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Supplementary Information

1. Behavioral-Health Variables

2. Working memory task

3. Neuroimaging Acquisition and Preprocessing

4. WM network Activation

5. Functional Connectivity of the WM network

6. Effective connectivity of the WM network

7. Reliability Analyses

8. Alternative Analyses

9. Supplementary Discussion

10. Code Availability

11. References

12. Supplementary Figures

13. Supplementary Tables

1. Behavioral-Health DatasetWe considered 116 behavioral and health variables from the Human Connectome Project

(HCP) dataset. We excluded variables with the raw scores of measures for which age-adjusted

scores were also available and categorical variables where more than 90% of the group

endorsed the same outcome (n=130). For psychometric tests with multiple correlated outcome

variables we selected those that are more commonly reported in the literature. All variables

were considered together in the global analysis but were grouped into thematic subsets

(modules) for the modular analyses. These modules comprised psychometric variables for

affective cognitive tasks (affective cognition module), non-affective cognitive tasks (non-affective

cognition module), sensorimotor processing tasks (sensorimotor processing module),

personality and mental health variables (personality and mental health module) and physical

health and lifestyle variables (physical health and lifestyle module). All the variables are shown

in Supplementary Table 1 organized by module. Where necessary, the directionality of some

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variables was reversed prior to entering the sparse canonical correlation analysis so that higher

values would always denote better cognitive performance, physical health or health promoting

behavior.

2. Working memory taskThe working memory (WM) task used in the HCP has a block design incorporating alternating

experimental (2-back) and sensorimotor control (0-back) conditions1. At the start of a block, a

written cue (lasting 2.5 seconds) informed participants about the type of condition to follow (2-

back or 0-back) and the designated target stimulus for the sensorimotor control condition. Four

different stimulus types (faces, places, tools and body parts) were presented in separate blocks.

Each stimulus was presented for 2 seconds, followed by a 500 millisecond (ms) inter-stimulus

interval. In the 2-back trials participants were asked to respond by pressing a button whenever

the current stimulus was the same as the one presented 2 trials back. In the 0-back condition,

participants were asked to respond by pressing a button each time they saw the designated

stimulus. Each run contained 8 blocks of 10 trials, each lasting 2.5 seconds, and 4 fixation

blocks each lasting 15 seconds.

3. Neuroimaging Acquisition and Preprocessing Task and resting-state functional magnetic resonance imaging (task- and rs-fMRI, respectively)

data were acquired on a Siemens Skyra 3T scanner according to HCP protocols. Both

acquisitions used an image matrix=104x90; voxel resolution=2 mm isotropic; slice thickness=2

mm; repetition time=720 ms; echo time=33.1 ms; field of view=208 x 180 mm; flip angle=52

degrees. Each task- and rs-fMRI dataset respectively comprised 405 and 1200 volumes.

All data were de-identified prior to release as described by van Essen and Barch2. Head

movement artifacts were minimized by using strict criteria regarding head motion and overall

quality control. Task- and rs-fMRI data were preprocessed using the HCP pipeline as described

by Glasser and colleagues3. Data preprocessing was carried out using tools from FSL4,

FreeSurfer5 and the HCP workbench6. For rs-fMRI, an additional preprocessing step was

applied to minimize head motion by removing structured artifacts using an automatic denoising

approach based on independent component analysis (ICA) followed by FMRIB's ICA-based X-

noiseifier7,8.

4. WM network activation

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General linear model analyses were implemented using Statistical Parametric Mapping

(SPM12) (www.fil.ion.ucl.ac.uk/spm/software/spm12/). The baseline was not explicitly modeled.

The preprocessed single-participant images were analyzed using a linear convolution model,

with vectors of onset representing the experimental (2-back) and the sensorimotor control

condition (0-back). Six movement parameters were also entered as nuisance covariates. Serial

correlations were removed using an AR(1) model. A high-pass filter (128 seconds) was applied

to remove low-frequency noise. In each participant, images were produced for the 2-back vs 0-

back contrast and for the 0-back vs. baseline contrast.

Brain regions activated by the WM-network were identified using a random-effects one-sample

t-test of the single-participant contrast images for the 2-back vs 0-back condition. The statistical

threshold was set to p<0.05 with family-wise error (FWE) correction at voxel level. All results

were reported in Montreal Neurological Institute (MNI) space. Using the above parameters we

identified WM-network nodes bilaterally in the dorsolateral prefrontal cortex (dlPFC), inferior

parietal lobule (PAR), dorsal anterior cingulate cortex (dACC), and the visual cortex (VC)

(Supplementary Table 2 and Supplementary Figure 1). These regions are entirely consistent

with those expected based on previous meta-analyses of this task9,10. In each individual, we

then created 4-mm radius spherical volumes-of-interest (VOIs) centered on the group peak

coordinates of each network node and extracted the mean beta values, separately for the

experimental and sensorimotor control conditions. The beta values of these two contrasts were

correlated below an absolute value of 0.3. In total 24 variables per individual, representing the

task-related activation module of the imaging dataset, were entered in the sCCAs.

5. Functional ConnectivityFunctional connectivity in fMRI is inferred on the basis of pairwise correlations of the blood-

oxygen-level-dependent (BOLD) signal between brain regions. We extracted and averaged the

time series from the 12 VOIs defined above (Section 4: WM network Activation). For each

participant, we computed the functional connectivity between each pair of VOIs using Pearson’s

correlation followed by a Fisher-Z transformation. These metrics were computed separately in

the resting-state and task-related fMRI datasets. In total there were 66 task-related and 66

resting-state functional connectivity variables per individual (respectively comprising the task-

related and resting-state functional connectivity modules) that were then used for the sCCAs.

6. Effective connectivity

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http://www.fil.ion.ucl.ac.uk/spm/software/spm12/

Effective connectivity is described as causal as it refers to the influence that one neural

grouping exerts over another; it is therefore time (i.e., dynamic) and experiment (i.e., task)

dependent11,12. We assessed effective connectivity in the WM-network using Dynamic Causal

Modeling (DCM) implemented in DCM12 within SPM12. In DCM for fMRI, the dynamics of the

neural states underlying regional BOLD responses are modeled by a bilinear differential

equation that describes how the neural states change as a function of their intrinsic (i.e., task-

independent) connections, the task-related modulatory effects on these intrinsic connections,

and the driving inputs13,14.

Model Selection and Specification of Model Architecture: The anatomic network was based on

prior evidence from meta-analytic studies of the n-back task9,10, our previous DCM studies of n-

back working memory15-17 and the current results (Section 4: WM network Activation). Therefore

for the anatomical DCM layout, we selected the VOIs in the dlPFC, PAR, dACC and VC that

were defined following the second-level analysis (Section 4: WM network Activation). Neural

network models of WM are not intended to reproduce all known aspects of WM. This is because

overly complex models will start fitting noise (over-fitting) and are less generalizable. A good

model represents the optimal balance between accuracy, complexity and generalizability 11,12. In

DCM, the WM-network is best modeled using the simplest possible circuit diagram that can

account for the observed data. Bayesian Model Selection, a robust statistical approach, is then

applied to derive model evidence to support model selection. Therefore, the bilateral

homologous VOIs were combined to form one time series and then adjusted for the F-contrast

of all conditions of interest (2-back and 0-back). Bidirectional intrinsic (i.e., task-independent)

connections were specified between the VOIs. Starting from this basic layout, a structured

model space was derived by considering the modulatory effect of the 2-back condition on each

connection to produce 12 alternative DCM models (Supplementary Figure 2A). For control

purposes we also computed a non-modulated model. The task-related information (driving

input) entered through the VC in all models17.

Bayesian Model Selection and Bayesian Model Averaging: We used random effects Bayesian

model selection (BMS) to compare the DCM models in terms of the (log) evidence of the model

(i.e., the probability of the observed data for any given model)18. We report the posterior

probabilities and protected exceedance probability of each model (i.e., the probability that this

model is more likely than any other tested models to generate the given group data) 18

(Supplementary Figure 2B). To accommodate uncertainty about the models, we used Bayesian

Model Averaging (BMA) to obtain average connectivity estimates (weighted by their posterior

model probability) across all models for each individual19. Models with a lower posterior

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probability contribute less to the estimation of the marginal posterior. In total we computed 44

DCM variables that were used in the sCCAs.

7. Reliability Analyses7.1 Resampling

We tested the robustness of the results of the global analysis by randomly resampling half of the

sample (n=411) 5000 times and repeating the sCCA each time resulting in a mean r-value=

0.53, standard-deviation=0.03. The robustness of each modular result is presented in

supplementary table 7. The results of all replication analyses were within two standard

deviations for any of the significant sCCAs. For the global analysis, we additionally treated each

of those samples as a training and test set and used the weights from each of the 5000

permutation to create two scores (one for the behavioral data, one for the imaging data) for

each of the 412 participants in the training set. The mean r-value of these scores in the test set

was 0.39 (SD = 0.06), indicating that the overall sCCA overfits only slightly. Moreover, the

average correlation of the individual variables between the test set and both the training set and

the entire set (see Supplementary Figure 4) was r>0.98. The test sets had slightly lower mean |

r| value and fairly low standard deviations (imaging variables with the behavioral score: overall

set, mean |r| = 0.05; training set: mean |r| = 0.05, mean sd = 0.03; test set: mean |r| = 0.07,

mean sd = 0.03; behavioral variables with the imaging score: overall set, mean |r| = 0.09;

training set: mean |r| = 0.10, mean sd = 0.04; test set: mean |r| =0.07, mean sd = 0.04).

7.2. Confounders We tested the effect of potential confounders (i.e. intra-cranial volume, acquisition sequence,

and age) on our results. The sCCA results were virtually unchanged if we did regress these

confounds out of either dataset, or if we only regressed them out of the functional dataset. The

detail of the correlations between the confounders and the imaging dataset is reported in

supplementary table 10. We used sex to exemplify the lack of effect for potential confounders.

As shown in supplementary table 8, there was no major difference in the main results between

men and women only.

7.3. Comparison with non-Sparse CCATo confirm that our results were independent of the analytic method used, we also conducted a

non-sparse CCA. For this we applied similar methods to Smith et al.20, by first performing a

principal component analysis in order to reduce the number of variables to 100 on each side

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and then perform non-sparse CCA. To test significance we performed 5000 permutations and

then repeated the non-Sparse CCA step for each of them. This CCA correlated at r=0.687 (p-

value=0.0002). As shown in supplementary table 9, individual variables correlated fairly similarly

to the imaging pattern as given in the CCA.

7.4. Accounting for familial structureThe HCP data contains numerous participants who were related (370 independent families from

1 to 4 members). In order to ensure that our findings were not overly influenced by this possible

genetic confounder, we performed an additional analysis accounting for family structure.

Following Smith et al.’s method20, we replaced all rows (in both imaging and non-imaging

datasets) from a given family with the average row across all family members, for each family,

and re-ran the sCCA. The global sCCA correlation remained highly significant (r=0.62,

p=0.0004). At the variable level, we found that the r-values of the individual variables with the

opposite variate from the original analysis (n=823) remained highly correlated with the r-values

from the model that accounted for family structure (n=370) (correlation of r-values of the 116

behavior-health variables with the imaging mode: r=0.97, correlation of the 200 imaging

variables with the behavioral mode: r=0.95). This suggests that our results are not directly

affected by familiality.

8. Alternative Analyses

8.1. DCM modular sCCA following removal of exceedance probabilities

In order to test whether the model exceedance probabilities contributed to the pattern of

correlation between the DCM and behavioral-health variates we conducted the analysis after

removing all information on exceedance probabilities. The results were not affected; as before

the DCM variate correlated with non-affective cognition (r=0.029, p=0.0006) but not with

affective cognition (r=0.20, p=0.15), physical health and lifestyle (r=0.18, p=0.85), personality &

mental health (r=0.20, p=0.41) or sensorimotor processing (r=0.19, p=0.62).

8.2. Alternative DCM model space

To ensure the reliability of the results in the sense that they may not depend on a specific model

space, we also explored an alternative model space (Supplementary Figure 3). These included:

(a) specification of anatomical WM-models with bilateral VOIs in the dlPFC, PAR, dACC and

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VC; (b) simultaneous modelling of WM modulation in all forward, all backward, and all lateral

inter-hemispheric connections; and (c) combinations of these 3 modulatory options.

The choice of model space had no effect on the global analysis. Similarly, the results of the

modular sCCA remained unchanged, the DCM variate showed significant correlation with the

non-affective cognition variate, indicating that the specific choice of model space does not affect

the main results of the study in a major way.

8.3. Resting-state networks

To clarify whether the association between the physical health and lifestyle module and

functional connectivity measures was specific to the working memory network, we computed an

additional sCCA between resting-state functional connectivity measures extracted from 8 major

resting-state networks and the physical health and lifestyle module. We examined the eight

resting-state networks that are the most reliably described in the literature21,22: the ventral and

dorsal default-mode networks, the left and right executive control network, the salience network,

the sensorimotor network, the visual network, and the auditory network. We defined these

networks using their respective masks freely available on

http://findlab.stanford.edu/functional_ROIs.html23. We calculated the resting-state functional

connectivity between each pair of network, for each individual. These computations resulted in

28 resting-state functional connectivity measures per subject. We found that the modular sCCA

significant (r=0.29, p=0.0002), showing that higher body mass index and higher blood pressure

had a detrimental effect on resting-state functional connectivity (both p<0.05, Bonferroni

Corrected). This finding confirms that lifestyle and physical health has not only an impact on the

working memory network activity but also on the whole brain intrinsic organization.

9. Supplementary DiscussionConsistent with the notion that the WM-network identified via the 2-back task has a domain-

general role24,25, we found that WM-network activation and effective connectivity were

associated with a wide range of higher-order functions relating to executive control of attention,

visual orientation and language. Specifically, meta-analyses of neuroimaging studies have

shown that the WM regions, as identified here, are part of a super-ordinal network subserving

cognitive control across multiple tasks26. This network appears to overlap with a super-ordinal

network for affect processing, particularly in the dorsal prefrontal cortex and dorsal anterior

cingulate cortex and portions of the visual cortex27,28. This observation is consistent with the

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correlation between WM-activation and the affective processing variate (Supplemental Table 5).

The mental health and personality variate was correlated with WM-network activation included

items reflecting personality dimensions and self-report ratings of affective state. Personality

dimensions, particularly those assessed with the NEO-Personality Inventory, are known to have

brain functional and structural correlates that also overlap with the WM-network29,30. The task-

related and resting-state functional connectivity of the WM showed some degree of correlation

with the affective, non-affective and mental health and personality variate (r range: 0.16-0.26)

which did not reach statistical significance. A possible explanation for this is that the functional

connectivity of a task-related network shows relative specificity for that particular task as they

may reflect the brain configuration necessary for task performance. Conversely, regional brain

activations are less likely to be task-specific shown by multiple meta-analyses of the

neuroimaging literature26-28,30. Task-activation, task- and resting-state functional connectivity

were correlated with the physical health and lifestyle variate. This close dependency between

physical traits and task-related brain activation and connectivity and resting state connectivity is

not specific to WM as it was also observed in connection to whole-brain resting-state

connectivity as shown in our supplemental analyses. The same correlation pattern has also

been reported in data from the 5000 participants of the UKBiobank31 and is likely to reflect the

fact that these imaging metrics are directly derived from changes in the hemodynamic brain

responses and seem sensitive to cardiometabolic factors that may affect blood oxygenation.

This observation is further supported by the lack of a significant correlation between physical

traits and DCM-derived connectivity which may be accounted for by the fact that DCM

measures derive from the transformation of neuronal activity into hemodynamic response13.

10. Code availability

The imaging and behavioral-health variables were z-standardized and entered into sCCAs

implemented in MatlabR2015b using an in-house script. This code is available under request to

Dr. Alex Ing ([email protected]).

11. References

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28. Lindquist KA, Satpute AB, Wager TD, Weber J, Barrett LF. The Brain Basis of Positive and Negative Affect: Evidence from a Meta-Analysis of the Human Neuroimaging Literature. Cereb Cortex 2016; 26(5): 1910-1922.

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12. Supplementary Figures

Figure 1. Overall study design. WMN: Working memory network

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Figure 2. Dynamic Causal Model used in main analyses. The DCM is a 4-node working

memory model with the driving input to the visual cortex. (A) Each arrow represents the

connections between the model nodes; each number represents an individual model. Green

arrows represent modulated forward connections and blue arrows represent modulated

backward connections. (B) Posterior probabilities of the Bayesian Model Selection comparing all

12 tested including the null-model (no WM modulation) as control. (C) Results of the Bayesian

Model Selection comparing the exceedance probabilities of all models tested. The Bayesian

omnibus risk is < 0.001.

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Figure 3. Alternative Dynamic Causal Models tested for reliability

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The DCM is an 8-node working memory model with the driving input to the visual cortex in either Hemisphere. Each arrow represents the connections between the model nodes. Green arrows represent modulated forward connections and blue arrows represent modulated backward connections and brown connections represent lateral connections. The tested models included a model that modulated all forward connections (FW), one that modulated all backward connections (BW), one that connected all lateral connections (LAT) and combinations of these 3 models.

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Figure 4. Reliability Analysis. The Y-axis represents the mean r-value of 5000 resampled test

sets (n=412 each). Each Y-value is the correlation (mean r-value) of an individual variable with

the opposing overall score (gained each time from the weights of the respective 5000 test sets).

(A) Each X-value is the correlation of an individual variable with the opposing overall score

within the overall sample (n=823). (B) Each X-value is the correlation (mean r-value) of an

individual variable with the opposing overall score within 5000 resampled training sets of n=411.

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13. Supplementary Tables

Table 1. Behavioral Variables organized by module

Physical Health and Lifestyle Module (n = 24)Category Variable Name Full Description of the VariableSleep PSQI_Latency30Min Pittsburg Sleep Quality Index: No asleep within

30minSleep PSQI_Score Sleep Quality (Pittsburg Sleep Quality Index: Total

Score)Sleep PSQI_WakeUp1 Pittsburg Sleep Quality Index: Trouble to wake upSleep PSQI_AmtSleep Amount of Sleep (Pittsburg Sleep Quality Index:

Total Score)Sleep PSQI_Min2Asleep1 Pittsburg Sleep Quality Index: Total ScoreWeight BMI1 Body mass IndexDrug Use SSAGA_Mj_Times_Used1 Times used marijuanaDrug Use SSAGA_Mj_Age_1st_Use1 Age at first marijuana useDrug Use THC1 Use of Marijuana (Test for Marijuana)Alcohol Use SSAGA_Alc_12_Max_Drinks1 Previous drinking problems (Max drinks in a single

day in past 12 months)Alcohol Use SSAGA_Alc_Hvy_Frq_Drk1 Binge drinking (Drinks per day in heaviest 12-

month period)Smoking Habit Total_Any_Tobacco_7days1 Tobacco Use (Total times used/smoked any

tobacco in past 7 days)Hemoglobin HbA1C1 Hemoglobin A1C

Hematocrit Hematocrit_1 Hematocrit Sample 1Hematocrit Hematocrit_2 Hematocrit Sample 2Blood Pressure BPSystolic1 Systolic Blood Pressure

Blood Pressure BPDiastolic1 Diastolic Blood Pressure

Endurance Endurance_AgeAdj Physical Endurance (2-min walk endurance test)Handedness Handedness Handedness

Family History FamHist_Moth_None No Maternal History of psychiatric illness

Family History FamHist_Fath_None No Paternal History of psychiatric illness

Female Only Menstrual_AgeBegan Females only: age at menarche

Female Only Menstrual_DaysSinceLast

Females only: Number of Days since last menstrual cycle ((only used in sex-specific supplemental analysis)

Female Only Menstrual_CycleLength Females only: Length of cycle (only used in sex-

specific supplemental analysis)

Mental Health & Personality Module (n = 17)

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Personality NEOFAC_C1 NEO-Five-Factor Model ConscientiousnessPersonality NEOFAC_E1 NEO-Five-Factor Model ExtroversionPersonality NEOFAC_A NEO-Five-Factor Model AgreeablenessPersonality NEOFAC_O NEO-Five-Factor Model OpennessPersonality NEOFAC_N NEO-Five-Factor Model NeuroticismLife Function ASR_Totp_T1 Adult self-report Total T scoreLife Function ASR_TAO_Sum1 Adult self-report : Sum of Thought, Attention, and

Other ProblemsLife Function ASR_Intn_T1 Adult self-report internalizing T scoreLife Function ASR_Extn_T1 Adult self-report externalizing T scoreLife Function ASR_Anxd_Pct1 Adult self-report anxious/depressedLife Function ASR_Witd_Pct1 Adult self-report withdrawnLife Function ASR_Soma_Pct1 Adult self-report somatic complainsLife Function ASR_Thot_Pct1 Adult self-report thought problemsLife Function ASR_Attn_Pct1 Adult self-report attention problemsLife Function ASR_Aggr_Pct1 Adult self-report aggressive behaviorLife Function ASR_Rule_Pct1 Adult self-rule breaking behaviorLife Function ASR_Intr_Pct1 Adult self-rule intrusive

Affective Cognition Module (n = 24)Emotion ER40SAD Penn Emotion Recognition: Correct Sad

IdentificationsEmotion ER40NOE Penn Emotion Recognition: Correct Neutral

IdentificationsEmotion ER40ANG Penn Emotion Recognition: Correct Anger

IdentificationsEmotion ER40HAP Penn Emotion Recognition: Correct Happy

IdentificationsEmotion ER40FEAR Penn Emotion Recognition: Correct Fear

IdentificationsEmotion ER40_CRT1 Penn Emotion Recognition: Correct Responses

median Response TimeEmotion ER40_CR Penn Emotion Recognition: Number of Correct

ResponsesEmotion Sadness_Unadj Sadness SurveyEmotion LifeSatisf_Unadj General Life Satisfaction SurveyEmotion MeanPurp_Unadj Meaning and Purpose SurveyEmotion PosAffect_Unadj Positive Affect SurveyEmotion PercReject_Unadj Perceived Rejection SurveyEmotion EmotSupp_Unadj Emotional Support SurveyEmotion PercStress_Unadj Perceived Stress SurveyEmotion SelfEff_Unadj Self-Efficacy SurveyEmotion AngAffect_Unadj1 Anger-Affect SurveyEmotion AngHostil_Unadj1 Anger Hostility SurveyEmotion AngAggr_Unadj1 Anger-Physical Aggression SurveyEmotion FearAffect_Unadj1 Fear-Affect SurveyEmotion FearSomat_Unadj1 Fear-Somatic Arousal Survey

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Emotion Friendship_Unadj Friendship SurveyEmotion Loneliness_Unadj1 Loneliness SurveyEmotion PercHostil_Unadj1 Perceived hostility SurveyEmotion InstruSupp_Unadj Instrumental Support Survey

Non-Affective Cognition Module (n = 37)Verbal Episodic Memory

IWRD_TOT Penn Word Memory Test: Number of Correct Responses

Verbal Episodic Memory

IWRD_RTC1 Penn Word Memory Test: Correct Responses median Response Time

Sustained Attention

SCPT_SEN Short Penn Continuous Performance Test Sensitivity

Sustained Attention

SCPT_SPEC Short Penn Continuous Performance Test Specificity

Sustained Attention

SCPT_TP Short Penn Continuous Performance Test True Positives

Sustained Attention

SCPT_LRNR1 Short Penn Continuous Performance Test Longest Run of Non-Responses

Sustained Attention

SCPT_TN Short Penn Continuous Performance Test True Negatives

Sustained Attention

SCPT_FP1 Short Penn Continuous Performance Test False Positives

Sustained Attention

SCPT_FN1 Short Penn Continuous Performance Test False Negatives

Spatial Orientation

VSPLOT_OFF1 Penn Line Orientation: Total Positions Off for all Trials

Spatial Orientation

VSPLOT_CRTE Penn Line Orientation: Median Reaction Time Divided by expected number of Clicks for correct trials

Spatial Orientation

VSPLOT_TC Penn Line Orientation: Total Number Correct

Impulsivity DDisc_AUC_40K Delay Discounting: Area under the curve for discounting of $K40

Impulsivity DDisc_AUC_200 Delay Discounting: Area under the curve for discounting of $200

Impulsivity DDisc_SV_1mo_40K Delay Discounting: Subjective Value for $K40 at 1 month

Impulsivity DDisc_SV_6mo_40K Delay Discounting: Subjective Value for $K40 at 6 months

Impulsivity DDisc_SV_1yr_40K Delay Discounting: Subjective Value for $K40 at 1 year

Impulsivity DDisc_SV_3yr_40K Delay Discounting: Subjective Value for $K40 at 3 years

Impulsivity DDisc_SV_5yr_40K Delay Discounting: Subjective Value for $K40 at 5 years

Impulsivity DDisc_SV_10yr_40K Delay Discounting: Subjective Value for $K40 at

19


10 yearsImpulsivity DDisc_SV_1mo_200 Delay Discounting: Subjective Value for $200 at 1

monthImpulsivity DDisc_SV_6mo_200 Delay Discounting: Subjective Value for $200 at 6

monthsImpulsivity DDisc_SV_1yr_200 Delay Discounting: Subjective Value for $200 at 1

yearImpulsivity DDisc_SV_3yr_200 Delay Discounting: Subjective Value for $200 at 3

yearsImpulsivity DDisc_SV_5yr_200 Delay Discounting: Subjective Value for $200 at 5

yearsImpulsivity DDisc_SV_10yr_200 Delay Discounting: Subjective Value for $200 at

10 yearsLanguage PicVocab_AgeAdj Picture Vocabulary TestWorking Memory

ListSort_Unadj List Sorting Working Memory Test

Processing Speed

ProcSpeed_AgeAdj Pattern Completion Processing Speed scale score

Fluid Intelligence

PMAT24_A_SI1 Penn Matrix Test: Total Skipped Items

Fluid Intelligence

PMAT24_A_CR Fluid Intelligence (Penn Matrix Test: Number of Correct Responses)

Fluid Intelligence

PMAT24_A_RTCR1 Penn Matrix Test: Median Reaction Time for Correct Responses

Cognitive Status

MMSE_Score Mini Mental Status Exam Total Score

Episodic Memory

PicSeq_AgeAdj Picture Sequence memory Test

Executive Function

CardSort_AgeAdj Dimensional Change Card Sort Scale Score

Executive Function

Flanker_AgeAdj Flanker Inhibitory Control and Attention Test

Language ReadEng_AgeAdj Reading Recognition Scale Score

Sensorimotor Processing Module (n = 10)Sensory Mars_Errs1 Errors on Mars Contrast SensitivitySensory Mars_Final Final Contrast Sensitivity ScoreSensory Mars_Log_Score Contrast Sensitivity ScoreStrength Strength_AgeAdj Strength test scale scoreLocomotion GaitSpeed_Comp 4-Meter Walk Gait Speed TestTaste Taste_AgeAdj Taste IntensityAudition Noise_Comp AuditionDexterity Dexterity_AgeAdj Pegboard Dexterity test scale scorePain PainInterf_Tscore Pain Intensity and InterferenceOlfaction Odor_AgeAdj Odor Identification

More information on the variables:

20


https://www.humanconnectome.org/documentation/Q3/HCP_Q3_Release_Appendix_VII.pdf1 directionality of values reversed in the sparse canonical correlation analysis so that higher values denote better performance, outcome or physical health or health promoting behavior

21

Table 2. Suprathreshold activation clusters in the 2-back task

Region Laterality Cluster Size

Co-ordinatest

X Y ZInferior Parietal Lobe Right 9104 44 -38 42 33.12

Left -42 -48 44 31.42Dorsolateral Prefrontal Cortex Right 27417 28 8 58 32.66

Left -28 4 60 30.18Dorsal Anterior Cingulate Cortex

Right 2 22 44 26.90Left -2 24 44 26.88

Right 198 4 12 26 11.89Inferior Occipital Gyrus Left 513 -12 -94 0 15.32

Right 35 16 -90 -8 8.03Lingual Gyrus Left 79 -28 -86 -16 8.77Middle Temporal Gyrus Left 76 -50 -38 -6 8.29

Right 46 50 -42 -2 7.41Statistical threshold p<0.05, familywise error correction at voxel level; k>30; coordinates are in MNI space

22

Table 3: Global Analysis: Behavioral-health variables most strongly correlated to the complete imaging variateMeasure Correlation Module of the measurePenn Progressive Matrices(Fluid Intelligence)1 0.41 Non-Affective Cognition

Reading Recognition2 0.33 Non-Affective CognitionLine Orientation3 0.32 Non-Affective CognitionPicture Vocabulary Test4 0.29 Non-Affective CognitionList Sorting (Working Memory Test)5 0.26 Non-Affective CognitionPicture Sequence Memory Test6 0.24 Non-Affective CognitionDimensional Card Sorting Test(Cognitive Flexibility)7 0.22 Non-Affective Cognition

Sustained Attention (Specificity)8 0.19 Non-Affective Cognition

Emotion Recognition9 0.18 Personality & Mental Health

Physical Endurance10 0.17 Physical Health & Lifestyle

Thought Problems11 -0.09 Personality & Mental Health

Aggressive Behavior12 -0.09 Personality & Mental Health

Hemoglobin A1C13 -0.10 Physical Health & Lifestyle

Conscientiousness14 -0.10 Personality & Mental Health

Times Drunk Last Year15 -0.11 Physical Health & LifestyleBody Mass Index16 -0.12 Physical Health & LifestyleWeekly Tobacco Use17 -0.15 Physical Health & LifestyleContinuous Attention (False positives) 18 -0.19 Non-Affective CognitionErrors Line Orientation19 -0.32 Non-Affective CognitionSkipped Items Penn Progressive Matrices20 -0.39 Non-Affective Cognition

The different measures correspond to the following variables within the HCP dataset: 1PMAT24_A_CR, 2ReadEng_AgeAdj, 3VSPLOT_TC, 4PicVocab_AgeAdj,5 ListSort_Unadj, 6PicSeq_Unadj, 7 CardSort_Unadj, 8SCPT_SPEC, 9ER40_CR,

10Endurance_AgeAdj, 11ASR_Thot_Pct, 12ASR_Aggr_Pct, 13HbA1C, 14NEOFAC_C,

15SSAGA_Alc_Hvy_Frq_Drk, 16BMI, 17Total_Any_Tobacco_7days, 18SCPT_FP/SCPT_FN, 19VSPLOT_OFF, 20PMAT24_A_RTCR

23

Table 4. Global Analysis: Imaging variables most strongly correlated to the complete behavioral-health variate

Region Laterality Correlation MeasuredlPFC R 0.38 Activation 2-back vs 0-back

dlPFC L 0.33 Activation 2-back vs 0-back

PAR L 0.31 Activation 2-back vs 0-back

dACC B 0.31 Activation 2-back vs 0-back

PAR R 0.31 Activation 2-back vs 0-back

VC L 0.23 Activation 2-back vs 0-back

Lingual Gyrus L 0.22 Activation 2-back vs 0-back

VC to dlPFC 0.20 Effective Connectivity (Intrinsic)

VC R 0.17 Activation 2-back vs 0-back

VC to PAR 0.16 Effective Connectivity(Bayesian Model Average)

dlPFC o dACC -0.10 Effective Connectivity (Intrinsic)

PAR with dACC -0.10 Functional Connectivity during the task

dlPFC to VC -0.11 Effective Connectivity (Intrinsic)

dACC -0.11 Activation Sensorimotor Condition

dlPFC L -0.12 Activation Sensorimotor Condition

PAR L -0.12 Activation Sensorimotor Condition

dACC to VC -0.13 Effective Connectivity (Intrinsic)

dlPFC R -0.20 Activation Sensorimotor Condition

PAR R -0.21 Activation Sensorimotor Condition

Top ten imaging variables most positively and most negatively correlated to the mode of the complete behavioral-health variate in the global sCCA. Abbreviations: dACC = dorsal Anterior Cingulate Cortex, dlPFC =Dorsolateral Prefrontal Cortex, PAR = (Inferior) Parietal Lobule, VC = Visual Cortex. Sensorimotor Condition refers to the contrast 0-back vs baseline.

24

Table 5. Results of the modular analyses

WM-Task Activation

(24)

WM- Dynamic Causal

Modeling(44)

WM-Task Functional

Connectivity(66)

WM-networksResting State

Functional Connectivity

(66)

Physical Health and Lifestyle (24)

r=0.27p=0.007

r=0.21p=0.60

r=0.31p=0.028

r=0.23p=0.012

Mental Health & Personality (17)

r=0.22p=0.004

r=0.21p =0.15

r=0.24p=0.52

r=0.16p=0.37

Affective Cognition (24)

r=0.23p =0.005

r=0.21p=0.29

r=0.26p=0.23

r=0.16p=0.45

Non-affective Cognition (37)

r=0.43p =0.00003

r=0.28p=0.002

r=0.24p=0.56

r=0.20p=0.07

Sensorimotor Processing (10)

r=0.18p =0.26

r=0.21p=0.21

r=0.26p=0.35

r=0.22p=0.008

Number of variables of the module is in parentheses. Bold indicates significant results at p<0.05, following 100000 permutations.; WM=working memory

25

Table 6. Modular Analyses, Correlations of behavioral variables with the respective imaging mode

Non-Affective Cognition

WM-Task Activation

DCM Module

WM-Task Functional

Connectivity

Resting State Functional

ConnectivityModule sCCA r-value 0.43 0.28 0.24 0.20Module sCCA p-value 0.0004 0.002 0.54 0.12

Variable Name r-value r-value r-value r-value

PMAT24_A_CR 0.34 0.21 0.20 0.19ReadEng_AgeAdj 0.30 0.20 0.13 0.05PicVocab_AgeAdj 0.28 0.12 0.16 0.06ListSort_Unadj 0.27 0.11 0.05 0.08VSPLOT_TC 0.27 0.20 0.16 0.06PicSeq_AgeAdj 0.23 0.10 0.08 0.04CardSort_AgeAdj 0.21 0.16 0.11 -0.03SCPT_SPEC 0.17 0.10 0.13 0.03SCPT_TN 0.17 0.10 0.13 0.03ProcSpeed_AgeAdj 0.16 0.12 0.11 -0.04SCPT_SEN 0.16 0.05 0.00 0.05SCPT_TP 0.16 0.05 0.00 0.05PMAT24_A_RTCR 0.16 0.16 0.14 0.16DDisc_SV_1mo_40K 0.15 0.01 0.04 0.10Flanker_AgeAdj 0.14 0.14 0.09 -0.04DDisc_SV_1yr_40K 0.13 0.02 0.05 0.05MMSE_Score 0.13 0.06 0.02 0.00IWRD_TOT 0.12 0.02 0.01 0.02DDisc_SV_6mo_200 0.12 -0.02 0.02 0.01DDisc_SV_3yr_200 0.11 -0.02 0.04 -0.01DDisc_SV_1yr_200 0.11 0.00 0.04 0.02DDisc_AUC_200 0.10 -0.02 0.04 0.00DDisc_SV_5yr_200 0.07 -0.01 0.03 -0.01DDisc_SV_3yr_40K 0.07 0.02 0.02 0.01DDisc_SV_10yr_200 0.07 -0.05 0.03 -0.02DDisc_SV_6mo_40K 0.07 -0.04 0.03 0.03DDisc_AUC_40K 0.06 0.00 0.04 0.02DDisc_SV_5yr_40K 0.04 0.01 0.05 0.01DDisc_SV_1mo_200 0.03 -0.01 -0.01 0.01VSPLOT_CRTE 0.02 0.09 0.03 -0.01DDisc_SV_10yr_40K 0.02 -0.06 0.03 0.01SCPT_LRNR -0.06 0.00 0.06 -0.01IWRD_RTC -0.11 -0.03 -0.02 0.01SCPT_FN -0.16 -0.05 0.00 -0.05SCPT_FP -0.17 -0.10 -0.13 -0.03VSPLOT_OFF -0.25 -0.21 -0.16 -0.10PMAT24_A_SI -0.32 -0.22 -0.20 -0.19

26

Affective Cognition

WM-Task Activation

DCM Module

WM-Task Functional

Connectivity



Variable Name

ER40_CR 0.20 0.19 0.06 0.05ER40FEAR 0.13 0.13 0.06 -0.01ER40SAD 0.12 0.09 0.06 0.10ER40NOE 0.11 0.07 -0.03 -0.03ER40ANG 0.07 0.11 0.02 0.05ER40HAP 0.06 0.10 0.09 0.02Sadness_Unadj 0.02 -0.02 -0.09 -0.02Loneliness_Unadj 0.00 0.01 -0.11 -0.07SelfEff_Unadj 0.00 0.06 0.11 0.00PercReject_Unadj 0.00 -0.04 -0.18 0.00EmotSupp_Unadj -0.01 0.03 0.16 0.05PosAffect_Unadj -0.02 0.05 0.08 0.04FearAffect_Unadj -0.02 0.00 -0.10 0.00Friendship_Unadj -0.02 0.04 0.14 0.06InstruSupp_Unadj -0.02 0.07 0.08 0.04PercStress_Unadj -0.03 -0.05 -0.15 -0.04LifeSatisf_Unadj -0.04 0.09 0.11 0.05MeanPurp_Unadj -0.04 0.01 0.07 0.11AngAffect_Unadj -0.04 0.01 -0.14 -0.05FearSomat_Unadj -0.05 0.07 -0.04 -0.04AngAggr_Unadj -0.06 -0.04 -0.10 -0.09ER40_CRT -0.07 -0.09 0.01 -0.03PercHostil_Unadj -0.07 -0.03 -0.26 -0.08AngHostil_Unadj -0.10 -0.08 -0.11 -0.02

Mental Health and Personality

WM-Task Activation

DCM Module

WM-Task Functional

Connectivity



Variable Name

NEOFAC_O 0.15 -0.01 0.02 -0.03NEOFAC_A 0.08 0.06 0.16 0.10ASR_Intr_Pct 0.06 -0.09 -0.11 -0.05ASR_Attn_Pct 0.01 -0.11 -0.12 -0.16NEOFAC_E 0.00 -0.03 0.08 0.03ASR_TAO_Sum -0.03 -0.12 -0.19 -0.13NEOFAC_N -0.04 -0.09 -0.16 -0.05ASR_Totp_T -0.04 -0.11 -0.23 -0.11

27

ASR_Anxd_Pct -0.04 -0.10 -0.14 -0.07ASR_Rule_Pct -0.04 -0.09 -0.10 -0.04ASR_Extn_T -0.04 -0.13 -0.20 -0.06ASR_Intn_T -0.05 -0.07 -0.18 -0.08ASR_Witd_Pct -0.07 -0.03 -0.15 -0.06ASR_Soma_Pct -0.08 -0.07 -0.12 -0.04ASR_Thot_Pct -0.08 -0.10 -0.12 -0.05ASR_Aggr_Pct -0.10 -0.21 -0.21 -0.05NEOFAC_C -0.11 0.04 0.07 0.13

Physical Health and Lifestyle

WM-Task Activation

DCM Module

WM-Task Functional

Connectivity



Variable Name

Endurance_AgeAdj 0.17 0.13 0.09 0.04FamHist_Moth_None 0.10 -0.05 0.06 0.06FamHist_Fath_None 0.09 0.00 0.05 -0.01SSAGA_Alc_12_Max_Drinks 0.09 0.05 -0.03 0.13

PSQI_AmtSleep 0.04 -0.03 0.02 -0.04Hematocrit_2 0.03 0.07 -0.04 0.15Menstrual_DaysSinceLast 0.02 -0.04 0.00 -0.04

Menstrual_CycleLength 0.02 0.02 0.00 0.00

Hematocrit_1 0.02 0.09 -0.04 0.16SSAGA_Mj_Age_1st_Use 0.01 -0.09 0.01 0.00

Handedness 0.00 -0.02 0.01 0.00BPSystolic -0.03 0.01 -0.12 -0.05Menstrual_AgeBegan -0.03 0.05 0.05 0.03SSAGA_Mj_Times_Used -0.03 0.05 -0.04 0.02

PSQI_Min2Asleep -0.04 0.05 -0.01 0.07BPDiastolic -0.04 0.05 -0.12 -0.09PSQI_Latency30Min -0.05 0.09 -0.02 0.07PSQI_Score -0.06 0.09 -0.03 0.10PSQI_WakeUp -0.07 0.03 -0.02 0.02HbA1C -0.08 -0.06 -0.07 -0.07BMI -0.09 -0.08 -0.31 -0.06SSAGA_Alc_Hvy_Frq_Drk -0.12 -0.09 -0.01 -0.03

THC -0.13 -0.04 -0.01 0.06Total_Any_Tobacco_7days -0.14 -0.05 -0.04 -0.05

28

Sensorimotor Processing

WM-Task Activation

DCM Module

WM-Task Functional

Connectivity



Variable Name

Dexterity_AgeAdj 0.13 0.03 0.24 0.01Mars_Final 0.13 0.14 -0.03 -0.04Mars_Log_Score 0.10 0.17 -0.02 -0.02Odor_AgeAdj 0.04 -0.01 0.08 0.02Strength_AgeAdj -0.03 0.11 -0.08 0.21PainInterf_Tscore -0.03 -0.02 -0.06 -0.01GaitSpeed_Comp -0.05 0.14 0.09 -0.01Noise_Comp -0.05 0.08 0.00 -0.05Taste_AgeAdj -0.06 -0.03 -0.01 -0.01Mars_Errs -0.06 0.04 -0.01 0.04Full name of the variables can be found in Table S1. Bold indicates significant results at

p<0.05, following 5000 permutations.

29

Table 7. Results of the reliability analyses for the significant modular sCCAs

WM-Task Activation DCM Module

WM-Task Functional

Connectivity


ConnectivityPhysical Health and Lifestyle

r823 = 0.27meanr411 =0.31Std411= 0.027

r823 = 0.31meanr411 = 0.36Std411= 0.033

r823 = 0.23meanr411 = 0.28Std411=0.028

Mental Health & Personality

r823 =0.22meanr411 = 0.24Std411= 0.033

Affective Cognition

r823 = 0.23meanr411 =0.27Std411= .028

Non-affective Cognition

r823 = 0.43meanr411 = 0.45Std411=0.024

r823 = 0.28meanr411 = 0.32Std411= 0.026

Sensorimotor Processing

r823 = 0.22meanr411 = 0.24Std411=0.031

Reliability analyses for the significant modular analyses using 5000 permutations, each of which randomly selects 411 participants and then reruns the same analysis. Abbreviations: r821

= the r value in the actual sCCA using all 823 participants. meanr411= the mean r value among the 5000 randomly chosen samples of 411 participants. Std411 the standard deviation of the r values among the 5000 randomly chosen samples of 411 participants.

30

Table 8. Global Analysis: No effect of sex

All Participants(n=823)

Female(n=462)

Male(n=361)

Global sCCA r-value 0.497 0.522 0.556

Global sCCA p-value 0.0004 0.0004 0.0004

Variable Name

PMAT24_A_CR 0.41 0.43 0.44

ReadEng_AgeAdj 0.33 0.36 0.36

VSPLOT_TC 0.32 0.32 0.37

PicVocab_AgeAdj 0.29 0.33 0.33

ListSort_Unadj 0.26 0.25 0.28

PicSeq_AgeAdj 0.24 0.28 0.27

PMAT24_A_RTCR 0.23 0.22 0.27

CardSort_AgeAdj 0.22 0.19 0.31

SCPT_SPEC 0.19 0.19 0.22

SCPT_TN 0.19 0.19 0.22

ER40_CR 0.18 0.23 0.18

Endurance_AgeAdj 0.17 0.24 0.16

ProcSpeed_AgeAdj 0.17 0.16 0.25

Flanker_AgeAdj 0.17 0.13 0.21

SCPT_SEN 0.15 0.11 0.29

SCPT_TP 0.15 0.11 0.29

NEOFAC_O 0.14 0.16 0.17

DDisc_SV_1mo_40K 0.14 0.17 0.20

Dexterity_AgeAdj 0.14 0.14 0.23

DDisc_SV_1yr_40K 0.13 0.12 0.22

MMSE_Score 0.12 0.10 0.17

ER40FEAR 0.12 0.13 0.12

IWRD_TOT 0.11 0.15 0.10

SSAGA_Alc_12_Max_Drinks 0.11 0.11 0.11

LifeSatisf_Unadj 0.11 0.14 0.18

Mars_Final 0.10 0.10 0.15

31

ER40ANG 0.09 0.14 0.09

DDisc_SV_1yr_200 0.09 0.09 0.16

ER40NOE 0.09 0.05 0.12

DDisc_SV_6mo_200 0.08 0.06 0.20

DDisc_SV_3yr_200 0.08 0.08 0.17

NEOFAC_A 0.08 0.10 0.13

Hematocrit_2 0.08 0.04 0.08

ER40HAP 0.08 0.13 0.05

Hematocrit_1 0.08 0.04 0.06

Mars_Log_Score 0.07 0.08 0.14

FamHist_Fath_None 0.07 0.12 0.06

ER40SAD 0.07 0.16 0.04

DDisc_AUC_200 0.07 0.07 0.18

FamHist_Moth_None 0.07 0.14 0.03

DDisc_SV_3yr_40K 0.07 0.08 0.15

SelfEff_Unadj 0.07 0.04 0.17

DDisc_SV_5yr_200 0.06 0.05 0.16

DDisc_AUC_40K 0.05 0.05 0.15

PosAffect_Unadj 0.05 0.05 0.14

FearSomat_Unadj 0.05 0.09 -0.01

Strength_AgeAdj 0.05 -0.03 0.05

DDisc_SV_6mo_40K 0.04 0.06 0.11

ASR_Intr_Pct 0.04 0.01 0.07

InstruSupp_Unadj 0.04 0.01 0.15

DDisc_SV_5yr_40K 0.04 0.03 0.14

EmotSupp_Unadj 0.04 0.05 0.11

DDisc_SV_10yr_200 0.04 0.02 0.13

VSPLOT_CRTE 0.03 -0.01 0.11

ASR_Attn_Pct 0.03 0.04 -0.08

Friendship_Unadj 0.03 0.01 0.14

Odor_AgeAdj 0.02 0.02 0.00

Menstrual_CycleLength 0.02 0.04

Menstrual_AgeBegan 0.02 0.03

32

Loneliness_Unadj 0.02 0.10 -0.16

DDisc_SV_1mo_200 0.02 0.04 0.10

PSQI_AmtSleep 0.01 -0.03 0.09

GaitSpeed_Comp 0.00 0.02 0.03

Menstrual_DaysSinceLast 0.00 -0.02

SSAGA_Mj_Age_1st_Use 0.00 -0.01 0.05

PSQI_Min2Asleep 0.00 -0.02 -0.01

SSAGA_Mj_Times_Used 0.00 0.01 -0.06

NEOFAC_E 0.00 -0.02 0.09

AngAffect_Unadj 0.00 0.02 -0.07

DDisc_SV_10yr_40K -0.01 -0.01 0.08

Handedness -0.01 0.04 0.00

ASR_Anxd_Pct -0.01 0.04 -0.15

PSQI_Latency30Min -0.01 -0.04 0.01

MeanPurp_Unadj -0.01 -0.04 0.12

ASR_TAO_Sum -0.02 0.01 -0.15

PSQI_Score -0.02 -0.05 0.01

FearAffect_Unadj -0.02 -0.01 -0.04

Sadness_Unadj -0.02 -0.01 -0.09

PercHostil_Unadj -0.02 -0.05 -0.09

ASR_Totp_T -0.03 -0.02 -0.14

ASR_Intn_T -0.03 0.00 -0.16

ASR_Rule_Pct -0.03 -0.04 -0.13

BPSystolic -0.04 -0.12 -0.04

PainInterf_Tscore -0.04 -0.07 -0.03

BPDiastolic -0.04 -0.10 0.00

SCPT_LRNR -0.04 -0.05 -0.02

AngAggr_Unadj -0.05 -0.06 -0.15

NEOFAC_N -0.05 -0.03 -0.15

ASR_Extn_T -0.05 -0.07 -0.10

ASR_Witd_Pct -0.05 -0.01 -0.17

PercReject_Unadj -0.05 -0.03 -0.16

PSQI_WakeUp -0.05 -0.10 0.00

33

Noise_Comp -0.06 0.01 -0.05

Mars_Errs -0.06 -0.05 -0.04

PercStress_Unadj -0.06 -0.03 -0.19

ASR_Soma_Pct -0.07 -0.05 -0.17

ER40_CRT -0.08 -0.12 -0.05

Taste_AgeAdj -0.08 -0.06 -0.09

IWRD_RTC -0.08 -0.06 -0.14

AngHostil_Unadj -0.08 -0.02 -0.24

THC -0.09 -0.13 -0.19

ASR_Thot_Pct -0.09 -0.07 -0.19

ASR_Aggr_Pct -0.09 -0.08 -0.19

HbA1C -0.10 -0.09 -0.07

NEOFAC_C -0.10 -0.08 -0.06

SSAGA_Alc_Hvy_Frq_Drk -0.11 -0.12 -0.12

BMI -0.12 -0.23 -0.02

Total_Any_Tobacco_7days -0.15 -0.17 -0.19

SCPT_FN -0.15 -0.11 -0.29

SCPT_FP -0.19 -0.19 -0.22

VSPLOT_OFF -0.32 -0.33 -0.36

PMAT24_A_SI -0.39 -0.41 -0.43

Full name of the variables can be found in Table S1.

34

Table 9. Global Analysis: Comparison sparse vs non-sparse canonical correlation analyses

sCCA(n=823)

Non-sparse CCA(n=823)

Variable Name

PMAT24_A_CR 0.41 0.59

ReadEng_AgeAdj 0.33 0.43

VSPLOT_TC 0.32 0.47

PicVocab_AgeAdj 0.29 0.43

ListSort_Unadj 0.26 0.32

PicSeq_AgeAdj 0.24 0.32

PMAT24_A_RTCR 0.23 0.39

CardSort_AgeAdj 0.22 0.34

SCPT_SPEC 0.19 0.09

SCPT_TN 0.19 0.09

ER40_CR 0.18 0.16

Endurance_AgeAdj 0.17 0.34

ProcSpeed_AgeAdj 0.17 0.27

Flanker_AgeAdj 0.17 0.28

SCPT_SEN 0.15 0.23

SCPT_TP 0.15 0.23

NEOFAC_O 0.14 0.11

DDisc_SV_1mo_40K 0.14 0.25

Dexterity_AgeAdj 0.14 0.18

DDisc_SV_1yr_40K 0.13 0.24

MMSE_Score 0.12 0.10

ER40FEAR 0.12 0.04

IWRD_TOT 0.11 0.06

SSAGA_Alc_12_Max_Drinks 0.11 0.21

LifeSatisf_Unadj 0.11 0.11

Mars_Final 0.10 0.15

ER40ANG 0.09 0.07

DDisc_SV_1yr_200 0.09 0.23

35

ER40NOE 0.09 0.22

DDisc_SV_6mo_200 0.08 0.27

DDisc_SV_3yr_200 0.08 0.26

NEOFAC_A 0.08 -0.01

Hematocrit_2 0.08 0.21

ER40HAP 0.08 0.03

Hematocrit_1 0.08 0.19

Mars_Log_Score 0.07 0.11

FamHist_Fath_None 0.07 0.08

ER40SAD 0.07 -0.02

DDisc_AUC_200 0.07 0.25

FamHist_Moth_None 0.07 0.21

DDisc_SV_3yr_40K 0.07 0.19

SelfEff_Unadj 0.07 0.15

DDisc_SV_5yr_200 0.06 0.20

DDisc_AUC_40K 0.05 0.22

PosAffect_Unadj 0.05 0.05

FearSomat_Unadj 0.05 0.10

Strength_AgeAdj 0.05 0.30

DDisc_SV_6mo_40K 0.04 0.10

ASR_Intr_Pct 0.04 0.06

InstruSupp_Unadj 0.04 0.08

DDisc_SV_5yr_40K 0.04 0.20

EmotSupp_Unadj 0.04 -0.04

DDisc_SV_10yr_200 0.04 0.20

VSPLOT_CRTE 0.03 -0.09

ASR_Attn_Pct 0.03 0.06

Friendship_Unadj 0.03 0.00

Odor_AgeAdj 0.02 -0.03

Menstrual_CycleLength 0.02 -0.18

Menstrual_AgeBegan 0.02 0.05

Loneliness_Unadj 0.02 -0.03

DDisc_SV_1mo_200 0.02 0.06

36

PSQI_AmtSleep 0.01 0.05

GaitSpeed_Comp 0.00 0.07

Menstrual_DaysSinceLast 0.00 0.20

SSAGA_Mj_Age_1st_Use 0.00 0.03

PSQI_Min2Asleep 0.00 0.01

SSAGA_Mj_Times_Used 0.00 0.00

NEOFAC_E 0.00 -0.08

AngAffect_Unadj 0.00 -0.04

DDisc_SV_10yr_40K -0.01 0.20

Handedness -0.01 -0.08

ASR_Anxd_Pct -0.01 -0.03

PSQI_Latency30Min -0.01 -0.07

MeanPurp_Unadj -0.01 -0.07

ASR_TAO_Sum -0.02 -0.04

PSQI_Score -0.02 -0.05

FearAffect_Unadj -0.02 -0.05

Sadness_Unadj -0.02 -0.09

PercHostil_Unadj -0.02 0.08

ASR_Totp_T -0.03 -0.04

ASR_Intn_T -0.03 -0.07

ASR_Rule_Pct -0.03 -0.12

BPSystolic -0.04 0.08

PainInterf_Tscore -0.04 -0.07

BPDiastolic -0.04 0.02

SCPT_LRNR -0.04 0.00

AngAggr_Unadj -0.05 -0.08

NEOFAC_N -0.05 -0.17

ASR_Extn_T -0.05 -0.06

ASR_Witd_Pct -0.05 0.00

PercReject_Unadj -0.05 -0.05

PSQI_WakeUp -0.05 -0.12

Noise_Comp -0.06 -0.22

Mars_Errs -0.06 -0.11

37

PercStress_Unadj -0.06 -0.17

ASR_Soma_Pct -0.07 -0.10

ER40_CRT -0.08 -0.14

Taste_AgeAdj -0.08 -0.17

IWRD_RTC -0.08 -0.14

AngHostil_Unadj -0.08 -0.04

THC -0.09 -0.14

ASR_Thot_Pct -0.09 -0.16

ASR_Aggr_Pct -0.09 -0.11

HbA1C -0.10 -0.10

NEOFAC_C -0.10 -0.12

SSAGA_Alc_Hvy_Frq_Drk -0.11 -0.22

BMI -0.12 -0.20

Total_Any_Tobacco_7days -0.15 -0.17

SCPT_FN -0.15 -0.23

SCPT_FP -0.19 -0.09

VSPLOT_OFF -0.32 -0.50

PMAT24_A_SI -0.39 -0.58

Full name of the variables can be found in Table S1.

38

Table 10. Global analysis: Correlation of demographic and potentially confounding variables with the imaging variate

Variable NameGlobal level

(n=823)

Education Level (SSAGA_Educ) 0.26

Acquisition Sequence of the HCP 0.12

Age in Years -0.13

Mean head movement during MRI scan -0.14

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