1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
|
"""
.. _et-fmri:
==================
Event-related fMRI
==================
Extracting the average time-series from one signal, time-locked to the
occurence of some type of event in another signal is a very typical operation
in the analysis of time-series from neuroscience experiments. Therefore, we
have an additional example of this kind of analysis in :ref:`grasshopper`
The following example is taken from an fMRI experiment in which a subject was
viewing a motion stimulus, while fMRI BOLD was recorded. The time-series in
this data set were extracted from motion-sensitive voxels near area MT (a
region containing motion-sensitive cells) in this subject's brain. 6 different
kinds of trials could occur in this experiment (designating different
directions and locations of motion). The following example shows the extraction
of the time-dependent responses of the voxels in this region to the different
stimuli.
We start by importing modules/functions used and define some variables we will
use in the analysis:
"""
import os
from matplotlib.mlab import csv2rec
import matplotlib.pyplot as plt
import nitime
import nitime.timeseries as ts
import nitime.analysis as nta
import nitime.viz as viz
TR = 2.
len_et = 15 # This is given in number of samples, not time!
"""
Next, we load the data into a recarray from the csv file, using csv2rec
"""
data_path = os.path.join(nitime.__path__[0], 'data')
data = csv2rec(os.path.join(data_path, 'event_related_fmri.csv'))
"""
We initialize TimeSeries objects with the data and the TR:
One TimeSeries is initialized for the BOLD data:
"""
t1 = ts.TimeSeries(data.bold, sampling_interval=TR)
"""
And another one for the events (the different stimuli):
"""
t2 = ts.TimeSeries(data.events, sampling_interval=TR)
"""
Note that this example uses the EventRelated analyzer (also used in the
:ref:`grasshopper` example), but here, instead of providing an :class:`Events`
object as input, another :class:`TimeSeries` object is provided, containing an
equivalent time-series with the same dimensions as the time-series on which the
analysis is done, with '0' wherever no event of interest occured and an integer
wherever an even of interest occured (sequential different integers for the
different kinds of events).
"""
E = nta.EventRelatedAnalyzer(t1, t2, len_et)
"""
Two different methods of the EventRelatedAnalyzer are used: :attr:`E.eta`
refers to the event-triggered average of the activity and :attr:`E.ets` refers
to the event-triggered standard error of the mean (where the degrees of freedom
are set by the number of trials). Note that you can also extract the
event-triggered data itself as a list, by referring instead to
:attr:`E.et_data`.
We pass the eta and ets calculations straight into the visualization function,
which plots the result:
"""
fig01 = viz.plot_tseries(E.eta, ylabel='BOLD (% signal change)', yerror=E.ets)
"""
.. image:: fig/event_related_fmri_01.png
In the following example an alternative approach is taken to calculating
the event-related activity, based on the finite impulse-response
model (see [Burock2000]_ for details)
"""
fig02 = viz.plot_tseries(E.FIR, ylabel='BOLD (% signal change)')
"""
.. image:: fig/event_related_fmri_02.png
Yet another method is based on a cross-correlation performed in the frequency
domain (thanks to Lavi Secundo for providing a previous implementation of this
idea). This method can speed up calculation substantially for long time-series,
because the calculation is done using a vector multiplication in the frequency
domain representation of the time-series, instead of a more computationally
expensive convolution-like operation
"""
fig03 = viz.plot_tseries(E.xcorr_eta, ylabel='BOLD (% signal change)')
"""
.. image:: fig/event_related_fmri_03.png
We call plt.show() in order to display all the figures:
"""
plt.show()
"""
.. [Burock2000] M.A. Burock and A.M.Dale (2000). Estimation and Detection of
Event-Related fMRI Signals with Temporally Correlated Noise: A
Statistically Efficient and Unbiased Approach. Human Brain Mapping,
11:249-260
"""
|
