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Connectome harmonic decomposition (CHD) generalises the mathematics of the Fourier transform to the network structure of the human brain. The traditional Fourier transform operates in the temporal domain (Fig. 1a): decomposition into temporal harmonics quantifies to what extent the signal varies slowly (low-frequency temporal harmonics) or quickly (high-frequency temporal harmonics) over time (Fig. 1b). Analogously, CHD re-represents a spatial signal in terms of harmonic modes of the human connectome, so that the spatial frequency (granularity) of each connectome harmonic quantifies to what extent the organization of functional brain signals deviates from the organization of the underlying structural network (Fig. 1c, d). Therefore, CHD is fundamentally different from, and complementary to, traditional approaches to functional MRI data analysis. This is because CHD does not view functional brain activity as composed of signals from discrete spatial locations, but rather as composed of contributions from distinct spatial frequencies: each connectome harmonic is a whole-brain pattern with a characteristic spatial scale (granularity)—from an entire hemisphere to just a few millimetres.

On one hand, this means that CHD is unsuitable to address questions pertaining to spatial localisation and the involvement of specific neuroanatomical regions; such questions have been extensively investigated within the traditional framework of viewing brain activity in terms of spatially discrete regions, and several previous studies have implicated specific neuroanatomical regions in supporting consciousness^{33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49}. On the other hand, CHD enables us to consider how brain activity across states of consciousness is shaped by the brain’s distributed network of structural connections, reflecting the contribution of global patterns at different spatial scales—each arising from the network topology of the human connectome. We emphasise that neither approach is inherently superior, but rather they each provide a unique perspective on brain function: one localised, the other distributed.

CRISPR gene editing created the G795A amino acid which was introduced to microglia derived from human stem cells. Researchers were able to transplant the donor microglia immune cells into humanized rodent models while administering an FDA-approved cancer drug called pexidartinib. The inclusion of the amino acid cause the donated microglia to thrive and resist the drug, while the host microglia died. The findings open the door for new methods of using microglia to treat a range of neurodegenerative disorders.

Part 2/2. Graph Theoretical Modelling of Brain Connectivity. Concepts and Workflow. GraphVar by Dr. Johann D. Kruschwitz.

Graph Theoretical Modelling of Brain Connectivity. Concepts and Workflow. GraphVar by Dr. Johann D. Kruschwitz.

(see below for links to each of the sections)

The goal of the Evolving Neural Networks workshop is to bring together experts from Systems and Computational Neuroscience, Machine Learning and the Evo-Devo field to discuss if and how knowing the evolutionary history of neural circuits can help us understand the way the brain works, as well as the relative importance of learned VS innate neural mechanisms.

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The workshop discusses the recent issue Systems Neuroscience Through The Lens of Evolutionary Theory edited by Paul Cisek and Ben Hayden published by the Royal Society:

https://royalsocietypublishing.org/toc/rstb/2022/377/1844

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Chinese scientists in a 10-year study found that people who adhered to the special six rules, the risk of dementia decreased by 90%. Even with a genetic predisposition to Alzheimer’s disease. The study involved 29 thousand people over 60 years of age, who were divided into three groups.

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Persons with autism spectrum conditions (ASC) often engage in video gaming, one of the most common leisure activity in this population. Escapism, aimed at the avoidance of negative experiences or self-development, is considered as one of the main gaming motivations. Furthermore, escapism is a self-regulatory strategy used while suffering from autistic burnout, consisting of exhaustion, reduced social skills, anhedonia, and withdrawal. The goal of the current study was to determine predictors of escapism in video gaming among adult gamers with ASC. It was hypothesized that two types of escapism – self-suppression and self-expansion – would differentiate gaming motivations, affective outcomes, anhedonia, and autistic burnout rates. A total of 189 persons participated in the study (M_age = 27.52, SD_age = 7.25), including 105 females. The results obtained indicated that self-suppression escapism was predicted by introjected regulation, positive and negative affect, and hedonic tone (F = 8.760, p < .001), while self-expansion was predicted by identified and integrated gaming motivations, hedonic tone, and positive affect (F = 23.066, p < .001). PLS-SEM analysis revealed good fit of the model with autistic burnout predicting self-suppression escapism. These results acknowledge the two-dimensional approach to escapism and highlight potential risk factors of self-suppression, especially among persons presenting symptoms of autistic burnout. Future research and clinical application directions are outlined.

Some philosophers search for the mark of the cognitive: a set of individually necessary and jointly suΜcient conditions identifying all and only the instances of cognition. They claim the mark is necessary to answer diΜcult questions concerning the nature and distribution of cognition.

Here, I will argue that, as things stand, given the current landscape of cognitive science, we are not able to identify a mark of the cognitive. I proceed as follows. First, I clarify some factors motivating the search for the mark of the cognitive, thereby highlighting the desiderata the mark is supposed to satisfy. Then, I highlight a tension in the literature over the mark. Given the literature, it is not clear whether the search aims for a mark capturing the intuitive notion of cognition or a genuine scientiΞc kind.