Neural Connectivity with Hidden Gaussian Graphical State-Model

The noninvasive procedures for neural connectivity are under questioning. Theoretical models sustain that the electromagnetic field registered at external sensors is elicited by currents at neural space. Nevertheless, what we observe at the sensor space is a superposition of projected fields, from the whole gray-matter. This is the reason for a major pitfall of noninvasive Electrophysiology methods: distorted reconstruction of neural activity and its connectivity or leakage. It has been proven that current methods produce incorrect connectomes. Somewhat related to the incorrect connectivity modelling, they disregard either Systems Theory and Bayesian Information Theory. We introduce a new formalism that attains for it, Hidden Gaussian Graphical State-Model (HIGGS). A neural Gaussian Graphical Model (GGM) hidden by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS is equivalent to a frequency domain Linear State Space Model (LSSM) but with sparse connectivity prior. The mathematical contribution here is the theory for high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS can attenuate the leakage effect in the most critical case: the distortion EEG signal due to head volume conduction heterogeneities. Its application in EEG is illustrated with retrieved connectivity patterns from human Steady State Visual Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence for noninvasive procedures of neural connectivity: concurrent EEG and Electrocorticography (ECoG) recordings on monkey. Open source packages are freely available online, to reproduce the results presented in this paper and to analyze external MEEG databases

Neurofeedback Therapy for Enhancing Visual Attention: State-of-the-Art and Challenges

We have witnessed a rapid development of brain-computer interfaces (BCIs) linking the brain to external devices. BCIs can be utilized to treat neurological conditions and even to augment brain functions. BCIs offer a promising treatment for mental disorders, including disorders of attention. Here we review the current state of the art and challenges of attention-based BCIs, with a focus on visual attention. Attention-based BCIs utilize electroencephalograms (EEGs) or other recording techniques to generate neurofeedback, which patients use to improve their attention, a complex cognitive function. Although progress has been made in the studies of neural mechanisms of attention, extraction of attention-related neural signals needed for BCI operations is a difficult problem. To attain good BCI performance, it is important to select the features of neural activity that represent attentional signals. BCI decoding of attention-related activity may be hindered by the presence of different neural signals. Therefore, BCI accuracy can be improved by signal processing algorithms that dissociate signals of interest from irrelevant activities. Notwithstanding recent progress, optimal processing of attentional neural signals remains a fundamental challenge for the development of efficient therapies for disorders of attention

Magnetoencephalography as a tool in psychiatric research: current status and perspective

The application of neuroimaging to provide mechanistic insights into circuit dysfunctions in major psychiatric conditions and the development of biomarkers are core challenges in current psychiatric research. In this review, we propose that recent technological and analytic advances in Magnetoencephalography (MEG), a technique which allows the measurement of neuronal events directly and non-invasively with millisecond resolution, provides novel opportunities to address these fundamental questions. Because of its potential in delineating normal and abnormal brain dynamics, we propose that MEG provides a crucial tool to advance our understanding of pathophysiological mechanisms of major neuropsychiatric conditions, such as Schizophrenia, Autism Spectrum Disorders, and the dementias. In our paper, we summarize the mechanisms underlying the generation of MEG signals and the tools available to reconstruct generators and underlying networks using advanced source-reconstruction techniques. We then survey recent studies that have utilized MEG to examine aberrant rhythmic activity in neuropsychiatric disorders. This is followed by links with preclinical research, which have highlighted possible neurobiological mechanisms, such as disturbances in excitation/inhibition parameters, which could account for measured changes in neural oscillations. In the final section of the paper, challenges as well as novel methodological developments are discussed which could pave the way for a widespread application of MEG in translational research with the aim of developing biomarkers for early detection and diagnosis

Melanopsin Sensitivity in the Human Visual System

The human retina contains long [L]-wavelength, medium [M]-wavelength, and short [S]-wavelength cones, rods, and intrinsically photosensitive retinal ganglion cells expressing the blue-sensitive (λmax = ~480 nm) photopigment melanopsin. Previous animal studies have pointed to a role of melanopsin in advancing circadian phase, melatonin suppression, the pupillary light reflex (PLR), light avoidance, and brightness discrimination, often relying on genetic tools to study melanopsin in isolation in animal models. This work addresses the question of human melanopsin sensitivity and function in vivo using a spectrally tunable light source and the method of silent substitution, allowing for the selective stimulation of melanopsin in the human retina, in combination of pupillometry, psychophysics, and BOLD functional neuroimaging (fMRI). In three studies, we find (1) that the temporal transfer function of melanopsin in controlling the pupil in humans is low-pass, peaking at slow temporal frequencies (0.01 Hz), with a sharp drop off at higher frequencies (1-2 Hz); (2) that signals originating from S cones get combined in an antagonistic fashion with melanopsin signals and signals from L and M cones cones, demonstrating spectral opponency in the control of the human PLR; (3) that nominally cone-silent melanopsin-directed spectral modulations stimulate cones in the partial shadow of the retinal blood vessels (termed penumbral cones), leading to the entoptic percept of the subjective retinal vasculature; and (4) that there is no measurable signal due to melanopsin stimulation in human visual cortical areas (V1, V2/V3, MT, LOC; measured with BOLD fMRI) at temporal frequencies most relevant to spatial vision (0.5–64 Hz) while modulations directed at L+M, L–M and S photoreceptor combinations yield characteristic temporal transfer functions in these areas. This work advances to our understanding of the functional significance of melanopsin function in the human visual system, contributing to the study of human health in relation to light and color

Deficits in high- (>60 Hz) gamma-band oscillations during visual processing in schizophrenia

Current theories of the pathophysiology of schizophrenia have focused on abnormal temporal coordination of neural activity. Oscillations in the gamma-band range (>25 Hz) are of particular interest as they establish synchronization with great precision in local cortical networks. However, the contribution of high gamma (>60 Hz) oscillations toward the pathophysiology is less established. To address this issue, we recorded magnetoencephalographic (MEG) data from 16 medicated patients with chronic schizophrenia and 16 controls during the perception of Mooney faces. MEG data were analysed in the 25–150 Hz frequency range. Patients showed elevated reaction times and reduced detection rates during the perception of upright Mooney faces while responses to inverted stimuli were intact. Impaired processing of Mooney faces in schizophrenia patients was accompanied by a pronounced reduction in spectral power between 60–120 Hz (effect size: d = 1.26) which was correlated with disorganized symptoms (r = −0.72). Our findings demonstrate that deficits in high gamma-band oscillations as measured by MEG are a sensitive marker for aberrant cortical functioning in schizophrenia, suggesting an important aspect of the pathophysiology of the disorder

Neural masses and fields in dynamic causal modeling

Dynamic causal modeling (DCM) provides a framework for the analysis of effective connectivity among neuronal subpopulations that subtend invasive (electrocorticograms and local field potentials) and non-invasive (electroencephalography and magnetoencephalography) electrophysiological responses. This paper reviews the suite of neuronal population models including neural masses, fields and conductance-based models that are used in DCM. These models are expressed in terms of sets of differential equations that allow one to model the synaptic underpinnings of connectivity. We describe early developments using neural mass models, where convolution-based dynamics are used to generate responses in laminar-specific populations of excitatory and inhibitory cells. We show that these models, though resting on only two simple transforms, can recapitulate the characteristics of both evoked and spectral responses observed empirically. Using an identical neuronal architecture, we show that a set of conductance based models—that consider the dynamics of specific ion-channels—present a richer space of responses; owing to non-linear interactions between conductances and membrane potentials. We propose that conductance-based models may be more appropriate when spectra present with multiple resonances. Finally, we outline a third class of models, where each neuronal subpopulation is treated as a field; in other words, as a manifold on the cortical surface. By explicitly accounting for the spatial propagation of cortical activity through partial differential equations (PDEs), we show that the topology of connectivity—through local lateral interactions among cortical layers—may be inferred, even in the absence of spatially resolved data. We also show that these models allow for a detailed analysis of structure–function relationships in the cortex. Our review highlights the relationship among these models and how the hypothesis asked of empirical data suggests an appropriate model class