Comment: Brain networks consist of different modules or subnetworks that are activated during various cognitive tasks. In this large study of 780 subjects between 8-22 years the authors show that modules display a characteristic pattern of development which is related to the function and topology of the modules. Individual variation of this pattern correlates with variation in cognitive performance.
Comment: Resting-state functional brain networks were studied in subjects sedated with Propofol and their clinical level of consciousness was assessed. Factorial analysis showed that Propofol levels were associated with a frontal parietal loss of functional connectivity, while decreased responsiveness was associated with lower thalamic frontal, and higher thalamic temporal / occipital connectivity.
Comment: Resting-state fMRI was used in 99 patients with acquired brain injury (ABI) with various levels of disturbed consciousness and healthy controls. Loss of functional connectivity of a subset of regions was correlated with loss of consciousness and outcome after three months. With support vector machine analysis recovery of consciousness could be predicted with an accuracy of 81.25%. The most predictive regions were the posterior cingulate gyrus and the precuneus. This study illustrates the importance of hub connectivity for consciousness, and the potential of machine learning applied to connectivity networks for clinical application.
Brain networks or ‘connectomes’ include a minority of highly connected hub nodes that are functionally valuable, because their topological centrality supports integrative processing and adaptive behaviours. Recent studies also suggest that hubs have higher metabolic demands and longer-distance connections than other brain regions, and therefore could be considered biologically costly. Assuming that hubs thus normally combine both high topological value and high biological cost, we predicted that pathological brain lesions would be concentrated in hub regions. To test this general hypothesis, we first identified the hubs of brain anatomical networks estimated from diffusion tensor imaging data on healthy volunteers (n = 56), and showed that computational attacks targeted on hubs disproportionally degraded the efficiency of brain networks compared to random attacks. We then prepared grey matter lesion maps, based on meta-analyses of published magnetic resonance imaging data on more than 20 000 subjects and 26 different brain disorders. Magnetic resonance imaging lesions that were common across all brain disorders were more likely to be located in hubs of the normal brain connectome (P5104, permutation test). Specifically, nine brain disorders had lesions that were significantly more likely to be located in hubs (P50.05, permutation test), including schizophrenia and Alzheimer’s disease. Both these disorders had significantly hub-concentrated lesion distributions, although (almost completely) distinct subsets of cortical hubs were lesioned in each disorder: temporal lobe hubs specifically were associated with higher lesion probability in Alzheimer’s disease, whereas in schizophrenia lesions were concentrated in both frontal and temporal cortical hubs. These results linking pathological lesions to the topological centrality of nodes in the normal diffusion tensor imaging connectome were generally replicated when hubs were defined instead by the meta-analysis of more than 1500 task-related functional neuroimaging studies of healthy volunteers to create a normative functional co-activation network. We conclude that the high cost/high value hubs of human brain networks are more likely to be anatomically abnormal than non-hubs in many (if not all) brain disorders.
Reference : Crossley NA, Mechelli A, Scott J, Carletti F, Fox PT, McGuire P, Bullmore ET. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain. 2014 Aug;137(Pt 8):2382-95.
어렸을때부터 건반 리듬게임을 좋아했다. 정작 오락실엔 가지 않고 컴터로 BM98을 즐겼으니 오리지날은 아니긴 하다만.
음악을 자주 접하며 자란지라 음악 듣는 것을 매우 좋아한다. 특히 뉴에이지 계열이나 피아노가 메인인 곡들. 피아노를 치고는 싶으나 시간과 노력의 부재로 인해 듣기만 해왔는데 이런 갈증을 해소시켜 주는 것들이 나름 이런 건반 리듬게임인 셈이다.
BM98 이후로도 계속해서 리듬게임이 출시되었으나 아무래도 리듬게임 자체가 멜로디보다는 비트가 중시되다보니 내가 원하는 취향의 곡들이 많지는 않기도 하고 내가 원하는건 좋은 음악을 연주 또는 연주하는 느낌을 받고자 하는 것이었기 때문에 하드한 게임을 떠나 한참을 잊고 있었다.
어느날 대만의 게임업체에서 만든 Deemo 라는 모바일 게임을 접하게 되었는데 바로 이거다 싶었다. 논문 또는 전자책 리더로 사용되던 아이패드에게 그 이외의 용도를 부여해주기도 하고, 부담없이 즐길 수 있기도 하고.
무엇보다도 라이트한 곡들이 많아서 좋았다. 가장 좋아하는 곡은 영상으로 나오는 Reflection. 그 외에도 좋아하는 곡들이 다수 있으나.. electronic 비스무리한 곡들은 그닥. 처음에 데모영상 볼때는 피아노곡만 있는줄 알았지. ㅎㅎ 속았다.
곡이야 귀에 익으면 또 좋아질 수 있으니 그렇다치고, 한두달 정도 플레이하고 나니 나름 또 아쉬운 점들도 있다. 가장 큰 아쉬움은 아이패드의 터치 반응이 게임을 못따라가는 느낌이 든다는 것. 실제로 물리적인 키가 있는 경우는 그렇지 않은데, 화면을 터치하면서 약간의 딜레이가 있는듯 위화감이 있고 키가 많이 내려와서 연속적으로 빨리 누르다보면 누른것들이 씹히기도 한다. 또 가끔씩 터치패널에 전자점프라도 일어나면 잘 해온 콤보 날아가는것도 한순간이니.
터치기기로는 역시 리듬게임은 좀 아닌가 싶기도 하고.
그래도 가끔씩 음악 듣는셈 치고 성취도 포기하고 연주하면 간단한 기분전환 정도는 되니 다행. 다른 피아노곡 위주 건반리듬게임 어디 없나.
In recent years, there’s been a resurgence in the field of Artificial Intelligence. It’s spread beyond the academic world with major players like Google, Microsoft, and Facebook creating their own research teams and making some impressive acquisitions.
Some this can be attributed to the abundance of raw data generated by social network users, much of which needs to be analyzed, as well as to the cheap computational power available via GPGPUs.
But beyond these phenomena, this resurgence has been powered in no small part by a new trend in AI, specifically in machine learning, known as “Deep Learning”. In this tutorial, I’ll introduce you to the key concepts and algorithms behind deep learning, beginning with the simplest unit of composition and building to the concepts of machine learning in Java.
(For full disclosure: I’m also the author of a Java deep learning library, available here, and the examples in this article are implemented using the above library. If you like it, you can support it by giving it a star on GitHub, for which I would be grateful. Usage instructions are available on the homepage.)