Key Brain Regions Research Articles

Effects of novel GLT-1 modulator, MC-100093, on neuroinflammatory and neurotrophic biomarkers in mesocorticolimbic brain regions of male alcohol preferring rats exposed chronically to ethanol

Chronic ethanol consumption can lead to increased extracellular glutamate concentrations in key reward brain regions, such as medial prefrontal cortex (mPFC) and nucleus accumbens (NAc), and consequently leading to oxidative stress and neuroinflammation. Previous studies from our lab tested β-lactam antibiotics and novel beta-lactam non-antibiotic, MC-100093, and showed these β-lactam upregulated the major astrocytic glutamate transporter, GLT-1, and consequently reduced ethanol intake and normalized glutamate homeostasis. This present study tested the effects of novel synthetic β-lactam non-antibiotic drug, MC-100093, in chronic ethanol intake and neuroinflammatory and trophic factors in subregions of the NAc (NAc core and shell) and mPFC (Prelimbic, PL; and Infralimbic, IL) of male P rats. MC-100093 treatment reduced ethanol intake after 5-week drinking regimen. Importantly, MC-100093 attenuated ethanol-induced downregulation of brain derived neurotrophic factor (BDNF) expression in these brain regions. In addition, MC-100093 attenuated ethanol-induced upregulation of pro-inflammatory cytokines such as TNF-a and HMGB1 in all these brain regions. Furthermore, MC-100093 treatment attenuated ethanol-induced increase in RAGE in these brain regions. MC-100093 prevented neuroinflammation caused by ethanol intake as well as increased neurotrophic factor in mesocorticolimbic brain regions. MC-100093 treatment reduced ethanol intake and this behavioral effect was associated with attenuation of reduced trophic factors and increased pro-inflammatory factors. MC-100093 is considered a small molecule that may have potential therapeutic effects for the treatment of the effects of chronic exposure to ethanol.

The Role of the Insula in Chronic Pain and Associated Structural Changes: An Integrative Review.

Chronic pain affects a substantial portion of the global population, significantly impacting quality of life and well-being. This condition involves complex mechanisms, including dysfunction of the autonomic nervous system, which plays a crucial role in pain perception.The insula, a key brain region involved in pain processing, plays a critical role in pain perception and modulation. Lesions in the insula can result in pain asymbolia, where pain perception remains intact but emotional responses are inappropriate. The insula is anatomically and functionally divided into anterior and posterior regions, with the posterior insula processing nociceptive input based on intensity and location before relaying it to the anterior insula for emotional mediation. Understanding the insula's intricate role in pain processing is crucial, as it is involved in encoding prediction errors and mediating emotional dimensions of pain perception.The focus of this review wason synthesizing existing literature on the role of the insula in chronic pain and associated structural changes. The goal was to integrate findings from various sources to provide a comprehensive overview of the topic.The search strategy included a combination of Medical Subject Headings (MeSH) and relevant keywords related to insulaand chronic pain. The following databases were surveyed: PubMed, Embase, Scopus, and Web of Science. We identified a total of 2515 articles, and after following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline based on eligibility criteria, 46 articles were used to synthesize this review. Our study highlights the pivotal role of the insula in chronic pain processing and associated structural changes, integrating findings from diverse studies and neuroimaging investigations. Beyond mere pain sensation, the insula contributes to emotional awareness, attention, and salience detection within the pain network.Various chronic pain conditions reveal alterations in insular activity and connectivity, accompanied by changes in gray matter volume and neurochemical profiles. Interventions targeting the insula show promise in alleviating chronic pain symptoms. However, further research is needed to understand underlying mechanisms, which can aid in developing more effective therapeutic interventions for pain.

Rapid Extraction of Prefrontal Cortex and Hippocampus for Molecular Analysis in Rats

Memory functions in laboratory animals are a crucial area of research in neuroscience, with a particular focus on identifying drugs that enhance various aspects of memory, particularly long-term declarative memory. This field involves studying cognition and memory from behavioral and molecular perspectives. Researchers evaluate changes in genes, enzymes, and proteins to discover the neurobiological mechanisms underlying memory formation and retrieval. The hippocampus and prefrontal cortex are key brain regions associated with memory and cognition, undergoing synaptogenesis following the acquisition of new information or skills. The production of specific proteins plays a crucial role in consolidating and storing memories, necessitating precise analysis of these brain regions to understand the molecular mechanisms involved in memory processes fully. However, the rapid degradation of these proteins by cellular enzymatic activities highlights the need for efficient and timely extraction techniques to ensure precise and reliable analysis. This methodological study aimed to introduce a rapid and accurate method for extracting the prefrontal cortex and hippocampus in rats.

Mechanical pain sensitivity is associated with hippocampal structural integrity.

Rodents and human studies indicate that the hippocampus, a brain region necessary for memory processing, responds to noxious stimuli. However, the hippocampus has yet to be considered a key brain region directly involved in the human pain experience. One approach to answer this question is to perform quantitative sensory testing on patients with hippocampal damage-ie, medial temporal lobe epilepsy. Some case studies and case series have performed such tests in a handful of patients with various types of epilepsy and have reported mixed results. Here, we aimed to determine whether mechanical pain sensitivity was altered in patients diagnosed with temporal lobe epilepsy. We first investigated whether mechanical pain sensitivity in patients with temporal lobe epilepsy differs from that of healthy individuals. Next, in patients with temporal lobe epilepsy, we evaluated whether the degree of pain sensitivity is associated with the degree of hippocampal integrity. Structural integrity was based on hippocampal volume, and functional integrity was based on verbal and visuospatial memory scores. Our findings show that patients with temporal lobe epilepsy have lower mechanical pain sensitivity than healthy individuals. Only left hippocampal volume was positively associated with mechanical pain sensitivity-the greater the hippocampal damage, the lower the sensitivity to mechanical pain. Hippocampal measures of functional integrity were not significantly associated with mechanical pain sensitivity, suggesting that the mechanisms of hippocampal pain processing may be different than its memory functions. Future studies are necessary to determine the mechanisms of pain processing in the hippocampus.

NrCAM-deficient mice exposed to chronic stress exhibit disrupted latent inhibition, a hallmark of schizophrenia.

The neuronal cell adhesion molecule (NrCAM) is widely expressed and has important physiological functions in the nervous system across the lifespan, from axonal growth and guidance to spine and synaptic pruning, to organization of proteins at the nodes of Ranvier. NrCAM lies at the core of a functional protein network where multiple targets (including NrCAM itself) have been associated with schizophrenia. Here we investigated the effects of chronic unpredictable stress on latent inhibition, a measure of selective attention and learning which shows alterations in schizophrenia, in NrCAM knockout (KO) mice and their wild-type littermate controls (WT). Under baseline experimental conditions both NrCAM KO and WT mice expressed robust latent inhibition (p = 0.001). However, following chronic unpredictable stress, WT mice (p = 0.002), but not NrCAM KO mice (F < 1), expressed latent inhibition. Analyses of neuronal activation (c-Fos positive counts) in key brain regions relevant to latent inhibition indicated four types of effects: a single hit by genotype in IL cortex (p = 0.0001), a single hit by stress in Acb-shell (p = 0.031), a dual hit stress x genotype in mOFC (p = 0.008), vOFC (p = 0.020), and Acb-core (p = 0.032), and no effect in PrL cortex (p > 0.141). These results indicating a pattern of differential effects of genotype and stress support a complex stress × genotype interaction model and a role for NrCAM in stress-induced pathological behaviors relevant to schizophrenia and other psychiatric disorders.

Developmentally sensitive multispectral cortical connectivity profiles serving visual selective attention

Throughout childhood and adolescence, the brain undergoes significant structural and functional changes that contribute to the maturation of multiple cognitive domains, including selective attention. Selective attention is crucial for healthy executive functioning and while key brain regions serving selective attention have been identified, their age-related changes in neural oscillatory dynamics and connectivity remain largely unknown. We examined the developmental sensitivity of selective attention circuitry in 91 typically developing youth aged 6 – 13 years old. Participants completed a number-based Simon task while undergoing magnetoencephalography (MEG) and the resulting data were preprocessed and transformed into the time-frequency domain. Significant oscillatory brain responses were imaged using a beamforming approach, and task-related peak voxels in the occipital, parietal, and cerebellar cortices were used as seeds for subsequent whole-brain connectivity analyses in the alpha and gamma range. Our key findings revealed developmentally sensitive connectivity profiles in multiple regions crucial for selective attention, including the temporoparietal junction (alpha) and prefrontal cortex (gamma). Overall, these findings suggest that brain regions serving selective attention are highly sensitive to developmental changes during the pubertal transition period.

Application of machine learning techniques in the diagnostic approach of PTSD using MRI neuroimaging data: A systematic review

BackgroundAt present, the diagnosis of post-traumatic stress disorder（PTSD） mainly relies on clinical symptoms and psychological scales, and finding objective indicators that are helpful for diagnosis has always been a challenge in clinical practice and academic research. Neuroimaging is a useful and powerful tool for discovering the biomarkers of PTSD,especially functional MRI (fMRI), structural MRI (sMRI) and Diffusion Weighted Imaging（DTI）are the most commonly used technologies, which can provide multiple perspectives on brain function, structure and its connectivity. Machine learning (ML) is an emerging and potentially powerful method, which has aroused people's interest because it is used together with neuroimaging data to define brain structural and functional abnormalities related to diseases, and identify phenotypes, such as helping physicians make early diagnosis. ObjectivesAccording to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) declaration, a systematic review was conducted to assess its accuracy in distinguishing between PTSD patients, TEHC(Trauma-Exposed Healthy Controls), and HC(healthy controls). MethodsWe searched PubMed, Embase, and Web of Science using common words for ML methods and PTSD until June 2023, with no language or time limits. This review includes 13 studies, with sensitivity, specificity, and accuracy taken from each publication or acquired directly from the authors. ResultsAll ML techniques have an diagnostic accuracy rate above 70%，and support vector machine（SVM） are the most commonly used techniques. This series of studies has revealed significant neurobiological differences in key brain regions among individuals with PTSD, TEHC, and HC. The connectivity patterns of regions such as the Insula and Amygdala hold particular significance in distinguishing these groups. TEHC exhibits more normal connectivity patterns compared to PTSD, providing valuable insights for the application of machine learning in PTSD diagnosis. ConclusionIn contrast to any currently available assessment and clinical diagnosis, ML techniques can be used as an effective and non-invasive support for early identification and detection of patients as well as for early screening of high-risk populations.

Manipulation of Glutamatergic Neuronal Activity in the Primary Motor Cortex Regulates Cardiac Function in Normal and Myocardial Infarction Mice.

Cardiac function is under neural regulation; however, brain regions in the cerebral cortex responsible for regulating cardiac function remain elusive. In this study, retrograde trans-synaptic viral tracing is used from the heart to identify a specific population of the excitatory neurons in the primary motor cortex (M1) that influences cardiac function in mice. Optogenetic activation of M1 glutamatergic neurons increases heart rate, ejection fraction, and blood pressure. By contrast, inhibition of M1 glutamatergic neurons decreased cardiac function and blood pressure as well as tyrosine hydroxylase (TH) expression in the heart. Using viral tracing and optogenetics, the median raphe nucleus (MnR) is identified as one of the key relay brain regions in the circuit from M1 that affect cardiac function. Then, a mouse model of cardiac injury is established caused by myocardial infarction (MI), in which optogenetic activation of M1 glutamatergic neurons impaired cardiac function in MI mice. Moreover, ablation of M1 neurons decreased the levels of norepinephrine and cardiac TH expression, and enhanced cardiac function in MI mice. These findings establish that the M1 neurons involved in the regulation of cardiac function and blood pressure. They also help the understanding of the neural mechanisms underlying cardiovascular regulation.

Long-term disruption of tissue levels of glutamate and glutamatergic neurotransmission neuromodulators, taurine and kynurenic acid induced by amphetamine.

Chronic amphetamine (AMPH) use leading to addiction results in adaptive changes within the central nervous system that persist well beyond the drug's elimination from the body and can precipitate relapse. Notably, alterations in glutamatergic neurotransmission play a crucial role in drug-associated behaviours. This study aimed to identify changes induced by amphetamine in glutamate levels and the neuromodulators of glutamatergic neurotransmission (taurine and kynurenic acid) observable after 14 and 28 days of abstinence in key brain regions implicated in addiction: the cortex (Cx), nucleus accumbens (Acb), and dorsolateral striatum (CPu-L). The rats were administered 12 doses of amphetamine (AMPH) intraperitoneally (i.p.) at 1.5mg/kg. The behavioural response was evaluated through ultrasonic vocalizations (USV). High-performance liquid chromatography (HPLC) was used to measure the levels of glutamate, taurine, and kynurenic acid in the Cx, Acb, and CPu-L after 14 and 28 days of abstinence. AMPH administration led to sensitisation towards AMPH's rewarding effects, as evidenced by changes in USV. There was a noticeable decrease in kynurenic acid levels and an increase in both taurine and glutamate in the CPu-L, along with an increase in glutamate levels in the Cx, 28 days following the final AMPH injection. The most significant changes in the tissue levels of glutamate, taurine, and kynurenic acid were seen in the CPu-L 28 days after the last dose of AMPH. The emergence of these changes exclusively after 28 days suggests that the processes initiated by AMPH use and subsequent abstinence take time to become apparent and may be enduring. This could contribute to the incubation of craving and the risk of relapse. Developing pharmacological strategies to counteract the reduction in kynurenic acid induced by psychostimulants may provide new avenues for therapy development.

Changes in effective connectivity during the visual-motor integration tasks: a preliminary f-NIRS study

BackgroundVisual-motor integration (VMI) is an essential skill in daily life. The present study aimed to use functional near-infrared spectroscopy (fNIRS) technology to explore the effective connectivity (EC) changes among brain regions during VMI activities of varying difficulty levels.MethodsA total of 17 healthy participants were recruited for the study. Continuous Performance Test (CPT), Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A), and Beery VMI test were used to evaluate attention performance, executive function, and VMI performance. Granger causality analysis was performed for the VMI task data to obtain the EC matrix for all participants. One-way ANOVA analysis was used to identify VMI load-dependent EC values among different task difficulty levels from brain network and channel perspectives, and partial correlation analysis was used to explore the relationship between VMI load-dependent EC values and behavioral performance.ResultsWe found that the EC values of dorsal attention network (DAN) → default mode network (DMN), DAN → ventral attention network (VAN), DAN → frontoparietal network (FPN), and DAN → somatomotor network (SMN) in the complex condition were higher than those in the simple and moderate conditions. Further channel analyses indicated that the EC values of the right superior parietal lobule (SPL) → right superior frontal gyrus (SFG), right middle occipital gyrus (MOG) → left SFG, and right MOG → right postcentral gyrus (PCG) in the complex condition were higher than those in the simple and moderate conditions. Subsequent partial correlation analysis revealed that the EC values from DAN to DMN, VAN, and SMN were positively correlated with executive function and VMI performance. Furthermore, the EC values of right MOG → left SFG and right MOG → right PCG were positively correlated with attention performance.ConclusionsThe DAN is actively involved during the VMI task and thus may play a critical role in VMI processes, in which two key brain regions (right SPL, right MOG) may contribute to the EC changes in response to increasing VMI load. Meanwhile, bilateral SFG and right PCG may also be closely related to the VMI performance.

Brain age of rhesus macaques over the lifespan

Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level biological age prediction and identify key brain regions responsible for the prediction. In this study, we present the methodology of constructing a rhesus macaque brain age model using a machine learning algorithm and discuss the key predictive brain regions in comparison to the human brain, to shed light on cross-species primate similarities and differences. Structural information of the brain (e.g., parcellated volumes) from brain magnetic resonance imaging of 43 rhesus macaques were used to develop brain atlas-based features to build a brain age model that predicts biological age. The best-performing model used 22 selected features and achieved an R2 of 0.72. We also identified interpretable predictive brain features including Right Fronto-orbital Cortex, Right Frontal Pole, Right Inferior Lateral Parietal Cortex, and Bilateral Posterior Central Operculum. Our findings provide converging evidence of the parallel and comparable brain regions responsible for both non-human primates and human biological age prediction.

The avBNSTGABA–VTA and avBNSTGABA–DRN pathways are respectively involved in the regulation of anxiety-like behaviors in parkinsonian rats

The anteroventral bed nucleus of stria terminalis (avBNST) is a key brain region which involves negative emotional states, such as anxiety. The most neurons in the avBNST are GABAergic, and it sends GABAergic projections to the ventral tegmental area (VTA) and the dorsal raphe nucleus (DRN), respectively. The VTA and DRN contain dopaminergic and serotonergic cell groups in the midbrain which regulate anxiety-like behaviors. However, it is unclear the role of GABAergic projections from the avBNST to the VTA and the DRN in the regulation of anxiety-like behaviors, particularly in Parkinson's disease (PD)-related anxiety. In the present study, unilateral 6-hydroxydopamine (6-OHDA) lesions of the substantia nigra pars compacta in rats induced anxiety-like behaviors, and decreased level of dopamine (DA) in the basolateral amygdala (BLA). Chemogenetic activation of avBNSTGABA–VTA or avBNSTGABA–DRN pathway induced anxiety-like behaviors and decreased DA or 5-HT release in the BLA in sham and 6-OHDA rats, while inhibition of avBNSTGABA–VTA or avBNSTGABA–DRN pathway produced anxiolytic-like effects and increased level of DA or 5-HT in the BLA. These findings suggest that avBNST inhibitory projections directly regulate dopaminergic neurons in the VTA and serotonergic neurons in the DRN, and the avBNSTGABA–VTA and avBNSTGABA–DRN pathways respectively exert impacts on PD-related anxiety-like behaviors.

White Matter Integrity and Motor Function Disruption Due to Traumatic Brain Injury in Piglets: Impacts on Motor-Related Brain Fibers.

Pediatric traumatic brain injury (TBI) often induces significant disability in patients, including long-term motor deficits. Early detection of injury severity is key in determining a prognosis and creating appropriate intervention and rehabilitation plans. However, conventional magnetic resonance imaging (MRI) scans, such as T2 Weighted (T2W) sequences, do not reliably assess the extent of microstructural white matter injury. Diffusion tensor imaging (DTI) tractography enables three-dimensional reconstruction of specific white matter tracts throughout the brain in order to detect white matter injury based on anisotropic diffusion. The objective of this study was to employ DTI tractography to detect acute changes to white matter integrity within the intersecting fibers of key motor-related brain regions following TBI. Piglets were assigned to either the sham craniectomy group (sham; n = 6) or the controlled cortical impact TBI group (TBI; n = 6). Gait and MRI were collected at seven days post-surgery (DPS). T2W sequences confirmed a localized injury predominately in the ipsilateral hemisphere in TBI animals. TBI animals, relative to sham animals, showed an increased apparent diffusion coefficient (ADC) and decreased fractional anisotropy (FA) in fiber bundles associated with key brain regions involved in motor function. TBI animals exhibited gait deficits, including stride and step length, compared to sham animals. Together these data demonstrate acute reductions in the white matter integrity, measured by DTI tractography, of fibers intersecting key brain regions that strongly corresponded with acute motor deficits in a pediatric piglet TBI model. These results provide the foundation for the further development of DTI-based biomarkers to evaluate motor outcomes following TBI.

Explainable AI-based Deep-SHAP for mapping the multivariate relationships between regional neuroimaging biomarkers and cognition

BackgroundMild cognitive impairment (MCI)/Alzheimer's disease (AD) is associated with cognitive decline beyond normal aging and linked to the alterations of brain volume quantified by magnetic resonance imaging (MRI) and amyloid-beta (Aβ) quantified by positron emission tomography (PET). Yet, the complex relationships between these regional imaging measures and cognition in MCI/AD remain unclear. Explainable artificial intelligence (AI) may uncover such relationships. MethodWe integrate the AI-based deep learning neural network and Shapley additive explanations (SHAP) approaches and introduce the Deep-SHAP method to investigate the multivariate relationships between regional imaging measures and cognition. After validating this approach on simulated data, we apply it to real experimental data from MCI/AD patients. ResultsDeep-SHAP significantly predicted cognition using simulated regional features and identified the ground-truth simulated regions as the most significant multivariate predictors. When applied to experimental MRI data, Deep-SHAP revealed that the insula, lateral occipital, medial frontal, temporal pole, and occipital fusiform gyrus are the primary contributors to global cognitive decline in MCI/AD. Furthermore, when applied to experimental amyloid Pittsburgh compound B (PiB)-PET data, Deep-SHAP identified the key brain regions for global cognitive decline in MCI/AD as the inferior temporal, parahippocampal, inferior frontal, supratemporal, and lateral frontal gray matter. ConclusionDeep-SHAP method uncovered the multivariate relationships between regional brain features and cognition, offering insights into the most critical modality-specific brain regions involved in MCI/AD mechanisms.

Structure-Function Interactions in the Hippocampus and Prefrontal Cortex Are Associated with Episodic Memory in Healthy Aging.

Aging comes with declines in episodic memory. Memory decline is accompanied by structural and functional alterations within key brain regions, including the hippocampus and lateral prefrontal cortex, as well as their affiliated default and frontoparietal control networks. Most studies have examined how structural or functional differences relate to memory independently. Here we implemented a multimodal, multivariate approach to investigate how interactions between individual differences in structural integrity and functional connectivity relate to episodic memory performance in healthy aging. In a sample of younger (N = 111; mean age, 22.11 years) and older (N = 78; mean age, 67.29 years) adults, we analyzed structural MRI and multiecho resting-state fMRI data. Participants completed measures of list recall (free recall of words from a list), associative memory (cued recall of paired words), and source memory (cued recall of the trial type, or the sensory modality in which a word was presented). The findings revealed that greater structural integrity of the posterior hippocampus and middle frontal gyrus were linked with a pattern of increased within-network connectivity, which together were related to better associative and source memory in older adulthood. Critically, older adults displayed better memory performance in the context of decreased hippocampal volumes when structural differences were accompanied by functional reorganization. This functional reorganization was characterized by a pruning of connections between the hippocampus and the limbic and frontoparietal control networks. Our work provides insight into the neural mechanisms that underlie age-related compensation, revealing that the functional architecture associated with better memory performance in healthy aging is tied to the structural integrity of the hippocampus and prefrontal cortex.

The Use of Transcranial Magnetic Stimulation in Attention Optimization Research: A Review from Basic Theory to Findings in Attention-Deficit/Hyperactivity Disorder and Depression.

This review explores the pivotal role of attention in everyday life, emphasizing the significance of studying attention-related brain functions. We delve into the development of methodologies for investigating attention and highlight the crucial role of brain neuroimaging and transcranial magnetic stimulation (TMS) in advancing attention research. Attention optimization theory is introduced to elucidate the neural basis of attention, identifying key brain regions and neural circuits involved in attention processes. The theory further explores neuroplasticity, shedding light on how the brain dynamically adapts and changes to optimize attention. A comprehensive overview of TMS is provided, elucidating the principles and applications of this technique in affecting brain activity through magnetic field stimulation. The application of TMS in attention research is discussed, outlining how it can be employed to regulate attention networks. The clinical applications of TMS are explored in attention-deficit/hyperactivity disorder (ADHD) and depression. TMS emerges as an effective clinical treatment for ADHD, showcasing its potential in addressing attention-related disorders. Additionally, the paper emphasizes the efficacy of TMS technology as a method for regulating depression, further underlining the versatility and therapeutic potential of TMS in clinical settings. In conclusion, this review underscores the interdisciplinary approach to attention research, integrating neuroimaging, neuroplasticity, and TMS. The presented findings contribute to our understanding of attention mechanisms and highlight the promising clinical applications of TMS in addressing attention-related disorders. This synthesis of theoretical and practical insights aims to propel further advancements in attention research and its therapeutic applications.

Influence of transient emotional episodes on affective and cognitive theory of mind.

Our emotions may influence how we interact with others. Previous studies have shown an important role of emotion induction in generating empathic reactions towards others' affect. However, it remains unclear whether (and to which extent) our own emotions can influence the ability to infer people's mental states, a process associated with Theory of Mind (ToM) and implicated in the representation of both cognitive (e.g. beliefs and intentions) and affective conditions. We engaged 59 participants in two emotion-induction experiments where they saw joyful, neutral and fearful clips. Subsequently, they were asked to infer other individuals' joy, fear (affective ToM) or beliefs (cognitive ToM) from verbal scenarios. Using functional magnetic resonance imaging, we found that brain activity in the superior temporal gyrus, precuneus and sensorimotor cortices were modulated by the preceding emotional induction, with lower response when the to-be-inferred emotion was incongruent with the one induced in the observer (affective ToM). Instead, we found no effect of emotion induction on the appraisal of people's beliefs (cognitive ToM). These findings are consistent with embodied accounts of affective ToM, whereby our own emotions alter the engagement of key brain regions for social cognition, depending on the compatibility between one's own and others' affect.

Cholestatic liver disease leads to significant adaptative changes in neural circuits regulating social behavior in mice to enhance sociability

Background & aimsCholestatic liver diseases (CLD) are commonly associated with behavioral changes, including social isolation, that negatively affects patient quality of life and remains unaltered by current therapies. It remains unclear whether CLD-associated social dysfunction stems from a direct effect on the brain, or from the psychological impact of CLD. The psychological component of disease is absent in animals, so we investigated the impact of CLD on social behavior and gene expression profiles in key social behavior-regulating brain regions in a mouse model. MethodsCLD due to bile duct ligation was used with the three-chamber sociability test for behavioral phenotyping. Differentially expressed gene (DEG) signatures were delineated in 3 key brain regions regulating social behavior using RNA-seq. Ingenuity Pathway Analysis (IPA®) was applied to streamline DEG data interpretation and integrate findings with social behavior-regulating pathways to identify important brain molecular networks and regulatory mechanisms disrupted in CLD. ResultsCLD mice exhibited enhanced social interactive behavior and significantly altered gene expression in each of the three social behavior-regulating brain regions examined. DEG signatures in BDL mice were associated with key IPA®-identified social behavior-regulating pathways including Oxytocin in Brain Signaling, GABA Receptor Signaling, Dopamine Receptor Signaling, and Glutamate Receptor Signaling. ConclusionsCLD causes complex alterations in gene expression profiles in key social behavior-regulating brain areas/pathways linked to enhanced social interactive behavior. These findings, if paralleled in CLD patients, suggest that CLD-associated reductions in social interactions predominantly relate to psychological impacts of disease and may inform new approaches to improve management.

Adaptive filter of frequency bands based coordinate attention network for EEG-based motor imagery classification.

In the brain-computer interface (BCI), motor imagery (MI) could be defined as the Electroencephalogram (EEG) signals through imagined movements, and ultimately enabling individuals to control external devices. However, the low signal-to-noise ratio, multiple channels and non-linearity are the essential challenges of accurate MI classification. To tackle these issues, we investigate the role of adaptive frequency bands selection and spatial-temporal feature learning in decoding motor imagery. We propose an Adaptive Filter of Frequency Bands based Coordinate Attention Network (AFFB-CAN) to improve the performance of MI classification. Specifically, we design the AFFB to adaptively obtain the upper and lower limits of frequency bands in order to alleviate information loss caused by manual selection. Next, we propose the CAN-based network to emphasize the key brain regions and temporal segments. And we design a multi-scale module to enhance temporal context learning. The conducted experiments on the BCI Competition IV-2a and 2b datasets reveal that our approach achieves an outstanding average accuracy, kappa values, and Macro F1-Score with 0.7825, 0.7104, and 0.7486 respectively. Similarly, for the BCI Competition IV-2b dataset, the average accuracy, kappa values, and F1-Score obtained are 0.8879, 0.7427, and 0.8734, respectively. The proposed AFFB-CAN method improves the performance of MI classification. In addition, our study confirms previous findings that motor imagery is mainly associated with µ and β rhythms. Moreover, we also find that γ rhythms also play an important role in MI classification.

Efficacy of a gamified digital therapy for speech production in people with chronic aphasia (iTalkBetter): behavioural and imaging outcomes of a phase II item-randomised clinical trial

Aphasia is among the most debilitating of symptoms affecting stroke survivors. Speech and language therapy (SLT) is effective, but many hours of practice are required to make clinically meaningful gains. One solution to this 'dosage' problem is to automate therapeutic approaches via self-supporting apps so people with aphasia (PWA) can amass practice as it suits them. However, response to therapy is variable and no clinical trial has yet identified the key brain regions required to engage with word-retrieval therapy. Between Sep 7, 2020 and Mar 1, 2022at University College London in the UK, we carried out a phase II, item-randomised clinical trial in 27 PWA using a novel, self-led app, 'iTalkBetter', which utilises confrontation naming therapy. Unlike previously reported apps, it has a real-time utterance verification system that drives its adaptive therapy algorithm. Therapy items were individually randomised to provide balanced lists of 'trained' and 'untrained' items matched on key psycholinguistic variables and baseline performance. PWA practised with iTalkBetter over a 6-week therapy block. Structural and functional MRI data were collected to identify therapy-related changes in brain states. A repeated-measures design was employed. The trial was registered at ClinicalTrials.gov (NCT04566081). iTalkBetter significantly improved naming ability by 13% for trained items compared with no change for untrained items, an average increase of 29 words (SD=26) per person; beneficial effects persisted at three months. PWA's propositional speech also significantly improved. iTalkBetter use was associated with brain volume increases in right auditory and left anterior prefrontal cortices. Task-based fMRI identified dose-related activity in the right temporoparietal junction. Our findings suggested that iTalkBetter significantly improves PWAs' naming ability on trained items. The effect size is similar to a previous RCT of computerised therapy, but this is the first study to show transfer to a naturalistic speaking task. iTalkBetter usage and dose caused observable changes in brain structure and function to key parts of the surviving language perception, production and control networks. iTalkBetter is being rolled-out as an app for all PWA and anomia: https://www.ucl.ac.uk/icn/research/research-groups/neurotherapeutics/projects/digital-interventions-neuro-rehabilitation-0 so that they can increase their dosage of practice-based SLT. National Institute for Health and Care Research, Wellcome Centre for Human Neuroimaging.