2017
DOI: 10.1016/j.jad.2016.10.021
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Predicting tDCS treatment outcomes of patients with major depressive disorder using automated EEG classification

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Cited by 77 publications
(61 citation statements)
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“…Consistent with this hypothesis, stimulation to the IPS, which shows strong prefrontal connections, lead to impairments on a stop signal task compared to stimulation of the tempoparietal junction, an area not connected to the prefrontal cortex 74 . Also consistent with this network hypothesis of TMS effects, recent work has shown that prefrontal stimulation alters parietal activity 75 , and that the therapeutic effects of prefrontal stimulation may be mediated in part through these changes in parietal activity 76 .…”
Section: Discussionmentioning
confidence: 61%
“…Consistent with this hypothesis, stimulation to the IPS, which shows strong prefrontal connections, lead to impairments on a stop signal task compared to stimulation of the tempoparietal junction, an area not connected to the prefrontal cortex 74 . Also consistent with this network hypothesis of TMS effects, recent work has shown that prefrontal stimulation alters parietal activity 75 , and that the therapeutic effects of prefrontal stimulation may be mediated in part through these changes in parietal activity 76 .…”
Section: Discussionmentioning
confidence: 61%
“…A number of studies have confirmed the role of tDCS in antidepressant treatment (Vigod et al, 2014 ; Al-Kaysi et al, 2017 ; Brennan et al, 2017 ). At present, the left and right DLPFC are typically used as anode and cathode stimulation sites for the majority of tDCS treatment methods, which can increase the excitability of the left DLPFC and inhibit the excitability of the right DLPFC to alleviate depressive symptoms (Meron et al, 2015 ).…”
Section: Transcranial Direct Current Stimulation (Tdcs)mentioning
confidence: 93%
“…These observed effects on EEG components, which are considered to have a source in medial frontal cortex, suggest tDCS to affect cortical functioning beyond focal changes at the stimulation site. Two studies by Al-Kaysi et al utilized machine learning methods in EEG data of 10 MDD patients to predict clinical outcomes following tDCS applications over the left DLPFC [ 30 , 31 ]. The authors demonstrated that a multichannel deep belief network can be used to accurately classify between the EEG data that was recorded after active and sham tDCS [ 30 ] and identified features from the baseline resting-state EEG that differentiate patients who respond to tDCS treatment from those who do not [ 31 ].…”
Section: Resultsmentioning
confidence: 99%
“…Two studies by Al-Kaysi et al utilized machine learning methods in EEG data of 10 MDD patients to predict clinical outcomes following tDCS applications over the left DLPFC [ 30 , 31 ]. The authors demonstrated that a multichannel deep belief network can be used to accurately classify between the EEG data that was recorded after active and sham tDCS [ 30 ] and identified features from the baseline resting-state EEG that differentiate patients who respond to tDCS treatment from those who do not [ 31 ]. Al-Kaysi et al [ 30 , 31 ] also found frontal channels to perform better in predicting clinical scores, while the parietal-occipital channels better in cognition score.…”
Section: Resultsmentioning
confidence: 99%