In addition to bottom-up input, the visual cortex receives large amounts of feedback from other cortical areas [1-3]. One compelling example of feedback activation of early visual neurons in the absence of bottom-up input occurs during the famous Kanizsa illusion, where a triangular shape is perceived, even in regions of the image where there is no bottom-up visual evidence for it. This illusion increases the firing activity of neurons in the primary visual cortex with a receptive field on the illusory contour [4]. Feedback signals are largely segregated from feedforward signals within each cortical area, with feedforward signals arriving in the middle layer, while top-down feedback avoids the middle layers and predominantly targets deep and superficial layers [1, 2, 5, 6]. Therefore, the feedback-mediated activity increase in V1 during the perception of illusory shapes should lead to a specific laminar activity profile that is distinct from the activity elicited by bottom-up stimulation. Here, we used fMRI at high field (7 T) to empirically test this hypothesis, by probing the cortical response to illusory figures in human V1 at different cortical depths [7-14]. We found that, whereas bottom-up stimulation activated all cortical layers, feedback activity induced by illusory figures led to a selective activation of the deep layers of V1. These results demonstrate the potential for non-invasive recordings of neural activity with laminar specificity in humans and elucidate the role of top-down signals during perceptual processing.

Electrophysiological recordings in animals have indicated that visual cortex γ-band oscillatory activity is predominantly observed in superficial cortical layers, whereas α-and β-band activity is stronger in deep layers. These rhythms, as well as the different cortical layers, have also been closely related to feedforward and feedback streams of information. Recently, it has become possible to measure laminar activity in humans with high-resolution functional MRI (fMRI). In this study, we investigated whether these different frequency bands show a differential relation with the laminar-resolved blood-oxygen level-dependent (BOLD) signal by combining data from simultaneously recorded EEG and fMRI from the early visual cortex. Our visual attention paradigm allowed us to investigate how variations in strength over trials and variations in the attention effect over subjects relate to each other in both modalities. We demonstrate that γ-band EEG power correlates positively with the superficial layers' BOLD signal and that β-power is negatively correlated to deep layer BOLD and α-power to both deep and superficial layer BOLD. These results provide a neurophysiological basis for human laminar fMRI and link human EEG and high-resolution fMRI to systems-level neuroscience in animals.cortical layers | oscillations | high-resolution fMRI | EEG T he different cortical layers have distinct anatomical connections with subcortical, upstream, and downstream cortical regions (1). Intracranial electrophysiological recordings in animals have linked neuronal oscillations in different frequency bands to the cortical feedforward and feedback information flow and to cortico-subcortical interactions (2-5). In line with these findings, electrophysiological recordings in animals have also demonstrated that changes in α-, β-, and γ-bands can be localized to specific cortical layers (6-12).These findings are almost exclusively based on nonhuman animal research. Although a recent study explored the feasibility of measuring laminar-specific activity with magnetoencephalography (MEG) (13), most recent advances in measuring laminar activity in humans have been made using high-resolution functional MRI (fMRI) (14-18). In this study, we make use of these recent developments to investigate the relationship between electrophysiology and the laminar-specific blood oxygen level-dependent (BOLD) signal. By simultaneously measuring EEG and high-resolution fMRI in humans performing a visual attention task, we demonstrate that changes in specific frequency bands in the EEG can be related to changes in the BOLD signal measured at different cortical depths.The BOLD signal is thought to be closely related to variations in local field potentials (19-21). A wide variety of features in the local field potential (LFP) or EEG, related to several cognitive processes, have now been found to correlate with the BOLD signal (19,20,(22)(23)(24)(25)(26). Most relevant for this study, α-and β-power changes in EEG correlate negatively with the BOLD signal whereas γ-band...

Interactions between top-down and bottom-up information streams are integral to brain function but challenging to measure noninvasively. Laminar resolution, functional MRI (lfMRI) is sensitive to depth-dependent properties of the blood oxygen level-dependent (BOLD) response, which can be potentially related to top-down and bottom-up signal contributions. In this work, we used lfMRI to dissociate the top-down and bottom-up signal contributions to the left occipitotemporal sulcus (LOTS) during word reading. We further demonstrate that laminar resolution measurements could be used to identify condition-specific distributed networks on the basis of whole-brain connectivity patterns specific to the depth-dependent BOLD signal. The networks corresponded to top-down and bottom-up signal pathways targeting the LOTS during word reading. We show that reading increased the top-down BOLD signal observed in the deep layers of the LOTS and that this signal uniquely related to the BOLD response in other language-critical regions. These results demonstrate that lfMRI can reveal important patterns of activation that are obscured at standard resolution. In addition to differences in activation strength as a function of depth, we also show meaningful differences in the interaction between signals originating from different depths both within a region and with the rest of the brain. We thus show that lfMRI allows the noninvasive measurement of directed interaction between brain regions and is capable of resolving different connectivity patterns at submillimeter resolution, something previously considered to be exclusively in the domain of invasive recordings.

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In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel’s Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.