BY KEVIN WEINER
New OHBM Communications Committee article on HuffPost Science:
It’s often hard to find easy-to-read articles about cool scientific findings that are written in a clear way - let alone articles that are understandable enough to use as bedtime reading with your child. But, here’s a little secret: there are articles out there that are actually written by scientists and approved by children before they are published. Read more
BY LEONARDO FERNANDINO
For over two decades functional magnetic resonance imaging (fMRI) has been the indisputable workhorse in human brain mapping. Its ability to localize brain activity with high spatial resolution (millimeters), coupled with its non-invasiveness, make it an excellent tool for mapping behavioral and cognitive phenomena onto detailed brain anatomy. However, since fMRI relies on changes in blood flow, volume and oxygen concentration as indicators of neural activity, millisecond-scale fluctuations in neuronal activity are not reflected in the signal. The temporal resolution of fMRI (typically > 500 ms) is therefore too coarse to track neural activity in real time and discriminate rapidly succeeding neural events.
For example, some fMRI studies indicate that auditory cortical activations in response to signed language are stronger in deaf participants than in hearing individuals, raising the possibility that the auditory cortex is rewired to process visual information in deaf individuals. An alternative interpretation, however, is that visual processing of sign language occurs entirely in the visual cortex for both groups, and that activations in the auditory cortex reflect only a later, conceptual processing stage. The speed with which these processes occur makes it challenging to determine the precise sequence in which different brain regions become active using fMRI (or any other method that relies on metabolic responses, such as fNIRS or PET).
Fortunately, other non-invasive techniques can provide such precise temporal information. Electroencephalography (EEG) and magnetoencephalography (MEG) directly track the electrical activity of brain cells by measuring its effects on the electrical and magnetic fields just outside the head. They allow researchers to record brain activity at a very high temporal resolution (milliseconds), close to the actual timescale of neural computations.
EEG measures changes in electrical potential generated by the brain, which reflect the bulk electrical activity of many pyramidal neurons depolarizing in synchrony, through electrode leads placed on the scalp and connected to an amplifier. Researchers can then estimate the locations of the neural sources of these signals. The accuracy of these estimates depends, among other things, on the number of electrodes used, with higher numbers resulting in more precise estimates. Typically, between 64 and 256 electrodes are used.
Nevertheless, MRI-assisted EEG source localization is becoming more common. In both cases, the inverse solution is typically computed through an iterative algorithm that searches for the combination of cortical sources whose forward-modeled signal best matches the observed signal.
The main advantage of MEG over EEG comes from the fact that, while electrical signals are blurred and distorted by the skull, magnetic fields can traverse it virtually unobstructed. Thus, the MEG signal has higher spatial resolution, which allows better estimation of its neural sources.
MEG’s main disadvantage is the cost: the scanner and magnetically shielded room run in the millions of dollars, while a high-density EEG system costs under US$150,000 (although recent advances in quantum sensing might herald the coming of low cost, room temperature MEG). Even leaving these financial considerations aside, MEG still cannot completely replace EEG (nor vice versa), since the two techniques have complementary strengths. Because a magnetic field is oriented perpendicularly to its generating current, MEG is virtually blind to activations outside the sulcal walls, while EEG is maximally sensitive to activations in the gyral crests (and somewhat sensitive to activations in the sulci). Thus, simultaneous recording of EEG and MEG can add valuable information to the source estimation procedure.
Both EEG and MEG have been used clinically in the detection and localization of seizure activity in epileptic patients, which can be crucial for diagnosis and surgical planning. In research, they have been used to study a wide variety of problems, from spatial attention and synesthesia to sentence comprehension. Researchers at the University of California San Diego, for example, used them to answer the question posed earlier, of whether the auditory cortex in deaf individuals is indeed rewired for visual processing. By recording the MEG responses to signed language in deaf and hearing participants, they showed that, in both groups, activity was confined to the visual cortex in the early perceptual stage (~100 ms), and only later, during the lexico-semantic stage (~300 ms), the auditory cortex became active. This result provides compelling evidence against the auditory rewiring hypothesis.
Like any technique, MEG and EEG have unique strengths and limitations. The advantage afforded by high temporal resolution is partly offset by the relatively low spatial resolution of the estimated source locations. Nevertheless, researchers who manage to incorporate these approaches into their toolkit should expect a rewarding boost in their ability to probe the living human brain.
BY GUEST AUTHOR R. ALLEN WAGGONER
I had the honor of knowing Dr. Kang Cheng not only as a scientific colleague, but also as one of my closest friends for more than 20 years. He was the picture of health, so I was as stunned as everyone else when I learned that he has passed away suddenly at the age of 54. He left behind his wife, Professor Mariko Miyata, and their young son Kai.
Kang was born in Ningbo, China, in the province of Zhejiang, in 1962. He did not start out as a neuroscientist but graduated from Zhejiang University in 1983, with a B.A. in Geology. After graduating, Kang worked at the Chinese Academy of Sciences, Chengdu Institute of Geography for six years, doing image processing of satellite images. His work in image processing led to several opportunities to come to RIKEN (The Institute for Physical and Chemical Research, in Wako-shi Japan) for short visits. One of those visits was in a neuroscience lab, the Information Science Laboratory, headed by Dr. Keiji Tanaka. Dr. Tanaka offered Kang the opportunity to join the lab on a more permanent basis, if he was interested in becoming a neuroscientist. Kang accepted this offer and moved to Japan in 1989.
Kang's neuroscience career ranged from histology and electrophysiology studies, to PET and, eventually, fMRI. In essence he left behind a career of making maps from satellite images for a career that would eventually lead to making brain maps from MRI images. The very first neuroscience study that he coauthored was published in Nature, not a bad start to a scientific career. That paper was on columnar organization in the inferotemporal cortex and columnar organization in the brain proved to be one of the primary themes in his scientific career. In 1995, Kang received his Ph.D. from Osaka University Department of Biophysical Engineering. His dissertation was entitled "Studies of Extrastriate Visual Cortex of Primates" and included both electrophysiological studies in monkeys and PET studies in humans. These studies were carried out in Dr. Tanaka's lab.
Kang stayed on in Dr. Tanaka's lab as a postdoc and when Dr. Tanaka received funding for a 4 Tesla MRI system in 1996, Kang became one of the primary members of the fMRI team within the lab. He spearheaded the effort to image cortical columns, which was and remains, a primary interest in the lab. His initial efforts lead to our 2001 Neuron paper on observing ocular dominance columns in humans, with fMRI. Ours was not the first paper reporting the observation of ocular dominance columns with fMRI, but many neuroscientists were skeptical of the earlier reports. In our paper, Kang described in detail many key methodological issues, such as the necessary shape of the Calcarine sulcus, the required slice orientation, and included illustrations of what the expected functional maps should look like if columns were successfully observed as well as when, for example, the slice orientation was not quite right. Kang's attention to detail won over the skeptics and this became the work for which Kang is best known.
Neuroimaging studies lie at the intersection of a variety of scientific disciplines. Kang (a neuroscientist) and I (an MRI physicist) working together was an example of this. But Kang recognized the importance of each member in the team having a basic understanding of all the methods being brought together. He felt that no aspect of the experiment or the analysis should be viewed as a black box, and that without at least a basic understanding of the contributions each discipline brings to neuroscience, communication with scientists from the various disciplines is very difficult. Thus our collaboration over the years included many long conversations about recent advances in MRI technology, the details of the neuroscience questions we were trying to answer, and whether the methods we, or others, were employing were even capable of yielding answers these questions. This also meant that we would often require new postdocs arriving in the lab to attend lectures aimed at lifting the cover off the black boxes in their background.
In 1997, RIKEN established the Brain Science Institute (BSI) and Dr. Tanaka's lab became one of the founding labs. In 2001, Kang was named Deputy Laboratory Head of the Laboratory for Cognitive Brain Mapping (the current name of Dr. Tanaka's lab). In 2006 Kang was named Head of the newly formed Support Unit for functional Magnetic Resonance Imaging. Kang continued to hold both appointments until the time of his passing. He was also an Adjunct Associate Professor in the Graduate School of Science and Engineering of Saitama University.
Kang's life was not limited to science; during his years at RIKEN he helped organize intramural clubs for badminton, basketball, and soccer. He also enjoyed singing karaoke. In fact, in his early days at RIKEN, one of the main ways that he learned to both read and speak Japanese was by going to karaoke with his coworkers and learning to sing Japanese songs. At Kang's wedding reception, after prompting by several friends, he even gave an impromptu rendition of one of his favorite karaoke songs. He was also an avid photographer, and enjoyed filling the inboxes of his friends with pictures of conference dinners, his family having fun, or cherry blossoms in the spring.
I first met Kang and Dr. Tanaka at the University of Western Ontario. They were visiting Ravi Menon's lab and invited me there to interview for an MRI physicist position in Dr. Tanaka's lab. Kang picked me up from the airport and since then (for the next 20 years) he always enjoyed telling people how nervous I was when we first met in the airport. From that beginning we became life-long friends and he was even a groomsman at my wedding.
Kang was a good friend not only to those of us who worked closely with him, but to many in the neuroscience community as well. He will be sorely missed by all who knew him.
Two memorial websites have been set up in Kang's honor:
- One by his friends and colleagues at RIKEN: http://www.brain.riken.jp/labs/cbms/kchengmemorial.html
- The other by his friends in the Overseas Chinese Society for Magnetic Resonance in Medicine: https://ocsmrm.wordpress.com/2016/11/13/memorial_to_prof_kang_cheng/
BY GUEST AUTHOR PETER BANDETTINI
This post originally appeared on The Brain Blog by Peter Bandettini and Eric Wong. Republished with permission.
I’ve been working to advance Functional MRI (fMRI) since its inception. On Sept 14, 1991, two years into graduate school, and a month after seeing preliminary Massachusetts General Hospital results at the SMR meeting in San Francisco, Eric Wong and I performed our first successful fMRI experiment (Bandettini 2012).
Functional MRI, to this day, over a quarter century later, remains as exciting to me as on Day 1 as developments and applications continue at a rapid rate. While human brain imaging methodologies have arisen and grown over the years, and all of them, I’m certain, have interesting stories behind them, I wanted to share why I feel fMRI is unique:
1. fMRI was a surprising, rapid discovery.
Elements leading up to the discovery of fMRI were the discovery of BOLD by Ogawa et al (Ogawa 2012), the discovery of the dependence of blood T2 on oxygenation by Thulborn et al (Thulborn 2012), the advent of arterial spin labelling techniques by Williams et al. (Williams, Detre et al. 1992), the technical capability to perform EPI (Cohen and Schmitt 2012), and for the Minnesota group, higher field strengths (Uğurbil 2012).
The first fMRI results came from Ken Kwong’s penchant for trying interesting experiments. With Ken’s experiment, the method was discovered rather than incrementally developed. In fact, the pulse sequence and basic parameters used by Ken for BOLD were not anything overly complex or new – simple T2* weighted gradient-echo EPI at 1.5 Tesla. There was minimal time series processing involved then – in stark contrast to processing methods today. Interestingly, aside from the explosion in the sophistication of time series processing, the details of Ken’s first experiment have not qualitatively changed in terms of general practice over the years. He was just the first to realize that such a straightforward thing could be done!
This discovery surprised and excited the MRI community. To provide an analogy, it was as if we realized that if one sets the exposure settings of a standard camera just right, rather than just getting a photograph, you can get a picture of, say, subatomic particles. While the MRI scanner vendors adopted a wait-and-see approach before putting any resources into developing fMRI, the clinical and basic neuroscientists were highly motivated to start scanning.
2. fMRI was a revolutionary advance in functional imaging capability.
Functional MRI was, and still is, the only non-invasive, whole-brain method that has enough sensitivity to see human brain activity with about 2mm detail as it is happening in real time. This made for good science fiction before 1991. No one imagined it would become reality so quickly.
3. fMRI is deeply multidisciplinary.
Functional MRI brought disparate disciplines together in a way that was unprecedented. Suddenly, cognitive neuroscientists were having intense conversations with MR physicists. Computer programmers were talking with clinicians. The best fMRI research today has a signature of advancing methodology and insight into brain function – requiring close collaborations between physicists, statisticians, programmers, and neuroscientists.
4. fMRI is riding on the back of the clinical MR industry.
A huge factor that many people overlook is that fMRI was able to launch and propagate so rapidly because it leveraged the massive clinical MRI industry. In the early 90’s there were at least 20,000 clinical MRI scanners worldwide. By 1998, most MRI scanners were equipped with EPI – for other more clinically relevant purposes such as following a bolus of gadolinium for perfusion imaging or visualizing the heart beating.
Even though fMRI had minimal clinical impact, almost every MRI scanner in every hospital in the world was a potential brain function imaging machine. There was no need for a manufacturer to make fMRI machines. They already existed! These scanners were priced at over $1M each and were paid for and supported by hospital revenue – not neuroscience research grants. Today, that’s changing somewhat as the fMRI market grows and research grant revenue towards fMRI increases, but the reality is that fMRI depends on the clinical MRI market to survive.
fMRI has tremendously benefited from essentially riding on the back of the clinical MRI industry. This relationship has clear drawbacks too. Many interesting pulse sequences and custom fMRI setups are not being disseminated worldwide because the scanner vendors do not yet see a large enough market of fMRI to necessitate adding more development resources. Until fMRI becomes a thriving clinical technique (hopefully soon), it will be at the mercy of the clinical focus of the MRI scanner vendors – namely Siemens, General Electric, and Philips.
5. The degrees of freedom in fMRI acquisition is vast and unexplored.
We can do so much more than collect a simple time series of T2* weighted echo planar images. The ability to derive physiologic and neuronal information from MRI is still being explored as there are so many “knobs” you can adjust on the acquisition side to highlight gray matter, white matter, CSF, flowing blood, perfusion, iron deposits, vascular territories, trauma, leaks in the blood brain barrier, hemorrhage, deoxygenated blood, metabolism, pulsation, macromolecules, temperature, water diffusion, diffusion anisotropy, and much more. Additionally, the information that may be useful to fMRI is also still relatively untapped. Along with mapping the magnitude of the hemodynamic response as is most commonly done, we can derive information about latency, fluctuations, oxidative metabolic rate changes, blood vessel sizes, oxygenation, and more.
6. Processing methods are exploding in variety and sophistication.
Functional MRI processing methods continue to surprise – as it seems that the field continues to find new and better ways to extract, compare, and display new hemodynamic and neuronal information in groups and individuals. With the emergence of massive shared data sets, ever more subtle information about individual differences and similarities is being plumbed with the help of modern machine learning approaches.
7. Functional MRI just works.
Functional MRI just works – almost every time! It’s a stunningly robust technique. The functional effect size to noise ratio (from 6/1 to 1/1) is still perhaps too small and subject-wise variability a bit to large (with current post processing techniques) for robust clinical use (at least 10/1 is considered essential) but is large enough to see a significant effect within a few minutes of averaging. If it took 6 hours of averaging to see something, ambitious people would still do it but it would be much more difficult and the field would be much more anemic at this point.
8. fMRI requires two highly serendipitous properties.
Another key to fMRI that is commonly taken for granted: It requires two very subtle yet all-important properties to be possible at all. The first is that hemoglobin has to change its magnetic susceptibility in a non-trivial manner between being oxygenated and deoxygenated. This is an extremely rare property of a biologic tissue. If our blood were copper based – as with mollusks – rather than iron based, this would not happen. We would not have BOLD contrast as copper based blood does not change susceptibility with oxygenation. The second all-important property is that, with activation, a localized flow increase in the active region creates a highly focal overabundance of oxygenation. Why didn’t nature just require that the oxygenation stay the same in the active regions? We are still trying to figure that out, but the fact that it does – every single time with every person – similarly across species – in the same precise way in a stunningly consistent manner is highly fortunate. Perhaps our brains could have evolved a system where localized activation-induced changes in flow increased to simply match the increased metabolic needs rather than apparently overshoot them. If this happened, there would be no BOLD changes. We are lucky!
9. fMRI fills a unique temporal and spatial niche.
The information that fMRI provides fills a large and interesting temporal and spatial niche in understanding brain organization. Our brains are highly modular, and fortunately, the larger modules (motor cortex, visual cortex, etc..) are easily large enough to be discerned with fMRI. If our largest brain modules happened to be no larger than ocular dominance columns, fMRI would have never taken off, and if it did, interpretation of the results would have been a challenge at best. We’ll likely gain enough sensitivity and resolution soon to routinely probe the columnar and layer level organization of the brain soon – which brings us to the next unique property…
10. The highest fMRI spatial resolution matches the intrinsic precision of hemodynamic control.
It appears that the highest resolution achievable to fMRI (limited by scanning technology) – that of cortical columns or layers – matches the intrinsic precision of hemodynamic control. In other words, the smallest homogeneously activated region that causes a focal change in blood flow is on the order of columns or layers (<1mm). This perhaps suggests that this is the smallest scale in which groups of neurons are activated together. This last point is potentially controversial as it may suggest that looking any finer than this scale at neuronal activity may not necessarily lend insight into modular brain organization. Either way, again it’s fortuitous that fMRI resolution limits match the hemodynamic control limits – at least in humans.
The OHBM Blog Team thanks Peter Bandettini for this guest post.
Interested in submitting a guest post for consideration? Email your post to the OHBM blog team at firstname.lastname@example.org.
BY NILS MUHLERT
Many neuroimaging projects are multi-disciplinary. They often involve collaborations between physicists, engineers, computer scientists, biologists, cognitive scientists, and clinicians amongst others. Inevitably, this leads some to go well beyond their core discipline, learning and making notable advances in complementary fields. Michel Thiebaut de Schotten’s career falls firmly within this camp. With a background in Psychology, Michel went on to make substantial advances in neurobiology and neuropsychiatry, co-authoring a fundamental book on white matter anatomy (Atlas of Human Brain Connections), and papers on the anatomy of spatial neglect, attention and word reading ability, before reinterpreting and re-analysing data from historical case studies in neurology. This breadth of experience lends itself well to OHBM, a society that provides a bridge between disciplines.
Following 8 years as postdoc and research fellow in King’s College London, Michel recently crossed the channel back to France, starting the ‘Brain Connectivity and Behaviour’ group in Paris’s famous Hôpital de la Salpêtrière at the Sorbonne. Michel has also recently taken over the role of OHBM treasurer, from the previous incumbent Kevin Murphy. Here, we find out more about Michel’s scientific journey and what he hopes to achieve in this new role.
Nils Muhlert (NM): Looking back over your studies, which would you say you are most proud of, and why?
Michel Thiebaut de Schotten (MTdS): Our most recent work on the subdivision of the brain based on structural connectivity is a real source of pride. My team and I have been working hard on this method. I feel that we are making a big theoretical step forward in the way that we look at structural connectivity. We now, finally, can unify white matter organisation with the functional specialisation of the grey matter. I am really excited about the future discoveries that this new method will bring.
NM: Sounds exciting! How does this new approach approach work?
MTdS: We identified units of cortex with a specific signature of connectivity with the rest of the brain and decoded their function using the tool ‘decode’ on neurosynth. Interestingly, areas defined by their connectivity exhibit variations in extent and localisation between brains but retain a robust pattern of connectivity. Hence, these methods offer an ideal new way to study the relationship between structural and functional variability by providing more individually tailored brain models (for more info, see our recent editorial).
NM: Your published work could easily be described as ‘multi-disciplinary’ - do you see yourself as more of a cognitive scientist, neurology-researcher, historian, or something else?
MTdS: I am a neuropsychologist and I am passionate about my work. This sometimes leads me to explore and use new methods or spend long hours reading antiquated manuscripts. My motto is to “strive for a better understanding of the research of the past in order to appropriately contribute to the research of the future”.
NM: Why did you want to become involved with the OHBM executive committee?
MTdS: Mostly out of curiosity. I am a great admirer of OHBM`s work, having attended every annual meeting since 2006. As each year passes, the work of OHBM has become more and more impressive with regard to both the content of the programme as well as to the overall organisation of the meeting. Having served as a dedicated Program/Treasurer Committee Member in the International School of Clinical Neuroanatomy in the past, I wished to expand on my experience as OHBM treasurer.
NM: Tell us a bit about the logistics of being treasurer for OHBM - what are the main roles and challenges?
MTdS: As Treasurer, the core attribute of my role is to verify, monitor and validate expenses, and prepare the budget for next year’s conference.
The main challenge of the role is mobilising committee members in order to reach a consensus on each proposition. Committee members are very intelligent expert scientists, professionally trained to question everything. That in itself makes the OHBM Committee a particularly challenging crowd to convince.
NM: What would you like to achieve as OHBM treasurer?
MTdS: In my term as OHBM Treasurer, I would like to accomplish three specific goals. I would like to (1)Set up a policy for the OHBM’s reserve funds, (2) Reduce the price of the educational courses’ registration to its bare minimum, (3) Reduce the price of the conference registration for students.
Many thanks Michel!
BY TZIPI HOROWITZ-KRAUS
Read Part 1 of this interview here.
Tzipi Horowitz-Kraus (THK): What do you consider to be the greatest scientific discovery that was made possible by neuroimaging?
Karl Friston (KF): That's a difficult question. I think we all have to acknowledge that there is no field in systems neuroscience that hasn't been profoundly touched by neuroimaging. However, I think the impact and import of neuroimaging is not about discovery, it is seismic in a slower and subtler way; basically, neuroimaging makes a lot of sense of stuff that we already knew. In other words, I would not point to what has been discovered as the validation of brain mapping but point to how it makes sense of the vast amount of knowledge that has been accrued through the centuries of anatomy, physiology and psychology. We knew a lot of things in the past but now we know how to put them together. Neuroimaging can contextualize mesoscopic – and indeed synaptic or molecular – findings and say why that is important and how it relates to this, and how those findings could drive the next wave of imaging neuroscience.
THK: What do you think are the most pressing issues in neuroimaging for your area of interest?
KF: More detailed and mechanistic modeling of distributed neuronal responses. For instance, getting the right kind of connectivity and determining how to best integrate structural and functional connectivity. Other issues include connecting behavior to physiology and connecting functional connectivity to the underlying synaptic processing, and connecting synaptic processing to microcircuits – and microcircuits to whole brain connectomes.
When people ask me “What is the most important issue for you?” I respond "the issues that I work on"; namely, biophysical modeling (at least in my day job). In short, getting better and better modeling tools that enable people to ask evidence-based questions about the mechanisms that underlie the functional integration they are interested in.
THK: What do you think is the future of neuroimaging for basic research, for translation and maybe for applications as well?
KF: There are many avenues for neuroimaging in the future and I guess it depends where you place yourself in the spectrum of basic to clinical neuroscience. I think neuroimaging is not a field, it's a tool: it provides data or evidence for ideas and hypotheses. In this sense, the integration of neuroimaging with other modalities of enquiry probably holds the greatest promise. For example, one can see this in the use of whole brain imaging to contextualize invasive electrophysiology – which takes us into the realm of basic neuroscience and, if we put pharmacology and genetics on top of that, molecular neuroscience. A nice example of this is the molecular basis of neuromodulation and its effect on effective connectivity at the synaptic and molecular level. To get to this future, we need mechanistic, biophysically grounded, models in place – that can generate and make predictions about the molecular biology of synaptic plasticity; for example, models based on the short-term changes in synaptic efficacy that also explain a BOLD response in the fusiform gyrus. When we get that far, I think the future will no longer be brain mapping – it will be brain metrology.
I started in schizophrenia research. The future for people like me is ultimately translational in nature. Clearly, it will be nice to predict outcome trajectories of neuropsychiatric syndromes based on a psychopathology and pathophysiology. I suspect that this ambition has led to the emergence of computational psychiatry in recent years. Interestingly, most people working in computational psychiatry come from neuroimaging. There is a clear reason why that might be the case: if you're a doctor and you're worried about your patients, a non-invasive neuroscience is very appealing. Neuroimaging is par excellence, the non-invasive tool that can harness computational and basic science advances; hopefully, in the service of refining our understanding and treatment of neuropsychiatric conditions.
THK: Fifty years from now where do you think the neuroscience field will be?
KF: I think the interesting challenges I see around at the moment are in artificial intelligence. I think there are going to be big advances in artificial intelligence – and they will inform us at many different levels in neuroscience, clinical management and possibly well-being. From a personal perspective, this is largely the focus of my Sunday job.
I love the idea of having sentient curious machines living in your computer and working with you. One can imagine interactions with e-creatures that live in an electronic world and that have a purpose beyond minimizing some cost function. They have a purpose that is epistemic – and they want to learn what they can do and learn about you – like a proactive personalized Wikipedia. They will know that you are information hungry and might create novel situations for you that you have to explore.
THK: So it will be an extension of yourself without limits. ..
KF: I was thinking more of an extension of your parents. :)
Karl Friston is a theoretical neuroscientist and authority on brain imaging. He invented statistical parametric mapping (SPM), voxel-based morphometry (VBM) and dynamic causal modelling (DCM). These contributions were motivated by schizophrenia research and theoretical studies of value-learning formulated as the dysconnection hypothesis of schizophrenia. Mathematical contributions include variational Laplacian procedures and generalized filtering for hierarchical Bayesian model inversion. Friston currently works on models of functional integration in the human brain and the principles that underlie neuronal interactions. His main contribution to theoretical neurobiology is a free-energy principle for action and perception (active inference). Friston received the first Young Investigators Award in Human Brain Mapping (1996) and was elected a Fellow of the Academy of Medical Sciences (1999). In 2000 he was President of the international Organization of Human Brain Mapping. In 2003 he was awarded the Minerva Golden Brain Award and was elected a Fellow of the Royal Society in 2006. In 2008 he received a Medal, College de France and an Honorary Doctorate from the University of York in 2011. He became of 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 (excellence in the life sciences) 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, a lifetime achievement award by OHBM (the Organization for Human Brain Mapping) in the field of human brain mapping. He holds Honorary Doctorates from the University of Zurich and Radboud University.
Special thanks to Jeanette Mumford for her assistance in transcribing and editing this interview.