Revisiting Hemispheric Asymmetry in Mood Regulation: Implications for rTMS for Major Depressive Disorder

Hemispheric differences in emotional processing have been observed for over half a century, leading to multiple theories classifying differing roles for the right and left hemisphere in emotional processing. Conventional acceptance of these theories has had lasting clinical implications for the treatment of mood disorders. The theory that the left hemisphere is broadly associated with positively valenced emotions, while the right hemisphere is broadly associated with negatively valenced emotions, drove the initial application of repetitive transcranial magnetic stimulation (rTMS) for the treatment of major depressive disorder (MDD). Subsequent rTMS research has led to improved response rates while adhering to the same initial paradigm of administering excitatory rTMS to the left prefrontal cortex (PFC) and inhibitory rTMS to the right PFC. However, accumulating evidence points to greater similarities in emotional regulation between the hemispheres than previously theorized, with potential implications for how rTMS for MDD may be delivered and optimized in the near future. This review will catalog the range of measurement modalities that have been used to explore and describe hemispheric differences, and highlight evidence that updates and advances knowledge of TMS targeting and parameter selection. Future directions for research are proposed that may advance precision medicine and improve efficacy of TMS for MDD.

Large hemispheric differences in the Hadley cell strength variability due to ocean coupling

By modulating the distribution of heat, precipitation and moisture, the Hadley cell holds large climate impacts at low and subtropical latitudes. Here we show that the interannual variability of the annual mean Hadley cell strength is ~ 30% less in the Northern Hemisphere than in the Southern Hemisphere. Using a hierarchy of ocean coupling experiments, we find that the smaller variability in the Northern Hemisphere stems from dynamic ocean coupling, which has opposite effects on the variability of the Hadley cell in the Southern and Northern Hemispheres; it acts to increase the variability in the Southern Hemisphere, which is inversely linked to equatorial upwelling, and reduce the variability in the Northern Hemisphere, which shows a direct relation with the subtropical wind-driven overturning circulation. The important role of ocean coupling in modulating the tropical circulation suggests that further investigation should be carried out to better understand the climate impacts of ocean-atmosphere coupling at low latitudes.

Age of Acquisition and Spoken Words: Examining Hemispheric Differences in Lexical Processing

The relationship between the age of acquisition (AoA) of words and their cerebral hemispheric representation is controversial because the experimental results have been contradictory. However, most of the lexical processing experiments were performed with stimuli consisting of written words. If we want to compare the processing of words learned very early in infancy—when children cannot read—with words learned later, it seems more logical to employ spoken words as experimental stimuli. This study, based on the auditory lexical decision task, used spoken words that were classified according to an objective criterion of AoA with extremely distant means (2.88 vs. 9.28 years old). As revealed by the reaction times, both early and late words were processed more efficiently in the left hemisphere, with no AoA × Hemisphere interaction. The results are discussed from a theoretical point of view, considering that all the experiments were conducted using adult participants.

Simulation of winter and summer climates with PRL Atmospheric General Circulation Model

A global, spectral Atmospheric General Circulation Model (AGCM) has been developed indigenously at Physical Research Laboratory (PRL) for climate studies. The model has six a levels in the vertical and has horizontal resolution of 21 waves with rhomboidal truncation. The model includes smooth topography, planetary boundary layer, deep convection, large scale condensation, interactive hydrology, radiation with interactive clouds and diurnal cycle. Sea surface temperature and sea ice values were fixed based on climatological data for different calender months. The model was integrated for six years starting with an isothermal atmosphere (2400K), zero winds initial conditions and forcing from incoming solar radiation. After one year the model stabilizes. The seasonal averages of various fields of the last five years are discussed in this paper. It is found that the model reproduces reasonably well the seasonal features of atmospheric circulation, seasonal variability and hemispheric differences.

Stroke Lateralization in Large Hemisphere Infarctions: Characteristics, Stroke-Related Complications, and Outcomes

Objectives: To assess the hemispheric differences in characteristics, stroke-related complications, and outcomes of patients with large hemisphere infarctions (LHI).Methods: We enrolled consecutive patients admitted within 24 h after the diagnosis of LHI (defined as an ischemic stroke involving more than 50% of the territory of the middle cerebral artery in computed tomography and/or magnetic resonance imaging). Univariate and multivariate analysis were performed to explore the association between lateralization and stroke-related complications and clinical outcomes.Results: A total of 314 patients with LHI were enrolled, with 171 (54.5%) having right hemispheric involvement. Right-sided patients with LHI had lower baseline National Institutes of Health Stroke Scale (NIHSS) score (18 vs. 22, p < 0.001), higher frequency of atrial fibrillation (69.0 vs. 52.4%, p = 0.003), and higher proportion of cardio-embolism (73.1 vs. 56.6%, p = 0.013) than the left. Right-sided LHI had higher incidence rates of malignant brain edema (MBE) (48.5 vs. 30.8%, p = 0.001) and a composite of cardiovascular events (29.8 vs. 17.5%, p = 0.011) during hospitalization. The incidence rate of 1-month mortality (34.5 vs. 23.8%, p = 0.036) was higher in right-sided patients with LHI, but there were no hemispheric differences in the incidence rates of 3-month mortality and unfavorable outcome (both p > 0.05). Multivariate analyses suggested right hemisphere involvement was independently associated with increased risk of MBE (adjusted OR 2.37, 95% CI 1.26–4.43, p = 0.007) and composite of cardiovascular events (adjusted OR 2.04, 95% CI 1.12–3.72, p = 0.020). However, it was not independently associated with 1-month death, 3-month mortality, and 3-month unfavorable outcome (all p > 0.05).Conclusions: Right-sided patients with LHI had higher frequency of atrial fibrillation and cardio-embolism than the left-sided patients. Right hemisphere involvement was independently associated with increased risk of MBE and composite of cardiovascular events during hospitalization, whereas stroke lateralization was not an independent predictor of mortality and unfavorable outcome in patients with LHI.

Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality

Hemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and function. Commonly employed pairwise comparisons between left and right are suitable for finding differences between the hemispheres, but they come with several caveats when assessing multiple asymmetries. What is more, they are not designed for identifying the characterizing features of each hemisphere. Here, we present a novel data-driven framework—based on machine learning-based classification—for identifying the characterizing features that underlie hemispheric differences. Using voxel-based morphometry data from two different samples (n = 226, n = 216), we separated the hemispheres along the midline and used two different pipelines: First, for investigating global differences, we embedded the hemispheres into a two-dimensional space and applied a classifier to assess if the hemispheres are distinguishable in their low-dimensional representation. Second, to investigate which voxels show systematic hemispheric differences, we employed two classification approaches promoting feature selection in high dimensions. The two hemispheres were accurately classifiable in both their low-dimensional (accuracies: dataset 1 = 0.838; dataset 2 = 0.850) and high-dimensional (accuracies: dataset 1 = 0.966; dataset 2 = 0.959) representations. In low dimensions, classification of the right hemisphere showed higher precision (dataset 1 = 0.862; dataset 2 = 0.894) compared to the left hemisphere (dataset 1 = 0.818; dataset 2 = 0.816). A feature selection algorithm in the high-dimensional analysis identified voxels that most contribute to accurate classification. In addition, the map of contributing voxels showed a better overlap with moderate to highly lateralized voxels, whereas conventional t test with threshold-free cluster enhancement best resembled the LQ map at lower thresholds. Both the low- and high-dimensional classifiers were capable of identifying the hemispheres in subsamples of the datasets, such as males, females, right-handed, or non-right-handed participants. Our study indicates that hemisphere classification is capable of identifying the hemisphere in their low- and high-dimensional representation as well as delineating brain asymmetries. The concept of hemisphere classifiability thus allows a change in perspective, from asking what differs between the hemispheres towards focusing on the features needed to identify the left and right hemispheres. Taking this perspective on hemispheric differences may contribute to our understanding of what makes each hemisphere special.

GTSception: A Deep Learning EEG Emotion Recognition Model Based on Fusion of Global, Time Domain and Frequency Domain Feature Extraction

With the rapid development of deep learning in recent years, automatic electroencephalography (EEG) emotion recognition has been widely concerned. At present, most deep learning methods do not normalize EEG data properly and do not fully extract the features of time and frequency domain, which will affect the accuracy of EEG emotion recognition. To solve these problems, we propose GTScepeion, a deep learning EEG emotion recognition model. In pre-processing, the EEG time slicing data including channels were pre-processed. In our model, global convolution kernels are used to extract overall semantic features, followed by three kinds of temporal convolution kernels representing different emotional periods, followed by two kinds of spatial convolution kernels highlighting brain hemispheric differences to extract spatial features, and finally emotions are dichotomy classified by the full connected layer. The experiments is based on the DEAP dataset, and our model can effectively normalize the data and fully extract features. For Arousal, ours is 8.76% higher than the current optimal emotion recognition model based on Inception. For Valence, the best accuracy of our model reaches 91.51%.

Evaluation of fNIRS signal components elicited by cognitive and hypercapnic stimuli

Functional near infrared spectroscopy (fNIRS) measurements are confounded by signal components originating from multiple physiological causes, whose activities may vary temporally and spatially (across tissue layers, and regions of the cortex). Furthermore, the stimuli can induce evoked effects, which may lead to over or underestimation of the actual effect of interest. Here, we conducted a temporal, spectral, and spatial analysis of fNIRS signals collected during cognitive and hypercapnic stimuli to characterize effects of functional versus systemic responses. We utilized wavelet analysis to discriminate physiological causes and employed long and short source-detector separation (SDS) channels to differentiate tissue layers. Multi-channel measures were analyzed further to distinguish hemispheric differences. The results highlight cardiac, respiratory, myogenic, and very low frequency (VLF) activities within fNIRS signals. Regardless of stimuli, activity within the VLF band had the largest contribution to the overall signal. The systemic activities dominated the measurements from the short SDS channels during cognitive stimulus, but not hypercapnic stimulus. Importantly, results indicate that characteristics of fNIRS signals vary with type of the stimuli administered as cognitive stimulus elicited variable responses between hemispheres in VLF band and task-evoked temporal effect in VLF, myogenic and respiratory bands, while hypercapnic stimulus induced a global response across both hemispheres.

Words and Faces on Left and Right: Perceptual Asymmetries as a Marker for SSRI Responsiveness

<p>Vulnerability to depression has been associated with greater relative right hemisphere frontal activity, as measured by EEG recordings of alpha activity. However, there is much heterogeneity in the patterns of hemispheric asymmetries in people at risk for depression. These different patterns of hemispheric asymmetries may be related to whether an individual responds to Selective Serotonin Reuptake Inhibitor (SSRI) medication. Response to SSRIs is associated with a pattern of overall relative LH activity, whereas non-response to SSRIs is associated with a pattern of overall relative RH activity. Very little is known about how these asymmetries in neural activity relate to asymmetries in cognition. The current study investigated hemispheric differences in the processing of emotional faces and words, in individuals not vulnerable to depression (a Never Depressed group) and in individuals vulnerable to depression (a Previously Depressed group). In the chimeric faces task, the Previously Depressed group had a significantly larger left hemispatial bias compared to the Never Depressed group. This may reflect relatively greater posterior RH activity/arousal in the Previously Depressed group. No differences were found between SSRI Responders and Non-responders in the chimeric faces task. In the divided visual field task, hemispheric differences in the processing of emotional words were found between the SSRI Responders and SSRI Non-responders. In contrast to SSRI Responders and Never Depressed controls, SSRI Non-responders showed a relative advantage for negative over positive words when they were presented to their LVF/RH; and an advantage for negative words presented to their LVF/RH compared to their RVF/LH. Additionally, they were more sensitive to perceiving the valence of a word that was presented to their LVF/RH. This suggests that their RH semantic systems may differ from that of SSRI Responders and Never Depressed controls. Genetic, hormonal and cognitive factors are discussed in relation to these patterns of hemispheric asymmetries and responsiveness to SSRI medication.</p>

Words and Faces on Left and Right: Perceptual Asymmetries as a Marker for SSRI Responsiveness

<p>Vulnerability to depression has been associated with greater relative right hemisphere frontal activity, as measured by EEG recordings of alpha activity. However, there is much heterogeneity in the patterns of hemispheric asymmetries in people at risk for depression. These different patterns of hemispheric asymmetries may be related to whether an individual responds to Selective Serotonin Reuptake Inhibitor (SSRI) medication. Response to SSRIs is associated with a pattern of overall relative LH activity, whereas non-response to SSRIs is associated with a pattern of overall relative RH activity. Very little is known about how these asymmetries in neural activity relate to asymmetries in cognition. The current study investigated hemispheric differences in the processing of emotional faces and words, in individuals not vulnerable to depression (a Never Depressed group) and in individuals vulnerable to depression (a Previously Depressed group). In the chimeric faces task, the Previously Depressed group had a significantly larger left hemispatial bias compared to the Never Depressed group. This may reflect relatively greater posterior RH activity/arousal in the Previously Depressed group. No differences were found between SSRI Responders and Non-responders in the chimeric faces task. In the divided visual field task, hemispheric differences in the processing of emotional words were found between the SSRI Responders and SSRI Non-responders. In contrast to SSRI Responders and Never Depressed controls, SSRI Non-responders showed a relative advantage for negative over positive words when they were presented to their LVF/RH; and an advantage for negative words presented to their LVF/RH compared to their RVF/LH. Additionally, they were more sensitive to perceiving the valence of a word that was presented to their LVF/RH. This suggests that their RH semantic systems may differ from that of SSRI Responders and Never Depressed controls. Genetic, hormonal and cognitive factors are discussed in relation to these patterns of hemispheric asymmetries and responsiveness to SSRI medication.</p>