6
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Parkinson’s Disease Affects Functional Connectivity within the Olfactory-Trigeminal Network

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background: Olfactory dysfunction (OD) is a frequent symptom of Parkinson’s disease (PD) that appears years prior to diagnosis. Previous studies suggest that PD-related OD is different from non-parkinsonian forms of olfactory dysfunction (NPOD) as PD patients maintain trigeminal sensitivity as opposed to patients with NPOD who typically exhibit reduced trigeminal sensitivity. We hypothesize the presence of a specific alteration of functional connectivity between trigeminal and olfactory processing areas in PD. Objective: We aimed to assess potential differences in functional connectivity within the chemosensory network in 15 PD patients and compared them to 15 NPOD patients, and to 15 controls. Methods: Functional MRI scanning session included resting-state and task-related scans where participants carried out an olfactory and a trigeminal task. We compared functional connectivity, using a seed-based correlation approach, and brain network modularity of the chemosensory network. Results: PD patients had impaired functional connectivity within the chemosensory network while no such changes were observed for NPOD patients. No group differences we found in modularity of the identified networks. Both patient groups exhibited impaired connectivity when executing an olfactory task, while network modularity was significantly weaker for PD patients than both other groups. When performing a trigeminal task, no changes were found for PD patients, but NPOD patients exhibited impaired connectivity. Conversely, PD patients exhibited a significantly higher network modularity than both other groups. Conclusion: In summary, the specific pattern of functional connectivity and chemosensory network recruitment in PD-related OD may explain distinct behavioral chemosensory features in PD when compared to NPOD patients and healthy controls.

          Related collections

          Most cited references88

          • Record: found
          • Abstract: found
          • Article: not found

          The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.

          To develop a 10-minute cognitive screening tool (Montreal Cognitive Assessment, MoCA) to assist first-line physicians in detection of mild cognitive impairment (MCI), a clinical state that often progresses to dementia. Validation study. A community clinic and an academic center. Ninety-four patients meeting MCI clinical criteria supported by psychometric measures, 93 patients with mild Alzheimer's disease (AD) (Mini-Mental State Examination (MMSE) score > or =17), and 90 healthy elderly controls (NC). The MoCA and MMSE were administered to all participants, and sensitivity and specificity of both measures were assessed for detection of MCI and mild AD. Using a cutoff score 26, the MMSE had a sensitivity of 18% to detect MCI, whereas the MoCA detected 90% of MCI subjects. In the mild AD group, the MMSE had a sensitivity of 78%, whereas the MoCA detected 100%. Specificity was excellent for both MMSE and MoCA (100% and 87%, respectively). MCI as an entity is evolving and somewhat controversial. The MoCA is a brief cognitive screening tool with high sensitivity and specificity for detecting MCI as currently conceptualized in patients performing in the normal range on the MMSE.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Fast unfolding of communities in large networks

            Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Complex network measures of brain connectivity: uses and interpretations.

              Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets. Copyright (c) 2009 Elsevier Inc. All rights reserved.
                Bookmark

                Author and article information

                Journal
                Journal of Parkinson's Disease
                JPD
                IOS Press
                18777171
                1877718X
                October 27 2020
                October 27 2020
                : 10
                : 4
                : 1587-1600
                Affiliations
                [1 ]Department of Anatomy, Université du Québec à Trois-Rivières, Trois-Rivières, Québec, Canada
                [2 ]Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
                [3 ]Faculty of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada
                [4 ]Institute of Psychology, University of Graz, Graz, Austria.
                [5 ]Research Center, Sacré-Coeur Hospital of Montrealéal, Québec, Canada
                Article
                10.3233/JPD-202062
                32597818
                f1cbf2d5-45a4-4f74-ad8c-0a9f1d849e7c
                © 2020
                History

                Comments

                Comment on this article