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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.’

Zapping the brain 

Brain activity involves electric currents, which generate magnetic fields that can be measured from outside the skull. 

The process also works in the other direction, the principle on which transcranial magnetic stimulation (TMS) is based. In TMS treatments, a coil is placed on the head to produce a powerful magnetic field that reaches the brain through skin and bone, without losing strength. The magnetic field pulse causes a short, weak electric field in the brain that affects neuron activity. 

It sounds wild, but it’s completely safe, says Professor of Applied Physics Risto Ilmoniemi, who has been developing and using TMS for decades.

‘The strength of the electric field is comparable to the brain’s own electric fields. The patient feels the stimulation, which is delivered in pulses, as light taps on their skin.’

Magnetic stimulation is used to treat severe depression and neuropathic pain. At least 200 million people around the world suffer from severe depression, while neuropathic pain is prevalent among spinal injury patients, diabetics and multiple sclerosis sufferers. Pharmaceuticals provide adequate relief to only half of all depression patients; this share is just 30% in the case of neuropathic pain sufferers.

How frequently pulses are given is based on the illness being treated. For depression, inter-neuron communication is stimulated with high-frequency pulse series, while less frequent pulses calm patient’s neurons for neuropathic pain relief. 

Stimulation is administered to the part of the brain where, according to the latest medical science, the neurons tied to the illness being treated are located.

About half of treated patients receive significant relief from magnetic stimulation. Ilmoniemi believes this could be much higher—with more coils and the help of algorithms.

Quantum optics sensors could revolutionise how we read neural signals 

Professor Lauri Parkkonen’s working group is developing a new kind of MEG device that adapts to the head size and shape and utilises sensors based on quantum optics. Unlike the SQUID sensors currently employed in MEG, they do not need to be encased in a thick layer of insulation, enabling measurements to be taken closer to the scalp surface. This makes it easier to perform precise measurements on children and babies especially.

The work has progressed at a brisk pace and yielded promising results: measurements made with optical sensors are already approaching the spatial accuracy of measurements made inside the cranium.  

Parkkonen believes that a MEG system based on optical sensors could also be somewhat cheaper and more compact and thus easier to place than traditional devices; such a MEG system could utilize a ‘person-sized’ magnetic shield instead of a large shielded room as the conventional MEG systems do.

‘This would bring it into reach of more researchers and hospitals.’

This article has been published in the (issuu.com), April 2022.

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