Most of our brain activity is not conscious – from processes that maintain our basic physiology to those that determine how we catch a baseball and play a piano well. Further, these unconscious processes include those that influence our basic perceptions of the world. Our opinions and deepest held beliefs – those that we prefer to feel that our conscious mind completely determines – are shaped largely by unconscious processes. The book, “Incognito: Secret Lives of the Brain” by David Eagleman, is an engaging account of those processes – packed with practical and interesting examples and insight. Eagleman is not only a neuroscientist, but an extremely clear and engaging writer. His writing, completely accessible to the non expert, is filled with solid neuroscience, packaged in a way that not only provides interesting information, but also builds perspective. It’s the first book that I’ve encountered that delves deeply into this particular subject. We mostly think of our brains as generating conscious thought, but, as he explains it’s just the small tip of the iceberg.
“Explaining the Brain” is a 2007 book by Carl Craver, who applies philosophical principles to comment on the current state of neuroscience. This is my first and only exposure to the philosophy of science, so my viewpoint is very naive, but here are some main points from the book that I found insightful.
The book starts by making a distinction between two broad goals in neuroscience: explanation, which is concerned with how the brain works; and control, which is concerned with practical things like diagnosis, repair, and augmentation of the brain. In my previous post on this blog, I tried to highlight that same distinction. This book focuses on explanation, which is essentially defined as the ability to fully describe the mechanisms by which a system operates.
A major emphasis is on the question of what it takes to establish a mechanism, and the notion of causality is integral to this question.
The future of healthcare both small and big. It’s big data, machine learning, and massive amounts of data coming from tiny robust devices or phone apps of individuals. It’s individualized medicine – not only for patients who need care but for healthy individuals. The data will come from devices that will become ever more ubiquitous – stickers on skin, tattoos, clothing, contact lenses, and more. This conference, organized by Applysci, and held on Feb 7 and 8, 2017 at Stanford University, involved a slate of some of the most creative, ambitious, and successful people in the digital health industry. I was both mesmerized and inspired.
I decided to venture outside my comfort zone of fMRI and brain imaging conferences to get a glimpse of the future of wearable technology and digital health by attending this conference. The speakers were mostly academics who have started companies related to their particular area of expertise. Others were solidly in industry or government. Some were quite famous and others were just getting started. All were great communicators – many having night jobs as writers. My goal for being here was to see how these innovations could complement fMRI – or vise versa. Were there new directions to go, strategies to consider, or experiments to try? What were the neural correlates of expanding one’s “umwelt?” – a fascinating concept elegantly described by one of the speakers, David Engleman.
On a personal level, I just love this stuff. I feel that use of the right data can truly provide insight into so many aspects of an individual’s health, fitness, and overall well-being, and can be used for prediction and classification. There’s so much untapped data that can be measured and understood on an individual level.
Many talks were focussed on flexible, pliable, wearable, and implantable devices that can measure, among other things, hemodynamics, neuronal activity, sweat content, sweat rate, body heat, solar radiation, body motion, heart rate, heart rate variability, skin conductance, blood pressure, electrocardiogram measures, then communicate this to the user and the cloud – all for analysis, feedback, and diagnosis. Other talks were on the next generation of brain analysis and imaging techniques. Others focussed on brain computer interfaces to allow for wired and wireless prosthetic interfacing. Frankly, the talks at this conference were almost all stunning. The prevailing theme that ran through each talk could be summarized as: In five or so years, not much will happen, but in ten to fifteen years, brace yourselves. The world will change! Technophiles see this future as a huge leap forward – as information will be more accessible and usable – reducing the cost of healthcare and, in some contexts – bypassing clinicians altogether and increasing the well-being of a very large fraction of the population. Others may see a dystopia wrought with the inevitable ethical issues of who can use and control the data.
Below are abbreviated notes, highlights, and personal thoughts from each of the talks that I attended. I don’t talk about the speakers themselves as they are easily googled – and most are more or less famous. I focus simply on what the highlights were for me.
In a very revealing paper: “Could a neuroscientist understand a microprocessor?”, Jonas and Kording tested a battery of neuroscientific methods to see if they were useful in helping to understand the workings of a basic microprocessor. This paper has already stirred quite a response, including from Numenta, the Spike, Arstechnica, the Atlantic, and lots of chatter on Twitter.
This is a fascinating paper. To a large degree, the answer to the title question as addressed by their methods (connectomics, lesion studies, tuning properties, LFPs, Granger causality, and dimensionality reduction), is simply ‘no’, but perhaps even more importantly, the paper brings focus to the question of what it means to ‘understand’ something that processes information, like a brain or a microprocessor. Continue reading “Understanding ‘Understanding’: Comments on “Could a neuroscientist understand a microprocessor?””
I recently had a meeting where the topic discussed was: “What would we like to see in the ideal cutting edge and future-focussed fMRI/DTI scanner?” While those who use fMRI are used to some progress being made in pulse sequences and scanner hardware, the technological capability exists to create something substantially better than we have now.
In this blog posting, I start out with a brief overview of what
we currently have now in terms of scanner technology. The second part of this blog is then focussed on what my ideal fMRI system would have. Lastly, the article ends with a summary outline of my wish list – so if you want to get the gist of this blog, scroll to the list at the bottom. Enjoy and enter your comments! Feedback, pushback, and more ideas are welcome!
A motivation for this blog is that since our graduate student days, Eric Wong and I have had hundreds of great conversations about MRI, fMRI, brain imaging, neuroscience, machine learning, and more. We finally decided to go ahead and start posting some of these, as well as thoughts of our own. It’s better – for us and hopefully others – to publicly share our thoughts, perspectives, and questions, than to keep them to ourselves. The posts are varied in topic and format. In certain areas, we know what we’re talking about, and in other others, we might be naïve or just wrong, so we welcome feedback! We also welcome guest blogs as we hope to grow the list of guest contributors and readers. Continue reading “Ten Unique Characteristics of fMRI”
Thanks to Peter Bandettini for the idea of starting a blog, and for offering to let me partner with him in this endeavor. We hope you find it interesting.
In this my first contribution to theBrainBlog, I would like to outline some of my initial thoughts about what a useful understanding of the human brain might look like. Continue reading “What does it mean to understand the brain?”