Brain connectivity analysis: a short survey

This short survey the reviews recent literature on brain connectivity studies. It encompasses all forms of static and dynamic connectivity whether anatomical, functional, or effective. The last decade has seen an ever increasing number of studies devoted to deduce functional or effective connectivity, mostly from functional neuroimaging experiments. Resting state conditions have become a dominant experimental paradigm, and a number of resting state networks, among them the prominent default mode network, have been identified. Graphical models represent a convenient vehicle to formalize experimental findings and to closely and quantitatively characterize the various networks identified. Underlying these abstract concepts are anatomical networks, the so-called connectome, which can be investigated by functional imaging techniques as well. Future studies have to bridge the gap between anatomical neuronal connections and related functional or effective connectivities

An overview of clustering methods with guidelines for application in mental health research

Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements. In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently introduced. How to choose algorithms to address common issues as well as methods for pre-clustering data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms, we provide information on R functions and librarie

Cortical resting state circuits: connectivity and oscillations

Ongoing spontaneous brain activity patterns raise ever-growing interest in the neuroscience community. Complex spatiotemporal patterns that emerge from a structural core and interactions of functional dynamics have been found to be far from arbitrary in empirical studies. They are thought to compose the network structure underlying human cognitive architecture. In this thesis, we use a biophysically realistic computer model to study key factors in producing complex spatiotemporal activation patterns. For the first time, we present a model of decreased physiological signal complexity in aging and demonstrate that delays shape functional connectivity in an oscillatory spiking-neuron network model for MEG resting-state data. Our results show that the inclusion of realistic delays maximizes model performance. Furthermore, we propose embracing a datadriven, comparative stance on decomposing the system into subnetworks.Últimamente, el interés de la comunidad científica sobre los patrones de la continua actividad espontanea del cerebro ha ido en aumento. Complejos patrones espacio-temporales emergen a partir de interacciones de un núcleo estructural con dinámicas funcionales. Se ha encontrado que estos patrones no son aleatorios y que componen la red estructural en la que la arquitectura cognitiva humana se basa. En esta tesis usamos un modelo computacional detallado para estudiar los factores clave en producir los patrones emergentes. Por primera vez, presentamos un modelo simplificado de la actividad cerebral en envejecimiento. También demostramos que la inclusión del desfase de transmisión en un modelo para grabaciones magnetoencefalográficas del estado en reposo maximiza el rendimiento del modelo. Para ello, aplicamos un modelo con una red de neuronas pulsantes (’spiking-neurons’) y con dinámicas oscilatorias. Además, proponemos adoptar una posición comparativa basada en los datos para descomponer el sistema en subredes

Machine Learning Applications for Drug Repurposing

The cost of bringing a drug to market is astounding and the failure rate is intimidating. Drug discovery has been of limited success under the conventional reductionist model of one-drug-one-gene-one-disease paradigm, where a single disease-associated gene is identified and a molecular binder to the specific target is subsequently designed. Under the simplistic paradigm of drug discovery, a drug molecule is assumed to interact only with the intended on-target. However, small molecular drugs often interact with multiple targets, and those off-target interactions are not considered under the conventional paradigm. As a result, drug-induced side effects and adverse reactions are often neglected until a very late stage of the drug discovery, where the discovery of drug-induced side effects and potential drug resistance can decrease the value of the drug and even completely invalidate the use of the drug. Thus, a new paradigm in drug discovery is needed. Structural systems pharmacology is a new paradigm in drug discovery that the drug activities are studied by data-driven large-scale models with considerations of the structures and drugs. Structural systems pharmacology will model, on a genome scale, the energetic and dynamic modifications of protein targets by drug molecules as well as the subsequent collective effects of drug-target interactions on the phenotypic drug responses. To date, however, few experimental and computational methods can determine genome-wide protein-ligand interaction networks and the clinical outcomes mediated by them. As a result, the majority of proteins have not been charted for their small molecular ligands; we have a limited understanding of drug actions. To address the challenge, this dissertation seeks to develop and experimentally validate innovative computational methods to infer genome-wide protein-ligand interactions and multi-scale drug-phenotype associations, including drug-induced side effects. The hypothesis is that the integration of data-driven bioinformatics tools with structure-and-mechanism-based molecular modeling methods will lead to an optimal tool for accurately predicting drug actions and drug associated phenotypic responses, such as side effects. This dissertation starts by reviewing the current status of computational drug discovery for complex diseases in Chapter 1. In Chapter 2, we present REMAP, a one-class collaborative filtering method to predict off-target interactions from protein-ligand interaction network. In our later work, REMAP was integrated with structural genomics and statistical machine learning methods to design a dual-indication polypharmacological anticancer therapy. In Chapter 3, we extend REMAP, the core method in Chapter 2, into a multi-ranked collaborative filtering algorithm, WINTF, and present relevant mathematical justifications. Chapter 4 is an application of WINTF to repurpose an FDA-approved drug diazoxide as a potential treatment for triple negative breast cancer, a deadly subtype of breast cancer. In Chapter 5, we present a multilayer extension of REMAP, applied to predict drug-induced side effects and the associated biological pathways. In Chapter 6, we close this dissertation by presenting a deep learning application to learn biochemical features from protein sequence representation using a natural language processing method

Pleiotropic effects of candidate genes on autism spectrum disorder and comorbidities: genetics, funcional studies and animal models

[eng] Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social communication and interaction, as well as repetitive and restricted patterns of behaviour. Although growing evidence supports a main contribution of genetic factors to its neurobiology and hundreds of candidate genes have been identified in recent years, the genetic architecture of the disorder is still not fully understood. Moreover, ASD frequently co-occurs with other developmental and psychiatric disorders, and shared genetic mechanisms are hypothesized to underlie these comorbidities. In this doctoral thesis, we aimed to study the contribution of several candidate genes to ASD and comorbidities. We have focused on the 14-3-3 gene family, RBFOX1 and the BEX/TCEAL gene family, performed genetic and functional studies and further characterized the neurobiological effects of their deficiency using animal models. First, our results suggest a role for the 14-3-3 genes in ASD and schizophrenia (SCZ). Ultra-rare variants in the 14-3-3 genes are enriched in ASD and common and rare variants in the YWHAE and YWHAZ genes, respectively, are associated with SCZ. We have also reported alterations in the expression of these genes in postmortem brains of ASD or SCZ patients. Furthermore, we have demonstrated a loss-of-function effect of a damaging variant in the YWHAZ gene present in two siblings with ASD and attention deficit/hyperactivity disorder (ADHD). In addition, we have characterized ywhaz expression in zebrafish across development and in adulthood and demonstrated that ywhaz depletion causes alterations in behaviour, in neuronal activity and connectivity and in monoamine signalling. The behavioural changes included freezing and were rescued with drug treatments that target monoamine neurotransmission. Second, we have demonstrated a relevant contribution of common variants in RBFOX1 to psychiatric disorders and traits. Also, we have shown that a high number of copy number variants (CNVs) spanning RBFOX1 are reported in patients with psychiatric conditions, the vast majority in patients with ASD or SCZ, and patients with these disorders also show a decreased expression of RBFOX1 in cortex. Finally, we have used knockout animal models to understand its role in psychiatric disorders, and demonstrated that both mice and zebrafish RBFOX1- deficient models present behavioural alterations that can be related to neurodevelopmental disorders such as ASD, ADHD and SCZ. Third, we found that all BEX/TCEAL genes are downregulated in postmortem brain regions of ASD and SCZ patients and that rare CNVs spanning several BEX/TCEAL genes have been reported in patients with severe neurodevelopmental problems. Furthermore, Bex3-deficient mice show anatomical and molecular alterations in brain, an excitatory/inhibitory imbalance and behavioural alterations that can be assimilated to ASD- and SCZ-like symptoms.[spa] El trastorno del espectro autista (TEA) es un trastorno del neurodesarrollo caracterizado por problemas en la comunicación e interacción social, así como patrones restrictivos y repetitivos de comportamiento. El peso de la genética en su etiología es cada vez más evidente, aunque la compleja arquitectura genética del trastorno sigue siendo una incógnita. Además, el diagnóstico de otros trastornos comórbidos es frecuente en pacientes con TEA, por lo que se hipotetiza una base genética común. El objetivo de esta tesis doctoral es elucidar la contribución de varios genes candidatos, concretamente la familia de genes 14-3-3, el gen RBFOX1 y la familia BEX/TCEAL, al TEA y otros trastornos comórbidos, realizando estudios genéticos y funcionales, así como caracterizando los efectos neurobiológicos de su deficiencia en modelos animales. En primer lugar, nuestros resultados sugieren que variantes ultra-raras en los genes 14-3-3 contribuyen al TEA y que variantes comunes y raras en los genes YWHAE y YWHAZ, respectivamente, están asociadas a esquizofrenia (SCZ). Además, la expresión de los genes 14- 3-3 está alterada en pacientes con TEA o SCZ. Hemos demostrado que una variante patogénica en el gen YWHAZ presente en dos hermanos con TEA y trastorno de déficit de atención e hiperactividad (TDAH) provoca una pérdida de función de la proteína. Asimismo, hemos demostrado que la deleción de ywhaz produce alteraciones en la actividad y conectividad neuronal, la señalización monoaminérgica y el comportamiento, pudiéndose este último recuperar mediante fármacos. En segundo lugar, hemos demostrado que variantes comunes en RBFOX1 están asociadas a diferentes trastornos psiquiátricos y que un número elevado de variantes del número de copias (CNV) afectan a RBFOX1 en pacientes con trastornos psiquiátricos, siendo especialmente frecuentes en pacientes con TEA o SCZ que, además, presentan una disminución en la expresión de RBFOX1 en corteza cerebral. Asimismo, hemos usado modelos animales genoanulados para estudiar la implicación de RBFOX1 en trastornos psiquiátricos, demostrando que tanto el modelo murino como los de pez cebra presentan alteraciones de comportamiento relacionadas con trastornos del neurodesarrollo, como ASD, TDAH y SCZ. Por último, la expresión de los genes BEX/TCEAL está disminuida en regiones cerebrales de pacientes con TEA o SCZ y, además, se han descrito CNVs que abarcan varios genes BEX/TCEAL en pacientes con trastornos severos del neurodesarrollo. Los ratones genoanulados para Bex3 muestran alteraciones anatómicas y moleculares en cerebro, un desequilibro excitación/inhibición y alteraciones de comportamiento asimilables a síntomas de TEA y SCZ

A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

Autism spectrum disorders (ASDs) denote a significant growing public health concern. Currently, one in 68 children has been diagnosed with ASDs in the United States, and most children are diagnosed after the age of four, despite the fact that ASDs can be identified as early as age two. The ultimate goal of this thesis is to develop a computer-aided diagnosis (CAD) system for the accurate and early diagnosis of ASDs using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the brain tissues are segmented based on three image descriptors: a visual appearance model that has the ability to model a large dimensional feature space, a shape model that is adapted during the segmentation process using first- and second-order visual appearance features, and a spatially invariant second-order homogeneity descriptor. Secondly, discriminatory features are extracted from the segmented brains. Cortex shape variability is assessed using shape construction methods, and white matter integrity is further examined through connectivity analysis. Finally, the diagnostic capabilities of these extracted features are investigated. The accuracy of the presented CAD system has been tested on 25 infants with a high risk of developing ASDs. The preliminary diagnostic results are promising in identifying autistic from control patients

29th Annual Computational Neuroscience Meeting: CNS*2020

Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202