11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Temporal Irreversibility of Large-Scale Brain Dynamics in Alzheimer’s Disease

      research-article

      Read this article at

      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

          Healthy brain dynamics can be understood as the emergence of a complex system far from thermodynamic equilibrium. Brain dynamics are temporally irreversible and thus establish a preferred direction in time (i.e., arrow of time). However, little is known about how the time-reversal symmetry of spontaneous brain activity is affected by Alzheimer's disease (AD). We hypothesized that the level of irreversibility would be compromised in AD, signaling a fundamental shift in the collective properties of brain activity toward equilibrium dynamics. We investigated the irreversibility from resting-state fMRI and EEG data in male and female human patients with AD and elderly healthy control subjects (HCs). We quantified the level of irreversibility and, thus, proximity to nonequilibrium dynamics by comparing forward and backward time series through time-shifted correlations. AD was associated with a breakdown of temporal irreversibility at the global, local, and network levels, and at multiple oscillatory frequency bands. At the local level, temporoparietal and frontal regions were affected by AD. The limbic, frontoparietal, default mode, and salience networks were the most compromised at the network level. The temporal reversibility was associated with cognitive decline in AD and gray matter volume in HCs. The irreversibility of brain dynamics provided higher accuracy and more distinctive information than classical neurocognitive measures when differentiating AD from control subjects. Findings were validated using an out-of-sample cohort. Present results offer new evidence regarding pathophysiological links between the entropy generation rate of brain dynamics and the clinical presentation of AD, opening new avenues for dementia characterization at different levels.

          SIGNIFICANCE STATEMENT By assessing the irreversibility of large-scale dynamics across multiple brain signals, we provide a precise signature capable of distinguishing Alzheimer’s disease (AD) at the global, local, and network levels and different oscillatory regimes. Irreversibility of limbic, frontoparietal, default-mode, and salience networks was the most compromised by AD compared with more sensory–motor networks. Moreover, the time-irreversibility properties associated with cognitive decline and atrophy outperformed and complemented classical neurocognitive markers of AD in predictive classification performance. Findings were generalized and replicated with an out-of-sample validation procedure. We provide novel multilevel evidence of reduced irreversibility in AD brain dynamics that has the potential to open new avenues for understating neurodegeneration in terms of the temporal asymmetry of brain dynamics.

          Related collections

          Most cited references108

          • 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: found

            The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease

            The National Institute on Aging and the Alzheimer's Association charged a workgroup with the task of revising the 1984 criteria for Alzheimer's disease (AD) dementia. The workgroup sought to ensure that the revised criteria would be flexible enough to be used by both general healthcare providers without access to neuropsychological testing, advanced imaging, and cerebrospinal fluid measures, and specialized investigators involved in research or in clinical trial studies who would have these tools available. We present criteria for all-cause dementia and for AD dementia. We retained the general framework of probable AD dementia from the 1984 criteria. On the basis of the past 27 years of experience, we made several changes in the clinical criteria for the diagnosis. We also retained the term possible AD dementia, but redefined it in a manner more focused than before. Biomarker evidence was also integrated into the diagnostic formulations for probable and possible AD dementia for use in research settings. The core clinical criteria for AD dementia will continue to be the cornerstone of the diagnosis in clinical practice, but biomarker evidence is expected to enhance the pathophysiological specificity of the diagnosis of AD dementia. Much work lies ahead for validating the biomarker diagnosis of AD dementia. Copyright © 2011. Published by Elsevier Inc.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

              An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
                Bookmark

                Author and article information

                Journal
                J Neurosci
                J Neurosci
                jneuro
                J. Neurosci
                The Journal of Neuroscience
                Society for Neuroscience
                0270-6474
                1529-2401
                1 March 2023
                1 March 2023
                : 43
                : 9
                : 1643-1656
                Affiliations
                [1] 1Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, 7911328, Santiago, Chile
                [2] 2Fundación para el Estudio de la Conciencia Humana (ECoH), 7550000, Santiago, Chile
                [3] 3Department of Physics, University of Buenos Aires, C1428EGA, Buenos Aires, Argentina
                [4] 4National Scientific and Technical Research Council (CONICET), C1033AAJ, Buenos Aires, Argentina
                [5] 5Cognitive Neuroscience Center (CNC), Universidad de San Andrés, C116ABJ, Buenos Aires, Argentina
                [6] 6Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, 08005 Barcelona, Spain
                [7] 7Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94143
                [8] 8Global Brain Health Institute, Trinity College, Dublin 2, Ireland
                [9] 9Department of Psychiatry, University of Oxford, Oxford OX3 7JX, United Kingdom
                [10] 10Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, 8000 Århus, Denmark
                [11] 11Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford OX3 9BX, United Kingdom
                [12] 12Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain
                [13] 13Institució Catalana de la Recerca i Estudis Avancats (ICREA), 08010 Barcelona, Spain
                [14] 14Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, D-04303 Leipzig, Germany
                [15] 15School of Psychological Sciences, Monash University, Melbourne 3168, Australia
                [16] 16Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
                [17] 17Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
                Author notes
                Correspondence should be addressed to Agustín Ibañez at agustin.ibanez@ 123456gbhi.org or Josephine Cruzat at josefinacruzatg@ 123456gmail.com

                Author contributions: J.C. and A.I. designed research; R.H., R.G.-G., S.M., and A.I. performed research; J.C., M.L.K., G.D., E.T., and A.I. contributed unpublished reagents/analytic tools; J.C., R.H., P.P., Y.S.-P., R.G.-G., and S.M. analyzed data; J.C., P.P., M.L.K., and A.I. wrote the paper.

                Author information
                https://orcid.org/0000-0002-3908-6898
                Article
                JN-RM-1312-22
                10.1523/JNEUROSCI.1312-22.2022
                10008060
                36732071
                10d9cd89-c9ea-41c4-b6c3-fd820674686a
                Copyright © 2023 Cruzat et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                : 4 July 2022
                : 12 December 2022
                : 25 December 2022
                Funding
                Funded by: NIA NIH
                Award ID: provided in ms
                Funded by: RCF | Tau Consortium (The Tau Consortium), doi 10.13039/100016948;
                Award ID: provided in ms
                Funded by: Alzheimer's Association (AA), doi 10.13039/100000957;
                Award ID: provided in ms
                Categories
                Research Articles
                Neurobiology of Disease

                alzheimer’s disease,dynamic networks,eeg,fmri,irreversibility dynamics,machine learning

                Comments

                Comment on this article