—STEVEN BLOCK, PROFESSOR OF BIOLOGICAL SCIENCES AND OF APPLIED PHYSICS, STANFORD UNIVERSITY
Imagine that we were trying to reverse engineer a computer without knowing anything about it (the “black box” approach). We might start by placing arrays of magnetic sensors around the device. We would notice that during operations that updated a database, significant activity was taking place in a particular circuit board. We would be likely to take note that there was also action in the hard disk during these operations. (Indeed, listening to the hard disk has always been one crude window into what a computer is doing.)
We might then theorize that the disk had something to do with the longterm memory that stores the databases and that the circuit board that is active during these operations was involved in transforming the data to be stored. This tells us approximately where and when the operations are taking place but relatively little about how these tasks are accomplished.
If the computer’s registers (temporary memory locations) were connected to front-panel lights (as was the case with early computers), we would see certain patterns of light flickering that indicated rapid changes in the states of these registers during periods when the computer was analyzing data but relatively slow changes when the computer was transmitting data. We might then theorize that these lights reflected changes in logic state during some kind of analytic behavior. Such insights would be accurate but crude and would fail to provide us with a theory of operation or any insights as to how information is actually coded or transformed.
The hypothetical situation described above mirrors the sort of efforts that have been undertaken to scan and model the human brain with the crude tools that have historically been available. Most models based on contemporary brain-scanning research (utilizing such methods as fMRI, MEG, and others discussed below) are only suggestive of the underlying mechanisms. Although these studies are valuable, their crude spatial and temporal resolution is not adequate for reverse engineering the salient features of the brain.
New Tools for Scanning the Brain. Now imagine, in our computer example above, that we are able to actually place precise sensors at specific points in the circuitry and that these sensors are capable of tracking specific signals at very high speeds. We would now have the tools needed to follow the actual information being transformed in real time, and we would be able to create a detailed description of how the circuits actually work. This is, in fact, exactly how electrical engineers go about understanding and debugging circuits such as computer boards (to reverse engineer a competitor’s product, for example), using logic analyzers that visualize computer signals.
Neuroscience has not yet had access to sensor technology that would achieve this type of analysis, but that situation is about to change. Our tools for peering into our brains are improving at an exponential pace. The resolution of noninvasive brain-scanning devices is doubling about every twelve months (per unit volume).31
We see comparable improvements in the speed of brain scanning image reconstruction:
The most commonly used brain-scanning tool is fMRI, which provides relatively high spatial resolution of one to three millimeters (not high enough to image individual neurons) but low temporal (time) resolution of a few seconds. Recent generations of fMRI technology provide time resolution of about one second, or a tenth of a second for a thin brain slice.
Another commonly used technique is MEG, which measures weak magnetic fields outside the skull, produced principally by the pyramidal neurons of the cortex. MEG is capable of rapid (one millisecond) temporal resolution but only very crude spatial resolution, about one centimeter.
Fritz Sommer, a principal investigator at Redwood Neuroscience Institute, is developing methods of combining fMRI and MEG to improve the spatiotemporal precision of the measurements. Other recent advances have demonstrated fMRI techniques capable of mapping regions called columnar and laminar structures, which are only a fraction of a millimeter wide, and of detecting tasks that take place in tens of milliseconds.32
fMRI and a related scanning technique using positrons called positronemission tomography (PET) both gauge neuronal activity through indirect means. PET measures regional cerebral blood flow (rCBF), while fMRI measures blood-oxygen levels.33 Although the relationship of these blood-flow amounts to neural activity is the subject of some controversy, the consensus is that they reflect local synaptic activity, not the spiking of neurons. The relationship of neural activity to blood flow was first articulated in the late nineteenth century.34 A limitation of fMRI, however, is that the relationship of blood flow to synaptic activity is not direct: a variety of metabolic mechanisms affect the relationship between the two phenomena.
However, both PET and fMRI are believed to be most reliable for measuring relative changes in brain state. The primary method they use is the “subtraction paradigm,” which can show regions that are most active during particular tasks.35 This procedure involves subtracting data produced by a scan when the subject is not performing an activity from data produced while the subject is performing a specified mental activity. The difference represents the change in brain state.
An invasive technique that provides high spatial and temporal resolution is “optical imaging,” which involves removing part of the skull, staining the living brain tissue with a dye that fluoresces upon neural activity, and then imaging the emitted light with a digital camera. Since optical imaging requires surgery, it has been used mainly in animal, particularly mouse, experiments.
Another approach to identifying brain functionality in different regions is transcranial magnetic stimulation (TMS), which involves applying a strongpulsed magnetic field from outside the skull, using a magnetic coil precisely positioned over the head. By either stimulating or inducing a “virtual lesion” of (by temporarily disabling) small regions of the brain, skills can be diminished or enhanced.36 TMS can also be used to study the relationship of different areas of the brain on specific tasks and can even induce sensations of mystical experiences.37 Brain scientist Allan Snyder has reported that about 40 percent of his test subjects hooked up to TMS display significant new skills, many of which are remarkable, such as drawing abilities.38
If we have the option of destroying the brain that we are scanning, dramatically higher spatial resolution becomes possible. Scanning a frozen brain is feasible today, though not yet at sufficient speed or bandwidth to fully map all interconnections. But again, in accordance with the law of accelerating returns, this potential is growing exponentially, as are all other facets of brain scanning.
Carnegie Mellon University’s Andreas Nowatzyk is scanning the nervous system of the brain and body of a mouse with a resolution of less than two hundred nanometers, which is approaching the resolution needed for full reverse engineering. Another destructive scanner called the “Brain Tissue Scanner” developed at the Brain Networks Laboratory at Texas A&M University is able to scan an entire mouse brain at a resolution of 250 nanometers in one month, using slices.39
Improving Resolution. Many new brain-scanning technologies now in development are dramatically improving both temporal and spatial resolution. This new generation of sensing and scanning systems is providing the tools needed to develop models with unprecedented fine levels of detail. Following is a small sample of these emerging imaging and sensing systems.
One particularly exciting new scanning camera is being developed at the University of Pennsylvania Neuroengineering Research Laboratory, led by Leif H. Finkel.40 The optical system’s projected spatial resolution will be high enough to image individual neurons and at one-millisecond time resolution, which is sufficient to record the firing of each neuron.
Initial versions are able to scan about one hundred cells simultaneously, at a depth of up to ten microns from the camera. A future version will image up to one thousand simultaneous cells, at a distance of up to 150 microns from the camera and at submillisecond time resolution. The system can scan neural tissue in vivo (in a living brain) while an animal
is engaged in a mental task, although the brain surface must be exposed. The neural tissue is stained to generate voltage-dependent fluorescence, which is picked up by the highresolution camera. The scanning system will be used to examine the brains of animals before and after they learn specific perceptual skills. This system combines the fast (one millisecond) temporary resolution of MEG while being able to image individual neurons and connections.
Methods have also been developed to noninvasively activate neurons or even a specific part of a neuron in a temporally and spatially precise manner. One approach, involving photons, uses a direct “two-photon” excitation, called “two-photon laser scanning microscopy” (TPLSM).41 This creates a single point of focus in three-dimensional space that allows very high-resolution scanning. It utilizes laser pulses lasting only one millionth of one billionth of a second (10−15 second) to detect the excitation of single synapses in the intact brain by measuring the intracellular calcium accumulation associated with the activation of synaptic receptors.42 Although the method destroys an insignificant amount of tissue, it provides extremely high-resolution images of individual dendritic spines and synapses in action.
This technique has been used to perform ultraprecise intracellular surgery. Physicist Eric Mazur and his colleagues at Harvard University have demonstrated its ability to execute precise modifications of cells, such as severing an interneuronal connection or destroying a single mitochondrion (the cell’s energy source) without affecting other cellular components. “It generates the heat of the sun,” says Mazur’s colleague Donald Ingber, “but only for quintillionths of a second, and in a very small space.”
Another technique, called “multielectrode recording,” uses an array of electrodes to record simultaneously the activity of a large number of neurons with very high (submillisecond) temporal resolution.43 Also, a noninvasive technique called second-harmonic generation (SHG) microscopy is able “to study cells in action,” explains lead developer Daniel Dombeck, a graduate student at Cornell University. Yet another technique, called optical coherence imaging (OCI), uses coherent light (lightwaves that are all aligned in the same phase) to create holographic three-dimensional images of cell clusters.
Scanning Using Nanobots. Although these largely noninvasive means of scanning the brain from outside the skull are rapidly improving, the most powerful approach to capturing every salient neural detail will be to scan it from inside. By the 2020s nanobot technology will be viable, and brain scanning will be one of its prominent applications. As described earlier nanobots are robots that will be the size of human blood cells (seven to eight microns) or even smaller.44 Billions of them could travel through every brain capillary, scanning each relevant neural feature from up close. Using high-speed wireless communication, the nanobots would communicate with one another and with computers compiling the brain-scan database. (In other words, the nanobots and computers will all be on a wireless local area network.)45
A key technical challenge to interfacing nanobots with biological brain structures is the blood-brain barrier (BBB). In the late nineteenth century, scientists discovered that when they injected blue dye into an animal’s bloodstream, all the organs of the animal turned blue with the exception of the spinal cord and brain. They accurately hypothesized a barrier that protects the brain from a wide range of potentially harmful substances in the blood, including bacteria, hormones, chemicals that may act as neurotransmitters, and other toxins. Only oxygen, glucose, and a very select set of other small molecules are able to leave the blood vessels and enter the brain.
Autopsies early in the twentieth century revealed that the lining of the capillaries in the brain and other nervous-system tissues is indeed packed much more tightly with endothelial cells than comparable-size vessels in other organs. More recent studies have shown that the BBB is a complex system that features gateways complete with keys and passwords that allow entry into the brain. For example, two proteins called zonulin and zot have been discovered that react with receptors in the brain to temporarily open the BBB at select sites. These two proteins play a similar role in opening receptors in the small intestine to allow digestion of glucose and other nutrients.
Any design for nanobots to scan or otherwise interact with the brain will have to consider the BBB. I describe here several strategies that will be workable, given future capabilities. Undoubtedly, others will be developed over the next quarter century.
An obvious tactic is to make the nanobot small enough to glide through the BBB, but this is the least practical approach, at least with nanotechnology as we envision it today. To do this, the nanobot would have to be twenty nanometers or less in diameter, which is about the size of one hundred carbon atoms. Limiting a nanobot to these dimensions would severely limit its functionality.
An intermediate strategy would be to keep the nanobot in the bloodstream but to have it project a robotic arm through the BBB and into the extracellular fluid that lines the neural cells. This would allow the nanobot to remain large enough to have sufficient computational and navigational resources. Since almost all neurons lie within two or three cell-widths of a capillary, the arm would need to reach only up to about fifty microns. Analyses conducted by Rob Freitas and others show that it is quite feasible to restrict the width of such a manipulator to under twenty nanometers.
Another approach is to keep the nanobots in the capillaries and use noninvasive scanning. For example, the scanning system being designed by Finkel and his associates can scan at very high resolution (sufficient to see individual interconnections) to a depth of 150 microns, which is several times greater than we need. Obviously this type of optical-imaging system would have to be significantly miniaturized (compared to contemporary designs), but it uses charge-coupled device sensors, which are amenable to such size reduction.
Another type of noninvasive scanning would involve one set of nanobots emitting focused signals similar to those of a two-photon scanner and another set of nanobots receiving the transmission. The topology of the intervening tissue could be determined by analyzing the impact on the received signal.
Another type of strategy, suggested by Robert Freitas, would be for the nanobot literally to barge its way past the BBB by breaking a hole in it, exit the blood vessel, and then repair the damage. Since the nanobot can be constructed using carbon in a diamondoid configuration, it would be far stronger than biological tissues. Freitas writes, “To pass between cells in cell-rich tissue, it is necessary for an advancing nanorobot to disrupt some minimum number of cell-to-cell adhesive contacts that lie ahead in its path. After that, and with the objective of minimizing biointrusiveness, the nanorobot must reseal those adhesive contacts in its wake, crudely analogous to a burrowing mole.”46
Yet another approach is suggested by contemporary cancer studies. Cancer researchers are keenly interested in selectively disrupting the BBB to transport cancer-destroying substances to tumors. Recent studies of the BBB show that it opens up in response to a variety of factors, which include certain proteins, as mentioned above; localized hypertension; high concentrations of certain substances; microwaves and other forms of radiation; infection; and inflammation. There are also specialized processes that ferry out needed substances such as glucose. It has also been found that the sugar mannitol causes a temporary shrinking of the tightly packed endothelial cells to provide a temporary breach of the BBB. By exploiting these mechanisms, several research groups are developing compounds that open the BBB.47 Although this research is aimed at cancer therapies, similar approaches can be used to open the gateways for nanobots that will scan the brain as well as enhance our mental functioning.
We could bypass the bloodstream and the BBB altogether by injecting the nanobots into areas of the brain that have direct access to neural tissue. As I mention below, new neurons migrate from the ventricles to other parts of the brain. Nanobots could follow the same migration path.
Rob Freitas has described several techniques for nanobots to monitor sensory signals.
48 These will be important both for reverse engineering the inputs to the brain, as well as for creating full-immersion virtual reality from within the nervous system.
To scan and monitor auditory signals, Freitas proposes “mobile nanodevices . . . [that] swim into the spiral artery of the ear and down through its bifurcations to reach the cochlear canal, then position themselves as neural monitors in the vicinity of the spiral nerve fibers and the nerves entering the epithelium of the organ of Corti [cochlear or auditory nerves] within the spiral ganglion. These monitors can detect, record, or rebroadcast to other nanodevices in the communications network all auditory neural traffic perceived by the human ear.”
For the body’s “sensations of gravity, rotation, and acceleration,” he envisions “nanomonitors positioned at the afferent nerve endings emanating from hair cells located in the . . . semicircular canals.”
For “kinesthetic sensory management . . . motor neurons can be monitored to keep track of limb motions and positions, or specific muscle activities, and even to exert control.”
“Olfactory and gustatory sensory neural traffic may be eavesdropped [on] by nanosensory instruments.”
“Pain signals may be recorded or modified as required, as can mechanical and temperature nerve impulses from . . . receptors located in the skin.”
Freitas points out that the retina is rich with small blood vessels, “permitting ready access to both photoreceptor (rod, cone, bipolar and ganglion) and integrator . . . neurons.” The signals from the optic nerve represent more than one hundred million levels per second, but this level of signal processing is already manageable. As MIT’s Tomaso Poggio and others have indicated, we do not yet understand the coding of the optic nerve’s signals. Once we have the ability to monitor the signals for each discrete fiber in the optic nerve, our ability to interpret these signals will be greatly facilitated. This is currently an area of intense research.