Mental illness, the
leading cause of disability in the western world, has long been a principal
focus of neuroscience research. A symposium
presented by the New York Academy of Sciences offered a progress report for
disorders ranging from autism to depression, anxiety to anorexia.
A theme shared by a
number of presentations was the growing ability to see through patterns of
thought, behavior, and emotion characterizing these diseases to their
underlying neurobiology. Advances in technology were part of the story—not only
in the laboratory, but in day-to-day interactions with patients, via mobile and
While the hope of new
interventions to help the mentally ill is a persistent subtext of research, Husseini
Manji, of Janssen Research & Development, suggested an even more
ambitious goal: “There is reason to be optimistic about changing the paradigm
from ‘diagnose and treat,’ to ‘predict and preempt,’” he said. (Janssen
Neuroscience, a pharmaceutical company, was a principal sponsor of the
a jump on psychosis
Prediction was at the
heart of Tyrone D. Cannon’s
presentation on psychotic disorders, with the tantalizing prospect of
prevention hovering just outside the frame.
related disorders typically appear in the late teens and early 20s, and several
decades of research have identified a prodrome (early symptoms suggesting onset
of a disease) that indicates high risk. Among people who show such symptoms as
declining social and academic function and psychosis-like changes in thought
and perception, 16 percent will develop psychosis within a year, 25 percent
within two years.
Cannon, director of the
Clinical Neuroscience Lab at Yale University, described research to sharpen
predictive power. With an algorithm that integrated unusual thought content and
suspiciousness, age, and neurocognitive variables like poor verbal learning and
speed of processing, researchers with the North America Prodrome
Longitudinal Study (NAPLS), a multiuniversity consortium, developed a risk
calculator whose accuracy compares favorably with algorithms used to predict
heart attack and cancer risk.
“There’s still room for
improvement,” he said. “What if we could integrate biomarker data?”
NAPLS researchers first looked at brain
structure. A small study suggested that
baseline MRI could predict conversion to psychosis among high-risk youth as
accurately as the demographic-neurocognitive variables used earlier.
Pursuing another line
of inquiry, they analyzed blood samples. An index combining markers of
inflammation, oxidative stress, and hypothalamic-pituitary-adrenal activation
was substantially more predictive than
the original algorithm.
“These are small
studies, in need of replication in large samples,” Cannon said. This research is under way, with four large
research groups pooling brain and blood data.
To be truly useful,
more accurate risk prediction
would demand effective preventive strategies now lacking. “We need insight into
the mechanism of onset to find the right targets,” he said.
research suggesting that synaptic pruning—a normal feature of adolescent brain
development—is amplified in
psychotic disorders, resulting in excessive cortical thinning. “What we
think is happening is that microglia [immune cells in the brain] are becoming
activated and engulfing dendrites at a higher than normal rate.” If this
hypothesis is confirmed, interventions that inhibit inflammatory signaling or
activate synapses to make them more robust against engulfment "could give
hope," he said.
S. Pine, who heads the section on development and affective neuroscience in
the National Institute of Mental Health Intramural Research Program, reiterated
the need to understand mechanisms underlying disease. “A big problem facing the
field is that we’re [classifying pathology] based purely on clinical signs and
symptoms; we need to move past this to an approach based on what we know about
Research should focus
on “core psychological processes that we can evoke in the lab to leverage
clinical insights,” he said.
Twenty years of
research into anxiety indicate a key component to be “bottom-up” capture of attention by a sudden
threat—much like the way one pulls a hand back from contact with a hot
stove—too fast for awareness. A conscious component (realizing you pulled back
because the stove was hot) comes after.
Repeated studies have
shown that people with high anxiety have an “attention bias” toward threat—a heightened tendency to monitor
potential danger in this way. One study, which tracked eye movement to gauge
attention, found that infants who were more distracted by threats were likely
to be more anxious ten years later. In a study of 1000 Israeli soldiers, those
with a similar attention bias when they entered the military were at heightened
risk of PTSD after combat.
In neural architecture, “the circuit
connecting the amygdala to the prefrontal cortex underlies this abnormally brisk
reflex,” Pine said. Understanding this component of anxiety might clarify some
limitations of interventions like cognitive behavioral therapy (CBT). “If you
want to change behavior people aren’t even aware of, you won’t do it by talking
to them,” he said. “You need a different approach to change attention
He and other researchers
developed an intervention, attention bias
modification treatment (ABMT) that uses a computer game to practice
redirecting attention away from a simulated threat (an angry face) "over
and over, thousands of times." More than 30 studies of this training have
found greater impact on adult anxiety symptoms than control treatment using
A small study found
that, by some measures, adding ABMT to CBT more effectively reduced children’s
anxiety symptoms than CBT alone. Results from a larger trial, which also
analyzed changes in brain function, should be available soon.
“The key idea is, by
tying therapies more tightly to underlying perturbations in particular
behaviors and brain circuits that give rise to them, we’re better able to
tailor treatment to the individual patient,” Pine said.
the digitized brain
In the last
presentation of the afternoon, Vaibhav Narayan,
of Janssen Research & Development, described developments in mobile
technology. While personal electronic devices that sense and track behavior and
physiology are useful in other medical areas, “they will have a
disproportionate impact in neuroscience,” he said.
“The brain is a highly
complex organ with complex circuitry, but its entire biology expresses itself
in a phenotypic layer of
behavior, symptoms, and memory that is ripe for being digitized.” Mobile
devices will untether indicators of disease onset and exacerbation from controlled
settings, enabling more continual measurement, even at home, he said.
Narayan offered, as a
simple example, a study tracking home computer use. Among people diagnosed with
mild cognitive impairment, hours at the computer declined steeply month by
month; among the cognitively stable, they stayed relatively constant.
“Something as simple as
measuring time spent on a computer gives an idea of what’s going on in the
brain. You can imagine a lot more sensitive measures, like interkey speed as
the user types in a password.”
But can technology detect
neurodegenerative brain changes before noticeable cognitive decline? “The only
real way to answer the question is through data,” Narayan said. Brain pathology is commonly reflected by
declining ability to take medications, use the phone, and handle everyday
finances. “You can use data-driven technology to measure these things,” he
technology in a smartphone can be tailored to measure verbal episodic memory,
the domain that correlates most strongly with dementia. Embedding assessments
of spatial episodic memory in map and GPS apps of people who agree to be
tracked can generate massive data sets that may lead to validated tests to
distinguish signs of pathology from normal declines in function.
disorders, change is considerably more dynamic. Bipolar symptoms, for example,
may be stable over time, then worsen precipitously in a matter of days. This,
Narayan said, suggests the possibility of prodromal intervention to pre-empt
the onset of disease. In addition, “every relapse is another opportunity to
detect, intervene, and improve the outcome,” he said. By tracking such
parameters as physical activity, social interaction, and sleep, a smartphone app
could pick up perturbations suggesting an imminent episode.
One ongoing study is
following more than 300 people with major depression for a year, continuously
collecting data on sleep, activity, and speech, in search of “a
technology-driven signature of relapse that we can detect,” he said.
Narayan pointed out
that dynamic data from devices, while sensitive to signs of illness, are not
specific, and must be combined with clinical data for a meaningful
biosignature. “It’s not just change
that’s predictive, but the rate of change," he said. "You have to
collect longitudinal data and be your own control.”
Along with the rapid
proliferation of mobile devices have come quality control issues, he said. “The
technology is moving a lot faster than the science… people are developing
of apps that claim to do things like monitor mood and offer therapeutic
intervention, but the number that have any data behind them is small.”
“The most important thing we need to do is tie applications
of digital technology to data, to evidence,” Narayan said. “We have to develop
this ecosystem in a thoughtful manner.”