1. Neuroscience

A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use

  1. Yihe Weng
  2. Johann Kruschwitz
  3. Laura M Rueda-Delgado
  4. Kathy L Ruddy
  5. Rory Boyle
  6. Luisa Franzen
  7. Emin Serin
  8. Tochukwu Nweze
  9. Jamie Hanson
  10. Alannah Smyth
  11. Tom Farnan
  12. Tobias Banaschewski
  13. Arun LW Bokde
  14. Sylvane Desrivières
  15. Herta Flor
  16. Antoine Grigis
  17. Hugh Garavan
  18. Penny A Gowland
  19. Andreas Heinz
  20. Rüdiger Brühl
  21. Jean-Luc Martinot
  22. Marie-Laure Paillère Martinot
  23. Eric Artiges
  24. Jane McGrath
  25. Frauke Nees
  26. Dimitri Papadopoulos Orfanos
  27. Tomas Paus
  28. Luise Poustka
  29. Nathalie Holz
  30. Juliane Fröhner
  31. Michael N Smolka
  32. Nilakshi Vaidya
  33. Gunter Schumann
  34. Henrik Walter
  35. Robert Whelan  Is a corresponding author
  36. IMAGEN Consortium
  1. School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
  2. Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Germany
  3. Collaborative Research Centre (SFB 940) 'Volition and Cognitive Control', Technische Universität Dresden, Germany
  4. School of Psychology, Queens University Belfast, United Kingdom
  5. Faculty of Psychology and Neuroscience, Maastricht University, Netherlands
  6. Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Germany
  7. Bernstein Center for Computational Neuroscience, Germany
  8. Department of Psychology, University of Utah, United States
  9. Department of Psychology, Learning Research & Development Center, University of Pittsburgh, United States
  10. Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany
  11. Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland
  12. Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology, & Neuroscience, SGDP Centre, King’s College London, United Kingdom
  13. Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Mannheim, Heidelberg University, Germany
  14. Department of Psychology, School of Social Sciences, University of Mannheim, Germany
  15. NeuroSpin, CEA, Université Paris-Saclay, France
  16. Departments of Psychiatry and Psychology, University of Vermont, United States
  17. Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, United Kingdom
  18. Physikalisch-Technische Bundesanstalt (PTB), Germany
  19. Institut National de la Santé et de la Recherche Médicale, INSERM U 1299 'Trajectoires développementales & psychiatrie', University Paris-Saclay, CNRS; Ecole Normale Supérieure Paris-Saclay, Centre Borelli, France
  20. AP-HP Sorbonne University, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, France
  21. Psychiatry Department, EPS Barthélémy Durand, France
  22. Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Germany
  23. Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hosptalier Universitaire Sainte-Justine, University of Montreal, Canada
  24. Departments of Psychiatry and Psychology, University of Toronto, Canada
  25. Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Germany
  26. Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Germany
  27. Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Germany
  28. Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, China
https://doi.org/10.7554/eLife.97150.3
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Cite this article
  1. Yihe Weng
  2. Johann Kruschwitz
  3. Laura M Rueda-Delgado
  4. Kathy L Ruddy
  5. Rory Boyle
  6. Luisa Franzen
  7. Emin Serin
  8. Tochukwu Nweze
  9. Jamie Hanson
  10. Alannah Smyth
  11. Tom Farnan
  12. Tobias Banaschewski
  13. Arun LW Bokde
  14. Sylvane Desrivières
  15. Herta Flor
  16. Antoine Grigis
  17. Hugh Garavan
  18. Penny A Gowland
  19. Andreas Heinz
  20. Rüdiger Brühl
  21. Jean-Luc Martinot
  22. Marie-Laure Paillère Martinot
  23. Eric Artiges
  24. Jane McGrath
  25. Frauke Nees
  26. Dimitri Papadopoulos Orfanos
  27. Tomas Paus
  28. Luise Poustka
  29. Nathalie Holz
  30. Juliane Fröhner
  31. Michael N Smolka
  32. Nilakshi Vaidya
  33. Gunter Schumann
  34. Henrik Walter
  35. Robert Whelan
  36. IMAGEN Consortium
(2024)
A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use
eLife 13:RP97150.
https://doi.org/10.7554/eLife.97150.3
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  3. Download .RIS
8 figures, 2 tables and 2 additional files

Figures

Figure 1
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Intra-individual coefficient of variation (ICV) changes over time.

(A) ICV changes over time. (B) Correlation of ICV between timepoints within participants. †, p<0.001.

Figure 2 with 1 supplement
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The predictive performances and networks of intra-individual coefficient of variation (ICV) per timepoint derived from Go trials.

Correlation between observed and predicted ICV in positive, negative, and combined networks at (A) age 14, (B) age 19, and (C) age 23. Predictive networks for ICV are at (D) age 14, (E) age 19, and (F) age 23. Connectome of positive and negative networks of ICV at (G) age 14, (H) age 19, and (I) age 23. The edges depicted above are those selected in at least 95% of cross-validation folds. Red, blue, and green spheres/lines/scatters represent positive, negative, and combined networks respectively. MF, medial frontal; FP, frontoparietal; DMN, default mode; MOT, motor; VI, visual I; VII, visual II; VAs, visual association; SAL, salience; SC, subcortical; CBL, cerebellar. R/L, right/left hemisphere. ***, p<0.001.

Figure 2—figure supplement 1
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The predictive networks predicting intra-individual coefficient of variation (ICV) per timepoint derived from Go trials.

The heatmaps show predictive networks with non-zero values at (A) age 14, (B) age 19, (C) age 23. Each heatmap cell shows the number of edges between or within functional networks. Radar plots show the proportion of the functional networks involved in predictive networks. The edges depicted above are those selected in at least 95% of cross-validation folds. Red, blue, and green represent positive, negative, and combined networks respectively. MF, medial frontal; FP, frontoparietal; DMN, default mode; MOT, motor; VI, visual I network; VII, visual II network; VAs, visual association network; SAL, salience; SC, subcortical; CBL, cerebellar. R/L, right/left hemisphere.

Figure 3 with 4 supplements
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The predictive performances and networks of intra-individual coefficient of variation (ICV) per timepoint derived from Successful stop trials.

Correlation between observed and predicted ICV in positive, negative, and combined networks at (A) age 14, (B) age 19, and (C) age 23. Predictive networks for ICV are at (D) age 14, (E) age 19, and (F) age 23. Connectome of positive and negative networks of ICV at (G) age 14, (H) age 19, and (I) age 23. The edges depicted above are those selected in at least 95% of cross-validation folds. Red, blue, and green spheres/lines/scatters represent positive, negative, and combined networks respectively. MF, medial frontal; FP, frontoparietal; DMN, default mode; MOT, motor; VI, visual I; VII, visual II; VAs, visual association; SAL, salience; SC, subcortical; CBL, cerebellar. R/L, right/left hemisphere. *, p<0.05; **, p<0.01; ***, p<0.001.

Figure 3—figure supplement 1
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The predictive networks predicting intra-individual coefficient of variation (ICV) per timepoint derived from Successful stop trials.

The heatmaps show predictive networks with non-zero values at (A) age 14, (B) age 19, and (C) age 23. Each heatmap cell shows the number of edges between or within functional networks. Radar plots show the proportion of the functional networks involved in predictive networks. The edges depicted above are those selected in at least 95% of cross-validation folds. Red, blue, and green represent positive, negative, and combined networks respectively. MF, medial frontal; FP, frontoparietal; DMN, default mode; MOT, motor; VI, visual I network; VII, visual II network; VAs, visual association network; SAL, salience; SC, subcortical; CBL, cerebellar. R/L, right/left hemisphere.

Figure 3—figure supplement 2
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Connectome of positive and negative networks predicting intra-individual coefficient of variation (ICV) at age 14 with mean framewise displacement (meanFD) from 0.2 mm to 0.5 mm.

Red and blue lines represent positive and negative networks respectively. MF, medial frontal; FP, frontoparietal; DMN, default mode; MOT, motor; VI, visual I; VII, visual II; VAs, visual association; SAL, salience; SC, subcortical; CBL, cerebellar. R/L, right/left hemisphere.

Figure 3—figure supplement 3
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Connectome of positive and negative networks predicting intra-individual coefficient of variation (ICV) at age 19 with mean framewise displacement (meanFD) from 0.2 mm to 0.5 mm.

Red and blue lines represent positive and negative networks respectively. MF, medial frontal; FP, frontoparietal; DMN, default mode; MOT, motor; VI, visual I; VII, visual II; VAs, visual association; SAL, salience; SC, subcortical; CBL, cerebellar. R/L, right/left hemisphere.

Figure 3—figure supplement 4
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Connectome of positive and negative networks predicting intra-individual coefficient of variation (ICV) at age 23 with mean framewise displacement (meanFD) from 0.2 mm to 0.5 mm.

Red and blue lines represent positive and negative networks respectively. MF, medial frontal; FP, frontoparietal; DMN, default mode; MOT, motor; VI, visual I; VII, visual II; VAs, visual association; SAL, salience; SC, subcortical; CBL, cerebellar. R/L, right/left hemisphere.

Figure 4 with 1 supplement
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The predictive performances of intra-individual coefficient of variation (ICV) across timepoints and generalization in STRATIFY.

Predictive performances of ICV (A) derived from Go trials and (B) derived from Successful stop trials. The top, middle, and bottom rows of (A) and (B) panels show the predictive performance: using models defined at age 14 to predict age 19 (i.e. 14 years → 19 years), using models defined at age 14 to predict age 23 (i.e. 14 years → 23 years), and using models defined at age 19 to predict age 23 (i.e. 19 years → 23 years) respectively. Generalization of predictive networks predicting ICV defined at age 23 in STRATIFY (i.e. 23 years → STRATIFY) derived from (C) Go trials and (D) Successful stop trials. The red, blue, and green scatter represent positive, negative, and combined networks. †, p<0.001.

Figure 4—figure supplement 1
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Generalization in subgroups in STRATIFY.

(A) Predictive performance in distinct patient groups in STRATIFY derived from Go and Successful stop trials. (B) The correlation between network strength and intra-individual coefficient of variation (ICV) across patient cohorts in STRATIFY derived from Go and Successful stop trials. AUD, alcohol use disorder; MDD, major depression disorder; BN, bulimia nervosa; AN, anorexia nervosa; HC, healthy controls. *, p<0.05; **, p<0.01; ***, p<0.001.

Figure 5 with 2 supplements
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Significant correlations between sustained attention and substance use across timepoints (false discovery rate [FDR] correction, q<0.05).

(A) Correlations between the intra-individual coefficient of variation (ICV) and cigarette and cannabis use (Cig+CB) across timepoints. Correlations between sustained attention network strength and Cig+CB across timepoints (B) derived from Go trials and (C) derived from Successful stop trials. Rhop: r value between network strength of the positive network. Rhon: r value between network strength of the negative network.

Figure 5—figure supplement 1
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Exploratory factor analysis of Timeline Followback (TLFB) at each timepoint.

(A) Total variance explained for exploratory factor analysis of TLFB items. (B) Rotated component matrix for exploratory factor analysis. Extraction method: Principal component analysis. Rotation method: Varimax with Kaiser normalization.

Figure 5—figure supplement 2
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Significant correlations between sustained attention and substance use across timepoints (false discovery rate [FDR] correction, q<0.05).

(A) Correlations between the intra-individual coefficient of variation (ICV) and cigarette and cannabis use (Cig+CB) across timepoints. Correlations between sustained attention network strength and Cig+CB across timepoints (B) derived from Go trials and (C) derived from Successful stop trials. Rhop: r value between network strength of the positive network. Rhon: r value between network strength of the negative network.

Figure 6
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A simplified bivariate latent change score model for substance use and ICV/brain activity.

SUB, substance use (alcohol, cigarette, and cannabis use); Brain, brain network strength of positive/negative network of sustained attention derived from Go trials/Successful stop trials. ICV, intra-individual coefficient of variation. T1, timepoint 1 (age 14); T2, timepoint 2 (age 19); T3, timepoint 3 (age 23). γ1, lagged effects of substance use on ICV or brain activity. γ2, lagged effects of ICV or brain activity on substance use. The square/circle represents the observation/true score in the model.

Figure 7 with 1 supplement
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Cigarette and cannabis score in Timeline Followback changes in individuals with high sustained attention (High SA) and low sustained attention (Low SA) from ages 14 to 23.

Participants were categorized into five equal groups based on the intra-individual coefficient of variation (ICV), strength of positive network, and strength of negative network at age 14. (A) Top ICV (Low SA) and bottom ICV (High SA) groups. (B) The top strength of the positive network (Low SA) and bottom strength of the positive network (High SA) groups derived from Go trials. (C) The top strength of the negative network (High SA) and bottom strength of the negative network (Low SA) groups derived from Go trials. Note that the higher strength of the negative network reflects lower ICV and higher sustained attention.

Figure 7—figure supplement 1
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Cigarette and cannabis score in Timeline Followback change in individuals with good working memory (Good WM) and poor working memory (Poor WM) from ages 14 to 23.

Participants were categorized into five equal groups based on the performance of strategy working memory task at age 14.

Figure 8
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Schematic of connectome-based predictive modeling.

(i) Feature selection. The correlation between each edge in the generalized psychophysiological interaction (gPPI) matrix and the behavioral phenotype is calculated while controlling for several covariates in the training set. These covariates include age, gender, mean framewise displacement (mean FD), scan sites, and mode-centered PDS (only for age 14). The r value with the associated p value for each edge is obtained using partial correlation, and a threshold of p=0.01 is used to select the edges. Positively or negatively correlated edges are regarded as positive or negative networks. Network strength is then calculated by summing the selected edges in the gPPI matrix for both positive and negative networks, as well as by subtracting the strength of the negative network from the strength of the positive network to obtain the combined network strength. (ii) Model building. Linear models are constructed between the network strength of the positive, negative, combined network, and behavioral phenotype in the training set. The network strength is then calculated for each participants in the testing set and input into the predictive model along with covariates to yield a predicted behavioral phenotype (e.g. predicted intra-individual coefficient of variation [ICV]) for each network. (iii) Model validation. The predictive performance is evaluated by calculating the correlation between predicted and observed values.

Tables

Table 1
Demographic information of adolescents in the linear mixed model across three timepoints.
Age 14Age 19Age 23
N (three timepoints)2148
Sex (M/F)1055/1093
Age (years)14.4±0.419±0.722.6±0.7
Mean FD (mm)0.28±0.320.18±0.170.18±0.12
GO RT (ms)466.6±80400.7±71.8403.9±73.8
ICV0.234±0.0380.224±0.0510.217±0.052
Stop RT (ms)461.5±114.8360±82.4363.6±78.2
SSD (ms)319.3±148.1188.1±132.4190±158.4
SSRT (ms)217.8±37.2213.3±43.3216.2±42.6
pOmission (%)4.4±10.52.6±8.63.7±11.1
pChoiceError (%)4.7±6.64.8±4.75.2±7.6
pCommission (%)47.9±6.347.5±647.2±6.9

  1. Note: These data pertain to the participants included in the behavioural analyses. N, number of subjects; FD, framewise displacement of MR images; ICV, intra-individual coefficient of variation (assay for sustained attention); SSRT, stop signal reaction time; GO RT, reaction time in Go trials; Stop RT, reaction time in stop fail trials; SSD, stop signal delay; pOmisssion, probability of go omissions (no response); pChoiceError, probability of choice errors on Go trials; pCommission, probability of commission on Stop trials.

Table 2
Bivariate latent change score model showing the bidirectional association between substance use and ICV/brain networks (false discovery rate corrected).
Cig+CBAlcohol use
Lagged effects of Cig+CB (γ1)Lagged effects of ICV/brain networks (γ2)Lagged effects of alcohol use (γ1)Lagged effects of ICV/brain networks (γ2)
Std. β (SE)Std. β (SE)Std. β (SE)Std. β (SE)
ICV0.017 (0.039)0.117 (0.031)***0.005 (0.029)0.057 (0.030)
SA GT PosNet–0.026 (0.030)0.087 (0.032)**0.025 (0.030)0.022 (0.036)
SA GT NegNet0.012 (0.026)–0.094 (0.035)**–0.012 (0.030)–0.059 (0.034)
SA SS PosNet0.005 (0.025)0.070 (0.036)0.101 (0.040)0.046 (0.039)
SA SS NegNet0.038 (0.028)–0.061 (0.031)–0.003 (0.035)–0.069 (0.031)

Additional files

Supplementary file 1

Participants' demographic information.

https://cdn.elifesciences.org/articles/97150/elife-97150-supp1-v1.xlsx
MDAR checklist
https://cdn.elifesciences.org/articles/97150/elife-97150-mdarchecklist1-v1.docx

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  1. Yihe Weng
  2. Johann Kruschwitz
  3. Laura M Rueda-Delgado
  4. Kathy L Ruddy
  5. Rory Boyle
  6. Luisa Franzen
  7. Emin Serin
  8. Tochukwu Nweze
  9. Jamie Hanson
  10. Alannah Smyth
  11. Tom Farnan
  12. Tobias Banaschewski
  13. Arun LW Bokde
  14. Sylvane Desrivières
  15. Herta Flor
  16. Antoine Grigis
  17. Hugh Garavan
  18. Penny A Gowland
  19. Andreas Heinz
  20. Rüdiger Brühl
  21. Jean-Luc Martinot
  22. Marie-Laure Paillère Martinot
  23. Eric Artiges
  24. Jane McGrath
  25. Frauke Nees
  26. Dimitri Papadopoulos Orfanos
  27. Tomas Paus
  28. Luise Poustka
  29. Nathalie Holz
  30. Juliane Fröhner
  31. Michael N Smolka
  32. Nilakshi Vaidya
  33. Gunter Schumann
  34. Henrik Walter
  35. Robert Whelan
  36. IMAGEN Consortium
(2024)
A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use
eLife 13:RP97150.
https://doi.org/10.7554/eLife.97150.3