Features

Karl Friston: Building the Future's Intelligent Ecosystem Based on Free Energy Theory

[:en]Karl Friston: Building the Future's Intelligent Ecosystem Based on Free Energy Theory[:]

Observations

Are You Addicted to GPT?

[:en]Are You Addicted to GPT?[:]

[:en]
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<h2>
	1. The Long and Arduous Road of Drug Development
</h2>


	Alzheimer&#39;s disease (AD), the most prevalent form of dementia, is rapidly evolving into a global public health crisis. Current statistics indicate that approximately 40 million people worldwide suffer from AD, with forecasts predicting an increase to 152 million by 2050. As patient numbers grow, the progressive decline in cognitive and memory functions significantly affects patients and their families, posing a substantial burden on socio-economic systems.



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	In response to AD&rsquo;s devastation, the medical community has persistently pursued solutions. Over recent decades, significant progress has been made in understanding AD&rsquo;s pathogenesis, with various hypotheses targeting proteins such as beta-amyloid (A&beta;) and tau. Although some promising drugs have entered clinical trials, to date, very few have been approved for the market, and these primarily provide symptomatic relief rather than a cure.



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	Among the popular monoclonal antibody drugs, Aduhelm was launched in 2022 but has since been gradually abandoned by its developers due to controversial effectiveness and safety concerns. Conversely, Leqembi, approved in 2023, has been shown to slow cognitive decline by 27% within 18 months in early-stage AD patients. However, its high cost and limited insurance coverage mean only about 398,000 patients worldwide may receive it by 2026.



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	Unlike hypertension patients who can manage their condition with antihypertensive drugs, the hope of curing AD with a few pills remains unrealized. Yet, are there other options? Recently, a novel non-pharmacological treatment has emerged quietly: 40Hz audiovisual stimulation therapy. This therapy, featuring a unique mechanism and easy operation, has shown promise in animal studies and preliminary clinical trials, offering new hope in the fight against AD.



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<h2>
	2. The 40Hz Audiovisual Stimulation: A New Pathway
</h2>


	Our brain is composed of approximately 86 billion neurons that work together like a vast symphony orchestra, performing complex cognitive functions. Neurons communicate through electrical signals, analogous to how orchestra members coordinate with the conductor and each other. During a performance, various musical instruments harmonize, creating a pleasing symphony. Similarly, in the brain, many neurons firing synchronously produce oscillations at specific frequencies, known as &quot;neural oscillations.&quot;



	<img alt="" class="aligncenter size-full wp-image-1901" height="414" src="https://admin.next-question.com/wp-content/uploads/2024/05/Picture1.png" width="784" />



	▷&nbsp;Different bands of neural oscillations. Figure source: Pandey, Pankaj, Richa Tripathi, and Krishna Prasad Miyapuram. &quot;Classifying oscillatory brain activity associated with Indian Rasa s using network metrics.&quot; Brain Informatics 9.1 (2022): 15.



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	Neural oscillations, like musical notes, occur at different frequencies. Researchers classify these into several bands: &delta; (1-4Hz), &theta; (4-8Hz), &alpha; (8-12Hz), &beta; (12-30Hz), and &gamma; (30-100Hz). Each band is linked to specific brain functions. For example, &alpha; waves are present during wakeful relaxation, whereas &gamma; waves are associated with higher cognitive processes such as perception, memory, and attention. In Alzheimer&rsquo;s disease (AD) patients, &gamma; oscillations are often disrupted, akin to a disjointed climactic movement in a musical performance. Studies utilizing electroencephalography and magnetoencephalography have shown significantly reduced &gamma; oscillations in both AD patients and animal models, indicating a decline in neuronal synchrony. This disruption impacts information transmission and integration across different brain regions, playing a crucial role in the progression of AD.



	<img alt="" class="aligncenter size-full wp-image-1900" height="270" src="https://admin.next-question.com/wp-content/uploads/2024/05/Picture2.png" width="480" />



	▷&nbsp;Differences in &gamma;&nbsp;rhythms between healthy individuals and AD patients. Figure source: https://brainhealth.org.uk/?p=624



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	So, can we restore the disrupted &gamma; oscillations in AD patients&rsquo; brains, allowing neurons to resume their rhythmic activity and play a harmonious symphony? The answer may be surprisingly simple: applying external 40Hz stimulation appears effective!



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	In 2016, a groundbreaking study led by Professor Li-Huei Tsai from MIT, published in the prestigious journal &quot;Nature,&quot; revealed significant therapeutic effects of 40Hz light stimulation in treating AD in mice. This discovery was based on AD mouse models that, although not yet displaying evident amyloid plaques, showed clear disruptions in &gamma; rhythms closely related to higher cognitive functions like perception, learning, and memory.



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	Researchers used optogenetic techniques to activate a key type of interneuron in the mouse brain. When these neurons were stimulated at a 40Hz frequency, the mice exhibited a significant reduction in the pathogenic amyloid-beta (A&beta;) protein, an effect not observed at other frequencies. Further analysis showed that 40Hz stimulation not only suppresses A&beta; production but also enhances phagocytic clearance by microglia. Stimulated microglia become more active, converging on plaques to perform a &quot;clean-up&quot; function, suggesting that regulating &gamma; rhythms could effectively combat AD through a dual mechanism.



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	Although optogenetics is a compelling technology, it involves injecting viruses into the brain and implanting optical fibers, making it currently suitable only for animal experiments and not yet for clinical use. However, scientists have found that simply exposing AD mice to 40Hz flickering light for just one hour can reduce brain A&beta; levels by nearly 60%. After receiving light exposure for one hour daily over seven days, the amyloid plaques in their brains were also significantly reduced.



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	What About Auditory Stimulation? In a 2019 study published in &quot;Cell,&quot; researchers explored the therapeutic effects of auditory stimulation further. The study found that 40Hz auditory stimulation reduced amyloid-beta (A&beta;) deposition in the auditory cortex of AD mice and synchronized neuronal activity in critical brain areas such as the hippocampus and medial prefrontal cortex. This synchronization with the 40Hz frequency exerted a therapeutic effect across a larger area. The mice also showed significant improvements in cognitive tasks, including spatial memory and object recognition.



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	When sound and light were combined, the therapeutic effects were enhanced. This multisensory stimulation not only induced a more robust immune response but also significantly reduced A&beta; in the medial prefrontal cortex. Whole-brain three-dimensional imaging revealed that combined stimulation significantly reduced plaques throughout the entire cortical brain and decreased levels of hyperphosphorylated tau protein. These findings suggest that 40Hz audiovisual stimulation therapy affects not just the primary sensory areas but also crucial hubs involved in learning and memory such as the hippocampus and prefrontal cortex, improving cognitive functions in AD mice behaviorally.



	<img alt="" class="aligncenter size-full wp-image-1899" height="498" src="https://admin.next-question.com/wp-content/uploads/2024/05/Picture3.png" width="498" />



	▷&nbsp;Audiovisual stimulation treatment for AD mice. Figure source: Martorell, Anthony J., et al. &quot;Multi-sensory &gamma;&nbsp;stimulation ameliorates Alzheimer&rsquo;s-associated pathology and improves cognition.&quot; Cell 177.2 (2019): 256-271.



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	Another study published in &quot;Neuron&quot; the same year investigated the mechanisms behind 40Hz light stimulation. Researchers discovered that 40Hz light stimulation enhanced &gamma; rhythms in other brain regions, including the hippocampus, visual cortex, somatosensory cortex, and prefrontal cortex. It also slowed the loss of neurons and synapses in these areas, improving the mice&#39;s spatial learning and memory capabilities.



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	Molecular-level studies revealed several potential protective mechanisms: suppression of pro-inflammatory gene expression in microglia, reduction of neuroinflammation, upregulation of genes related to synaptic function in neurons, enhancement of synaptic plasticity, induction of neuroprotective factors, and reduction of neuronal DNA damage, thereby helping to maintain genomic stability.



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	Notably, 40Hz stimulation regulates not only the pathological changes associated with Alzheimer&#39;s disease but also shows a similar regulatory effect in normally aging mice, suggesting that it could broadly improve neural functions.



	<img alt="" class="aligncenter size-full wp-image-1898" height="663" src="https://admin.next-question.com/wp-content/uploads/2024/05/Picture4.png" style="" title="" width="663" />



	▷&nbsp;Treatment mechanisms. Figure source: Adaikkan, Chinnakkaruppan, et al. &quot;&gamma;&nbsp;entrainment binds higher-order brain regions and offers neuroprotection.&quot; Neuron 102.5 (2019): 929-943.



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<h2>
	3. Skepticism from Other Teams
</h2>


	As the academic community marveled at the remarkable efficacy of such a simple therapy, a study published in March 2023 by Gy&ouml;rgy Buzs&aacute;ki&#39;s team at New York University in Nature Neuroscience delivered a significant blow to the 40Hz treatment credibility.



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	Their research involved recording neural activity in various brain regions, including the visual cortex, olfactory cortex, and hippocampus. The study found that 40Hz light stimulation did not induce &gamma; oscillations and had a minimal impact on deeper brain regions. Further experiments indicated that AD mice seemed to dislike and actively avoid the 40Hz flickering light.



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	The researchers also examined the effect of 40Hz light stimulation on AD pathology. Despite employing multiple experimental methods, they observed no changes in the quantity of amyloid plaques, no significant differences in microglial morphology, and no statistical differences in the levels of A&beta;40 and A&beta;42 between the treatment and control groups. Although some pathological indicators showed improvement, these changes were not stable or consistent.



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	Does this mean that 40Hz audiovisual stimulation therapy for AD is ineffective? Not necessarily. Numerous factors can influence the outcomes of biological experiments. Even if all data are accurate and reliable, differences in key experimental steps, mouse strains, or slight changes in the environment can lead to vastly different results. Additionally, the study focused only on molecular pathological changes within the brain and did not explore the impact of the 40Hz therapy on cognitive functions, which are clearly more crucial. As the authors themselves stated, their results should not be viewed as evidence against the therapy&#39;s effectiveness in treating AD, and further exploration is needed to develop the 40Hz audiovisual stimulation therapy effectively.



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<h2>
	4. New Research Tops &quot;Nature&quot; Again
</h2>


	In response to ongoing skepticism, Professor Tsai&#39;s team published a groundbreaking study in &quot;Nature&quot; in March 2024, uncovering a novel therapeutic mechanism for the 40Hz stimulation therapy.



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	For normal brain function, an efficient &quot;garbage disposal system&quot; is essential to continuously remove waste. The glymphatic system, a crucial component of this system, depends on the unobstructed flow of cerebrospinal fluid (CSF) and interstitial fluid. It has been found that abnormal &gamma; oscillations may disrupt this process, potentially exacerbating Alzheimer&#39;s disease (AD).



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	The study discovered that just one hour of 40Hz audiovisual stimulation significantly increases the influx of CSF and the efflux of interstitial fluid in the cortical brain of AD mice. This effect is mediated by the key protein AQP4 on astrocytes. Inhibiting AQP4 with the small molecule TGN020 diminished the inflow of CSF and impaired the clearance of amyloid-beta (A&beta;). These findings were further validated in mice genetically modified to lack the AQP4 gene. Additional research demonstrated that 40Hz stimulation enhances CSF flow by promoting the polarization of AQP4 on the cell membrane.



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	Further investigations have shown that arterial pulsation regulates CSF flow and that lymphatic vessels facilitate the drainage of CSF from the brain, also aiding in the transport of A&beta;. Experiments indicate that 40Hz audiovisual stimulation also boosts arterial pulsation and increases the diameter of lymphatic vessels in the brains of mice, improving A&beta; clearance.



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	So, who coordinates all these processes? Subsequent experiments have revealed that these are regulated by VIPergic interneurons, which secrete the vasoactive intestinal peptide (VIP). Prior studies have indicated that the VIP pathway plays a role in regulating arterial diameter, astrocytic metabolism, and aquaporin transport, among other physiological processes. Inhibiting VIP neurons prior to 40Hz stimulation resulted in decreased A&beta; clearance, AQP4 polarization, and arterial pulsation.



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	This research systematically elucidates a novel mechanism: 40Hz stimulation, via &gamma; oscillations, promotes the synergistic action of a neural-vascular-glia circuit centered around VIPergic interneurons, thereby accelerating AQP4-mediated CSF flow and the removal of pathogenic proteins. This multi-level regulatory process&mdash;from gene expression to cellular function and tissue level&mdash;reveals intrinsic connections between neural activity, CSF circulation, and AD pathology, further affirming the efficacy of 40Hz audiovisual stimulation in combating AD.



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<h2>
	5. Rapid Clinical Translation
</h2>


	Building on these groundbreaking discoveries, Professor Li-Huei Tsai and her collaborators founded Cognito Therapeutics to officially begin the journey of transitioning 40Hz audiovisual therapy from the laboratory to clinical settings.



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	At the 2021 Alzheimer&#39;s/Parkinson&#39;s Disease Congress, Cognito unveiled the results of a multicenter, randomized, controlled Phase II clinical trial (NCT-03556280). After six months of treatment, patients with mild to moderate Alzheimer&rsquo;s disease showed good tolerance to the company&#39;s 40Hz audiovisual stimulation device, named Gamma Sense. Patients in the treatment group, compared to those in the placebo or sham group, experienced an 84% reduction in cognitive decline, an 83% slowdown in the deterioration of daily living skills, and a 61% reduction in related brain atrophy.



	<img alt="" class="aligncenter size-large wp-image-1897" height="475" src="https://admin.next-question.com/wp-content/uploads/2024/05/Picture5-1024x949.png" style="" title="" width="512" />



	▷&nbsp;Clinical trial, showing a slowdown in cognitive decline.



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	In the same year, the FDA designated Gamma Sense as a Breakthrough Device. Additional findings were published in the March 2024 issue of &quot;Frontiers in Neuroscience.&quot;



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	Following the initial success of the Phase II trial, a Phase III clinical trial has been aggressively pursued. This trial aims to enroll over 500 patients who will receive daily audiovisual therapy for 12 consecutive months, with the trial&#39;s completion expected in 2025.



	<img alt="" class="aligncenter size-large wp-image-1896" height="341" src="https://admin.next-question.com/wp-content/uploads/2024/05/Picture6-1024x682.png" style="" title="" width="512" />



	▷&nbsp;40Hz audiovisual stimulation device, The Spectris. Source: COGNITO THERAPEUTICS.



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<h2>
	6. Conclusion
</h2>


	Undoubtedly, 40Hz audiovisual stimulation therapy offers unique advantages. Unlike traditional pharmacological treatments, it modulates neural oscillations&mdash;a more upstream component&mdash;in a synergistic manner across multiple levels, providing neuroprotection and potentially slowing down the progression of Alzheimer&rsquo;s disease at various stages. As a non-invasive physical therapy, it is easy to administer, safe, and can be performed by patients at home. This convenience greatly improves patient compliance and reduces treatment costs.



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	However, it is crucial to acknowledge that this therapy is still in the clinical trial phase. Early research has been limited by small sample sizes and short follow-up periods. Should Phase III clinical trials continue to demonstrate efficacy and long-term safety, and if personalized treatment adaptations are incorporated, 40Hz audiovisual stimulation therapy could emerge as a truly beneficial, non-invasive, safe, and affordable method to delay the progression of AD.



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	It is also worth noting that 40Hz audiovisual stimulation therapy does not conflict with existing drug therapies; instead, it may enhance them. We can envision a scenario where patients receive regular injections of antibody medications while also using a daily head-mounted audiovisual stimulation device. This approach could enhance the effects of both treatments in a coordinated, precision medicine approach.



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	While the prospects are promising, the path toward clinical translation is long and fraught with challenges. We look forward to ongoing efforts in both basic and clinical research that will further elucidate the mechanisms of the treatment, assess tolerance for long-term use, confirm the durability of the effects, and evaluate suitability across different populations. Through these endeavors, this innovative therapy may flourish and help humanity combat the formidable challenge of Alzheimer&rsquo;s disease.



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[:]

Why is 40Hz Remarkably Effective?

[:en]Why is 40Hz Remarkably Effective?[:]

From The Journals

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	In the early stages of human societal development, communities were primarily close-knit, where every word and action of an individual spread among neighbors, creating tightly connected small communities. In these settings, interactions among individuals were not accidental but characterized by long-term continuity. Everyone consistently acted in ways that would shape their image and reputation within the community. This continuous interaction, resembling an expansive network, tightly interwove each individual&rsquo;s fate with the others&#39;. Against this backdrop, cooperation and mutual assistance emerged as crucial cornerstones in the early development of human societies.


	This perspective is rooted in the theory of the evolution of cooperation through repeated interactions, which posits that acts of cooperation are based on the anticipation of future cooperative opportunities. Such long-term social engagements foster cooperation among individuals. However, this seemingly plausible explanation overlooks a critical detail: even within long-term interactions, individuals may engage in ambiguous reciprocal behaviors (i.e., the fuzzy reciprocation strategy), potentially undermining the foundation of cooperation. In other words, the mere expectation of future interactions does not ensure the stability of cooperation.


	Another hypothesis, group competition, suggests that competition among groups fosters the evolution of cooperation. Yet, this theory also has its limitations. While inter-group competition can encourage internal cooperation, its effectiveness wanes when groups become exceedingly similar. Furthermore, even with discernible differences between groups, group competition may not efficiently bolster cooperation since cooperative groups tend to compete with each other rather than with non-cooperative groups.


	A recent article in Nature addresses the limitations of these theories, illustrating that their integration can produce a synergistic effect. While the basis for cooperation in repeated interactions is fragile, group competition can mitigate the negative impact of ambiguous reciprocation strategies. Indeed, evolutionary strategies often involve cooperative behaviors towards in-group members and non-cooperative behaviors towards out-group members.


	<img loading="lazy" decoding="async" alt="" class="aligncenter size-full wp-image-1847" height="473" src="https://admin.next-question.com/wp-content/uploads/2024/04/Picture1-1.png" width="967" srcset="https://admin.next-question.com/wp-content/uploads/2024/04/Picture1-1.png 967w, https://admin.next-question.com/wp-content/uploads/2024/04/Picture1-1-300x147.png 300w, https://admin.next-question.com/wp-content/uploads/2024/04/Picture1-1-768x376.png 768w" sizes="(max-width: 967px) 100vw, 967px" /> 
	▷ Paper: Efferson, Charles, et al. &quot;Super-additive cooperation.&quot;&nbsp;Nature&nbsp;(2024): 1-8.


	The experiment was conducted in West Papua, New Guinea, where the research team selected two local communities, the Ngenika and the Perepka, to participate in a two-person sequential social dilemma game. Unlike the traditional prisoner&#39;s dilemma game, this experiment&#39;s game allowed for cooperative actions to vary continuously within a certain range, more accurately reflecting the social dilemmas humans encounter in the real world.


	In this game, each participant received a certain amount of &quot;resources.&quot; The game unfolded in two steps: initially, the first mover could choose to transfer some of their resources to the second mover, which would double during the transfer; then, based on the initial transfer, the second mover could choose to transfer some of their resources back to the first mover, which would also double. This setup not only presented an opportunity for cooperation but also posed a strategic challenge.


	The experiment explored three scenarios: one featuring only repeated interaction, one with only group competition, and one that combined both. Additionally, the study examined variations in strategic space dimensions to simulate potential cooperative strategies.


	The results revealed that participants engaged in higher initial resource transfers (indicating more cooperative behavior) when paired with in-group members, and lower initial resource transfers (indicating less cooperative behavior) when paired with out-group members. Importantly, the response strategies of the second movers varied significantly: responses to in-group members often increased cooperation (by augmenting resource transfers to reward the first mover&rsquo;s cooperation), while responses to out-group members generally reduced cooperation (by decreasing resource transfers in reaction to the first mover&rsquo;s level of cooperation).


	This pattern aligned perfectly with the research team&rsquo;s model predictions, suggesting that the combination of repeated interaction and group competition could create a powerful synergistic effect, fostering in-group cooperation and mitigating the negative impact of ambiguous reciprocity on cooperation.


	This experiment challenges the notion that a single mechanism, such as repeated interactions or group competition alone, can account for human cooperation. It introduces a new perspective: human cooperation may have evolved under the combined influences of repeated interaction and group competition. The findings demonstrate that even in one-off interactions, cooperative behaviors are shaped by the historical legacy effects of these two mechanisms, implying that the cooperative behaviors we observe today may be a direct result of social motivations that evolved in the distant past through in-group repeated interactions and inter-group competition.


	From the Ngenika to the Perepka, from forests to cities, and from ancient tribes to modern societies, the essence of cooperation has consistently permeated human interaction. This experiment serves as a mirror, reflecting the intricate and nuanced evolutionary backdrop of human cooperative behavior. The essence of human cooperation is not a product of simple algorithms but is cultivated through the complexities of repeated interactions and honed in the crucible of group competition.


	&nbsp;


	&nbsp;

[:en]
	&nbsp;



	&nbsp;



	In the early stages of human societal development, communities were primarily close-knit, where every word and action of an individual spread among neighbors, creating tightly connected small communities. In these settings, interactions among individuals were not accidental but characterized by long-term continuity. Everyone consistently acted in ways that would shape their image and reputation within the community. This continuous interaction, resembling an expansive network, tightly interwove each individual&rsquo;s fate with the others&#39;. Against this backdrop, cooperation and mutual assistance emerged as crucial cornerstones in the early development of human societies.



	This perspective is rooted in the theory of the evolution of cooperation through repeated interactions, which posits that acts of cooperation are based on the anticipation of future cooperative opportunities. Such long-term social engagements foster cooperation among individuals. However, this seemingly plausible explanation overlooks a critical detail: even within long-term interactions, individuals may engage in ambiguous reciprocal behaviors (i.e., the fuzzy reciprocation strategy), potentially undermining the foundation of cooperation. In other words, the mere expectation of future interactions does not ensure the stability of cooperation.



	Another hypothesis, group competition, suggests that competition among groups fosters the evolution of cooperation. Yet, this theory also has its limitations. While inter-group competition can encourage internal cooperation, its effectiveness wanes when groups become exceedingly similar. Furthermore, even with discernible differences between groups, group competition may not efficiently bolster cooperation since cooperative groups tend to compete with each other rather than with non-cooperative groups.



	A recent article in Nature addresses the limitations of these theories, illustrating that their integration can produce a synergistic effect. While the basis for cooperation in repeated interactions is fragile, group competition can mitigate the negative impact of ambiguous reciprocation strategies. Indeed, evolutionary strategies often involve cooperative behaviors towards in-group members and non-cooperative behaviors towards out-group members.



	<img alt="" class="aligncenter size-full wp-image-1847" height="473" src="https://admin.next-question.com/wp-content/uploads/2024/04/Picture1-1.png" width="967" /> 
	▷ Paper: Efferson, Charles, et al. &quot;Super-additive cooperation.&quot;&nbsp;Nature&nbsp;(2024): 1-8.



	The experiment was conducted in West Papua, New Guinea, where the research team selected two local communities, the Ngenika and the Perepka, to participate in a two-person sequential social dilemma game. Unlike the traditional prisoner&#39;s dilemma game, this experiment&#39;s game allowed for cooperative actions to vary continuously within a certain range, more accurately reflecting the social dilemmas humans encounter in the real world.



	In this game, each participant received a certain amount of &quot;resources.&quot; The game unfolded in two steps: initially, the first mover could choose to transfer some of their resources to the second mover, which would double during the transfer; then, based on the initial transfer, the second mover could choose to transfer some of their resources back to the first mover, which would also double. This setup not only presented an opportunity for cooperation but also posed a strategic challenge.



	The experiment explored three scenarios: one featuring only repeated interaction, one with only group competition, and one that combined both. Additionally, the study examined variations in strategic space dimensions to simulate potential cooperative strategies.



	The results revealed that participants engaged in higher initial resource transfers (indicating more cooperative behavior) when paired with in-group members, and lower initial resource transfers (indicating less cooperative behavior) when paired with out-group members. Importantly, the response strategies of the second movers varied significantly: responses to in-group members often increased cooperation (by augmenting resource transfers to reward the first mover&rsquo;s cooperation), while responses to out-group members generally reduced cooperation (by decreasing resource transfers in reaction to the first mover&rsquo;s level of cooperation).



	This pattern aligned perfectly with the research team&rsquo;s model predictions, suggesting that the combination of repeated interaction and group competition could create a powerful synergistic effect, fostering in-group cooperation and mitigating the negative impact of ambiguous reciprocity on cooperation.



	This experiment challenges the notion that a single mechanism, such as repeated interactions or group competition alone, can account for human cooperation. It introduces a new perspective: human cooperation may have evolved under the combined influences of repeated interaction and group competition. The findings demonstrate that even in one-off interactions, cooperative behaviors are shaped by the historical legacy effects of these two mechanisms, implying that the cooperative behaviors we observe today may be a direct result of social motivations that evolved in the distant past through in-group repeated interactions and inter-group competition.



	From the Ngenika to the Perepka, from forests to cities, and from ancient tribes to modern societies, the essence of cooperation has consistently permeated human interaction. This experiment serves as a mirror, reflecting the intricate and nuanced evolutionary backdrop of human cooperative behavior. The essence of human cooperation is not a product of simple algorithms but is cultivated through the complexities of repeated interactions and honed in the crucible of group competition.



	&nbsp;



	&nbsp;

[:zh]


[:]

Nature: United by a Common Foe or Sharing Scarce Resources—What Drove the Evolution of Cooperation

[:en]Nature: United by a Common Foe or Sharing Scarce Resources—What Drove the Evolution of Cooperation[:]

[:en]The 21st century is celebrated as the Information Age, a time when information has become an indispensable tool for survival and interaction with the world around us. But do we truly grasp the essence of "information"? It drives the era forward, yet its substantial impact on our lives and thought processes remains to be fully appreciated.

Claude Shannon, a distinguished mathematician, laid the groundwork for information theory, offering a theoretical framework to discuss the quantification, storage, and transmission of information. According to this theory, information serves as a "clue" that significantly reduces the uncertainty surrounding unknown events. If knowledge about certain events decreases the uncertainty of another, then that knowledge is considered information.

Consider a newcomer at a company who initially knows little about it and feels uncertain about his colleagues. After sharing a few meals, he observes their behavior and gradually discerns their interests and personalities. This newfound information makes the colleagues less unfamiliar and notably diminishes the newcomer's sense of uncertainty.

In our everyday lives and interpersonal interactions, information is ubiquitous, profoundly shaping our communication and cognitive processes. From the perspective of information theory, the human brain is seen as an information-processing entity, with information acting as the neural system's fundamental currency. Neurons exchange information through electrical and neurochemical signals, constantly refining and integrating this data within the brain to shape our cognition and memory.

Thus arises the question: Are electrical and chemical signals themselves information?

Neuroscientists have yet to agree on this matter [1]. Referring to information as the "base currency" in a rhetorical sense may not capture its true essence.

Recently, information theory has embraced a new viewpoint: information is not a monolithic entity but consists of various forms. This approach, known as "information decomposition," [2] seeks to break down information into three categories: unique information, redundant information, and synergistic information. This methodology offers a fresh perspective on the nature of information, aiding in a more thorough understanding of the brain's informational architecture and cognitive processes.

<img src="https://admin.next-question.com/wp-content/uploads/2024/04/Picture6.png" alt="" width="993" height="313" class="aligncenter size-full wp-image-1864" />
▷ Original paper：Luppi, Andrea I., et al. "Information decomposition and the informational architecture of the brain." Trends in Cognitive Sciences (2024).

<h2>1. Information Decomposition: Multifaceted Information</h2>

The three types of information—unique, redundant, and synergistic—each possess distinct characteristics and fulfill different roles in the process of information processing.

Consider visual information processing as an example. The human visual system comprises central and peripheral visual fields. The peripheral visual field captures a broad range of environmental information, including the general location and blurred details of objects. The information in the peripheral visual field of each eye is unique because closing one eye (for example, the left eye) results in the loss of that eye's peripheral information, leaving the brain to rely solely on the other eye (right eye). For instance, while driving, peripheral vision allows the driver to notice vehicles on the sides and behind through the rearview mirror. Should the driver close his left eye due to discomfort, he might not see a vehicle approaching quickly from the left rear side, potentially leading to an unforeseen accident.

In contrast, the central visual field, which is the focal area of human vision, contains most of the detailed information we perceive. For a driver, the vehicle directly ahead in the same lane is visible within the central visual field of both eyes. Closing the left eye momentarily still allows the driver, using just the right eye, to observe the vehicle ahead. Such information is categorized as "redundant information," valued for its robustness since the same information is provided by multiple sources. This overrepresentation ensures that information is still accessible even if one source is compromised. However, the drawback of redundant information is its failure to fully leverage the brain's information-gathering capacity.

Synergistic information is the final category. Stereoscopic vision cannot be produced by a single eye. How, then, does the world appear three-dimensional to us? This effect relies on the collaboration between both eyes. Due to their different positions on the head, each eye views the same object from slightly varied perspectives. The visual cortex receives two distinct two-dimensional images, and by analyzing the binocular disparity—the differences between these images—it calculates the object's distance, thereby creating a perception of depth and three-dimensionality.

In driving scenarios, the cooperation of both eyes is crucial for accurately judging the distance to vehicles or obstacles ahead. Damage to either eye significantly compromises safety. Synergistic information's key feature is its efficiency, maximizing the interaction between different parts of the brain's neural system to achieve an effect where the whole is greater than the sum of its parts, thereby playing a vital role in managing complex tasks.

<img src="https://admin.next-question.com/wp-content/uploads/2024/04/Picture7.png" alt="" width="650" height="431" class="aligncenter size-full wp-image-1863" />
▷ Figure 1: The acorn and banana in the image symbolize unique information, the rectangle denotes redundant information, and the cube illustrates synergistic information, which requires the combined effort of both eyes to perceive depth. Figure source: Reference [2]

As a highly complex system, the human body's approach to information decomposition not only offers a more nuanced perspective on the structure of information but also finds applications in other domains, such as cellular automata, socio-economic data analysis, and artificial neural networks [3].

<h2>2. How does the brain integrate information?</h2>

Integration is a key concept in neuroscience and cognitive science, presenting researchers with two distinct interpretations: integration as oneness and integration as cooperation.

The oneness perspective views strong correlations or synchrony between activities in different brain regions, as observed in brain data analysis, as an indication of high integration. This is based on the idea that integrated elements operate as a single unit. The more synchronous and similar the activities between two elements are, the higher their level of integration is considered. Conversely, the cooperation perspective argues that when different elements complement each other, the system's information processing capacity benefits from their interactions, signifying integration.

In terms of information decomposition, integration is seen as the counterpoint to unique information. Oneness aligns with redundant information, while cooperation aligns with synergistic information. However, traditional neuroscience often relies on correlation to deduce the integration within the nervous system, a method that struggles to distinguish between oneness and cooperation accurately. High correlation typically suggests information redundancy, whereas low correlation can indicate either uniqueness or synergistic information.

To differentiate redundancy from synergy, researchers have developed the synergy-redundancy index, which reflects the balance between these two in a system. If the system's total information output exceeds the sum of its individual parts, synergy is inferred. If the sum of the parts' contributions exceeds the system's total output, redundancy is present. While intuitive, this approach has limitations in situations where synergy and redundancy coexist, and it cannot precisely identify synergistic information.

Unlike correlation-based methods or the synergy-redundancy index, information decomposition provides a more accurate analysis of information processing by calculating the system's transfer entropy (TE). For instance, if TE data show a higher transfer from time series data X to Y than from Y to X in a system, X is deemed to have a significant "influence" on Y.

Considering epilepsy, characterized by disordered functional connectivity in the brain, traditional interpretations view the highly synchronous signals among patients as indicative of oneness. This suggests that redundancy in different brain regions could contribute to epileptic seizures. However, when analyzing EEG data from epilepsy patients using information decomposition, researchers discovered increases in both redundant and synergistic information transfer from subcortical to cortical regions during seizures. Furthermore, an analysis of deep electrode recordings revealed that an increase in unique information transfer from specific subcortical areas to cortical regions might be a primary trigger for cortical oscillations, offering clearer evidence for identifying seizure onset zones [4].

<img src="https://admin.next-question.com/wp-content/uploads/2024/04/Picture8.png" alt="" width="954" height="753" class="aligncenter size-full wp-image-1862" />
▷ Figure 2: Information decomposition offers a unified framework for cognitive science, where each bi-directional arrow crossing the central triangular area represents opposing concepts in cognitive and neuroscience. One end of the arrow corresponds to a category within the information decomposition framework, and the other end differentiates between two types of information, such as integration and disintegration—the latter implying unique information, and the former encompassing both redundant and synergistic information. Localization refers to instances where an information source solely contains unique information, making it exclusively available from that source. In contrast, sources with only redundant information disseminate their information across multiple locations. Differentiation occurs when system parts exhibit distinct behaviors or do not demonstrate oneness, indicating they might be independent or complementary. Figure source: Reference [2]

<h2>3. How does the brain balance redundancy and synergy?</h2>

Redundancy and synergy, significant modes of interaction within the brain, have been distinguished by extensive research. A meta-analysis utilizing the NeuroSynth database, which includes over 15,000 imaging studies, revealed that redundant information is crucial for sensory-motor processing. As the brain's input-output mechanism, stable sensory-motor processing is essential for survival, with redundant interactions providing the necessary robustness.

Conversely, synergistic information serves as a "global workspace" in the brain, crucial for executing higher cognitive functions. Areas of the brain exhibiting high synergy demonstrate increased aerobic glycolysis rates and a broader range of neurotransmitter receptor expressions, supporting flexible and rapid energy supply, synapse formation, and neural regulation [5].

At the macroscopic scale, human resting-state fMRI data indicate that synergy is more prevalent overall [5], although specific brain regions vary. The frontal and parietal association cortices, integral for integrating multimodal information, primarily operate through synergy. The frontal association cortex is involved in long-term planning and decision-making, while the parietal association cortex focuses on spatial localization, hand-eye coordination, and other tasks. In contrast, redundancy is more common in primary cortical areas that process unimodal information, such as the visual, somatomotor, and auditory cortices. Compared to other primates, humans possess a more developed association cortex, suggesting a greater integration of information through cooperation and synergy, underscoring our cognitive advantages.

Microscale research corroborates these macroscopic findings. Electrophysiological recordings in the prefrontal cortex reveal complex and flexible neuronal responses to stimuli and task changes, highlighting the importance of neuronal interactions. However, in the visual cortex, especially in areas V4 and V1, neuronal interactions contribute less to activity patterns [6, 7].

Artificial neural network research further supports these observations, showing that redundancy is predominant in early network stages. As learning progresses, neurons specialize, contributing more unique information. When networks learn multiple tasks, requiring flexibility to integrate different information sources, synergy strengthens. Damage to highly synergistic neurons significantly impairs network performance. Conversely, training with randomly deactivated neurons enhances redundancy and robustness, improving resistance to damage [8].

In summary, while redundancy offers robustness and underpins sensory-motor functions in humans and many primates, it does not maximize the neural system's information-processing capacity. Synergy, associated with higher-order information processing, is more efficient and flexible, playing a crucial role in advancing human cognitive capabilities. Damage to parts of this cooperative network is likely to impair higher cognitive functions.

<img src="https://admin.next-question.com/wp-content/uploads/2024/04/Picture9.png" alt="" width="835" height="910" class="aligncenter size-full wp-image-1861" />
▷ Figure 3: (A) The image shows brain areas where redundancy (blue) and synergy (red) predominate. Blue areas, related to primary sensory-motor processing, exhibit a modular network organization with structured connections for single modality processing, like visual information in the visual cortex. Red areas, responsible for complex cognitive processing, are associated with synaptic density, dendritic spine-related genes, and enhanced plasticity. (B) The evolutionary trajectory of synergy. While redundant information remains relatively stable across species, the human brain shows more developed synergistic information, likely due to expanded cortical areas, compared to other primates. Figure source: Reference [2]

<h2>4. A New Blueprint for Artificial Intelligence Design</h2>

The framework of information decomposition not only provides a comprehensive lens for examining brain information processing but also sheds light on human evolutionary advantages. Moreover, it paves the way for researchers to design AI systems that more closely mimic human intelligence.

Artificial neural networks have shown impressive capabilities across various domains. A pivotal question emerges: Do these systems rely on synergy, akin to the human brain?

Recent advances in artificial intelligence have largely been driven by increases in model scale. Researchers note that as AI models grow in scale and their ability to handle multitasking improves, they display more synergistic behaviors, hinting at AI systems becoming more human-like. However, the inherent fragility of synergistic actions introduces risks for AI, suggesting that future designs of AI systems should differentiate between types of information, and evolve the information decomposition framework into a universal tool for deciphering complex systems. This approach could also illuminate the inner workings of AI models, often considered "black boxes."

Conversely, artificial intelligence offers a fertile ground for testing theories of information. Observations indicate that synergy intensifies when confronting complex challenges. If AI systems were engineered to favor synergistic actions, perhaps through evolutionary algorithms, might they navigate complex tasks with greater efficacy? Moreover, an AI system designed solely for synergistic actions would provide a unique lens through which to examine the benefits and limitations of synergy—a scenario beyond the reach of any biological system.

<img src="https://admin.next-question.com/wp-content/uploads/2024/04/Picture10-1024x652.png" alt="" width="1024" height="652" class="aligncenter size-large wp-image-1860" />
▷ Figure 4: Envisioning the information decomposition framework as a Rosetta Stone bridging biology and artificial intelligence. Just as the Rosetta Stone, inscribed with Greek, Ancient Egyptian, and other scripts, was vital for decoding Ancient Egyptian history, information processing and decomposition elucidate the connections between brain structure, functional organization, and cognitive and behavioral outcomes. Similarly, in AI systems, this framework can clarify the links between system architecture, computational abilities, and performance outcomes. Whether in the biological brain or an AI system, information processing and decomposition, transcending the medium, can serve as a universal language. Figure source: Reference [2]

<h2>5. Conclusion</h2>

The rapid advancements in artificial intelligence technology often astonish us in our everyday lives. However, the complexity of the human brain vastly exceeds that of current AI capabilities, and the framework of information decomposition brings us one step closer to deciphering the brain's enigmas.

What techniques will we uncover in the future to further decompose information? How can we leverage information decomposition to develop more sophisticated AI systems? Could a deeper insight into the brain's information architecture enable us to address the myriad mental health issues affecting society? Perhaps, with enough understanding of the brain's fundamental mechanisms, the creation of an artificial brain might transition from fantasy to reality.[:]

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Deep within the human brain, there&#8217;s a fascinating phenomenon that has captivated us for centuries—an intricate interplay between music and the brain. From soothing lullabies to exhilarating symphonies, music possesses boundless power to evoke emotions, ignite creativity, and offer us diverse sensory experiences.
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Scientists are currently dedicated to unraveling the mystery of how music influences human neural circuits, shedding light on its impact on various regions and functions of the brain. We are honored to have invited Professor Du Yi from Institute of Psychology, Chinese Academy of Sciences, to explore the effects of music on the brain from an anti-aging perspective.
<img loading="lazy" decoding="async" class="aligncenter wp-image-787" src="https://admin.next-question.com/wp-content/uploads/2023/11/1699193665366.png" alt="" width="600" height="274" srcset="https://admin.next-question.com/wp-content/uploads/2023/11/1699193665366.png 717w, https://admin.next-question.com/wp-content/uploads/2023/11/1699193665366-300x137.png 300w" sizes="(max-width: 600px) 100vw, 600px" />
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<h4>Question: Please briefly introduce your research background.</h4>
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Professor Du: I took my undergraduate courses in medicine, but when it came to choosing my doctoral program, I developed a strong interest in psychology, especially in unraveling the mysteries of human brains. Consequently, I switched to a Ph.D. program in psychology. During my doctoral studies, my research primarily focused on auditory studies using rats as subjects, encompassing both physiological and behavioral research. However, in the later stages of my Ph.D. program and during my postdoctoral research, I became increasingly fascinated by the human brain and its processing of advanced auditory signals, such as language and music. As a result, after establishing my own laboratory, I have been dedicated to researching how the human brain comprehends language, appreciates music, and the neural mechanisms underlying these processes.
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As people age, the structure and function of the brain will degenerate. One significant change that impacts the quality of life for the elderly is the deterioration of hearing. In noisy environments like streets or restaurants, it becomes increasingly challenging for the elderly to discern what others are saying. This not only affects their social interactions but also their emotional well-being and even cause premature dementia. Therefore, we are deeply interested in understanding how the elderly manage speech comprehension in such noisy settings and whether they employ additional cognitive mechanisms for this purpose.
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In a paper we published in Nature Communications in 2016, we discovered that despite the decline in perceptual processing abilities among the elderly, they tend to engage additional brain regions, particularly those associated with speech motor control, to actively predict what others will say. This proactive, top-down prediction mechanism helps the elderly better compensate for their declining perceptual abilities, enabling them to comprehend language more effectively, especially in complex environments.
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Building upon this background, in our study published in PNAS in 2017, we found that young individuals, particularly young musicians, excel at speech comprehension in complex environments. We were interested in uncovering the underlying neural mechanisms, and our results revealed that young musicians possess stronger perceptual-motor integration capabilities. They rely not only on auditory input but also on predicting and compensating for speech motor movements and integrating cross-modal information to aid their speech comprehension in intricate environments.
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These two studies suggest a potential connection between perception and motor. It appears that the elderly may rely more on advanced, top-down perceptual-motor predictions and integration, while young musicians, attributable to their musical training experience, have advatanges in utilizing perceptual-motor integration for speech comprehension. Naturally, this leads us to inquire whether the elderly, through systematic musical training, can also enhance their cross-modal information integration abilities. Will such training potentially aid them in improving their speech comprehension within complex environments?
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Based on this premise, we embarked on a series of subsequent studies and found that compared to elder non-musicians, elder musicians are better at speech perception in noisy environments. In 2021, we published related behavioral research in Ear and Hearing, and followed up with new studies whose most recent findings have been published in Science Advances.
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<h4>Question: You mentioned that you recently published a study in Science Advances. Could you please introduce the new findings from this research?</h4>
&nbsp;
Professor Du: The recently published study in Science Advances is a featured cover article. We recruited three groups of subjects: one consisted of young non-musicians as a control group, but our primary focus was on the other two groups—elder non-musicians and elder musicians.
&nbsp;
We had them perform a visual-auditory speech syllable discrimination task in noisy environment, while monitoring their brain activity using fMRI. They would hear different syllables while simultaneously viewing matching lip movements on a screen. Their task was to determine what exactly they have heard against background noise of varying intensities.
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We found that behaviorally, the performance of elder musicians was closer to that of young individuals, with no significant differences in their performance, even in moderate and light noise backgrounds. In other words, musical training experience can assist the elderly in achieving performance similar to that of young individuals. This finding leads to the question: what are the underlying mechanisms?
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Firstly, compared to elder non-musicians, elder musicians exhibited neural processing patterns (also known as representation patterns) that were more similar to those of young individuals in both the left and right perceptual-motor regions when processing syllables in noisy environment. Sometimes, there’s even no significant differences between the two groups. In fact, their performance was significantly better than that of elder non-musicians who displayed a noticeable decline in distinguishing different speech sounds compared to young individuals. Their brains struggled to distinguish between, for example, “ba” and “da”. In contrast, elder musicians maintained strong neural differentiation abilities, similar to young individuals. This indicates that musical training experience can enhance and rejuvenate brain function which we refer to as functional preservation.
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Furthermore, we also discovered that elder musicians, in contrast to elder non-musicians, activated a broader range of brain regions within the frontoparietal network. These brain regions are associated with working memory and cognitive control. This suggests that elder musicians are capable of engaging more advanced brain regions for behavioral compensation.
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Simultaneously, elder musicians exhibited greater suppression of a brain region known as the angular gyrus compared to elder non-musicians. As a core component of the default brain network, angular gyrus is associated with autobiographical memory and long-term memory. Therefore, it tends to be activated when we are not engaged in specific tasks and relatively suppressed when we perform cognitive tasks. When we engage in cognitive tasks that require our attention to external stimuli, it becomes necessary to suppress brain regions in default network to prevent them from interfering.
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We found that elder musicians excel at suppressing this brain region, indicating their ability to focus more attentively on external stimuli and suppress irrelevant information. This enhanced suppression also assists them in achieving better behavioral performance. Elder musicians achieve improved performance by employing additional activation or inhibition of certain brain regions, a mechanism referred to as functional compensation.
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This mechanism is related to the first one mentioned earlier &#8212; functional preservation. We found that these two mechanisms are interconnected and mutually influence each other. When an elderly possess stronger abilities to activate the frontoparietal network and to inhibit the angular gyrus, his/her neural representation in the perceptual-motor brain regions also improves, rejuvenated to the level of young individuals. This suggests that functional compensation can support enhanced functional preservation. Based on these two mechanisms, musical training experience can assist elder individuals in speech perception in noisy environments and improve their behavioral performance.
<img loading="lazy" decoding="async" class="aligncenter wp-image-784" src="https://admin.next-question.com/wp-content/uploads/2023/11/81.png" alt="" width="600" height="761" srcset="https://admin.next-question.com/wp-content/uploads/2023/11/81.png 1080w, https://admin.next-question.com/wp-content/uploads/2023/11/81-237x300.png 237w, https://admin.next-question.com/wp-content/uploads/2023/11/81-808x1024.png 808w, https://admin.next-question.com/wp-content/uploads/2023/11/81-768x974.png 768w" sizes="(max-width: 600px) 100vw, 600px" />
<h4>Question: Based on your research, how often does one need to play a musical instrument to potentially help preserve cognitive function and counteract the negative effects of aging?</h4>
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Professor Du: In this study, we asked the elderly to maintain a minimum of one hour of training per week within the last three years. Although they self-reported a substantial training frequency over the preceding three to four decades, it was challenging to track their training durations during the 1960s and 1970s due to historical reasons. Therefore, our focus was solely on the training frequency in the most recent three years.
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<h4>Question: Is it possible to achieve cognitive protection effects through temporary practice if one had no prior experience in playing a musical instrument?</h4>
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Professor Du: That&#8217;s a great question. What we&#8217;re currently conducting is a cross-sectional study where we directly compare the elderly with long-term musical training to those without such experience to observe differences, and we have found some. Additionally, there are numerous other longitudinal studies, both long-term and short-term, indicating that brief periods of musical training lasting from three to six months can also enhance language processing abilities for the elderly.
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<h4>Question: Can we achieve the same effects by listening to music?</h4>
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Professor Du: We all hope that passive music listening can yield similar positive effects, but current findings do not allow us to draw that conclusion definitively. Our study focuses on the effects of actively engaging in musical training, which involves multiple systems including visual, auditory, motor, emotional, and attentional processes. This multi-system engagement may have a more pronounced impact. However, listening to music also has its own benefits, such as mood regulation, stress relief, and potential improvement of focus and attention.
&nbsp;
<h4>Question: The participants in your study were engaged in a visual-auditory syllable discrimination task. Have you considered conducting other types of tasks?</h4>
&nbsp;
Professor Du: Both musicians and non-musicians in our study have completed other types of tasks. One of these tasks involved auditory working memory, where participants were presented with sequences of numbers of varying lengths and asked to repeat them in the original order or reversed order. Additionally, we assessed their executive control abilities, including the classic Stroop effect (the interference of literal meaning in font color).
&nbsp;
We found that elder musicians exhibited superior auditory working memory compared to elder non-musicians. While we didn&#8217;t observe better performance in cognitive control tasks like the Stroop effect in our study, it&#8217;s noteworthy that other research has indicated that musical experience can enhance working memory and executive control capabilities.
&nbsp;
The reason why we conducted the visual-auditory syllable discrimination task in our study was that speech processing involves real-time processing for everyone, and identifying consonants, vowels, and tones is crucial for recognition. Therefore, syllable discrimination tasks serve as a fundamental unit for speech processing.
&nbsp;
<h4>Question: Compared to non-musicians, musicians exhibit superior speech perception abilities in noisy environments. Do you believe this is solely attributable to their musical training, or could there be other factors at play?</h4>
&nbsp;
Professor Du: We chose musical training for our study because it represents a multi-system training approach that is relatively enjoyable. Moreover, it has been associated with improvements in various cognitive functions, such as working memory, selective attention, and cognitive control, among others. This includes findings from our research: stronger inhibition of the default brain network and enhanced modulation of the frontoparietal attention network among musicians. Therefore, we believe it is likely associated with musical training.
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However, since our study is cross-sectional, it&#8217;s important to acknowledge that there may be other differences between musicians and non-musicians in various aspects, and we cannot definitively rule out whether these differences are caused by innate factors or acquired ones.
&nbsp;
It&#8217;s possible that some individuals become musicians because of better innate structural and functional foundations or other traits that guide them toward a musical path. Therefore, our current cross-sectional study cannot definitively confirm the involvement of other factors, especially personal traits that might contribute to superior speech processing abilities. To address this question, conducting a longitudinal study would be ideal to establish whether these effects are indeed the results of musical training.
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<h4>Question: Are there any other studies examining similar topics? If so, are there any similarities and discrepancies between those studies and yours?</h4>
&nbsp;
Professor Du: Many researchers have indeed investigated whether musical training can assist the elderly in improving their speech perception, and they have found similar results at the behavioral level. What sets our new study apart from theirs is that we were able to identify cognitive neural mechanisms supporting both functional preservation and compensation in the brain. This allowed us to provide better explanation in terms of mechanisms to improved behavioral performance.
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<h4>Question: Has this study shed new light on anti-aging and brain health? Are there potential clinical application value of the findings, such as developing targeted interventions or treatment methods to enhance speech perception or other cognitive abilities among the elderly?</h4>
&nbsp;
Professor Du: We believe that musical training can have a neuroprotective effect on the brain by bringing it back to youth.
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While our study focused on the effects of long-term musical training, in the future, we are interested in investigating the minimum durations and frequencies of training that might also yield similar outcomes.
&nbsp;
This also reminds us that regardless of age, if you can start learning a musical instrument or even singing as early as possible, it may potentially slow down the aging process of the brain. Therefore, we currently advocate for the general public to consider learning music if plausible and to start engaging with music.
&nbsp;
In terms of clinical value, we are interested in exploring whether music therapy could be employed in the context of certain cognitive degenerative disorders among the elderly, such as Alzheimer&#8217;s disease, to improve their memory and language abilities.
&nbsp;
<h4>Question: Could music-based games potentially have similar effects?</h4>
&nbsp;
Professor Du: That&#8217;s a great approacg. Games like Rhythm Master can be effective training methods. We also refer to this as digital therapy. Using music as a form of digital therapy holds great promise for the future because gamified training like this is undoubtedly more intriguing and appealing for the elderly get fully engaged.
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Interviewed &amp; Edited by: Ashley Wang 
Proofread by: Xu Yunke

Interview with Prof. Du Yi: Can music rejuvenate the brain

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It has taken decades for computer scientists to finally materialize automatic speech recognition AI models that are closer to the human level. Such purely engineered AI models have completely abandoned earlier modeling frameworks based on linguistic theory, and entirely adopted data-driven, end-to-end, large-scale pre-trained deep neural networks. So how much does such a model resemble the auditory pathways of the human brain?
&nbsp;
To address this issue, Professor Yuanning Li&#8217;s team at the School of Biomedical Engineering, ShanghaiTech University, in collaboration with Professor Edward Chang at the University of California, San Francisco, and Professor Jinsong Wu/Junfeng Lu&#8217;s team at Fudan University, integrated a variety of technological approaches such as self-supervised pre-trained in-depth speech modeling, high-density intracranial electroencephalograms, and single-neuron simulation modeling, and thoroughly investigated the computational and representational similarities between AI speech models and the auditory pathways of the human brain utilizing a cross-language (English and Chinese) control experiment paradigm.
&nbsp;
Researchers can extract features at different levels &#8211; from purely acoustic spectrogram features to phonetic vowels and consonants and articulations, to relative pitches containing contextual information, etc. &#8211; and then use sliding time windows to predict neural responses. If a particular type of feature accurately predicts neural activity in a particular region, it is usually assumed that the neural activity encoded in that region expresses that type of feature.
&nbsp;
Over the past decade or so, using intracranial electrophysiological recording experiments as well as neural coding models, researchers have identified several important features of neural coding, e.g., the activity of different neural populations in the secondary auditory cortex of the superior temporal gyrus encodes features ranging from envelopes of voice to specific vowel and consonant phonemes, and so on.
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In this study, in addition to the secondary auditory cortex, which is closely related to speech, using intracranial high-density EEG recording technology and high-precision single-neuron biophysical simulation modeling, the researchers obtained neural responses covering the entire auditory pathway from the auditory nerves to the brainstem to the auditory cortex.
&nbsp;
Given that AI models and brain auditory circuits are capable of receiving the same speech input and performing similar cognitive functions, are there computational and representational similarities between the two? This is the key question on which this study focuses.
&nbsp;
To address this problem, the researchers have constructed a new deep neural coding model. This is a purely data-driven model that extracts feature representations from deep neural networks pre-trained with speeches, applies these data-driven features to construct a new linear coding model, and performs correlation analysis with authentic brain auditory response signals to investigate the similarity between the intrinsic feature representations of deep neural networks and the activities of different neural populations within the brain auditory pathway.
&nbsp;
By comparing the performance of neural activity prediction at different nodes of the auditory pathway of the neural coding models built based on these models, it was found that there is indeed a great similarity between the hierarchical structure of the end-to-end speech pre-training network, and that of the auditory circuit.
&nbsp;
First, for the entire auditory pathway, the encoding prediction model based on deep neural network features is comprehensively better than the traditional linear feature model based on linguistic theory. This indicates that the entire auditory pathway has strong nonlinear features. Second, models with different levels of complexity correspond to different regions in the auditory pathway. In addition, it was found that for the same self-supervised speech model, its overall hierarchical structure corresponds to the AN-IC-STG hierarchical structure of the auditory pathway.
&nbsp;
Having established the representational similarities between the deep speech model and the auditory pathway, the researchers further explored the computational mechanisms driving these representational similarities, focusing on the best-performing HuBERT model.
&nbsp;
The results show that as the network deepens, the attentional weights aligned to long-range contextual structures become progressively larger. It is worth emphasizing that the HuBERT model used here is fully self-supervised, and the training process does not include any explicit information about the context structure as well as the speech content information. This result suggests that a self-supervised trained speech model can learn key context structure information related to language and semantics in natural speech.
&nbsp;
In the secondary auditory cortex of the superior temporal gyrus, which is closely related to speech processing, the higher the alignment of the self-attentional weights with the contextual structures in speech, the better the neural network&#8217;s prediction of brain activity, while conversely, in the primary auditory cortex as well as in these regions of the auditory nerve and brainstem, the greater the expression of localized transient information in the temporal domain, and the greater the resemblance between neural networks and brain signals.
&nbsp;
Last but not least, the study further analyzed whether the self-supervised model was able to learn more advanced contextual information. The results show that the self-supervised model is able to learn higher-level contextual information related to linguistic specificity and that this specificity information is significantly correlated with computation and representation in the phonetic cortex of the brain.
&nbsp;
This study provides a new biological perspective on unlocking the “black box” of deep neural networks, especially the self-attention model called Transformer.
&nbsp;
<a href="https://www.nature.com/articles/s41593-023-01468-4">Read the paper on Nature Neuroscience</a>

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