Nanotools for Brain Mapping

ALIVISATOS ET AL. VOL. 7 ’ NO. 3 ’ 1850–1866 ’ 2013

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March 20, 2013

C 2013 American Chemical Society

Nanotools for Neuroscience and BrainActivity MappingA. Paul Alivisatos,† Anne M. Andrews,‡,§ Edward S. Boyden,^ Miyoung Chun, ) George M. Church,#

Karl Deisseroth,z,0 John P. Donoghue,9 Scott E. Fraser,O Jennifer Lippincott-Schwartz,b Loren L. Looger,4

SotirisMasmanidis,‡,2,* Paul L.McEuen,3Arto V. Nurmikko,1Hongkun Park,"Darcy S. Peterka,` Clay Reid,††

Michael L. Roukes,‡‡,§§ Axel Scherer,‡‡,^^,*Mark Schnitzer,z, ) ) Terrence J. Sejnowski,22Kenneth L. Shepard,##

Doris Tsao,zz Gina Turrigiano,00 Paul S. Weiss,‡,99,* Chris Xu,OO Rafael Yuste,`,bb,* and Xiaowei Zhuang44

†Department of Chemistry, University of California, Berkeley, California 94720, and Lawrence Berkeley Laboratory, Berkeley, California 94720-1460,‡California NanoSystems Institute and §Department of Psychiatry, and Semel Institute for Neuroscience & Human Behavior, Department of Chemistry &Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, ^Media Laboratory, Department of Biological Engineering, Brain and CognitiveSciences, and McGovern Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, )The Kavli Foundation, Oxnard, California 93030,#Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, Wyss Institute for Biologically Inspired Engineering and BiophysicsProgram, Harvard University, Boston, Massachusetts 02115, zHoward Hughes Medical Institute and 0Departments of Bioengineering and Psychiatry, StanfordUniversity, Stanford California 94305, 9Department of Neuroscience, Division of Engineering, Department of Computer Science, Brown University, Providence,Rhode Island 02912, ODepartments of Biological Sciences, Biomedical Engineering, Physiology and Biophysics, Stem Cell Biology and Regenerative Medicine, andPediatrics, Radiology and Ophthalmology, University of Southern California, Los Angeles, California 90089, bCell Biology and Metabolism Program, Eunice KennedyShriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland 20892, 4Howard Hughes Medical Institute,Janelia Farm Research Campus, Ashburn, Virginia 20147, 2Department of Neurobiology, University of California, Los Angeles, California 90095, 3Department ofPhysics, Laboratory of Atomic and Solid State Physics, and Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, New York 14853, 1Departmentof Physics and Division of Engineering, Brown University, Providence, Rhode Island 02912, "Department of Chemistry and Chemical Biology and Department ofPhysics, Harvard University, Cambridge, Massachusetts 02138, `Howard Hughes Medical Institute and Department of Biological Sciences, Columbia University, NewYork, New York 10027, ††Allen Institute for Brain Science, Seattle, Washington 98103, ‡‡Kavli Nanoscience Institute, §§Departments of Physics, Applied Physics, andBioengineering, and ^^Departments of Electrical Engineering, Applied Physics, and Physics, California Institute of Technology, MC 149-33, Pasadena, California91125, ) )Departments of Applied Physics and Biology, James H. Clark Center, Stanford University, Stanford, California 94305, 22Howard Hughes Medical Institute,Computational Neurobiology Laboratory, Salk Institute, La Jolla, California 92037, and Division of Biological Sciences, University of California, San Diego, La Jolla,California 92093, ##Department of Electrical Engineering, Columbia University, New York, New York 10027, zzDivision of Biology, California Institute of Technology,Pasadena, California 91125, 00Department of Biology and Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02254, 99Department ofChemistry & Biochemistry, Department of Materials Science & Engineering, University of California, Los Angeles, California 90095, OOSchool of Applied andEngineering Physics, Cornell University, Ithaca, New York 14853, bbKavli Institute for Brain Science, Columbia University, New York, New York 10027, and44Howard Hughes Medical Institute, Departments of Chemistry and Chemical Biology and Physics, Harvard University, Cambridge, Massachusetts 02138

The Brain Activity Mapping (BAM)Project1�5 has three goals in termsof building tools for neuroscience

capable of (1)measuring the activity of large

sets of neurons in complex brain circuits, (2)computationally analyzing and modelingthese brain circuits, and (3) testing thesemodels by manipulating the activities of

Mapping Project. In this Nano Focus, we discuss how recent developments in nanoscale analysis tools and in

the design and synthesis of nanomaterials have generated optical, electrical, and chemical methods that can

readily be adapted for use in neuroscience. These approaches represent exciting areas of technical

development and research. Moreover, unique opportunities exist for nanoscientists, nanotechnologists,

and other physical scientists and engineers to contribute to tackling the challenging problems involved in

understanding the fundamentals of brain function.

* Address correspondence [email protected],[email protected],[email protected],[email protected].

Published online10.1021/nn4012847

ABSTRACT

Neuroscience is at a crossroads. Great effort is being invested into deciphering specific neural interactions and circuits. At the same time, there exist few

general theories or principles that explain brain function. We attribute this disparity, in part, to limitations in current methodologies. Traditional

neurophysiological approaches record the activities of one neuron or a few neurons at a time. Neurochemical approaches focus on single neurotransmitters.

Yet, there is an increasing realization that neural circuits operate at emergent levels, where the interactions between hundreds or thousands of neurons,

utilizing multiple chemical transmitters, generate functional states. Brains function at the nanoscale, so tools to study brains must ultimately operate at

this scale, as well. Nanoscience and nanotechnology are poised to provide a rich toolkit of novel methods to explore brain function by enabling

simultaneous measurement and manipulation of activity of thousands or even millions of neurons. We and others refer to this goal as the Brain Activity

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chosen sets of neurons in these

brain circuits.As described below, many differ-

ent approaches can, and likely will,be taken to achieve these goals asneural circuits of increasing size andcomplexity are studied and probed.The BAM project will focus both

on dynamic voltage activity and onchemical neurotransmission. Withan estimated 85 billion neurons,100 trillion synapses, and 100 che-mical neurotransmitters in the hu-man brain,6 this is a daunting task.Thus, the BAM project will start withmodel organisms, neural circuits(vide infra), and small subsets ofspecific neural circuits in humans.Among the approaches that show

promise for the required dynamic,parallel measurements are opticaland electro-optical methods that canbe used to sense neural cell activitysuch as Ca2þ,7 voltage,8�10 and(already some) neurotransmitters;11

electrophysiological approaches thatsense voltages and some electro-chemically active neurotransmit-ters;12�17 next-generation photonics-based probes with multifunctionalcapabilities;18 synthetic biologyapproaches for recording historiesof function;19�21 and nanoelectronicmeasurements of voltage and localbrain chemistry.22�39 We anticipatethat tools developed will also be ap-plied to glia and more broadly tonanoscale andmicroscale monitoringof metabolic processes.Entirely new tools will ultimately

be required both to study neuronsand neural circuits with minimalperturbation and to study the hu-man brain. These tools might in-clude “smart”, active nanoscale de-vices embedded within the brainthat report on neural circuit activitywirelessly and/or entirely newmodalities of remote sensing ofneural circuit dynamics fromoutsidethe body. Remarkable advances innanoscience and nanotechnologythus have key roles to play in trans-duction, reporting, power, and com-munications.One of the ultimate goals of the

BAM project is that the knowledge

acquired and tools developed willprove useful in the intervention andtreatment of a wide variety of dis-eases of the brain, including depres-sion, epilepsy, Parkinson's, schizo-phrenia, and others. We note thattens of thousands of patients havealready been treated with invasive(i.e., through the skull) treatments.While we hope to reduce the needfor suchmeasures, greatly improvedand more robust interfaces to thebrain would impact effectiveness andlongevity where such treatmentsremain necessary.

Neuroscience at a Crossroads. Under-standing how the brain works is oneof the greatest challenges facingscience and engineering. After morethan a century of sustained progressin biological sciences and medicine,one could argue that mankind hasmade significant advances in ourunderstanding of how biologicalsystems operate and how differentparts of the body function and,when damaged, generate disease.At the same time, a comprehensiveunderstanding of the brain remainsan elusive, distant frontier. To arriveat a general theory of brain functionwould be an historic event, compar-able to inferring quantum theoryfrom huge sets of complex spectraand inferring evolutionary theoryfrom vast biological field work. Notonly would a theory of brain func-tion be a fundamental advance inbiology, but it would enable under-standing of the pathophysiology ofneurological and neuropsychiatricdiseases. Many of these devastatingbrain-based pathologies have neithercures nor effective treatments, inlarge part because it is difficult toprovide a treatment for a dysfunc-tional organ when one does notknow how it works. Finally, the his-toric importance of generating atheory of brain function is high-lighted by the fact that as humans,more than any other species, we aredefined by the higher cognitiveabilities generated by our brains.Thus, scientific understanding ofour brains will enable deeper knowl-edge of ourselves and of our minds.

Neuroscientists have worked onthis key problem for the last century,and yet a comprehensive theory ofbrain function remains elusive. En-ormous progress has been made inunderstanding the molecular andcellular components of neural cir-cuits in humans and experimentalanimals. One goal to this end is todrive the development and testingof theories of brain function thatrequire better spatial and temporalsampling and intervention than ispresently possible. Greater precisionand parallelism in electrical and che-mical sensing, as well as the abilityto excite and to probe neural circuitsactively, as proposed here, wouldbridge the nanoscale to the micro-scale to the macroscale and wouldcomplement ongoing connectionalmapping of brain circuits.

Need for High-Resolution, Network-Level Brain Activity Mapping Approaches.There has been remarkable progressin the ability to ascribe specific func-tional roles to specific neuroanato-mical regions, axonal tracts, cells,synapses, and molecules. For exam-ple, large-scale maps of gene ex-pression in the brain, such as theAllen Brain Atlas40 or GENSATProject41 provide enormous insightinto the brain's architecture atthe genetic level with precise

Greater precision and

parallelism in electrical

and chemical sensing,

as well as the ability to

excite and to probe

neural circuits actively,

as proposed here, would

bridge the nanoscale to

the microscale to the

macroscale and would

complement ongoing

connectional mapping

of brain circuits.

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anatomical resolution. However, nocomparably high-resolutionmaps ofbrain-wide neuronal activity areavailable. On one hand, noninvasivemapping techniques such as func-tional magnetic resonance imaging(fMRI), positron emission tomogra-phy (PET), and electroencephalo-graphy (EEG) reveal a wealth ofinformation about functional brainorganization and connectivity.42�44

These methods offer coarse-grainviews that do not fully capture theunderlying networks' properties. Onthe other hand, our ability to per-ceive and to ponder the cosmos, toremember information, to feel plea-sure fromdaily experiences, tomakedecisions;and deficits in perform-ing some of these tasks when facedwith disease;involves a complexinterplay of large, distributed neuro-nal populations signaling on millise-cond time scales. Science has barelyscratched the surface of this fast,network-level regime. The huge po-tential payoff for understanding thebrain and diagnosing and treatingneurological disorders means thattechniques for measuring brain ac-tivity are scaling up at a rapid pace.45

Thanks in part to these advances,the development of technology toenable a paradigm shift from experi-ments that routinely record tens ofneurons at a time to experimentsthat can record millions of cells isimportant and timely. From a com-putational perspective, it is obviousthat information processing in thebrain relies on a cascade of events,46

and sampling these events a fewcells at a time, as has been the normin neuroscience, cannot capture theemergent properties of such a deep-ly interconnected network as thebrain. The search for spatiotemporalpatterns and correlations in spiketrains of recorded neurons can beenormously enhanced by raisingthe number (N) of simultaneouslyaccessible units.22,47 For example,assuming a uniform connectionprobability, the likelihood of findingsynaptically coupled cells increasesquadratically with N. Testing thefunctional implications of small-

world models of interacting neuralnetworks48 would likewise benefitfrom having access to greater N.Molecular-level analyses of cellularorganization reveal the immenseheterogeneity of neuronal subpo-pulations. Many of these subpopu-lations;such as cholinergic inter-neurons in the striatum49

;repre-sent only a small fraction of cells ina given area, yet they are known toplay important roles in regulatingbehavior.50 This suggests that inorder to sample several of these rarebut important units reliably, so as tounderstand their function in vivo,large-scale measurements of neuro-nal activity are necessary. Further,because the brain is composed ofmany specialized neuron types thatperform specific functions withinmicrocircuits, techniques must bedeveloped that enable both theidentities and activities of neuronsto be measured.

In spite of this progress, whydoes neuroscience still lack a gen-eral theory? One contributor is thesheer complexity of brain circuits.Even in the simplest organisms, ner-vous systems are composed of cir-cuits built with many different sub-types of neurons connected inpatterns of prohibitive complexity.These “impenetrable jungles wheremany investigators have lost them-selves”, as Ramon y Cajal, one of theearliest neuroanatomists termedthem,51 have difficult experimentalaccess, and the sheer diversity ofneural circuits and their compo-nents makes it difficult to drawstrong conclusions or generaliza-tions on which to build general the-ories. In fact, neuroscientists havetraditionally analyzed the structureand function of these circuits oneneuron at a time, using electricalrecordings from individual neurons,for example, while an experimentalanimal is performing a specific be-havior. At the same time, any neuralcircuit is composed of thousands orhundreds of thousands of neurons,which are heavily interconnected.Because of these structures, it islikely that neural circuits operate at

an emergent level, one generatedby the functional interactions be-tween large populations of neurons.Thus, measurements from indivi-dual neurons would not give insightinto function, just as one cannotunderstand the function of a build-ing by analyzing the molecular struc-tures of its bricks. In fact, emergentproperties have been encounteredin many areas of science and engi-neering. The laws of thermo-dynamics, statistical mechanics,and the generation of magneticproperties are examples of fields ofscience that require understandingof emergent phenomena that resultfrom interactions across many indi-vidual particles. The goal of the BAMproject is to provide the data setsand the critical tests to enable thedevelopment and testing, respec-tively, of theories and models ofneural circuits and brain function.

To elucidate emergent proper-ties, neuroscientists will need noveltechniques that enable simulta-neous monitoring of the activitiesof many or all of the cells in neuralcircuits. While whole-brain imagingtechniques, such as fMRI, enablebird's eye views of the activity ofbrain areas, they lack the spatialand temporal resolution requiredto provide functional informationon individual neurons and theirinteractions. New techniques areneeded, and while neuroscientistsare generating many novel appro-aches, we believe that nanoscienceand nanotechnology are ideallypoised to make fundamental contri-butions to this problem and to helpgenerate the toolkits of methods thatcould be used to measure and tomanipulate the activities of increas-ingly larger sets of neurons in com-plex and widespread neural circuits.

The Nanoscience and NanotechnologyRevolution. The nanoscience and na-notechnology revolution beganwith the ability to “see” at the atomicscale with the inventions of thescanning tunneling microscope, theatomic force microscope, and re-lated tools.52�55 It then progre-ssed with the ability to manipulate

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individual atoms and molecules, aswell as to direct assemblies of mol-ecules into precise structures.56�60

In the years since, remarkableprogress has been made in devel-oping novel materials, tools, andmethods that have opened up newpossibilities across science, engi-neering, and medicine. Some pro-gress has already beenmade towardaddressing problems in neuro-science via nanotechnology.

In the past decade, substantialinvestmentshavebeenmade throughthe National Nanotechnology In-itiative in the United States andsimilar programs in countriesaround the world. Support con-tinues in the hope that the dra-matic advances we have seen innanoscience and nanotechnologywill continue and will now be ap-plied to other fields of science,engineering, and medicine, aswell as to manufacturing andcommercialization.61

Top-Down (Lithographic) Devices. Mini-aturization through lithographic andother means has been a continuingtrend fueled largely by the need todevelop ever more functional sys-tems. Over the past decades, thesesystems have catered to the needs ofconsumer electronics,with theoppor-tunities of usingmicrochips to controland to interpret information on mas-sive scales. More recently, this trendhas been driven by our need to beconnected and to communicate,

leading to more portable systems inwhich size, weight, and power are at apremium. The same trend that en-ables theminiaturization of electronicand radio frequency communicationssystems has also influenced opticaland fluidic systems, with the emer-gence of printed silicon photonicsand microfluidics that can decreasethe sizes of lasers, modulators, anddetectors, as well as pumps, valves,and mixers. As a general rule, thesize of printed systems has been re-duced by a factor of 100� in volumeevery 10 years (Bell's Law).62 Thesereductions in system size correspondto improvements in the fidelity oflithographic processes, enabling thegeometric doubling in the number ofindividual devices on a chip every 18months (Moore's Law).63 It could beargued that Moore's Law is driven bythe “real-estate” value on the chip,which in the case of silicon electronicshas remained constant over the past40 years, at a cost of approximately$5/cm2.

Miniaturization in electronic de-vices has led to the ability to createstructures with 22 nm lateral widthover 300 mm wafers, produced oncommercial scales at approximately20 wafers/h. Individual transistorsare now 200 nm in size, and ampli-fication circuits are on the order ofmicrometers. For the specific appli-cation of studying the brain, it is nowpossible to contemplate using thesecapabilities to increase the numbersof neurons interrogated by reducingthe sizes of electrophysiologicalprobes and to develop systems thatcan manage large amounts of dataaccumulated and/or transmittedduring such measurements. Thisdramatic reduction in the size ofelectronic systems enables the con-struction of devices that can be im-planted with less intrusiveness andenables the development of smallelectrophysiological tools to mea-sure and to control individual neu-rons. Simultaneously, thisminiaturiza-tion is associated with increases inthe operating frequencies of elec-tronics and reductions in the sizesof antennas and power required,

resulting in smaller communicationssystems.

Indeed, systems being devel-oped for future use in communica-tions may be of great value forneuroscience, as well. Multiferroicantennas are one example in whichdevices can be 1000� smaller thanconventional antennas and may beable to be powered remotely.64

More broadly, ultrasmall nanoelec-tronic chips might be used to com-bine modalities of detection ofsignals from operating neural cir-cuits and the wireless broadcastingof this information at extremely highdata rates to decoders for real-timerecording and deciphering of neuralcodes.

Bottom-Up Methods, Self-Assembly,and Chemical Patterning. Smaller-than-standard lithographic scales can bereached using self- and directed as-sembly.65�68 Tremendous progresshas been made in functionalizing awide variety of materials, includingsemiconductors, insulators, metals,glasses, nanoparticles, and porousmaterials. With a single molecularlayer, the chemical, physical, andbiological properties of materialscan be controlled and tailored.

Given the need for more thanelectronic or optical function, it is cri-tical to control the exposed chemistryon devices. Tremendous advanceshave been made in the last 30 yearsin the chemical functionalization on awide range of substrates.65�67 Suchadvancescanbeappliedboth toguideassembly and to control biologicaland other interactions.

Likewise, nanoparticles and othernanostructures can be specificallyfunctionalized so as to target speci-fic locations and to be stabilizedthere such as lodging themselvesin cell membranes.69,70 In this way,physical placement of nanostruc-tures will not necessarily be re-quired, but instead reporting orpost-mortem analyses can be usedto determine the absolute and rela-tive locations of nanostructuresused in BAM sensing. Potential usesof nanoparticles and nanostructuresare discussed further below.

To elucidate emergent

properties,

neuroscientists will

need novel techniques

that enable

simultaneous

monitoring of the

activities of many or all

of the cells in neural

circuits.

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In addition, functional moleculescan be used to sense and to trans-duce potentials, pressure, and spe-cific aspects of chemical environ-ments. We anticipate that artificialand hybrid neurotransmitter recep-tors will be critical in reporting thelocal environment in the brain. Tre-mendous opportunities exist if onecan explore the 10 nm synapse scaleto understand dynamic neuro-transmission while simultaneouslyrecording the activities of thousandsof neurons in a neural circuit. Ifnanostructures can be targeted tospecific cell or synapse types bymeans of chemical signatures onthe cell surfaces, it may be possibleto monitor and to control the activ-ities of neurons in cell-type-specificmanners. We anticipate that, ulti-mately, such measurements couldalso be used in feedback circuitsto control diseases in which localchemistries play critical roles, suchas Parkinson's and schizophrenia.The implications of developingthese and other BAM technologiesare significant.

Development of Nanoscale Tools forNeuroscience. In light of the advancesin resolution of these top-down andbottom-up miniaturization strate-gies, the endeavor towardmatchingthe sizes of devices that measureand control neuronal activity withthe sizes of individual neurons iscompelling and appears inevitable.Below, we enumerate a few of theareas in which these contributionsare anticipated.

Electrophysiology. The major ob-stacles that presently limit the useof nanoscale probes are the engi-neering challenges of building effi-cient power and communicationssystems to interface such neuralprobes with the outside world andat the same time avoid tissue da-mage and undesirable cell re-sponses. Whether quantum dots orwafer-bondedmicrosystems are used,it is important to avoid heating andtoxicity in the vicinity of the mea-surement probes. The most impor-tant physical barrier that limits thesizes of intracellular neural interfaces

is the impedance of the electrodes;whether these are on nano-particles embeddedwithin cell wallsor on more conventional electro-physiological patch-clamp systems.For extracellular recording, the im-pedance is even more important, asit determines signal-to-noise char-acteristics and, thus, the sensitivitiesof the neural probes. The sensitivitytoward neuronal signals depends onthe impedance, which can thenbe transduced to measurement de-vices outside of the brain.

Brain Activity Mapping with Nanofab-ricated Electrode Arrays. One of thetechnologies that can greatly facil-itate brain activity mapping is theextracellular microelectrode, whichcan resolve single-neuron firingin vivo without penetrating the cell.Once inserted in the brain, the de-tection range of this type of sensor istypically limited to neurons whosecell bodies are closer than ∼50 μmfromthemicroelectrode surface.23,71,72

Thus, in order to construct systems-scale views of brain function fromsuch measurements, the objectivehas been to increase the numberand density of recording sites. Scal-ing up these device attributes hasbeen spurred by parallel advances inelectronic instrumentation for read-ing out signals via low-noise ampli-fiers, multiplexers, and wireless tran-smitters24�30 and performing basicsignal processing functions on-chipto reduce the burden of data collec-tion.31,32 Innovations in microelec-trode manufacturing techniqueshave made it possible to deploysimultaneously nearly 1000 mea-surement sites distributed acrossseveral cortical areas of the sameanimal, allowing the firing patternsof hundreds of neurons to be

monitored in parallel.33 Leveragingmicro- and nanofabrication technol-ogy raises the prospect for creatingvastly greater numbers of electrodesand smaller, less invasive implanta-ble devices.34�37 A promising cate-gory of these micromachined devicesis the planar electrode array, whichis patterned on a crystalline,38,73 cer-amic,39 or polymer74,75 support struc-ture (Figure 1). Positions of micro-electrodes on these thin penetratingshafts and spacing between twoor more shafts can be tailored tosimplify targeting of multiple anato-mical areas or subregions in tan-dem.76�78 Themeasurement of neuro-nal activity with three-dimensional(3D) microelectrode arrays repre-sents a major advance in brain activ-ity mapping techniques, by pro-viding a tool to probe how intra-and inter-regional neural circuitsbehave cooperatively to computeinformation. We envision scalingup this 3D architecture to samplearbitrarily complex networks.

Advantages and Challenges of Elec-trode-Array-Based Mapping Approaches.The use of implantable electrodesis complementary to optical-basedbrain activity mapping: Electrodescan access deep brain structuresthat are challenging to reach withopticalmethods; they do not requirelabeling cells with a dye; and theyoffer higher sampling speed than vol-tage or calcium indicators (althoughadvances in optical recording tech-niques are circumventing many ofthese issues).7,79,80 Furthermore, themanufacturing processes used involtage-sensingelectrodedevelopmentcan translate to other modes of in-terrogatingneuronal activity, such aschemical sensors.81,82

Figure 1. Nanofabricated planar electrode array for high-density neuronal voltagerecording. False-color SEM image of a portion of a 64-channel array patterned on asilicon substrate. Scale bar = 50 μm. Modified from ref 37. Copyright 2011 Du et al.

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Electrodes also present two ma-jor challenges for brain activity map-ping. First, as they measure extra-cellular electric fields from all nearbyactive neurons, deriving single-unitinformation from these signals isnot trivial,83 and a fast, automated“spike sorting” algorithm for hand-ling data from a large number ofelectrodes receiving correlated sig-nals remains elusive. Even afterspike sorting is successful, extracel-lular signals cannot directly differ-entiate the origins of actionpotentials at the level of geneticallyspecific neuronal subpopulations.One approach that partially ad-dresses this limitation is to relyon indirect identification methodssuch as extracellular action potentialshape, spike time characteristics,and pharmacological response. Thismay be valuable for identifying cellsalong broadly defined categories,such as pyramidal neurons versus

interneuron84 or dopaminergic ver-

sus nondopaminergic.85 However,this method is not without pitfalls.86

Perhaps an early goal of the BAM

electrode technology effort wouldbe to catalog exhaustively extracel-lular electrophysiological markers ofgenetically identified neuronal sub-populations to lend greater validityto this indirect approach. Alterna-tively, a more direct way of identi-fying neurons is by probing theirresponses to a gene, region, orpathway-specific pharmacolog-ical or optogenetic modulator ofactivity.87�89 Microfabricated neuralprobes that can record activity anddeliver drugs or light have beendeveloped and can help address thisissue.90�94

New developments in nano�biointerfacing and 3D microfabricationtechniques might provide the meansto overcome some of the limitationsof planar microelectrode-based ex-tracellularelectrophysiology.Nanoscaleneedle electrodes (Figure 2) can pro-vide high-fidelity electrophysiologicalinterfaces to cardiomyocytes95,96 andmammalian neurons,97 with clearcell-to-electrode registry. Theseelectrodes can even perform intra-cellular recording and stimulation of

neurons in a highly scalable fashionin vitro and ex vivo.97 One possibilityis to couple these nanoscale electro-des together with the modular 3Dbrain interfacing technology thathas recently been developed.98

These 3D devices, which are as-sembled from lock-and-key digitallydesigned elements, enable position-ing of electrodes with micrometer-scale precision yet can extend acentimeter or more in linear dimen-sion and deliver and collect informa-tion from thousands of pointsthroughout the brain. While thetechnology was originally devel-oped for brain-wide optogeneticmapping,99 it can also be used forpractically any sensing and stimula-tion modalities, including electro-des, chemical sensors, and camerapixels. When coupled together, thenanoscale electrodes and 3D inter-face system will enable high-precision observation of synapse-and subthreshold-resolution neur-al activity in neurons through-out complex intact brain circuits,thereby addressing many of the

Figure 2. Three-dimensional nanoelectrode array (3D-NEA) for in vivo interrogation of neuronal networks. (a) Scanningelectronmicroscope (SEM) image of the nine silicon nanoneedles that constitute the active region of a 3D-NEA. Dimensions ofthe nanoneedle electrodes are designed to facilitate single-cell intracellular electrical coupling. False colors showmetal-coatedtips (gray) and insulating silicon oxide (blue). Reprintedwith permission from ref 97. Copyright 2012 Nature Publishing Group.(b) Scanning electron micrograph of a rat cortical cell (3 days in vitro, false colored yellow) on top of an electrode pad (falsecolored blue). Reprinted with permission from ref 97. Copyright 2012 Nature Publishing Group. (c) Stimulation and recordingof rat cortical neurons. Upper traces show that action potentials (blue: measured by a patch pipet) could be reliably stimulatedby voltage pulses applied to the nanoelectrodes (magenta). Similarly, lower traces show that the nanoelectrodes can recordaction potentials (magenta) stimulated by a patch pipet (blue). Reprinted with permission from ref 97. Copyright 2012 NaturePublishing Group. (d) Scanning electron micrograph of a representative 3D brain-interfacing device consisting of 24 probes,each containing arrays of active sites distributed along their length. Inset: optical image of the 3D probe array. Reprinted withpermission from ref 98. Copyright 2012 Optical Society of America.

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early technological goals of theBAM project.

Flexible and active electronicsoffer another potential option forinterfaces to neural circuits and thebrain.100�106 Significant progresshas been made in the areas of ultra-thin, flexible, light, biocompatiblecircuits. Hundreds of contacts tothe brain can be made on double-sided active semiconductor nano-membrane electronics transferredonto silk or other flexible substrates(Figure 3).106 Early examples of thesedevices already integrate electrodes,sensors, amplifiers, and multiplexersat the hundreds- to thousands-of-devices scale and have been testedin vivo. Substrates and other partscan be made to be biodegradable/bioresorbable.105 The possibilitiesof folding these flexible structuresmay enable less invasive means ofintroduction.

An additional challenge is elec-trode longevity, which is criticalfor ensuring the success of brain�machine interfaces and long-termstudies linking brain activity to

behavior.105,107�109 The ability to re-cord units from chronically implantedmicroelectrodes degrades on thetime scale of days to years,110�112

and themechanisms underlying thisdegradation have not been fullydetermined. On short time scales,disruption of the blood-brain barrierand cellular milieu from the stabwound may negatively affect re-cording performance113�115 but donot appear to prohibit high-yieldmeasurements.116 On longer timescales, this injury triggers a cascadeof molecular signaling events lead-ing to a sustained inflammatory re-sponse around the implant.117,118 Avariety of approaches are being ex-plored to mitigate signal-to-noisereduction resulting from acute andchronic injury responses. Some ofthese include using flexible or wire-less interconnects to decouple theelectrode from shearing forces causedbybrainmicromotion in the skull,119,120

anti-inflammatory probe coat-ings,121,122 and techniques to coaxneurons to form stable interfaceswith the implanted devices.123,124

Nanotechnology has the potentialto augment these efforts signifi-cantly by enabling extremely minia-turized sensors that have negligibleadverse interactions with surround-ing neural tissue or vasculature.125

An important milestone in addres-sing electrode longevity challengescould be a minimally invasive wire-less nanoscale probe than can at-tach itself to a single neuron andreport its firing activity for greaterthan 1 year. Finally, many importantobservations of brain activity cantake place on short behavioral timescales of minutes or hours. In thisacute recording situation, the inter-face longevity issue is less criticalthan the need to sample as manyneurons as possible.126,127 This cre-ates an intriguing opportunity topursue in parallel a different set ofBAM tools for acute, ultra-large-scale electrophysiology and tools forchronic, ultrastable electrophysiology.Insights from these different techno-logical development strategies couldbe combined in the final embodi-ment of the BAM electrode initiative.

Nanoparticle Labeling and Reporting.Over the past few decades, the de-velopment of more reproducibleand accurate tools for monitoringand controlling chemical reactionshas enabled the synthesis of an en-ormous variety of nanoparticles, na-nomaterials, and nanostructures,with controlled composition, orga-nization, shape, and functionaliza-tion. Examples include the evolu-tion of fullerenes, carbon nano-tubes, and graphene, materials sys-tems in which desirable electricaland mechanical attributes can beobtained by careful vapor deposi-tion of carbon with accurate controlover geometry and bonding. Simi-larly, carefully controlled chemicalreactions have led to light-emittingmaterials defined in nanoparticleswhose surfaces can be passivatedto ensure high luminescence effi-ciencies. Today, quantum dots withemission wavelengths spanning thespectrum fromultraviolet to infraredcan be found and are used foroptical imaging, as efficient light

Figure 3. (a) Flexible, high-density active electrode arrays composed of semicon-ductor nanomembrane electronics transferred onto polymer substrates wereplaced on the visual cortex of a cat brain or (b) into the interhemispheric fissure(inset). Reprinted with permission from ref 105. Copyright 2011 Nature PublishingGroup.

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sources, for photovoltaic energyharvesting systems, and for quan-tum physics experiments. As theseparticles are small enough to quan-tum-confine carriers in the semicon-ductor materials from which theyare made, their geometries deter-mine the blue shift in the band gapof the light emitter, and their emis-sion wavelength can be tuned geo-metrically by changing their size orshape. Moreover, as the dimensionsof these nanoparticles are small, it ispossible to build up extremely largeelectrical or optical fields in quan-tum dots and wires, leading toswitching and sensing opportunitiesfar beyond simple photoluminescentlight sources.128 For imaging pur-poses, nanoparticle surfaces havebeen functionalized with specificbinding chemistries to target andhighlight local chemistries.129,130

Unlike more conventional organicdye molecules, quantum dots areless subject to many of the stabilityproblems that can plague fluoro-phores, such as photobleachingand oxidation. We can also envisionnanodevices with active optoelec-tronic properties, so that they be-come self-powered neural signaltransmitters, perhaps tapping intothe brain's metabolic pathways foroperating power.

Chemical Measurements in the Brain.Direct chemical measurements canbe made in the brains of live, behav-ing animals with probes that extractanalytes (e.g., microdialysis)131�134

or extracellular fluid135 or that takemeasurements locally and directly(e.g., electrochemical measurementsfor electroactive compounds suchas dopamine).12�17 Currently, thespatial and temporal resolutionsfor chemical measurements basedon extraction are ∼100 μm andtens of seconds to minutes, res-pectively, whereas local electro-chemical methods can measureat the few micrometer scaleand hundreds of milliseconds,respectively.

Indirect in vivo measurementsalso can be made of nonelectro-chemically active neurotransmitters

(e.g., glutamate, acetyl choline) andother molecules by coupling enzy-matic reactions that produce elec-trochemically active products tosensors.136�139 These biosensormethods have somewhat lower spa-tial and temporal resolution thandirect electrochemical sensors butcould be coupled to the parallelplatforms envisaged for the BAMproject. Likewise, as platforms aredeveloped for this project, artificialreceptors will be developed140,141

and coupled for electronic measure-ments142,143 to sense the spatial andtemporal profiles of neurotransmit-ters in vivo.

New Imaging Tools. Optical interro-gation of populations of neurons inintact animals critically depends ontwo things: the ability to deliver lightefficiently to the brain and the abil-ity to get light out of the brain. Theseproblems are difficult in the single-cell context and become more chal-lenging when parallel simultaneousmeasurements are required. Today,both conventional fluorescence mi-croscopy and nonlinear optical mi-croscopy play important roles inimaging neural activity in behavinganimals.144 High-speed, miniatur-ized epi-fluorescence microscopes(<2.0 g mass) have been fabricatedfrom mass-producible optical parts,such as light-emitting diodes (LEDs)and nanofabricated semiconductorsensors (Figure 4).145 An adult mousecan readily bear such a microscopeon the head during active behavior,which routinely allows imaging of cal-cium dynamics in >1000 neurons permouse (Figure 5).146 To reach deepbrain areas, this approach relies onoptical needles based onmicrolenses.

Alternatively, to maintain single-cell precision and localized excitationvolumes within tissue that scatterslight, one can use nonlinear opticalmicroscopies in the near-infrared(NIR) region because of highertransmission relative to visiblewavelengths.147

Despite the reduced scatteringof NIR photons compared with visi-ble photons, the exponential loss ofballistic photons in scattering media

Figure 4. Miniature, mass-producible fluorescence microscope. (a) Cross-sectionalschematic of the microscope design. Purple and green arrows show excitation andemissionpathways, respectively. (b)Microscope (1.9 g) shown fully assembledwithits LED light source,micro-optics, and camera. Insets show, clockwise frombottom-left, the fluorescence filter cube with excitation and emission filters and dichroicmirror; the mounted camera chip; and the LED light source. Scale bars for a, b, andinsets are 5 mm. Reprinted with permission from ref 145. Copyright 2011 NaturePublishing Group.

The development of

more reproducible and

accurate tools for

monitoring and

controlling chemical

reactions has enabled

the synthesis of an

enormous variety of

nanoparticles,

nanomaterials, and

nanostructures, with

controlled composition,

organization, shape,

and functionalization.

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greatly limits our ability to penetratedeep within the brain.148 One strat-egy to increase image contrast andtotal depth is to increase the non-linearity of the excitation process,which can dramatically reduce back-ground signal. This can be donedirectly, as in the case of long-wa-velength three-photon fluorescencemicroscopy,149 or through cascadednonlinearities enabled by new fluo-rophores.150 Both techniques stillrequire delivery of ballistic photonsand suffer in scattering media. How-ever, in the past decade, there havebeen notable improvements in theability to deliver light deep withincomplex media.151,152 In these ex-periments, the wavefront of the in-coming light beam is dynamicallymodified to compensate effectivelyfor the aberrations and scatteringbodies in the sample. The morecomplex the media, the more com-plex the wavefront modificationsrequired for correction, and thegreater the number of iterations ofcorrections that will be required tocompensate. The most dramatic ex-amples have been performed usingsingle-photon illumination withhigh-speed deformable mirror de-vices (DMD), which can modulatethe wavefront at rates of ∼20 kHz,and have achieved near diffraction-limited performance imaging througha dynamically changing turbid en-vironment. The same methods havebeen used successfully in directinglight and imaging through multi-mode fibers,153 offering the poten-tial of minimally invasive imaging ofdeep internal structures. Unfortu-nately, the same successes havenot yet been achieved with non-linear microscopies, as the modula-tion devices are significantly slower.This difference occurs because, inorder to maintain excitation effi-ciency, phase-only modulators areused, and the best devices for thispurpose are phase-only liquid-crys-tal spatial light modulators wherethe liquid crystal properties limitspeed to frame rates up to 500 Hz.Within the materials/nanosciencecommunities, there is a tremendous

opportunity for applying solid-stateor polymer technologies to addressthis problem. For optical switching,many materials can provide fastphase modulation, with switchingtimes in the GHz. The developmentof a high resolution (>1 Mpixel)phasemodulator arraymight enabledeep-tissue optical imaging. Thesesignificantly higher speeds make di-gital/optical phase conjugation orcompressive sampling more power-ful when working with scatteringmedia and enable deeper imagingin dynamically changing sampleswith temporal resolution below thedecorrelation time.

High-resolution, high-speed ar-rays would also enable the creationof holograms for multisite excitationthroughout the volume, which is apossible means of attaining high-speed, parallel measurements. Tem-poral coding of individual targetsites might also enable high-speed,parallel measurements. However, innonlinear optical imaging, hologramsthat illuminate large volumes of tis-sue have the notable disadvantageof reducing the instantaneous light

intensity at any one location. In com-parison, time-multiplexing schemes,such as those using acousto-opticdeflectors, have the distinct advan-tage that they keep the instanta-neous illumination high. Further-more, as the focal point moves dee-per into the sample, the emittedphotons scatter during escape.When the focal depth increases be-yond ∼10 scattering lengths, theescaping photons lose memory oftheir origins and approach thephoton diffusion limit. Here, the ef-fective source size of the illuminatedobject scales proportionally with thedepth of the target, making simpleimaging insufficient to recover theindividual signals. With fast modula-tion of the hologram, one could beatindividual targets at different rates,enabling temporal encoding of themultiplexed illumination such thatunique signatures exist for eachoverlapping target.

Along with improvements in theinput of light to the system, we alsoneed to improve the detectors mea-suring light coming out of the sam-ple. Custom-designed microcavities

Figure 5. Ca2þ imaging in >1200 CA1 pyramidal cells in freely moving mice. (a)Integrated microscope (Figure 4) is equipped with a microendoscope and imagesCA1 neurons expressing the Ca2þ indicator GCaMP3 via the Camk2a promoter. Thebase plate andmicroendoscope are fixed to the cranium, for repeated access to thesame field of view. Reprintedwith permission from ref 146. Copyright 2013 NaturePublishing Group. (b) 1202 CA1 pyramidal cells (red somata) identified by Ca2þ

imaging in a freelymovingmouse, atop amean fluorescence image (green) of CA1.Vessels appear as dark shadows. Image courtesy of Yaniv Ziv and Lacey Kitch,Stanford University. (c) Example traces of Ca2þ dynamics from 15 cells. Scale bars:5% ΔF/F (vertical) and 10 s (horizontal). Reprinted with permission from ref 146.Copyright 2013 Nature Publishing Group.

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and photonic crystals offer the pos-sibility of extremely sensitive detec-tion of the local environment andactivity.18 Additionally, these micro-cavities can be used to enhance thebrightness and spectral propertiesof coupled fluorophores, throughthe Purcell effect.154 Large fields ofview (FOVs) are required to collectemitted photons efficiently fromdeepwithin the samples. Here, thereare opportunities to harness thenanoscale electronics communityto develop large FOV multichannelavalanche photodiode (APD) arraysthat would offer significant multi-plexing advantages over traditionaldetectors. The high time resolu-tion and channel independenceprovided by such arrays would bewell-suited to demultiplex complexsignals arising from deep in vivo

multisite excitation.Many cellular structures regulat-

ing neuronal activity (including sy-napses, tight junctions, actin, micro-filaments, and receptor complexes)are composed of densely arrayedcomponents with spacings that arecomplex and far below the diffrac-tion limit (Figure 6). New opticalimaging tools with nanoscale reso-lution, such as PALM155 andSTORM,156

are helping scientists explore thesenanoscale objects within cells. Thesetechniques can resolve structures inmicroscopic images with∼20 nm orbetter spatial precision. They thuspromise to help uncover the organi-zational principles of macromolecu-lar complexes within specializedcells of the nervous system. As anexample, recent work employingthese techniques has revealed thedynamic behavior and organizationof the actin cytoskeleton insidecells, which is relevant for under-standing how neurons probe theirinvolvement during neuronal out-growth and in response to injury157

and how they differentiate axonalprocesses.158 These techniques alsopermit characterization of receptorclustering and stoichiometry at theplasma membrane under diverseconditions159,160 as well as proteinorganization inside synapses,161

which are critical for understandinghow synapses respond to changesin neuronal activity.

Optogenetics. Brain activity map-ping is tightly linked to optoge-netics;the use of light to control

well-defined events within targetedelements of intact biological sys-tems (reviewed in ref 162);in sev-eral important ways (Figure 7).163

Control of brain activity in a precise,targeted, high-speed manner (e.g.,

Figure 6. Actin retrograde flow rates at the leading edge of a PtK1 cell. Thephotoactivatable protein tdEos tagged to actin was expressed in PtK1 cells.Individual molecules were visualized through photoactivation with ultravioletlight. They were then tracked over time to reveal movement of individual actinmoleculeswithin actin filaments at the edge of the cells. A flowmapof rates of actinfilament movement from the cell surface is shown. Vector colors reflect flow speed(color bar), and arrows reflect direction. The scale bar is 10 μm. Reproduced withpermission from ref 157. Copyright 2011 Nature Publishing Group.

Figure 7. Brain activity mapping may be enabled, in part, by optogenetic prep-arations as shownhere in a freelymovingmouse; green light is delivered to deeporsuperficial brain areas via fiber optics. Optogenetic control of microbial protein-expressing targeted neurons enables (1) determination of causal significance ofactivity patterns; (2) in some cases, phototagging identification of cells fromwhichelectrical spikes are recorded; and (3) in some preparations, imaging of neuralresponses to control or stimulation. Advances in computational optics andnanoscale device engineering will further enable delivery of complex spatiallymodulated light patterns to the target tissue. Figure adapted from Inbal Goshenand Karl Deisseroth, Stanford University/HHMI.

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as enabled by optogenetics withmicrobial opsin genes,89,162,164�166

which encode light-activated channeland pump regulators of transmem-brane ion conductance) in principlewill allow assessment of the causalsignificance of brain activity patternsobserved with recording or imaging.In thisway, activitymappingbecomesnot simple, passive observation ofactivity correlating with behaviorbut observation coupled with in-sight into causal significance;a keydistinction.

Further, optogenetics enablesthe identification of cellular geneticidentity by “phototagging”. Thismethod could help liberate high-speed readouts of neural activityfrom the need to be intrinsicallygenetically targeted. For example,multiunit electrical recording itselfcarries essentially no informationon the genetic phenotype of theneurons generating the recordedspikes; however, when linked tophototagging (in which a geneti-cally encoded control tool is intro-duced to drive spiking), correspond-ing electrical spikes with their ownunique waveforms are observedafter light pulse initiation at suffi-ciently brief intervals and can insome cases thereafter be inferredto belong to the genetically tar-geted class.167 In this way, optoge-netics can enhance and enable pureactivity mapping itself.

Optogenetic tools are them-selves nanoscale devices that canbe engineered for new classes ofbrain activity mapping function,building on molecular structure�function relationships. Not onlycanmany different kinds of ion flow,spectral responses, and kinetics beachieved through directed engi-neering,162�164 but also fundamen-tally new classes of function canarise from this kind of work (e.g.,turning a microbial opsin into avoltage sensor for activity map-ping9). Moreover, the success of op-togenetics has inspired discussionsof other possible classes of control(e.g., magnetogenetics, acousto-genetics) in which other modalities

of energy delivery would be cap-tured by distinct classes of engi-neered, targetable-molecular-energyantenna-like elements expressedin specific classes of cells.163,168,169

Optogenetics has already foundwidespread utility in mapping cir-cuits causally involved in both nor-mal function and in the elicitationand correction of disease-relatedphenomena including anxiety, de-pression, fear memory, parkinson-ism, and social dysfunction.170�174

We have pointed out that additionaltechnologies also need to be devel-oped further for this approach toreach its full potential:20 (1)methodsfor determination of global (brain-wide) wiring diagrams of cells thatare observed and controlled in vivo;(2) volumetric, genetically targetedmethods both to visualize andto control activity within intacttissue;98,175,176 and (3) non-opticalmethods that leave a recoverabletrace of activity within cells to side-step the light-scattering problem,which could involve a gene encod-ing a designer polymerase trans-duced into a genetically targetedsubset of neurons,19�21 especially ifthe polymerasewere engineered forincreased error rate in elevatedCa2þ,20 which can track neural activ-ity patterns at high speeds, even inthe nucleus.177 Together, these con-cepts highlight how optogeneticsapproaches could address key goalsof brain activity mapping but re-quire integration with other estab-lished and novel technologies.

Biological Hybrids and Synthetic Biol-ogy. Functional metagenomics cansurvey the biosphere for extraordin-ary new nanocomponents (typicallyproteins), which, via protein engi-neering and laboratory selections,can be fused and optimized tomakecomplex systems very much athome in nanometer- to centimeter-scale biological networks. These bio-nano “parts” can include not onlyfluorescent ion sensors and light-responsive channels (vide supra)but also light-emitting sensors (luci-ferases) and fluorescent voltage(action potential) indicators like

Archaerhodopsin 3 (Arch) with anoptical signal-to-noise ratio >10 and41 ms time response.9

Synthetic biology can potentiallyprovide hybrid system interfaceswith inorganic fabricated compo-nents, including building or brid-ging 3D optical fiber arrays91,98 toprovide effectively high optical sur-face area and multiplexing;for ex-ample, thin natural light waveguides in glass sponges.178 Buildingoptical fibers around the extensivebrain vasculature might be less dis-ruptive than inserting them as solidarrays from the surface (or couldcomplement such arrays). Dynamicviral capsids, DNA nanorobots,179

and/or engineered cells can navi-gate the blood-brain barrier, andcerebrospinal fluid or trans-synapticclefts180 can provide targeting spe-cificity for brain activity input/out-put and/or neuronal connectivitydata. Polymerases can provide ana-log-to-digital “ticker tape” recordingsensors for light, ATP (e.g., in activesynapses), and ions.1,181 Fluorescentin situ sequencing (FISSEQ)182 orhybridization (FISH)183 could enablealignment of electrode or opticalfiber arrays with the anatomical con-nectome, RNA transcriptome, andticker tapes all in one set of serialsections;reminiscent of the AllenBrain atlas,40 but without the arti-facts (and cost) of aligning “similar”regions from numerous differentbrains. Insertion of (up to billions of)synthetic DNA barcodes is helpfulnot only for neuronal lineageanalysis184 and synaptic connect-ivity180 but also for integrating themultidisciplinary brain data (videsupra) with the controlled sensoryinputs and behavioral outputs inBAM experiments on individual ani-mals over diverse (normal andpathological) genetic and pharma-cological backgrounds.

Connecting Neuroscience at the Molec-ular and Dynamical Systems Levels. Brainactivity mapping electrode technol-ogy will enable scientists and clin-icians to generate an inherentlydifferent type of data set than whatgenomic sequencing or brain-wide

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gene expression maps provide,which are static or slowly evolvingsnapshots of molecular information.In contrast, BAM technology will re-veal an extremely complex choreo-graphy of neuronal activation that isconstantly in flux. One of the majorchallenges for BAM technology de-velopment is to unify this electro-physiological view with the equallycomplex and important molecularlandscape of the brain. After all,behavior, learning, and cognitionrequire synergy between chemicalneurotransmission, biochemical re-actions, and electrical impulses. It iscritical that, in addition to enablingrecordings from unprecedentedlylarge numbers of neurons, BAM alsoenhances these incredibly powerfulcomplementary levels of analysis.The development of multifunctionalnanoscale probes that can simulta-neously record and pharmacologi-cally or optogenetically perturbmolecularly defined neurons, withhigh spatial and temporal precision,may be a necessary step in provid-ing critical tests of proposed me-chanisms and theories of neuralcircuit and brain function.

Theory, Modeling, and Computation.One of the goals of brain activitymapping is to provide large datasets to be used in modeling and de-veloping theories of the emergentproperties of neural circuits and de-tailed connections between differ-ent levels of operations andhierarchical abstractions, includingnatural, synthetic, and practical spa-tiotemporal patterns. Likewise, tech-nology to be developed can providecritical tests of these models andtheories by active interaction with

neural circuits. We anticipate thatopen access to these data sets willdraw worldwide interest in and at-tention to the task of developingthis understanding. The data setswill be large but comparable tothose being produced by currentastronomical observations, in geno-mics, and other areas. As in theseother large data sets, compressionand leveraging tools developed inthe field of sparsity will likely beused heavily.185

Equally important for achieving adeeper understanding of brain func-tion is the comparable developmentof a conceptual framework andmathe-matical theory for brain activity inhigh-dimensional spaces. Currenttheories of brain function based onrecordings from single neurons arelimited in scope. Population codeshave been deduced by combiningrecordings from many neurons re-corded separately. Observing popu-lation codes as they unfold in realtime should reveal deeper and dy-namically shifting relationships be-tween the neurons and the ongoingprocessing occurring in dense braincircuits. The population dynamics ofthe neural ensemble carries a farricher representation of sensory sti-muli and actions.

Simulations of brain activity willbe another important tool for refin-ing our ideas about brain function,based on experimental recordingsand additional constraints from thecellular andmolecular levels of brainorganization. The new methods forrecording and manipulating neu-rons, including intracellular bio-chemical pathways, will enable usto design experiments that testcompeting hypotheses. For exam-ple, the responses of neurons inthe visual cortex have latencies thatrange from 20 to 100 ms, but wehave the impression of a single mo-ment when a stimulus flashes. Isthere a deeper neural correlateamong a large population of neu-rons that is more closely associatedwith our subjective impression oftime? How do we store and recallthe temporal structures that occur in

music? The BAM Project may bringus closer to answering ultimatequestions about how we think andmake decisions, which involve thecoordinated activity in large num-bers of neurons widely distributedthroughout the human brain. Un-derstanding the principles of neuralcomputation will also lead to newdevices based on these principles.

PROSPECTS

In these and other areas, thereare tremendous opportunities fornanoscience and nanotechnologyto contribute to neuroscience. Wehave collected answers to frequentlyasked questions on the BAM Projectas this exciting proposal now stands.We hope that the BAM Project willbring the past decade's national andinternational investments in science,technology, and people in nano-science and nanotechnology to bearon important and challenging pro-blems in brain science.

Conflict of Interest: The authors declareno competing financial interest.

Acknowledgment. We gratefully ac-knowledge the Kavli Foundation for sup-port and encouragement of this initiativeand the discussions that led up to it. Theauthors acknowledge helpful discussionswith Prof. Adam Cohen, Dr. Tim Harris,Prof. John Rogers, and Dr. Alan Rudolph,as well as many of our colleagues. WethankMs. Holly Bunje for help in preparingthe manuscript.

Brain activity mapping

technology will reveal

an extremely complex

choreography of

neuronal activation that

is constantly in flux.

We hope that the Brain

Activity Mapping

Project will bring the

last decade's national

and international

investments in science,

technology, and people

in nanoscience and

nanotechnology to bear

on important and

challenging problems in

brain science.

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REFERENCES AND NOTES1. Alivisatos, A. P.; Chun, M.; Church,

G. M.; Greenspan, R. J.; Roukes, M. L;Chun, M.; Yuste, R. The Brain Activ-ity Map Project and the Challengeof Functional Connectomics. Neu-ron 2012, 74, 970–974.

2. Andrews, A. M.; Weiss, P. S. Nano inthe Brain: Nano-Neuroscience. ACSNano 2012, 6, 8643–8644.

3. Alivisatos, A. P.; Chun, M.; Church,G. M.; Deisseroth, K.; Donoghue, J. P.;Greenspan,R. J.;McEuen,P. L.; Roukes,M.; Sejnowski, T. J.; Weiss, P. S.; et al.The Brain Activity Map. Science 2013,10.1126/science.1236939.

4. Underwood, E. Brain Project DrawsPresidential Interest, but Mixed Re-actions. Science 2013, 339, 1022–1023.

5. A Frequently Asked Questions fileis available for the Brain ActivityMapping Project at http://www.kavlifoundation.org/BAM.

6. Azevedo, F. A. C.; Carvalho, L. R. B.;Grinberg, L. T.; Farfel, J. M.; Ferretti,R. E. L.; Leite, R. E. P.; Filho, W. J.;Lent, R.; Herculano-Houzel, S. EqualNumbers of Neuronal and Non-neuronal Cells Make the HumanBrain an Isometrically Scaled-UpPrimate Brain. J. Comp. Neurol.2009, 513, 532–541.

7. Akerboom, J.; Chen, T. W.; Wardill,T. J.; Tian, L.; Marvin, J. S.; Mutlu, S.;Calderon, N. C.; Esposti, F.; Bor-ghuis, B. G.; Sun, X. R.; et al. Opti-mization of a GCaMP CalciumIndicator for Neural Activity Ima-ging. J. Neurosci. 2012, 32, 13819–13840.

8. Peterka, D. S.; Takahashi, H.; Yuste,R. Imaging Voltage in Neurons.Neuron 2011, 69, 9–21.

9. Kralj, J. M.; Douglass, A. D.;Hochbaum, D. R.; Maclaurin, D.;Cohen, A. E. Optical Recording ofAction Potentials in MammalianNeurons Using aMicrobial Rhodop-sin. Nat. Methods 2011, 9, 90–95.

10. Vokoun, C. R.; Jackson, M. B.; Basso,M. A. Intralaminar and InterlaminarActivity within the Rodent SuperiorColliculus Visualized with VoltageImaging. J. Neurosci. 2010, 30,10667–10682.

11. Marvin, J. S.; Borghuis, B. G.; Tian, L.;Cichon, J.; Harnett, M. T.; Aker-boom, J.; Gordus, A.; Renninger,S. L.; Chen, T.-W.; Bargmann, C. I.;et al. An Optimized FluorescentProbe for Visualizing GlutamateNeurotransmission. Nat. Methods2013, 10, 162–170.

12. Cheer, J. F.; Heien, M. L.; Garris, P. A.;Carelli, R. M.; Wightman, R. M. Si-multaneous Dopamine and Single-Unit Recordings Reveal Accum-bens GABAergic Responses: Impli-cations for Intracranial Self-Stimulation. Proc. Natl. Acad. Sci.U.S.A. 2005, 102, 19150–19155.

13. Roitman, M. F.; Wheeler, R. A.;Wightman, R. M.; Carelli, R. M.Real-Time Chemical Responses in

the Nucleus Accumbens Differenti-ate Rewarding and Aversive Stimu-li. Nat. Neurosci. 2008, 11, 1376–1377.

14. Makos, M. A.; Han, K. A.; Heien, M. L.;Ewing, A. G. Using In Vivo Electro-chemistry To Study the Physiologi-cal Effects of Cocaine and OtherStimulants on the Drosophila mel-anogaster Dopamine Transporter.ACS Chem. Neurosci. 2010, 1, 74–83.

15. Baganz, N.; Horton, R.; Martin, K.;Holmes, A.; Daws, L. C. RepeatedSwim Impairs Serotonin Clearancevia a Corticosterone-Sensitive Me-chanism:OrganicCationTransporter3, the Smoking Gun. J. Neurosci.2010, 30, 15185–15195.

16. Kishida, K. T.; Sandberg, S. G.;Lohrenz, T.; Comair, Y. G.; Sáez, I.;Phillips, P. E.; Montague, P. R. Sub-second Dopamine Detection inHuman Striatum. PLoS One 2011,6, e23291.

17. Xiao, N.; Venton, B. J. Rapid, Sensi-tive Detection of Neurotransmit-ters at Microelectrodes Modifiedwith Self-Assembled SWCNT For-ests. Anal. Chem. 2012, 84, 7816–7822.

18. Li, M.; He, F.; Liao, Q.; Liu, J.; Xu, L.;Jiang, L.; Song, Y.; Wang, S.; Zhu, D.Ultrasensitive DNA DetectionUsing Photonic Crystals. Angew.Chem., Int. Ed. 2008, 47, 7258–7262.

19. Kording, K. P. Of Toasters and Mo-lecular Ticker Tapes. PLoS Comput.Biol. 2011, 7, e1002291.

20. Deisseroth, K. Optogenetics andPsychiatry: Applications, Chal-lenges, and Opportunities. Biol.Psychiatry 2012, 71, 1030–1032.

21. Zamft, B. M.; Marblestone, A. H.;Kording, K.; Schmidt, D.; Martin-Alarcon, D.; Tyo, K.; Boyden, E. S.;Church, G. Measuring Cation De-pendent DNA Polymerase FidelityLandscapes by Deep Sequencing.PLoS One 2012, 7, e43876.

22. Buzsaki, G. Large-Scale Recordingof Neuronal Ensembles. Nat. Neu-rosci. 2004, 7, 446–451.

23. Gray, C.M.;Maldonado, P. E.; Wilson,M.; McNaughton, B. TetrodesMarkedly Improve the Reliabilityand Yield of Multiple Single-UnitIsolation from Multi-Unit Record-ings inCat StriateCortex. J. Neurosci.Methods 1995, 63, 43–54.

24. Szuts, T. A.; Fadeyev, V.; Kachiguine,S.; Sher, A.; Grivich, M. V.; Agrochao,M.; Hottowy, P.; Dabrowski, W.;Lubenov, E. V.; Siapas, A. G.; et al.A Wireless Multi-channel NeuralAmplifier for FreelyMoving Animals.Nat. Neurosci. 2011, 14, 263–269.

25. Olsson, R. H.; Buhl, D. L.; Sirota,A. M.; Buzsaki, G.; Wise, K. D.Band-Tunable and Multiplexed In-tegrated Circuits for SimultaneousRecording and Stimulation withMicroelectrode Arrays. IEEE Trans.Biomed. Eng. 2005, 52, 1303–1311.

26. Harrison, R. R. The Design of Inte-grated Circuits To Observe BrainActivity. Proc. IEEE 2008, 96, 1203–1216.

27. Miranda, H.; Gilja, V.; Chestek, C. A.;Shenoy, K. V.; Meng, T. H.; Hermes,D. A High-Rate Long-Range Wire-less Transmission System for Si-multaneous Multichannel NeuralRecording Applications. IEEE Trans.Biomed. Circ. Syst. 2010, 4, 181–191.

28. Najafi, K.;Wise, K. D.; Mochizuki, T. AHigh-Yield IC-Compatible Multi-channel Recording Array. IEEETrans. Electron Devices 1985, 32,1206–1211.

29. Seidl, K.; Lemke, B.; Ramirez, H.;Herwik, S.; Ruther, P.; Paul, O.CMOS-Based High-Density SiliconMicroprobe for Stress Mapping inIntracortical Applications. MEMS2010 23rd IEEE International Con-ference onMicro Electro MechanicalSystems, Technical Digest 2010,35–38.

30. Viventi, J.; Kim, D. H.; Vigeland, L.;Frechette, E. S.; Blanco, J. A.; Kim,Y. S.; Avrin, A. E.; Tiruvadi, V. R.;Hwang, S. W.; Vanleer, A. C.; et al.Flexible, Foldable, Actively Multi-plexed, High-Density Electrode Ar-ray for Mapping Brain Activity inVivo.Nat. Neurosci. 2011, 14, 1599–1605.

31. Karkare, V.; Gibson, S.; Markovic, D.A 130-mu W, 64-Channel NeuralSpike-Sorting DSP Chip. IEEE J. Solid-State Circ. 2011, 46, 1214–1222.

32. Olsson, R. H., III; Buhl, D. L.; Sirota,A. M.; Buzsaki, G.; Wise, K. D. Band-Tunable and Multiplexed Inte-grated Circuits for SimultaneousRecording and Stimulation withMicroelectrode Arrays. IEEE Trans.Biomed. Eng. 2005, 52, 1303–1311.

33. Nicolelis, M. A. L.; Dimitrov, D.;Carmena, J. M.; Crist, R.; Lehew, G.;Kralik, J. D.; Wise, S. P. Chronic,Multisite, Multielectrode Record-ings in Macaque Monkeys. Proc.Natl. Acad. Sci. U.S.A. 2003, 100,11041–11046.

34. Wise, K. D.; Najafi, K. Microfabrica-tion Techniques for IntegratedSensors and Microsystems. Science1991, 254, 1335–1342.

35. Cheung, K. C.; Djupsund, K.; Dan, Y.;Lee, L. P. Implantable MultichannelElectrode Array Based on SOI Tech-nology. J. Microelectromech. Syst.2003, 12, 179–184.

36. Wise, K. D.; Sodagar, A. M.; Yao, Y.;Gulari, M. N.; Perlin, G. E.; Najafi, K.Microelectrodes, Microelectronics,and Implantable Neural Microsys-tems. Proc. IEEE 2008, 96, 1184–1202.

37. Du, J.; Blanche, T. J.; Harrison, R. R.;Lester, H. A.; Masmanidis, S. C. Mul-tiplexed, High Density Electrophy-siology with Nanofabricated NeuralProbes. PLoS One 2011, 6, e26204.

38. Wise, K. D.; Najafi, K.; Drake, K. L. AMultichannel Probe for Intracortical

NANO

FOCUS


www.acsnano.org

1863

Single-Unit Recording. IEEE Trans.Biomed. Eng. 1984, 31, 583–583.

39. Moxon, K. A.; Leiser, S. C.; Gerhardt,G. A.; Barbee, K. A.; Chapin, J. K.Ceramic-Based Multisite ElectrodeArrays for Chronic Single-NeuronRecording. IEEE Trans. Biomed.Eng. 2004, 51, 647–656.

40. Lein, E. S.; Hawrylycz, M. J.; Ao, N.;Ayres,M.; Bensinger, A.; Bernard, A.;Boe, A. F.; Boguski, M. S.; Brockway,K. S.; Byrnes, E. J.; et al. Genome-Wide Atlas of Gene Expression inthe Adult Mouse Brain. Nature2007, 445, 168–176.

41. Heintz, N. BAC to the Future: TheUse of BAC Transgenic Mice forNeuroscience Research. Nat. Rev.Neurosci. 2001, 2, 861–870.

42. Bullmore, E.; Sporns, O. ComplexBrain Networks: Graph TheoreticalAnalysis of Structural and Func-tional Systems. Nat. Rev. Neurosci.2009, 10, 186–198.

43. Logothetis, N. K. What We Can DoandWhatWe Cannot Dowith fMRI.Nature 2008, 453, 869–878.

44. Buzsaki, G.; Anastassiou, C. A.; Koch,C. The Origin of Extracellular Fieldsand Currents;EEG, ECoG, LFP andSpikes.Nat. Rev. Neurosci. 2012, 13,407–420.

45. Stevenson, I. H.; Kording, K. P. HowAdvances in Neural Recording Af-fect Data Analysis. Nat. Neurosci.2011, 14, 139–142.

46. Ikegaya, Y.; Aaron, G.; Cossart, R.;Aronov, D.; Lampl, I.; Ferster, D.;Yuste, R. Synfire Chains and Corti-cal Songs: Temporal Modules ofCortical Activity. Science 2004,304, 559–564.

47. Carandini, M. From Circuits to Be-havior: A Bridge Too Far? Nat. Neu-rosci. 2012, 15, 507–509.

48. Watts, D. J.; Strogatz, S. H. CollectiveDynamics of `Small-World' Net-works. Nature 1998, 393, 440–442.

49. Aosaki, T.; Tsubokawa, H.; Ishida, A.;Watanabe, K.; Graybiel, A. M.; Ki-mura, M. Responses of TonicallyActive Neurons in the Primate'sStriatum Undergo SystematicChanges during Behavioral Sensor-imotor Conditioning. J. Neurosci.1994, 14, 3969–3984.

50. Witten, I. B.; Lin, S. C.; Brodsky, M.;Prakash, R.; Diester, I.; Anikeeva, P.;Gradinaru, V.; Ramakrishnan, C.;Deisseroth, K. Cholinergic Inter-neurons Control Local Circuit Ac-tivity and Cocaine Conditioning.Science 2010, 330, 1677–1681.

51. Ramón y Cajal, S. Recuerdos de MiVida: Historia de Mi Labor Cientifica.Alianza Editorial (Madrid, 1923).

52. Binnig, G.; Rohrer, H. Scanning Tun-neling Microscopy. Helv. Phys. Acta1982, 55, 726–735.

53. Binnig, G.; Quate, C. F.; Gerber, Ch.Phys. Rev. Lett. 1986, 56, 930–933.

54. Jackson, N. B.; Chaurand, P. R.; Ful-ghum, J. E.; Hernandez, R.; Higgins,D. A.; Hwang, R.; Kneipp, K.; Korets-ky, A. P.; Larabell, C. A.; Stranick,

S. J.; et al. Visualizing Chemistry: TheProgress and Promise of AdvancedChemical Imaging; National Acade-mies Press: Washington, DC, 2006.

55. Weiss, P. S. A Conversation with Dr.Heinrich Rohrer: STM Co-Inventorand One of the Founding Fathersof Nanoscience. ACS Nano 2007, 1,3–5.

56. Eigler, D. M.; Schweizer, E. K. Posi-tioning Single Atoms with a Scan-ning TunnelingMicroscope.Nature1990, 344, 524–526.

57. Piner, R. D.; Zhu, J.; Xu, F.; Hong,S. H.; Mirkin, C. A. “Dip-Pen” Nano-lithography. Science 1999, 283,661–663.

58. Takami, T.; Ye, T.; Pathem, B. K.;Arnold, D. P.; Sugiura, K.-i.; Bian, Y.;Jiang, J.; Weiss, P. S. ManipulatingDouble-Decker Molecules at theLiquid�Solid Interface. J. Am. Chem.Soc. 2010, 132, 16460–16466.

59. Claridge, S. A.; Schwartz, J. J.; Weiss,P. S. Electrons, Photons, and Force:Quantitative Single-Molecule Mea-surements from Physics to Biology.ACS Nano 2011, 5, 693–729.

60. Claridge, S. A.; Liao, W.-S.; Thomas,J. C.; Zhao, Y.; Cao, H.; Cheunkar, S.;Serino, A. C.; Andrews, A. M.; Weiss,P. S. From the Bottom Up: Dimen-sional Control andCharacterizationin Molecular Monolayers. Chem. Soc.Rev. 2013, 42, 2725–2745.

61. Nanotechnology Research Direc-tions for Societal Needs in 2020:Retrospective and Outlook; Roco,M. C., Mirkin, C. A., Hersam, M. C.,Eds.; Science Policy Reports, Springer:Berlin, 2011.

62. Bell, C. G.; Chen, R.; Rege, S. TheEffect of Technology on Near TermComputer Structures. Computer1972, 2, 29–38.

63. Moore, G. E. Cramming More Com-ponents onto Integrated Circuits.Electronics 1965, 38, 4–7.

64. Keller, S. M.; Carman, G. P. Electro-magnetic Wave Propagation in (Bia-nisotropic) Magnetoelectric mate-rials. J. Intellect. Mater. Syst. Struct.2013, 10.1177/1045389X12467518.

65. Smith, R. K.; Lewis, P. A.; Weiss, P. S.Patterning Self-Assembled Mono-layers. Prog. Surf. Sci. 2004, 75, 1–68.

66. Love, J. C.; Estroff, L. A.; Kriebel, J. K.;Nuzzo, R. G.; Whitesides, G. M. Self-Assembled Monolayers of Thio-lates on Metals as a Form of Nano-technology. Chem. Rev. 2005, 105,1103–1169.

67. Saavedra, H. M.; Mullen, T. J.;Zhang, P. P.; Dewey, D. C.; Claridge,S. A.; Weiss, P. S. Hybrid Ap-proaches in Nanolithography. Rep.Prog. Phys. 2010, 73, 036501.

68. Vaish, A.; Shuster, M. J.; Cheunkar,S.; Singh, Y. S.; Weiss, P. S.; Andrews,A. M. Native Serotonin MembraneReceptors Recognize 5-Hydroxy-tryptophan-Functionalized Sub-strates: Enabling Small-MoleculeRecognition. ACS Chem. Neurosci.2010, 1, 495–504.

69. VanDersarl, J. J.; Xu, A. M.; Melosh,N. A. Nanostraws for Direct FluidicIntracellular Access. Nano Lett.2012, 12, 3881–3886.

70. Zheng, Y. B.; Payton, J. L.; Song,T.-B.; Pathem, B. K.; Zhao, Y.; Ma,H.; Yang, Y.; Jensen, L.; Jen, A. K. Y.;Weiss, P. S. Surface-EnhancedRaman Spectroscopy To ProbePhotoreaction Pathways and Ki-netics of Isolated Reactants onSurfaces: Flat versus Curved Sub-strates. Nano Lett. 2012, 12, 5362–5368.

71. Gold, C.; Henze, D. A.; Koch,C. Using Extracellular ActionPotential Recordings To ConstrainCompartmental Models. J. Comp.Neurosci. 2007, 23, 39–58.

72. Harris, K. D.; Henze, D. A.; Csicsvari,J.; Hirase, H.; Buzsaki, G. Accuracy ofTetrode Spike Separation As Deter-mined by Simultaneous Intracellu-lar and Extracellular Measurements.J. Neurophysiol. 2000, 84, 401–414.

73. Chan, H. Y.; Aslam, D. M.; Wiler, J. A.;Casey, B. A Novel DiamondMicrop-robe for Neuro-Chemical and-Electrical Recording in Neural Pro-sthesis. J. Microelectromech. Syst.2009, 18, 511–521.

74. Mercanzini, A.; Cheung, K.; Buhl,D. L.; Boers, M.; Maillard, A.; Colin,P.; Bensadoun, J. C.; Bertsch, A.;Renaud, P. Demonstration of Cor-tical Recording Using Novel Flex-ible Polymer Neural Probes. Sens.Actuators, A 2008, 143, 90–96.

75. Takeuchi, S.; Suzuki, T.; Mabuchi, K.;Fujita, H. 3D Flexible MultichannelNeural Probe Array. J. Micromech.Microeng. 2004, 14, 104–107.

76. Merriam, M. E.; Dehmel, S.; Srivan-navit, O.; Shore, S. E.; Wise, K. D. A3-D 160-Site Microelectrode Arrayfor Cochlear Nucleus Mapping.IEEE Trans. Biomed. Eng. 2011, 58,397–403.

77. Du, J. G.; Roukes, M. L.; Masmanidis,S. C. Dual-Side and Three-Dimensional Microelectrode ArraysFabricated from Ultra-thin SiliconSubstrates. J. Micromech. Microeng.2009, 19, 075008.

78. Hoogerwerf, A. C.; Wise, K. D. A3-Dimensional Microelectrode Arrayfor Chronic Neural Recording. IEEETrans. Biomed. Eng. 1994, 41, 1136–1146.

79. Flusberg, B. A.; Lung, J. C.; Cocker,E. D.; Anderson, E. P.; Schnitzer, M. J.In Vivo Brain Imaging Using aPortable 3.9 Gram Two-PhotonFluorescence Microendoscope. Opt.Lett. 2005, 30, 2272–2274.

80. Dombeck, D. A.; Blanchard-Desce,M.; Webb, W. W. Optical Recordingof Action Potentials with Second-Harmonic Generation Microscopy.J. Neurosci. 2004, 24, 999–1003.

81. Szapacs, M. E.; Mathews, T. A.;Tessarollo, L.; Lyons,W. E.;Mamounas,L. A.; Andrews, A. M. Exploring theRelationship between Serotonin andBrain Derived Neurotrophic Factor:

NANO

FOCUS


www.acsnano.org

1864

Analysis of BDNF Protein and Extra-neuronal 5-HT in Mice with ReducedSerotonin Transporter or BDNF Ex-pression. J. Neurosci. Methods 2004,140, 81–92.

82. Wassum, K. M.; Tolosa, V. M.; Wang,J.; Walker, E.; Monbouquette, H. G.;Maidment, N. T. Silicon Wafer-Based PlatinumMicroelectrode Ar-ray Biosensor for Near Real-TimeMeasurement of Glutamate in Vivo.Sensors 2008, 8, 5023–5036.

83. Segev, R.; Goodhouse, J.; Puchalla,J.; Berry, M. J. Recording SpikesFrom a Large Fraction of the Gang-lion Cells in a Retinal Patch. Nat.Neurosci. 2004, 7, 1155–1162.

84. Bartho, P.; Hirase, H.; Monconduit,L.; Zugaro, M.; Harris, K. D.; Buzsaki,G. Characterization of NeocorticalPrincipal Cells and Interneurons byNetwork Interactions and Extracel-lular Features. J. Neurophysiol.2004, 92, 600–608.

85. Zhang, T. A.; Placzek, A. N.; Dani,J. A. In Vitro Identification and Elec-trophysiological Characterizationof Dopamine Neurons in the Ven-tral Tegmental Area. Neurophar-macology 2010, 59, 431–436.

86. Margolis, E. B.; Lock, H.; Hjelmstad,G. O.; Fields, H. L. The Ventral Teg-mental Area Revisited: Is There anElectrophysiological Marker forDopaminergic Neurons? J. Phy-siol. (Oxford, U.K.) 2006, 577, 907–924.

87. Lima,S.Q.;Hromadka, T.; Znamenskiy,P.; Zador, A.M. PINP: ANewMethodof Tagging Neuronal Populationsfor Identification during In VivoElectrophysiological Recording. PLoSOne 2009, 4, e6099.

88. Lerchner, W.; Xiao, C.; Nashmi, R.;Slimko, E. M.; van Trigt, L.; Lester,H. A.; Anderson, D. J. ReversibleSilencing of Neuronal Excitabi-lity in Behaving Mice by a Geneti-cally Targeted, Ivermectin-GatedCl� Channel. Neuron 2007, 54,35–49.

89. Boyden, E. S.; Zhang, F.; Bamberg,E.; Nagel, G.; Deisseroth, K. Millise-cond-Timescale, Genetically Tar-geted Optical Control of NeuralActivity. Nat. Neurosci. 2005, 8,1263–1268.

90. Seidl, K.; Spieth, S.; Herwik, S.;Steigert, J.; Zengerle, R.; Paul, O.;Ruther, P. In-Plane Silicon Probesfor Simultaneous Neural Recordingand Drug Delivery. J. Micromech.Microeng. 2010, 20, 105006.

91. Zorzos, A. N.; Boyden, E. S.; Fonstad,C. G. Multiwaveguide ImplantableProbe for Light Delivery to Sets ofDistributed Brain Targets. Opt. Lett.2010, 35, 4133–4135.

92. Retterer, S. T.; Smith, K. L.; Bjornsson,C. S.; Neeves, K. B.; Spence, A. J. H.;Turner, J. N.; Shain, W.; Isaacson,M. S. Model Neural Prostheses withIntegratedMicrofluidics: A PotentialIntervention Strategy for Con-trolling Reactive Cell and Tissue

Responses. IEEE Trans. Biomed.Eng. 2004, 51, 2063–2073.

93. Royer, S.; Zemelman, B. V.; Barbic,M.; Losonczy, A.; Buzsaki, G.;Magee, J. C. Multi-array SiliconProbes with Integrated OpticalFibers: Light-Assisted Perturbationand Recording of Local Neural Cir-cuits in the Behaving Animal. Eur. J.Neurosci. 2010, 31, 2279–2291.

94. Anikeeva, P.; Andalman, A. S.;Witten, I.; Warden, M.; Goshen, I.;Grosenick, L.; Gunaydin, L. A.; Frank,L. M.; Deisseroth, K. Optetrode:A Multichannel Readout for Opto-genetic Control in Freely MovingMice. Nat. Neurosci. 2012, 15, 163–170.

95. Duan, X.; Gao, R.; Xie, P.; Cohen-Karni, T.; Qing, Q.; Choe, H. S.; Tian,B.; Jiangm, X.; Lieber, C. M. Intracel-lular Recordings of Action Poten-tials by an Extracellular NanoscaleField-Effect Transistor. Nat. Nano-technol. 2012, 7, 174–179.

96. Xie, C.; Lin, Z.; Hanson, L.; Cui, Y.;Cui, B. Intracellular Recording ofAction Potentials by NanopillarElectroporation. Nat. Nanotechnol.2012, 7, 185–190.

97. Robinson, J. T.; Jorgolli, M.; Shalek,A. K.; Yoon, M.-H.; Gertner, R. S.;Park, H. Vertical Nanowire Elec-trode Arrays as a Scalable Platformfor Intracellular Interfacing to Neu-ronal Circuits. Nat. Nanotechnol.2012, 7, 180–184.

98. Zorzos, A. N.; Scholvin, J.; Boyden,E. S.; Fonstad,C.G. Three-DimensionalMultiwaveguide Probe Array forLight Delivery to Distributed BrainCircuits. Opt. Lett. 2012, 37, 4841–4843.

99. Bernstein, J. G.; Boyden, E. S. Opto-genetic Tools for Analyzing theNeural Circuits of Behavior. TrendsCognit. Sci. 2011, 15, 592–600.

100. Margalit, E. Visual and ElectricalEvoked Response Recorded fromSubdural Electrodes Implantedabove the Visual Cortex in NormalDogs under Two Methods of An-esthesia. J. Neurosci. Methods 2003,123, 129–137.

101. Yeager, J. D.; Phillips, D. J.; Rector,D. M.; Bahr, D. F. Characterization ofFlexible ECoG Electrode Arrays forChronic Recording in Awake Rats. J.Neurosci. Methods 2008, 173, 279–285.

102. Kim, D.-H.; Rogers, J. A. Bend,Buckle, and Fold: Mechanical Engi-neering with Nanomembranes. ACSNano 2009, 3, 498–501.

103. Huang, M.; Ritz, C. S.; Novakovic, B.;Yu, D.; Zhang, Y.; Flack, F.; Savage,D. E.; Evans, P. G.; Knezevic, I.; Liu, F.;et al. Mechano-Electronic Superlat-tices in Silicon Nanoribbons. ACSNano 2009, 3, 721–727.

104. Kim, D.-H.; Viventi, J.; Amsden, J. J.;Xiao, J.; Vigeland, L.; Kim, Y.-S.; Blanco,J. A.; Panilaitis, B.; Frechette, E. S.;Contreras, D.; et al. DissolvableFilms of Silk Fibroin for Ultrathin

Conformal Bio-Integrated Electro-nics. Nat. Mater. 2010, 9, 511–517.

105. Viventi, J.; Kim, D.-H.; Vigeland, L.;Frechette, E. S.; Blanco, J. A.; Kim,Y.-S.; Avrin, A. E.; Tiruvadi, V. R.;Hwang, S. W.; Vanleer, A. C.; et al.Flexible, Foldable, Actively Multi-plexed, High-Density ElectrodeArray for Mapping Brain Activity inVivo. Nat. Neurosci. 2011, 14, 1599–1605.

106. Kim, D.-H.; Lu, N.; Ma, R.; Kim, Y.-S.;Kim, R. H.; Wang, S.; Wu, J.; Won,S. M.; Tao, H.; Islam, J. A.; et al.Epidermal Electronics. Science 2011,333, 838–843.

107. Schwartz, A. B. Cortical Neural Pros-thetics. Annu. Rev. Neurosci. 2004,27, 487–507.

108. Nicolelis, M. A. L. Brain�MachineInterfaces To Restore Motor Func-tion and Probe Neural Circuits.Nat. Rev. Neurosci. 2003, 4, 417–422.

109. Prochazka, A.; Mushahwar, V. K.;McCreery, D. B. Neural Prostheses.J. Physiol. (Oxford, U.K.) 2001, 533,99–109.

110. Dickey, A. S.; Suminski, A.; Amit, Y.;Hatsopoulos, N. G. Single-Unit Sta-bility Using Chronically ImplantedMultielectrode Arrays. J. Neurophy-siol. 2009, 102, 1331–1339.

111. Vetter, R. J.; Williams, J. C.; Hetke,J. F.; Nunamaker, E. A.; Kipke, D. R.Chronic Neural Recording UsingSilicon-Substrate MicroelectrodeArrays Implanted in Cerebral Cor-tex. IEEE Trans. Biomed. Eng. 2004,51, 896–904.

112. Liu, X. D.; McCreery, D. B.; Bullara,L. A.; Agnew, W. F. Evaluation of theStability of Intracortical Microelec-trode Arrays. IEEE Trans. Neural Syst.Rehabil. 2006, 14, 91–100.

113. Edell, D. J.; Toi, V. V.; McNeil, V. M.;Clark, L. D. Factors Influencing theBiocompatibility of Insertable Sili-con Microshafts in Cerebral-Cortex.IEEE Trans. Biomed. Eng. 1992, 39,635–643.

114. Szarowski, D. H.; Andersen, M. D.;Retterer, S.; Spence, A. J.; Isaacson,M.; Craighead, H. G.; Turner, J. N.;Shain, W. Brain Responses to Micro-Machined Silicon Devices. BrainRes. 2003, 983, 23–35.

115. Mitala, C. M.; Wang, Y. X.; Borland,L. M.; Jung, M.; Shand, S.; Watkins,S.; Weber, S. G.; Michael, A. C. Im-pact of Microdialysis Probes onVasculature and Dopamine in theRat Striatum: A Combined Fluores-cence and Voltammetric Study. J.Neurosci. Methods 2008, 174, 177–185.

116. Scott, K. M.; Du, J.; Lester, H. A.;Masmanidis, S. C. Variability ofAcute Extracellular Action PotentialMeasurements with Multisite Sili-con Probes. J. Neurosci. Methods2012, 211, 22–30.

117. Turner, J. N.; Shain, W.; Szarowski,D. H.; Andersen, M.; Martins, S.;Isaacson, M.; Craighead, H. Cerebral

NANO

FOCUS


www.acsnano.org

1865

Astrocyte Response to Microma-chined Silicon Implants. Exp. Neu-rol. 1999, 156, 33–49.

118. Ward, M. P.; Rajdev, P.; Ellison, C.;Irazoqui, P. P. Toward a Comparisonof Microelectrodes for Acute andChronic Recordings. Brain Res.2009, 1282, 183–200.

119. Biran, R.; Martin, D. C.; Tresco, P. A.The Brain Tissue Response to Im-planted Silicon Microelectrode Ar-rays Is Increased When the DeviceIs Tethered to the Skull. J. Biomed.Mater. Res., PartA2007, 82, 169–178.

120. Sodagar, A. M.; Perlin, G. E.; Yao, Y.;Najafi, K.; Wise, K. D. An Implantable64-Channel Wireless Microsystemfor Single-Unit Neural Recording.IEEE J. Solid State Circ. 2009, 44,2591–2604.

121. He, W.; McConnell, G. C.; Schneider,T. M.; Bellamkonda, R. V. A NovelAnti-inflammatory Surface forNeural Electrodes. Adv. Mater.2007, 19, 3529–3533.

122. Spataro, L.; Dilgen, J.; Retterer, S.;Spence, A. J.; Isaacson, M.; Turner,J. N.; Shain, W. DexamethasoneTreatment Reduces Astroglia Re-sponses to Inserted Neuropros-thetic Devices in Rat Neocortex.Exp. Neurol. 2005, 194, 289–300.

123. He, W.; Bellamkonda, R. V. Nano-scale Neuro-Integrative Coatingsfor Neural Implants. Biomaterials2005, 26, 2983–2990.

124. Cullen, D. K.; Patel, A. R.; Doorish,J. F.; Smith, D. H.; Pfister, B. J. Devel-oping a Tissue-Engineered Neural-Electrical Relay Using EncapsulatedNeuronal Constructs on Conduct-ing Polymer Fibers. J. Neural Eng.2008, 5, 374–384.

125. Kozai, T. D. Y.; Marzullo, T. C.; Hooi,F.; Langhals, N. B.; Majewska, A. K.;Brown, E. B.; Kipke, D. R. Reductionof Neurovascular Damage Result-ing from Microelectrode Insertioninto the Cerebral Cortex UsingIn Vivo Two-Photon Mapping. J.Neural Eng. 2010, 7, 046011.

126. Niell, C. M.; Stryker, M. P. Modula-tion of Visual Responses by Beha-vioral State in Mouse Visual Cortex.Neuron 2010, 65, 472–479.

127. Harvey, C. D.; Coen, P.; Tank, D. W.Choice-Specific Sequences in Par-ietal Cortex during a Virtual-Navi-gation Decision Task. Nature 2012,484, 62–68.

128. Empedocles, S. A.; Bawendi, M. G.Quantum-Confined Stark Effect inSingle CdSe Nanocrystallite Quan-tum Dots. Science 1997, 278, 2114–2117.

129. Rosenthal, S. J.; Tomlinson, I.; Ad-kins, E. M.; Schroeter, S.; Adams, S.;Swafford, L.; McBride, J.; Wang, Y.;DeFelice, L. J.; Blakely, R. D. Target-ing Cell Surface Receptors withLigand-Conjugated Nanocrystals.J. Am. Chem. Soc. 2002, 124, 4586–4594.

130. Beikmann, B. S.; Tomlinson, I. D.;Rosenthal, S. J.; Andrews, A. M.

Serotonin Uptake Is Largely Mediatedby Platelets versus Lymphocytes in Per-ipheral Blood Cells. ACS Chem. Neuros-

ci. 2013, 4, 161–170.131. Mathews, T. A.; Fedele, D. E.;

Coppelli, F. M.; Avila, A. M.; Murphy,D. L.; Andrews, A. M. Gene Dose-Dependent Alterations in Extra-neuronal Serotonin but Not Dopa-mine in Mice with ReducedSerotonin Transporter Expression.J. Neurosci. Methods 2004, 140,169–181.

132. Guiard, B. P.; David, D. J.; Deltheil, T.;Chenu, F.; Le Maitre, E.; Renoir, T.;Leroux-Nicollet, I.; Sokoloff, P.;Lanfumey, L.; Hamon, M.; et al.Brain-Derived Neurotrophic Factor-Deficient Mice Exhibit a Hippocam-pal Hyperserotonergic Phenotype.Int. J. Neuropsychopharmacol. 2008,11, 79–92.

133. Liu, Y.; Zhang, J.; Xu, X.; Zhao, M. K.;Andrews, A. M.; Weber, S. G. Capil-lary Ultrahigh Performance LiquidChromatography with ElevatedTemperature for Sub-One MinuteSeparations of Basal Serotonin inSubmicroliter Brain MicrodialysateSamples. Anal. Chem. 2010, 82,9611–9616.

134. Wang, M.; Slaney, T.; Mabrouk, O.;Kennedy, R. T. Collection of Nano-liter Microdialysate Fractions inPlugs for Off-Line In Vivo ChemicalMonitoring with up to 2 s TemporalResolution. J. Neurosci. Methods2010, 190, 39–48.

135. Slaney, T. R.; Mabrouk, O. S.; Porter-Stransky, K. A.; Aragona, B. J.;Kennedy, R. T. Chemical Gradientswithin Brain Extracellular SpaceMeasured using Low Flow Push-Pull Perfusion Sampling in Vivo.ACS Chem. Neurosci. 2013, 4, 321–329.

136. Mattinson, C. E.; Burmeister, J. J.;Quintero, J. E.; Pomerleau, F.; Huettl,P.; Gerhardt, G. A. Tonic and PhasicRelease of Glutamate and Acetyl-choline Neurotransmission in Sub-regions of the Rat Prefrontal CortexUsing Enzyme-Based Microelec-trode Arrays. J. Neurosci. Methods2011, 202, 199–208.

137. Wassum, K. M.; Tolosa, V. M.; Tseng,T. C.; Balleine, B. W.; Monbouquette,H. G.; Maidment, N. T. TransientExtracellular Glutamate Events inthe Basolateral Amygdala Track Re-ward-Seeking Actions. J. Neurosci.2012, 32, 2734–2746.

138. Sacchi, S.; Rosini, E.; Caldinelli, L.;Pollegioni, L. Biosensors for D-AminoAcid Detection. Methods Mol. Biol.2012, 794, 313–324.

139. Opris, I.; Hampson, R. E.; Gerhardt,G. A.; Berger, T. W.; Deadwyler, S. A.Columnar Processing in PrimatepFC: Evidence for Executive ControlMicrocircuits. J. Cognit. Neurosci.2012, 24, 2334–2347.

140. Shuster, M. J.; Vaish, A.; Szapacs,M. E.; Anderson, M. E.; Weiss,P. S.; Andrews, A. M. Biospecific

Recognition of Tethered Small Mol-ecules Diluted in Self-AssembledMonolayers. Adv. Mater. 2008, 20,164–167.

141. Vaish, A.; Shuster, M. J.; Singh, Y. S.;Weiss, P. S.; Andrews, A. M. NativeSerotonin Membrane ReceptorsRecognize 5-Hydroxytryptophan-Functionalized Substrates: En-abling Small-Molecule Recognition.ACS Chem. Neurosci. 2010, 1, 495–504.

142. Chui, C. O.; Shin, K. S. IntegratedAmplifying Nanowire FET for Sur-face and Bulk Sensing. Proc. SPIE2011, 8106, 81060C-1-8.

143. Tian, B.; Lieber, C. M. SyntheticNanoelectronic Probes for Biologi-cal Cells and Tissues. Annu. Rev.Anal. Chem. 2013, 6, 31–51.

144. Wilt, B. A.; Fitzgerald, J. E.; Schnitzer,M. J. Photon Shot Noise Limits onOptical Detection of Neuronal Spikesand Estimation of Spike Timing. Bio-phys. J. 2013, 104, 51–62.

145. Ghosh, K. K.; Burns, L. D.; Cocker,E. D.; Nimmerjahn, A.; Ziv, Y.; ElGammal, A.; Schnitzer, M. J. Minia-turized Integration of a Fluores-cence Microscope. Nat. Methods2011, 8, 871–878.

146. Ziv, Y.; Burns, L. D.; Cocker, E. D.;Hamel, E. O.; Ghosh, K. K.; Kitch, L. J.;El Gammal, A.; Schnitzer, M. J. Long-Term Dynamics of CA1 Hippocam-pal Place Codes. Nat. Neurosci.2013, 16, 264–266.

147. Denk, W.; Strickler, J. H.; Webb,W. W. Two-Photon Laser ScanningFluorescence Microscopy. Science1990, 248, 73–76.

148. Theer, P.; Denk, W. On the Funda-mental Imaging-Depth Limit inTwo-Photon Microscopy. J. Opt.Soc. Am. A 2006, 23, 3139–3149.

149. Horton, N. G.; Wang, K.; Kobat, D.;Clark, C. G.; Wise, F. W.; Schaffer,C. B.; Xu, C. In Vivo Three-PhotonMicroscopy of Subcortical Struc-tures within an Intact Mouse Brain.Nat. Photonics 2013, 7, 205–209.

150. Chen, Z.; Wei, L.; Zhu, X.; Min, W.Extending the Fundamental Ima-ging-Depth Limit of Multi-PhotonMicroscopy by Imaging with Photo-activatable Fluorophores. Opt. Exp.2012, 20, 18525–18536.

151. Conkey,D. B.; Brown,A. N.; Caravaca-Aguirre, A. M.; Piestun, R. GeneticAlgorithm Optimization for Focus-ing through Turbid Media in NoisyEnvironments. Opt. Exp. 2012, 20,4840–4849.

152. Vellekoop, I. M.; van Putten, E. G.;Lagendijk, A.; Mosk, A. P. DemixingLight Paths inside DisorderedMeta-materials.Opt. Exp. 2008, 16, 67–80.

153. Papadopoulos, I. N.; Farahi, S.; Mo-ser, C.; Psaltis, D. Focusing andScanning Light through a Multi-mode Optical Fiber Using DigitalPhase Conjugation. Opt. Exp.2012, 20, 10583–10590.

154. Noda, S.; Fujita, M.; Asano, T.Spontaneous-Emission Control by

NANO

FOCUS


www.acsnano.org

1866

Photonic Crystals and Nanocavities.Nat. Photonics 2007, 1, 449–458.

155. Betzig, E.; Patterson, G. H.; Sougrat,R.; Lindwasser, O. W.; Olenych, S.;Bonifacino, J. S.; Davidson, M. W.;Lippincott-Schwartz, J.; Hess, H. F.Imaging Intracellular FluorescentProteins at Nanometer Resolution.Science 2006, 313, 1642–1645.

156. Rust, M. J.; Bates, M.; Zhuang, X. W.Sub-diffraction-Limit Imaging byStochastic Optical ReconstructionMicroscopy (STORM). Nat. Methods2006, 3, 793–795.

157. Burnette, D.; Manley, S.; Sengupta,P.; Sougrat, R.; Davidson, M.; Kachar,B.; Lippincott-Schwartz, J. A Role forActin Arcs in the Leading EdgeAdvance of Migrating Cells. Nat.Cell Biol. 2011, 13, 371–381.

158. Xu, K.; Zhong, G.; Zhuang, X. Actin,Spectrin, and Associated ProteinsForm a Periodic Cytoskeletal Struc-ture in Axons. Science 2013, 339,452–456.

159. Sengupta, P.; Jovanovic-Talisman,T.; Skoko, D.; Renz, M.; Veatch, S.;Lippincott-Schwartz, J. ProbingProtein Heterogeneity in thePlasma Membrane Using PALMand Pair Correlation Analysis. Nat.Methods 2011, 8, 969–975.

160. Renz, M.; Daniels, B. R.; Vámosi, G.;Arias, I. M.; Lippincott-Schwartz, J.Plasticity of the AsialoglycoproteinReceptor Deciphered by EnsembleFRET Imaging and Single-MoleculeCounting PALM Imaging. Proc. Natl.Acad. Sci. U.S.A. 2012, 109, 2989–2997.

161. Dani, A.; Huang, B.; Bergan, J.; Dulac,C.; Zhuang, X. Superresolution Ima-ging of Chemical Synapses in theBrain. Neuron 2010, 68, 843–856.

162. Yizhar, O.; Fenno, L.; Davidson, T.;Mogri, M.; Deisseroth, K. Optoge-netics in Neural Systems. Neuron2011, 71, 9–34.

163. Bernstein, J. G.; Garrity, P. A.; Boy-den, E. S. Optogenetics and Ther-mogenetics: Technologies for Con-trolling the Activity of Targeted Cellswithin Intact Neural Circuits. Curr.Opin. Neurobiol. 2012, 22, 61–71.

164. Zhang, F.; Vierock, J.; Yizhar,O.; Fenno,L. E.; Tsunoda, S.; Kianianmomeni, A.;Prigge, M.; Berndt, A.; Cushman, J.;Polle, J.; et al. The Microbial OpsinFamily of Optogenetic Tools. Cell2011, 147, 1446–1457.

165. Han, X.; Boyden, E. S. Multiple-ColorOptical Activation, Silencing, andDesynchronization of Neural Activ-ity, with Single-Spike Temporal Re-solution. PLoS ONE 2007, 2, e299.

166. Chow, B. Y.; Han, X.; Dobry, A. S.;Qian, X.; Chuong, A. S.; Li, M.;Henninger, M. A.; Belfort, G. M.;Lin, Y.; Monahan, P. E.; et al. High-PerformanceGenetically TargetableOptical Neural Silencing by Light-Driven Proton Pumps. Nature 2010,463, 98–102.

167. Lima, S.Q.; Hromádka, T.; Znamenskiy,P.; Zador, A. M. PINP: A NewMethod

of Tagging Neuronal Populationsfor Identification during In VivoElectrophysiological Recording. PLoSONE 2009, 4, e6099.168.B5.

168. Stanley, S. A.; Gagner, J. E.; Daman-pour, S.; Yoshida, M.; Dordick, J. S.;Friedman, J. M. Radio-Wave Heat-ing of Iron Oxide Nanoparticles CanRegulate Plasma Glucose in Mice.Science 2012, 336, 604–608.

169. Huang, H.; Delikanli, S.; Zeng, H.;Ferkey, D. M.; Pralle, A. RemoteControl of Ion Channels andNeurons through Magnetic-FieldHeating of Nanoparticles. Nat.Nanotechnol. 2010, 5, 602–606.

170. Goshen, I.; Brodsky, M.; Prakash, R.;Wallace, J.; Gradinaru, V.; Ramak-rishnan, C.; Deisseroth, K. Dynamicsof Retrieval Strategies for RemoteMemories. Cell 2011, 147, 678–689.

171. Liu, X.; Ramirez, S.; Pang, P. T.;Puryear, C. B.; Govindarajan, A.;Deisseroth, K.; Tonegawa, S. Opto-genetic Stimulation of a Hippo-campal Engram Activates FearMemory Recall. Nature 2012,484, 381–385.

172. Tye, K. M.; Deisseroth, K. Optoge-netic Investigation of Neural Cir-cuits Underlying Brain Disease inAnimal Models. Nat. Rev. Neurosci.2012, 13, 251–266.

173. Tye, K. M.; Mirzabekov, J. J.; Warden,M. R.; Tsai, H. C.; Finkelstein, J.; Kim,S. Y.; Ferenczi, E.; Adhikari, A.;Thompson, K. R.; Andalman, A. S.;et al. Dopamine Neurons Modulatethe Neural Encoding and Expres-sion of Depression-Related Beha-vior. Nature 2013, 493, 537–541.

174. Warden, M. W.; Selimbeyoglu, A.;Mirzabekov, J. J.; Lo, M.; Thompson,K. R.; Kim, S. Y.; Adhikari, A.; Tye,K. M.; Frank, L. M.; Deisseroth, K. APrefrontal Cortex-Brainstem Pro-jection Controlling Response to Be-havioral Challenge. Nature 2012,492, 428–432.

175. Packer, A. M.; Peterka, D. S.; Hirtz,J. J.; Prakash, R.; Deisseroth, K.;Yuste, R. Two-Photon Optogeneticsof Dendritic Spines and NeuronalCircuits in 3D.Nat. Methods 2012, 9,1202–1205.

176. Prakash, R.; Yizhar, O.; Grewe, B.;Ramakrishnan, C.; Wang, N.; Goshen,I.; Packer, A. M.; Peterka, D. S.; Yuste,R.; Schnitzer, M. J.; et al. Two-PhotonOptogenetic Toolbox for FastInhibition, Excitation, and BistableModulation. Nat. Methods 2012, 9,1171–1179.

177. Deisseroth, K.; Mermelstein, P. G.;Xia, H.; Tsien, R. W. Signaling fromSynapse to Nucleus: The Logic be-hind the Mechanisms. Curr. Opin.Neurobiol. 2003, 13, 354–365.

178. Sundar, V. C.; Yablon, A. D.; Grazul,J. L.; Ilan, M.; Aizenberg, J. Fibre-Optical Features of a Glass Sponge.Nature 2003, 424, 899–900.

179. Douglas, S. M.; Bachelet, I.; Church,G. M. A Logic-Gated Nanorobotfor Targeted Transport ofMolecular

Payloads. Science 2012, 335, 831–834.

180. Zador, A. M.; Dubnau, J.; Oyibo,H. K.; Zhan, H.; Cao, G.; Peikon, I. D.Sequencing the Connectome. PLoSBiol. 2012, 10, e1001411.

181. Zamft, B. M.; Marblestone, A. H.;Kording, K.; Schmidt, D.; Martin-Alarcon, D.; Tyo, K.; Boyden, E. S.;Church, G. Measuring Cation De-pendent DNA Polymerase. PLoSOne 2012, 7, e43876.

182. Mitra, R. D.; Shendure, J.; Olejnik, J.;Krzymanska-Olejnik, E.; Church,G. M. Fluorescent In Situ Sequen-cing on Polymerase Colonies. Anal.Biochem. 2003, 320, 55–65.

183. Beliveau, B. J.; Joyce, E. F.; Aposto-lopoulos, N.; Yilmaz, F.; Fonseka,C. Y.; McCole, R. B.; Chang, Y.; Li,J. B.; Senaratne, T. N.; Williams, B. R.;et al. Versatile Design and SynthesisPlatform for Visualizing Genomeswith Oligopaint FISH Probes. Proc.Natl. Acad. Sci. U.S.A. 2012, 109,21301–21306.

184. Golden, J. A.; Fields-Berry, S. C.;Cepko, C. L. Construction and Char-acterization of a Highly ComplexRetroviral Library for Lineage Anal-ysis. Proc. Natl. Acad. Sci. U.S.A.1995, 92, 5704–5708.

185. Loh, P. R.; Baym, M.; Berger, B. Com-pressive Genomics. Nat. Biotechnol.2012, 30, 627–630.

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