REDUCING THE NEGATIVE ATTENTIONAL BIAS IN...
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REDUCING THE NEGATIVE ATTENTIONAL BIAS IN DEPRESSION WITH CLOSED-LOOP REAL-TIME fMRI NEUROFEEDBACK TRAINING Introduction 2 s VISIT 1: Pre NF ✍ VISIT 2: NF 1 % VISIT 3: NF 2 VISIT 4: NF 3 % VISIT 5: Post NF ✍ VISIT 6: 1M FU ✍ VISIT 7: 3M FU ✍ Study design Anne C. Mennen 1 , Nicholas B. Turk-Browne 2 , Darsol Seok 3 , Megan T. deBettencourt 4 , Kenneth A. Norman 1,5 , Yvette I. Sheline 3 1 Princeton Neuroscience Institute, Princeton University, 2 Department of Psychology, Yale University, 3 Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 4 Institute for Mind and Biology, University of Chicago, 5 Department of Psychology, Princeton University Preliminary results: NF transfer to other measures Conclusions and future directions Funded by NIH Training Grant T32MH065214 to A.C.M., Intel Corporation, & University of Pennsylvania Endowment Contact: [email protected] Poster #41 Depressed individuals are biased to attend to negative stimuli [1,2], which has inspired attention training research aimed at improving depressive symptoms. However, meta- analyses of behavioral training paradigms reveal mixed efficacies [3-5]. More recently, research has suggested that the negative bias in depression is caused by a problem with disinhibiting negative information [6].To address this issue, we use a closed-loop real-time fMRI task [7] to train sustained attention by forcing subjects to pull themselves out of negative states. Additional tasks were administered before and after neurofeedback to understand how changes in neurofeedback related to other clinical, neural, and behavioral measures. What differences do we see between depressed and control subjects before neurofeedback? Can we improve depression severity by training depressed subjects to get themselves out of negative states? Do the improvements in neurofeedback relate to improvements in other domains? Over the course of training, depressed subjects improved in terms of depression severity, amygdala reactivity to negative faces, and negative stickiness during neurofeedback. Additionally, the improvement during neurofeedback was related to the improvement in depression and amygdala reactivity. Future analyses will focus on other behavioral and neural estimates (e.g., resting state, eye-tracking etc.), and analyzing potential links to NF and severity improvement. Data collection is still ongoing, as we aim to collect 16 subjects per group. References: [1] Gotlib, Ian H., et al. 2004. “Attentional Biases for Negative Interpersonal Stimuli in Clinical Depression.” Journal of Abnormal Psychology 113 (1): 127–35. [2] Mogg, Karin, and Brendan P. Bradley. 2005. “Attentional Bias in Generalized Anxiety Disorder Versus Depressive Disorder.” Cognitive Therapy and Research 29 (1): 29–45. [3] Cristea, Ioana A., et al. 2015. “Efficacy of Cognitive Bias Modification Interventions in Anxiety and Depression: Meta-Analysis.” The British Journal of Psychiatry: The Journal of Mental Science 206 (1): 7–16. [4] Hallion, Lauren S., and Ayelet Meron Ruscio. 2011. “A Meta-Analysis of the Effect of Cognitive Bias Modification on Anxiety and Depression.” Psychological Bulletin 137 (6): 940–58. [5] Jones, Emma B., and Louise Sharpe. 2017. “Cognitive Bias Modification: A Review of Meta-Analyses.” Journal of Affective Disorders 223 (December): 175–83. [6] Mennen, A. C., et al. 2019. “Attentional bias in depression: understanding mechanisms to improve training and treatment.” Current Opinion in Psychology 29: 266- 273. [7] deBettencourt, Megan T., et al. 2015. “Closed-Loop Training of Attention with Real-Time Brain Imaging.” Nature Neuroscience 18 (3): 1–9. [8] Kellough, Jennifer L., et al. 2008. “Time Course of Selective Attention in Clinically Depressed Young Adults: An Eye Tracking Study.” Behaviour Research and Therapy 46: 1238–43. [9] Chai, Xiaoqian J., et al. 2015. “Functional and Structural Brain Correlates of Risk for Major Depression in Children with Familial Depression.” NeuroImage Clinical 8 (May): 398–407. [10] Schnyer, David M., et al. 2012. “Neurocognitive Therapeutics: From Concept to Application in the Treatment of Negative Attention Bias.” Biology of Mood & Anxiety Disorders 5 (1). https://doi.org/10.1186/s13587-015-0016-y. GROUP 1: healthy control (HC) n=11 GROUP 2: depressed (MDD) n=14 Task Clinical assessments Gaze task [8] Go/no go attention task [7] fMRI – resting state fMRI – Face- matching [9] fMRI – real-time neurofeedback [7,10] Symbol ✍ % attend face attend scene neutral stimuli emotional stimuli 1 3 4 2 3 4 2 1 Press for indoor; DON’T press for outdoor 10% catch trials Go/no go task Stimulus conditions x 8 blocks/run 1 2 2 1 4 4 4 4 Outside of scanner: all conditions randomized Neurofeedback neutral S/F model training negative faces for NF x 50 trials/block indoor scenes ● 1 s 1 s 1 s Press L or R to match the face on the top 3 s 3 s x 6 trials/block objects negative Face-matching task x 3 reps/run neutral positive Look anywhere on the screen + 30 s 1 s 30 s x 20 trials Gaze task Data preprocessed with fMRIPrep, FreeSurfer, & AFNI ROI: left amygdala (LA) Measurement: beta values during negative face blocks correct sustained attention to neutral scene negative stickiness to negative face Bin classification Closed-loop neurofeedback Example neurofeedback: scene – face decoded classification opacity (t)à brain (t+2) classification (t)à opacity (t+1) scene – face evidence as discrete states NF 1: largest difference is in most negative state NF 3: largest difference is in most correct state MDD > HC HC > MDD conditional probability: given the classification state at t, what is the state 5 s later? Quantifying neurofeedback performance Face-matching task: group differences in amygdala reactivity to negative faces HC subjects improve correct sustained attention MDD subjects become less stuck in negative state Depression severity decreases over time Pre NF: MDD shows increased reaction to negative faces over block Post NF: no group difference Improvement in neurofeedback is related to change in amygdala reactivity How does neurofeedback performance relate to response to negative faces in MDD and HC? Combine across runs & subjects to compare groups over time Improvement in NF negative stickiness is positively related to improvement in depression
Transcript of REDUCING THE NEGATIVE ATTENTIONAL BIAS IN...
REDUCING THE NEGATIVE ATTENTIONAL BIAS IN DEPRESSION WITH CLOSED-LOOP REAL-TIME fMRI NEUROFEEDBACK TRAINING
Introduction
2 s
VISIT 1:Pre NF✍👀🛑
VISIT 2:NF 1💭%📈
VISIT 3:NF 2👀🛑📈
VISIT 4:NF 3📈%💭
VISIT 5:Post NF✍👀🛑
VISIT 6:1M FU✍👀🛑
VISIT 7:3M FU✍
Study design
Anne C. Mennen1, Nicholas B. Turk-Browne2, Darsol Seok3, Megan T. deBettencourt4, Kenneth A. Norman1,5, Yvette I. Sheline31Princeton Neuroscience Institute, Princeton University, 2Department of Psychology, Yale University, 3Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 4Institute for Mind and Biology, University of Chicago, 5Department of Psychology, Princeton University
Preliminary results: NF transfer to other measures Conclusions and future directions
Funded by NIH Training Grant T32MH065214 to A.C.M., Intel Corporation, & University of Pennsylvania Endowment
Contact: [email protected] #41
Depressed individuals are biased to attend to negative stimuli [1,2], which has inspired attention training research aimed at improving depressive symptoms. However, meta-analyses of behavioral training paradigms reveal mixed efficacies [3-5]. More recently, research has suggested that the negative bias in depression is caused by a problem with disinhibiting negative information [6].To address this issue, we use a closed-loop real-time fMRI task [7] to train sustained attention by forcing subjects to pull themselves out of negative states. Additional tasks were administered before and after neurofeedback to understand how changes in neurofeedback related to other clinical, neural, and behavioral measures.
What differences do we see between depressed and control subjects before neurofeedback?
Can we improve depression severity by training depressed subjects to get themselves out of negative states?
Do the improvements in neurofeedback relate to improvements in other domains?
Over the course of training, depressed subjects improved in terms of depression severity, amygdala reactivity to negative faces, and negative stickiness during neurofeedback. Additionally, the improvement during neurofeedback was related to the improvement in depression and amygdala reactivity.
Future analyses will focus on other behavioral and neural estimates (e.g., resting state, eye-tracking etc.), and analyzing potential links to NF and severity improvement.
Data collection is still ongoing, as we aim to collect 16 subjects per group.References: [1] Gotlib, Ian H., et al. 2004. “Attentional Biases for Negative Interpersonal Stimuli in Clinical Depression.” Journal ofAbnormal Psychology 113 (1): 127–35. [2] Mogg, Karin, and Brendan P. Bradley. 2005. “Attentional Bias in Generalized Anxiety DisorderVersus Depressive Disorder.” Cognitive Therapy and Research 29 (1): 29–45. [3] Cristea, Ioana A., et al. 2015. “Efficacy of Cognitive BiasModification Interventions in Anxiety and Depression: Meta-Analysis.” The British Journal of Psychiatry: The Journal of Mental Science206 (1): 7–16. [4] Hallion, Lauren S., and Ayelet Meron Ruscio. 2011. “A Meta-Analysis of the Effect of Cognitive Bias Modification onAnxiety and Depression.” Psychological Bulletin 137 (6): 940–58. [5] Jones, Emma B., and Louise Sharpe. 2017. “Cognitive BiasModification: A Review of Meta-Analyses.” Journal of Affective Disorders 223 (December): 175–83. [6] Mennen, A. C., et al. 2019.“Attentional bias in depression: understanding mechanisms to improve training and treatment.” Current Opinion in Psychology 29: 266-273. [7] deBettencourt, Megan T., et al. 2015. “Closed-Loop Training of Attention with Real-Time Brain Imaging.” Nature Neuroscience18 (3): 1–9. [8] Kellough, Jennifer L., et al. 2008. “Time Course of Selective Attention in Clinically Depressed Young Adults: An EyeTracking Study.” Behaviour Research and Therapy 46: 1238–43. [9] Chai, Xiaoqian J., et al. 2015. “Functional and Structural BrainCorrelates of Risk for Major Depression in Children with Familial Depression.” NeuroImage Clinical 8 (May): 398–407. [10] Schnyer,David M., et al. 2012. “Neurocognitive Therapeutics: From Concept to Application in the Treatment of Negative Attention Bias.” Biologyof Mood & Anxiety Disorders 5 (1). https://doi.org/10.1186/s13587-015-0016-y.
GROUP 1:healthy control
(HC) n=11
GROUP 2:depressed
(MDD)n=14
Task Clinical assessments
Gaze task [8] Go/no go attention task [7]
fMRI –resting state
fMRI –Face-
matching [9]
fMRI –real-time
neurofeedback [7,10]
Symbol ✍ 👀 🛑 💭 % 📈
attend face
attend scene
neut
ral
stim
uli
emot
iona
l st
imul
i
1 3 4 2 3 4 2 1
Pressforindoor;DON’Tpressforoutdoor
10% catch trials
Go/no go task
Stimulus conditions x 8 blocks/run
1 2 2 1 4 4 4 4
Outside of scanner: all conditions randomized
Neurofeedbackneutral S/F model training negative faces for NF
x 50 trials/block
indoor scenes ●
1 s 1 s 1 s
PressLorRtomatchthefaceonthetop
3 s 3 s x 6 trials/block
objects negative
Face-matching task
x 3 reps/runneutral positive
Lookanywhereonthescreen
+30 s 1 s 30 s x 20 trials
Gaze task
Data preprocessed with fMRIPrep, FreeSurfer, & AFNIROI: left amygdala (LA)Measurement: beta values during negative face blocks
correct sustained attention to neutral scene
negative stickiness to negative face
Bin classification
Closed-loop neurofeedbackExample
neurofeedback:
scene – face decoded
classification
opac
ity (t
)àbr
ain (t
+2) classification (t)à
opacity (t+1)
scene – face evidence as discrete states
NF 1: largest difference is in most negative state
NF 3: largest difference isin most correct state MDD > HC
HC > MDD
conditional probability: given the classification
state at t, what is the state 5 s later?
Quantifying neurofeedback performance
Face-matching task: group differences in amygdala reactivity to negative faces
HC subjects improve correct sustained attention
MDD subjects become less stuck in negative state
Depression severity decreases over time
PreNF:MDDshowsincreasedreactiontonegativefacesoverblock
PostNF:nogroupdifference
Improvement in neurofeedback is related to change in amygdala reactivity
How does neurofeedback performance relate to response to negative faces in MDD and HC?
Combine across runs &
subjects to compare groups
over time
Improvement in NF negative stickiness is positively related to improvement in depression
