Brain functional connectivity predicts depression and anxiety during childhood and adolescence: A connectome-based predictive modeling approach. Morfini, F., Kucyi, A., Zhang, J., Bauer, C. C., Bloom, P. A., Pagliaccio, D., Hubbard, N. A., Rosso, I. M., Yendiki, A., Ghosh, S. S., Pizzagalli, D. A., Gabrieli, J. D., Whitfield-Gabrieli, S., & Auerbach, R. P. Imaging Neuroscience, 3:IMAG.a.145, September, 2025. ![Brain functional connectivity predicts depression and anxiety during childhood and adolescence: A connectome-based predictive modeling approach [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Identifying brain-based correlates of risk for future depression and anxiety severity in youth could improve prevention and treatment efforts. We tested whether connectome-based predictive modeling (CPM) based on resting-state functional connectivity (FC) at baseline: (a) predicts future depression and anxiety severity during childhood and (b) generalizes to adolescence. We used two independent, longitudinal datasets including children from the Adolescent Brain Cognitive Development (ABCD) study and adolescents from the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA). ABCD included a cohort of 11,875 children ages 9–11 years old, and BANDA enrolled 215 adolescents ages 14–17 years, of which ~70% reported a depressive or anxiety disorder. CPM with internal (within ABCD) and external validation (from ABCD to BANDA) used baseline whole-brain FC to predict depression and anxiety severity at a 1-year follow-up assessment. ABCD-derived functional connections, which we term “Symptoms Network”, were validated within BANDA to test model applicability in adolescence, which is a peak period for the emergence of internalizing disorders. Participants with complete data were included from ABCD (n = 3,718, 52.9% girls, ages 10.0 ± 0.6) and BANDA (n = 150, 61.3% girls, ages 15.4 ± 0.9). In ABCD, we found that FC predicted 1-year follow-up symptoms severity (ρ = 0.058, p = 0.040), measured with the Child Behavior Checklist Anxious/Depressed subscale. External validation in BANDA indicated that the Symptoms Network predicted 1-year follow-up symptoms severity (ρ = 0.222, p = 0.007), measured with the Revised Child Depression and Anxiety Scale t-transformed total score. In both ABCD and BANDA, FC enhanced the prediction of future symptom severity beyond baseline clinical and demographic information (baseline severity, sex, and age), including when correcting for mean head motion. The ABCD-derived connections included contributions from somatomotor, attentional, and subcortical regions and were characterized by heterogeneous FC within adolescents, where the same region pairs were characterized by positive FC for some participants but by negative FC for others. In conclusion, FC may provide inroads for early identification of internalizing symptoms, which could inform preventative-intervention approaches prior to the emergence of affective disorders during a critical period of neuromaturation. However, the small effect sizes and heterogeneity in results underscore the challenges of employing brain-based biomarkers for clinical applications and emphasize the need for individualized approaches for understanding neurodevelopment and mental health.
@article{morfini_brain_2025,
title = {Brain functional connectivity predicts depression and anxiety during childhood and adolescence: {A} connectome-based predictive modeling approach},
volume = {3},
issn = {2837-6056},
shorttitle = {Brain functional connectivity predicts depression and anxiety during childhood and adolescence},
url = {https://doi.org/10.1162/IMAG.a.145},
doi = {10.1162/IMAG.a.145},
abstract = {Identifying brain-based correlates of risk for future depression and anxiety
severity in youth could improve prevention and treatment efforts. We tested
whether connectome-based predictive modeling (CPM) based on resting-state
functional connectivity (FC) at baseline: (a) predicts future depression and
anxiety severity during childhood and (b) generalizes to adolescence. We used
two independent, longitudinal datasets including children from the Adolescent
Brain Cognitive Development (ABCD) study and adolescents from the Boston
Adolescent Neuroimaging of Depression and Anxiety (BANDA). ABCD included a
cohort of 11,875 children ages 9–11 years old, and BANDA enrolled 215
adolescents ages 14–17 years, of which {\textasciitilde}70\% reported a depressive or
anxiety disorder. CPM with internal (within ABCD) and external validation (from
ABCD to BANDA) used baseline whole-brain FC to predict depression and anxiety
severity at a 1-year follow-up assessment. ABCD-derived functional connections,
which we term “Symptoms Network”, were validated within BANDA to
test model applicability in adolescence, which is a peak period for the
emergence of internalizing disorders. Participants with complete data were
included from ABCD (n = 3,718, 52.9\% girls, ages 10.0 ± 0.6) and
BANDA (n = 150, 61.3\% girls, ages 15.4 ± 0.9). In ABCD, we found
that FC predicted 1-year follow-up symptoms severity (ρ = 0.058, p = 0.040), measured with the Child
Behavior Checklist Anxious/Depressed subscale. External validation in BANDA
indicated that the Symptoms Network predicted 1-year follow-up symptoms severity
(ρ = 0.222, p =
0.007), measured with the Revised Child Depression and Anxiety Scale t-transformed total score. In both ABCD and BANDA, FC
enhanced the prediction of future symptom severity beyond baseline clinical and
demographic information (baseline severity, sex, and age), including when
correcting for mean head motion. The ABCD-derived connections included
contributions from somatomotor, attentional, and subcortical regions and were
characterized by heterogeneous FC within adolescents, where the same region
pairs were characterized by positive FC for some participants but by negative FC
for others. In conclusion, FC may provide inroads for early identification of
internalizing symptoms, which could inform preventative-intervention approaches
prior to the emergence of affective disorders during a critical period of
neuromaturation. However, the small effect sizes and heterogeneity in results
underscore the challenges of employing brain-based biomarkers for clinical
applications and emphasize the need for individualized approaches for
understanding neurodevelopment and mental health.},
urldate = {2026-02-04},
journal = {Imaging Neuroscience},
author = {Morfini, Francesca and Kucyi, Aaron and Zhang, Jiahe and Bauer, Clemens C.C. and Bloom, Paul A. and Pagliaccio, David and Hubbard, Nicholas A. and Rosso, Isabelle M. and Yendiki, Anastasia and Ghosh, Satrajit S. and Pizzagalli, Diego A. and Gabrieli, John D.E. and Whitfield-Gabrieli, Susan and Auerbach, Randy P.},
month = sep,
year = {2025},
pages = {IMAG.a.145},
file = {Full Text PDF:/Users/mexico/Zotero/storage/BJ7DKE28/Morfini et al. - 2025 - Brain functional connectivity predicts depression and anxiety during childhood and adolescence A co.pdf:application/pdf;Snapshot:/Users/mexico/Zotero/storage/TIQQMK33/IMAG.a.html:text/html},
}
Downloads: 0
{"_id":"Ydi7x6YQLRNGPYEjn","bibbaseid":"morfini-kucyi-zhang-bauer-bloom-pagliaccio-hubbard-rosso-etal-brainfunctionalconnectivitypredictsdepressionandanxietyduringchildhoodandadolescenceaconnectomebasedpredictivemodelingapproach-2025","author_short":["Morfini, F.","Kucyi, A.","Zhang, J.","Bauer, C. C.","Bloom, P. A.","Pagliaccio, D.","Hubbard, N. A.","Rosso, I. M.","Yendiki, A.","Ghosh, S. S.","Pizzagalli, D. A.","Gabrieli, J. D.","Whitfield-Gabrieli, S.","Auerbach, R. P."],"bibdata":{"bibtype":"article","type":"article","title":"Brain functional connectivity predicts depression and anxiety during childhood and adolescence: A connectome-based predictive modeling approach","volume":"3","issn":"2837-6056","shorttitle":"Brain functional connectivity predicts depression and anxiety during childhood and adolescence","url":"https://doi.org/10.1162/IMAG.a.145","doi":"10.1162/IMAG.a.145","abstract":"Identifying brain-based correlates of risk for future depression and anxiety severity in youth could improve prevention and treatment efforts. We tested whether connectome-based predictive modeling (CPM) based on resting-state functional connectivity (FC) at baseline: (a) predicts future depression and anxiety severity during childhood and (b) generalizes to adolescence. We used two independent, longitudinal datasets including children from the Adolescent Brain Cognitive Development (ABCD) study and adolescents from the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA). ABCD included a cohort of 11,875 children ages 9–11 years old, and BANDA enrolled 215 adolescents ages 14–17 years, of which ~70% reported a depressive or anxiety disorder. CPM with internal (within ABCD) and external validation (from ABCD to BANDA) used baseline whole-brain FC to predict depression and anxiety severity at a 1-year follow-up assessment. ABCD-derived functional connections, which we term “Symptoms Network”, were validated within BANDA to test model applicability in adolescence, which is a peak period for the emergence of internalizing disorders. Participants with complete data were included from ABCD (n = 3,718, 52.9% girls, ages 10.0 ± 0.6) and BANDA (n = 150, 61.3% girls, ages 15.4 ± 0.9). In ABCD, we found that FC predicted 1-year follow-up symptoms severity (ρ = 0.058, p = 0.040), measured with the Child Behavior Checklist Anxious/Depressed subscale. External validation in BANDA indicated that the Symptoms Network predicted 1-year follow-up symptoms severity (ρ = 0.222, p = 0.007), measured with the Revised Child Depression and Anxiety Scale t-transformed total score. In both ABCD and BANDA, FC enhanced the prediction of future symptom severity beyond baseline clinical and demographic information (baseline severity, sex, and age), including when correcting for mean head motion. The ABCD-derived connections included contributions from somatomotor, attentional, and subcortical regions and were characterized by heterogeneous FC within adolescents, where the same region pairs were characterized by positive FC for some participants but by negative FC for others. In conclusion, FC may provide inroads for early identification of internalizing symptoms, which could inform preventative-intervention approaches prior to the emergence of affective disorders during a critical period of neuromaturation. However, the small effect sizes and heterogeneity in results underscore the challenges of employing brain-based biomarkers for clinical applications and emphasize the need for individualized approaches for understanding neurodevelopment and mental health.","urldate":"2026-02-04","journal":"Imaging Neuroscience","author":[{"propositions":[],"lastnames":["Morfini"],"firstnames":["Francesca"],"suffixes":[]},{"propositions":[],"lastnames":["Kucyi"],"firstnames":["Aaron"],"suffixes":[]},{"propositions":[],"lastnames":["Zhang"],"firstnames":["Jiahe"],"suffixes":[]},{"propositions":[],"lastnames":["Bauer"],"firstnames":["Clemens","C.C."],"suffixes":[]},{"propositions":[],"lastnames":["Bloom"],"firstnames":["Paul","A."],"suffixes":[]},{"propositions":[],"lastnames":["Pagliaccio"],"firstnames":["David"],"suffixes":[]},{"propositions":[],"lastnames":["Hubbard"],"firstnames":["Nicholas","A."],"suffixes":[]},{"propositions":[],"lastnames":["Rosso"],"firstnames":["Isabelle","M."],"suffixes":[]},{"propositions":[],"lastnames":["Yendiki"],"firstnames":["Anastasia"],"suffixes":[]},{"propositions":[],"lastnames":["Ghosh"],"firstnames":["Satrajit","S."],"suffixes":[]},{"propositions":[],"lastnames":["Pizzagalli"],"firstnames":["Diego","A."],"suffixes":[]},{"propositions":[],"lastnames":["Gabrieli"],"firstnames":["John","D.E."],"suffixes":[]},{"propositions":[],"lastnames":["Whitfield-Gabrieli"],"firstnames":["Susan"],"suffixes":[]},{"propositions":[],"lastnames":["Auerbach"],"firstnames":["Randy","P."],"suffixes":[]}],"month":"September","year":"2025","pages":"IMAG.a.145","file":"Full Text PDF:/Users/mexico/Zotero/storage/BJ7DKE28/Morfini et al. - 2025 - Brain functional connectivity predicts depression and anxiety during childhood and adolescence A co.pdf:application/pdf;Snapshot:/Users/mexico/Zotero/storage/TIQQMK33/IMAG.a.html:text/html","bibtex":"@article{morfini_brain_2025,\n\ttitle = {Brain functional connectivity predicts depression and anxiety during childhood and adolescence: {A} connectome-based predictive modeling approach},\n\tvolume = {3},\n\tissn = {2837-6056},\n\tshorttitle = {Brain functional connectivity predicts depression and anxiety during childhood and adolescence},\n\turl = {https://doi.org/10.1162/IMAG.a.145},\n\tdoi = {10.1162/IMAG.a.145},\n\tabstract = {Identifying brain-based correlates of risk for future depression and anxiety\nseverity in youth could improve prevention and treatment efforts. We tested\nwhether connectome-based predictive modeling (CPM) based on resting-state\nfunctional connectivity (FC) at baseline: (a) predicts future depression and\nanxiety severity during childhood and (b) generalizes to adolescence. We used\ntwo independent, longitudinal datasets including children from the Adolescent\nBrain Cognitive Development (ABCD) study and adolescents from the Boston\nAdolescent Neuroimaging of Depression and Anxiety (BANDA). ABCD included a\ncohort of 11,875 children ages 9–11 years old, and BANDA enrolled 215\nadolescents ages 14–17 years, of which {\\textasciitilde}70\\% reported a depressive or\nanxiety disorder. CPM with internal (within ABCD) and external validation (from\nABCD to BANDA) used baseline whole-brain FC to predict depression and anxiety\nseverity at a 1-year follow-up assessment. ABCD-derived functional connections,\nwhich we term “Symptoms Network”, were validated within BANDA to\ntest model applicability in adolescence, which is a peak period for the\nemergence of internalizing disorders. Participants with complete data were\nincluded from ABCD (n = 3,718, 52.9\\% girls, ages 10.0 ± 0.6) and\nBANDA (n = 150, 61.3\\% girls, ages 15.4 ± 0.9). In ABCD, we found\nthat FC predicted 1-year follow-up symptoms severity (ρ = 0.058, p = 0.040), measured with the Child\nBehavior Checklist Anxious/Depressed subscale. External validation in BANDA\nindicated that the Symptoms Network predicted 1-year follow-up symptoms severity\n(ρ = 0.222, p =\n0.007), measured with the Revised Child Depression and Anxiety Scale t-transformed total score. In both ABCD and BANDA, FC\nenhanced the prediction of future symptom severity beyond baseline clinical and\ndemographic information (baseline severity, sex, and age), including when\ncorrecting for mean head motion. The ABCD-derived connections included\ncontributions from somatomotor, attentional, and subcortical regions and were\ncharacterized by heterogeneous FC within adolescents, where the same region\npairs were characterized by positive FC for some participants but by negative FC\nfor others. In conclusion, FC may provide inroads for early identification of\ninternalizing symptoms, which could inform preventative-intervention approaches\nprior to the emergence of affective disorders during a critical period of\nneuromaturation. However, the small effect sizes and heterogeneity in results\nunderscore the challenges of employing brain-based biomarkers for clinical\napplications and emphasize the need for individualized approaches for\nunderstanding neurodevelopment and mental health.},\n\turldate = {2026-02-04},\n\tjournal = {Imaging Neuroscience},\n\tauthor = {Morfini, Francesca and Kucyi, Aaron and Zhang, Jiahe and Bauer, Clemens C.C. and Bloom, Paul A. and Pagliaccio, David and Hubbard, Nicholas A. and Rosso, Isabelle M. and Yendiki, Anastasia and Ghosh, Satrajit S. and Pizzagalli, Diego A. and Gabrieli, John D.E. and Whitfield-Gabrieli, Susan and Auerbach, Randy P.},\n\tmonth = sep,\n\tyear = {2025},\n\tpages = {IMAG.a.145},\n\tfile = {Full Text PDF:/Users/mexico/Zotero/storage/BJ7DKE28/Morfini et al. - 2025 - Brain functional connectivity predicts depression and anxiety during childhood and adolescence A co.pdf:application/pdf;Snapshot:/Users/mexico/Zotero/storage/TIQQMK33/IMAG.a.html:text/html},\n}\n\n","author_short":["Morfini, F.","Kucyi, A.","Zhang, J.","Bauer, C. C.","Bloom, P. A.","Pagliaccio, D.","Hubbard, N. A.","Rosso, I. M.","Yendiki, A.","Ghosh, S. S.","Pizzagalli, D. A.","Gabrieli, J. D.","Whitfield-Gabrieli, S.","Auerbach, R. P."],"key":"morfini_brain_2025","id":"morfini_brain_2025","bibbaseid":"morfini-kucyi-zhang-bauer-bloom-pagliaccio-hubbard-rosso-etal-brainfunctionalconnectivitypredictsdepressionandanxietyduringchildhoodandadolescenceaconnectomebasedpredictivemodelingapproach-2025","role":"author","urls":{"Paper":"https://doi.org/10.1162/IMAG.a.145"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/f/ZRjsQ58xmRS385CAj/mypubs.bib","dataSources":["qg54cFyjST4rE4gZr","fB4GuzdCZcPR6LeBn","hKTEXvGKSBsqbXw2D"],"keywords":[],"search_terms":["brain","functional","connectivity","predicts","depression","anxiety","during","childhood","adolescence","connectome","based","predictive","modeling","approach","morfini","kucyi","zhang","bauer","bloom","pagliaccio","hubbard","rosso","yendiki","ghosh","pizzagalli","gabrieli","whitfield-gabrieli","auerbach"],"title":"Brain functional connectivity predicts depression and anxiety during childhood and adolescence: A connectome-based predictive modeling approach","year":2025}