Elsevier

NeuroImage

Volume 52, Issue 3, September 2010, Pages 766-776
NeuroImage

Review
Can structure predict function in the human brain?

https://doi.org/10.1016/j.neuroimage.2010.01.071Get rights and content

Abstract

Over the past decade, scientific interest in the properties of large-scale spontaneous neural dynamics has intensified. Concurrently, novel technologies have been developed for characterizing the connective anatomy of intra-regional circuits and inter-regional fiber pathways. It will soon be possible to build computational models that incorporate these newly detailed structural network measurements to make predictions of neural dynamics at multiple scales. Here, we review the practicality and the value of these efforts, while at the same time considering in which cases and to what extent structure does determine neural function. Studies of the healthy brain, of neural development, and of pathology all yield examples of direct correspondences between structural linkage and dynamical correlation. Theoretical arguments further support the notion that brain network topology and spatial embedding should strongly influence network dynamics. Although future models will need to be tested more quantitatively and against a wider range of empirical neurodynamic features, our present large-scale models can already predict the macroscopic pattern of dynamic correlation across the brain. We conclude that as neuroscience grapples with datasets of increasing completeness and complexity, and attempts to relate the structural and functional architectures discovered at different neural scales, the value of computational modeling will continue to grow.

Introduction

The brain is composed of anatomically distinct elements interconnected by a dense web of structural links. This structural network shapes how neural dynamics—the processes underlying human cognitive function—unfold over time. Structure–function relationships are pervasive in biology and range in scale from the folding of proteins up to the biomechanics of mammalian skeletons. Structure invariably informs and constrains biological function. In what ways does structure predict function in the human brain? We review evidence at microscopic and macroscopic scales, and frame an answer from the perspectives of network theory and computational modeling.

Large-scale computational models now combine neuroanatomical and physiological connectivity data with unprecedented comprehensiveness and detail. What can these models tell us about the relationship between anatomical connectivity and dynamic interactions that develop upon the network over time?

The question gains in significance because of the accumulation of highly resolved neural connectivity data recorded from individual participants. Until the recent arrival of noninvasive diffusion imaging techniques, mapping of human brain connectivity depended largely on gross dissection or on postmortem histology. These methods left large gaps in our understanding of the structural substrate of cognition. The comprehensive description of human brain connectivity—the connectome (Sporns et al., 2005)—has now become a feasible scientific goal. The availability of detailed large-scale connectivity data offers the opportunity to understand the links between brain structure and brain function at the regional level, and parallel approaches to mapping the connectivity of single neurons will facilitate a more complete understanding of the functioning of local neural circuits.

The paper is structured as follows. In the second section we define key terms such as “structural connectivity” and “functional connectivity”, we make a distinction between two levels of brain organization, and we introduce the computational framework of network modeling approaches. The third section addresses the structure–function relationships observed among individual neurons and among small populations of neurons. We examine the evidence for precise and patterned synaptic targeting, and the potential role of such precise structure in local circuit dynamics. In the fourth section we review the evidence for a link between structure and function at the large scale. We focus on empirical studies of spontaneous and task-evoked neural interactions. We further review how structure–function relationships depend on the spatiality of the brain and how they change across time, or as a consequence of local or distributed network damage. Throughout, we attempt to establish links between empirical findings and results of network analysis and computational modeling. We close with some considerations of empirical and modeling developments in the near future.

Section snippets

Definitions, scales, and models

When asking whether “structure” determines “function” in a given context it is necessary to specify one's usage of the key terms. In the present context, we take “structure” to refer to the spatial and topological arrangement of connections between neuronal elements. The notion of “function” is more delicate. By the “function” of a particular neuron or brain region we do not refer to the set of behavioral or psychological functions (e.g. attention, memory) subserved by a given neural circuit or

Thinking inside the voxel: Structure and function of neural circuits

Ten cubic millimeters of human cerebral cortex—the approximate volume of a standard fMRI voxel—contains on the order of 105 neurons and 109 synapses (Pakkenberg and Gundersen, 1997). In human sensory cortices, such a voxel will typically contain between 10 and 40 functional domains (assuming each domain has a diameter between 300 and 600 μm). Functional domains are constituted by sets of neurons that show similar responses to variations in somatic, auditory or visual stimulation (Mountcastle,

From single voxels to the whole brain: Structure and function of large-scale systems

The analysis of spontaneous neural dynamics offers an opportunity to measure the aggregate level of relation between structural and functional connectivity in a relatively task-neutral manner. Several studies have performed a combined analysis of structural connectivity (SC) derived from diffusion imaging and tractography, and functional connectivity (FC) derived from spontaneous fluctuations of the BOLD response (reviewed in Damoiseaux and Greicius, 2009). The first such study examined SC and

Network topology of SC and FC

Network concepts have been used to define principles of the structural and functional organization of the cerebral cortex for many decades. The “mosaic organization” of the cortex into specialized regions that become functionally integrated during perception and cognition (Zeki, 1978, Zeki and Shipp, 1988), as well as the idea that large-scale connectivity in the primate brain is structurally (Felleman and Van Essen, 1991) and functionally (Mesulam, 1998) organized into multiple processing

Conclusions

Rapid advances in recording and data processing methods are beginning to yield structural and functional connection maps of brain networks at multiple scales and with unprecedented accuracy and resolution. Connectome datasets will facilitate a far clearer understanding of the relationship between structure and function in the human brain. Initial results are encouraging, in that many of the characteristics of functional brain dynamics can be traced to structural patterns in connectivity. In

Acknowledgments

The authors (CJH, JPT, OS) gratefully acknowledge support from the JS McDonnell Foundation.

References (145)

  • GreiciusM.D. et al.

    Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus

    Biol. Psych.

    (2007)
  • HeB.J. et al.

    Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits in spatial neglect

    Neuron

    (2007)
  • HestrinS. et al.

    Electrical synapses define networks of neocortical GABAergic neurons

    Trends Neurosci.

    (2005)
  • HuttenlocherP.R.

    Morphometric study of human cerebral cortex development

    Neuropsychologia

    (1990)
  • IngberL. et al.

    Multiple scales of statistical physics of the neocortex: application to electroencephalography

    Math Comput. Model.

    (1990)
  • KennedyD.P. et al.

    The intrinsic functional organization of the brain is altered in autism

    Neuroimage

    (2008)
  • KnockS.A. et al.

    The effects of physiologically plausible connectivity structure on local and global dynamics in large scale brain models

    J. Neurosci. Methods

    (2009)
  • KochM.A. et al.

    An investigation of functional and anatomical connectivity using magnetic resonance imaging

    Neuroimage

    (2002)
  • KohnA. et al.

    Correlations and brain states: from electrophysiology to functional imaging

    Curr. Opin. Neurobiol.

    (2009)
  • LichtmanJ.W. et al.

    Seeing circuits assemble

    Neuron

    (2008)
  • MeunierD. et al.

    Age-related changes in modular organization of human brain functional networks

    Neuroimage

    (2009)
  • MicheloyannisS. et al.

    Small-world networks and disturbed functional connectivity in schizophrenia

    Schizophr. Res.

    (2006)
  • MonkC.S. et al.

    Abnormalities of intrinsic functional connectivity in autism spectrum disorders

    Neuroimage

    (2009)
  • AchardS. et al.

    Efficiency and cost of economical brain functional networks

    PLoS Comput. Biol.

    (2007)
  • AlstottJ. et al.

    Modeling the impact of lesions in the human brain

    PLoS Comput. Biol.

    (2009)
  • AngelucciA. et al.

    Circuits for local and global signal integration in primary visual cortex

    J. Neurosci.

    (2002)
  • BassettD.S. et al.

    Small-world brain networks

    Neuroscientist

    (2006)
  • BassettD.S. et al.

    Adaptive reconfiguration of fractal small-world human brain functional networks

    Proc. Natl. Acad. Sci. U. S. A.

    (2006)
  • BassettD.S. et al.

    Hierarchical organization of human cortical networks in health and schizophrenia

    J. Neurosci.

    (2008)
  • BassettD.S. et al.

    Human brain networks in health and disease

    Curr. Opin. Neurol.

    (2009)
  • BertschingerN. et al.

    Real-time computation at the edge of chaos in recurrent neural networks

    Neural Comput.

    (2004)
  • BoskingW.H. et al.

    Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex

    J. Neurosci.

    (1997)
  • BraitenbergV. et al.

    Statistics and Geometry of Neuronal Connectivity

    (1998)
  • BreakspearM.

    Nonlinear phase desynchronization in human electroencephalographic data

    Hum. Brain Mapp.

    (2002)
  • BreakspearM. et al.

    Modulation of excitatory synaptic coupling facilitates synchronization and complex dynamics in a biophysical model of neuronal dynamics

    Network Comput. Neural. Syst.

    (2003)
  • BreakspearM. et al.

    Dynamics of a neural system with a multiscale architecture

    Phil. Trans. Roy. Soc. B.

    (2005)
  • BreakspearM. et al.

    Neuronal dynamics and brain connectivity

  • BreakspearM. et al.

    Kinetic models of brain activity

    Brain Imaging Behav.

    (2008)
  • BucknerR.L. et al.

    The brain's default network: anatomy, function, and relevance to disease

    Ann. N.Y. Acad. Sci.

    (2008)
  • BucknerR.L. et al.

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

    J. Neurosci.

    (2009)
  • BullmoreE. et al.

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

    Nat. Rev. Neurosci.

    (2009)
  • BuonomanoD.V. et al.

    State-dependent computations: spatiotemporal processing in cortical networks

    Nat. Rev. Neurosci.

    (2009)
  • ChenZ.J. et al.

    Revealing modular architecture of human brain structural networks by using cortical thickness from MRI

    Cereb. Cortex

    (2008)
  • DamoiseauxJ.S.

    Consistent resting-state networks across healthy subjects

    Proc. Natl. Acad. Sci. U. S. A.

    (2006)
  • DamoiseauxJ.S. et al.

    Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity

    Brain Struct. Funct.

    (2009)
  • DecoG. et al.

    Key role of coupling, delay, and noise in resting brain fluctuations

    Proc. Natl. Acad. Sci. U. S. A.

    (2009)
  • FairD.A. et al.

    Development of distinct control networks through segregation and integration

    Proc. Natl. Acad. Sci. U. S. A.

    (2007)
  • FairD.A. et al.

    The maturing architecture of the brain's default network

    Proc. Natl. Acad. Sci. U. S. A.

    (2008)
  • FairD.A. et al.

    Functional brain networks develop from a “local to distributed” organization

    PLoS Comput. Biol.

    (2009)
  • FellemanD.J. et al.

    Distributed hierarchical processing in the primate cerebral cortex

    Cereb. Cortex

    (1991)
  • Cited by (0)

    View full text