Effects of environmental complexity on neurogenesis in the hippocampal dentate gyrus

Introduction. Postnatal neurogenesis in the hippocampal dentate gyrus, can be modulated by numerous determinants, such as hormones, transmitters and stress. Among the factors positively interfering with neurogenesis, the complexity of the environment appears to play a particularly striking role. Adult mice reared in an enriched environment produce more neurons and exhibit better performance in hippocampus-specific learning tasks. While the effects of complex environments on hippocampal neurogenesis are well documented, there is a lack of information on the effects of living under socio-sensory deprivation conditions. Due to the immaturity of rats and mice at birth, studies dealing with the effects of environmental enrichment on hippocampal neurogenesis were carried out in adult animals, i.e. during a period of relatively low rate of neurogenesis. The impact of environment is likely to be more dramatic during the first postnatal weeks, because at this time granule cell production is remarkably higher than at later phases of development. The aim of the present research was to clarify whether and to what extent isolated or enriched rearing conditions affect hippocampal neurogenesis during the early postnatal period, a time window characterized by a high rate of precursor proliferation and to elucidate the mechanisms underlying these effects. The experimental model chosen for this research was the guinea pig, a precocious rodent, which, at 4-5 days of age can be independent from maternal care. Experimental design. Animals were assigned to a standard (control), an isolated, or an enriched environment a few days after birth (P5-P6). On P14-P17 animals received one daily bromodeoxyuridine (BrdU) injection, to label dividing cells, and were sacrificed either on P18, to evaluate cell proliferation or on P45, to evaluate cell survival and differentiation. Methods. Brain sections were processed for BrdU immunhistochemistry, to quantify the new born and surviving cells. The phenotype of the surviving cells was examined by means of confocal microscopy and immunofluorescent double-labeling for BrdU and either a marker of neurons (NeuN) or a marker of astrocytes (GFAP). Apoptotic cell death was examined with the TUNEL method. Serial sections were processed for immunohistochemistry for i) vimentin, a marker of radial glial cells, ii) BDNF (brain-derived neurotrofic factor), a neurotrophin involved in neuron proliferation/survival, iii) PSA-NCAM (the polysialylated form of the neural cell adhesion molecule), a molecule associated with neuronal migration. Total granule cell number in the dentate gyrus was evaluated by stereological methods, in Nissl-stained sections. Results. Effects of isolation. In P18 isolated animals we found a reduced cell proliferation (-35%) compared to controls and a lower expression of BDNF. Though in absolute terms P45 isolated animals had less surviving cells than controls, they showed no differences in survival rate and phenotype percent distribution compared to controls. Evaluation of the absolute number of surviving cells of each phenotype showed that isolated animals had a reduced number of cells with neuronal phenotype than controls. Looking at the location of the new neurons, we found that while in control animals 76% of them had migrated to the granule cell layer, in isolated animals only 55% of the new neurons had reached this layer. Examination of radial glia cells of P18 and P45 animals by vimentin immunohistochemistry showed that in isolated animals radial glia cells were reduced in density and had less and shorter processes. Granule cell count revealed that isolated animals had less granule cells than controls (-32% at P18 and -42% at P45). Effects of enrichment. In P18 enriched animals there was an increase in cell proliferation (+26%) compared to controls and a higher expression of BDNF. Though in both groups there was a decline in the number of BrdU-positive cells by P45, enriched animals had more surviving cells (+63) and a higher survival rate than controls. No differences were found between control and enriched animals in phenotype percent distribution. Evaluation of the absolute number of cells of each phenotype showed that enriched animals had a larger number of cells of each phenotype than controls. Looking at the location of cells of each phenotype we found that enriched animals had more new neurons in the granule cell layer and more astrocytes and cells with undetermined phenotype in the hilus. Enriched animals had a higher expression of PSA-NCAM in the granule cell layer and hilus Vimentin immunohistochemistry showed that in enriched animals radial glia cells were more numerous and had more processes.. Granule cell count revealed that enriched animals had more granule cells than controls (+37% at P18 and +31% at P45). Discussion. Results show that isolation rearing reduces hippocampal cell proliferation but does not affect cell survival, while enriched rearing increases both cell proliferation and cell survival. Changes in the expression of BDNF are likely to contribute to he effects of environment on precursor cell proliferation. The reduction and increase in final number of granule neurons in isolated and enriched animals, respectively, are attributable to the effects of environment on cell proliferation and survival and not to changes in the differentiation program. As radial glia cells play a pivotal role in neuron guidance to the granule cell layer, the reduced number of radial glia cells in isolated animals and the increased number in enriched animals suggests that the size of radial glia population may change dynamically, in order to match changes in neuron production. The high PSA-NCAM expression in enriched animals may concur to favor the survival of the new neurons by facilitating their migration to the granule cell layer. Conclusions. By using a precocious rodent we could demonstrate that isolated/enriched rearing conditions, at a time window during which intense granule cell proliferation takes place, lead to a notable decrease/increase of total granule cell number. The time-course and magnitude of postnatal granule cell production in guinea pigs are more similar to the human and non-human primate condition than in rats and mice. Translation of current data to humans would imply that exposure of children to environments poor/rich of stimuli may have a notably large impact on dentate neurogenesis and, very likely, on hippocampus dependent memory functions.

Seizure prediction and control in epilepsy

The first part of my thesis presents an overview of the different approaches used in the past two decades in the attempt to forecast epileptic seizure on the basis of intracranial and scalp EEG. Past research could reveal some value of linear and nonlinear algorithms to detect EEG features changing over different phases of the epileptic cycle. However, their exact value for seizure prediction, in terms of sensitivity and specificity, is still discussed and has to be evaluated. In particular, the monitored EEG features may fluctuate with the vigilance state and lead to false alarms. Recently, such a dependency on vigilance states has been reported for some seizure prediction methods, suggesting a reduced reliability. An additional factor limiting application and validation of most seizure-prediction techniques is their computational load. For the first time, the reliability of permutation entropy [PE] was verified in seizure prediction on scalp EEG data, contemporarily controlling for its dependency on different vigilance states. PE was recently introduced as an extremely fast and robust complexity measure for chaotic time series and thus suitable for online application even in portable systems. The capability of PE to distinguish between preictal and interictal state has been demonstrated using Receiver Operating Characteristics (ROC) analysis. Correlation analysis was used to assess dependency of PE on vigilance states. Scalp EEG-Data from two right temporal epileptic lobe (RTLE) patients and from one patient with right frontal lobe epilepsy were analysed. The last patient was included only in the correlation analysis, since no datasets including seizures have been available for him. The ROC analysis showed a good separability of interictal and preictal phases for both RTLE patients, suggesting that PE could be sensitive to EEG modifications, not visible on visual inspection, that might occur well in advance respect to the EEG and clinical onset of seizures. However, the simultaneous assessment of the changes in vigilance showed that: a) all seizures occurred in association with the transition of vigilance states; b) PE was sensitive in detecting different vigilance states, independently of seizure occurrences. Due to the limitations of the datasets, these results cannot rule out the capability of PE to detect preictal states. However, the good separability between pre- and interictal phases might depend exclusively on the coincidence of epileptic seizure onset with a transition from a state of low vigilance to a state of increased vigilance. The finding of a dependency of PE on vigilance state is an original finding, not reported in literature, and suggesting the possibility to classify vigilance states by means of PE in an authomatic and objectic way. The second part of my thesis provides the description of a novel behavioral task based on motor imagery skills, firstly introduced (Bruzzo et al. 2007), in order to study mental simulation of biological and non-biological movement in paranoid schizophrenics (PS). Immediately after the presentation of a real movement, participants had to imagine or re-enact the very same movement. By key release and key press respectively, participants had to indicate when they started and ended the mental simulation or the re-enactment, making it feasible to measure the duration of the simulated or re-enacted movements. The proportional error between duration of the re-enacted/simulated movement and the template movement were compared between different conditions, as well as between PS and healthy subjects. Results revealed a double dissociation between the mechanisms of mental simulation involved in biological and non-biologial movement simulation. While for PS were found large errors for simulation of biological movements, while being more acurate than healthy subjects during simulation of non-biological movements. Healthy subjects showed the opposite relationship, making errors during simulation of non-biological movements, but being most accurate during simulation of non-biological movements. However, the good timing precision during re-enactment of the movements in all conditions and in both groups of participants suggests that perception, memory and attention, as well as motor control processes were not affected. Based upon a long history of literature reporting the existence of psychotic episodes in epileptic patients, a longitudinal study, using a slightly modified behavioral paradigm, was carried out with two RTLE patients, one patient with idiopathic generalized epilepsy and one patient with extratemporal lobe epilepsy. Results provide strong evidence for a possibility to predict upcoming seizures in RTLE patients behaviorally. In the last part of the thesis it has been validated a behavioural strategy based on neurobiofeedback training, to voluntarily control seizures and to reduce there frequency. Three epileptic patients were included in this study. The biofeedback was based on monitoring of slow cortical potentials (SCPs) extracted online from scalp EEG. Patients were trained to produce positive shifts of SCPs. After a training phase patients were monitored for 6 months in order to validate the ability of the learned strategy to reduce seizure frequency. Two of the three refractory epileptic patients recruited for this study showed improvements in self-management and reduction of ictal episodes, even six months after the last training session.

Role of BDNF secretion and signaling in the activity-dependent refinement of synaptic connections

Neuronal networks exhibit diverse types of plasticity, including the activity-dependent regulation of synaptic functions and refinement of synaptic connections. In addition, continuous generation of new neurons in the “adult” brain (adult neurogenesis) represents a powerful form of structural plasticity establishing new connections and possibly implementing pre-existing neuronal circuits (Kempermann et al, 2000; Ming and Song, 2005). Neurotrophins, a family of neuronal growth factors, are crucially involved in the modulation of activity-dependent neuronal plasticity. The first evidence for the physiological importance of this role evolved from the observations that the local administration of neurotrophins has dramatic effects on the activity-dependent refinement of synaptic connections in the visual cortex (McAllister et al, 1999; Berardi et al, 2000; Thoenen, 1995). Moreover, the local availability of critical amounts of neurotrophins appears to be relevant for the ability of hippocampal neurons to undergo long-term potentiation (LTP) of the synaptic transmission (Lu, 2004; Aicardi et al, 2004). To achieve a comprehensive understanding of the modulatory role of neurotrophins in integrated neuronal systems, informations on the mechanisms about local neurotrophins synthesis and secretion as well as ditribution of their cognate receptors are of crucial importance. In the first part of this doctoral thesis I have used electrophysiological approaches and real-time imaging tecniques to investigate additional features about the regulation of neurotrophins secretion, namely the capability of the neurotrophin brain-derived neurotrophic factor (BDNF) to undergo synaptic recycling. In cortical and hippocampal slices as well as in dissociated cell cultures, neuronal activity rapidly enhances the neuronal expression and secretion of BDNF which is subsequently taken up by neurons themselves but also by perineuronal astrocytes, through the selective activation of BDNF receptors. Moreover, internalized BDNF becomes part of the releasable source of the neurotrophin, which is promptly recruited for activity-dependent recycling. Thus, we described for the first time that neurons and astrocytes contain an endocytic compartment competent for BDNF recycling, suggesting a specialized form of bidirectional communication between neurons and glia. The mechanism of BDNF recycling is reminiscent of that for neurotransmitters and identifies BDNF as a new modulator implicated in neuro- and glio-transmission. In the second part of this doctoral thesis I addressed the role of BDNF signaling in adult hippocampal neurogenesis. I have generated a transgenic mouse model to specifically investigate the influence of BDNF signaling on the generation, differentiation, survival and connectivity of newborn neurons into the adult hippocampal network. I demonstrated that the survival of newborn neurons critically depends on the activation of the BDNF receptor TrkB. The TrkB-dependent decision regarding life or death in these newborn neurons takes place right at the transition point of their morphological and functional maturation Before newborn neurons start to die, they exhibit a drastic reduction in dendritic complexity and spine density compared to wild-type newborn neurons, indicating that this receptor is required for the connectivity of newborn neurons. Both the failure to become integrated and subsequent dying lead to impaired LTP. Finally, mice lacking a functional TrkB in the restricted population of newborn neurons show behavioral deficits, namely increased anxiety-like behavior. These data suggest that the integration and establishment of proper connections by newly generated neurons into the pre-existing network are relevant features for regulating the emotional state of the animal.

Complexity of N-Myc transcriptional function in childhood neuroblastoma

Myc is a transcription factor that can activate transcription of several hundreds genes by direct binding to their promoters at specific DNA sequences (E-box). However, recent studies have also shown that it can exert its biological role by repressing transcription. Such studies collectively support a model in which c-Myc-mediated repression occurs through interactions with transcription factors bound to promoter DNA regions but not through direct recognition of typical E-box sequences. Here, we investigated whether N-Myc can also repress gene transcription, and how this is mechanistically achieved. We used human neuroblastoma cells as a model system in that N-MYC amplification/over-expression represents a key prognostic marker of this tumour. By means of transcription profile analyses we could identify at least 5 genes (TRKA, p75NTR, ABCC3, TG2, p21) that are specifically repressed by N-Myc. Through a dual-step-ChIP assay and genetic dissection of gene promoters, we found that N-Myc is physically associated with gene promoters in vivo, in proximity of the transcription start site. N-Myc association with promoters requires interaction with other proteins, such as Sp1 and Miz1 transcription factors. Furthermore, we found that N-Myc may repress gene expression by interfering directly with Sp1 and/or with Miz1 activity (i.e. TRKA, p75NTR, ABCC3, p21) or by recruiting Histone Deacetylase 1 (Hdac1) (i.e. TG2). In vitro analyses show that distinct N-Myc domains can interact with Sp1, Miz1 and Hdac1, supporting the idea that Myc may participate in distinct repression complexes by interacting specifically with diverse proteins. Finally, results show that N-Myc, through repressed genes, affects important cellular functions, such as apoptosis, growth, differentiation and motility. Overall, our results support a model in which N-Myc, like c-Myc, can repress gene transcription by direct interaction with Sp1 and/or Miz1, and provide further lines of evidence on the importance of transcriptional repression by Myc factors in tumour biology.

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.

Methods for the estimation of the cortical activity and connectivity during cognitive tasks in humans

The research activity characterizing the present thesis was mainly centered on the design, development and validation of methodologies for the estimation of stationary and time-varying connectivity between different regions of the human brain during specific complex cognitive tasks. Such activity involved two main aspects: i) the development of a stable, consistent and reproducible procedure for functional connectivity estimation with a high impact on neuroscience field and ii) its application to real data from healthy volunteers eliciting specific cognitive processes (attention and memory). In particular the methodological issues addressed in the present thesis consisted in finding out an approach to be applied in neuroscience field able to: i) include all the cerebral sources in connectivity estimation process; ii) to accurately describe the temporal evolution of connectivity networks; iii) to assess the significance of connectivity patterns; iv) to consistently describe relevant properties of brain networks. The advancement provided in this thesis allowed finding out quantifiable descriptors of cognitive processes during a high resolution EEG experiment involving subjects performing complex cognitive tasks.

Implicit computational complexity and probabilistic classes

The thesis applies the ICC tecniques to the probabilistic polinomial complexity classes in order to get an implicit characterization of them. The main contribution lays on the implicit characterization of PP (which stands for Probabilistic Polynomial Time) class, showing a syntactical characterisation of PP and a static complexity analyser able to recognise if an imperative program computes in Probabilistic Polynomial Time. The thesis is divided in two parts. The first part focuses on solving the problem by creating a prototype of functional language (a probabilistic variation of lambda calculus with bounded recursion) that is sound and complete respect to Probabilistic Prolynomial Time. The second part, instead, reverses the problem and develops a feasible way to verify if a program, written with a prototype of imperative programming language, is running in Probabilistic polynomial time or not. This thesis would characterise itself as one of the first step for Implicit Computational Complexity over probabilistic classes. There are still open hard problem to investigate and try to solve. There are a lot of theoretical aspects strongly connected with these topics and I expect that in the future there will be wide attention to ICC and probabilistic classes.

Institutional Complexity and Technology Transfer: A Theoretical and Empirical Analysis

This Doctoral Thesis unfolds into a collection of three distinct papers that share an interest in institutional theory and technology transfer. Taking into account that organizations are increasingly exposed to a multiplicity of demands and pressures, we aim to analyze what renders this situation of institutional complexity more or less difficult to manage for organizations, and what makes organizations more or less successful in responding to it. The three studies offer a novel contribution both theoretically and empirically. In particular, the first paper “The dimensions of organizational fields for understanding institutional complexity: A theoretical framework” is a theoretical contribution that tries to better understand the relationship between institutional complexity and fields by providing a framework. The second article “Beyond institutional complexity: The case of different organizational successes in confronting multiple institutional logics” is an empirical study which aims to explore the strategies that allow organizations facing multiple logics to respond more successfully to them. The third work “ How external support may mitigate the barriers to university-industry collaboration” is oriented towards practitioners and presents a case study about technology transfer in Italy.

Extending Implicit Computational Complexity and Abstract Machines to Languages with Control

The Curry-Howard isomorphism is the idea that proofs in natural deduction can be put in correspondence with lambda terms in such a way that this correspondence is preserved by normalization. The concept can be extended from Intuitionistic Logic to other systems, such as Linear Logic. One of the nice conseguences of this isomorphism is that we can reason about functional programs with formal tools which are typical of proof systems: such analysis can also include quantitative qualities of programs, such as the number of steps it takes to terminate. Another is the possiblity to describe the execution of these programs in terms of abstract machines. In 1990 Griffin proved that the correspondence can be extended to Classical Logic and control operators. That is, Classical Logic adds the possiblity to manipulate continuations. In this thesis we see how the things we described above work in this larger context.

Probabilistic Recursion Theory and Implicit Computational Complexity

In this thesis we provide a characterization of probabilistic computation in itself, from a recursion-theoretical perspective, without reducing it to deterministic computation. More specifically, we show that probabilistic computable functions, i.e., those functions which are computed by Probabilistic Turing Machines (PTM), can be characterized by a natural generalization of Kleene's partial recursive functions which includes, among initial functions, one that returns identity or successor with probability 1/2. We then prove the equi-expressivity of the obtained algebra and the class of functions computed by PTMs. In the the second part of the thesis we investigate the relations existing between our recursion-theoretical framework and sub-recursive classes, in the spirit of Implicit Computational Complexity. More precisely, endowing predicative recurrence with a random base function is proved to lead to a characterization of polynomial-time computable probabilistic functions.