A Lesion-Derived Brain Network for Emotion Regulation

[1H-13C]-NMR-Based Metabolic Kinetics Reveals Brain Neurochemical Alterations in Mice After Retinal Ischemia-Reperfusion Injury

Retinal ischemia-reperfusion injury (RIRI) is a pathological process that occurs in various blinding eye diseases and is often accompanied by anxiety and depression. However, the underlying metabolic mechanism of mood disorders remains unclear. This study aimed to investigate the metabolic dynamics of the brain after RIRI. C57BL/6 J mice were used to establish the RIRI model and assessed after 1 and 7 days. Mood-related behaviors were examined using open-field, elevated plus-maze, and forced swimming tests. Retinal injury histology was assessed using retinal hematoxylin and eosin staining. Retinal apoptosis was measured via the TdT-mediated dUTP nick-end labeling staining. The 13C-labeled metabolite information for six brain regions of interest was obtained using the [1H-13C]-NMR technique. Retinal tissue damage and cell apoptosis in the retina were observed 1 and 7 days after RIRI. One day after RIRI, mice displayed anxiety- and depression-like behaviors, and multiple metabolites involved in the glutamine (Gln)/glutamate (Glu)-γ-aminobutyric acid (GABA) and tricarboxylic acid (TCA) cycles exhibited reductions in all studied brain regions, with frontal cortex (FC) and temporal cortex (TC) being the most markedly altered. Metabolites and behavioral indicators nearly returned to normal after 7 days. Significant positive correlations between Gln/Glu-GABA and TCA cycle metabolites were observed in the RIRI brain. The results revealed that within a short period after RIRI, there was a reduction in brain metabolites and a disruption of the Gln/Glu-GABA and TCA cycles, which may contribute to mood disorders in mice.

MRI-based neuroimaging alterations in immune-related skin diseases: a comprehensive review

The skin, as the largest organ in the human body, serves as the primary physical barrier and plays a crucial role in the immune defense process. Immune-related skin diseases encompass a spectrum of complex dermatological conditions characterized by aberrant immune responses, including the production of autoantibodies and dysregulation of inflammatory mediators. Growing evidence suggest a heightened prevalence of comorbid neuropsychiatric disorders among patients with immune-related skin diseases, indicative of potential shared pathogenesis. In recent years, the brain connectome, delineating the intricate network of neural connections, has gained prominence in elucidating various neurological and psychiatric conditions. Researchers have begun to investigate whether alterations in brain connectivity occur in patients with immune-related skin diseases, thereby exploring the connectome perspective in understanding the pathogenesis of these diseases. This review aims to synthesize and analyze recent neuroimaging studies about immune-related skin diseases, including systemic lupus erythematosus, psoriasis, chronic spontaneous urticaria, and atopic dermatitis. We mainly discussed the connectome studies related to these diseases and summarized the underlying mechanisms associated with their clinical manifestations, progression, and treatment. Then from our perspectives, we believe that interdisciplinary collaborations encompassing fields such as neurology, psychiatry, dermatology, and neuroimaging will be instrumental in advancing our understanding of immune-related skin diseases. Furthermore, multi-omics approaches will enable a comprehensive exploration of the molecular mechanisms underlying these neural changes, facilitating the identification of novel diagnostic biomarkers and therapeutic targets.

fNIRS neurofeedback facilitates emotion regulation: Exploring individual differences over the ventrolateral prefrontal cortex

The ventrolateral prefrontal cortex (VLPFC) plays a pivotal role in emotion regulation, yet the effectiveness of neurofeedback (NF) training targeting the VLPFC remains uncertain, suggesting significant individual differences in outcomes. In this study, we aimed to clarify these differences by enrolling 90 participants, randomly assigned to either an experimental group or a sham group (n = 48/42). Participants in the experimental group underwent VLPFCNF training over eight sessions across two consecutive days, while those in the sham group received random signals from functional near-infrared spectroscopy (fNIRS). To investigate individual variability, participants in the experimental group were further categorized as high or low-efficacy groups based on their training efficiency, determined by the regression slope of VLPFC activity over the sessions. Our results revealed a significant reduction in negative emotions and increased VLPFC activity during emotion regulation in the high-efficacy group, compared to both the low-efficacy group and sham group. Importantly, the benefit in emotion regulation, as reflected by decreased negativity ratings, was predicted by NF training efficiency. Furthermore, the enhancement of VLPFC activity during emotion regulation fully mediated the relationship between NF training efficiency and emotion regulation benefits. Participants with higher VLPFCNF training efficiency exhibited greater engagement of the VLPFC during emotion regulation, leading to superior emotional outcomes. Additionally, VLPFCNF training efficiency was linked to the habitual use of reappraisal strategies in daily life. This study provides novel causal evidence that VLPFCNF training can effectively enhance emotion regulation, highlighting the importance of individual differences in training outcomes. Our findings suggest that NF training targeting the VLPFC offers a promising and personalized intervention strategy for improving emotion regulation, with potential applications for treating emotional disorders. This research underscores the potential of personalized NF approaches, offering new avenues for tailored therapeutic interventions in the future.

Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations

Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights with advanced algorithmic methods in pursuit of an enhanced understanding and applications of emotion recognition. Methods: The study was conducted following PRISMA guidelines, involving a rigorous selection process that resulted in the inclusion of 64 empirical studies that explore neuroimaging modalities such as fMRI, EEG, and MEG, discussing their capabilities and limitations in emotion recognition. It further evaluates deep learning architectures, including neural networks, CNNs, and GANs, in terms of their roles in classifying emotions from various domains: human-computer interaction, mental health, marketing, and more. Ethical and practical challenges in implementing these systems are also analyzed. Results: The review identifies fMRI as a powerful but resource-intensive modality, while EEG and MEG are more accessible with high temporal resolution but limited by spatial accuracy. Deep learning models, especially CNNs and GANs, have performed well in classifying emotions, though they do not always require large and diverse datasets. Combining neuroimaging data with behavioral and cognitive features improves classification performance. However, ethical challenges, such as data privacy and bias, remain significant concerns. Conclusions: The study has emphasized the efficiencies of neuroimaging and deep learning in emotion detection, while various ethical and technical challenges were also highlighted. Future research should integrate behavioral and cognitive neuroscience advances, establish ethical guidelines, and explore innovative methods to enhance system reliability and applicability.

The Influence of Emotional Intelligence on Psychiatric Nurses' Care Behavior, and the Chain Mediating Role of Compassion Fatigue and Perception of Management

PURPOSE

To investigate psychiatric nurses' emotional intelligence and its effect on care behavior, while also examining the mediating effect of compassion fatigue and perception of management.

METHOD

This cross-sectional study was conducted in several specialized psychiatric hospitals with 360 psychiatric nurses. Self-report surveys were administered, which included the Caring Factor Survey-Caring of Manager, Caring Behaviors Inventory, Compassion Fatigue Short Scale, and Wong Law Emotional Intelligence Scale. Data were analyzed using descriptive, correlation, and path analyses.

RESULTS

Mean score for care behavior was 4.78 (SD = 0.83), which was at a moderate level. Compassion fatigue exhibited negative correlations with other variables. Results of mediation effect analysis showed that emotional intelligence directly predicted care behavior (p < 0.001). Emotional intelligence also indirectly affected care behavior through the mediating roles of perception of management and compassion fatigue, and affected care behavior through the chain mediation effect of perception of management and compassion fatigue.

CONCLUSION

Nurse managers should pay attention to the influence of emotional intelligence on care behavior and improve care behavior by strengthening leadership and alleviating compassion fatigue. [Journal of Psychiatric Nursing and Mental Health Services, 63(2), 35-43.].

Mechanisms underlying the spontaneous reorganization of depression network after stroke

Exploring the causal relationship between focal brain lesions and post-stroke depression (PSD) can provide therapeutic insights. However, a gap exists between causal and therapeutic information. Exploring post-stroke brain repair processes post-stroke could bridge this gap. We defined a depression network using the normative connectome and investigated the predictive capacity of lesion-induced network damage on depressive symptoms in discovery cohort of 96 patients, at baseline and six months post-stroke. Stepwise functional connectivity (SFC) was used to examine topological changes in the depression network over time to identify patterns of network reorganization. The predictive value of reorganization information was evaluated for follow-up symptoms in discovery and validation cohort 1 (22 worsening PSD patients) as well as for treatment responsiveness in validation cohort 2 (23 antidepressant-treated patients). We evaluated the consistency of significant reorganization areas with neuromodulation targets. Spatial correlations of network reorganization patterns with gene expression and neurotransmitter maps were analyzed. The predictive power of network damage for symptoms diminished at follow-up compared to baseline (Δadjusted R2 = -0.070, p < 0.001). Reorganization information effectively predicted symptoms at follow-up in the discovery cohort (adjust R2 = 0.217, 95 %CI: 0.010 to 0.431), as well as symptom exacerbation (r = 0.421, p = 0.033) and treatment responsiveness (r = 0.587, p = 0.012) in the validation cohorts. Regions undergoing significant reorganization overlapped with neuromodulatory targets known to be effective in treating depression. The reorganization of the depression network was associated with immune-inflammatory responses gene expressions and gamma-aminobutyric acid. Our findings may yield important insights into the repair mechanisms of PSD and provide a critical context for developing post-stroke treatment strategies.

A potential target for noninvasive neuromodulation of PTSD symptoms derived from focal brain lesions in veterans

Neuromodulation trials for the treatment of posttraumatic stress disorder (PTSD) have yielded mixed results, and the optimal neuroanatomical target remains unclear. Here we analyzed three datasets to study brain circuitry causally linked to PTSD in military veterans. In veterans with penetrating traumatic brain injury, lesion locations that reduced probability of PTSD were preferentially connected to a circuit including the medial prefrontal cortex, amygdala and anterolateral temporal lobe. In veterans without lesions, PTSD was specifically associated with increased connectivity within this circuit. Reduced functional connectivity within this circuit after transcranial magnetic stimulation correlated with symptom reduction, even though the circuit was not directly targeted. This lesion-based 'PTSD circuit' may serve as a target for clinical trials of neuromodulation in veterans with PTSD.

Mapping Lesion-Related Human Aggression to a Common Brain Network

BACKGROUND

Aggression exacts a significant toll on human societies and is highly prevalent among neuropsychiatric patients. The neural mechanisms of aggression are unclear and treatment options are limited.

METHODS

Using a recently validated lesion network mapping technique, we derived an aggression-associated network by analyzing data from 182 patients who had experienced penetrating head injuries during their service in the Vietnam War. To test whether damage to this lesion-derived network would increase the risk of aggression-related neuropsychiatric symptoms, we used the Harvard Lesion Repository (N = 852). To explore potential therapeutic relevance of this network, we used an independent deep brain stimulation dataset of 25 patients with epilepsy, in which irritability and aggression are known potential side effects.

RESULTS

We found that lesions associated with aggression occurred in many different brain locations but were characterized by a specific brain network defined by functional connectivity to a hub region in the right prefrontal cortex. This network involves positive connectivity to the ventromedial prefrontal cortex, dorsolateral prefrontal cortex, frontal pole, posterior cingulate cortex, anterior cingulate cortex, temporal-parietal junction, and lateral temporal lobe and negative connectivity to the amygdala, hippocampus, insula, and visual cortex. Among all 24 neuropsychiatric symptoms included in the Harvard Lesion Repository, criminality demonstrated the most alignment with our aggression-associated network. Deep brain stimulation site connectivity to this same network was associated with increased irritability.

CONCLUSIONS

We conclude that brain lesions associated with aggression map to a specific human brain circuit, and the functionally connected regions in this circuit provide testable targets for therapeutic neuromodulation.

Lesion voxels to lesion networks: The enduring value of the Vietnam Head Injury Study

The Vietnam Head Injury Study has been curated by Dr Jordan Grafman since the 1980s in an effort to study patients with penetrating traumatic brain injuries suffered during the Vietnam War. Unlike many datasets of ischemic stroke lesions, the VHIS collected extraordinarily deep phenotyping and was able to sample lesion locations that are not constrained to typical vascular territories. For decades, this dataset has helped researchers draw causal links between neuroanatomical regions and neuropsychiatric symptoms. The value of the VHIS has only increased over time as techniques for analyzing the dataset have developed and evolved. Tools such as voxel lesion symptom mapping allowed one to relate symptoms to individual brain voxels. With the advent of the human connectome, tools such as lesion network mapping allow one to relate symptoms to connected brain networks by combining lesion datasets with new atlases of human brain connectivity. In a series of recent studies, lesion network mapping has been combined with the Vietnam Head Injury dataset to identify brain networks associated with spirituality, religiosity, consciousness, memory, emotion regulation, addiction, depression, and even transdiagnostic mental illness. These findings are enhancing our ability to make diagnoses, identify potential treatment targets for focal brain stimulation, and understand the human brain generally. Our techniques for studying brain lesions will continue to improve, as will our tools for modulating brain circuits. As these advances occur, the value of well characterized lesion datasets such as the Vietnam Head Injury Study will continue to grow. This study aims to review the history of the Vietnam Head Injury Study and contextualize its role in modern-day localization of neurological symptoms.

Electric field distribution predicts efficacy of accelerated intermittent theta burst stimulation for late-life depression

Introduction

Repetitive transcranial magnetic stimulation (rTMS) is a promising intervention for late-life depression (LLD) but may have lower rates of response and remission owing to age-related brain changes. In particular, rTMS induced electric field strength may be attenuated by cortical atrophy in the prefrontal cortex. To identify clinical characteristics and treatment parameters associated with response, we undertook a pilot study of accelerated fMRI-guided intermittent theta burst stimulation (iTBS) to the right dorsolateral prefrontal cortex in 25 adults aged 50 or greater diagnosed with LLD and qualifying to receive clinical rTMS.

Methods

Participants underwent baseline behavioral assessment, cognitive testing, and structural and functional MRI to generate individualized targets and perform electric field modeling. Forty-five sessions of iTBS were delivered over 9 days (1800 pulses per session, 50-min inter-session interval). Assessments and testing were repeated after 15 sessions (Visit 2) and 45 sessions (Visit 3). Primary outcome measure was the change in depressive symptoms on the Inventory of Depressive Symptomatology-30-Clinician (IDS-C-30) from Visit 1 to Visit 3.

Results

Overall there was a significant improvement in IDS score with the treatment (Visit 1: 38.6; Visit 2: 31.0; Visit 3: 21.3; mean improvement 45.5%) with 13/25 (52%) achieving response and 5/25 (20%) achieving remission (IDS-C-30 < 12). Electric field strength and antidepressant effect were positively correlated in a subregion of the ventrolateral prefrontal cortex (VLPFC) (Brodmann area 47) and negatively correlated in the posterior dorsolateral prefrontal cortex (DLPFC).

Conclusion

Response and remission rates were lower than in recently published trials of accelerated fMRI-guided iTBS to the left DLPFC. These results suggest that sufficient electric field strength in VLPFC may be a contributor to effective rTMS, and that modeling to optimize electric field strength in this area may improve response and remission rates. Further studies are needed to clarify the relationship of induced electric field strength with antidepressant effects of rTMS for LLD.

Functional and structural lesion network mapping in neurological and psychiatric disorders: a systematic review

Background

The traditional approach to studying the neurobiological mechanisms of brain disorders and localizing brain function involves identifying brain abnormalities and comparing them to matched controls. This method has been instrumental in clinical neurology, providing insight into the functional roles of different brain regions. However, it becomes challenging when lesions in diverse regions produce similar symptoms. To address this, researchers have begun mapping brain lesions to functional or structural networks, a process known as lesion network mapping (LNM). This approach seeks to identify common brain circuits associated with lesions in various areas. In this review, we focus on recent studies that have utilized LNM to map neurological and psychiatric symptoms, shedding light on how this method enhances our understanding of brain network functions.

Methods

We conducted a systematic search of four databases: PubMed, Scopus, and Web of Science, using the term "Lesion network mapping." Our focus was on observational studies that applied lesion network mapping in the context of neurological and psychiatric disorders.

Results

Following our screening process, we included 52 studies, comprising a total of 6,814 subjects, in our systematic review. These studies, which utilized functional connectivity, revealed several regions and network overlaps across various movement and psychiatric disorders. For instance, the cerebellum was found to be part of a common network for conditions such as essential tremor relief, parkinsonism, Holmes tremor, freezing of gait, cervical dystonia, infantile spasms, and tics. Additionally, the thalamus was identified as part of a common network for essential tremor relief, Holmes tremor, and executive function deficits. The dorsal attention network was significantly associated with fall risk in elderly individuals and parkinsonism.

Conclusion

LNM has proven to be a powerful tool in localizing a broad range of neuropsychiatric, behavioral, and movement disorders. It holds promise in identifying new treatment targets through symptom mapping. Nonetheless, the validity of these approaches should be confirmed by more comprehensive prospective studies.