Synchronization patterns suggest different functional organization in parietal reach region and dorsal premotor cortex

Abstract

Chakrabarti S, Martinez-Vazquez P, Gail A. Synchronization patterns suggest different functional organization in parietal reach region and dorsal premotor cortex. J Neurophysiol 112: 3138-3153, 2014. First published September 17, 2014 doi:10.1152/jn.00621.2013.-The parietal reach region (PRR) and dorsal premotor cortex (PMd) form part of the fronto-parietal reach network. While neural selectivity profiles of single-cell activity in these areas can be remarkably similar, other data suggest that both areas serve different computational functions in visually guided reaching. Here we test the hypothesis that different neural functional organizations characterized by different neural synchronization patterns might be underlying the putatively different functional roles. We use cross-correlation analysis on single-unit activity (SUA) and multiunit activity (MUA) to determine the prevalence of synchronized neural ensembles within each area. First, we reliably find synchronization in PRR but not in PMd. Second, we demonstrate that synchronization in PRR is present in different cognitive states, including "idle" states prior to task-relevant instructions and without neural tuning. Third, we show that local field potentials (LFPs) in PRR but not PMd are characterized by an increased power and spike field coherence in the beta frequency range (12-30 Hz), further indicating stronger synchrony in PRR compared with PMd. Finally, we show that neurons with similar tuning properties tend to be correlated in their random spike rate fluctuations in PRR but not in PMd. Our data support the idea that PRR and PMd, despite striking similarity in single-cell tuning properties, are characterized by unequal local functional organization, which likely reflects different network architectures to support different functional roles within the fronto-parietal reach network.

Directed Interaction Between Monkey Premotor and Posterior Parietal Cortex During Motor-Goal Retrieval from Working Memory

“…Two spectral components dominated the LFP signals: the beta-band (13–28 Hz, LFP-beta) and frequencies lower than 10 Hz (LFP-LF). Consistent with our previous observations ( Chakrabarti et al 2014 ), LFP-beta was stronger in PRR than PMd, as obvious already from simultaneously recorded raw data (Fig. 2 A ; see Supplementary Fig.…”

“…LFP-beta in PMd, instead, while known to be spatially selective during evoked potentials ( Mehring et al 2003 ; O’Leary and Hatsopoulos 2006 ), was strongly attenuated in power during sustained memory phases, especially compared with PRR (Fig. 2 and ( Chakrabarti et al 2014 )). Correspondingly, Salazar and colleagues showed that beta-range interaction was dominated by parietal-to-frontal influences (parietal senders, frontal receivers), and that frontoparietal beta-frequency coherence was content-specific ( Salazar et al 2012 ).…”

“…Computational models and human imaging data, on the other hand, suggest that prefrontal projections could help boosting WM capacity in the memory-storing parietal cortex ( Edin et al 2009 ). Rather than prefrontal cortex, our study focused on premotor cortex which shares high similarity in single neuron selectivity during rule-guided reach planning ( Gail et al 2009 ; Chakrabarti et al 2014 ). Our data suggest that premotor-to-parietal interaction particularly at low signal frequencies is indicative of motor-goal retrieval from spatial WM during memory-guided reaching in monkeys, while reverberating beta-frequency activity in parietal cortex could support storage, with limitations, as discussed below.…”

“…Instead, DI-beta was characteristic for movement-withholding states, that is, instructed delays, even without any trial-specific information being specified yet (fixation period of all cueing conditions). This means, while reverberating oscillatory activities at beta frequencies, that is, local beta-range signal power, in principle might well be suited to actively maintain spatial WM content in posterior parietal cortex ( Jensen et al 2002 ; Van Der Werf et al 2008 ; Palva et al 2010 ; Chakrabarti et al 2014 ), the strength of DI with premotor cortex at beta-range frequencies during WM maintenance is lacking such content-specificity. This could have 2 reasons.…”

Comparing open-source toolboxes for processing and analysis of spike and local field potentials data

“…Combination of spiking activity and LFP is often used to study rhythmic neuronal synchronization since spike-LFP measures are more sensitive than spike-spike synchronization measures (Vinck et al, 2012; Chakrabarti et al, 2014). To this end Brainstorm, Chronux, FieldTrip and Elephant implement a spike-field coherence (SFC), a frequency-domain pairwise non-directional measure.…”