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This chapter provides a summary of what has been reviewed in this volume. It is all too common to hear in neuroscience and psychology that attention is a vague concept not amenable to a truly scientific explanation; or that, even if we did have an understanding of the mechanisms of attention, this would do little to illuminate the problems that arise in psychology or social neuroscience. The goal of this volume is to argue that both of these assertions are false. Although certainly there is much more to be learned, attention is an organ system and thus can be studied network by network, even though—as in all systems—there are interactions among the constituent parts. Attention networks have anatomical and functional independence, but they also interact in many practical situations. Damage to a node of these networks, irrespective of the source, produces distinctive neuropsychological deficits.

Attention is an old topic in psychology, but viewing it as an organ system allows the growing exploration of its physical basis. One goal of this volume is to examine the anatomy and development of brain networks of attention. This chapter defines three networks—alerting, orienting, and executive—and gives a summary of the tools used in exploring them. Attention networks are common to everyone, but their efficiency can be measured and it does differ among individuals.

This chapter discusses the evolutionary and developmental history of attention networks. In the case of self-regulation, efforts to derive its physical basis depend on the rather surprising relationship between a measure of executive attention derived from the attention network test and parental reports of effortful control. The use of conflict in neuroimaging studies has made it possible to discover many nodes of the neural network that carry out resolution of conflict, including the important role of anterior cingulate connectivity in influencing brain areas involved in cognition and emotion. This discovery led to a proposed brain circuitry underlying self-regulation. The association of genetic variations with individual differences in the efficiency of the network provides a further method for discovery of genes that serve to build the nodes and connections of the network during development. This link provides a molecular perspective to the physical basis of a complex psychological construct. The ability to find candidate genes related to the attention network rests on pharmacological findings linking different neuromodulators to the various networks.

Functional brain imaging has been widely used to describe regional abnormalities in cerebral blood flow and metabolism in Parkinson’s disease (PD) patients scanned in the rest state with PET, SPECT, or perfusion MRI techniques. This general brain mapping approach has also been used to identify PD-related functional changes occurring at the network level. This chapter summarizes recent advances in the use of disease-related spatial covariance patterns (metabolic brain networks) to study disease progression and to identify robust imaging biomarkers with which to assess therapeutic outcome in PD patients. Specific attention is given to clinical applications of network-based techniques in the study of dopaminergic pharmacotherapy and stereotaxic surgical interventions for the disorder.

This chapter discusses the use of network theory to study anatomical systems at different levels. It starts with the analysis of intercellular networks in different kinds of tissues; the way in which they are constructed; and the role network theory in distinguishing various kinds of tissues, such as healthy from inflamed and cancerous ones. The study of vascular networks is presented in a concise way, as exemplified by networks inside bones and brain vascular networks. Then, the chapter turns to the study of brain networks. This part is divided into two sections, one for brain networks in non-humans and another devoted to human brain networks. In both cases, the analyses of local and global structural properties of these networks are accounted for; in particular, the role of network theory for the analysis of different aspects of the human brain, such as intelligence, neurodegenerative diseases, schizophrenia, and stroke lesions.

The human brain is a fine-tuned and balanced structural, functional, and dynamic electrochemical system. Any alterations, from slight slowing of partial brain networks to severe disruptions in structural, functional, and dynamic connectivity across local and large-scale brain networks will result in slight to severe changes in cognitive ability, awareness, and consciousness. Using future noninvasive technologies, the common goal is to relate typical or atypical resting-state, sensory-motor functions, cognition, and consciousness to underlying typical or altered quantified brain structure, biochemistry, pathways, functional brain networks, and connectivity. This will pose enormous ethical challenges of quantitative diagnostic and prognostic strategies in future neurologic and psychiatric clinical practice.

Based on neuro-imaging findings, we present a brain model for intelligence and a model for creativity and discuss how genius may emerge from the overlapping and the unique aspects of these models. Intelligence is associated with integrity of the dorsolateral prefrontal cortex and creative achievement with lower volumes of the orbitofrontal cortex. Increased creative drive sometimes associated with frontotemporal dementia is related to damage in the left anterior temporal lobe. Intelligence is also associated with integrity of white-matter tracts including the arcuate fasciculus and corpus callosum. Divergent thinking and openness to experience are associated with lower measures of integrity within white-matter tracts linking the thalamus with frontal projection zones. Although there is some overlap, intelligence and creativity appear to involve largely different brain networks. The intelligence findings suggest the importance of network integrity that may facilitate knowledge acquisition and retention. The creativity findings suggest a disinhibition of networks that facilitates the generation of novel associations among knowledge stores. Whether there is a specific network for genius is not yet apparent. Complex phenomena like intelligence, creativity, and genius can be studied scientifically with modern neuroscience methods even as their definitions evolve with better empirical observations.

This chapter provides an in-depth discussion of how current neuroimaging techniques can aid the clinician in the diagnosis of patients with parkinsonian symptoms. Although clinical assessment and structural imaging can rule out secondary causes, several functional imaging techniques are available to help the clinician with a critical diagnostic question—whether the symptoms are due to idiopathic Parkinson’s disease (PD) versus an alternative parkinsonian syndrome. If the symptoms are due to an alternative non-PD syndrome, the clinician must determine which one. The chapter reviews how the presenting symptom complex and the differential diagnosis can be used to choose the most informative imaging strategy. Clinical applications of imaging for parkinsonism are discussed in terms of the predominant presenting features, such as akinetic rigidity, tremor, or cognitive dysfunction (dementia). New metabolic imaging data on the use of brain networks to classify individual subjects are also discussed.

The levels of social impairment experienced by different individuals with autism spectrum disorder strongly correlate with the degrees of dysfunction within each individual’s superior temporal sulcus (STS), a key region in social perception, in general, and in the perception of biological motion, in specific. From a developmental perspective, while STS responsiveness becomes increasingly selective for human movement in typical children, observers with autism show less selective tuning. Brain imaging research is reviewed that supports an interactive development model of the social brain and social perception.

Psychopathology might be one domain where we can get some empirical perch on the Systematicity debate. In patients with schizophrenia transformational systematicity and other types of systematicity often break down. Systems neuroscience has provided some reason to believe that the best explanation for this break down is in terms of the degradation of key brain subsymbolic network properties such as small-world graphical structures. It is argued that if such systems neuroscience explanations for failures of systematicity in schizophrenia are robust then this is a victory for network approaches over symbol-and-rule approaches that themselves provide little insight into said failures. Finally, there is speculation that the relevant dynamical and graphical relations in such cases extend beyond the brain to include body and environment.

This chapter sketches in very general terms the cognitive architecture of both language comprehension and production, as well as the neurobiological infrastructure that makes the human brain ready for language. Focus is on spoken language, since that compares most directly to processing music. It is worth bearing in mind that humans can also interface with language as a cognitive system using sign and text (visual) as well as Braille (tactile); that is to say, the system can connect with input/output processes in any sensory modality. Language processing consists of a complex and nested set of subroutines to get from sound to meaning (in comprehension) or meaning to sound (in production), with remarkable speed and accuracy. The first section outlines a selection of the major constituent operations, from fractionating the input into manageable units to combining and unifying information in the construction of meaning. The next section addresses the neurobiological infrastructure hypothesized to form the basis for language processing. Principal insights are summarized by building on the notion of “brain networks” for speech–sound processing, syntactic processing, and the construction of meaning, bearing in mind that such a neat three-way subdivision overlooks important overlap and shared mechanisms in the neural architecture subserving language processing. Finally, in keeping with the spirit of the volume, some possible relations are highlighted between language and music that arise from the infrastructure developed here. Our characterization of language and its neurobiological foundations is necessarily selective and brief. Our aim is to identify for the reader critical questions that require an answer to have a plausible cognitive neuroscience of language processing. Published in the Strungmann Forum Reports Series.

Chapter 4 opens Part III of the book, ‘Multilayer Networks’, which comprises chapters 4–15. The chapter starts with an informal definition of multiplex networks, multi-slice networks and networks of networks, and motivates the research interest on multilayer networks by providing a general overview of the multiple applications of the multilayer network framework in different disciplines and contexts, including social networks, complex infrastructures, financial networks, molecular networks and network medicine, brain networks, ecological networks and climate networks. This chapter discusses the major examples of multilayer network datasets studied so far in the different disciplines and highlights the main research questions emerging from the study of these real datasets.

In this dialogue, the Harvard neuroscientist, Alvaro Pascual-Leone initially reflects on the importance of ‘unlearning’ and forgetting. He then gives a detailed explanation of, and how he carries out, transcraneal magnetic stimulation (TMS) and how he uses this technology to fight diseases, as well as explaining his experiments on inattentional blindness. He then discusses how the brain acts as a hypothesis generator and whether the brain, the mind and the soul are different things or not. Later reflect on the questions: Is the mind and what we are a consequence of the brain’s structure?  Do changes in the brain change our reality? And why are a person’s dreams important? Then he explains how freewill and decision-making work from the brain, and relates his vision of intelligence and where it may be generated from, explaining the differences between the mind and the brain. He finally reflects on what is known so far about the brain’s “dark energy” and the way we are continuously being surprised by the wonders of the brain's plasticity.

There are different opinions on the concept of individual differences; some believe that experience can be solely taken into consideration to find individual differences; others, that genes play an important role in identifying the individual differences. This chapter explores how genes and social experience or environment interact to shape brain networks of control and changes in behavior. In this context, the authors present a detailed analysis of particular genes and their relation to the structure and function of specific neural networks. On the basis of their findings, they suggest that genes and experience help shape human behavior together.

This chapter focuses on the neurocognitive development of social decision making. It presents evidence that the development of social decision making is related to changes in different, but interacting, brain networks. The chapter suggests that structural changes in brain development during adolescence are linked to changes in brain networks, and that these changes lead to the development of adolescent social behavior.

This chapter explains the concept of effort on the basis of evidence from experimental and cognitive psychology, demonstrates how it has been used in conducting studies of brain activity, and goes on to examine the individual differences that play a role in determining the efficiency of brain networks associated with effortful control. It also reviews certain educational training methods; those when used among children can change these networks, along with conditional changes developed in adults through meditation training. The findings reveal that meditation helps in producing better attentional performance and the subjective condition related to effort. The chapter also investigates how these training methods play a role in determining the concept of flow.