Rapid instructed task learning (RITL) is the uniquely human ability to transform task information into goal-directed behavior without relying on trial-and-error learning. RITL is a core cognitive process supported by functional brain networks. In patients with schizophrenia, RITL ability is impaired, but the role of functional network connectivity in these RITL deficits is unknown. We investigated task-based connectivity of eight a priori network pairs in participants with schizophrenia (n = 29) and control participants (n = 31) during the performance of an RITL task. Multivariate pattern analysis was used to determine which network connectivity patterns predicted diagnostic group. Of all network pairs, only the connectivity between the cingulo-opercular network (CON) and salience network (SAN) during learning classified patients and control participants with significant accuracy (80%). CON-SAN connectivity during learning was significantly associated with task performance in participants with schizophrenia. These findings suggest that impaired interactions between identification of salient stimuli and maintenance of task goals contributes to RITL deficits in participants with schizophrenia.
BraunU.PlichtaM. M.EsslingerC.SauerC.HaddadL.GrimmO.. . . Meyer-LindenbergA. (2012). Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. NeuroImage, 59, 1404–1412. doi:10.1016/j.neuroimage.2011.08.044
3.
CaoH.PlichtaM. M.SchäferA.HaddadL.GrimmO.SchneiderM.. . . TostH. (2014). Test–retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing, and resting state. NeuroImage, 84, 888–900. doi:10.1016/J.NEUROIMAGE.2013.09.013
4.
ChangC. C.LinC. J. (2012). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, Article 27. doi:10.1145/1961189.1961199
5.
CheinJ. M.SchneiderW. (2012). The brain’s learning and control architecture. Current Directions in Psychological Science, 21, 78–84. doi:10.1177/0963721411434977
6.
ColeM. W.BraverT. S.MeiranN. (2017). The task novelty paradox: Flexible control of inflexible neural pathways during rapid instructed task learning. Neuroscience and Biobehavioral Reviews, 81, 4–15. doi:10.1016/j.neubiorev.2017.02.009
7.
ColeM. W.ItoT.SchultzD.MillR.ChenR.CocuzzaC. (2019). Task activations produce spurious but systematic inflation of task functional connectivity estimates. NeuroImage, 189, 1–18. doi:10.1016/j.neuroimage.2018.12.054
ColeM. W.LaurentP.StoccoA. (2013b). Rapid instructed task learning: A new window into the human brain’s unique capacity for flexible cognitive control. Cognitive, Affective, & Behavioral Neuroscience, 13, 1–22. doi:10.3758/s13415-012-0125-7
10.
ColeM. W.PatrickL. M.MeiranN.BraverT. S. (2017). A role for proactive control in rapid instructed task learning. Acta Psychologica, 184, 20–30. doi:10.1016/j.actpsy.2017.06.004
11.
CollinsA. G. E.AlbrechtM. A.WaltzJ. A.GoldJ. M.FrankM. J. (2017). Interactions among working memory, reinforcement learning, and effort in value-based choice: A new paradigm and selective deficits in schizophrenia. Biological Psychiatry, 82, 431–439. doi:10.1016/j.biopsych.2017.05.017
12.
DosenbachN. U.FairD. A.MiezinF. M.CohenA. L.WengerK. K.DosenbachR. A.. . . PetersenS. E. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences, USA, 104, 11073–11078. doi:10.1073/pnas.0704320104
13.
FirstM. B.SpitzerR. L.GibbonM.WilliamsJ. B. W. (1995). Structured Clinical Interview for DSM-IV Axis I Disorders–Non-Patient Edition (SCID-I/NP, Version 2.0). New York, NY: Biometrics Research Department, New York State Psychiatric Institute.
14.
GlasserM. F.SotiropoulosS. N.WilsonJ. A.CoalsonT. S.FischlB.AnderssonJ. L.. . . JenkinsonM. (2013). The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage, 80, 105–124. doi:10.1016/j.neuroimage.2013.04.127
15.
GodwinD.JiA.KandalaS.MamahD. (2017). Functional connectivity of cognitive brain networks in schizophrenia during a working memory task. Frontiers in Psychiatry, 8, Article 294. doi:10.3389/fpsyt.2017.00294
16.
HampshireA.DawsR. E.NevesI. D.SoreqE.SandroneS.ViolanteI. R. (2019). Probing cortical and sub-cortical contributions to instruction-based learning: Regional specialisation and global network dynamics. NeuroImage, 192, 88–100. doi:10.1016/J.NEUROIMAGE.2019.03.002
17.
HeinrichsR. W.ZakzanisK. K. (1998). Neurocognitive deficit in schizophrenia: A quantitative review of the evidence. Neuropsychology, 12, 426–445.
18.
KottaramA.JohnstonL. A.CocchiL.GanellaE. P.EverallI.PantelisC.. . . ZaleskyA. (2019). Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network. Human Brain Mapping, 40, 2212–2228. doi:10.1002/hbm.24519
19.
KriegeskorteN.SimmonsW. K.BellgowanP. S.BakerC. I. (2009). Circular analysis in systems neuroscience: The dangers of double dipping. Nature Neuroscience, 12, 535–540. doi:10.1038/nn.2303
20.
MenonV.UddinL. Q. (2010). Saliency, switching, attention and control: A network model of insula function. Brain Structure and Function, 214, 655–667. doi:10.1007/s00429-010-0262-0
21.
MohrH.WolfenstellerU.BetzelR. F.MisicB.SpornsO.RichiardiJ.RugeH. (2016). Integration and segregation of large-scale brain networks during short-term task automatization. Nature Communications, 7, Article 13217. doi:10.1038/ncomms13217
MoranL. V.TagametsM. A.SampathH.O’DonnellA.SteinE. A.KochunovP.HongL. E. (2013). Disruption of anterior insula modulation of large-scale brain networks in schizophrenia. Biological Psychiatry, 74, 467–474. doi:10.1016/j.biopsych.2013.02.029
24.
MurphyK.BirnR. M.HandwerkerD. A.JonesT. B.BandettiniP. A. (2009). The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?NeuroImage, 44, 893–905. doi:10.1016/j.neuroimage.2008.09.036
25.
OishiK.TomaK.BagarinaoE. T.MatsuoK.NakaiT.ChiharaK.FukuyamaH. (2005). Activation of the precuneus is related to reduced reaction time in serial reaction time tasks. Neuroscience Research, 52, 37–45. doi:10.1016/J.NEURES.2005.01.008
26.
PereiraF.MitchellT.BotvinickM. (2009). Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45, S199–S209. doi:10.1016/j.neuroimage.2008.11.007
27.
PowerJ. D.CohenA. L.NelsonS. M.WigG. S.BarnesK. A.ChurchJ. A.. . . PetersenS. E. (2011). Functional network organization of the human brain. Neuron, 72, 665–678. doi:10.1016/j.neuron.2011.09.006
28.
PowerJ. D.MitraA.LaumannT. O.SnyderA. Z.SchlaggarB. L.PetersenS. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320–341. doi:10.1016/j.neuroimage.2013.08.048
29.
RugeH.WolfenstellerU. (2010). Rapid formation of pragmatic rule representations in the human brain during instruction-based learning. Cerebral Cortex, 20, 1656–1667. doi:10.1093/cercor/bhp228
30.
RugeH.WolfenstellerU. (2016). Towards an understanding of the neural dynamics of intentional learning: Considering the timescale. NeuroImage, 142, 668–673. doi:10.1016/j.neuroimage.2016.06.006
31.
SaykinA. J.ShtaselD. L.GurR. E.KesterD. B.MozleyL. H.StafiniakP.GurR. C. (1994). Neuropsychological deficits in neuroleptic naive patients with first-episode schizophrenia. Archives of General Psychiatry, 51, 124–131.
32.
SchnackH. G.KahnR. S. (2016). Detecting neuroimaging biomarkers for psychiatric disorders: Sample size matters. Frontiers in Psychiatry, 7, Article 50. doi:10.3389/fpsyt.2016.00050
33.
SchreiberK.KrekelbergB. (2013). The statistical analysis of multi-voxel patterns in functional imaging. PLOS ONE, 8(7), Article 069328. doi:10.1371/journal.pone.0069328
34.
SheffieldJ. M.KandalaS.BurgessG. C.HarmsM. P.BarchD. M. (2016). Cingulo-opercular network efficiency mediates the association between psychotic-like experiences and cognitive ability in the general population. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1, 498–506. doi:10.1016/j.bpsc.2016.03.009
35.
SheffieldJ. M.KandalaS.TammingaC. A.PearlsonG. D.KeshavanM. S.SweeneyJ. A.. . . BarchD. M. (2017). Transdiagnostic associations between functional brain network integrity and cognition. JAMA Psychiatry, 74, 605–613. doi:10.1001/jamapsychiatry.2017.0669
36.
SheffieldJ. M.RugeH.KandalaS.BarchD. M. (2018). Rapid instruction-based task learning (RITL) in schizophrenia. Journal of Abnormal Psychology, 127, 513–528. doi:10.1037/abn0000354
37.
SupekarK.CaiW.KrishnadasR.PalaniyappanL.MenonV. (2019). Dysregulated brain dynamics in a triple-network saliency model of schizophrenia and its relation to psychosis. Biological Psychiatry, 85, 60–69. doi:10.1016/j.biopsych.2018.07.020
38.
TorreyE. F. (2002). Studies of individuals with schizophrenia never treated with antipsychotic medications: A review. Schizophrenia Research, 58, 101–115. doi:10.1016/S0920-9964(02)00381-X
39.
WaltzJ. A.GoldJ. M. (2016). Motivational deficits in schizophrenia and the representation of expected value. Current Topics in Behavioral Neurosciences, 27, 375–410. doi:10.1007/7854_2015_385
40.
WechslerD. (2001). Wechsler Test of Adult Reading: WTAR. The Psychological Corporation.
41.
Whitfield-GabrieliS.ThermenosH. W.MilanovicS.TsuangM. T.FaraoneS. V.McCarleyR. W.. . . SeidmanL. J. (2009). Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proceedings of the National Academy of Sciences, USA, 106, 1279–1284. doi:10.1073/pnas.0809141106
42.
WinklerA. M.RidgwayG. R.WebsterM. A.SmithS. M.NicholsT. E. (2014). Permutation inference for the general linear model. NeuroImage, 92, 381–397. doi:10.1016/j.neuroimage.2014.01.060
43.
WolfenstellerU.RugeH. (2012). Frontostriatal mechanisms in instruction-based learning as a hallmark of flexible goal-directed behavior. Frontiers in Psychology, 3, Article 192. doi:10.3389/fpsyg.2012.00192
44.
WoodwardN. D.RogersB.HeckersS. (2011). Functional resting-state networks are differentially affected in schizophrenia. Schizophrenia Research, 130, 86–93. doi:10.1016/j.schres.2011.03.010
45.
YarkoniT. (2009). Big correlations in little studies: Inflated fMRI correlations reflect low statistical power—Commentary on Vul et al.(2009). Perspectives on Psychological Science, 4, 294–298. doi:10.1111/j.1745-6924.2009.01127.x.
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.
