As Olaf Sporns mentions in his preface, networks have become of central interest in the natural sciences, particularly in the study of complex biological systems. And no complex biological system has spurred more interest than the human brain. Hence, Sporns has written a captivating book on “the story of brain connectivity” to introduce networks to neuroscientists and make neuroscience attractive to specialists on theoretical network models. With the goal of keeping his book accessible to both audiences, Sporns has purposely avoided mathematical formalisms and has opted for a narrative recount of recent research and open problems. This sacrifice of technical depth is intended to make the subject appealing to prospective readers, who can find additional information by following the provided references in the extensive 41-page bibliography at the end of this monograph.

The book is somewhat disorganized as a textbook for self-learning, and it often provides more questions than answers; yet, it renders a good overview of what network science can tell us about the brain. “[Network] approaches can provide fundamental insights into the means by which simple elements organize into dynamic patterns” (p. 2); hence, in theory, they can be extremely useful for analyzing the quadrillion synapses in the human brain. Compare that with the mere billions of base pairs in the human genome, and you will realize the dimensions of the challenge ahead.

The first part of the book includes three introductory chapters on network science and brain networks. A brief survey of network measures and models is provided, as well as an informative description of empirical techniques for brain observation (for example, EEG, PET, and fMRI). Three modes of brain connectivity are also introduced: structural, functional, and effective connectivity. Each of these modes focuses on different aspects of the rich spatiotemporal dynamics of the brain.

A second set of chapters turns its attention to the brain anatomic networks, from issues surrounding function localization to the consideration of the economical use of limited resources in the brain (that is, analyzing brain networks as physical objects that consume space and energy). The study of the brain’s connectome is bound to reveal structural connections in unprecedented detail in the future; it will hopefully disclose key insights on the functional specialization of nodes within the network and the balance among localized processing, fault tolerance, and functional integration in the human brain. At this point, however, we must resign ourselves to what is known about the coarse-grained topology of structural brain networks and their apparent modular small-world architecture.

The third collection of chapters in Sporns’ monograph focuses on network dynamics, the patterns of dynamic interactions that emerge from the brain’s physical wiring. The four chapters in this part analyze the spontaneous (or endogenous) neural activity in the brain that is not driven by external stimuli; the recurrent (or reentrant) processes that contribute to brain responses to external stimuli and might one day help us comprehend the poorly understood relationship between brain and cognition as a network phenomenon beyond simplistic neural reductionism; how network dynamics are affected by physical injuries and some diseases associated with the abnormal topological organization of a brain network, such as Alzheimer’s disease, schizophrenia, or autism; and, finally, how brain dynamics are shaped by self-organized growth and the brain’s marvelous plasticity that maintains its high sensitivity to inputs and information capacity.

The book’s final chapters return to issues related to the complexity of brain networks. They mention relatively unexplored phenomena that pervade neural networks and might help establish a neural basis for “the unity of mind and experience,” often with curious names such as metastability and self-organized criticality. They address why complexity matters: neural complexity “combines segregation and integration in a [hypothetically] nearly decomposable, modular small-world network.” If the link between consciousness and patterns of brain connectivity is someday disentangled and consciousness emerges as a property of a complex network, machine consciousness might be within our reach (p. 298). Finally, Sporns also discusses how the brain is shaped by its natural context, as it is embodied within a system (our body) equipped with sensors and actuators. He considers perception-action cycles as the fundamental building blocks for learning and development, in line with the intelligent agents perspective of artificial intelligence (AI) and robotics.

In summary, since networks provide general models for studying complex systems and the interactions among their elements, one can state that “it’s networks all the way down” when talking about complex biological systems such as the brain. Hence, it is no surprise that Sporns views “the study of brain networks as a promising direction for uncovering the mechanisms by which the collective actions of a large number of neurons give rise to the complexity of the human mind” (p. 325). It might still take awhile to achieve such lofty goals, since there is much to be done before that, but I cannot agree more with the author.