Alexandre Gramfort - Profile on Academia.edu (original) (raw)
Papers by Alexandre Gramfort
NeuroImage, 2011
Although the spatial organization of visual areas can be revealed by functional Magnetic Resonanc... more Although the spatial organization of visual areas can be revealed by functional Magnetic Resonance Imaging (fMRI), the synoptic, non-invasive access to the temporal characteristics of the information flow amongst distributed visual processes remains a technical and methodological challenge. Using frequency-encoded steady-state visual stimulation together with a combination of time-resolved functional magnetic source imaging from magnetoencephalography (MEG) and anatomical magnetic resonance imaging (MRI), this study evidences maps of visuotopic sustained oscillatory neural responses distributed across the visual cortex. Our results further reveal relative phase delays across responding striate and extra-striate visual areas, which thereby shape the chronometry of neural processes amongst these regions. The methodology developed in this study points at further developments in time-resolved analyses of distributed visual processes in the millisecond range, and to new ways of exploring...
Brain decoding and networks studies: well beyond standard analysis in fMRI with Python and scikits.learn
Frontiers in Neuroinformatics, 2011
We are interested by under-determined inverse problems, and more specifically by source localizat... more We are interested by under-determined inverse problems, and more specifically by source localization in magneto and electro- encephalography (M/EEG). In these problems, despite we have a physical model of diffusion (or "mixing") of the sources, the (very) under- determined aspect of the problem leads to a very diffucult inversion. The need of finding strong and physically relevant prior on the
2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, 2007
Magneto-encephalography (MEG) and electro-encephalograhy (EEG) experiments provide huge amounts o... more Magneto-encephalography (MEG) and electro-encephalograhy (EEG) experiments provide huge amounts of data and lead to the manipulations of high dimensional objects like time series or topographies. In the past, essentially in the last decade, various methods for extracting the structure in complex data have been developed and successfully exploited for visualization or classification purposes. Here we propose to use one of these methods, the Laplacian eigenmaps, on EEG data and prove that it provides an powerful approach to visualize and understand the underlying structure of evoked potentials or multitrial time series.
Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals
NeuroImage, 2015
Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electro... more Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain-computer interfaces (BCI) systems, in nearly all source localization methods for spatial whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for spatial whitening and source localization of MEG signals.
Lecture Notes in Computer Science, 2012
The prediction of behavioral covariates from functional MRI (fMRI) is known as brain reading. Fro... more The prediction of behavioral covariates from functional MRI (fMRI) is known as brain reading. From a statistical standpoint, this challenge is a supervised learning task. The ability to predict cognitive states from new data gives a model selection criterion: prediction accuracy. While a good prediction score implies that some of the voxels used by the classifier are relevant, one cannot state that these voxels form the brain regions involved in the cognitive task. The best predictive model may have selected by chance non-informative regions, and neglected relevant regions that provide duplicate information. In this contribution, we address the support identification problem. The proposed approach relies on randomization techniques which have been proved to be consistent for support recovery. To account for the spatial correlations between voxels, our approach makes use of a spatially constrained hierarchical clustering algorithm. Results are provided on simulations and a visual experiment.
A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding
Lecture Notes in Computer Science, 2012
ABSTRACT Functional Magnetic Resonance Imaging (fMRI) provides an unique opportunity to study bra... more ABSTRACT Functional Magnetic Resonance Imaging (fMRI) provides an unique opportunity to study brain functional architecture, while being minimally invasive. Reverse inference, a.k.a. decoding, is a recent approach that has been used with success for deciphering activity patterns that are thought to fit the neuroscientific concept of population coding. This method can be used to assess the specificity of several brain regions for certain cognitive tasks, by evaluating the accuracy of the prediction of a behavioral variable of interest – the target – based on the activations measured in these regions. In this paper, we compare two important sub-problems that naturally arise: namely, which model is best suited for intra- versus inter-subject kind of decoding. While inter-subject prediction aims at finding predictive regions that are stable across subjects,it is plagued by the additional inter-subject variability (lack of voxel-to-voxel correspondence), so that the best suited prediction algorithms used in reverse inference may not be the same in both cases. We benchmark different prediction algorithms in both intra- and inter-subjects analysis, and we show that using spatial regularization improves reverse inference in the challenging context of inter-subject prediction.
Non-negative Tensor Factorization for single-channel EEG artifact rejection
2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2013
Non-negative matrix factorization for single-channel EEG artifact rejection
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
2012 Second International Workshop on Pattern Recognition in NeuroImaging, 2012
Word reading involves multiple cognitive processes. To infer which word is being visualized, the ... more Word reading involves multiple cognitive processes. To infer which word is being visualized, the brain first processes the visual percept, deciphers the letters, bigrams, and activates different words based on context or prior expectation like word frequency. In this contribution, we use supervised machine learning techniques to decode the first step of this processing stream using functional Magnetic Resonance Images (fMRI). We build a decoder that predicts the visual percept formed by four letter words, allowing us to identify words that were not present in the training data. To do so, we cast the learning problem as multiple classification problems after describing words with multiple binary attributes. This work goes beyond the identification or reconstruction of single letters or simple geometrical shapes [1], [2] and addresses a challenging estimation problem, that is the prediction of multiple variables from a single observation, hence facing the problem of learning multiple predictors from correlated inputs.
2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009
The inverse problem with distributed dipoles models in M/EEG is strongly ill-posed requiring to s... more The inverse problem with distributed dipoles models in M/EEG is strongly ill-posed requiring to set priors on the solution. Most common priors are based on a convenient ℓ 2 norm. However such methods are known to smear the estimated distribution of cortical currents. In order to provide sparser solutions, other norms than ℓ 2 have been proposed in the literature, but they often do not pass the test of real data. Here we propose to perform the inverse problem on multiple experimental conditions simultaneously and to constrain the corresponding active regions to be different, while preserving the robust ℓ 2 prior over space and time. This approach is based on a mixed norm that sets a ℓ 1 prior between conditions. The optimization is performed with an efficient iterative algorithm able to handle highly sampled distributed models. The method is evaluated on two synthetic datasets reproducing the organization of the primary somatosensory cortex (S1) and the primary visual cortex (V1), and validated with MEG somatosensory data.
2014 International Workshop on Pattern Recognition in Neuroimaging, 2014
MEG/EEG source imaging allows for the noninvasive analysis of brain activity with high temporal a... more MEG/EEG source imaging allows for the noninvasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, a priori information is required to find a unique source estimate. For the analysis of evoked brain activity, spatial sparsity of the neuronal activation can be assumed. Due to the convexity, 1-norm based constraints are often used for this, which however lead to source estimates biased in amplitude and often suboptimal in terms of source selection. As an alternative, non-convex regularization functionals such as p-quasinorms with 0 < p < 1 can be used. In this work, we present a MEG/EEG inverse solver based on a 2,0.5-quasinorm penalty promoting spatial sparsity as well as temporal stationarity of the brain activity. For solving the resulting non-convex optimization problem, we propose the iterative reweighted Mixed Norm Estimate, which is based on reweighted convex optimization and combines a block coordinate descent scheme and an active set strategy to solve each surrogate problem efficiently. We provide empirical evidence based on simulations and analysis of MEG data that the proposed method outperforms the standard Mixed Norm Estimate in terms of active source identification and amplitude bias.
Brain : a journal of neurology, 2014
In recent years, numerous electrophysiological signatures of consciousness have been proposed. He... more In recent years, numerous electrophysiological signatures of consciousness have been proposed. Here, we perform a systematic analysis of these electroencephalography markers by quantifying their efficiency in differentiating patients in a vegetative state from those in a minimally conscious or conscious state. Capitalizing on a review of previous experiments and current theories, we identify a series of measures that can be organized into four dimensions: (i) event-related potentials versus ongoing electroencephalography activity; (ii) local dynamics versus inter-electrode information exchange; (iii) spectral patterns versus information complexity; and (iv) average versus fluctuations over the recording session. We analysed a large set of 181 high-density electroencephalography recordings acquired in a 30 minutes protocol. We show that low-frequency power, electroencephalography complexity, and information exchange constitute the most reliable signatures of the conscious state. When...
NeuroImage, 2014
Multisensory interactions are ubiquitous in cortex and it has been suggested that sensory cortice... more Multisensory interactions are ubiquitous in cortex and it has been suggested that sensory cortices may be supramodal i.e. capable of functional selectivity irrespective of the sensory modality of inputs (Pascual-Leone and Hamilton, 2001; Renier et al., 2013; Ricciardi and Pietrini, 2011; Voss and Zatorre, 2012). Here, we asked whether learning to discriminate visual coherence could benefit from supramodal processing. To this end, three groups of participants were briefly trained to discriminate which of a red or green intermixed population of random-dot-kinematograms (RDKs) was most coherent in a visual display while being recorded with magnetoencephalography (MEG). During training, participants heard no sound (V), congruent acoustic textures (AV) or auditory noise (AVn); importantly, congruent acoustic textures shared the temporal statistics - i.e. coherence - of visual RDKs. After training, the AV group significantly outperformed participants trained in V and AVn although they wer...
Random Pursuit Denoising (RPDN) with application to MEEG signals
Débruitage Aveugle par Décompositions Parcimonieuses et Aléatoires
Electro-Metabolic Coupling Investigated with Jitter Invariant Dictionary Learning
Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for ... more Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the noninvasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For the analysis of evoked brain activity, spatial sparsity of the neuronal activation is a common assumption. It is often taken into account using convex constraints based on the l1-norm. The resulting source estimates are however biased in amplitude and often suboptimal in terms of source selection due to high correlations in the forward model. In this work, we demonstrate that an inverse solver based on a block-separable prior with a Frobenius norm per block and a l0.5-quasinorm over blocks addresses both of these issues. For solving the resulting non-convex optimization problem, we propose the iterative reweighted Mixed Norm Estimate (irMxNE), i.e., an optimization scheme based on iterative reweighted convex surrogate optimization problems, which are solved efficiently using a block coordinate descent scheme and an active set strategy. We provide empirical evidence based on simulations and analysis of MEG data that the proposed method improves on MxNE in terms of amplitude bias and support recovery.
P118 Brain decoding and networks studies: well beyond standard analysis in fMRI with Python and scikits. learn
Scattering Transform Layer One Linearizes functional MRI activation in Visual Areas ICML Workshop on Statistics, Machine Learning and Neuroscience 2012
NeuroImage, 2011
Although the spatial organization of visual areas can be revealed by functional Magnetic Resonanc... more Although the spatial organization of visual areas can be revealed by functional Magnetic Resonance Imaging (fMRI), the synoptic, non-invasive access to the temporal characteristics of the information flow amongst distributed visual processes remains a technical and methodological challenge. Using frequency-encoded steady-state visual stimulation together with a combination of time-resolved functional magnetic source imaging from magnetoencephalography (MEG) and anatomical magnetic resonance imaging (MRI), this study evidences maps of visuotopic sustained oscillatory neural responses distributed across the visual cortex. Our results further reveal relative phase delays across responding striate and extra-striate visual areas, which thereby shape the chronometry of neural processes amongst these regions. The methodology developed in this study points at further developments in time-resolved analyses of distributed visual processes in the millisecond range, and to new ways of exploring...
Brain decoding and networks studies: well beyond standard analysis in fMRI with Python and scikits.learn
Frontiers in Neuroinformatics, 2011
We are interested by under-determined inverse problems, and more specifically by source localizat... more We are interested by under-determined inverse problems, and more specifically by source localization in magneto and electro- encephalography (M/EEG). In these problems, despite we have a physical model of diffusion (or "mixing") of the sources, the (very) under- determined aspect of the problem leads to a very diffucult inversion. The need of finding strong and physically relevant prior on the
2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, 2007
Magneto-encephalography (MEG) and electro-encephalograhy (EEG) experiments provide huge amounts o... more Magneto-encephalography (MEG) and electro-encephalograhy (EEG) experiments provide huge amounts of data and lead to the manipulations of high dimensional objects like time series or topographies. In the past, essentially in the last decade, various methods for extracting the structure in complex data have been developed and successfully exploited for visualization or classification purposes. Here we propose to use one of these methods, the Laplacian eigenmaps, on EEG data and prove that it provides an powerful approach to visualize and understand the underlying structure of evoked potentials or multitrial time series.
Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals
NeuroImage, 2015
Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electro... more Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain-computer interfaces (BCI) systems, in nearly all source localization methods for spatial whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for spatial whitening and source localization of MEG signals.
Lecture Notes in Computer Science, 2012
The prediction of behavioral covariates from functional MRI (fMRI) is known as brain reading. Fro... more The prediction of behavioral covariates from functional MRI (fMRI) is known as brain reading. From a statistical standpoint, this challenge is a supervised learning task. The ability to predict cognitive states from new data gives a model selection criterion: prediction accuracy. While a good prediction score implies that some of the voxels used by the classifier are relevant, one cannot state that these voxels form the brain regions involved in the cognitive task. The best predictive model may have selected by chance non-informative regions, and neglected relevant regions that provide duplicate information. In this contribution, we address the support identification problem. The proposed approach relies on randomization techniques which have been proved to be consistent for support recovery. To account for the spatial correlations between voxels, our approach makes use of a spatially constrained hierarchical clustering algorithm. Results are provided on simulations and a visual experiment.
A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding
Lecture Notes in Computer Science, 2012
ABSTRACT Functional Magnetic Resonance Imaging (fMRI) provides an unique opportunity to study bra... more ABSTRACT Functional Magnetic Resonance Imaging (fMRI) provides an unique opportunity to study brain functional architecture, while being minimally invasive. Reverse inference, a.k.a. decoding, is a recent approach that has been used with success for deciphering activity patterns that are thought to fit the neuroscientific concept of population coding. This method can be used to assess the specificity of several brain regions for certain cognitive tasks, by evaluating the accuracy of the prediction of a behavioral variable of interest – the target – based on the activations measured in these regions. In this paper, we compare two important sub-problems that naturally arise: namely, which model is best suited for intra- versus inter-subject kind of decoding. While inter-subject prediction aims at finding predictive regions that are stable across subjects,it is plagued by the additional inter-subject variability (lack of voxel-to-voxel correspondence), so that the best suited prediction algorithms used in reverse inference may not be the same in both cases. We benchmark different prediction algorithms in both intra- and inter-subjects analysis, and we show that using spatial regularization improves reverse inference in the challenging context of inter-subject prediction.
Non-negative Tensor Factorization for single-channel EEG artifact rejection
2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2013
Non-negative matrix factorization for single-channel EEG artifact rejection
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
2012 Second International Workshop on Pattern Recognition in NeuroImaging, 2012
Word reading involves multiple cognitive processes. To infer which word is being visualized, the ... more Word reading involves multiple cognitive processes. To infer which word is being visualized, the brain first processes the visual percept, deciphers the letters, bigrams, and activates different words based on context or prior expectation like word frequency. In this contribution, we use supervised machine learning techniques to decode the first step of this processing stream using functional Magnetic Resonance Images (fMRI). We build a decoder that predicts the visual percept formed by four letter words, allowing us to identify words that were not present in the training data. To do so, we cast the learning problem as multiple classification problems after describing words with multiple binary attributes. This work goes beyond the identification or reconstruction of single letters or simple geometrical shapes [1], [2] and addresses a challenging estimation problem, that is the prediction of multiple variables from a single observation, hence facing the problem of learning multiple predictors from correlated inputs.
2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009
The inverse problem with distributed dipoles models in M/EEG is strongly ill-posed requiring to s... more The inverse problem with distributed dipoles models in M/EEG is strongly ill-posed requiring to set priors on the solution. Most common priors are based on a convenient ℓ 2 norm. However such methods are known to smear the estimated distribution of cortical currents. In order to provide sparser solutions, other norms than ℓ 2 have been proposed in the literature, but they often do not pass the test of real data. Here we propose to perform the inverse problem on multiple experimental conditions simultaneously and to constrain the corresponding active regions to be different, while preserving the robust ℓ 2 prior over space and time. This approach is based on a mixed norm that sets a ℓ 1 prior between conditions. The optimization is performed with an efficient iterative algorithm able to handle highly sampled distributed models. The method is evaluated on two synthetic datasets reproducing the organization of the primary somatosensory cortex (S1) and the primary visual cortex (V1), and validated with MEG somatosensory data.
2014 International Workshop on Pattern Recognition in Neuroimaging, 2014
MEG/EEG source imaging allows for the noninvasive analysis of brain activity with high temporal a... more MEG/EEG source imaging allows for the noninvasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, a priori information is required to find a unique source estimate. For the analysis of evoked brain activity, spatial sparsity of the neuronal activation can be assumed. Due to the convexity, 1-norm based constraints are often used for this, which however lead to source estimates biased in amplitude and often suboptimal in terms of source selection. As an alternative, non-convex regularization functionals such as p-quasinorms with 0 < p < 1 can be used. In this work, we present a MEG/EEG inverse solver based on a 2,0.5-quasinorm penalty promoting spatial sparsity as well as temporal stationarity of the brain activity. For solving the resulting non-convex optimization problem, we propose the iterative reweighted Mixed Norm Estimate, which is based on reweighted convex optimization and combines a block coordinate descent scheme and an active set strategy to solve each surrogate problem efficiently. We provide empirical evidence based on simulations and analysis of MEG data that the proposed method outperforms the standard Mixed Norm Estimate in terms of active source identification and amplitude bias.
Brain : a journal of neurology, 2014
In recent years, numerous electrophysiological signatures of consciousness have been proposed. He... more In recent years, numerous electrophysiological signatures of consciousness have been proposed. Here, we perform a systematic analysis of these electroencephalography markers by quantifying their efficiency in differentiating patients in a vegetative state from those in a minimally conscious or conscious state. Capitalizing on a review of previous experiments and current theories, we identify a series of measures that can be organized into four dimensions: (i) event-related potentials versus ongoing electroencephalography activity; (ii) local dynamics versus inter-electrode information exchange; (iii) spectral patterns versus information complexity; and (iv) average versus fluctuations over the recording session. We analysed a large set of 181 high-density electroencephalography recordings acquired in a 30 minutes protocol. We show that low-frequency power, electroencephalography complexity, and information exchange constitute the most reliable signatures of the conscious state. When...
NeuroImage, 2014
Multisensory interactions are ubiquitous in cortex and it has been suggested that sensory cortice... more Multisensory interactions are ubiquitous in cortex and it has been suggested that sensory cortices may be supramodal i.e. capable of functional selectivity irrespective of the sensory modality of inputs (Pascual-Leone and Hamilton, 2001; Renier et al., 2013; Ricciardi and Pietrini, 2011; Voss and Zatorre, 2012). Here, we asked whether learning to discriminate visual coherence could benefit from supramodal processing. To this end, three groups of participants were briefly trained to discriminate which of a red or green intermixed population of random-dot-kinematograms (RDKs) was most coherent in a visual display while being recorded with magnetoencephalography (MEG). During training, participants heard no sound (V), congruent acoustic textures (AV) or auditory noise (AVn); importantly, congruent acoustic textures shared the temporal statistics - i.e. coherence - of visual RDKs. After training, the AV group significantly outperformed participants trained in V and AVn although they wer...
Random Pursuit Denoising (RPDN) with application to MEEG signals
Débruitage Aveugle par Décompositions Parcimonieuses et Aléatoires
Electro-Metabolic Coupling Investigated with Jitter Invariant Dictionary Learning
Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for ... more Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the noninvasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For the analysis of evoked brain activity, spatial sparsity of the neuronal activation is a common assumption. It is often taken into account using convex constraints based on the l1-norm. The resulting source estimates are however biased in amplitude and often suboptimal in terms of source selection due to high correlations in the forward model. In this work, we demonstrate that an inverse solver based on a block-separable prior with a Frobenius norm per block and a l0.5-quasinorm over blocks addresses both of these issues. For solving the resulting non-convex optimization problem, we propose the iterative reweighted Mixed Norm Estimate (irMxNE), i.e., an optimization scheme based on iterative reweighted convex surrogate optimization problems, which are solved efficiently using a block coordinate descent scheme and an active set strategy. We provide empirical evidence based on simulations and analysis of MEG data that the proposed method improves on MxNE in terms of amplitude bias and support recovery.
P118 Brain decoding and networks studies: well beyond standard analysis in fMRI with Python and scikits. learn
Scattering Transform Layer One Linearizes functional MRI activation in Visual Areas ICML Workshop on Statistics, Machine Learning and Neuroscience 2012