#Importing required libraries and dataset
	import matplotlib.pyplot as plt
	from nilearn import image
	from nilearn import datasets
	from nilearn.plotting import plot_anat, show
	from matplotlib.patches import Rectangle
	
	#Fetching the Dataset
	haxby_dataset = datasets.fetch_haxby()
	
	#Building the mean image because we have no anatomic data
	func = haxby_dataset.func[0]
	mean_img = image.mean_img(func)
	
	z_slice = -14
	
	fig = plt.figure(figsize=(4, 6.4), facecolor='k')
	
	display = plot_anat(mean_img, display_mode='z', cut_coords=[z_slice], figure=fig)
	
	#Plotting the Brain Activities for ventral visual/temporal cortex, House and Face
	vt = haxby_dataset.mask_vt[0]
	house = haxby_dataset.mask_house[0]
	face = haxby_dataset.mask_face[0]
	
	display.add_contours(vt, contours=1, antialiased=False, linewidths=2., levels=[0], colors=['red'])
	display.add_contours(house, contours=1, antialiased=False, linewidths=2., levels=[0], colors=['blue'])
	display.add_contours(face, contours=1, antialiased=False, linewidths=2., levels=[0], colors=['yellow'])
	
	#Generating Legends
	p_v = Rectangle((0, 0), 1, 1, fc="red")
	p_h = Rectangle((0, 0), 1, 1, fc="blue")
	p_f = Rectangle((0, 0), 1, 1, fc="yellow")
	plt.legend([p_v, p_h, p_f], ["ventral temporal", "house", "face"])
	
	show()