Tag Archives: Methods

The weather and the brain – using new methods to understand developmental disorders

 

The latest article was written by our brilliant lab member Danyal Akarca. It describes some of his MPhil research which aims to explore transient brain networks in individuals with a particular type of genetic mutation. Dan finished his degree in Pre-Clinical Medicine before joining our lab and has since been fascinated by the intersection of genetic disorders and the dynamics of brain networks.

The brain is a complex dynamic system. It can be very difficult to understand how specific differences within that system can be associated with the cognitive and behavioural difficulties that some children experience. This is because even if we group children together on the basis that they all have a particular developmental disorder, that group of children will likely have a heterogeneous aetiology. That is, even though they all fall into the same category, there may be a wide variety of different underlying brain causes. This makes these disorders notoriously difficult to study.

Developmental disorders that have a known genetic cause can be very useful for understanding these brain-cognition relationships, because by definition they all have the same causal mechanism (i.e. the same gene is responsible for the difficulties that each child experiences). We have been studying a language disorder caused by a mutation to a gene called ZDHHC9. These children have broader cognitive difficulties, and more specific difficulties with speech production, alongside a form of childhood epilepsy called rolandic epilepsy.

In our lab, we have explored how brain structure is organised differently in individuals with this mutation, relative to typically developing controls. Since then our attention has turned to applying new analysis methods to explore differences in dynamic brain function. We have done this by directly recording magnetic fields generated by the activity of neurons, through a device known as a magnetoencephalography (MEG) scanner. The scanner uses magnetic fields generated by the brain to infer electrical activity.

The typical way that MEG data is interpreted, is by comparing how electrical activity within the brain changes in response to a stimulus. These changes can take many forms, including how well synchronised different brain areas are, or the how size of the magnetic response differs across individuals. However, in our current work, we are trying to explore how the brain configures itself within different networks, in a dynamic fashion. This is especially interesting to us, because we think that the ZDHHC9 gene has an impact on the excitability of neurons in particular parts of the brain, specifically in those areas that are associated with language. These changes in network dynamics might be linked to the kinds of cognitive difficulties that these individuals have.

We used an analysis method called “Group Level Exploratory Analysis of Networks” – or GLEAN for short – and has recently been developed at the Oxford centre for Human Brain Activity. The concept behind GLEAN is that the brain changes between different patterns of activation in a fashion that is probabilistic. This is much like the concept of the weather – just as the weather can change from day to day in some probabilistic way, so too may the brain change in its activation.

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This analysis method not only allows us to observe what regions of the brain are active when the participants are in the MEG scanner. It also allows us to see the probabilistic way in which they can change between each other. For example, just as it is more likely to transition from rain one day to cloudiness the next day, relative to say rain to blistering sun, we find that brain activation patterns can be described in a very similar way over sub-second timescales. We can characterise those dynamic transitions in lots of different ways, such as how long you stay in a specific brain state or how long does it take to return to a state once you’ve transitioned away. (A more theoretical account of this can be found in another recent blog post in our Methods section – “The resting brain… that never rests”.) We have found that a number networks differ between individuals with the mutation and our control subjects.

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(These are two brain networks that show the most differences in activation – largely in the parietal and frontotemporal regions of the brain.)

Interestingly, these networks strongly overlap with areas of the brain that are known to express the gene (we found this out by using data from the Allen Atlas). This is the first time that we know of that researchers have been able to link a particular gene, to differences dynamic electrical brain networks, to a particular pattern of cognitive difficulties. And we are really excited!

 

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Coder’s Little Time Saver

We all know the problem; it’s getting late in the office, all your colleagues left hours ago, and your eyes are watering from staring at the analysis output of a script that should have finished running ages ago. Yet for some inexplicable reason, it’s still not done. Wouldn’t it be great if you could just nip out to get some fresh air and be informed when the script is finally done? Well actually, you can! Here’s a handy little tip explaining how to embed email alerts in MATLAB and Python scripts:

MATLAB
MATLAB comes with a handy function that supports sending emails within scripts. But before we can actually get to the email sending, we need to configure some server information. Here is an example for a gmail account:


mail = 'Me@gmail.com'; %Your GMail email address
password = 'secret'; %Your GMail password
setpref('Internet','SMTP_Server','smtp.gmail.com');

setpref('Internet','E_mail',mail);
setpref('Internet','SMTP_Username',mail);
setpref('Internet','SMTP_Password',password);
props = java.lang.System.getProperties;
props.setProperty('mail.smtp.auth','true');
props.setProperty('mail.smtp.socketFactory.class', 'javax.net.ssl.SSLSocketFactory');
props.setProperty('mail.smtp.socketFactory.port','465');

Now, we are ready to send an email:

sendmail(‘someone@coldmail.com’,’Hello there!’);

The second argument in the sendmail function corresponds to the subject line of the email. If you are keen to let MATLAB send a more elaborate email, you can also include a text body:


sendmail('someone@coldmail.com','Hello there!','Have you seen that great post on the Forging Connections Blog?');

It is even possible to send attachments:

sendmail('recipient@someserver.com','Hello there!','Have you seen that great post on the Forging Connections Blog?',{'/Users/Fred/image.jpeg’});

By including these few lines of code in your time-consuming MATLAB script, you can now get notified when it is time to go back to the office for the results.

Python
Python offers a simple solution to send emails from within scripts via the smtplib module. Here is a function that provides the configuration for gmail:
def send_email():
import smtplib


gmail_user = "Fred@gmail.com"
gmail_pwd = "Password"
FROM = 'Fred@gmail.com'
TO = ['Luke@gmail.com']
SUBJECT = "Meeting at 3pm"
TEXT = "Are you coming to the meeting?"

# Prepare actual message
message = """\From: %s\nTo: %s\nSubject: %s\n\n%s
""" % (FROM, ", ".join(TO), SUBJECT, TEXT)
try:
server = smtplib.SMTP("smtp.gmail.com", 587)
server.ehlo()
server.starttls()
server.login(gmail_user, gmail_pwd)
server.sendmail(FROM, TO, message)
server.close()
print 'successfully sent the mail'
except:
print "failed to send mail"

For more information see http://www.tutorialspoint.com/python/python_sending_email.htm and http://stackoverflow.com/questions/10147455/trying-to-send-email-gmail-as-mail-provider-using-python