You have probably heard the parable of the blind men and the elephant. One feels the trunk and says it’s a snake, another feels a leg and claims it’s a tree. It warns of how focusing on single parts can obscure the whole.
Neuroscience made the same mistake for decades, viewing the brain as a collection of specialised regions, each working on a distinct function. Our understanding of what each region did often stemmed from incredible accidents, like the case of Phineas Gage, a 19th-century railway worker who survived having an iron rod blown through his brain. His personality change was blamed on frontal lobe damage. More recently, we have gained insights from brain stimulation studies that tied the amygdala to emotions, the occipital lobe to vision, and so on.
Brain regions do specialise, but that isn’t the whole picture. Advances in imaging technologies in the late 1990s and early 2000s, notably functional MRI and PET, allowed scientists to observe the whole brain in action. What they discovered transformed neuroscience. Brain regions don’t operate alone – instead, complex behaviours emerge from synchronised activity across multiple, overlapping networks.
“The mapping of brain networks has played a major role in shifting neuroscientific thinking,” says Luiz Pessoa at the University of Maryland.
The shift began in 2001 when Marcus Raichle, now at Washington University in St. Louis, Missouri, described the default mode network (DMN), a system of interconnected regions that lights up when your mind disengages from a task.
Two years later, Kristen McKiernan, then at the Medical College of Wisconsin, and her colleagues showed that the DMN increased in activity during easier and familiar tasks, such as daydreaming and self-reflection.
Suddenly, researchers had a “resting state” baseline against which to measure all brain activity. They also began to link the DMN with complex behaviours such as emotional intelligence and theory of mind. Meanwhile, the discovery of other networks – for attention, language, emotion, memory and planning – reshaped thinking about mental health and neurodiversity. Network differences are now associated with a plethora of neurological conditions, including Parkinson’s disease, post-traumatic stress disorder, depression and anxiety. It is also linked to ADHD.
Network science has become a field in its own right, improving our understanding of everything from autism – which is increasingly being characterised as a difference within the social salience network, a brain system that detects and prioritises relevant social cues to help us produce appropriate responses – to Alzheimer’s disease, where new research suggests abnormal proteins may spread along network pathways. We even have it to thank for inspiring the development of artificial neural networks in AI systems like ChatGPT.
Neural networks have transformed how we understand the brain, as well as how we diagnose and treat the problems affecting it. We may not yet be viewing the whole elephant, but the picture is certainly coming into focus.
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