% 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{Kraue:1005733,
      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-2023-01605},
      year         = {2023},
      abstract     = {To study information processing in the cerebral cortex,
                      multiple complementary approaches exist to characterize the
                      coordinated population dynamics. One approach is to
                      investigate the correlated spiking activity of individual
                      neurons. Another approach is to analyze the local field
                      potential (LFP) as an aggregate signature of the neuronal
                      population dynamics. However, it is an open question how
                      these two scales of observation relate to each other.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 propagating waves [2, 3]. These are commonly observed
                      as planar waves that travel across the primary motor cortex,
                      preferably on a rostral-caudal axis [3]. Significant
                      patterns of precise synchronous spiking (on a ms scale) that
                      have been identified in the motor cortex [4] also display a
                      preferred spatial orientation [5]. Indeed, estimated
                      functional connectivity measured from spike trains using a
                      Granger causality approach occurs in a directed manner that
                      aligns with the mean propagation axis of LFP waves [6].
                      These findings raise the question of a direct relation
                      between a single spike pattern and a co-occurring LFP
                      wave.To investigate this question, we analyzed
                      multi-electrode-array (Utah array) recordings of the motor
                      cortex (MI/PMd) from a macaque monkey during an instructed
                      reach-to-grasp task [7]. In the beta-band LFP recordings
                      (15-25 Hz), we identified wave directions and planarity
                      based on the gradient of the instantaneous phase using an
                      automated analysis pipeline approach (Cobrawap) [8,9].
                      Independently, we detected all repeating synchronous spike
                      patterns in the same data sets using the SPADE method [10,
                      11]. We identified the dominant spatial axis of the
                      synchronous spike pattern as the first eigenvector of a
                      principal component analysis (PCA) over the electrode grid
                      coordinates of the involved neurons. We show that this axis
                      tends to be perpendicular to the propagation direction of
                      simultaneously occurring planar waves (cf. Fig.). This
                      relationship does not only appear on average as suggested by
                      previous work [5,6] but also on a pattern-by-pattern basis.
                      Finally, we discuss extensions of this analysis approach to
                      non-synchronous spike patterns.References:[1]: Kilavik et
                      al. (2012). doi:10.1093/cercor/bhr299[2]: Denker et al.
                      (2018). doi:10.1038/s41598-018-22990-7[3]: Rubino et al.
                      (2006). doi:10.1038/nn1802[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]: Brochier et al. (2018).
                      doi:10.1038/sdata.2018.55[8]: Gutzen et al. (2021).
                      doi:10.12751/NNCN.BC2020.0030[9]: Capone et al. (2022).
                      doi:10.48550/arXiv.2104.07445[10]: Torre et al. (2013).
                      doi:10.3389/fncom.2013.00132[11]: Stella et al. (2022).
                      doi:10.1523/ENEURO.0505-21.2022Acknowledgements:Founded by
                      EU Grant 785907 (HBP SGA2), EU Grant 945539 (HBP SGA3), ANR
                      Grant GRASP (France), Helmholtz IVF Grant ZT-I-0003 (HAF),
                      and the Joint-Lab “Supercomputing and Modeling for the
                      Human Brain”.},
      month         = {Mar},
      date          = {2023-03-22},
      organization  = {15th Goettingen Meeting of the German
                       Neuroscience Society, Goettingen
                       (Germany), 22 Mar 2023 - 24 Mar 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) / 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:(EU-Grant)785907 / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1005733},
}