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Motor signs in late life depression relate to altered subcortical synaptic density and gray matter volume: an 11C-UCB-J PET-MR study

Neuropsychopharmacology volume 51, pages 661–671 (2026)Cite this article

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Abstract

Motor signs are common in patients with a major depressive disorder (MDD) and associated with functional disability and falls. They appear to be more pronounced in patients with late life depression, but it is unclear whether this is caused by global brain aging or by a specific pathology associated with depression. We therefore sought to investigate associations between motor signs in late life depression and aging related changes in synapses, gray and white matter. From the monocentric Leuven Late Life depression study, we included 75 participants (41 healthy controls, 34 currently depressed MDD patients) aged ≥ 60 years. Motor assessment included the MDS-Unified Parkinson’s Disease Rating Scale part III (MDS-UPDRSIII), Scale for Assessment and Rating of Ataxia (SARA), gait analysis and digitized drawing. Brain synaptic vesicle glycoprotein 2A binding as a proxy for synaptic density was determined in predefined cortico-subcortical volumes of interest (VOI) using 11C-UCB-J PET in 62 participants (25 patients). Brain T1 and FLAIR MR images were used to quantify gray matter volume and white matter hyperintensity volume in 69 participants (32 patients). Multiple linear regression analyses were performed with motor outcome as the dependent variable, diagnosis and VOI 11C-UCB-J SUVR and their interaction, age, whole brain white matter hyperintensity volume and gray matter VOI as independent variables. The study demonstrated that patients had significant impairments on all motor assessments, compared to healthy controls. Specifically in patients, right globus pallidus synaptic density was associated with MDS-UPDRSIII score and drawing speed; thalamic gray matter volume predicted SARA score, gait and drawing speed; and white matter hyperintensity volume predicted MDS-UPDRSIII score. We conclude that motor signs in late life depression are associated with specific synaptic density and gray matter volume differences in basal ganglia-thalamic structures.

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Fig. 1: Participant flowchart.
Fig. 2: Scatter plots illustrating significant (*) and trend associations of 11C-UCB-J SUVR x diagnosis interaction with motor outcome.
Fig. 3: Summary figure: associations of motor outcome with synaptic density, GMV and WMH in patients, contrasted to healthy controls.
Fig. 4: Scatter plots illustrating the relation between MDS-UPDRSIII composite scores and white matter hyperintensities.

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Data availability

Grouped data supporting the study’s findings are provided in the supplementary materials. Due to ethical regulations concerning the specific participant profiles of older persons with depression, individual-level data are not made publicly available. However, anonymized data relevant to the study may be obtained from the corresponding author upon reasonable request.

References

  1. Sobin C, Sackeim HA. Psychomotor symptoms of depression. Am J Psychiatry. 1997;154:4–17.

    Article  PubMed  CAS  Google Scholar 

  2. Rogers MA, Bradshaw JL, Phillips JG, Chiu E, Vaddadi K, Presnel I, et al. Parkinsonian motor characteristics in unipolar major depression. J Clin Exp Neuropsychol. 2000;22:232–44.

  3. American Psychiatric Association D-TF Diagnostic and statistical manual of mental disorders: DSM-5™, 5th ed.: American Psychiatric Publishing; 2013.

  4. Buyukdura JS, McClintock SM, Croarkin PE. Psychomotor retardation in depression: Biological underpinnings, measurement, and treatment. Prog neuro-Psychopharmacol Biol Psychiatry. 2011;35:395–409.

    Article  Google Scholar 

  5. Belvederi Murri M, Triolo F, Coni A, Tacconi C, Nerozzi E, Escelsior A, et al. Instrumental assessment of balance and gait in depression: A systematic review. Psychiatry Res. 2020;284:112687.

    Article  PubMed  Google Scholar 

  6. Stubbs B, Stubbs J, Gnanaraj SD, Soundy A. Falls in older adults with major depressive disorder (MDD): a systematic review and exploratory meta-analysis of prospective studies. Int Psychogeriatr. 2016;28:23–9.

    Article  PubMed  Google Scholar 

  7. Fried EI, Nesse RM. The impact of individual depressive symptoms on impairment of psychosocial functioning. PLoS One. 2014;9:e90311.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Novick JS, Stewart JW, Wisniewski SR, Cook IA, Manev R, Nierenberg AA, et al. Clinical and demographic features of atypical depression in outpatients with major depressive disorder: preliminary findings from STAR*D. J Clin Psychiatry. 2005;66:1002–11.

    Article  PubMed  Google Scholar 

  9. Pier MPBI, Hulstijn W, Sabbe BGC. Psychomotor retardation in elderly depressed patients. J Affect Disord. 2004;81:73–7.

    Article  PubMed  CAS  Google Scholar 

  10. Shahpesandy H. Different manifestation of depressive disorder in the elderly. Neuro Endocrinol Lett. 2005;26:691–5.

    PubMed  Google Scholar 

  11. Brodaty H, Luscombe G, Parker G, Wilhelm K, Hickie I, Austin MP, et al. Increased rate of psychosis and psychomotor change in depression with age. Psychol Med. 1997;27:1205–13.

    Article  PubMed  CAS  Google Scholar 

  12. Schrijvers D, Hulstijn W, Sabbe BGCC. Psychomotor symptoms in depression: A diagnostic, pathophysiological and therapeutic tool. J Affect Disord. 2008;109:1–20.

    Article  PubMed  Google Scholar 

  13. Shah PJ, Ogilvie AD, Goodwin GM, Ebmeier KP. Clinical and psychometric correlates of dopamine D2 binding in depression. Psychol Med. 1997;27:1247–56.

    Article  PubMed  CAS  Google Scholar 

  14. Martinot MLPP, Bragulat V, Artiges E, Dollé F, Hinnen F, Jouvent R, et al. Decreased presynaptic dopamine function in the left caudate of depressed patients with affective flattening and psychomotor retardation. Am J Psychiatry. 2001;158:314–6.

    Article  PubMed  CAS  Google Scholar 

  15. Bragulat V, Paillère-Martinot ML, Artiges E, Frouin V, Poline JB, Martinot JL. Dopaminergic function in depressed patients with affective flattening or with impulsivity: [18F]Fluoro-l-dopa positron emission tomography study with voxel-based analysis. Psychiatry Res - Neuroimaging. 2007;154:115–24.

    Article  CAS  Google Scholar 

  16. Meyer JH, McNeely HE, Sagrati S, Boovariwala A, Martin K, Verhoeff NP, et al. Elevated putamen D(2) receptor binding potential in major depression with motor retardation: an [11C]raclopride positron emission tomography study. Am J Psychiatry. 2006;163:1594–602.

    Article  PubMed  Google Scholar 

  17. Wüthrich F, Lefebvre S, Mittal VA, Shankman SA, Alexander N, Brosch K, et al. The neural signature of psychomotor disturbance in depression. Mol Psychiatry. 2024;29:317–26.

  18. Liberg B, Klauser P, Harding IH, Adler M, Rahm C, Lundberg J, et al. Functional and structural alterations in the cingulate motor area relate to decreased fronto-striatal coupling in major depressive disorder with psychomotor disturbances. Front Psychiatry. 2014;5:176.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Mayberg HS, Lewis PJ, Regenold W, Wagner HN Jr. Paralimbic hypoperfusion in unipolar depression. J Nucl Med. 1994;35:929–34.

  20. Videbech P, Ravnkilde B, Pedersen TH, Hartvig H, Egander A, Clemmensen K, et al. The Danish PET/depression project: Clinical symptoms and cerebral blood flow. A regions-of-interest analysis. Acta Psychiatr Scandinavica. 2002;106:35–44.

    Article  CAS  Google Scholar 

  21. Yin Y, Wang M, Wang Z, Xie C, Zhang H, Zhang H, et al. Decreased cerebral blood flow in the primary motor cortex in major depressive disorder with psychomotor retardation. Prog Neuropsychopharmacol Biol Psychiatry. 2018;81:438–44.

    Article  PubMed  Google Scholar 

  22. Walther S, Hugli S, Hofle O, Federspiel A, Horn H, Bracht T, et al. Frontal white matter integrity is related to psychomotor retardation in major depression. Neurobiol Dis. 2012;47:13–9.

    Article  PubMed  Google Scholar 

  23. Cantisani A, Stegmayer K, Bracht T, Federspiel A, Wiest R, Horn H, et al. Distinct resting-state perfusion patterns underlie psychomotor retardation in unipolar vs. bipolar depression. Acta Psychiatr Scandinavica. 2016;134:329–38.

    Article  CAS  Google Scholar 

  24. Pan F, Xu Y, Zhou W, Chen J, Wei N, Lu S, et al. Disrupted intrinsic functional connectivity of the cognitive control network underlies disease severity and executive dysfunction in first-episode, treatment-naive adolescent depression. J Affect Disord. 2020;264:455–63.

    Article  PubMed  Google Scholar 

  25. Zitser J, Casaletto KB, Staffaroni AM, Sexton C, Weiner-Light S, Wolf A, et al. Mild motor signs matter in typical brain aging: the value of the UPDRS score within a functionally intact cohort of older adults. Front Aging Neurosci. 2021;13:594637.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Louis ED, Bennett DA. Mild Parkinsonian signs: An overview of an emerging concept. Mov Disord. 2007;22:1681–8.

    Article  PubMed  Google Scholar 

  27. Buchanan SM, Richards M, Schott JM, Schrag A. Mild Parkinsonian signs: a systematic review of clinical, imaging, and pathological associations. Mov Disord. 2021;36:2481–93.

    Article  PubMed  Google Scholar 

  28. Van Cauwenberge MGA, Delva A, Vande Casteele T, Laroy M, Radwan A, Vansteelandt K, et al. Mild Motor Signs in Healthy Aging Are Associated with Lower Synaptic Density in the Brain. Mov Disord. 2023;38:1786–94.

  29. Emsell L, Laroy M, Van Cauwenberge M, Vande Casteele T, Vansteelandt K, Van Laere K, et al. The Leuven late life depression (L3D) study: PET-MRI biomarkers of pathological brain ageing in late-life depression: study protocol. BMC Psychiatry. 2021;21:64.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Van Cauwenberge MGA, Vande Casteele T, Laroy M, Vansteelandt K, Van den Stock J, Bouckaert F, et al. Motor dysfunction in late life depression: A mood or movement disorder? J Affect Disord. 2025;381:680–91.

  31. Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, et al. Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord. 2008;23:2129–70.

    Article  PubMed  Google Scholar 

  32. Schmitz-Hübsch T, du Montcel ST, Baliko L, Berciano J, Boesch S, Depondt C, et al. Scale for the assessment and rating of ataxia: development of a new clinical scale. Neurology. 2006;66:1717–20.

    Article  PubMed  Google Scholar 

  33. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin psychiatry. 1998;59:22–33. quiz 4-57

    PubMed  Google Scholar 

  34. Henschel L, Conjeti S, Estrada S, Diers K, Fischl B, Reuter M. FastSurfer - A fast and accurate deep learning based neuroimaging pipeline. Neuroimage. 2020;219:117012.

    Article  PubMed  Google Scholar 

  35. Safai A, Prasad S, Chougule T, Saini J, Pal PK, Ingalhalikar M. Microstructural abnormalities of substantia nigra in Parkinson’s disease: A neuromelanin sensitive MRI atlas based study. Hum Brain Mapp. 2020;41:1323–33.

    Article  PubMed  Google Scholar 

  36. Ma D, Popuri K, Bhalla M, Sangha O, Lu D, Cao J, et al. Quantitative assessment of field strength, total intracranial volume, sex, and age effects on the goodness of harmonization for volumetric analysis on the ADNI database. Hum Brain Mapp. 2019;40:1507–27.

    Article  PubMed  Google Scholar 

  37. Mertens N, Maguire RP, Serdons K, Lacroix B, Mercier J, Sciberras D, et al. Validation of Parametric Methods for [(11)C]UCB-J PET Imaging Using Subcortical White Matter as Reference Tissue. Mol Imaging Biol. 2020;22:444–52.

    Article  PubMed  CAS  Google Scholar 

  38. Thomas BA, Erlandsson K, Modat M, Thurfjell L, Vandenberghe R, Ourselin S, et al. The importance of appropriate partial volume correction for PET quantification in Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2011;38:1104–19.

    Article  PubMed  Google Scholar 

  39. Tabachnick BG, Fidell LS Using Multivariate Statistics. Pearson; 2014, p. 1056.

  40. Leucht S, Samara M, Heres S, Davis JM. Dose equivalents for antipsychotic drugs: the DDD method. Schizophrenia Bull. 2016;42:S90–S4.

    Article  Google Scholar 

  41. Li X, Xing Y, Martin-Bastida A, Piccini P, Auer DP. Patterns of grey matter loss associated with motor subscores in early Parkinson’s disease. Neuroimage Clin. 2018;17:498–504.

    Article  PubMed  Google Scholar 

  42. Levy-Tzedek S, Arbelle D, Forman D, Zlotnik Y. Improvement in upper-limb UPDRS motor scores following fast-paced arm exercise: A pilot study. Restor Neurol Neurosci. 2018;36:535–45.

    PubMed  PubMed Central  Google Scholar 

  43. Holmes SE, Honhar P, Tinaz S, Naganawa M, Hilmer AT, Gallezot J-D, et al. Synaptic loss and its association with symptom severity in Parkinson’s disease. npj Parkinson’s Dis. 2024;10:42.

    Article  Google Scholar 

  44. Matuskey D, Tinaz S, Wilcox KC, Naganawa M, Toyonaga T, Dias M, et al. Synaptic changes in Parkinson disease assessed with in vivo imaging. Ann Neurol. 2020;87:329–38.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Delva A, Van Weehaeghe D, Koole M, Van Laere K, Vandenberghe W. Loss of presynaptic terminal integrity in the substantia nigra in early Parkinson’s Disease. Mov Disord. 2020;35:1977–86.

    Article  PubMed  CAS  Google Scholar 

  46. Lauterbach EC Mood Disorders and the Globus Pallidus. In: Bédard M-A, Agid Y, Chouinard S, Fahn S, Korczyn AD, Lespérance P, editors. Mental and behavioral dysfunction in movement disorders. Totowa, NJ: Humana Press; 2003, pp. 305–20.

  47. Alexander GE, DeLong MR, Strick PL. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci. 1986;9:357–81.

    Article  PubMed  CAS  Google Scholar 

  48. Mizuno Y, Ashok AH, Bhat BB, Jauhar S, Howes OD. Dopamine in major depressive disorder: A systematic review and meta-analysis of in vivo imaging studies. J Psychopharmacol. 2023;37:1058–69.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Meyer JH, Krüger S, Wilson AA, Christensen BK, Goulding VS, Schaffer A, et al. Lower dopamine transporter binding potential in striatum during depression. Neuroreport. 2001;12:4121–5.

    Article  PubMed  CAS  Google Scholar 

  50. Kazmi H, Walker Z, Booij J, Khan F, Shah S, Sudre CH, et al. Late onset depression: dopaminergic deficit and clinical features of prodromal Parkinson’s disease: a cross-sectional study. J Neurol Neurosurg Psychiatry. 2021;92:158–64.

    Article  PubMed  Google Scholar 

  51. Van Cauwenberge MGA, Bouckaert F, Vansteelandt K, Adamson C, De Winter FL, Sienaert P, et al. A longitudinal study of the association between basal ganglia volumes and psychomotor symptoms in subjects with late life depression undergoing ECT. Transl Psychiatry. 2021;11:199.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Belge JB, Van Diermen L, Schrijvers D, Sabbe B, Constant E, de Timary P, et al. The basal ganglia: A central hub for the psychomotor effects of electroconvulsive therapy. J Affect Disord. 2020;265:239–46.

    Article  PubMed  CAS  Google Scholar 

  53. Dong J, Hawes S, Wu J, Le W, Cai H. Connectivity and functionality of the globus pallidus externa under normal conditions and Parkinson’s disease. Front Neural Circuits. 2021;15:645287.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Albin RL, Young AB, Penney JB. The functional anatomy of basal ganglia disorders. Trends Neurosci. 1989;12:366–75.

    Article  PubMed  CAS  Google Scholar 

  55. Williams D. Why so slow? Models of parkinsonian bradykinesia. Nat Rev Neurosci. 2024;25:573–86.

    Article  PubMed  CAS  Google Scholar 

  56. Marsden CD, Obeso JA. The functions of the basal ganglia and the paradox of stereotaxic surgery in Parkinson’s disease. Brain. 1994;117:877–97.

    Article  PubMed  Google Scholar 

  57. Poston KL, Eidelberg D. FDG PET in the evaluation of Parkinson’s Disease. PET Clin. 2010;5:55–64.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Giossi C, Rubin JE, Gittis A, Verstynen T, Vich C. Rethinking the external globus pallidus and information flow in cortico-basal ganglia-thalamic circuits. Eur J Neurosci. 2024;60:6129–44.

    Article  PubMed  CAS  Google Scholar 

  59. Naismith S, Hickie I, Ward PB, Turner K, Scott E, Little C, et al. Caudate nucleus volumes and genetic determinants of homocysteine metabolism in the prediction of psychomotor speed in older persons with depression. Am J Psychiatry. 2002;159:2096–8.

    Article  PubMed  Google Scholar 

  60. Desmidt T, Andersson F, Brizard B, Cottier JP, Patat F, Gissot V, et al. Cerebral blood flow velocity positively correlates with brain volumes in long-term remitted depression. Prog Neuropsychopharmacol Biol Psychiatry. 2018;81:243–9.

    Article  PubMed  Google Scholar 

  61. Wilson J, Allcock L, Mc Ardle R, Taylor JP, Rochester L. The neural correlates of discrete gait characteristics in ageing: A structured review. Neurosci Biobehav Rev. 2019;100:344–69.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Dumurgier J, Crivello F, Mazoyer B, Ahmed I, Tavernier B, Grabli D, et al. MRI atrophy of the caudate nucleus and slower walking speed in the elderly. NeuroImage. 2012;60:871–8.

    Article  PubMed  Google Scholar 

  63. Callisaya ML, Beare R, Phan TG, Chen J, Srikanth VK. Global and regional associations of smaller cerebral gray and white matter volumes with gait in older people. PLOS ONE. 2014;9:e84909.

    Article  PubMed  PubMed Central  Google Scholar 

  64. van Diermen L, van den Ameele S, Kamperman AM, Sabbe BCG, Vermeulen T, Schrijvers D, et al. Prediction of electroconvulsive therapy response and remission in major depression: meta-analysis. Br J Psychiatry. 2018;212:71–80.

    Article  PubMed  Google Scholar 

  65. van Diermen L, Schrijvers D, Cools O, Birkenhäger TK, Fransen E, Sabbe BGCC. Distinguishing subgroups based on psychomotor functioning among patients with major depressive disorder. Neuropsychobiology. 2018;76:199–208.

    Google Scholar 

  66. van Diermen L, Walther S, Cools O, Fransen E, Birkenhäger TK, Sabbe BCG, et al. Observer-rated retardation but not agitation corresponds to objective motor measures in depression. Acta Neuropsychiatr. 2018;30:359–64.

  67. Beheydt LL, Schrijvers D, Docx L, Bouckaert F, Hulstijn W, Sabbe B. Psychomotor retardation in elderly untreated depressed patients. Front Psychiatry. 2015;5:196.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Zapparoli L, Mariano M, Paulesu E. How the motor system copes with aging: a quantitative meta-analysis of the effect of aging on motor function control. Commun Biol. 2022;5:79.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Li KZ, Lindenberger U. Relations between aging sensory/sensorimotor and cognitive functions. Neurosci Biobehav Rev. 2002;26:777–83.

    Article  PubMed  Google Scholar 

  70. Asan L, Falfán-Melgoza C, Beretta CA, Sack M, Zheng L, Weber-Fahr W, et al. Cellular correlates of gray matter volume changes in magnetic resonance morphometry identified by two-photon microscopy. Sci Rep. 2021;11:4234.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Korczyn AD. Vascular parkinsonism—characteristics, pathogenesis and treatment. Nat Rev Neurol. 2015;11:319–26.

    Article  PubMed  Google Scholar 

  72. Taylor WD, Aizenstein HJ, Alexopoulos GS. The vascular depression hypothesis: mechanisms linking vascular disease with depression. Mol Psychiatry. 2013;18:963–74.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Pimontel MA, Reinlieb ME, Johnert LC, Garcon E, Sneed JR, Roose SP. The external validity of MRI-defined vascular depression. Int J Geriatr Psychiatry. 2013;28:1189–96.

    Article  PubMed  Google Scholar 

  74. Dalby RB, Frandsen J, Chakravarty MM, Ahdidan J, Sorensen L, Rosenberg R, et al. Correlations between Stroop task performance and white matter lesion measures in late-onset major depression. Psychiatry Res. 2012;202:142–9.

    Article  PubMed  Google Scholar 

  75. Park JH, Lee SB, Lee JJ, Yoon JC, Han JW, Kim TH, et al. Depression Plays a Moderating Role in the Cognitive Decline Associated With Changes of Brain White Matter Hyperintensities. J Clin Psychiatry. 2018;79:17m11763.

  76. Reinlieb ME, Persaud A, Singh D, Garcon E, Rutherford BR, Pelton GH, et al. Vascular depression: overrepresented among African Americans? Int J Geriatr Psychiatry. 2014;29:470–7.

    Article  PubMed  Google Scholar 

  77. Brown PJ, Roose SP, O’Boyle KR, Ciarleglio A, Maas B, Igwe KC, et al. Frailty and its correlates in adults with late life depression. Am J Geriatr Psychiatry. 2020;28:145–54.

    Article  PubMed  Google Scholar 

  78. Hickie I, Scott E, Mitchell P, Wilhelm K, Austin MP, Bennett B. Subcortical hyperintensities on magnetic resonance imaging: Clinical correlates and prognostic significance in patients with severe depression. Biol Psychiatry. 1995;37:151–60.

    Article  PubMed  CAS  Google Scholar 

  79. Respino M, Jaywant A, Kuceyeski A, Victoria LW, Hoptman MJ, Scult MA, et al. The impact of white matter hyperintensities on the structural connectome in late-life depression: Relationship to executive functions. Neuroimage Clin. 2019;23:101852.

  80. Coloigner J, Batail JM, Commowick O, Corouge I, Robert G, Barillot C, et al. White matter abnormalities in depression: A categorical and phenotypic diffusion MRI study. Neuroimage Clin. 2019;22:101710.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Bracht T, Federspiel A, Schnell S, Horn H, Höfle O, Wiest R, et al. Cortico-cortical white matter motor pathway microstructure is related to psychomotor retardation in major depressive disorder. PLoS ONE. 2012;7:1–8.

    Article  Google Scholar 

  82. Hyett MP, Perry A, Breakspear M, Wen W, Parker GB. White matter alterations in the internal capsule and psychomotor impairment in melancholic depression. PLoS One. 2018;13:e0195672.

    Article  PubMed  PubMed Central  Google Scholar 

  83. van Agtmaal MJM, Houben A, Pouwer F, Stehouwer CDA, Schram MT. Association of microvascular dysfunction with late-life depression: a systematic review and meta-analysis. JAMA Psychiatry. 2017;74:729–39.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Carvalho de Abreu DC, Pieruccini-Faria F, Son S, Montero-Odasso M, Camicioli R. Is white matter hyperintensity burden associated with cognitive and motor impairment in patients with parkinson’s disease? A systematic review and meta-analysis. Neurosci Biobehav Rev. 2024;161:105677.

    Article  PubMed  Google Scholar 

  85. Vande Casteele T, Laroy M, Van Cauwenberge M, Vanderlinden G, Vansteelandt K, Koole M, et al. Late life depression is not associated with Alzheimer-type tau: preliminary evidence from a next-generation tau ligand PET-MR study. Am J Geriatr Psychiatry. 2025;33:47–62.

    Article  PubMed  Google Scholar 

  86. Vande Casteele T, Laroy M, Van Cauwenberge M, Koole M, Dupont P, Sunaert S, et al. Preliminary evidence for preserved synaptic density in late-life depression. Transl Psychiatry. 2024;14:145.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Wilson H, Pagano G, de Natale ER, Mansur A, Caminiti SP, Polychronis S, et al. Mitochondrial Complex 1, Sigma 1, and Synaptic Vesicle 2A in Early Drug-Naive Parkinson’s Disease. Mov Disord. 2020;35:1416–27.

    Article  PubMed  CAS  Google Scholar 

  88. Johansen A, Armand S, Plavén-Sigray P, Nasser A, Ozenne B, Petersen IN, et al. Effects of escitalopram on synaptic density in the healthy human brain: a randomized controlled trial. Mol Psychiatry. 2023;28:4272–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Rossi R, Arjmand S, Bærentzen SL, Gjedde A, Landau AM. Synaptic Vesicle Glycoprotein 2A: Features and Functions. Front Neurosci. 2022;16:864514.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Serrano ME, Kim E, Petrinovic MM, Turkheimer F, Cash D. Imaging synaptic density: the next holy grail of neuroscience? Front Neurosci. 2022;16:796129.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Shanaki Bavarsad M, Spina S, Oehler A, Allen IE, Suemoto CK, Leite REP, et al. Comprehensive mapping of synaptic vesicle protein 2A (SV2A) in health and neurodegenerative diseases: a comparative analysis with synaptophysin and ground truth for PET-imaging interpretation. Acta Neuropathol. 2024;148:58.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

We thank all participants for their contribution. We thank Jef Van Loock, Kwinten Porters and Michel Koole for their support at the PET-MR facility, Kim Serdons and radiopharmacy UZ Leuven team for tracer productions, Ruben Houbrechts at Icometrix® for technical support with WMH segmentation, Hans-Leo Teulings for support with Movalyzer®, Jari Mees, Aki Takamiya and Jeroen Blommaert for imaging software assistance.

Funding

M.V.C received support from KU Leuven grant PDMT2/24/089. M.L. received support from Research Foundation Flanders (FWO) grant 1168821 N. M.V., F.B., J.V.d.S., and L.E. are supported by FWO, grant G0C0319N, KU Leuven Fund C24/18/095, and the Sequoia Fund for Research on Aging and Mental Health. There was no industrial sponsoring related to the study.

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Authors and Affiliations

  1. KU Leuven, Leuven Brain Institute, Department of Neurosciences, Neuropsychiatry, Leuven, Belgium

    Margot G. A. Van Cauwenberge, Thomas Vande Casteele, Maarten Laroy, Kristof Vansteelandt, Jan Van den Stock, Filip Bouckaert, Louise Emsell & Mathieu Vandenbulcke

  2. Department of Neurology, General Hospital Diest, Diest, Belgium

    Margot G. A. Van Cauwenberge

  3. Department of Neurology, University Hospitals Leuven, Leuven, Belgium

    Margot G. A. Van Cauwenberge

  4. Geriatric Psychiatry, University Psychiatric Center KU Leuven, Leuven, Belgium

    Thomas Vande Casteele, Maarten Laroy, Kristof Vansteelandt, Jan Van den Stock, Filip Bouckaert, Louise Emsell & Mathieu Vandenbulcke

  5. Division of Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium

    Koen Van Laere

  6. KU Leuven, Department of Imaging & Pathology, Nuclear Medicine and Molecular Imaging, Leuven, Belgium

    Koen Van Laere

  7. KU Leuven, Department of Imaging & Pathology, Translational MRI, Leuven, Belgium

    Louise Emsell

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  1. Margot G. A. Van Cauwenberge
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Contributions

Concept: M.V.C., F.B., L.E., M.V.; Funding/resources: M.V.C., M.L., F.B., J.V.d.S, L.E., M.V., Data Curation & project administration: M.V.C., T.V.C., M.L., J.V.d.S., F.B., L.E., M.V.; Methodology: T.V.C, M.L., M.V.C.; Data analysis & statistical design: M.V.C. Statistical review: K.V.; Manuscript draft writing: M.V.C. Validation, visualization: M.V.C., K.V., K.V.L., L.E., M.V.; Supervision: F.B., L.E., M.V., Manuscript review/editing: M.V.C., T.V.C., M.L., K.V., J.V.d.S, F.B., L.E., M.V.

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Correspondence to Margot G. A. Van Cauwenberge.

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Van Cauwenberge, M.G.A., Vande Casteele, T., Laroy, M. et al. Motor signs in late life depression relate to altered subcortical synaptic density and gray matter volume: an 11C-UCB-J PET-MR study. Neuropsychopharmacol. 51, 661–671 (2026). https://doi.org/10.1038/s41386-025-02229-6

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