Unraveling Motivation and Attention

by Randolph S. Marshall, Career Corner Editor

A commentary on the recent Brain and Behavior article, “Effects of Motivation on Reward and Attentional Networks: an fMRI Study”, by Ivanov et al.

How does the anticipation of a reward interact with cognitive demand? This is the basic question that was asked by K-23 awardee IIllyan Ivanov. In his article just published in Brain and Behavior, Ivanov and colleagues used BOLD fMRI to examine regional brain activation in a 3-pronged experiment that pitted the motivational system against the attentional system. Both the motivation of an anticipated reward and higher levels of attention are known to speed up cognitive reaction times behaviorally, but what is the influence of the motivational system on cognitive control as a task requires more cognitive muscle? Does reward anticipation enhance performance or interfere with it?  What if there is not only promise of reward, but risk of monetary loss? These questions are important both for our understanding of systems biology, and for implications of treatment of individuals with attention deficit/hyperactivity disorder, obsessive-compulsive disorder, and drug addiction where attention and motivation may be altered.

In this study of 16 healthy adults, behavior results were as anticipated: shorter reaction times were seen with reward anticipation, particularly with the easier, “congruent” task trials. The imaging results confirmed that attentional network regions (right ACC, right primary motor cortex, supplemental motor and somatosensory association cortices bilaterally, right middle frontal gyrus and right thalamus) activated more during the higher cognitive demands of the non-congruent trials whereas key components of the motivational network (bilateral insula and ventral striatum) engaged with the unique “surprising non-reward” component of the task. Furthermore, the interaction effects showed that cognitive conflict elicited greater activation, but only in the absence of reward incentives – as if subjects worked harder to avoid possible loss. Conversely, reward anticipation decreased activity in the attentional networks possibly due to improved information processing.

Surprisingly, the more difficult task components decreased activity in the striatum and the orbito-frontal cortex suggesting that harder trails may have been experienced as less rewarding. These results were interpreted as showing that in the context of a difficult task one can maximize performance through both increasing efforts to obtain rewards on easier trials and committing more attentional effort to avoid punishment and losses during more difficult trials. The authors conclude that there is not a direct correlation between motivational incentives and improvement of performance, but that their interplay will highly depend on the context.

I interviewed Dr. Ivanov about his experiment, and asked him to talk about the process of beginning his career in clinical neuroscience. Dr. Ivanov is currently Assistant Professor in Child Psychiatry at Mt. Sinai Medical Center in New York. He completed a K-23/R02 sponsored by grant in 2010, sponsored by NIDA/AACAP and now is completing his work on an R03 to study the effects of motivation and attention in more depth.

Marshall:  What was the most interesting finding for you in this study?

Ivanov: The interaction effect, which suggested that incentives may boost information processing but can also be a distractor and possibly hamper performance on cognitive tasks. This is interesting because new studies suggest that if you have strong stimuli (e.g. a drug like methylphenidate) this interaction effect may be reversed as we hope to show in a follow up study.

Marshall: Was clinical relevance an important motivator for you in pursuing this project, or were you more interested in the systems biology aspect?

Ivanov: I would say both. As a clinician I was interested in the main idea which was whether we could tap into risk factors that would help us understand the motivational and attentional systems. I wanted to know if there is a biological signature or hallmark for what treatment might be helpful in children at risk for later substance abuse.

Marshall: How important was mentorship in the design and implementation of this work?

Ivanov: Crucial, especially with neuroimaging.  The amount of time and the amount of knowledge needed was very high.  I had both inside and outside mentors. I studied with the Director of Child Psychiatry at Mt. Sinai, Jeffrey Newcorn, and with outside mentors also, which turned out to be a very good thing. I worked with Tom Crowley, an adult psychiatrist at Denver, and Edith London from UCLA, who was a mentor for my K-23.  I also went to the Wellcome Trust Centre for Neuroimaging in London a couple of times to work with Karl Friston. Through this process what you find is that you accumulate a group of people around the country or the world who you can then count on later for advice and support.

Marshall: What was the hardest part about getting this project done?

Ivanov: I didn’t know much about neuroimaging when I started. I was naïve about the time needed to complete a neuroimaging study in young children. It’s not like clinical work, in which we get used to working quickly.  Getting used to working in that scientific environment is different.  It is also very demanding moving humans into human research, particularly youths. You have to work with kids and family through the whole process.  Children have their natural curiosity, but entering the fMRI scanner is not an everyday experience and they can be fearful – having a skilled research team is crucial.

Marshall: What is the next hypothesis to test? Is it a direct follow up of this project or will you work on a parallel project?

Ivanov: We may be able to set up a treatment trial. We want to ask, do you see clinical   subgroups with particular biological signatures that might optimize our treatments for high risk groups.

Marshall: What advice would you give a young investigator looking to get a first K-award or similar grant funded?

Ivanov: Get a good mentor. A good mentor will help flesh out your ideas.  Also, you have to find an area you are really interested in and feel really passionate about.  And when you start thinking about the process, don’t have the goal right away of producing the paper that will turn science around.  Concentrate on learning, increasing your background knowledge, and developing your network. The best outcome for the K is to develop the confidence and skills that will let you succeed in the future.

Exploring underlying mechanisms of cranial electrotherapy stimulation

by Randolph S. Marshall, Career Corner Editor

A commentary on the recent Brain and Behavior article, “Effects of crainal electrotherapy stimulation on resting state brain activity”, by Feusner et. al.

This interesting article by Dr. Feusner (K23 recipient) and colleagues addresses a perennial problem in neuroscience – how to verify the scientific validity of an empirically proven therapy. Feusner et. al. set out to explain the underlying mechanism of cranial electrotherapy stimulation (CES), a long-standing empirical treatment for mood alteration which received FDA approval in 1979. CES has been used for a variety of indications including anxiety, insomnia, depression, and pain, but without clear physiological explanation of its effect. Although several studies have reported beneficial results, it has remained unclear how subsensory alternating current delivered to the earlobes at .5 or 100Hz can alter behavioral symptoms. Preclinical work had shown effects of CES on slowing of alpha waves on EEG in monkeys, associated with reduced adverse reactions to stressful stimuli, but it was unclear whether changes in brain waves were a cause or an effect of improved clinical states. Given uncertainty about mechanisms, the authors proposed to look at effects of CES on brain activity by delivering CES to healthy control subjects while in an fMRI scanner.

One of the strengths of this study, and a general principle for successful investigations of this kind, was the generation of plausible a priori hypotheses based on other studies. Clear statement of hypotheses such as the following establish the scientific context of the study, and let the reader know what to look for in interpreting the results. The hypotheses were:

1. CES would cause a general deactivation in cortical and thalamic regions because of prior evidence that the stimulation reduces alpha power on EEG.
2. CES would produce alteration in connectivity networks such as default mode network (DMN) because CES 100Hz affects the EEG beta band, which correlates most highly with the DMN.
3. CES would alter other connectivity networks such as the dorsal fronto-parietal network (FPN) because there was clinical evidence of CES affect on attention, and the sensorimotor network (SMN) because of clinical evidence for CES effect on pain.

Although there was no randomization, the study design was to use baseline (“off”) periods compared to “on” periods during stimulation. Subject blinding was done by forced choice testing prior to scanning to ensure that participants couldn’t tell if the CES machine was on or off. Subjects had no knowledge of status while in the scanner, and thus there would be no behavior confounder for changes in BOLD fMRI activity.

The authors found that CES correlated with a decreased activation in several brain regions – bilateral SMA, right supramarginal gyrus, right superior parietal and left superior frontal. No increases in regional brain activation were found. The connectivity results demonstrated that the 100Hz stimulation altered the DMN. The FPN and SMN showed no change with CES, and only the higher frequency stimulation produced alterations in connectivity.

Based on these results, the authors concluded that the study demonstrated positive proof that there is a biological effect of CES. The electrical current was proposed to reach the cortex where it would disrupt brain oscillation patterns. The reduction in BOLD activity in several brain regions was consistent with previous EEG studies demonstrating reduction in alpha frequency signal. The altered connectivity for 100 Hz and not .5 Hz was consistent with knowledge that 100Hz frequency affects the beta band, which correlates with DMN activity.  The authors closed the discussion with several unanswered questions, including how the CES alteration of brain activation and connectivity translates to clinical effect, and whether the fMRI BOLD effects they observed would be similar in subjects who had a depression, anxiety, insomnia, or pain.

Comment:

This was an excellent study from the perspective of initiating a line of inquiry. Aside from the results themselves helping to advance our understanding of the effects of external electrical stimulation on brain activity, this is a strategic line of inquiry from a career perspective. Studies such as these are both hypothesis-testing and hypothesis-generating. What better way to justify your next study or grant than to generate an interesting question by answering the one at hand? High-level neuroscience journals are rife with this type of investigation. In addition, from a grants perspective, early phase clinical trial proposals are now expected to include a physiological “surrogate marker” of efficacy. Developing an imaging method to assess underlying mechanisms of clinical phenomena can therefore be a fruitful line of investigation. Congratulations to Dr. Feusner et al, and good luck with your next study!

Questions for discussion:

1. Did the results as reported in the paper have ecological validity, i.e. seem plausible, interesting, and relevant to clinical practice? What additional studies would need to be done to convince you that the results are valid? Which hypotheses were left unproven and why?
2. What weaknesses can be identified in the study design? What are the strengths/advantages of this design?
3. What is the next hypothesis to test? How would the next study be best designed?

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