The potential to create personalized digital twins of your brain and body is a hot topic in neuroscience and medicine today. These computer models are designed to simulate how parts of your brain interact and the way the brain may reply to stimulation, disease, or medication.
The extraordinary complexity of the brain’s billions of neurons makes this a really difficult task, after all, even within the era of AI and massive data. Until now, whole-brain models have struggled to capture what makes each brain unique.
People’s brains are all wired barely in another way, so everyone has a novel network of neural connections that represents a sort of “brain fingerprint.”
Nevertheless, most so-called brain twins are currently more like distant cousins. Their performance is barely any closer to the true thing than if the model were using the wiring diagram of a random stranger.
This matters because digital twins are increasingly proposed as tools for testing treatments by computer simulation, before applying them to real people. If these models fail to capture fundamental principles of every patient’s unique brain organization, their predictions won’t be personalized—and in worst cases could possibly be misleading.
In our latest study, published in Nature Neuroscience, we show that realistic digital brain twins require something that many existing models overlook: competition between the brain’s different systems.
Our findings suggest that without competition, digital twins risk being overly generic, missing out on what makes you “you.”
Excess of Cooperation
The human brain isn’t static. The ebb and flow of its activity could be mapped non-invasively using neuroimaging methods similar to functional MRI. A pc model could be built from this, specific to that person and simulating how the regions of their brain interact. That is the thought of the digital twin.
The brain is commonly described as a highly cooperative system. Yet on a regular basis experiences similar to focusing attention or switching between tasks tells us intuitively that brain systems compete for limited resources. Our brains cannot do all the pieces without delay, and not all regions could be energetic together on a regular basis.
Despite this, the overwhelming majority of brain simulations over the past 20 years haven’t taken these competitive interactions between regions under consideration. Quite, they’ve “forced” neighboring regions to cooperate. This could push the simulated brain into overly synchronized states which are rarely seen in real brains.
In a large comparative study of humans, macaque monkeys, and mice, our international team of researchers used non-invasive brain activity recordings to point out that probably the most realistic whole-brain models not only require cooperative interactions inside specialized brain circuits, but long-range competitive interactions between different circuits.
To attain this, we compared two varieties of brain model: one wherein all interactions between brain regions were cooperative, and one other wherein regions could either excite or suppress one another’s activity. In humans, monkeys, and mice, the models that included competitive interactions consistently outperformed cooperative-only models.
Using a large-scale evaluation of over 14,000 neuroimaging studies, we found that spontaneous activity within the competitive models more faithfully reflected known cognitive circuits, similar to those involved in attention or memory. This implies competition is crucial for enabling the brain to flexibly activate appropriate mixtures of regions—an indicator of intelligent behavior.
Visual summary of our study:
When whole-brain models of humans, macaques, and mice are allowed to treat interactions between some brain regions as competitive, they consistently accomplish that—generating activity patterns that closely resemble those related to real cognitive processes. Luppi et al/Nature Neuroscience, CC BY
We concluded that competitive interactions act as a stabilizing force, allowing different brain systems to take turns in shaping the direction of the brain’s ebbs and flows without interference or distraction. This ability to avoid runaway activity can also contribute to the remarkable energy-efficiency of the mammalian brain, which is many orders of magnitude more efficient than modern AI systems.
Crucially, models with competitive interactions weren’t only more accurate but in addition more individual-specific. This implies they were higher at capturing the unique brain fingerprint that distinguishes one person’s brain from one other’s.
No Longer Lost in Translation?
The undeniable fact that our findings hold across humans and other mammals suggests they reflect fundamental principles of how intelligent systems work. In each case, we found models with competitive interactions generated brain activity patterns that closely resembled those related to real cognitive processes.
This might have major implications for translational neuroscience. Animal models are routinely used to check treatments before human trials, yet differences between species often limit how well these results translate. Around 90 percent of treatments for neuropsychiatric disorders are “lost in translation,” failing in human clinical trials after showing promise in animal trials.
Combining brain imaging data from human patients with whole-brain modeling could seriously change this. A framework that works across species would supply a robust bridge between basic research and clinical application.
If someone needs intervention within the brain, for instance as a consequence of epilepsy or a tumor, their digital twin could possibly be used to explore how the patient’s brain activity would change when stimulated with different levels of medication or electrical impulses. This might significantly improve on existing trial-and-error approaches with real patients, and thus provide higher treatments.
The overall principles of brain organization across species also offer a path for understanding how one can shape the following generation of artificial intelligence. Within the not-too-distant future, we may have the option to construct digital twins which are more faithful in reproducing the salient features of the human brain—and potentially, AI models which are more faithful to the human mind.