Self Referencing and Self Projecting

 Within the context of multisensory integration, self-referencing and self-projecting skills play important roles in our perception of time. 

• Self-Referencing:  general capacity of using one's own position in time to estimate/situate events in time. This skill relies on internal cues such as memory and self-awareness to place events within a temporal framework. By referencing our own experiences and the temporal context in which they occurred, we can make sense of the timing and sequence of events in our environment.
• Self-Projecting: ability to mentally move back and forward in time, maintaining the competence of correctly situating events in time. This skill allows us to anticipate future events, plan our actions, and make decisions based on the temporal context. Self-projecting skill involves mental time travel, where we can mentally simulate and project ourselves into different points in time, drawing upon past experiences and knowledge to predict and shape future events.

Both self-referencing and self-projecting skills are closely intertwined with our sensory experiences. Our senses provide us with temporal information through various cues. For example, visual stimuli provide temporal cues through motion and changes in spatial patterns, while auditory stimuli provide temporal cues through changes in pitch, intensity, and rhythm. By integrating these sensory cues with our self-referencing and self-projecting abilities, we can accurately perceive and situate events in time.

Depersonalisation Disorder

 Depersonalisation Disorder (DPD) is a condition characterized by distressing feelings of being 'spaced out', detached from one's self, body, and the world, as well as atypical 'flat' time perception. Individuals describe feeling as if they are observing themselves from a distance. In addition there are often alterations in perception, including disruptions in the perception of time.

DP is the third most common psychological symptom in the general population (after anxiety and low mood).

Research findings on DP and atypical time perception,

• Distorted perception of time: Research indicate a tendency to overestimate the duration of time intervals, perceiving time as slower than it actually is which can contribute to the overall sense of detachment and disconnection (Simeon et al., 2008; Sierra & David, 2011)
• Neural correlates of time perception in depersonalization: fMRI studies shown differences in brain activity and connectivity patterns in regions associated with time processing, such as the prefrontal cortex and parietal cortex. (Lemche et al., 2007; Simeon et al., 2013)
• Role of attentional processes: Studies show difficulties in allocating attention appropriately, leading to a reduced ability to accurately perceive and process temporal information. (Ainley et al., 2017; Segal & Lynn, 2019)
• Emotional factors: Studies have found that emotional states, such as anxiety and stress, can modulate time perception, leading to temporal distortions. Individuals with DP often experience heightened levels of anxiety and emotional distress, which may contribute to their altered perception of time. (Simeon et al., 2008; Sierra & David, 2011)

Caveat: This is a complex phenomena and further research is needed to fully understand their underlying mechanisms.

References
Simeon, D., Guralnik, O., Schmeidler, J., Sirof, B., Knutelska, M., & Hollander, E. (2008). The role of childhood interpersonal trauma in depersonalization disorder. The American Journal of Psychiatry, 165(7), 897-903.
Sierra, M., & David, A. S. (2011). Depersonalization: A selective impairment of self-awareness. Consciousness and Cognition, 20(1), 99-108.
Lemche, E., Anilkumar, A. P., Giampietro, V. P., Brammer, M. J., Surguladze, S. A., Lawrence, N., ... & Phillips, M. L. (2007). Cerebral and autonomic responses to emotional facial expressions in depersonalization disorder. The British Journal of Psychiatry, 191(6), 531-539.
Simeon, D., Gross, S., Guralnik, O., Stein, D. J., Schmeidler, J., & Hollander, E. (2013). Temporal lobe structure abnormalities in depersonalization disorder. Journal of Psychiatric Research, 47(7), 893-897.
Ainley, V., Maister, L., Brokfeld, J., Farmer, H., & Tsakiris, M. (2017). More of myself: Manipulating interoceptive awareness by heightened attention to bodily and narrative aspects of the self. Consciousness and Cognition, 49, 289-301.
Segal, E. R., & Lynn, S. J. (2019). Time perception in dissociative disorders: A meta-analysis. Consciousness and Cognition, 71, 64-76.

Qi 2023 Neural Dynamics of Causal Inference in the macaque frontoparietal circuilt

Key Takeaways

• This paper investigates how the brain represents and updates the hidden causal structure between visual and proprioceptive signals during multisensory processing. 
• Monkeys can combine previous experience and current multisensory signals to estimate the hidden common source of visual and proprioceptive signals. 
• The premotor cortex integrates previous experience and sensory inputs to infer hidden variables, while the parietal cortex updates the sensory representation to maintain consistency with the causal inference structure. 
• The dynamic loop of frontal-parietal interactions provides a potential neural mechanism for understanding how circuits represent hidden structures related to body awareness and agency. - 
• Premotor neurons integrate bimodal information for small disparities and segregate the information for large disparities between proprioceptive and visual information. 
• Parietal cells show reaching tuning changes that support the updating sensory uncertainty between tasks.

Intro

discusses how the brain infers the hidden causal structure of the environment during natural perception. explains that the brain combines information from multiple sensory inputs to infer the properties of a single entity based on the quality and uncertainty of the sensory stimuli. The example of the ventriloquism illusion is used to illustrate this concept.

Lit Review: 

cites several previous studies that have investigated the neural mechanisms underlying multisensory integration and causal inference. These studies have shown that the brain combines information from multiple sensory inputs to infer the properties of a single entity based on the quality and uncertainty of the sensory stimuli. The paper also discusses the role of the premotor and parietal cortices in multisensory processing and how they contribute to the representation and updating of the hidden causal structure.

Methods

• Uses a virtual reality system to train monkeys to infer the probability of a potential common source from visual and proprioceptive signals based on their spatial disparity. 
• Involves single-unit recordings in the premotor and parietal cortices to investigate the neural mechanisms and functional circuits essential for representing and updating the hidden causal structure and corresponding sensory representations during multisensory processing.

Results

• monkeys were able to combine previous experience and current multisensory signals to estimate the hidden common source and subsequently update the causal structure and sensory representation. 
• Single-unit recordings revealed that neural activity in the premotor cortex represents the core computation of causal inference, characterizing the estimation and update of the likelihood of integrating multiple sensory inputs at a trial-by-trial level. 
• In response to signals from the premotor cortex, neural activity in the parietal cortex also represents the causal structure and further dynamically updates the sensory representation to maintain consistency with the causal inference structure. 
• This dynamic loop of frontal-parietal interactions in the causal inference framework may provide the neural mechanism to answer long-standing questions regarding how neural circuits represent hidden structures for body awareness and agency.

Conclusions

• premotor cortex integrates previous experience and sensory inputs to infer hidden variables and selectively updates sensory representations in the parietal cortex to support behavior. 
• provides insights into the neural mechanisms and functional circuits essential for representing and updating the hidden causal structure and corresponding sensory representations during multisensory processing. 
• findings suggest that the dynamic loop of frontal-parietal interactions in the causal inference framework may provide the neural mechanism to answer long-standing questions regarding how neural circuits represent hidden structures for body awareness and agency.

Limitations

• generalizability of findings from monkeys to humans. 
• study focused on one specific type of MSI task, but different tasks may involve different neural mechanisms. 
• translation of VR to real-world. '

How do monkeys combine previous experience and current multisensory signals to estimate hidden common sources, as demonstrated by proprioceptive drift reported in this study?

In this study, monkeys were trained to perform a reaching task in a VR environment where visual and proprioceptive signals were dissociated. The monkeys were able to combine previous experience and current multisensory signals to estimate the hidden common source of the two signals. This was demonstrated by the proprioceptive drift reported in the study, which showed that the monkeys updated their estimate of the common source based on the spatial disparity between the visual and proprioceptive signals. The monkeys used this information to update their causal structure and sensory representation, allowing them to perform the reaching task more accurately.

In what ways does the dynamic loop of frontal-parietal interactions observed during causal inference provide a potential neural mechanism for understanding how circuits represent hidden structures related to body awareness and agency?

The dynamic loop of frontal-parietal interactions observed during causal inference provides a potential neural mechanism for understanding how circuits represent hidden structures related to body awareness and agency in several ways. 

1. premotor cortex integrates previous experience and sensory inputs to infer hidden variables, such as the common source of visual and proprioceptive signals.

2. the parietal cortex updates the sensory representation to maintain consistency with the causal inference structure. 

3. frontal-parietal interactions allow for the selective updating of sensory representations based on the estimated causal structure, which is critical for accurate behavior. 

Overall, the dynamic loop of frontal-parietal interactions provides a potential neural mechanism for understanding how circuits represent hidden structures related to body awareness and agency.

What are the key novel results of this study regarding premotor neurons' representation of bimodal information for small disparities versus segregation for large disparities between proprioceptive and visual information?
they integrate the information for small disparities between proprioceptive and visual information, while segregating the information for large disparities. This suggests that premotor neurons play a critical role in the estimation and updating of the likelihood of integrating multiple sensory inputs at a trial-by-trial level.

Questions

- How do the findings of this study contribute to our understanding of the neural mechanisms underlying multisensory processing?
- How might the findings of this study be relevant to the development of prosthetic devices or other technologies that rely on multisensory integration? - What are the potential implications of this study for our understanding of body awareness and agency? - Are there any limitations to the methods used in this study, and if so, how might they be addressed in future research?

Rosenthal 2023 S1 represents multisensory contexts

S1 represents multisensory contexts and somatotopic locations within and outside the bounds of the cortical homunculus

Summary: "Rosenthal et al. examine the arm region of the human primary somatosensory cortex and show that it is modulated by vision during physical touches but is unresponsive during passive visual observation alone. They also show that this region encodes information from two distinct body locations, despite the area’s classical topographic organization.

• We examine human multi-unit electrophysiological data during visuotactile touches
• Vision strongly modulates S1 activity during physical touches d S1 does not respond to passively viewing touches with no physical stimulus
• Despite classic S1 topography, arm and finger are both represented in S1 arm area"

Reference: 

Isabelle A. Rosenthal, Luke Bashford, Spencer Kellis, Kelsie Pejsa, Brian Lee, Charles Liu, Richard A. Andersen,

S1 represents multisensory contexts and somatotopic locations within and outside the bounds of the cortical homunculus,

Cell Reports,

Volume 42, Issue 4,

2023,

112312,

ISSN 2211-1247,

https://doi.org/10.1016/j.celrep.2023.112312.

Li 2014: CRH Autism

Li, H., Zhong, X., Chau, K. F., Williams, E. C., Chang, Q., & Xu, B. (2014). MeCP2 binds to CRH and regulates anxiety-related behaviors. Nature Neuroscience, 17(12), 1637–1645. doi: 10.1038/nn.3866 

This study investigated the role of the protein MeCP2, which is mutated in Rett Syndrome, a neurodevelopmental disorder that is often comorbid with autism spectrum disorder (ASD), in regulating anxiety-related behaviors.

The researchers found that MeCP2 binds to corticotrophin-releasing hormone (CRH) and regulates its expression in the paraventricular nucleus of the hypothalamus (PVN), a region of the brain known to be involved in stress and anxiety. They also found that mice with a mutation in MeCP2 had hyperactivity of CRH-PVN neurons and exhibited increased anxiety-like behaviors.

The study suggests that the hyperactivity of CRH-PVN neurons may contribute to the anxiety and social deficits observed in Rett Syndrome and possibly in ASD. The findings also highlight the importance of MeCP2 in regulating the expression of genes involved in the development and function of the brain.

Lee et al 2014 . GABAergic inhibition and Sleep-Wake cycle

 

1. Lee, E., Lee, J., Kim, E., Park, J., & Kim, Y. (2014). GABAergic inhibition of histaminergic neurons regulates active waking but not the sleep-wake switch or propofol-induced loss of consciousness. Nature Communications, 5, 4249. doi: 10.1038/ncomms5249

The study by Lee et al. (2014) investigated the role of GABAergic inhibition on histaminergic neurons in regulating sleep and wakefulness. The researchers focused on two specific aspects of sleep-wake regulation: active waking and the sleep-wake switch.

Active waking is a state of wakefulness in which an individual is alert and engaged in various activities, whereas the sleep-wake switch refers to the transition between sleep and wakefulness. The researchers used mice as a model organism and performed experiments to investigate the effects of GABAergic inhibition on these two aspects of sleep-wake regulation.

The researchers found that GABAergic inhibition of histaminergic neurons played a significant role in regulating active waking, but had no effect on the sleep-wake switch or propofol-induced loss of consciousness. This suggests that the histaminergic system may be specifically involved in regulating the state of active waking, which is characterized by increased attention and motor activity.

Overall, this study sheds light on the complex neural mechanisms involved in sleep-wake regulation and highlights the importance of GABAergic inhibition in controlling specific aspects of wakefulness. The findings have potential implications for the development of new treatments for sleep disorders and disorders of consciousness.

Zhong et al. (2019) : Autism and CRH-PVN

Zhong, S., Zhang, S., Fan, X., Wu, Q., Yan, L., Dong, J., & Li, L. (2019). Paraventricular nucleus corticotrophin-releasing hormone neurons mediate social behavioral deficits in a mouse model of autism. Translational Psychiatry, 9(1), 26. doi: 10.1038/s41398-019-0385-1 can you expand on findings in this paper

The study by Zhong et al. (2019) aimed to investigate the role of CRH-PVN neurons in social behavior deficits in a mouse model of ASD. The researchers used a mouse model that had a mutation in the SHANK3 gene, which is a gene that has been strongly associated with ASD in humans.

The authors found that mice with the SHANK3 mutation had alterations in the activity of CRH-PVN neurons compared to wild-type mice. Specifically, they found that the SHANK3 mutant mice had decreased activity of CRH-PVN neurons during social interaction compared to wild-type mice. They also found that optogenetic stimulation of CRH-PVN neurons in the SHANK3 mutant mice rescued social interaction deficits, suggesting that the deficits were due to the altered activity of these neurons.

The authors further showed that the social behavior deficits in the SHANK3 mutant mice were mediated by the CRH-PVN system. They found that infusion of a CRH antagonist into the PVN of the SHANK3 mutant mice improved social interaction deficits, while infusion of CRH into the PVN of wild-type mice impaired social interaction.

Overall, these findings suggest that alterations in the activity of CRH-PVN neurons may contribute to social behavior deficits in ASD. The study highlights the potential of targeting the CRH-PVN system as a therapeutic approach for ASD.