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      Neuropsychiatric symptoms predict hypometabolism in preclinical Alzheimer disease

      research-article
      , MRCP, , MD, , MSc, , MD, , MSc, , MSc, , BSc, , MD, , MD, , FRCP, , MD, PhD, , MD , For the Alzheimer's Disease Neuroimaging Initiative
      (Collab), , MD (Collab), , MD (Collab), , MD, PhD (Collab), , MD (Collab), , MD (Collab), , MD (Collab), , PsyD (Collab), , MD, PhD (Collab), , PhD (Collab), , PhD (Collab)
      Neurology
      Lippincott Williams & Wilkins

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          Abstract

          Objective:

          To identify regional brain metabolic dysfunctions associated with neuropsychiatric symptoms (NPS) in preclinical Alzheimer disease (AD).

          Methods:

          We stratified 115 cognitively normal individuals into preclinical AD (both amyloid and tau pathologies present), asymptomatic at risk for AD (either amyloid or tau pathology present), or healthy controls (no amyloid or tau pathology present) using [ 18F]florbetapir PET and CSF phosphorylated tau biomarkers. Regression and voxel-based regression models evaluated the relationships between baseline NPS measured by the Neuropsychiatric Inventory (NPI) and baseline and 2-year change in metabolism measured by [ 18F]fluorodeoxyglucose (FDG) PET.

          Results:

          Individuals with preclinical AD with higher NPI scores had higher [ 18F]FDG uptake in the posterior cingulate cortex (PCC), ventromedial prefrontal cortex, and right anterior insula at baseline. High NPI scores predicted subsequent hypometabolism in the PCC over 2 years only in individuals with preclinical AD. Sleep/nighttime behavior disorders and irritability and lability were the components of the NPI that drove this metabolic dysfunction.

          Conclusions:

          The magnitude of NPS in preclinical cases, driven by sleep behavior and irritability domains, is linked to transitory metabolic dysfunctions within limbic networks vulnerable to the AD process and predicts subsequent PCC hypometabolism. These findings support an emerging conceptual framework in which NPS constitute an early clinical manifestation of AD pathophysiology.

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          Most cited references26

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          The neural bases of emotion regulation.

          Emotions are powerful determinants of behaviour, thought and experience, and they may be regulated in various ways. Neuroimaging studies have implicated several brain regions in emotion regulation, including the ventral anterior cingulate and ventromedial prefrontal cortices, as well as the lateral prefrontal and parietal cortices. Drawing on computational approaches to value-based decision-making and reinforcement learning, we propose a unifying conceptual framework for understanding the neural bases of diverse forms of emotion regulation.
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            Neurobiology of emotion perception II: Implications for major psychiatric disorders.

            To date, there has been little investigation of the neurobiological basis of emotion processing abnormalities in psychiatric populations. We have previously discussed two neural systems: 1) a ventral system, including the amygdala, insula, ventral striatum, ventral anterior cingulate gyrus, and prefrontal cortex, for identification of the emotional significance of a stimulus, production of affective states, and automatic regulation of emotional responses; and 2) a dorsal system, including the hippocampus, dorsal anterior cingulate gyrus, and prefrontal cortex, for the effortful regulation of affective states and subsequent behavior. In this critical review, we have examined evidence from studies employing a variety of techniques for distinct patterns of structural and functional abnormalities in these neural systems in schizophrenia, bipolar disorder, and major depressive disorder. In each psychiatric disorder, the pattern of abnormalities may be associated with specific symptoms, including emotional flattening, anhedonia, and persecutory delusions in schizophrenia, prominent mood swings, emotional lability, and distractibility in bipolar disorder during depression and mania, and with depressed mood and anhedonia in major depressive disorder. We suggest that distinct patterns of structural and functional abnormalities in neural systems important for emotion processing are associated with specific symptoms of schizophrenia and bipolar and major depressive disorder.
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              Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis.

              The quantitative analysis of magnetic resonance imaging (MRI) data has become increasingly important in both research and clinical studies aiming at human brain development, function, and pathology. Inevitably, the role of quantitative image analysis in the evaluation of drug therapy will increase, driven in part by requirements imposed by regulatory agencies. However, the prohibitive length of time involved and the significant intraand inter-rater variability of the measurements obtained from manual analysis of large MRI databases represent major obstacles to the wider application of quantitative MRI analysis. We have developed a fully automatic "pipeline" image analysis framework and have successfully applied it to a number of large-scale, multicenter studies (more than 1,000 MRI scans). This pipeline system is based on robust image processing algorithms, executed in a parallel, distributed fashion. This paper describes the application of this system to the automatic quantification of multiple sclerosis lesion load in MRI, in the context of a phase III clinical trial. The pipeline results were evaluated through an extensive validation study, revealing that the obtained lesion measurements are statistically indistinguishable from those obtained by trained human observers. Given that intra- and inter-rater measurement variability is eliminated by automatic analysis, this system enhances the ability to detect small treatment effects not readily detectable through conventional analysis techniques. While useful for clinical trial analysis in multiple sclerosis, this system holds widespread potential for applications in other neurological disorders, as well as for the study of neurobiology in general.
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                Author and article information

                Contributors
                Journal
                Neurology
                Neurology
                neurology
                neur
                neurology
                NEUROLOGY
                Neurology
                Lippincott Williams & Wilkins (Hagerstown, MD )
                0028-3878
                1526-632X
                09 May 2017
                09 May 2017
                : 88
                : 19
                : 1814-1821
                Affiliations
                From the Translational Neuroimaging Laboratory (K.P.N., T.A.P., S.M., C.-O.C., A.L.B., M.S., M.S.K., P.R.-N.) and Alzheimer's Disease Research Unit (K.P.N., X.L., M.B., P.R.-N., S.G.), McGill University Research Centre for Studies in Aging, Montreal, Quebec, Canada; Department of Neurology (K.P.N., N.K.), National Neuroscience Institute, Singapore; Montreal Neurological Institute (P.R.-N.); Department of Neurology and Neurosurgery (P.R.-N.), McGill University, Montreal, Quebec, Canada; Department of Neurology (X.L.), The Second Affiliated Hospital of Chongqing Medical University, Chongqing; and Department of Neurology (M.B.), Yantai Yuhuangding Hospital Affiliated to Qingdao Medical University, Shandong, PR China.
                Author notes
                Correspondence to Dr. Gauthier: serge.gauthier@ 123456mcgill.ca

                Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article. The Article Processing Charge was funded by the authors.

                Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The ADNI investigators contributed to the design and implementation of ADNI and/or provided data. The ADNI list is available at Neurology.org.

                Article
                NEUROLOGY2016776104
                10.1212/WNL.0000000000003916
                5419982
                28404803
                0b09b612-d3ea-4f88-af6b-bdcd3af2e662
                Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology

                This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

                History
                : 14 October 2016
                : 17 February 2017
                Funding
                Funded by: Canadian Institutes of Health Research
                Award ID: [MOP-11-51-31]
                Funded by: Alzheimer's Association
                Award ID: (NIRG-08-92090)
                Funded by: Medical Research Council
                Funded by: National Institute on Aging
                Award ID: W81XWH-12-2-0012
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