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Reverse inference problem

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This is a talk slide presented at Japan Neuroscience Meeting in 2017.

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Reverse inference problem

  1. 1. Introduction: Revisiting reverse inference problem in functional MRI J. Chikazoe National Institute for Physiological Sciences
  2. 2. Annual Meeting of the Japan Neuroscience Society Disclosure of Conflict of Interest Name of first author: Junichi Chikazoe I have no COI with regard to the presentation.
  3. 3. Forward and reverse inference Forward inference Reverse inference Mental state induced by task (e.g. inhibition vs. control) P(A|M) A: Brain activity M: Mental state P(M|A) Mental state
  4. 4. Informal Reverse inference examples Conclusion: Participants may feel disgust because go/no-go task is too demanding. Response inhibition (task: go/no-go) The strongest activation in the insula Disgust (Wright et al., 2004)
  5. 5. Informal Reverse inference examples Conclusion: Participants may have to sustain task rule during the performance of go/no-go task. Response inhibition (task: go/no-go) The strongest activation in the insula Working memory (Engström et al., 2015)
  6. 6. Informal Reverse inference examples Conclusion: Participants may have ‘sweet’ feelings because successful responses in a difficult task can be taken as reward. Response inhibition (task: go/no-go) The strongest activation in the insula Gustation (Sweet) (Chikazoe et al., in preparation)
  7. 7. Informal Reverse inference examples Conclusion: Participants may have ‘sour’ feelings because… Response inhibition (task: go/no-go) The strongest activation in the insula Gustation (Sour) (Chikazoe et al., in preparation)
  8. 8. What is the issue of reverse inference? Bayes formula: P(M|A) = P(A|M) x P(M) + P(A|~M) x P(~M) P(A|M) x P(M) M: Mental process A: Brain activation
  9. 9. What is the issue of reverse inference? Bayes formula: P(M|A) = P(A|M) x P(M) + P(A|~M) x P(~M) P(A|M) x P(M) M: Mental process A: Activation estimated by arbitrarily picking up previous studies
  10. 10. What is the issue of reverse inference? Bayes formula: P(M|A) = P(A|M) x P(M) + P(A|~M) x P(~M) P(A|M) x P(M) M: Mental process A: Activation estimated by arbitrarily picking up previous studies estimated by our belief
  11. 11. Toward formal reverse inference What should we do?
  12. 12. Importance of pattern analysis for exploring shared neural correlates across modalities J. Chikazoe National Institute for Physiological Sciences
  13. 13. Cognitive components and their neural correlates Cognitive components Associated brain regions Decision making Posterior IPL Anterior MPFC PCC TPJ Self-referential processing Memory Multiple-to-multiple correspondence is observed.
  14. 14. Multiple functions in the same region -Most of cognitive functions may require multiple brain regions.  ( cf. connectionism )
  15. 15. Multiple functions in the same region -Most of cognitive functions may require multiple brain regions.  ( cf. connectionism ) →Global activation patterns may differ across functions.
  16. 16. Multiple functions in the same region -Most of cognitive functions may require multiple brain regions.  ( cf. connectionism ) →Global activation patterns may differ across functions. -The same region may have the similar computational processes but each neuron in that region may be assigned to different functions.
  17. 17. Multiple functions in the same region -Most of cognitive functions may require multiple brain regions.  ( cf. connectionism ) →Global activation patterns may differ across functions. -The same region may have the similar computational processes but each neuron in that region may be assigned to different functions. →Local activation patterns may differ across functions.
  18. 18. Multiple functions in the same region -Most of cognitive functions may require multiple brain regions.  ( cf. connectionism ) →Global activation patterns may differ across functions. -The same region may have the similar computational processes but each neuron in that region may be assigned to different functions. →Local activation patterns may differ across functions. -The same neural correlates may be shared across functions.
  19. 19. Multiple functions in the same region -Most of cognitive functions may require multiple brain regions.  ( cf. connectionism ) →Global activation patterns may differ across functions. -The same region may have the similar computational processes but each neuron in that region may be assigned to different functions. →Local activation patterns may differ across functions. -The same neural correlates may be shared across functions.
  20. 20. Positivity and negativity Kringelbach and Rolls 2004 A meta-analysis demonstrated that positive value is represented in the medial OFC, while negative value is represented in the lateral OFC.
  21. 21. Contradicting evidence from monkey electrophysiological studies Positivity- and negativity-sensitive neurons are interspersed in the OFC. (Morrison et al., 2009)
  22. 22. Averaged brain activity does not have sufficient specificity FMRI data showed overlap between the positivity- and negativity-sensitive regions. (Chikazoe et al., 2014)OverlapPositive Negative
  23. 23. Global or local activation patterns Global activation patterns Positivity Negativity
  24. 24. Global or local activation patterns Global activation patterns Local activation patterns Positivity Negativity
  25. 25. Neurosynth (created by Dr. Yarkoni) Meta-analysis P(pain|activation) Automated coordinate extraction Related studiesTerm-based search ‘Pain’ Yarkoni et al., 2011, Nature Methods P(M|A) = P(A|M) x P(M) + P(A|~M) x P(~M) P(A|M) x P(M) estimated, based on almost all fMRI studies set to 0.5 (uninformative prior)
  26. 26. Global activation patterns Positivity-related activity Positive Negativity-related activity Negative 0.13 0.07
  27. 27. Global activation patterns Positivity-related activity Positive Negativity-related activity Negative 0.13 0.070.05 0.08
  28. 28. Representational similarity analysis Local activation pattern analysis revealed that positivity and negativity could be discriminated. vectorize Creating RSM Local activation patterns Trial k Trial l Trial-by-trial correlation Value representational similarity matrix Value PosNeg Neu Neg Pos Neu Value SimilarDissimilar
  29. 29. Bayesian regression analysis H0 : β3 = 0 vs. H1 : β3 > 0 (H0 corresponds to ‘no relationship between neural and valence representations’) (Chikazoe et al., 2014)
  30. 30. Bayes factor estimation Representational similarity matrix BF10 (univariate) BF10 (pattern) Visual X Visual Visual X Gustatory Gustatory X Gustatory <.01 <.01 <.01 19 (Decisive for null) (Decisive for null) (Decisive for null) (Strong for H1) (Strong for H1)(Strong for H1) 39>100 Visual value Gustatory value Gustatory value Visualvalue Visualvalue Pos PosNeg Neg Neu Neu Gustatoryvalue Pos Neg Neu Pos PosNeg Neg Neu Neu PosNeg Neu
  31. 31. Summary -Global and local activation patterns were useful for formal reverse inference. -Shared neural correlates should satisfy cross-condition correspondence as well as within-condition correspondence.
  32. 32. Acknowledgements Cornell University Dr. Adam Anderson Dr. Eve de Rosa Ross Makello Columbia University Dr. Nikolaus Kriegeskorte National Institute for Physiological Sciences Dr. Norihiro Sadato Takaaki Yoshimoto Dr. Balbir Awana Ryutaro Uchiyama Funded by the Imaging Science Project of the Center for Novel Science Initiatives (CNSI)(# IS281004)
  33. 33. Decoding as a special case of reverse inference Bayes formula: P(M|A) = P(A|M∩Task) x P(M|Task) + P(A|~M ∩Task) x P(~M|Task) P(A|M∩Task) x P(M|Task) M: Mental process A: Activation pattern (Hutzler 2014) For “decoding” or local activation pattern analysis, we do (can) not consider other tasks (experiments).

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