Hostname: page-component-cb9f654ff-65tv2 Total loading time: 0 Render date: 2025-08-01T05:40:02.102Z Has data issue: false hasContentIssue false
  • English
  • Français

Deep phenotyping of attention impairments and the ‘Inattention Biotype’ in Major Depressive Disorder

Published online by Cambridge University Press:  03 September 2019

Arielle S. Keller ,
Tali M. Ball  and
Leanne M. Williams
Arielle S. Keller
Affiliation:
Graduate Program in Neurosciences, Stanford University, Stanford, CA, USA Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
Tali M. Ball
Affiliation:
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
Leanne M. Williams*
Affiliation:
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA MIRECC, VA Palo Alto Health Care System, Palo Alto, CA, USA
*
Author for correspondence: Leanne M. Williams, E-mail: leawilliams@stanford.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Attention impairment is an under-investigated feature and diagnostic criterion of Major Depressive Disorder (MDD) that is associated with poorer outcomes. Despite increasing knowledge regarding mechanisms of attention in healthy adults, we lack a detailed characterization of attention impairments and their neural signatures in MDD.

Methods

Here, we focus on selective attention and advance a deep multi-modal characterization of these impairments in MDD, using data acquired from n = 1008 patients and n = 336 age- and sex-matched healthy controls. Selective attention impairments were operationalized and anchored in a behavioral performance measure, assessed within a battery of cognitive tests. We sought to establish the accompanying neural signature using independent measures of functional magnetic resonance imaging (15% of the sample) and electroencephalographic recordings of oscillatory neural activity.

Results

Greater impairment on the behavioral measure of selective attention was associated with intrinsic hypo-connectivity of the fronto-parietal attention network. Not only was this relationship specific to the fronto-parietal network unlike other large-scale networks; this hypo-connectivity was also specific to selective attention performance unlike other measures of cognition. Selective attention impairment was also associated with lower posterior alpha (8–13 Hz) power at rest and was related to more severe negative bias (frequent misidentifications of neutral faces as sad and lingering attention on sad faces), relevant to clinical features of negative attributions and brooding. Selective attention impairments were independent of overall depression severity and of worrying or sleep problems.

Conclusions

These results provide a foundation for the clinical translational development of objective markers and targeted therapeutics for attention impairment in MDD.

Keywords

Attentionbehavioral testingdepressionEEGfunctional MRI

Information

Type
Original Articles
Information
Psychological Medicine , Volume 50 , Issue 13 , October 2020 , pp. 2203 - 2212
Check for updates
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Alarcón, G, Pfeifer, JH, Fair, DA and Nagel, BJ (2018) Adolescent gender differences in cognitive control performance and functional connectivity between default mode and fronto-parietal networks within a self-referential context. Frontiers in Behavioral Neuroscience 12, 73.10.3389/fnbeh.2018.00073CrossRefGoogle Scholar
Clasen, PC, Wells, TT, Ellis, AJ and Beevers, CJ (2013) Attentional biases and the persistence of sad mood in major depressive disorder. Journal of Abnormal Psychology 122, 74–85.CrossRefGoogle Scholar
Corbetta, M and Shulman, GL (2002) Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience 3, 201–215.CrossRefGoogle Scholar
Falkenberg, LE, Specht, K and Westerhausen, R (2011) Attention and cognitive control networks assessed in a dichotic listening fMRI study. Brain and Cognition 76, 276–285.10.1016/j.bandc.2011.02.006CrossRefGoogle Scholar
Fernandes, BS, Williams, LM, Steiner, J, Leboyer, M, Carvalho, AF and Berk, M (2017) The new field of ‘precision psychiatry’. BMC Medicine 15, 80.CrossRefGoogle Scholar
Gatt, JM, Nemeroff, CB, Schofield, PR, Paul, RH, Clark, CR, Gordon, E and Williams, LM (2010) Early life stress combined with serotonin 3A receptor and brain-derived neurotrophic factor valine 66 to methionine genotypes impacts emotional brain and arousal correlates of risk for depression. Biological Psychiatry 68, 818–824.10.1016/j.biopsych.2010.06.025CrossRefGoogle Scholar
Giesbrecht, B, Woldorff, MG, Song, AW and Mangun, GR (2003) Neural mechanisms of top-down control during spatial and feature attention. NeuroImage 19, 496–512.CrossRefGoogle Scholar
Gotlib, IH and Joormann, J (2010) Cognition and depression: current status and future directions. Annual Reviews in Clinical Psychology 6, 285–312.10.1146/annurev.clinpsy.121208.131305CrossRefGoogle Scholar
Grieve, SM, Korgaonkar, MS, Etkin, A, Harris, A, Koslow, SH, Wisniewski, S, Schatzberg, A, Nemeroff, CB, Gordon, E and Williams, LM (2013) Brain imaging predictors and the international study to predict optimized treatment for depression: study protocol for a randomized control trial. Trials 14, 224.10.1186/1745-6215-14-224CrossRefGoogle Scholar
He, Y, Lim, S, Fortunato, S, Sporns, O, Zhang, L, Qiu, J, Xie, P and Zuo, XN (2018) Reconfiguration of cortical networks in MDD uncovered by multiscale community detection with fMRI. Cerebral Cortex 28, 1383–1395.CrossRefGoogle Scholar
Hollon, SD and Ponniah, K (2010) A review of empirically supported psychological therapies for mood disorders in adults. Depression and Anxiety 27, 891–932.10.1002/da.20741CrossRefGoogle Scholar
Iwata, M, Ota, KT and Duman, RS (2013) The inflammasome: pathways linking psychological stress, depression, and systemic illnesses. Brain Behavior, and Immunity 31, 105–114.10.1016/j.bbi.2012.12.008CrossRefGoogle Scholar
Jaeger, J, Berns, S, Uzelac, S and Davis-Conway, S (2006) Neurocognitive deficits and disability in major depressive disorder. Psychiatry Research 145, 39–48.10.1016/j.psychres.2005.11.011CrossRefGoogle Scholar
Kerr, CE, Jones, SR, Wan, Q, Pritchett, DL, Wasserman, RH, Wexler, A, Villaneuva, JJ, Shaw, JR, Lazar, SW, Kaptchuk, TJ, Littenberg, R, Hämäläinen, MS and Moore, CI (2011) Effects of mindfulness meditation training on anticipatory alpha modulation in primary somatosensory cortex. Brain Research Bulletin 85, 96–103.10.1016/j.brainresbull.2011.03.026CrossRefGoogle Scholar
Klimesch, W (2012) Alpha-band oscillations, attention, and controlled access to stored information. Trends in Cognitive Sciences 16, 606–617.10.1016/j.tics.2012.10.007CrossRefGoogle Scholar
Lim, J, Tan, JC, Parimal, S, Dinges, DF and Chee, MW (2010) Sleep deprivation impairs object-selective attention: a view from the ventral visual cortex. Public Library of Science (PLoS) One 5, e9087.Google Scholar
Luo, Q, Deng, Z, Qin, J, Wei, D, Cun, L, Qiu, J, Hitchman, G and Xie, P (2015) Frequency dependent topological alterations of intrinsic functional connectome in major depressive disorder. Scientific Reports 5, 9710.10.1038/srep09710CrossRefGoogle Scholar
Nobre, AC, Sebestyen, GN, Gitelman, DR, Mesulam, MM, Frackowiak, RSJ and Frith, CD (1997) Functional localization of the system for visuospatial attention using positron emission tomography. Brain 120(pt.3), 515–533.CrossRefGoogle Scholar
Parker, G and Hadzi-Pavlovic, D (1996) Development and structure of the CORE system. In Parker, G and Hadzi-Pavlovic, D (eds), Melancholia: A Disorder of Movement and Mood. Cambridge, England: Cambridge University Press, pp. 223–236.10.1017/CBO9780511759024CrossRefGoogle Scholar
Payne, L and Sekuler, R (2014) On the importance of ignoring: alpha oscillations protect selective processing. Current Directions in Psychological Science 23, 171–177.10.1177/0963721414529145CrossRefGoogle Scholar
Pfurtscheller, G, Stancák, A Jr and Neuper, C (1996) Event-related synchronization (ERS) in the alpha band–an electrophysiological correlate of cortical idling: a review. International Journal of Psychophysiology 24, 39–46.CrossRefGoogle Scholar
Prado, J, Carp, J and Weissman, DH (2011) Variations of response time in a selective attention task are linked to variations of functional connectivity in the attentional network. Neuroimage 54, 541–549. doi: 10.1016/j.neuroimage.2010.08.022.CrossRefGoogle Scholar
Ptak, R (2012) The frontoparietal attention network of the human brain. Neuroscientist 18, 502–515.10.1177/1073858411409051CrossRefGoogle Scholar
Qin, J, Wei, M, Liu, H, Yan, R, Luo, G, Yao, Z and Qing, L (2014) Abnormal brain anatomical topological organization of the cognitive-emotional and the frontoparietal circuitry in major depressive disorder. Magnetic Resonance in Medicine 72, 1397–1407.10.1002/mrm.25036CrossRefGoogle Scholar
Robinson, PN (2012) Deep phenotyping for precision medicine. Human Mutation 33, 777–780.CrossRefGoogle Scholar
Saveami, R, Etkin, A, Duchemin, AM, Goldstein-Piekarski, A, Gyurak, A, Debattista, C, Schatzberg, AF, Sood, S, Day, CV, Palmer, DM, Rekshan, WR, Gordon, E, Rush, AJ and Williams, LM (2015) The international Study to Predict Optimized Treatment in Depression (iSPOT-D): outcomes from the acute phase of antidepressant treatment. Journal of Psychiatric Research 61, 1–12.10.1016/j.jpsychires.2014.12.018CrossRefGoogle Scholar
Serences, JT and Kastner, S (2014) A multi-level account of selective attention. In Nobre, AC and Kastner, S (eds), The Oxford Handbook of Attention. New York, NY: Oxford University Press, pp. 76–104.Google Scholar
Shilyansky, C, Williams, LM, Gyurak, A, Harris, A, Usherwood, T and Etkin, A (2016) Effect of antidepressant treatment on cognitive impairments associated with depression: a randomized longitudinal study. The Lancet. Psychiatry 3, 425–435.10.1016/S2215-0366(16)00012-2CrossRefGoogle Scholar
Strauss, GP, Allen, DN, Jorgensen, ML and Cramer, SL (2005) Test-retest reliability of standard and emotional stroop tasks: an investigation of color-word and picture-word versions. Assessment 12, 330–337.CrossRefGoogle Scholar
Williams, LM (2016) Precision psychiatry: a neural circuit taxonomy for depression and anxiety. The Lancet. Psychiatry 3, 472–480.CrossRefGoogle Scholar
Williams, LM, Rush, AJ, Koslow, SH, Wisniewski, SR, Cooper, NJ, Nemeroff, CB, Schatzberg, AF and Gordon, E (2011) International study to predict optimized treatment for depression (iSPOT-D), a randomized clinical trial: rationale and protocol. Trials 12, 4.CrossRefGoogle Scholar
World Health Organization (2017) Depression and other common mental disorders: global health estimates. Available at http://apps.who.int/iris/bitstream/10665/254610/1/WHO-MSD-MER-2017.2-eng.pdf?ua=1 (Accessed 15 March 2018).Google Scholar
Yarkoni, T., Poldrack, R.A., Nichols, T.E., Van Essen, D.C. and Wager, T.D. (2011) NeuroSynth: a platform for large-scale automated synthesis of human functional neuroimaging data. Frontiers in Neuroinformatics Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00058.CrossRefGoogle Scholar
Supplementary material: File

Keller et al. supplementary material

Keller et al. supplementary material

Download Keller et al. supplementary material(File)
File 969.4 KB