Models for the study of neuronal activity during cognitive tasks

Assessment of brain connectivity among different brain areas during cognitive or motor tasks is a crucial problem in neuroscience today. Aim of this research study is to use neural mass models to assess the effect of various connectivity patterns in cortical EEG power spectral density (PSD), and investigate the possibility to derive connectivity circuits from EEG data. To this end, two different models have been built. In the first model an individual region of interest (ROI) has been built as the parallel arrangement of three populations, each one exhibiting a unimodal spectrum, at low, medium or high frequency. Connectivity among ROIs includes three parameters, which specify the strength of connection in the different frequency bands. Subsequent studies demonstrated that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). For this reason in the second model an individual ROI is simulated only with a single population. Both models have been validated by comparing the simulated power spectral density with that computed in some cortical regions during cognitive and motor tasks. Another research study is focused on multisensory integration of tactile and visual stimuli in the representation of the near space around the body (peripersonal space). This work describes an original neural network to simulate representation of the peripersonal space around the hands, in basal conditions and after training with a tool used to reach the far space. The model is composed of three areas for each hand, two unimodal areas (visual and tactile) connected to a third bimodal area (visual-tactile), which is activated only when a stimulus falls within the peripersonal space. Results show that the peripersonal space, which includes just a small visual space around the hand in normal conditions, becomes elongated in the direction of the tool after training, thanks to a reinforcement of synapses.

Interactions between normative systems and software cognitive agents. A formalization in temporal modal defeasible logic and its implementation

Sustainable computer systems require some flexibility to adapt to environmental unpredictable changes. A solution lies in autonomous software agents which can adapt autonomously to their environments. Though autonomy allows agents to decide which behavior to adopt, a disadvantage is a lack of control, and as a side effect even untrustworthiness: we want to keep some control over such autonomous agents. How to control autonomous agents while respecting their autonomy? A solution is to regulate agents’ behavior by norms. The normative paradigm makes it possible to control autonomous agents while respecting their autonomy, limiting untrustworthiness and augmenting system compliance. It can also facilitate the design of the system, for example, by regulating the coordination among agents. However, an autonomous agent will follow norms or violate them in some conditions. What are the conditions in which a norm is binding upon an agent? While autonomy is regarded as the driving force behind the normative paradigm, cognitive agents provide a basis for modeling the bindingness of norms. In order to cope with the complexity of the modeling of cognitive agents and normative bindingness, we adopt an intentional stance. Since agents are embedded into a dynamic environment, things may not pass at the same instant. Accordingly, our cognitive model is extended to account for some temporal aspects. Special attention is given to the temporal peculiarities of the legal domain such as, among others, the time in force and the time in efficacy of provisions. Some types of normative modifications are also discussed in the framework. It is noteworthy that our temporal account of legal reasoning is integrated to our commonsense temporal account of cognition. As our intention is to build sustainable reasoning systems running unpredictable environment, we adopt a declarative representation of knowledge. A declarative representation of norms will make it easier to update their system representation, thus facilitating system maintenance; and to improve system transparency, thus easing system governance. Since agents are bounded and are embedded into unpredictable environments, and since conflicts may appear amongst mental states and norms, agent reasoning has to be defeasible, i.e. new pieces of information can invalidate formerly derivable conclusions. In this dissertation, our model is formalized into a non-monotonic logic, namely into a temporal modal defeasible logic, in order to account for the interactions between normative systems and software cognitive agents.

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.

Spectrum Management and Cognitive Radios

The ever increasing demand for new services from users who want high-quality broadband services while on the move, is straining the efficiency of current spectrum allocation paradigms, leading to an overall feeling of spectrum scarcity. In order to circumvent this problem, two possible solutions are being investigated: (i) implementing new technologies capable of accessing the temporarily/locally unused bands, without interfering with the licensed services, like Cognitive Radios; (ii) release some spectrum bands thanks to new services providing higher spectral efficiency, e.g., DVB-T, and allocate them to new wireless systems. These two approaches are promising, but also pose novel coexistence and interference management challenges to deal with. In particular, the deployment of devices such as Cognitive Radio, characterized by the inherent unplanned, irregular and random locations of the network nodes, require advanced mathematical techniques in order to explicitly model their spatial distribution. In such context, the system performance and optimization are strongly dependent on this spatial configuration. On the other hand, allocating some released spectrum bands to other wireless services poses severe coexistence issues with all the pre-existing services on the same or adjacent spectrum bands. In this thesis, these methodologies for better spectrum usage are investigated. In particular, using Stochastic Geometry theory, a novel mathematical framework is introduced for cognitive networks, providing a closed-form expression for coverage probability and a single-integral form for average downlink rate and Average Symbol Error Probability. Then, focusing on more regulatory aspects, interference challenges between DVB-T and LTE systems are analysed proposing a versatile methodology for their proper coexistence. Moreover, the studies performed inside the CEPT SE43 working group on the amount of spectrum potentially available to Cognitive Radios and an analysis of the Hidden Node problem are provided. Finally, a study on the extension of cognitive technologies to Hybrid Satellite Terrestrial Systems is proposed.

Effect of loss of CDKL5 on brain development in a new Cdkl5 knockout mouse model

Rett's Syndrome (RTT) is a severe neurodevelopmental disorder, characterized by cognitive disability that appears in the first months/years of life. Recently, mutations in the X-linked cyclin-dependent kinase-like 5 (CDKL5) gene have been detected in RTT patients characterized by early-onset seizures. CDKL5 is highly expressed in the brain starting from early postnatal stages to adulthood, suggesting the importance of this kinase for proper brain maturation and function. However, the role/s of CDKL5 in brain development and the molecular mechanisms whereby CDKL5 exerts its effects are still largely unknown. In order to characterize the role of CDKL5 on brain development, we created a mice carrying a targeted conditional knockout allele of Cdkl5. A first behavioral characterization shows that Cdkl5 knockout mice recapitulate several features that mimic the clinical features described in CDKL5 patients and are a useful tool to investigate phenotypic and functional aspects of Cdkl5 loss. We used the Cdkl5 knockout mouse model to dissect the role of CDKL5 on hippocampal development and to establish the mechanism/s underlying its actions. We found that Cdkl5 knockout mice showed increased precursor cell proliferation in the hippocampal dentate gyrus. Interestingly, this region was also characterized by an increased rate of apoptotic cell death that caused a reduction in the final neuron number in spite of the proliferation increase. Moreover, loss of Cdkl5 led to decreased dendritic development of new generated granule cells. Finally, we identified the Akt/GSK3-beta signaling as a target of Cdkl5 in the regulation of neuronal precursor proliferation, survival and maturation. Overall our findings highlight a critical role of CDKL5/AKT/GSK3-beta signaling in the control of neuron proliferation, survival and differentiation and suggest that CDKL5-related alterations of these processes during brain development underlie the neurological symptoms of the CDKL5 variant of RTT.

Pharmacological Rescue of Dendritic Pathology in the Ts65Dn Mouse Model of Down Syndrome

Down syndrome (DS) is a genetic pathology characterized by brain hypotrophy and severe cognitive disability. Although defective neurogenesis is an important determinant of cognitive impairment, a severe dendritic pathology appears to be an equally important factor. It is well established that serotonin plays a pivotal role both on neurogenesis and dendritic maturation. Since the serotonergic system is profoundly altered in the DS brain, we wondered whether defects in the hippocampal development can be rescued by treatment with fluoxetine, a selective serotonin reuptake inhibitor and a widely used antidepressant drug. A previous study of our group showed that fluoxetine fully restores neurogenesis in the Ts65Dn mouse model of DS and that this effect is accompanied by a recovery of memory functions. The goal of the current study was to establish whether fluoxetine also restores dendritic development and maturation. In mice aged 45 days, treated with fluoxetine in the postnatal period P3-P15, we examined the dendritic arbor of newborn and mature granule cells of the dentate gyrus (DG). The granule cells of trisomic mice had a severely hypotrophic dendritic arbor, fewer spines and a reduced innervation than euploid mice. Treatment with fluoxetine fully restored all these defects. Moreover the impairment of excitatory and inhibitory inputs to CA3 pyramidal neurons was fully normalized in treated trisomic mice, indicating that fluoxetine can rescue functional connectivity between the DG and CA3. The widespread beneficial effects of fluoxetine on the hippocampal formation suggest that early treatment with fluoxetine can be a suitable therapy, possibly usable in humans, to restore the physiology of the hippocampal networks and, hence, memory functions. These findings may open the way for future clinical trials in children and adolescents with DS.

Sleep motor activity in parkinsonian syndromes at onset: a prospective study to determine potential diagnostic and prognostic markers

Aim of this study is to describe the possible diagnostic value of sleep disturbances in the differential diagnosis of neurodegenerative diseases characterized by parkinsonism at onset. 42 consecutive patients with parkinsonian features and disease duration up to 3 years were included in the BO-ProPark study. Each patient was evaluated twice, at baseline (T0) and 16 months later (T1). Patients were diagnosed as Parkinson disease (PD, 27 patients), PD plus (PD with cognitive impairment/dementia or dysautonomia, 4 patients) and parkinsonian syndrome (PS, 11 patients). All patients underwent a full night video-polysomnography scored by a neurologist blinded to the clinical diagnosis. Sleep efficiency and total sleep time were reduced in all patients; wake after sleep onset was higher in patients with atypical parkinsonisms than in PD patients. No significant differences between groups of patients were detected in other sleep parameters. The mean percentage of epochs with enhanced tonic muscle EMG activity during REM sleep was higher in PD plus and PS than in PD. No difference in phasic muscle EMG activity during REM sleep was seen between the two groups. REM behaviour disorder was more frequent in PD plus and PS than in PD patients. Our data suggest that REM sleep motor control is more frequently impaired at disease onset in patients with PS and PD plus compared to PD patients. The presence of RBD or an enhanced tonic muscle EMG activity in a patient with recent onset parkinsonian features should suggest a diagnosis of atypical parkinsonism, rather than PD. More data are needed to establish the diagnostic value of these features in the differential diagnosis of parkinsonisms. The evaluation of sleep disorders may be a useful tool in the differential diagnosis of parkinsonism at onset.

Cooperative Cognitive Wireless Networks over TV White and Gray Spaces

Wireless networks rapidly became a fundamental pillar of everyday activities. Whether at work or elsewhere, people often benefits from always-on connections. This trend is likely to increase, and hence actual technologies struggle to cope with the increase in traffic demand. To this end, Cognitive Wireless Networks have been studied. These networks aim at a better utilization of the spectrum, by understanding the environment in which they operate, and adapt accordingly. In particular recently national regulators opened up consultations on the opportunistic use of the TV bands, which became partially free due to the digital TV switch over. In this work, we focus on the indoor use of of TVWS. Interesting use cases like smart metering and WiFI like connectivity arise, and are studied and compared against state of the art technology. New measurements for TVWS networks will be presented and evaluated, and fundamental characteristics of the signal derived. Then, building on that, a new model of spectrum sharing, which takes into account also the height from the terrain, is presented and evaluated in a real scenario. The principal limits and performance of TVWS operated networks will be studied for two main use cases, namely Machine to Machine communication and for wireless sensor networks, particularly for the smart grid scenario. The outcome is that TVWS are certainly interesting to be studied and deployed, in particular when used as an additional offload for other wireless technologies. Seeing TVWS as the only wireless technology on a device is harder to be seen: the uncertainity in channel availability is the major drawback of opportunistic networks, since depending on the primary network channel allocation might lead in having no channels available for communication. TVWS can be effectively exploited as offloading solutions, and most of the contributions presented in this work proceed in this direction.

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.

Evaluation of the transcriptomic response of an Alzheimer's disease murine model induced at different ages: searching for predictors

Alzheimer’s disease (AD) is a chronic, progressive neurodegenerative disease, characterized by the impairment of mnesic and cognitive functions, that represents the most frequent type of dementia in older people worldwide. Aging is the most important risk factor for the sporadic form of the pathology and it is associated to the progressive impairment of the proteostasis network. The endoplasmic reticulum (ER), the main cellular actor involved in proteostasis, appears significantly compromised in AD due to the accumulation of β-amyloid (Aβ) protein and phosphorylated-tau protein. Increasing proteins misfolding activates a specific cellular response known as Unfolded Protein response (UPR) which orchestrates the recovery of ER function. The aim of the present study was to investigate the role of UPR and aging process in a murine model of AD induced by intracerebroventricular (i.c.v.) injection of Aβ1-42 oligomers at 3 or 18 months. The oligomers injection in aged animals caused the increased of memory impairment, oxidative stress, and the depletion of glutathione reserve. Furthermore, the RNA-sequencing analysis was performed and the bioinformatic analysis showed the enrichment of several pathways involved in neurodegeneration and protein regulations. The following analysis highlighted the significant dysregulation of the three branches of the UPR, the protein kinase RNA-like ER kinase (PERK), inositol-requiring protein 1α (IRE1α) and activating transcription factor 6 (ATF-6). In turn, ER stress affected the PI3K/Akt/Gsk3β and MAPK/ERK pathways, highlighting Mapkapk5 as a potential marker of the neurodegenerative process, which regulation could lead to the definition of new pharmacological and neuroprotective strategies to counteract AD.

Hierarchical active inference for cognitive architectures

This thesis explores the methods based on the free energy principle and active inference for modelling cognition. Active inference is an emerging framework for designing intelligent agents where psychological processes are cast in terms of Bayesian inference. Here, I appeal to it to test the design of a set of cognitive architectures, via simulation. These architectures are defined in terms of generative models where an agent executes a task under the assumption that all cognitive processes aspire to the same objective: the minimization of variational free energy. Chapter 1 introduces the free energy principle and its assumptions about self-organizing systems. Chapter 2 describes how from the mechanics of self-organization can emerge a minimal form of cognition able to achieve autopoiesis. In chapter 3 I present the method of how I formalize generative models for action and perception. The architectures proposed allow providing a more biologically plausible account of more complex cognitive processing that entails deep temporal features. I then present three simulation studies that aim to show different aspects of cognition, their associated behavior and the underlying neural dynamics. In chapter 4, the first study proposes an architecture that represents the visuomotor system for the encoding of actions during action observation, understanding and imitation. In chapter 5, the generative model is extended and is lesioned to simulate brain damage and neuropsychological patterns observed in apraxic patients. In chapter 6, the third study proposes an architecture for cognitive control and the modulation of attention for action selection. At last, I argue how active inference can provide a formal account of information processing in the brain and how the adaptive capabilities of the simulated agents are a mere consequence of the architecture of the generative models. Cognitive processing, then, becomes an emergent property of the minimization of variational free energy.

Cancers of unknown primary site (CUPs) as a model of metastatic process.

Cancers of unknown primary site (CUPs) are a rare group of metastatic tumours, with a frequency of 3-5%, with an overall survival of 6-10 month. The identification of tumour primary site is usually reached by a combination of diagnostic investigations and immunohistochemical testing of the tumour tissue. In CUP patients, these investigations are inconclusive. Since international guidelines for treatment are based on primary site indication, CUP treatment requires a blind approach. As a consequence, CUPs are usually empiric treated with poorly effective. In this study, we applied a set of microRNAs using EvaGreen-based Droplet Digital PCR in a retrospective and prospective collection of formalin-fixed paraffin-embedded tissue samples. We assessed miRNA expression of 155 samples including primary tumours (N=94), metastases of known origin (N=10) and metastases of unknown origin (N=50). Then, we applied the shrunken centroids predictive algorithm to obtain the CUP’s site(s)-of-origin. The molecular test was successfully applied to all CUP samples and provided a site-of-origin identification for all samples, potentially within a one-week time frame from sample inclusion. In the second part of the study we derived two CUP cell lines, and corresponding patient-derived xenografts (PDXs). CUP cell lines and PDXs underwent histological, molecular, and genomic characterization confirming the features of the original tumour. Tissues-of-origin prediction was obtained from the tumour microRNA expression profile and confirmed by single cell RNA sequencing. Genomic testing analysis identified FGFR2 amplification in both models. Drug-screening assays were performed to test the activity of FGFR2-targeting drug and the combination treatment with the MEK inhibitor trametinib, which proved to be synergic and exceptionally active, both in vitro and in vivo. In conclusion, our study demonstrated that miRNA expression profiling could be employed as diagnostic test. Then we successfully derived two CUP models from patients, used for therapy tests, bringing personalized therapy closer to CUP patients.