It is increasingly appreciated that a complete description of brain functioning will necessarily involve the characterization of large-scale interregional temporal synchronization of neuronal assemblies. The need to capture the dynamic formation of such large-scale networks has yielded a renewed interest in the human EEG in combination with a suite of methods for estimating functional connectivity along with the graph theoretical approaches for characterizing network structure. While initial work has established generally good reproducibility for a limited selection of these graph theoretical measures, there remains an obvious need to document the reproducibility of a more extensive array of commonly used graph metrics. We sought to evaluate the test-retest reliability of a much richer suite of graph theoretic measures as applied to weighted networks derived from high-density resting-state human EEG. Our findings were promising overall, with some important qualifications when considering the frequency bands of interest and the method used to calculate functional connectivity as well as some substantial variance between individual graph metrics. In general, the reliability of networks in the α and β frequency bands was improved when functional connectivity was defined solely on the basis of relative phase distributions. In the δ and θ bands, reliability was substantially better when functional connectivity was based on coherence, which incorporates both phase and amplitude information.

Large-scale brain signals exhibit rich intermittent patterning, reflecting the fact that the cortex actively eschews fixed points in favor of itinerant wandering with frequent state transitions. Fluctuations in endogenous cortical activity occur at multiple time scales and index a dynamic repertoire of network states that are continuously explored, even in the absence of external sensory inputs. Here, we quantified such moment-to-moment brain signal variability at rest in a large, cross-sectional sample of children ranging in age from seven to eleven years. Our findings revealed a monotonic rise in the complexity of electroencephalogram (EEG) signals as measured by sample entropy, from the youngest to the oldest age cohort, across a range of time scales and spatial regions. From year to year, the greatest changes in intraindividual brain signal variability were recorded at electrodes covering the anterior cortical zones. These results provide converging evidence concerning the age-dependent expansion of functional cortical network states during a critical developmental period ranging from early to late childhood.

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In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox (the “Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education” package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere.

The phenomenal experiences of pleasure and displeasure that accompany emotions have occupied a precarious position within affective neuroscience. Beginning with some of the earliest scientific investigations of this topic, the experiential qualities of emotion (affect) have posed a difficult conceptual and methodological problem, to the point that some have argued for their exclusion from research, largely on pragmatic grounds. We first briefly review the major historical and technical factors that have contributed to this state of affairs and argue that, despite the inherent challenges, affect can be naturalized into an empirical neurobiological framework. We review several hypotheses concerning the potential adaptive functions of internal affective states and consider how these might relate to behavior from the perspective of emergent, largescale brain dynamics. Finally, we survey promising experimental approaches that are currently on the horizon for investigating internal affective experiences in humans using systematic and objective methods. We conclude that while careful investigation of phenomenal affective qualities is not as trivial as some early investigators might have hoped, it represents a methodologically tractable problem whose solution is important if we are to achieve a complete understanding of emotion's biological underpinnings.

The rhythmic delivery of visual stimuli evokes large-scale neuronal entrainment in the form of steady-state oscillatory field potentials. The spatiotemporal properties of stimulus drive appear to constrain the relative degrees of neuronal entrainment. Specific frequency ranges, for example, are uniquely suited for enhancing the strength of stimulus-driven brain oscillations. When it comes to the nature of the visual stimulus itself, studies have used a plethora of inputs ranging from spatially unstructured empty fields to simple contrast patterns (checkerboards, gratings, stripes) and complex arrays (human faces, houses, natural scenes). At present, little is known about how the global spatial statistics of the input stimulus influence entrainment of scalp-recorded electrophysiological signals. In this study, we used rhythmic entrainment source separation of scalp EEG to compare stimulus-driven phase alignment for distinct classes of visual inputs, including broadband spatial noise ensembles with varying second-order statistics, natural scenes, and narrowband sine-wave gratings delivered at a constant flicker frequency. The relative magnitude of visual entrainment was modulated by the global properties of the driving stimulus. Entrainment was strongest for pseudo-naturalistic broadband visual noise patterns in which luminance contrast is greatest at low spatial frequencies (a power spectrum slope characterized by 1/ƒ). Rhythmically modulated visual stimuli entrain the activity of neuronal populations, but the effect of global stimulus statistics on this entrainment is unknown. We assessed entrainment evoked by ) visual noise ensembles with different spectral slopes,) complex natural scenes, and ) narrowband sinusoidal gratings. Entrainment was most effective for broadband noise with naturalistic luminance contrast. This reveals some global properties shaping stimulus-driven brain oscillations in the human visual system.

Contemporary neuroscience suggests that perception is perhaps best understood as a dynamically iterative process that does not honor cleanly segregated "bottom-up" or "top-down" streams. We argue that there is substantial empirical support for the idea that affective influences infiltrate the earliest reaches of sensory processing and even that primitive internal affective dimensions (e.g., goodness-to-badness) are represented alongside physical dimensions of the external world.

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