Neurosystems: brain rhythms and cognitive processing

Cannon, Jonathan; McCarthy, Michelle M.; Lee, Shane; Lee, Jung; Börgers, Christoph; Whittington, Miles A.; Kopell, Nancy

doi:10.1111/ejn.12453

Cited by 210 publications

(193 citation statements)

References 142 publications

(200 reference statements)

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“…This hypothesis is consistent with recent physiological and computational studies which have suggested that a critical parameter ensuring adequate communication between brain areas is coherence between their main rhythms (reviewed in (Cannon et al, 2014). It will be interesting in future multisite recordings to test whether this change in frequency really leads to changes in synchrony with other brain structures.…”

supporting

confidence: 88%

“…Such correlation can be found for the data obtained from animals running on various types of run-ways and with various velocities (Slawinska and Kasicki, 1998). However, analyzing the short locomotor bouts of similar pattern, we still found a significant shift of theta frequency during the locomotor stereotypy provoked by the high quinpirole and quinpirole + cocaine injections (Table 2 and have a consistent phase difference (see (Cannon et al, 2014;Fries, 2005;Varela et al, 2001) for review). called "coherence filtering" (see (Cannon et al, 2014) for review).…”

Section: Accepted Manuscriptsupporting

confidence: 69%

“…However, analyzing the short locomotor bouts of similar pattern, we still found a significant shift of theta frequency during the locomotor stereotypy provoked by the high quinpirole and quinpirole + cocaine injections (Table 2 and have a consistent phase difference (see (Cannon et al, 2014;Fries, 2005;Varela et al, 2001) for review). called "coherence filtering" (see (Cannon et al, 2014) for review). According to this concept, if area A projects to area B, with area B containing both "principal" (excitatory) neurons and inhibitory interneurons that are reciprocally interconnected, the input from A will be influencing B much more strongly if it is coherent with (has the same main frequency than) the major rhythm of area B.…”

Section: Accepted Manuscriptmentioning

confidence: 79%

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Activation of D2 autoreceptors alters cocaine-induced locomotion and slows down local field oscillations in the rat ventral tegmental area

et al. 2016

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supporting

confidence: 88%

Section: Accepted Manuscriptsupporting

confidence: 69%

Section: Accepted Manuscriptmentioning

confidence: 79%

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Activation of D2 autoreceptors alters cocaine-induced locomotion and slows down local field oscillations in the rat ventral tegmental area

et al. 2016

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“…They are thought to constrain and organize neural activity within and between functional networks across a wide range of temporal and spatial scales (14)(15)(16)(17). Oscillations at specific frequencies have been associated with different states of arousal (18), as well as different sensory (5) and cognitive processes (19,20).…”

mentioning

confidence: 99%

A study of problems encountered in Granger causality analysis from a neuroscience perspective

Stokes

Purdon

2017

Proc. Natl. Acad. Sci. U.S.A.

268

189

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, we misunderstood the method described in Barnett and Seth (25) to compute the conditional Granger causality. We realize now that the spectral factorization method they describe can be used to obtain the conditional Granger causality with a single model fit, which would avoid the computational problems associated with separate model fits. We apologize for this error." Granger causality methods were developed to analyze the flow of information between time series. These methods have become more widely applied in neuroscience. Frequency-domain causality measures, such as those of Geweke, as well as multivariate methods, have particular appeal in neuroscience due to the prevalence of oscillatory phenomena and highly multivariate experimental recordings. Despite its widespread application in many fields, there are ongoing concerns regarding the applicability of Granger causality methods in neuroscience. When are these methods appropriate? How reliably do they recover the system structure underlying the observed data? What do frequency-domain causality measures tell us about the functional properties of oscillatory neural systems? In this paper, we analyze fundamental properties of Granger-Geweke (GG) causality, both computational and conceptual. Specifically, we show that (i) GG causality estimates can be either severely biased or of high variance, both leading to spurious results; (ii) even if estimated correctly, GG causality estimates alone are not interpretable without examining the component behaviors of the system model; and (iii) GG causality ignores critical components of a system's dynamics. Based on this analysis, we find that the notion of causality quantified is incompatible with the objectives of many neuroscience investigations, leading to highly counterintuitive and potentially misleading results. Through the analysis of these problems, we provide important conceptual clarification of GG causality, with implications for other related causality approaches and for the role of causality analyses in neuroscience as a whole.Granger causality | time series analysis | neural oscillations | connectivity | system identification G ranger causality is a statistical tool developed to analyze the flow of information between time series. Neuroscientists have applied Granger causality methods to diverse sources of data, including electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and local field potentials (LFP). These studies have investigated functional neural systems at scales of organization from the cellular level (1-3) to whole-brain network activity (4), under a range of conditions, including sensory stimuli (5-7), varying levels of consciousness (8-10), cognitive tasks (11), and pathological states (12, 13). In such analyses, the time series data are interpreted to reflect neural activity from a particular source, and Granger causality is used to characterize the directionality, directness, and dynamics of influence between sources.Oscillations are a ...

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“…In dynomic terms, I will take labeling to be the slowing down of γ to β followed by β-α coupling, involving a basal ganglia-thalamic-cortical loop (see Cannon et al, 2014 for the rhythmogenesis of β in the basal ganglia). This would disinhibit the thalamic medio-dorsal nucleus via the β band.…”

Section: Labelingmentioning

confidence: 99%