Abstract
This chapter provides a critical appraisal of the concept of a circuit-centered neurophenotype that is conceptualized within a genome-to-phenome framework. Representing many of the kinds of variables that complicate this model are a few select ones that are sampled in this chapter. They include the following: (a) The problem of minimal circuit definition—the many scales of neural circuitry and the difficulty of demarcating circuits within some forms of neural architecture; (b) the modulation of neural circuits and the alterations of circuit architecture through non-gene-regulated factors such as synaptic plasticity, extra-synaptic neuromodulation, and bioelectric dynamics; and (c) technical and methodological considerations in circuit delineation—as is coming to light in the field of microscale connectomics. Adding these complex variables from neuroscience into the fray make for great attenuation of the notion of a circuit neurophenotype in the behavioral neurosciences, and this is given some depth of coverage in this chapter. However, the chapter does not dismiss the utility of the concept of circuit neurophenotypes. It concludes with a discussion of the kinds of additional information that may be needed in order for the concept to be better validated.
Keywords
- Circuit neurophenotypes
- Circuit definition
- Circuit scale
- Circuit architecture
- Circuit morphology
- Synaptic plasticity
- Neuromodulation
- Bioelectric dynamics
- Gap junction communication
- Microscale connectomics
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Notes
- 1.
See for example the Special Issue of the Journal of Abnormal Psychology (2013), Vol. 122, No. 3.
- 2.
It is also commonly said in neuroscience that the Cajalian “neuron hypothesis” and the Golgi-labeled sparse neuron diagrams that Cajal elegantly illustrated are deep historical roots of this notion.
- 3.
Certain mathematical models may offer ways around this problem—see Chap. 14 in this volume—though in this instance, the number of neural variables that would be needed for the equations are currently improbable in terms of recording and discovery.
- 4.
Lecture given at Boston University, titled “The Promises and Perils of Connectomics,” December 4th, 2015.
References
Adams DS, Uzel SG, Akagi J et al (2016) Bioelectric signalling via potassium channels: a mechanism for craniofacial dysmorphogenesis in KCNJ2-associated Andersen-Tawil syndrome. J Physiol 594(12):3245–3270
Bargmann CI (2012) Beyond the connectome: how neuromodulators shape neural circuits. BioEssays 34(6):458–465
Bargmann CI, Marder E (2013) From the connectome to brain function. Nat Methods 10(6):483–490
Beane WS, Morokuma J, Lemire JM et al (2013) Bioelectric signaling regulates head and organ size during planarian regeneration. Development 140(2):313–322
Behrens TE, Sporns O (2012) Human connectomics. Curr Opin Neurobiol 22(1):144–153
Berardi NA, Maffei L (2015) Brain structural and functional development: genetics and experience. Dev Med Child Neurol 57(Suppl 2):4–9
Bliss TV, Lomo T (1973) Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J Physiol 232(2):331–356
Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198
Burns R, Roncal WG, Kleissas D et al (2013) The open connectome project data cluster: scalable analysis and vision for high-throughput neuroscience. Sci Stat Database Manag
Castellanos FX, Di Martino A, Craddock RC et al (2013) Clinical applications of the functional connectome. Neuroimage 80:527–540
Craddock N, Jones L, Jones IR et al (2010) Strong genetic evidence for a selective influence of GABAA receptors on a component of the bipolar disorder phenotype. Mol Psychiatry 15(2):146–153
Craddock RC, Jbabdi S, Yan CG et al (2013) Imaging human connectomes at the macroscale. Nat Methods 10(6):524–539
Deco G, Kringelbach ML (2014) Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron 84(5):892–905
Emmons-Bell M, Durant F, Hammelman J et al (2015) Gap junctional blockade stochastically induces different species-specific head anatomies in genetically wild-type Girardia dorotocephala flatworms. Int J Mol Sci 16(11):27865–27896
Fornito A, Bullmore ET (2012) Connectomic intermediate phenotypes for psychiatric disorders. Front Psychiatry 3(32):1–15
Fornito A, Zalesky A, Breakspear M (2015) The connectomics of brain disorders. Nat Rev Neurosci 16(3):159–172
Fries P (2005) A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn Sci 9(10):474–480
Getting PA (1989) Emerging principles governing the operation of neural networks. Annu Rev Neurosci 12:185–204
Hassan BA, Hiesinger PR (2015) Beyond molecular codes: simple rules to wire complex brains. Cell 163(2):285–291
Helmstaedter M (2013) Cellular-resolution connectomics: challenges of dense neural circuit reconstruction. Nat Methods 10(6):501–507
Hyman SE (2002) Neuroscience, genetics, and the future of psychiatric diagnosis. Psychopathology 35(2–3):139–144
Izquierdo EJ, Beer RD (2013) Connecting a connectome to behavior: an ensemble of neuroanatomical models of C. elegans klinotaxis. PLoS Comput Biol 9(2):e1002890
Johnson MB, Kawasawa YI, Mason CE et al (2009) Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron 62(4):494–509
Kandel ER (2007) In search of memory: the emergence of a new science of mind, 1st edn. W. W. Norton and Company, New York
Kasthuri N, Lichtman JW (2007) The rise of the ‘projectome’. Nat Methods 4(4):307–308
Kasthuri N, Hayworth KJ, Berger DR et al (2015) Saturated reconstruction of a volume of neocortex. Cell 162(3):648–661
Kim IJ, Zhang Y, Yamagata M et al (2008) Molecular identification of a retinal cell type that responds to upward motion. Nature 452(7186):478–482
Kirov G, Pocklington AJ, Holmans P et al (2012) De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol Psychiatry 17(2):142–153
Levin M (2007) Gap junctional communication in morphogenesis. Prog Biophys Mol Biol 94(1–2):186–206
Levin M (2012) Molecular bioelectricity in developmental biology: new tools and recent discoveries: control of cell behavior and pattern formation by transmembrane potential gradients. BioEssays 34(3):205–217
Levin M (2013) Reprogramming cells and tissue patterning via bioelectrical pathways: molecular mechanisms and biomedical opportunities. Wiley Interdiscip Rev Syst Biol Med 5(6):657–676
Levin M (2014) Molecular bioelectricity: How endogenous voltage potentials control cell behavior and instruct pattern regulation in vitro. Mol Biol Cell 25:3835–3850
Levin M, Stevenson CG (2012) Regulation of cell behavior and tissue patterning by bioelectrical signals: challenges and opportunities for biomedical engineering. Annu Rev Biomed Eng 14:295–323
Lichtman (2015) “The Promises and Perils of Connectomics.” Lecture given at Boston University, December 4th, 2015
Lichtman JW, Denk W (2011) The big and the small: challenges of imaging the brain’s circuits. Science 334(6056):618–623
Lichtman JW, Pfister H, Shavit N (2014) The big data challenges of connectomics. Nat Neurosci 17(11):1448–1454
Linden DE (2012) The challenges and promise of neuroimaging in psychiatry. Neuron 73(1):8–22
Marder E (2012) Neuromodulation of neuronal circuits: back to the future. Neuron 76(1):1–11
Marder E, Bucher D (2007) Understanding circuit dynamics using the stomatogastric nervous system of lobsters and crabs. Annu Rev Physiol 69:291–316
Marder E, Thirumalai V (2002) Cellular, synaptic and network effects of neuromodulation. Neural Netw 15(4–6):479–493
Marder E, O’Leary T, Shruti S (2014) Neuromodulation of circuits with variable parameters: single neurons and small circuits reveal principles of state-dependent and robust neuromodulation. Annu Rev Neurosci 37:329–346
Markram H (2012) The human brain project. Sci Am 306(6):50–55
Mathalon DH, Sohal VS (2015) Neural oscillations and synchrony in brain dysfunction and neuropsychiatric disorders: it’s about time. JAMA Psychiatry 72(8):840–844
Miniaci MC, Kim JH, Puthanveettil SV et al (2008) Sustained CPEB-dependent local protein synthesis is required to stabilize synaptic growth for persistence of long-term facilitation in Aplysia. Neuron 59(6):1024–1036
Monakow CV (1969) Die lokalisation im grosshirn und derabbau derfunktion durchkortikale herde. In: Harris G, Pribram KH (eds) Mood, states and mind. Penguin Books, London, England, pp 27–37
Morgan JL, Lichtman JW (2013) Why not connectomics? Nat Methods 10(6):494–500
Morris SE, Cuthbert BN (2012) Research domain criteria: cognitive systems, neural circuits, and dimensions of behavior. Dialogues Clin Neurosci 14(1):29–37
Mustard JL, Levin M (2014) Bioelectrical mechanisms for programming growth and form: taming physiological networks for soft body robotics. Soft Robot 1(3):169–191
Nesse RM, Stein DJ (2012) Towards a genuinely medical model for psychiatric nosology. BMC Med 10:5
Oldham MC, Konopka G, Iwamoto K et al (2008) Functional organization of the transcriptome in human brain. Nat Neurosci 11(11):1271–1282
Parikshak NN, Gandal MJ, Geschwind DH (2015) Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat Rev Genet 16(8):441–458
Paulsen O, Sejnowski TJ (2000) Natural patterns of activity and long-term synaptic plasticity. Curr Opin Neurobiol 10(2):172–179
Pereda AE, Curti S, Hoge G et al (2013) Gap junction-mediated electrical transmission: regulatory mechanisms and plasticity. Biochim Biophys Acta 1828(1):134–146
Raman K, Wagner A (2011) The evolvability of programmable hardware. J R Soc Interface 8(55):269–281
Rockland KS (2015) About connections. Front Neuroanat 9(61):1–7
Seung HS (2009) Reading the book of memory: sparse sampling versus dense mapping of connectomes. Neuron 62(1):17–29
Singer W (1999) Neuronal synchrony: a versatile code for the definition of relations? Neuron 24(1):49–65, 111–125
Sirevaag AM, Greenough WT (1988) A multivariate statistical summary of synaptic plasticity measures in rats exposed to complex, social and individual environments. Brain Res 441(1–2):386–392
Siri B, Quoy M, Delord B et al (2007) Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons. J Physiol Paris 101(1–3):136–148
Sporns O (2013) Making sense of brain network data. Nat Methods 10(6):491–493
Thompson A (1998) On the automatic design of robust electronics through artificial evolution. In: Sipper M, Mange D, Pérez-Uribe A (eds) Proceedings of Evolvable systems: from biology to hardware: second international conference, ICES 98, Lausanne, Switzerland, 23–25 September 1998. Springer, Berlin, Heidelberg, pp 13–24
Thompson A, Layzell P (1999) Analysis of unconventional evolved electronics. Commun ACM 42(4):71–79
Thompson A, Layzell P (2000) Evolution of robustness in an electronics design. In: Miller J, Thompson A, Thomson P et al (eds) Proceedings of Evolvable systems: from biology to hardware: third international conference, ICES 2000, Edinburgh, Scotland, UK, 17–19 April 2000. Springer, Berlin, Heidelberg, pp 218–228
Vazquez-Reina A, Michael G, Huang D, Lichtman J, Miller E (2011) Segmentation fusion for connectomics. In: IEEE international conference on computer vision, Barcelona, Spain, 6–13 November 2011, pp 177–184
Weimann JM, Marder E (1994) Switching neurons are integral members of multiple oscillatory networks. Curr Biol 4(10):896–902
Xia M, He Y (2011) Magnetic resonance imaging and graph theoretical analysis of complex brain networks in neuropsychiatric disorders. Brain Connect 1(5):349–365
Zalesky A, Fornito A, Harding IH et al (2010) Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 50(3):970–983
Zucker RS, Regehr WG (2002) Short-term synaptic plasticity. Annu Rev Physiol 64:355–405
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Jagaroo, V., Bosl, W., Santangelo, S.L. (2016). Appraising Circuit-Centered Neurophenotypes. In: Jagaroo, V., Santangelo, S. (eds) Neurophenotypes. Innovations in Cognitive Neuroscience. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-3846-5_3
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