SlideShare a Scribd company logo
1 of 33
Introduction:
Revisiting reverse inference problem in
functional MRI
J. Chikazoe
National Institute for Physiological
Sciences
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.
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
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)
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)
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)
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)
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
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
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
Toward formal reverse inference
What should we do?
Importance of pattern analysis for
exploring shared neural correlates
across modalities
J. Chikazoe
National Institute for Physiological
Sciences
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.
Multiple functions in the same region
-Most of cognitive functions may require multiple brain
regions.  ( cf. connectionism )
Multiple functions in the same region
-Most of cognitive functions may require multiple brain
regions.  ( cf. connectionism )
→Global activation patterns may differ across
functions.
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.
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.
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.
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.
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.
Contradicting evidence from monkey
electrophysiological studies
Positivity- and negativity-sensitive neurons are
interspersed in the OFC.
(Morrison et al., 2009)
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
Global or local activation patterns
Global activation patterns
Positivity Negativity
Global or local activation patterns
Global activation patterns
Local activation patterns
Positivity Negativity
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)
Global activation patterns
Positivity-related activity Positive
Negativity-related activity Negative
0.13
0.07
Global activation patterns
Positivity-related activity Positive
Negativity-related activity Negative
0.13
0.070.05
0.08
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
Bayesian regression analysis
H0 : β3 = 0 vs. H1 : β3 > 0
(H0 corresponds to ‘no relationship between neural and
valence representations’)
(Chikazoe et al., 2014)
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
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.
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)
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).

More Related Content

What's hot

(2021.10) 機械学習と機械発見 データ中心型の化学・材料科学の教訓とこれから
(2021.10) 機械学習と機械発見 データ中心型の化学・材料科学の教訓とこれから (2021.10) 機械学習と機械発見 データ中心型の化学・材料科学の教訓とこれから
(2021.10) 機械学習と機械発見 データ中心型の化学・材料科学の教訓とこれから
Ichigaku Takigawa
 
論文紹介資料「Quantum Deep Field : Data-Driven Wave Function ...」
論文紹介資料「Quantum Deep Field : Data-Driven Wave Function ...」論文紹介資料「Quantum Deep Field : Data-Driven Wave Function ...」
論文紹介資料「Quantum Deep Field : Data-Driven Wave Function ...」
DaikiKoge
 

What's hot (20)

【石】[Win版]卒研発表スライド
【石】[Win版]卒研発表スライド【石】[Win版]卒研発表スライド
【石】[Win版]卒研発表スライド
 
[DL輪読会]Deep Neural Networks as Gaussian Processes
[DL輪読会]Deep Neural Networks as Gaussian Processes[DL輪読会]Deep Neural Networks as Gaussian Processes
[DL輪読会]Deep Neural Networks as Gaussian Processes
 
[DL輪読会]SOM-VAE: Interpretable Discrete Representation Learning on Time Series
[DL輪読会]SOM-VAE: Interpretable Discrete Representation Learning on Time Series[DL輪読会]SOM-VAE: Interpretable Discrete Representation Learning on Time Series
[DL輪読会]SOM-VAE: Interpretable Discrete Representation Learning on Time Series
 
機械学習は化学研究の"経験と勘"を合理化できるか?
機械学習は化学研究の"経験と勘"を合理化できるか?機械学習は化学研究の"経験と勘"を合理化できるか?
機械学習は化学研究の"経験と勘"を合理化できるか?
 
「アクティブビジョンと フリストン自由エネルギー原理」@北大20170111
「アクティブビジョンと フリストン自由エネルギー原理」@北大20170111「アクティブビジョンと フリストン自由エネルギー原理」@北大20170111
「アクティブビジョンと フリストン自由エネルギー原理」@北大20170111
 
機械学習の応用例にみる認知症診断と将来の発症予測
機械学習の応用例にみる認知症診断と将来の発症予測機械学習の応用例にみる認知症診断と将来の発症予測
機械学習の応用例にみる認知症診断と将来の発症予測
 
研究発表を準備する(2022年版)
研究発表を準備する(2022年版)研究発表を準備する(2022年版)
研究発表を準備する(2022年版)
 
Reinforcement Learning @ NeurIPS2018
Reinforcement Learning @ NeurIPS2018Reinforcement Learning @ NeurIPS2018
Reinforcement Learning @ NeurIPS2018
 
低ランク性および平滑性を用いたテンソル補完 (Tensor Completion based on Low-rank and Smooth Structu...
低ランク性および平滑性を用いたテンソル補完 (Tensor Completion based on Low-rank and Smooth Structu...低ランク性および平滑性を用いたテンソル補完 (Tensor Completion based on Low-rank and Smooth Structu...
低ランク性および平滑性を用いたテンソル補完 (Tensor Completion based on Low-rank and Smooth Structu...
 
MASTERING ATARI WITH DISCRETE WORLD MODELS (DreamerV2)
MASTERING ATARI WITH DISCRETE WORLD MODELS (DreamerV2)MASTERING ATARI WITH DISCRETE WORLD MODELS (DreamerV2)
MASTERING ATARI WITH DISCRETE WORLD MODELS (DreamerV2)
 
(2021.10) 機械学習と機械発見 データ中心型の化学・材料科学の教訓とこれから
(2021.10) 機械学習と機械発見 データ中心型の化学・材料科学の教訓とこれから (2021.10) 機械学習と機械発見 データ中心型の化学・材料科学の教訓とこれから
(2021.10) 機械学習と機械発見 データ中心型の化学・材料科学の教訓とこれから
 
Mother Waveletの設定と分解能について 修正第4版
Mother Waveletの設定と分解能について 修正第4版Mother Waveletの設定と分解能について 修正第4版
Mother Waveletの設定と分解能について 修正第4版
 
情報幾何勉強会 EMアルゴリズム
情報幾何勉強会 EMアルゴリズム 情報幾何勉強会 EMアルゴリズム
情報幾何勉強会 EMアルゴリズム
 
Collaboration with Statistician? 矩陣視覺化於探索式資料分析
Collaboration with Statistician? 矩陣視覺化於探索式資料分析Collaboration with Statistician? 矩陣視覺化於探索式資料分析
Collaboration with Statistician? 矩陣視覺化於探索式資料分析
 
(文献紹介) 画像復元:Plug-and-Play ADMM
(文献紹介) 画像復元:Plug-and-Play ADMM(文献紹介) 画像復元:Plug-and-Play ADMM
(文献紹介) 画像復元:Plug-and-Play ADMM
 
論文紹介資料「Quantum Deep Field : Data-Driven Wave Function ...」
論文紹介資料「Quantum Deep Field : Data-Driven Wave Function ...」論文紹介資料「Quantum Deep Field : Data-Driven Wave Function ...」
論文紹介資料「Quantum Deep Field : Data-Driven Wave Function ...」
 
非線形データの次元圧縮 150905 WACODE 2nd
非線形データの次元圧縮 150905 WACODE 2nd非線形データの次元圧縮 150905 WACODE 2nd
非線形データの次元圧縮 150905 WACODE 2nd
 
コネクショニズムと汎化 (全脳アーキテクチャ若手の会 第29回勉強会)
コネクショニズムと汎化 (全脳アーキテクチャ若手の会 第29回勉強会)コネクショニズムと汎化 (全脳アーキテクチャ若手の会 第29回勉強会)
コネクショニズムと汎化 (全脳アーキテクチャ若手の会 第29回勉強会)
 
距離学習を導入した二値分類モデルによる異常音検知
距離学習を導入した二値分類モデルによる異常音検知距離学習を導入した二値分類モデルによる異常音検知
距離学習を導入した二値分類モデルによる異常音検知
 
自由エネルギー原理と視覚的意識 2019-06-08
自由エネルギー原理と視覚的意識 2019-06-08自由エネルギー原理と視覚的意識 2019-06-08
自由エネルギー原理と視覚的意識 2019-06-08
 

Similar to Reverse inference problem

Tehovnik%2 c%20chen%202015
Tehovnik%2 c%20chen%202015Tehovnik%2 c%20chen%202015
Tehovnik%2 c%20chen%202015
Declara, INC
 
Investigating the Functional Utility of the Left Parietal ERP Old/New Effect:...
Investigating the Functional Utility of the Left Parietal ERP Old/New Effect:...Investigating the Functional Utility of the Left Parietal ERP Old/New Effect:...
Investigating the Functional Utility of the Left Parietal ERP Old/New Effect:...
Stuart Fairbairns
 
Kimpo et al 2014_eLife
Kimpo et al 2014_eLifeKimpo et al 2014_eLife
Kimpo et al 2014_eLife
Rhea Kimpo
 
19 Jun 2004 1434 AR AR217-NE27-07.tex AR217-NE27-07.sgm LaTeX
19 Jun 2004 1434 AR AR217-NE27-07.tex AR217-NE27-07.sgm LaTeX19 Jun 2004 1434 AR AR217-NE27-07.tex AR217-NE27-07.sgm LaTeX
19 Jun 2004 1434 AR AR217-NE27-07.tex AR217-NE27-07.sgm LaTeX
AnastaciaShadelb
 
Individual functional atlasing of the human brain with multitask fMRI data: l...
Individual functional atlasing of the human brain with multitask fMRI data: l...Individual functional atlasing of the human brain with multitask fMRI data: l...
Individual functional atlasing of the human brain with multitask fMRI data: l...
Ana Luísa Pinho
 
Localization of function psychology IB
Localization of function psychology IBLocalization of function psychology IB
Localization of function psychology IB
Mette Morell
 

Similar to Reverse inference problem (20)

Sherlock.pdf
Sherlock.pdfSherlock.pdf
Sherlock.pdf
 
MfD_connectivity_2015_Ohrnberger_Caciagli.pptx
MfD_connectivity_2015_Ohrnberger_Caciagli.pptxMfD_connectivity_2015_Ohrnberger_Caciagli.pptx
MfD_connectivity_2015_Ohrnberger_Caciagli.pptx
 
Tehovnik%2 c%20chen%202015
Tehovnik%2 c%20chen%202015Tehovnik%2 c%20chen%202015
Tehovnik%2 c%20chen%202015
 
Reply to teacher.pdf
Reply to teacher.pdfReply to teacher.pdf
Reply to teacher.pdf
 
Execitive function and fluid inteligence after frontal lobe lesions
Execitive function and fluid inteligence after frontal lobe lesionsExecitive function and fluid inteligence after frontal lobe lesions
Execitive function and fluid inteligence after frontal lobe lesions
 
NCP_Thesis_Francesca_Bocca.pdf
NCP_Thesis_Francesca_Bocca.pdfNCP_Thesis_Francesca_Bocca.pdf
NCP_Thesis_Francesca_Bocca.pdf
 
Posner task results
Posner task resultsPosner task results
Posner task results
 
The Baby and the Bathwater: Signal and Noise in Psychiatric Neuroimaging
The Baby and the Bathwater: Signal and Noise in Psychiatric NeuroimagingThe Baby and the Bathwater: Signal and Noise in Psychiatric Neuroimaging
The Baby and the Bathwater: Signal and Noise in Psychiatric Neuroimaging
 
Sources of Avoidance Motivation
Sources of Avoidance MotivationSources of Avoidance Motivation
Sources of Avoidance Motivation
 
poster
posterposter
poster
 
Investigating the Functional Utility of the Left Parietal ERP Old/New Effect:...
Investigating the Functional Utility of the Left Parietal ERP Old/New Effect:...Investigating the Functional Utility of the Left Parietal ERP Old/New Effect:...
Investigating the Functional Utility of the Left Parietal ERP Old/New Effect:...
 
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
 
Kimpo et al 2014_eLife
Kimpo et al 2014_eLifeKimpo et al 2014_eLife
Kimpo et al 2014_eLife
 
19 Jun 2004 1434 AR AR217-NE27-07.tex AR217-NE27-07.sgm LaTeX
19 Jun 2004 1434 AR AR217-NE27-07.tex AR217-NE27-07.sgm LaTeX19 Jun 2004 1434 AR AR217-NE27-07.tex AR217-NE27-07.sgm LaTeX
19 Jun 2004 1434 AR AR217-NE27-07.tex AR217-NE27-07.sgm LaTeX
 
Week 4 the neural basis of consciousness introduction to the visual system
Week 4  the neural basis of consciousness  introduction to the visual systemWeek 4  the neural basis of consciousness  introduction to the visual system
Week 4 the neural basis of consciousness introduction to the visual system
 
Individual functional atlasing of the human brain with multitask fMRI data: l...
Individual functional atlasing of the human brain with multitask fMRI data: l...Individual functional atlasing of the human brain with multitask fMRI data: l...
Individual functional atlasing of the human brain with multitask fMRI data: l...
 
Neurology examoutline
Neurology examoutlineNeurology examoutline
Neurology examoutline
 
Localization of function psychology IB
Localization of function psychology IBLocalization of function psychology IB
Localization of function psychology IB
 
science journal.pdf
science journal.pdfscience journal.pdf
science journal.pdf
 
COGS 107B - Winter 2010 - Lecture 17 - PFC, Attention
COGS 107B - Winter 2010 - Lecture 17 - PFC, AttentionCOGS 107B - Winter 2010 - Lecture 17 - PFC, Attention
COGS 107B - Winter 2010 - Lecture 17 - PFC, Attention
 

Recently uploaded

Abortion Clinic in Kempton Park +27791653574 WhatsApp Abortion Clinic Service...
Abortion Clinic in Kempton Park +27791653574 WhatsApp Abortion Clinic Service...Abortion Clinic in Kempton Park +27791653574 WhatsApp Abortion Clinic Service...
Abortion Clinic in Kempton Park +27791653574 WhatsApp Abortion Clinic Service...
mikehavy0
 
obat aborsi Tarakan wa 081336238223 jual obat aborsi cytotec asli di Tarakan9...
obat aborsi Tarakan wa 081336238223 jual obat aborsi cytotec asli di Tarakan9...obat aborsi Tarakan wa 081336238223 jual obat aborsi cytotec asli di Tarakan9...
obat aborsi Tarakan wa 081336238223 jual obat aborsi cytotec asli di Tarakan9...
yulianti213969
 
obat aborsi Bontang wa 082135199655 jual obat aborsi cytotec asli di Bontang
obat aborsi Bontang wa 082135199655 jual obat aborsi cytotec asli di  Bontangobat aborsi Bontang wa 082135199655 jual obat aborsi cytotec asli di  Bontang
obat aborsi Bontang wa 082135199655 jual obat aborsi cytotec asli di Bontang
siskavia95
 
Reconciling Conflicting Data Curation Actions: Transparency Through Argument...
Reconciling Conflicting Data Curation Actions:  Transparency Through Argument...Reconciling Conflicting Data Curation Actions:  Transparency Through Argument...
Reconciling Conflicting Data Curation Actions: Transparency Through Argument...
Bertram Ludäscher
 
Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...
Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...
Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...
Klinik kandungan
 
一比一原版(UCD毕业证书)加州大学戴维斯分校毕业证成绩单原件一模一样
一比一原版(UCD毕业证书)加州大学戴维斯分校毕业证成绩单原件一模一样一比一原版(UCD毕业证书)加州大学戴维斯分校毕业证成绩单原件一模一样
一比一原版(UCD毕业证书)加州大学戴维斯分校毕业证成绩单原件一模一样
wsppdmt
 
如何办理(Dalhousie毕业证书)达尔豪斯大学毕业证成绩单留信学历认证
如何办理(Dalhousie毕业证书)达尔豪斯大学毕业证成绩单留信学历认证如何办理(Dalhousie毕业证书)达尔豪斯大学毕业证成绩单留信学历认证
如何办理(Dalhousie毕业证书)达尔豪斯大学毕业证成绩单留信学历认证
zifhagzkk
 
Simplify hybrid data integration at an enterprise scale. Integrate all your d...
Simplify hybrid data integration at an enterprise scale. Integrate all your d...Simplify hybrid data integration at an enterprise scale. Integrate all your d...
Simplify hybrid data integration at an enterprise scale. Integrate all your d...
varanasisatyanvesh
 
原件一样(UWO毕业证书)西安大略大学毕业证成绩单留信学历认证
原件一样(UWO毕业证书)西安大略大学毕业证成绩单留信学历认证原件一样(UWO毕业证书)西安大略大学毕业证成绩单留信学历认证
原件一样(UWO毕业证书)西安大略大学毕业证成绩单留信学历认证
pwgnohujw
 
如何办理(WashU毕业证书)圣路易斯华盛顿大学毕业证成绩单本科硕士学位证留信学历认证
如何办理(WashU毕业证书)圣路易斯华盛顿大学毕业证成绩单本科硕士学位证留信学历认证如何办理(WashU毕业证书)圣路易斯华盛顿大学毕业证成绩单本科硕士学位证留信学历认证
如何办理(WashU毕业证书)圣路易斯华盛顿大学毕业证成绩单本科硕士学位证留信学历认证
acoha1
 
Displacement, Velocity, Acceleration, and Second Derivatives
Displacement, Velocity, Acceleration, and Second DerivativesDisplacement, Velocity, Acceleration, and Second Derivatives
Displacement, Velocity, Acceleration, and Second Derivatives
23050636
 
如何办理(UCLA毕业证书)加州大学洛杉矶分校毕业证成绩单学位证留信学历认证原件一样
如何办理(UCLA毕业证书)加州大学洛杉矶分校毕业证成绩单学位证留信学历认证原件一样如何办理(UCLA毕业证书)加州大学洛杉矶分校毕业证成绩单学位证留信学历认证原件一样
如何办理(UCLA毕业证书)加州大学洛杉矶分校毕业证成绩单学位证留信学历认证原件一样
jk0tkvfv
 
Abortion pills in Riyadh Saudi Arabia (+966572737505 buy cytotec
Abortion pills in Riyadh Saudi Arabia (+966572737505 buy cytotecAbortion pills in Riyadh Saudi Arabia (+966572737505 buy cytotec
Abortion pills in Riyadh Saudi Arabia (+966572737505 buy cytotec
Abortion pills in Riyadh +966572737505 get cytotec
 

Recently uploaded (20)

Jual Obat Aborsi Bandung (Asli No.1) Wa 082134680322 Klinik Obat Penggugur Ka...
Jual Obat Aborsi Bandung (Asli No.1) Wa 082134680322 Klinik Obat Penggugur Ka...Jual Obat Aborsi Bandung (Asli No.1) Wa 082134680322 Klinik Obat Penggugur Ka...
Jual Obat Aborsi Bandung (Asli No.1) Wa 082134680322 Klinik Obat Penggugur Ka...
 
How to Transform Clinical Trial Management with Advanced Data Analytics
How to Transform Clinical Trial Management with Advanced Data AnalyticsHow to Transform Clinical Trial Management with Advanced Data Analytics
How to Transform Clinical Trial Management with Advanced Data Analytics
 
Case Study 4 Where the cry of rebellion happen?
Case Study 4 Where the cry of rebellion happen?Case Study 4 Where the cry of rebellion happen?
Case Study 4 Where the cry of rebellion happen?
 
Abortion Clinic in Kempton Park +27791653574 WhatsApp Abortion Clinic Service...
Abortion Clinic in Kempton Park +27791653574 WhatsApp Abortion Clinic Service...Abortion Clinic in Kempton Park +27791653574 WhatsApp Abortion Clinic Service...
Abortion Clinic in Kempton Park +27791653574 WhatsApp Abortion Clinic Service...
 
obat aborsi Tarakan wa 081336238223 jual obat aborsi cytotec asli di Tarakan9...
obat aborsi Tarakan wa 081336238223 jual obat aborsi cytotec asli di Tarakan9...obat aborsi Tarakan wa 081336238223 jual obat aborsi cytotec asli di Tarakan9...
obat aborsi Tarakan wa 081336238223 jual obat aborsi cytotec asli di Tarakan9...
 
obat aborsi Bontang wa 082135199655 jual obat aborsi cytotec asli di Bontang
obat aborsi Bontang wa 082135199655 jual obat aborsi cytotec asli di  Bontangobat aborsi Bontang wa 082135199655 jual obat aborsi cytotec asli di  Bontang
obat aborsi Bontang wa 082135199655 jual obat aborsi cytotec asli di Bontang
 
Ranking and Scoring Exercises for Research
Ranking and Scoring Exercises for ResearchRanking and Scoring Exercises for Research
Ranking and Scoring Exercises for Research
 
Reconciling Conflicting Data Curation Actions: Transparency Through Argument...
Reconciling Conflicting Data Curation Actions:  Transparency Through Argument...Reconciling Conflicting Data Curation Actions:  Transparency Through Argument...
Reconciling Conflicting Data Curation Actions: Transparency Through Argument...
 
Digital Transformation Playbook by Graham Ware
Digital Transformation Playbook by Graham WareDigital Transformation Playbook by Graham Ware
Digital Transformation Playbook by Graham Ware
 
SCI8-Q4-MOD11.pdfwrwujrrjfaajerjrajrrarj
SCI8-Q4-MOD11.pdfwrwujrrjfaajerjrajrrarjSCI8-Q4-MOD11.pdfwrwujrrjfaajerjrajrrarj
SCI8-Q4-MOD11.pdfwrwujrrjfaajerjrajrrarj
 
Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...
Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...
Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...
 
一比一原版(UCD毕业证书)加州大学戴维斯分校毕业证成绩单原件一模一样
一比一原版(UCD毕业证书)加州大学戴维斯分校毕业证成绩单原件一模一样一比一原版(UCD毕业证书)加州大学戴维斯分校毕业证成绩单原件一模一样
一比一原版(UCD毕业证书)加州大学戴维斯分校毕业证成绩单原件一模一样
 
Unsatisfied Bhabhi ℂall Girls Vadodara Book Esha 7427069034 Top Class ℂall Gi...
Unsatisfied Bhabhi ℂall Girls Vadodara Book Esha 7427069034 Top Class ℂall Gi...Unsatisfied Bhabhi ℂall Girls Vadodara Book Esha 7427069034 Top Class ℂall Gi...
Unsatisfied Bhabhi ℂall Girls Vadodara Book Esha 7427069034 Top Class ℂall Gi...
 
如何办理(Dalhousie毕业证书)达尔豪斯大学毕业证成绩单留信学历认证
如何办理(Dalhousie毕业证书)达尔豪斯大学毕业证成绩单留信学历认证如何办理(Dalhousie毕业证书)达尔豪斯大学毕业证成绩单留信学历认证
如何办理(Dalhousie毕业证书)达尔豪斯大学毕业证成绩单留信学历认证
 
Simplify hybrid data integration at an enterprise scale. Integrate all your d...
Simplify hybrid data integration at an enterprise scale. Integrate all your d...Simplify hybrid data integration at an enterprise scale. Integrate all your d...
Simplify hybrid data integration at an enterprise scale. Integrate all your d...
 
原件一样(UWO毕业证书)西安大略大学毕业证成绩单留信学历认证
原件一样(UWO毕业证书)西安大略大学毕业证成绩单留信学历认证原件一样(UWO毕业证书)西安大略大学毕业证成绩单留信学历认证
原件一样(UWO毕业证书)西安大略大学毕业证成绩单留信学历认证
 
如何办理(WashU毕业证书)圣路易斯华盛顿大学毕业证成绩单本科硕士学位证留信学历认证
如何办理(WashU毕业证书)圣路易斯华盛顿大学毕业证成绩单本科硕士学位证留信学历认证如何办理(WashU毕业证书)圣路易斯华盛顿大学毕业证成绩单本科硕士学位证留信学历认证
如何办理(WashU毕业证书)圣路易斯华盛顿大学毕业证成绩单本科硕士学位证留信学历认证
 
Displacement, Velocity, Acceleration, and Second Derivatives
Displacement, Velocity, Acceleration, and Second DerivativesDisplacement, Velocity, Acceleration, and Second Derivatives
Displacement, Velocity, Acceleration, and Second Derivatives
 
如何办理(UCLA毕业证书)加州大学洛杉矶分校毕业证成绩单学位证留信学历认证原件一样
如何办理(UCLA毕业证书)加州大学洛杉矶分校毕业证成绩单学位证留信学历认证原件一样如何办理(UCLA毕业证书)加州大学洛杉矶分校毕业证成绩单学位证留信学历认证原件一样
如何办理(UCLA毕业证书)加州大学洛杉矶分校毕业证成绩单学位证留信学历认证原件一样
 
Abortion pills in Riyadh Saudi Arabia (+966572737505 buy cytotec
Abortion pills in Riyadh Saudi Arabia (+966572737505 buy cytotecAbortion pills in Riyadh Saudi Arabia (+966572737505 buy cytotec
Abortion pills in Riyadh Saudi Arabia (+966572737505 buy cytotec
 

Reverse inference problem

  • 1. Introduction: Revisiting reverse inference problem in functional MRI J. Chikazoe National Institute for Physiological Sciences
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. Toward formal reverse inference What should we do?
  • 12. Importance of pattern analysis for exploring shared neural correlates across modalities J. Chikazoe National Institute for Physiological Sciences
  • 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. Multiple functions in the same region -Most of cognitive functions may require multiple brain regions.  ( cf. connectionism )
  • 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. 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. 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. 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. 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. 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. Contradicting evidence from monkey electrophysiological studies Positivity- and negativity-sensitive neurons are interspersed in the OFC. (Morrison et al., 2009)
  • 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. Global or local activation patterns Global activation patterns Positivity Negativity
  • 24. Global or local activation patterns Global activation patterns Local activation patterns Positivity Negativity
  • 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. Global activation patterns Positivity-related activity Positive Negativity-related activity Negative 0.13 0.07
  • 27. Global activation patterns Positivity-related activity Positive Negativity-related activity Negative 0.13 0.070.05 0.08
  • 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. 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. 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. 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. 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. 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).

Editor's Notes

  1. Before moving to each presentation, I would like to briefly explain the purpose of this symposium.
  2. I will briefly explain what forward and reverse inference are. In forward inference, probability of brain activation given mental state is inferred. This is the basic form of fMRI studies. In reverse inference, mental state is inferred from brain activation.
  3. Let’s say we conducted an fmri study investigating brain regions associated with response inhibition. In our dataset, we found the strongest activation in the insula. We can say the insula is associated with response inhibition. This is forward inference. From this activation, we may want to infer reversely. For example, the insula is known to be associated with disgust. By applying reverse inference, we may draw a conclusion That participants may feel disgust because response inhibition task is too demanding.
  4. However, another study demonstrated strong relationship between the insula and working memory. So, in this case our conclusion will be that participants may have to sustain task rule during the performance of go/no-go task.
  5. Another study provided another evidence. The insula is known as the primary gustatory cortex. Sweet liquid evoked insula activation. Based on this result we may draw a conclusion that participants may have sweet feelings because successful responses in a difficult task can be taken as reward.
  6. But the same study demonstrated sour taste activates the insula. So, we may have to draw a different conclusion from this.
  7. Such a situation may look like a comedy, but what is the issue of the reverse inference? In reverse inference, we estimate the conditional probability of a mental process given a brain activation result.
  8. In the informal reverse inference, we estimate conditional probability of brain activation given a mental state by arbitrarily picking up previous studies.
  9. Furthremore, prior probability is estimated by our own belief. So, the conclusion is strongly biased.
  10. In this symposium, we will discuss how we can apply reverse inference formally.
  11. First, I would like to talk about relationship between cognitive components and their neural correlates. In most cases, 1-to-1 correspondence is not observed. For example, self-referential processing is associated with anterior MPFC as well as PCC and TPJ. Conversely, the anterior MPFC is related with self-referential processing, decision making and memory. In this way, multiple-to-multiple correspondence is observed.
  12. This suggests multiple functions in the same region. For example, most of cognitive functions may require multiple brain regions. In this case, global activation patterns may differ across functions. Another possibility is that the same region may have the similar computational processes but each neuron in that region may be assigned to different functions. In this case local activation patterns may differ across functions. Another possibility is that the same construct may
  13. This suggests multiple functions in the same region. For example, most of cognitive functions may require multiple brain regions. In this case, global activation patterns may differ across functions. Another possibility is that the same region may have the similar computational processes but each neuron in that region may be assigned to different functions. In this case local activation patterns may differ across functions. Another possibility is that the same construct may
  14. This suggests multiple functions in the same region. For example, most of cognitive functions may require multiple brain regions. In this case, global activation patterns may differ across functions. Another possibility is that the same region may have the similar computational processes but each neuron in that region may be assigned to different functions. In this case local activation patterns may differ across functions. Another possibility is that the same construct may
  15. This suggests multiple functions in the same region. For example, most of cognitive functions may require multiple brain regions. In this case, global activation patterns may differ across functions. Another possibility is that the same region may have the similar computational processes but each neuron in that region may be assigned to different functions. In this case local activation patterns may differ across functions. Another possibility is that the same construct may
  16. This suggests multiple functions in the same region. For example, most of cognitive functions may require multiple brain regions. In this case, global activation patterns may differ across functions. Another possibility is that the same region may have the similar computational processes but each neuron in that region may be assigned to different functions. In this case local activation patterns may differ across functions. Another possibility is that the same construct may
  17. This suggests multiple functions in the same region. For example, most of cognitive functions may require multiple brain regions. In this case, global activation patterns may differ across functions. Another possibility is that the same region may have the similar computational processes but each neuron in that region may be assigned to different functions. In this case local activation patterns may differ across functions. Another possibility is that the same construct may
  18. From here, I would like to talk about my study. I am interested in value representations.
  19. Our fmri study showed consistent results. In this study, emotionally positive or negative stimuli were presented, and participants rated positivity and negativity for each stimulus. This slide shows univariate analysis results. Yellow indicates brain regions sensitive to positive stimuli, and blue indicates negativity-sensitive regions. Green indicates overlap. This shows large overlap between positive and negative regions.
  20. How can we discriminate such activation patterns? One possible solution will be employing multivoxel pattern analysis. For that purpose, we can use global activation patterns or local activation patterns.
  21. How can we discriminate such activation patterns? One possible solution will be employing multivoxel pattern analysis. For that purpose, we can use global activation patterns or local activation patterns.
  22. Neurosynth created by Dr. Yarkoni is a very powerful tool to perform reverse inference using global activation patterns. This online software automatically searches published fMRI studies and stores information of text and activation coordinate. Based on a huge number of published papers, probability of activation given a mental state is estimated. This prior probability can be calculated from the empirical published data, but for the purpose of comparison, This is set to 0.5.
  23. Using Neursynth, I’ve got correlation between my imaging result and reverse inference map created by Neurosynth. This shows positivity-related activity is more similar to the reverse inference map associated with the term positive while
  24. Negativity-related activity map is similar to the reverse inference map related to the term negative.
  25. Another direction is performing MVPA using local activation patterns. I extracted the brain activity data from medial OFC and then vectorized them. Correlations between those vectors are calculated, resulting in representational similarity matrix. The combination of negative and negative or positive and positive shows higher correlation, While the combination of positive and negative shows lower correlation. This indicates
  26. In the previous paper, we decomposed neural representational similarity in the OFC into several components such as visual feature, categories and valence using multiple regression analysis. This time, we applied bayesian regression analysis on this equation. The null hypothesis is no relationship between neural and valence representations.
  27. We calculated bayes factor for 3 matrices. The first one is within-visual comparison, the second one is within gustatory comparison, And the third one is cross-modal comparison. We compared bayes factor obtained by univariate and pattern analysis. While univariate analysis failed to show strong association between neural and valence representations in the OFC, Multivariate analysis showed strong association between them. Importantly, not only within modal comparison, but also cross modal comparison shows strong association between neural and valence representations in the OFC.
  28. When analyzing local activation patterns, we cannot compare them to other studies. This means all the terms in the Bayes formula were conditioned by task.