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@INPROCEEDINGS{Kraue:1015190,
      author       = {Krauße, Sven and Gutzen, Robin and Stella, Alessandra and
                      Brochier, Thomas and Riehle, Alexa and Grün, Sonja and
                      Denker, Michael},
      title        = {{R}elating the orientation of cortical traveling waves and
                      co-occurring spike patterns},
      reportid     = {FZJ-2023-03586},
      year         = {2023},
      abstract     = {The collective population dynamics of the cerebral cortex
                      can be studied at different levels. One option is to study
                      individual neurons' collective correlated spiking activity.
                      A complementary approach on the mesoscopic scale is to
                      analyze the local field potential (LFP) as an aggregate
                      signature of the neuronal population activity. However, the
                      exact relation between these two observation levels remains
                      an open research question.The LFP activity in the motor
                      cortex exhibits functionally relevant oscillations in the
                      beta frequency band (e.g., [1]). It has been shown that the
                      phases of beta oscillations typically form traveling waves
                      [2]. While different spatial patterns of such waves are
                      identified [3], the most common are planar waves that travel
                      across the primary motor cortex, predominantly along the
                      rostral-caudal axis [2].There are several indications of
                      spatio-temporal organization of motor cortex activity in
                      different signal types. Repeating patterns of precise
                      synchronous spiking (on a ms scale) identified in the motor
                      cortex [4] also display a preferred spatial orientation [5].
                      Correlated spiking activity measured by functional
                      connectivity occurs in the same direction as the average
                      propagation axis of LFP waves [6]. In more local recordings,
                      it was found that the spiking activity phase locks to beta
                      LFP oscillations. The phase locking is even more pronounced
                      for spikes involved in significant synchronous spiking as
                      identified by Unitary Events [7].To investigate the direct
                      relation of synchronous spike patterns to beta LFP phase
                      waves, we analyze multi-electrode array (Utah array)
                      recordings of the motor cortex (M1/PMd) from a macaque
                      monkey during an instructed reach-to-grasp task [8]. We
                      analyze the LFP In the beta band (15-30 Hz) for wave
                      directions and their planarity based on the gradient of the
                      instantaneous phase using an automated analysis pipeline
                      approach (Cobrawap) [9,10]. Independently, we detect
                      repeating synchronous spike patterns in the same data sets
                      using the SPADE method [11, 12]. We show that the average
                      pattern orientation axis tends to be perpendicular to the
                      propagation direction of simultaneously occurring planar
                      waves, as suggested by previous work [5,6]. Moreover, this
                      relation is observed pattern-by-pattern, most prominently
                      during movement preparation. These findings provide direct
                      evidence of how spatially organized oscillatory LFP activity
                      can be interpreted in the context of precisely coordinated
                      spike patterns.References:[1] Kilavik et al. (2012).
                      doi:10.1093/cercor/bhr299[2] Rubino et al. (2006).
                      doi:10.1038/nn1802[3] Denker et al. (2018).
                      doi:10.1038/s41598-018-22990-7[4] Riehle et al. (1997).
                      doi:10.1126/science.278.5345.1950[5] Torre et al. (2016).
                      doi:10.1523/JNEUROSCI.4375-15.2016[6] Takahashi et al.
                      (2015). doi:10.1038/ncomms8169[7] Denker (2011).
                      doi:10.1093/cercor/bhr040[8] Brochier et al. (2018).
                      doi:10.1038/sdata.2018.55[9] Gutzen et al. (2022).
                      doi:10.48550/arXiv.2211.08527 $RRID:SCR_022966[10]$ Capone
                      et al. (2022). doi:10.48550/arXiv.2104.07445[11] Torre et
                      al. (2013). doi:10.3389/fncom.2013.00132[12] Stella et al.
                      (2022).
                      doi:10.1523/ENEURO.0505-21.2022Acknowledgments:Funded by EU
                      Grant 785907 (HBP SGA2), EU Grant 945539 (HBP SGA3), ANR
                      Grant GRASP (France), Helmholtz IVF Grant ZT-I-0003 (HAF),
                      the Joint-Lab “Supercomputing and Modeling for the Human
                      Brain”, and the Ministry of Culture and Science of the
                      State of North Rhine-Westphalia, Germany (NRW-network
                      'iBehave', grant number: NW21-049).},
      month         = {Sep},
      date          = {2023-09-26},
      organization  = {Bernstein Conference 2023, Berlin
                       (Germany), 26 Sep 2023 - 30 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          = {5235 - Digitization of Neuroscience and User-Community
                      Building (POF4-523) / 5231 - Neuroscientific Foundations
                      (POF4-523) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539) / Algorithms of Adaptive Behavior and
                      their Neuronal Implementation in Health and Disease
                      (iBehave-20220812) / HBP SGA2 - Human Brain Project Specific
                      Grant Agreement 2 (785907) / JL SMHB - Joint Lab
                      Supercomputing and Modeling for the Human Brain (JL
                      SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5235 / G:(DE-HGF)POF4-5231 /
                      G:(EU-Grant)945539 / G:(DE-Juel-1)iBehave-20220812 /
                      G:(EU-Grant)785907 / G:(DE-Juel1)JL SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1015190},
}