Objective To investigate the differences in emotional processing characteristics and electroencephalography （EEG） power values in patients with Parkinson disease （PD）. Methods A total of 24 PD patients were enrolled as subjects， and 30 healthy individuals were enrolled as control group. With the use of the EPIE experimental paradigm， SAM questionnaire was used to determine the scores of emotional valence and arousal， and EEG was used for real-time monitoring of cortical EEG signals. The two groups were compared in terms of the differences in valence/arousal and EEG power values under different emotions and their correlation. Results The PD group had significantly higher BAI and BDI scores than the control group[BAI（16.92±3.83）vs（11.62±3.65），t=4.521，P<0.05；BDI（22.69±2.30）vs（14.17±4.06），t=7.981，P<0.05]. In the negative mood， there were significant differences in valence/arousal between the two groups （t=4.505，-7.705，bothP<0.05）. There were significant differences between the two groups in power values at Fp1，Fp2，F7，F3，F4，T3，T4， and T5（t=-4.12，-12.43，5.76，-2.90，-4.72，-5.34，-5.81，-2.65，all P<0.05）. In the negative mood， for the control group， valence score was correlated with Fp1 （r=-0.837， P<0.01）， Fp2 （r=-0.920， P<0.01），F4（r=-0.604，P=0.008），P3（r=-0.658，P=0.003），and P4（r=-0.546，P=0.019）， and arousal score was correlated with Fp1（r=0.887， P<0.01）， Fp2 （r=0.958， P=0.003），F4（r=0.683，P=0.003），P3 （r=0.721， P=0.003），and P4 （r=0.610，P=0.007）； for the PD group， valence score was correlated with Fp2（r=-0.490，P=0.015） and F7（r=-0.564，P=0.004）， and arousal score was correlated with Fp2 （r=0.440， P=0.031） and F7（r=0.853，P<0.01）. Conclusion Patients with PD have negative emotional processing abnormalities associated with right PFC and left lateral FL.
Electroencephalography

Valacyclovir-associated neurotoxicity (VAN) is a recognized adverse effect in elderly patients with renal impairment but remains underdiagnosed due to its nonspecific presentation and overlap with acute neurologic emergencies. We report a 78-year-old Filipino female with end-stage renal disease on maintenance hemodialysis who developed acute disorientation, agitation, vivid visual hallucinations and generalized weakness shortly after initiation of valacyclovir for herpes zoster. Given the abrupt onset of neuropsychiatric symptoms, viral encephalitis was initially considered. Magnetic resonance imaging of the brain showed no evidence of acute infarction or encephalitis, while electroencephalography demonstrated diffuse generalized slowing consistent with an encephalopathic process. Review of the medication history revealed valacyclovir dosing that exceeded recommendations for patients with end-stage renal disease. Valacyclovir was discontinued and emergent hemodialysis was initiated resulting in marked improvement in sensorium after the second session and complete resolution of symptoms after the third. This case shows VAN as an important diagnostic mimic of acute encephalopathy in elderly patients with renal failure and emphasizes the critical role of early medication review in preventing unnecessary investigations and enabling prompt, reversible management.

Neural activities differentiating bodies versus non-body stimuli have been identified in the occipitotemporal cortex of both humans and nonhuman primates. However, the neural mechanisms of coding the similarity of different individuals' bodies of the same species to support their categorical representations remain unclear. Using electroencephalography (EEG) and magnetoencephalography (MEG), we investigated the temporal and spatial characteristics of neural processes shared by different individual body silhouettes of the same species by quantifying the repetition suppression of neural responses to human and animal (chimpanzee, dog, and bird) body silhouettes showing different postures. Our EEG results revealed significant repetition suppression of the amplitudes of early frontal/central activity at 180-220 ms (P2) and late occipitoparietal activity at 220-320 ms (P270) in response to animal (but not human) body silhouettes of the same species. Our MEG results further localized the repetition suppression effect related to animal body silhouettes in the left supramarginal gyrus and left frontal cortex at 200-440 ms after stimulus onset. Our findings suggest two neural processes that are involved in spontaneous categorical representations of animal body silhouettes as a cognitive basis of human-animal interactions.
Humans ; Animals ; Male ; Electroencephalography ; Magnetoencephalography ; Female ; Young Adult ; Adult ; Pattern Recognition, Visual/physiology* ; Brain Mapping ; Photic Stimulation ; Brain/physiology* ; Dogs

Rhythm, as a prominent characteristic of auditory experiences such as speech and music, is known to facilitate attention, yet its contribution to working memory (WM) remains unclear. Here, human participants temporarily retained a 12-tone sequence presented rhythmically or arrhythmically in WM and performed a pitch change-detection task. Behaviorally, while having comparable accuracy, rhythmic tone sequences showed a faster response time and lower response boundaries in decision-making. Electroencephalographic recordings revealed that rhythmic sequences elicited enhanced non-phase-locked beta-band (16 Hz-33 Hz) and theta-band (3 Hz-5 Hz) neural oscillations during sensory encoding and WM retention periods, respectively. Importantly, the two-stage neural signatures were correlated with each other and contributed to behavior. As beta-band and theta-band oscillations denote the engagement of motor systems and WM maintenance, respectively, our findings imply that rhythm facilitates auditory WM through intricate oscillation-based interactions between the motor and auditory systems that facilitate predictive attention to auditory sequences.
Humans ; Memory, Short-Term/physiology* ; Male ; Beta Rhythm/physiology* ; Female ; Theta Rhythm/physiology* ; Young Adult ; Auditory Perception/physiology* ; Adult ; Electroencephalography ; Acoustic Stimulation ; Reaction Time/physiology* ; Brain/physiology* ; Attention/physiology*

General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.
Animals ; Machine Learning ; Electroencephalography/methods* ; Ketamine/administration & dosage* ; Rats ; Male ; Propofol/administration & dosage* ; Rats, Sprague-Dawley ; Anesthesia, General/methods* ; Brain/physiology* ; Intraoperative Neurophysiological Monitoring/methods*

Depression is increasingly prevalent among adolescents and can profoundly impact their lives. However, the early detection of depression is often hindered by the time-consuming diagnostic process and the absence of objective biomarkers. In this study, we propose a novel approach for depression detection based on an affective brain-computer interface (aBCI) and the resting-state electroencephalogram (EEG). By fusing EEG features associated with both emotional and resting states, our method captures comprehensive depression-related information. The final depression detection model, derived through decision fusion with multiple independent models, further enhances detection efficacy. Our experiments involved 40 adolescents with depression and 40 matched controls. The proposed model achieved an accuracy of 86.54% on cross-validation and 88.20% on the independent test set, demonstrating the efficiency of multimodal fusion. In addition, further analysis revealed distinct brain activity patterns between the two groups across different modalities. These findings hold promise for new directions in depression detection and intervention.
Humans ; Male ; Female ; Adolescent ; Case-Control Studies ; Depression/diagnosis* ; Early Diagnosis ; Rest ; Electroencephalography/methods* ; Brain-Computer Interfaces ; Models, Psychological ; Reproducibility of Results ; Affect/physiology* ; Photic Stimulation/methods* ; Video Recording ; Brain/physiopathology*

Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize the brain stimulation process, is becoming a hot research topic. Because the EEG signal is non-stationary, the commonly used EEG phase-based prediction methods have large variances, which may reduce the accuracy of the phase prediction. In this study, we proposed a machine learning-based EEG phase prediction network, which we call EEG phase prediction network (EPN), to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data. We verified the performance of EPN on pre-recorded data, simulated EEG data, and a real-time experiment. Compared with widely used state-of-the-art models (optimized multi-layer filter architecture, auto-regress, and educated temporal prediction), EPN achieved the lowest variance and the greatest accuracy. Thus, the EPN model will provide broader applications for EEG phase-based closed-loop neuromodulation.
Humans ; Electroencephalography/methods* ; Brain/physiology* ; Machine Learning ; Signal Processing, Computer-Assisted ; Male ; Adult ; Neural Networks, Computer ; Brain Waves/physiology*

Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its early prediction is important for prevention and diagnosis. However, it still lacks effective predictors and approaches. Here, a classical model of pharmacoresistant temporal lobe epilepsy (TLE) was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats. Ictal electroencephalograms (EEGs) recorded before phenytoin treatment were analyzed. Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats, a convolutional neural network predictive model was constructed to predict pharmacoresistance, and achieved 78% prediction accuracy. We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power, which was verified in seizure EEGs from pharmacoresistant TLE patients. Prospectively, therapies targeting the subiculum in those predicted as "pharmacoresistant" individual rats significantly reduced the subsequent occurrence of pharmacoresistance. These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model. This may be of translational importance for the precise management of pharmacoresistant TLE.
Epilepsy, Temporal Lobe/diagnosis* ; Animals ; Drug Resistant Epilepsy/drug therapy* ; Electroencephalography/methods* ; Rats ; Anticonvulsants/pharmacology* ; Neural Networks, Computer ; Male ; Humans ; Phenytoin/pharmacology* ; Adult ; Disease Models, Animal ; Female ; Rats, Sprague-Dawley ; Young Adult ; Convolutional Neural Networks

While multiple step saccades (MSS) are occasionally reported in the healthy population, they are more evident in patients with Parkinson's disease (PD). Therefore, MSS has been suggested as a biological marker for the diagnosis of PD. However, the lack of clarity on the neural mechanism underlying the generation of MSS largely impedes their application in the clinic. We have proposed recently that MSS are triggered by the discrepancy between desired and executed saccades. Accordingly, brain regions involved in saccadic planning and execution might play a role in the generation of MSS. To test this hypothesis, we explored the role of the prefrontal (PFC) and posterior parietal cortex (PPC) in generating MSS by conducting two experiments: electroencephalographic recording and single-pulse transcranial magnetic stimulation in the PFC or PPC of humans while participants were performing a gap saccade task. We found that the PFC and PPC are involved in the generation of MSS.
Humans ; Parietal Lobe/physiology* ; Saccades/physiology* ; Prefrontal Cortex/physiology* ; Male ; Transcranial Magnetic Stimulation ; Female ; Electroencephalography ; Adult ; Young Adult

Attempts have been made to modulate motor sequence learning (MSL) through repetitive transcranial magnetic stimulation, targeting different sites within the sensorimotor network. However, the target with the optimum modulatory effect on neural plasticity associated with MSL remains unclarified. This study was therefore designed to compare the role of the left primary motor cortex and the left supplementary motor area proper (SMAp) in modulating MSL across different complexity levels and for both hands, as well as the associated neuroplasticity by applying intermittent theta burst stimulation together with the electroencephalogram and concurrent transcranial magnetic stimulation. Our data demonstrated the role of SMAp stimulation in modulating neural communication to support MSL, which is achieved by facilitating regional activation and orchestrating neural coupling across distributed brain regions, particularly in interhemispheric connections. These findings may have important clinical implications, particularly for motor rehabilitation in populations such as post-stroke patients.
Humans ; Transcranial Magnetic Stimulation ; Motor Cortex/physiology* ; Male ; Electroencephalography ; Neuronal Plasticity/physiology* ; Female ; Adult ; Evoked Potentials, Motor/physiology* ; Young Adult ; Learning/physiology*