scholarly journals Characterization of major depressive disorder using a multiparametric classification approach based on high resolution structural images

2014 ◽  
Vol 39 (2) ◽  
2019 ◽  
Vol 50 (13) ◽  
pp. 2203-2212 ◽  
Author(s):  
Arielle S. Keller ◽  
Tali M. Ball ◽  
Leanne M. Williams

AbstractBackgroundAttention impairment is an under-investigated feature and diagnostic criterion of Major Depressive Disorder (MDD) that is associated with poorer outcomes. Despite increasing knowledge regarding mechanisms of attention in healthy adults, we lack a detailed characterization of attention impairments and their neural signatures in MDD.MethodsHere, we focus on selective attention and advance a deep multi-modal characterization of these impairments in MDD, using data acquired from n = 1008 patients and n = 336 age- and sex-matched healthy controls. Selective attention impairments were operationalized and anchored in a behavioral performance measure, assessed within a battery of cognitive tests. We sought to establish the accompanying neural signature using independent measures of functional magnetic resonance imaging (15% of the sample) and electroencephalographic recordings of oscillatory neural activity.ResultsGreater impairment on the behavioral measure of selective attention was associated with intrinsic hypo-connectivity of the fronto-parietal attention network. Not only was this relationship specific to the fronto-parietal network unlike other large-scale networks; this hypo-connectivity was also specific to selective attention performance unlike other measures of cognition. Selective attention impairment was also associated with lower posterior alpha (8–13 Hz) power at rest and was related to more severe negative bias (frequent misidentifications of neutral faces as sad and lingering attention on sad faces), relevant to clinical features of negative attributions and brooding. Selective attention impairments were independent of overall depression severity and of worrying or sleep problems.ConclusionsThese results provide a foundation for the clinical translational development of objective markers and targeted therapeutics for attention impairment in MDD.


2018 ◽  
Vol 39 (5) ◽  
pp. 1957-1971 ◽  
Author(s):  
Jintao Sheng ◽  
Yuedi Shen ◽  
Yanhua Qin ◽  
Lei Zhang ◽  
Binjia Jiang ◽  
...  

2020 ◽  
Vol 87 (9) ◽  
pp. S257
Author(s):  
Gaurav Verma ◽  
Yael Jacob ◽  
Laurel Morris ◽  
James Murrough ◽  
Priti Balchandani

2011 ◽  
Vol 26 (S2) ◽  
pp. 2085-2085
Author(s):  
T. Frodl

IntroductionThe underlying neurobiology of major depressive disorder (MDD) is likely to represent an interaction between genetic susceptibility and environmental factors like stress. There is growing evidence that epigenetic processes might mediate the effects of the social environment during childhood on gene expression.ObjectivesWe investigated in multimodal high-resolution MRI-genetic studies whether microstructural and functional brain changes are the result of gene-environment interactions.MethodsPatients with major depressive disorder (MDD), high-risk subjects for developing MDD and healthy participants were investigated using high-resolution magnetic resonance imaging (MRI), high angular resolution diffusion imaging (HARDI) and functional MRI. Furthermore, we assessed early life adversity and measured the serotonin transporter polymorphisms (5-HTTLPR).ResultsWe demonstrated that patients with MDD have smaller hippocampal and frontal cortex volumes associated with gen-environment interactions. Healthy Subjects at risk for developing depression, who manage to stay healthy, show better activation of the frontal cognitive control system. Those who had stronger fibre connections between frontal and temporal brain regions also better managed incidences of adversity in early life.ConclusionsStress x gene interactions seem to account for at least some of the structural brain changes. Resilience against environmental stressors might be associated with stronger neural fibre connections and more effective cognitive control networks.


2020 ◽  
Vol 87 ◽  
pp. 831-839 ◽  
Author(s):  
Shigeo Miyata ◽  
Hirotaka Yamagata ◽  
Koji Matsuo ◽  
Shusaku Uchida ◽  
Kenichiro Harada ◽  
...  

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