Hello everyone. I’m Mikio Obuchi, a PhD student at Dartmouth College. Today I am excited
to present my work, "Predicting brain functional connectivity using mobile sensing". Yes, this
sounds very cool and challenging. By the way, this background image is my brain.
Many researchers including our group have utilized mobile sensing in order to study
people’s mental health including depression and schizophrenia. The reason why we are using
mobile sensing is that it has a variety of sensors such as GPS, microphone, phone usage
and so on. And by processing those data, we can extract a lot of behavioral or contextual
information. Also many researchers use EMA, ecological momentary assessment, to measure
people’s mental states during the studies.
The most unique part of this work is that I am integrating mobile sensing and brain
imaging using an fMRI for the first time. Our goal here is to see if we can predict
brain connectivity between regions in the brain associated with anxiety by using mobile
sensing.
Many people suffer from mental illness. Depression and anxiety are very common mental illnesses,
as you can see from the statistics here.
The question is, how should we assess the mental states of participants if we want to
study them? Typically researchers often use validated
surveys such as GAD7 for generalized anxiety disorder. The survey can assess anxiety with
high sensitivity and specificity and are typically used as the ground truth of an individuals
mental state.
In this study, we wanted to explore a new way to measure people’s mental well-being.
And we found that brain imaging data would be a good objective data that can be related
to people’s mental health. Neuroscientists have studied a lot of brain mechanisms and
people's cognitive processes. And, we wanted to build a bridge to bring their knowledge
to our mobile sensing area.
You might be thinking, how do we measure brain activity? There are a lot of methods but
in the current study we are using fMRI as it has high spatial resolution and is non-invasive.
Functional MRI indirectly measures neural activity through local blood oxygenation level
dependent signal.
In our study, we observe temporal correlation between brain regions, termed resting-state
functional connectivity. Participants are asked to stay still, or be in a resting-state,
in the scanner for about 10 to 20 minutes while we scan their brain. Functional connectivity
is also quite simple.
Brain regions are functionally connected when neural populations are active at the same
time. Let’s say we take X axis as time and Y axis as neural activity. If that up-down
pattern is similar between two regions, calculated as temporal correlation, we call them functionally
connected. In very simple terms, we can interpret it
as the level of synchrony between those two regions.
Ok. In order to study people’s mental illness, we had to choose the regions.
As a first step, we decided to take a look at functional connectivity between region called vmPFC
and amygdala. That’s totally fine if you haven’t heard
of these regions but the only thing that you need to know is, there is research conducted
by Kim that says that connectivity between them is negatively associated with people’s
anxiety.
To collect the data, we did a study on our campus with 92 first-year undergrads for one
academic term. We first scanned students at the beginning
of the term, and they started the mobile sensing part after that.
For mobile sensing data, we have activity, audio, location, sleep, and phone unlock data.
To generate features, our strategy was to basically compute the mean, standard deviation
and sample entropy for each sensing data that we have. We also divided the day into four
different periods to better interpretability.
We performed two analyses, with the first one is correlation analysis to see the
relationship between functional connectivity and each sensing feature. Also, the second analysis leveraged all sensing
data and try to predict brain connectivity.
First we performed Spearman’s correlation between brain connectivity and each feature.
We used the BH’s false discovery rate to control for false positives. Among all sensors, microphone
data had the most correlated features. For instance, if you take a look at the top one, it says conversation duration
is positively correlated with the connectivity and r-value is 0.365.
So more conversation, higher connectivity which is associated with lower anxiety.
We don’t have time to go through everything but from other sensing features, the result
showed that a student has stronger connectivity if they regularly spend time at other student’s
dorms or use their phone in the morning.
The second analysis is the prediction of brain connectivity with machine learning.
As a first step, we set this question as a binary classification problem so we split
the participants based on the median score, and we will be predicting is a person belongs
to a higher or lower functional connectivity group.
We used 8 different algorithms like SVM, Naive Bayes, tree-based model and so on.
Also, we used 3 10 fold nest cross-validation to evaluate the model performance.
And we used a technique called sequential forward selection to select useful features.
Here are the results of our balanced binary classification, where baseline will be 50%.
The best model was SVC with RBF kernel, and the F1 score was 0.793, and the accuracy is
0.791. And the right figure is the confusion matrix
for that model. It seems pretty good.
Because my time is limited, I do not have time to show important features, but many
of them came from microphone data, and I think it makes sense considering that there are
a lot of features that have relatively higher correlation coefficients.
Ok. So in this work, I think I successfully demonstrated that mobile sensing is related
to brain imaging data. And we have opened up the new concept of mental health
sensing at UbiComp by incorporating new objective data in the form of brain imaging. However,
we are aware of many limitations.
The first one is our N is small and they are all undergrads from our campus so the result
cannot be easily generalized. The second one is brain localization could
be critical. We have to be really careful when we try to interpret our results because
the particular brain region may play a very different role during the cognitive processes.
The last one is fMRI is not ubiquitous. We have to think about how to integrate fMRI
if we want to design a self-monitoring or intervention app in the future.
Ok, that’s it. Thank you for listening.