Abell 30 is the brighter of only two planetary nebulae that show extended X-ray emission. Our recent ROSAT high-resolution imager observation of A30 reveals a central source at a 4 p level and emission knots at a 2 p level. These emission features are within the same region as the H-deÐcient knots and Ðlaments resolved in a previous HST WFPC2 [O III] image. The ROSAT position of the X-ray peak is o †set by from the HST position of the central star of A30. Since the ROSAT pointing may be uncer-2A .8 tain by up to 10A, we assume that the X-ray peak is aligned with the central star. The two brighter X-ray emission knots then become aligned with prominent [O III] features in the nebula. Ground-based echelle observations and HST WFPC2 images of A30 reveal a bipolar pair of knots and a clumpy expanding disk. The morphology and velocity structure of the bipolar knots and disk show evidence of the stellar wind ablating the knots and clumps. An efficient mixing of the shocked stellar wind and the ablated material is needed to produce the low plasma temperature, 4.5 ] 105 K, and the high electron density, D1000 cm~3, derived from the observed X-ray Ñux and spectral distribution. Subject headings : planetary nebulae : individual (A30) È stars : AGB and post-AGB È X-rays : ISM
Our thoughts arise from coordinated patterns of interactions between brain structures that change with our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brain’s functional connectome that display homologous lower-level dynamic correlations. Here we test the hypothesis that high-level cognition is reflected in high-order dynamic correlations in brain activity patterns. We develop an approach to estimating high-order dynamic correlations in timeseries data, and we apply the approach to neuroimaging data collected as human participants either listen to a ten-minute story or listen to a temporally scrambled version of the story. We train across-participant pattern classifiers to decode (in held-out data) when in the session each neural activity snapshot was collected. We find that classifiers trained to decode from high-order dynamic correlations yield the best performance on data collected as participants listened to the (unscrambled) story. By contrast, classifiers trained to decode data from scrambled versions of the story yielded the best performance when they were trained using first-order dynamic correlations or non-correlational activity patterns. We suggest that as our thoughts become more complex, they are reflected in higher-order patterns of dynamic network interactions throughout the brain.
5Our thoughts arise from coordinated patterns of interactions between brain structures that change with 6 our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different 7 subgraphs of the brain's connectome that display homologous lower-level dynamic correlations. We tested 8 the hypothesis that high-level cognition is supported by high-order dynamic correlations in brain activity 9 patterns. We developed an approach to estimating high-order dynamic correlations in timeseries data, and 10 we applied the approach to neuroimaging data collected as human participants either listened to a ten-11 minute story, listened to a temporally scrambled version of the story, or underwent a resting state scan. We 12 trained across-participant pattern classifiers to decode (in held-out data) when in the session each neural 13 activity snapshot was collected. We found that classifiers trained to decode from high-order dynamic 14 correlations yielded the best performance on data collected as participants listened to the (unscrambled) 15 story. By contrast, classifiers trained to decode data from scrambled versions of the story or during 16 the resting state scan yielded the best performance when they were trained using first-order dynamic 17 correlations or non-correlational activity patterns. We suggest that as our thoughts become more complex, 18 they are supported by higher-order patterns of dynamic network interactions throughout the brain.
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