Observations

How to Bridge the Gap Between Artificial Intelligence and Human Intelligence?

[:en]How to Bridge the Gap Between Artificial Intelligence and Human Intelligence?[:]

A Quiet Revolution: How Focused Ultrasound is Redefining Non-Invasive Brain Treatment

[:en]A Quiet Revolution: How Focused Ultrasound is Redefining Non-Invasive Brain Treatment[:]

What Will Future Life Forms Look Like?

[:en]What Will Future Life Forms Look Like?[:]

Acupuncture: The Revival of Ancient Wisdom in Modern Neuroscience

[:en]Acupuncture: The Revival of Ancient Wisdom in Modern Neuroscience[:]

From The Journals

[:en]<div style="line-height: 1.5;">The human brain operates with remarkably low energy consumption while performing vast computational processes. Understanding this requires a deep comprehension of the synaptic connections, spatial structures, and static networks of neural circuits, along with the dynamic cognitive processes built upon them. These tasks far exceed the traditional scope of brain region functionality studies. Connectomics research aims to achieve this by precisely mapping how each cell connects with others.
With advancements in scientific research, especially the rapid improvements in computational and data processing capabilities, research methods for the nervous system are also evolving. In this context, technology giants like Google have begun investing in this field, applying advanced technologies to neuroscience research, thereby driving the rapid development of connectomics. Scientists have now mapped detailed connectomes of various organisms' brains, rapidly changing our understanding of brain functioning.
Ten years ago, Google started focusing on AI innovations and collaborating with large datasets, thereby establishing a connectomics team. In reconstructing brain structures, they generated massive amounts of data. To effectively handle this data, the Google team developed the flood-filling networks (FFN) model to replace the traditional manual method of annotating cells in images [1]. These networks can automatically reconstruct neurons through tissue layers. Based on this, Google developed the SegCLR algorithm to automatically identify different parts and cell types within these networks [2]. The Google connectomics team also created TensorStore [3], an open-source C++ and Python software library for storing and managing large multidimensional datasets. This tool has achieved functionality far beyond connectomics and is now widely used at Google and in the broader machine learning community.
In 2018, Google partnered with the Max Planck Institute for Neurobiology in Germany to develop a deep learning-based system that can automatically map brain neurons [1]. They reconstructed the scanned images of one million cubic micrometers of a zebra finch brain.

<img class="aligncenter size-full wp-image-2441" src="https://admin.next-question.com/wp-content/uploads/2024/09/Picture1-2.png" alt="" width="999" height="508" />

▷ Illustration of segmenting each neuron in a small part of the zebra finch brain using FFN. Source: google.

In 2019, Google collaborated with the Howard Hughes Medical Institute and the University of Cambridge, using the FFN model and TPU chips to slice a fruit fly brain into thousands of 40-nanometer ultra-thin sections. They used a transmission electron microscope to generate images of each slice, producing over 40 trillion pixels of fruit fly brain images. These 2D images were then aligned to reconstruct a complete 3D image of the fruit fly brain [4]. In 2020, Google released the fruit fly hemibrain connectome, which included images covering 25,000 neurons and revealed their synaptic connections, accounting for about one-third of the fruit fly brain by volume [5].

<img class="aligncenter size-large wp-image-2440" src="https://admin.next-question.com/wp-content/uploads/2024/09/Picture2-2-1024x768.png" alt="" width="1024" height="768" />

▷ Fruit fly hemibrain connectome.

Recently, Google collaborated with the Lichtman Laboratory at Harvard University to publish an article in Science titled "A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution."

<img class="aligncenter size-full wp-image-2439" src="https://admin.next-question.com/wp-content/uploads/2024/09/Picture3-2.png" alt="" width="951" height="429" />

▷ Shapson-Coe, Alexander, et al. "A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution." Science 384.6696 (2024): eadk4858.

This study released the latest H01 dataset, a 1.4 PB rendered image of a small sample of human brain tissue. The H01 sample was imaged at a resolution of 4nm using serial section electron microscopy and then reconstructed and annotated through automated computational techniques, ultimately revealing the preliminary structure of the human cerebral cortex. The H01 dataset is not only the largest sample of its kind imaged and reconstructed to this extent in any organism, but it is also the first large-scale sample to study the synaptic connectivity of the human cerebral cortex, spanning multiple cell types across all cortical layers.

&nbsp;

<img class="aligncenter size-full wp-image-2438" src="https://admin.next-question.com/wp-content/uploads/2024/09/Picture4-2.png" alt="" width="602" height="615" />

▷ Figure 1: The H01 dataset of the human cerebral cortex.

The research team encountered many obstacles in creating this image. One major issue was finding suitable brain tissue samples. Since the human brain begins to degrade rapidly after death, postmortem brain tissue is often unsuitable for research. Therefore, the team chose a tissue sample taken during brain surgery from an epilepsy patient, aiming to help control their seizures. Additionally, identifying the structure of neural circuits at the synaptic level requires higher resolution electron microscopy. Serial EM, with its automation and rapid imaging capabilities, can image cubic millimeter volumes of tissue at nanoscale resolution, making it a powerful tool for reconstructing the fine neural circuits of the human brain.

The outcomes of this work primarily include the following four aspects:

&nbsp;
<h2>1.Reconstruction of Human Brain Samples and Neuron Connectivity</h2>
The human brain sample used in this study was taken from the left anterior temporal lobe of an epilepsy patient during surgery. The sample was about the size of half a grain of rice. After fixation, staining, and embedding, the sample was sectioned using an automatic tape-collecting ultramicrotome (ATUM), producing 5,019 tissue sections with an average thickness of 33.9 nm, totaling 0.170 mm in thickness. These sections were then placed on silicon wafers and imaged under a custom-built 61-beam parallel scanning electron microscope at a resolution of 4x4 nm², rapidly capturing images with a total imaging volume of 1.05 mm³. The raw data size of each section reached up to 350 terabytes, and the overall dataset size reached 1.4 petabytes.

The subsequent computational challenge was to analyze such a massive dataset. The researchers successfully addressed this issue using artificial intelligence algorithms. The team computationally stitched and aligned this data to generate a single 3D volume. Despite the overall high quality of the data, these alignment channels needed to be robust to handle challenges such as imaging artifacts, missing sections, variations in microscope parameters, and physical stretching and compression of the tissue. Once aligned, a multi-scale FFN (flood-filling network) algorithm, utilizing thousands of Google Cloud TPUs, was applied to generate 3D segmentations of each individual cell in the tissue. FFN is the first automated segmentation technique capable of producing sufficiently accurate reconstructions. Additionally, other machine learning pipelines were used to identify and annotate 130 million synapses, dividing each 3D segment into different "subregions" (e.g., axons, dendrites, or cell bodies) and identifying other structures of interest, such as myelin and cilia.

However, the results of automated reconstruction were not perfect. Given the massive amount of neuronal structural data, it was impractical for individual lab members to manually proofread the entire dataset. Therefore, researchers from Princeton University and the Allen Institute developed an online proofreading platform called CAVE (Connectome Annotation and Versioning Engine), as an extension of Neuroglancer. This platform allows users to proofread and update annotated data to improve the dataset as part of their research. Researchers can select specific neurons or their connections and manually refine and annotate their reconstructions. CAVE is already used for smaller datasets, including the fly connectome.

For this project, researchers updated the algorithms to handle such large datasets. The interface permits proofreading, which is a crucial final step in completing the connectome reconstruction. Anyone can apply to become a proofreader, and proofreaders can download data related to the cells they are working on for subsequent analysis. Additionally, VAST (Volume Annotation and Segmentation Tool) is available as a general free software for viewing, segmenting, and annotating large datasets. For diversified analysis, researchers also provided various databases allowing users to query cell and synapse data specifically. To support complex query conditions, they developed a standalone program called CREST (Connectome Reconstruction and Exploration Simple Tool), which identifies and explores cell connections based on many cell characteristics.

<img class="aligncenter size-full wp-image-2437" src="https://admin.next-question.com/wp-content/uploads/2024/09/Picture5-2.png" alt="" width="747" height="996" />▷ Source: Viren Jain
<h2>2. Composition of Cells and Synapses in the Human Cerebral Cortex</h2>
Through identification and quantitative analysis of all nucleated cells in the sample, 49,080 neurons and glial cells, along with 8,100 vascular-related cells, were identified. The number of glial cells (32,315) was about twice that of neurons (16,087). These neurons were mainly divided into spiny (65.5%), non-spiny, and non-pyramidal types. The neuronal density in the human cerebral cortex is about 16,000/mm³, which is approximately one-third lower than previously estimated by optical microscopy in the human temporal cortex and nearly ten times lower than the density in the mouse associative cortex.

Based on the size and distribution density of the cell bodies, the researchers divided the sample into six cortical layers and white matter. The distribution of glial cells varied among these layers. Notably, protoplasmic astrocytes were densely distributed from layers 2 to 6. However, in the shallower part of layer 1, they tended to be more mixed, with higher density and smaller size. Fibrous astrocytes in the white matter were more elongated than those occupying the cortex. Two other types of glial cells, microglia and oligodendrocyte precursor cells (OPCs), had nearly uniform density across all layers. Another type of glial cell, oligodendrocytes (n=20,139), exhibited a gradient distribution, with the lowest density in the upper layers and the highest density in the white matter, likely related to their role in myelination. Microglia, OPCs, and oligodendrocytes all showed an affinity for blood vessels. A large number of myelinated axons were radially distributed between the white matter and the superficial cortical layers, horizontally distributed on two orthogonal axes. The reconstructed blood vessels did not show layer-specific distribution.

Synapses are the bridges of the neural network. The human brain has 86 billion neurons, and synapses allow the transmission of electrical signals from one neuron to the next. To identify synaptic sites, the researchers trained a classifier based on the U-Net architecture to label three categories: background, presynaptic, and postsynaptic. They also trained a binary ResNet-50 classifier to categorize each identified synapse as excitatory or inhibitory based on its electron microscopy appearance, postsynaptic structure type, and presynaptic neuron type (if known).

In the reconstructed human brain tissue, approximately 102.5 million (67.1%) synapses were identified as excitatory, and about 50.3 million (32.9%) as inhibitory (Fig.2). Further analysis showed that most of these synapses were located on dendrites (99.4%), with only a small portion on axon initial segments (0.2%) or cell bodies (0.4%). This data is accessible on the Neuroglancer platform. By analyzing the reconstructed synapses, the researchers found that excitatory synapses were mainly distributed in layers 1 and 3, while inhibitory synapses were predominantly found in layer 1. Additionally, the ratio of excitatory to inhibitory inputs received by pyramidal neurons and interneurons varied slightly across layers, reflecting the complex regulatory mechanisms of neural networks.

<img class="aligncenter size-full wp-image-2438" src="https://admin.next-question.com/wp-content/uploads/2024/09/Picture4-2.png" alt="" width="602" height="615" />

▷ Figure 2: Distribution of cells and synapses in the human cerebral cortex and white matter.
<h2>3.Morphological Subtypes of Layer 6 Triangular Neurons</h2>
The deeper layers of the cerebral cortex are less understood than the superficial layers due to the complexity and diversity of cell types present. In this study, the researchers focused on spiny neurons in the deep cortical layers, particularly triangular neurons, which are not well understood in primates. In the reconstructed human brain tissue, triangular neurons were predominantly located in layers 5 and 6 (n = 876), accounting for about one-third of the spiny neurons. These cells are characterized by their large basal dendrites extending from the soma in various directions, forming a notable directional distribution, thus referred to as "compass neurons."

The study further distinguished two types of neurons based on the direction of their basal dendrites: one type with large basal dendrites extending anteriorly and another with large basal dendrites extending posteriorly at a mirror-symmetric angle. These two groups of cells formed a mirror-symmetric distribution in the cerebral white matter (Fig.3). Additionally, the direction of the basal dendrites exhibited a bimodal distribution, with most cells extending either significantly forward or markedly backward, while others were more tangentially oriented. The distribution of these basal dendrites corresponded to the functional regions of the cerebral cortex and might be related to the principal axis of the myelinated axons in the subjacent white matter. These neurons tended to cluster with nearest neighbor cells having the same orientation, suggesting that this spatial distribution is not random. The statistical correlation of these mirror-symmetric neurons indicates that such cell clustering might have some underlying, yet unclear, functional significance.

&nbsp;

<img class="aligncenter size-large wp-image-2435" src="https://admin.next-question.com/wp-content/uploads/2024/09/Picture7-1-1024x546.png" alt="" width="1024" height="546" />

▷ Figure 3: Two mirror-symmetric subgroups of triangular neurons in the deep layers of the human cerebral cortex.
<h2>4.Multiple Synaptic Connections</h2>
Previous research has shown that axons in the rodent cerebral cortex occasionally form multiple synaptic connections on the same postsynaptic cell. To investigate whether the same phenomenon occurs in the human cerebral cortex, researchers used CREST to systematically identify strong connections in reconstructed human brain tissue. The results indicated that, in the human cerebral cortex, single axons occasionally establish multiple synapses on the same postsynaptic cell. Researchers systematically discovered this phenomenon in both excitatory and inhibitory axons. Although such strong connections are rare, they are extremely potent, with over 50 individual synaptic connections possible between pairs of neurons. Typically, 96.5% of axon-target cell contacts have only one synapse, while 0.092% of axon connections may have four or more synapses.

Furthermore, analysis of 2,743 neurons revealed that 39% of neurons had at least one dendritic input with seven or more synapses, indicating that strong axonal inputs are a common feature of human brain neurons. Interestingly, these multiple synaptic connections are often formed by axons maintaining close contact with dendrites over long distances (Fig.4).

The authors also explored whether the number of synapses established by an axon on target cells exceeded the expectations of a random model. The results showed that the incidence of strong connections observed was significantly higher than the model's predictions (Fig.4). Although the phenomenon of single axons establishing multiple synapses on the same postsynaptic cell is uncommon in the human cerebral cortex, these rare, strong connections might be an important component of complex neuron-to-neuron communication.

<img class="aligncenter size-large wp-image-2434" src="https://admin.next-question.com/wp-content/uploads/2024/09/Picture8-1-1024x871.png" alt="" width="1024" height="871" />

▷ Figure 4: Exceptionally strong synaptic connections in the human cerebral cortex.

Using nanoscale resolution to explore the six-layer structure of the human cortex, researchers revealed details of brain tissue at the cellular to subcellular level, investigating the complex relationships among neurons, synapses, glia, and blood vessels. Additionally, the authors provided several software tools, such as the browser-based Neuroglancer and its derivatives CREST and CAVE, as well as VAST, greatly facilitating data visualization and annotation.

However, the study has certain limitations. Fresh samples from healthy individuals are unlikely to be obtained through this neurosurgical approach. Although the patient's temporal lobe did not show substantial pathological changes under light microscopy, long-term epilepsy or its drug treatment may have subtle effects on cortical tissue connectivity or structure.

Some unusual features were observed in this tissue, including some very large spines, axon varicosities filled with unusual substances, and a few axons forming extensive "axon helices." It is currently unclear whether these rare structures are related to the disease or its treatment or are common features of the human brain. Comparing samples obtained from individuals with different underlying conditions will help us better understand these phenomena. Moreover, since the structure of the human cerebral cortex partly depends on past experiences, there is structural variability among individuals, but the extent of this variability is still unknown.

Considering the greater variability in human experience, behavior, and genetics, and the fact that humans and other vertebrates have many identified neuron categories rather than single identified neuron types, comparing neural circuits among human brains may be more challenging.

</div>[:]

Mapping the Largest Fragment of the Human Brain

[:en]Mapping the Largest Fragment of the Human Brain[:]

Latest News

Interviews With Scientists

Interview with Prof. Moritz Helmstaedter: The decade-long debate has concluded, Has the European Brain Project failed?

[:en]Interview with Prof. Moritz Helmstaedter: The decade-long debate has concluded, Has the European Brain Project failed?[:zh]十年争议落幕，欧洲脑计划失败了吗？｜追问专访·Moritz Helmstaedter[:]

Interview with Professor Si Wu: Unlocking the "Door of Wisdom" in Artificial Intelligence

[:en]Interview with Professor Si Wu: Unlocking the "Door of Wisdom" in Artificial Intelligence[:zh]吴思：打开人工智能的“智慧之门”[:]

Interview with Prof. Wang Xiaojing: Fostering interdisciplinary talents for computational neuroscience

[:en]Interview with Prof. Wang Xiaojing: Fostering interdisciplinary talents for computational neuroscience[:zh] 汪小京 · 为计算神经科学培养跨学科人才[:]

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.
&nbsp;
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>
&nbsp;
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.
&nbsp;
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.
&nbsp;
<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?
&nbsp;
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.
&nbsp;
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.
&nbsp;
<h4>Question: Can we achieve the same effects by listening to music?</h4>
&nbsp;
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.
&nbsp;
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.
&nbsp;
<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.
&nbsp;
<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.
&nbsp;
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.
&nbsp;
Interviewed &amp; Edited by: Ashley Wang 
Proofread by: Xu Yunke

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

[:en]Interview with Prof. Du Yi: Can music rejuvenate the brain[:zh]杜忆 · 音乐让大脑“返老还童”？[:]

Interview with Prof. Wu Mengyue: Do machines understand sounds better than humans?

[:en]Interview with Prof. Wu Mengyue: Do machines understand sounds better than humans?[:zh]吴梦玥 · 机器比人类更会理解声音吗？[:]

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How Does the Large Language Model Proclaim the Death of Psychology?

[:en]How Does the Large Language Model Proclaim the Death of Psychology?[:zh]万字长文，大语言模型如何宣告心理学的死亡?[:]

Ultrasound Telepathy: A New Breakthrough! Utilizing Intent to Control Direction, Achieving 'Precognition without Motion'

[:en]Ultrasound Telepathy: A New Breakthrough! Utilizing Intent to Control Direction, Achieving 'Precognition without Motion'[:zh]“超声读心”新突破！用意念控制方向，实现“未动先知”[:]

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?
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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This study provides a new biological perspective on unlocking the “black box” of deep neural networks, especially the self-attention model called Transformer.
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<a href="https://www.nature.com/articles/s41593-023-01468-4">Read the paper on Nature Neuroscience</a>

How similar are AI speech models to the auditory pathways of humans?

[:en]How similar are AI speech models to the auditory pathways of humans?[:zh]AI语音模型与人的听觉有多相似？上科大/UCSF/复旦联合团队解析深度语音模型与人脑听觉通路的表征与计算相似性[:]

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[:en]Because the Mountain is There - A Decade of Human Brain Projects[:zh]因为山就在那里——人类脑计划的十年[:]

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