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Science ◽  
2022 ◽  
Vol 375 (6577) ◽  
pp. 167-172
Yang Yang ◽  
Diana Arseni ◽  
Wenjuan Zhang ◽  
Melissa Huang ◽  
Sofia Lövestam ◽  

Hi-res view of human Aβ42 filaments Alzheimer’s disease is characterized by a loss of memory and other cognitive functions and the filamentous assembly of Aβ and tau in the brain. The assembly of Aβ peptides into filaments that end at residue 42 is a central event. Yang et al . used electron cryo–electron microscopy to determine the structures of Aβ42 filaments from human brain (see the Perspective by Willem and Fändrich). They identified two types of related S-shaped filaments, each consisting of two identical protofilaments. These structures will inform the development of better in vitro and animal models, inhibitors of Aβ42 assembly, and imaging agents with increased specificity and sensitivity. —SMH

2022 ◽  
Leor Zmigrod

A quick scan of the political landscape reveals that people differ in the ideologies they embrace and advocate. Why do individuals prefer certain ideologies over others? A formal analysis of psychological needs and consumption desires suggests that it is possible to compute the subjective utility of selecting one ideology over another, as though it were a purchasing decision. Given resources, constraints, and available options, individuals can rationally choose the ideology that best matches or resonates with their interests. It is a compelling framework that can take into account how diverse ideologies satisfy people’s diverse and multidimensional psychological and material needs. This psycho-economic model is ambitious and informative, and I will argue that it can be even more encompassing and enlightening if it is expanded to incorporate two critical components of ideological cognition: (1) the nature of ideological conviction and extremism and (2) the dynamic, probabilistic mental computations that underlie belief formation, preservation, and change. Firstly, I will argue that a formal model of ideological choice cannot escape the question of the strength of ideological commitment. In other words, we need to ask not only about which ideologies individuals choose but also about how strongly they adhere to these ideologies once those are chosen. An analysis of ideological choice needs to be accompanied by an analysis of ideological conviction. Secondly, in order to build a robust sense of the rationality behind ideological thinking, it is useful to incorporate principles of uncertainty and probability-based belief updating into the formal model of ideological worldviews. Bayesian models highlight how human brains seek to build predictive models of the world by updating their beliefs and preferences in ways that are proportional to their prior expectations and sensory experiences. Consequently, incorporating Bayesian principles into the formal model of ideological choice will provide a more wholistic understanding of what happens when a mind enters the market for belief systems – and why a mind can, at times, purchase toxic doses of the ideologies that sellers and entrepreneurs offer on display.

2022 ◽  
Joana Cabral ◽  
Francesca Castaldo ◽  
Jakub Vohryzek ◽  
Vladimir Litvak ◽  
Christian Bick ◽  

A rich repertoire of oscillatory signals is detected from human brains with electro- and magnetoencephalography (EEG/MEG). However, the principles underwriting coherent oscillations and their link with neural activity remain unclear. Here, we hypothesise that the emergence of transient brain rhythms is a signature of weakly stable synchronization between spatially distributed brain areas, occurring at network-specific collective frequencies due to non-negligible conduction times. We test this hypothesis using a phenomenological network model to simulate interactions between neural mass potentials (resonating at 40Hz) in the structural connectome. Crucially, we identify a critical regime where metastable oscillatory modes emerge spontaneously in the delta (0.5-4Hz), theta (4-8Hz), alpha (8-13Hz) and beta (13-30Hz) frequency bands from weak synchronization of subsystems, closely approximating the MEG power spectra from 89 healthy individuals. Grounded in the physics of delay-coupled oscillators, these numerical analyses demonstrate the role of the spatiotemporal connectome in structuring brain activity in the frequency domain.

2021 ◽  
Vol 1 (1) ◽  
pp. 031-038
Keiko Ikemoto

The latest psychopharmacological study showed effectiveness of a novel non-D2-receptor-binding drug, SEP-363856, for the treatment of schizophrenia. The compound is trace amine-associated receptor 1 (TAAR1) full agonist and also 5-hydroxytryptamin 1A (5-HT 1A) receptor partial agonist. I found the TAAR1 ligand neuron, D-neuron, in the striatum and nucleus accumbens (Acc), a neuroleptic acting site, of human brains, though failed to find in the homologous area of monkey brains. To study human D-neuron functions, total of 154 post-mortem brains, and a modified immunohistochemical method using high qualified antibodies against monoamine-related substances, was applied. The number of D-neuron in the caudate nucleus, putamen, and Acc was reduced in post-mortem brains with schizophrenia. The reduction was significant (p<0.05) in Acc. I proposed “D-cell hypothesis of schizophrenia”, that NSC dysfunction-based D-neuron reduction is cellular and molecular basis of mesolimbic dopamine (DA) hyperactivity, progressive pathophysiology and prospectiveness of TAAR1 medicinal chemistry, emphasizing importance of D-neuron.

2021 ◽  
Keiji Wakamatsu ◽  
Yoichi Chiba ◽  
Ryuta Murakami ◽  
Koichi Matsumoto ◽  
Yumi Miyai ◽  

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Hui Ding ◽  
Yajun Chen ◽  
Linling Wang

In today’s era, online teaching plays an important part in the college English teaching. Deep learning, famous for its ability of imitating the learning process of human brains and obtaining the internal essential features or rules of voice, videos, images, and other data, can be applied to assist and improve the college English online teaching which involves a wide use of those data. Based on the combination of the multilayer neural network model and the k-means clustering algorithm, this paper designs a kind of deep learning method that can be used to assist and improve the college English online teaching. Experiments were designed to test the reliability of this deep learning method. The results show that the optimization algorithm designed in this paper, which can adjust the learning rate, will improve the common probability gradient descent algorithm. Besides, it is proved that the deep learning’s efficiency of the CNN model is significantly higher than that of the MLP model. With the help of this deep learning method, it becomes feasible to apply the technologies related to the artificial intelligence to help teachers deeply analyze and diagnose students’ English learning behavior, replace the teachers in part to answer students’ questions in time, and automatically grade assignments in the process of the college English online teaching. Surveys and exams were then conducted to evaluate the effect of the application of the college English online teaching model based on deep learning on the students’ learning cognition and their academic performance. The results show that the college English online teaching model based on deep learning can stimulate students’ learning motivation and improve their academic performance.

Nature Aging ◽  
2021 ◽  
Shiva Kazempour Dehkordi ◽  
Jamie Walker ◽  
Eric Sah ◽  
Emma Bennett ◽  
Farzaneh Atrian ◽  

2021 ◽  
Lei Ding ◽  
Guofa Shou ◽  
Yoon-Hee Cha ◽  
John A. Sweeney ◽  
Han Yuan

AbstractSpontaneous neural activity in human as assessed with resting-state functional magnetic resonance imaging (fMRI) exhibits brain-wide coordinated patterns in the frequency of <0.1Hz. However, fast brain-wide networks at the timescales of neuronal events (milliseconds to sub-seconds) and their spatial, spectral, and propagational characteristics remain unclear due to the temporal constraints of hemodynamic signals. With milli-second resolution and whole-head coverage, scalp-based electroencephalography (EEG) provides a unique window into brain-wide networks with neuronal-timescale dynamics, shedding light on the organizing principles of brain function. Using state-of-the-art signal processing techniques, we reconstructed cortical neural tomography from resting-state EEG and extracted component-based co-activation patterns (cCAPs). These cCAPs revealed brain-wide intrinsic networks and their dynamics, indicating the configuration/reconfiguration of resting human brains into recurring and propagating functional states, which are featured with the prominent spatial phenomena of global patterns and anti-state pairs of co-(de)activations. Rich oscillational structures across a wide frequency band (i.e., 0.6Hz, 5Hz, and 10Hz) were embedded in the dynamics of these functional states. We further identified a superstructure that regulated between-state propagations and governed a significant aspect of brain-wide network dynamics. These findings demonstrated how resting-state EEG data can be functionally decomposed using cCAPs to reveal rich structures of brain-wide human neural activations.

2021 ◽  
Shun Li ◽  
Florian olde Heuvel ◽  
Rida Rehman ◽  
Zhenghui Li ◽  
Oumayma Aousji ◽  

AbstractImmune system molecules are expressed by neurons, often for unknown functions. We have identified IL-13 and its receptor IL-13Ra1 as neuronal, synaptic proteins in mouse, rat, and human brains, whose engagement upregulates the phosphorylation of NMDAR and AMPAR subunits and, in turn, increases synaptic activity and CREB-mediated transcription. We demonstrate that increased IL-13 is a hallmark of traumatic brain injury (TBI) in mice as well as in two distinct cohorts of human patients. We also provide evidence that IL-13 upregulation protects neurons from excitotoxic death. We show IL-13 upregulation occurring in several cohorts of human brain samples and in CSF. Thus, IL-13 is a previously unrecognized physiological modulator of synaptic physiology of neuronal origin, with implications for the establishment of synaptic plasticity and the survival of neurons under injury conditions. Furthermore, we suggest that the neuroprotection afforded through the upregulation of IL-13 represents a new entry point for interventions in the pathophysiology of TBI.

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