Neuro-cognitive Ramifications of Fasting and Feeding in Obese and Non-obese Cases

Mostafavi, Seyed‐Ali; Khaleghi, Ali; Vand, Safa Rafiei; Alavi, Seyyed Salman; Mohammadi, Mohammad Reza

doi:10.9758/cpn.2018.16.4.481

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Eventually, we applied Fast Fourier Transform (FFT) to compute the phase and amplitude of the SSEPs. Readers can refer to ( 28 , 30 , 33 ) for more information about the method of extracting SSEPs and calculating their amplitude and phase.…”

Section: Methodsmentioning

confidence: 99%

Auditory Steady-State Evoked Potentials in Post Traumatic Stress Disorder: Introduction of a Potential Biomarker

Jahromi,

Sotoudeh,

Mostafaie

et al. 2024

IJPS

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Objective: The lack of steady-state evoked potential (SSEP) studies on post-traumatic stress disorder (PTSD) has led to undiscovered useful information about the pathophysiology of the disorder. Thus, we explored SSEP patterns in PTSD patients during a stop-signal task to disclose possible impairments in these informative brain potentials. Method: 25 adult patients with PTSD and 25 healthy adults participated in this research. Subjects were assessed with electroencephalography while the tone signal stimuli at 40 Hz were used to evoke SSEPs and subjects performed a stop-signal task. The amplitude and phase of SSEPs were then computed in different brain regions. The subjects were also evaluated using the Mississippi PTSD questionnaire. Appropriate statistical methods such as repeated measure ANOVA were used to compare the two groups, and the correlation between SSEPs and clinical symptoms was assessed using Pearson correlation analysis. Results: Patients showed considerably poorer performance in the cognitive task (P < 0.01), accompanied by raised SSEP phase and amplitude in the anterior and midline regions compared to healthy controls (P < 0.05). The Mississippi total score was positively correlated with the SSEP amplitude in the midline region (r = 0.62, P < 0.05). Furthermore, based on ROC analysis, the SSEP amplitude in the midline region provided an excellent AUC value (AUC = 0.850) for distinguishing patients with PTSD from normal subjects. Conclusion: Current findings suggest that abnormalities in the anterior and midline cortical neural networks are involved in the pathophysiology of PTSD. Importantly, midline abnormalities may provide a clinically-relevant measure for researchers wishing to assess the use of biomarkers for early diagnosis of PTSD as well as to evaluate new therapeutic and management approaches in the treatment of PTSD.

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Section: Methodsmentioning

confidence: 99%

Auditory Steady-State Evoked Potentials in Post Traumatic Stress Disorder: Introduction of a Potential Biomarker

Jahromi,

Sotoudeh,

Mostafaie

et al. 2024

IJPS

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“…Steady-state visual evoked potential (SSVEP) is an electrophysiological potential produced by the electrical activity of cerebral cortex which is extracted if a repetitive visual stimulation is delivered to a subject [26]. Previous studies have proven the high diagnostic value of this informative potential in different psychiatric disorders, including schizophrenia [27], ADHD [28] and bipolar disorder [29].…”

Section: Introductionmentioning

confidence: 99%

Differential Diagnosis of Attention-Deficit/Hyperactivity Disorder and Bipolar Disorder using Steady-State Visual Evoked Potentials

2024

ijacsa

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Bipolar disorderand Attentiondeficit/Hyperactivity disorder (ADHD) are two prevalent disorders whose symptoms are similar. In order to reduce the misdiagnosis between bipolar disorder and ADHD, a machine learning-based system using electroencephalography (EEG) and steady state potentials (i.e., steady-state visual evoked potential [SSVEP]) was evaluated to classify ADHD, bipolar disorder and normal conditions. Indeed, this research was conducted for the first time with the aim of designing a machine learning system for EEG detection of ADHD, bipolar disorder, and normal conditions using SSVEPs. For this purpose, both linear and nonlinear dynamics of extracted SSVEPs were analyzed. Indeed, after data preprocessing, spectral analysis and recurrence quantification analysis (RQA) were applied to SSVEPs. Then, feature selection was utilized through the DISR. Finally, we utilized various machine learning techniques to classify the linear and nonlinear features extracted from SSVEPs into three classes of ADHD, bipolar disorder and normal: k-nearest neighbors (KNN), support vector machine (SVM), linear discriminant analysis (LDA) and Naïve Bayes. Experimental results showed that SVM classifier with linear kernel yielded an accuracy of 78.57% for ADHD, bipolar disorder and normal classification through the leave-onesubject-out (LOSO) cross-validation. Although this research is the first to evaluate the utilization of signal processing and machine learning approaches in SSVEP classification of these disorders, it has limitations that future studies should investigate to enhance the efficacy of proposed system.

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Deficits in auditory and visual steady-state responses in adolescents with bipolar disorder

Wang

Khaleghi

2022

Journal of Psychiatric Research

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Neuro-cognitive Ramifications of Fasting and Feeding in Obese and Non-obese Cases

Cited by 3 publications

References 17 publications

Auditory Steady-State Evoked Potentials in Post Traumatic Stress Disorder: Introduction of a Potential Biomarker

Auditory Steady-State Evoked Potentials in Post Traumatic Stress Disorder: Introduction of a Potential Biomarker

Differential Diagnosis of Attention-Deficit/Hyperactivity Disorder and Bipolar Disorder using Steady-State Visual Evoked Potentials

Deficits in auditory and visual steady-state responses in adolescents with bipolar disorder

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