Chalas 2023 - Hierarchically nested networks

s: Chalas, N., Omigie, D., Poeppel, D., van Wassenhove, V., Hierarchically

nested networks optimize the analysis of audiovisual speech, ISCIENCE (2023), doi: https://

doi.org/10.1016/j.isci.2023.106257.

What's unique about this paper

• This paper is unique in that it provides an analysis of large-scale oscillatory networks operating at multiple temporal scales, and how they respond to changes in their environment. 

• Additionally, the research presented here explores ways these systems can be modulated or controlled by external factors as well as methods for predicting and controlling network behavior based on data from recordings taken over time.

Takeaways

• Large-scale oscillatory networks operating at multiple temporal scales are sensitive to the external environment. 
• Oscillations refer to a regular, repeating pattern of activity in neurons or neural circuits over time. 
• Temporal scale refers to how quickly these patterns occur - for example, some may happen very rapidly (on the order of milliseconds) while others might take much longer (seconds or minutes).
• This sensitivity means that changes in environmental conditions can have an effect on their behavior and performance.

Data Used

• • The data used for experiments in this research paper includes recordings of neural activity from large-scale networks operating at multiple temporal scales.
• • This could include measurements such as the firing rate or frequency of neurons, electrical signals between different parts of a network, and other types of information about how these systems are functioning over time.

Main Approaches

• The main approaches discussed in this research paper are related to understanding how large-scale oscillatory networks operating at multiple temporal scales respond to changes in their environment. 
• This includes looking at the effects of different types of stimuli on these systems, as well as exploring ways that they can be modulated or controlled by external factors. 
• Additionally, researchers have studied methods for predicting and controlling network behavior based on data from recordings taken over time

Results

The results of this paper indicate that large-scale oscillatory networks operating at multiple temporal scales are sensitive to changes in their environment, and can be modulated or controlled by external factors. Additionally, researchers have developed methods for predicting and controlling network behavior based on data from recordings taken over time.

What does this paper conclude

This paper concludes that large-scale oscillatory networks operating at multiple temporal scales are sensitive to changes in their environment, and can be modulated or controlled by external factors. Additionally, researchers have developed methods for predicting and controlling network behavior based on data from recordings taken over time.

Peng 2018 - Sonic Hedgehog and Axon Guidance

hiasm guidance

• Shh protein is produced by RGCs in the retina and transported to the optic chiasm
• Shh is secreted by contralateral RGCs at the chiasm to repel ipsilateral RGCs
• The repulsive effect of Shh requires the receptor Boc and signaling mediator Smo
• Remotely produced cues and axon-axon interactions are important in chiasm guidance

• The research paper focuses on the role of Shh protein in guiding retinal ganglion cells (RGCs) to their target at the optic chiasm. 

• It is found that RGCs produce and secrete this protein, which acts as a repellent for other contralateral RGCs present at the chiasm. 

• This repulsive effect requires two components: Boc receptor and Smo signaling mediator.

• Additionally, it has been observed that both remote cues from outside sources and axon-axon interactions are important factors influencing guidance of these cells towards their destination.

 

Uesaka 2016 - Innervation of the Gut

Key takeways
• The gastrointestinal (GI) tract is innervated by intrinsic enteric neurons and extrinsic efferent and afferent nerves. 

• Enteric nervous system (ENS) consists of two main ganglionated layers; myenteric and submucosal ganglia, containing numerous types of enteric neurons and glial cells. 

• Axons arising from the ENS regulate many gut functions while Schwann cell precursors along with a subpopulation of cells in myenterics migrate to form the mucosal region for further development.
• Recent progress has been made in understanding molecular mechanisms that control both intrinsic as well as extrinsc innervation processes during GI tract's development

 

Muktar 2018 - Untangling Cortical Complexity During Development

 

• the cerebral cortex is composed of billions of morphologically and functionally distinct neurons which need to be precisely organized during development in order for them to encode complex cognitive functions.
• Paper also discussed various methods used for characterizing neuronal subtypes such as fate mapping, genome-wide analysis and transcriptome profiling which can help us better understand cortical complexity.
• Insight into how these findings could be applied practically when studying neurological disorders or memory formation by understanding precisely how different types of neurons interact with each other during development.

Fate mapping is a method used to characterize neuronal subtypes in the cerebral cortex. It involves tracing and labeling cells of different types during development, which allows researchers to track their fate as they differentiate into distinct cell types. This technique can be used to study how neurons are produced, organized and connected during development in order for them encode complex cognitive functions such as memory formation or neurological conditions like autism.

Marin 2003 - Cell Migration in the Forebrain

 

Key takeaways

• The forebrain is composed of intricate structures that are necessary for the most advanced functions in mammals.
• Two types of migrations take place in the forebrain: radial migration and tangential migration.
• Radial migration creates a cytoarchitectonical framework, while tangential migration increases complexity by allowing dispersion of multiple neuronal types.
• This paper reviews cellular and molecular mechanisms underlying these two forms of migrations to understand how they shape normal or pathological development in the forebrain.

Steop 2021 - Addictive Nature of Human Multisensory Evoked Pupil Response

Van der Stoep N, Van der Smagt MJ, Notaro C, Spock Z, Naber M. The additive nature of the human multisensory evoked pupil response. Sci Rep. 2021 Jan 12;11(1):707. doi: 10.1038/s41598-020-80286-1.

Abstract: This research paper investigates the nature of multisensory pupil response by combining methodological approaches from previous studies and using suprathreshold stimuli, to determine whether it is linear (additive) or non-linear (sub-additive/super-additive).

Key Takeaways

• The study investigated the nature of multisensory pupil response by combining methodological approaches from previous studies and using suprathreshold stimuli. It provides insights into how spatial orienting may be modulated by saliency, focused spatial attention or motor coordination through changes in pupil size.
• Reaction time (RT) data indicated MSI as shown by race model inequality violation, however, the multisensory pupil response in both experiments could best be explained by linear summation of unisensory responses. 
• This suggests that for supra-threshold stimuli, the multisensory pupil response is additive in nature and cannot be used to measure MSI since only a departure from additivity can unequivocally demonstrate an interaction between senses.

Data Used: two experiments were conducted using auditory and visual stimuli to evoke an (onset) response in observers. The data used for these experiments included reaction time (RT), unisensory  pupil responses, and multisensory pupil responses

Main Approaches in Paper

• Combining methodological approaches from previous studies and using suprathreshold stimuli to investigate the nature of multisensory pupil response.Comparing RT data with unisensory and multisensory responses, as deviations from additivity can be used as a strict criterion for MSI.
• Investigating whether spatial orienting is modulated by saliency, focused spatial attention or motor coordination through changes in pupil size.

Results in Brief

• Results indicate that the multisensory pupil response for supra-threshold stimuli is additive in nature and cannot be used as a measure of MSI, since only a departure from additivity can unequivocally demonstrate an interaction between senses. 
• Additionally, it was found that spatial orienting may be modulated by saliency, focused spatial attention or motor coordination through changes in pupil size.

IN MORE DETAIL

INTRO  

Multisensory integration (MSI) is the process by which the brain combines information from different senses to create a unified perception.

MSI importance in spatial orienting eg:  using senses to quickly determine the location of an approaching car when crossing the street and adjust our actions accordingly.

MSI mediated by SC, which contains multisensory neurons that respond to input from different sensory modalities and contribute to the multisensory enhancement of orienting behavior. 

• Integrates sensory input and generates eye-movements to unisensory and multisensory events. 
• Involved in  transient changes in pupil size. It has been suggested that the pupil’s response to sensory events plays an important role in orienting responses as it is modulated by saliency, focused spatial attention, and motor coordination.
• The underlying neural computation of these multisensory neural responses has been characterized as linear (additive: equal to the sum of the unisensory responses), or non-linear (i.e. sub-additive: less than the sum, or super-additive: larger than the sum).

Implications of the multisensory pupil response for clinical applications.

• If the multisensory pupil response is larger or smaller than the linear sum of the unisensory pupil responses, then it can be concluded that the multisensory response is driven by integrated sensory input and that patients can integrate sensory input.
• However, if the multisensory pupil response is additive, then it is likely that the observed multisensory behaviour is the result of the independent processing of sensory input. This is important for clinical applications, as it can be used to determine whether a patient is able to integrate sensory input.

Conflicting results of research on the nature of the multisensory pupil response.

The multisensory pupil response is the change in pupil size in response to a multisensory stimulus. 

Previous research in monkeys showed the multisensory pupil response to AV (audiovisual) events to be additive or sub-additive. But recent study in humans suggests super-additive responses which suggests that multisensory pupil response is driven by MSI. 

Conflicting results suggest that more research needed. 

Experiment 1: (Figure 1 left panel)

Figure 1 (left panel) shows the experimental setup for Experiment 1. Twelve Participants took part in a response and no-response block, where they were instructed to respond as fast as possible to an onset of sound or light in the response block, and passively observe stimuli in the no-response block. Pupil responses were recorded during both blocks. 

Experiment 2: Figure 1, right panel 
Figure 1 (right panel) illustrates the experimental setup for Experiment 2 which was similar to that of experiment one but with two additional conditions: bright visual stimulus and dark visual stimulus. In this second experiment participants had to respond when there was a change detected either visually or auditorily while withholding their response if neither changed occurred

Results of Experiment 1

Figure 2 shows the results of Experiment 1. The left panel illustrates response times (RTs) for auditory only, bright visual stimulus and dark visual stimulus conditions in both active and passive blocks. It can be seen that responses were faster to multisensory stimuli than unisensory stimuli - i.e., RTs decreased when a sound was presented together with either a bright or dark target compared to just one modality alone (A > AV Bright , A > AV Dark ). The right panel displays pupil size data from Experiment 1 which indicates an additive nature of the multisensory pupil response as there is no significant difference between audiovisual targets versus sum of uni-modal targets across all participants tested in this experiment (AV vs cSum). This suggests that any observed speedup due to MSI could not explain beyond statistical facilitation effects such as crossmodal spatial attention or switch costs etc..

Response Time: The results of the experiments were measured using response times (RTs). The results indicated that participants responded faster to multisensory stimuli than unisensory stimuli in both the bright and dark stimuli.

The results were measured using a Bayesian repeated measures Analysis of Variance (ANOVA) and post-hoc tests corrected for multiple testing. This ANOVA indicated very strong evidence for an effect of Target Modality compared to a null model assuming no effect. Essentially results indicated that responses in the multisensory conditions were faster than in the unisensory conditions, for both the dark and light conditions.

Multisensory Response Enhancement (MRE)

Multisensory response enhancement (MRE) is a measure of the speed-up in the multisensory condition relative to the fastest unisensory condition.

It is calculated by analysing the grey area in Figure 2B . Bayesian one-sample t-tests were used to determine if the MRE was larger than zero in the bright and dark AV target condition. The results showed that the MRE was larger than zero in both conditions, with a mean of 30 ms and 39 ms in the bright and dark conditions respectively.

There was only anecdotal evidence for a difference in the amount of MRE between the dark and bright condition.

To investigate whether the observed MRE could be explained by statistical facilitation, the cumulative response time distribution in the multisensory condition (the blue line in Figure 2C) was compared to the sum of the unisensory cumulative RT distributions (the race model, the black line in Figure 2C). If responses in the multisensory condition are faster than the upper limit of the race model, the race model inequality is violated.

This means that multisensory response enhancement cannot be explained by independent processing of sensory input, which is indicative of MSI

Bayesian one-sided one-sample t-tests indicated there was strong evidence for the amount of RMI violation being larger than zero in both the Dark and Bright condition (see Figure 2E).

RMI violation was observed both for dark and bright AV targets There was no evidence for or against a difference in violation area between the Dark and Bright condition.

Race model inequality (RMI) violation is a measure used to determine whether multisensory response enhancement can be explained by independent processing of sensory input. 

It compares the cumulative response time distribution in the multisensory condition with that of the sum of unisensory responses, known as race models. 

If responses in the multisensory condition are faster than those predicted by race models, then RMI has been violated and this indicates that there is evidence for MSI - i.e., an interaction between senses which cannot simply be explained away through statistical facilitation or other processes such as crossmodal spatial attention or switch costs.

"Overall, these results are indicative of MSI in the Bright and Dark target condition as the observed multisensory response enhancement cannot simply be explained by independent processing of sensory input"

Pupillometry Data 

Discusses the pupillometric measures used to investigate the nature of multisensory pupil response. Pupil size is a measure that can be used as an indicator for various sensory processes, and has been shown to be modulated by saliency, focused spatial attention or motor coordination. 

In this study, eye-link 1000 was used to collect eye position and pupil size data from participants' right eyes with a sampling rate of 1000 Hz in order to compare responses between unisensory (sound/light alone) stimuli and audiovisual targets presented simultaneously. The amount of speed-up in the multisensory condition relative to fastest uni-sensual condition was analysed using Multivariate Response Enhancement (MRE). Additionally, Bayesian one sample t tests were conducted on RMI violation area which compared cumulative RT distributions under different conditions - i.e., whether MSI could explain any observed differences beyond statistical facilitation effects such as crossmodal spatial attention or switch costs etc..

Discussion Expt 1

The results of the experiment showed that the response time data indicated multisensory integration (MSI) as shown by race model inequality violation. However, the multisensory pupil response in both experiments could best be explained by linear summation of the unisensory pupil responses. This means that the multisensory pupil response was equal to the sum of the unisensory pupil responses, indicating that the multisensory pupil response is additive in nature. This suggests that the multisensory pupil response may not be a good measure of MSI in populations for which response time data collection is not feasible. The researchers then conducted a second experiment using a change-detection paradigm similar to a previous study that did show super-additivity of the multisensory pupil response. The second experiment was conducted to further investigate the nature of the multisensory pupil response.

Experiment 2

Figure 3 shows the results of Experiment 2. The left panel illustrates response times (RTs) for auditory only, bright visual stimulus and dark visual stimulus conditions in both active and passive blocks. It can be seen that responses were faster to multisensory stimuli than unisensory stimuli - i.e., RTs decreased when a sound was presented together with either a bright or dark target compared to just one modality alone (A > AV Bright , A > AV Dark ). The right panel displays pupil size data from Experiment 2 which indicates an additive nature of the multisensory pupil response as there is no significant difference between audiovisual targets versus sum of uni-modal targets across all participants tested in this experiment (AV vs cSum). This suggests that any observed speedup due to MSI could not explain beyond statistical facilitation effects such as crossmodal spatial attention or switch costs etc..

Figure 4 shows the results of a Bayesian repeated measures Analysis of Variance (ANOVA) for RTs in the dark target condition. It indicates very strong evidence for an effect of Target Modality compared to a null model assuming no effect (A, V Dark , AV Dark ; BF 10 = 625,233). Post-hoc tests corrected for multiple testing indicated that responses in the AV Dark condition were faster than those observed with either unisensory stimulus alone - i.e., A or VDark conditions (M = 249 ms vs 285 and 319 respectively; SD's 40/47/57). This demonstrates additivity of the audiovisual pupil response as there is no significant difference between multisensory targets versus sum of unimodal targets across all participants tested in this experiment.

Figure 5 shows the results of a Bayesian one sample t test on RMI violation area which compared cumulative RT distributions under different conditions. It indicates that there was no significant difference between audiovisual targets versus sum of uni-modal targets across all participants tested in this experiment (AV vs cSum). This suggests that any observed speedup due to MSI could not explain beyond statistical facilitation effects such as crossmodal spatial attention or switch costs etc..

Terms Explained

• MSI: Multisensory Integration
• SC: subcortical structure that contains multisensory neurons that respond to input from different sensory modalities and contribute to the multisensory enhancement of orienting behavior
• Additivity: measure of how the multisensory response compares to the sum of the unisensory responses
• Additive Response: If the multisensory response is equal to the sum of the unisensory responses
• SubAdditive Response: If the multisensory response is less than the sum of the unisensory responses. 
• SuperAdditive Response:  If the multisensory response is larger than the sum of the unisensory responses

Monahan 2022 - CCAPS-62

The paper discusses the internal validity and measurement invariance of a psychological symptom assessment tool (CCAPS-62) among autistic and non-autistic college students. 

Key Takeaways

• The CCAPS-62 had a strong model fit and was invariant across groups.
• This study examined these two concepts in relation to autistic college students compared with non-autistic college students using the Counseling Center Assessment of Psychological Symptoms (CCAPS)-62 item tool.

The main approach discussed in this paper is confirmatory factor analysis, which is a statistical technique used to evaluate the internal validity and measurement invariance of an instrument. This method was applied to data from 1,268 autistic college students and 3,776 non-autistic college students using the Counseling Center Assessment of Psychological Symptoms (CCAPS)-62 item tool.

The results of this paper indicate that the CCAPS-62 had a strong model fit and was invariant across groups, meaning it is suitable for use with both autistic and non-autistic college students. This suggests that scores on the same construct are comparable between different populations or subgroups of people, indicating good internal validity and measurement invariance for this instrument.

This paper has contributed to the understanding of how well an instrument such as CCAPS-62 can be used across different populations or subgroups.

• Internal validity refers to the extent that an instrument measures what it is intended to measure, while measurement invariance means that scores on the same construct are comparable between different populations or subgroups of people.

 

Larkum 2018 - Cortical Layering

A Perspective on Cortical Layering and Layer-Spanning Neuronal Elements

Larkum Matthew E., Petro Lucy S., Sachdev Robert N. S., Muckli Lars

Frontiers in Neuroanatomy, 2018

https://www.frontiersin.org/articles/10.3389/fnana.2018.00056
10.3389/fnana.2018.00056

 

Key takeaways 

• Reevaluate understanding of layered structures in cortex. Move from viewing it as simply an arrangement of neurons and synaptic connections, to one which considers its functional anatomy. 
• Consider not only where axons terminate but also biophysical properties such as dendritic integration processes spanning multiple layers simultaneously when interpreting any given recordings or explaining behavior observed using fMRI techniques. 
• Develop models that can accurately describe how inputs between and within cortical areas are transformed into laminar specific output signals throughout the system by examining all cell types of cortex, capturing their properties, understanding how they integrate synaptic inputs etc. 
• Application: The interpretation of BOLD signals from high-res fMRI recordings, for instance, cannot be attributed simply to the spiking neuronal activity occurring in the same layers as the BOLD signal.
With the increased spatial resolution of fMRI in recent years, BOLD is now measured at different cortical depths and can therefore be used to characterize the summed energy consumption in different layers of cortex. That is, fMRI, which measures BOLD contrast, is better linked to neuronal activity by summed energy consumption than by spiking neuron output.

• 
  

Conceptual Shift:  

This perspective requires us to look beyond simple point-to-point connectivity between cells and instead consider complex interactions occurring across various compartments throughout layer structures when interpreting any given recordings or explaining behavior observed in humans subjects using high resolution fMRI techniques

What Defines a layer as a subunit of function? Need to understand cortical layering in terms of its functional anatomy. It suggests that we should consider not only where axons terminate but also take into account biophysical properties such as dendritic integration processes spanning multiple layers simultaneously, or local field potentials measuring energy related synaptic currents located directly abutting terminal sites rather than spiking neuron outputs from cell bodies.

This means looking beyond simple point-to-point connectivity between cells and instead considering complex interactions occurring across various compartments throughout layer structures when interpreting any given recordings or explaining behavior observed in humans subjects using high resolution fMRI techniques.

Figure 1 talks about the shifting perspectives with respect to Cortical Layering. Fig 1A looks at the 19th-20th century perspectives which focused on cytoarchitecture and neuronal projections. Fig 1B looks at the advances in  functional anatomy or connectivity, but this came at the expense of complexity of components, which were treated as simple point neurons. Fig 1C is a combined perspective that takes into account the broad influences of long- and short-range connectivity and the functional components that span multiple layers simultaneously. Neurons can be considered to be the functional components of the cortex. Neurons have dendritic compartments, which span across the cortical layers, meaning that they can receive inputs from neurons in different layers. Small boxes represent neurons that can still be described as point neurons, meaning that they are relatively simple and can be described with a single point. The functional components (compartmental neurons) that span multiple layers simultaneously should also be taken into account. This means that the neurons should be able to receive inputs from neurons in different layers and process them in order to produce an output.The proposed combined perspective should take into account the broad influences of long-range (Feedback, red and Feed forward, blue) and short-range (Recurrent, green) connectivity.

The Somato-Centric-Perspective 

(Box1)  in layered structures was first proposed by Cajal in 1894 and states that information flows unidirectionally across neurons in the nervous system. This has led to a reductive description of how neurons operate, which is apparent in the way neuroscientists talk about "activity" in the brain and the way the operation of neurons is formalized.

The somato-centric perspective is based on the idea that axon terminals are the "input" to a neuron, and the neuron's "output" is emitted from its "conceptual cell body". This perspective has been used for more than a century because it fits the intuition that cell bodies are both physically prominent and provide a convenient locus for recording action potentials. However, recent advances in optical methods have revealed that the output of a neuron almost always manifests as the release of transmitter at the axon terminals, while the input is best described as synaptic currents located directly abutting the terminals.

Brings us the last key takeaway around fMRI interpretation. 

With the advent of methodologies that can more precisely resolve the layering of the cortex, it becomes necessary to shift perspectives in order to interpret the signals. Because of the close apposition of input and output, the choice of label for synapses reduces quickly to semantics. It is increasingly well understood that the "function" of a neuron (i.e., the transformation from input to output) occurs via the process of dendritic integration that in the cortex frequently occurs in active and layer-spanning dendritic trees. Thus, the computation of a cortical neuron is actually a complex spatio-temporal phenomenon that transforms inputs arriving over various layers to output delivered to various other layers. In this transformation process, the cell body specifies neither the location of the processing nor the output of the neuron and could in principle be collapsed to a dimensionless node in any specific layer without substantially changing the input/output function of the neuron. It is therefore not correct from a functional perspective to attribute the "activity" of a cell to the layer in which the cell body is located. 

The suggestion is therefore that we should move away from viewing it as simply an arrangement of neurons and synaptic connections, to one which takes into account its functional anatomy - i.e., how inputs arriving at different layers affect activity within cortex. This means considering not only where axons terminate but also taking into account biophysical properties such as dendritic integration processes spanning multiple layers simultaneously, or local field potentials measuring energy related synaptic currents located directly abutting terminal sites rather than spiking neuron outputs from cell bodies . The Box 1 diagram further highlights both potential gains that can be made by informed interpretations as well as pitfalls when proceeding without such understanding; it is ideal for a case study because unlike traditional fMRI this approach calls for an appreciation of cortical operation from laminar perspective. 

Combining Components & Connectivity in Describing Cortex. 
 

The suggestion is that we should consider not only where axons terminate but also take into account biophysical properties such as dendritic integration processes spanning multiple layers simultaneously, or local field potentials measuring energy related synaptic currents located directly abutting terminal sites rather than spiking neuron outputs from cell bodies. 

This means looking beyond simple point-to-point connectivity between cells and instead considering complex interactions occurring across various compartments throughout layer structures when interpreting any given recordings or explaining behavior observed in humans subjects using high resolution fMRI techniques. The goal is to develop a model which can accurately describe how inputs between and within cortical areas are transformed into laminar specific output signals throughout the system.

Figure 2  looks at approaches for combining components with cortical layering.  It is based on a hypothesis for the possible ramifications of the associative properties of cortical pyramidal neurons with dendritic calcium spikes at the network level. This hypothesis suggests that the active properties of the apical dendrites associate feed-forward and feedback information streams arriving at different layers.  Figure 2A shows this hypothesis with blue arrows indicating feed forward information streams and red arrows indicating feedback. Figure 2B shows the missing components (gray) needed for an expanded theory of the one shown in part A. This expanded theory should include the intrinsic properties of neurons, dendrites and synaptic inputs. Feedback and feed forward axonal input are indicated with red and blue lines, respectively. Figure 2C provides an example of abstractions of neurons needed for new theories within the new perspective. Here, A = dendrites and B = Somata. 

A Case Study using Ultra-High Resolution fMRI

Ultra-High res fMRI can be used to separate different depth layers in cortex can lead to appreciation of cortical operation from laminar perspective. 

Underpinning these functional cognitive brain imaging studies, there exists a broad field advancing laminar differences in cerebral blood flow, neurovascular coupling, vascularity etc which will all benefit from interpreting layer specific data taking into account synaptic inputs distribution across layers as well as properties of neurons spanning multiple layers. The promise here lies in allowing us insight into how feed forward and feedback pathways interact during complex cognitive states like emotion or mental time travel using non invasive approaches since we are not yet able to image at cellular/subcellular level nor manipulate specific pathways optogenetically.

Figure 3 discusses use of high-res fMRI   to measure cortical depth levels in the human brain. It explains that fMRI can be used to label voxels (small 3D cubes) according to their cortical depth, and that this can be used to separate the layers of the cortex into upper, middle, and lower layers. This is done by separating feedforward and feedback processing, which are two different types of neural pathways.

The figure also says that while the bands of cortical depth measured with fMRI are still insufficient to separate all six anatomical layers of the cortex, there are important gradients that are functionally different in their processing of internal mental states. This means that, even though the bands of cortical depth measured with fMRI are not enough to separate all six layers of the cortex, they can still be used to measure important gradients in the processing of internal mental states.

Also, the fMRI data used in the example is at 0.8 mm3. very small voxel size which allows for very precise measurements of cortical depth levels in the human brain.

So what still needs to be done? 

Need to develop a model which can accurately describe how inputs between and within cortical areas are transformed into laminar specific output signals throughout the system.  

1. examine all cell types of cortex and capture their properties as well as understand how they integrate synaptic inputs (Figures 2B,C). This task should be started in vitro but eventually needs validation under awake behaving conditions. 
2. Once you get biophysical facts about components of cortex, then necessary to create abstractions so that functionality is captured by a model.
3. Combining these two pieces with connectivity information allows us interpret layer-specific data collected from brain whether electrical recordings or imaging activity at various levels/depths more effectively than before since now we have an understanding not only where axons terminate but also take into account biophysical processes such as dendritic integration spanning multiple layers simultaneously etc when interpreting any given recording or explaining behavior observed using fMRI techniques 

 

Rowland 2023 - Hemianopia and Multisensory Training

 Amerliorating Hemianopia with Multisensory Training (Rowland et al., 2023)

This paper presented evidence that visual-auditory stimulation therapy is a rapid and effective method for restoring visual function in patients with hemianopia.

Glennon 2022 - Cochear Implant Performance.

 

The paper discussed in my Multisensory Integration seminar class this week. 

Locus coeruleus activity improves cochlear implant performance  Glennon et al, 2022 

 

Takeaways

Key takeaway is that there is a lot of variability in CI outcomes (the time taken for hearing to be restored and perceptual accuracy after long-term CI use) with the researchers thinking this could be due to differences in neuroplasticity.