Literature DB >> 25363311

Graph theory reveals hyper-functionality in visual cortices of Seasonal Affective Disorder patients.

Viola Borchardt1, Anna Linda Krause, Tuomo Starck, Juuso Nissilä, Markku Timonen, Vesa Kiviniemi, Martin Walter.   

Abstract

OBJECTIVES: Seasonal affective disorder (SAD) is a subtype of recurrent unipolar or bipolar depressive disorder with a higher prevalence in winter than in summer. The biological underpinnings of SAD are so far poorly understood. Studies examining SAD have found disturbances between the molecular and connectivity scales. The aim of the study was to explore changes in functional connectivity typical for SAD.
METHODS: We investigated unmedicated, untreated SAD patients and healthy controls using resting-state functional magnetic resonance imaging (rs-fMRI) utilizing graph theory, a data driven and hypothesis free approach, to model functional networks of the brain.
RESULTS: Comparing whole brain network properties using graph theory we observed globally affected network topologies with increasing pathlength in SAD. Nodal changes, however, were highly restricted to bilateral inferior occipital cortex. Interestingly, we found a lateralization where hyper-connectedness was restricted to right inferior occipital cortex and hyper-efficiency was found in the left inferior occipital cortex. Furthermore, we found these nodes became more "hub like" in patients, suggesting a greater functional role.
CONCLUSIONS: Our work stresses the importance of abnormal intrinsic processing during rest, primarily affecting visual areas and subsequently changing whole brain networks, and thus providing an important hint towards potential future therapeutic approaches.

Entities:  

Keywords:  Affective disorders; MRI; brain imaging; depression; seasonal affective disorder

Mesh:

Year:  2014        PMID: 25363311     DOI: 10.3109/15622975.2014.966144

Source DB:  PubMed          Journal:  World J Biol Psychiatry        ISSN: 1562-2975            Impact factor:   4.132


  13 in total

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3.  A spectroscopic approach toward depression diagnosis: local metabolism meets functional connectivity.

Authors:  Liliana Ramona Demenescu; Lejla Colic; Meng Li; Adam Safron; B Biswal; Coraline Danielle Metzger; Shijia Li; Martin Walter
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4.  Anticipation-related brain connectivity in bipolar and unipolar depression: a graph theory approach.

Authors:  Anna Manelis; Jorge R C Almeida; Richelle Stiffler; Jeanette C Lockovich; Haris A Aslam; Mary L Phillips
Journal:  Brain       Date:  2016-06-30       Impact factor: 13.501

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6.  Altered functional connectivity density in major depressive disorder at rest.

Authors:  Bin Zhang; Meng Li; Wen Qin; Liliana Ramona Demenescu; Coraline Danielle Metzger; Bernhard Bogerts; Chunshui Yu; Martin Walter
Journal:  Eur Arch Psychiatry Clin Neurosci       Date:  2015-08-12       Impact factor: 5.270

7.  Preprocessing strategy influences graph-based exploration of altered functional networks in major depression.

Authors:  Viola Borchardt; Anton Richard Lord; Meng Li; Johan van der Meer; Hans-Jochen Heinze; Bernhard Bogerts; Michael Breakspear; Martin Walter
Journal:  Hum Brain Mapp       Date:  2016-02-17       Impact factor: 5.038

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Authors:  Joseph J Shaffer; Casey P Johnson; Jess G Fiedorowicz; Gary E Christensen; John A Wemmie; Vincent A Magnotta
Journal:  Brain Imaging Behav       Date:  2018-06       Impact factor: 3.978

9.  Psychoradiological patterns of small-world properties and a systematic review of connectome studies of patients with 6 major psychiatric disorders

Authors:  Xueling Suo; Du Lei; Lei Li; Wenbin Li; Jing Dai; Song Wang; Manxi He; Hongyan Zhu; Graham J. Kemp; Qiyong Gong
Journal:  J Psychiatry Neurosci       Date:  2018-11-01       Impact factor: 6.186

10.  Dynamic disconnection of the supplementary motor area after processing of dismissive biographic narratives.

Authors:  Viola Borchardt; Anna L Krause; Meng Li; Marie-José van Tol; Liliana Ramona Demenescu; Anna Buchheim; Coraline D Metzger; Catherine M Sweeney-Reed; Tobias Nolte; Anton R Lord; Martin Walter
Journal:  Brain Behav       Date:  2015-09-14       Impact factor: 2.708

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