scholarly journals Characterization of Cerebellar Atrophy and Resting State Functional Connectivity Patterns in Sporadic Adult-Onset Ataxia of Unknown Etiology (SAOA)

2019 ◽  
Vol 18 (5) ◽  
pp. 873-881 ◽  
Author(s):  
Xueyan Jiang ◽  
J. Faber ◽  
I. Giordano ◽  
J. Machts ◽  
Ch. Kindler ◽  
...  
2020 ◽  
Author(s):  
Carola Dell'Acqua ◽  
Shadi Ghiasi ◽  
Simone Messerotti ◽  
Alberto Greco ◽  
Claudio Gentili ◽  
...  

Background: The understanding of neurophysiological correlates underlying the risk of developing depression may have a significant impact on its early and objective identification. Research has identified abnormal resting-state electroencephalography (EEG) power and functional connectivity patterns in major depression. However, the entity of dysfunctional EEG dynamics in dysphoria is yet unknown. Methods: 32-channel EEG was recorded in 26 female individuals with dysphoria and in 38 age-matched, female healthy controls. EEG power spectra and alpha asymmetry in frontal and posterior channels were calculated in a 4-minute resting condition. An EEG functional connectivity analysis was conducted through phase locking values, particularly mean phase coherence. Results: While individuals with dysphoria did not differ from controls in EEG spectra and asymmetry, they exhibited dysfunctional brain connectivity. Particularly, in the theta band (4-8 Hz), participants with dysphoria showed increased connectivity between right frontal and central areas and right temporal and left occipital areas. Moreover, in the alpha band (8-12 Hz), dysphoria was associated with increased connectivity between right and left prefrontal cortex and between frontal and central-occipital areas bilaterally. Limitations: All participants belonged to the female gender and were relatively young. Mean phase coherence did not allow to compute the causal and directional relation between brain areas. Conclusions: An increased EEG functional connectivity in the theta and alpha bands characterizes dysphoria. These patterns may be associated with the excessive self-focus and ruminative thinking that typifies depressive symptoms. EEG connectivity patterns may represent a promising measure to identify individuals with a higher risk of developing depression.


PLoS ONE ◽  
2019 ◽  
Vol 14 (7) ◽  
pp. e0219656 ◽  
Author(s):  
Charlotte Martial ◽  
Stephen Karl Larroque ◽  
Carlo Cavaliere ◽  
Sarah Wannez ◽  
Jitka Annen ◽  
...  

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