Beyond Emotions: Oscillations of the Amygdala and Their Implications for Electrical Neuromodulation

Beta-band power modulation in the human amygdala differentiates between go/no-go responses in an arm-reaching task

Objective. Traditionally known for its involvement in emotional processing, the amygdala's involvement in motor control remains relatively unexplored, with sparse investigations into the neural mechanisms governing amygdaloid motor movement and inhibition. This study aimed to characterize the amygdaloid beta-band (13-30 Hz) power between 'Go' and 'No-go' trials of an arm-reaching task.Approach. Ten participants with drug-resistant epilepsy implanted with stereoelectroencephalographic (SEEG) electrodes in the amygdala were enrolled in this study. SEEG data was recorded throughout discrete phases of a direct reach Go/No-go task, during which participants reached a touchscreen monitor or withheld movement based on a colored cue. Multitaper power analysis along with Wilcoxon signed-rank and Yates-correctedZtests were used to assess significant modulations of beta power between the Response and fixation (baseline) phases in the 'Go' and 'No-go' conditions.Main results. In the 'Go' condition, nine out of the ten participants showed a significant decrease in relative beta-band power during the Response phase (p⩽ 0.0499). In the 'No-go' condition, eight out of the ten participants presented a statistically significant increase in relative beta-band power during the response phase (p⩽ 0.0494). Four out of the eight participants with electrodes in the contralateral hemisphere and seven out of the eight participants with electrodes in the ipsilateral hemisphere presented significant modulation in beta-band power in both the 'Go' and 'No-go' conditions. At the group level, no significant differences were found between the contralateral and ipsilateral sides or between genders.Significance.This study reports beta-band power modulation in the human amygdala during voluntary movement in the setting of motor execution and inhibition. This finding supplements prior research in various brain regions associating beta-band power with motor control. The distinct beta-power modulation observed between these response conditions suggests involvement of amygdaloid oscillations in differentiating between motor inhibition and execution.

Intermuscular coherence reveals that affective emotional pictures modulate neural control mechanisms during the initiation of arm pointing movements

Introduction

Several studies in psychology provided compelling evidence that emotions significantly impact motor control. Yet, these evidences mostly rely on behavioral investigations, whereas the underlying neurophysiological processes remain poorly understood.

Methods

Using a classical paradigm in motor control, we tested the impact of affective pictures associated with positive, negative or neutral valence on the kinematics and patterns of muscle activations of arm pointing movements performed from a standing position. The hand reaction and movement times were measured and electromyography (EMG) was used to measure the activities from 10 arm, leg and trunk muscles that are involved in the postural maintenance and arm displacement in pointing movements. Intermuscular coherence (IMC) between pairs of muscles was computed to measure changes in patterns of muscle activations related to the emotional stimuli.

Results

The hand movement time increased when an emotional picture perceived as unpleasant was presented as compared to when the emotional picture was perceived as pleasant. When an unpleasant emotional picture was presented, beta (β, 15-35 Hz) and gamma (γ, 35-60 Hz) IMC decreased in the recorded pairs of postural muscles during the initiation of pointing movements. Moreover, a linear relationship between the magnitude of the intermuscular coherence in the pairs of posturo-focal muscles and the hand movement time was found in the unpleasant scenarios.

Discussion

These findings reveal that emotional stimuli can significantly affect the content of the motor command sent by the central nervous system to muscles when performing voluntary goal-directed movements.

Prestimulus amygdala spectral activity is associated with visual face awareness

Alpha cortical oscillations have been proposed to suppress sensory processing in the visual, auditory, and tactile domains, influencing conscious stimulus perception. However, it is unknown whether oscillatory neural activity in the amygdala, a subcortical structure involved in salience detection, has a similar impact on stimulus awareness. Recording intracranial electroencephalography (EEG) from 9 human amygdalae during face detection in a continuous flash suppression task, we found increased spectral prestimulus power and phase coherence, with most consistent effects in the alpha band, when faces were undetected relative to detected, similarly as previously observed in cortex with this task using scalp-EEG. Moreover, selective decreases in the alpha and gamma bands preceded face detection, with individual prestimulus alpha power correlating negatively with detection rate in patients. These findings reveal for the first time that prestimulus subcortical oscillations localized in human amygdala may contribute to perceptual gating mechanisms governing subsequent face detection and offer promising insights on the role of this structure in visual awareness.

Temporal interference stimulation targeting right frontoparietal areas enhances working memory in healthy individuals

Background

Temporal interference (TI) stimulation is a novel technique that enables the non-invasive modulation of deep brain regions. However, the implementation of this technology in humans has not been well-characterized or examined, including its safety and feasibility.

Objective

We aimed to examine the feasibility, safety, and blinding of using TI on human participants in this pilot study.

Materials and methods

In a randomized, single-blinded, and sham-controlled pilot study, healthy young participants were randomly divided into four groups [TI and transcranial alternating current stimulation (tACS) targeting the right frontoparietal region, TI-sham, and tACS-sham]. Each participant was asked to complete N-back (N = 1 to 3) tasks before, during, and after one session of stimulation to assess their working memory (WM). The side effects and blinding efficacy were carefully assessed. The accuracy, reaction time (RT), and inverse efficiency score (IES, reaction time/accuracy) of the N-back tasks were measured.

Results

No severe side effects were reported. Only mild-to-moderate side effects were observed in those who received TI, which was similar to those observed in participants receiving tACS. The blinding efficacy was excellent, and there was no correlation between the severity of the reported side effects and the predicted type of stimulation that the participants received. WM appeared to be only marginally improved by TI compared to tACS-sham, and this improvement was only observed under high-load cognitive tasks. WM seemed to have improved a little in the TI-sham group. However, it was not observed significant differences between TI and TI-sham or TI and tACS in all N-back tests.

Conclusion

Our pilot study suggests that TI is a promising technique that can be safely implemented in human participants. Studies are warranted to confirm the findings of this study and to further examine the effects of TI-sham stimulation as well as the effects of TI on deeper brain regions.

Diagnosis of Subcortical Ischemic Vascular Cognitive Impairment With No Dementia Using Radiomics of Cerebral Cortex and Subcortical Nuclei in High-Resolution T1-Weighted MR Imaging

Purpose

To investigate whether the combination of radiomics derived from brain high-resolution T1-weighted imaging and automatic machine learning could diagnose subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND) accurately.

Methods

A total of 116 right-handed participants involving 40 SIVCIND patients and 76 gender-, age-, and educational experience-matched normal controls (NM) were recruited. A total of 7,106 quantitative features from the bilateral thalamus, hippocampus, globus pallidus, amygdala, nucleus accumbens, putamen, caudate nucleus, and 148 areas of the cerebral cortex were automatically calculated from each subject. Six methods including least absolute shrinkage and selection operator (LASSO) were utilized to lessen the redundancy of features. Three supervised machine learning approaches of logistic regression (LR), random forest (RF), and support vector machine (SVM) employing 5-fold cross-validation were used to train and establish diagnosis models, and 10 times 10-fold cross-validation was used to evaluate the generalization performance of each model. Correlation analysis was performed between the optimal features and the neuropsychological scores of the SIVCIND patients.

Results

Thirteen features from the right amygdala, right hippocampus, left caudate nucleus, left putamen, left thalamus, and bilateral nucleus accumbens were included in the optimal subset. Among all the three models, the RF produced the highest diagnostic performance with an area under the receiver operator characteristic curve (AUC) of 0.990 and an accuracy of 0.948. According to the correlation analysis, the radiomics features of the right amygdala, left caudate nucleus, left putamen, and left thalamus were found to be significantly correlated with the neuropsychological scores of the SIVCIND patients.

Conclusions

The combination of radiomics derived from brain high-resolution T1-weighted imaging and machine learning could diagnose SIVCIND accurately and automatically. The optimal radiomics features are mostly located in the right amygdala, left caudate nucleus, left putamen, and left thalamus, which might be new biomarkers of SIVCIND.

Anticonvulsant vs. Proconvulsant Effect of in situ Deep Brain Stimulation at the Epileptogenic Focus

Since deep brain stimulation (DBS) at the epileptogenic focus (in situ) denotes long-term repetitive stimulation of the potentially epileptogenic structures, such as the amygdala, the hippocampus, and the cerebral cortex, a kindling effect and aggravation of seizures may happen and complicate the clinical condition. It is, thus, highly desirable to work out a protocol with an evident quenching (anticonvulsant) effect but free of concomitant proconvulsant side effects. We found that in the basolateral amygdala (BLA), an extremely wide range of pulsatile stimulation protocols eventually leads to the kindling effect. Only protocols with a pulse frequency of ≤1 Hz or a direct current (DC), with all of the other parameters unchanged, could never kindle the animal. On the other hand, the aforementioned DC stimulation (DCS), even a pulse as short as 10 s given 5 min before the kindling stimuli or a pulse given even to the contralateral BLA, is very effective against epileptogenicity and ictogenicity. Behavioral, electrophysiological, and histological findings consistently demonstrate success in seizure quenching or suppression as well as in the safety of the specific DBS protocol (e.g., no apparent brain damage by repeated sessions of stimulation applied to the BLA for 1 month). We conclude that in situ DCS, with a novel and rational design of the stimulation protocol composed of a very low (∼3% or 10 s/5 min) duty cycle and assuredly devoid of the potential of kindling, may make a successful antiepileptic therapy with adequate safety in terms of little epileptogenic adverse events and tissue damage.

Spectral power and theta-gamma coupling in the basolateral amygdala related with methamphetamine conditioned place preference in mice

The basolateral amygdala (BLA) plays a crucial role in conditioned place preference (CPP) for addictive drugs. However, neural signaling associated with methamphetamine (METH) craving and seeking remained to be investigated. This study characterized local field potential (LFP) oscillatory patterns in the BLA and conditioned place preference induced by METH-related context. Male Swiss albino ICR mice were deeply anesthetized for LFP intracranial electrode implantation in the BLA. Control and METH groups received sessions to learn to associate saline-paired and METH-paired compartments of the CPP apparatus with saline and METH injections, respectively, for 10 days. LFP signals and exploring behavior were recorded simultaneously during pre- and post-conditioning phases. Time spent in METH-paired compartment was normalized and expressed as CPP scores. Fast Fourier Transform (FFT) algorithm was used to analyze LFP powers of 8 discrete frequency ranges (delta, theta, alpha, beta, gamma I-IV). During post-conditioning phase of METH CPP with METH cues, statistical analysis revealed that METH group significantly increased time spent in METH-paired compartment. Significant suppressions of theta and alpha powers were observed. Phase-amplitude cross frequency coupling analyses confirmed significant increases in maximal modulation index (MI), frequency for phase of slow wave and MI of theta-gamma II coupling. Taken together, LFP oscillation in the BLA was sensitive in association with METH CPP. These research findings might suggest the underlying mechanisms of drug reward learning and adaptive changes in the BLA in acquisition of METH CPP and dependence.

Hyperscanning EEG and Classification Based on Riemannian Geometry for Festive and Violent Mental State Discrimination

Interactions between two brains constitute the essence of social communication. Daily movements are commonly executed during social interactions and are determined by different mental states that may express different positive or negative behavioral intent. In this context, the effective recognition of festive or violent intent before the action execution remains crucial for survival. Here, we hypothesize that the EEG signals contain the distinctive features characterizing movement intent already expressed before movement execution and that such distinctive information can be identified by state-of-the-art classification algorithms based on Riemannian geometry. We demonstrated for the first time that a classifier based on covariance matrices and Riemannian geometry can effectively discriminate between neutral, festive, and violent mental states only on the basis of non-invasive EEG signals in both the actor and observer participants. These results pave the way for new electrophysiological discrimination of mental states based on non-invasive EEG recordings and cutting-edge machine learning techniques.

From Emotions to Mood Disorders: A Survey on Gait Analysis Methodology

Mood disorders affect more than 300 million people worldwide and can cause devastating consequences. Elderly people and patients with neurological conditions are particularly susceptible to depression. Gait and body movements can be affected by mood disorders, and thus they can be used as a surrogate sign, as well as an objective index for pervasive monitoring of emotion and mood disorders in daily life. Here we review evidence that demonstrates the relationship between gait, emotions and mood disorders, highlighting the potential of a multimodal approach that couples gait data with physiological signals and home-based monitoring for early detection and management of mood disorders. This could enhance self-awareness, enable the development of objective biomarkers that identify high risk subjects and promote subject-specific treatment.