Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition ‘dipolarity’ defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).

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We derive an asymptotic Newton algorithm for Quasi-Maximum Likelihood estimation of the ICA mixture model, using the ordinary gradient and Hessian. The probabilistic mixture framework yields an algorithm that can accommodate non-stationary environments and arbitrary source densities. We prove asymptotic stability when the source models match the true sources. An example application to EEG segmentation is given.

Abstract.We propose an extension of the mixture of factor (or independent component) analyzers model to include strongly super-gaussian mixture source densities. This allows greater economy in representation of densities with (multiple) peaked modes or heavy tails than using several Gaussians to represent these features. We derive an EM algorithm to find the maximum likelihood estimate of the model, and show that it converges globally to a local optimum of the actual non-gaussian mixture model without needing any approximations. This extends considerably the class of source densities that can be used in exact estimation, and shows that in a sense super-gaussian densities are as natural as Gaussian densities. We also derive an adaptive Generalized Gaussian algorithm that learns the shape parameters of Generalized Gaussian mixture components. Experiments verify the validity of the algorithm.

Background Electrophysiological and hemodynamic activity is altered in attention-deficit/hyperactivity disorder (ADHD) during tasks requiring cognitive control. Frontal midline theta oscillations are a cortical correlate of cognitive control influencing behavioral outcomes including reaction times. Reaction time variability (RTV) is consistently increased in ADHD and is known to share genetic effects with the disorder. The etiological relationship between the cognitive control system, RTV, and ADHD is unknown. In a sample of twins selected for ADHD and matched control subjects, we aimed to quantify the strength of the phenotypic, genetic, and environmental relationships between event-related midline theta oscillations, RTV, and ADHD. Methods Our sample included 134 participants aged 12 to 15 years: 67 twin pairs (34 monozygotic; 33 dizygotic) with concordance or discordance for ADHD symptomatology assessed at 8, 10, and 12 years of age. Our main outcome measures were frontal midline theta activity, derived from both channel and source decomposed electroencephalographic data, and behavioral performance on a response-choice arrow flanker task known to elicit theta activity. Results Variability in stimulus event-related theta phase from frontal midline cortex is strongly related to both RTV and ADHD, both phenotypically and genetically. Conclusions This is the first finding to confirm the genetic link between the frontal midline cognitive control system and ADHD and the first to identify a genetically related neurophysiological marker of RTV in ADHD. Variability in the timing of the theta signal in ADHD may be part of a dysfunctional brain network that impairs regulation of task-relevant responses in the disorder.

An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input–output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture.

De novo tissue generation stimulated by three angiogenic growth factors administered in a factorial design was studied in an in vivo murine tissue engineering chamber. A silicone chamber was implanted around the epigastric pedicle and filled with Matrigel with 100 ng/ml of recombinant mouse vascular endothelial growth factor-120 (VEGF 120 ), recombinant human basic fibroblastic growth factor (FGF-2), or recombinant rat platelet-derived growth factor-BB (PDGF-BB) added as single, double, or triple combinations. Angiogenesis, supporting tissue ingrowth, and adipogenesis were assessed at 2 and 6 weeks by immunohistochemistry and morphometry. At 2 weeks angiogenesis was synergistically enhanced by VEGF 120 ؉ FGF-2 (P ‫؍‬ 0.019). FGF-2 (P ‫؍‬ 0.008) and PDGF-BB (P ‫؍‬ 0.01) significantly increased connective tissue/inflammatory cell infiltrate (macrophages, pericytes, and preadipocytes) in double and triple combinations compared with control. At 6 weeks sequential addition of growth factors increased the percent volume of adipose tissue (P < 0.0005, each main effect), with a synergistic increase in adipose tissue in combination treatments (P < 0.0005). Groups containing 300 ng/ml of single growth factors produced significantly less adipose tissue than the triple growth factor combination (P < 0.0005, VEGF 120 and PDGF-BB; P < 0.001, FGF-2). In conclusion, angiogenic growth factor combinations increased early angiogenesis and cell infiltration resulting in synergistically increased adipose tissue growth at 6 weeks. Two way and higher level synergies are likely to be important in therapeutic applications of angiogenic growth factors.

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