In this ‘behind the paper’ post, Stephanie Williams discusses how the new equipment, techniques and methods developed in her lab helped them…
In this ‘behind the paper’ post, Joe Ziminski discusses his collaborative experience while studying how our brain changes when we learn.
The human brain retains an amazing capacity for change, or ‘plasticity’ throughout adulthood. This adaptability underpins our human experience, allowing us to continue to learn and grow throughout our lives. In our recent paper, we investigated how anatomical and chemical plasticity shape the brain’s activity to allow us to learn new skills.
My interest in brain plasticity was first inspired by the book ‘The Brain that Changes Itself (Norman Doidge, MD)’, which I read during my undergraduate studies. This collection of tales from neuroscientific research exemplified the astounding adaptability of humans, underpinned by the flexible and dynamic biology of our brains. During my PhD, I dived into researching how the activity of neurons, the key cells in our brain that underpin our thoughts, feelings, and behaviors, are shaped by neurochemical factors — for example GABA, a neurotransmitter that shuts down neuronal activity.
Having studied the brain at close range using microscopic methods, I joined the Adaptive Brain lab to broaden my perspective and investigate how adaptation across the entire brain may underpin human learning. In our paper, we non-invasively imaged the brain using MRI (the large scanning machines you may have seen in hospitals) to determine how brain connectivity changes due to training on a perceptual decision task. Further, we used MRI to measure myelin, an insulating sheath that wraps around part of a neuron to increase its firing speed, as well as the concentration of the inhibitory neurochemical GABA. MRI allowed us to probe the complex interactions between these biochemical features and how their plasticity may shape brain activity and learning.
We imaged the brains of participants at baseline, pre-training and post-training sessions. Between the pre-training and post-training sessions, the participants learnt a difficult perceptual discrimination task that involved detecting patterns embedded in background noise. This allowed us to compare changes in the brain before and after learning.
A typical day would involve juggling the acquisition of experimental data with the processing of data, a typical experience for a neuroscience researcher. The data-collection phase took around 6 months. I would train participants on the computer-based visual task at our Cambridge lab, and cycle to the Wolfson Brain Imaging Centre to run the MRI-sessions. The radiographers at the imaging centre would run the technical side of the MRI data collection, while myself and colleagues from the lab (generously accompanying me to assist in these time-sensitive experiments) would handle study-specific procedures such running the task. Working with the excellent radiographers at the Wolfson Brain Imaging Centre was a highlight of the project and I have many fond memories of this period.
The processing of MRI data types was a challenging but enjoyable experience. MRI is a versatile technique and the different types of data it produces (capturing myelin, GABA or neuronal-activity-related signals) require particular care and attention to ensure high data-quality is maintained. Fortunately, I was greatly assisted in this endeavor by experts in our lab and by our collaborators. In particular, the help and advice I received from co-authors Dr Poly Frangou and Dr Vasilis Karlaftis was invaluable. This impressed upon me the importance of collaboration and leveraging the expertise of those both across and within teams when working with complex data. This experience was also valuable in improving my programming abilities, leading directly to an interest in scientific software development and my recent role as a research software engineer.
The experimental design was built upon previous work in the lab demonstrating GABA changes in a brain region called the occipital temporal lobe. These GABA changes in the occipital temporal lobe, a key brain region involved in processing visual shape, were observed across a single learning session. A memorable moment of the study was the point when a key piece of analysis indicated this relationship held even when learning occurred across a longer period of multiple days. As the majority of experiments do not yield the expected results and often require extensive rounds of revision and further exploration, the rare moments when things slot together neatly are extremely satisfying and make the long periods of exploration worthwhile.
As an example of such revision of hypotheses, we predicted that the occipital temporal cortex that demonstrated learning-related GABA plasticity might also interact with myelin change. This was based on work from animals indicating some mechanistic relationship between these plasticity mechanisms. However, we did not observe a significant relationship between these signals in the occipital temporal lobe. Unexpectedly, however, we observed the exciting discovery of robust changes in myelin within the pulvinar, a thalamic nucleus involved in visual attention. Further, we discovered that these changes in myelin shaped connectivity between the pulvinar and occipital temporal cortex — linked to both the GABA change in this area and behavior improvement of participants on the task. This unexpected finding changed the direction of our research and led to new insights into the broader network involved in learning and visual attention.
Overall, the most important thing I will take away from the project is the value of collaborative research. Throughout, I had the opportunity to work with many colleagues who provided guidance on methods, techniques, and intellectually stimulating discussions on the project’s direction. This is crucial in modern neuroscience, as an ever-increasing range of complex techniques are employed to study the brain. A collaborative approach ultimately led to new discoveries and ongoing projects to further elucidate the underlying mechanisms of brain plasticity and learning.
About the author
Joe Ziminski completed his PhD in Eisuke Koya’s lab at the University of Sussex, before undertaking a post-doctoral position in the Adaptive Brain Lab with Prof. Zoe Kourtzi, where the present work was completed. He currently works as a research software engineer at the Sainsbury’s Wellcome Centre in London. 0000-0003-4286-6868