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Incidental Findings in Functional Imaging: A View from Psychology and Neuroscience

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Incidental Findings in Functional Imaging: A View from Psychology and Neuroscience. (Or, what concerns PhD scientists doing fMRI research?). Kevin Ochsner S ocial C ognitive N euroscience Lab Columbia University. Roadmap. What we study kinds of questions we ask kinds of data we collect

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slide1
Incidental Findings in Functional Imaging:

A View from Psychology and Neuroscience

(Or, what concerns PhD scientists doing fMRI research?)

Kevin Ochsner

Social Cognitive Neuroscience Lab

Columbia University

slide2
Roadmap
  • What we study
    • kinds of questions we ask
    • kinds of data we collect
  • Two kinds of incidental findings
    • neural
    • behavior
  • Policy issues and implications
slide3
Learning, Memory and Decision-making

Emotion +

Self - Control

Nonverbal Behavior

Attitudes + Intergroup Relations

slide4
Address questions by linking data collected at multiple levels of analysis.....

Level

Data

age, race, beliefs, moods, personality, or other individual differences (questionnaires)

Person level descriptors

specific behaviors

(computerized tasks)

Social/Cognitive/Affective Performance

Infer their operation

Psychological Processes

None

Brain activity or structure

(fMRI, MRI or other

imaging techniques)

Neural Systems

slide5
Example: Performance Level
  • Cognitive Reappraisal
  • Rethink the meaning of an event or action

Instruction

Think about image in way that makes you feel less negative….

Reappraisal

“He’s just tired/annoyed, is hearty, will be right as rain….”

slide7
Example: Neural Level

Turn on regions involved in higher cognition

Prefrontal Increases

Turn off regions involved in generating emotion

Amygdala Decreases

slide8
Example: Psychological Level

Turn on regions involved in higher cognition

Prefrontal Increases

Reappraisal involves specific linguistic, memory and inhibitory processes

Turn off regions involved in generating emotion

Amygdala Decreases

Reappraisal modulates specific affect-generating processes

slide9
Roadmap
  • What we study
    • kinds of questions we ask
    • kinds of data we collect
  • Two kinds of incidental findings
    • neural
    • behavioral
  • Policy issues and implications
slide10
Neural incidental findings
  • IFs found in structural images of the brain
    • during recruitment/screening
      • participants may indicate they, “want a diagnosis”
      • researcher/consent indicates that scans are not medically diagnostic and researchers are not clinicians
    • during data collection and analysis
      • participants may ask if scans look normal/OK
      • may request and/or typically be given brain picture
      • how/who identifies potential IFs?
      • strong preference for mandatory reads by M.D.
    • post-discovery
      • when found, PhDs are neither qualified to - nor comfortable with - communicating IFs to participants
      • strong preference for policies allowing/requiring communication via M.D. (e.g. neuroradiologist)
slide11
Example
  • How does this play out in actual practice?
    • Highlight anticipated vs. unanticipated issues that arise
    • Case study from CU MRI Center:
      • ~2 weeks after data collection, PI notices potential IF
      • PI’s protocol stipulated that reads not mandatory
      • If technician/researcher notes potential IF, a radiologist would read scan & contact participant as needed
      • Contacts Center, which has changed directorship
      • The stipulated M.D. no longer associated with center
      • Told by interim director that reporting process being revised
      • ~2 weeks later told to contact participant
      • Because participant did not give phone #, PI emailed to arrange phone conversation
slide12
Example
  • How does this play out in actual practice?
    • Cont’d......
      • Participant responds, but does not give phone number
      • 2 days later PI informs IRB, who stipulates PI has violated protocol and should wait til IRB reviews matter
      • 2 days later IRB indicates that PI should have an M.D. contact the participant, in accord with protocol
      • PI seeks Center’s assistance in securing new M.D., which takes ~3 weeks (summer! busy doctors!)
      • Participant responds to PI’s emails indicating he/she has moved across the country
      • Same day M.D. contacts participant who follows up with personal physician
slide13
Example
  • What this highlights
    • Anticipated: Plan was in place for dealing with IFs
    • Unanticipated: Change in Center directorship/policy; confusion about reading scan and contacting participant
    • Differences in philosophy
      • Prior director was PhD who had relationship with radiologist to read scans & make contact
      • New/Interim director followed M.D. model of PI’s making contact directly
    • Do differently?
      • Contact IRB before making contact with participant
      • Need for PIs, imaging centers and IRBsto have general plans in place for unanticipated circumstances
      • Minimize problems with standardized, blanket policies
slide14
Behavioral incidental findings
  • Behavioral IFs from self-reports or observations
    • Important to consider because many, if not all, of our type of studies collect both behavioral and neural data that could be sources of potential IFs
    • Behavioral IF typically include responses/actions indicating serious psychological or physical distress or potential for harm/self-harm
    • When anticipated: protocols can require concurrent screening of responses and offer referrals to appropriate evaluation/counseling
    • When unanticipated: what should a PI and IRB do when a finding arises and the protocol did not include an appropriate action plan?
slide15
Example
  • How does this play out in actual practice?
    • Highlight anticipated vs. unanticipated issues that arise
    • Case study from CU MRI Center:
      • A few weeks after data collection, PI’s research team notices what they believe could be a potential behavioral IF
slide16
Beck Depression Inventory, BDI

(0-9 = none; 9-18 = mild; 19-29 = moderate; 30-63 = severe)

slide17
Example
  • How does this play out in actual practice?
    • Highlight anticipated vs. unanticipated issues that arise
    • Case study from CU MRI Center:
      • A few weeks after data collection, PI’s research team notices what they believe could be a potential behavioral IF
  • A high score on a depression inventory, and in particular an item indicating suicidal ideation
  • 2. PI’s protocol does not stipulate any policy for dealing with behavioral IFs
    • What should PI do?
slide18
Example
  • What this highlights
    • Importance of considering IF policies for behavioral - not just neural – data – esp. when many labs collect both types of data simultaneously
      • Not always appreciated (my lab, e.g., has plans in place for some but not all protocols)
      • Many measures are collected, and IFs on each one are hard to quantify, so policy harder to set
slide19
Roadmap
  • What we study
    • kinds of questions we ask
    • kinds of data we collect
  • Two kinds of incidental findings
    • neural
    • behavioral
  • Policy issues and implications
slide20
Policy
  • Concluding thoughts
    • General policies are useful for reducing confusion in the face of both anticipate and unanticipated events
    • Should indicate whether scans are read, by whom, and how communication to participants takes place
    • Communication btw. PIs, IRBs and imaging centers is essential
    • Standing operating committees with M.D.s and Ph.D.s to set std policy/protocol, esp. for new events as they arise
    • May be worthwhile to consider policies on specific types of behavioral IFS as well
    • Can be valuable to ask basic scientists for their input
      • Helps identify new issues
      • Bears on how scientists understand how to implement protocols
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