On average, anxious patients show altered attention to threat—including early vigilance toward threat and later avoidance of threat—accompanied by altered functional connectivity across brain regions. However, substantial heterogeneity within clinical, neural, and attentional features of anxiety is overlooked in typical group-level comparisons. We used a well-validated method for data-driven parsing of neural connectivity to reveal connectivity-based subgroups among 60 adults with transdiagnostic anxiety. Subgroups were externally compared on attentional patterns derived from independent behavioral measures. Two subgroups emerged. Subgroup A (68% of patients) showed stronger executive network influences on sensory processing regions and a paradigmatic “vigilance–avoidance” pattern on external behavioral measures. Subgroup B was defined by a larger number of limbic influences on sensory regions and exhibited a more atypical and inconsistent attentional profile. Neural connectivity-based categorization revealed an atypical, limbic-driven pattern of connectivity in a subset of anxious patients that generalized to atypical patterns of selective attention.
American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.
2.
AmirN.TaylorC. T.DonohueM. C. (2011). Predictors of response to an attention modification program in generalized social phobia. Journal of Consulting and Clinical Psychology, 79, 533–541. doi:10.1037/a0023808
3.
Bar-HaimY.LamyD.PergaminL.Bakermans-KranenburgM. J.van IJzendoornM. H. (2007). Threat-related attentional bias in anxious and nonanxious individuals: A meta-analytic study. Psychological Bulletin, 133(1), 1–24. doi:10.1037/0033-2909.133.1.1
4.
Bar-HaimY.MoragI.GlickmanS. (2011). Training anxious children to disengage attention from threat: A randomized controlled trial. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 52, 861–869. doi:10.1111/j.1469-7610.2011.02368.x
5.
BeltzA. M.GatesK. M. (2017). Network mapping with GIMME. Multivariate Behavioral Research, 52, 789–804. doi:10.1080/00273171.2017.1373014
6.
BeltzA. M.MoserJ. S.ZhuD. C.BurtS. A.KlumpK. L. (2018). Using person-specific neural networks to characterize heterogeneity in eating disorders: Illustrative links between emotional eating and ovarian hormones. International Journal of Eating Disorders, 51, 730–740.
7.
BeltzA. M.WrightA. G.SpragueB. N.MolenaarP. C. (2016). Bridging the nomothetic and idiographic approaches to the analysis of clinical data. Assessment, 23(4), 447–458. doi:10.1177/1073191116648209
8.
BestM. W.MilanovicM.IfteneF.BowieC. R. (2019). A randomized controlled trial of executive functioning training compared with perceptual training for schizophrenia spectrum disorders: Effects on neurophysiology, neurocognition, and functioning. American Journal of Psychiatry, 176, 297–306. doi:10.1176/appi.ajp.2018.18070849
9.
BishopS. J. (2007). Neurocognitive mechanisms of anxiety: An integrative account. Trends in Cognitive Sciences, 11(7), 307–316. doi:10.1016/j.tics.2007.05.008
10.
BrowningM.HolmesE. A.HarmerC. J. (2010). The modification of attentional bias to emotional information: A review of the techniques, mechanisms, and relevance to emotional disorders. Cognitive, Affective & Behavioral Neuroscience, 10, 8–20. doi:10.3758/CABN.10.1.8
11.
ClementzB. A.SweeneyJ. A.HammJ. P.IvlevaE. I.EthridgeL. E.PearlsonG. D.. . . TammingaC. A. (2016). Identification of distinct psychosis biotypes using brain-based biomarkers. American Journal of Psychiatry, 173, 373–384. doi:10.1176/appi.ajp.2015.14091200
12.
de RaedtR.KosterE. H. W. (2010). Understanding vulnerability for depression from a cognitive neuroscience perspective: A reappraisal of attentional factors and a new conceptual framework. Cognitive, Affective & Behavioral Neuroscience, 10, 50–70. doi:10.3758/CABN.10.1.50
FristonK. J. (1994). Functional and effective connectivity in neuroimaging: A synthesis. Human Brain Mapping, 2, 56–78.
15.
GatesK. M.LaneS. T.VarangasE.GiovanelloK.GuiskewiczK. (2017). Unsupervised classification during time series model building. Multivariate Behavioral Research, 52, 129–148.
16.
GatesK. M.MolenaarP. C. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63, 310–319. doi:10.1016/j.neuroimage.2012.06.026
17.
GatesK. M.MolenaarP. C.HillaryF. G.SlobounovS. (2011). Extended unified SEM approach for modeling event-related fMRI data. NeuroImage, 54, 1151–1158. doi:10.1016/j.neuroimage.2010.08.051
18.
GatesK. M.MolenaarP. C.IyerS. P.NiggJ. T.FairD. A. (2014). Organizing heterogeneous samples using community detection of GIMME-derived resting state functional networks. PLOS ONE, 9(3), Article e91322. doi:10.1371/journal.pone.0091322
19.
GotlibI. H.JoormannJ. (2010). Cognition and depression: Current status and future directions. Annual Review of Clinical Psychology, 6, 285–312. doi:10.1146/annurev.clinpsy.121208.131305
20.
GraftonB.MacLeodC. (2014). Enhanced probing of attentional bias: The independence of anxiety-linked selectivity in attentional engagement with and disengagement from negative information. Cognition & Emotion, 28, 1287–1302. doi:10.1080/02699931.2014.881326
21.
GrisanzioK. A.Goldstein-PiekarskiA. N.WangM. Y.Rashed AhmedA. P.SamaraZ.WilliamsL. M. (2018). Transdiagnostic symptom clusters and associations with brain, behavior, and daily function in mood, anxiety, and trauma disorders. JAMA Psychiatry, 75, 201–209. doi:10.1001/jamapsychiatry.2017.3951
22.
HeerenA.MogoaseC.PhilippotP.McNallyR. J. (2015). Attention bias modification for social anxiety: A systematic review and meta-analysis. Clinical Psychology Review, 40, 76–90. doi:10.1016/j.cpr.2015.06.001
23.
KaiserR. H.Andrews-HannaJ. R.WagerT. D.PizzagalliD. A. (2015). Large-scale network dysfunction in major depressive disorder: A meta-analysis of resting-state functional connectivity. JAMA Psychiatry, 72, 603–611. doi:10.1001/jamapsychiatry.2015.0071
24.
KaralunasS. L.FairD.MusserE. D.AykesK.IyerS. P.NiggJ. T. (2014). Subtyping attention-deficit/hyperactivity disorder using temperament dimensions: Toward biologically based nosologic criteria. JAMA Psychiatry, 71, 1015–1024. doi:10.1001/jamapsychiatry.2014.763
25.
KendlerK. S.PrescottC. A.MyersJ.NealeM. C. (2003). The structure of genetic and environmental risk factors for common psychiatric and substance use disorders in men and women. Archives of General Psychiatry, 60, 929–937. doi:10.1001/archpsyc.60.9.929
26.
KesslerR. C.ChiuW. T.DemlerO.MerikangasK. R.WaltersE. E. (2005). Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 617–627. doi:10.1001/archpsyc.62.6.617
27.
KimJ.ZhuW.ChangL.BentlerP. M.ErnstT. (2007). Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data. Human Brain Mapping, 28(2), 85–93. doi:10.1002/hbm.20259
28.
KruijtA. W.ParsonsS.FoxE. (2018). A meta-analysis of bias at baseline in RCTs of attention bias modification: No evidence for dot-probe bias towards threat in clinical anxiety and PTSD. Journal of Abnormal Psychology, 128, 563–573. doi:10.1037/abn0000406
29.
LaheyB. B.Van HulleC. A.SinghA. L.WaldmanI. D.RathouzP. J. (2011). Higher-order genetic and environmental structure of prevalent forms of child and adolescent psychopathology. Archives of General Psychiatry, 68(2), 181–189. doi:10.1001/archgenpsychiatry.2010.192
30.
LairdA. R.FoxP. M.EickhoffS. B.TurnerJ. A.RayK. L.McKayD. R.. . . FoxP. T. (2011). Behavioral interpretations of intrinsic connectivity networks. Journal of Cognitive Neuroscience, 23, 4022–4037. doi:10.1162/jocn_a_00077
31.
LaneS. T.GatesK. M.FisherZ.ArizmendiC.MolenaarP. C. M. (2015). gimme: Group iterative multiple model estimation (Version 0.7-1)[Computer software]. Retrieved from http://cran.r-project.org/package=gimme
32.
LaneS. T.GatesK. M.PikeH. K.BeltzA. M.WrightA. G. C. (2019). Uncovering general, shared, and unique temporal patterns in ambulatory assessment data. Psychological Methods, 24, 54–69. doi:10.1037/met0000192
33.
LangP. J.BradleyM. M.CuthbertB. N. (2008). International Affective Picture System (IAPS): Affective ratings of pictures and instruction manual. Gainesville, FL: University of Florida.
34.
LegersteeJ. S.TulenJ. H. M.KallenV. L.DielemanG. C.TreffersP. D.VerhulstF. C.UtensE. M. (2009). Threat-related selective attention predicts treatment success in childhood anxiety disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 196–205. doi:10.1097/CHI.0b013e31819176e4
35.
LinetzkyM.Pergamin-HightL.PineD. S.Bar-HaimY. (2015). Quantitative evaluation of the clinical efficacy of attention bias modification treatment for anxiety disorders. Depression and Anxiety, 32, 383–391. doi:10.1002/da.22344
36.
MacLeodC.RutherfordE.CampbellL.EbsworthyG.HolkerL. (2002). Selective attention and emotional vulnerability: Assessing the causal basis of their association through the experimental manipulation of attentional bias. Journal of Abnormal Psychology, 111, 107–123.
37.
McCormickD. A.BalT. (1994). Sensory gating mechanisms of the thalamus. Current Opinion in Neurobiology, 4, 550–556.
38.
McNallyR. J. (2018). Attentional bias for threat: Crisis or opportunity?Clinical Psychology Review, 69, 4–13. doi:10.1016/j.cpr.2018.05.005
39.
McTeagueL. M.LangP. J. (2012). The anxiety spectrum and the reflex physiology of defense: From circumscribed fear to broad distress. Depression and Anxiety, 29, 264–281. doi:10.1002/da.21891
40.
MillerM. B.Van HornJ. D.WolfordG. L.HandyT. C.Valsangkar-SmythM.InatiS.. . . GazzanigaM. S. (2002). Extensive individual differences in brain activations associated with episodic retrieval are reliable over time. Journal of Cognitive Neuroscience, 14, 1200–1214. doi:10.1162/089892902760807203
41.
MoggK.BradleyB. P.MilesF.DixonR. (2004). Time course of attentional bias for threat scenes: Testing the vigilance-avoidance hypothesis. Cognition and Emotion, 18, 689–700.
42.
MoggK.WatersA. M.BradleyB. P. (2017). Attention bias modification (ABM): Review of effects of multisession ABM training on anxiety and threat-related attention in high-anxious individuals. Clinical Psychological Science, 5, 698–717. doi:10.1177/2167702617696359
43.
MolenaarP. C.CampbellC. G. (2009). The new person-specific paradigm in psychology. Current Directions in Psychology, 18, 112–117.
44.
MumfordJ. A.RamseyJ. D. (2014). Bayesian networks for fMRI: A primer. NeuroImage, 86, 573–582. doi:10.1016/j.neuroimage.2013.10.020
45.
NicholsT. T.GatesK. M.MolenaarP. C.WilsonS. J. (2014). Greater BOLD activity but more efficient connectivity is associated with better cognitive performance within a sample of nicotine-deprived smokers. Addiction Biology, 19, 931–940. doi:10.1111/adb.12060
46.
PetersenS. E.PosnerM. I. (2012). The attention system of the human brain: 20 years after. Annual Review of Neuroscience, 35, 73–89. doi:10.1146/annurev-neuro-062111-150525
47.
PonsP.LatapyM. (2006). Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications, 10(2), 191–218.
48.
PosnerM. I.SnyderC. R.DavidsonB. J. (1980). Attention and the detection of signals. Journal of Experimental Psychology, 109, 160–174.
49.
PriceM.ToneE. B.AndersonP. L. (2011). Vigilant and avoidant attention biases as predictors of response to cognitive behavioral therapy for social phobia. Depression and Anxiety, 28, 349–353. doi:10.1002/da.20791
50.
PriceR. B.AllenK. B.SilkJ. S.LadouceurC. D.RyanN. D.DahlR. E.. . . SiegleG. J. (2016). Vigilance in the laboratory predicts avoidance in the real world: A dimensional analysis of neural, behavioral, and ecological momentary data in anxious youth. Developmental Cognitive Neuroscience, 19, 128–136. doi:10.1016/j.dcn.2016.03.001
51.
PriceR. B.BrownV.SiegleG. J. (2019). Computational modeling applied to the dot-probe task yields improved reliability and mechanistic insights. Biological Psychiatry, 85, 606–612. doi:10.1016/j.biopsych.2018.09.022
52.
PriceR. B.CummingsL.GilchristD.GraurS.BanihashemiL.KuoS.SiegleG. J. (2018). Toward personalized, brain-based behavioral intervention for transdiagnostic anxiety: Transient neural responses to negative images predict outcomes following a targeted computer-based intervention. Journal of Consulting and Clinical Psychology, 86, 1031–1045.
53.
PriceR. B.EldrethD. A.MohlmanJ. (2011). Deficient prefrontal attentional control in late-life generalized anxiety disorder: An fMRI investigation. Translational Psychiatry, 1, e46. doi:10.1038/tp.2011.46
54.
PriceR. B.GatesK.KraynakT. E.ThaseM. E.SiegleG. J. (2017). Data-driven subgroups in depression derived from directed functional connectivity paths at rest. Neuropsychopharmacology, 42, 2623–2632. doi:10.1038/npp.2017.97
55.
PriceR. B.KuckertzJ. M.SiegleG. J.LadouceurC. D.SilkJ. S.RyanN. D.. . . AmirN. (2015). Empirical recommendations for improving the stability of the dot-probe task in clinical research. Psychological Assessment, 27, 365–376.
56.
PriceR. B.LaneS.GatesK.KraynakT. E.HornerM. S.ThaseM. E.SiegleG. J. (2017). Parsing heterogeneity in the brain connectivity of depressed and healthy adults during positive mood. Biological Psychiatry, 81, 347–357. doi:10.1016/j.biopsych.2016.06.023
57.
PriceR. B.RosenD.SiegleG. J.LadouceurC. D.TangK.AllenK. B.. . . SilkJ. S. (2015). From anxious youth to depressed adolescents: Prospective prediction of 2-year depression symptoms via attentional bias measures. Journal of Abnormal Psychology, 125, 267–278. doi:10.1037/abn0000127
58.
PriceR. B.SiegleG. J.SilkJ. S.LadouceurC. D.McFarlandA.DahlR. E.RyanN. D. (2014). Looking under the hood of the dot-probe task: An fMRI study in anxious youth. Depression and Anxiety, 31, 178–187.
59.
PriceR. B.WallaceM.KuckertzJ. M.AmirN.GraurS.CummingsL.. . . Bar-HaimY. (2016). Pooled patient-level meta-analysis of children and adults completing a computer-based anxiety intervention targeting attentional bias. Clinical Psychology Review, 50, 37–49.
60.
PriceR. B.WoodyM. L.PannyB. P.SiegleG. J. (2019). Pinpointing mechanisms of a mechanistic treatment: Dissociable roles for overt and covert attentional processes in acute and long-term outcomes following Attention Bias Modification. Clinical Psychological Science, 7, 1042–1062 .
61.
RatcliffR.McKoonG. (2008). The diffusion decision model: Theory and data for two-choice decision tasks. Neural Computation, 20, 873–922.
62.
R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
63.
RodebaughT. L.ScullinR. B.LangerJ. K.DixonD. J.HuppertJ. D.BernsteinA.. . . LenzeE. J. (2016). Unreliability as a threat to understanding psychopathology: The cautionary tale of attentional bias. Journal of Abnormal Psychology, 125, 840–851. doi:10.1037/abn0000184
64.
RosenD.PriceR. B.SilkJ. S. (2019). The vigilance-avoidance model in pediatric anxiety disorders: Are we looking in the wrong place? An integrative review. Manuscript submitted for publication.
65.
SalumG. a.MoggK.BradleyB. P.GadelhaA.PanP.TamanahaA. C.. . . PineD. S. (2012). Threat bias in attention orienting: Evidence of specificity in a large community-based study. Psychological Medicine, 1–13. doi:10.1017/S0033291712001651
66.
SmithS. M.MillerK. L.Salimi-KhorshidiG.WebsterM.BeckmannC. F.NicholsT. E.. . . WoolrichM. W. (2011). Network modelling methods for FMRI. NeuroImage, 54, 875–891. doi:10.1016/j.neuroimage.2010.08.063
67.
SomervilleL. H.WagnerD. D.WigG. S.MoranJ. M.WhalenP. J.KelleyW. M. (2013). Interactions between transient and sustained neural signals support the generation and regulation of anxious emotion. Cerebral Cortex, 23, 49–60. doi:10.1093/cercor/bhr373
68.
WatsonD.WeberK.AssenheimerJ. S.ClarkL. A.StraussM. E.McCormickR. A. (1995). Testing a tripartite model: I. Evaluating the convergent and discriminant validity of anxiety and depression symptom scales. Journal of Abnormal Psychology, 104, 3–14.
69.
WeierichM. R.TreatT. A.HollingworthA. (2008). Theories and measurement of visual attentional processing in anxiety. Cognition and Emotion, 22, 985–1018.
70.
WhiteL. K.SequeiraS.BrittonJ. C.BrotmanM. A.GoldA. L.BermanE.. . . PineD. S. (2017). Complementary features of attention bias modification therapy and cognitive-behavioral therapy in pediatric anxiety disorders. American Journal of Psychiatry, 174, 775–784. doi:10.1176/appi.ajp.2017.16070847
71.
WilkinsonS.HoltzheimerP. E.GaoS.KirwinD.PriceR. (2019). Leveraging neuroplasticity to enhance adaptive learning: The potential for synergistic somatic-behavioral treatment combinations to improve clinical outcomes in depression. Biological Psychiatry, 85, 454–465.
72.
YangZ.XuY.XuT.HoyC. W.HandwerkerD. A.ChenG.. . . BandettiniP. A. (2014). Brain network informed subject community detection in early-onset schizophrenia. Scientific Reports, 4, Article 5549. doi:10.1038/srep05549
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
