Human Brain Mapping in Quebec City was an excellent, if not slightly overwhelming, conference which brought together the often discrepant worlds of neuroscience, psychology, psychiatry, physics, engineering, mathematics, and computer science. (Did I miss anyone?) For a first-timer – and someone relatively new to the world of neuroimaging – it was a whirlwind opportunity to try to absorb as much wisdom as possible about how best to peer through the frosted looking glass and make the most of what we see.
It was remarkable to hear about the advancements and contentious issues of brain imaging from many of the people responsible for developing the techniques and analytical approaches first-hand. Personally having a better idea about the best ways forward, while being careful to respect the limitations of each approach, it is clear that this was the kind of transdisciplinary conference that will continue to have relevance as the young science slowly ages. In particular, Stephen Smith, Christian Beckman, Victor Solo and Martin Lindquist, all warned the ~2300 attendees to be wary of strong claims that some approaches reveal ‘causal’ connections in the brain (e.g. Granger causality, DCM, ICA). Despite these caveats, however, they reminded us that with careful consideration, these approaches can help clarify brain structure and function. Some of this interesting recent work can be found here and here.
A related theme throughout the conference was the analysis of neural networks and the myriad ways in which this can be done (correctly or otherwise). For instance, Ed Bullmore focused on the benefits of using brain graphs – a series of interconnected (measured by connecting lines or ‘edges’) brain areas (or ‘nodes’) – to map neural networks. He described the vast usefulness of this approach both visually as well as semi-quantitatively (e.g. once mapped, it becomes possible to directly compare one neural network to another, and also to non-neural networks such as LinkedIn), and pointed out that this approach highlights the apparent trade-offs between physical connection costs and topological efficiency (e.g. connections within the brain are not maximally energy efficient, and this may be due to the increased gains in processing seen with focused, wide-spread, network activity. Incidentally, there is some evidence that people with schizophrenia, for instance, show greater disorganized network activity and reduced efficiency – though interpretations of these findings are still somewhat contentious). Together, these studies and many others contribute to a better understanding of the human connectome.
One of the highlights of the conference was the Talairach Lecture by Karl Deisseroth on one of the most promising neuroscience techniques to emerge in the last decade – optogenetics. Though it is a newer brain imaging technique and has obvious direct connections to magnetic resonance imaging, I have to confess I was positively surprised to find a researcher focused on non-human animal work have such a prominent role at this conference. While the gist of the lecture and the opening of the conference (with pictures) was summed up nicely and briefly by a fellow HBMer, it’s worth noting that Dr Deisseroth emphasized that his team’s ground-breaking work would have been absolutely impossible without many prior advances in science. More importantly, he underscored the fact that much of that research (such as advancements in microbial opsins and a better understanding of how photoreceptors can help control neural activity) was initially not motivated by any clear hypotheses related to neuroscience nor, in fact, was there any clear practical ‘use’ to such research.
All in all, I found this conference to be highly informative. Though it was very heavy on brain imaging methods, and much less focused on underlying neurobiology and functional activity, there was still more information than could possibly be absorbed across many domains. Importantly, while this is an essential meeting for MRI methodologists, neurobiology-leaning neuroscientists like myself had an excellent opportunity to broaden their methodological armament – if ever so slightly.
Bullmore ET, & Bassett DS (2011). Brain graphs: graphical models of the human brain connectome. Annual review of clinical psychology, 7, 113-40 PMID: 21128784
Fenno L, Yizhar O, & Deisseroth K (2011). The development and application of optogenetics. Annual review of neuroscience, 34, 389-412 PMID: 21692661
Lindquist MA, & Sobel ME (2011). Graphical models, potential outcomes and causal inference: Comment on Ramsey, Spirtes and Glymour. NeuroImage, 57 (2), 334-6 PMID: 20970507
Lynall ME, Bassett DS, Kerwin R, McKenna PJ, Kitzbichler M, Muller U, & Bullmore E (2010). Functional connectivity and brain networks in schizophrenia. The Journal of neuroscience : the official journal of the Society for Neuroscience, 30 (28), 9477-87 PMID: 20631176
Smith SM, Miller KL, Salimi-Khorshidi G, Webster M, Beckmann CF, Nichols TE, Ramsey JD, & Woolrich MW (2011). Network modelling methods for FMRI. NeuroImage, 54 (2), 875-91 PMID: 20817103