It’s about Time
Read:: - [ ] Cohen (2011) - It’s about Time 🛫2023-09-11 !!2 rd citation todoist Print:: ❌ Zotero Link:: Zotero Files:: attachment Reading Note:: Web Rip:: url:: https://www.frontiersin.org/articles/10.3389/fnhum.2011.00002
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(1) Cross-frequency coupling. Cross-frequency coupling refers to a relationship between activities in two different frequency bands. For example, the power of gamma (∼30–80 Hz) oscillations may vary as a function of the phase of theta (∼4–8 Hz). Cross-frequency coupling may be used for information coding if the lower frequency oscillations coordinate the activity of sub- tp
2) Inter-regional oscillatory synchronization. In addition to dynamics across frequency bands within the same region of space, information may be embedded in the temporal relationship of activity over space. Inter-regional phase synchronization (a frequency band-specific measure of functional connectivity) may underlie information transfer and co-processing (Knight, 2007; Womelsdorf et al., 2007). tp
populations of cells that use higher frequency oscillations to process information. tp
There are several ways in which cross-frequency coupling can be quantified (Mormann et al., 2005; Canolty et al., 2006; Cohen, 2008; Tort et al., 2010); different methods may be suited for different purposes, but all methods generally test for a modulation of activity in one frequency band as a function of activity in another (typically, relatively lower) frequency band. tp
For example, increases in gamma–theta synchronization strength were reported to increase with working memory load, although there was no reported significant change in overall oscillation power at those specific frequency bands (Axmacher et al., 2010). These findings support a model of working memory that predicts that theta acts to coordinate activation of stimulus representations stored in gamma band activity (Lisman, 2010). tp
(3) Microstates and other transient electrophysiological events. Microstates refer to brief periods of cortical electrophysiological activity that are topographically stable over tens to hundreds of milliseconds (Lehmann et al., 2006). Microstates fluctuate 1–2 orders of magnitude faster than the hemodynamic response, and have been linked to visual perception, error processing, and resting state (Muller et al., 2005; Britz and Michel, 2010). tp
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in neurocognitive function. Indeed, for some neurocognitive processes, time may be as important, or possibly more important, than space in terms of the underlying neurocomputational mechanisms.
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It is naïve to think that these two dimensions are sufficient for characterizing neurocognitive function. The range and flexibility of cognitive, emotional, perceptual, and other mental processes is huge, and the scale of typical functional localization claims – on the order of several cubic centimeters – is large compared to the number of cells with unique physiological, neurochemical, morphological, and connectional properties contained in each MRI voxel.
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Further, there are no one-to-one mappings between cognitive processes and brain regions: Different cognitive processes can activate the same brain region, and activation of several brain regions can be associated with single cognitive processes. In the analogy of Plato’s cave, our current approach to understanding the biological foundations of cognition is like looking at shadows cast on a region of the wall of the cave without observing how they change dynamically over time. This makes it difficult to disentangle shadows cast by different but overlapping shapes (Figure 1).
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Here I will argue that too much attention has been focused on investigating neurocognitive function based on attempts to localize processes in space (i.e., functional localization). Instead, fruitful insights might arise from considering time to be an important factor
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There are many spatial scales in the brain that differ in size by several orders of magnitude, ranging from single neurons to cortical columns to meso- and macroscopic populations. It is unclear what the appropriate spatial scale is for functional localization, or whether different neurocognitive processes can or should be localized at different spatial scales. Dynamics at some spatial scales may or may not be relevant for dynamics at other spatial scales (Kiebel et al., 2008).
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It should be noted that some physiological functions appear to be more reliably localized in the brain, for example the superchiasmatic nucleus may be the “location” of our circadian rhythm. Clearly, these areas influence cognitive function, but the cognitive processes typically under investigation in cognitive neuroscience studies do not appear to be precisely localizable.
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It is likely that the brain uses more dimensions for information processing than just space and activation magnitude. This is not meant to imply that space is irrelevant for information coding/processing, or that functional localization is inappropriate or invalid. Rather, after this initial period of studying functional localization and learning about its merits and limitations, it is perhaps useful to consider time as an important factor
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from the activity recorded from a single electrode, there are already multiple domains of information, including frequency (the speed of the oscillation), power (the amount of the energy in a frequency band at a point in time), and phase angle (the position of the oscillation along the sine wave, driven by the state of excitation of the population of neurons; Figure 2; see also Makeig et al., 2004).
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time may be as important – if not more important – than space for information processing, particularly at the level of small populations of cells (spatial scale of millimeters to a few centimeters). As described below, “time” refers to rapid dynamics in electrochemical signals that are often but not necessarily oscillatory
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Time as latency in functional MRI (e.g., the hemodynamic response peaks about 6–8 s after a stimulus) or an event-related component (e.g., the average voltage deflection 300–600 ms after a stimulus) is not taking into account the rich information that appears to be embedded in the temporal dynamics of neural activity.
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Oscillations occur in multiple temporal and spatial scales, ranging from ultra-slow oscillations with a periodicity of tens of seconds over much of the cortex during deep sleep (Steriade, 2006) to ultra-fast oscillations with a periodicity of a few milliseconds within patches of somatosensory cortex (Curio, 2000). Oscillations that seem most relevant for cognitive processes range from delta (∼1–4 Hz) and theta (∼4–8 Hz) to gamma (∼30–100 Hz; for general reviews of neural oscillations, see Varela et al., 2001; Buzsaki and Draguhn, 2004; Traub et al., 2004).
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Although the literature on time-based coding schemes and sophisticated analyses of electrophysiological data is overshadowed by the literature on fMRI-based localization studies, there are too many relevant and insightful findings to discuss all them all here. Instead, this section will highlight three examples of how mathematical analyses of the temporal dynamics of human electrophysiological recordings have shed insight into neurocognitive function. These examples also illustrate cases in which standard localization- and hemodynamic-based analyses would be unlikely to reveal these brain dynamics (e.g., because no overall increase in space-averaged activity occurs).
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it seems likely that functionally different networks can emerge from the same population of anterior cingulate cortex neurons, depending on task demands (Fujisawa et al., 2008).