Selective Activation of the Deep Layers of the Human Primary Visual Cortex by Top-Down Feedback

Sensitivity and specificity considerations for fMRI encoding, decoding, and mapping of auditory cortex at ultra-high field

Following rapid technological advances, ultra-high field functional MRI (fMRI) enables exploring correlates of neuronal population activity at an increasing spatial resolution. However, as the fMRI blood-oxygenation-level-dependent (BOLD) contrast is a vascular signal, the spatial specificity of fMRI data is ultimately determined by the characteristics of the underlying vasculature. At 7T, fMRI measurement parameters determine the relative contribution of the macro- and microvasculature to the acquired signal. Here we investigate how these parameters affect relevant high-end fMRI analyses such as encoding, decoding, and submillimeter mapping of voxel preferences in the human auditory cortex. Specifically, we compare a T₂* weighted fMRI dataset, obtained with 2D gradient echo (GE) EPI, to a predominantly T₂ weighted dataset obtained with 3D GRASE. We first investigated the decoding accuracy based on two encoding models that represented different hypotheses about auditory cortical processing. This encoding/decoding analysis profited from the large spatial coverage and sensitivity of the T₂* weighted acquisitions, as evidenced by a significantly higher prediction accuracy in the GE-EPI dataset compared to the 3D GRASE dataset for both encoding models. The main disadvantage of the T₂* weighted GE-EPI dataset for encoding/decoding analyses was that the prediction accuracy exhibited cortical depth dependent vascular biases. However, we propose that the comparison of prediction accuracy across the different encoding models may be used as a post processing technique to salvage the spatial interpretability of the GE-EPI cortical depth-dependent prediction accuracy. Second, we explored the mapping of voxel preferences. Large-scale maps of frequency preference (i.e., tonotopy) were similar across datasets, yet the GE-EPI dataset was preferable due to its larger spatial coverage and sensitivity. However, submillimeter tonotopy maps revealed biases in assigned frequency preference and selectivity for the GE-EPI dataset, but not for the 3D GRASE dataset. Thus, a T₂ weighted acquisition is recommended if high specificity in tonotopic maps is required. In conclusion, different fMRI acquisitions were better suited for different analyses. It is therefore critical that any sequence parameter optimization considers the eventual intended fMRI analyses and the nature of the neuroscience questions being asked.

Using Laser Capture Microdissection to Isolate Cortical Laminae in Nonhuman Primate Brain

Laser capture microdissection (LCM) is a technique that allows procurement of an enriched cell population from a heterogeneous tissue sample under direct microscopic visualization. Fundamentally, laser capture microdissection consists of three main steps: (1) visualizing the desired cell population by microscopy, (2) melting a thermolabile polymer onto the desired cell populations using infrared laser energy to form a polymer-cell composite (capture method) or photovolatizing a region of tissue using ultraviolet laser energy (cutting method), and (3) removing the desired cell population from the heterogeneous tissue. In this chapter, we discuss the infrared capture method only. LCM technology is compatible with a wide range of downstream applications such as mass spectrometry, DNA genotyping and RNA transcript profiling, cDNA library generation, proteomics discovery, and signal pathway mapping. This chapter profiles the ArcturusXT^™ laser capture microdissection instrument, using isolation of specific cortical lamina from nonhuman primate brain regions, and sample preparation methods for downstream proteomic applications.

Hemifield columns co-opt ocular dominance column structure in human achiasma

In the absence of an optic chiasm, visual input to the right eye is represented in primary visual cortex (V1) in the right hemisphere, while visual input to the left eye activates V1 in the left hemisphere. Retinotopic mapping In V1 reveals that in each hemisphere left and right visual hemifield representations are overlaid (Hoffmann et al., 2012). To explain how overlapping hemifield representations in V1 do not impair vision, we tested the hypothesis that visual projections from nasal and temporal retina create interdigitated left and right visual hemifield representations in V1, similar to the ocular dominance columns observed in neurotypical subjects (Victor et al., 2000). We used high-resolution fMRI at 7T to measure the spatial distribution of responses to left- and right-hemifield stimulation in one achiasmic subject. T₂-weighted 2D Spin Echo images were acquired at 0.8mm isotropic resolution. The left eye was occluded. To the right eye, a presentation of flickering checkerboards alternated between the left and right visual fields in a blocked stimulus design. The participant performed a demanding orientation-discrimination task at fixation. A general linear model was used to estimate the preference of voxels in V1 to left- and right-hemifield stimulation. The spatial distribution of voxels with significant preference for each hemifield showed interdigitated clusters which densely packed V1 in the right hemisphere. The spatial distribution of hemifield-preference voxels in the achiasmic subject was stable between two days of testing and comparable in scale to that of human ocular dominance columns. These results are the first in vivo evidence showing that visual hemifield representations interdigitate in achiasmic V1 following a similar developmental course to that of ocular dominance columns in V1 with intact optic chiasm.

Local expectation violations result in global activity gain in primary visual cortex

During natural perception, we often form expectations about upcoming input. These expectations are usually multifaceted - we expect a particular object at a particular location. However, expectations about spatial location and stimulus features have mostly been studied in isolation, and it is unclear whether feature-based expectation can be spatially specific. Interestingly, feature-based attention automatically spreads to unattended locations. It is still an open question whether the neural mechanisms underlying feature-based expectation differ from those underlying feature-based attention. Therefore, establishing whether the effects of feature-based expectation are spatially specific may inform this debate. Here, we investigated this by inducing expectations of a specific stimulus feature at a specific location, and probing the effects on sensory processing across the visual field using fMRI. We found an enhanced sensory response for unexpected stimuli, which was elicited only when there was a violation of expectation at the specific location where participants formed a stimulus expectation. The neural consequences of this expectation violation, however, spread to cortical locations processing the stimulus in the opposite hemifield. This suggests that an expectation violation at one location in the visual world can lead to a spatially non-specific gain increase across the visual field.

An active inference theory of allostasis and interoception in depression

In this paper, we integrate recent theoretical and empirical developments in predictive coding and active inference accounts of interoception (including the Embodied Predictive Interoception Coding model) with working hypotheses from the theory of constructed emotion to propose a biologically plausible unified theory of the mind that places metabolism and energy regulation (i.e. allostasis), as well as the sensory consequences of that regulation (i.e. interoception), at its core. We then consider the implications of this approach for understanding depression. We speculate that depression is a disorder of allostasis, whose myriad symptoms result from a 'locked in' brain that is relatively insensitive to its sensory context. We conclude with a brief discussion of the ways our approach might reveal new insights for the treatment of depression.This article is part of the themed issue 'Interoception beyond homeostasis: affect, cognition and mental health'.

Prediction Errors but Not Sharpened Signals Simulate Multivoxel fMRI Patterns during Speech Perception

Successful perception depends on combining sensory input with prior knowledge. However, the underlying mechanism by which these two sources of information are combined is unknown. In speech perception, as in other domains, two functionally distinct coding schemes have been proposed for how expectations influence representation of sensory evidence. Traditional models suggest that expected features of the speech input are enhanced or sharpened via interactive activation (Sharpened Signals). Conversely, Predictive Coding suggests that expected features are suppressed so that unexpected features of the speech input (Prediction Errors) are processed further. The present work is aimed at distinguishing between these two accounts of how prior knowledge influences speech perception. By combining behavioural, univariate, and multivariate fMRI measures of how sensory detail and prior expectations influence speech perception with computational modelling, we provide evidence in favour of Prediction Error computations. Increased sensory detail and informative expectations have additive behavioural and univariate neural effects because they both improve the accuracy of word report and reduce the BOLD signal in lateral temporal lobe regions. However, sensory detail and informative expectations have interacting effects on speech representations shown by multivariate fMRI in the posterior superior temporal sulcus. When prior knowledge was absent, increased sensory detail enhanced the amount of speech information measured in superior temporal multivoxel patterns, but with informative expectations, increased sensory detail reduced the amount of measured information. Computational simulations of Sharpened Signals and Prediction Errors during speech perception could both explain these behavioural and univariate fMRI observations. However, the multivariate fMRI observations were uniquely simulated by a Prediction Error and not a Sharpened Signal model. The interaction between prior expectation and sensory detail provides evidence for a Predictive Coding account of speech perception. Our work establishes methods that can be used to distinguish representations of Prediction Error and Sharpened Signals in other perceptual domains.

Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex

Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding attentional processes, predictive coding, executive control, and a gamut of other brain functions. However, the underlying circuit mechanism remains poorly understood and represents a major challenge in neuroscience. We approached this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intralaminar, interlaminar, interareal, and whole cortex) scales. The model was tested by reproducing, as well as providing insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas, including the observation that feedforward pathways are associated with enhanced gamma (30 to 70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low-beta (8 to 15 Hz) oscillations. Furthermore, the model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of interareal signaling, as reported in recent monkey and human experiments, and suggests a mechanism for the observed context-dependent hierarchy dynamics. Together, this work highlights the necessity of multiscale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions.

An Orientation Dependent Size Illusion Is Underpinned by Processing in the Extrastriate Visual Area, LO1

We use the simple, but prominent Helmholtz's squares illusion in which a vertically striped square appears wider than a horizontally striped square of identical physical dimensions to determine whether functional magnetic resonance imaging (fMRI) BOLD responses in V1 underpin illusions of size. We report that these simple stimuli which differ in only one parameter, orientation, to which V1 neurons are highly selective elicited activity in V1 that followed their physical, not perceived size. To further probe the role of V1 in the illusion and investigate plausible extrastriate visual areas responsible for eliciting the Helmholtz squares illusion, we performed a follow-up transcranial magnetic stimulation (TMS) experiment in which we compared perceptual judgments about the aspect ratio of perceptually identical Helmholtz squares when no TMS was applied against selective stimulation of V1, LO1, or LO2. In agreement with fMRI results, we report that TMS of area V1 does not compromise the strength of the illusion. Only stimulation of area LO1, and not LO2, compromised significantly the strength of the illusion, consistent with previous research that LO1 plays a role in the processing of orientation information. These results demonstrate the involvement of a specific extrastriate area in an illusory percept of size.

Encoding of Stimulus Probability in Macaque Inferior Temporal Cortex

Optimal perceptual decisions require sensory signals to be combined with prior information about stimulus probability. Although several theories propose that probabilistic information about stimulus occurrence is encoded in sensory cortex, evidence from neuronal recordings has not yet fully supported this view. We recorded activity from single neurons in inferior temporal cortex (IT) while monkeys performed a task that involved discriminating degraded images of faces and fruit. The relative probability of the cue being a face versus a fruit was manipulated by a latent variable that was not revealed to the monkeys and that changed unpredictably over the course of each recording session. In addition to responding to stimulus identity (face or fruit), population responses in IT encoded the long-term stimulus probability of whether a face or a fruit stimulus was more likely to occur. Face-responsive neurons showed reduced firing rates to expected faces, an effect consistent with "expectation suppression," but expected stimuli were decoded from multivariate population signals with greater accuracy. These findings support "predictive coding" theories, whereby neural signals in the mammalian visual system actively encode and update predictions about the local sensory environment.

The relationship between oscillatory EEG activity and the laminar-specific BOLD signal

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.

Aging into Perceptual Control: A Dynamic Causal Modeling for fMRI Study of Bistable Perception

Aging is accompanied by stereotyped changes in functional brain activations, for example a cortical shift in activity patterns from posterior to anterior regions is one hallmark revealed by functional magnetic resonance imaging (fMRI) of aging cognition. Whether these neuronal effects of aging could potentially contribute to an amelioration of or resistance to the cognitive symptoms associated with psychopathology remains to be explored. We used a visual illusion paradigm to address whether aging affects the cortical control of perceptual beliefs and biases. Our aim was to understand the effective connectivity associated with volitional control of ambiguous visual stimuli and to test whether greater top-down control of early visual networks emerged with advancing age. Using a bias training paradigm for ambiguous images we found that older participants (n = 16) resisted experimenter-induced visual bias compared to a younger cohort (n = 14) and that this resistance was associated with greater activity in prefrontal and temporal cortices. By applying Dynamic Causal Models for fMRI we uncovered a selective recruitment of top-down connections from the middle temporal to Lingual gyrus (LIN) by the older cohort during the perceptual switch decision following bias training. In contrast, our younger cohort did not exhibit any consistent connectivity effects but instead showed a loss of driving inputs to orbitofrontal sources following training. These findings suggest that perceptual beliefs are more readily controlled by top-down strategies in older adults and introduce age-dependent neural mechanisms that may be important for understanding aberrant belief states associated with psychopathology.