Behavioural and structural analysis of the interaction between language rule learning and temporal attention in Huntington's Disease.

(1)

1

UNIVERSITÀ DEGLI STUDI DI PISA

Dipartimento di Biologia

Laurea Magistrale in Neuroscience

BEHAVIOURAL AND STRUCTURAL ANALYSIS OF THE

INTERACTION BETWEEN LANGUAGE RULE

LEARNING AND TEMPORAL ATTENTION IN

HUNTINGTON’S DISEASE

Relatori:

Prof.ssa Ruth De Diego-Balaguer, Prof.ssa Maria Concetta Morrone

Tesi di

Laura Fornari

(2)

(3)

3

List of abbreviations

ACA= Anterior Cingulate Area ACC= Anterior Cingulate Cortex AF= Arcuate Fascicle

ANOVA= Analysis of Variance BA= Brodmann Area

BG= Basal Ganglia CAP= CAG-Age Product CMA= Cingulate Motor Area

CSTC= Cortico-Striato-Thalamo-Cortical Circuit DCS= Diagnostic confidence score

DLPFC= Dorsolateral Pre-Frontal Cortex DTI= Diffusion Tensor Imaging

EEG= Electroencephalography EF= Executive Functions ERP= Event Related Potentials

fMRI= Functional Magnetic Resonance Imaging FEF= Frontal Eye Field

FEW= Family Wise Error GM= Grey matter

GPi= Internal Globus Pallidus HD= Huntington’s Disease HF= Hazard Function HTT= Huntingtin

IFC= Inferior Frontal Cortex ITC= Inferior Temporal Cortex

IFOF= Inferior Fronto Occipital Fascicle ILF= Inferior Longitudinal Fascicle IP= Intraparietal Sulcus

IPC= Inferior Parietal Cortex LOFC= Lateral Orbitofrontal Cortex

(5)

5 M1= Primary Motor Cortex

MDpl= Mediodorsal nucleus of the Thalamus, lateral part MOFC= Medial Orbitofrontal Cortex

MRI= Magnetic Resonance Imaging NAD= Non-Adjacent Dependencies PC= Posterior Occipital Cortex PD= Parkinson’s Disease

PET= Positron Emission Tomography PM= Procedural Memory

PMC= Premotor Cortex

Pre-HD= Presympromatic Huntington’s Disease Patients SD= Standard Deviation

SEF= Supplementary Eye Field SL= Statistical Learning

SLF= Superior Longitudinal Fascicle SLI= Specific Language Impairment SMA= Supplementary Motor Area SNr= Substantia Nigra Pars Reticulata SOA= Stimulus Onset Asynchrony TFC= Total Functional Capacity TIV= Total Intracranial Volume

TMS= Transcranial Magnetic Stimulation TPJ= Temporoparietal Junction

UHDRS= Unified Huntington’s Disease Rating Scale

VAmc= Ventral Anterior Nucleus of Thalamus, Magnocellular Part VApc= Ventral Anterior Nucleus of Thalamus, Parvocellular Part VBM= Voxel Based Morphometry

VFC= Ventral Frontal Cortex

VLcr= Ventrolateral Nucleus of Thalamus, Caudal Part, Rostral Division VLm= Ventrolateral Nucleus of Thalamus, Medial Part

(6)

6

Preface

This master thesis was part of a bigger project begun in 2013, aiming to study the different

symptomatic profiles in Huntington’s disease gene-carriers and to make a detailed characterization of the variety of cognitive, motor and psychiatric deficits of the patients. To do this, a broad battery of tests tapping these aspects and several neuroimaging measures were collected from a sample of subjects selected from a network of hospitals around Barcelona.

This thesis focused on the data from the tests assessing temporal orienting of attention and learning of non-adjacent relations (NAD) from an auditory linguistic material. The work was carried out during a 9 months internship at the Cognition and Brain Plasticity research unit

(www.brainvitge.org) in Barcelona. Particularly, I worked under the supervision of professor De Diego-Balaguer, in a group that studies the neurobiological bases of language learning.

During my internship I did not participate in the collection of the data, but had to analyze all the data that had been collected since the beginning of the project, both behavioral and structural. Given the complexity of the tasks, this was possible only with the help and support of the other members of the team, who explained me how to use different statistical software and introduced me to the complex theoretical background behind temporal attention and NAD learning. Thanks to them I was then able to pursue my research quite autonomously and to interpret the results in the light of

existing literature.

Starting from the discovery that Huntington’s disease (HD) gene carriers have an impairment in NAD learning (De Diego-Balaguer et al., 2008), we hypothesized that this impairment could be due to a deficit in the temporal orienting of attention (TO). To assess this, we tested the same group of subjects through two behavioural tests: one on temporal orienting and one on NAD learning. Then we looked for correlations in the results. Moreover, we performed a structural analysis to see which brain areas with significant neurodegeneration in HD with respect to controls were related to the behavioural differences obtained in the previous analyses.

(7)

7

1-Introduction

Today our knowledge of the neurobiological bases of language is still very scarce, especially if we compare it to the one of the neural substrates of vision, memory and motion. This is mainly due to a lack of animal models and to the extreme complexity of language.

In fact, our ability to communicate through a structured and meaningful ensemble of sounds results from an incredibly huge variety of cognitive processes and from the interaction of different brain areas. Surely the neural substrates of language are not exclusive for it, but also subserve other aims. Most of them were initially responsible of more basic behaviours and were successively recycled once linguistic abilities started to appear during evolution (Ullman, 2016).

If we want to understand how language processing works, we have first to study the different mechanisms which underpin this higher order skill, such as attention, time perception, executive functions and procedural memory and the brain areas connected to them. Between these brain areas the striatum appears to be of fundamental importance, since it contributes to all the aforementioned behaviours.

Language acquisition requires a lot of different abilities; among them we decided to focus on the learning of associations between non-adjacent elements (NAD) in the phrase. Huntington’s disease (HD) patients, who have a striatal degeneration, show an impairment in NADs acquisition (De Diego-Balaguer et al., 2008); however, the underlying cognitive cause of this impairment is still to be elucidated. Temporal orienting of attention has been proposed to have an important role in speech processing and more recently in the acquisition of NADs. This is because tracking those dependencies requires to focus on the two associated non-adjacent elements, while ignoring other intermediate information that is variable and non-relevant to acquire the rule (De Diego-Balaguer et al., 2016; R. Gómez & Maye, 2005). Since the striatum, the main target of neurodegeneration in HD, is a key structure in temporal processing and HD patients show also attention deficits (Couette et al., 2008), the aim of this study was to assess whether the NAD learning impairment observed in HD patients could be due to difficulties in temporal orienting of attention. Moreover, we wanted to see the degeneration of which cortical and subcortical structures correlated with the behavioural deficits observed.

(8)

8

In the following paragraphs I will describe in details cortico-striatal connections and Huntington’s disease as a model of striatal degeneration. Then I will introduce non-adjacent dependencies and explain how their learning could be influenced by temporal orienting of attention.

1.1. Anatomy of the cortico-striatal circuits

The striatum is part of the bigger complex of the basal ganglia, which also includes the globus pallidus, the substantia nigra and the subthalamic nucleus. It is separated into putamen and caudate nucleus by an internal capsule and it is the main input structure in the basal ganglia.

It receives projections from the cortex, the thalamus and the brainstem. Axons from the striatum project to the substantia nigra pars reticulata and to the internal pallidum, the two major output nuclei, which in turn project to different thalamic and brainstem areas.

Figure 1.2. Coronal (A) and sagittal (B) section of the basal ganglia. In the coronal section also the surrounding

structures are outlined. Four main cortico-basal ganglia-talamo-cortical pathways connect the basal ganglia to the cortex and thalamus: skeletomotor, oculomotor, associative and limbic (see figure 1.3.). These four pathways, as indicated by their names, respectively regulate movement, executive functions and mood (Kandel, Principles of Neural Science, fifth edition).

(9)

9

Figure 1.3. Schema of the main cortico-basal ganglia-thalamocortical circuits. (ACA, anterior cingulate area; CMA,

cingulate motor area; DLPFC, dorsolateral prefrontal cortex; FEF, frontal eye field; GPi, internal segment of the globus pallidus; LOFC, lateral orbitofrontal cortex; M1, primary motor cortex; MDpl, mediodorsal nucleus of thalamus, lateral part; MOFC, medial orbitofrontal cortex; PMC, premotor cortex; SEF, supplementary eye field; SMA, supplementary motor area; SNr, substantia nigra pars reticulata; VAmc, ventral anterior nucleus of thalamus, magnocellular part; VApc, ventral anterior nucleus of thalamus, parvocellular part; VLcr, ventrolateral nucleus of thalamus, caudal part, rostral division; VLm, ventrolateral nucleus of thalamus, medial part; VLo, ventrolateral nucleus of thalamus, pars oralis.) (Kandel, Principles of Neural Science, fifth edition).

The most well-known role of the BG is motor control; this is because pathologies involving BG degeneration are mainly characterized by motor disorder (e.g. Parkinson’s and Huntington’s disease). The motor circuits originate from precentral areas which include primary motor cortex (M1), premotor cortex (PMC), supplementary motor area (SMA) and cingulate motor area (CMA); all these areas project to the putamen in a somatotopical manner. Neurons in the putamen project to the caudoventral portions of both segments of the pallidum and to the lateral portions of the substantia nigra pars reticulata. In turn, these two output nuclei project to specific motor-related areas of the thalamus. The thalamus then projects back to the cortical motor and premotor areas, closing the loop. Between motor prefrontal areas, the supplementary motor complex, composed by the SMA and pre-SMA, is particularly important for the preparation and execution of voluntary motor actions. The SMA contains a complete map of the contralateral body, which however is not

(10)

10

as detailed as the one found in M1. Lesions in SMC are connected to problems in initiating or suppressing movements.

The oculomotor circuit originates from the Frontal eye field (FEF) and supplementary eye field (SEF), reaches the posterior caudate, the external globus pallidus, the subtalamic nucleus and the substantia nigra pars reticulata and then goes back to the cerebral cortex through the thalamus. This circuit is involved in the control of eye movement.

From a cognitive point of view the associative or prefrontal pathway is of particular relevance, since it has been connected to executive functions. It is composed by a dorsolateral prefrontal circuit and by a lateral orbitofrontal circuit. The dorsolateral prefrontal circuit originates in the Brodmann Areas (BA) 9 and 10 of the frontal cortex and projects to the head of caudate nucleus, which in turns projects directly and indirectly to the dorsomedial portion of the internal pallidal segment and to the rostral substantia nigra pars reticulata. Axons coming from these regions reach the ventral anterior and mediodorsal nuclei of the thalamus and the prefrontal cortex. This circuit is involved in problem solving and in the use of verbal skills. The lateral orbitofrontal circuit originates from the lateral prefrontal cortex and projects to the ventromedial caudate nucleus and is probably connected to social behaviour (Hudspeth, Jessell, Kandel, Schwartz, & Siegelbaum, 2013).

The limbic circuit takes origin from the anterior cingulate and medial orbitofrontal cortices that project to the ventral striatum, which is also connected to the hippocampus, amygdala and entorhinal cortex. The ventral striatum projects to the ventral and rostromedial pallidum and rostrodorsal substantia nigra pars reticulata. Then, the pathway goes back to the anterior cingulate cortex passing through the thalamus. This circuit is responsible of motivated behaviour (Hudspeth et al., 2013).

A way to better understand the function of these cortico-striatal circuits is to study neurodegenerative diseases that disrupt them, such as Huntington’s or Parkinson disease.

1.2. Huntington’s disease

Huntington’s disease (HD) is an autosomal dominant neurodegenerative disorder which has often been studied as a model of striatal dysfunction, since the disease primarily targets this structure. It has a midlife onset and is characterized by psychiatric, cognitive and motor symptoms (hyperkinesia), which lead to dementia and death approximately after 20 years from the disease onset. It affects men and women equally with an incidence of 5-10 cases every 100 000 individuals.

(11)

11

It is due to an excess in the number of repetitions of a CAG triplets in the gene (IT15) that codifies for the protein Huntingtin (HTT), which results in the expansion of a polyglutamine tract. The higher is the number of repetitions of this triplet, the earlier the disease will develop.

In healthy individuals the repetitions can be from 6 to 35 and a number higher than 35 is considered pathogenic. Because of a lack of an ideal animal model of HD the pathogenesis is still unclear, as well as the role of Huntingtin in brain. We know that HTT is necessary for a correct brain development, that is probably involved in endocytosis and vescicle trafficking (Landles & Bates, 2004) and has an anti-apoptotic role (Rigamonti et al., 2000). Aberrant glutamine expansion can render it neurotoxic, since it induces its aggregation in a prionic fashion, leading to the formation of insoluble aggregates (Cattaneo et al., 2001). The accumulation of these aggregates inside neurons appears to be particularly lethal for striatal medium spiny neurons, the main output neurons in striatum.

A formal diagnosis of HD is done according to the Unified Huntington’s Disease Rating Scale (UHDRS) and is based on the presence of specific motor (e.g., chorea, dystonia, bradykinesia, rigidity), cognitive and behavioural signs. However, slight motor, psychiatric and cognitive symptoms can be already detected years before the formal diagnosis (Harrington et al., 2014; Ross et al., 2014). The period that precedes the formal diagnosis is called presymptomatic (pre-HD) and can be further distinguished into a symptom-free period (premanifest phase) and a prodromal phase, in which mild symptoms related to the pathology start to appear.

The motor section of the UHDRS assesses oculomotor function, dysarthria, chorea, dystonia, gait, and postural stability with standardized ratings and contains in total 31 elements (Harrington et al., 2014; Kieburtz et al., 2001). The evaluation of each one of the 31 motor skills is based on a diagnostic confidence score (DCS), which can go from 0 (no motor abnormalities due to HD), to 4 (confidence ≥ 99% that the motor abnormalities are due to HD). The UHDR total motor score is the sum of all the individual motor ratings and can go from 0 to 124 (Ross et al., 2014), with higher scores indicating more severe motor impairment than lower scores.

Pre-HD are defined as those gene-carriers with a DCS lower than 4 in all the items, while the manifest phase begins with the first rating of a DCS score equal to 4 (Biglan et al., 2013) (see figure 1.1). From this moment onward, the distinction of the five different stages of the disease is done according to the total functional capacity score (TFC), the section of the UHDRS which assesses the autonomy of the patient in everyday life. TFC goes from 0 (totally dependent) to 13 (fully independent). Early stage HD gene-carriers have a TFC score that ranges from 10 to 13 (see figure 1.1 ) and generally are still able to work .

(12)

12

The dichotomy between pre-HD and manifest stages, although useful to establish a uniformity between clinical studies, can be misleading. Indeed, the degenerative decline begins many years before the specific motor abnormalities used for the official diagnose of the disease. This is due to the fact that the aberrant HTT is expressed since early development in HD gene-carriers.

Some magnetic resonance imaging (MRI) studies have proved that changes in the striatum, and cortical grey and white matter are already detectable in pre-HD (Nopoulos et al., 2010; Paulsen et al., 2010; Tabrizi et al., 2011). However, how these changes correlate with cognitive deficits is still not clear.

Some studies have proved the correlation between striatal (Papp et al., 2013; Paulsen et al., 2010; Wolf et al., 2013) or white matter volume (Papp et al., 2013; Paulsen et al., 2010) and executive functions. In a study on pre-HD, Rosas et al. (Rosas et al., 2005) also found a correlation between cortical thickness reduction in some brain areas and executive functions. However data are controversial, since there are studies that did not find significant correlations between cognitive processes and cortical morphometry (Wolf et al., 2013). This variability in the results is due to the heterogeneity of symptoms and disease progression in pre-HD (Harrington et al., 2014).

Figure 1.1. HD progression and rating scales used to evaluate the total functional capacity (TFC) and motor abilities

(DCS). (Rosset al., 2014).

1.3. Learning of non-adjacent dependencies in Huntington’s Disease

Non-adjacent dependencies (NAD) are statistical associations between two events that are not contiguous in space and time, since they are separated by one or more intervening elements (Pacton, Sobaco, & Perruchet, 2015). They can be represented through an AXC structure, were X identifies one or more intervening elements. NAD are present in every sensory modality: they can be found in

(13)

13

the auditory domain (e.g. language and music) and in the visual domain and we constantly need to analyze them. As claimed by Turk-Browne and colleagues (Turk-Browne et al., 2005):

“People are constantly bombarded with noise in space and time that needs to be segregated in order to extract a coherent representation of the world, and people rarely encounter a sequence of relevant stimuli without any interruptions”.

From a linguistic point of view NAD are associations between words which are separated by intervening elements that are statistically independent from them. This kind of structures are common in natural languages, where, for example, we can find dependencies between auxiliaries and inflexional morphemes (e.g. is screaming, is swimming), as well as dependencies for number agreement (e.g. the dogs... are barking).

NAD learning in Huntington’s Disease has been examined in a study by De Diego-Balaguer and colleagues, using an artificial language that contained associations between non-adjacent syllables (De Diego-Balaguer et al., 2008). This study showed that even HD patients at the earliest stage of the disease have poor rule learning abilities if compared to controls. On the other hand, pre-HD, despite having a normal performance, have a reduced ability to generalize the rule acquired (‘transfer capacity’).

De Diego-Balaguer and colleagues examined the impact of working memory and executive functions on NAD learning, discovering a correlation between the impairment in rule learning and poor working memory and sequencing abilities. This suggested that the difficulties in NAD learning could be attributed to a degeneration of the associative cortico-striatal loop, which is involved in executive functions. They also analyzed structural data, even if focusing only on the striatum (particularly on the bicaudate ratio) and proved the existence of a correlation between the striatal degeneration and the impairment in NAD learning. However, it is probable that the poor performance of HD patients was not only due to striatal, but also to cortical degeneration.

In De Diego-Balaguer and colleagues’ work, the contribution of attention to NAD learning was not explicitly considered, since participants had just to passively listen to a stream of words. However, the existence of a link between temporal orienting of attention and NAD learning in the healthy population had already been documented in previous literature.

For example, Gómez (Gómez, 2002) noticed that people learned better the order of the elements in three-word phrases (AXC) when the variability of the intervening element (X) was increased. This observation led her to hypothesize that NAD learning occurs because a high variability of the surrounding elements induces our attention to focus on the less variable structures. This process is

(14)

14

due to statistical learning, which allow us to track transitional probabilities between the different components of the phrase in a rapid and involuntary way, combined with attention to the time when co-occurring elements appear. A stronger proof of attentional involvement in NAD learning was provided by Perruchet and colleagues (Pacton et al., 2008), who showed that attention could determine which kind of association was learned from a written series of segmented items (they used digits) that contained both adjacent and non-adjacent dependencies. Particularly, they were able to observe that if participants were asked to pay attention to NAD they learnt the non-adjacent associations, but not the adjacent ones and vice versa.

Given this previous knowledge, our study was the first that tried to explain the difficulties in non-adjacency rule learning observed in Huntington’s Disease (De Diego-Balaguer et al., 2008) through a deficit in temporal orienting of attention.

Even if we specifically focused on the role of TO in language rule acquisition, from a more general perspective, temporal orienting appears to be necessary for both speech perception and production (Kotz & Schwartze, 2010).

1.4. Temporal orienting of attention

Speech is essentially an auditory phenomenon and as all auditory signals it develops in time. To decode the information carried by this stream of sounds we need to interpret its temporal structure, so to segment words and establish relationships between them. NAD, which appear quite often in natural languages, can be considered among the fundamental relationships to track in order to decode speech or learn a new language. Consequently, temporal orienting of attention is hypothesized to play a relevant role in NAD tracking (Pacton et al., 2008) and therefore in language learning and comprehension.

While spatial orienting of attention has been extensively studied, research about temporal orienting is still scarce. For this reason, if we want to understand how temporal attention works, we must start by describing a model built on visuo-spatial attention (Corbetta et al., 2002).

Corbetta and colleagues distinguished two networks: a ventral frontoparietal network, related to stimulus driven attention (exogenous), that responds when a task relevant stimulus appears outside the focus of attention and a dorsal frontoparietal network, related to goal-directed attention (endogenous) which maintains people focused on the features and tasks to be performed. Endogenous cues are a form of top-down control of attention that instruct the observer about the

(15)

15

position of the forthcoming target (e.g. an arrow located in the center of a screen whose orientation indicates on which side the target will appear). Exogenous cues, on the contrary, are responsible of a stimulus-driven, bottom-up control of attention, since they simply are attention-grabbing stimuli that automatically drive our sight towards a certain point in space (e.g. a light flashed it the exact point where the target is going to appear later on). Both exogenous and endogenous cues facilitate our response to the target, even if the exogenous one have a more rapid effect (within 50 ms), because, unlike the endogenous one, they do not need cognitive elaboration.

Figure 1.5. Brain areas included in the 2 visuospatial attention networks proposed by Corbetta and Shulman. (Chica et

al., 2013)

Even if this model was elaborated to describe spatial orienting, it could be valid also for the temporal dimension, since the existence of a general attention system independent of the stimulus dimensions seems likely. In fact, some fMRI studies have proved that Corbetta’s model can also be applied to other sensory modalities, such as to the auditory domain (Driver et al. 2004; Coull & Nobre 1998). Consequently, also for temporal attention we can distinguish an exogenous and an endogenous form (Rohenkohl et al., 2011).

According to Coull and colleagues, exogenous TO is induced by rhythmic stimuli or stimuli with a predictable temporal structure, while endogenous TO is induced by symbolic cues which direct attention to a precise time point. Coull and Nobre designed a temporal expectation task in which participants could predict not only the position in space, but also the moment of target appearance

(16)

16

thanks to endogenous cues. This study demonstrated that temporal prediction speeded up the reaction time to the target. Moreover, thanks to the use of PET and fMRI, it showed an overlap between brain areas activated for temporal orienting and spatial orienting of attention according to endogenous cues. Nevertheless, Coull and colleagues reported some hemispheric asymmetries between the two attentional networks, since spatial attention seemed to activate more right parietal areas, while temporal attention induced a higher activation in the left parietal areas.

Since temporal orienting of attention is deeply linked to time perception, if we want to understand TO we cannot prescind from considering time processing mechanisms. A possible explanation of the mechanisms that allow for temporal orienting is given by the dynamic attending theory (DAT) (Large & Jones, 1999). According to the DAT, the allocation of exogenous attention in time depends on the synchronization between self-sustained internally generated oscillations (attending rhythms) and the external temporal structure of the auditory stimulus (e.g. music or speech). In a second step, this allows a volountary (endogenous) orienting of attention toward time points when important information is going to appear. With respect to language, only when our internal oscillations manage to synchronize with the speech rhythm (exogenous signal given by the succession of syllable vocalic nuclei) we are able to focus on salient time points and to extract information (Kotz & Schwartze, 2010). The attentional oscillation might be controlled by the time-sensing network, whose activity depends in turns on the external rhythmed stimulation (Kotz & Schwartze, 2010; Meck & Benson, 2002; Schwartze et al., 2011).

Therefore, due to their influence on attention, networks involved in time perception might be involved as well in language acquisition (Kotz & Schwartze, 2010), included NAD learning. Finally, in order to better understand the interaction between time perception and TO in relation to NAD learning we should analyze the neural substrates linked to timing.

1.5. Time perception

1.5.1 Forms of time perception

Time perception is surely one of the most fascinating and complex themes in the field of cognitive neuroscience.

Timing mechanisms can be implicit or explicit (see figure 1.6). Explicit timing is engaged whenever subjects are requested to deliberate estimate the duration of a time interval or to compare it with a

(17)

17

reference one. Explicit timing task can require a perceptual evaluation of time intervals or a motor response; in the second case participants are asked to represent a time duration with a sustained, delayed or periodic motor act (paced finger tapping task).

On the other hand, implicit timing is engaged whenever an action or a sensory stimulus are temporally structured, so to allow temporal predictions, but the task does not explicitly require time estimation. (Coull & Nobre, 2008). In fact, implicit timing is an ability that benefits participants performance in achieving non-temporal task goals. Also in this case we can distinguish a motor timing, in which time perception originates from the regularity of a certain motor output (emergent timing) and a perceptual timing, which arises from temporal expectations.

For what concerns temporal expectations, they can be automatic (exogenous) or controlled (endogenous). Exogenous expectations arise from the rhythmicity or more generally from the regularity of a certain stimulus and include the foreperiod, the sequential and the rhythmic effects.

The foreperiod effect, also known as hazard function effect (HF), is due to our perception of the unidirectional flow of time. In other words, when we expect an event to occur, but it has not happened yet, the likelihood that it will happen in the next moment increases with time passing by ( Coull et al., 2011; Niemi & Naatanen, 1981). This rising likelihood induces an increase in response preparation to that event, resulting in a faster response when it finally occurs (in experimental psychology the HF effect can be measured through the reaction time to the appearance of a certain target). The foreperiod effect can be eliminated by introducing trials in which the target does not appear (catch trials). In fact, this change disrupts participants’ certainty about target appearance and prevents their temporal expectancy to raise (Correa et al., 2004; Correa et al., 2006)

The sequential effect can be instead observed when intervals of different duration are alternated. It consists in a speed up of the RT to the stimulus presentation when the stimulus onset asynchrony (SOA) of a trial is equal or longer to the one immediately preceding. On the contrary, RT is slowed down in case the SOA of a trial is shorter than the previous one (Coull et al., 2000; Los & Van Den Heuvel, 2001). This is probably due to the fact that subjects’ preparation reaches the peak at the same foreperiod as in the previous trial. Finally, the rhythmic effect benefits the participants’ performance when they are required to respond to a stimulus that appears at fixed time intervals.

On the other hand, endogenous expectations involve the deliberate use of a symbolic cue to predict the stimulus onset and so to orient attention in time, in a goal-directed manner. For example, subjects can learn the associations between two different cues (e.g. a high pitch tone and a low pitch tone) and two different interstimulus intervals. The ability to orient attention in time linked to

(18)

18

endogenous temporal expectation, as previously described, was tested by Coull and colleagues through the use of visual informative cues (Coull & Nobre, 1998).

Figure 1.6. Functional taxonomy of timing (Coull & Nobre, 2008).

Time sensing is supposed to play an important role in language rule learning, mainly because of its influence on exogenous and endogenous temporal orienting of attention. However, our knowledge about the interaction between time perception and TO is still in its infancy.

1.5.2 Neuroanatomical substrates of timing

Different brain areas connected to time perception have been identified through fMRI experiments: supplementary motor area, basal ganglia, cerebellum, right inferior frontal and parietal cortices (Coull & Nobre, 2008).

For what concerns the explicit evaluation of time intervals, perceptual timing task seem to activate specifically the pre-supplementary motor area (pre-SMA), right inferior frontal cortex and basal ganglia (Pouthas et al., 2005; Tregellas et al., 2006).

Matell & Meck (Matell & Meck, 2004) inserted basal ganglia in a model of time processing called the pacemaker accumulator model, which was proposed in 1963 by Treisman (Treisman, 1963). This model postulates the existence of a pacemaker able to produce ticks at a constant rate

(19)

19

(similarly to a metronome) and an accumulator that stores and counts the ticks during a certain time interval (see figure 1.7.). Between the pacemaker and the accumulator there’s a switch, that allows for the pulses to accumulate when a temporal estimation is required. The duration estimation is based on the number of pulses accumulated and allows a following comparison of time intervals.

Figure1.7. Schema of the pacemaker accumulator model proposed by Treisman (Caselli et al., 2009).

According to Matell & Meck the striatum could constitute the clock described in the model, since the oscillatory activity of striatal neurons at a population level (phase coding), could give origin to the regular pulses whose existence was hypothesized by Treisman.

Moreover, the pacemaker could be represented by dopamine pulses coming from the substantia nigra pars compacta, which modulate the activity of striatal neurons.

A proof in support of this idea is the fact that altering the amount of dopamine that reaches the striatum can alter time estimation. An increase in dopamine induces an acceleration of the pacemaker rate (Matell et al, 2004), causing an overestimation of time intervals; on the contrary dopamine reduction slows down the pacemaker rate, causing an underestimation of time intervals. The switch represents an attentional gate, while the accumulator could be represented by working memory.

Another way to understand striatal role in timing is to study diseases that specifically involve striatal neurodegeneration, such as Parkinson’s or Huntington’s disease. However, research in this field has not brought to clear findings. Malapani and colleagues (Malapani et al. 1998) reported an overestimation of short intervals and an underestimation of the long ones in Parkinson’s disease, in an experiment where they were presented one after the other during the training phase.

(20)

20

Nevertheless, they reckoned that this was probably related more to a memory disruption, impairing the recall of the exact duration of time intervals, than to a real inability to estimate duration.

The basal ganglia, together with SMA, premotor cortex, inferior parietal cortex, dorsolateral prefrontal cortex (DLPFC) and cerebellum are also involved in explicit motor timing. In fact they activate during the continuation phase of a finger tapping-task (Lewis et al., 2004) or during sustained motor response when subjects are asked to reproduce the duration of a reference time interval (Bueti et al., 2008). However, data about BG role in motor timing are controversial, since there are studies which report unimpaired performance in a paced finger tapping task in patients with unilateral BG lesions (Aparicio et al., 2005) or in Parkinson’s Disease patients (Spencer & Ivry, 2005).

In our study we decided to focus on implicit timing, by choosing a task that evaluated the effect of temporal expectations on RT in Huntington’s Disease patients. Implicit timing includes both: i) exogenous expectation, which arises from the temporal features of external stimuli or from the constant movement of objects and ii) endogenous expectation, which is engaged by informative cues indicating when a certain event will occur (e.g. the amber traffic light predicts that soon the light will turn red).

Examples of exogenous expectation are the hazard function, rhythm and sequential effect, each one relying on multiple brain areas. Given the complexity of the experimental procedures necessary to study exogenous temporal orienting and the great variability of the results obtained, a definition of the neural substrates of exogenous expectation remains controversial.

Kotz and Schwartze attribute rhythm perception to a cortico-striato-thalamo-cortical system (CSTCS), which include basal ganglia, cerebellum, pre-SMA, SMA and thalamus (Kotz & Schmidt-Kassow, 2015). According to this view, the CSTCS circuit, which was initially involved exclusively in time processing related to motor coordination, has been recycled for speech production and perception during evolution, in collaboration with the cerebello-thalamo-cortical system (CTCS).

On the other hand, the hazard function effect seems to be related more to the parietal cortices and to the right DLPFC. A study by Vallesi and colleagues (2007) proved that transcranial magnetic stimulation in DLPFC is sufficient to significantly reduce the HF effect (Vallesi et al., 2007). Moreover, Coull and colleagues (Coull et al., 2016) used a cued visual reaction time task in an fMRI design in order to extensively study all the cortical areas whose activity positively correlates with benefits in the HF. They found a correlation between the HF effect and activity in left inferior

(21)

21

parietal cortex (IPC) and right inferior frontal cortex (IFC), since the activation of these areas increases with the foreperiod duration when participants are waiting for the target to appear but do not know exactly when this is going to happen. This suggests that both the left IPC and the right IFC are responsible of the dynamic updating of temporal probabilities over time, which allows a reorienting of attention to the following foreperiod if the target has not appeared yet. In line with these discoveries, a study done on patients after surgical removal of unilateral tumor from parietal, prefrontal or premotor cortex found a reduction in the foreperiod effect (Vallesi et al., 2007).

The role of the basal ganglia in the Hazard Function effect is still unclear, since some studies reported a reduced benefit of long foreperiod in Parkinson’s disease, possibly due to a reduction in the dopaminergic inputs reaching the striatum (Jurkowski et al., 2005), while others did not (Triviño et al., 2010). The difficulty in establishing BG contribution to HF effect is mainly due to the fact that many brain areas seem to be involved (Coull et al., 2016).

Another form of exogenous expectation is the prediction of the moment a particular event will take place based on the evaluation of the speed of moving objects involved. This ability seems to rely mostly on the posterior regions of the lateral cerebellum (Beudel et al., 2008; O’Reilly et al., 2008)

Endogenous temporal expectations, which are engaged when subjects can rely on explicit

informative cues to optimize their performance in a non-temporal task, activate mainly areas involved in motor preparation, such as the SMA and the premotor cortex (Praamstra et al., 2006). Schubotz (Schubotz, 2007) has proposed that this activation of premotor areas is independent of action execution and simply reflects the effect of temporal prediction. In other words, she proposes that our motor system can work also as a prediction system. Consequently, the activation of premotor and parietal areas during endogenous temporal orienting may be related not only to motor preparation, but also to temporal expectation.

Coull and Nobre (Coull & Nobre, 1998) proved that also the cerebellum, the striatum (particularly the left putamen) and left parietal cortices are involved in endogenous temporal expectation.

Also in the case of endogenous temporal expectation, studying neurodegenerative diseases that affect the striatum can be helpful. However, as it happens for explicit timing, results are not so clear. According to study by Praamstra & Pope (Praamstra & Pope, 2007) Parkinson’s Disease patients have unimpaired implicit timing abilities, since they show a normal reaction time to the appearance of a target when predicted by endogenous cue. However, they display an altered slow preparatory brain potential (contingent negative variation) and an altered oscillatory synchrony in the alpha and beta band (which are modulated by temporal expectation). Another experiment by

(22)

22

Beudel and colleagues (Beudel et al., 2008) showed that Parkinson’s Disease patients have a preserved ability to use endogenous cues.

In synthesis, despite our knowledge of neural substrates of time perception is still scarce, the varied scenario presented clearly suggests that our timing abilities results from the activity of a great number of brain areas.

1.5.3 Time perception in Huntington’s Disease

Up to now very little is known about the effect that HD can have on time perception and particularly on implicit timing. However, some research has been conducted on explicit timing, both on patients and on animal models of HD. Particularly, a study by Höhn and colleagues (Höhn et al., 2011) on transgenic rats models reported an altered performance in a time discrimination task (discrimination between time intervals of 2 and 8 s). This impairment was significant already in pre-symptomatic subjects (which still did not show any motor deficit) and was attributed to the degeneration of prefrontostriatal circuits. The same study also reported an enhanced plasticity in these circuits, with increased paired-pulse facilitation, short term depression and long-term potentiation recorded in the striatum after the stimulation of the prelimbic cortex.

For what concerns studies on patients, they are scarce and have focused mainly on the explicit evaluation of short time intervals (below 1.2 s). For example, Paulsen and colleagues (Paulsen et al., 2004) showed that pre-HD close to the disease onset (predicted on the base of the number of CAG repetition) had difficulties in establishing if the duration of a certain interval was shorter or longer than 1.2 s. On the contrary, they observed that pre-HD far from the disease onset were unimpaired. Moreover, another study on presymptomatic gene-carriers (Hinton et al., 2007) reported a worsening in their motor timing abilities (measured through a paced finger-tapping task) as the estimated onset of the disease approached. On the other hand Beste and colleagues (Beste et al., 2007) found that pre-HD have a reduced accuracy in the reproduction of time intervals (time estimation), but are unimpaired discriminating between two different durations (time discrimination). These apparently conflicting results are due to the different sample considered; in fact, in the first two studies pre-HD were separated according to the distance from disease onset, while this distinction was not applied in the last study.

Beste and colleagues hypothesized that the greater impairment observed in time estimation was due to the fact that the task required a higher implication of the motor system, so that timing was

(23)

23

embedded in motor processes. Unfortunately, in tasks requiring motor timing, it appears impossible to evaluate how much the impairment observed is due to the effect of HD on time perception and how much it derives from a disruption in the motor performance. In this study they assumed that actually both the effects of HD derived from the death of striatal medium spiny neurons, because they are important for both time estimation and movement execution. However, this remained a hypothesis, since the study took into account only behavioural data, without looking for a correlation with striatal and cortical degeneration.

As far as we know, the present study was the first one to analyze implicit timing in Huntington’s disease patients through its influence on endogenous temporal orienting of attention and to try to find a correlation between the behavioural performance and structural data. This in the perspective that the impairment in NAD learning observed in HD could be the result of a degeneration of cortico-striatal circuits involved in time perception.

1.6. The present study

Starting from a broad range of factors that could possibly influence NAD acquisition, we decided to focus on the impact of temporal orienting of attention.

The aim of the current study was to examine temporal orienting of attention and rule learning in Huntington disease gene-carriers in order to determine: i) whether and how they are impaired; ii) how difficulties in NAD learning may be affected by temporal orienting deficits; iii) which brain areas that degenerate in HD are involved in temporal attention and NAD learning in gene-carriers.

In order to assess this, an artificial-language task and a temporal orienting task were performed on the same group of gene carriers (pre-symptomatic and early stage patients) and controls matched for gender, age and years of education. Moreover, a voxel-based morphometry (VBM) analysis and a cortical thickness analysis were conducted on the T1 scans of HD gene-carriers to see whether behavioural results correlated with structural changes in their brain.

(24)

24

2-Methods

2.1 Participants

• Attention experiment

Sixty-five participants were tested: thirty-three Huntington gene carriers and thirty-two healthy controls matched for gender, age and years of education. HD gene-carriers were defined as carriers of the genetic mutation with ≥ 36 repeats. Twenty-one of the gene-carriers were manifest early-stage HD patients (early-stage 1), while twelve of them were premanifest HD (pre-HD). None of the gene-carriers or controls reported previous history of neurological disorder other than HD for the gene-carriers. All the participants signed an informed consent to participate in this study, which was approved by the Ethical Committee of the University of Barcelona. Demographic characteristics of each group are reported in Table 2.1.

Neuropsychological evaluation

Gene-carriers’ cognitive abilities where evaluated through different tests: the verbal letter fluency test (FAS) (Butters et al., 1986), the Trail Making test (Tombaugh, 2004), the Stroop Test (Golden & Freshwater, 1978), the Symbol-Digit Test (Wechsler & De Lemos, 1981) and the Digit Span Test (forward and backward) (Wechsler, 1997). For some participants, due to timing constrains, not all the tests could be administered. Early stage patients performed significantly worse with respect to mean normative data in all these tests; this confirmed that they already had a clear cognitive impairment. On the other hand, presymptomatic gene-carriers had an unimpaired performance in most of the tests, but showed deficits in the Digit Span Forward Test and in the Symbol Digit test. The difficulties observed in the Symbol Digit test, which evaluated working memory in a task which required to form associations between symbols and numbers, indicated that pre-HD already had deficits in associative learning. Therefore, provided that the present study aimed to examine a possible impairment in NAD, this result allowed us to consider presymptomatics and early stage patients as a single group. Results of the neuropsychological evaluation are reported in table 2.2 (the specific N for each test is indicated).

(25)

25

Table 2.1 Main demographical characteristics of participants in the Attention experiment

Mean age in years. SD= Standard Deviation. CAG= number of CAG repeats in the HD gene. CAP= CAP score.

Table 2.2 Results of the neuropsychological assessment in gene-carriers subdivided between pre-HD and early stage

patients.

Function Test Group

Presymptomatics Early stage patients

N Mean value ± SD

Motor ability Motor UHDRS 12 2.4±3.8 21 20.1±10.1 Functional ability TFC 12 12.9±0.3 21 11.6±1.4 Selective attention and

inhibition of cognitive interference

Stroop Interference 12 8.8±13.8 19 0.4±5.5**

Speed Processing and Cognitive Flexibility

TMT A (seconds) 11 32.2±11.4 21 64.1±23.7** TMT B (seconds) 11 63.9±36.2 21 251.7±197.1** Working Memory Digit Span Forward 10 5.6±1.1* 19 4.53±0.8**

Digit Span Backwards 10 4.4±1.2 19 3.1±0.7** Symbol Digit 12 50.5±11.3* 21 28.1±11.1** Verbal Fluency FAS 12 45.3±14.4 21 24.7±9.5** TFC= Total Functional Capacity; UHDRS = Uniﬁed Huntington's Disease Rating Scale; TMT= Trail Making Test; FAS= Verbal fluency test.Pathological scores compared with the mean normative data are marked with ** p<0.01

n Mean Age ± SD Gender Handedness Mean Years of Education±SD

Mean CAG±SD Mean Disease Burden ±SD Mean CAP ±S D F M left right Gene-carriers 33 46.3±12.0 (70-27) 23 10 2 31 12.0±3.0(16-8) 44.2±3.1 (50-39) 380.3±112.4 (634.5-148.5) 101.3±21.7 (142.4-47.4) Controls 32 44.3±10.4 (69-28) 16 16 1 31 12.8±2.6(16-8) - - -

(26)

26 • NAD learning experiment:

Twenty-eight out of the 33 gene-carriers that took part in the attention task also performed the language task (12 presymptomatics, 16 early stage patients) and 31 controls out of 32. Two participants (1 early stage patient and 1 control) were added to these groups, since they took only the language experiment. One control, 1 pre-HD and 2 early stage patients were excluded because of technical errors. The final sample was composed by 11 pre-HD, 15 early stage patients and 31 controls matched for gender, age and years of education.

2.2 Apparatus & Stimuli

2.2.1 Attention Task

Stimuli and procedure:

To assess if temporal attention is impaired in HD gene-carriers we presented participants with a speedy detection task in which they were asked to press a key as soon as possible as they heard a target sound. Target appearance was predicted by an auditory cue, which allowed participants to orient attention to a precise time point and prepare to target appearance. This task tested implicit timing, since it looked at participants’ ability to achieve a non-temporal goal thanks to the use of temporal predictors (Coull et al., 2011). Both the effect of exogenous temporal expectation (foreperiod effect, probabilistic information linked to the passage of time) and of endogenous temporal expectation (determined by the auditory cues) were tested.

The experiment included two parts separated by a short break; each part contained 64 trials, subdivided in two blocks (32 with a late cue and 32 with an early cue, counterbalanced across participants). In total there were 128 trials. The type of temporal cue (early/late) shown in each trial was kept constant during a whole block. In each block 25 % of trials were catch trials (8 CT: 6 valid and 2 invalid) and 25% were invalid trials (6 normal and 2 invalid CT). Catch trials were trials in which participants heard a different tone instead of the expected target tone and had to refrain from responding. In synthesis the whole experiment was composed by 72 valid trials, 24 invalid trials, 24 valid CT and 8 invalid CT. Before starting the experimental session, participants underwent a

(27)

27

training session composed by 16 trials (8 with the late cue and 8 with the early cue) to make sure they understood task instructions.

Figure 2.1: Design of each type of trial in the temporal preparation task. a) Valid trial, early cue-short SOA. b) Valid

trial, late cue- long SOA. c) Valid catch trial, late cue-long SOA.

The experiment was performed on a 15-inch screen laptop computer in a silent room. E-prime software was used to program the experiment, run the experimental task and collect data on reaction time and accuracy of responses. Each trial (see Figure 2.1) began with a central fixation square (‘◼’), followed by a temporal auditory cue represented by a 50 ms tone. If this cue was a high-pitched tone (1000 HZ), it predicted that the target (a second tone) would appear after a short SOA (350 ms). On the contrary, if the cue was a low-pitched one (400 Hz), it predicted that the target would appear after a long SOA (1350ms). The screen stayed black until the target tone was played. The target tone was a 700 Hz sound that lasted for 50 ms, after which the screen remained blank again until participants responded, or for 1900 ms. The cue-to-target interval was valid in 75% of the trials. In invalid trials, the expectancy created by the cue did not match with the actual SOA length. Participants were explicitly informed about the meaning of each cue (high-pitched tone/early cue, low-pitched tone/late cue) and received visual feedback both during the training session and during the actual experimental trials. Whenever they answered too early (before target appearance), a message showed up on the screen telling them that they answered too soon. Whenever they answered to a catch trial, a message was displayed telling them not to press when the target did not appear. Eventually, if they did not answer within the 1900 ms window after the target appearance, a message was displayed telling them to answer faster. Between each block participants could take a break and proceed to the next one by clicking on the space bar.

(28)

28

Normally RT in the long foreperiods is shorter (Klemmer, 1956; Woodrow, 1914); this is probably due to the flow of time itself, that increases participants preparation and speeds up the motor response. Moreover, RT to the invalid cue should be slower with respect to the valid, since participants can prepare their response (Coull & Nobre, 1998). However, while literature reports this validity effect when the foreperiod is short, almost no effect has been observed in the case of long foreperiods. This effect is due of HF: when the target is expected early but appears later, it induces a reorienting of attention in time that speeds up RT in the long invalid condition, so that there’s no more an advantage of the valid trials compared to the invalid on long foreperiods (no validity effect) (Correa et al., 2006; Coull et al., 2000; Coull & Nobre, 1998).

Figure 2.2 Differences in methodology and results between temporal orienting tasks with and without catch trials. A) Experimental conditions associated to the reorienting process; a valid long condition (expect-late cue

followed by a target at the long interval), and an invalid long condition (expect-early cue followed by target at the long interval. B) Behavioural results in task without catch trials. Similar RTs are observed between valid and invalid trials at the long interval. C) Experimental conditions associated to the dispreparation process (same as in B, but with the incorporation of catch trials in which no target is presented). D) Behavioural results in a task with catch trials. Slower RTs are found in invalid trials compared to valid trials at the long interval (adapted from Correa, 2010. In Martinez-Alvarez PhD dissertation, 2018).

(29)

29

2.2.2 NAD learning Task

In order to see whether the learning of non-adjacent dependencies (NAD) is impaired in HD gene-carriers we presented participants with an artificial language learning paradigm already used in previous research (Gómez, 2002; López-Barroso et al., 2016; Romberg & Saffran, 2013).

López-Barroso and colleagues showed that implicit NAD is independent of attention, while explicit NAD learning depends on the attentional load. In fact, in the attended condition (phrases containing a target word to detect) participants were able to explicitly recall both the position of words and the associations between them (NAD), while in the unattended condition they only learned positional information, but not non-adjacent dependencies.

Provided this theoretical background, a further aim of the present study was to see if a potential impairment in non-adjacency rule learning in HD gene-carriers correlated with the performance in the temporal attention task.

The NAD learning experiment was based on a smaller group of participants (26 gene-carriers and 31 controls) with respect to the attention one. Starting from this sample, six controls were furtherly excluded from the analysis due to low accuracy (<50% of correct responses) in the online phase (4) or because their mean RT in the online phase was more than 2SD higher with respect to the mean of the control group (2). 1 gene-carrier (pre-symptomatic) was also excluded because of low accuracy in the online phase. The final sample was composed by 25 gene-carriers (10 presymptomatics and 15 early stage patients) and 25 controls. The two groups were matched for gender, age and years of education.

Stimuli and Procedure:

24 CVCV bisyllabic novel words were created following Spanish phonotactics. Words were recorded in isolation to avoid intonations cues, in a sound attenuated booth, by a female Spanish native speaker. Afterward they were combined with a sound editor software (Adobe Audition) to form the phrases, taking for each phrase three words from the pool of novel words and inserting a 100 ms interval between words. The average duration of each word was 483.8 ms (±39.7 ms).

The experiment was performed on a 15-inch screen laptop computer in a silent room. The auditory phrases were presented during the learning and test phases, through headphones at a comfortable

(30)

30

level and set constant across participants with the Presentation software. The experiment included 2 parts: a learning phase and a test phase. The test phase was furtherly divided in an online implicit test and an offline explicit evaluation of the learned rules. In each phase the bisyllabic words were combined differently, in order to initially allow learning and then test the learning of NAD.

2.2.2.1 Learning Phase

For the learning phase, words were combined to form rule phrases (AXC) and filler (XXX) phrases. Following the structure used in previous studies (Gómez, 2002; Gómez & Maye, 2005; López-Barroso et al., 2016), rule phrases were AXC phrases (e.g. tagi-male-sira, tagi-fuse-sira), where the initial word (A) determined the third (C), regardless of the middle element (X). Six of the words from the pool were used to form three different AXC rules (i.e. A1_C1: tagi_sira, A2_C2: jupo_runi, A3_C3: pine_ladu). The remaining 18 words were used as middle words for all A_C structures. Each rule structure used only 12 of the 18 available middle elements during the learning phase and the online test; the remaining 6, different for each structure, were only used to test generalization of the rule in a following recognition phase (offline test). Filler phrases were created by combining the 18 elements that randomly appeared in the middle of the rule phrases. They were combined with the constraint that the same word could not appear twice in the same phrase and each X had the same probability to appear in each position. Each filler phrase was presented only once in the learning phase.

A 100 ms warning tone was used as an arousing signal to prepare the participants for the upcoming presentation of the phrase, which started 400 ms after the tone.

Since the aim of the study was to test the impact of attention on NAD learning, while listening to the stream, participants were asked to press the left arrow if the last element of the sentence was a particular target word and the right one if it they heard a different word. The target word remained printed on the screen throughout the task, was always a C word and remained the same for a given participant throughout the experiment. There were two possible target words (runi and ladu), counterbalanced across participants. Therefore, only one from the three AXC structures contained the target (e.g. A1 fully predicted the presence of the target C1, while A2 and A3 fully predicted its absence). Thus, the transitional probability between A and C was 1, between A and X was 0.083 (since in rule sentences each A could be followed by 12 different X) and between X and X or X and C was 0.05 (since in filler sentence each X can be followed by 17 different possible X, while in rule sentence each X can be followed by 3 possible C). The following trial was not delivered until the

(31)

31

participants answered the previous one. In total, 54 phrases were presented: 18 filler (XXX) and 36 rule (AXC), divided between 12 target (attended condition) and 24 non-target (unattended condition).

This word-monitoring task did not allow us only to test the impact of attention, but also to record the reaction time to each phrase presentation as an indirect measure of learning. Participants were asked to answer as soon as they knew the response, meaning that the experiment contained cues that allowed for prediction. This suggestion was given to help them in performing the task, which otherwise would have been too complicated. Nevertheless, participants were not explicitly informed about the presence of the rules.

2.2.2.2 Test phase:

Online implicit test

After the learning phase, an online test was performed with no break or any other indication distinguishing it from the learning phase. Participants continued to listen to rule phrases as previously, but filler phrases were substituted by non-rule phrases with a C in the final position (XXC). In total participants listened to 54 phrases (12 AXC target, 24 AXC non-target, 6 XXC target and 12 XXC non-target).

In XXC sentences the first two elements were randomly assigned with the same constraint as in XXX, whereas the last element was always a C element from any of the three rule structures. This structure did not allow to predict the presence or the absence of the target according to the first element. The introduction of non-rule phrases followed the rationale of the classical serial reaction time task. If participants learned the specific dependencies in AXC structures they should respond faster in rule phrases than in non-rule, since rule presence allows for prediction. In contrast with the classical serial reaction time task, here learning was not tested introducing real violations to the rules (i.e. A1_C2). This allowed to spare the prediction of rule phrases for the following explicit judgement test.

Offline explicit test

Immediately after the rule learning task an offline test was performed to test a recognition and a generalization of the rules. In order to do so, the test included both rule phrases and new rule

(32)

32

phrases for each one of the three rules previously learnt. New rule sentences consisted of each of the three A_C structures combined with 6 words belonging to the X category that never appeared in that specific structure during the learning phase and the online test. Therefore, the transitional probability between these X and each element of the structure in the learning phase was zero. Moreover, two types of rule violations were used following Endress & Bonatti (2007):

- Violation of dependency: the first element (A) and the last (C) were placed in the correct position, but belonged to different rule structures (e.g. A1XC2, A2XC3). This allowed us to test if participants learned the specific association between a certain A and C.

- Violation of category: the first and the last word of a rule sentence were swapped with one another (e.g. C1XA1, C3XA3). This were violations of order position which allowed us to test whether participants learned the position of the words in rule phrases.

During the offline test 36 intermixed test phrases were presented: 9 rule phrases, 9 new rule phrases, 9 dependency violations and 9 category violations. Participants were asked to discriminate rule phrases from violations. They were instructed to press the left arrow on the keyboard for phrases that belonged to the pre-exposed language or the right arrow for phrases that did not. There was no maximum time to answer, but they were instructed to respond as fast as possible. Each phrase was delivered immediately after the response to the previous one. The offline test evaluated whether attention (target presence) had an effect on explicit learning; this would have been indicated by a higher ability to discriminate rule phrases from violations in the target condition.

2.2.3 Structural Analysis

T1 images were collected and used for a voxel-based morphometry (VBM) and a cortical thickness analysis. VBM included both the cortex and subcortical structures (basal ganglia). Both methods had previously been used to identify structural changes in Huntington’s disease (see Mühlau et al., 2007 for VBM and Rosas et al., 2008 for cortical thickness).

MRI data acquisition:

MRI data were acquired through a 3T whole-body MRI scanner (Siemens Magnetom Trio; Hospital Clínic, Barcelona), using a 32-channel phased array head coil. Structural images comprised a conventional high-resolution 3D T1 image (magnetization-prepared rapid-acquisition gradient echo sequence (MPRAGE), 208 sagittal slices, repetition time (TR) = 1970 ms, echo time (TE) = 2.34

(33)

33

ms, inversion time (IT) = 1050 ms, flip angle = 9º, field of view (FOV) = 256 mm, 1mm isotropic voxel).

VBM analysis

Statistical analyses for neuroimaging data were carried out using the SPM12 software package (Welcome Department of Imaging Neuroscience Group, London, United Kingdom) running on MATLAB (v12.b, Mathworks, Natick, MA). Results were then visualized with Xjview.

Cortical thickness analysis

Vertex-wise cortical thickness (CT) maps were estimated using Freesurfer v6.0 software (http://surfer.nmr.mgh.harvard.edu/) by means of a fully automated surface reconstruction of the grey/white matter boundary and the pial surface (Fischl et al., 1999). Then, CT was calculated as the closest distance between the grey/white matter boundary and the pial surface at each vertex across the surface (Fischl & Dale, 2000). CT maps were projected onto the average subject surface (fsaverage) to align the cortical folding patterns. Finally, a 10 mm FWHM surface-based smoothing kernel was applied. The maps were then parcellated into 31 neuroanatomical regions in each hemisphere basing on DKT atlas (Klein & Tourville, 2012).

(34)

34

3-Results

3.1. Temporal Attention Task

In order to investigate the effect of temporal orienting of attention (TO) in Huntington gene-carriers, a 2x2x2 repeated-measures mixed ANOVA was performed. Group (gene-carriers vs. controls) was taken as between-subjects factor and SOA (short vs. long) and validity (valid vs. invalid) as within-subjects factors. The dependent variable was the reaction time (RT) calculated from the onset of the target. Trials with RTs which was 2,5 standard deviations above or below the mean of each subject were excluded.

Note that the combination between Cue-type (valid or invalid) and SOA (short or long) gave rise to four distinct experimental conditions: valid/short SOA (E350), valid/long SOA (L1350), invalid/short SOA(L350), invalid/long SOA (E1350) (the letter indicates the expected presentation of the target, early or late, while the number indicates SOA length, in ms.) (see figure 3.1 and table 3.1).

SOA effect was calculated by subtracting the RT in the long SOAs from the RT in the short SOAs (considering the mean of valid and invalid trials), while Validity effect was calculated separately on the long and short SOA by subtracting RT in the valid trials from RT in the invalid ones (e.g. L350-E350 for the short SOA, E1350-L1350 for the long SOA). The validity effect measures the improvement in the behavioral response due to the presentation of by a valid temporal cue (faster RT for valid than invalid trials). In contrast, the SOA effect is due to a slower RT in the short SOA with respect to the long one.

Figure 3.1 The bar chart shows the mean RT for each condition in HD and controls. It appears clearly that in HD

there’s a significant difference between the short invalid and the long invalid condition, which is absent in controls. Orange: Invalid conditions, Grey: Valid conditions; Uniform bars: Short SOA; Shaded bars: Long SOA.