John Brockman - What Should We Be Worried About?

Given the high potential value of neural data to science, it’s likely that privacy models will be negotiated to move forward with projects. There could be pressure to achieve scale quickly, both in the amount and types of data collected and the validity and utility of the data (still at issue in some areas of personalized genomics). Raw data streams need to be linked to neurophysiological states. Already an ecosystem of open and closed research models is evolving to accommodate different configurations of those conducting and participating in research. One means of realizing scale is through crowd sourcing, both for data acquisition and analysis. This could be particularly true here as low-cost tools for neural-data monitoring become available to consumers and interested individuals contribute their information to an open data commons. Different levels of privacy preferences are accommodated, as a small percentage of those comfortable sharing their data opt in to create a valuable public good usable by all. Even more than has happened in genomics (but not in longitudinal phenotypic data), open-access data could become a norm in neural studies.

Perhaps not initially, but in a later mainstream future for neural data, we might have a granular tiered permissioning system for enhanced privacy and security. A familiar example is the access tiers (family, friends) in such social networks as Facebook and Google Plus. With neural data, we could have similar (and greater) specificity—for example, allowing professional colleagues into certain neural data streams at certain times of day. However, there may be limitations related to a current lack of understanding of neural data streams generally and how signals may be transmitted, processed, and controlled.

The malicious hacking of neural data streams is a potential problem. Issues could arise in both hacking external data streams and devices (like any other data security breach) and hacking communication going back into the human. The latter is too far ahead for useful speculation, but the precedent could be that of spam, malware, and computer viruses. These are “Red Queen” problems, where perpetrators and responders compete in lockstep, effectively running to stay in place, often innovating incrementally to temporarily outcompete the other. Malicious neural-data-stream hacking will likely not occur in a vacuum; we can expect unfortunate side effects, and we’ll need responses analogous to antivirus software.

Rather than being an inhibitory worry, the area of neural-data privacy rights invites us to advance to a new node in societal progress. The potential long-term payoff of the continuous bioneurometric-information climate is significant. Objective metrics data collection and its permissioned broadcast might greatly improve both knowledge of the self and our ability to understand and collaborate with others. As personalized genomics has helped destigmatize health issues, neural data could help destigmatize mental health and behavioral issues, especially by letting us infer the issues of the many from the data of the few. Improved interactions with the self and others could free us to focus on higher levels of capacity, spending less time, emotion, and cognitive load on evolutionary-relic communications problems while transitioning to a truly advanced society.

STANISLAS DEHAENE

Neuroscientist, experimental cognitive psychologist, Collège de France, Paris; author, Reading in the Brain: The Science and Evolution of a Human Invention

Like many other neuroscientists, I receive my weekly dose of bizarre e-mails. My correspondents seem to have a good reason to worry, though: They think their brain is being tapped. Thanks to new “neurophonic” technologies, someone is monitoring their mind. They can’t think a thought without its being immediately broadcast to Google, the CIA, news agencies worldwide… or their spouses.

This is a paranoid worry, to be sure. Or is it? Neuroscience is making giant strides, and you don’t have to be schizophrenic to wonder whether it will ever crack the lockbox of your mind. Will there be a time, perhaps in the near future, when your innermost feelings and intimate memories will be laid bare for others to scroll through? I believe that the answer is a cautious no—at least for a while.

Brain-imaging technologies are no doubt powerful. More than fifteen years ago, at the dawn of functional magnetic resonance imaging, I was already marveling at the fact that we could detect a single motor action: Any time a person clicked a button with the left or right hand, we could see the corresponding motor cortex being activated, and we could tell with more than 98-percent accuracy which hand the person had used. We could also tell which language the scanned person spoke. In response to spoken sentences in French, English, Hindi, or Japanese, brain activation would either invade a large swath of the left hemisphere, including Broca’s area, or stay within the confines of the auditory cortex—a sure sign that the person did or did not understand what was being said. Recently we also managed to tell whether someone had learned to read a given script simply by monitoring the activation of the “visual word form area,” a brain region that holds our knowledge of legal letter strings.

Whenever I lectured on this research, I insisted on our methods’ limitations. Action and language are macrocodes of the brain, I explained. They mobilize gigantic cortical networks that lie centimeters apart and are therefore easily resolved by our coarse brain-imagers. Most of our fine-grained thoughts, however, are encrypted in a microcode of submillimeter neuronal-activity patterns. The neural configurations that distinguish my thought of a giraffe from my thought of an elephant are minuscule, unique to my brain, and intermingled in the same brain regions. Therefore they will forever escape decoding, at least by noninvasive imaging methods.

In 2008, Tom Mitchell’s beautiful Science paper proved me partly wrong. [i] “Predicting Human Brain Activity Associated with the Meanings of Nouns,” Science 320, 1191 (2008). His research showed that snapshots of state-of-the-art functional MRI contained a lot of information about specific thoughts. When a person thought of different words or pictures, the brain-activity patterns they evoked differed so much that a machine-learning algorithm could tell them apart much better than would be expected by chance. Strikingly, many of these patterns were macroscopic, and they were even similar in different people’s brains. This is because when we think of a word, we do not merely activate a small set of neurons in the temporal lobes that serves as an internal pointer to its meaning. The activation also spreads to distant sensory and motor cortices that encode each word’s concrete network of associations. In all of us, the verb “kick” activates the foot region of the motor cortex, “banana” evokes a smell and a color, and so on. These associations and their cortical patterns are so predictable that even new, untrained words can be identified by their brain signature.

Why is such brain decoding an interesting challenge for neuroscientists? It is, above all, a proof that we understand enough about the brain to partially decrypt it. For instance, we now know enough about number sense to tell exactly where in the brain the knowledge of a number is encrypted. And, sure enough, when Evelyn Eger, in my lab, captured high-resolution MRI images of this parietal-lobe region, she could tell whether the scanned person had viewed two, four, six, or eight dots, or even the corresponding Arabic digits. [j] “Deciphering Cortical Number Coding from Human Brain Activity Patterns,” Curr. Biol. 19, 1608-15 (2009).