Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis

Stavisky, Sergey D.; Willett, Francis R.; Wilson, Guy H; Murphy, Brian; Rezaii, Paymon; Avansino, Donald T.; Memberg, William D.; Miller, Jonathan P.; Kirsch, Robert F.; Hochberg, Leigh R.; Ajiboye, A Bolu; Druckmann, Shaul; Shenoy, Krishna V.; Henderson, Jaimie M.

doi:10.7554/elife.46015

Cited by 79 publications

(65 citation statements)

References 87 publications

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“…The difference between reach- and grasp-related neuronal dynamics seems to stem from the fundamentally different kinematics and functions of these movements, rather than from effector-specific differences, since dynamics are observed for reach-like finger movements. That rotational dynamics are observed in reach-to-grasp likely reflects the reaching component of the behavior, consistent with the observation that movement signals are broadcast widely throughout motor cortex ( Musall et al, 2019 ; Stavisky et al, 2019 ; Willett et al, 2020 ).…”

Section: Discussionsupporting

confidence: 83%

Neural population dynamics in motor cortex are different for reach and grasp

Suresh

Goodman

Okorokova

et al. 2020

eLife

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Low-dimensional linear dynamics are observed in neuronal population activity in primary motor cortex (M1) when monkeys make reaching movements. This population-level behavior is consistent with a role for M1 as an autonomous pattern generator that drives muscles to give rise to movement. In the present study, we examine whether similar dynamics are also observed during grasping movements, which involve fundamentally different patterns of kinematics and muscle activations. Using a variety of analytical approaches, we show that M1 does not exhibit such dynamics during grasping movements. Rather, the grasp-related neuronal dynamics in M1 are similar to their counterparts in somatosensory cortex, whose activity is driven primarily by afferent inputs rather than by intrinsic dynamics. The basic structure of the neuronal activity underlying hand control is thus fundamentally different from that underlying arm control.

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Section: Discussionsupporting

confidence: 83%

Neural population dynamics in motor cortex are different for reach and grasp

Suresh

Goodman

Okorokova

et al. 2020

eLife

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“…The hand area of MC also does not exhibit rotational dynamics during grasping-only behaviour ( Suresh et al, 2020 ), though it does exhibit rotational dynamics during reach-to-grasp ( Abbaspourazad et al, 2021 ; Rouse and Schieber, 2018 ; Vaidya et al, 2015 ) which may reflect the reaching component of the behaviour. More broadly there is a growing body of work characterizing cortical neural dynamics across different behavioral tasks which have revealed rotational ( Abbaspourazad et al, 2021 ; Aoi et al, 2020 ; Gao et al, 2016 ; Kao et al, 2015 ; Libby and Buschman, 2021 ; Remington et al, 2018 ; Sani et al, 2021 ; Sohn et al, 2019 ; Stavisky et al, 2019 ; Vaidya et al, 2015 ), helical ( Russo et al, 2020 ), stationary ( Machens et al, 2010 ), and ramping dynamics ( Finkelstein et al, 2021 ; Kaufman et al, 2016 ; Machens et al, 2010 ) and these dynamics appear to support various classes of computations. Thus, finding of rotational dynamics across the fronto-parietal circuit in the present study was not trivial.…”

Section: Discussionmentioning

confidence: 99%

Rotational dynamics in motor cortex are consistent with a feedback controller

Kalidindi

Cross

Lillicrap

et al. 2021

eLife

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Recent studies have identified rotational dynamics in motor cortex (MC), which many assume arise from intrinsic connections in MC. However, behavioral and neurophysiological studies suggest that MC behaves like a feedback controller where continuous sensory feedback and interactions with other brain areas contribute substantially to MC processing. We investigated these apparently conflicting theories by building recurrent neural networks that controlled a model arm and received sensory feedback from the limb. Networks were trained to counteract perturbations to the limb and to reach toward spatial targets. Network activities and sensory feedback signals to the network exhibited rotational structure even when the recurrent connections were removed. Furthermore, neural recordings in monkeys performing similar tasks also exhibited rotational structure not only in MC but also in somatosensory cortex. Our results argue that rotational structure may also reflect dynamics throughout the voluntary motor system involved in online control of motor actions.

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“…However, these animal models fall short with respect to more complex freely generated motor sequences such as speech; this is primarily because none of the dominant models employed are capable of learning vocal behavior resembling the complexity of human speech [ 2 , 22 ]. For this reason, speech production studies, unlike other motor behavioral fields, have been limited exclusively to invasive [ 11 , 23 , 24 , 25 ] and non-invasive [ 26 ], clinical studies in humans.…”

Section: Introductionmentioning

confidence: 99%

Local field potentials in a pre-motor region predict learned vocal sequences

et al. 2021

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Neuronal activity within the premotor region HVC is tightly synchronized to, and crucial for, the articulate production of learned song in birds. Characterizations of this neural activity detail patterns of sequential bursting in small, carefully identified subsets of neurons in the HVC population. The dynamics of HVC are well described by these characterizations, but have not been verified beyond this scale of measurement. There is a rich history of using local field potentials (LFP) to extract information about behavior that extends beyond the contribution of individual cells. These signals have the advantage of being stable over longer periods of time, and they have been used to study and decode human speech and other complex motor behaviors. Here we characterize LFP signals presumptively from the HVC of freely behaving male zebra finches during song production to determine if population activity may yield similar insights into the mechanisms underlying complex motor-vocal behavior. Following an initial observation that structured changes in the LFP were distinct to all vocalizations during song, we show that it is possible to extract time-varying features from multiple frequency bands to decode the identity of specific vocalization elements (syllables) and to predict their temporal onsets within the motif. This demonstrates the utility of LFP for studying vocal behavior in songbirds. Surprisingly, the time frequency structure of HVC LFP is qualitatively similar to well-established oscillations found in both human and non-human mammalian motor areas. This physiological similarity, despite distinct anatomical structures, may give insight into common computational principles for learning and/or generating complex motor-vocal behaviors.

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Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis

Cited by 79 publications

References 87 publications

Neural population dynamics in motor cortex are different for reach and grasp

Neural population dynamics in motor cortex are different for reach and grasp

Rotational dynamics in motor cortex are consistent with a feedback controller

Local field potentials in a pre-motor region predict learned vocal sequences

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