Human brain mapping in mental health

1. 정신현상을 뇌기능매핑
기법으로 이해 가능할까?
: 뇌영상분석기법의 비판적 한계와 극복방안
TAE YOUNG LEE
SEOUL NATIONAL UNIVERSITY HOSPITAL

2. Precision medicine

3. Precision medicine in psychiatry
Figure adapted fron Gottesman 2003; Birnbaum 2013

4. Challenges of developing neuroimaging
markers for psychiatric disorders

5. Lacking gold standards for psychiatric diagnoses
Figure adapted from Issler and Chen 2015; Hasler and Northoff 2011
Phenotypes Genotypes Endophenotypes

6. Identification of pathological features
Subtle, elusive Availability Replication
Figure adapted Mehta et al. 2013; Bissell 2013

7. Validation of neuroimaging markers
Figure adapted from Cornblatt et al. 2015; wikipedia neuron
Longitudinal study in vivo VS in vitro

8. Noise
Thermal noise
- Responsible for variation in background of image
- Eddy currents, scanner heating
Power fluctuations
- Typically caused by scanner problems
Artifact-induced problems
Variation in subject
- Timing of processes
Head motion
Physiological fluctuations
- Respiration
- Cardiac pulsation
Differences across brain regions
- Functional differences
- Large vessel effects
Figure adapted from ecs.utdallas.edu

9. Multiple comparisons
Figure adapted from Bennett et al. 2009; Eklund 2016; Woo et al. 2014; Reddan et al. 2017

10. P-hacking
1. Stop collecting data once p < .05
2. Analyze many measures, but report only those with p < .05
3. Collect and analyze many conditions, but only report those with p < .05
4. Use covariates to get p < .05
5. Exclude participants to get p < .05
6. Transform the data to get p < .05

11. A framework for the development of
neuroimaging biomarkers in psychiatry

12. Identifying a clinically relevant question
Figure adapted from Cornblatt et al. 2015
Neuroimaging
marker
development
Phase I
Target
identification
Phase II
Internal
validation
Phase III
External
validation
Phase IV
Demonstrating
of
clinical utility
Bench-to-bedside
Real-world setting

13. Ensuring the Neuroimaging marker
measures the intended biological process
Disorder
Yes No
Yes
True
positive
(TP)
False
Positive
(FP)
No
False
Negative
(FN)
True
Negative
(TN)
TestResults
Reliability vs Validity

14. Ensuring the Neuroimaging marker
measures the intended biological process
Disorder
Yes No
Yes
True
positive
(TP)
False
Positive
(FP)
No
False
Negative
(FN)
True
Negative
(TN)
TestResults
Sensitivity: TP / TP + FN
Specificity: TN / TN + FP
Positive Predictive Value
: TP / TP + FP
Negative Predictive Value
: TN / TN + FN

15. Ensuring the Neuroimaging marker
measures the intended biological process
SCZ : prevalence 1% in 50,000,000
HC : 49,500,000, SCZ : 500,000
MRI sensitivity 99%, specificity 95%
SCZ : TP (500,000 * 0.99) = 495,000
FP (49,500,000 * 0.1) = 4,950,000
= 5,445,000 = Hipp def. (+)
Positive Predictive Value
= 495,000 / 495,000 + 4,950,000
= 0.091%

16. Demonstrating the Neuroimaging marker’s
predictive value
‘Sufficient high’ predictive value is depends on the specific clinical question
Figure adapted from Abi-Dargham 2016

17. Future directions

18. Transparent research
Registry of clinical trialsReproducible research
Figure adapted from Peng 2011; clinicaltrials.gov

19. A new classification scheme
Functional Domains
Negative Valence
(e.g., fear, anxiety,
loss)
Positive Valence
Systems
(e.g., reward,
learning, habit)
Cognitive Systems
(e.g., attention,
perception,
memory)
Systems for Social
Processes
(e.g., attachment,
communication,
perception of self &
others)
Arousal and
Regulatory
Systems
(e.g., arousal,
circadian rhythms)
Units of Analysis
Genes Molecules Cells Circuits Physiology Behavior Self-Reports Paradigms
PET
SPECT
MRS
MRI
fMRI
DTI
EEG
ECoG
MEG

20. Standardization of neuroimaging methods

21. Balance the studies design
Theory
Hypothesis
Observation
Confirmation
Theory
Hypothesis
Pattern
Observation
Hypothesis-driven analysis Data-driven analysis

22. Thank you for your attention
Tae Young Lee
leetaey@gmail.com