Author: Nicco Reggente

woman doing a grocery task, showcasing the need for VR for cognition and memory

VR for Cognition and Memory

Manuscript, Proceedings, Protocol-002

This blog post is based on a recent book chapter “VR for Cognition and Memory” in Current Topics in Behavioral Neuroscience: Virtual Reality in Behavioral Neuroscience: New Insights and Methods. This work presents a review of research on VR’s ability to provide ecologically valid environments to study memory and cognition and discusses how features like interactivity, locomotion, and contextual control engage the brain’s memory systems more naturally than lab studies.

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Reggente N. (2023). VR for Cognition and Memory. Current topics in behavioral neurosciences, 10.1007/7854_2023_425. Advance online publication. https://doi.org/10.1007/7854_2023_425

Reggente N. VR for Cognition and Memory [published online ahead of print, 2023 Jul 14]. Curr Top Behav Neurosci. 2023;10.1007/7854_2023_425. doi:10.1007/7854_2023_425

Reggente, Nicco. “VR for Cognition and Memory.” Current topics in behavioral neurosciences, 10.1007/7854_2023_425. 14 Jul. 2023, doi:10.1007/7854_2023_425

Revolutionizing Cognition Research with Virtual Reality

For decades, scientists have worked tirelessly to elucidate the intricate neural machinery supporting human cognition. This endeavor is certainly not for the faint of heart, as formidable challenges present themselves at every turn.

“To study cognition holistically means investigating interconnections between its rich repertoire of functions, including attention, reasoning, language, and memory. Memory is a particularly crucial facet, as it supports and subserves all other aspects of cognition; no cognitive task can be accomplished without memory.”

A holistic understanding demands that we study cognition as it operates in its natural habitat – the real world. Otherwise, as the parable of the blind men and the elephant warns, we risk gross mischaracterizations. Researchers must therefore conduct experiments in “verisimilar contexts (i.e. contexts appearing as the RW)” to achieve ecological validity.

Virtual reality (VR) presents an unprecedented opportunity in this regard. By simulating the real world, we can now study memory and cognition with enhanced veridicality.

“The environmental customization afforded by VR makes it an ideal tool for studying cognition in an ecologically valid fashion. Through the lens of memory studies, this chapter showcases the ways in which VR has advanced a meaningful and applicable understanding of cognition.”

The article presents a thorough review of research that showcases how VR is revolutionizing the study of cognition and memory.

Bridging the Gap Between Lab and Real-World Cognition

Traditional lab experiments often possess limited generalizability, whereas VR can provide naturalistic environments and tasks that echo real-world demands, easily bolstering ecological validity. Previous work has made a compelling case for how VR enhances the ecological validity of fMRI memory research.

VR experiences engage recollection-based memory retrieval akin to real events, unlike lab stimuli which rely more on familiarity. Indeed, VR experiences appear to be retrieved via recollection-based processes similar to those that support autobiographical/recollection memory, whereas retrieval of conventional screen experiences seems more similar to familiarity. This makes VR apt for integrated cognition and memory research.

VR Permits for Information to be Situation in Space

Most importantly, VR permits realistic navigation around virtual environments (c.f.), affording users with a sense of space (the scaffolding of memory). Both philosophers and psychologists alike postulate that brains have evolved solely to support purposeful and predictable movement. Many posit that the ontogeny of episodic memory relates to the onset of locomotion during infancy that scales with Hippocampal development (which also provides a mechanism for infantile amnesia and age-related episodic memory loss). One source of evidence to support this proposition is in the life cycle of the bluebell tunicate. This filter feeder begins to digest a substantial chunk of its cerebral ganglion once identifying a suitable undersea perch to spend the rest of its existence. This phenomenon suggests that once it has served its purpose as a neural network supporting movement, the cerebral ganglion yields greater utility to the organism as nutrition.

From chemotaxis to cognitive maps, a representation of space is necessary for meaningful movement. A neural instantiation of a map that provides spatial bookmarks of an organism’s experiences, demarcating the locations of nutrition and enemies within an environment, is a fundamental component of brains. Indeed, there is a primacy of spatial content in the neural representation of events. Spatial information is often recalled earliest in the retrieval process, and the degree to which individuals report confidence in their autobiographical memories is predicted by their knowledge of the spatial layout of the setting in which the memory occurred. The Method of Loci (a.k.a. Memory Palace) mnemonic has long been appreciated for its ability to increase memory by imagining to-be-remembered information placed at familiar locations. Past work used a VR implantation of this technique to suggest that the principal component behind mnemonic efficacy is the explicit binding of the objects to a spatial location and revealed a tight relationship between spatial memory (SM) and free recall of encoded objects. These observations showcase that space and memory are inextricably linked at conceptual and neuronal levels – a notion that has become entrenched in popular culture; the phrase “out of space” is often used when indicating a computer’s memory is full.

a fantastical virtual environment that could be used to study the relationship between objects, positions, and memory.

If space is the inescapable wallpaper that serves as the backdrop for all experience, then it follows that as our spatial or environmental context changes, so should the neural activity underlying diverse cognitive processes. Given that VR can easily change environments, it provides an unparalleled landscape with which to study the intersection of space, memory, and cognition.

Additionally, VR enables human analogs of spatial memory research previously limited to animal models, like virtual radial arm mazes. This facilitates powerful translational research from rodents to humans.

Key Features of VR That Facilitate Cognition Research

Below are some features highlighted by the chapter that are exclusive to VR. Such features permit real-world scenarios with increased experimental control and significantly less costs.

  • VR provides absolute control over the environment. This permits isolation and systematic manipulation of spatial contexts, immersion, emotions, embodiment, etc.
  • Rapid teleportation between environments induces robust context-dependent learning, a fundamental principle in memory encoding.
  • Interactivity and locomotion increase embodiment and navigational involvement, enhancing hippocampal memory systems.
  • Implicit metrics like gaze, paths, and object interactions generate objective measures of memory and attention unbiased by subjective reporting.
  • Brain imaging during VR reveals in vivo neural correlates of cognition impossible with real-world navigation.
  • VR spatial mnemonics such as the Method of Loci can provide performance improvements over just imagination by standardizing and controlling the environments.

Applications of VR for Assessing and Enhancing Cognition

Conventional measures of memory typically focus on core content (i.e., the “what”) instead of the true binding that happens in actual episodes (i.e., “what,” where,” and “when”). They also often use verbal materials, which makes the test sensitive to performance in non-memory domains, permitting for compensatory strategies which could erroneously reveal normal “memory.” Subjective reports rarely scale with performance on traditional memory tests, warranting criticism that such measures wrongly estimate memory capacities for everyday situations. For example, patients reporting topographical memory deficits have preserved ability in tabletop tests of spatial or geographical knowledge. Additionally, cognitive complaints in amnesiacs typically show little correlation with verbal memory tests used in clinical settings.

VR tasks, however, have been more reliable in tracking self and caregiver reports of deficits that impact quality of life. The points below highlight other aspects of VR that can increase the ecological validity of both the detection and amelioration of memory deficits.

  • VR scenarios like virtual stores and routes enable sensitive, ecologically valid tools to identify mild cognitive impairment early.
  • VR spatial navigation paradigms can differentiate Alzheimer’s from milder impairment based on hippocampal recruitment patterns.
  • VR enables safe exposure therapy for memory deficits induced by trauma and realistic training for brain injury rehabilitation.
  • Spatial mnemonic techniques adapted to VR boost memory beyond baseline abilities in healthy individuals.
  • VR puzzles engage aging minds, increasing motivation. Long-term regimes may prevent decline. As one study found, “6 months of VR training powerfully increased long-term recall.”
  • VR training could augment real-world cognition and rehabilitate deficits, with proven memory transfer effects.

In conclusion, VR enables an unprecedented ability to understand real-world cognition, precisely diagnose impairments, and develop interventions that enhance memory and cognition. The immersive, interactive nature of VR environments engages our brains’ memory systems far more naturally than traditional lab studies.

The inherently engaging qualities of VR, coupled with its ability to implicitly quantify and enhance memory, make it a powerful tool in populations spanning from pediatrics to the elderly.

Indeed, VR may catalyze discoveries about the very mechanisms underlying human consciousness itself, which intimately relies on episodic memory. By augmenting these processes, VR could profoundly transform our experience and understanding of consciousness. The future of cognition research has never looked more exciting.

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A diagram showing how ultrasound for neuromodulation works

Current State of Potential Mechanisms Supporting Low-Intensity Focused Ultrasound for Neuromodulation

Proceedings, Protocol-003, Review
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Published in Frontiers in Human Neuroscience in 2022, this review intended to answer how ultrasound for neuromodulation works

Our review titled, Current state of potential mechanisms supporting low intensity focused ultrasound for neuromodulation, attempts to address the following questions: 1) How can we alter the amount of mechanical energy or other properties of the mechanical energy using the sonication parameters available with each device, 2) How are neuronal tissue affected by mechanical energy, and 3) How do those sonication parameters change the type of neuromodulation (i.e., excitatory or suppressive)? We reviewed the theoretical mechanisms of action for neuromodulation and the empirical findings tracking all the sonication parameters used to elucidate the possible link between the proposed mechanisms of action and the choice of sonication parameters. This is still an emerging field, but a tabulation of the empirical findings and theoretical models is needed to help clinicians and researchers choose the best paradigm to use.

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Frontiers in Human Neuroscience: Brain Imaging and Stimulation Open Source Article

Current State of Potential Mechanisms Supporting Low-Intensity Focused Ultrasound for Neuromodulation PDF

Cite This Work

DellItalia, John, et al. “Current state of potential mechanisms supporting low intensity focused ultrasound for neuromodulation.” Frontiers in Human Neuroscience: 228.

DellItalia, J., Sanguinetti, J. L., Monti, M. M., Bystritsky, A., & Reggente, N. Current state of potential mechanisms supporting low intensity focused ultrasound for neuromodulation. Frontiers in Human Neuroscience, 228.

DellItalia, John, Joseph L. Sanguinetti, Martin M. Monti, Alexander Bystritsky, and Nicco Reggente. “Current state of potential mechanisms supporting low intensity focused ultrasound for neuromodulation.” Frontiers in Human Neuroscience: 228

DellItalia, J., Sanguinetti, J.L., Monti, M.M., Bystritsky, A. and Reggente, N., Current state of potential mechanisms supporting low intensity focused ultrasound for neuromodulation. Frontiers in Human Neuroscience, p.228.

DellItalia J, Sanguinetti JL, Monti MM, Bystritsky A, Reggente N. Current state of potential mechanisms supporting low intensity focused ultrasound for neuromodulation. Frontiers in Human Neuroscience.:228.

Science Without Jargon

Science should be accessible to everyone. However, dense jargon-filled articles can make it difficult for non-experts to engage with research. Making science accessible promotes scientific literacy and informed decision-making. In this post, we summarize our recent article for a lay audience.

 

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How ultrasound for neuromodulation works

Non-invasive brain stimulation has been used to modulate the activity of neural tissue without the need for surgical procedures to implant devices or permanently alter the neural tissue. Non-invasive brain stimulation has been used for neuromodulation across empirical research and clinical practices using transcranial electrical stimulation and transcranial magnetic stimulation. These types of neural modulation use electric (i.e., transcranial electrical stimulation) or magnetic (i.e., transcranial magnetic stimulation) fields applied outside the skull to induce changes in the electrochemical activity underneath the device within and around neurons. These fields tend to affect larger areas and affect all the neural tissue that the fields pass through. Thus, this limits which brain regions can be targeted precisely or individually.

An alternative to the putative non-invasive brain stimulation is devices using ultrasound. Ultrasound has been used for decades by clinicians to image various parts of the body, but recently ultrasound devices have begun to be used for neuromodulation. Ultrasound doesn’t use electric or magnetic fields, rather it generates acoustic waves that are a mechanical force. This mechanical force can be focused on a precise area with only the maximal mechanical energy converging on millimeter-sized region. This allows for deeper and/or smaller brain regions to be targeted compared to transcranial electrical stimulation and transcranial magnetic stimulation. However, the different energy source compared to electric or magnetic fields requires a different understanding of how neuromodulation occurs. Without this understanding, effective uses of ultrasound in empirical research and clinical practices will be limited.

Ultrasound’s acoustic waves have the characteristic properties of wavelength, amplitude, and frequency. Wavelength is the distance between two peaks within the wave, the amplitude is the height of the wave, and frequency is the number of peaks in a second. Each of these properties affects the total amount of mechanical energy delivered by the ultrasound device and other sonication parameters. The total energy can be measured by either the average amount in a spatial region or the average amount delivered over time typically converted into the units of watts per centimeter squared. In addition to the intensity, the total energy delivered over time is affected by the duty cycle, which is the percentage of time the sonication occurs. The duty cycle also determines if a paradigm is pulsed or continuous. Pulsed paradigms are any duty cycle below 100 percent, which allows for breaks between the sonication, compared to a continuous application of ultrasound. The frequency of the ultrasound’s acoustic waves is related to the sonication parameters of center frequency and pulse repetition frequency. The center frequency is set by the device manufacturer, which is the frequency delivered by the device and this frequency is related to the spatial precision of the acoustic wave delivered. The pulse repetition frequency is the frequency of the acoustic wave delivered by the pulsed paradigm. The final commonly adjusted sonication parameter is sonication duration (i.e., total time of acoustic wave delivered).

The mechanical energy delivered by the ultrasound device has seven proposed mechanisms to affect the activity of groups of neurons. Neurons are connected and each neuron’s activity either helps to excite other neurons connected to it or suppresses the activity of the neurons connected to it. These signals involve both electrical and chemical signaling. Since ultrasound is mechanical, the mechanisms of action proposed describes: 1) effects of mechanical energy on the temperature., 2) how the neurons detect and transform that mechanical energy to electrical or chemical signaling (i.e., mechanosensitive ion channels), or 3) how mechanical energy interacts with the elasticity of neurons to change the electrical properties or structure of neurons (e.g., direct flexoelectricity, change of membrane conformational states, or sonoporation).

Ultrasound has been used for decades to destroy malignant tissue by using enough intensity to generate larger changes in temperature. These large changes in temperature are not seen in the intensity ranges used in non-invasive brain stimulation. Despite the lower intensity used, there is still a mechanical force acting on the neurons. Some neurons have specific mechanisms for detecting external mechanical forces. These are most well understood in our tactile sensations. When our hand presses against a surface, specialized neurons detect the mechanical force by getting stretched which allows for chemical and electrical signaling to occur. The amount and distribution of neurons with similar properties in the brain is an active area of research. In addition to these specialized neurons, the mechanical energy from ultrasound can change the electrical properties of neurons by distorting the shape. The specific configuration of the membrane allows for electrical signals (i.e., direct flexoelectricity) to be produced as the mechanical energy changes the alignment of the interior and exterior parts of the membrane. On top of these alignment changes, there are pressure changes which can also generate both chemical and electrical changes from the changes in membrane conformational states.

Additionally, the neuron’s membranes can have changes to their permeability called sonoporation allowing for electrical changes that can elicit the neuron to fire. This process was investigated how the ultrasound’s pulse repetition frequency, intensity, or duty cycle could produce excitatory or suppressive effects. The key sonication parameter that best predicted differences in neuronal activity was duty cycle. Higher duty cycles between 10% to 70%, typically excited neurons, while lower duty below 10% created suppressive effects. Unfortunately, this one parameter did not predict the suppressive findings well, but the excitatory findings were almost all exclusively found between 10% to 70% duty cycle.

While duty cycle was predictive of some results found in the literature, it left most of the results unexplained. More models and theories are needed to expand the understanding of the mechanisms of action. Hopefully, this review gives a basic knowledge base to clinicians and researchers to use in their treatments or experiments. As the understanding of the mechanisms of action expand, more nuanced treatments and experiments can be used.

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Figures & Captions

Feel free to use these figures in your articles, blogs, and presentations. If you do, please cite this work.

Figure 1.

Low intensity focused ultrasound general principles. (A) A depiction of a typical LIFU experimental setup. A participant is seated (2) with an US device (5) firmly pressed against their head held in place by an arm (3). The US device is controlled by a computer system (4) and targeted using infrared system (1). (B) Depiction of the mechanical wave properties (amplitude, wavelength, and frequency) used in US stimulation. (C) Spatial intensities of the mechanical wave. (D) Temporal intensities of the mechanical wave. (E) Two exemplary pulsation schemes: pulsed (in yellow) and continuous (in teal). Both the pulsing schemes have a customizable sonication duration with inter stimulation interval with the DC parameter (i.e., the ratio of tone burst duration over pulse repetition period) determining the pulsing scheme.

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Figure 2.

Proposed ultrasonic stimulation’s mechanisms for neuromodulation. Depicted in column 1 are six neuronal membranes (four with an ion channel [rows A,C,D,E] and two neuronal membranes [rows B,F] with polar lipid bilayer) and a neuron with the microtubules highlighted (row G). Depicted in column 2, these membranes have four types of electrophysiological-mechanical coupling during an action potential: change in membrane conformation state, thermodynamic waves, direct flexoelectricity, and opening of mechanosensitive ion channels (see Section above). Column 3 depicts these same four electrophysiological-mechanical coupling during US stimulation along with three other possible mechanisms of US’s neuromodulation: thermal modulation, sonoporation and cavitation, and microtubule resonance (see Section above).

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Figure 3.

Neuronal intramembrane cavitation excitation model. Plaksin et al. (20142016) proposed the NICE model hypothesizing sonoporation (see Figure 2F) as US’s mechanism of neuromodulation. The US’s DC (see Figure 1E) determines the polarity of neuromodulation. A low DC (i.e., below 5%) during a stimulation’s off-periods will preferentially activate thalamic reticular neurons (TRN), thalamocortical neurons (TCN), and low-threshold spiking (LTS) interneurons via T-type voltage-gated calcium channels (see Section above for full description) producing an inhibitory effect. A high DC (i.e., over 20%) during the on-periods will preferentially activate regular spiking (RS) pyramidal cells and fast spiking (FS) interneurons while suppressing the LTS interneurons producing an overall excitatory effect. This excitatory effect is simulated using a basic network model of LTS, FS, and RS neurons connected with excitatory and inhibitory synapses and thalamic inputs. The network model predicts an optimum excitation of 70% DC.

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Figure 4.

Excitatory and suppressive empirical findings’ relationships to DC, PRF, ISPPA, fc, and SD. DC, PRF, ISPPA, fc, and SD are used as grouping factors for excitatory and suppressive findings. We used density plots for each study, but studies with multiple sonication parameters have each one plotted. In the top panel, high DC, above 10%, has the vast majority of the excitatory findings. While ow DC, less than 10%, contains the majority of the suppressive findings, there are still approximately 30% of the suppressive findings above 10% DC. The top panel is highlighted in red because DC is the one sonication parameter that has any distinction between excitatory and suppressive findings. In the four bottom panels, PRF, ISPPA, fc, and SD has no clear distinction between excitatory and suppressive findings.

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