Learning about the challenges of computational neuroscience

The following was written by Thomas Witelski, Associate Director at SAMSI and Professor at Duke University in the Mathematics Department.

At some level, everyone is aware of the pressing medical and societal
challenges of neuroscience from media coverage of the growing impact of
neurological diseases like Alzheimer’s and Parkinson’s. Understanding the
brain at a scientific level has been identified as one of the central
challenges for this century’s research, as reflected in the magnitude of
resources invested in the NIH’s BRAIN initiative and the European Union’s
Human Brain Project.

attendees sitting in the auditorium

The opening workshop for CCNS was held at the NC Biotech Center.

In August, a diverse community of researchers converged at the NC
Biotechnology Center for SAMSI’s opening workshop for the Challenges in
Computational Neuroscience (CCNS) program. The presentations by leading
researchers on clinical, cognitive, computational and theoretical aspects of
brain research yielded many very lively discussions. Some talks addressed
technical issues, but many pointed to big fundamental questions on
exploring what might be nature’s most intricate black box.

Martin Lindquist speaking at the podium

Martin Lindquist, Johns Hopkins, speaking at the opening workshop.

A long history of anatomical studies has established the general features
comprising the human brain, but great challenges lie ahead in making clear
how the structure and functions of the brain relate to each other. Many of
the talks in the CCNS workshop addressed methods in neuroimaging. Martin
Lindquist (Johns Hopkins Univ) gave a lecture over viewing the various
modalities for functional imaging of brains in vivo, including functional
magnetic resonance imaging (fMRI), positron emission tomography (PET), and
electro/magneto-encephalography (EEG/MEG). These techniques differ in the
technologies used to collect data, but more importantly, they fundamentally
differ in the physiological types of behavior they monitor — in terms of
either blood flow, metabolic activity or electrical activity in the brain.
The methods have different limitations and trades-off in terms of spatial
and temporal resolutions, and represent the current state-of-the art in
clinical methods of collecting neuroimaging data.

Several talks in the meeting addressed fundamental statistical and
mathematical questions on image processing and how to use collected data
(possibly coming from multiple scans) to obtain the most accurate possible
maps of the brain’s structure. Of particular interest is the use of
neuroimaging data to infer the networks of connections among parts of the
brain, called the field of connectomics. In this direction, Max Descoteaux
(Univ of Sherbrooke) showed how diffusion in MRI images could be used to
identify structural connections within the white matter of the brain.

Another major branch of neuroscience research explored in the workshop is
based on “bottom-up” modeling of time series of neural activity in networks
of connected neurons. Physiologically-based models of chemical/electrical
activity like the Hodgkin-Huxley equations can effectively reproduce the
dynamics observed in individual neurons. Equivalent reduced models, like
the “leaky-integrate-and-fire” neuron, can then be used to give statistical
descriptions for the patterns of spikes typically recorded in EEG data.
Workshop presentations in this area included talks by Robert Kass (Carnegie
Mellon), Kenneth Miller (Columbia) and Uri Eden (Boston Univ).

Returning to studies at the “whole-brain” level, many speakers touched on
the computational challenges involved in analyzing the huge datasets that
have been collected in connection with some clinical studies. The importance
of using mathematical and statistical methods to interpret clinical
neuroscience was also highlighted in talks on neurodevelopment by Raquel Gur
(Univ Pennsylvania), behavioral studies by Ruben Gur (Univ Pennsylvania)
and the influence of anesthesia on brain activity by Emery Brown (Harvard).

two people looking at the poster

Looking at a poster during the CCNS Opening Workshop.

Many of the advanced topics addressed in the workshop were also introduced
in a Neuroscience Summer School that was held in connection with the CCNS
program in July. The research focuses begun in the workshop are being
carried forward in several working groups, two graduate courses and further

Five things I learned at the Forensics Tutorial

fingerprint analysis lab

Participants got to try their hand at doing a fingerprint analysis.

I don’t know about you, but I am fascinated by forensics. Crime scenes are puzzles waiting for someone to solve. So, I, along with some other staff members, sat in on part of the Forensics tutorial held in August.

Here are five things I learned while I listened in on the tutorial:

1)Unlike “CSI” or whatever your favorite cop show might be (mine is “Dexter”), the methods used at the crime scene are not nearly as scientific as you would expect. That’s where statisticians and applied mathematicians come in. There is a plethora of areas where adding statistical or mathematical methodologies to the field of forensics can make a tremendous impact in helping law enforcement catch the right people.

2)A lot of forensic evidence comes from inductive inferential processes, which is not a very good/reproducible way to gather evidence. Bill Tobin, from Forensic Engineering International, told us that they do not share error rates and there is no “peer review” for the evidence.See one of his presentations here.

3)There are many factors that are often overlooked or are not explored in firearm toolmark evidence (Firearm toolmarks are used to associate or eliminate a particular firearm as a murder weapon comparing characteristics imported to bullets and cartridge cases when it cycles through a gun.) but there are ways that forensic experts could use a more scientific approach to make this a more credible piece of evidence. See Cliff Spiegelman’s talk for more information about this.

Andy Parker dusting for fingerprints

Andy Parker shows the group how he dusts for footprints.

4)Andy Parker from the Wake County Crime Bureau Investigation team is awesome! He talked about what his job entailed and showed us several slides (sorry, they cannot be shared) of dead bodies and had us guess if it was a murder, suicide or death by accident. Then he explained how one could conclude which was correct. Later, he had us all gather around and showed us how they dust for prints, including footprints. It was really neat!

Herbert David Sheets

Herbert David Sheets, Canisius College and University at Buffalo, talks about bitemarks.

5)The uniqueness of bite marks is not been scientifically established, and the uniqueness of a bite mark to make a unique pattern in someone’s skin has also not been scientifically established. See Herbert David Sheets’ presentation for more info.

Overall, this is a fascinating topic that applied mathematicians and statisticians have traditionally not been involved with, but that needs your attention. Saving an innocent person from being in prison and/or catching a criminal who may still be free is vital to keeping our society whole. You can still get involved by attending one of SAMSI’s subsequent workshops which will be posted on the SAMSI website sometime in the near future.