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.

Dynamic directed interictal connectivity in left and right temporal lobe epilepsy.

OBJECTIVE There is increasing evidence that epileptic activity involves widespread brain networks rather than single sources and that these networks contribute to interictal brain dysfunction. We investigated the fast-varying behavior of epileptic networks during interictal spikes in right and left temporal lobe epilepsy (RTLE and LTLE) at a whole-brain scale using directed connectivity. METHODS In 16 patients, 8 with LTLE and 8 with RTLE, we estimated the electrical source activity in 82 cortical regions of interest (ROIs) using high-density electroencephalography (EEG), individual head models, and a distributed linear inverse solution. A multivariate, time-varying, and frequency-resolved Granger-causal modeling (weighted Partial Directed Coherence) was applied to the source signal of all ROIs. A nonparametric statistical test assessed differences between spike and baseline epochs. Connectivity results between RTLE and LTLE were compared between RTLE and LTLE and with neuropsychological impairments. RESULTS Ipsilateral anterior temporal structures were identified as key drivers for both groups, concordant with the epileptogenic zone estimated invasively. We observed an increase in outflow from the key driver already before the spike. There were also important temporal and extratemporal ipsilateral drivers in both conditions, and contralateral only in RTLE. A different network pattern between LTLE and RTLE was found: in RTLE there was a much more prominent ipsilateral to contralateral pattern than in LTLE. Half of the RTLE patients but none of the LTLE patients had neuropsychological deficits consistent with contralateral temporal lobe dysfunction, suggesting a relationship between connectivity changes and cognitive deficits. SIGNIFICANCE The different patterns of time-varying connectivity in LTLE and RTLE suggest that they are not symmetrical entities, in line with our neuropsychological results. The highest outflow region was concordant with invasive validation of the epileptogenic zone. This enhanced characterization of dynamic connectivity patterns could better explain cognitive deficits and help the management of epilepsy surgery candidates.

Assessment of the Spatial and Temporal Distribution of Functional Connectivity in Resting-State BOLD fMRI

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.

Spatial patterns of genetic diversity in Mediterranean eagle owl Bubo bubo populations

Little information is available on the patterns of genetic connectivity in owls. We studied the genetic structure of the eagle owl Bubo bubo (Linnaeus, 1758) in southeastern Spain at two different spatial scales. Seven microsatellites previously described for this species were used, although only six loci amplified correctly. The observed low genetic variation could be explained by the short dispersal distance, high mortality rate and high degree of monogamy shown by this large nocturnal predator. As expected, the highest genetic isolation was detected in the geographically most isolated population. Significant genetic differentiation was found among study units separated by less than 50 km. The territorial analysis showed interesting connectivity patterns related with the gene flow and turnover rate of the breeding individuals. The lowest genetic diversity was found in the region with the largest population, which could imply incipient inbreeding.

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

Assessing landscape functional connectivity in a forest carnivore using path selection functions

Context Understanding connectivity patterns in relation to habitat fragmentation is essential to landscape management. However, connectivity is often judged from expert opinion or species occurrence patterns, with very few studies considering the actual movements of individuals. Path selection functions provide a promising tool to infer functional connectivity from animal movement data, but its practical application remains scanty. Objectives We aimed to describe functional connectivity patterns in a forest carnivore using path-level analysis, and to explore how connectivity is affected by land cover patterns and road networks. Methods We radiotracked 22 common genets in a mixed forest-agricultural landscape of southern Portugal. We developed path selection functions discriminating between observed and random paths in relation to landscape variables. These functions were used together with land cover information to map conductance surfaces. Results Genets moved preferentially within forest patches and close to riparian habitats. Functional connectivity declined with increasing road density, but increased with the proximity of culverts, viaducts and bridges. Functional connectivity was favoured by large forest patches, and by the presence of riparian areas providing corridors within open agricultural land. Roads reduced connectivity by dissecting forest patches, but had less effect on riparian corridors due to the presence of crossing structures. Conclusions Genet movements were jointly affected by the spatial distribution of suitable habitats, and the presence of a road network dissecting such habitats and creating obstacles in areas otherwise permeable to animal movement. Overall, the study showed the value of path-level analysis to assess functional connectivity patterns in human-modified landscapes.

Isolation and characterisation of novel microsatellite and mitochondrial DNA markers for the Eastern Water Dragon (Physignathus lesueurii)

Habitat fragmentation as a result of urbanisation is a growing problem for native lizard species. The Eastern Water Dragon (Physignathus lesueurii) is a social arboreal agamid lizard, native to Australia. This species represents an ideal model species to investigate the effect of urbanisation because of their prominent abundance in the urban landscape. Here we describe the isolation and characterisation of a novel set of 74 di-, tri-, and tetramicrosatellites from which 18 were selected and optimised into two multiplexes. The 18 microsatellites generated a total 148 alleles across the two populations. The number of alleles per locus varied from 2 to 18 alleles and measures of Ho and He varied from 0.395 to 0.877 and from 0.441 to 0.880, respectively. We also present primers for four novel mitochondrial DNA (mtDNA) markers. The combined length of the four mtDNA marker pairs was 2,528 bp which included 15 nucleotides changes. In comparison to threatened species, which are generally characterised by small population sizes, the Eastern Water Dragon represents an ideal model species to investigate the effect of urbanisation on their behavioural ecology and connectivity patterns among populations.

Analyzing multiple spike trains with nonparametric granger causality

Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons limultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.

Regulation of GABAergic neuron identity and diversity in the developing midbrain

Gamma-aminobutyric acid (GABA) is the most abundant inhibitory neurotransmitter in the vertebrate brain. In the midbrain, GABAergic neurons contribute to the regulation of locomotion, nociception, defensive behaviours, fear and anxiety, as well as sensing reward and addiction. Despite the clinical relevance of this group of neurons, the mechanisms regulating their development are largely unknown. In addition, their migration and connectivity patterns are poorly characterized. This study focuses on the molecular mechanisms specifying the GABAergic fate, and the developmental origins of midbrain GABAergic neurons. First, we have characterized the function of a zink-finger transcription factor Gata2. Using a tissue-specific mutagenesis in mouse midbrain and anteror hindbrain, we showed that Gata2 is a crucial determinant of the GABAergic fate in midbrain. In the absence of Gata2, no GABAergic neurons are produced from the otherwise competent midbrain neuroepithelium. Instead, the Gata2-mutant cells acquire a glutamatergic neuron phenotype. Ectopic expression of Gata2 was also sufficient to induce GABAergic in chicken midbrain. Second, we have analyzed the midbrain phenotype of mice mutant for a proneural gene Ascl1, and described the variable and region-dependent requirements for Ascl1 in the midbrain GABAergic neurogenesis. These studies also have implications on the origin of distinct anatomical and functional GABAergic subpopulations in midbrain. Third, we have identified unique developmental properties of GABAergic neurons that are associated with the midbrain dopaminergic nuclei, the substantia nigra pars reticulata (SNpr) and ventral tegmental area (VTA). Namely, the genetic regulation of GABAergic fate in these cells is distinct from the rest of midbrain. In accordance to this phenomenon, our detailed fate-mapping analyses indicated that the SNpr-VTA GABAergic neurons are generated outside midbrain, in the neuroepithelium of anterior hindbrain.

Social insect colony as a biological regulatory system: modelling information flow in dominance networks

Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the `feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony.

Evolving graphs: dynamical models, inverse problems and propagation

Applications such as neuroscience, telecommunication, online social networking, transport and retail trading give rise to connectivity patterns that change over time. In this work, we address the resulting need for network models and computational algorithms that deal with dynamic links. We introduce a new class of evolving range-dependent random graphs that gives a tractable framework for modelling and simulation. We develop a spectral algorithm for calibrating a set of edge ranges from a sequence of network snapshots and give a proof of principle illustration on some neuroscience data. We also show how the model can be used computationally and analytically to investigate the scenario where an evolutionary process, such as an epidemic, takes place on an evolving network. This allows us to study the cumulative effect of two distinct types of dynamics.

Shared neural circuits for mentalizing about the self and others

Although many examples exist for shared neural representations of self and other, it is unknown how such shared representations interact with the rest of the brain. Furthermore, do high-level inference-based shared mentalizing representations interact with lower level embodied/simulation-based shared representations? We used functional neuroimaging (fMRI) and a functional connectivity approach to assess these questions during high-level inference-based mentalizing. Shared mentalizing representations in ventromedial prefrontal cortex, posterior cingulate/precuneus, and temporo-parietal junction (TPJ) all exhibited identical functional connectivity patterns during mentalizing of both self and other. Connectivity patterns were distributed across low-level embodied neural systems such as the frontal operculum/ventral premotor cortex, the anterior insula, the primary sensorimotor cortex, and the presupplementary motor area. These results demonstrate that identical neural circuits are implementing processes involved in mentalizing of both self and other and that the nature of such processes may be the integration of low-level embodied processes within higher level inference-based mentalizing.

Multiple Pathways Analysis of Brain Functional Networks from EEG Signals: An Application to Real Data

In the present study, we propose a theoretical graph procedure to investigate multiple pathways in brain functional networks. By taking into account all the possible paths consisting of h links between the nodes pairs of the network, we measured the global network redundancy R (h) as the number of parallel paths and the global network permeability P (h) as the probability to get connected. We used this procedure to investigate the structural and dynamical changes in the cortical networks estimated from a dataset of high-resolution EEG signals in a group of spinal cord injured (SCI) patients during the attempt of foot movement. In the light of a statistical contrast with a healthy population, the permeability index P (h) of the SCI networks increased significantly (P < 0.01) in the Theta frequency band (3-6 Hz) for distances h ranging from 2 to 4. On the contrary, no significant differences were found between the two populations for the redundancy index R (h) . The most significant changes in the brain functional network of SCI patients occurred mainly in the lower spectral contents. These changes were related to an improved propagation of communication between the closest cortical areas rather than to a different level of redundancy. This evidence strengthens the hypothesis of the need for a higher functional interaction among the closest ROIs as a mechanism to compensate the lack of feedback from the peripheral nerves to the sensomotor areas.

Alterações da default mode network provocadas pela ingestão de Ayahuasca investigadas por Ressonância Magnética Funcional

Ayahuasca is psychotropic beverage that has been used for ages by indigenous populations in South America, notably in the Amazon region, for religious and medicinal purposes. The tea is obtained by the decoction of leaves from the Psychotria viridis with the bark and stalk of a shrub, the Banisteriopsis caapi. The first is rich in N-N-dimethyltryptamine (DMT), which has an important and well-known hallucinogenic effect due to its agonistic action in serotonin receptors, specifically 5-HT2A. On the other hand, β-carbolines present in B. caapi, particularly harmine and harmaline, are potent monoamine oxidase inhibitors (MAOi). In addition, the tetrahydroharmine (THH), also present in B. caapi, acts as mild selective serotonin reuptake inhibitor and a weak MAOi. This unique composition induces a number of affective, sensitive, perceptual and cognitive changes in individuals under the effect of Ayahuasca. On the other hand, there is growing interest in the Default Mode Network (DMN), which has been consistently observed in functional neuroimaging studies. The key components of this network include structures in the brain midline, as the anterior medial frontal cortex, ventral medial frontal cortex, posterior cingulate cortex, precuneus, and some regions within the inferior parietal lobe and middle temporal gyrus. It has been argued that DMN participate in tasks involving self-judgments, autobiographical memory retrieval, mental simulations, thinking in perspective, meditative states, and others. In general, these tasks require an internal focus of attention, hence the conclusion that the DMN is associated with introspective mental activity. Therefore, this study aimed to evaluate by functional magnetic resonance imaging (fMRI) changes in DMN caused via the ingestion of Ayahuasca by 10 healthy subjects while submitted to two fMRI protocols: a verbal fluency task and a resting state acquisition. In general, it was observed that Ayahuasca causes a reduction in the fMRI signal in central nodes of DMN, such as the anterior cingulate cortex, the medial prefrontal cortex, the posterior cingulate cortex, precuneus and inferior parietal lobe. Furthermore, changes in connectivity patterns of the DMN were observed, especially a decrease in the functional connectivity of the precuneus. Together, these findings indicate an association between the altered state of consciousness experienced by individuals under the effect of Ayahuasca, and changes in the stream of spontaneous thoughts leading to an increased introspective mental activity

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.