Wednesday, November 20, 2019
Tuesday, November 19, 2019
The difference between an expert's brain and a novice's
November 18, 2019
Cold Spring Harbor Laboratory
In learning new tasks, neuron
networks in the brain of mice become more refined and selective. Charting
changes in neural activity can help inform the design of better computational
models for understanding decision making and cognition.
When mice learn to do a new
task, their brain activities change over time as they advance from 'novice' to
'expert.' The changes are reflected in the wiring of cell circuits and
activities of neurons.
Using a two-photon imaging
microscope and a wealth of genetic tools, researchers from Cold Spring Harbor
Laboratory (CSHL), Columbia University, University College London, and Flatiron
Institute found that neural networks become more focused as mice got better at
performing a trained task. They used the data to construct computational models
that can inform their understanding of the neuroscience behind decision-making.
"We recorded the activity
from hundreds of neurons all at the same time, and studied what the neurons did
over learning," said CSHL Associate Professor Anne Churchland.
"Nobody really knew how animals or humans learn the structure of a task
and how the neural activity supports that."
The team, including Farzaneh
Najafi, the first author on the study and a postdoctoral fellow in Churchland's
lab, started by training mice on perceptual decision-making tasks. The mice
received multisensory stimuli in the form of a sequence of clicks and flashes
that were presented together. Their job was to tell researchers whether those
are happening at a high or low rate by licking one of three waterspouts in
front of them.
They licked the middle spout
to start the trial, one side to report a high-rate decision and the other side
for a low-rate decision. When the mice made the correct decision, they received
a reward.
"Most decision-making
studies focused on the period where the animals are really experts. But we were
able to see how they arrive at the state by measuring the neurons in their
brain all the way through learning," said Churchland, the senior author on
the study. "We found that in all the animals, their learning occurs
gradually over about four weeks. And we found that what supports learning is
activity changes in a whole bunch of neurons."
The neurons, the team
discovered, became more selective in responding to an activity associated with
a particular task. The also started reacting faster and more immediately.
"They'll respond really
strongly in advance of one choice and much less so in advance of the other
choice," Churchland said.
When the animals are just
beginning to learn, the neurons don't respond until around the time it makes
the choice. But as the animal gains expertise, the neurons respond much further
in advance.
"We can kind of read the
animal's mind in a way, we can predict what the animal is going to do before he
does it," Churchland said. "When you're a novice at something your
brain is doing all different things, so you have neurons engaged in all
different things. But then when you're an expert, you hone in on exactly what
you're going to do and we can pick up that activity."
The researchers decoded neural
activity by training a small artificial network called the 'Linear Support
Vector Machine' using machine learning algorithms. It collects performance data
from multiple trials and combines it with the activity of all the neurons,
weighing them to make a guess about what the animal's going to do. As the
animal gets better at the task, its neural networks get more refined, precise
and specific. The researchers are able to mirror that onto the artificial
network, which can then predict the animal's decision with about 90 percent
accuracy.
The learning models also offer
another way of looking at specific types of neurons in the brain involved in
cognition, like excitatory and inhibitory neurons, which trigger positive and
negative changes, respectively. In this study, published in Neuron (Cell
Press), the team found that the inhibitory neurons are part of very selective
sub-networks in the brain, and they're strongly selective for the choice that
the animal's going to make.
These neurons are part of a
biophysical model that helps researchers understand how decision-making works.
As researchers refine these models, they're able to make more sense of how
cognition informs behavior.
"We've learned a lot
about perceptual decision-making, the decisions that a subject would get right
and wrong, how long it takes to make those decisions, what the neural activity
would look like during decision-making-by making different kinds of models that
make really concrete predictions," Churchland said. "Now we can
understand, hopefully better, why these very selective sub-networks are there,
how they help us make better decisions, and how they are wired up during
learning."
Story Source:
Materials provided by Cold Spring Harbor Laboratory.
Original written by Charlotte Hu. Note: Content may be edited for style
and length.
Related Multimedia:
Journal Reference:
Farzaneh Najafi, Gamaleldin F.
Elsayed, Robin Cao, Eftychios Pnevmatikakis, Peter E. Latham, John P.
Cunningham, Anne K. Churchland. Excitatory and Inhibitory Subnetworks Are
Equally Selective during Decision-Making and Emerge Simultaneously during
Learning. Neuron, 2019; DOI: 10.1016/j.neuron.2019.09.045
Link between inflammation and mental sluggishness shown in new study
November 15, 2019
University of Birmingham
Summary:
Scientists have uncovered a
possible explanation for the mental sluggishness that often accompanies
illness.
Scientists at the University
of Birmingham in collaboration with the University of Amsterdam have uncovered
a possible explanation for the mental sluggishness that often accompanies
illness.
An estimated 12M UK citizens
have a chronic medical condition, and many of them report severe mental fatigue
that they characterize as 'sluggishness' or 'brain fog'. This condition is
often as debilitating as the disease itself.
A team in the University's
Centre for Human Brain Health investigated the link between this mental fog and
inflammation -- the body's response to illness. In a study published in Neuroimage,
they show that inflammation appears to have a particular negative impact on the
brain's readiness to reach and maintain an alert state.
Dr Ali Mazaheri and Professor
Jane Raymond of the University's Centre for Human Brain Health, are the senior
authors of the study. Dr Mazaheri says: "Scientists have long suspected a
link between inflammation and cognition, but it is very difficult to be clear
about the cause and effect. For example, people living with a medical condition
or being very overweight might complain of cognitive impairment, but it's hard
to tell if that's due to the inflammation associated with these conditions or
if there are other reasons."
"Our research has
identified a specific critical process within the brain that is clearly
affected when inflammation is present."
The study focussed
specifically on an area of the brain which is responsible for visual attention.
A group of 20 young male volunteers took part and received a salmonella typhoid
vaccine that causes temporary inflammation but has few other side effects. They
were tested for cognitive responses to simple images on a computer screen a few
hours after the injection so that their ability to control attention could be
measured. Brain activity was measured while they performed the attention tests.
On a different day, either
before or after, they received an injection with water (a placebo) and did the
same attention tests. On each test day they were unaware of which injection
they had received. Their inflammation state was measured by analysing blood
taken on each day.
The tests used in the study
assessed three separate attention processes, each involving distinct parts of
the brain. These processes are: "alerting" which involves reaching and
maintaining an alert state; "orienting" which involves selecting and
prioritising useful sensory information; and "executive control" used
to resolving what to pay attention to when available information is
conflicting.
The results showed that
inflammation specifically affected brain activity related to staying alert,
while the other attention processes appeared unaffected by inflammation.
"These results show quite
clearly that there's a very specific part of the brain network that's affected
by inflammation," says Dr Mazaheri. "This could explain 'brain
fog'."
Professor Raymond says,
"This research finding is major step forward in understanding the links
between physical, cognitive, and mental health and tells us that even the
mildest of illnesses may reduce alertness."
Dr Leonie Balter the first
author of the study which was completed as part of her PhD, concluded :
"Getting a better understanding of the relationships between inflammation
and brain function will help us investigate other ways to treat some of these
conditions. For example, further research might show that patients with
conditions associated with chronic inflammation, such as obesity, kidney
disease or Alzheimer's, could benefit from taking anti-inflammatory drugs to
help preserve or improve cognitive function."
"Furthermore, subtle
changes in brain function may be used as an early marker cognitive
deterioration in patients with inflammatory diseases."
The next step for the team
will be to test the effects of inflammation on other areas of brain function
such as memory.
Story Source:
Materials provided by University of Birmingham. Note:
Content may be edited for style and length.
Journal Reference:
Leonie JT. Balter, Jos A.
Bosch, Sarah Aldred, Mark T. Drayson, Jet JCS. Veldhuijzen van Zanten, Suzanne
Higgs, Jane E. Raymond, Ali Mazaheri. Selective effects of acute low-grade
inflammation on human visual attention. NeuroImage, 2019; 202: 116098
DOI: 10.1016/j.neuroimage.2019.116098
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