Human being functional magnetic resonance imaging (fMRI) mind networks have a complex topology comprising integrative parts, e. was associated with high inter-modular degree and long connection distance. Nodes in superior and lateral cortex with high inter-modular degree and long connection distance experienced local transcriptional profiles enriched for oxidative metabolism and mitochondria, and for genes specific to supragranular layers of human being cortex. In contrast, primary and secondary sensory cortical nodes in posterior cortex with high intra-modular degree and short connection distance experienced transcriptional profiles enriched for RNA translation and nuclear parts. We conclude that, as predicted, topologically integrative hubs, mediating long-distance contacts between modules, are more costly in terms of mitochondrial glucose metabolism. This article is definitely part of the themed issue Interpreting Daring: a dialogue between cognitive and cellular neuroscience. = 300) was sub-sampled from the primary cohort for structural and practical MRI assessments and more detailed cognitive testing. Here, we used fMRI data from 40 participants sampled from the top two age strata of the secondary cohort (20C24 years), with 10 males and 10 women in each of the two strata. Participants were excluded if they were currently being treated for any psychiatric disorder or for drug or alcohol dependence; experienced a current or past history of neurological disorders including epilepsy or head injury causing loss of consciousness; experienced a learning disability requiring professional educational support and/or medical treatment; or experienced a security contraindication prohibiting MRI. MRI scanning was carried out at the following three sites: (i) the Wellcome Trust Centre for Neuroimaging, London, (ii) the Wolfson Mind Imaging Centre, Cambridge, and (iii) the Medical Study Council Cognition and Mind Sciences Unit, Cambridge. All sites were identically operating 3 MUC12 T whole-body MRI systems (Magnetom TIM Trio, Siemens Healthcare, Erlangen, Germany; VB17 software version) with standard 32-channel radio-frequency (RF) get head coil and RF body coil for tranny. Resting-state fMRI data were acquired using a multi-echo echoplanar imaging sequence with on-line reconstruction [30]: repetition time (TR) = 2.42 s; GRAPPA with acceleration element = 2; flip angle = 90; matrix size = 64 64 34; FOV = 240 240 mm; in-plane resolution = 3.75 3.75 mm; slice thickness = 3.75 mm with 10% gap, GSK 525768A supplier sequential slice acquisition, 34 oblique slices; bandwidth = 2368 Hz/pixel; echo instances (TE) = 13, 30.55 and 48.1 ms. For pre-processing of these data, we used multi-echo independent component analysis (ME-ICA) [3,30] to identify the sources of variance in the fMRI time series that scaled linearly with TE and could therefore become confidently regarded as representing Daring contrast. Other sources of fMRI variance, such as head movement, which were not Daring GSK 525768A supplier dependent, and consequently did not level with TE, were recognized by ME-ICA and discarded. The retained independent parts, representing Daring contrast, were optimally recomposed to generate a broadband denoised fMRI time series at each voxel [3]. We GSK 525768A supplier used a wavelet transform for estimating practical connection in these data because of prior evidence indicating that cortical fMRI time series often have slowly decaying positive autocorrelation [31,32]. This approach also allowed us to focus on functional associations between brain areas based on a physiologically relevant rate of recurrence range or wavelet level. We used a discrete wavelet transform (Daubechies 4 wavelet), resulting in a Daring signal oscillating in the rate of recurrence range 0.025C0.111 Hz (scales 2 and 3) [33]. Pre-processing and ME-ICA was performed with the AFNI tool meica.py [3] which we slightly modified for a more stable ICA and more traditional component selection. The forked launch is based on the original ME-ICA V2.5 and was released on GitHub (doi://10.5281/zenodo.50505). Wavelet decompositions were implemented using an open source, R-based software library: brainwaver v. 1.6, which is freely downloadable at: https://cran.r-project.org/web/packages/brainwaver/index.html (b) Functional magnetic resonance imaging connection and network analysis To define regional nodes or parcels of cortex for network analysis, we used a backtracking algorithm [34] to parcellate the Freesurfer average (fsaverage) mind, subdividing regions of the DesikanCKilliany surface-based anatomical atlas of the human brain [35] into 308 smaller contiguous areas (nodes) with approximately homogeneous sizes (500 mm2 on the surface). This parcellation template image in standard space was transformed to the native space of each individual’s fMRI dataset and regional.