Elsevier

NeuroImage

Volume 80, 15 October 2013, Pages 263-272
NeuroImage

Brainnetome: A new -ome to understand the brain and its disorders

https://doi.org/10.1016/j.neuroimage.2013.04.002Get rights and content

Highlights

  • The brainnetome is a new “-ome” in which brain network is basic research unit.

  • We review brainnetome techniques, brainnetome atlas, and brain network methods.

  • We illustrate the essential role of the brainnetome in neuropsychiatric disorders.

  • We demonstrate how the brainnetome meets genome.

  • We summarize the challenges in the brainnetome and strategic priorities in China.

Abstract

The human brain can be studied as a hierarchy of complex networks on different temporal and spatial scales. On each scale, from gene, protein, synapse, neuron and microcircuit, to area, pathway and the whole brain, many advances have been made with the development of related techniques. Brain network studies on different temporal and spatial scales are booming. However, such studies have focused on single levels, and can only reflect limited aspects of how the brain is formed and how it works. Therefore, it is increasingly urgent to integrate a variety of techniques, methods and models, and to merge fragmented findings into a uniform research framework or platform. To this end, we have proposed the concept of the brainnetome and several related programs/projects have been launched in China. In this paper, we offer a brief review on the methodologies of the brainnetome, which include techniques on different scales, the brainnetome atlas, and methods of brain network analysis. We then take Alzheimer's disease and schizophrenia as examples to show how the brainnetome can be studied in neurological and psychiatric disorders. We also review the studies of how risk genes for brain diseases affect the brain networks. Finally, we summarize the challenges for the brainnetome, and what actions and measures have been taken to address these challenges in China. It is envisioned that the brainnetome will open new avenues and some long-standing issues may be solved by combining the brainnetome with other “omes”.

Introduction

The human brain is the most complex network system in the world. It comprises about one hundred billion neurons, with thousands of trillions of connections between them. Its complexity is not only reflected in the numbers of neurons and connections, but also in how the brain is wired on different scales and how such patterns of their connections produce cognitive functions, thoughts, feelings, and behaviors. There have long been efforts to make a connection map of the brain, recently called the “Human Connectome” (Sporns et al., 2005). It is of central significance for understanding how the brain works at a detailed level and what happens when something goes wrong (Insel, 2010). A similar opinion can be traced back to an earlier study (Crick and Jones, 1993). Now, both the academic community and government are aware of its importance. This has been demonstrated by a number of programs and projects launched in different countries. The Human Connectome Project was launched by the National Institutes of Health in the USA. A similar project, CONNECT, was launched by the European Community.

Actually, the complex links within the human brain are presented in the physical (static) architecture as well as dynamic activity. Mathematically, a “network” can be used to model a system that contains multiple components interacting with one other. The neuroscience community refers “brain network” to the brain system that consists of relational units at different tempo-spatial scales. We strongly suggest that the unique features of networks (in the structural and dynamic view) are very important for brain science, so we proposed the brainnetome (Brain-net-ome) as a new “ome” in which the brain network is the basic research unit to investigate the hierarchy in human brain from genetics and neuronal circuits to behaviors. Since the two components of the brainnetome, nodes and their connections, can be defined at different scales with different techniques, the brainnetome is as complex as any other –ome, such as the genome and proteome. It includes at least the following five research themes: (1) Identification of Brain Networks. One goal of the brainnetome is to identify brain networks with multimodal neuroimaging techniques, from the finest scale (such as ultramicrotomy, and staining techniques), to the most macroscopic (such as functional MRI, diffusion MRI and electroencephalography); and to explore the relationships among them. In particular, a new human brain atlas beyond Brodmann's will be established by combining connectivity with cytoarchitecture and other information on the microscale. (2) Dynamics and Characteristics of Brain Networks. The brainnetome will investigate the dynamics and characteristics of brain networks during developmental, aging and evolutionary processes and how they are affected by such factors as learning, training, language, culture, diseases, and stimuli. (3) Network Manifestations of Functions and Malfunctions of the Brain. One unique characteristic of the brainnetome is to explore the core brain regions and their connectivity patterns for each cognitive function and to show how they are affected in neurological and psychiatric diseases, and by drugs and other stimuli. A specific goal is to explore how the symptoms of neurological and psychiatric diseases are due to altered brain networks. (4) Genetic Basis of Brain Networks. The brainnetome will investigate the effects of genetic variations on the brain networks associated with behaviors, cognitive functions or cognitive disorders. It will also explore the influence of genetic factors on the developmental processes of specific brain networks through twin and pedigree studies. Moreover, it will investigate the biological mechanisms by which genes modulate the brain networks with gene-modified animal models. (5) Simulating and Modeling for the brainnetome. An essential goal of the brainnetome is to simulate and model brain networks with informatics and simulation technologies to understand the basic organizing principles of the brain. To this end, it is necessary to develop theories and methodologies and to integrate the existing and new supercomputing hardware with software and visualization tools. The concept of the brainnetome has received impetus from a number of programs and projects in China. In 2010, a project with the same name (that is, the Brainnetome Project) was launched in China. Recently, the European Union has just launched the Human Brain Project and the USA is considering launching the “Brain Activity Mapping” Project. These two projects include the studies of neuronal circuits and brain networks. Table 1 lists some worldwide projects related to the brainnetome.

In this paper, we present the methodologies and advances of the brainnetome and focus on the studies in our laboratory. We first give a brief review of the methodologies used in the brainnetome, which include techniques on different scales, the brainnetome atlas, and the methods of brain network analysis. Then we take Alzheimer's disease and schizophrenia as examples to show how the brainnetome can be studied in neurology and psychiatry, called the clinical brainnetome. After that, we review studies of how risk genes for brain diseases affect brain networks. Finally, we give a perspective on the brainnetome.

Section snippets

Methodologies for the brainnetome

The human brain is a massively complex system with a hierarchy of different but tightly integrated levels of organisms: from gene, protein, synapse, neurons, and neuronal circuits, to brain areas, pathways and the whole brain. The brain network, in general, should be investigated at each of these levels. Here, we roughly define the macroscale as the level of brain areas and pathway, and the microscale as the level of genes, neurons and neural circuits. The brainnetome is proposed to

Clinical brainnetome

An increasing number of studies have revealed disturbances of the organized architecture of brain structure/function in various brain disorders (Bullmore, 2012). The malfunctioning of connections and brain networks may underlie many brain disorders. These network studies, especially those using modern imaging techniques in vivo, have revealed that brain disorders can, for the first time, be studied as abnormalities in the connections between brain areas, or as problems in the coordination of

Genetic basis for the brainnetome

Convergent evidence from multimodal imaging studies has demonstrated that brain networks are heritable both structurally and functionally (Meyer-Lindenberg, 2009). Specifically, a twin study based on diffusion MRI found that genetic factors may explain 40% to 80% of the observed variability of integrity in the white matter tracts (Chiang et al., 2011). A pedigree study reported that the heritability of functional connectivity within the default mode network can reach 42% (Glahn et al., 2010).

Perspectives

The human brain can be studied as hierarchical complex networks on different temporal and spatial scales. On the microscale level, recent evidence shows that the human brain functions by the interactions between neurons on different temporal and spatial scales. It is becoming increasingly apparent that such a network structure and dynamic interaction produce the physiological activity of the human brain and finally lead to human cognitive behavior. On the macroscale level, more and more

Acknowledgments

I thank Ming Song, Bing Liu, Yong Liu, Yuan Zhou, Bing Hou, Lingzhong Fan, Xin Zhang, Nianming Zuo, Yue Cui and Yong Fan for their help with manuscript preparation. I also thank Professors Iain Bruce and Sumner MacLean for the proof-reading of the manuscript. This work was partially supported by the National Key Basic Research and Development Program (973) (Grant No. 2011CB707800), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB02030300), and the

References (87)

  • B. Liu et al.

    Haplotypes of catechol-O-methyltransferase modulate intelligence-related brain white matter integrity

    Neuroimage

    (2010)
  • C.M. Lu et al.

    Use of fNIRS to assess resting state functional connectivity

    J. Neurosci. Methods

    (2010)
  • S. Marenco et al.

    Imaging genetics of structural brain connectivity and neural integrity markers

    Neuroimage

    (2010)
  • Y. Mei et al.

    Molecular tools and approaches for optogenetics

    Biol. Psychiatry

    (2012)
  • C.M. Michel et al.

    Towards the utilization of EEG as a brain imaging tool

    Neuroimage

    (2012)
  • S.M. Nelson et al.

    A parcellation scheme for human left lateral parietal cortex

    Neuron

    (2010)
  • S. Palva et al.

    Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs

    Trends Cogn. Sci.

    (2012)
  • W. Pettersson-Yeo et al.

    Dysconnectivity in schizophrenia: where are we now?

    Neurosci. Biobehav. Rev.

    (2011)
  • J. Reithler et al.

    Multimodal transcranial magnetic stimulation: using concurrent neuroimaging to reveal the neural network dynamics of noninvasive brain stimulation

    Prog. Neurobiol.

    (2011)
  • A. Roebroeck et al.

    Mapping directed influence over the brain using Granger causality and fMRI

    Neuroimage

    (2005)
  • M.E. Thomason et al.

    COMT genotype affects prefrontal white matter pathways in children and adolescents

    Neuroimage

    (2010)
  • A.J. Trachtenberg et al.

    The effects of APOE on the functional architecture of the resting brain

    Neuroimage

    (2012)
  • L. Wang et al.

    Changes in hippocampal connectivity in the early stages of Alzheimer's disease: evidence from resting state fMRI

    Neuroimage

    (2006)
  • Z. Wang et al.

    Baseline and longitudinal patterns of hippocampal connectivity in mild cognitive impairment: evidence from resting state fMRI

    J. Neurol. Sci.

    (2011)
  • J. Wang et al.

    Tractography-based parcellation of the human left inferior parietal lobule

    Neuroimage

    (2012)
  • Q. Wang et al.

    Anatomical insights into disrupted small-world networks in schizophrenia

    Neuroimage

    (2012)
  • Z. Wang et al.

    Changes in thalamus connectivity in mild cognitive impairment: evidence from resting state fMRI

    Eur. J. Radiol.

    (2012)
  • G. Winterer et al.

    Association of 5' end neuregulin-1 (NRG1) gene variation with subcortical medial frontal microstructure in humans

    Neuroimage

    (2008)
  • K. Yoshida et al.

    Influence of the serotonin transporter gene-linked polymorphic region on the antidepressant response to fluvoxamine in Japanese depressed patients

    Prog. Neuropsychopharmacol. Biol. Psychiatry

    (2002)
  • A. Zalesky et al.

    Whole-brain anatomical networks: does the choice of nodes matter?

    Neuroimage

    (2010)
  • K. Zhang et al.

    The combined effects of the 5-HTTLPR and 5-HTR1A genes modulates the relationship between negative life events and major depressive disorder in a Chinese population

    J. Affect. Disord.

    (2009)
  • Y. Zhou et al.

    Functional dysconnectivity of the dorsolateral prefrontal cortex in first-episode schizophrenia using resting-state fMRI

    Neurosci. Lett.

    (2007)
  • Y. Zhou et al.

    Functional disintegration in paranoid schizophrenia using resting-state fMRI

    Schizophr. Res.

    (2007)
  • Y. Zhou et al.

    Altered resting-state functional connectivity and anatomical connectivity of hippocampus in schizophrenia

    Schizophr. Res.

    (2008)
  • H. Akil et al.

    Medicine. The future of psychiatric research: genomes and neural circuits

    Science

    (2010)
  • G. Allen et al.

    Reduced hippocampal functional connectivity in Alzheimer disease

    Arch. Neurol.

    (2007)
  • R.L. Buckner et al.

    Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease

    J. Neurosci.

    (2009)
  • E.T. Bullmore et al.

    Brain graphs: graphical models of the human brain connectome

    Annu. Rev. Clin. Psychol.

    (2011)
  • E. Bullmore et al.

    Complex brain networks: graph theoretical analysis of structural and functional systems

    Nat. Rev. Neurosci.

    (2009)
  • A. Caspi et al.

    Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene

    Science

    (2003)
  • F. Crick et al.

    Backwardness of human neuroanatomy

    Nature

    (1993)
  • X. Delbeuck et al.

    Alzheimer's disease as a disconnection syndrome?

    Neuropsychol. Rev.

    (2003)
  • C. Esslinger et al.

    Neural mechanisms of a genome-wide supported psychosis variant

    Science

    (2009)
  • Cited by (0)

    View full text