Neural correlates of goal-directed and non-goal-directed movements

Abstract

What are the neural correlates that distinguish goal-directed (G) from non-goal-1 directed movements (nG)? We investigated this question in the monkey frontal eye field, 2 which is implicated in voluntary control of saccades. We found that only for G-saccades, the 3 variability in spike rate across trials decreased, the regularity of spike timings within trials 4 increased, the neural activity increased earlier from baseline and had a concurrent reduction 5 of LFP beta band power. 6 7 Most movements are goal directed while others, such as fidgets, may not be. However, the 8 neural mechanism that entail these different movements is poorly studied. The macaque frontal 9 eye fields (FEF) in particular has neurons that discharge before visually guided saccades, saccades 10 made in total darkness such as learned saccades or memory-guided saccades, but not before 11 spontaneous saccades in total darkness 1 . Here we discovered that when monkeys make saccades 12 that have no obvious goal in a lit environment, FEF movement and vis-mov neurons do, in fact, 13 discharge. We asked if these seeming non-goal-directed saccades made in the light were actually 14 made to a goal that we did not discern, or if there were differences in neural activities that 15 distinguished between non-goal-directed (nG) and goal directed (G) saccades. We studied two 16 characteristics of neural response not directly visible in the firing rate but which precede 17 movements: a decrease in neural response variability 2 and a decrease in local field potential beta 18 oscillatory activity 3,4 . Previous studies have shown that decreases in response variability are 19 correlated with attention 5 , preparation of visually guided saccades 2 , the onset of a visual stimulus 6 , 20 etc; and decreases in beta power have been correlated with motor preparation, and inhibitory 21 control 7,8 among other processes 9 . Nevertheless, despite these efforts, their roles in goal-directed 22We performed all our analyses on previously published datasets of frontal eye field neurons 3,10 . 130Please refer to that study for full details. We briefly describe the experimental procedures and methods here. 132Subjects: Two adult monkeys, J (male, Macaca radiata) and G (female, Macaca mulatta) were used for 133 the experiments and were cared for in accordance with the Committee for the Purpose of Control and

Neural mechanisms underlying the temporal control of sequential saccade planning in the frontal eye fields

“…The detailed methods pertaining to this dataset has been published in a previous study (Basu & Murthy 2020; Sendhilnathan et al, 2021). A brief overview is given below.…”

“…The activity of FEF movement neurons closely follows accumulator dynamics and represents a decisionmaking stage, which has been found to be capacity-limited in computational models Ray et al, 2012;Sigman & Dehaene, 2005). Further, FEF is a higher-order control center for goal-directed saccadic planning (Sendhilnathan, Basu, Goldberg, Schall, & Murthy, 2019;Sendhilnathan, Basu, & Murthy, 2017, 2020. Finally, FEF movement neurons encode two saccade plans in parallel (Basu and Murthy, 2019).…”

“…To investigate the neural architecture of saccade-related bottlenecks, we recorded neural activity from the frontal eye field (FEF) of macaque monkeys performing a sequential saccade task. FEF is a good candidate region to study the neural imprints of processing bottlenecks since it is a higher-order control center for goal-directed saccadic planning (Sendhilnathan et al, 2021; Sendhilnathan et al, 2017, 2020). Further, the activity of FEF movement neurons follow the dynamics of accumulator models and resemble the central capacity-limited stage observed in computational models of dual-task studies (Hanes and Schall, 1996; Ray et al, 2012; Sigman and Dehaene, 2005).…”

Assessing within‐trial and across‐trial neural variability in macaque frontal eye fields and their relation to behaviour

“…Previous studies have established that across‐trial variability in neural activity decreases during neural computations such as in response to the onset of a stimulus (Churchland et al, 2010). Decrease in across‐trial variability has also been correlated with broad spatial tuning after stimulus onset (Chang, Armstrong, Armstrong, & Moore, 2012), expected reward (Falkner, Goldberg, & Krishna, 2013), attention (Cohen & Maunsell, 2009; Herrero, Gieselmann, Sanayei, & Thiele, 2013; Hussar & Pasternak, 2010; Mitchell, Sundberg, & Reynolds, 2007; Sendhilnathan, Basu, Goldberg, Schall, & Murthy, 2019), task familiarity (Qi & One, 2012), sensorimovement learning (Mandelblat‐Cerf, Paz, & Vaadia, 2009), movement preparation (Churchland, Yu, Ryu, Santhanam, & Shenoy, 2006), task engagement (Hussar & Pasternak, 2010) and behaviour (Churchland et al, 2010; Falkner et al., 2013; Purcell, Heitz, Heitz, Cohen, & Schall, 2012; Steinmetz & Moore, 2010). Taken together, these studies raise the possibility that neural computations suppress the chaos in the system making it more ‘stable’ following an input (Abbott, Rajan, & Sompolinsky, 2011; Churchland et al, 2010).…”

Preparatory activity links frontal eye field activity with small amplitude motor unit recruitment of neck muscles during gaze planning

“…Some of the methods that were used in this study have been described in detail elsewhere (49)(50)(51). Here, we describe them briefly.…”