Sparse DCM for whole-brain effective connectivity from resting-state fMRI data

Contemporary neuroscience has embraced network science and dynamical systems to study the complex and self-organized structure of the human brain. Despite the developments in non-invasive neuroimaging techniques, a full understanding of the directed interactions in whole brain networks, referred to as effective connectivity, as well as their role in the emergent brain dynamics is still lacking. The main reason is that estimating brain connectivity requires solving a formidable large-scale inverse problem from indirect and noisy measurements. Building on the dynamic causal modelling framework, the present study offers a novel method for estimating whole-brain effective connectivity from resting-state functional magnetic resonance data. To this purpose sparse estimation methods are adapted to infer the parameters of our novel model, which is based on a linearized, region-specific haemodynamic response function. The resulting algorithm, referred to as sparse DCM, is shown to compare favorably with state-of-the art methods when tested on both synthetic and real data. We also provide a graph-theoretical analysis on the whole-brain effective connectivity estimated using data from a cohort of healthy individuals, which reveals properties such as asymmetry in the connectivity structure as well as the different roles of brain areas in favoring segregation or integration

Network Theoretical Approach to Describe Epileptic Processes

Epilepsy is characterized by recurrent unprovoked seizures. Recent studies suggest that seizure generation may be caused by the abnormal activity of the entire network. This new paradigm requires new tools and methods for its study. In this sense, synchronization by linear as well as nonlinear measures are used to determine network structure and functional connectivity of neurophysiological data. Electroencephalography (EEG) data can be analyzed using each electrode’s activity as a node of the underlying cortical network. The information provided by the synchronization matrix is the basic brick upon which several lines of analysis can be performed thereafter. Detection of community structures, identification of centrality nodes, transformation of the underlying network into a simpler one, and the identification of the basic network architecture are only some of the many lines of basic works that can be done in order to characterize the epilepsy as a network disease. This chapter describes new approaches in network epilepsy, provides mathematical concepts in order to understand the complex network analyses, and reviews the advances in network analyses and its application to epilepsy research

Best practices for fNIRS publications

The application of functional near-infrared spectroscopy (fNIRS) in the neurosciences has been expanding over the last 40 years. Today, it is addressing a wide range of applications within different populations and utilizes a great variety of experimental paradigms. With the rapid growth and the diversification of research methods, some inconsistencies are appearing in the way in which methods are presented, which can make the interpretation and replication of studies unnecessarily challenging. The Society for Functional Near-Infrared Spectroscopy has thus been motivated to organize a representative (but not exhaustive) group of leaders in the field to build a consensus on the best practices for describing the methods utilized in fNIRS studies. Our paper has been designed to provide guidelines to help enhance the reliability, repeatability, and traceability of reported fNIRS studies and encourage best practices throughout the community. A checklist is provided to guide authors in the preparation of their manuscripts and to assist reviewers when evaluating fNIRS papers

Conditional network measures using multivariate partial coherence analysis for spike train data with application to multi-electrode array recordings

This thesis proposes a novel approach for functional connectivity studies of neuronal signal recordings based on statistical signal processing analysis in the frequency domain using Multivariate Partial Coherence (MVPC) combined with network theory measures. MVPC is applied to spike trains signals to make inferences about the underlying network structure. The presence of connections between single unit spike trains is estimated using both coherence and MVPC analysis. Scalability of MVPC analysis is investigated through application to simulated spike train data with up to 100 simultaneous spike trains generated from a network of excitatory and inhibitory cortical neurons. Stable MVPC estimates were obtained with up to 198 predictors in partial coherence estimates, using a combination of simulated cortical neuron data and additional Poisson spike train predictors. MVPC provides higher order partial coherence analysis for multi-channel spike trains signals, removing effects of common influences in pairwise connectivity estimates. Network measures applied to binary and weighted adjacency measures derived from coherence and partial coherence are compared to determine the differences in unconditional and conditional networks of spike train interactions. A combination of MVPC analysis along with network theory analysis provides a systematic approach for multi-channel spike train signals. The proposed method is applied to simulated and multi-electrode array (MEA) spike train data. The MEA data consists of 19 single unit channels recorded from a study of connectivity in a model of kainic acid (KA) induced epileptiform activity for mesial temporal lobe epilepsy (mTLE) in a rat. The network theory analysis uses basic measures on both conditional and unconditional network, which highlights the differences in network structure and characteristics between the two representations. Complex analysis on conditional networks is useful in describing the properties of integration and segregation in the network

Human brain networks: consensus, reproducibility, inter-modal comparison and epilepsy pathology

Classical and contemporary research in neuroscience postulates that connectivity is a fundamental component of human brain function. Recently, advances in computational neuroimaging have enabled reconstruction of macroscopic human brain structural connectivity in vivo using diffusion MRI. Studies show that the structural network topology may discriminate between neurological phenotypes or relate to individual brain function. To investigate disease effectively, it is necessary to determine the network methodological and biological variability. Reproducibility was calculated for two state-of-the-art reconstruction pipelines in healthy subjects. High reproducibility of connection weights was observed, which increased with connection strength. A high agreement between pipelines was found across network density thresholds. In addition, a robust core network was identified coinciding with a peak in similarity across thresholds, and replicated with alternative atlases. This study demonstrates the utility of applying multiple structural network pipelines to diffusion data in order to identify the most important connections. Focal epilepsy is characterised by seizures that can spread to contiguous and non-contiguous sites. Diffusion MRI and cortico-cortical evoked potentials were acquired in focal epilepsy patients to reconstruct and correlate their structural and effective brain networks and examine connectivity of the ictal-onset zone and propagative regions. Automated methods are described to reconstruct comparable largescale structural and effective networks. A high overlap and low correlation was observed between network modalities. Low correlation may be due to imperfections in methodology, such as difficulty tracing U-fibers using tractography. Effective connectivity amplitude, baseline fluctuation, and outward connectivity tended to be higher at ictal-onset regions, while higher structural connectivity between ictal-onset regions was observed. Furthermore, a high prevalence of structural and effective connections to sites of non-contiguous seizure spread was found. These results support the concept of highly excitable cortex underlying ictal-onset regions which promotes non-contiguous seizure spread via high outward connectivity

Cerebral language networks and neuropsychological profile in children with frontotemporal lobe epilepsy : a multimodal neuroimaging and neuropsychological approach

Thèse de doctorat présentée en vue de l'obtention du doctorat en psychologie (Ph.D).L'enfance et l'adolescence sont des périodes uniques de la vie où les changements neuronaux favorisent l'établissement de réseaux cérébraux matures et le développement des capacités intellectuelles. Le langage est un domaine cognitif qui est, non seulement essentiel pour la communication interhumaine, mais qui contribue également au développement de nombreuse capacités et prédit de manière significative la réussite académique. Les régions cérébrales frontotemporales sont des régions clés du réseau langagier du cerveau. Il a été démontré que les neuropathologies telles que l'épilepsie des lobes frontal et temporal (ELF et ELT) interfèrent avec le développement des réseaux cérébraux du langage et provoquent des circuits cérébraux aberrants. Les patrons exacts de réorganisation des réseaux cérébraux fonctionnels ne sont toutefois, pas entièrement compris et l'association avec le profil neuropsychologique reste spéculative. Par conséquent, l'objectif principal de cette thèse est d'accroître la compréhension des altérations du réseau langagier et d'améliorer les connaissances de l'association de l'architecture du réseau et des capacités cognitives chez les enfants et les adolescents avec ELF ou ELT. La présente thèse est composée de trois articles scientifiques, les deux premiers présentant des travaux méthodologiques qui ont permis d'optimiser les méthodes appliquées dans le troisième article, l'étude empirique principale menée auprès d'enfants avec ELF et ELT. Le premier article présente le bilan neuropsychologique pédiatrique comme un outil important pour estimer les capacités cognitives et dresser un profil cognitif avec ses forces et ses faiblesses. Dans le deuxième article, l'analyse factorielle parallèle (PARAFAC) est présentée et validée comme une nouvelle technique employée pour corriger les artefacts de mouvement qui contaminent le signal hémodynamique évalué par la spectroscopie fonctionnelle proche infrarouge (fNIRS). Une meilleure qualité du signal permet une interprétation fiable de la réponse cérébrale en plis de déduire des métriques d'organisation du réseau cérébral. Le troisième article consiste en une étude empirique, où le traitement cérébral du langage, est comparé entre des enfants avec ELF et ELT, et des pairs neuroptypiques. Les schémas de connectivité fonctionnelle indiquent que le groupe de patients présente moins de connexions intra-hémisphériques dans l'hémisphère gauche et entre les hémisphères, et des connexions accrues dans l'hémisphère droit par rapport au groupe témoin. Les mesures de l'architecture du réseau révèlent en outre une efficacité de traitement local plus élevée dans l'hémisphère droit chez les enfants atteints de ELF et ELT par rapport aux enfants en bonne santé. L'architecture du réseau local de l'hémisphère gauche et la capacité intellectuelle globale dans le groupe de patients sont négativement liées, tandis que dans le groupe contrôle, aucune association de ce type n'est identifiable. Ces résultats suggèrent que la réorganisation du réseau de langage chez les enfants avec ELF ou ELT semble dans certains cas soutenir un meilleur résultat cognitif, soit lorsque l'efficacité du traitement local dans l'hémisphère gauche est diminuée. Au contraire, une plus grande efficacité de traitement local semble être une caractéristique d'un réseau de langage cérébral associé à de moins bonnes capacités cognitives. Les travaux de recherche de cette thèse de doctorat fournissent des lignes directrices pour l'utilisation de l'évaluation neuropsychologique pédiatrique, à la fois dans un contexte clinique et scientifique. L'introduction de PARAFAC pour corriger les artefacts de mouvement dans le signal fNIRS est un ajout important au pipeline de prétraitement qui permet d'augmenter la qualité du signal pour une analyse ultérieure. De futurs projets pourront s'appuyer sur cette validation initiale et étendre l'utilisation de PARAFAC pour les analyses du signal fNIRS. Sur cette base méthodologique solide, le travail empirique confirme l'incidence accrue de circuits cérébraux aberrants liés au traitement du langage chez les enfants atteints de ELF et de ELT, et soutient en outre l'efficacité du réseau local en tant que déterminant clé de l'impact de la plasticité cérébrale précoce sur les capacités cognitives. Afin de mieux comprendre les altérations du réseau en réponse aux neuropathologies et leur impact, des études avec des échantillons plus grands et de différents groupes d'âge, devraient étudier plus spécifiquement le rôle des facteurs cliniques (e.g., le type d'épilepsie, la latéralisation de l'épilepsie, le contrôle des crises, etc.) et aborder leurs influences sur le développement. À long terme, cela augmentera le pronostic des phénotypes cliniques chez les patients pédiatriques atteints de ELF et de ELT, et offrira des opportunités d'interventions précoces pour soutenir un développement typique.Childhood and adolescence are unique periods in life where neuronal changes support the establishment of mature brain networks and the development of intellectual capacities. Language is one cognitive domain that is not only an essential part of inter-human communication but also contributes to the development of other capacities and significantly influences academic achievement. Frontotemporal brain areas are key regions of the brain's language network. Neuropathologies such as frontal and temporal lobe epilepsies (FLE and TLE) have been shown to interfere with developing brain language networks and cause aberrant cerebral circuits. The exact patterns of functional brain network reorganization are not fully understood and the association with the neuropsychological profile remains speculative. Therefore, the main objective of this thesis was to increase comprehension of language network alterations and enhance the knowledge on the association of network topology and cognitive capacities in children and adolescents with FLE or TLE. This thesis consists of three scientific articles, with the first two presenting methodological work that allowed for the optimization of the methods applied in the third article, which is the main empirical study conducted on children with FLE and TLE. The first article presents the pediatric neuropsychological assessment as a valuable tool to estimate cognitive capacities and draw a cognitive profile with strengths and weaknesses. In the second article, parallel factor analysis (PARAFAC) is presented and validated as a novel technique to correct motion artifacts that contaminate the hemodynamic signal assessed with functional near-infrared spectroscopy (fNIRS). A better signal quality is the basis for a reliable interpretation of the cerebral response and derive metrics of brain network organization. The third article consists of an empirical study where cerebral language processing is compared between children with FLE and TLE, and neuroptypical peers. Patterns of functional connectivity indicate that the patient group demonstrates fewer intra-hemispheric connections in the left hemisphere and between hemispheres, and increased connections within the right hemisphere as compared to the control group. Metrics of network architecture further reveal a higher local processing efficiency within the right hemisphere in children with FLE and TLE compared to healthy peers. Local network architecture of the left hemisphere and the overall intellectual capacity in the patient group is negatively related, while in the control group no such association is identifiable. These findings suggest that language network reorganization in children with FLE or TLE in some cases seems to support a better cognitive outcome, namely when local processing efficiency in the left hemisphere is decreased. On the contrary, a higher local processing efficiency seems to be a characteristic of a brain language network that goes along with worse cognitive capacities. The research work of this doctoral thesis provides guidelines for the use of pediatric neuropsychological assessment both in a clinical and scientific context. The introduction of PARAFAC to correct motion artifact in the fNIRS signal is an important add-on to the preprocessing pipeline that allows to increase signal quality for subsequent analysis. Future projects will be able to build on this initial validation and extend PARAFAC's use for fNIRS analysis. On this solid methodological foundation, the empirical work confirms the increased incidence of aberrant brain circuits related to language processing in children with FLE and TLE, and further supports local network efficiency as a key determinant of the impact of early brain plasticity on cognitive capacities. In order to further understand network alterations in response to neuropathologies and their impact, studies with larger samples sizes and different age groups should further investigate the specific role of clinical factors (e.g., epilepsy type, epilepsy lateralization, seizure control, etc.) and address developmental influences. Ultimately, this will increase prognosis of clinical phenotypes in pediatric patients with FLE and TLE, and offer opportunities for early interventions to support a healthy development

Characterization of Neural Activity using Complex Network Theory. Application to the Identification of the Altered Neural Substrates in Schizophrenia

La esquizofrenia es un desorden psiquiátrico caracterizado por alteraciones en el pensamiento y en la capacidad de respuesta emocional. Comprende una gran variedad de síntomas, sin embargo, no está claro que todos compartan un sustrato neurológico común. Por ello, el objetivo de esta Tesis Doctoral es desarrollar un marco de referencia desde la perspectiva de la Teoría de Redes Complejas para investigar las interacciones neurales alteradas de la esquizofrenia haciendo uso de la señal electroencefalográfica. Así, dos bases de datos independientes de registros electroencefalográficos fueron registras durante una tarea cognitiva. Nuestros hallazgos son consistentes con estudios previos al tiempo que muestran una hiperactivación del intervalo de estímulo previa a una reorganización neural disminuida durante la cognición, principalmente asociado a caminos neurales secundarios. Los hallazgos de esta Tesis ponen de manifiesto la gran heterogeneidad de la esquizofrenia, posiblemente asociada a la existencia de subgrupos dentro de la misma.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Motor learning in developmental coordination disorder: behavioral and neuroimaging study

Developmental coordination disorder (DCD) is characterized by motor learning deficits that are poorly understood within whole-body activities context. Here we present results of one of the largest non-randomized interventional trials combining brain imaging and motion capture techniques to examine motor skill acquisition and its underpinning mechanisms in adolescents with and without DCD. A total of 86 adolescents with low fitness levels (including 48 with DCD) were trained on a novel stepping task for a duration of 7 weeks. Motor performance during the stepping task was assessed under single and dual-task conditions. Concurrent cortical activation in the prefrontal cortex (PFC) was measured using functional near-infrared spectroscopy (fNIRS). Additionally, structural and functional magnetic resonance imaging (MRI) was conducted during a similar stepping task at the beginning of the trial. The results indicate that adolescents with DCD performed similarly to their peers with lower levels of fitness in the novel stepping task and demonstrated the ability to learn and improve motor performance. Both groups showed significant improvements in both tasks and under single- and dual-task conditions at post-intervention and follow-up compared to baseline. While both groups initially made more errors in the Stroop task under dual-task conditions, at follow-up, a significant difference between single- and dual-task conditions was observed only in the DCD group. Notably, differences in prefrontal activation patterns between the groups emerged at different time points and task conditions. Adolescents with DCD exhibited distinct prefrontal activation responses during the learning and performance of a motor task, particularly when complexity was increased by concurrent cognitive tasks. Furthermore, a relationship was observed between MRI brain structure and function measures and initial performance in the novel stepping task. Overall, these findings suggest that strategies that address task and environmental complexities, while simultaneously enhancing brain activity through a range of tasks, offer opportunities to increase the participation of adolescents with low fitness in physical activity and sports

New Horizons in Time-Domain Diffuse Optical Spectroscopy and Imaging

Jöbsis was the first to describe the in vivo application of near-infrared spectroscopy (NIRS), also called diffuse optical spectroscopy (DOS). NIRS was originally designed for the clinical monitoring of tissue oxygenation, and today it has also become a useful tool for neuroimaging studies (functional near-infrared spectroscopy, fNIRS). However, difficulties in the selective and quantitative measurements of tissue hemoglobin (Hb), which have been central in the NIRS field for over 40 years, remain to be solved. To overcome these problems, time-domain (TD) and frequency-domain (FD) measurements have been tried. Presently, a wide range of NIRS instruments are available, including commonly available commercial instruments for continuous wave (CW) measurements, based on the modified Beer–Lambert law (steady-state domain measurements). Among these measurements, the TD measurement is the most promising approach, although compared with CW and FD measurements, TD measurements are less common, due to the need for large and expensive instruments with poor temporal resolution and limited dynamic range. However, thanks to technological developments, TD measurements are increasingly being used in research, and also in various clinical settings. This Special Issue highlights issues at the cutting edge of TD DOS and diffuse optical tomography (DOT). It covers all aspects related to TD measurements, including advances in hardware, methodology, the theory of light propagation, and clinical applications