Karl J. Friston

Last updated

Karl Friston
Born
Karl John Friston

(1959-07-12) 12 July 1959 (age 65) [1]
York, England
NationalityBritish
Education Gonville and Caius College, Cambridge (BA, 1980)
Known for Statistical parametric mapping, voxel-based morphometry, dynamic causal modelling, free energy principle, active inference
SpouseAnn Elisabeth Leonard [1]
Awards
Scientific career
Fields Neuroscience, Mathematical and theoretical biology, Variational Bayesian methods
Institutions University College London [3]
Website www.fil.ion.ucl.ac.uk/~karl

Karl John Friston FRS FMedSci FRSB (born 12 July 1959) is a British neuroscientist and theoretician at University College London. He is an authority on brain imaging and theoretical neuroscience, especially the use of physics-inspired statistical methods to model neuroimaging data and other random dynamical systems. [3] [4] [5] [6] [7] [8] [9] [10] Friston is a key architect of the free energy principle and active inference. In imaging neuroscience he is best known for statistical parametric mapping and dynamic causal modelling. Friston also acts as a scientific advisor to numerous groups in industry.

Contents

Friston is one of the most highly cited living scientists [11] and in 2016 was ranked No. 1 by Semantic Scholar in the list of top 10 most influential neuroscientists. [12]

Education

Karl Friston attended the Ellesmere Port Grammar School, later renamed Whitby Comprehensive, from 1970 to 1977. Friston studied natural sciences (physics and psychology) at the University of Cambridge in 1980, and completed his medical studies at King's College Hospital, London. [1]

Career

Friston subsequently qualified under the Oxford University Rotational Training Scheme in Psychiatry, and is now a professor of neuroscience at University College London. [13] He was a Wellcome Trust Principal Fellow and is currently Scientific Director of the Wellcome Trust Centre for Neuroimaging. [14] He also holds an honorary consultant post at the National Hospital for Neurology and Neurosurgery. He invented statistical parametric mapping: SPM is an international standard for analysing imaging data and rests on the general linear model and random field theory (developed with Keith Worsley). In 1994 his group developed voxel-based morphometry. [15] VBM detects differences in neuroanatomy and is used clinically and as a surrogate in genetic studies.

These technical contributions were motivated by schizophrenia research and theoretical studies of value-learning (with Gerry Edelman). In 1995, this work was formulated as the dysconnection hypothesis of schizophrenia (with Chris Frith). In 2003, he invented dynamic causal modelling (DCM), which is used to infer the architecture of distributed systems like the brain. Mathematical contributions include Variational Laplace [16] and Generalized filtering, which use variational Bayesian methods for time-series analysis. Friston is principally known for models of functional integration in the human brain and the principles that underlie neuronal interactions. His main contribution to theoretical neurobiology is a variational free energy principle [17] (Active inference in the Bayesian brain [18] ). According to Google Scholar, Friston's h-index is 263. [3]

In 2020 he applied dynamic causal modelling as a Systems biology approach to Epidemiological modelling. [19] He subsequently became a member of Independent SAGE, an independent, public-facing alternative to the COVID-19 pandemic government advisory body Scientific Advisory Group for Emergencies.

In 2024, Friston appeared on StarTalk, the podcast from Neil deGrasse Tyson.

Awards and achievements

In 1996, Friston received the first Young Investigators Award in Human Brain Mapping, and was elected a Fellow of the Academy of Medical Sciences (1999) in recognition of contributions to the bio-medical sciences. In 2000 he was President of the international Organization for Human Brain Mapping. In 2003 he was awarded the Minerva Golden Brain Award and was elected a Fellow of the Royal Society in 2006 and received a Collège de France Medal in 2008. His nomination for the Royal Society reads

Karl Friston pioneered and developed the single most powerful technique for analysing the results of brain imaging studies and unravelling the patterns of cortical activity and the relationship of different cortical areas to one another. Currently over 90% of papers published in brain imaging use his method (SPM or Statistical Parametric Mapping) and this approach is now finding more diverse applications, for example, in the analysis of EEG and MEG data. His method has revolutionised studies of the human brain and given us profound insights into its operations. None has had as major an influence as Friston on the development of human brain studies in the past twenty-five years. [2]

He became a Fellow of the Royal Society of Biology in 2012, received the Weldon Memorial Prize and Medal in 2013 for contributions to mathematical biology and was elected as a member of EMBO in 2014 and the Academia Europaea in 2015. He was the 2016 recipient of the Charles Branch Award for unparalleled breakthroughs in Brain Research and the Glass Brain Award from the Organization for Human Brain Mapping. He holds Honorary Doctorates from the universities of York, Zurich, Liège and Radboud University.

Related Research Articles

Functional integration is the study of how brain regions work together to process information and effect responses. Though functional integration frequently relies on anatomic knowledge of the connections between brain areas, the emphasis is on how large clusters of neurons – numbering in the thousands or millions – fire together under various stimuli. The large datasets required for such a whole-scale picture of brain function have motivated the development of several novel and general methods for the statistical analysis of interdependence, such as dynamic causal modelling and statistical linear parametric mapping. These datasets are typically gathered in human subjects by non-invasive methods such as EEG/MEG, fMRI, or PET. The results can be of clinical value by helping to identify the regions responsible for psychiatric disorders, as well as to assess how different activities or lifestyles affect the functioning of the brain.

<span class="mw-page-title-main">FreeSurfer</span> Brain imaging software package

FreeSurfer is brain imaging software originally developed by Bruce Fischl, Anders Dale, Martin Sereno, and Doug Greve. Development and maintenance of FreeSurfer is now the primary responsibility of the Laboratory for Computational Neuroimaging at the Athinoula A. Martinos Center for Biomedical Imaging. FreeSurfer contains a set of programs with a common focus of analyzing magnetic resonance imaging (MRI) scans of brain tissue. It is an important tool in functional brain mapping and contains tools to conduct both volume based and surface based analysis. FreeSurfer includes tools for the reconstruction of topologically correct and geometrically accurate models of both the gray/white and pial surfaces, for measuring cortical thickness, surface area and folding, and for computing inter-subject registration based on the pattern of cortical folds.

<span class="mw-page-title-main">Voxel-based morphometry</span> Computational neuroanatomy method

Voxel-based morphometry is a computational approach to neuroanatomy that measures differences in local concentrations of brain tissue, through a voxel-wise comparison of multiple brain images.

Connectomics is the production and study of connectomes: comprehensive maps of connections within an organism's nervous system. More generally, it can be thought of as the study of neuronal wiring diagrams with a focus on how structural connectivity, individual synapses, cellular morphology, and cellular ultrastructure contribute to the make up of a network. The nervous system is a network made of billions of connections and these connections are responsible for our thoughts, emotions, actions, memories, function and dysfunction. Therefore, the study of connectomics aims to advance our understanding of mental health and cognition by understanding how cells in the nervous system are connected and communicate. Because these structures are extremely complex, methods within this field use a high-throughput application of functional and structural neural imaging, most commonly magnetic resonance imaging (MRI), electron microscopy, and histological techniques in order to increase the speed, efficiency, and resolution of these nervous system maps. To date, tens of large scale datasets have been collected spanning the nervous system including the various areas of cortex, cerebellum, the retina, the peripheral nervous system and neuromuscular junctions.

Bayesian approaches to brain function investigate the capacity of the nervous system to operate in situations of uncertainty in a fashion that is close to the optimal prescribed by Bayesian statistics. This term is used in behavioural sciences and neuroscience and studies associated with this term often strive to explain the brain's cognitive abilities based on statistical principles. It is frequently assumed that the nervous system maintains internal probabilistic models that are updated by neural processing of sensory information using methods approximating those of Bayesian probability.

Psychophysiological interaction (PPI) is a brain connectivity analysis method for functional brain imaging data, mainly functional magnetic resonance imaging (fMRI). It estimates context-dependent changes in effective connectivity (coupling) between brain regions. Thus, PPI analysis identifies brain regions whose activity depends on an interaction between psychological context and physiological state of the seed region.

Sophia Frangou is a professor of psychiatry at the Icahn School of Medicine at Mount Sinai where she heads the Psychosis Research Program. She is a Fellow of the Royal College of Psychiatrists and vice-chair of the RCPsych Panamerican Division. She is a Fellow of the European Psychiatric Association (EPA) and of the American Psychiatric Association (APA). She served as vice-president for Research of the International Society for Bipolar Disorders from 2010 to 2014. She has also served on the Council of the British Association for Psychopharmacology. She is founding member of the EPA NeuroImaging section and founding chair of the Brain Imaging Network of the European College of Neuropsychopharmacology. She is one of the two Editors of European Psychiatry, the official Journal of the European Psychiatric Association.

Anders Martin Dale is a prominent neuroscientist and professor of radiology, neurosciences, psychiatry, and cognitive science at the University of California, San Diego (UCSD), and is one of the world's leading developers of sophisticated computational neuroimaging techniques. He is the founding Director of the Center for Multimodal Imaging Genetics (CMIG) at UCSD.

<span class="mw-page-title-main">Resting state fMRI</span> Type of functional magnetic resonance imaging

Resting state fMRI is a method of functional magnetic resonance imaging (fMRI) that is used in brain mapping to evaluate regional interactions that occur in a resting or task-negative state, when an explicit task is not being performed. A number of resting-state brain networks have been identified, one of which is the default mode network. These brain networks are observed through changes in blood flow in the brain which creates what is referred to as a blood-oxygen-level dependent (BOLD) signal that can be measured using fMRI.

The following outline is provided as an overview of and topical guide to brain mapping:

The free energy principle is a theoretical framework suggesting that the brain reduces surprise or uncertainty by making predictions based on internal models and updating them using sensory input. It highlights the brain's objective of aligning its internal model and the external world to enhance prediction accuracy. This principle integrates Bayesian inference with active inference, where actions are guided by predictions and sensory feedback refines them. It has wide-ranging implications for comprehending brain function, perception, and action.

Dynamic functional connectivity (DFC) refers to the observed phenomenon that functional connectivity changes over a short time. Dynamic functional connectivity is a recent expansion on traditional functional connectivity analysis which typically assumes that functional networks are static in time. DFC is related to a variety of different neurological disorders, and has been suggested to be a more accurate representation of functional brain networks. The primary tool for analyzing DFC is fMRI, but DFC has also been observed with several other mediums. DFC is a recent development within the field of functional neuroimaging whose discovery was motivated by the observation of temporal variability in the rising field of steady state connectivity research.

In neuroscience, predictive coding is a theory of brain function which postulates that the brain is constantly generating and updating a "mental model" of the environment. According to the theory, such a mental model is used to predict input signals from the senses that are then compared with the actual input signals from those senses. Predictive coding is member of a wider set of theories that follow the Bayesian brain hypothesis.

Dynamic causal modeling (DCM) is a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison. It uses nonlinear state-space models in continuous time, specified using stochastic or ordinary differential equations. DCM was initially developed for testing hypotheses about neural dynamics. In this setting, differential equations describe the interaction of neural populations, which directly or indirectly give rise to functional neuroimaging data e.g., functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) or electroencephalography (EEG). Parameters in these models quantify the directed influences or effective connectivity among neuronal populations, which are estimated from the data using Bayesian statistical methods.

<span class="mw-page-title-main">John-Dylan Haynes</span> British-German brain researcher (born 1971)

John-Dylan Haynes is a British-German brain researcher.

Bayesian model reduction is a method for computing the evidence and posterior over the parameters of Bayesian models that differ in their priors. A full model is fitted to data using standard approaches. Hypotheses are then tested by defining one or more 'reduced' models with alternative priors, which usually – in the limit – switch off certain parameters. The evidence and parameters of the reduced models can then be computed from the evidence and estimated (posterior) parameters of the full model using Bayesian model reduction. If the priors and posteriors are normally distributed, then there is an analytic solution which can be computed rapidly. This has multiple scientific and engineering applications: these include scoring the evidence for large numbers of models very quickly and facilitating the estimation of hierarchical models.

<span class="mw-page-title-main">Vince Calhoun</span> American engineer and neuroscientist (Born 1967)

Vince Daniel Calhoun is an American engineer and neuroscientist. He directs the Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), a partnership between Georgia State University, Georgia Institute of Technology, and Emory University, and holds faculty appointments at all three institutions. He was formerly the President of the Mind Research Network and a Distinguished Professor of Electrical and Computer Engineering at the University of New Mexico.

Catherine J. Price is a British neuroscientist and academic. She is Professor of Cognitive Neuroscience and director of the Wellcome Trust Centre for Neuroimaging at University College London.

<span class="mw-page-title-main">Rosalyn Moran</span> Irish and British neuroscientist and computational psychiatrist

Rosalyn J. Moran is an Irish and British neuroscientist and computational psychiatrist. She is deputy director of the King's College London Institute for Artificial Intelligence. Her research looks to understand neural algorithms through brain connectivity.

References

  1. 1 2 3 "FRISTON, Prof. Karl John". Who's Who 2014, A & C Black, an imprint of Bloomsbury Publishing plc, 2014; online edn, Oxford University Press.(subscription required)
  2. 1 2 "EC/2006/16: Friston, Karl John". London: The Royal Society. Archived from the original on 29 March 2017.
  3. 1 2 3 Karl J. Friston publications indexed by Google Scholar
  4. ↑ Friston, K (2003). "Learning and inference in the brain". Neural Networks. 16 (9): 1325–52. CiteSeerX   10.1.1.160.2313 . doi:10.1016/j.neunet.2003.06.005. PMID   14622888. S2CID   17163442.
  5. ↑ Friston, K (2002). "Functional integration and inference in the brain". Progress in Neurobiology. 68 (2): 113–43. doi:10.1016/s0301-0082(02)00076-x. PMID   12450490. S2CID   7203119.
  6. ↑ Friston, K (2005). "A theory of cortical responses". Philosophical Transactions of the Royal Society B: Biological Sciences. 360 (1456): 815–36. doi:10.1098/rstb.2005.1622. PMC   1569488 . PMID   15937014.
  7. ↑ Karl J. Friston's publications indexed by the Scopus bibliographic database. (subscription required)
  8. ↑ Penny, W; Ghahramani, Z; Friston, K (2005). "Bilinear dynamical systems". Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences . 360 (1457): 983–93. doi:10.1098/rstb.2005.1642. PMC   1854926 . PMID   16087442. Open Access logo PLoS transparent.svg
  9. ↑ Harrison, L. M.; David, O; Friston, K. J. (2005). "Stochastic models of neuronal dynamics". Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 360 (1457): 1075–91. doi:10.1098/rstb.2005.1648. PMC   1854931 . PMID   16087449.
  10. ↑ David, O; Harrison, L; Friston, K. J. (2005). "Modelling event-related responses in the brain". NeuroImage. 25 (3): 756–70. doi:10.1016/j.neuroimage.2004.12.030. PMID   15808977. S2CID   11725486.
  11. ↑ "Highly Cited Researchers (h>100) according to their Google Scholar Citations public profiles" . Retrieved 28 July 2022.
  12. ↑ Bohannon, John (11 November 2016). "A computer program just ranked the most influential brain scientists of the modern era". sciencemag.org. Retrieved 5 January 2017.
  13. ↑ "Iris View Profile". University College London. Retrieved 20 July 2014.
  14. ↑ "Professor Karl Friston – Selected papers".{{cite journal}}: Cite journal requires |journal= (help)
  15. ↑ Wright, I.C. (1995). "A Voxel-Based Method for the Statistical Analysis of Gray and White Matter Density Applied to Schizophrenia". NeuroImage. 2 (4): 244–252. doi:10.1006/nimg.1995.1032. PMID   9343609. S2CID   45664559.
  16. ↑ K Friston, J Mattout, N Trujillo-Barreto, J Ashburner, and W Penny, "Variational free energy and the Laplace approximation," NeuroImage, vol. 34, no. 1, pp. 220-34, 2007
  17. ↑ Raviv, Shaun (13 November 2018). "The Genius Neuroscientist Who Might Hold the Key to True AI". WIRED. Retrieved 16 November 2018.
  18. ↑ Friston, Karl (2018). "Of woodlice and men: A Bayesian account of cognition, life and consciousness. An interview with Karl Friston (by Martin Fortier & Daniel Friedman)". ALIUS Bulletin. 2: 17–43.
  19. ↑ Spinney, Laura (31 May 2020). "Covid-19 expert Karl Friston: "Germany may have more immunological "dark matter""". The Guardian. Retrieved 8 April 2021.
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