The impact of white matter hyperintensities on the structural connectome in late-life depression: Relationship to executive functions

Matteo Respino, Abhishek Jaywant, Amy Kuceyeski, Lindsay W Victoria, Matthew J Hoptman, Matthew A Scult, Lindsey Sankin, Monique Pimontel, Conor Liston, Martino Belvederi Murri, George S Alexopoulos, Faith M Gunning, Matteo Respino, Abhishek Jaywant, Amy Kuceyeski, Lindsay W Victoria, Matthew J Hoptman, Matthew A Scult, Lindsey Sankin, Monique Pimontel, Conor Liston, Martino Belvederi Murri, George S Alexopoulos, Faith M Gunning

Abstract

Background: White matter hyperintensities (WMH) represent ischemic white matter damage in late-life depression (LLD) and are associated with cognitive control dysfunction. Understanding the impact of WMH on the structural connectivity of gray matter and the cognitive control correlates of WMH-related structural dysconnectivity can provide insight into the pathophysiology of LLD.

Methods: We compared WMH burden and performance on clinical measures of cognitive control in patients with LLD (N = 44) and a control group of non-depressed older adults (N = 59). We used the Network Modification (NeMo) Tool to investigate the impact of WMH on structural dysconnectivity in specific gray matter regions, and how such connectivity was related to cognitive control functions.

Results: Compared to the control group, LLD participants had greater WMH burden, poorer performance on Trail Making Test (TMT) A & B, and greater self-reported dysexecutive behavior on the Frosntal Systems Behavior Scale-Executive Function subscale (FrSBe-EF). Within the LLD group, disrupted connectivity in the left supramarginal gyrus, paracentral lobule, thalamus, and pallidum was associated with psychomotor slowing (TMT-A). Altered connectivity in the left supramarginal gyrus, paracentral lobule, precentral gyrus, postcentral gyrus, thalamus, and pallidum was associated with poor attentional set-shifting (TMT-B). A follow-up analysis that isolated set-shifting ability (TMT-B/A ratio) confirmed the association with dysconnectivity in the bilateral paracentral lobule, right thalamus, left precentral gyrus, postcentral gyrus, and pallidum; additionally, it revealed associations with dysconnectivity in the right posterior cingulate, and left anterior cingulate, middle frontal cortex, and putamen.

Conclusions: In LLD, WMH are associated with region-specific disruptions in cortical and subcortical gray matter areas involved in attentional aspects of cognitive control systems and sensorimotor processing, which in turn are associated with slower processing speed, and reduced attentional set-shifting.

Clinical trials registration: https://ichgcp.net/clinical-trials-registry/NCT01728194.

Keywords: Late life depression, cognitive control, executive functions; MRI; Structural connectivity; White matter hyperintensities.

Copyright © 2019. Published by Elsevier Inc.

Figures

Fig. 1
Fig. 1
The Network Modification Tool.
Fig. 2
Fig. 2
Scatterplots depicting the relationship between Trail Making Test-B (TMT-B) performance in seconds and change in connectivity (ChaCo) in the (a) left supramarginal gyrus, (b) left thalamus, and (c) left pallidum.
Fig. 3
Fig. 3
Scatterplots depicting the relationship between Trail Making Test-B/A ratio (TMT-B/A) and change in connectivity (ChaCo) in the (a) right posterior cingulate, (b) left anterior cingulate, and (c) left middle frontal cortex.

References

    1. Aizenstein H.J., Andreescu C., Edelman K.L., Cochran J.L., Price J., Butters M.A. fMRI correlates of white matter hyperintensities in late-life depression. Am. J. Psychiatry. 2011;168:1075–1082.
    1. Ajilore O., Lamar M., Leow A., Zhang A., Yang S., Kumar A. Graph theory analysis of cortical-subcortical networks in late-life depression. Am. J. Geriatr. Psychiatry. 2014;22(2):195–206.
    1. Alexopoulos G.S., Kiosses D.N., Klimstra S., Kalayam B., Bruce M.L. Clinical presentation of the “depression-executive dysfunction syndrome” of late life. Am. J. Geriatr. Psychiatry. 2002;10(1):98–106.
    1. Alexopoulos G.S., Hoptman M.J., Kanellopoulos D., Murphy C.F., Lim K.O., Gunning F.M. Functional connectivity in the cognitive control network and the default mode network in late-life depression. J. Affect. Disord. 2012;139(1):56–65.
    1. Alnæs D., Sneve M.H., Richard G., Skåtun K.C., Kaufmann T., Nordvik J.E. Functional connectivity indicates differential roles for the intraparietal sulcus and the superior parietal lobule in multiple object tracking. Neuroimage. 2015;123:129–137.
    1. Anguera J.A., Gunning F.M., Areán P.A. Improving late life depression and cognitive control through the use of therapeutic video game technology: a proof-of-concept randomized trial. Depress Anxiety. 2017;34(6):508–517.
    1. Aron A.R., Monsell S., Sahakian B.J., Robbins T.W. A componential analysis of task-switching deficits associated with lesions of left and right frontal cortex. Brain. 2004;127(7):1561–1573.
    1. Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 1995;57(1):289–300.
    1. Bennett I.J., Madden D.J. Disconnected aging: cerebral white matter integrity and age-related differences in cognition. Neuroscience. 2014;276:187–205.
    1. Bhalla R.K., Butters M.A., Mulsant B.H., Begley A.E., Zmuda M.D., Schoderbek B. Persistence of neuropsychologic deficits in the remitted state of late-life depression. Am. J. Geriatr. Psychiatry. 2006;14(5):419–427. doi: 10.1097/01.JGP.0000203130.45421.69. Available from.
    1. Bissonette G.B., Powell E.M., Roesch M.R. Neural structures underlying set-shifting: roles of medial prefrontal cortex and anterior cingulate cortex. Behav. Brain Res. 2013;250:91–101. doi: 10.1016/j.bbr.2013.04.037. Available from.
    1. Braver T.S., Barch D.M., Gray J.R., Molfese D.L., Snyder A. Anterior cingulate cortex and response conflict: effects of frequency, inhibition and errors. Cereb. Cortex. 2001;11:825–836.
    1. Cassady K., Gagnon H., Lalwani P., Simmonite M., Foerster B., Park D. Sensorimotor network segregation declines with age and is linked to GABA and to sensorimotor performance. Neuroimage. 2019;186:234–244.
    1. Corbetta M., Shulman G.L., Miezin F.M., Petersen S.E. Superior parietal cortex activation during spatial attention shifts and visual feature conjunction. Science. 1995;270(5237):802–805.
    1. Cummings J.L. Frontal-subcortical circuits and human behavior. Arch. Neurol. 1993;50(8):873–880.
    1. Dombrovski A.Y., Szanto K., Clark L., Aizenstein H.J., Chase H.W., Reynolds C.F. Corticostriatothalamic reward prediction error signals and executive control in late-life depression. Psychol. Med. 2015;45(7):1413–1424.
    1. Drane D.L., Yuspeh R.L., Huthwaite J.S., Klingler L.K. Demographic characteristics and normative observations for Derived-Trail making test indices. Neuropsychiatry Neuropsychol. Behav. Neurosci. 2002;15(1):39–43.
    1. Drysdale A.T., Grosenick L., Downar J., Dunlop K., Mansouri F., Meng Y. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 2017;23(1):28–38.
    1. Dubin M.J., Liston C., Avissar M.A., Ilieva I., Gunning F.M. Network-guided transcranial magnetic stimulation for depression. Curr. Behav. Neurosci. Rep. 2017;4(1):70–77.
    1. Fava M., Mahableshwarkar A.R., Jacobson W., Zhong W., Keefe R.S., Olsen C.K. What is the overlap between subjective and objective cognitive impairments in MDD? Ann. Clin. Psychiatry. 2018;30(3):176–184.
    1. Fields R.D. White matter in learning, cognition and psychiatric disorders. Trends Neurosci. 2008;31:361–370.
    1. Filley C.M. White matter and behavioral neurology. Ann. N. Y. Acad. Sci. 2005;1064:162–183.
    1. Filley C.M., Fields R.D. White matter and cognition: making the connection. J. Neurophysiol. 2016;116(5):2093–2104.
    1. Folstein M.F., Folstein S.E., McHugh P.R., Roth M., Shapiro M.B., Post F. Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 1975;12(3):189–198.
    1. Gasquoine P.G. Localization of function in anterior cingulate cortex: from psychosurgery to functional neuroimaging. Neurosci. Biobehav. Rev. 2013;37(3):340–348. doi: 10.1016/j.neubiorev.2013.01.002. [Internet]. Elsevier Ltd. Available from:
    1. Golden C.J., Freshwater S.M. Stoelting Co.; Wood Dale, IL: 2002. The Stroop Color and Word Test: A Manual for Clinical and Experimental Uses.
    1. Grace J. Encyclopedia of Clinical Neuropsychology. 2011. Frontal systems behavior scale; pp. 1090–1093.
    1. Griffanti L., Zamboni G., Khan A., Li L., Bonifacio G., Sundaresan V. BIANCA (brain intensity AbNormality classification algorithm): a new tool for automated segmentation of white matter hyperintensities. Neuroimage. 2016;141:191–205.
    1. Gunning-Dixon F.M., Hoptman M.J., Lim K.O., Murphy C.F., Klimstra S., Latoussakis V. Macromolecular white matter abnormalities in geriatric depression: a magnetization transfer imaging study. Am. J. Geriatr. Psychiatry. 2008;16(4):255–262.
    1. Gunning-Dixon F.M., Walton M., Cheng J., Acuna J., Klimstra S., Zimmerman M.E. MRI signal hyperintensities and treatment remission of geriatric depression. J. Affect. Disord. 2010;126(3):395–401.
    1. Hamilton M.C. Hamilton depression rating scale (HAM-D) Redloc. 1960;23:56–62.
    1. Heinen R., Bouvy W., Mendrik A., Viergever M., Biessels G., De Bresser J. Robustness of automated methods for brain volume measurements across different MRI field strengths. PLoS One. 2016;11(10)
    1. Hinman J.D., Abraham C.R. What's behind the decline? The role of white matter in brain aging. Neurochem. Res. 2007;32:2023–2031.
    1. Jenkinson M., Beckmann C.F., Behrens T.E.J., Woolrich M.W., Smith S.M. FSL. Neuroimage. 2012;62(2):782–790.
    1. Kohler S., Thomas A.J., Lloyd A., Barber R., Almeida O.P., O'Brien J.T. White matter hyperintensities, cortisol levels, brain atrophy and continuing cognitive deficits in late-life depression. Br. J. Psychiatry. 2010;196(2):143–149.
    1. Krishnan K.R., Hays J.C., Blazer D.G. MRI-defined vascular depression. Am. J. Psychiatry. 1997;154(4):497–501.
    1. Kuceyeski A., Zhang Y., Raj A. Linking white matter integrity loss to associated cortical regions using structural connectivity information in Alzheimer's disease and fronto-temporal dementia: the loss in connectivity (LoCo) score. Neuroimage. 2012;61(4):1311–1323.
    1. Kuceyeski A., Maruta J., Relkin N., Raj A. The network modification (NeMo) tool: elucidating the effect of white matter integrity changes on cortical and subcortical structural connectivity. Brain Connect. 2013;3(5):451–463.
    1. Kuceyeski A., Navi B.B., Kamel H., Relkin N., Villanueva M., Raj A. Exploring the brain's structural connectome: a quantitative stroke lesion-dysfunction mapping study. Hum. Brain Mapp. 2015;36:2147–2160.
    1. Kuceyeski A., Monohan E., Morris E., Fujimoto K., Vargas W., Gauthier S.A. Baseline biomarkers of connectome disruption and atrophy predict future processing speed in early multiple sclerosis. NeuroImage Clin. 2018;19(May):417–424.
    1. Leech R., Sharp D.J. The role of the posterior cingulate cortex in cognition and disease. Brain. 2014;137:12–32.
    1. Lesser I.M., Boone K.B., Mehringer C.M., Wohl M.A., Miller B.L., Berman N.G. Cognition and white matter hyperintensities in older depressed patients. Am. J. Psychiatry. 1996;153(10):1280–1287.
    1. Lockhart S.N., Luck S.J., Geng J., Beckett L., Disbrow E.A., Carmichael O. White matter hyperintensities among older adults are associated with futile increase in frontal activation and functional connectivity during spatial search. PLoS One. 2015;10(3)
    1. Manning K.J., Alexopoulos G.S., Banerjee S., Morimoto S.S., Seirup J.K., Klimstra S.A. Executive functioning complaints and escitalopram treatment response in late-life depression. Am. J. Geriatr. Psychiatry. 2015;23(5):440–445.
    1. Marzinzik F., Wahl M., Schneider G.H., Kupsch A., Curio G., Klostermann F. The human thalamus is crucially involved in executive control operations. J. Cogn. Neurosci. 2008;20(10):1903–1914.
    1. Minzenberg M.J., Laird A.R., Thelen S., Carter C.S., Glahn D.C. Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. Arch. Gen. Psychiatry. 2009;66(8):811–822.
    1. Morimoto S.S., Gunning F.M., Wexler B.E., Hu W., Ilieva I., Liu J. Executive dysfunction predicts treatment response to neuroplasticity-based computerized cognitive remediation (nCCR-GD) in elderly patients with major depression. Am. J. Geriatr. Psychiatry. 2016;24(10):816–820.
    1. Naarding P., Tiemeier H., Breteler M.M.B., Schoevers R.A., Jonker C., Koudstaal P.J. Clinically defined vascular depression in the general population. Psychol. Med. 2007;37(3):383–392.
    1. Orr C., Hester R. Error-related anterior cingulate cortex activity and the prediction of conscious error awareness. Front. Hum. Neurosci. 2012;6:1–12.
    1. Pardini M., Bonzano L., Bergamino M., Bommarito G., Feraco P., Murugavel A. Cingulum bundle alterations underlie subjective fatigue in multiple sclerosis. Mult. Scler. J. 2015;21(4):442–447.
    1. Pimontel M.A., Rindskopf D., Rutherford B.R., Brown P.J., Roose S.P., Sneed J.R. A meta-analysis of executive dysfunction and antidepressant treatment response in late-life depression. Am. J. Geriatr. Psychiatry. 2016;24(1):31–41.
    1. Pugh K.G., Lipsitz L.A. The microvascular frontal-subcortical syndrome of aging. Neurobiol. Aging. 2002;23(3):421–431.
    1. Rahm C., Liberg B., Wiberg-Kristoffersen M., Aspelin P., Msghina M. Rostro-caudal and dorso-ventral gradients in medial and lateral prefrontal cortex during cognitive control of affective and cognitive interference. Scand. J. Psychol. 2013;54:66–71.
    1. Reijmer Y.D., Schultz A.P., Leemans A., O'Sullivan M.J., Gurol M.E., Sperling R. Decoupling of structural and functional brain connectivity in older adults with white matter hyperintensities. Neuroimage. 2015;117:222–229.
    1. Reitan R.M. Validity of the trail making test as an Indicator of organic brain damage. Percept. Mot. Skills. 1958;8(3):271–276.
    1. Salthouse T.A. What cognitive abilities are involved in trail-making performance? Intelligence. 2011;39(4):222–232.
    1. Scott R.B., Harrison J., Boulton C., Wilson J., Gregory R., Parkin S. Global attentional-executive sequelae following surgical lesions to globus pallidus interna. Brain. 2002;125(3):562–574.
    1. Sexton C.E., MacKay C.E., Ebmeier K.P. A systematic review and meta-analysis of magnetic resonance imaging studies in late-life depression. Am. J. Geriatr. Psychiatry. 2013;21(2):184–195.
    1. Sheline Y.I., Price J.L., Vaishnavi S.N., Mintun M.A., Barch D.M., Epstein A.A. Regional white matter hyperintensity burden in automated segmentation distinguishes late-life depressed subjects from comparison subjects matched for vascular risk factors. Am. J. Psychiatry. 2008;165:524–532.
    1. Stroop J.R. Stroop color word test. J. Exp. Physiol. 1935;18:643–662.
    1. Taylor W.D., Payne M.E., Krishnan K.R.R., Wagner H.R., Provenzale J.M., Steffens D.C. Evidence of white matter tract disruption in MRI hyperintensities. Biol. Psychiatry. 2001;50(3):179–183.
    1. Taylor W.D., Aizenstein H.J., Alexopoulos G.S. The vascular depression hypothesis: mechanisms linking vascular disease with depression. Mol. Psychiatry. 2013;18(9):963–974.
    1. Thomas A.J., O'Brien J.T., Davis S., Ballard C., Barber R., Kalaria R.N. Ischemic basis for deep white matter hyperintensities in major depression: a neuropathological study. Arch. Gen. Psychiatry. 2002;59(9):785–792.
    1. van Agtmaal M.J.M., Houben A.J.H.M., Pouwer F., Stehouwer C.D.A., Schram M.T. Association of microvascular dysfunction with late-life depression: A systematic review and meta-analysis. JAMA Psychiatry. 2017;74(7):729–739.
    1. Van Der Werf Y.D., Scheltens P., Lindeboom J., Witter M.P., Uylings H.B.M., Jolles J. Deficits of memory, executive functioning and attention following infarction in the thalamus; a study of 22 cases with localised lesions. Neuropsychologia. 2003;41(10):1330–1344.
    1. Vasudev A., Saxby B.K., O'Brien J.T., Colloby S.J., Firbank M.J., Brooker H. Relationship between cognition, magnetic resonance white matter hyperintensities, and cardiovascular autonomic changes in late-life depression. Am. J. Geriatr. Psychiatry. 2012;20(8):691–699.
    1. Vataja R., Pohjasvaara T., Mäntylä R., Ylikoski R., Leskelä M., Kalska H. Depression–executive dysfunction syndrome in stroke patients. Am. J. Geriatr. Psychiatry. 2005;13(2):99–107.
    1. Wahlund L.O., Barkhof F., Fazekas F., Bronge L., Augustin M., Sjögren M. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke. 2001;32(6):1318–1322.
    1. Wang L., Krishnan K.R., Steffens D.C., Potter G.G., Dolcos F., McCarthy G. Depressive state- and disease-related alterations in neural responses to affective and executive challenges in geriatric depression. Am. J. Psychiatry. 2008;165(7):863–871.
    1. Wei W., Wang X.J. Inhibitory control in the Cortico-basal ganglia-Thalamocortical loop: complex regulation and interplay with memory and decision processes. Neuron. 2016;92(5):1093–1105.
    1. Wu M., Andreescu C., Butters M.A., Tamburo R., Reynolds C.F., 3rd, Aizenstein H. Default-mode network connectivity and white matter burden in late-life depression. Psychiatry Res. 2011;194(1):39–46.
    1. Wu X., Lai Y., Zhang Y., Yao L., Wen X. Breakdown of sensorimotor network communication in leukoaraiosis. Neurodegener. Dis. 2015;15(6):322–330.
    1. Xiong Y., Yang J., Wong A., Wong C.H.K., Chan S.S.W., Li H.H.S. Operational definitions improve reliability of the age-related white matter changes scale. Eur. J. Neurol. 2011;18(5):744–749.
    1. Zhang P., Wang J., Xu Q., Song Z., Dai J., Wang J. Altered functional connectivity in post-ischemic stroke depression: a resting-state functional magnetic resonance imaging study. Eur. J. Radiol. 2018;100:156–165. June 2017.

Source: PubMed

3
Tilaa