Monday, March 16, 2015

Update on the BROADEN Trial of DBS for Treatment-Resistant Depression

Website for the BROADEN™ study, which was terminated

In these days of irrational exuberance about neural circuit models, it's wise to remember the limitations of current deep brain stimulation (DBS) methods to treat psychiatric disorders. If you recall (from Dec. 2013), Neurotech Business Report revealed that "St. Jude Medical failed a futility analysis of its BROADEN trial of DBS for treatment of depression..."

A recent comment on my old post about the BROADEN Trial1 had an even more pessimistic revelation: there was only a 17.2% chance of a successful study outcome:
Regarding Anonymous' comment on January 30, 2015 11:01 AM, as follows in part:
"Second, the information that it failed FDA approval or halted by the FDA is prima facie a blatant lie and demonstratively false. St Jude, the company, withdrew the trial."

Much of this confusion could be cleared up if the study sponsors practiced more transparency.
A bit of research reveals that St. Judes' BROADEN study was discontinued after the results of a futility analysis predicted the probability of a successful study outcome to be no greater than 17.2%. (According to a letter from St. Jude)

Medtronic hasn't fared any better. Like the BROADEN study, Medtronics' VC DBS study was discontinued owing to inefficacy based on futility Analysis.

If the FDA allowed St. Jude to save face with its shareholders and withdraw the trial rather than have the FDA take official action, that's asserting semantics over substance.

If you would like to read more about the shortcomings of these major studies, please read (at least):
Deep Brain Stimulation for Treatment-resistant Depression: Systematic Review of Clinical Outcomes,
Takashi Morishita & Sarah M. Fayad &
Masa-aki Higuchi & Kelsey A. Nestor & Kelly D. Foote
The American Society for Experimental NeuroTherapeutics, Inc. 2014
DOI 10.1007/s13311-014-0282-1

The Anonymous Commenter kindly linked to a review article (Morishita et al., 2014), which indeed stated:
A multicenter, prospective, randomized trial of SCC DBS for severe, medically refractory MDD (the BROADEN study), sponsored by St. Jude Medical, was recently discontinued after the results of a futility analysis (designed to test the probability of success of the study after 75 patients reached the 6-month postoperative follow-up) statistically predicted the probability of a successful study outcome to be no greater than 17.2 % (letter from St. Jude Medical Clinical Study Management).

I (and others) had been looking far and wide for an update on the BROADEN Trial, whether in or published by the sponsors. Instead, the authors of an outside review article (who seem to be involved in DBS for movement disorders and not depression) had access to a letter from St. Jude Medical Clinical Studies.

Another large randomized controlled trial that targeted different brain structures (ventral capsule/ventral striatum, VC/VS) also failed a futility analysis (Morishita et al., 2014):
Despite the very encouraging outcomes reported in the open-label studies described above, a recent multicenter, prospective, randomized trial of VC/VS DBS for MDD sponsored by Medtronic failed to show significant improvement in the stimulation group compared with a sham stimulation group 16 weeks after implantation of the device. This study was discontinued owing to perceived futility, and while investigators remain hopeful that modifications of inclusion criteria and technique might ultimately result in demonstrable clinical benefit in some cohort of severely debilitated, medically refractory patients with MDD, no studies investigating the efficacy of VC/VS DBS for MDD are currently open.
In this case, however, the results were published (Dougherty et al., 2014):
There was no significant difference in response rates between the active (3 of 15 subjects; 20%) and control (2 of 14 subjects; 14.3%) treatment arms and no significant difference between change in Montgomery-Åsberg Depression Rating Scale scores as a continuous measure upon completion of the 16-week controlled phase of the trial. The response rates at 12, 18, and 24 months during the open-label continuation phase were 20%, 26.7%, and 23.3%, respectively.

Additional studies (with different stimulation parameters, better target localization, more stringent subject selection criteria) are needed, one would say. Self-reported outcomes from the patients themselves range from “...the side effects caused by the device were, at times, worse than the depression itself” to “I feel like I have a second chance at life.”

So where do we go now?? Here's a tip: all the forward-looking investors are into magnetic nanoparticles these days (see Magnetic 'rust' controls brain activity)...

UPDATE to the update (March 22 2015): The Vancouver Sun reported (on 3/17/2015) that the sponsor ended the trial:
A procedure that treats depression by using electrodes implanted deep in the brain won’t be available to the public soon, says the researcher who pioneered the procedure more than a decade ago with a team at the University of Toronto.

Neurologist Dr. Helen Mayberg, now at Emory University in Atlanta, said in Vancouver Tuesday that 80 per cent of her recent patients find sustained relief from severe depression after fine wires are surgically implanted to deliver electrical current to a specific part of the brain.

But a medical equipment maker halted its tests to commercialize the discovery six months after implanting devices in 125 recruits in 2013.

Data from that work has not yet been released by St. Jude Medical Inc. based in St. Paul, Minn., although a spokesman for the company said Tuesday that it will be made public. The patients still have the implanted devices and the study was not stopped for safety reasons.


1 BROADEN is an tortured acronym for BROdmann Area 25 DEep brain Neuromodulation. The target was subgenual cingulate cortex (aka BA 25). The trial was either halted by the FDA or withdrawn by the sponsor.


Dougherty DD, Rezai AR, Carpenter LL, Howland RH, Bhati MT, O'Reardon JP, Eskandar EN, Baltuch GH, Machado AD, Kondziolka D, Cusin C, Evans KC, Price LH, Jacobs K, Pandya M, Denko T, Tyrka AR, Brelje T, Deckersbach T, Kubu C, Malone DA Jr. (2014). A Randomized Sham-Controlled Trial of Deep Brain Stimulation of the Ventral Capsule/Ventral Striatum for Chronic Treatment-Resistant Depression. Biol Psychiatry Dec 13. [Epub ahead of print].

Morishita, T., Fayad, S., Higuchi, M., Nestor, K., & Foote, K. (2014). Deep Brain Stimulation for Treatment-resistant Depression: Systematic Review of Clinical Outcomes. Neurotherapeutics, 11 (3), 475-484. DOI: 10.1007/s13311-014-0282-1

DBS for MDD targets as of November 2013
(Image credit: P. HUEY/SCIENCE)

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Monday, March 09, 2015

Daylight Savings Time and "The Dress"

Could one's chronotype (degree of "morningness" vs. "eveningness") be related to your membership on Team white/gold vs. Team blue/black?

Dreaded by night owls everywhere, Daylight Savings Time forces us to get up an hour earlier. Yes, [my time to blog and] I have been living under a rock, but this evil event and an old tweet by Vaughan Bell piqued my interest in melanopsin and intrinsically photosensitive retinal ganglion cells.

I thought this was a brilliant idea, perhaps differences in melanopsin genes could contribute to differences in brightness perception. More about that in a moment.

{Everyone already knows about #thedress from Tumblr and Buzzfeed and Twitter obviously}

In the initial BuzzFeed poll, 75% saw it as white and gold, rather than the actual colors of blue and black. Facebook's more systematic research estimated this number was only 58% (and influenced by probably exposure to articles that used Photoshop). Facebook also reported differences by sex (males more b/b), age (youngsters more b/b), and interface (more b/b on computer vs. iPhone and Android).

Dr. Cedar Riener wrote two informative posts about why people might perceive the colors differently, but Dr. Bell was not satisfied with this and other explanations. Wired consulted two experts in color vision:
“Our visual system is supposed to throw away information about the illuminant and extract information about the actual reflectance,” says Jay Neitz, a neuroscientist at the University of Washington. “But I’ve studied individual differences in color vision for 30 years, and this is one of the biggest individual differences I’ve ever seen.”
“What’s happening here is your visual system is looking at this thing, and you’re trying to discount the chromatic bias of the daylight axis,” says Bevil Conway, a neuroscientist who studies color and vision at Wellesley College. “So people either discount the blue side, in which case they end up seeing white and gold, or discount the gold side, in which case they end up with blue and black.”

Finally, Dr. Conway threw out the chronotype card:
So when context varies, so will people’s visual perception. “Most people will see the blue on the white background as blue,” Conway says. “But on the black background some might see it as white.” He even speculated, perhaps jokingly, that the white-gold prejudice favors the idea of seeing the dress under strong daylight. “I bet night owls are more likely to see it as blue-black,” Conway says.

Melanopsin and Intrinsically Photosensitive Retinal Ganglion Cells

Rods and cones are the primary photoreceptors in the retina that convert light into electrical signals. The role of the third type of photoreceptor is very different. Intrinsically photosensitive retinal ganglion cells (ipRGCs) sense light without vision and:
  • ...contribute to the regulation of pupil size and other behavioral responses to ambient lighting conditions...
  • ...contribute to photic regulation of, and acute photic suppression of, release of the hormone melatonin...

Recent research suggests that ipRGCs may play more of a role in visual perception than was originally believed. As Vaughan said, melanopsin (the photopigment in ipRGCs) is involved in brightness discrimination and is most sensitive to blue light. Brown et al. (2012) found that melanopsin knockout mice showed a change in spectral sensitivity that affected brightness discrimination; the KO mice needed higher green radiance to perform the task as well as the control mice.

The figure below shows the spectra of human cone cells most sensitive to Short (S), Medium (M), and Long (L) wavelengths.

Spectral sensitivities of human cone cells, S, M, and L types. X-axis is in nm.

The peak spectral sensitivity for melanopsin photoreceptors is in the blue range. How do you isolate the role of melanopsin in humans?  Brown et al. (2012) used metamers, which are...
...light stimuli that appear indistinguishable to cones (and therefore have the same color and photopic luminance) despite having different spectral power distributions.  ... to maximize the melanopic excitation achievable with the metamer approach, we aimed to circumvent rod-based responses by working at background light levels sufficiently bright to saturate rods.

They verified their approach in mice, then used a four LED system to generate stimuli that diffed in presumed melanopsin excitation, but not S, M, or L cone excitation. All six of the human participants perceived greater brightness as melanopsin excitation increased (see Fig. 3E below). Also notice the individual differences in test radiance with the fixed 11% melanopic excitation (on the right of the graph).

Modified from Fig. 3E (Brown et al. (2012). Across six subjects, there was a strong correlation between the test radiance at equal brightness and the melanopic excitation of the reference stimulus (p < 0.001).1

Maybe Team white/gold and Team blue/black differ on this dimension? And while we're at it, is variation in melanopsin related to circadian rhythms, chronotype, even seasonal affective disorder (SAD)? 2 There is some evidence in favor of the circadian connections. Variants of the melanopsin (Opn4) gene might be related to chronotype and to SAD, which is much more common in women. Another Opn4 polymorphism may be related to pupillary light responses, which would affect light and dark adaptation. These genetic findings should be interpreted with caution, however, until replicated in larger populations.

Could This Device Hold the Key to “The Dress”?

ADDENDUM (March 10 2015): NO, according to Dr. Geoffry K. Aguirre of U. Penn.: Speaking as a guy with a 56-primary version of This Device to study melanopsin, I think the answer to your question is 'no'…” His PNAS paper, Opponent melanopsin and S-cone signals in the human pupillary light response, is freely available.3

A recent method developed by Cao, Nicandro and Barrionuevo (2015) increases the precision of isolating ipRGC function in humans. The four-primary photostimulator used by Brown et al. (2012) assumed that the rod cells were saturated at the light levels they used. However, Cao et al. (2015) warn that “a four-primary method is not sufficient when rods are functioning together with melanopsin and cones.” So they:
...introduced a new LED-based five-primary photostimulating method that can independently control the excitation of melanopsin-containing ipRGC, rod and cone photoreceptors at constant background photoreceptor excitation levels.

Fig. 2 (Cao et al., 2015). The optical layout and picture of the five-primary photostimulator.

Their Journal of Vision article is freely available, so you can read all about the methods and experimental results there (i.e., I'm not even going to try to summarize them here).

So the question remains: beyond the many perceptual influences that everyone has already discussed at length (e.g., color constancy, Bayesian priors, context, chromatic bias, etc.), could variation in ipRGC responses influence how you see “The Dress”?


1Fig 3E (continued). The effect was unrelated to any impact of melanopsin on pupil size. Subjects were asked to judge the relative brightness of three metameric stimuli (melanopic contrast −11%, 0%, and +11%) with respect to test stimuli whose spectral composition was invariant (and equivalent to the melanopsin 0% stimulus) but whose radiance changed between trials.

2 This would test Conway's quip that night owls are more likely to see the dress as blue and black.

3 Aguirre also said that a contribution from melanopsin (to the dress effect) was doubtful, at least from any phasic effect: “It's a slow signal with poor spatial resolution and subtle perceptual effects.” It remains to be seen whether any bias towards discarding blue vs. yellow illuminant information is affected by chronotype.

Interesting result from Spitschan, Jain, Brainard, & Aguirre 2014):
The opposition of the S cones is revealed in a seemingly paradoxical dilation of the pupil to greater S-cone photon capture. This surprising result is explained by the neurophysiological properties of ipRGCs found in animal studies.


Brown, T., Tsujimura, S., Allen, A., Wynne, J., Bedford, R., Vickery, G., Vugler, A., & Lucas, R. (2012). Melanopsin-Based Brightness Discrimination in Mice and Humans. Current Biology, 22 (12), 1134-1141 DOI: 10.1016/j.cub.2012.04.039

Cao, D., Nicandro, N., & Barrionuevo, P. (2015). A five-primary photostimulator suitable for studying intrinsically photosensitive retinal ganglion cell functions in humans. Journal of Vision, 15 (1), 27-27 DOI: 10.1167/15.1.27

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Thursday, February 19, 2015

One Brain Network for All Mental Illness

What do schizophrenia, bipolar disorder, major depression, addiction, obsessive compulsive disorder, and anxiety have in common? A loss of gray matter in the dorsal anterior cingulate cortex (dACC) and bilateral anterior insula, according to a recent review of the structural neuroimaging literature (Goodkind et al., 2015). These two brain regions are important for executive functions, the top-down cognitive processes that allow us to maintain goals and flexibly alter our behavior in response to changing circumstances. The authors modestly concluded they had identified a “Common Neurobiological Substrate for Mental Illness.”

One problem with this view is that the specific pattern of deficits in executive functions, and their severity, differ across these diverse psychiatric disorders. For instance, students with anxiety perform worse than controls in verbal selection tasks, while those with depression actually perform better (Snyder et al., 2014). Another problem is that gray matter volume in the dorsolateral prefrontal cortex, a key region for working memory (a core impairment in schizophrenia and to a lesser extent, in major depression and non-psychotic bipolar disorder), was oddly unaffected in the meta-analysis.

The NIMH RDoC movement (Research Domain Criteria) aims to explain the biological basis of psychiatric symptoms that cut across traditional DSM diagnostic categories. But I think some of the recent research that uses this framework may carry the approach too far (Goodkind et al., 2015):
Our findings ... provide an organizing model that emphasizes the import of shared endophenotypes across psychopathology, which is not currently an explicit component of psychiatric nosology. This transdiagnostic perspective is consistent...with newer dimensional models such as the NIMH’s RDoC Project.

However, not even the Director of NIMH believes this is true:
"The idea that these disorders share some common brain architecture and that some functions could be abnormal across so many of them is intriguing," said Thomas Insel, MD...


"I wouldn't have expected these results. I've been working under the assumption that we can use neuroimaging to help classify the different forms of mental illness," Insel said. "This makes it harder."

Anterior Cingulate and Anterior Insula and Everyone We Know

The dACC and anterior insula are ubiquitously activated 1 in human neuroimaging studies (leading Micah Allen to dub it the ‘everything’ network), and comprise either a salience network or task-set network (or even two separate cingulo-opercular systems) in resting state functional connectivity studies. But the changes reported in the newly published work were structural in nature. They were based on a meta-analysis of 193 voxel-based morphometry (VBM) studies that quantified gray matter volume across the entire brain in psychiatric patient groups, and compared this to controls.

Goodkind et al., (2015) included a handy flow chart for how they selected the papers for their review.

I could be wrong, but it looks like 34 papers were excluded because they found no differences between patients and controls. This would of course bias the results towards greater differences between patients and controls. And we don't know which of the six psychiatric diagnoses were included in the excluded batch. Was there an over-representation of null results in OCD? Anxiety? Depression?

What Does VBM Measure, Anyway?

Typically, VBM measures gray matter volume, which in the cortex is determined by surface area (which can vary due to differences in folding patterns) and by thickness (Kanai & Rees, 2011). These can be differentially related to some ability or characteristic. For example, Song et al. (2015) found that having a larger surface area in early visual cortex (V1 and V2) was correlated with better performance in a perceptual discrimination task, while larger cortical thickness was actually correlated with worse performance. Other investigators warn that volume really isn't the best measure of structural differences between patients and controls, and that cortical thickness is better (Ehrlich et al., 2012):
Cortical thickness is assumed to reflect the arrangement and density of neuronal and glial cells, synaptic spines, as well as passing axons. Postmortem studies in patients with schizophrenia showed reduced neuronal size and a decrease in interneuronal neuropil, dendritic trees, cortical afferents, and synaptic spines, while no reduction in the number of neurons or signs of gliosis could be demonstrated.
This leads us to the huge gap between dysfunction in cortical and subcortical microcircuits and gross changes in gray matter volume.

Psychiatric Disorders Are Circuit Disorders

This motto tells us that mental illnesses are disorder of neural circuits, in line with the funding priorities of NIMH and the BRAIN Initiative. But structural MRI studies tell us nothing about the types of neurons that are affected. Or how their size, shape, and synaptic connections might be altered. Basically, volume loss in dACC and anterior insula could be caused by any number of reasons, and by different mechanisms across the disorders under consideration. Goodkind et al., (2015) state:
Our connection of executive functioning to integrity of a well-established brain network that is perturbed across a broad range of psychiatric diagnoses helps ground a transdiagnostic understanding of mental illness in a context suggestive of common neural mechanisms for disease etiology and/or expression.

But actually, we might find a reduction in the density of von Economo neurons in the dACC of individuals with early-onset schizophrenia (Brüne et al., 2010), but not in persons with other disorders. Or a reduction in the density of GAD67 mRNA-expressing neurons in ACC cortical layer 5 in schizophrenia, but not in bipolar disorder. On the other hand, we could see something like an alteration in the synapses onto parvalbumin inhibitory interneurons (due to stress) that cuts across multiple diagnoses.

And it's not always the case that bigger is better: smaller cortical volumes can also be associated with better performance (Kanai & Rees, 2011).

As Kanai and Rees (2011) noted in their review:
...a direct link between microstructures and macrostructures has not been established in the human brain. A histological study directly compared whether histopathological measurements of resected temporal lobe tissue correlated with grey matter density as used in typical VBM studies. However, none of the histological measures — including neuronal density — showed a clear relationship with the grey matter volume. 

So where do we go from here? Bridging the technological gulf between exceptionally invasive methods (like optogenetics and chemogenetics in animals) and non-invasive ones (TMS, MRI in humans) is a minor funding priority of the BRAIN Initiative. Another more manageable strategy for the present would be a comprehensive review of imaging, genetic, and post-mortem neuroanatomical studies of brains from people who lived with schizophrenia, bipolar disorder, major depression, addiction, obsessive compulsive disorder, and anxiety. This has been done most extensively (perhaps) for schizophrenia (e.g., Meyer-Lindenberg, 2010; Arnsten, 2011). Certain types of electrophysiological studies in primate prefrontal cortex may provide another bridge, although this has been disputed.

Goodkind and colleagues have indeed uncovered some “biological commonalities that may have been underappreciated in prior work,” but it's also clear there are “some fairly obvious distinctions between schizophrenia and bipolar disorder” at a clinical level (to give one example). In the rush to cut up psychiatric nosology along the RDoC dotted lines, let's not forget the limitations of current methods that are designed to do the carving.

Further Reading

Other comprehensive reviews:

Large-scale brain networks and psychopathology: a unifying triple network model

Does the salience network play a cardinal role in psychosis? An emerging hypothesis of insular dysfunction

Salience processing and insular cortical function and dysfunction

Critiques of phrenology-like VBM studies:

Now Is That Gratitude?

Should Policy Makers and Financial Institutions Have Access to Billions of Brain Scans?

Anthropomorphic Neuroscience Driven by Researchers with Large TPJs

Liberals Are Conflicted and Conservatives Are Afraid

Great discussion of a failure to replicate VBM studies (at Neuroskeptic):

Failed Replications: A Reality Check for Neuroscience?


1 To quote Russ Poldrack:
In Tal Yarkoni's recent paper in Nature Methods, we found that the anterior insula was one of the most highly activated part of the brain, showing activation in nearly 1/3 of all imaging studies!
2 Links to recent J Neurosci articles via @prerana123 and @MyCousinAmygdala.


Brüne M, Schöbel A, Karau R, Benali A, Faustmann PM, Juckel G, Petrasch-Parwez E. (2010). Von Economo neuron density in the anterior cingulate cortex is reduced inearly onset schizophrenia. Acta Neuropathol. 119(6):771-8.

Ehrlich S, Brauns S, Yendiki A, Ho BC, Calhoun V, Schulz SC, Gollub RL, Sponheim SR. (2012). Associations of cortical thickness and cognition in patients with schizophrenia and healthy controls. Schizophr Bull. 38(5):1050-62.

Goodkind, M., Eickhoff, S., Oathes, D., Jiang, Y., Chang, A., Jones-Hagata, L., Ortega, B., Zaiko, Y., Roach, E., Korgaonkar, M., Grieve, S., Galatzer-Levy, I., Fox, P., & Etkin, A. (2015). Identification of a Common Neurobiological Substrate for Mental Illness. JAMA Psychiatry DOI: 10.1001/jamapsychiatry.2014.2206

Kanai, R., & Rees, G. (2011). The structural basis of inter-individual differences in human behaviour and cognition. Nature Reviews Neuroscience, 12 (4), 231-242. DOI: 10.1038/nrn3000

Song C, Schwarzkopf DS, Kanai R, Rees G. (2015). Neural population tuning links visualcortical anatomy to human visual perception. Neuron 85(3):641-56.

Snyder HR, Kaiser RH, Whisman MA, Turner AE, Guild RM, Munakata Y. (2014). Opposite effects of anxiety and depressive symptoms on executive function: the case of selecting among competing options. Cogn Emot. 28(5):893-902.

Fig. 3 (Meyer-Lindenberg, 2010). Schematic summary of putative alterations in dorsolateral prefrontal cortex circuitry in schizophrenia.

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Saturday, January 31, 2015

Against Initiatives: "don't be taken in by the boondoggle"

 ...or should I say braindoggle...

I've been reading The Future of the Brain, a collection of Essays by the World's Leading Neuroscientists edited by Gary Marcus and Jeremy Freeman. Amidst the chapters on jaw-dropping technical developments, Big Factory Science, and Grand Neuroscience Initiatives, one stood out for its contrarian stance (and personally reflective tone). Here's Professor Leah Krubitzer, who heads the Laboratory of Evolutionary Biology at University of California, Davis:

“From a personal rather than scientific standpoint, the final important thing I've learned is don't be taken in by the boondoggle, don't get caught up in technology, and be very suspicious of "initiatives." Science should be driven by questions that are generated by inquiry and in-depth analysis rather than top-down initiatives that dictate scientific directions. I have also learned to be suspicious of labels declaring this the "decade of" anything: The brain, The mind, Consciousness. There should be no time limit on discovery. Does anyone really believe we will solve these complex, nonlinear phenomena in ten years or even one hundred? Tightly bound temporal mandates can undermine the important, incremental, and seemingly small discoveries scientists make every day doing critical, basic, nonmandated research. These basic scientific discoveries have always been the foundation for clinical translation. By all means funding big questions and developing innovative techniques is worthwhile, but scientists and the science should dictate the process.”

...although it should be said that a bunch of scientists did at least contribute to the final direction taken by the BRAIN Initiative (Brain Research through Advancing Innovative NeurotechnologiesSM)...

An AS @ UVA Project
by Meagan Hess
May 2004

Top image: vintage spoof Monopoly game issued during the 1936 US presidential campaign.

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Tuesday, January 27, 2015

This Blog Is Brought to You by the Number 9 and the Letter K

The Neurocritic (the blog) began 9 years ago today.

I've enjoyed the journey immensely and look forward to the years to come, by Nodes of Ranvier (the band — not the myelin sheath gaps).

Node of Ranvier

And now a word from our sponsors,  Episode 3979 of Sesame Street...

The Number 9

The Letter k

Thank you for watching! (and reading).

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Monday, January 26, 2015

Is it necessary to use brain imaging to understand teen girls' sexual decision making?

“It is feasible to recruit and retain a cohort of female participants to perform a functional magnetic resonance imaging [fMRI] task focused on making decisions about sex, on the basis of varying levels of hypothetical sexual risk, and to complete longitudinal prospective diaries following this task. Preliminary evidence suggests that risk level differentially impacts brain activity related to sexual decision making in these women [i.e., girls aged 14-15 yrs], which may be related to past and future sexual behaviors.”

-Hensel et al. (2015)

Can the brain activity of adolescents predict whether they are likely to make risky sexual decisions in the future?  I think this is the goal of a new pilot study by researchers at Indiana University and the Kinsey Institute (Hensel et al., 2015). While I have no reason to doubt the good intentions of the project, certain aspects of it make me uncomfortable.

But first, I have a confession to make. I'm not an expert in adolescent sexual health like first author Dr. Devon Hensel. Nor do I know much about pediatrics, adolescent medicine, health risk behaviors, sexually transmitted diseases, or the epidemiology of risk, like senior author Dr. J. Dennis Fortenberry (who has over 300 publications on these topics).  His papers include titles such as Time from first intercourse to first sexually transmitted infection diagnosis among adolescent women and Sexual learning, sexual experience, and healthy adolescent sex. Clearly, these are very important topics with serious personal and public health implications. But are fMRI studies of a potentially vulnerable population the best way to address these societal problems?

The study recruited 14 adolescent girls (mean age = 14.7 yrs) from health clinics in lower- to middle-income neighborhoods. Most of the participants (12 of the 14) were African-American, most did not drink or do drugs, and most had not yet engaged in sexual activity.  However, the clinics served areas with “high rates of early childbearing and sexually transmitted infection” so the implication is that these young women are at greater risk of poor outcomes than those who live in different neighborhoods.

Detailed sexual histories were obtained from the girls upon enrollment (see below). They also kept a diary of sexual thoughts and behaviors for 30 days.

Given the sensitive nature of the information revealed by minors, it's especially important to outline the informed consent procedures and the precautions taken to protect privacy. Yes, a parent or guardian gave their approval, and the girls completed informed consent documents that were approved by the local IRB. But I wanted to see more about this in the Methods. For example, did the parent or guardian have access to their daughters' answers and/or diaries, or was that private? This could have influenced the willingness of the girls to disclose potentially embarrassing behavior or “verboten” activities (prohibited by parental mores, church teachings, legal age of consent,1 etc.). 

I don't know, maybe the standard procedures are obvious to those within the field of sexual health behavior, but they weren't to me.

Turning to more familiar territory, the experimental design for the neuroimaging study involved presentation of four different types of stimuli: (1) faces of adolescent males; (2) alcoholic beverages; (3) restaurant food; (4) household items (e.g., frying pan). My made-up examples of the stimuli are shown below.

Each picture was presented with information that indicated the item's risk level (“high” or “low”):
  • Adolescent male faces: number of previous sexual partners and typical condom use (yes/no)
  • Alcoholic beverages: number of alcohol units and whether there was a designated driver (yes/no)
  • Food: calorie content and whether the restaurant serving the food had been cited in the past year for health code violations (yes/no)
  • Household items: whether the object could be returned to the store (yes/no)

For each picture, participants rated how likely they were to: (1) have sex with the male, (2) drink the beverage, (3) eat the food, or (4) purchase the product (1 = very unlikely to 4 = very likely). There were 35 exemplars of each category, and each stimulus was presented in both “high” and “low” risk contexts. So oddly, the pizza was 100 calories and from a clean restaurant on one trial, compared to 1,000 calories and from a roach-infested dump on another trial.

The faces task was adapted from a study in adult women (Rupp et al., 2009) where the participants gave a mean likelihood rating of 2.45 for sex with low risk men vs. 1.41 for high risk men (significantly less likely for the latter). The teen girls showed the opposite result: 2.85 for low risk teen boys vs. 3.85 for high risk teen boys (significantly more likely) the “bad boy” effect?

But the actual values were quite confusing. At one point the authors say they omitted the alcohol condition: “The present study focused on the legal behaviors (e.g., sexual behavior, buying item, and eating food) in which adolescents could participate.”

But in the Fig. 1 legend, they say the opposite (that the alcohol condition was included):
Panel (A) provides the average likelihood of young women's endorsing low- and high-risk decisions in the boy, alcohol, food, and household item (control) stimulus categories.

Then they say that the low-risk male faces were rated as the most unlikely (i.e., least preferred) of all stimuli.  But Fig. 1 itself shows that the low-risk food stimuli were rated as the most unlikely...

Regardless of the precise ratings, the young women were more drawn to all stimuli when they were in the high risk condition. The authors tried to make a case for more "risky" sexual choices among participants with higher levels of overt or covert sexual reporting, but the numbers were either impossibly low (for behavior) or thought-crimes only (for dreams/fantasy). So it's really hard to see how brain activity of any sort could be diagnostic of actual behavior at this point in their lives.

And the neuroimaging results were confusing as well. First, the less desirable low-risk stimuli elicited greater responses in cognitive and emotional control regions:
Neural activity in a cognitive-affective network, including prefrontal and anterior cingulate (ACC) regions, was significantly greater during low-risk decisions.

But then, we see that the more desirable high-risk sexual stimuli elicited greater responses in cognitive/emotional control regions:
Compared with other decisions, high-risk sexual decisions elicited greater activity in the anterior cingulate, and low-risk sexual decision elicited greater activity in regions of the visual cortex. 

This pattern went in the opposite direction from what was seen in adult women (Rupp et al., 2009), and it implicated a different region of the ACC. It's difficult to draw comparisons, though, because the adult and adolescent groups diverged in age, demographic characteristics, and sexual experience.

Figure adapted from Hensel et al., 2015 (left) and Rupp et al., 2009 (right).

So is it feasible to use fMRI to understand teen girls' sexual decision making? Maybe, from the point of view of logistics and subject compliance, which is no mean feat. But is it necessary, or even informative? Certainly not, in my view. It's not clear what neuroimaging will add to the picture, beyond the participants' fully disclosed sexual histories. Finally, is it ethical to use brain imaging to understand teen girls' sexual decision making? While the future predictive value of the fMRI data is uncertain, linking a biomarker to sensitive sexual information requires extra protection, especially when from a potentially vulnerable adolescent population.


1 In the state of Indiana, it is illegal for an individual 18 years of age or older to have sex with one of the participants in the present study. So if a young women engaged in sexual activity with an 18 year old senior, he could potentially go to jail. Not that this was necessarily the case for anyone here.


Hensel, D., Hummer, T., Acrurio, L., James, T., & Fortenberry, J. (2015). Feasibility of Functional Neuroimaging to Understand Adolescent Women's Sexual Decision Making. Journal of Adolescent Health. DOI: 10.1016/j.jadohealth.2014.11.004

Rupp, H., James, T., Ketterson, E., Sengelaub, D., Janssen, E., & Heiman, J. (2009). The role of the anterior cingulate cortex in women's sexual decision making. Neuroscience Letters, 449 (1), 42-47 DOI: 10.1016/j.neulet.2008.10.083

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Sunday, January 18, 2015

Interfering With Traumatic Memories of the Boston Marathon Bombings

The Boston Marathon bombings of April 15, 2013 killed three people and injured hundreds of others near the finish line of the iconic footrace. The oldest and most prominent marathon in the world, Boston attracts over 20,000 runners and 500,000 spectators. The terrorist act shocked and traumatized and unified the city.

What should the survivors do with their traumatic memories of the event? Many with disabling post-traumatic stress disorder (PTSD) receive therapy to lessen the impact of the trauma. Should they forget completely? Is it possible to selectively “alter” or “remove” a specific memory? Studies in rodents are investigating the use of pharmacological manipulations (Otis et al., 2014) and behavioral interventions (Monfils et al., 2009) to disrupt the reconsolidation of a conditioned fear memory. Translating these interventions into clinically effective treatments in humans is an ongoing challenge.

The process of reconsolidation may provide a window to altering unwanted memories. When an old memory is retrieved, it enters a transiently labile state, when it's susceptible to change before becoming consolidated and stored again (Nader & Hardt et al., 2009). There's some evidence that the autonomic response to a conditioned fear memory can be lessened by an “updating” procedure during the reconsolidation period (Schiller et al., 2010).1 How this might apply to the recollection of personally experienced trauma memories is uncertain.

Remembering the Boston Bombings

Can you interfere with recall of a traumatic event by presenting competing information during the so-called reconsolidation window? A new study by Kredlow and Otto (2015) recruited 113 Boston University undergraduates who were in Boston on the day of the bombings. In the first testing session, participants wrote autobiographical essays recounting the details of their experience, prompted by specific questions. In principle, this procedure re-activated the traumatic memory, rendering it vulnerable to updating during the reconsolidation window (~6 hours).

The allotted time for the autobiographical essay was 4 min. After that, separate groups of subjects read either a neutral story, a negative story, or a positive story (for 5 min). The fourth group did not read a story. Presentation of a story that is not one's own would presumably “update” the personal memory of the bombings.

A second session occurred one week later. The participants were again asked to write an autobiographical essay for 4 min, under the same conditions as Session #1. They were also asked about their physical proximity to the bombings, whether they watched the marathon in person, feared for anyone's safety, and knew anyone who was injured or killed. Nineteen subjects were excluded for various reasons, leaving the final n=94.

One notable weakness is that we don't know anything about the mental health of these undergrads, except that they completed the 10 item Positive and Negative Affective Schedule (PANAS-SF) before each session. And they were “provided with mental health resources” after testing (presumably links to resources, since the study was conducted online).

In terms of proximity, 10% of the participants were within one block of the bombings (“Criterion A” stressor), placing them at risk for developing of PTSD. Most (95%) feared for someone's safety and 12% knew someone who was injured or killed (also considered Criterion A). But we don't know if anyone had a current or former PTSD diagnosis.

The authors predicted that reading the negative stories during the “autobiographical reconsolidation window” would yield the greatest reduction in episodic details recalled from Session #1 (S1) to Session #2 (S2), relative to the No-Story condition. This is because the negative story and the horrific memories are both negative in valence [although I'm not sure of what mechanism would account for this effect].2
Specifically, we hypothesized that learning a negative affective story during the reconsolidation window compared to no interference would interfere with the reconsolidation of memories of the Boston Marathon bombings. In addition, we expected the neutral and positive stories to result in some interference, but not as much as the negative story.

The essays were coded for the number of memory details recalled in S1 and S2 (by 3-5 raters3), and the main measure was the number of details recalled in S2 for each of the four conditions. Other factors taken into account were the number of words used in S1, and time between the Boston Marathon and the testing session (both of which influenced the number of details recalled).

The results are shown in Table 1 below. the authors reported comparisons between Negative Story vs. No Story (p<.05, d = 0.62), Neutral Story vs. No Story (p=.20, d = 0.39), and Positive Story vs. No Story (p=.83, d = 0.06). The effect sizes are “medium-ish” for both the Negative and Neutral comparisons, but only “significant” for Negative.

I would argue that the comparison between Negative Story vs. Neutral Story which was not reported is the only way to evaluate the valence aspect of the prediction, i.e. whether the reduction in details recalled was specific to reading a negative story vs. potentially any story. I wasn't exactly sure why they didn't do an ANOVA in the first place, either.

Nonetheless, Kredlow and Otto (2015) suggest that their study...
...represent[s] a step toward translating reconsolidation interference work to the clinic, as, to our knowledge, no published studies to date have examined nonpharmacological reconsolidation interference for clinically-relevant negative memories. Additional studies should examine reconsolidation interference paradigms, such as this one, in clinical populations.

If this work was indeed extended to clinical populations, I would suggest conducting the study under more controlled conditions (in the lab, not online), which would also allow close monitoring of any distress elicited by writing the autobiographical essay (essentially a symptom provocation design). As the authors acknowledge, it would be especially important to evaluate not only the declarative, detail-oriented aspects of the traumatic memories, but also any change in their emotional impact.

Further Reading

Brief review of memory reconsolidation

Media’s role in broadcasting acute stress following the Boston Marathon bombings

Autobiographical Memory for a Life-Threatening Airline Disaster

I Forget...


1 But this effect hasn't replicated in other studies (e.g., Golkar et al., 2012).

2 Here, the authors say:
...some degree of similarity between the original memory and interference task may be required to achieve interference effects. This is in line with research suggesting that external and internal context is an important factor in extinction learning and may also be relevant to reconsolidation. As such, activating the affective context in which a memory was originally consolidated may facilitate reconsolidation interference.
This is a very different strategy than the “updating of fear memories” approach, where a safety signal occurs before extinction. But conditioned fear (blue square paired with mild shock) is very different from episodic memories of a bombing scene.

3 Details of the coding system:
A group consensus coding system was used to code the memories. S1 and S2 memory descriptions for each participant were compared and coded for recall of memory details. One point was given for each detail from the S1 memory description that was recalled in the S2 memory description. Each memory pair was coded by between three to five raters until a consensus between three raters was reached. Raters were blind to participant randomization, but not to each other's ratings. Consensus was reached in 83% of memory pairs.


Kredlow MA, & Otto MW (2015). Interference with the reconsolidation of trauma-related memories in adults. Depression and anxiety, 32 (1), 32-7 PMID: 25585535

Monfils MH, Cowansage KK, Klann E, LeDoux JE. (2009). Extinction-reconsolidation boundaries: key to persistent attenuation of fear memories. Science 324:951-5.

Nader K, Hardt O. (2009). A single standard for memory: the case for reconsolidation. Nat Rev Neurosci. 10:224-34.

Otis JM, Werner CT, Mueller D. (2014). Noradrenergic Regulation of Fear and Drug-Associated Memory Reconsolidation. Neuropsychopharmacology. [Epub ahead of print]

Schiller D, Monfils MH, Raio CM, Johnson DC, Ledoux JE, & Phelps EA (2010). Preventing the return of fear in humans using reconsolidation update mechanisms. Nature 463: 49-53.

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