Post on 17-Mar-2018
Reliable individual functional networks and their relationship to behavior
Emily S. Finn, PhDSection on Functional Imaging MethodsLaboratory of Brain & Cognition, NIMHemily.finn@nih.gov
Taking Connectivity to a Skeptical Future Educational Workshop | OHBM Annual MeetingJune 25, 2017
@esfinn
Outline
Introduction:Why study individual differences?
1 How to get good brain data
2 How to get good behavioral data
3
Outline
Introduction:Why study individual differences?
1 How to get good brain data
2
How to relate brain to behavior
4
How to get good behavioral data
3
Outline
Introduction:Why study individual differences?
1 How to get good brain data
2
How to relate brain to behavior
4
Learn more5How to get good behavioral data
3
Outline
Introduction:Why study individual differences?
1 How to get good brain data
2
How to relate brain to behavior
4
Learn more5How to get good behavioral data
3
Why study individual differences?
Controls
Patients
Why study individual differences?
Controls
Patients
• Basic neuroscience: mapping from individual brains to individual behaviors
Why study individual differences?
Controls
Patients
• Basic neuroscience: mapping from individual brains to individual behaviors
• Eventual clinical applications:
Why study individual differences?
Categorical approach
Controls
Patients
• Basic neuroscience: mapping from individual brains to individual behaviors
• Eventual clinical applications:
Why study individual differences?
Categorical approach
Controls
Patients
• Basic neuroscience: mapping from individual brains to individual behaviors
• Eventual clinical applications:
Why study individual differences?
Categorical approach
Controls
Patients
• Basic neuroscience: mapping from individual brains to individual behaviors
• Eventual clinical applications:
Why study individual differences?
Categorical approach
Controls
Patients
• Basic neuroscience: mapping from individual brains to individual behaviors
• Eventual clinical applications:
p < 0.05*
Why study individual differences?
Categorical approach Dimensional approach
Controls
Patients
• Basic neuroscience: mapping from individual brains to individual behaviors
• Eventual clinical applications:
p < 0.05*
Why study individual differences?
Categorical approach Dimensional approach
Controls
Patients
• Basic neuroscience: mapping from individual brains to individual behaviors
• Eventual clinical applications:
p < 0.05*
Why study individual differences?Motor
Mt
EmotionEm
LanguageLg
Working memoryWM
RestingR1
+
RestingR2
+
Why study individual differences?Motor
Mt
EmotionEm
LanguageLg
Working memoryWM
RestingR1
RestingR2
Why study individual differences?Motor
Mt
EmotionEm
LanguageLg
Working memoryWM
RestingR1
RestingR2
Why study individual differences?
Finn et al., Nat Neurosci (2015)
Why study individual differences?
0.93 0.84 0.63
0.72 0.79 0.67
0.64 0.60 0.54
R1
WM
Mt
R2 Lg Em
Data
base
Target
ID rate
0.5 1.0
Finn et al., Nat Neurosci (2015)
Why study individual differences?
You always look most like yourself, regardless of what you’re doing
0.93 0.84 0.63
0.72 0.79 0.67
0.64 0.60 0.54
R1
WM
Mt
R2 Lg Em
Data
base
Target
ID rate
0.5 1.0
Finn et al., Nat Neurosci (2015)
Why study individual differences?
You always look most like yourself, regardless of what you’re doing
0.93 0.84 0.63
0.72 0.79 0.67
0.64 0.60 0.54
R1
WM
Mt
R2 Lg Em
Data
base
Target
ID rate
0.5 1.0
Finn et al., Nat Neurosci (2015)
Why study individual differences?
You always look most like yourself, regardless of what you’re doing
0.93 0.84 0.63
0.72 0.79 0.67
0.64 0.60 0.54
R1
WM
Mt
R2 Lg Em
Data
base
Target
ID rate
0.5 1.0
Individual differences in FC predict individual differences in behavior
Finn et al., Nat Neurosci (2015)
Outline
Introduction:Why study individual differences?
1 How to get good brain data
2
How to relate brain to behavior
4
Learn more5How to get good behavioral data
3
Outline
Introduction:Why study individual differences?
1 How to get good brain data
2
How to relate brain to behavior
4
Learn more5How to get good behavioral data
3
Q. Do you need HCP-quality data?
Q. Do you need HCP-quality data?A. Not really
Q. Do you need HCP-quality data?A. Not really
ID is fairly robust even at more standard spatial & temporal resolutions:
Q. Do you need HCP-quality data?A. Not really
Airan et al., Hum Brain Mapp (2016)
ID is fairly robust even at more standard spatial & temporal resolutions:
Q. Do you need HCP-quality data?A. Not really
Airan et al., Hum Brain Mapp (2016)
ID is fairly robust even at more standard spatial & temporal resolutions:
Courtesy of Jason Druzgal
Q. Do you need HCP-quality data?A. Not really
• More nodes —> higher identification rate
Airan et al., Hum Brain Mapp (2016)
ID is fairly robust even at more standard spatial & temporal resolutions:
Courtesy of Jason Druzgal
Q. Do you need HCP-quality data?A. Not really
• More nodes —> higher identification rate
‣ Parcellation method (random vs. functional) did not matter
Airan et al., Hum Brain Mapp (2016)
ID is fairly robust even at more standard spatial & temporal resolutions:
Courtesy of Jason Druzgal
Q. Do you need HCP-quality data?A. Not really
• More nodes —> higher identification rate
‣ Parcellation method (random vs. functional) did not matter‣ Caution: Higher resolution may amplify effects of anatomical diffs/registration error
Airan et al., Hum Brain Mapp (2016)
ID is fairly robust even at more standard spatial & temporal resolutions:
Courtesy of Jason Druzgal
Q. Do you need HCP-quality data?A. Not really
• More nodes —> higher identification rate
‣ Parcellation method (random vs. functional) did not matter‣ Caution: Higher resolution may amplify effects of anatomical diffs/registration error‣ Parcellations in the 200-300 node range seem like a good compromise
Airan et al., Hum Brain Mapp (2016)
ID is fairly robust even at more standard spatial & temporal resolutions:
Courtesy of Jason Druzgal
Q. What about amount of data?
Q. What about amount of data?A. Scan duration matters!
Q. What about amount of data?A. Scan duration matters!
Longer acquisitions are better:
Q. What about amount of data?A. Scan duration matters!
Longer acquisitions are better:• higher reliability within subjects
Birn et al., NeuroImage (2013)
Q. What about amount of data?A. Scan duration matters!
Finn et al., Nat Neurosci (2015)
• higher identifiability across subjects
Longer acquisitions are better:• higher reliability within subjects
Birn et al., NeuroImage (2013)
Q. What about amount of data?A. Scan duration matters!
Finn et al., Nat Neurosci (2015)
• higher identifiability across subjects
‣ higher sampling rate (shorter TR) cannot make up for shorter scan duration
Longer acquisitions are better:• higher reliability within subjects
Birn et al., NeuroImage (2013)
Q. What about amount of data?A. Scan duration matters!
Finn et al., Nat Neurosci (2015)
• higher identifiability across subjects
‣ higher sampling rate (shorter TR) cannot make up for shorter scan duration
Airan et al., Hum Brain Mapp (2016)
Longer acquisitions are better:• higher reliability within subjects
Birn et al., NeuroImage (2013)
Q. What about amount of data?A. Scan duration matters!
Finn et al., Nat Neurosci (2015)
• higher identifiability across subjects
‣ higher sampling rate (shorter TR) cannot make up for shorter scan duration
Airan et al., Hum Brain Mapp (2016)
Shah et al., Brain & Behav (2016)
Longer acquisitions are better:• higher reliability within subjects
Birn et al., NeuroImage (2013)
Q. Does scan condition matter?
Q. Does scan condition matter?A. Yes!
Q. Does scan condition matter?A. Yes!Rest has become the default condition for FC & individual differences, but tasks may increase signal-to-noise
Q. Does scan condition matter?A. Yes!Rest has become the default condition for FC & individual differences, but tasks may increase signal-to-noise
0.93 0.84 0.63
0.72 0.79 0.67
0.64 0.60 0.54
R1
WM
Mt
R2 Lg Em
Data
base
Target
ID rate
0.5 1.0
Q. Does scan condition matter?A. Yes!Rest has become the default condition for FC & individual differences, but tasks may increase signal-to-noise
Q. Does scan condition matter?A. Yes!Rest has become the default condition for FC & individual differences, but tasks may increase signal-to-noise
Finn et al., NeuroImage (2017)
Q. Does scan condition matter?A. Yes!Rest has become the default condition for FC & individual differences, but tasks may increase signal-to-noise
Finn et al., NeuroImage (2017)
Q. Does scan condition matter?A. Yes!Rest has become the default condition for FC & individual differences, but tasks may increase signal-to-noise
Finn et al., NeuroImage (2017)
Q. Does scan condition matter?A. Yes!Rest has become the default condition for FC & individual differences, but tasks may increase signal-to-noise
Finn et al., NeuroImage (2017)
Q. Does scan condition matter?A. Yes!Rest has become the default condition for FC & individual differences, but tasks may increase signal-to-noise
‣ for a set scan duration, tasks may be more reliable than rest‣ tasks may converge faster on a subject’s “true” profile
Finn et al., NeuroImage (2017)
Q. Is rest best?
Q. Is rest best?A. Probably not
Q. Is rest best?A. Probably not
Tasks may stabilize individuals’ functional architecture, increase SNR:
Q. Is rest best?A. Probably not
‣ some task pairs give better ID than the two rest sessions
Tasks may stabilize individuals’ functional architecture, increase SNR:
Q. Is rest best?A. Probably not
‣ some task pairs give better ID than the two rest sessions
Target
Data
base
Day 1 Day 2
ID ra
te
Day
1D
ay 2
Tasks may stabilize individuals’ functional architecture, increase SNR:
Finn et al., NeuroImage (2017)
Q. Is rest best?A. Probably not
‣ some task pairs give better ID than the two rest sessions
Target
Data
base
Day 1 Day 2
ID ra
te
Day
1D
ay 2
‣ consider a combination of rest and task
Tasks may stabilize individuals’ functional architecture, increase SNR:
Finn et al., NeuroImage (2017)
Q. Is rest best?A. Probably not
‣ some task pairs give better ID than the two rest sessions
Target
Data
base
Day 1 Day 2
ID ra
te
Day
1D
ay 2
‣ consider a combination of rest and task
Tasks may stabilize individuals’ functional architecture, increase SNR:
Finn et al., NeuroImage (2017)
Finn et al., Nat Neurosci (2015)
Q. Is rest best? A. Probably not
Q. Is rest best? A. Probably not
Consider naturalistic tasks:
Q. Is rest best? A. Probably not
Consider naturalistic tasks:
Inscapes: Vanderwal et al., NeuroImage 2015headspacestudios.org
Q. Is rest best? A. Probably not
Consider naturalistic tasks:
Q. Is rest best? A. Probably not
Consider naturalistic tasks:
‣ ID rate is just as good as (if not better than) rest
Q. Is rest best? A. Probably not
Consider naturalistic tasks:
Session 1
Rest Inscapes Ocean’s 11
Session 2
‣ ID rate is just as good as (if not better than) rest
Q. Is rest best? A. Probably not
Consider naturalistic tasks:
Session 1
Rest Inscapes Ocean’s 11
Session 2
‣ ID rate is just as good as (if not better than) rest
Vanderwal et al., NeuroImage (2017)
Q. Is rest best? A. Probably not
Consider naturalistic tasks:
Session 1
Rest Inscapes Ocean’s 11
Session 2
‣ ID rate is just as good as (if not better than) rest
Vanderwal et al., NeuroImage (2017)
Q. Is rest best? A. Probably not
Q. Is rest best? A. Probably not
Tasks also have purely practical advantages:
‣ increase subject compliance (i.e., decrease head motion), especially in certain populations
Q. Is rest best? A. Probably not
Tasks also have purely practical advantages:
‣ increase subject compliance (i.e., decrease head motion), especially in certain populations
Huijbers et al., NeuroImage (2017)
Q. Is rest best? A. Probably not
Tasks also have purely practical advantages:
‣ increase subject compliance (i.e., decrease head motion), especially in certain populations
Child
ren
Adul
ts
Vanderwal et al., NeuroImage (2015)Huijbers et al., NeuroImage (2017)
Q. Is rest best? A. Probably not
Tasks also have purely practical advantages:
Outline
Introduction:Why study individual differences?
1 How to get good brain data
2
How to relate brain to behavior
4
Learn more5How to get good behavioral data
3
Outline
Introduction:Why study individual differences?
1 How to get good brain data
2
How to relate brain to behavior
4
Learn more5How to get good behavioral data
3
How to choose behavior
How to choose behaviorIs it stable?
How to choose behaviorIs it stable?
• Trait vs. state• State variables may be better
suited to within-subject analysis
How to choose behaviorIs it stable?
• Trait vs. state• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
How to choose behaviorIs it stable?
• Trait vs. state• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
How to choose behaviorIs it stable?
• Trait vs. state• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
How to choose behaviorIs it stable?
Does it show a good distribution in your population?
• Trait vs. state• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
How to choose behaviorIs it stable?
Does it show a good distribution in your population?
• Trait vs. state• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
How to choose behaviorIs it stable?
Does it show a good distribution in your population?
• Trait vs. state• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
How to choose behaviorIs it stable?
Does it show a good distribution in your population?
• Trait vs. state• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
How to choose behaviorIs it stable?
Does it show a good distribution in your population?
• Trait vs. state• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
How to choose behaviorIs it stable?
Does it show a good distribution in your population?
Is it something you expect to be reflected in brain function?
• Trait vs. state• State variables may be better
suited to within-subject analysis
Betzel et al., Sci Rep (2017)
Behavior: Mitigating confounds
Behavior: Mitigating confoundsMany behaviors/phenotypes are correlated with head motion!
Behavior: Mitigating confoundsMany behaviors/phenotypes are correlated with head motion!
Siegel et al., Cerebral Cortex (2016)
Negatively: Positively:
Behavior: Mitigating confoundsMany behaviors/phenotypes are correlated with head motion!
Siegel et al., Cerebral Cortex (2016)
Negatively: Positively:
Geerligs et al., Hum Brain Mapp (2017)
Age, vascular health:
Behavior: Mitigating confoundsMany behaviors/phenotypes are correlated with head motion!
Behavior: Mitigating confoundsMany behaviors/phenotypes are correlated with head motion!
‣ Check for correlation in your sample
Behavior: Mitigating confoundsMany behaviors/phenotypes are correlated with head motion!
‣ Check for correlation in your sample‣ Consider excluding particularly high-motion subjects
Behavior: Mitigating confoundsMany behaviors/phenotypes are correlated with head motion!
‣ Check for correlation in your sample‣ Consider excluding particularly high-motion subjects‣ Choose appropriate preprocessing techniques
Behavior: Mitigating confoundsMany behaviors/phenotypes are correlated with head motion!
‣ Check for correlation in your sample‣ Consider excluding particularly high-motion subjects‣ Choose appropriate preprocessing techniques
Ciric et al., NeuroImage (2017)
Behavior: Mitigating confoundsMany behaviors/phenotypes are correlated with head motion!
‣ Check for correlation in your sample‣ Consider excluding particularly high-motion subjects‣ Choose appropriate preprocessing techniques‣ Use motion as an explicit covariate
Ciric et al., NeuroImage (2017)
Outline
Introduction:Why study individual differences?
1 How to get good brain data
2
How to relate brain to behavior
4
Learn more5How to get good behavioral data
3
Outline
Introduction:Why study individual differences?
1 How to get good brain data
2
How to relate brain to behavior
4
Learn more5How to get good behavioral data
3
Brain ↔ behavior: Cross-validate!
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
2
7.5
4
6.3
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
2
7.5
4
6.3
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
Pred
icte
d be
hav
Observed behav
2
7.5
4
6.3
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
Pred
icte
d be
hav
Observed behav
2
7.5
4
6.3
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
Pred
icte
d be
hav
Observed behav
2
7.5
4
6.3
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
Pred
icte
d be
hav
Observed behav
2
7.5
4
6.3
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
Pred
icte
d be
hav
Observed behav
2
7.5
4
6.3
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
Pred
icte
d be
hav
Observed behav
2
7.5
4
6.3
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
Pred
icte
d be
hav
Observed behav
2
7.5
4
6.3
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
Pred
icte
d be
hav
Observed behav
2
7.5
4
6.3
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
Pred
icte
d be
hav
Observed behav
2
7.5
4
6.3
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
‣ even better: cross-dataset
Sustained attention model
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
Pred
icte
d be
hav
Observed behav
2
7.5
4
6.3
Rosenberg et al., Nature Neuroscience (2016)Rosenberg et al., Trends in Cog Sci (2017)
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
‣ even better: cross-dataset
Sustained attention model
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
Pred
icte
d be
hav
Observed behav
2
7.5
4
6.3
Rosenberg et al., Nature Neuroscience (2016)Rosenberg et al., Trends in Cog Sci (2017)
Observed behav
Pred
icted
beh
av
n = 25 adults
Brain ↔ behavior: Cross-validate!Find a brain-behavior relationship in one sample, see if it holds in another sample
‣ leave-one-subject-out (within dataset)
‣ even better: cross-dataset
Sustained attention model
Connectome-based Predictive ModelingShen et al., Nature Protocols (2017)
Pred
icte
d be
hav
Observed behav
2
7.5
4
6.3
Rosenberg et al., Nature Neuroscience (2016)Rosenberg et al., Trends in Cog Sci (2017)
Observed behav
Pred
icted
beh
av
n = 25 adults
Observed ADHD score
n = 113 children
Pred
icted
ADH
D sc
ore
Q. What is the best brain state?
Q. What is the best brain state?A. Maybe it depends on your behavior
Q. What is the best brain state?A. Maybe it depends on your behavior
Certain task conditions generate better predictions of behavior:
Q. What is the best brain state?A. Maybe it depends on your behavior
n = 716, 10-fold cross-validationConnectome-based Predictive Modeling (CPM; Shen et al., Nat Protocols 2017)
Mod
el in
put d
ata
Target behavior
Certain task conditions generate better predictions of behavior:
Q. What is the best brain state?A. Maybe it depends on your behavior
n = 716, 10-fold cross-validationConnectome-based Predictive Modeling (CPM; Shen et al., Nat Protocols 2017)
Mod
el in
put d
ata
Target behavior
Certain task conditions generate better predictions of behavior:
Consider tailoring scan condition to behavior of interest
➡“Stress test”?
Take-home points
Introduction:Why study individual differences?
1 How to get good brain data
2 How to get good behavioral data
3
How to relate brain to behavior
4
Learn more5
Take-home points
Introduction:Why study individual differences?
1 How to get good brain data
2 How to get good behavioral data
3
How to relate brain to behavior
4
Learn more5
• Data quality ≠ critical
Take-home points
Introduction:Why study individual differences?
1 How to get good brain data
2 How to get good behavioral data
3
How to relate brain to behavior
4
Learn more5
• Data quality ≠ critical• Longer scans are better!
Take-home points
Introduction:Why study individual differences?
1 How to get good brain data
2 How to get good behavioral data
3
How to relate brain to behavior
4
Learn more5
• Data quality ≠ critical• Longer scans are better!• Consider using tasks
Take-home points
Introduction:Why study individual differences?
1 How to get good brain data
2 How to get good behavioral data
3
How to relate brain to behavior
4
Learn more5
• Data quality ≠ critical• Longer scans are better!• Consider using tasks ‣ improve compliance
Take-home points
Introduction:Why study individual differences?
1 How to get good brain data
2 How to get good behavioral data
3
How to relate brain to behavior
4
Learn more5
• Data quality ≠ critical• Longer scans are better!• Consider using tasks ‣ improve compliance‣ increase sensitivity
Take-home points
Introduction:Why study individual differences?
1 How to get good brain data
2 How to get good behavioral data
3
How to relate brain to behavior
4
Learn more5
• Data quality ≠ critical• Longer scans are better!• Consider using tasks ‣ improve compliance‣ increase sensitivity
• Choose an interesting behavior with a good distribution
Take-home points
Introduction:Why study individual differences?
1 How to get good brain data
2 How to get good behavioral data
3
How to relate brain to behavior
4
Learn more5
• Data quality ≠ critical• Longer scans are better!• Consider using tasks ‣ improve compliance‣ increase sensitivity
• Choose an interesting behavior with a good distribution
• Consider potential confounds and take steps to mitigate them
Take-home points
Introduction:Why study individual differences?
1 How to get good brain data
2 How to get good behavioral data
3
How to relate brain to behavior
4
Learn more5
• Data quality ≠ critical• Longer scans are better!• Consider using tasks ‣ improve compliance‣ increase sensitivity
• Choose an interesting behavior with a good distribution
• Consider potential confounds and take steps to mitigate them
• Cross-validate whenever possible
Take-home points
Introduction:Why study individual differences?
1 How to get good brain data
2 How to get good behavioral data
3
How to relate brain to behavior
4
Learn more5
• Data quality ≠ critical• Longer scans are better!• Consider using tasks ‣ improve compliance‣ increase sensitivity
• Choose an interesting behavior with a good distribution
• Consider potential confounds and take steps to mitigate them
• Cross-validate whenever possible
• Consider tailoring your scan condition to behavior of interest
Take-home points
Introduction:Why study individual differences?
1 How to get good brain data
2 How to get good behavioral data
3
How to relate brain to behavior
4
Learn more5
• Data quality ≠ critical• Longer scans are better!• Consider using tasks ‣ improve compliance‣ increase sensitivity
• Choose an interesting behavior with a good distribution
• Consider potential confounds and take steps to mitigate them
• Cross-validate whenever possible
• Consider tailoring your scan condition to behavior of interest
Open data sets with brain & behavior
➡ Use these on their own or in combination with your own data to generate or test hypotheses, see if a finding generalizes, etc
Philadelphia Neurodevelopmental Cohort
Further reading
Building a science of individual differences from fMRIDubois & Adolphs, Trends in Cognitive Sciences (2016)
From regions to connections and networks: new bridges between brain and behaviorMisic & Sporns, Current Opinion in Neurobiology (2016)
Can brain state be manipulated to emphasize individual differences in functional connectivity?Finn et al., NeuroImage (2017)
Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscienceGabrieli, Ghosh & Gabrieli, Neuron (2015)
Selected reviews:
Learn more at OHBM 2017
Symposium: Collect your thoughts: Individual differences in the networks underlying intelligenceTues 8:00-9:15am
Symposium:Relating connectivity to inter- and intra-individual differences in attention and cognitionWeds 8:00-9:15am
Poster #4042: Can brain state be manipulated to emphasize individual differences in functional connectivity? Finn et al.
Poster #4040: Large-scale functional connectivity networks predict attention fluctuations Rosenberg et al.
Poster #2110: Functional connectivity-based predictors of naturalistic reading comprehension Jangraw et al.
Poster #4029: FMRI connectivity is differentially associated with performance across tasks in a multi-task study Topolski et al.
@esfinn