% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Essink:1017081,
      author       = {Essink, Simon and Ito, Junji and Riehle, Alexa and
                      Brochier, Thomas and Grün, Sonja},
      title        = {{B}imodal distribution of preferred directions to hand
                      movements in visuo-parietal areas},
      reportid     = {FZJ-2023-03923},
      year         = {2023},
      abstract     = {Decades of intense research established that neurons in
                      monkey motor cortex are tuned to hand movement direction
                      with a cosine-like function that can be characterized by a
                      preferred direction (PD) [1]. Although PDs across neurons
                      were assumed to be uniformly distributed, recent experiments
                      showed that PDs are bimodally distributed if hand movements
                      are constrained to a horizontal work area [2,3]. Several
                      modeling studies [4,5,6] attribute this biased distribution
                      to the limb biomechanics.In electrophysiological recordings
                      via multiple Utah electrode arrays along the dorsal visual
                      stream of macaque monkeys [7], we reproduce the biased
                      distribution of PDs in motor cortex and further elucidate if
                      such a bias extends to visual and parietal areas.Macaque
                      monkeys were trained to perform a visually guided sequential
                      reaching task using a robotic exoskeleton system (KINARM
                      Exoskeleton Laboratory, BKIN Technologies) that constrains
                      movements to the horizontal plane. Both eye and hand
                      movements were recorded along with extracellular potentials
                      from 224 channels across visual (V1/V2), parietal (DP, 7A)
                      and motor (M1/PMd) areas. After spike sorting, we relate
                      spiking activity of single units to the instantaneous hand
                      movement direction through Poisson Generalized Linear Models
                      (GLMs) [8], thus estimating the directional tuning curve and
                      the preferred direction of each unit. The resulting
                      distributions of PDs per area were tested for bimodality
                      using the Rayleigh r statistic.We confirm the bimodality (at
                      forward-left and backwards-right directions) of the
                      distribution of PDs for neurons in the motor cortex.
                      Interestingly, we observe the same tendencies in all visual
                      and parietal areas and find statistical significance of the
                      results.We then investigate whether our observations are a
                      genuine expression of the hand movement or rather arise in
                      response to co-occurring sensory and/or behavioral events
                      (e.g. appearance of visual stimuli toward which the monkeys
                      moved their hands). To exclude such confounds, we chose to
                      fit more complex GLMs to the neural activity that account
                      for the impact of various modalities (visual input, eye/hand
                      position, saccade, and hand movement) on the activity. Even
                      after such a control, we observe significant bimodal
                      distributions of PDs in V1/V2, DP and 7A being attributed
                      only to the hand movement regressors, suggesting an
                      influence of limb biomechanics even in the lower hierarchies
                      of the dorsal visual stream.References [1] Georgopoulos, A.,
                      Kalaska, J., Caminiti, R. $\&$ Massey, J. On the relations
                      between the direction of two-dimensional arm movements and
                      cell discharge in primate motor cortex. J. Neurosci. 2,
                      1527–1537 (1982)., 10.1523/JNEUROSCI.02-11-01527.1982 [2]
                      Scott, S. H., Gribble, P. L., Graham, K. M. $\&$ Cabel, D.
                      W. Dissociation between hand motion and population vectors
                      from neural activity in motor cortex. Nature 413, 161–165
                      (2001)., 10.1038/35093102 [3] Suminski, A. J., Mardoum, P.,
                      Lillicrap, T. P. $\&$ Hatsopoulos, N. G. Temporal evolution
                      of both premotor and motor cortical tuning properties
                      reflect changes in limb biomechanics. Journal of
                      Neurophysiology 113, 2812–2823 (2015).,
                      10.1152/jn.00486.2014 [4] Lillicrap, T. P. $\&$ Scott, S. H.
                      Preference Distributions of Primary Motor Cortex Neurons
                      Reflect Control Solutions Optimized for Limb Biomechanics.
                      Neuron 77, 168–179 (2013)., 10.1016/j.neuron.2012.10.041
                      [5] Verduzco-Flores, S. O. $\&$ De Schutter, E.
                      Self-configuring feedback loops for sensorimotor control.
                      eLife 11, e77216 (2022)., 10.7554/eLife.77216 [6] Codol, O.,
                      Michaels, J. A., Kashefi, M., Pruszynski, J. A. $\&$
                      Gribble, P. L. MotorNet: a Python toolbox for controlling
                      differentiable biomechanical effectors with artificial
                      neural networks. bioarxiv (2023), 10.1101/2023.02.17.528969
                      [7] de Haan, M. J., Brochier, T., Grün, S., Riehle, A. $\&$
                      Barthélemy, F. V. Real-time visuomotor behavior and
                      electrophysiology recording setup for use with humans and
                      monkeys. Journal of Neurophysiology 120, 539–552 (2018).,
                      10.1152/jn.00262.2017 [8] McCullagh, P. $\&$ Nelder, J. A.
                      Generalized Linear Models. (Springer US, 1989).,
                      10.1007/978-1-4899-3242-6},
      month         = {Sep},
      date          = {2023-09-26},
      organization  = {Bernstein Conference 2023, Berlin
                       (Germany), 26 Sep 2023 - 29 Sep 2023},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / 5235 -
                      Digitization of Neuroscience and User-Community Building
                      (POF4-523) / GRK 2416 - GRK 2416: MultiSenses-MultiScales:
                      Neue Ansätze zur Aufklärung neuronaler multisensorischer
                      Integration (368482240) / HBP SGA2 - Human Brain Project
                      Specific Grant Agreement 2 (785907) / HBP SGA3 - Human Brain
                      Project Specific Grant Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5235 /
                      G:(GEPRIS)368482240 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2023-03923},
      url          = {https://juser.fz-juelich.de/record/1017081},
}