averaged waveforms, which differed in Short and Long ISIs (Cˇeponiene˙ et al., 2005). For Short ISI, only one electrophysiological response (P1 and N2) was elicited because of the integration of two tones within a pair. P1 was the most positive value, while N2 was the most negative one in the particular time windows shown in **Table 2** for

Dacewicz et al. Auditory Temporal Training in SLI

deviants and control deviants. For Long ISI, on the other hand, doubled electrophysiological responses were obtained, reflecting responses to two separate tones within a pair identified as P1-N2 and P1'-N2' (e.g., Clunies-Ross et al., 2015). P1 and N2 were elicited by the first tone within the doublet, whereas, P1' and N2' by the second tone. In this study, we analyzed only N2 and N2' components (in Short and Long ISI, respectively) because they reflected the electrophysiological response to the second tone within a deviant doublet (change onset). For this second tone, we got the MMN by subtracting the control deviant from the deviant. For each participant, the maximum peak amplitudes and latencies for N2 and N2' were analyzed in the particular time windows (**Table 2**).

Finally, the amplitudes of particular ERPs (in µV) were determined as the maximum peak in the particular time windows with regard to the baseline. The latencies of MMN and P3a in both conditions were obtained by subtracting time intervals corresponding to the onset of stimulus change (i.e., 100 or 250 ms for Short and Long ISI, respectively).

### RESULTS

### Statistical Analyses

To analyze differences in amplitudes (or latencies) of particular components in the two conditions, we conducted mixed-design analysis of variance (ANOVAs). Greenhouse-Geisser correction was applied when the sphericity assumption was violated. After ANOVAs, post hoc analysis was performed with the Bonferroni correction adjusted for multiple comparisons.

The data were analyzed in two stages.

In Stage 1, we examined the components identified on the difference wave, i.e., MMN and P3a for amplitudes (or latencies). In these ANOVAs, the within-subject factors were: Session (pretest vs. post-test), Condition (Short ISI vs. Long ISI), whereas the between-subject factor was Group (EG vs. CG).

In Stage 2, to understand the intervention-related changes in MMN obtained in Stage 1, we examined the amplitudes (or latencies) of the N2 and N2' waveforms observed for deviants and control deviants in the two groups, considering Short and Long ISI separately. The rationale was to clarify whether the trainingrelated MMN amplitude changes resulted from increased or decreased amplitudes (or latencies) of N2 and N2' elicited by deviants or control deviants. In the former case, the increased amplitudes would correspond to higher sensitivity of deviant detection in the oddball task, whereas in the latter case, the decreased amplitudes would result from repeated exposure. This may reflect reduced resources involved in the perception of a predictable auditory pattern. In Stage 2, the within-subject factors were: Session (pre-test vs. post-test), Block (deviants vs. control deviants) and Group (EG vs. CG) as a between-subject factor.

### Stage 1—Difference Wave

Mean amplitudes for MMN and P3a are presented in **Figures 4**, **5**, **6**.

#### MMN

ANOVA conducted on amplitudes revealed significant main effects of Condition and Session modified by Session × Group interaction (see **Table 3**, **Figure 5**). The other factors and interactions were nonsignificant.

The amplitudes were higher (more negative, M = −4.41; SD = 2.25) in the Short ISI than in the Long ISI (M = −3.36; SD = 1.73), irrespective of the Session and Group. Increased MMN in post-test was observed only in EG (p < 0.001). In CG, the difference between MMN amplitudes in pre- and posttest was nonsignificant (p = 0.387). The same pattern of results was observed for Short and Long ISIs. Although in pre-test, the two groups did not differ significantly in MMN amplitudes (p = 0.674), in post-test they were higher (more negative) in EG

TABLE 3 | Results of analyses of variances (ANOVAs) with repeated measures: F-values, p-values and effect sizes (η 2 ) for the mismatch negativity (MMN) (top) and P3a (bottom) mean peak amplitudes (or latencies) including Session (pre-test vs. post-test) and Condition (Short ISI vs. Long ISI) as within-subject factors and Group (experimental group, EG vs. control group, CG) as a between-subject factor.

Significant effects are marked in gray background.

than in CG (p = 0.031). Mean and SD for MMN amplitudes are presented in **Table 4**.

ANOVA conducted on latencies revealed a tendency toward significance (p = 0.055) for Condition with shorter MMN

latencies in Long ISI (M = 193.34; SD = 25.69) than in Short ISI (M = 202.92; SD = 21.57). Mean (with SD) for MMN latencies are presented in **Table 4** and **Figure 5**.

#### P3a

ANOVA conducted on amplitudes (**Table 3**) revealed a significant main effect of Session, with higher amplitudes in post- (M = 5.00; SD = 1.63) than pre-test (M = 4.37; SD = 1.27), independent of Group and Condition (p = 0.044).

In ANOVA on latencies (**Table 3**), main effects of Session and Condition were observed. Shorter P3a latencies were observed in post- (M = 343.49; SD = 41.12) than pre-test (M = 378.36; SD = 51.54; p = 0.001) and in Short ISI (M = 347.71; SD = 26.76) than in Long ISI (M = 374.13; SD = 66.48; p = 0.025) in both groups. Mean (with SD) for P3a amplitudes are presented in **Table 4** and **Figure 6**.

In summary, increased amplitudes in post-test in comparison to pre-test were observed only in EG for MMN, but in both groups for P3a. Shorter P3a latencies were observed in postcompared to pre-test and in the Short ISI than in the Long ISI Condition.

### Stage 2—N2 and N2'

### N2 (Short ISI Condition)

In ANOVA (Group × Session × Block) on amplitudes, a main effect of Block modified by the Session × Block interaction was observed (see **Table 5**). N2 amplitudes were higher in post- (M = −8.91; SD = 4.17) than in pre-test (M = −7.62; SD = 3.39) but only for deviants (p = 0.006). For control deviants, this difference was nonsignificant (p = 0.531). Moreover, only in posttest were the amplitudes for deviants (M = −8.91; SD = 4.17) higher than for control deviants (M = −7.15; SD = 2.59; p < 0.001). In pre-test, the amplitudes for deviants and control deviants did not differ significantly (p = 0.676).

The Session × Group interaction resulted from higher amplitudes in post- than pre-test in EG (p = 0.001, see **Table 5**). In CG the difference between sessions was nonsignificant (p = 0.135). Moreover, in post-test in EG, amplitudes were higher than in CG (p = 0.003). Pre-test between-groups differences were nonsignificant (p = 0.644). Mean (with SD) for N2 amplitudes are presented in **Table 6**.

In ANOVA on N2 latencies, all effects were nonsignificant (see **Table 6**).

### N2' (Long ISI Condition)

In ANOVA (Group × Session × Block) on amplitudes, the main effect of Session was significant (p = 0.005), whereas Block tended towards significance (p = 0.051). Two interactions, Session × Group and Group × Block, were observed (see **Table 5**).

The Session × Group interaction resulted from higher amplitudes in post- than in pre-test in EG (p = 0.001). In CG, these differences were nonsignificant (p = 0.517). Mean (with SD) for N2' amplitudes are presented in **Table 6**.

Although the groups did not differ significantly in amplitudes for deviants (p = 0.083) or control deviants (p = 0.617), the Group × Block interaction reflected higher amplitudes in EG in response to deviants (p = 0.005) than to control deviants. These differences in CG were nonsignificant (p = 0.904). Mean (with SD) for N2' amplitudes are presented in **Table 6**.

No significant effects in ANOVA on N2' latencies were observed (see **Table 5**).

In summary, enhanced N2 amplitudes post-test compared to pre-test were observed for deviants (presented in oddball), indicating increased deviant detection sensitivity. These differences were nonsignificant for control deviants. In EG, enhanced N2 and N2' amplitudes for both deviants and control deviants were found in post-test. These amplitudes were higher in response to deviants than to control deviants.

### DISCUSSION

This study showed important training-related influences posttest as compared to pre-test: (1) in EG in post-test increased MMN was accompanied by enhanced N2 and N2' amplitudes for deviants (in oddball); (2) in both groups post-test, higher P3a amplitudes and shorter latencies were observed. Additionally, in Short ISI, generally higher MMN amplitudes were observed irrespective of training effect.

The applied MMN paradigm allowed the study of the ''genuine'' timing, minimizing the influence of other cognitive processes which are highly involved in any behavioral task. It is known that MMN is generated automatically, even without overt attention being paid to the presented stimuli and it reflects the brain's pre-attentive ability to detect any violation of the regularity of auditory stimulation (Näätänen and Picton, 1987; Winkler, 2007). Due to its low cognitive demands (such as attention, decision making, executive function, etc.) it is considered a reliable and objective measure in children. In our study, the use of MMN allowed the verification of whether temporal training caused changes in electrophysiological responses as compared to the control non-temporal training.

Below, we discuss the observed relationships in terms of training-induced effects and general rapid auditory processing.

## Training-Related Changes in Rapid Auditory Processing

#### MMN

After training, increased MMN amplitudes in both conditions were obtained only in EG (**Figure 4**). This result is congruent with data from other studies indicating increased MMN amplitudes after temporal-auditory interventions (Kujala et al., 2001; Heim et al., 2016). For example, Heim et al. (2016) reported normalized ERP amplitudes and latencies of MMN in children

TABLE 5 | Results of ANOVAs with repeated measures: F-values, p-values and effect sizes (η 2 ) for the N2 (left) and N2' (right) mean peak amplitudes (or latencies) including Session (pre-test vs. post-test) and Block (Deviants vs. Control deviants) as within-subject factors and Group (EG vs. CG) as a between-subject factor.

Significant effects are marked in gray background.

TABLE 6 | N2 (left) and N2' (right) mean peak amplitudes in µV (with SD) and latencies in ms in EG and CG, in pre-test and post-test and in Short and Long inter-stimulus interval (ISI) conditions.

with language-learning impairment after the administration of FFW (see ''Introduction'' section). Nevertheless, they also found enhanced MMN post-test in healthy controls, probably due to task re-exposure. As in our study, increased MMN was only found in EG, excluding any effect of repeated measurement but suggesting, rather, that the changes were training-related. Moreover, Kujala et al. (2001) found increased MMN amplitude in response to paired tones following 14 sessions of auditory discrimination training in children with dyslexia. They reported a correlation between increased MMN amplitude and improved reading skills. In contrast, in our study we did not find any statistically significant correlations between enhanced MMN and improvement in behavioral measures reported in our previous article (Szelag et al., 2015). This lack of correlation in young children may be caused by the high variability of behavioral indicators which are highly cognitively demanding.

In our study, the training-related improvements indexed by MMN amplitudes in EG were post-test by both enhanced N2 and N2' amplitudes for the deviants, but not for the control deviants (see ''Results'' section, Stage 2). This may suggest increased temporal acuity after temporal training and better detection of the regular deviants. These relationships were not observed in CG.

At this point, some conclusions can be drawn about the potential impact of the control block design on the obtained results. As mentioned before, our control block allows the stimuli's physical properties to be controlled while ignoring the adaptation effect. One should agree that the reported MMN amplitudes could potentially be artificially elevated because of habituation due to repetition of the control deviants. As significant differences between sessions for control deviant amplitudes were not found, one may expect that the adaptation effect modified both the pre- and post-test measurements in a similar way. Therefore, we expect that our control block design will only have weakly influenced the training-related changes, which were the main focus of our study. However, we would recommend that future studies be more careful in designing the control block, as suggested by, e.g., Jacobsen and Schröger (2003).

The effects of our temporal training may be important for improving the rapid auditory processing of nonverbal information. There are convincing indications that such processing is the neural basis of language and speech (see ''Introduction'' section). The training-induced improvements in TIP may result in a transfer of the improvement from the time domain to the language domain in which timing is incorporated. Thus, improving timing in nonverbal information may facilitate speech functions. This seems to be crucial for future clinical applications of temporal training procedures in speech therapy in subjects with language disorders (aphasia or SLI; Szelag et al., 2014; 2015; Szymaszek et al., 2017).

According to previous studies, other auditory trainings for children with language disorders (e.g., FFW, Earobics, phonological interventions) may induce changes on different levels of auditory processing. For example, Pihko et al. (2007), in a MEG study, observed a stronger response to syllables, as reflected in the enhancement of both the P1 component and MMN, corresponding to speech improvement indexed by behavioral measurements in bilingual SLI children. Trainingrelated changes were also observed in ABRs (auditory brainstem responses) in children with SLI or CAPD (central auditory processing disorders; Filippini et al., 2012) indicating that auditory processing may be enhanced at very early stages (subcortical stages).

The transfer between improved auditory processing and language processing after auditory interventions (musical trainings) may be associated with enhanced sensitivity to acoustic features creating a base for speech improvement (Besson et al., 2011).

#### P3a

Some training-related changes were also observed in both groups in the P3a component, which reflects involuntary attention shifting (Escera and Corral, 2007). After both interventions, enhanced amplitudes and shortened P3a latencies were found, irrespective of condition. Our results are similar to those obtained by Lovio et al. (2012) who reported P3a enhancement after both the experimental (phonological awareness) and control (math games) interventions, whereas MMN increment were observed only following the experimental intervention. This pattern of training-related improvements is similar to that found in the present study, i.e., enhanced MMN in EG and increased P3a in both groups.

This training-related P3a increment in our study in both groups may be caused by some features common to the two interventions. These are: (1) similar attentional engagement; (2) comparable mental load; and (3) involvement of auditory discrimination (compare the ''Materials and Methods'' section). Thus, after both kinds of intense training, involuntary shifting of attention to the presented auditory stimuli might be enhanced, independent of the condition. Moreover, some researchers have claimed that language improvement induced by audiovisual trainings in children with language disorders may be moderated by attentional processes (Cohen et al., 2005; McArthur et al., 2010). Stevens et al. (2008) observed enhanced ERPs associated with selective attention in children with SLI after FFW administration resulted in receptive language improvement. Some support for this observation also comes from children with dyslexia showing diminished P300 amplitudes as well as better behavioral performance in a lexical decision task after audiovisual training (Jucla et al., 2010). The interpretation is that after such training, attentional resources are more efficiently allocated during word recognition.

Regardless of the training approach taken, there is room to create new intervention tools for children with language learning impairments.

### Two Temporal Mechanisms of Auditory Perception in SLI

Looking at the waveforms displayed in **Figure 4** for deviants (red lines) and control deviants (blue lines), we can assume that two mechanisms of auditory processing were active depending on the duration of the ISI separating the two stimuli presented in rapid succession. This may be reflected in two different patterns of neural responses. For Short ISI (paired tones separated by an ISI of 50 ms), one electrophysiological response was elicited and indicated by a complex P1-N2 response which is dominant in children (Ervast et al., 2015). This reflects a situation in which the response to the second tone within a pair began before the termination of the response to the first tone, which is reported in studies using paired stimuli separated by a relatively short ISI (**Figure 4**). Evidence from previous literature has suggested the existence of a 200 ms long TWI following the onset of the first sound, in which a unitary sound representation is formed (Näätänen and Winkler, 1999;Horváth et al., 2007). In contrast, for longer ISIs (above 200 ms) two distinct neural responses were obtained: P1-N2 followed by P1'-N2' for the first and second tone within a pair, respectively. The electrophysiological response to the second tone seemed to appear after termination of the response to the first tone. Similarly, two separated MMN potentials were elicited when the stimulus onset asynchrony exceeded the duration of the TWI (Long ISI Condition; **Figure 4**).

Such unitary sound representation has been reported in previous studies. For example, Wang et al. (2005) and Fox et al. (2010) indicated the integration of two paired tones presented with relatively short ISIs. Fox et al. (2010) obtained two distinct neural responses in children aged 7–9 when paired tones were separated by a 200 ms ISI. For ISIs of 25, 50 or 100 ms, only one neural response was elicited. Furthermore, Wang et al. (2005), using the double deviant method in children aged 5–8, reported two separate MMN responses elicited by two kinds of deviant presented in a row differing either in pitch or intensity, but only when the separating ISI was 250 ms. At shorter ISIs (i.e., 50, 100, or 200 ms) they found only one MMN, indicating an integrated neural response to two different kinds of deviants. In summary, the above research on temporal integration focused on an approximately 200 ms border interval known as the TIW. This research has clearly indicated the elicitation of one neural response when such an interval is short or near the TIW limit, whereas, two distinct neural responses are observed when the time window following the onset of a sound exceeds the TIW.

The important observation is that the children with SLI studied here presented the same pattern of responses as their normally developing peers (reported in previous studies), i.e., one integrated MMN response at shorter ISIs and two separate responses at longer ISIs. We may speculate that despite the TIP deficits in children with SLI evidenced in behavioral methods, the neural basis of TIP seems preserved. As reported in our previous studies (Szelag et al., 2015), children with SLI displayed deteriorated perception of the temporal order of two sounds presented in rapid succession, i.e., they displayed higher temporal order thresholds (values of approx. 200 ms) than their typically developing peers (approx. 100 ms). Our results are promising as they show that the neural mechanism of TIP evidenced in the electrophysiological method reported here is intact. One might expect that this preserved neural base may constitute the framework for the efficacy of TIP training, resulting in improved language skills.

### Short vs. Long ISI Detection Reflected in MMN

Higher MMN was observed in Short ISI as compared to Long ISI (**Figure 4**). As higher amplitudes are usually observed in easier tasks than in more difficult ones (e.g., Näätänen, 2000), one may expect that for children with SLI, processing in Short ISI was easier than in Long ISI. This was confirmed by greater MMN amplitudes in the former case, irrespective of Session and Group (see ''Results'' section, Stage 1, **Figure 4**). It seems that processing within the TIW (Short ISI) fosters easier detection of violation of regularities. Conversely, if the stimulus-onsetasynchrony exceeds the TIW, smaller MMN (or even no MMN) occurred. The Long ISI Condition required a longer memory trace which made the detection of violation more difficult.

According to Näätänen (2000), MMN occurs when the memory trace of the standard is still active during deviant presentation, thus, MMN may depend on sensory memory persistence. The overall duration of the paired-stimulus pattern in Short ISI (150 ms) was twice as short as that in Long ISI (300 ms), resulting in greater sensory memory load in the latter case, creating more a difficult perceptual situation. On the other hand, according to Winkler (2007), increased MMN amplitude in response to the paired-tone patterns may indicate the improvement of temporal grouping of incoming sounds, which seems crucial in the process of auditory object formation.

We emphasized the advantage of the MMN paradigm in testing TIP in children as it does not require the participant to actively perform the task or even to attend to the stimuli. In this respect, this paradigm has a large advantage over behavioral studies. It is worth mentioning that results of electrophysiological and behavioral measurements examining the same phenomena cannot be compared directly. Considering the behavioral data previously reported in children with SLI (Szelag et al., 2015), an opposite relation of detection may emerge—i.e., shorter ISIs may make the temporal ordering more difficult than long ISIs. Szelag et al. (2015) indicated that thresholds of temporal order for two sounds required an ISI of approx. 200 ms, whereas at shorter ISIs it was reported at a chance level. This suggests that longer ISIs created an easier perceptual situation than shorter ones. Such divergence between the electrophysiological results presented here and the behavioral data published previously may be due to the different mechanisms implemented in the sequencing of the auditory stimuli presented with the various ISIs, i.e., perceptual auditory streaming or identification of separate sounds.

### CONCLUSION

In summary, using ERP we have demonstrated that temporal training may enhance the detection of changes in rapid auditory streams in children with SLI. Improved temporal resolution in two temporal windows was measured in terms of increased MMN. Our study also indicated that intense cognitive training (both temporal and non-temporal) in children with SLI may ameliorate involuntary attention shifting as reflected by increased P3a amplitudes.

Results indicate that, not only in normal children (studied by, e.g., Fox et al., 2010) but also in children with SLI, auditory perception of sequences of rapidly changing stimuli depends on the duration of the ISI separating the presented stimuli. At a relatively short ISI (50 ms), one electrophysiological response was elicited, whereas at a long ISI (200 ms) two separate electrophysiological responses occurred.

### AUTHOR CONTRIBUTIONS

AD: subject recruitment, data acquisition, conducting therapy sessions, analysis and interpretation of data, contribution to manuscript writing. AS: data acquisition, conducting therapy sessions, analysis and interpretation of data, manuscript writing. KN: conducting therapy sessions, analysis and interpretation of data. ES: conceptualization and study design, analysis and interpretation of data, manuscript writing. All authors: final approval.

### FUNDING

This research was supported by grant INNOTECH-K1/IN1/ 30/159041/NCBR/12 from The National Centre for Research and Development (Narodowe Centrum Badan i Rozwoju) and ´ Statutable to the Nencki Institute.

### ACKNOWLEDGMENTS

The authors would like to thank the neuropsychologists and speech-language pathologists who recruited children with SLI for the study. We thank Elzbieta Chru´scicka and ˙ Monika Kastory-Bronowska from the Early Intervention Centre in Warsaw for their assistance in the recruitment

### REFERENCES

of children. The authors would like to thank Anna Bombinska for her technical assistance during data collection.

with acoustically modified speech. Science 271, 81–84. doi: 10.1126/science.271. 5245.81

**Conflict of Interest Statement**: ES and AS are the creators of the Dr. Neuronowskir software package, realized as part of a project at the Nencki Institute with funding from the National Centre for Research and Development in Poland. The rights to the software lie with the Nencki Institute, which has an agreement with Harpo Ltd., the company commercializing this software. ES and AS are not the owners of this technology nor do they have a direct financial arrangement with Harpo Ltd. The authors state that this does not affect the scientific validity of the results.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Dacewicz, Szymaszek, Nowak and Szelag. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Temporal Processing of Joyful and Disgusting Food Pictures by Women With an Eating Disorder

#### Caroline Gagnon\*, Catherine Bégin, Vincent Laflamme and Simon Grondin

École de Psychologie, Université Laval, Québec, QC, Canada

The present study used the presentation of food pictures and judgements about their duration to assess the emotions elicited by food in women suffering from an eating disorder (ED). Twenty-three women diagnosed with an ED, namely anorexia (AN) or bulimia nervosa (BN), and 23 healthy controls (HC) completed a temporal bisection task and a duration discrimination task. Intervals were marked with emotionally prerated pictures of joyful and disgusting food, and pictures of neutral objects. The results showed that, in the bisection task, AN women overestimated the duration of food pictures in comparison to neutral ones. Also, compared to participants with BN, they perceived the duration of joyful food pictures as longer, and tended to overestimate the duration of the disgusting ones. These effects on perceived duration suggest that AN women experienced an intense reaction of fear when they were confronted to food pictures. More precisely, by having elevated the arousal level and activated the defensive system, food pictures seemed to have speeded up the rhythm of the AN participants' internal clock, which led to an overestimation of images' duration. In addition, the results revealed that, in both tasks, ED women presented a lower temporal sensitivity than HC, which was related to their ED symptomatology (i.e., BMI, restraint and concern) and, particularly, to their weaker cognitive abilities in terms of attention, processing speed and working memory. Considered all together, the findings of the present experiment highlight the role of fear and anxiety in the manifestations of AN and point out the importance of considering non-temporal factors in the interpretation of time perception performance.

#### Edited by:

Mark A. Williams, Macquarie University, Australia

#### Reviewed by:

Fuat Balc*ı*, Koç University, Turkey Joseph Glicksohn, Bar-Ilan University, Israel

#### \*Correspondence:

Caroline Gagnon<EMAIL_ADDRESS>
Received: 30 November 2017 Accepted: 19 March 2018 Published: 06 April 2018

#### Citation:

Gagnon C, Bégin C, Laflamme V and Grondin S (2018) Temporal Processing of Joyful and Disgusting Food Pictures by Women With an Eating Disorder. Front. Hum. Neurosci. 12:129. doi: 10.3389/fnhum.2018.00129 Keywords: time perception, emotion, eating disorders, food, bisection, discrimination

## INTRODUCTION

Time perception refers to the subjective experience of time, which indicates how a person interprets the duration of an event. The sense of time is modulated by intrinsic (e.g., age, gender, menstrual cycle; Block et al., 2000; Morita et al., 2005; Glicksohn and Hadad, 2012; Pütz et al., 2012; Ferreira et al., 2016) and extrinsic contexts (e.g., medication, drugs, rhythmical stimuli; Droit-Volet et al., 2010; Lake and Meck, 2013; Shahabifar and Movahedinia, 2016). Among the intrinsic factors, emotions are particularly important. As suggested by the saying "time flies when we are having fun," time perception is intimately connected to emotional life (Droit-Volet and Gil, 2009).

Viewed from a pacemaker-accumulator (internal-clock) interpretation of time processing (Gibbon, 1977; Gibbon et al., 1984), emotions can affect time perception through two of the latter's components: the pacemaker or the attentional mechanisms. Several researches have shown that