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).

More colors for Matplotlib

 

The default Matplotlib color scheme is not good. While searching for something better for my thesis, I stumbled upon Bridget Riley's painting "Gather" in the RISD museum. Is uses five colors: white, black, ochre, blue, turquoise, and a rust-colored red. But, from a distance they combine to create something far more vibrant. The blue and the turquoise hues are similar, so perhaps not the best to combine in a plot, but the others form an attractive and distinct pallet:

White	#f1f0e9
Black	#44525c
Rust	#eb7a59
Ochre	#eea300
Azure	#5aa0df
Turquoise	#00bac9

Surprisingly, when I ran this through a color-blindness simulator , some colors become even more distinct, so it is fairly color-blind friendly. I added colors to expand the pallet, to handle gradients and plots with extra lines.

Yellow	#efcd2b
Moss	#77ae64
Mauve	#b56ab6
Magenta	#cc79a7
Violet	#8d5ccd
Indigo	#606ec3
Viridian	#11be8d
Chartreuse	#b59f1a

These are less distinct, and not all combinations are color-blind friendly. Here's a bit of Python to define and tests these colors:

from pylab import *
from matplotlib import patches
WHITE      = mpl.colors.to_rgb('#f1f0e9')
RUST       = mpl.colors.to_rgb('#eb7a59')
OCHRE      = mpl.colors.to_rgb('#eea300')
AZURE      = mpl.colors.to_rgb('#5aa0df')
TURQUOISE  = mpl.colors.to_rgb('#00bac9')
BLACK      = mpl.colors.to_rgb('#44525c')
YELLOW     = mpl.colors.to_rgb('#efcd2b')
INDIGO     = mpl.colors.to_rgb('#606ec3')
VIOLET     = mpl.colors.to_rgb('#8d5ccd')
MAUVE      = mpl.colors.to_rgb('#b56ab6')
MAGENTA    = mpl.colors.to_rgb('#cc79a7')
CHARTREUSE = mpl.colors.to_rgb('#b59f1a')
MOSS       = mpl.colors.to_rgb('#77ae64')
VIRIDIAN   = mpl.colors.to_rgb('#11be8d')
GATHER  = [WHITE,RUST,OCHRE,AZURE,TURQUOISE,BLACK]
COLORS  = [BLACK,WHITE,YELLOW,OCHRE,CHARTREUSE,MOSS,VIRIDIAN,TURQUOISE,AZURE,INDIGO,VIOLET,MAUVE,MAGENTA,RUST]
CYCLE   = [BLACK,RUST,AZURE,OCHRE,TURQUOISE,MAUVE,YELLOW,INDIGO]
mpl.rcParams['axes.prop_cycle'] = mpl.cycler(color=CYCLE)
def test_panel(COLORS):
    ax = gca()
    NCOLORS = len(COLORS)
    axis('off')
    xlim(0,NCOLORS)
    ylim(NCOLORS,0)
    for j in range(NCOLORS):
        for i in range(NCOLORS):
            ax.add_patch(patches.Rectangle((i,j),1,1,linewidth=1,
                edgecolor='none',facecolor=COLORS[i]))
            ax.add_patch(patches.Rectangle((i+.25,j+.25),.5,.5,linewidth=1,
                edgecolor='none',facecolor=COLORS[j]))
    axis('equal')
figure(figsize=(6,3),dpi=200)
subplots_adjust(0,0,1,1,0.1,0)
subplot(111)
test_panel(COLORS)
savefig('colorblind_test_panel.png',bbox_inches='tight',pad_inches=0)
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Two squences form decent color maps. They aren't perceptually uniform, but pass the color blindness simulator tests. Adding violet, mauve, and moss, to the original Gather pallet creates a hue wheel. This one flunks color-blindness test (see Crameri et al. ). Here they are in python:

riley2 = matplotlib.colors.LinearSegmentedColormap.from_list('riley2',
    [INDIGO,VIOLET,MAUVE,MAGENTA,RUST,OCHRE])
riley3 = matplotlib.colors.LinearSegmentedColormap.from_list('riley3',
   [OCHRE,CHARTREUSE,MOSS,VIRIDIAN,TURQUOISE,AZURE])
# Smoothed out mauve, violet, rust, ochre, moss, turquoise, azure, in a loop
hues = ['#8c62cc', '#9560c8', '#9e62c3', '#a765be', '#b068b6', '#bb6caa', '#c66f98',
        '#d27384', '#dd7670', '#e67c5c', '#ea8348', '#ec8b34', '#ec9421', '#e99c13',
        '#dea212', '#c9a620', '#b0a934', '#96ab4a', '#7cae60', '#62b076', '#48b38c',
        '#2eb5a2', '#1ab7b6', '#12b6c5', '#1cb2ce', '#2dadd4', '#41a7d8', '#529fdb',
        '#6194db', '#6d86d8', '#7977d4', '#836ad0']
riley = matplotlib.colors.LinearSegmentedColormap.from_list('riley',hues)
# Make new maps behave like native Matplotlib maps
plt.register_cmap(name='riley2',cmap=riley2)
plt.register_cmap(name='riley3',cmap=riley3)
plt.register_cmap(name='riley' ,cmap=riley )
# Show as figure
figure(figsize=(5,1),dpi=300)
subplot(311)
imshow([linspace(0,1,256)],cmap='riley2',aspect='auto')
axis('off'); tight_layout()
subplot(312)
imshow([linspace(0,1,256)],cmap='riley3',aspect='auto')
axis('off'); tight_layout()
subplot(313)
imshow([linspace(0,1,256)],cmap='riley',aspect='auto')
axis('off'); tight_layout()
subplots_adjust(hspace=0.3)
savefig('moremaps.png')
</pre></div>