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ALFF response interaction with learning during feedback in individuals with multiple sclerosis

Published:January 06, 2023DOI:https://doi.org/10.1016/j.msard.2023.104510

      Abstract

      Amplitude of low-frequency fluctuations (ALFF) is defined as changes of BOLD signal during resting state (RS) brain activity. Previous studies identified differences in RS activation between healthy and multiple sclerosis (MS) participants. However, no research has investigated the relationship between ALFF and learning in MS. We thus examine this here. Twenty-five MS and nineteen healthy participants performed a paired-associate word learning task where participants were presented with extrinsic or intrinsic performance feedback. Compared to healthy participants, MS participants showed higher local brain activation in the right thalamus. We also observed a positive correlation in the MS group between ALFF and extrinsic feedback within the left inferior frontal gyrus, and within the left superior temporal gyrus in association with intrinsic feedback. Healthy participants showed a positive correlation in the right fusiform gyrus between ALFF and extrinsic feedback. Findings suggest that while MS participants do not show a feedback learning impairment compared to the healthy participants, ALFF differences might suggest a general maladaptive pattern of task unrelated thalamic activation and adaptive activation in frontal and temporal regions. Results indicate that ALFF can be successfully used at capturing pathophysiological changes in local brain activation in MS in association with learning through feedback.

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      References

        • Azevedo C.J.
        • Cen S.Y.
        • Khadka S.
        • Liu S.
        • Kornak J.
        • Shi Y.
        • Zheng L.
        • Hauser S.L.
        • Pelletier D.
        Thalamic atrophy in multiple sclerosis: a magnetic resonance imaging marker of neurodegeneration throughout disease.
        Ann. Neurol. 2018; 83: 223-234https://doi.org/10.1002/ANA.25150
        • Benedict R.H.B.
        The thalamus and multiple sclerosis.
        Neurology. 2013; 80: 210-219
        • Biswal B.
        • Zerrin Yetkin F.
        • Haughton V.M.
        • Hyde J.S.
        Functional connectivity in the motor cortex of resting human brain using echo-planar mri.
        Magn. Reson. Med. 1995; 34: 537-541https://doi.org/10.1002/mrm.1910340409
        • Bonavita S.
        • Gallo A.
        • Sacco R.
        • Corte M.D.
        • Bisecco A.
        • Docimo R.
        • Lavorgna L.
        • Corbo D.
        • Costanzo A.D.
        • Tortora F.
        • Cirillo M.
        • Esposito F.
        • Tedeschi G.
        Distributed changes in default-mode resting-state connectivity in multiple sclerosis.
        Mult. Scler. 2011; 17: 411-422https://doi.org/10.1177/1352458510394609
        • Braga R.M.
        • DiNicola L.M.
        • Becker H.C.
        • Buckner R.L.
        Situating the left-lateralized language network in the broader organization of multiple specialized large-scale distributed networks.
        J. Neurophysiol. 2020; 124: 1415-1448https://doi.org/10.1152/JN.00753.2019
        • Brandstadter R.
        • Fabian M.
        • Leavitt V.M.
        • Krieger S.
        • Yeshokumar A.
        • Katz Sand I.
        • Klineova S.
        • Riley C.S.
        • Lewis C.
        • Pelle G.
        • Lublin F.D.
        • Miller A.E.
        • Sumowski J.F.
        Word-finding difficulty is a prevalent disease-related deficit in early multiple sclerosis.
        Mult. Scler. 2020; 26: 1752-1764https://doi.org/10.1177/1352458519881760
        • Cagna C.
        • Ceceli A.O.
        • Sandry J.
        • Bhanji J.P.
        • Tricomi E.
        • Dobryakova E.
        Cognitive fatigue alters cortico-striatal functional connectivity during feedback-based learning in multiple sclerosis.
        SSRN Electr. J. 2022; https://doi.org/10.2139/ssrn.4112860
        • Chiaravalloti N.D.
        • DeLuca J.
        Cognitive impairment in multiple sclerosis.
        Lancet Neurol. 2008; 7: 1139-1151https://doi.org/10.1016/S1474-4422(08)70259-X
        • Coltheart M.
        The MRC psycholinguistic database.
        Q. J. Exp. Psychol. 1981; 33 (A): 497-505
        • Cox R.W.
        AFNI: software for analysis and visualization of functional magnetic resonance neuroimages.
        Comput. Biomed. Res. 1996; 29: 162-173
        • d'Ambrosio A.
        • Hidalgo de la Cruz M.
        • Valsasina P.
        • Pagani E.
        • Colombo B.
        • Rodegher M.
        • Comi G.
        • Filippi M.
        • Rocca M.A.
        Structural connectivity-defined thalamic subregions have different functional connectivity abnormalities in multiple sclerosis patients: implications for clinical correlations.
        Hum. Brain. Mapp. 2017; 38: 6005-6018https://doi.org/10.1002/hbm.23805
        • Deichmann R.
        • Gottfried J.A.
        • Hutton C.
        • Turner R.
        Optimized EPI for fMRI studies of the orbitofrontal cortex.
        Neuroimage. 2003; 19: 430-441https://doi.org/10.1016/s1053-8119(03)00073-9
        • Dobryakova E.
        • Tricomi E.
        Basal ganglia engagement during feedback processing after a substantial delay.
        Cogn. Affect. Behav. Neurosci. 2013; 13: 725-736https://doi.org/10.3758/s13415-013-0182-6
        • Dobryakova E.
        • Zuckerman S.
        • Sandry J.
        Neural correlates of extrinsic and intrinsic outcome processing during learning in individuals with TBI: a pilot investigation.
        Brain Imaging Behav. 2021;
        • Eshaghi A.
        • Marinescu R.V.
        • Young A.L.
        • Firth N.C.
        • Prados F.
        • Jorge Cardoso M.
        • Tur C.
        • de Angelis F.
        • Cawley N.
        • Brownlee W.J.
        • de Stefano N.
        • Laura Stromillo M.
        • Battaglini M.
        • Ruggieri S.
        • Gasperini C.
        • Filippi M.
        • Rocca M.A.
        • Rovira A.
        • Sastre-Garriga J.
        • Ciccarelli O.
        Progression of regional grey matter atrophy in multiple sclerosis.
        Brain. 2018; 141: 1665-1677https://doi.org/10.1093/BRAIN/AWY088
        • Gu X.-Q.
        • Liu Y.
        • Gu J.-B.
        • Li L.-F.
        • Fu L.-L.
        • Han X.-M.
        Correlations between hippocampal functional connectivity, structural changes, and clinical data in patients with relapsing-remitting multiple sclerosis: a case-control study using multimodal magnetic resonance imaging.
        Neural Regen. Res. 2022; 17: 1115https://doi.org/10.4103/1673-5374.324855
        • Hawellek D.J.
        • Hipp J.F.
        • Lewis C.M.
        • Corbetta M.
        • Engel A.K.
        Increased functional connectivity indicates the severity of cognitive impairment in multiple sclerosis.
        Proc. Natl. Acad. Sci. 2011; 108: 19066-19071https://doi.org/10.1073/pnas.1110024108
        • Hidalgo de la Cruz M.
        • d'Ambrosio A.
        • Valsasina P.
        • Pagani E.
        • Colombo B.
        • Rodegher M.
        • Falini A.
        • Comi G.
        • Filippi M.
        • Rocca M.A.
        Abnormal functional connectivity of thalamic sub-regions contributes to fatigue in multiple sclerosis.
        Mult. Scler. J. 2018; 24: 1183-1195https://doi.org/10.1177/1352458517717807
        • Hwang K.
        • Bertolero M.A.
        • Liu W.B.
        • D'Esposito M
        The human thalamus is an integrative hub for functional brain networks.
        J. Neurosci. 2017; 37: 5594-5607https://doi.org/10.1523/JNEUROSCI.0067-17.2017
        • Kipp M.
        • Wagenknecht N.
        • Beyer C.
        • Samer S.
        • Wuerfel J.
        • Nikoubashman O.
        Thalamus pathology in multiple sclerosis: from biology to clinical application.
        Cellular Mol. Life Sci. 2015; 72: 1127-1147https://doi.org/10.1007/S00018-014-1787-9/FIGURES/2
        • Landauer T.K.
        • Foltz P.W.
        • Laham D.
        An introduction to latent semantic analysis.
        Discourse Process. 1998; 25: 259-284
        • Levy S.
        • Sandry J.
        • Beck E.S.
        • Brandstadter R.
        • Katz Sand I.
        • Sumowski J.F.
        Pattern of thalamic nuclei atrophy in early relapse-onset multiple sclerosis.
        Mult. Scler. Relat. Disord. 2022; : 67https://doi.org/10.1016/J.MSARD.2022.104083
        • Lin F.
        • Zivadinov R.
        • Hagemeier J.
        • Weinstock-Guttman B.
        • Vaughn C.
        • Gandhi S.
        • Jakimovski D.
        • Hulst H.E.
        • Benedict R.H.
        • Bergsland N.
        • Fuchs T.
        • Dwyer M.G.
        Altered nuclei-specific thalamic functional connectivity patterns in multiple sclerosis and their associations with fatigue and cognition.
        Mult. Scler. J. 2019; 25: 1243-1254https://doi.org/10.1177/1352458518788218
        • Liu H.
        • Chen H.
        • Wu B.
        • Zhang T.
        • Wang J.
        • Huang K.
        • Song G.
        • Zhan J.
        Functional cortical changes in relapsing-remitting multiple sclerosis at amplitude configuration: a resting-state fMRI study.
        Neuropsychiatr. Dis. Treat. 2016; 12 (Volume): 3031-3039https://doi.org/10.2147/NDT.S120909
        • Liu Y.
        • Liang P.
        • Duan Y.
        • Jia X.
        • Wang F.
        • Yu C.
        • Qin W.
        • Dong H.
        • Ye J.
        • Li K.
        Abnormal baseline brain activity in patients with neuromyelitis optica: a resting-state fMRI study.
        Eur. J. Radiol. 2011; 80: 407-411https://doi.org/10.1016/j.ejrad.2010.05.002
        • Liu Y.
        • Liang P.
        • Duan Y.
        • Jia X.
        • Yu C.
        • Zhang M.
        • Wang F.
        • Zhang M.
        • Dong H.
        • Ye J.
        • Butzkueven H.
        • Li K.
        Brain plasticity in relapsing–remitting multiple sclerosis: evidence from resting-state fMRI.
        J. Neurol. Sci. 2011; 304: 127-131https://doi.org/10.1016/J.JNS.2011.01.023
        • Liu Y.
        • Meng B.
        • Zeng C.
        • Wang J.
        • Li Y.
        • Yin P.
        • Sah S.K.
        • Li Y.
        Abnormal baseline brain activity in patients with multiple sclerosis with simple spinal cord involvement detected by resting-state functional magnetic resonance imaging.
        J. Comput. Assist. Tomogr. 2015; 39: 866-875https://doi.org/10.1097/RCT.0000000000000299
        • Loitfelder M.
        • Filippi M.
        • Rocca M.
        • Valsasina P.
        • Ropele S.
        • Jehna M.
        • Fuchs S.
        • Schmidt R.
        • Neuper C.
        • Fazekas F.
        • Enzinger C.
        Abnormalities of resting state functional connectivity are related to sustained attention deficits in MS.
        PLoS ONE. 2012; 7: e42862https://doi.org/10.1371/journal.pone.0042862
        • Ontaneda D.
        • Raza P.C.
        • Mahajan K.R.
        • Arnold D.L.
        • Dwyer M.G.
        • Gauthier S.A.
        • Greve D.N.
        • Harrison D.M.
        • Henry R.G.
        • Li D.K.B.
        • Mainero C.
        • Moore W.
        • Narayanan S.
        • Oh J.
        • Patel R.
        • Pelletier D.
        • Rauscher A.
        • Rooney W.D.
        • Sicotte N.L.
        • Azevedo C.J.
        Deep grey matter injury in multiple sclerosis: a NAIMS consensus statement.
        Brain. 2021; 144https://doi.org/10.1093/BRAIN/AWAB132
        • Parisi L.
        • Rocca M.a
        • Mattioli F.
        • Copetti M.
        • Capra R.
        • Valsasina P.
        • Stampatori C.
        • Filippi M.
        Changes of brain resting state functional connectivity predict the persistence of cognitive rehabilitation effects in patients with multiple sclerosis.
        Mult. Scler. 2014; 20: 686-694https://doi.org/10.1177/1352458513505692
        • Plata-Bello J.
        • Pérez-Martín Y.
        • Castañón-Pérez A.
        • Modroño C.
        • Hernández-Martín E.
        • González-Platas M.
        • Marcano F.
        • González-Mora J.L.
        The relationship between amplitude of low frequency fluctuations and gray matter volume of the mirror neuron system: differences between low disability multiple sclerosis patients and healthy controls.
        IBRO Rep. 2018; 5: 60-66https://doi.org/10.1016/j.ibror.2018.09.002
        • Rocca M.A.
        • Valsasina P.
        • Absinta M.
        • Riccitelli G.
        • Rodegher M.E.
        • Misci P.
        • Rossi P.
        • Falini A.
        • Comi G.
        • Filippi M.
        Default-mode network dysfunction and cognitive impairment in progressive MS.
        Neurology. 2010; 74: 1252-1259https://doi.org/10.1212/WNL.0b013e3181d9ed91
        • Rocca M.A.
        • Valsasina P.
        • Absinta M.
        • Riccitelli G.
        • Rodegher M.E.
        • Misci P.
        • Rossi P.
        • Falini A.
        • Comi G.
        • Filippi M.
        Default-mode network dysfunction and cognitive impairment in progressive MS.
        Neurology. 2010; 74: 1252-1259https://doi.org/10.1212/WNL.0b013e3181d9ed91
        • Schlund M.W.
        • Pace G.
        Relations between traumatic brain injury and the environment: feedback reduces maladaptive behaviour exhibited by three persons with traumatic brain injury.
        Brain Inj. 1999; 13: 889-897
        • Schoonheim M.M.
        • Broeders T.A.A.
        • Geurts J.J.G.
        The network collapse in multiple sclerosis: an overview of novel concepts to address disease dynamics.
        Neuroimage Clin. 2022; 35https://doi.org/10.1016/J.NICL.2022.103108
        • Schoonheim M.M.
        • Hulst H.E.
        • Brandt R.B.
        • Strik M.
        • Wink A.M.
        • Uitdehaag B.M.J.
        • Barkhof F.
        • Geurts J.J.G.
        Thalamus structure and function determine severity of cognitive impairment in multiple sclerosis.
        Neurology. 2015; 84: 776-783https://doi.org/10.1212/WNL.0000000000001285
        • Schoonheim M.M.
        • Meijer K.A.
        • Geurts J.J.G.
        Network collapse and cognitive impairment in multiple sclerosis.
        Front. Neurol. 2015; 6: 82https://doi.org/10.3389/fneur.2015.00082
        • Sumowski J.F.
        • Benedict R.
        • Enzinger C.
        • Filippi M.
        • Geurts J.J.
        • Hamalainen P.
        • Hulst H.
        • Inglese M.
        • Leavitt V.M.
        • Rocca M.A.
        • Rosti-Otajarvi E.M.
        • Rao S.
        Cognition in multiple sclerosis.
        Neurology. 2018; 90: 278-288https://doi.org/10.1212/WNL.0000000000004977
        • Tricomi E.
        • Fiez J.a.
        Feedback signals in the caudate reflect goal achievement on a declarative memory task.
        Neuroimage. 2008; 41: 1154-1167https://doi.org/10.1016/j.neuroimage.2008.02.066
        • Tsagkas C.
        • Chakravarty M.M.
        • Gaetano L.
        • Naegelin Y.
        • Amann M.
        • Parmar K.
        • Papadopoulou A.
        • Wuerfel J.
        • Kappos L.
        • Sprenger T.
        • Magon S.
        Longitudinal patterns of cortical thinning in multiple sclerosis.
        Hum. Brain Mapp. 2020; 41: 2198-2215https://doi.org/10.1002/HBM.24940
        • van Geest Q.
        • Westerik B.
        • van der Werf Y.D.
        • Geurts J.J.G.
        • Hulst H.E.
        The role of sleep on cognition and functional connectivity in patients with multiple sclerosis.
        J. Neurol. 2017; 264: 72-80https://doi.org/10.1007/s00415-016-8318-6
        • Zang Y.F.
        • Yong H.
        • Chao-Zhe Z.
        • Qing-Jiu C.
        • Man-Qiu S.
        • Meng L.
        • Li-Xia T.
        • Tian-Zi J.
        • Yu-Feng W.
        Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI.
        Brain Dev. 2007; 29: 83-91https://doi.org/10.1016/j.braindev.2006.07.002
        • Zhou F.
        • Zhuang Y.
        • Wu L.
        • Zhang N.
        • Zeng X.
        • Gong H.
        • Zee C.-S.
        Increased thalamic intrinsic oscillation amplitude in relapsing–remitting multiple sclerosis associated with the slowed cognitive processing.
        Clin. Imaging. 2014; 38: 605-610https://doi.org/10.1016/j.clinimag.2014.05.006