Advertisement

The interplay between structural and functional connectivity in early stage Parkinson's disease patients

  • Amgad Droby
    Correspondence
    Corresponding author at: Laboratory of Early Markers of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Medical Center, 6 Weizmann Street, Tel Aviv 64239, Israel.
    Affiliations
    Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

    Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel

    Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
    Search for articles by this author
  • Shai Nosatzki
    Affiliations
    Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

    Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Israel
    Search for articles by this author
  • Yariv Edry
    Affiliations
    Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

    Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Israel
    Search for articles by this author
  • Avner Thaler
    Affiliations
    Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

    Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel

    Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
    Search for articles by this author
  • Nir Giladi
    Affiliations
    Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

    Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel

    Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
    Search for articles by this author
  • Anat Mirelman
    Affiliations
    Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

    Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel

    Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
    Search for articles by this author
  • Inbal Maidan
    Affiliations
    Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

    Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel

    Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
    Search for articles by this author
Published:October 10, 2022DOI:https://doi.org/10.1016/j.jns.2022.120452

      Highlights

      • Neural mechanisms underlying cognitive deficits in early PD remain unclear.
      • We investigated the relationship between structural and functional connectivity.
      • PD showed reduced functional connectivity during cognitive task performance.
      • No differences in structural connectivity were observed between HCs and PD.
      • The obtained results suggest that FC might precede neural degeneration in PD.

      Abstract

      The mechanisms underlying cognitive disturbances in Parkinson's disease (PD) are poorly understood but likely to depend on the ongoing degenerative processes affecting structural and functional connectivity (FC). This pilot study examined patterns of FC alterations during a cognitive task using EEG and structural characteristics of white matter (WM) pathways connecting these activated regions in early-stage PD. Eleven PD patients and nine healthy controls (HCs) underwent EEG recording during an auditory oddball task and MRI scans. Source localization was performed and Gaussian mixture model was fitted to identify brain regions with high power during task performance. These areas served as seed regions for connectivity analysis. FC among these regions was assessed by measures of magnitude squared coherence (MSC), and phase-locking value (PLV), while structural connectivity was evaluated using fiber tracking based on diffusion tensor imaging (DTI). The paracentral lobule (PL), superior parietal lobule (SPL), superior and middle frontal gyrus (SMFG), parahippocampal gyrus, superior and middle temporal gyri (STG, MTG) demonstrated increased activation during task performance. Compared to HCs, PD showed lower FC between SMFG and PL and between SMFG and SPL in MSC (p = 0.012 and p = 0.036 respectively). No significant differences between the groups were observed in PLV and the measured DTI metrics along WM tracts. These findings demonstrate that in early PD, cognitive performance changes might be attributed to FC alterations, suggesting that FC is affected early on in the degenerative process, whereas structural damage is more prominent in advanced stages as a result of the disease burden accumulation.

      Graphical abstract

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Journal of the Neurological Sciences
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Svenningsson P.
        • Westman E.
        • Ballard C.
        • Aarsland D.
        Cognitive impairment in patients with Parkinson’s disease: diagnosis, biomarkers, and treatment.
        Lancet Neurol. 2012; 11: 697-707
        • Williams-Gray C.H.
        • Foltynie T.
        • Brayne C.E.
        • Robbins T.W.
        • Barker R.A.
        Evolution of cognitive dysfunction in an incident Parkinson’s disease cohort.
        Brain. 2007; 130: 1787-1798
        • Janvin C.C.
        • Larsen J.P.
        • Aarsland D.
        • Hugdahl K.
        Subtypes of mild cognitive impairment in Parkinson’s disease: progression to dementia.
        Mov. Disord. 2006; 21: 1343-1349
        • Klein J.C.
        • Eggers C.
        • Kalbe E.
        • et al.
        Neurotransmitter changes in dementia with Lewy bodies and Parkinson disease dementia in vivo.
        Neurology. 2010; 16: 885-892
        • Monchi O.
        • Martinu K.
        • Strafella A.P.
        The contribution of neuroimaging for the study of cognitive deficits in Parkinson’s disease.
        Clin EEG Neurosci. 2010; 41: 76-81
        • Bonanni L.
        • Perfetti B.
        • Bifolchetti S.
        • et al.
        Quantitative electroencephalogram utility in predicting conversion of mild cognitive impairment to dementia with Lewy bodies.
        Neurobiol. Aging. 2015; 36: 434-445
        • Klassen B.T.
        • Hentz J.G.
        • Shill H.A.
        • et al.
        Quantitative EEG as a predictive biomarker for Parkinson disease dementia.
        Neurology. 2011; 12: 118-124
        • Bocquillon P.
        • Bourriez J.L.
        • Palmero-Soler E.
        • Defebvre L.
        • Derambure P.
        • Dujardin K.
        Impaired early attentional processes in Parkinson’s disease: a high-resolution event-related potentials study.
        PLoS One. 2015; 10e0131654
        • Palmero-Soler E.
        • Dolan K.
        • Hadamschek V.
        • Tass P.A.
        swLORETA: a novel approach to robust source localization and synchronization tomography.
        Phys. Med. Biol. 2007; 7: 1783-1800
      1. Pascual-Marqui RD, Esslen M, Kochi K, Lehmann D. Functional imaging with low-resolution brain electromagnetic tomography (LORETA): a review. Methods Find Exp Clin Pharmacol 2002;24 Suppl C:91–95.

        • Pascual-Marqui R.D.
        Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details.
        Methods Find Exp Clin Pharmacol. 2002; 24: 5-12
        • Bocquillon P.
        • Bourriez J.L.
        • Palmero-Soler E.
        • et al.
        Role of basal ganglia circuits in resisting interference by distracters: a swLORETA study.
        PLoS One. 2012; 7e34239
        • Bringas Vega M.L.
        • Liu S.
        • Zhang M.
        • et al.
        Flanker task-elicited event-related potential sources reflect human recombinant erythropoietin differential effects on Parkinson’s patients.
        Parkinsons Dis. 2020; 2020: 8625794
        • Fox M.D.
        • Raichle M.E.
        Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging.
        Nat. Rev. Neurosci. 2007; 8: 700-711
        • Siems M.
        • Siegel M.
        Dissociated neuronal phase- and amplitude-coupling patterns in the human brain.
        Neuroimage. 2020; 1116538
        • Honey C.J.
        • Sporns O.
        • Cammoun L.
        • et al.
        Predicting human resting-state functional connectivity from structural connectivity.
        Proc. Natl. Acad. Sci. U. S. A. 2009; 10: 2035-2040
        • Skudlarski P.
        • Jagannathan K.
        • Calhoun V.D.
        • Hampson M.
        • Skudlarska B.A.
        • Pearlson G.
        Measuring brain connectivity: diffusion tensor imaging validates resting state temporal correlations.
        Neuroimage. 2008; 15: 554-561
        • Burke R.E.
        • O’Malley K.
        Axon degeneration in Parkinson’s disease.
        Exp. Neurol. 2013; 246: 72-83
        • Blesa J.
        • Trigo-Damas I.
        • Dileone M.
        • Del Rey N.L.
        • Hernandez L.F.
        • Obeso J.A.
        Compensatory mechanisms in Parkinson’s disease: circuits adaptations and role in disease modification.
        Exp. Neurol. 2017; 298: 148-161
        • Chung S.J.
        • Kim H.R.
        • Jung J.H.
        • Lee P.H.
        • Jeong Y.
        • Sohn Y.H.
        Identifying the functional brain network of Motor Reserve in Early Parkinson’s disease.
        Mov. Disord. 2020; 35: 577-586
        • Herrington T.M.
        • Briscoe J.
        • Eskandar E.
        Structural and functional network dysfunction in Parkinson disease.
        Radiology. 2017; 285: 725-727
        • Tessitore A.
        • Cirillo M.
        • De M.R.
        Functional connectivity signatures of Parkinson’s disease.
        J. Parkinsons Dis. 2019; 9: 637-652
        • Hattori T.
        • Orimo S.
        • Aoki S.
        • et al.
        Cognitive status correlates with white matter alteration in Parkinson’s disease.
        Hum. Brain Mapp. 2012; 33: 727-739
        • Kamagata K.
        • Motoi Y.
        • Abe O.
        • et al.
        White matter alteration of the cingulum in Parkinson disease with and without dementia: evaluation by diffusion tensor tract-specific analysis.
        AJNR Am. J. Neuroradiol. 2012; 33: 890-895
        • Yang Y.
        • Ye C.
        • Sun J.
        • et al.
        Alteration of brain structural connectivity in progression of Parkinson’s disease: a connectome-wide network analysis.
        Neuroimage Clin. 2021; 31102715
        • Chen B.
        • Fan G.G.
        • Liu H.
        • Wang S.
        Changes in anatomical and functional connectivity of Parkinson’s disease patients according to cognitive status.
        Eur. J. Radiol. 2015; 84: 1318-1324
        • Goetz C.G.
        • Tilley B.C.
        • Shaftman S.R.
        • et al.
        Movement Disorder Society-sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results.
        Mov. Disord. 2008; 15: 2129-2170
        • Nasreddine Z.S.
        • Phillips N.A.
        • Bedirian V.
        • et al.
        The Montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment.
        J. Am. Geriatr. Soc. 2005; 53: 695-699
        • Grech R.
        • Cassar T.
        • Muscat J.
        • et al.
        Review on solving the inverse problem in EEG source analysis.
        J Neuroeng Rehabil. 2008; 7: 25
        • Herrmann M.J.
        • Rommler J.
        • Ehlis A.C.
        • Heidrich A.
        • Fallgatter A.J.
        Source localization (LORETA) of the error-related-negativity (ERN/ne) and positivity (Pe).
        Brain Res. Cogn. Brain Res. 2004; 20: 294-299
        • Dickson D.S.
        • Wicha N.Y.Y.
        P300 amplitude and latency reflect arithmetic skill: an ERP study of the problem size effect.
        Biol. Psychol. 2019; 148107745
        • Cavanagh J.F.
        • Frank M.J.
        Frontal theta as a mechanism for cognitive control.
        Trends Cogn. Sci. 2014; 18: 414-421
        • Cooper P.S.
        • Karayanidis F.
        • McKewen M.
        • et al.
        Frontal theta predicts specific cognitive control-induced behavioural changes beyond general reaction time slowing.
        Neuroimage. 2019; 1: 130-140
        • Tanaka S.
        • Honda M.
        • Sadato N.
        Modality-specific cognitive function of medial and lateral human Brodmann area 6.
        J. Neurosci. 2005; 12: 496-501
        • Al-Jumeily D.
        • Iram S.
        • Vialatte F.B.
        • Fergus P.
        • Hussain A.
        A novel method of early diagnosis of Alzheimer’s disease based on EEG signals.
        ScientificWorldJournal. 2015; 2015931387
        • Miskovic V.
        • Keil A.
        Reliability of event-related EEG functional connectivity during visual entrainment: magnitude squared coherence and phase synchrony estimates.
        Psychophysiology. 2015; 52: 81-89
        • Lachaux J.P.
        • Rodriguez E.
        • Martinerie J.
        • Varela F.J.
        Measuring phase synchrony in brain signals.
        Hum. Brain Mapp. 1999; 8: 194-208
        • Jian W.
        • Chen M.
        • McFarland D.J.
        EEG based zero-phase phase-locking value (PLV) and effects of spatial filtering during actual movement.
        Brain Res. Bull. 2017; 130: 156-164
        • Andersson J.L.R.
        • Sotiropoulos S.N.
        An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging.
        Neuroimage. 2016; 15: 1063-1078
        • Basser P.J.
        • Mattiello J.
        • LeBihan D.
        MR diffusion tensor spectroscopy and imaging.
        Biophys. J. 1994; 66: 259-267
        • Basser P.J.
        • Pierpaoli C.
        Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. 1996.
        J. Magn. Reson. 2011; 213: 560-570
        • Mattes D.
        • Haynor D.R.
        • Vesselle H.
        • Lewellen T.K.
        • Eubank W.
        PET-CT image registration in the chest using free-form deformations.
        IEEE Trans. Med. Imaging. 2003; 22: 120-128
        • Tax C.M.
        • Jeurissen B.
        • Vos S.B.
        • Viergever M.A.
        • Leemans A.
        Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data.
        Neuroimage. 2014; 1: 67-80
        • Yeatman J.D.
        • Dougherty R.F.
        • Myall N.J.
        • Wandell B.A.
        • Feldman H.M.
        Tract profiles of white matter properties: automating fiber-tract quantification.
        PLoS One. 2012; 7e49790
      2. Y.Benjamini YH. Benjamini and Y FDR.pdf., 57 ed 1995:289–300.

        • Polich J.
        Clinical application of the P300 event-related brain potential.
        Phys. Med. Rehabil. Clin. N. Am. 2004; 15: 133-161
        • van DR Arns M.
        • Jongsma M.L.
        • Kessels R.P.
        P300 development across the lifespan: a systematic review and meta-analysis.
        PLoS One. 2014; 9e87347
        • Wright M.J.
        • Geffen G.M.
        • Geffen L.B.
        ERP measures of stimulus processing during an auditory oddball task in Parkinson’s disease: evidence for an early information processing deficit.
        Parkinsonism Relat. Disord. 1996; 2: 13-21
      3. Johnson R J. On the neural generators of the P300 component of the event-related potential., 30 ed. 1993:90–97.

        • Karanian J.M.
        • Slotnick S.D.
        False memory for context and true memory for context similarly activate the parahippocampal cortex.
        Cortex. 2017; 91: 79-88
        • Mullette-Gillman O.A.
        • Detwiler J.M.
        • Winecoff A.
        • Dobbins I.
        • Huettel S.A.
        Infrequent, task-irrelevant monetary gains and losses engage dorsolateral and ventrolateral prefrontal cortex.
        Brain Res. 2011; 13: 53-61
        • Pascual B.
        • Masdeu J.C.
        • Hollenbeck M.
        • et al.
        Large-scale brain networks of the human left temporal pole: a functional connectivity MRI study.
        Cereb. Cortex. 2015; 25: 680-702
        • Tinaz S.
        • Lauro P.M.
        • Ghosh P.
        • Lungu C.
        • Horovitz S.G.
        Changes in functional organization and white matter integrity in the connectome in Parkinson’s disease.
        Neuroimage Clin. 2017; 13: 395-404
        • Barbagallo G.
        • Caligiuri M.E.
        • Arabia G.
        • et al.
        Structural connectivity differences in motor network between tremor-dominant and nontremor Parkinson’s disease.
        Hum. Brain Mapp. 2017; 38: 4716-4729
        • Haghshomar M.
        • Dolatshahi M.
        • Ghazi S.F.
        • Sanjari M.H.
        • Shirin S.M.
        • Aarabi M.H.
        Disruption of inferior longitudinal fasciculus microstructure in Parkinson’s disease: a systematic review of diffusion tensor imaging studies.
        Front. Neurol. 2018; 9: 598
        • Scherfler C.
        • Frauscher B.
        • Schocke M.
        • et al.
        White and gray matter abnormalities in idiopathic rapid eye movement sleep behavior disorder: a diffusion-tensor imaging and voxel-based morphometry study.
        Ann. Neurol. 2011; 69: 400-407
        • Agosta F.
        • Canu E.
        • Stefanova E.
        • et al.
        Mild cognitive impairment in Parkinson’s disease is associated with a distributed pattern of brain white matter damage.
        Hum. Brain Mapp. 2014; 35: 1921-1929
      4. Parkinson Progression Marker Initiative. The Parkinson Progression Marker Initiative (PPMI). Prog Neurobiol. 2011; 95(4):629–35. doi: https://doi.org/10.1016/j.pneurobio.2011.09.005.

        • Zhang Y.
        • Burock M.A.
        Diffusion tensor imaging in Parkinson’s disease and parkinsonian syndrome: a systematic review.
        Front. Neurol. 2020; 11531993
        • Poston K.L.
        • YorkWilliams S.
        • Zhang K.
        • et al.
        Compensatory neural mechanisms in cognitively unimpaired Parkinson disease.
        Ann. Neurol. 2016; 79: 448-463
        • Aarsland D.
        • Zaccai J.
        • Brayne C.
        A systematic review of prevalence studies of dementia in Parkinson’s disease.
        Mov. Disord. 2005; 20: 1255-1263
        • Rosenberg-Katz K.
        • Maidan I.
        • Jacob Y.
        • Giladi N.
        • Mirelman A.
        • Hausdorff J.M.
        Alterations in conflict monitoring are related to functional connectivity in Parkinson’s disease.
        Cortex. 2016; 82: 277-286
        • Sanjari M.H.
        • Dolatshahi M.
        • Mohebi F.
        • Aarabi M.H.
        Structural white matter alterations as compensatory mechanisms in Parkinson’s disease: a systematic review of diffusion tensor imaging studies.
        J. Neurosci. Res. 2020; 98: 1398-1416
        • Schirinzi T.
        • Madeo G.
        • Martella G.
        • et al.
        Early synaptic dysfunction in Parkinson’s disease: insights from animal models.
        Mov. Disord. 2016; 31: 802-813