Erik D. Fagerholm Group: Computational Neuroscience

About

Our group creates computational models to better understand the ways in which information is processed in dynamical networks. In particular, we are interested in linking the statistical laws underlying the evolution of neural dynamics at different scales of operation in the brain. We translate these models into novel diagnosis and treatment options for neurological disorders.

Specifically, our work focuses on:

Dynamics of Neural Systems: Investigating the energy landscapes and dissipative structures that underlie neural activity, aiming to quantify how the brain stores and transmits information during cognitive processes.

Information Theoretic metrics in Neuroscience: Developing metrics such as 'selection entropy' to explore the hidden information within neuronal patterns, providing insight into how the structures of large-scale neural systems are organized for maximum computational efficiency.

Neural Timeseries Analysis: Creating methodologies to estimate anisotropy and phase-amplitude interactions directly from neural timeseries data, enhancing our understanding of the temporal and spatial dynamics of neural activity.

Application to Clinical Neuroscience: Applying computational models to clinical contexts, such as fine-tuning neural excitation/inhibition for tailored treatment strategies in psychiatric disorders such as treatment-resistant depression and focal epilepsy.

A) in this example we describe the electrical signals emanating from a neural region prone to epileptic seizures using a drift-diffusion model
B) the diffusive properties of the neural structure are estimated via diffusion tensor imaging (DTI)
C) this model allows us to optimize the stimulation parameters to be used in the mitigation of seizures via neuromodulation techniques such as deep brain stimulation

Anisotropy in neural signals: Developing novel methodologies to estimate the degree to which neural signals propagate anisotropically through the structures of the brain, as a way of providing insight into the temporal and spatial dynamics of neural activity.

The scaling laws of neural systems: A main avenue of research in our group is to uncover the mathematical transformations governing changes in network topology over the vast range of scales in the brain — from the microscopic operation of individual cells, to the mesoscopic function of neural circuits, through to the macroscopic order of entire brain regions.

Changes in scale in neural systems can be seen in various ways - for instance there is the phylogenetic change in size — across evolutionary timescales: left: vervet monkey, right: human

Changes in scale can also be seen ontogenetically — across the lifetime of an individual organism: left: infant human, right: adult human

We can also consider the changes in scale resulting from various levels of spatial resolution with which we are able to image a neural system: left to right shows increasing resolution in an adult human

Publications

selected publications

Fagerholm, E., at al. Estimating the Energy of Dissipative Neural Systems. (2024) https://doi.org/10.31219/osf.io/4kcw5
Dezhina, Z., Smallwood, J., Xu, T., Turkheimer, F.E., Moran, R.J., Friston, K.J., Leech, R. and Fagerholm, E.D., 2023. Establishing brain states in neuroimaging data. PLOS Computational Biology, 19(10), p.e1011571.
Fagerholm, E. D., et al. Selection entropy: The information hidden within neuronal patterns. Physical Review Research 5, 023197 (2023).
Fagerholm, E. D., et al. A primer on entropy in neuroscience. Neuroscience & Biobehavioral Reviews (2023): 105070.
Fagerholm, E.D., et al. Estimating anisotropy directly via neural timeseries. The Journal of Computational Neuroscience (2022).
Turkheimer FE, Liu J, Fagerholm E.D., …, 2022, The art of pain: A quantitative color analysis of the self-portraits of Frida Kahlo. Front. Hum. Neurosci. 16:1000656.
Turkheimer, F.E., Liu, J., Fagerholm, E.D., Dazzan, P., Loggia, M. and Bettelheim, E., 2022. The Art of Pain: A Quantitative Colour Analysis of the Self-Portraits of Frida Kahlo., accepted at Frontiers in Human Neuroscience
Fagerholm, Erik D., et al. "Rendering neuronal state equations compatible with the principle of stationary action." The Journal of Mathematical Neuroscience 11.1 (2021): 1-15.
Fagerholm, Erik D., et al. "Fine-tuning neural excitation/inhibition for tailored ketamine use in treatment-resistant depression." Translational psychiatry 11.1 (2021): 1-10.
Fagerholm, Erik D., et al. "Neural systems under change of scale." Frontiers in Computational Neuroscience 15 (2021): 33.
Friston, K.J., Fagerholm, E.D.,…, 2021. Parcels and particles: Markov blankets in the brain. Network Neuroscience, 5(1), pp.211-251.
Turkheimer, F.E. ,…, Fagerholm, E.D. ,…, 2021. A complex systems perspective on neuroimaging studies of behaviour and its disorders. The Neuroscientist,
Fagerholm, Erik D., et al. "Neural diffusivity and pre-emptive epileptic seizure intervention." PLOS Computational Biology 16.12 (2020): e1008448.
Fagerholm, Erik D., et al. "Conservation laws by virtue of scale symmetries in neural systems." PLoS computational biology 16.5 (2020): e1007865.
Fagerholm, Erik D., et al. "Dynamic causal modelling of phase-amplitude interactions." Neuroimage 208 (2020): 116452.
Moran RJ, Fagerholm E.D., et al. Estimating required ‘lockdown’ cycles before immunity to SARS-CoV-2: model-based analyses of susceptible population sizes, ‘S0’, in seven European countries, including the UK and Ireland Wellcome Open Res 2020, 5:85,
Turkheimer, F.E., Fagerholm, E.D. ,…, 2020. A GABA Interneuron Deficit Model of the Art of Vincent van Gogh. Frontiers in Psychiatry, 11, p.685.
Fagerholm, Erik D., et al. "The characteristic patterns of neuronal avalanches in mice under anaesthesia and at rest: An investigation using constrained artificial neural networks." Plos one 13.5 (2018): e0197893.
Fagerholm, Erik D., et al. "Cortical entropy, mutual information and scale-free dynamics in waking mice." Cerebral cortex 26.10 (2016): 3945-3952.
Dinov M. ,…, Fagerholm E.D. ,…, Novel modelling of task vs. rest brain state predictability using a dynamic time warping spectrum: comparisons and contrasts with other standard measures of brain dynamics. Frontiers in computational neuroscience. 2016 May 12;10:46.
Fagerholm, Erik D., et al. "Disconnection of network hubs and cognitive impairment after traumatic brain injury." Brain 138.6 (2015): 1696-1709.
Fagerholm, Erik D., et al. "Cascades and cognitive state: focused attention incurs subcritical dynamics." Journal of Neuroscience 35.11 (2015): 4626-4634.
Scott, G., Fagerholm, E.D. ,…, 2014. Voltage imaging of waking mouse cortex reveals emergence of critical neuronal dynamics. Journal of Neuroscience, 34(50), pp.16611-16620.
Schalek, ,…, Fagerholm E.D. ,…, J., 2011. Development of high-throughput, high-resolution 3D reconstruction of large-volume biological tissue using automated tape collection ultramicrotomy and scanning electron microscopy. Microscopy and Microanalysis, 17(S2), pp.966-967.