Thanks to visit codestin.com
Credit goes to link.springer.com

Skip to main content
Springer Nature Link
Log in

Intrinsic Functional Connectivity Associated with γ‑Aminobutyric Acid and Glutamate/Glutamine in the Lateral Prefrontal Cortex and Internalizing Psychopathology in Adolescents

  • Original Article
  • Published:
Neuroscience Bulletin Aims and scope Submit manuscript

Abstract

In this study, we systematically tested the hypothesis that during the critical developmental period of adolescence, on a macro scale, the concentrations of major excitatory and inhibitory neurotransmitters (glutamate/glutamine and γ‑aminobutyric acid [GABA]) in the dorsal and ventral lateral prefrontal cortex are associated with the brain’s functional connectivity and an individual’s psychopathology. Neurotransmitters were measured via magnetic resonance spectroscopy while functional connectivity was measured with resting-state fMRI (n = 121). Seed-based and network-based analyses revealed associations of neurotransmitter concentrations and functional connectivities between regions/networks that are connected to prefrontal cortices via structural connections that are thought to be under dynamic development during adolescence. These regions tend to be boundary areas between functional networks. Furthermore, several connectivities were found to be associated with individual’s levels of internalizing psychopathology. These findings provide insights into specific neurochemical mechanisms underlying the brain’s macroscale functional organization, its development during adolescence, and its potential associations with symptoms associated with internalizing psychopathology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+
from £29.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

References

  1. Lenroot RK, Giedd JN. Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neurosci Biobehav Rev 2006, 30: 718–729.

    Article  PubMed  Google Scholar 

  2. Uytun MC. Development period of prefrontal cortex. Prefrontal Cortex. Ch. 1, InTech, 2018

  3. Kilb W. Development of the GABAergic system from birth to adolescence. Neuroscientist 2012, 18: 613–630.

    Article  PubMed  Google Scholar 

  4. Caballero A, Tseng KY. GABAergic function as a limiting factor for prefrontal maturation during adolescence. Trends Neurosci 2016, 39: 441–448.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Selemon LD. A role for synaptic plasticity in the adolescent development of executive function. Transl Psychiatry 2013, 3: e238.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Larsen B, Luna B. Adolescence as a neurobiological critical period for the development of higher-order cognition. Neurosci Biobehav Rev 2018, 94: 179–195.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Page CE, Coutellier L. Prefrontal excitatory/inhibitory balance in stress and emotional disorders: Evidence for over-inhibition. Neurosci Biobehav Rev 2019, 105: 39–51.

    Article  PubMed  CAS  Google Scholar 

  8. Miller EK, Cohen JD. An integrative theory of prefrontal cortex function. Annu Rev Neurosci 2001, 24: 167–202.

    Article  PubMed  CAS  Google Scholar 

  9. Banich MT. Executive function. Curr Dir Psychol Sci 2009, 18: 89–94.

    Article  Google Scholar 

  10. Cole MW, Ito T, Braver TS. Lateral prefrontal cortex contributes to fluid intelligence through multinetwork connectivity. Brain Connect 2015, 5: 497–504.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Dosenbach NUF, Fair DA, Cohen AL, Schlaggar BL, Petersen SE. A dual-networks architecture of top-down control. Trends Cogn Sci 2008, 12: 99–105.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Deco G, Jirsa VK, McIntosh AR. Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci 2011, 12: 43–56.

    Article  PubMed  CAS  Google Scholar 

  13. Wang Z, Dai Z, Gong G, Zhou C, He Y. Understanding structural-functional relationships in the human brain: A large-scale network perspective. Neuroscientist 2015, 21: 290–305.

    Article  PubMed  Google Scholar 

  14. Buckner RL, Krienen FM, Thomas Yeo BT. Opportunities and limitations of intrinsic functional connectivity MRI. Nat Neurosci 2013, 16: 832–837.

    Article  PubMed  Google Scholar 

  15. Guo CC, Kurth F, Zhou J, Mayer EA, Eickhoff SB, Kramer JH. One-year test-retest reliability of intrinsic connectivity network fMRI in older adults. Neuroimage 2012, 61: 1471–1483.

    Article  PubMed  Google Scholar 

  16. Cole MW, Bassett DS, Power JD, Braver TS, Petersen SE. Intrinsic and task-evoked network architectures of the human brain. Neuron 2014, 83: 238–251.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Nasrallah FA, Singh KKDR, Yeow LY, Chuang KH. GABAergic effect on resting-state functional connectivity: Dynamics under pharmacological antagonism. Neuroimage 2017, 149: 53–62.

    Article  PubMed  CAS  Google Scholar 

  18. Abdallah CG, Averill CL, Salas R, Averill LA, Baldwin PR, Krystal JH, et al. Prefrontal connectivity and glutamate transmission: Relevance to depression pathophysiology and ketamine treatment. Biol Psychiatry Cogn Neurosci Neuroimaging 2017, 2: 566–574.

    PubMed  PubMed Central  Google Scholar 

  19. Stagg CJ, Bachtiar V, Amadi U, Gudberg CA, Ilie AS, Sampaio-Baptista C, et al. Local GABA concentration is related to network-level resting functional connectivity. Elife 2014, 3: e01465.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Kiemes A, Davies C, Kempton MJ, Lukow PB, Bennallick C, Stone JM, et al. GABA, glutamate and neural activity: A systematic review with meta-analysis of multimodal 1H-MRS-fMRI studies. Front Psychiatry 2021, 12: 644315.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Andersen JV, Schousboe A. Milestone Review: Metabolic dynamics of glutamate and GABA mediated neurotransmission - The essential roles of astrocytes. J Neurochem 2023, 166: 109–137.

    Article  PubMed  CAS  Google Scholar 

  22. Petrides M. Lateral prefrontal cortex: Architectonic and functional organization. Philos Trans R Soc Lond B Biol Sci 2005, 360: 781–795.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Blumenfeld RS, Nomura EM, Gratton C, D’Esposito M. Lateral prefrontal cortex is organized into parallel dorsal and ventral streams along the rostro-caudal axis. Cereb Cortex 2013, 23: 2457–2466.

    Article  PubMed  Google Scholar 

  24. Rushworth MFS. Anatomical and functional subdivision within the primate lateral prefrontal cortex. Psychobiology 2000, 28: 187–196.

    Article  Google Scholar 

  25. Fuster JM (2015) The prefrontal cortex, 5th edn Elsevier/Academic Press, Amsterdam; Boston.

    Google Scholar 

  26. Wang K, Smolker HR, Brown MS, Snyder HR, Hankin BL, Banich MT. Association of γ-aminobutyric acid and glutamate/glutamine in the lateral prefrontal cortex with patterns of intrinsic functional connectivity in adults. Brain Struct Funct 2020, 225: 1903–1919.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Li M, Danyeli LV, Colic L, Wagner G, Smesny S, Chand T, et al. The differential association between local neurotransmitter levels and whole-brain resting-state functional connectivity in two distinct cingulate cortex subregions. Hum Brain Mapp 2022, 43: 2833–2844.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Baumbach P, Meißner W, Reichenbach JR, Gussew A. Functional connectivity and neurotransmitter impairments of the salience brain network in chronic low back pain patients: A combined resting-state functional magnetic resonance imaging and 1 H-MRS study. Pain 2022, 163: 2337–2347.

    Article  PubMed  Google Scholar 

  29. Sousa SS, Amaro E Jr, Crego A, Gonçalves ÓF, Sampaio A. Developmental trajectory of the prefrontal cortex: A systematic review of diffusion tensor imaging studies. Brain Imaging Behav 2018, 12: 1197–1210.

    Article  PubMed  Google Scholar 

  30. Lebel C, Treit S, Beaulieu C. A review of diffusion MRI of typical white matter development from early childhood to young adulthood. NMR Biomed 2019, 32: e3778.

    Article  PubMed  Google Scholar 

  31. Amemiya K, Naito E, Takemura H. Age dependency and lateralization in the three branches of the human superior longitudinal Fasciculus. Cortex 2021, 139: 116–133.

    Article  PubMed  Google Scholar 

  32. Simmonds DJ, Hallquist MN, Asato M, Luna B. Developmental stages and sex differences of white matter and behavioral development through adolescence: A longitudinal diffusion tensor imaging (DTI) study. Neuroimage 2014, 92: 356–368.

    Article  PubMed  Google Scholar 

  33. Thomason ME, Thompson PM. Diffusion imaging, white matter, and psychopathology. Annu Rev Clin Psychol 2011, 7: 63–85.

    Article  PubMed  Google Scholar 

  34. Piekarski DJ, Colich NL, Ho TC. The effects of puberty and sex on adolescent white matter development: A systematic review. Dev Cogn Neurosci 2023, 60: 101214.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wang X, Pathak S, Stefaneanu L, Yeh FC, Li S, Fernandez-Miranda JC. Subcomponents and connectivity of the superior longitudinal Fasciculus in the human brain. Brain Struct Funct 2016, 221: 2075–2092.

    Article  PubMed  Google Scholar 

  36. Martino J, De Witt Hamer PC, Berger MS, Lawton MT, Arnold CM, de Lucas EM, et al. Analysis of the subcomponents and cortical terminations of the perisylvian superior longitudinal Fasciculus: A fiber dissection and DTI tractography study. Brain Struct Funct 2013, 218: 105–121.

    Article  PubMed  Google Scholar 

  37. Heilbronner SR, Haber SN. Frontal cortical and subcortical projections provide a basis for segmenting the Cingulum bundle: Implications for neuroimaging and psychiatric disorders. J Neurosci 2014, 34: 10041–10054.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Constantinidis C, Luna B. Neural substrates of inhibitory control maturation in adolescence. Trends Neurosci 2019, 42: 604–616.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Tamnes CK, Herting MM, Goddings AL, Meuwese R, Blakemore SJ, Dahl RE, et al. Development of the cerebral cortex across adolescence: A multisample study of inter-related longitudinal changes in cortical volume, surface area, and thickness. J Neurosci 2017, 37: 3402–3412.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Lebel C, Deoni S. The development of brain white matter microstructure. Neuroimage 2018, 182: 207–218.

    Article  PubMed  Google Scholar 

  41. Gong G, He Y, Evans AC. Brain connectivity: Gender makes a difference. Neuroscientist 2011, 17: 575–591.

    Article  PubMed  Google Scholar 

  42. Kaczkurkin AN, Raznahan A, Satterthwaite TD. Sex differences in the developing brain: Insights from multimodal neuroimaging. Neuropsychopharmacology 2019, 44: 71–85.

    Article  PubMed  Google Scholar 

  43. Hankin BL, Snyder HR, Gulley LD, Schweizer TH, Bijttebier P, Nelis S, et al. Understanding comorbidity among internalizing problems: Integrating latent structural models of psychopathology and risk mechanisms. Dev Psychopathol 2016, 28: 987–1012.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Murrough JW, Abdallah CG, Mathew SJ. Targeting glutamate signalling in depression: Progress and prospects. Nat Rev Drug Discov 2017, 16: 472–486.

    Article  PubMed  CAS  Google Scholar 

  45. Duman RS, Sanacora G, Krystal JH. Altered connectivity in depression: GABA and glutamate neurotransmitter deficits and reversal by novel treatments. Neuron 2019, 102: 75–90.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Wang L, Hermens DF, Hickie IB, Lagopoulos J. A systematic review of resting-state functional-MRI studies in major depression. J Affect Disord 2012, 142: 6–12.

    Article  PubMed  CAS  Google Scholar 

  47. Dutta A, McKie S, William Deakin JF. Resting state networks in major depressive disorder. Psychiatry Res 2014, 224: 139–151.

    Article  PubMed  Google Scholar 

  48. Hatoum AS, Morrison CL, Mitchell EC, Lam M, Benca-Bachman CE, Reineberg AE, et al. Genome-wide association study shows that executive functioning is influenced by GABAergic processes and is a neurocognitive genetic correlate of psychiatric disorders. Biol Psychiatry 2023, 93: 59–70.

    Article  PubMed  CAS  Google Scholar 

  49. Vanes LD, Moutoussis M, Ziegler G, Goodyer IM, Fonagy P, Jones PB, et al. White matter tract myelin maturation and its association with general psychopathology in adolescence and early adulthood. Hum Brain Mapp 2020, 41: 827–839.

    Article  PubMed  Google Scholar 

  50. Yeterian EH, Pandya DN, Tomaiuolo F, Petrides M. The cortical connectivity of the prefrontal cortex in the monkey brain. Cortex 2012, 48: 58–81.

    Article  PubMed  Google Scholar 

  51. Fuster JM (2015) Anatomy of the prefrontal cortex The Prefrontal Cortex, Elsevier, Amsterdam, pp 9–62.

    Book  Google Scholar 

  52. Xu R, Bichot NP, Takahashi A, Desimone R. The cortical connectome of primate lateral prefrontal cortex. Neuron 2022, 110: 312-327.e7.

    Article  PubMed  CAS  Google Scholar 

  53. Eid M, Geiser C, Koch T, Heene M. Anomalous results in G-factor models: Explanations and alternatives. Psychol Methods 2017, 22: 541–562.

    Article  PubMed  Google Scholar 

  54. Snyder HR, Silton RL, Hankin BL, Smolker HR, Kaiser RH, Banich MT, et al. The dimensional structure of internalizing psychopathology: Relation to diagnostic categories. Clin Psychol Sci 2023, 11: 1044–1063.

    Article  PubMed  Google Scholar 

  55. Smolker HR, Snyder HR, Hankin BL, Banich MT. Gray-matter morphometry of internalizing-symptom dimensions during adolescence. Clin Psychol Sci 2022, 10: 941–959.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Banich MT, Smith LL, Smolker HR, Hankin BL, Silton RL, Heller W, et al. Common and specific dimensions of internalizing disorders are characterized by unique patterns of brain activity on a task of emotional cognitive control. Int J Psychophysiol 2020, 151: 80–93.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Thomas Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 2011, 106: 1125–1165.

    Article  PubMed Central  Google Scholar 

  58. Snyder HR, Friedman NP, Hankin BL. Transdiagnostic mechanisms of psychopathology in youth: Executive functions, dependent stress, and rumination. Cognit Ther Res 2019, 43: 834–851.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Mescher M, Merkle H, Kirsch J, Garwood M, Gruetter R. Simultaneous in vivo spectral editing and water suppression. NMR Biomed 1998, 11: 266–272.

    Article  PubMed  CAS  Google Scholar 

  60. Provencher SW. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med 1993, 30: 672–679.

    Article  PubMed  CAS  Google Scholar 

  61. Edden RAE, Puts NAJ, Harris AD, Barker PB, John Evans C. Gannet: A batch-processing tool for the quantitative analysis of gamma-aminobutyric acid–edited MR spectroscopy spectra. J Magn Reson Imaging 2014, 40: 1445–1452.

    Article  PubMed  Google Scholar 

  62. Harris AD, Puts NAJ, Edden RAE. Tissue correction for GABA-edited MRS: Considerations of voxel composition, tissue segmentation, and tissue relaxations. J Magn Reson Imaging 2015, 42: 1431–1440.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Stagg CJ, Best JG, Stephenson MC, O’Shea J, Wylezinska M, Tamas Kincses Z, et al. Polarity-sensitive modulation of cortical neurotransmitters by transcranial stimulation. J Neurosci 2009, 29: 5202–5206.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Kraljević N, Langner R, Küppers V, Raimondo F, Patil KR, Eickhoff SB, et al. Network and state specificity in connectivity-based predictions of individual behavior. Hum Brain Mapp 2024, 45: e26753.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM. Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers. Neuroimage 2014, 90: 449–468.

    Article  PubMed  Google Scholar 

  66. Nickerson LD, Smith SM, Öngür D, Beckmann CF. Using dual regression to investigate network shape and amplitude in functional connectivity analyses. Front Neurosci 2017, 11: 115.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Smith SM, Nichols TE, Vidaurre D, Winkler AM, Behrens TEJ, Glasser MF, et al. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat Neurosci 2015, 18: 1565–1567.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Marrelec G, Krainik A, Duffau H, Pélégrini-Issac M, Lehéricy S, Doyon J, et al. Partial correlation for functional brain interactivity investigation in functional MRI. NeuroImage 2006, 32: 228–237.

    Article  PubMed  Google Scholar 

  69. Lieberman MD, Cunningham WA. Type I and Type II error concerns in fMRI research: re-balancing the scale. Soc Cogn Affect Neurosci 2009, 4: 423–428.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Noble S, Scheinost D, Todd Constable R. Cluster failure or power failure? Evaluating sensitivity in cluster-level inference. Neuroimage 2020, 209: 116468.

    Article  PubMed  Google Scholar 

  71. Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. Neuroimage 2014, 92: 381–397.

    Article  PubMed  Google Scholar 

  72. Winkler AM, Webster MA, Vidaurre D, Nichols TE, Smith SM. Multi-level block permutation. Neuroimage 2015, 123: 253–268.

    Article  PubMed  Google Scholar 

  73. Eklund A, Nichols TE, Knutsson H. Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proc Natl Acad Sci U S A 2016, 113: 7900–7905.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Hayasaka S, Nichols TE. Validating cluster size inference: Random field and permutation methods. Neuroimage 2003, 20: 2343–2356.

    Article  PubMed  Google Scholar 

  75. Meyer TJ, Miller ML, Metzger RL, Borkovec TD. Development and validation of the Penn state worry questionnaire. Behav Res Ther 1990, 28: 487–495.

    Article  PubMed  CAS  Google Scholar 

  76. Brown TA, Antony MM, Barlow DH. Psychometric properties of the Penn State Worry Questionnaire in a clinical anxiety disorders sample. Behav Res Ther 1992, 30: 33–37.

    Article  PubMed  CAS  Google Scholar 

  77. Watson D, Weber K, Assenheimer JS, Clark LA, Strauss ME, McCormick RA. Testing a tripartite model: I .Evaluating the convergent and discriminant validity of anxiety and depression symptom scales. J Abnorm Psychol 1995, 104: 3–14.

    Article  PubMed  CAS  Google Scholar 

  78. Buckby JA, Yung AR, Cosgrave EM, Killackey EJ. Clinical utility of the Mood and Anxiety Symptom Questionnaire (MASQ) in a sample of young help-seekers. BMC Psychiatry 2007, 7: 50.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Rogers WM, Schmitt N. Parameter recovery and model fit using multidimensional composites: A comparison of four empirical parceling algorithms. Multivar Behav Res 2004, 39: 379–412.

    Article  Google Scholar 

  80. Onu M, Badea L, Roceanu A, Tivarus M, Bajenaru O. Increased connectivity between sensorimotor and attentional areas in Parkinson’s disease. Neuroradiology 2015, 57: 957–968.

    Article  PubMed  Google Scholar 

  81. Haghighat H, Mirzarezaee M, Araabi BN, Khadem A. Functional networks abnormalities in autism spectrum disorder: Age-related hypo and hyper connectivity. Brain Topogr 2021, 34: 306–322.

    Article  PubMed  Google Scholar 

  82. Rollins NK, Glasier P, Seo Y, Morriss MC, Chia J, Wang Z. Age-related variations in white matter anisotropy in school-age children. Pediatr Radiol 2010, 40: 1918–1930.

    Article  PubMed  Google Scholar 

  83. Taki Y, Thyreau B, Hashizume H, Sassa Y, Takeuchi H, Wu K, et al. Linear and curvilinear correlations of brain white matter volume, fractional anisotropy, and mean diffusivity with age using voxel-based and region-of-interest analyses in 246 healthy children. Hum Brain Mapp 2013, 34: 1842–1856.

    Article  PubMed  Google Scholar 

  84. Dubois J, Dehaene-Lambertz G, Kulikova S, Poupon C, Hüppi PS, Hertz-Pannier L. The early development of brain white matter: A review of imaging studies in fetuses, newborns and infants. Neuroscience 2014, 276: 48–71.

    Article  PubMed  CAS  Google Scholar 

  85. Grayson DS, Fair DA. Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature. NeuroImage 2017, 160: 15–31.

    Article  PubMed  Google Scholar 

  86. Cole MW, Reynolds JR, Power JD, Repovs G, Anticevic A, Braver TS. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat Neurosci 2013, 16: 1348–1355.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Haber SN, Liu H, Seidlitz J, Bullmore E. Prefrontal connectomics: From anatomy to human imaging. Neuropsychopharmacology 2022, 47: 20–40.

    Article  PubMed  CAS  Google Scholar 

  88. Lannoy S, Pfefferbaum A, Le Berre AP, Thompson WK, Brumback T, Schulte T, et al. Growth trajectories of cognitive and motor control in adolescence: How much is development and how much is practice? Neuropsychology 2022, 36: 44–54.

    Article  PubMed  Google Scholar 

  89. Genc S, Raven EP, Drakesmith M, Blakemore SJ, Jones DK. Novel insights into axon diameter and myelin content in late childhood and adolescence. Cereb Cortex 2023, 33: 6435–6448.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Frankle WG, Laruelle M, Haber SN. Prefrontal cortical projections to the midbrain in Primates: Evidence for a sparse connection. Neuropsychopharmacology 2006, 31: 1627–1636.

    Article  PubMed  CAS  Google Scholar 

  91. Kornhuber J, Kim JS, Kornhuber ME, Kornhuber HH. The cortico-nigral projection: Reduced glutamate content in the substantia nigra following frontal cortex ablation in the rat. Brain Res 1984, 322: 124–126.

    Article  PubMed  CAS  Google Scholar 

  92. Apps R, Watson TC (2013) Cerebro-cerebellar connections. Handbook of the Cerebellum and Cerebellar Disorders, Springer, Netherlands, Dordrecht, pp 1131–1153.

    Book  Google Scholar 

  93. Schmahmann JD. The cerebrocerebellar system. Essentials of Cerebellum and Cerebellar Disorders. Cham: Springer International Publishing, 2016: 101–115.

  94. Zuo XN, He Y, Betzel RF, Colcombe S, Sporns O, Milham MP. Human connectomics across the life span. Trends Cogn Sci 2017, 21: 32–45.

    Article  PubMed  Google Scholar 

  95. Raichle ME. The brain’s default mode network. Annu Rev Neurosci 2015, 38: 433–447.

    Article  PubMed  CAS  Google Scholar 

  96. Fedorenko E, Thompson-Schill SL. Reworking the language network. Trends Cogn Sci 2014, 18: 120–126.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledegments

This work was supported by NIMH grant R01 105501 (PI: MTB and BLK).

Author information

Authors and Affiliations

  1. Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, China

    Kai Wang & Yu Cheng

  2. School of Psychology, South China Normal University, Guangzhou, 510631, China

    Kai Wang & Yu Cheng

  3. Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, 80309‑0344, USA

    Kai Wang, Harry R. Smolker & Marie T. Banich

  4. Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, 80309‑0345, USA

    Marie T. Banich

  5. Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, 80303, USA

    Harry R. Smolker

  6. Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA

    Mark S. Brown

  7. Department of Psychology, Brandeis University, Waltham, MA, 02453, USA

    Hannah R. Snyder

  8. Psychology Department, University of Illinois-Urbana Champaign, Champaign, IL, 61820, USA

    Benjamin L. Hankin

Authors
  1. Kai Wang
    View author publications

    Search author on:PubMed Google Scholar

  2. Harry R. Smolker
    View author publications

    Search author on:PubMed Google Scholar

  3. Mark S. Brown
    View author publications

    Search author on:PubMed Google Scholar

  4. Hannah R. Snyder
    View author publications

    Search author on:PubMed Google Scholar

  5. Yu Cheng
    View author publications

    Search author on:PubMed Google Scholar

  6. Benjamin L. Hankin
    View author publications

    Search author on:PubMed Google Scholar

  7. Marie T. Banich
    View author publications

    Search author on:PubMed Google Scholar

Corresponding authors

Correspondence to Kai Wang or Marie T. Banich.

Ethics declarations

Conflict of interest

The authors declare no competing financial interests.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, K., Smolker, H.R., Brown, M.S. et al. Intrinsic Functional Connectivity Associated with γ‑Aminobutyric Acid and Glutamate/Glutamine in the Lateral Prefrontal Cortex and Internalizing Psychopathology in Adolescents. Neurosci. Bull. 41, 1553–1569 (2025). https://doi.org/10.1007/s12264-025-01408-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1007/s12264-025-01408-1

Keywords

Profiles

  1. Kai Wang View author profile
  2. Marie T. Banich View author profile