Laminar Organization of Working Memory Signals in Human Visual Cortex

Laminar dissociation of feedforward and feedback in high-level ventral visual cortex during imagery and perception

Visual imagery and perception share neural machinery but rely on different information flow. While perception is driven by the integration of sensory feedforward and internally generated feedback information, imagery relies on feedback only. This suggests that although imagery and perception may activate overlapping brain regions, they do so in informationally distinctive ways. Using lamina-resolved MRI at 7 T, we measured the neural activity during imagery and perception of faces and scenes in high-level ventral visual cortex at the mesoscale of laminar organization that distinguishes feedforward from feedback signals. We found distinctive laminar profiles for imagery and perception of scenes and faces in the parahippocampal place area and the fusiform face area, respectively. Our findings provide insight into the neural basis of the phenomenology of visual imagery versus perception and shed new light into the mesoscale organization of feedforward and feedback information flow in high-level ventral visual cortex.

Neuronal representation of visual working memory content in the primate primary visual cortex

The human ability to perceive vivid memories as if they "float" before our eyes, even in the absence of actual visual stimuli, captivates the imagination. To determine the neural substrates underlying visual memories, we investigated the neuronal representation of working memory content in the primary visual cortex of monkeys. Our study revealed that neurons exhibit unique responses to different memory contents, using firing patterns distinct from those observed during the perception of external visual stimuli. Moreover, this neuronal representation evolves with alterations in the recalled content and extends beyond the retinotopic areas typically reserved for processing external visual input. These discoveries shed light on the visual encoding of memories and indicate avenues for understanding the remarkable power of the mind's eye.

Predictions and errors are distinctly represented across V1 layers

Popular accounts of mind and brain propose that the brain continuously forms predictions about future sensory inputs and combines predictions with inputs to determine what we perceive.1,2,3,4,5,6 Under "predictive processing" schemes, such integration is supported by the hierarchical organization of the cortex, whereby feedback connections communicate predictions from higher-level deep layers to agranular (superficial and deep) lower-level layers.7,8,9,10 Predictions are compared with input to compute the "prediction error," which is transmitted up the hierarchy from superficial layers of lower cortical regions to the middle layers of higher areas, to update higher-level predictions until errors are reconciled.11,12,13,14,15 In the primary visual cortex (V1), predictions have thereby been proposed to influence representations in deep layers while error signals may be computed in superficial layers. Despite the framework's popularity, there is little evidence for these functional distinctions because, to our knowledge, unexpected sensory events have not previously been presented in human laminar paradigms to contrast against expected events. To this end, this 7T fMRI study contrasted V1 responses to expected (75% likely) and unexpected (25%) Gabor orientations. Multivariate decoding analyses revealed an interaction between expectation and layer, such that expected events could be decoded with comparable accuracy across layers, while unexpected events could only be decoded in superficial laminae. Although these results are in line with these accounts that have been popular for decades, such distinctions have not previously been demonstrated in humans. We discuss how both prediction and error processes may operate together to shape our unitary perceptual experiences.

Uncovering the Role of the Early Visual Cortex in Visual Mental Imagery

The question of whether the early visual cortex (EVC) is involved in visual mental imagery remains a topic of debate. In this paper, I propose that the inconsistency in findings can be explained by the unique challenges associated with investigating EVC activity during imagery. During perception, the EVC processes low-level features, which means that activity is highly sensitive to variation in visual details. If the EVC has the same role during visual mental imagery, any change in the visual details of the mental image would lead to corresponding changes in EVC activity. Within this context, the question should not be whether the EVC is 'active' during imagery but how its activity relates to specific imagery properties. Studies using methods that are sensitive to variation in low-level features reveal that imagery can recruit the EVC in similar ways as perception. However, not all mental images contain a high level of visual details. Therefore, I end by considering a more nuanced view, which states that imagery can recruit the EVC, but that does not mean that it always does so.

Developing an AI-empowered head-only ultra-high-performance gradient MRI system for high spatiotemporal neuroimaging

Recent advances in neuroscience requires high-resolution MRI to decipher the structural and functional details of the brain. Developing a high-performance gradient system is an ongoing effort in the field to facilitate high spatial and temporal encoding. Here, we proposed a head-only gradient system NeuroFrontier, dedicated for neuroimaging with an ultra-high gradient strength of 650 mT/m and 600 T/m/s. The proposed system features in 1) ultra-high power of 7MW achieved by running two gradient power amplifiers using a novel paralleling method; 2) a force/torque balanced gradient coil design with a two-step mechanical structure that allows high-efficiency and flexible optimization of the peripheral nerve stimulation; 3) a high-density integrated RF system that is miniaturized and customized for the head-only system; 4) an AI-empowered compressed sensing technique that enables ultra-fast acquisition of high-resolution images and AI-based acceleration in q-t space for diffusion MRI (dMRI); and 5) a prospective head motion correction technique that effectively corrects motion artifacts in real-time with 3D optical tracking. We demonstrated the potential advantages of the proposed system in imaging resolution, speed, and signal-to-noise ratio for 3D structural MRI (sMRI), functional MRI (fMRI) and dMRI in neuroscience applications of submillimeter layer-specific fMRI and dMRI. We also illustrated the unique strength of this system for dMRI-based microstructural mapping, e.g., enhanced lesion contrast at short diffusion-times or high b-values, and improved estimation accuracy for cellular microstructures using diffusion-time-dependent dMRI or for neurite microstructures using q-space approaches.

Dynamic fading memory and expectancy effects in the monkey primary visual cortex

In order to investigate the involvement of the primary visual cortex (V1) in working memory (WM), parallel, multisite recordings of multi-unit activity were obtained from monkey V1 while the animals performed a delayed match-to-sample (DMS) task. During the delay period, V1 population firing rate vectors maintained a lingering trace of the sample stimulus that could be reactivated by intervening impulse stimuli that enhanced neuronal firing. This fading trace of the sample did not require active engagement of the monkeys in the DMS task and likely reflects the intrinsic dynamics of recurrent cortical networks in lower visual areas. This renders an active, attention-dependent involvement of V1 in the maintenance of WM contents unlikely. By contrast, population responses to the test stimulus depended on the probabilistic contingencies between sample and test stimuli. Responses to tests that matched expectations were reduced which agrees with concepts of predictive coding.

Human brain representations of internally generated outcomes of approximate calculation revealed by ultra-high-field brain imaging

Much of human culture's advanced technology owes its existence to the ability to mentally manipulate quantities. Neuroscience has described the brain regions overall recruited by numerical tasks and the neuronal codes representing individual quantities during perceptual tasks. Nevertheless, it remains unknown how quantity representations are combined or transformed during mental computations and how specific quantities are coded in the brain when generated as the result of internal computations rather than evoked by a stimulus. Here, we imaged the brains of adult human subjects at 7 Tesla during an approximate calculation task designed to disentangle in- and outputs of the computation from the operation itself. While physically presented sample numerosities were distinguished in activity patterns along the dorsal visual pathway and within frontal and occipito-temporal regions, a representation of the internally generated result was most prominently detected in higher order regions such as angular gyrus and lateral prefrontal cortex. Behavioral precision in the task was related to cross-decoding performance between sample and result representations in medial IPS regions. This suggests the transformation of sample into result may be carried out within dorsal stream sensory-motor integration regions, and resulting outputs maintained for task purposes in higher-level regions in a format possibly detached from sensory-evoked inputs.

Predictive processing of scenes and objects

Real-world visual input consists of rich scenes that are meaningfully composed of multiple objects which interact in complex, but predictable, ways. Despite this complexity, we recognize scenes, and objects within these scenes, from a brief glance at an image. In this review, we synthesize recent behavioral and neural findings that elucidate the mechanisms underlying this impressive ability. First, we review evidence that visual object and scene processing is partly implemented in parallel, allowing for a rapid initial gist of both objects and scenes concurrently. Next, we discuss recent evidence for bidirectional interactions between object and scene processing, with scene information modulating the visual processing of objects, and object information modulating the visual processing of scenes. Finally, we review evidence that objects also combine with each other to form object constellations, modulating the processing of individual objects within the object pathway. Altogether, these findings can be understood by conceptualizing object and scene perception as the outcome of a joint probabilistic inference, in which "best guesses" about objects act as priors for scene perception and vice versa, in order to concurrently optimize visual inference of objects and scenes.

Expectation Cues and False Percepts Generate Stimulus-Specific Activity in Distinct Layers of the Early Visual Cortex

Perception has been proposed to result from the integration of feedforward sensory signals with internally generated feedback signals. Feedback signals are believed to play an important role in driving false percepts, that is, seeing things that are not actually there. Feedforward and feedback influences on perception can be studied using layer-specific fMRI, which we used here to interrogate neural activity underlying high-confidence false percepts while healthy human participants (N = 25, male and female) performed a perceptual orientation discrimination task. Auditory cues implicitly signaled the most likely upcoming orientation (referred to here as expectations). These expectations induced orientation-specific templates in the deep and superficial layers of V2, without affecting perception. In contrast, the orientation of falsely perceived stimuli with high confidence was reflected in the middle input layers of V2, suggesting a feedforward signal contributing to false percepts. The prevalence of high-confidence false percepts was related to everyday hallucination severity in a separate online sample (N = 100), suggesting a possible link with abnormal perceptual experiences. These results reveal a potential feedforward mechanism underlying false percepts, reflected by spontaneous stimulus-like activity in the input layers of the visual cortex, independent of top-down signals reflecting cued orientations.SIGNIFICANCE STATEMENT False percepts have been suggested to arise through excessive feedback signals. However, feedforward contributions to false percepts have remained largely understudied. Laminar fMRI has been shown to be useful in distinguishing feedforward from feedback activity as it allows the imaging of different cortical layers. In the present study we demonstrate that although cued orientations are encoded in the feedback layers of the visual cortex, the content of the false percepts are encoded in the feedforward layers and did not rely on these cued orientations. This shows that false percepts can in principle emerge from random feedforward signals in the visual cortex, with possible implications for disorders hallmarked by hallucinations like schizophrenia and Parkinson's disease.

Meaning in hand: Investigating shared mechanisms of motor imagery and sensorimotor simulation in language processing

There is substantial evidence to support grounded theories of semantic representation, however the mechanisms of simulation in most theories are underspecified. In the present study, we used an individual differences approach to test whether motor imagery may share some mechanisms with sensorimotor simulations engaged during semantic processing. We quantified individual differences in motor imagery ability via implicit imagery tasks and explicit imagery questionnaires and tested their relationship to sensorimotor effects in syntactic classification tasks. In Experiment 1 (N = 185) we tested relationships between motor imagery and semantic processing of body-object interaction meaning (BOI; the degree to which you can interact with a word's referent) and foot/leg action meaning. We observed two interactions between imagery ability measured on the Florida Praxis Imagery Questionnaire (FPIQ) and BOI effects in semantic processing (response time and accuracy). In both interactions poorer imagery ability was associated with null BOI effects, whereas better imagery was associated with BOI effects. We also observed faster and more accurate responses to verbs associated with more foot/leg action meaning than verbs with less foot/leg action meaning, but this foot/leg action effect did not significantly interact with individual differences in motor imagery. In Experiment 2 (N = 195) we tested whether the interactions observed in Experiment 1 were dependent on the object-directed nature of the actions, or whether similar effects would be observed for hand actions not associated with objects. We also expanded our investigation beyond hand and foot imagery to consider whole body imagery. We observed an interaction between performance on a hand laterality judgement task (HLJT; assessing hand motor imagery) and sensorimotor effects in semantic processing of verbs associated with hand/arm action meaning. Participants with the fastest responses on the most difficult trials of the HLJT showed no significant difference in their response times to words with high and low hand/arm action meaning. We also observed faster and more accurate responses to high relative to low embodiment verbs, but this sensorimotor effect did not interact with individual differences in motor imagery. The results suggest specific (and not general) associations, in that some, but not all forms of hand and object-directed motor imagery are related to sensorimotor effects in language processing of hand/arm action verbs and nouns describing objects that are easy to interact with. As such, hand and object-directed motor imagery may share mechanisms with sensorimotor simulation during semantic processing.

Parietal-driven visual working memory representation in occipito-temporal cortex

Human fMRI studies have documented extensively that the content of visual working memory (VWM) can be reliably decoded from fMRI voxel response patterns during the delay period in both the occipito-temporal cortex (OTC), including early visual areas (EVC), and the posterior parietal cortex (PPC).1,2,3,4 Further work has revealed that VWM signal in OTC is largely sustained by feedback from associative areas such as prefrontal cortex (PFC) and PPC.4,5,6,7,8,9 It is unclear, however, if feedback during VWM simply restores sensory representations initially formed in OTC or if it can reshape the representational content of OTC during VWM delay. Taking advantage of a recent finding showing that object representational geometry differs between OTC and PPC in perception,10 here we find that, during VWM delay, the object representational geometry in OTC becomes more aligned with that of PPC during perception than with itself during perception. This finding supports the role of feedback in shaping the content of VWM in OTC, with the VWM content of OTC more determined by information retained in PPC than by the sensory information initially encoded in OTC.

Brain Signatures of Embodied Semantics and Language: A Consensus Paper

According to embodied theories (including embodied, embedded, extended, enacted, situated, and grounded approaches to cognition), language representation is intrinsically linked to our interactions with the world around us, which is reflected in specific brain signatures during language processing and learning. Moving on from the original rivalry of embodied vs. amodal theories, this consensus paper addresses a series of carefully selected questions that aim at determining when and how rather than whether motor and perceptual processes are involved in language processes. We cover a wide range of research areas, from the neurophysiological signatures of embodied semantics, e.g., event-related potentials and fields as well as neural oscillations, to semantic processing and semantic priming effects on concrete and abstract words, to first and second language learning and, finally, the use of virtual reality for examining embodied semantics. Our common aim is to better understand the role of motor and perceptual processes in language representation as indexed by language comprehension and learning. We come to the consensus that, based on seminal research conducted in the field, future directions now call for enhancing the external validity of findings by acknowledging the multimodality, multidimensionality, flexibility and idiosyncrasy of embodied and situated language and semantic processes.

Ultra-High Field Imaging of Human Visual Cognition

Functional magnetic resonance imaging (fMRI), the key methodology for mapping the functions of the human brain in a noninvasive manner, is limited by low temporal and spatial resolution. Recent advances in ultra-high field (UHF) fMRI provide a mesoscopic (i.e., submillimeter resolution) tool that allows us to probe laminar and columnar circuits, distinguish bottom-up versus top-down pathways, and map small subcortical areas. We review recent work demonstrating that UHF fMRI provides a robust methodology for imaging the brain across cortical depths and columns that provides insights into the brain's organization and functions at unprecedented spatial resolution, advancing our understanding of the fine-scale computations and interareal communication that support visual cognition.

Modelling perception as a hierarchical competition differentiates imagined, veridical, and hallucinated percepts

Mental imagery is a process by which thoughts become experienced with sensory characteristics. Yet, it is not clear why mental images appear diminished compared to veridical images, nor how mental images are phenomenologically distinct from hallucinations, another type of non-veridical sensory experience. Current evidence suggests that imagination and veridical perception share neural resources. If so, we argue that considering how neural representations of externally generated stimuli (i.e. sensory input) and internally generated stimuli (i.e. thoughts) might interfere with one another can sufficiently differentiate between veridical, imaginary, and hallucinatory perception. We here use a simple computational model of a serially connected, hierarchical network with bidirectional information flow to emulate the primate visual system. We show that modelling even first approximations of neural competition can more coherently explain imagery phenomenology than non-competitive models. Our simulations predict that, without competing sensory input, imagined stimuli should ubiquitously dominate hierarchical representations. However, with competition, imagination should dominate high-level representations but largely fail to outcompete sensory inputs at lower processing levels. To interpret our findings, we assume that low-level stimulus information (e.g. in early visual cortices) contributes most to the sensory aspects of perceptual experience, while high-level stimulus information (e.g. towards temporal regions) contributes most to its abstract aspects. Our findings therefore suggest that ongoing bottom-up inputs during waking life may prevent imagination from overriding veridical sensory experience. In contrast, internally generated stimuli may be hallucinated when sensory input is dampened or eradicated. Our approach can explain individual differences in imagery, along with aspects of daydreaming, hallucinations, and non-visual mental imagery.

Spoken language processing activates the primary visual cortex

Primary visual cortex (V1) is generally thought of as a low-level sensory area that primarily processes basic visual features. Although there is evidence for multisensory effects on its activity, these are typically found for the processing of simple sounds and their properties, for example spatially or temporally-congruent simple sounds. However, in congenitally blind individuals, V1 is involved in language processing, with no evidence of major changes in anatomical connectivity that could explain this seemingly drastic functional change. This is at odds with current accounts of neural plasticity, which emphasize the role of connectivity and conserved function in determining a neural tissue's role even after atypical early experiences. To reconcile what appears to be unprecedented functional reorganization with known accounts of plasticity limitations, we tested whether V1's multisensory roles include responses to spoken language in sighted individuals. Using fMRI, we found that V1 in normally sighted individuals was indeed activated by comprehensible spoken sentences as compared to an incomprehensible reversed speech control condition, and more strongly so in the left compared to the right hemisphere. Activation in V1 for language was also significant and comparable for abstract and concrete words, suggesting it was not driven by visual imagery. Last, this activation did not stem from increased attention to the auditory onset of words, nor was it correlated with attentional arousal ratings, making general attention accounts an unlikely explanation. Together these findings suggest that V1 responds to spoken language even in sighted individuals, reflecting the binding of multisensory high-level signals, potentially to predict visual input. This capability might be the basis for the strong V1 language activation observed in people born blind, re-affirming the notion that plasticity is guided by pre-existing connectivity and abilities in the typically developed brain.

Targeted visual cortex stimulation (TVCS): a novel neuro-navigated repetitive transcranial magnetic stimulation mode for improving cognitive function in bipolar disorder

A more effective and better-tolerated site for repetitive transcranial magnetic stimulation (rTMS) for treating cognitive dysfunction in patients with bipolar disorder (BD) is needed. The primary visual cortex (V1) may represent a suitable site. To investigate the use of the V1, which is functionally linked to the dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC), as a potential site for improving cognitive function in BD. Seed-based functional connectivity (FC) analysis was used to locate targets in the V1 that had significant FC with the DLPFC and ACC. Subjects were randomly assigned to 4 groups, namely, the DLPFC active-sham rTMS (A1), DLPFC sham-active rTMS (A2), ACC active-sham rTMS (B1), and ACC sham-active rTMS groups (B2). The intervention included the rTMS treatment once daily, with five treatments a week for four weeks. The A1 and B1 groups received 10 days of active rTMS treatment followed by 10 days of sham rTMS treatment. The A2 and B2 groups received the opposite. The primary outcomes were changes in the scores of five tests in the THINC-integrated tool (THINC-it) at week 2 (W2) and week 4 (W4). The secondary outcomes were changes in the FC between the DLPFC/ACC and the whole brain at W2 and W4. Of the original 93 patients with BD recruited, 86 were finally included, and 73 finished the trial. Significant interactions between time and intervention type (Active/Sham) were observed in the scores of the accuracy of the Symbol Check in the THINC-it tests at baseline (W0) and W2 in groups B1 and B2 (F = 4.736, p = 0.037) using a repeated-measures analysis of covariance approach. Group B1 scored higher in the accuracy of Symbol Check at W2 compared with W0 (p < 0.001), while the scores of group B2 did not differ significantly between W0 and W2. No significant interactions between time and intervention mode were seen between groups A1 and A2, nor was any within-group significance of FC between DLPFC/ACC and the whole brain observed between baseline (W0) and W2/W4 in any group. One participant in group B1 experienced disease progression after 10 active and 2 sham rTMS sessions. The present study demonstrated that V1, functionally correlated with ACC, is a potentially effective rTMS stimulation target for improving neurocognitive function in BD patients. Further investigation using larger samples is required to confirm the clinical efficacy of TVCS.

Mnemonic representations in human lateral geniculate nucleus

There is a growing appreciation for the role of the thalamus in high-level cognition. Motivated by findings that internal cognitive state drives activity in feedback layers of primary visual cortex (V1) that target the lateral geniculate nucleus (LGN), we investigated the role of LGN in working memory (WM). Specifically, we leveraged model-based neuroimaging approaches to test the hypothesis that human LGN encodes information about spatial locations temporarily encoded in WM. First, we localized and derived a detailed topographic organization in LGN that accords well with previous findings in humans and non-human primates. Next, we used models constructed on the spatial preferences of LGN populations in order to reconstruct spatial locations stored in WM as subjects performed modified memory-guided saccade tasks. We found that population LGN activity faithfully encoded the spatial locations held in memory in all subjects. Importantly, our tasks and models allowed us to dissociate the locations of retinal stimulation and the motor metrics of memory-guided saccades from the maintained spatial locations, thus confirming that human LGN represents true WM information. These findings add LGN to the growing list of subcortical regions involved in WM, and suggest a key pathway by which memories may influence incoming processing at the earliest levels of the visual hierarchy.

Subjective signal strength distinguishes reality from imagination

Humans are voracious imaginers, with internal simulations supporting memory, planning and decision-making. Because the neural mechanisms supporting imagery overlap with those supporting perception, a foundational question is how reality and imagination are kept apart. One possibility is that the intention to imagine is used to identify and discount self-generated signals during imagery. Alternatively, because internally generated signals are generally weaker, sensory strength is used to index reality. Traditional psychology experiments struggle to investigate this issue as subjects can rapidly learn that real stimuli are in play. Here, we combined one-trial-per-participant psychophysics with computational modelling and neuroimaging to show that imagined and perceived signals are in fact intermixed, with judgments of reality being determined by whether this intermixed signal is strong enough to cross a reality threshold. A consequence of this account is that when virtual or imagined signals are strong enough, they become subjectively indistinguishable from reality.

Relating neural oscillations to laminar fMRI connectivity in visual cortex

Laminar functional magnetic resonance imaging (fMRI) holds the potential to study connectivity at the laminar level in humans. Here we analyze simultaneously recorded electroencephalography (EEG) and high-resolution fMRI data to investigate how EEG power modulations, induced by a task with an attentional component, relate to changes in fMRI laminar connectivity between and within brain regions in visual cortex. Our results indicate that our task-induced decrease in beta power relates to an increase in deep-to-deep layer coupling between regions and to an increase in deep/middle-to-superficial layer connectivity within brain regions. The attention-related alpha power decrease predominantly relates to reduced connectivity between deep and superficial layers within brain regions, since, unlike beta power, alpha power was found to be positively correlated to connectivity. We observed no strong relation between laminar connectivity and gamma band oscillations. These results indicate that especially beta band, and to a lesser extent, alpha band oscillations relate to laminar-specific fMRI connectivity. The differential effects for alpha and beta bands indicate that they relate to different feedback-related neural processes that are differentially expressed in intra-region laminar fMRI-based connectivity.

Laminar functional magnetic resonance imaging in vision research

Magnetic resonance imaging (MRI) scanners at ultra-high magnetic fields have become available to use in humans, thus enabling researchers to investigate the human brain in detail. By increasing the spatial resolution, ultra-high field MR allows both structural and functional characterization of cortical layers. Techniques that can differentiate cortical layers, such as histological studies and electrode-based measurements have made critical contributions to the understanding of brain function, but these techniques are invasive and thus mainly available in animal models. There are likely to be differences in the organization of circuits between humans and even our closest evolutionary neighbors. Thus research on the human brain is essential. Ultra-high field MRI can observe differences between cortical layers, but is non-invasive and can be used in humans. Extensive previous literature has shown that neuronal connections between brain areas that transmit feedback and feedforward information terminate in different layers of the cortex. Layer-specific functional MRI (fMRI) allows the identification of layer-specific hemodynamic responses, distinguishing feedback and feedforward pathways. This capability has been particularly important for understanding visual processing, as it has allowed researchers to test hypotheses concerning feedback and feedforward information in visual cortical areas. In this review, we provide a general overview of successful ultra-high field MRI applications in vision research as examples of future research.

Reward and loss incentives improve spatial working memory by shaping trial-by-trial posterior frontoparietal signals

Integrating motivational signals with cognition is critical for goal-directed activities. The mechanisms that link neural changes with motivated working memory continue to be understood. Here, we tested how externally cued and non-cued (internally represented) reward and loss impact spatial working memory precision and neural circuits in human subjects using fMRI. We translated the classic delayed-response spatial working memory paradigm from non-human primate studies to take advantage of a continuous numeric measure of working memory precision, and the wealth of translational neuroscience yielded by these studies. Our results demonstrated that both cued and non-cued reward and loss improved spatial working memory precision. Visual association regions of the posterior prefrontal and parietal cortices, specifically the precentral sulcus (PCS) and intraparietal sulcus (IPS), had increased BOLD signal during incentivized spatial working memory. A subset of these regions had trial-by-trial increases in BOLD signal that were associated with better working memory precision, suggesting that these regions may be critical for linking neural signals with motivated working memory. In contrast, regions straddling executive networks, including areas in the dorsolateral prefrontal cortex, anterior parietal cortex and cerebellum displayed decreased BOLD signal during incentivized working memory. While reward and loss similarly impacted working memory processes, they dissociated during feedback when money won or avoided in loss was given based on working memory performance. During feedback, the trial-by-trial amount and valence of reward/loss received was dissociated amongst regions such as the ventral striatum, habenula and periaqueductal gray. Overall, this work suggests motivated spatial working memory is supported by complex sensory processes, and that the IPS and PCS in the posterior frontoparietal cortices may be key regions for integrating motivational signals with spatial working memory precision.

The promise of layer-specific neuroimaging for testing predictive coding theories of psychosis

Predictive coding potentially provides an explanatory model for understanding the neurocognitive mechanisms of psychosis. It proposes that cognitive processes, such as perception and inference, are implemented by a hierarchical system, with the influence of each level being a function of the estimated precision of beliefs at that level. However, predictive coding models of psychosis are insufficiently constrained-any phenomenon can be explained in multiple ways by postulating different changes to precision at different levels of processing. One reason for the lack of constraint in these models is that the core processes are thought to be implemented by the function of specific cortical layers, and the technology to measure layer specific neural activity in humans has until recently been lacking. As a result, our ability to constrain the models with empirical data has been limited. In this review we provide a brief overview of predictive processing models of psychosis and then describe the potential for newly developed, layer specific neuroimaging techniques to test and thus constrain these models. We conclude by discussing the most promising avenues for this research as well as the technical and conceptual challenges which may limit its application.

Perceptual reality monitoring: Neural mechanisms dissociating imagination from reality

There is increasing evidence that imagination relies on similar neural mechanisms as externally triggered perception. This overlap presents a challenge for perceptual reality monitoring: deciding what is real and what is imagined. Here, we explore how perceptual reality monitoring might be implemented in the brain. We first describe sensory and cognitive factors that could dissociate imagery and perception and conclude that no single factor unambiguously signals whether an experience is internally or externally generated. We suggest that reality monitoring is implemented by higher-level cortical circuits that evaluate first-order sensory and cognitive factors to determine the source of sensory signals. According to this interpretation, perceptual reality monitoring shares core computations with metacognition. This multi-level architecture might explain several types of source confusion as well as dissociations between simply knowing whether something is real and actually experiencing it as real. We discuss avenues for future research to further our understanding of perceptual reality monitoring, an endeavour that has important implications for our understanding of clinical symptoms as well as general cognitive function.

Layer-specific activation in human primary somatosensory cortex during tactile temporal prediction error processing

The human brain continuously generates predictions of incoming sensory input and calculates corresponding prediction errors from the perceived inputs to update internal predictions. In human primary somatosensory cortex (area 3b), different cortical layers are involved in receiving the sensory input and generation of error signals. It remains unknown, however, how the layers in the human area 3b contribute to the temporal prediction error processing. To investigate prediction error representation in the area 3b across layers, we acquired layer-specific functional magnetic resonance imaging (fMRI) data at 7T from human area 3b during a task of index finger poking with no-delay, short-delay and long-delay touching sequences. We demonstrate that all three tasks increased activity in both superficial and deep layers of area 3b compared to the random sensory input. The fMRI signal was differentially modulated solely in the deep layers rather than the superficial layers of area 3b by the delay time. Compared with the no-delay stimuli, activity was greater in the deep layers of area 3b during the short-delay stimuli but lower during the long-delay stimuli. This difference activity features in the superficial and deep layers suggest distinct functional contributions of area 3b layers to tactile temporal prediction error processing. The functional segregation in area 3b across layers may reflect that the excitatory and inhibitory interplay in the sensory cortex contributions to flexible communication between cortical layers or between cortical areas.

Using high spatial resolution fMRI to understand representation in the auditory network

Following rapid methodological advances, ultra-high field (UHF) functional and anatomical magnetic resonance imaging (MRI) has been repeatedly and successfully used for the investigation of the human auditory system in recent years. Here, we review this work and argue that UHF MRI is uniquely suited to shed light on how sounds are represented throughout the network of auditory brain regions. That is, the provided gain in spatial resolution at UHF can be used to study the functional role of the small subcortical auditory processing stages and details of cortical processing. Further, by combining high spatial resolution with the versatility of MRI contrasts, UHF MRI has the potential to localize the primary auditory cortex in individual hemispheres. This is a prerequisite to study how sound representation in higher-level auditory cortex evolves from that in early (primary) auditory cortex. Finally, the access to independent signals across auditory cortical depths, as afforded by UHF, may reveal the computations that underlie the emergence of an abstract, categorical sound representation based on low-level acoustic feature processing. Efforts on these research topics are underway. Here we discuss promises as well as challenges that come with studying these research questions using UHF MRI, and provide a future outlook.

Advances in spiral fMRI: A high-resolution study with single-shot acquisition

Spiral fMRI has been put forward as a viable alternative to rectilinear echo-planar imaging, in particular due to its enhanced average k-space speed and thus high acquisition efficiency. This renders spirals attractive for contemporary fMRI applications that require high spatiotemporal resolution, such as laminar or columnar fMRI. However, in practice, spiral fMRI is typically hampered by its reduced robustness and ensuing blurring artifacts, which arise from imperfections in both static and dynamic magnetic fields. Recently, these limitations have been overcome by the concerted application of an expanded signal model that accounts for such field imperfections, and its inversion by iterative image reconstruction. In the challenging ultra-high field environment of 7 Tesla, where field inhomogeneity effects are aggravated, both multi-shot and single-shot 2D spiral imaging at sub-millimeter resolution was demonstrated with high depiction quality and anatomical congruency. In this work, we further these advances towards a time series application of spiral readouts, namely, single-shot spiral BOLD fMRI at 0.8 mm in-plane resolution. We demonstrate that high-resolution spiral fMRI at 7 T is not only feasible, but delivers both excellent image quality, BOLD sensitivity, and spatial specificity of the activation maps, with little artifactual blurring. Furthermore, we show the versatility of the approach with a combined in/out spiral readout at a more typical resolution (1.5 mm), where the high acquisition efficiency allows to acquire two images per shot for improved sensitivity by echo combination.

Imaging human engrams using 7 Tesla magnetic resonance imaging

The investigation of the physical traces of memories (engrams) has made significant progress in the last decade due to optogenetics and fluorescent cell tagging applied in rodents. Engram cells were identified. The ablation of engram cells led to the loss of the associated memory, silent memories were reactivated, and artificial memories were implanted in the brain. Human engram research lags behind engram research in rodents due to methodological and ethical constraints. However, advances in multivariate analysis techniques of functional magnetic resonance imaging (fMRI) data and machine learning algorithms allowed the identification of stable engram patterns in humans. In addition, MRI scanners with an ultrahigh field strength of 7 Tesla (T) have left their prototype state and became more common around the world to assist human engram research. Although most engram research in humans is still being performed with a field strength of 3T, fMRI at 7T will push engram research. Here, we summarize the current state and findings of human engram research and discuss the advantages and disadvantages of applying 7 versus 3T fMRI to image human memory traces.

Brain structure and perfusion in relation to serum renal function indexes in healthy young adults

Prior neuroimaging studies of the relationship between the kidney and the brain have been limited to clinical populations and have largely relied on a single modality. We sought to examine the kidney-brain associations in healthy subjects using a combined analysis of multi-modal imaging data. Structural, diffusion, and perfusion magnetic resonance imaging (MRI) scans were performed to measure cortical thickness, white matter integrity, and cerebral blood flow in 157 healthy young adults. Peripheral venous blood samples were collected to measure serum renal function indexes. Correlation analyses were performed to investigate the relations between brain MRI measures and renal function indexes. Results showed that higher serum uric acid level was associated with increased cortical thickness in the transverse temporal gyrus. We also found that decreased serum creatinine level was linked to lower white matter integrity in the sagittal stratum, anterior corona radiata, superior corona radiata, and external capsule. Furthermore, we observed that increased serum uric acid level was related to hyperperfusion in the opercular and triangular parts of inferior frontal gyrus and supramarginal gyrus, and hypoperfusion in the calcarine sulcus, cuneus and lingual gyrus. More importantly, mediation analysis revealed that the relationship between serum uric acid and working memory performance was mediated by perfusion in the supramarginal gyrus and lingual gyrus. These findings not only may extend current knowledge regarding the relationship between the kidney and the brain, but also may inform real-world clinical practice by identification of potential brain regions vulnerable to renal dysfunction.

Structural Covariance of Depth-Dependent Intracortical Myelination in the Human Brain and Its Application to Drug-Naïve Schizophrenia: A T1w/T2w MRI Study

Aberrations in intracortical myelination are increasingly being considered as a cardinal feature in the pathophysiology of schizophrenia. We investigated the network-level distribution of intracortical myelination across various cortex depths. We enrolled 126 healthy subjects and 106 first-episode drug-naïve schizophrenia patients. We used T1w/T2w ratio as a proxy of intracortical myelination, parcellated cortex into several equivolumetric surfaces based on cortical depths and mapped T1w/T2w ratios to each surface. Non-negative matrix factorization was used to generate depth-dependent structural covariance networks (dSCNs) of intracortical myelination from 2 healthy controls datasets-one from our study and another from 100-unrelated dataset of the Human Connectome Project. For patient versus control comparisons, partial least squares approach was used; we also related myelination to clinical features of schizophrenia. We found that dSCNs were highly reproducible in 2 independent samples. Network-level myelination was reduced in prefrontal and cingulate cortex and increased in perisylvian cortex in schizophrenia. The abnormal network-level myelination had a canonical correlation with symptom burden in schizophrenia. Moreover, myelination of prefrontal cortex correlated with duration of untreated psychosis. In conclusion, we offer a feasible and sensitive framework to study depth-dependent myelination and its relationship with clinical features.

Perceived and mentally rotated contents are differentially represented in cortical depth of V1

Primary visual cortex (V1) in humans is known to represent both veridically perceived external input and internally-generated contents underlying imagery and mental rotation. However, it is unknown how the brain keeps these contents separate thus avoiding a mixture of the perceived and the imagined which could lead to potentially detrimental consequences. Inspired by neuroanatomical studies showing that feedforward and feedback connections in V1 terminate in different cortical layers, we hypothesized that this anatomical compartmentalization underlies functional segregation of external and internally-generated visual contents, respectively. We used high-resolution layer-specific fMRI to test this hypothesis in a mental rotation task. We found that rotated contents were predominant at outer cortical depth bins (i.e. superficial and deep). At the same time perceived contents were represented stronger at the middle cortical bin. These results identify how through cortical depth compartmentalization V1 functionally segregates rather than confuses external from internally-generated visual contents. These results indicate that feedforward and feedback manifest in distinct subdivisions of the early visual cortex, thereby reflecting a general strategy for implementing multiple cognitive functions within a single brain region.

In order to test whether there is a cortical depth compartmentalization in the processing of external and internally-generated visual contents, Iamshchinina et al use high-resolution fMRI at 7 T in participants performing a mental rotation task. They demonstrate that feedforward and feedback representations during mental rotation manifest at differentiable grey matter depth in early visual cortex, thereby reflecting a general strategy for implementing multiple cognitive functions within a single brain region.

Persistent Activity During Working Memory From Front to Back

Working memory (WM) extends the duration over which information is available for processing. Given its importance in supporting a wide-array of high level cognitive abilities, uncovering the neural mechanisms that underlie WM has been a primary goal of neuroscience research over the past century. Here, we critically review what we consider the two major "arcs" of inquiry, with a specific focus on findings that were theoretically transformative. For the first arc, we briefly review classic studies that led to the canonical WM theory that cast the prefrontal cortex (PFC) as a central player utilizing persistent activity of neurons as a mechanism for memory storage. We then consider recent challenges to the theory regarding the role of persistent neural activity. The second arc, which evolved over the last decade, stemmed from sophisticated computational neuroimaging approaches enabling researchers to decode the contents of WM from the patterns of neural activity in many parts of the brain including early visual cortex. We summarize key findings from these studies, their implications for WM theory, and finally the challenges these findings pose. Our goal in doing so is to identify barriers to developing a comprehensive theory of WM that will require a unification of these two "arcs" of research.

Linking cortical circuit models to human cognition with laminar fMRI

Laboratory animal research has provided significant knowledge into the function of cortical circuits at the laminar level, which has yet to be fully leveraged towards insights about human brain function on a similar spatiotemporal scale. The use of functional magnetic resonance imaging (fMRI) in conjunction with neural models provides new opportunities to gain important insights from current knowledge. During the last five years, human studies have demonstrated the value of high-resolution fMRI to study laminar-specific activity in the human brain. This is mostly performed at ultra-high-field strengths (≥ 7 T) and is known as laminar fMRI. Advancements in laminar fMRI are beginning to open new possibilities for studying questions in basic cognitive neuroscience. In this paper, we first review recent methodological advances in laminar fMRI and describe recent human laminar fMRI studies. Then, we discuss how the use of laminar fMRI can help bridge the gap between cortical circuit models and human cognition.

The Neural Codes Underlying Internally Generated Representations in Visual Working Memory

Humans can construct rich subjective experience even when no information is available in the external world. Here, we investigated the neural representation of purely internally generated stimulus-like information during visual working memory. Participants performed delayed recall of oriented gratings embedded in noise with varying contrast during fMRI scanning. Their trialwise behavioral responses provided an estimate of their mental representation of the to-be-reported orientation. We used multivariate inverted encoding models to reconstruct the neural representations of orientation in reference to the response. We found that response orientation could be successfully reconstructed from activity in early visual cortex, even on 0% contrast trials when no orientation information was actually presented, suggesting the existence of a purely internally generated neural code in early visual cortex. In addition, cross-generalization and multidimensional scaling analyses demonstrated that information derived from internal sources was represented differently from typical working memory representations, which receive influences from both external and internal sources. Similar results were also observed in intraparietal sulcus, with slightly different cross-generalization patterns. These results suggest a potential mechanism for how externally driven and internally generated information is maintained in working memory.

Perceptual Learning beyond Perception: Mnemonic Representation in Early Visual Cortex and Intraparietal Sulcus

The ability to discriminate between stimuli relies on a chain of neural operations associated with perception, memory and decision-making. Accumulating studies show learning-dependent plasticity in perception or decision-making, yet whether perceptual learning modifies mnemonic processing remains unclear. Here, we trained human participants of both sexes in an orientation discrimination task, while using functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) to separately examine training-induced changes in working memory (WM) representation. fMRI decoding revealed orientation-specific neural patterns during the delay period in primary visual cortex (V1) before, but not after, training, whereas neurodisruption of V1 during the delay period led to behavioral deficits in both phases. In contrast, both fMRI decoding and disruptive effect of TMS showed that intraparietal sulcus (IPS) represented WM content after, but not before, training. These results suggest that training does not affect the necessity of sensory area in representing WM information, consistent with the sensory recruitment hypothesis in WM, but likely alters the coding format of the stored stimulus in this region. On the other hand, training can render WM content to be maintained in higher-order parietal areas, complementing sensory area to support more robust maintenance of information.SIGNIFICANCE STATEMENT There has been accumulating progresses regarding experience-dependent plasticity in perception or decision-making, yet how perceptual experience moulds mnemonic processing of visual information remains less explored. Here, we provide novel findings that learning-dependent improvement of discriminability accompanies altered WM representation at different cortical levels. Critically, we suggest a role of training in modulating cortical locus of WM representation, providing a plausible explanation to reconcile the discrepant findings between human and animal studies regarding the recruitment of sensory or higher-order areas in WM.

Distraction in Visual Working Memory: Resistance is Not Futile

Over half a century of research focused on understanding how working memory is capacity constrained has overshadowed the fact that it is also remarkably resistant to interference. Protecting goal-relevant information from distraction is a cornerstone of cognitive function that involves a multifaceted collection of control processes and storage mechanisms. Here, we discuss recent advances in cognitive psychology and neuroscience that have produced new insights into the nature of visual working memory and its ability to resist distraction. We propose that distraction resistance should be an explicit component in any model of working memory and that understanding its behavioral and neural correlates is essential for building a comprehensive understanding of real-world memory function.

Estimation of laminar BOLD activation profiles using deconvolution with a physiological point spread function

BACKGROUND

The specificity of gradient echo (GE)-BOLD laminar fMRI activation profiles is degraded by intracortical veins that drain blood from lower to upper cortical layers, propagating activation signal in the same direction. This work describes an approach to obtain layer specific profiles by deconvolving the measured profiles with a physiological Point Spread Function (PSF).

NEW METHOD

It is shown that the PSF can be characterised by a TE-dependent peak to tail (p2t) value that is independent of cortical depth and can be estimated by simulation. An experimental estimation of individual p2t values and the sensitivity of the deconvolved profiles to variations in p2t is obtained using laminar data measured with a multi-echo 3D-FLASH sequence. These profiles are echo time dependent, but the underlying neuronal response is the same, allowing a data-based estimation of the PSF.

RESULTS

The deconvolved profiles are highly similar to the gold-standard obtained from extremely high resolution 3D-EPI data, for a range of p2t values of 5-9, which covers both the empirically determined value (6.8) and the value obtained by simulation (6.3). -Comparison with Existing Method(s) Corrected profiles show a flatter shape across the cortex and a high level of similarity with the gold-standard, defined as a subset of profiles that are unaffected by intracortical veins.

CONCLUSIONS

We conclude that deconvolution is a robust approach for removing the effect of signal propagation through intracortical veins. This makes it possible to obtain profiles with high laminar specificity while benefitting from the higher efficiency of GE-BOLD sequences.

Why do imagery and perception look and feel so different?

Despite the past few decades of research providing convincing evidence of the similarities in function and neural mechanisms between imagery and perception, for most of us, the experience of the two are undeniably different, why? Here, we review and discuss the differences between imagery and perception and the possible underlying causes of these differences, from function to neural mechanisms. Specifically, we discuss the directional flow of information (top-down versus bottom-up), the differences in targeted cortical layers in primary visual cortex and possible different neural mechanisms of modulation versus excitation. For the first time in history, neuroscience is beginning to shed light on this long-held mystery of why imagery and perception look and feel so different. This article is part of the theme issue 'Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation'.

An in-vivo study of BOLD laminar responses as a function of echo time and static magnetic field strength

Layer specific functional MRI requires high spatial resolution data. To compensate the associated poor signal to noise ratio it is common to integrate the signal from voxels at a given cortical depth. If the region is sufficiently large then physiological noise will be the dominant noise source. In this work, activation profiles in response to the same visual stimulus are compared at 1.5 T, 3 T and 7 T using a multi-echo, gradient echo (GE) FLASH sequence, with a 0.75 mm isotropic voxel size and the cortical integration approach. The results show that after integrating over a cortical volume of 40, 60 and 100 mm³ (at 7 T, 3 T, and 1.5 T, respectively), the signal is in the physiological noise dominated regime. The activation profiles obtained are similar for equivalent echo times. BOLD-like noise is found to be the dominant source of physiological noise. Consequently, the functional contrast to noise ratio is not strongly echo-time or field-strength dependent. We conclude that laminar GE-BOLD fMRI at lower field strengths is feasible but that larger patches of cortex will need to be examined, and that the acquisition efficiency is reduced.

Ultra-high field fMRI reveals origins of feedforward and feedback activity within laminae of human ocular dominance columns

Ultra-high field MRI can functionally image the cerebral cortex of human subjects at the submillimeter scale of cortical columns and laminae. Here, we investigate both in concert, by imaging ocular dominance columns (ODCs) in primary visual cortex (V1) across different cortical depths. We ensured that putative ODC patterns in V1 (a) are stable across runs, sessions, and scanners located in different continents, (b) have a width (~1.3 mm) expected from post-mortem and animal work and (c) are absent at the retinotopic location of the blind spot. We then dissociated the effects of bottom-up thalamo-cortical input and attentional feedback processes on activity in V1 across cortical depth. Importantly, the separation of bottom-up information flows into ODCs allowed us to validly compare attentional conditions while keeping the stimulus identical throughout the experiment. We find that, when correcting for draining vein effects and using both model-based and model-free approaches, the effect of monocular stimulation is largest at deep and middle cortical depths. Conversely, spatial attention influences BOLD activity exclusively near the pial surface. Our findings show that simultaneous interrogation of columnar and laminar dimensions of the cortical fold can dissociate thalamocortical inputs from top-down processing, and allow the investigation of their interactions without any stimulus manipulation.

Prior expectations evoke stimulus-specific activity in the deep layers of the primary visual cortex

The way we perceive the world is strongly influenced by our expectations. In line with this, much recent research has revealed that prior expectations strongly modulate sensory processing. However, the neural circuitry through which the brain integrates external sensory inputs with internal expectation signals remains unknown. In order to understand the computational architecture of the cortex, we need to investigate the way these signals flow through the cortical layers. This is crucial because the different cortical layers have distinct intra- and interregional connectivity patterns, and therefore determining which layers are involved in a cortical computation can inform us on the sources and targets of these signals. Here, we used ultra-high field (7T) functional magnetic resonance imaging (fMRI) to reveal that prior expectations evoke stimulus-specific activity selectively in the deep layers of the primary visual cortex (V1). These findings are in line with predictive processing theories proposing that neurons in the deep cortical layers represent perceptual hypotheses and thereby shed light on the computational architecture of cortex.

The way we perceive the world is strongly influenced by our expectations, but the neural circuitry through which the brain achieves this remains unknown. A study using ultra-high field fMRI reveals that prior expectations evoke stimulus-specific signals in the deep layers of the primary visual cortex.

Revisit once more the sensory storage account of visual working memory

Recent work has highlighted the role of early visual areas in visual working memory (VWM) storage and put forward a sensory storage account of VWM. Using a distractor interference paradigm, however, we previolsy showed that the contribution of early visual areas to VWM storage may not be essential. Instead, higher cortical regions such as the posterior parietal cortex may play a more significant role in VWM storage. This is consistent with reviews of other available behavioral, neuroimaging and neurophysiology results. Recently, a number of studies brought forward new evidence regarding this debate. Here I review these new pieces of evidence in detail and show that there is still no strong and definitive evidence supporting an essential role of the early visual areas in VWM storage. Instead, converging evidence suggests that early visual areas may contribute to the decision stage of a VWM task by facilitating target and probe comparison. Aside from further clarifying this debate, it is also important to note that whether or not VWM storage uses a sensory code depends on how it is defined, and that behavioral interactions between VWM and perception tasks do not necessarily support the involvement of sensory regions in VWM storage.

An Attempt at a Unified Theory of the Neocortical Microcircuit in Sensory Cortex

The neocortex performs a wide range of functions, including working memory, sensory perception, and motor planning. Despite this diversity in function, evidence suggests that the neocortex is made up of repeating subunits ("macrocolumns"), each of which is largely identical in circuitry. As such, the specific computations performed by these macrocolumns are of great interest to neuroscientists and AI researchers. Leading theories of this microcircuit include models of predictive coding, hierarchical temporal memory (HTM), and Adaptive Resonance Theory (ART). However, these models have not yet explained: (1) how microcircuits learn sequences input with delay (i.e., working memory); (2) how networks of columns coordinate processing on precise timescales; or (3) how top-down attention modulates sensory processing. I provide a theory of the neocortical microcircuit that extends prior models in all three ways. Additionally, this theory provides a novel working memory circuit that extends prior models to support simultaneous multi-item storage without disrupting ongoing sensory processing. I then use this theory to explain the functional origin of a diverse set of experimental findings, such as cortical oscillations.

Comparison of BOLD and CBV using 3D EPI and 3D GRASE for cortical layer functional MRI at 7 T

PURPOSE

Functional MRI (fMRI) at the mesoscale of cortical layers and columns requires both sensitivity and specificity, the latter of which can be compromised if the imaging method is affected by vascular artifacts, particularly cortical draining veins at the pial surface. Recent studies have shown that cerebral blood volume (CBV) imaging is more specific to the actual laminar locus of neural activity than BOLD imaging using standard gradient-echo EPI sequences. Gradient and spin-echo (GRASE) BOLD imaging has also shown greater specificity when compared with standard gradient-echo EPI BOLD. Here we directly compare CBV and BOLD contrasts in high-resolution imaging of the primary motor cortex for laminar functional MRI in four combinations of signal labeling, CBV using slice-selective slab-inversion vascular space occupancy (VASO) and BOLD, each with 3D gradient-echo EPI and zoomed 3D-GRASE image readouts.

METHODS

Activations were measured using each sequence and contrast combination during a motor task. Activation profiles across cortical depth were measured to assess the sensitivity and specificity (pial bias) of each method.

RESULTS

Both CBV imaging using gradient-echo 3D-EPI and BOLD imaging using 3D-GRASE show similar specificity and sensitivity and are therefore useful tools for mesoscopic functional MRI in the human cortex. The combination of GRASE and VASO did not demonstrate high levels of sensitivity, nor show increased specificity.

CONCLUSION

Three-dimensional EPI with VASO contrast and 3D-GRASE with BOLD contrast both demonstrate sufficient sensitivity and specificity for laminar functional MRI to be used by neuroscientists in a wide range of investigations of depth-dependent neural circuitry in the human brain.

Cortical laminar resting-state signal fluctuations scale with the hypercapnic blood oxygenation level-dependent response

Calibrated functional magnetic resonance imaging can remove unwanted sources of signal variability in the blood oxygenation level‐dependent (BOLD) response. This is achieved by scaling, using information from a perfusion‐sensitive scan during a purely vascular challenge, typically induced by a gas manipulation or a breath‐hold task. In this work, we seek for a validation of the use of the resting‐state fluctuation amplitude (RSFA) as a scaling factor to remove vascular contributions from the BOLD response. Given the peculiarity of depth‐dependent vascularization in gray matter, BOLD and vascular space occupancy (VASO) data were acquired at submillimeter resolution and averaged across cortical laminae. RSFA from the primary motor cortex was, thus, compared to the amplitude of hypercapnia‐induced signal changes (tSD_hc) and with the M factor of the Davis model on a laminar level. High linear correlations were observed for RSFA and tSD_hc (R² = 0.92 ± 0.06) and somewhat reduced for RSFA and M (R² = 0.62 ± 0.19). Laminar profiles of RSFA‐normalized BOLD signal changes yielded good agreement with corresponding VASO profiles. Overall, this suggests that RSFA contains strong vascular components and is also modulated by baseline quantities contained in the M factor. We conclude that RSFA may replace the scaling factor tSD_hc for normalizing the laminar BOLD response.

Laminar specific fMRI reveals directed interactions in distributed networks during language processing

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.

Dissociable laminar profiles of concurrent bottom-up and top-down modulation in the human visual cortex

Recent developments in human neuroimaging make it possible to non-invasively measure neural activity from different cortical layers. This can potentially reveal not only which brain areas are engaged by a task, but also how. Specifically, bottom-up and top-down responses are associated with distinct laminar profiles. Here, we measured lamina-resolved fMRI responses during a visual task designed to induce concurrent bottom-up and top-down modulations via orthogonal manipulations of stimulus contrast and feature-based attention. BOLD responses were modulated by both stimulus contrast (bottom-up) and by engaging feature-based attention (top-down). Crucially, these effects operated at different cortical depths: Bottom-up modulations were strongest in the middle cortical layer and weaker in deep and superficial layers, while top-down modulations were strongest in the superficial layers. As such, we demonstrate that laminar activity profiles can discriminate between concurrent top-down and bottom-up processing, and are diagnostic of how a brain region is activated.