Computational Biology 2015
Department of Computer science，University of Warwick CV4 7AL, UK
Winner prize: the one who gets the highest score to solve question 3 and 5 will be awarded 500
Chinese Yuan: the best computational biologist in 2015.
Image formation and pre-processing
1． Image formation.
(a) Can you give a description on how T1 and T2* images are acquired ?
(b) For a given voxel of gray matter and white matter in brain, we want to decide the percentage
of its gray matter and white matter using TR=2000 msec, TE=20 msec. We know that
T1(wihte matter) = 600 msec, T1(gray matter)=1000 msec, and T2(white matter)= T2(gray
matter) =20 msec, M0=3Tesla. The net magnetization we read out from MRI machine for
the voxel is 1.0643 Tesla. Work out the percentage of the gray matter of the voxel.
(c) Assume that fMRI is a LTI systems, and the stimuli v(t) used in the experiment is as
in Fig. 1
Fig. 1. Stimuli vs. time
v(t ) 
25
(t

i
 ti ) where ti is random numbers uniformly distributed between [0
1
100] and (0) =1 and 0 otherwise. Using the standard HRF, work out the response
function x(t), using matlab to plot it.
(d) For a given T1 image, work out its Fourier transform and plot it out in K-space.
2. Pre-processing
(a) Summarize how many steps we have to go through to obtain a proper T1 and T2* image.
Briefly explain the contents of each step.
(b)
Two
raw
files
can
be
downloaded
from
http://www.dcs.warwick.ac.uk/~feng/Comp_Biol.html. One is for reconstructing T1 data
for a whole brain and the other is for reconstructing T2* data at resting state. Submit your
files to Ye Yao and comment on every step you did.
Granger causality and time series
3. Background: For most people, adolescence is synonymous with emotional turmoil. Yet the
current scientific evidence suggests that emotional reactivity per se does not change much in the
transition from childhood to adulthood. In fact, most research up-to-date has shown that the
observed change in emotional behaviour is due to continuous developmental improvement in the
regulation of emotional responses, along with changes in its neural bases. Here, we used fMRIbased neurofeedback (NF) to investigate the suitability of NF to directly influence plasticity in the
developing emotion regulation (ER) brain networks. We taught a group of 17 7-16 year-olds to upregulate the bilateral insula, a key ER region and found that all participants learned to increase
activation during the regulate trials in comparison to rest trials. Importantly, a subsequent Granger
Causality Analyses of information flow within the wider ER network found that during regulation
trials。 This suggests that: 1) NF training had a differential effect on regulation and rest network
connections, and specifically that 2) our training was not only superficially concentrated on surface
effects but also relevant with regards to the underlying neurocognitive bases.
Together these
findings highlight the feasibility of using NF in paediatric populations and its possible use for
shaping key social cognitive networks during development. In particular, 8 ROIs are identified
which are significantly activated during regulation trials. They are left amygdala (lAMY): -21, -3, 7; lINS: -39, 14, 3; rINS: 36, 15, 3; left mid-cingulate cortex (MCC): -6, 18, 39; left middle frontal
gyrus (lMFG): -38, 34, 27; left medial frontal gyrus/supplementary motor area (lSMA): -2, 16, 50;
left intra-parietal lobule (lIPL): -60, -48, 35; left precentral gyrus (lPreG): -48, 2, 32, where x,y,z
are in MNI coordination.
From the data set http://www.dcs.warwick.ac.uk/~feng/Comp_Bio.html
17 individuals with a recording of 200 time points (resting) and 160 time points (regulation).
• left amygdala (lAMY): -21, -3, -7;
[17 * 50], resting, [17*40], regulation
• lINS: -39, 14, 3;
[17 * 50], resting, [17*40], regulation
• rINS: 36, 15, 3;
[17 * 50], resting, [17*40], regulation
• left mid-cingulate cortex (MCC): -6, 18, 39;
[17 * 50], resting, [17*40], regulation
• left middle frontal gyrus (lMFG): -38, 34, 27;
[17 * 50], resting, [17*40], regulation
• left supplementary motor area (lSMA): -2, 16,50; [17 * 50], resting, [17*40], regulation
• left intra-parietal lobule (lIPL): -60, -48, 35;
[17 * 50], resting, [17*40], regulation
• left precentral gyrus (lPreG): -48, 2, 32,
[17 * 50], resting, [17*40], regulation
•
Work out the causal relationship between these eight regions for resting and tasking. Attempt all
parts and submit a report that should include the program listings (with comments).
Multi-comparison correction and Networks
4. Multi-comparison correction
Generating a figure of 100x100 pixels with an imbedded image and added noise as in Fig. 2 below..
(a)
(b)
(c)
(d)
Fig. 2. Raw images.
Can you construct t-statistics to recover the original image?
If no correction is used, reported your results as in Fig. 3, the upper panel.
If FWER is applied with alpha=5%, reported your results as in Fig. 3 middle panel.
If FDR is used with alpha=5%, reported your results as in Fig. 3 bottom panel.
F
ig. 3. Plot your results as here.
(e) Summarize your results. Explain which approach will you use in dealing with real data and why?
5. Functional Networks
Download
a
fMRI
resting
state
file
from
http://www.dcs.warwick.ac.uk/~feng/teaching/Comp_Biol.html which contains 100 patients and
100 matched healthy controls of 90 AAI regions.
(a) Using the data to construct the functional networks of healthy controls and schizophrenia
patients.
(b) Carry out both FWER and FDR corrections and report your results and explain each steps.
(c) Can you develop a method to verify your obtained results?
(d) Write down in details on how you do it and submit the final results to Ye Yao.
Attempt all parts and submit a report that should include the program listings (with comments).
Please visit the following web site for a submission cover sheet:
http://www.dcs.warwick.ac.uk/undergraduate/cover.html
Completed report should be submitted to the filing cabinet in the Terminal Room CS006 in the
Computer Science Department before 12 noon on Thursday Week 10 (i.e., 12th March 2015).