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

Wang, Xiao; Gu, Manyi; Khaleghi, Ali

doi:10.1016/j.jpsychires.2022.04.041

Cited by 24 publications

(15 citation statements)

References 56 publications

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“…40 Hz SSEP represents the neurons' propensity for the oscillation at gamma frequency resulted from periodical external stimulations ( 28 , 30 ). The current observation of SSEP abnormalities may be related to N-methyl-D-aspartate receptor (NMDAR) and glutamatergic deficits in PTSD.…”

Section: Discussionmentioning

confidence: 99%

“…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%

“…SSEPs have been investigated in major psychiatric disorders such as depression ( 27 ), bipolar disorder ( 28 ), schizophrenia ( 29 ), and attention deficit/hyperactivity disorder ( 30 ) and have yielded interesting findings about the brain function in these patients. However, to the best of our knowledge, despite the evidence of brain dysfunction and cognitive deficits in PTSD, these valuable potentials have not yet been used to investigate and evaluate the brain function of PTSD patients.…”

mentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…Although audiovisual dysfunction is not a typical clinical symptom of BD, studies have shown that neurophysiological deficits and poorer task performance in adolescent BD imply that visual and auditory pathways do not transfer sensory data well to the audiovisual cortex and that the frontal cortex does not integrate incoming signals well into unified and coherent perceptual actions ( 45 ). Our results showed abnormal connectivity between the VN and higher cognitive networks (i.e., the DMN, DAN, and VAN), the AUN and higher cognitive networks, and within the VN across multiple states in early-onset BD.…”

Section: Discussionmentioning

confidence: 99%

Abnormal dynamic functional network connectivity in patients with early-onset bipolar disorder

Zhou

2023

Front. Psychiatry

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ObjectiveTo explore the changes in dynamic functional brain network connectivity (dFNC) in patients with early-onset bipolar disorder (BD).MethodsResting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 39 patients with early-onset BD and 22 healthy controls (HCs). Four repeated and stable dFNC states were characterised by independent component analysis (ICA), sliding time windows and k-means clustering, and three dFNC temporal metrics (fraction of time, mean dwell time and number of transitions) were obtained. The dFNC temporal metrics and the differences in dFNC between the two groups in different states were evaluated, and the correlations between the differential dFNC metrics and neuropsychological scores were analysed.ResultsThe dFNC analysis showed four connected patterns in all subjects. Compared with the HCs, the dFNC patterns of early-onset BD were significantly altered in all four states, mainly involving impaired cognitive and perceptual networks. In addition, early-onset BD patients had a decreased fraction of time and mean dwell time in state 2 and an increased mean dwell time in state 3 (p < 0.05). The mean dwell time in state 3 of BD showed a positive correlation trend with the HAMA score (r = 0.4049, p = 0.0237 × 3 > 0.05 after Bonferroni correction).ConclusionPatients with early-onset BD had abnormal dynamic properties of brain functional network connectivity, suggesting that their dFNC was unstable, mainly manifesting as impaired coordination between cognitive and perceptual networks. This study provided a new imaging basis for the neuropathological study of emotional and cognitive deficits in early-onset BD.

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“…Various relevant principles from physics and mathematics with the aid of computer science are growingly employed to study the human brain and, subsequently, the abnormalities and neuropathologies related to the mental illnesses ( 5 , 6 ). In fact, computational psychiatry and computational neuroscience approaches utilize these nonmedical concepts to explore the complex behaviors and symptoms in patients with different psychological conditions ( 7 , 8 ). Nonlinear analysis and the concept of complexity is one of these important disciplines that has been used in the last few decades to inform us about complex pathological mechanisms in brain functioning resulting from various psychiatric conditions ( 9 ).…”

mentioning

confidence: 99%

Brain Complexity and Psychiatric Disorders

Hernández,

Ponce-Meza,

Saavedra-López

et al. 2023

IJPS

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Objective: In recent years, researchers and neuroscientists have begun to use a variety of nonlinear techniques for analyzing neurophysiologic signals derived from fMRI, MEG, and EEG in order to describe the complex dynamical aspects of neural mechanisms. In this work, we first attempted to describe different algorithms to estimate neural complexity in a simple manner understandable for psychiatrists, psychologists, and neuroscientists. Then, we reviewed the findings of the brain complexity analysis in psychiatric disorders and their clinical implications. Method: A non-systematic comprehensive literature search was conducted for original studies on the complexity analysis of neurophysiological signals such as electroencephalogram, magnetoencephalogram, and blood-oxygen-level-dependent obtained from functional magnetic resonance imaging or functional near infrared spectroscopy. The search encompassed online scientific databases such as PubMed and Google Scholar. Results: Complexity measures mainly include entropy-based methods, the correlation dimension, fractal dimension, Lempel-Ziv complexity, and the Lyapunov exponent. There are important differences in the physical notions between these measures. Our literature review shows that dementia, autism, and adult ADHD exhibit less complexity in their neurophysiologic signals than healthy controls. However, children with ADHD, drug-naïve young schizophrenic patients with positive symptoms, and patients with mood disorders (i.e., depression and bipolar disorder) exhibit higher complexity in their neurophysiologic signals compared to healthy controls. In addition, contradictory findings still exist in some psychiatric disorders such as schizophrenia regarding brain complexity, which can be due to technical issues, large heterogeneity in psychiatric disorders, and interference of typical factors. Conclusion: In summary, complexity analysis may present a new dimension to understanding psychiatric disorders. While complexity analysis is still far from having practical applications in routine clinical settings, complexity science can play an important role in comprehending the system dynamics of psychiatric disorders.

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

Cited by 24 publications

References 56 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

Abnormal dynamic functional network connectivity in patients with early-onset bipolar disorder

Brain Complexity and Psychiatric Disorders

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