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T-patterns in the study of movement and behavioral disorders
Physiology & Behavior 215 (2020) 112790
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Physiology & Behavior journal homepage: www.elsevier.com/locate/physbeh
Review
T-patterns in the study of movement and behavioral disorders a
a
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Stefania Aiello , Giuseppe Crescimanno , Giuseppe Di Giovanni , Maurizio Casarrubea
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Laboratory of Behavioral Physiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), Human Physiology Section “Giuseppe Pagano”, University of Palermo, Palermo, Italy Laboratotry of Neurophysiology, Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, Msida, Malta c School of Biosciences, Cardiff University, Cardiff, United Kingdom a
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A R T I C LE I N FO
A B S T R A C T
Keywords: Behavioral disorder Movement disorder Multivariate techniques T-pattern analysis TPA
Aim of the present review is to offer an outline of the application of T-pattern analysis (TPA) in the study of neurological disorders characterized by anomalies of movement and, more in general, of behavior. TPA is a multivariate technique to detect real time patterns of behavior on the basis of statistically significant constraints among the events in sequence. TPA is particularly suitable to analyse the structure of behavior. The application of TPA to study movement and behavioral disorders is able to offer, with a high level of detail, hidden characteristics of behavior otherwise impossible to detect. For its intrinsic features, TPA is completely different not only from quantitative evaluations of behavior such as assessments of frequencies, durations, percent distributions etc. of individual behavioral components, but also from the largest extent of multivariate approaches based, for instance, on the analysis of transition matrices. Various applications of TPA in the study of behavior in human patients and in animal models of neurological disorders are discussed. TPA is a suitable tool to study the movement and behavioral disorders. This review represents a useful background for researchers, therapists, physicians etc. who intend to use this technique
“As soon as the relation between two entities A and B becomes conditional on C's value or state then a necessary component of organization is present […] so also is the assumption that we are speaking of a whole composed of parts” [1]. William Ross Ashby, 1903–1972 1. Introduction Behavior is the reaction of a living being to external or internal causal factors [2,3]. In humans and animals, this reaction is coordinated by the central nervous system (CNS) in a contextualized and coherent fashion [4]. Any complex or simple action, like raising a hand, smiling, walking or speaking, involves several and complex processes in the CNS; therefore, an injury or a malfunction at any level of the CNS may result in substantial behavioral or movement disorder. These anomalies, as isolated episodes or, otherwise, related to delicate phases of psychomotor development, sometimes lead to a range of disorders, often socially dysfunctional, such as aggressiveness, impulsiveness, hyperactivity or stereotypies. In neuropsychiatry, the term “behavioral disorder” is extremely broad and it includes a wide range of diseases with heterogeneous etiology. On this subject, the Diagnostic and
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Statistical Manual of Mental Disorders [5] indicates, just to name a few, the Neurodevelopmental Disorders such as Autism Spectrum Disorders, Attention-Deficit Hyperactivity Disorder (ADHD), motor disorders associated with self-injurious behaviors, Tourette's syndrome and other tic disorders, psychotic disorders, bipolar disorders, depressive disorders, anxiety disorders, Obsessive-Compulsive Spectrum Disorders, feeding and eating disorders, Substance-Related and Addictive Disorders, Neurocognitive Disorders, Personality Disorders. On the basis of the Official Information Sheet from the World Health Organization, the social economic burden of these disorders is dramatic and increasing with substantial impact on health, daily life, employment and social relations in general [6]. Consequently, in these fields, it becomes of fundamental importance and actuality a scientific research, aimed at identifying new approaches, tools and methodologies for a better and a deeper understanding of the physio-pathological basis of these conditions. During the last decades an approach known as T-pattern analysis (TPA) has been employed with increasing frequency to study neurological and neuropsychiatric disorders associated with movement and/or behavioral anomalies. Aim of the present review is to offer an outline of the use of TPA in this field. Various applications of TPA in the study of behavior in human patients and in animal models of neurological disorders will be discussed.
Corresponding author. E-mail address: [email protected] (M. Casarrubea).
https://doi.org/10.1016/j.physbeh.2019.112790 Received 12 November 2019; Received in revised form 18 December 2019; Accepted 19 December 2019 Available online 21 December 2019 0031-9384/ © 2019 Elsevier Inc. All rights reserved.
Physiology & Behavior 215 (2020) 112790
S. Aiello, et al.
For its intrinsic features, TPA is completely different from conventional quantitative evaluations of behavior such as assessments of frequencies, durations, percent distributions etc. of individual behavioral components. Actually, it is worth noting that a lot of researches and clinical studies in the field of movement and behavioral disorders have made available a huge amount of data on the individual aspects of the behavioral anomalies associated with these conditions and, importantly, on the effects induced by independent variables such as the use of specific drugs or other treatments. Alongside these studies, during the last decades, it has gradually increased the need to evaluate neurological and neuropsychiatric disorders by using approaches able to provide information on the temporal aspects of behavior, in its comprehensive structure, rather than on individual parameters, detached from the real organization of the subject's activity. As a matter of fact, individual elements of any behavior, physiological or pathological, simple or complex, unfold over time. On the basis of the words of Irenäus Eibl-Eibesfeldt, “Behavior consists of patterns in time. Investigations of behavior deal with sequences that, in contrast to bodily characteristics, are not always visible” [7]. One of the methods able to provide detailed information about the temporal features of behavioral disorders associated with neurological and/or neuropsychiatric diseases is the T-pattern detection and analysis (TPA), a multivariate technique widely and fruitfully applied, during the last decades, to study several aspects of animal and human behavior [8–13]. To better understand the difference between a classic quantitative approach and TPA various clarifications and technical details will be provided in the following section. The purpose of this review is to make available a framework on TPA and its usefulness in the study of neuropsychiatric conditions associated with behavioral disorders. Besides human pathologies, the application of TPA to study animal models of neurological disorders it will be discussed. By providing an overview on the literature concerning this subject, this review aims to be a useful tool for researchers who intend to utilize this approach to study neurological and neuropsychiatric conditions characterized by movement and/or behavioral disorders.
Fig. 2. Bottom line near the X-axis: the same string of 35 hypothetical components showed in Fig. 1. Upper line: by removing the “background noise” events (gray letters), the sequence s-e-m becomes immediately evident.
related to the level of complexity. For example, the rhythmic succession of movements of the lower limbs during the walking of a human being can easily be noticed. But, if to this rhythmic alternation are added, as frequently happens, movements of other parts of the body, such as the arms, the head, the eyes, everything becomes more complex and it will be much more difficult to notice if and which movements are in relation and/or with the context in which they take place. Focusing again on Fig. 1, this string of events hides a sequence that is repeated a number of times within the T0-Tx period. Even by means of a careful and scrupulous observation, the identification of such a sequence will be very difficult. Fig. 2 has the same string of events as in the previous figure with the only difference that “background noise” events have been eliminated: even at very first glance it will be evident that the “s-em” sequence is repeated several times within the same time window. This example leads to a crucial consideration: if it is so difficult to find a sequence between a few letters in a two-dimensional space, such as a computer display or a sheet of paper, it will be much more difficult to find structural relationships between the various components of the behavior of a living being, where different events unfold over time. Furthermore, as to Fig. 2, it could be interesting to assess whether the three events “s”, “e” and “m” follow one another by pure chance or, again, if relatively invariant temporal distances between them exist. The TPA is able to provide answers for these and many other questions. A detailed description of theories, concepts and procedures behind TPA and related applications both in human and animal subjects can be found in our reviews [12,13] and in our recent book [10,11]. The following two sections present an overview of the current literature related to the application of T-pattern detection and analysis in the study of movement and behavioral disorders in human patients and animal models as well (Table 1).
2. T-pattern detection and analysis: a brief overview TPA is a multivariate technique able to detect real time patterns of behavior on the basis of the detection of statistically significant constraints among the events in sequence. A hypothetical series of events taking place during a given observation period T0-Tx is illustrated in Fig. 1. From a quantitative point of view, each of these events could be described in terms of frequency, percent distribution, etc. For example, it is possible to count the amount of different events and their overall number. In the example shown in Fig. 1, we have a total of thirty-five events, fourteen different from each other. The most frequent among them is "x", with a frequency of eight which, out of the thirty-five total events, covers a slice of 22.86%, followed by the event "e" which, with a frequency of five, represents 14.29%, and so on. Such a purely quantitative evaluation provides considerable exhaustiveness in descriptive terms; these data, however, concern isolated elements, decontextualized from the structure of the real behavior, from its sequential organization and, therefore, from its temporal characteristics. The introduction of the temporal dimension makes the analysis more difficult but it greatly enriches the study. Of course, not all the behavioral sequences are difficult to observe with the naked eye. This is
3. Application of T-pattern analysis in the study of behavioral disorders in humans About four decades ago TPA has been developed to study human behavior [11,12]. Hence, not surprisingly, most of the studies utilizing such an approach have been carried out on human subjects [12,13]. This section discusses a number of different TPA utilizations in a wide range of behavioral disorders in humans. One of the first TPA applications in the study of behavioral disorders in humans comes from Melvin Lyon and Colleagues [14]. These Authors have analysed subjects affected by schizophrenia, comparing them a same number of age-, sex-, and education-matched normal control subjects. Results revealed that schizophrenic patients, in comparison to control subjects, had a larger number of significant T-patterns, more different types of patterns, and more branching of patterns at a higher level (i.e. more complex T-patterns). Thus, it is clear that schizophrenic patients are characterized by an increased variability and complexity of their behavior (e.g. indiscriminate and/or repetitive responses). This result is quite interesting because it is not in line with the conventional DSM procedures (in 1994, year of Lyon's article publication [14], DSMIII-R) but in agreement with studies of two-choice behavior in schizophrenia based on previous studies by the same Authors concerning possible relationship to dopamine/acetylcholine imbalance in the brain [15]. The Authors conclude that diagnostic procedures in schizophrenia might benefit from tests and more researches oriented toward these findings. The same research group, in following years has continued to apply TPA in the study of neuropsychiatric disorders. Indeed, about ten
Fig. 1. Short string of 35 hypothetical behavioral components (letters) occurring during a given T0–Tx time window (X-axis). A hidden recurring sequence of events is present. Even so, albeit (1) such a sequence recurs various times, (2) the amount of its constitutive components is small and, (3) represented in a bidimensional space, the detection of such sequences is an extremely difficult task. 2
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Table 1 Synoptic table presenting the applications of T-pattern analysis in the study movement and/or behavioral disorders in human patients (Section 3) or in animal models of neurological disorders (Section 4). Subject
Research field
References
Human Human Human Human Human Human Human Human Human Human Human Human Mouse Starling Starling Rat Mouse Rat Rat Rat Rat Rat Rat Rat Rat Rat Rat Mouse Mouse
Schizophrenia Schizophrenia, mood disorders, anxiety disorders Self injurious behavior Self injurious behavior Suicidal behavior Attention-deficit hyperactivity disorder Autism spectrum disorders Autism spectrum disorders Autism spectrum disorders Dementia Routine activties in working environment Routine activties in working environment Stereotypic behavior Stereotypic behavior Stereotypic behavior Obsessive-compulsive behaviors Obsessive-compulsive behaviors Social interaction Anxiety-related behavior in the Elevated plus maze Anxiety-related behavior in the Elevated plus maze Anxiety-related behavior in the Elevated plus maze Anxiety-related behavior in the Elevated plus maze Anxiety-related behavior in the Hole-Board Anxiety-related behavior in the Hole-Board Anxiety-related behavior in the Hole-Board Parkinson's disease Feeding disorders Histamine activity in the CNS Tourette's syndrome
Lyon et al. (1994) Lyon and Kemp, (2004) Kemp et al. (2008) Sandman et al. (2012) Haynal-Reymond et al. (2005) Masunami et al. (2009) Tardif et al.(1995) Warreyn et al. (2007) Willemsen-Swinkels et al. (2002) Woods et al. (2014) Su et al. (2013) Brdiczka and Begole, (2009) Bonasera et al. (2008) Brilot et al. (2009) Feenders and Bateson (2012) De Haas et al. (2011) De Haas et al. (2012) Casarrubea et al. (2017) Casarrubea et al. (2013) Casarrubea et al. (2013) Casarrubea et al. (2014) Casarrubea et al. (2016) Casarrubea et al. (2010) Casarrubea et al. (2011) Casarrubea et al. (2015) Casarrubea et al. (2019) Casarrubea et al. (2019) Santangelo et al. (2017) Santangelo et al. (2018)
years after the publication of the first above cited seminal work concerning the utilization of TPA in the study of psychoses, Lyon and Kemp [16] produced another paper aimed at the comparison of responses from schizophrenic subjects, subjects with mood or anxiety disorders, and healthy control subjects. In this study a specific task requiring the pressing of buttons with two possible choices was used. In comparison with healthy controls, neuropsychiatric patients showed a higher amount of T-patterns, by far more complex, demonstrating repetitive stereotyped responses. Also, Lyon and Kemp revealed that neuroleptic clozapine has an evident effect inducing a reduction of the number and complexity of T-patterns [16]. The importance of this study lies in that, as the Authors concluded, the considerable increase of detected patterns may represent an important sign of neuropsychiatric conditions such as schizophrenia and mania. Two additional interesting studies from the same research group investigated the temporal structure of behavior in patients with self-injurious activities, that is, intentional behavioral acts dramatically aimed at self-harming. In the first of these studies, Kemp and Colleagues investigated the features of temporal patterns of self-injury and their possible relationships with the level of stress hormones [17]. Outcomes of the study indicate that the detected T-patterns in patients may be related with blood levels of POMC-derived stress hormones; in addition, the authors conclude that TPA represents a very useful tool to detect otherwise unnoticeable relationships between patterns of self-injury and specific biological parameters such as blood levels of specific hormones; in the second study Sandman and Colleagues [18] demonstrated that subjects affected by self-injurious activities present more complex T-patterns; it has been hypothesized that self-injurious behavior contributes to complex organized behavioral patterns. Interestingly, the Authors suggest that these results may represent a background for interventions directed at modifying the individual's environment to ameliorate the comprehensive symptomatology. In the context of self-injurious activities, the attempt of suicide is, by far, the most dramatic behavioral expression. In this delicate field of research, Haynal-Reymond and Colleagues [19] applied the temporal pattern analysis to study non-verbal communication in
doctor-suicidal patient interviews. In brief, these Authors gathered about 60 video recordings of patients with attempt of suicide admitted to the emergency room of an hospital and studied these videos by using T-pattern detection and analysis. The conclusion of the Authors is twofold: first, they confirmed the existence of an important non-verbal interaction between patient and the therapist; second, it is suggested that such a non-verbal communication is potentially able to provide important signs regarding the affective/emotional condition of the subjects and, importantly, their suicidal tendency [19]. Such a seminal application of TPA is, in our opinion, extremely important because suicide attempts do represent critical behaviors often difficult to prevent. In addition, suicide attempts, independently of the final result of the action, always left dramatic and enduring consequences in terms of familiar and social environment. A deeper knowledge of the underlying behavior would be able to provide important tools for doctors, psychiatrists and psychologists to prevent these behaviors, dealing with patients in a more effective way. TPA has been fruitfully employed to study specific pathological conditions such as autism spectrum disorders (ASD) or attention-deficit hyperactivity disorder (ADHD). On the basis of previous studies showing that children affected by ADHD are affected by an abnormal sensitivity to punishments and/or rewards [2023], Masunami and Colleagues [24] hypothesized that ADHD children and normal children present a completely different decision-making strategy and that this strategy is highly dependent on the sensitivity to rewards and punishments in the gambling task. Results revealed that ADHD children had fewer T-patterns with punishments and have an important tendency to have many T-patterns with rewards. The authors conclude that ADHD patients are characterized by a highly impaired decision-making strategy provoked by their abnormal sensitivity to rewards and punishments. Tardif and Colleagues [25] applied T-patterns to investigate the interaction between adults and autistic children during a play situation. The behavior of 10 autistics children was studied to this purpose. The peculiar result of this research lies in the detection of specific T-patterns in autistic children and that these patterns appear to be dependent on the IQ and encompass a number of 3
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TPA. This is an original application of different multivariate approaches. Actually, the utilization of analyses based on transition matrices (Markov processes) and T-patterns in the same paper is a challenging and time-demanding task, for these reasons utilized only in very few other studies (e.g. see [36, 37]); even so, it is important to bear in mind that synergic utilization of different multivariate approaches in the study of a given behavior is able to provide an unrivaled level of detail. Brilot et al. [34] clearly show that both methods can be fruitfully applied together to identify stereotypies and to accurately quantify stereotypic behavior. In detail, the mean number of T-patterns detected was found to be correlated with the detection of abnormal behaviors. It is suggested that animals, characterized by stereotypic behavior, should have a reduced behavioral repertoire with more time spent to perform stereotypies. Interestingly the same research group showed, in the following paper, that stereotypies are noticeably more common in wildcaught starlings [35] and suggest an important role of escape motivation. De Haas and Colleagues have produced two interesting papers concerning the detection of obsessive-compulsive behaviors and repetitive behaviors. In the first paper [38] the Authors have utilized rats administered with the dopamine receptor agonist quinpirole for five consecutive weeks. The behavior of animals was then assessed in an open-field apparatus. Results of TPA showed that, differently from the very well known rituals observable in obsessive-compulsive patients, quinpirole-induced behavior consisted of a smaller behavioral repertoire. On the other hand, similarly to humans, quinpirole-treated animals performed these behaviors with a high rate of repetition. The Authors concluded that quinpirole rat model of obsessive compulsive disorder has an interesting but not exceptional translational utility because it is able to mimic only partially the compulsive behavior observed in human patients. In a following report [39], the same research group used quinpirole to assess possible effects of this molecule in different mouse strain, i.e. A/J and C57BL/6 J strain. Results of TPA clearly demonstrated that the A/J strain, much more than C57BL/6 J one, presents several stereotypies and resembles behavioral characteristics of human patients. These studies by De Haas and Colleagues [38, 39] have a considerable translational value because represent an excellent example of application of T-pattern detection and analysis in the identification and comparison of animal models of human neurological diseases. The application of TPA in situation involving the interaction between two or more subjects is a particularly interesting field of research. In a recent paper, Casarrubea and Colleagues [40], applying TPA in the study of social interaction in rats, have unveiled the existence of T-patterns in situations where at a first glance it would appear evident a complete lack of interaction. From a translational perspective this study could open new frontiers in the study of autism in humans. In addition, the detection of interactions and causal relationships linking the behavior of two subjects apparently not in interactive activities may represent a stimulating topic of discussion to reconsider from a critical/ theoretical point of view what should be deemed interaction and what should be studied as interaction in rodents. Numerous studies have applied TPA in the study of different rodent models of anxiety, social interaction, disorders of feeding behavior, Tourette‘s syndrome, Parkinson's disease. Casarrubea and Colleagues have deeply analyse the anxiety-related behavior of mice or rats in different experimental contexts such as hole board [41-43], plus maze [44-47] and open-field [37, 48]. Concerning the hole-board, it has been demonstrated, for the first time, the existence of T-patterns of rat behavior in hole-board, so providing a more comprehensive and useful description of rodent's anxietyrelated behavior in this experimental assay. The Authors also suggest that TPA could be used to improve the reliability of hole-board, in the evaluation of anxiety-related behavior in rodents [41]. The assessment of diazepam-induced anxiolysis has further demonstrated that even little pharmacological manipulation of the anxiety level can be studied and appraised with great detail if TPA is utilized [42]. Notably, TPA has been also able to reveal complex behavioral changes induced by acute nicotine administration [43] as well.
extraneous behaviors (e.g. handling of objects unrelated to the interaction or unspecified gazes looking elsewhere). Interestingly, if the autistic child is engaged in physical interaction, a minor number of Tpatterns, more simple in their structure, were detected. Another interesting study investigated the temporal features of joint attention behaviors in autistic children [26]. The authors investigated the most frequently occurring patterns in children affected by autism spectrum disorders (ASD) and normal ones. Results showed that children with ASD made less eye contact with their mothers and waited longer before the utilization of a verbal behavior. Even so, the authors underline the detection of the same sequences, from a structural point of view, both in autistic and normal children. This is an interesting result because various evidences demonstrate that ASD children are able to request joint attention behaviors [27, 28]. Moreover, the application of TPA to analyse the interaction between the autistic children and the adult unveiled several patterns otherwise impossible to be detected. Accordingly, in the study of ASD children, the detection of hidden patterns of behavior is an approach potentially able to offer new tools to deal with these serious neurological disorders. Willemsen-Swinkels and Colleagues [29] applied TPA to study autistic children trying to answer questions concerning attachments and autistic behavior. Results show that autistic children are able to develop attachment relationships with their primary caregiver; the Authors suggest that a disorganized attachment, validly assessed by means of TPA, is associated with autism and mental retardation and should not be simply considered the only reflection of autistic behavior. An elegant research from Woods and Colleagues [30] assessed the existence of complex behavioral features in persons with dementia. Results clearly indicate a potential utilization of TPA to detect specific patterns in patients. The Authors also suggest that the identifications of these patterns may represent an important propaedeutic moment to the development of individualized interventions. Finally, two studies, by Su and Colleagues [31] and by Brdiczka and Begole [32] deserve attention. Albeit these papers are not directly related with the analysis of time patterns in the evaluation of movement and/or behavioral disorders, they have potential interesting implications in this field. In detail, Su and Colleagues [31] analysed the routine activities of employees in their working environment. Results suggest that the increased number of T-patterns detected is linked with diminished workload; on the other hand, an increased number of T-patterns may be a negative symptom of productivity breakdown [31]. Brdiczka and Begole [32] presented interesting statistics of detected Tpatterns and derived correlations with participant perceptions of workload, autonomy, and productivity [32]. 4. Application of T-pattern analysis in the study of animal models characterized by movement and behavioral disorders Applications of TPA in animal behavior represent a unique resource showing a series of important and interesting data regarding the behavior of animals characterized by movement and behavioral disorders. Several papers have investigated stereotypies and repetitive behaviors in rodents. On this subject, Steve Bonasera and Colleagues [33] are among the first Authors to apply TPA to investigate movement and behavior disorders in animal models. These Authors applied TPA to assess route-tracing stereotypies in mice, namely, an important behavioral correlate of striatal function. Notably, the authors evaluated the behavior of rodents after the administration of psychoactive compounds. Results showed that TPA is a versatile and solid pattern detection tool able to provide precious information concerning the observed behavior in rodents. Stereotypic behavior patterns have been investigated also by Brilot et al. [34] and by Feenders and Bateson [35]. These studies have utilized behavioral observations carried out in the European Starling, that is the Sturnus Vulgaris. Both the papers clearly show that TPA is an excellent tool to reveal hidden patterns and stereotypies in behavior. As to the paper of Brilot et al. [34], the behavior of the European Starling was evaluated by means of Markov Chains and 4
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As to the well known elevated plus maze, the application of the TPA has been suggested to be able to improve the reliability of such an important behavioral assay [46] and able to predict the effects of putative anxiety modulators and different anxiety levels [44]. Consistently, it has been demonstrated that different rodent strain, characterized by a different emotional profile, have a completely different behavior once observed on the elevated plus maze, both considering the behavior in the whole apparatus [45] or in its most important area, i.e., the central platform, namely, the zone where “what to do next” selection processes do take place [47]. Beyond anxiety-related behavior, Casarrubea and Colleagues have also utilized TPA to study behavioral anomalies in protocols with manipulation of histamine levels in mice [48], in a mouse model of Tourette's Syndrome [37], in a rat model of Parkinson's Disease [49] and in a rat model of feeding disorder [50]. Concerning histamine levels in mice, the utilization of TPA showed a clear-cut enhancement of behavioral complexity in mice. It has been suggested a possible role of histamine on the organization of repetitive behavioral sequences of freely moving mice. In detail, TPA has been able to support a putative involvement of histamine in the pathophysiology of tics and related disorders [48]. As to rodent model of Tourette's syndrome, TPA highlighted a behavioral fragmentation in the D1CT-7 mouse model of this neurological disorder: transgenic mice showed a lower behavioral organization, a fragmented behavior, and patterns pervasively disturbed by intrusive tic-like twitches [37]. 6OHDA is a compound able to seriously damage basal ganglia circuitry. The effects induced by injection of 6-OHDA in the substantia nigra pars compacta on the behavioral sequencing have shown important differences between normal and lesioned subjects. Lesioned animals appear to be able to produce roughly the same amount of behavioral components produced by non lesioned subjects. However, as revealed by TPA, the important difference between normal and lesioned subjects lies in the impossibility of the latter to sequence behavioral patterns coherent to the context [49]. Finally, concerning feeding behavior, Casarrubea and Colleagues have compared the behavior of rats under normal and hyperglycidic diet [50]. TPA unveiled a noticeable behavioral reorganization induced by the high-carbohydrates treatment: fifty different patterns were detected in subjects under standard diet, on the other hand, more than seven hundreds of different T-patterns were showed in animals under hyperglycidic diet. The Authors suggested strong correlations of animals under high-carbohydrates with an increased anxiety level, suggestive of a pervasive craving-related behavior [50].
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Warreyn, H. Roeyers, U. Van Wetswinkel, I. De Groote, Temporal coordination of joint attention behavior in preschoolers with autism spectrum disorder, J. Autism Dev. Disord. 37 (2007) 501–512. [27] L. Camaioni, P. Perucchini, F. Muratori, B. Parrini, A. Cesari, The communicative use of pointing in autism: developmental profile and factors related to change, European Psychiatry 18 (2003) 6–12. [28] P. Mundy, Sigman M, C. Kasari, A longitudinal study of joint attention and language development in autistic children, J. Autism Dev. Disord. 20 (1990) 115–128. [29] S.H. Willemsen-Swinkels, M.J. Bakermans-Kranenburg, J.K. Buitelaar, M.H. van IJzendoorn, H. van Engeland, Insecure and disorganised attachment in children with a pervasive developmental disorder: relationship with social interaction and heart rate, J. Child Psychol. Psychiatry 41 (2000) 759–767. [30] D.L. Woods, M. Yefimova, M.L. Brecht, A method for measuring person-centered interventions: detecting and characterizing complex behavioral symptoms of persons with dementia, Clin. Gerontol. 37 (2014) 139–150. [31] N.M. Su, O. Brdiczka, B. Begole, The routineness of routines: measuring rhythms of media interaction, Hum-Comput. Interact 28 (2013) 287–334.
5. Conclusion The application of TPA in the study of neurological disorders, characterized by anomalies of movement and/or of behavior, is able to offer a different and useful point of view on the studied pathological condition. Such a usefulness orbits around one of the most important features of this multivariate technique: its ability to assess the real-time structure of behavior on the basis of the detection of sequences of events characterized by statistically significant constraints among the interval length separating them. Declaration of Competing Interest None References [1] W.R. Ashby, Principles of the self-organizing system, in: H. Von Foerster, G.W. Jr. Zopf (Eds.), Proceedings of the Principles of Self-Organization: Transactions of the University of Illinois Symposium, Pergamon Press, London, UK, 1962, pp. 255–278. [2] N. Tinbergen, Behavior as a reaction to external stimuli, in: N. Tinbergen (Ed.), The Study of Instinct, Clarendon Press/Oxford University Press, USNew York, NY, 1951. [3] N. Tinbergen, The internal factors responsible for the spontaneity of behaviour, in: N Tinbergen (Ed.), The Study of Instinct, Clarendon Press/Oxford University Press,
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[32] O. Brdiczka, N.M. Su, B. Begole, Using temporal patterns (t-patterns) to derive stress factors of routine tasks, CHI EA ’09 (2009) 4081–4086. ACM. [33] S.J. Bonasera, A.K. Schenk, E.J. Luxenberg, L.H. Tecott, A novel method for automatic quantification of psycho stimulant-evoked route-tracing stereotypy: application to MUS musculus, Psychopharmacology (Berl.) 196 (2008) 591–602. [34] B.O. Brilot, L. Asher, G. Feenders, M. Bateson, Quantification of abnormal repetitive behaviour in captive european starlings (Sturnus vulgaris), Behav. Process 82 (2009) 256–264. [35] G. Feenders, M. Bateson, The development of stereotypic behavior in caged european starlings, sturnus vulgaris, Dev. Psychobiol. 54 (2012) 773–784. [36] M. Casarrubea, F. Sorbera, G. Crescimanno, Multivariate data handling in the study of rat behavior: an integrated approach, Behav. Res. Methods 41 (2009) 772–781. [37] A. Santangelo, M. Bortolato, L.J. Mosher, G. Crescimanno, G. Di Giovanni, E. Cassioli, V. Ricca, M. Casarrubea, Behavioral fragmentation in the D1CT-7 mouse model of tourette's syndrome, CNS Neurosci. Ther. 24 (8) (2018) 703–711. [38] R. de Haas, A. Nijdam, T.A. Westra, M.J. Kas, H.G. Westenberg, Behavioral pattern analysis and dopamine release in quinpirole-induced repetitive behavior in rats, J. Psychopharmacol 25 (2011) 1712–1719. [39] R. de Haas, A. Seddik, H. Oppelaar, H.G. Westenberg, M.J. Kas, Marked inbred mouse strain difference in the expression of quinpirole induced compulsive like behavior based on behavioral pattern analysis, Eur. Neuropsychopharmacol. 22 (2012) 657–663. [40] M. Casarrubea, F. Faulisi, A. Cudia, D. Cancemi, M. Cardaci, M.S. Magnusson, G. Crescimanno, Discovery of recurring behavioural sequences in wistar rat social activity: possible support to studies on autism spectrum disorders, Neurosci. Lett. 653 (2017) 58–63, https://doi.org/10.1016/j.neulet.2017.05.031. [41] M. Casarrubea, F. Sorbera, M. Magnusson, G. Crescimanno, Temporal patterns analysis of rat behavior in hole-board, Behav. Brain Res. 208 (2010) 124–131. [42] M. Casarrubea, F. Sorbera, M. Magnusson, G. Crescimanno, T-pattern analysis of diazepam-induced modifications on the temporal organization of rat behavioral response to anxiety in hole-board, Psychopharmacology (Berl.) 215 (2011) 177–189. [43] M. Casarrubea, C. Davies, F. Faulisi, M. Pierucci, R. Colangeli, L. Partdrig,
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Contents lists available at ScienceDirect
Physiology & Behavior journal homepage: www.elsevier.com/locate/physbeh
Review
T-patterns in the study of movement and behavioral disorders a
a
b,c
Stefania Aiello , Giuseppe Crescimanno , Giuseppe Di Giovanni , Maurizio Casarrubea
a,⁎
T
Laboratory of Behavioral Physiology, Department of Biomedicine, Neuroscience and Advanced Diagnostics (Bi.N.D.), Human Physiology Section “Giuseppe Pagano”, University of Palermo, Palermo, Italy Laboratotry of Neurophysiology, Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, Msida, Malta c School of Biosciences, Cardiff University, Cardiff, United Kingdom a
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A R T I C LE I N FO
A B S T R A C T
Keywords: Behavioral disorder Movement disorder Multivariate techniques T-pattern analysis TPA
Aim of the present review is to offer an outline of the application of T-pattern analysis (TPA) in the study of neurological disorders characterized by anomalies of movement and, more in general, of behavior. TPA is a multivariate technique to detect real time patterns of behavior on the basis of statistically significant constraints among the events in sequence. TPA is particularly suitable to analyse the structure of behavior. The application of TPA to study movement and behavioral disorders is able to offer, with a high level of detail, hidden characteristics of behavior otherwise impossible to detect. For its intrinsic features, TPA is completely different not only from quantitative evaluations of behavior such as assessments of frequencies, durations, percent distributions etc. of individual behavioral components, but also from the largest extent of multivariate approaches based, for instance, on the analysis of transition matrices. Various applications of TPA in the study of behavior in human patients and in animal models of neurological disorders are discussed. TPA is a suitable tool to study the movement and behavioral disorders. This review represents a useful background for researchers, therapists, physicians etc. who intend to use this technique
“As soon as the relation between two entities A and B becomes conditional on C's value or state then a necessary component of organization is present […] so also is the assumption that we are speaking of a whole composed of parts” [1]. William Ross Ashby, 1903–1972 1. Introduction Behavior is the reaction of a living being to external or internal causal factors [2,3]. In humans and animals, this reaction is coordinated by the central nervous system (CNS) in a contextualized and coherent fashion [4]. Any complex or simple action, like raising a hand, smiling, walking or speaking, involves several and complex processes in the CNS; therefore, an injury or a malfunction at any level of the CNS may result in substantial behavioral or movement disorder. These anomalies, as isolated episodes or, otherwise, related to delicate phases of psychomotor development, sometimes lead to a range of disorders, often socially dysfunctional, such as aggressiveness, impulsiveness, hyperactivity or stereotypies. In neuropsychiatry, the term “behavioral disorder” is extremely broad and it includes a wide range of diseases with heterogeneous etiology. On this subject, the Diagnostic and
⁎
Statistical Manual of Mental Disorders [5] indicates, just to name a few, the Neurodevelopmental Disorders such as Autism Spectrum Disorders, Attention-Deficit Hyperactivity Disorder (ADHD), motor disorders associated with self-injurious behaviors, Tourette's syndrome and other tic disorders, psychotic disorders, bipolar disorders, depressive disorders, anxiety disorders, Obsessive-Compulsive Spectrum Disorders, feeding and eating disorders, Substance-Related and Addictive Disorders, Neurocognitive Disorders, Personality Disorders. On the basis of the Official Information Sheet from the World Health Organization, the social economic burden of these disorders is dramatic and increasing with substantial impact on health, daily life, employment and social relations in general [6]. Consequently, in these fields, it becomes of fundamental importance and actuality a scientific research, aimed at identifying new approaches, tools and methodologies for a better and a deeper understanding of the physio-pathological basis of these conditions. During the last decades an approach known as T-pattern analysis (TPA) has been employed with increasing frequency to study neurological and neuropsychiatric disorders associated with movement and/or behavioral anomalies. Aim of the present review is to offer an outline of the use of TPA in this field. Various applications of TPA in the study of behavior in human patients and in animal models of neurological disorders will be discussed.
Corresponding author. E-mail address: [email protected] (M. Casarrubea).
https://doi.org/10.1016/j.physbeh.2019.112790 Received 12 November 2019; Received in revised form 18 December 2019; Accepted 19 December 2019 Available online 21 December 2019 0031-9384/ © 2019 Elsevier Inc. All rights reserved.
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For its intrinsic features, TPA is completely different from conventional quantitative evaluations of behavior such as assessments of frequencies, durations, percent distributions etc. of individual behavioral components. Actually, it is worth noting that a lot of researches and clinical studies in the field of movement and behavioral disorders have made available a huge amount of data on the individual aspects of the behavioral anomalies associated with these conditions and, importantly, on the effects induced by independent variables such as the use of specific drugs or other treatments. Alongside these studies, during the last decades, it has gradually increased the need to evaluate neurological and neuropsychiatric disorders by using approaches able to provide information on the temporal aspects of behavior, in its comprehensive structure, rather than on individual parameters, detached from the real organization of the subject's activity. As a matter of fact, individual elements of any behavior, physiological or pathological, simple or complex, unfold over time. On the basis of the words of Irenäus Eibl-Eibesfeldt, “Behavior consists of patterns in time. Investigations of behavior deal with sequences that, in contrast to bodily characteristics, are not always visible” [7]. One of the methods able to provide detailed information about the temporal features of behavioral disorders associated with neurological and/or neuropsychiatric diseases is the T-pattern detection and analysis (TPA), a multivariate technique widely and fruitfully applied, during the last decades, to study several aspects of animal and human behavior [8–13]. To better understand the difference between a classic quantitative approach and TPA various clarifications and technical details will be provided in the following section. The purpose of this review is to make available a framework on TPA and its usefulness in the study of neuropsychiatric conditions associated with behavioral disorders. Besides human pathologies, the application of TPA to study animal models of neurological disorders it will be discussed. By providing an overview on the literature concerning this subject, this review aims to be a useful tool for researchers who intend to utilize this approach to study neurological and neuropsychiatric conditions characterized by movement and/or behavioral disorders.
Fig. 2. Bottom line near the X-axis: the same string of 35 hypothetical components showed in Fig. 1. Upper line: by removing the “background noise” events (gray letters), the sequence s-e-m becomes immediately evident.
related to the level of complexity. For example, the rhythmic succession of movements of the lower limbs during the walking of a human being can easily be noticed. But, if to this rhythmic alternation are added, as frequently happens, movements of other parts of the body, such as the arms, the head, the eyes, everything becomes more complex and it will be much more difficult to notice if and which movements are in relation and/or with the context in which they take place. Focusing again on Fig. 1, this string of events hides a sequence that is repeated a number of times within the T0-Tx period. Even by means of a careful and scrupulous observation, the identification of such a sequence will be very difficult. Fig. 2 has the same string of events as in the previous figure with the only difference that “background noise” events have been eliminated: even at very first glance it will be evident that the “s-em” sequence is repeated several times within the same time window. This example leads to a crucial consideration: if it is so difficult to find a sequence between a few letters in a two-dimensional space, such as a computer display or a sheet of paper, it will be much more difficult to find structural relationships between the various components of the behavior of a living being, where different events unfold over time. Furthermore, as to Fig. 2, it could be interesting to assess whether the three events “s”, “e” and “m” follow one another by pure chance or, again, if relatively invariant temporal distances between them exist. The TPA is able to provide answers for these and many other questions. A detailed description of theories, concepts and procedures behind TPA and related applications both in human and animal subjects can be found in our reviews [12,13] and in our recent book [10,11]. The following two sections present an overview of the current literature related to the application of T-pattern detection and analysis in the study of movement and behavioral disorders in human patients and animal models as well (Table 1).
2. T-pattern detection and analysis: a brief overview TPA is a multivariate technique able to detect real time patterns of behavior on the basis of the detection of statistically significant constraints among the events in sequence. A hypothetical series of events taking place during a given observation period T0-Tx is illustrated in Fig. 1. From a quantitative point of view, each of these events could be described in terms of frequency, percent distribution, etc. For example, it is possible to count the amount of different events and their overall number. In the example shown in Fig. 1, we have a total of thirty-five events, fourteen different from each other. The most frequent among them is "x", with a frequency of eight which, out of the thirty-five total events, covers a slice of 22.86%, followed by the event "e" which, with a frequency of five, represents 14.29%, and so on. Such a purely quantitative evaluation provides considerable exhaustiveness in descriptive terms; these data, however, concern isolated elements, decontextualized from the structure of the real behavior, from its sequential organization and, therefore, from its temporal characteristics. The introduction of the temporal dimension makes the analysis more difficult but it greatly enriches the study. Of course, not all the behavioral sequences are difficult to observe with the naked eye. This is
3. Application of T-pattern analysis in the study of behavioral disorders in humans About four decades ago TPA has been developed to study human behavior [11,12]. Hence, not surprisingly, most of the studies utilizing such an approach have been carried out on human subjects [12,13]. This section discusses a number of different TPA utilizations in a wide range of behavioral disorders in humans. One of the first TPA applications in the study of behavioral disorders in humans comes from Melvin Lyon and Colleagues [14]. These Authors have analysed subjects affected by schizophrenia, comparing them a same number of age-, sex-, and education-matched normal control subjects. Results revealed that schizophrenic patients, in comparison to control subjects, had a larger number of significant T-patterns, more different types of patterns, and more branching of patterns at a higher level (i.e. more complex T-patterns). Thus, it is clear that schizophrenic patients are characterized by an increased variability and complexity of their behavior (e.g. indiscriminate and/or repetitive responses). This result is quite interesting because it is not in line with the conventional DSM procedures (in 1994, year of Lyon's article publication [14], DSMIII-R) but in agreement with studies of two-choice behavior in schizophrenia based on previous studies by the same Authors concerning possible relationship to dopamine/acetylcholine imbalance in the brain [15]. The Authors conclude that diagnostic procedures in schizophrenia might benefit from tests and more researches oriented toward these findings. The same research group, in following years has continued to apply TPA in the study of neuropsychiatric disorders. Indeed, about ten
Fig. 1. Short string of 35 hypothetical behavioral components (letters) occurring during a given T0–Tx time window (X-axis). A hidden recurring sequence of events is present. Even so, albeit (1) such a sequence recurs various times, (2) the amount of its constitutive components is small and, (3) represented in a bidimensional space, the detection of such sequences is an extremely difficult task. 2
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Table 1 Synoptic table presenting the applications of T-pattern analysis in the study movement and/or behavioral disorders in human patients (Section 3) or in animal models of neurological disorders (Section 4). Subject
Research field
References
Human Human Human Human Human Human Human Human Human Human Human Human Mouse Starling Starling Rat Mouse Rat Rat Rat Rat Rat Rat Rat Rat Rat Rat Mouse Mouse
Schizophrenia Schizophrenia, mood disorders, anxiety disorders Self injurious behavior Self injurious behavior Suicidal behavior Attention-deficit hyperactivity disorder Autism spectrum disorders Autism spectrum disorders Autism spectrum disorders Dementia Routine activties in working environment Routine activties in working environment Stereotypic behavior Stereotypic behavior Stereotypic behavior Obsessive-compulsive behaviors Obsessive-compulsive behaviors Social interaction Anxiety-related behavior in the Elevated plus maze Anxiety-related behavior in the Elevated plus maze Anxiety-related behavior in the Elevated plus maze Anxiety-related behavior in the Elevated plus maze Anxiety-related behavior in the Hole-Board Anxiety-related behavior in the Hole-Board Anxiety-related behavior in the Hole-Board Parkinson's disease Feeding disorders Histamine activity in the CNS Tourette's syndrome
Lyon et al. (1994) Lyon and Kemp, (2004) Kemp et al. (2008) Sandman et al. (2012) Haynal-Reymond et al. (2005) Masunami et al. (2009) Tardif et al.(1995) Warreyn et al. (2007) Willemsen-Swinkels et al. (2002) Woods et al. (2014) Su et al. (2013) Brdiczka and Begole, (2009) Bonasera et al. (2008) Brilot et al. (2009) Feenders and Bateson (2012) De Haas et al. (2011) De Haas et al. (2012) Casarrubea et al. (2017) Casarrubea et al. (2013) Casarrubea et al. (2013) Casarrubea et al. (2014) Casarrubea et al. (2016) Casarrubea et al. (2010) Casarrubea et al. (2011) Casarrubea et al. (2015) Casarrubea et al. (2019) Casarrubea et al. (2019) Santangelo et al. (2017) Santangelo et al. (2018)
years after the publication of the first above cited seminal work concerning the utilization of TPA in the study of psychoses, Lyon and Kemp [16] produced another paper aimed at the comparison of responses from schizophrenic subjects, subjects with mood or anxiety disorders, and healthy control subjects. In this study a specific task requiring the pressing of buttons with two possible choices was used. In comparison with healthy controls, neuropsychiatric patients showed a higher amount of T-patterns, by far more complex, demonstrating repetitive stereotyped responses. Also, Lyon and Kemp revealed that neuroleptic clozapine has an evident effect inducing a reduction of the number and complexity of T-patterns [16]. The importance of this study lies in that, as the Authors concluded, the considerable increase of detected patterns may represent an important sign of neuropsychiatric conditions such as schizophrenia and mania. Two additional interesting studies from the same research group investigated the temporal structure of behavior in patients with self-injurious activities, that is, intentional behavioral acts dramatically aimed at self-harming. In the first of these studies, Kemp and Colleagues investigated the features of temporal patterns of self-injury and their possible relationships with the level of stress hormones [17]. Outcomes of the study indicate that the detected T-patterns in patients may be related with blood levels of POMC-derived stress hormones; in addition, the authors conclude that TPA represents a very useful tool to detect otherwise unnoticeable relationships between patterns of self-injury and specific biological parameters such as blood levels of specific hormones; in the second study Sandman and Colleagues [18] demonstrated that subjects affected by self-injurious activities present more complex T-patterns; it has been hypothesized that self-injurious behavior contributes to complex organized behavioral patterns. Interestingly, the Authors suggest that these results may represent a background for interventions directed at modifying the individual's environment to ameliorate the comprehensive symptomatology. In the context of self-injurious activities, the attempt of suicide is, by far, the most dramatic behavioral expression. In this delicate field of research, Haynal-Reymond and Colleagues [19] applied the temporal pattern analysis to study non-verbal communication in
doctor-suicidal patient interviews. In brief, these Authors gathered about 60 video recordings of patients with attempt of suicide admitted to the emergency room of an hospital and studied these videos by using T-pattern detection and analysis. The conclusion of the Authors is twofold: first, they confirmed the existence of an important non-verbal interaction between patient and the therapist; second, it is suggested that such a non-verbal communication is potentially able to provide important signs regarding the affective/emotional condition of the subjects and, importantly, their suicidal tendency [19]. Such a seminal application of TPA is, in our opinion, extremely important because suicide attempts do represent critical behaviors often difficult to prevent. In addition, suicide attempts, independently of the final result of the action, always left dramatic and enduring consequences in terms of familiar and social environment. A deeper knowledge of the underlying behavior would be able to provide important tools for doctors, psychiatrists and psychologists to prevent these behaviors, dealing with patients in a more effective way. TPA has been fruitfully employed to study specific pathological conditions such as autism spectrum disorders (ASD) or attention-deficit hyperactivity disorder (ADHD). On the basis of previous studies showing that children affected by ADHD are affected by an abnormal sensitivity to punishments and/or rewards [2023], Masunami and Colleagues [24] hypothesized that ADHD children and normal children present a completely different decision-making strategy and that this strategy is highly dependent on the sensitivity to rewards and punishments in the gambling task. Results revealed that ADHD children had fewer T-patterns with punishments and have an important tendency to have many T-patterns with rewards. The authors conclude that ADHD patients are characterized by a highly impaired decision-making strategy provoked by their abnormal sensitivity to rewards and punishments. Tardif and Colleagues [25] applied T-patterns to investigate the interaction between adults and autistic children during a play situation. The behavior of 10 autistics children was studied to this purpose. The peculiar result of this research lies in the detection of specific T-patterns in autistic children and that these patterns appear to be dependent on the IQ and encompass a number of 3
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TPA. This is an original application of different multivariate approaches. Actually, the utilization of analyses based on transition matrices (Markov processes) and T-patterns in the same paper is a challenging and time-demanding task, for these reasons utilized only in very few other studies (e.g. see [36, 37]); even so, it is important to bear in mind that synergic utilization of different multivariate approaches in the study of a given behavior is able to provide an unrivaled level of detail. Brilot et al. [34] clearly show that both methods can be fruitfully applied together to identify stereotypies and to accurately quantify stereotypic behavior. In detail, the mean number of T-patterns detected was found to be correlated with the detection of abnormal behaviors. It is suggested that animals, characterized by stereotypic behavior, should have a reduced behavioral repertoire with more time spent to perform stereotypies. Interestingly the same research group showed, in the following paper, that stereotypies are noticeably more common in wildcaught starlings [35] and suggest an important role of escape motivation. De Haas and Colleagues have produced two interesting papers concerning the detection of obsessive-compulsive behaviors and repetitive behaviors. In the first paper [38] the Authors have utilized rats administered with the dopamine receptor agonist quinpirole for five consecutive weeks. The behavior of animals was then assessed in an open-field apparatus. Results of TPA showed that, differently from the very well known rituals observable in obsessive-compulsive patients, quinpirole-induced behavior consisted of a smaller behavioral repertoire. On the other hand, similarly to humans, quinpirole-treated animals performed these behaviors with a high rate of repetition. The Authors concluded that quinpirole rat model of obsessive compulsive disorder has an interesting but not exceptional translational utility because it is able to mimic only partially the compulsive behavior observed in human patients. In a following report [39], the same research group used quinpirole to assess possible effects of this molecule in different mouse strain, i.e. A/J and C57BL/6 J strain. Results of TPA clearly demonstrated that the A/J strain, much more than C57BL/6 J one, presents several stereotypies and resembles behavioral characteristics of human patients. These studies by De Haas and Colleagues [38, 39] have a considerable translational value because represent an excellent example of application of T-pattern detection and analysis in the identification and comparison of animal models of human neurological diseases. The application of TPA in situation involving the interaction between two or more subjects is a particularly interesting field of research. In a recent paper, Casarrubea and Colleagues [40], applying TPA in the study of social interaction in rats, have unveiled the existence of T-patterns in situations where at a first glance it would appear evident a complete lack of interaction. From a translational perspective this study could open new frontiers in the study of autism in humans. In addition, the detection of interactions and causal relationships linking the behavior of two subjects apparently not in interactive activities may represent a stimulating topic of discussion to reconsider from a critical/ theoretical point of view what should be deemed interaction and what should be studied as interaction in rodents. Numerous studies have applied TPA in the study of different rodent models of anxiety, social interaction, disorders of feeding behavior, Tourette‘s syndrome, Parkinson's disease. Casarrubea and Colleagues have deeply analyse the anxiety-related behavior of mice or rats in different experimental contexts such as hole board [41-43], plus maze [44-47] and open-field [37, 48]. Concerning the hole-board, it has been demonstrated, for the first time, the existence of T-patterns of rat behavior in hole-board, so providing a more comprehensive and useful description of rodent's anxietyrelated behavior in this experimental assay. The Authors also suggest that TPA could be used to improve the reliability of hole-board, in the evaluation of anxiety-related behavior in rodents [41]. The assessment of diazepam-induced anxiolysis has further demonstrated that even little pharmacological manipulation of the anxiety level can be studied and appraised with great detail if TPA is utilized [42]. Notably, TPA has been also able to reveal complex behavioral changes induced by acute nicotine administration [43] as well.
extraneous behaviors (e.g. handling of objects unrelated to the interaction or unspecified gazes looking elsewhere). Interestingly, if the autistic child is engaged in physical interaction, a minor number of Tpatterns, more simple in their structure, were detected. Another interesting study investigated the temporal features of joint attention behaviors in autistic children [26]. The authors investigated the most frequently occurring patterns in children affected by autism spectrum disorders (ASD) and normal ones. Results showed that children with ASD made less eye contact with their mothers and waited longer before the utilization of a verbal behavior. Even so, the authors underline the detection of the same sequences, from a structural point of view, both in autistic and normal children. This is an interesting result because various evidences demonstrate that ASD children are able to request joint attention behaviors [27, 28]. Moreover, the application of TPA to analyse the interaction between the autistic children and the adult unveiled several patterns otherwise impossible to be detected. Accordingly, in the study of ASD children, the detection of hidden patterns of behavior is an approach potentially able to offer new tools to deal with these serious neurological disorders. Willemsen-Swinkels and Colleagues [29] applied TPA to study autistic children trying to answer questions concerning attachments and autistic behavior. Results show that autistic children are able to develop attachment relationships with their primary caregiver; the Authors suggest that a disorganized attachment, validly assessed by means of TPA, is associated with autism and mental retardation and should not be simply considered the only reflection of autistic behavior. An elegant research from Woods and Colleagues [30] assessed the existence of complex behavioral features in persons with dementia. Results clearly indicate a potential utilization of TPA to detect specific patterns in patients. The Authors also suggest that the identifications of these patterns may represent an important propaedeutic moment to the development of individualized interventions. Finally, two studies, by Su and Colleagues [31] and by Brdiczka and Begole [32] deserve attention. Albeit these papers are not directly related with the analysis of time patterns in the evaluation of movement and/or behavioral disorders, they have potential interesting implications in this field. In detail, Su and Colleagues [31] analysed the routine activities of employees in their working environment. Results suggest that the increased number of T-patterns detected is linked with diminished workload; on the other hand, an increased number of T-patterns may be a negative symptom of productivity breakdown [31]. Brdiczka and Begole [32] presented interesting statistics of detected Tpatterns and derived correlations with participant perceptions of workload, autonomy, and productivity [32]. 4. Application of T-pattern analysis in the study of animal models characterized by movement and behavioral disorders Applications of TPA in animal behavior represent a unique resource showing a series of important and interesting data regarding the behavior of animals characterized by movement and behavioral disorders. Several papers have investigated stereotypies and repetitive behaviors in rodents. On this subject, Steve Bonasera and Colleagues [33] are among the first Authors to apply TPA to investigate movement and behavior disorders in animal models. These Authors applied TPA to assess route-tracing stereotypies in mice, namely, an important behavioral correlate of striatal function. Notably, the authors evaluated the behavior of rodents after the administration of psychoactive compounds. Results showed that TPA is a versatile and solid pattern detection tool able to provide precious information concerning the observed behavior in rodents. Stereotypic behavior patterns have been investigated also by Brilot et al. [34] and by Feenders and Bateson [35]. These studies have utilized behavioral observations carried out in the European Starling, that is the Sturnus Vulgaris. Both the papers clearly show that TPA is an excellent tool to reveal hidden patterns and stereotypies in behavior. As to the paper of Brilot et al. [34], the behavior of the European Starling was evaluated by means of Markov Chains and 4
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As to the well known elevated plus maze, the application of the TPA has been suggested to be able to improve the reliability of such an important behavioral assay [46] and able to predict the effects of putative anxiety modulators and different anxiety levels [44]. Consistently, it has been demonstrated that different rodent strain, characterized by a different emotional profile, have a completely different behavior once observed on the elevated plus maze, both considering the behavior in the whole apparatus [45] or in its most important area, i.e., the central platform, namely, the zone where “what to do next” selection processes do take place [47]. Beyond anxiety-related behavior, Casarrubea and Colleagues have also utilized TPA to study behavioral anomalies in protocols with manipulation of histamine levels in mice [48], in a mouse model of Tourette's Syndrome [37], in a rat model of Parkinson's Disease [49] and in a rat model of feeding disorder [50]. Concerning histamine levels in mice, the utilization of TPA showed a clear-cut enhancement of behavioral complexity in mice. It has been suggested a possible role of histamine on the organization of repetitive behavioral sequences of freely moving mice. In detail, TPA has been able to support a putative involvement of histamine in the pathophysiology of tics and related disorders [48]. As to rodent model of Tourette's syndrome, TPA highlighted a behavioral fragmentation in the D1CT-7 mouse model of this neurological disorder: transgenic mice showed a lower behavioral organization, a fragmented behavior, and patterns pervasively disturbed by intrusive tic-like twitches [37]. 6OHDA is a compound able to seriously damage basal ganglia circuitry. The effects induced by injection of 6-OHDA in the substantia nigra pars compacta on the behavioral sequencing have shown important differences between normal and lesioned subjects. Lesioned animals appear to be able to produce roughly the same amount of behavioral components produced by non lesioned subjects. However, as revealed by TPA, the important difference between normal and lesioned subjects lies in the impossibility of the latter to sequence behavioral patterns coherent to the context [49]. Finally, concerning feeding behavior, Casarrubea and Colleagues have compared the behavior of rats under normal and hyperglycidic diet [50]. TPA unveiled a noticeable behavioral reorganization induced by the high-carbohydrates treatment: fifty different patterns were detected in subjects under standard diet, on the other hand, more than seven hundreds of different T-patterns were showed in animals under hyperglycidic diet. The Authors suggested strong correlations of animals under high-carbohydrates with an increased anxiety level, suggestive of a pervasive craving-related behavior [50].
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5. Conclusion The application of TPA in the study of neurological disorders, characterized by anomalies of movement and/or of behavior, is able to offer a different and useful point of view on the studied pathological condition. Such a usefulness orbits around one of the most important features of this multivariate technique: its ability to assess the real-time structure of behavior on the basis of the detection of sequences of events characterized by statistically significant constraints among the interval length separating them. Declaration of Competing Interest None References [1] W.R. Ashby, Principles of the self-organizing system, in: H. Von Foerster, G.W. Jr. Zopf (Eds.), Proceedings of the Principles of Self-Organization: Transactions of the University of Illinois Symposium, Pergamon Press, London, UK, 1962, pp. 255–278. [2] N. Tinbergen, Behavior as a reaction to external stimuli, in: N. Tinbergen (Ed.), The Study of Instinct, Clarendon Press/Oxford University Press, USNew York, NY, 1951. [3] N. Tinbergen, The internal factors responsible for the spontaneity of behaviour, in: N Tinbergen (Ed.), The Study of Instinct, Clarendon Press/Oxford University Press,
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