Thorough specification of the neurophysiologic processes underlying behavior and of their manifestation in EEG - demonstration with the go/no-go task

Goded Shahaf, Hillel Pratt, Goded Shahaf, Hillel Pratt

Abstract

In this work we demonstrate the principles of a systematic modeling approach of the neurophysiologic processes underlying a behavioral function. The modeling is based upon a flexible simulation tool, which enables parametric specification of the underlying neurophysiologic characteristics. While the impact of selecting specific parameters is of interest, in this work we focus on the insights, which emerge from rather accepted assumptions regarding neuronal representation. We show that harnessing of even such simple assumptions enables the derivation of significant insights regarding the nature of the neurophysiologic processes underlying behavior. We demonstrate our approach in some detail by modeling the behavioral go/no-go task. We further demonstrate the practical significance of this simplified modeling approach in interpreting experimental data - the manifestation of these processes in the EEG and ERP literature of normal and abnormal (ADHD) function, as well as with comprehensive relevant ERP data analysis. In-fact we show that from the model-based spatiotemporal segregation of the processes, it is possible to derive simple and yet effective and theory-based EEG markers differentiating normal and ADHD subjects. We summarize by claiming that the neurophysiologic processes modeled for the go/no-go task are part of a limited set of neurophysiologic processes which underlie, in a variety of combinations, any behavioral function with measurable operational definition. Such neurophysiologic processes could be sampled directly from EEG on the basis of model-based spatiotemporal segregation.

Keywords: ADHD; EEG/ERP; analysis; go/no-go; modeling; neurophysiologic processes; representation.

Figures

Figure 1
Figure 1
Representation of temporary stimulus-response relations. (A) The riddle: how are temporary associations formed? (B) Spatial multiplication of each stimulus and response (in each of the figures presenting the expanding model, the additive model blocks and connections are emphasized with a gray square). (C) Lateral inhibition for selection of one representation in one locus. (D) Inter-modality connection according to localization.
Figure 1
Figure 1
Representation of temporary stimulus-response relations. (A) The riddle: how are temporary associations formed? (B) Spatial multiplication of each stimulus and response (in each of the figures presenting the expanding model, the additive model blocks and connections are emphasized with a gray square). (C) Lateral inhibition for selection of one representation in one locus. (D) Inter-modality connection according to localization.
Figure 2
Figure 2
Memory maintenance process. (A) Possible hippocampal contribution for the maintenance of relations. (B) One locus representation without inhibition of the other representation.
Figure 3
Figure 3
Perception process. (A) Distinction between primary and higher sensory regions and units. (B) Excitation and inhibition from primary to higher sensory regions. (C) Possible thalamic involvement in perception.
Figure 4
Figure 4
Responsiveness process. (A) Distinction between higher and primary motor regions. (B) Plausible regions participating in promoting responsiveness. Note that connection strengths are depicted as follows: solid line (—) a connection that can take effect on target by itself; dashed line (a) – a connection that requires additional connection to take effect upon the target; bold line (—) – a connection with overriding effect over regular connections (see the rationale in the text).
Figure 5
Figure 5
Analysis of repetitive events. (A) Averaged activity in each electrode and each sample is filtered into different frequency bands; shown are the delta (1.5–4 Hz) and alpha (7–13 Hz) frequency bands. (B) The largest half-waves in each frequency band are selected. (C) Only those that are also largest in activity compared with simultaneous activity in other electrodes are selected. (D) Only peaks that are repetitive across samples from the same experimental group (with temporal tolerance) are selected as repetitive events.
Figure 6
Figure 6
Summary of repetitive events in the various experimental conditions: ADHD-go (top right), ADHD-no-go (top left), control-go (bottom right), control-no-go (bottom left). Three types of repetitive activity can be found by applying the analysis method described: (i) early alpha event with average peak time (across samples) below 200 ms, (ii) early delta event with average peak time below 200 ms, and (iii) late delta event with average peak time above 200 ms. The raw data of average event time across subjects and standard deviation are presented in the background table.
Figure 7
Figure 7
In the top inset note that activity peaks in the delta and in the alpha ranges appear to be part of larger continuous activity. Summarizing the results presented in Figure 6, there is repetitive early alpha activity in at-least one experimental condition in eight electrodes (left) and repetitive prolonged delta activity in at-least one experimental condition in seven electrodes (right). Six of these electrodes overlap and there is also partial temporal overlap.
Figure 8
Figure 8
“Responsiveness index” – a single electrode biomarker. The bottom chart presents the distribution of the various subjects in terms of ERP responsiveness index and their DSM global grade. The responsiveness index actually denotes the late activity in the no-go condition (200–600 ms post-stimulus) at the Cz electrode. The average late activities for the various electrodes are represented in the top main left topographic image to clarify it distributed nature. The early activities are represented in the small topographic image to emphasize the differences between the two distributions. Note that the separation between groups is better than the one obtained for the best psychophysical index – presented in the inset on the right (also correlation with DSM is stronger). Note that one ADHD subject actually shows both electrophysiological and DSM characteristics more similar to control subjects. Note that the one deviant subject has ERP activity which does not involve clear N1, N2, and P2 activities (bottom left inset).

References

    1. Alexander D. M., Hermens D. F., Keage H. A., Clark C. R., Williams L. M., Kohn M. R., et al. (2008). Event-related wave activity in the EEG provides new marker of ADHD. Clin. Neurophysiol. 119, 163–17910.1016/j.clinph.2007.09.119
    1. Barry R. J., Johnstone S. J., Clarke A. R. (2003). A review of electrophysiology in attention-deficit/hyperactivity disorder: II. Event-related potentials. Clin. Neurophysiol. 114, 184–19810.1016/S1388-2457(02)00363-2
    1. Bokura H., Yamaguchi S., Kobayashi S. (2001). Electrophysiological correlates for response inhibition in a Go/NoGo task. Clin. Neurophysiol. 112, 2224–223210.1016/S1388-2457(01)00691-5
    1. Bruin K. J., Wijers A. A. (2002). Inhibition, response mode, and stimulus probability: a comparative event-related potential study. Clin. Neurophysiol. 113, 1172–118210.1016/S1388-2457(02)00141-4
    1. da Silva F. L. (1991). Neural mechanisms underlying brain waves: from neural membranes to networks. Electroencephalogr. Clin. Neurophysiol. 79, 81–9310.1016/0013-4694(91)90044-5
    1. de Charms R. C., Zador A. (2000). Neural representation and the cortical code. Annu. Rev. Neurosci. 23, 613–64710.1146/annurev.neuro.23.1.613
    1. DeLong M. R., Wichmann T. (2007). Circuits and circuit disorders of the basal ganglia. Arch. Neurol. 64, 20–2410.1001/archneur.64.1.20
    1. D’Esposito M. (2007). From cognitive to neural models of working memory. Philos. Trans. R. Soc. Lond. B Biol. Sci. 362, 761–77210.1098/rstb.2007.2086
    1. Eytan D., Brenner N., Marom S. (2003). Selective adaptation in networks of cortical neurons. J. Neurosci. 23, 9349–9356
    1. Fisher T., Aharon-Peretz J., Pratt H. (2011). Dis-regulation of response inhibition in adult Attention Deficit Hyperactivity Disorder (ADHD): an ERP study. Clin. Neurophysiol. 122, 2390–239910.1016/j.clinph.2011.05.010
    1. Galarreta M., Hestrin S. (2001). Electrical synapses between GABA-releasing interneurons. Nat. Rev. Neurosci. 2, 425–43310.1038/35077566
    1. Gonzales C., Chesselet M. F. (1990). Amygdalonigral pathway: an anterograde study in the rat with Phaseolus vulgaris leucoagglutinin (PHA-L). J. Comp. Neurol. 297, 182–20010.1002/cne.902970203
    1. Grieve K. L., Acuna C., Cudeiro J. (2000). The primate pulvinar nuclei: vision and action. Trends Neurosci. 23, 35–3910.1016/S0166-2236(99)01482-4
    1. Grinvald A., Lieke E. E., Frostig R. D., Hildesheim R. (1994). Cortical point-spread function and long-range lateral interactions revealed by real-time optical imaging of Macaque monkey primary visual cortex. J. Neurosci. 14, 2545–2568
    1. Halgren E., Squires N. K., Wilson C. L., Rohrbaugh J. W., Babb T. L., Crandall P. H. (1980). Endogenous potentials generated in the human hippocampal formation and amygdala by infrequent events. Science 210, 803–80510.1126/science.7434000
    1. Hubel D. H., Wiesel T. N. (1959). Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148, 574–591
    1. Irle E., Markowitsch H. J. (1982). Connections of the hippocampal formation, mamillary bodies, anterior thalamus and cingulated cortex. A retrograde study using horseradish peroxidase in the cat. Exp. Brain Res. 47, 79–9410.1007/BF00235889
    1. Jones E. G., Coulter J. D., Burton H., Porter R. (1977). Cells of origin and terminal distribution of corticostriatal fibers arising in the sensory-motor cortex of monkeys. J. Comp. Neurol. 173, 53–8010.1002/cne.901730105
    1. Kurata K. (2005). Activity properties and location of neurons in the motor thalamus that project to the cortical motor areas in monkeys. J. Neurophysiol. 94, 550–56610.1152/jn.01034.2004
    1. LeDoux J. E., Cicchetti P., Xagoraris A., Romanski L. M. (1990). The lateral amygdaloid nucleus: sensory interface of the amygdala in fear conditioning. J. Neurosci. 10, 1062–1069
    1. London M., Hausser M. (2005). Dendritic computation. Annu. Rev. Neurosci. 28, 503–52210.1146/annurev.neuro.28.061604.135703
    1. Loo S. K., Barkley R. A. (2005). Clinical Utility of EEG in Attention Deficit Hyperactivity Disorder. Appl. Neuropsychol. 12, 64–7610.1207/s15324826an1202_2
    1. Markram H. (2006). The blue brain project. Nat. Rev. Neurosci. 7, 153–16010.1038/nrn1848
    1. Mesulam M. M. (1998). From sensation to cognition. Brain 121, 1013–105210.1093/brain/121.6.1013
    1. Mountcastle V. B. (1997). The columnar orgalization of the neocortex. Brain 120, 701–72210.1093/brain/120.4.701
    1. Nieuwenhuis S., Yeung N. (2003). Electrophysiological correlates of anterior cingulate function in a go/no-go task: effects of response conflict and trial type frequency. Cogn. Affect. Behav. Neurosci. 3, 17–26
    1. Nunez P. L., Srinivasan R. (2006). A theoretical basis for standing and traveling brain waves measured with human EEG with implications for an integrated consciousness. Clin. Neurophysiol. 117, 2424–243510.1016/j.clinph.2006.06.754
    1. Olejniczak P. (2006). Neurophysiologic basis of EEG. J. Clin. Neurophysiol. 23, 186–18910.1097/01.wnp.0000220079.61973.6c
    1. Pandya D. N. (1995). Anatomy of the auditory cortex. Rev. Neurol. 151, 486–494
    1. Pessoa L., Adolphs R. (2010). Emotion processing and the amygdala: from a ‘low road’ to ‘many roads’ of evaluating biological significance. Nat. Rev. Neurosci. 11, 773–78210.1038/nrn2920
    1. Prox V., Dietrich D. E., Zhang Y., Emrich H. M., Ohlmeier M. D. (2007). Attentional processing in adults with ADHD as reflected by event-related potentials. Neurosci. Lett. 419, 236–24110.1016/j.neulet.2007.04.011
    1. Rispal-Padel L., Massion J. (1970). Relations between the ventrolateral nucleus and the motor cortex in the cat. Exp. Brain Res. 10, 331–33910.1007/BF02324762
    1. Schultz W. (1998). Predictive reward signal of dopamine neurons. J. Neurophysiol. 80, 1–27
    1. Shahaf G., Reches A., Pinchuk N., Fisher T., Ben Bashat G., Kanter A., et al. (2012). Introducing a novel approach of network oriented analysis of ERPs, demonstrated on adult attention deficit hyperactivity disorder. Clin. Neurophysiol. 123, 1568–158010.1016/j.clinph.2011.12.010
    1. Shao Z., Burkhalter A. (1996). Different balance of excitation and inhibition in forward and feedback circuits of rat visual cortex. J. Neurosci. 16, 7353–7365
    1. Siapas A. G., Lubenov E. V., Wilson M. A. (2005). Prefrontal phase locking to hippocampal theta oscillations. Neuron 46, 141–15110.1016/j.neuron.2005.02.028
    1. Sirota A., Montgomery S., Fujisawa S., Isomura Y., Zugaro M., Buzsaki G. (2008). Entrainment of neocortical neurons and gamma oscillations by the hippocampal theta rhythm. Neuron 60, 683–69710.1016/j.neuron.2008.09.014
    1. Smith J. L., Johnstone S. J., Barry R. J. (2004). Inhibitory processing during the Go/NoGo task: an ERP analysis of children with attention-deficit/hyperactivity disorder. Clin. Neurophysiol. 115, 1320–133110.1016/j.clinph.2003.12.027
    1. Steriade M. (1997). Synchronized activities of coupled oscillators in the cerebral cortex and thalamus at different levels of vigilance. Cereb. Cortex 7, 583–60410.1093/cercor/7.6.583
    1. Sunohara G. A., Malone M. A., Rovet J., Humphries T., Roberts W., Taylor M. J. (1999). Effect of methylphenidate on attention in children with attention deficit hyperactivity disorder (ADHD): ERP evidence. Neuropsychopharmacology 21, 218–22810.1016/S0893-133X(99)00023-8
    1. Van der Werf Y. D., Witter M. P., Groenewegen H. J. (2002). The intralaminar and midline nuclei of the thalamus. Anatomical and functional evidence for participation in processes of arousal and awareness. Brain Res. Brain Res. Rev. 39, 107–14010.1016/S0165-0173(02)00181-9
    1. Verbaten M. N., Overtoom C. C., Koelega H. S., Swaab-Barneveld H., van der Gaag R. J., Buitelaar J., et al. (1994). Methylphenidate influences on both early and late ERP waves of ADHD children in a continuous performance test. J. Abnorm. Child Psychol. 22, 561–57810.1007/BF02168938
    1. Wells J. (1966). Activity properties and location of neurons in the motor thalamus that project to the cortical motor areas in monkeys. Exp. Neurol. 14, 338–35010.1016/0014-4886(66)90119-1
    1. Wild-Wall N., Oades R. D., Schmidt-Wessels M., Christiansen H., Falkenstein M. (2009). Neural activity associated with executive functions in adolescents with attention-deficit/hyperactivity disorder (ADHD). Int. J. Psychophysiol. 74, 19–2710.1016/j.ijpsycho.2009.06.003

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