Collective neural dynamics in primate motor cortex

As of the 29th of May, 2016, I officially have a Ph.D. in neuroscience! The thesis, Collective Neural Dynamics in Primate Motor Cortex, is available from the Brown University library [PDF].

I studied how single-neuron activity relates to large-scale collective neural dynamics during movement planning and execution. The thesis covers three research projects, which have been (will be) published as stand-alone papers:

• Chapter 2, pp. 88-121: Contribution of LFP dynamics to spiking variability in motor cortex during movement execution. read more...
  

Rule, M.E., Vargas-Irwin, C., Donoghue, J.P. and Truccolo, W., 2015. Contribution of LFP dynamics to single-neuron spiking variability in motor c ortex during movement execution. Frontiers in systems neuroscience, 9, p.89.

• Chapter 3, pp. 122:168: Dissociation between single-neuron spiking β-rhythmicity and transient β-LFP oscillations during movement preparation in primate motor cortex. read more…
  

Rule, M.E., Vargas-Irwin, C.E., Donoghue, J.P. and Truccolo, W., 2017. Dissociation between sustained single-neuron spiking and transient β-LFP oscillations in primate motor cortex. Journal of neurophysiology, 117(4), pp.1524-1543.

• Chapter 4, pp. 169-213: Phase diversity and spatiotemporal wavedynamics in primate motor cortex local field potentials. read more…

Rule, M.E., Vargas-Irwin, C., Donoghue, J.P. and Truccolo, W., 2018. Phase reorganization leads to transient β-LFP spatial wave patterns in motor cortex during steady-state movement preparation. Journal of neurophysiology, 119(6), pp.2212-2228.

The introduction contains background on primate motor cortex (Chapter 1, pp. 7-88), including its constituent areas, how they connect with the rest of the brain, and how neurons connect to each-other within each area. It surveys what is known (as of 2016) about motor cortex population dynamics, LFP oscillations, and spatiotemporal waves. The section on statistical methods (Chapter 1.5, pp. 61-87) provides background for signal processing to extract single-neuron spikes and LFPs from multi-electrode array recordings. It also covers how to apply Generalized Linear Point-Process Models (PP-GLM) to analyze spiking neural data.

I'd also like to share two new illustrations from the introduction not published elsewhere:

Figure 1.1 

(high resolution PDF, SVG)

 

Figure 1.1: Anatomy of visually-guided reaching and grasping. During visually guided reaching and grasping, the arm and hand area of M1 coordinates with the dorsal and ventral premotor areas PMd and PMv. In this illustration, reciprocally connected motor and parietal areas are shaded in common colors. Premotor areas receive segregated streams of visual information from parietal cortex. Area PMd receives information about spatial geometry important for reaching from dorsal parietal areas (shaded in blue). Area PMv receives information about object geometry important for grasping from the parietal areas shaded in orange. Area M1 also receives feedback from somatosensory cortex (areas 3a,1,2, shaded in grey). Connections between parietal and premotor cortex are taken from Tanné-Gariépy et al. (2002), and anatomical boundaries of premotor areas are taken from Dum and Strick (2002).

Figure 1.13 

(high resolution PDF, SVG)

Figure 1.13: Signal processing for reaching and grasping. (A) Kinematics are recorded using a motion capture setup, which tracks the position of infrared reflective markers mounted on the forearm. Motion capture data is cleaned, and occluded markers are inferred based on a model of arm kinematics. Smoothed velocity trajectories are extracted, and normalized “pathlet” features as in Hatsopoulos et al. (2007) are generated for the grip aperture and wrist endpoint separately. The drawing of the monkey is credited to John Mislow and is from Figure 2 in Vargas-Irwin et al. (2010). (B) Neural signals are recorded on implanted microelectrode arrays (MEAs). Spikes are extracted from the high frequency components of the electrical signal, and sorted off-line. Local field potentials are extracted from the low-pass electrical signal, and separated into various bands. The Hilbert transform can be used to construct the analytic signal from narrow-band LFP oscillations, from which the instantaneous phase and amplitude envelope can be extracted.

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For individual figure components re-published from elsewhere, please consult the original rightsholder. All other materials in this thesis are licensed freely under the Creative Commons Attribution license. Materials in chapters 2-4 may be cited by referring to the corresponding publications. All other content may be cited as:

Rule, M. (2016). Collective neural dynamics in primate motor cortex. Ph.D. Thesis. Brown University, Providence, Rhode Island. Available at Brown University Library, doi.org/10.7301/Z0KS6Q07

Many thanks to all who have accompanied and supported me on this journey.