The human brain consists of some 86 billion neurons, nerve cells that process and convey information through electrical nerve impulses.
That’s why measuring neural electrical activity is often the best way to study the brain, says Hanna Renvall. She is Aalto University and HUS Helsinki University Hospital Assistant Professor in Translational Brain Imaging and heads the HUS BioMag Laboratory.
Electroencephalography, or EEG, is the most used brain imaging technique in the world. Renvall's favourite, however, is magnetoencephalography or MEG, which measures the magnetic fields generated by the brain’s electrical activity.
MEG signals are easier to interpret than EEG because the skull and other tissues don’t distort magnetic fields as much. This is precisely what makes the technique so great, Renvall explains.
‘MEG can locate the active part of the brain with much greater accuracy, at times achieving millimetre-scale precision.’
An MEG device looks a lot like bonnet hairdryers found in hair salons. The SQUID sensors that perform the measurements are concealed and effectively insulated inside the bonnet because they only function at truly freezing temperatures, close to absolute zero.
The world's first whole-head MEG device was built by a company that emerged from Helsinki University of Technology’s Low Temperature Laboratory – and is now the leading equipment manufacturer in this field.
MEG plays a major role in the European Union’s new AI-Mind project, whose Finnish contributors are Aalto and HUS. The goal of the €14-million project is to learn ways to identify those patients, whose dementia could be delayed or even prevented.
For this to happen, neuroscience and neurotechnology need help from artificial intelligence experts.
Fingerprinting the brain
Dementia is a broad-reaching neural function disorder that significantly erodes the sufferer’s ability to cope with everyday life. Some 10 million people are afflicted in Europe, and as the population ages this number is growing. The most common illness that causes dementia is Alzheimer’s disease, which is diagnosed in 70–80% of dementia patients.
Researchers believe that communication between neurons begins to deteriorate well before the initial clinical symptoms of dementia present themselves. This can be seen in MEG data—if you know what to look for.
MEG is at its strongest when measuring the brain’s response to stimuli like speech and touch that occur at specific moments and are repetitive.
Interpreting resting-state measurements is considerably more complex.
That’s why the AI-Mind project uses a tool referred to as the fingerprint of the brain. It was created when Renvall and Professor Riitta Salmelin and her colleagues began to investigate whether MEG measurements could detect a person’s genotype. More than 100 sibling pairs took part in the study that sat subjects in an MEG, first for a couple of minutes with their eyes closed and then for a couple of minutes with their eyes open. They also submitted blood samples for a simple genetic analysis.
When researchers compared the graphs and genetic markers, they noticed that, even though there was substantial variance between individuals, siblings’ graphs were similar.
Next, Aalto University Artificial Intelligence Professor Samuel Kaski’sgroup tested whether a computer could learn to identify graph sections that were as similar as possible between siblings while also being maximally different when compared to other test subjects.
The machine did it—and more, surprisingly.
‘It learned to distinguish the individual perfectly based on just the graphs, irrespective of whether the imaging had been performed with the test subject’s eyes open or closed,’ Hanna Renvall says.
‘For humans, graphs taken with eyes closed or open look very different, but the machine could identify their individual features. We’re extremely excited about this brain fingerprinting and are now thinking about how we could teach the machine to recognise neural network deterioration in a similar manner.’
