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      Subject-specific features of excitation/inhibition profiles in neurodegenerative diseases

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          Abstract

          Brain pathologies are characterized by microscopic changes in neurons and synapses that reverberate into large scale networks altering brain dynamics and functional states. An important yet unresolved issue concerns the impact of patients’ excitation/inhibition profiles on neurodegenerative diseases including Alzheimer’s Disease, Frontotemporal Dementia, and Amyotrophic Lateral Sclerosis. In this work, we used The Virtual Brain (TVB) simulation platform to simulate brain dynamics in healthy and neurodegenerative conditions and to extract information about the excitatory/inhibitory balance in single subjects. The brain structural and functional connectomes were extracted from 3T-MRI (Magnetic Resonance Imaging) scans and TVB nodes were represented by a Wong-Wang neural mass model endowing an explicit representation of the excitatory/inhibitory balance. Simulations were performed including both cerebral and cerebellar nodes and their structural connections to explore cerebellar impact on brain dynamics generation. The potential for clinical translation of TVB derived biophysical parameters was assessed by exploring their association with patients’ cognitive performance and testing their discriminative power between clinical conditions. Our results showed that TVB biophysical parameters differed between clinical phenotypes, predicting higher global coupling and inhibition in Alzheimer’s Disease and stronger N-methyl-D-aspartate (NMDA) receptor-dependent excitation in Amyotrophic Lateral Sclerosis. These physio-pathological parameters allowed us to perform an advanced analysis of patients’ conditions. In backward regressions, TVB-derived parameters significantly contributed to explain the variation of neuropsychological scores and, in discriminant analysis, the combination of TVB parameters and neuropsychological scores significantly improved the discriminative power between clinical conditions. Moreover, cluster analysis provided a unique description of the excitatory/inhibitory balance in individual patients. Importantly, the integration of cerebro-cerebellar loops in simulations improved TVB predictive power, i.e., the correlation between experimental and simulated functional connectivity in all pathological conditions supporting the cerebellar role in brain function disrupted by neurodegeneration. Overall, TVB simulations reveal differences in the excitatory/inhibitory balance of individual patients that, combined with cognitive assessment, can promote the personalized diagnosis and therapy of neurodegenerative diseases.

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          "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician.

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            An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging

            In this paper we describe a method for retrospective estimation and correction of eddy current (EC)-induced distortions and subject movement in diffusion imaging. In addition a susceptibility-induced field can be supplied and will be incorporated into the calculations in a way that accurately reflects that the two fields (susceptibility- and EC-induced) behave differently in the presence of subject movement. The method is based on registering the individual volumes to a model free prediction of what each volume should look like, thereby enabling its use on high b-value data where the contrast is vastly different in different volumes. In addition we show that the linear EC-model commonly used is insufficient for the data used in the present paper (high spatial and angular resolution data acquired with Stejskal–Tanner gradients on a 3 T Siemens Verio, a 3 T Siemens Connectome Skyra or a 7 T Siemens Magnetome scanner) and that a higher order model performs significantly better. The method is already in extensive practical use and is used by four major projects (the WU-UMinn HCP, the MGH HCP, the UK Biobank and the Whitehall studies) to correct for distortions and subject movement.
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              A component based noise correction method (CompCor) for BOLD and perfusion based fMRI.

              A component based method (CompCor) for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regions-of-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time series data. Two approaches for the determination of the noise ROI are considered. The first method uses high-resolution anatomical data to define a region of interest composed primarily of white matter and cerebrospinal fluid, while the second method defines a region based upon the temporal standard deviation of the time series data. With the application of CompCor, the temporal standard deviation of resting-state perfusion and BOLD data in gray matter regions was significantly reduced as compared to either no correction or the application of a previously described retrospective image based correction scheme (RETROICOR). For both functional perfusion and BOLD data, the application of CompCor significantly increased the number of activated voxels as compared to no correction. In addition, for functional BOLD data, there were significantly more activated voxels detected with CompCor as compared to RETROICOR. In comparison to RETROICOR, CompCor has the advantage of not requiring external monitoring of physiological fluctuations.
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                Author and article information

                Contributors
                Journal
                Front Aging Neurosci
                Front Aging Neurosci
                Front. Aging Neurosci.
                Frontiers in Aging Neuroscience
                Frontiers Media S.A.
                1663-4365
                05 August 2022
                2022
                : 14
                : 868342
                Affiliations
                [1] 1Brain Connectivity Center, IRCCS Mondino Foundation , Pavia, Italy
                [2] 2Department of Brain and Behavioral Sciences, University of Pavia , Pavia, Italy
                [3] 3Unit of Behavioral Neurology, IRCCS Mondino Foundation , Pavia, Italy
                [4] 4Department of Radiology, IRCCS Policlinico San Donato , Milan, Italy
                [5] 5Department of Biomedical Sciences for Health, University of Milan , Milan, Italy
                [6] 6Advanced Imaging and Radiomic Center, IRCCS Mondino Foundation , Pavia, Italy
                [7] 7Dementia Research Center, IRCCS Mondino Foundation , Pavia, Italy
                [8] 8Institut de Neurosciences des Systèmes, INSERM, INS, Aix-Marseille University , Marseille, France
                [9] 9NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, University College London (UCL) Queen Square Institute of Neurology , London, United Kingdom
                Author notes

                Edited by: Changiz Geula, Northwestern University, United States

                Reviewed by: Carmen Jiménez-Mesa, University of Granada, Spain; Luigi Lorenzini, Academic Medical Center, Netherlands

                *Correspondence: Anita Monteverdi, anita.monteverdi01@ 123456universitadipavia.it

                This article was submitted to Alzheimer’s Disease and Related Dementias, a section of the journal Frontiers in Aging Neuroscience

                Article
                10.3389/fnagi.2022.868342
                9391060
                35992607
                3ff5a5b0-c836-4715-863b-224854633759
                Copyright © 2022 Monteverdi, Palesi, Costa, Vitali, Pichiecchio, Cotta Ramusino, Bernini, Jirsa, Gandini Wheeler-Kingshott and D’Angelo.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 02 February 2022
                : 05 July 2022
                Page count
                Figures: 5, Tables: 4, Equations: 6, References: 84, Pages: 17, Words: 10561
                Funding
                Funded by: Ministero della Salute, doi 10.13039/501100003196;
                Funded by: Horizon 2020 Framework Programme, doi 10.13039/100010661;
                Funded by: Multiple Sclerosis Society, doi 10.13039/501100000381;
                Funded by: Wings for Life, doi 10.13039/100012066;
                Funded by: Horizon 2020 Framework Programme, doi 10.13039/100010661;
                Funded by: Biomedical Research Council, doi 10.13039/501100012415;
                Categories
                Neuroscience
                Original Research

                Neurosciences
                brain dynamics,excitatory/inhibitory balance,alzheimer’s disease,frontotemporal dementia,amyotrophic lateral sclerosis,mri,connectivity

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