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

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

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

Big Data Analytics and Information Science for Business and Biomedical Applications II

The analysis of big data in biomedical, business and financial research has drawn much attention from researchers worldwide. This collection of articles aims to provide a platform for an in-depth discussion of novel statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions to these areas are showcased

Quantifying neurodegeneration from medical images with machine learning and graph theory

Neurodegeneration (or brain atrophy) is part of the pathological cascade of Alzheimer’s disease (AD) and is strongly associated with cognitive decline. In clinics, atrophy is measured through visual assessments of specific brain regions on medical images according to established rating scales. In this thesis, we developed a model based on recurrent convolutional neural networks (AVRA: Automatic visual ratings of atrophy) that could predict scores from magnetic resonance images (MRI) according to commonly used clinical rating scales, namely: Scheltens’ scale for medial temporal atrophy (MTA), Pasquier’s frontal subscale of global cortical atrophy (GCA-F), and Koedam’s posterior atrophy (PA) scale. AVRA was trained on over 2000 images rated by a single neuroradiologist and demonstrated similar inter-rater agreement levels on all three scales to what has reported between two "human raters" in previous studies. We further applied different versions of AVRA, trained systematically on data with different levels of heterogeneity, in external data from multiple European memory clinics. We observed a general performance drop in the out-of-distribution (OOD) data compared to test sets sampled from the same cohort as the training data. By training AVRA on data from multiple sources, we show that the performance in external cohorts generally increased. AVRA demonstrated a notably low agreement in one memory clinic, despite good quality images, which suggests that it may be challenging to assess how well a machine learning model generalizes to OOD data. For additional validation of our model, we compared AVRA’s MTA ratings to two external radiologists’ and the volumes of the hippocampi and inferior lateral ventricles. The images came from a longitudinal cohort that comprised individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) followed up over six years. AVRA showed substantial agreement to one of the radiologists, and lower rating agreement to the other. The two radiologists also showed low agreement between each other. All sets of ratings were strongly associated with the subcortical volumes, suggesting that all three raters were reliable. We further observed that individuals with SCD and (probably) underlying AD pathology had a faster MTA progression than MCI patients with non-AD biomarker profile. Finally, we evaluated a method to quantify patterns of atrophy through the use of graph theory. We compared structural gray matter networks between groups of healthy controls and AD patients, con- structed from different subsamples and with different network construc- tion methods. Our experiments suggested that structural gray matter networks may not be very stable. Our networks required more than 150 subjects/group to show convergence in the included network properties, which is a greater sample size than used in the majority of the studies applying these methods. The different graph construction methods did not yield consistent differences between the control and AD networks, which may explain why findings have been inconsistent across previous studies. To conclude, we demonstrated that a machine learning model can successfully learn to mimic a radiologist’s assessment of atrophy without intra-rater variability. The challenge going forward is to assert model consistency across clinics, scanners and image quality—nuisances that humans are better at ignoring than deep learning models