scholarly journals Genes involved in cholesterol cascades are linked to brain connectivity in one third of autistic patients

2020 ◽  
Author(s):  
Javier Rasero ◽  
Antonio Jimenez-Marin ◽  
Ibai Diez ◽  
Mazahir T. Hasan ◽  
Jesus M. Cortes
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Brain Connectivity ◽  
Autism Spectrum ◽  
The Other ◽  
Brain Atlas ◽  
Consensus Clustering ◽  
Major Drawback ◽  
Large Populations

AbstractThe large heterogeneity in the symptomatology and severity of autism spectrum disorder (ASD) is a major drawback for the design of effective therapies. Beyond behavioral phenotypes, subtype stratification strategies that can be applied to large populations are needed, these combining different neurobiological characteristics and based on the large-scale organization of the human brain, as well as neurogenetic fingerprints. Here, we make use of ABIDE, the largest publicly available database of functional neuroimaging in ASD, to which we have applied rigorous data harmonization between the different scanning institutions in order to employ analyses based on consensus clustering and to evaluate the patterns of brain connectivity. As a result, we identified three subtypes of ASD, the first of which was characterized by a mixture of hyper- and hypo-connectivity, stronger network segregation and weaker integration, and it represented approximately 13% of all patients. The second subtype was associated with 31% of the patients, and it was characterized by hyperconnectivity but no topological differences with respect to the group of typically developing controls. The third was the most numerous subtype, assigned to 52% of all patients, and it was characterized by hypoconnectivity, decreased network segregation and increased integration. We also defined a neurobiological signature for each of these subtypes, detailing the connectivity and structures most specific to each subtype. Strikingly, at the behavioral level, none of the neuropsychological scores used in the diagnosis of ASD is capable of differentiating any of the subtypes from the other two. Finally, we use the Allen Human Brain Atlas of gene transcription brain maps to show that subtype 2 has an extraordinary enrichment in biological processes related to the synthesis, regulation and transport of cholesterol and other lipoproteins, one of the mechanisms previously attributed to ASD. We also show that this lipid-susceptible ASD subtype could be represented by the dysfunctionality of the network, unlike the other two subtypes that have more structural alterations in the connectome. Thus, our study provide compelling support for prospects of cholesterol-related therapies in this subset of autistic individuals.

scholarly journals Resources available for autism research in the big data era: a systematic review

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e2880 ◽  
Author(s):  
Reem Al-jawahiri ◽  
Elizabeth Milne
Keyword(s):  
Human Brain ◽  
Brain Connectivity ◽  
Open Data ◽  
Autism Spectrum ◽  
Data Types ◽  
Participant Recruitment ◽  
Data Resource ◽  
Autism Research ◽  
Great Heterogeneity ◽  
Google Search

Recently, there has been a move encouraged by many stakeholders towards generating big, open data in many areas of research. One area where big, open data is particularly valuable is in research relating to complex heterogeneous disorders such as Autism Spectrum Disorder (ASD). The inconsistencies of findings and the great heterogeneity of ASD necessitate the use of big and open data to tackle important challenges such as understanding and defining the heterogeneity and potential subtypes of ASD. To this end, a number of initiatives have been established that aim to develop big and/or open data resources for autism research. In order to provide a useful data reference for autism researchers, a systematic search for ASD data resources was conducted using the Scopus database, the Google search engine, and the pages on ‘recommended repositories’ by key journals, and the findings were translated into a comprehensive list focused on ASD data. The aim of this review is to systematically search for all available ASD data resources providing the following data types: phenotypic, neuroimaging, human brain connectivity matrices, human brain statistical maps, biospecimens, and ASD participant recruitment. A total of 33 resources were found containing different types of data from varying numbers of participants. Description of the data available from each data resource, and links to each resource is provided. Moreover, key implications are addressed and underrepresented areas of data are identified.

scholarly journals Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks

2014 ◽  
Vol 369 (1653) ◽  
pp. 20130531 ◽  
Author(s):  
Petra E. Vértes ◽  
Aaron Alexander-Bloch ◽  
Edward T. Bullmore
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Large Scale ◽  
Hebbian Learning ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Network ◽  
Generative Models ◽  
Adaptive Behaviour ◽  
Topological Term

Rich clubs arise when nodes that are ‘rich’ in connections also form an elite, densely connected ‘club’. In brain networks, rich clubs incur high physical connection costs but also appear to be especially valuable to brain function. However, little is known about the selection pressures that drive their formation. Here, we take two complementary approaches to this question: firstly we show, using generative modelling, that the emergence of rich clubs in large-scale human brain networks can be driven by an economic trade-off between connection costs and a second, competing topological term. Secondly we show, using simulated neural networks, that Hebbian learning rules also drive the emergence of rich clubs at the microscopic level, and that the prominence of these features increases with learning time. These results suggest that Hebbian learning may provide a neuronal mechanism for the selection of complex features such as rich clubs. The neural networks that we investigate are explicitly Hebbian, and we argue that the topological term in our model of large-scale brain connectivity may represent an analogous connection rule. This putative link between learning and rich clubs is also consistent with predictions that integrative aspects of brain network organization are especially important for adaptive behaviour.

scholarly journals The metastable human brain associated with autistic-like traits

2019 ◽  
Author(s):  
Takumi Sase ◽  
Keiichi Kitajo
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Autism Spectrum ◽  
Autism Spectrum Quotient ◽  
Healthy Humans ◽  
Large Scale Network ◽  
Individual Traits ◽  
Scale Network ◽  
Phase Amplitude ◽  
Attention To Detail

AbstractRecent studies suggest that the resting brain utilizes metastability such that the large-scale network can spontaneously yield transition dynamics across a repertoire of oscillatory states. By analyzing resting-state electroencephalographic signals and the autism-spectrum quotient acquired from healthy humans, we show experimental evidence of how autistic-like traits may be associated with the metastable human brain. Observed macroscopic brain signals exhibited slow and fast oscillations forming phase-amplitude coupling (PAC) with dynamically changing modulation strengths, resulting in oscillatory states characterized by different PAC strengths. In individuals with the ability to maintain a strong focus of attention to detail and less attention switching, these transient PAC dynamics tended to stay in a state for a longer time, to visit a lower number of states, and to oscillate at a higher frequency than in individuals with a lower attention span. We further show that attractors underlying the transient PAC could be multiple tori and consistent across individuals, with evidence that the dynamic changes in PAC strength can be attributed to changes in the strength of phase-phase coupling, that is, to dynamic functional connectivity in an electrophysiological sense. Our findings suggest that the metastable human brain can organize spontaneous events dynamically and selectively in a hierarchy of macroscopic oscillations with multiple timescales, and that such dynamic organization might encode a spectrum of individual traits covering typical and atypical development.Significance StatementMetastability in the brain is thought to be a mechanism involving spontaneous transitions among oscillatory states of the large-scale network. We show experimental evidence of how autistic-like traits may be associated with the metastable human brain by analyzing resting-state electroencephalographic signals and scores for the autism-spectrum quotient acquired from healthy humans. We found that slow and fast neural oscillations can form phase-amplitude coupling with dynamically changing modulation strengths, and that these transient dynamics can depend on the ability to maintain attention to detail and to switch attention. These results suggest that the metastable human brain can encode a spectrum of individual traits by realizing the dynamic organization of spontaneous events in a hierarchy of macroscopic oscillations with multiple timescales.

Nerve and Brain

Mind Shift ◽  
2021 ◽  
pp. 63-79
Author(s):  
John Parrington
Keyword(s):  
Human Brain ◽  
Glial Cells ◽  
Large Scale ◽  
Brain Size ◽  
Adult Brain ◽  
The Other ◽  
Basic Unit ◽  
The Brain ◽  
Size And Structure

This chapter evaluates the basic unit of the human brain: the nerve cell, or neuron. These cells are also the main units of the peripheral nervous system, which sends messages from the brain to the other tissues and organs that make up our bodies. Neurons are the most well-known cells in the brain but they are not the only type of cell in this organ. The other main types are the glial cells, also known as neuroglia. Recent studies of the role of glial cells in the brain are revealing potentially important differences between humans and other species in the functions of these cells. The chapter then turns to the large-scale structure of the brain. The most dramatic changes in brain size and structure occurred in the final phase of human evolutionary change. Indeed, Neanderthals had brains similar in size to those of modern humans. An important feature of the human brain is that a larger fraction of its growth occurs outside the womb. Although humans reach adult brain size in childhood, brain development continues for decades afterwards.

scholarly journals The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core

2016 ◽  
Author(s):  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Viktor K. Jirsa ◽  
Petra Ritter
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Spectral Characteristics ◽  
Network Models ◽  
Brain Network ◽  
Network Modelling ◽  
Novel Approach ◽  
Underlying Mechanisms

AbstractIn the human brain, spontaneous activity during resting state consists of rapid transitions between functional network states over time but the underlying mechanisms are not understood. We use connectome based computational brain network modeling to reveal fundamental principles of how the human brain generates large-scale activity observable by noninvasive neuroimaging. By including individual structural and functional neuroimaging data into brain network models we construct personalized brain models. With this novel approach, we reveal that the human brain during resting state operates at maximum metastability, i.e. in a state of maximum network switching. In addition, we investigate cortical heterogeneity across areas. Optimization of the spectral characteristics of each local brain region revealed the dynamical cortical core of the human brain, which is driving the activity of the rest of the whole brain. Personalized brain network modelling goes beyond correlational neuroimaging analysis and reveals non-trivial network mechanisms underlying non-invasive observations. Our novel findings significantly pertain to the important role of computational connectomics in understanding principles of brain function.

scholarly journals Central neurogenetic signatures of the visuomotor integration system

2020 ◽  
Vol 117 (12) ◽  
pp. 6836-6843 ◽  
Author(s):  
Elisenda Bueichekú ◽  
Maite Aznárez-Sanado ◽  
Ibai Diez ◽  
Federico d’Oleire Uquillas ◽  
Laura Ortiz-Terán ◽  
...  
Keyword(s):  
Human Brain ◽  
Neural Development ◽  
Fragile X ◽  
Autism Spectrum ◽  
Brain Atlas ◽  
Functional Impairments ◽  
Visuomotor Integration ◽  
Human Cortex ◽  
Genetic Expression ◽  
Brain Transcriptome

Visuomotor impairments characterize numerous neurological disorders and neurogenetic syndromes, such as autism spectrum disorder (ASD) and Dravet, Fragile X, Prader–Willi, Turner, and Williams syndromes. Despite recent advances in systems neuroscience, the biological basis underlying visuomotor functional impairments associated with these clinical conditions is poorly understood. In this study, we used neuroimaging connectomic approaches to map the visuomotor integration (VMI) system in the human brain and investigated the topology approximation of the VMI network to the Allen Human Brain Atlas, a whole-brain transcriptome-wide atlas of cortical genetic expression. We found the genetic expression of four genes—TBR1, SCN1A, MAGEL2, and CACNB4—to be prominently associated with visuomotor integrators in the human cortex. TBR1 gene transcripts, an ASD gene whose expression is related to neural development of the cortex and the hippocampus, showed a central spatial allocation within the VMI system. Our findings delineate gene expression traits underlying the VMI system in the human cortex, where specific genes, such as TBR1, are likely to play a central role in its neuronal organization, as well as on specific phenotypes of neurogenetic syndromes.

scholarly journals More similarities than differences between ADHD and ASD in functional brain connectivity

2020 ◽  
Author(s):  
Joe Bathelt ◽  
Matthan Caan ◽  
Hilde Geurts
Keyword(s):  
Machine Learning ◽  
Functional Neuroimaging ◽  
Learning Algorithm ◽  
Brain Connectivity ◽  
Total Sample ◽  
Autism Spectrum ◽  
Future Research ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Functional Brain

Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly comorbid neurodevelopmental conditions. There is an ongoing debate regarding the nature of their overlap. Behavioral symptoms and cognitive profiles indicate differences between the conditions, but genetic studies and neuroimaging investigations suggest at least some shared etiology. The current study investigated if functional connectivity can be used to distinguish ADHD and ASD using a machine-learning approach. Towards this aim, we trained a machine learning algorithm to distinguish ASD and ADHD cases from each other and from comparison cases in a total sample of 805 cases, comprising of 243 ASD cases, 164 ADHD cases, and 398 comparison cases between 7 and 21 years of age. We compared the performance of the best performing machine learning algorithm (l2-regularised support vector classification) when classifying unseen cases of ADHD, ASD, and CMP. The results indicated lower classification performance when distinguishing ADHD from ASD compared to classifying diagnostic groups vs a typical comparison group. The model trained to distinguish ASD and comparison cases performed equally well when tasked with classifying ADHD vs CMP. A Bayesian analysis gave strong evidence for similarity ADHD and ASD. The ADHD and ASD group showed overlap in connections of the right ventral attention network, the salience network, and the default mode network. In sum, these results suggest a substantial overlap in functional brain connectivity between ADHD and ASD. We discuss the implications of these findings for the quest to identify functional neuroimaging biomarkers and provide recommendation for future research.

scholarly journals Cross-species neuroscience: closing the explanatory gap

2020 ◽  
Vol 376 (1815) ◽  
pp. 20190633
Author(s):  
Helen C. Barron ◽  
Rogier B. Mars ◽  
David Dupret ◽  
Jason P. Lerch ◽  
Cassandra Sampaio-Baptista
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Explanatory Gap ◽  
Temporal Precision ◽  
Common Space ◽  
Non Invasive ◽  
Spatio Temporal ◽  
The Explanatory Gap

Neuroscience has seen substantial development in non-invasive methods available for investigating the living human brain. However, these tools are limited to coarse macroscopic measures of neural activity that aggregate the diverse responses of thousands of cells. To access neural activity at the cellular and circuit level, researchers instead rely on invasive recordings in animals. Recent advances in invasive methods now permit large-scale recording and circuit-level manipulations with exquisite spatio-temporal precision. Yet, there has been limited progress in relating these microcircuit measures to complex cognition and behaviour observed in humans. Contemporary neuroscience thus faces an explanatory gap between macroscopic descriptions of the human brain and microscopic descriptions in animal models. To close the explanatory gap, we propose adopting a cross-species approach. Despite dramatic differences in the size of mammalian brains, this approach is broadly justified by preserved homology. Here, we outline a three-armed approach for effective cross-species investigation that highlights the need to translate different measures of neural activity into a common space. We discuss how a cross-species approach has the potential to transform basic neuroscience while also benefiting neuropsychiatric drug development where clinical translation has, to date, seen minimal success. This article is part of the theme issue ‘Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity’.

scholarly journals Human brain atlasing: past, present and future

2017 ◽  
Vol 30 (6) ◽  
pp. 504-519 ◽  
Author(s):  
Wieslaw L Nowinski
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Brain Atlas ◽  
Future Directions ◽  
Human Connectome Project ◽  
Health And Disease ◽  
The Brain ◽  
Brain Atlases

We have recently witnessed an explosion of large-scale initiatives and projects addressing mapping, modeling, simulation and atlasing of the human brain, including the BRAIN Initiative, the Human Brain Project, the Human Connectome Project (HCP), the Big Brain, the Blue Brain Project, the Allen Brain Atlas, the Brainnetome, among others. Besides these large and international initiatives, there are numerous mid-size and small brain atlas-related projects. My contribution to these global efforts has been to create adult human brain atlases in health and disease, and to develop atlas-based applications. For over two decades with my R&D lab I developed 35 brain atlases, licensed to 67 companies and made available in about 100 countries. This paper has two objectives. First, it provides an overview of the state of the art in brain atlasing. Second, as it is already 20 years from the release of our first brain atlas, I summarise my past and present efforts, share my experience in atlas creation, validation and commercialisation, compare with the state of the art, and propose future directions.

The Use of Medical Record Linkage for Population and Genetic Studies

1969 ◽  
Vol 08 (01) ◽  
pp. 07-11 ◽  
Author(s):  
H. B. Newcombe
Keyword(s):  
Record Linkage ◽  
Large Scale ◽  
Medical Record Linkage ◽  
Canadian Province ◽  
Genetic Studies ◽  
Parental Characteristics ◽  
Family Histories ◽  
The Family ◽  
Large Populations ◽  
Machine Readable

Methods are described for deriving personal and family histories of birth, marriage, procreation, ill health and death, for large populations, from existing civil registrations of vital events and the routine records of ill health. Computers have been used to group together and »link« the separately derived records pertaining to successive events in the lives of the same individuals and families, rapidly and on a large scale. Most of the records employed are already available as machine readable punchcards and magnetic tapes, for statistical and administrative purposes, and only minor modifications have been made to the manner in which these are produced.As applied to the population of the Canadian province of British Columbia (currently about 2 million people) these methods have already yielded substantial information on the risks of disease: a) in the population, b) in relation to various parental characteristics, and c) as correlated with previous occurrences in the family histories.

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