July 2019, ScientificAmerican.com 33result in what seem to be the utterings of voices in one’s head.
Like schizophrenia, major depressive disorder is not caused
by a single abnormal brain region. Three specific modules
appear to be affected in depression: the frontoparietal control,
salience and default mode modules. In fact, the symptoms of
depression—emotional disinhibition, altered sensitivity to emo-
tional events and rumination—map to these modules.
As a result, normal communication among the three modules
becomes destabilized. Activities from module to module typical-
ly tug back and forth to balance the cognitive processing of sen-
sory inputs with more introspective thoughts. In depression,
though, the default mode dominates, and the afflicted person
lapses into ruminative thought. The music of the brain thus
becomes in creasingly unbalanced, with one family of instru-
ments governing the symphony. These observations have broad-
ened our understanding of the network properties of depression
to the extent that a connectivity pattern in a brain can allow us to
diagnose certain subtypes of the disorder and determine which
areas should be treated with electrical-stimulation technology.NETWORKS EVOLVE
besides studying development, network neuroscientists have
begun to ask why brain networks have taken their present form
over tens of thousands of years. The areas identified as hubs are
also the locations in the human brain that have expanded the
most during evolution, making them up to 30 times the size
they are in macaques. Larger brain hubs most likely permit
greater integration of processing across modules and so sup-
port more complex computations. It is as if evolution increased
the number of musicians in a section of the orchestra, fostering
more intricate melodies.
Another way neuroscientists have explored these questions is
by creating computer-generated networks and subjecting them to
evolutionary pressures. In our lab, we have begun to probe the
evolutionary origins of hubs. This exercise started with a network
in which all edges were placed uniformly at random. Next, the
network was rewired, mimicking natural selection to form segre-
gated modules and display a property known in network science
as small-worldness, in which paths form to let distant network
nodes communicate with surprising ease. Thousands of such net-
works then evolved, each of which ultimately contained hubs
strongly connected to multiple modules but also tightly intercon-
nected to one another, forming what is called a club. Nothing in
the selection process explicitly selected for a club of hubs—they
simply emerged from this iterative process.
This simulation demonstrates that one potential solution to
evolving a brain capable of exchanging information among
modules requires hubs with strong connections. Notably, real
networks—brains, airports, power grids—also have durable,
tightly interconnected hubs, exactly as predicted by evolution-
ary experiments. That observation does not mean evolution nec-
essarily occurred in the same way as the simulation, but it shows
a possible means by which one of nature’s tricks might operate.
STATES OF MIND
when nobel prize–winning physicist Richard Feynman died in
1988, his blackboard read, “What I cannot create, I do not under-
stand.” He created a beautiful aphorism, yet it misses a pivotal
idea: it should be revised to “What I cannot create and control, I do
not understand.” Absent such control, we still know enough to
enjoy a symphony, even if we do not qualify to be the conductor.
When it comes to the brain, we have a basic understanding
of its form and the importance of its network architecture. We
know that our brain determines who we are, but we are just
beginning to understand how it all happens. To rephrase math-
ematician Pierre-Simon Laplace’s explanation of determinism
and mechanics and apply it to the brain, one’s present brain,
and so one’s mental state, can be thought of as a compilation of
past states that can be used to predict the future. A neuroscien-
tist who knew all the principles of brain function and every-
thing about someone’s brain could predict that person’s mental
conditions—the future, as well as the past, would be present
inside the person’s mind.
This knowledge could be used to prevent pain and suffering,
given that many mental illnesses are associated with network
abnormalities. With enough engineering ingenuity, we may
develop implanted devices that alter or even generate new
brain networks or edit genomes to prevent the disorganized
networks associated with mental disorders from occurring in
the first place. Such an achievement would enable us to treat
diseases and to restore brain function after stroke or injury and
enhance it in healthy individuals.
Before those futuristic scenarios materialize, two major gaps
must be filled: we need to know more about how personal
genetics, early-life development and environment determine
one’s brain’s structure and how that structure leads to function-
al capacities. Neuroscientists have some knowledge from the
human genome about the structure that gives rise to functional
networks but still need to learn precisely how this process
occurs. We are starting to grasp the way brain networks devel-
op and are shaped by the environment but are not close to
explaining the entire complexity of this process. The brain’s
wiring, its structural connectivity, constrains how various mod-
ules interact with one another, but our knowledge remains lim-
ited. As we fill in these gaps, chances improve for interventions
to guide brain functioning into healthy trajectories.
What holds us back, for the moment, is our still blurry vision
of the brain—it is as if we are outside the concert hall and have
seen only sketches of the instruments. Inside each brain region
that neuroscientists study are millions of neurons firing every
millisecond, and we are able just to indirectly measure their
average activity levels every second or so. Thus far we can
roughly identify the human brain’s structural connections.
Luckily, scientists and engineers have taken steps to deliver ever
clearer data that will enable a deeper look into perhaps the
most complex network in the known universe: your brain.MORE TO EXPLORE
Network Neuroscience. Danielle S. Bassett and Olaf Sporns in Nature Neuroscience,
Vol. 20, pages 353–364; March 2017.
Graph Theory Methods: Applications in Brain Networks, Olaf Sporns in Dialogues
in Clinical Neuroscience, Vol. 20, No. 2, pages 111–121; June 2018.
A Mechanistic Model of Connector Hubs, Modularity and Cognition. Maxwell A.
Bertolero et al. in Nature Human Behaviour, Vol. 2, pages 765–767; October 2018.
FROM OUR ARCHIVES
100 Trillion Connections. Carl Zimmer; January 2011.
scientificamerican.com/magazine/sa