Mathematical models of cognitive processes

The research activity carried out during the PhD course was focused on the development of mathematical models of some cognitive processes and their validation by means of data present in literature, with a double aim: i) to achieve a better interpretation and explanation of the great amount of data obtained on these processes from different methodologies (electrophysiological recordings on animals, neuropsychological, psychophysical and neuroimaging studies in humans), ii) to exploit model predictions and results to guide future research and experiments. In particular, the research activity has been focused on two different projects: 1) the first one concerns the development of neural oscillators networks, in order to investigate the mechanisms of synchronization of the neural oscillatory activity during cognitive processes, such as object recognition, memory, language, attention; 2) the second one concerns the mathematical modelling of multisensory integration processes (e.g. visual-acoustic), which occur in several cortical and subcortical regions (in particular in a subcortical structure named Superior Colliculus (SC)), and which are fundamental for orienting motor and attentive responses to external world stimuli. This activity has been realized in collaboration with the Center for Studies and Researches in Cognitive Neuroscience of the University of Bologna (in Cesena) and the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA). PART 1. Objects representation in a number of cognitive functions, like perception and recognition, foresees distribute processes in different cortical areas. One of the main neurophysiological question concerns how the correlation between these disparate areas is realized, in order to succeed in grouping together the characteristics of the same object (binding problem) and in maintaining segregated the properties belonging to different objects simultaneously present (segmentation problem). Different theories have been proposed to address these questions (Barlow, 1972). One of the most influential theory is the so called “assembly coding”, postulated by Singer (2003), according to which 1) an object is well described by a few fundamental properties, processing in different and distributed cortical areas; 2) the recognition of the object would be realized by means of the simultaneously activation of the cortical areas representing its different features; 3) groups of properties belonging to different objects would be kept separated in the time domain. In Chapter 1.1 and in Chapter 1.2 we present two neural network models for object recognition, based on the “assembly coding” hypothesis. These models are networks of Wilson-Cowan oscillators which exploit: i) two high-level “Gestalt Rules” (the similarity and previous knowledge rules), to realize the functional link between elements of different cortical areas representing properties of the same object (binding problem); 2) the synchronization of the neural oscillatory activity in the γ-band (30-100Hz), to segregate in time the representations of different objects simultaneously present (segmentation problem). These models are able to recognize and reconstruct multiple simultaneous external objects, even in difficult case (some wrong or lacking features, shared features, superimposed noise). In Chapter 1.3 the previous models are extended to realize a semantic memory, in which sensory-motor representations of objects are linked with words. To this aim, the network, previously developed, devoted to the representation of objects as a collection of sensory-motor features, is reciprocally linked with a second network devoted to the representation of words (lexical network) Synapses linking the two networks are trained via a time-dependent Hebbian rule, during a training period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from linguistic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with some shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits). PART 2. The ability of the brain to integrate information from different sensory channels is fundamental to perception of the external world (Stein et al, 1993). It is well documented that a number of extraprimary areas have neurons capable of such a task; one of the best known of these is the superior colliculus (SC). This midbrain structure receives auditory, visual and somatosensory inputs from different subcortical and cortical areas, and is involved in the control of orientation to external events (Wallace et al, 1993). SC neurons respond to each of these sensory inputs separately, but is also capable of integrating them (Stein et al, 1993) so that the response to the combined multisensory stimuli is greater than that to the individual component stimuli (enhancement). This enhancement is proportionately greater if the modality-specific paired stimuli are weaker (the principle of inverse effectiveness). Several studies have shown that the capability of SC neurons to engage in multisensory integration requires inputs from cortex; primarily the anterior ectosylvian sulcus (AES), but also the rostral lateral suprasylvian sulcus (rLS). If these cortical inputs are deactivated the response of SC neurons to cross-modal stimulation is no different from that evoked by the most effective of its individual component stimuli (Jiang et al 2001). This phenomenon can be better understood through mathematical models. The use of mathematical models and neural networks can place the mass of data that has been accumulated about this phenomenon and its underlying circuitry into a coherent theoretical structure. In Chapter 2.1 a simple neural network model of this structure is presented; this model is able to reproduce a large number of SC behaviours like multisensory enhancement, multisensory and unisensory depression, inverse effectiveness. In Chapter 2.2 this model was improved by incorporating more neurophysiological knowledge about the neural circuitry underlying SC multisensory integration, in order to suggest possible physiological mechanisms through which it is effected. This endeavour was realized in collaboration with Professor B.E. Stein and Doctor B. Rowland during the 6 months-period spent at the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA), within the Marco Polo Project. The model includes four distinct unisensory areas that are devoted to a topological representation of external stimuli. Two of them represent subregions of the AES (i.e., FAES, an auditory area, and AEV, a visual area) and send descending inputs to the ipsilateral SC; the other two represent subcortical areas (one auditory and one visual) projecting ascending inputs to the same SC. Different competitive mechanisms, realized by means of population of interneurons, are used in the model to reproduce the different behaviour of SC neurons in conditions of cortical activation and deactivation. The model, with a single set of parameters, is able to mimic the behaviour of SC multisensory neurons in response to very different stimulus conditions (multisensory enhancement, inverse effectiveness, within- and cross-modal suppression of spatially disparate stimuli), with cortex functional and cortex deactivated, and with a particular type of membrane receptors (NMDA receptors) active or inhibited. All these results agree with the data reported in Jiang et al. (2001) and in Binns and Salt (1996). The model suggests that non-linearities in neural responses and synaptic (excitatory and inhibitory) connections can explain the fundamental aspects of multisensory integration, and provides a biologically plausible hypothesis about the underlying circuitry.

Cognitive and affective processes in social actions and decisions

The question of how we make, and how we should make judgments and decisions has occupied thinkers for many centuries. This thesis has the aim to add new evidences to clarify the brain’s mechanisms for decisions. The cognitive and the emotional processes of social actions and decisions are investigated with the aim to understand which brain areas are mostly involved. Four experimental studies are presented. A specific kind of population is involved in the first study (as well as in study III) concerning patients with lesion of ventromedial prefrontal cortex (vmPFC). This region is collocated in the ventral surface of frontal lobe, and it seems have an important role in social and moral decision in forecasting the negative emotional consequences of choice. In study I, it is examined whether emotions, specifically social emotions subserved by the vmPFC, affect people’s willingness to trust others. In study II is observed how incidental emotions could encourage trusting behaviour, especially when individuals are not aware of emotive stimulation. Study III has the aim to gather a direct psychophysiological evidence, both in healthy and neurologically impaired individuals, that emotions are crucially involved in shaping moral judgment, by preventing moral violations. Study IV explores how the moral meaning of a decision and its subsequent action can modulate the basic component of action such as sense of agency.

Models for the Study of Cortical Activity During Cognitive ans Motor Tasks

This thesis is mainly devoted to show how EEG data and related phenomena can be reproduced and analyzed using mathematical models of neural masses (NMM). The aim is to describe some of these phenomena, to show in which ways the design of the models architecture is influenced by such phenomena, point out the difficulties of tuning the dozens of parameters of the models in order to reproduce the activity recorded with EEG systems during different kinds of experiments, and suggest some strategies to cope with these problems. In particular the chapters are organized as follows: chapter I gives a brief overview of the aims and issues addressed in the thesis; in chapter II the main characteristics of the cortical column, of the EEG signal and of the neural mass models will be presented, in order to show the relationships that hold between these entities; chapter III describes a study in which a NMM from the literature has been used to assess brain connectivity changes in tetraplegic patients; in chapter IV a modified version of the NMM is presented, which has been developed to overcomes some of the previous version’s intrinsic limitations; chapter V describes a study in which the new NMM has been used to reproduce the electrical activity evoked in the cortex by the transcranial magnetic stimulation (TMS); chapter VI presents some preliminary results obtained in the simulation of the neural rhythms associated with memory recall; finally, some general conclusions are drawn in chapter VII.

Cognitive Assessment and Rehabilitation of subjects with Traumatic Brain Injury

This thesis regards the study and the development of new cognitive assessment and rehabilitation techniques of subjects with traumatic brain injury (TBI). In particular, this thesis i) provides an overview about the state of art of this new assessment and rehabilitation technologies, ii) suggests new methods for the assessment and rehabilitation and iii) contributes to the explanation of the neurophysiological mechanism that is involved in a rehabilitation treatment. Some chapters provide useful information to contextualize TBI and its outcome; they describe the methods used for its assessment/rehabilitation. The other chapters illustrate a series of experimental studies conducted in healthy subjects and TBI patients that suggest new approaches to assessment and rehabilitation. The new proposed approaches have in common the use of electroencefalografy (EEG). EEG was used in all the experimental studies with a different purpose, such as diagnostic tool, signal to command a BCI-system, outcome measure to evaluate the effects of a treatment, etc. The main achieved results are about: i) the study and the development of a system for the communication with patients with disorders of consciousness. It was possible to identify a paradigm of reliable activation during two imagery task using EEG signal or EEG and NIRS signal; ii) the study of the effects of a neuromodulation technique (tDCS) on EEG pattern. This topic is of great importance and interest. The emerged founding showed that the tDCS can manipulate the cortical network activity and through the research of optimal stimulation parameters, it is possible move the working point of a neural network and bring it in a condition of maximum learning. In this way could be possible improved the performance of a BCI system or to improve the efficacy of a rehabilitation treatment, like neurofeedback.

Seeing emotions in others: Improving cognitive and neuronal mechanisms of facial expression processing

In the conceptual framework of affective neuroscience, this thesis intends to advance the understanding of the plasticity mechanisms of other’s emotional facial expression representations. Chapter 1 outlines a description of the neurophysiological bases of Hebbian plasticity, reviews influential studies that adopted paired associative stimulation procedures, and introduces new lines of research where the impact of cortico-cortical paired associative stimulation protocols on higher order cognitive functions is investigated. The experiments in Chapter 2 aimed to test the modulatory influence of a perceptual-motor training, based on the execution of emotional expressions, on the subsequent emotion intensity judgements of others’ high (i.e., full visible) and low-intensity (i.e., masked) emotional expressions. As a result of the training-induced learning, participants showed a significant congruence effect, as indicated by relatively higher expression intensity ratings for the same emotion as the one that was previously trained. Interestingly, although judged as overall less emotionally intense, surgical facemasks did not prevent the emotion-specific effects of the training to occur, suggesting that covering the lower part of other’s face do not interact with the training-induced congruence effect. In Chapter 3 it was implemented a transcranial magnetic stimulation study targeting neural pathways involving re-entrant input from higher order brain regions into lower levels of the visual processing hierarchy. We focused on cortical visual networks within the temporo-occipital stream underpinning the processing of emotional faces and susceptible to plastic adaptations. Importantly, we tested the plasticity-induced effects in a state dependent manner, by administering ccPAS while presenting different facial expressions yet afferent to a specific emotion. Results indicated that the discrimination accuracy of emotion-specific expressions is enhanced following the ccPAS treatment, suggesting that a multi-coil TMS intervention might represent a suitable tool to drive brain remodeling at a neural network level, and consequently influence a specific behavior.