Hemodynamic Traveling Waves in Human Visual Cortex

Functional MRI (fMRI) experiments rely on precise characterization of the blood oxygen level dependent (BOLD) signal. As the spatial resolution of fMRI reaches the sub-millimeter range, the need for quantitative modelling of spatiotemporal properties of this hemodynamic signal has become pressing. Here, we find that a detailed physiologically-based model of spatiotemporal BOLD responses predicts traveling waves with velocities and spatial ranges in empirically observable ranges. Two measurable parameters, related to physiology, characterize these waves: wave velocity and damping rate. To test these predictions, high-resolution fMRI data are acquired from subjects viewing discrete visual stimuli. Predictions and experiment show strong agreement, in particular confirming BOLD waves propagating for at least 5–10 mm across the cortical surface at speeds of 2–12 mm s-1. These observations enable fundamentally new approaches to fMRI analysis, crucial for fMRI data acquired at high spatial resolution

Clinical Applications of Stochastic Dynamic Models of the Brain, Part I: A Primer

Biological phenomena arise through interactions between an organism's intrinsic dynamics and stochastic forces-random fluctuations due to external inputs, thermal energy, or other exogenous influences. Dynamic processes in the brain derive from neurophysiology and anatomical connectivity; stochastic effects arise through sensory fluctuations, brainstem discharges, and random microscopic states such as thermal noise. The dynamic evolution of systems composed of both dynamic and random effects can be studied with stochastic dynamic models (SDMs). This article, Part I of a two-part series, offers a primer of SDMs and their application to large-scale neural systems in health and disease. The companion article, Part II, reviews the application of SDMs to brain disorders. SDMs generate a distribution of dynamic states, which (we argue) represent ideal candidates for modeling how the brain represents states of the world. When augmented with variational methods for model inversion, SDMs represent a powerful means of inferring neuronal dynamics from functional neuroimaging data in health and disease. Together with deeper theoretical considerations, this work suggests that SDMs will play a unique and influential role in computational psychiatry, unifying empirical observations with models of perception and behavior

Structural and effective connectivity reveals potential network-based influences on category-sensitive visual areas

Visual category perception is thought to depend on brain areas that respond specifically when certain categories are viewed. These category-sensitive areas are often assumed to be modules (with some degree of processing autonomy) and to act predominantly on feedforward visual input. This modular view can be complemented by a view that treats brain areas as elements within more complex networks and as influenced by network properties. This network-oriented viewpoint is emerging from studies using either diffusion tensor imaging to map structural connections or effective connectivity analyses to measure how their functional responses influence each other. This literature motivates several hypotheses that predict category-sensitive activity based on network properties. Large, long-range fiber bundles such as inferior fronto-occipital, arcuate and inferior longitudinal fasciculi are associated with behavioural recognition and could play crucial roles in conveying backward influences on visual cortex from anterior temporal and frontal areas. Such backward influences could support top-down functions such as visual search and emotion-based visual modulation. Within visual cortex itself, areas sensitive to different categories appear well-connected (e.g., face areas connect to object- and motion sensitive areas) and their responses can be predicted by backward modulation. Evidence supporting these propositions remains incomplete and underscores the need for better integration of DTI and functional imaging

Inference of direct and multistep effective connectivities from functional connectivity of the brain and of relationships to cortical geometry

Background The problem of inferring effective brain connectivity from functional connectivity is under active investigation, and connectivity via multistep paths is poorly understood. New method A method is presented to calculate the direct effective connection matrix (deCM), which embodies direct connection strengths between brain regions, from functional CMs (fCMs) by minimizing the difference between an experimental fCM and one calculated via neural field theory from an ansatz deCM based on an experimental anatomical CM. Results The best match between fCMs occurs close to a critical point, consistent with independent published stability estimates. Residual mismatch between fCMs is identified to be largely due to interhemispheric connections that are poorly estimated in an initial ansatz deCM due to experimental limitations; improved ansatzes substantially reduce the mismatch and enable interhemispheric connections to be estimated. Various levels of significant multistep connections are then imaged via the neural field theory (NFT) result that these correspond to powers of the deCM; these are shown to be predictable from geometric distances between regions. Comparison with existing methods This method gives insight into direct and multistep effective connectivity from fCMs and relating to physiology and brain geometry. This contrasts with other methods, which progressively adjust connections without an overarching physiologically based framework to deal with multistep or poorly estimated connections. Conclusions deCMs can be usefully estimated using this method and the results enable multistep connections to be investigated systematically

Linear systems analysis for laminar fMRI: evaluating BOLD amplitude scaling for luminance contrast manipulations

A fundamental assumption of nearly all functional magnetic resonance imaging (fMRI) analyses is that the relationship between local neuronal activity and the blood oxygenation level dependent (BOLD) signal can be described as following linear systems theory. With the advent of ultra-high field (7T and higher) MRI scanners, it has become possible to perform sub-millimeter resolution fMRI in humans. A novel and promising application of sub-millimeter fMRI is measuring responses across cortical depth, i.e. laminar imaging. However, the cortical vasculature and associated directional blood pooling towards the pial surface strongly influence the cortical depth-dependent BOLD signal, particularly for gradient-echo BOLD. This directional pooling may potentially affect BOLD linearity across cortical depth. Here we assess whether the amplitude scaling assumption for linear systems theory holds across cortical depth. For this, we use stimuli with different luminance contrasts to elicit different BOLD response amplitudes. We find that BOLD amplitude across cortical depth scales with luminance contrast, and that this scaling is identical across cortical depth. Although nonlinearities may be present for different stimulus configurations and acquisition protocols, our results suggest that the amplitude scaling assumption for linear systems theory across cortical depth holds for luminance contrast manipulations in sub-millimeter laminar BOLD fMRI

Unraveling the spatiotemporal brain dynamics during a simulated reach-to-eat task

The reach-to-eat task involves a sequence of action components including looking, reaching, grasping, and feeding. While cortical representations of individual action components have been mapped in human functional magnetic resonance imaging (fMRI) studies, little is known about the continuous spatiotemporal dynamics among these representations during the reach-to-eat task. In a periodic event-related fMRI experiment, subjects were scanned while they reached toward a food image, grasped the virtual food, and brought it to their mouth within each 16-s cycle. Fourier-based analysis of fMRI time series revealed periodic signals and noise distributed across the brain. Independent component analysis was used to remove periodic or aperiodic motion artifacts. Timefrequency analysis was used to analyze the temporal characteristics of periodic signals in each voxel. Circular statistics was then used to estimate mean phase angles of periodic signals and select voxels based on the distribution of phase angles. By sorting mean phase angles across regions, we were able to show the real-time spatiotemporal brain dynamics as continuous traveling waves over the cortical surface. The activation sequence consisted of approximately the following stages: (1) stimulus related activations in occipital and temporal cortices; (2) movement planning related activations in dorsal premotor and superior parietal cortices; (3) reaching related activations in primary sensorimotor cortex and supplementary motor area; (4) grasping related activations in postcentral gyrus and sulcus; (5) feeding related activations in orofacial areas. These results suggest that phase-encoded design and analysis can be used to unravel sequential activations among brain regions during a simulated reach-to-eat task

Quasi-periodic patterns of brain intrinsic activity coordinate the functional connections in humans

The brain is a complex self-organizing biophysical system and intrinsically very active. How such intrinsic activity organizes the brain in humans is widely being studied during resting-state using functional magnetic resonance imaging (rsfMRI) and the functional connectivity (FC) metric. FC, calculated as the Pearson correlation between rsfMRI timeseries from different brain areas, indicates coherent activity on average over time, and can reflect some spatial aspects of the brain’s intrinsic organization. For example, based on the FC profile of each area, the cerebral cortex can be parcellated into a few resting-state networks (RSNs) or exhibit a few functional connectivity gradients (FCGs). Brain is a complex system and exhibits varied dynamic spatiotemporal regimes of coherent activity, which are still poorly understood. A subset of such regimes should be giving rise to FC, yet they might entail significantly insightful aspects about the brain’s self-organizing processes, which cannot be captured by FC. Among such dynamic regimes is the quasi-periodic pattern (QPP), obtained by identifying and averaging similar ~20s-long segments of rsfMRI timeseries. QPP involves a cycle of activation and deactivation of different areas with different timings, such that the overall activity within QPP resembles RSNs and FCGs, suggesting QPP might be contributing to FC. To robustly detect multiple QPPs, method improvements were implemented and three primary QPPs were thoroughly characterized. Within these QPPs activity propagates along the functional gradients at the cerebral cortex and most subcortical regions, in a well-coordinated way, because of the consistencies and synchronies across all brain regions which reasonably accord with the consensus on the structural connections. Nuanced timing differences between regions and the closed flow of activity throughout the brain suggest drivers for these patterns. When three QPPs are removed from rsfMRI timeseries, FC within and particularly between RSNs remarkably reduces, illustrating their dominant contribution. Together, our results suggest a few recurring spatiotemporal patterns of intrinsic activity might be dominantly coordinating the functional connections across the whole brain and serving self-organization. These intrinsic patterns possibly interact with the external tasks, affecting performance, or might provide more sensitive biomarkers in certain disorders and diseases.Ph.D

Modeling and analysis of mechanisms underlying high-resolution functional MRI of cortical columns

High spatial resolution functional MRI (fMRI) and advanced multivariate analysis techniques are promising tools for studying the cortical basis of human cognitive processes at the level of columns and layers. However the true spatial specificity of high-resolution fMRI has not been quantified, and the basis for decoding from fine scale structures using large voxels and relatively low magnetic field strength is unknown. It is also not yet known what method and voxel size is optimal for decoding and what voxel size is optimal for high-resolution imaging. In this thesis we present four studies that answer part of these questions using a model-based approach of imaging cortical columns. We started our investigation of model-based analysis of high-resolution fMRI of cortical columns by addressing the specific problem of how it is possible to decode information thought to be mediated by cortical columns using large voxels at low field strength. Multivariate machine learning algorithms applied to human functional MRI (fMRI) data can decode information conveyed by cortical columns, despite the voxel-size being large relative to the width of columns. Several mechanisms have been proposed to underlie decoding of stimulus orientation or the stimulated eye. These include: (I) aliasing of high spatial-frequency components, including the main frequency component of the columnar organization, (II) contributions from local irregularities in the columnar organization, (III) contributions from large-scale non-columnar organizations, (IV) functionally selective veins with biased draining regions, and (V) complex spatio-temporal filtering of neuronal activity by fMRI voxels. Here we sought to assess the plausibility of two of the suggested mechanisms: (I) aliasing and (II) local irregularities, using a naive model of BOLD as blurring and MRI voxel sampling. To this end, we formulated a mathematical model that encompasses both the processes of imaging ocular dominance (OD) columns and the subsequent linear classification analysis. Through numerical simulations of the model, we evaluated the distribution of functional differential contrasts that can be expected when considering the pattern of cortical columns, the hemodynamic point spread function, the voxel size, and the noise. We found that with data acquisition parameters used at 3 Tesla, sub-voxel supra-Nyquist frequencies, including frequencies near the main frequency of the OD organization (0.5 cycles per mm), cannot contribute to the differential contrast. The differential functional contrast of local origin is dominated by low-amplitude contributions from low frequencies, associated with irregularities of the cortical pattern. Realizations of the model with parameters that reflected a best-case scenario and the reported BOLD point-spread at 3 Tesla (3.5 mm) predicted decoding performances lower than those that have been previously obtained at this magnetic field strength. We conclude that low frequency components that underlie local irregularities in the columnar organization are likely to play a role in decoding. We further expect that fMRI-based decoding relies, in part, on signal contributions from large-scale, non-columnar functional organizations, and from complex spatio-temporal filtering of neuronal activity by fMRI voxels, involving biased venous responses. Our model can potentially be used for evaluating and optimizing data-acquisition parameters for decoding information conveyed by cortical columns. Having developed a model of imaging ODCs we then used this model to estimate the spatial specificity of BOLD fMRI, specifically at high field (7 T). Previous attempts at characterizing the spatial specificity of the blood oxygenation level dependent functional MRI (BOLD fMRI) response by estimating its point-spread function (PSF) have conventionally relied on spatial representations of visual stimuli in area V1. Consequently, their estimates were confounded by the width and scatter of receptive fields of V1 neurons. Here, we circumvent these limits by instead using the inherent cortical spatial organization of ocular dominance columns (ODCs) to determine the PSF for both Gradient Echo (GE) and Spin Echo (SE) BOLD imaging at 7 Tesla. By applying Markov Chain Monte Carlo sampling on a probabilistic generative model of imaging ODCs, we quantified the PSFs that best predict the spatial structure and magnitude of differential ODCs’ responses. Prior distributions for the ODC model parameters were determined by analyzing published data of cytochrome oxidase patterns from post-mortem histology of human V1 and of neurophysiological ocular dominance indices. The most probable PSF full-widths at half-maximum were 0.82 mm (SE) and 1.02 mm (GE). Our results provide a quantitative basis for the spatial specificity of BOLD fMRI at ultra-high fields, which can be used for planning and interpretation of high-resolution differential fMRI of fine-scale cortical organizations. Our BOLD fMRI PSF findings show that the PSF is considerably smaller than what was reported previously. This in turn raised the question of the role of the imaging PSF, which now has become relevant. Next, we show that the commonly used magnitude point-spread function fails to accurately represent the true effects of k-space sampling and signal decay, and propose an alternative model that accounts more accurately for these effects. The effects of k-space sampling and signal decay on the effective spatial resolution of MRI and functional MRI (fMRI) are commonly assessed by means of the magnitude point-spread function (PSF), defined as the absolute values (magnitudes) of the complex MR imaging PSF. It is commonly assumed that this magnitude PSF signifies blurring, which can be quantified by its full-width at half-maximum (FWHM). Here we show that the magnitude PSF fails to accurately represent the true effects of k-space sampling and signal decay. Firstly, a substantial part of the width of the magnitude PSF is due to MRI sampling per se. This part is independent of any signal decay and its effect depends on the spatial frequency composition of the imaged object. Therefore, it cannot always be expected to introduce blurring. Secondly, MRI reconstruction is typically followed by taking the absolute values (magnitude image) of the reconstructed complex image. This introduces a non-linear stage into the process of image formation. The complex imaging PSF does not fully describe this process, since it does not reflect the stage of taking the magnitude image. Its corresponding magnitude PSF fails to correctly describe this process, since convolving the original pattern with the magnitude PSF is different from the true process of taking the absolute following a convolution with the complex imaging PSF. Lastly, signal decay can have not only a blurring, but also a high-pass filtering effect. This cannot be reflected by the strictly positive width of the magnitude PSF. As an alternative, we propose to model the imaging process by decomposing it into a signal decay-independent MR sampling part and an approximation of the signal decay effect. We approximate the latter as a convolution with a Gaussian PSF or, if the effect is that of high-pass filtering, as reversing the effect of a convolution with a Gaussian PSF. We show that for typical high-resolution fMRI at 7 Tesla, signal decay in Spin-Echo has a moderate blurring effect (FWHM = 0.89 voxels, corresponds to 0.44 mm for 0.5 mm wide voxels). In contrast, Gradient-Echo acts as a moderate high-pass filter that can be interpreted as reversing a Gaussian blurring with FWHM = 0.59 voxels (0.30 mm for 0.5 mm wide voxels). Our improved approximations and findings hold not only for Gradient-Echo and Spin-Echo fMRI but also for GRASE and VASO fMRI. Our findings support the correct planning, interpretation, and modeling of high-resolution fMRI. In our first study we used our model to analyze imaging of cortical columns under a very specific scenario. We studied a best case scenario for decoding the stimulated eye from ODCs imaged at 3T using large voxels. In order to do so, we formalized available knowledge about fMRI of cortical columns. In particular, the ability of fMRI to resolve cortical columnar organization depends on several interdependent factors, e.g. the spatial scale of the columnar pattern, the point-spread of the BOLD response, voxel size and the signal-to-noise ratio. In our fourth study we aim to analyze how these factors contribute and combine in imaging of arbitrary cortical columnar patterns at varying field strengths and voxel sizes. In addition, we compared different pattern imaging approaches. We show how detection, decoding and reconstruction of a fine scale organization depend on the parameters of the model, and we predict optimal voxel sizes for each approach under various scenario. The capacity of fMRI to resolve cortical columnar organizations depends on several factors, e.g. the spatial scale of the columnar pattern, the point-spread of the fMRI response, the voxel size, and the SNR considering thermal and physiological noise. How these factors combine, and what is the voxel size that optimizes fMRI of cortical columns remain unknown. Here we combine current knowledge into a quantitative model of fMRI of patterns of cortical columns. We compare different approaches for imaging patterns of cortical columns, including univariate and multivariate based detection, multi-voxel pattern analysis (MVPA) based decoding, and reconstruction of the pattern of cortical columns. We present the dependence of their performance on the parameters of the imaged pattern and the data acquisition, and predict voxel sizes that optimize fMRI under various scenarios. To this end, we modeled differential imaging of realistic patterns of cortical columns with different spatial scales and degrees of irregularity. We quantified the capacity to detect and decode stimulus-specific responses by analyzing the distribution of voxel-wise differential responses relative to noise. We quantified the accuracy with which the spatial pattern of cortical columns can be reconstructed as the correlation between the underlying columnar pattern and the imaged pattern. For regular patterns, optimal voxel widths for detection, decoding and reconstruction were close to half the main cycle length of the organization. Optimal voxel widths for irregular patterns were less dependent on the main cycle length, and differed between univariate detection, multivariate detection and decoding, and reconstruction. We compared the effects of different factors of Gradient Echo fMRI at 3 Tesla (T), Gradient Echo fMRI at 7T and Spin-Echo fMRI at 7T, and found that for all measures (detection, decoding, and reconstruction), the width of the fMRI point-spread has the most significant effect. In contrast, different response amplitudes and noise characteristics played a comparatively minor role. We recommend specific voxel widths for optimal univariate detection, for multivariate detection and decoding, and for reconstruction under these three data-acquisition scenarios. Our study supports the planning, optimization, and interpretation of fMRI of cortical columns and the decoding of information conveyed by these columns

Etude du traitement visuel rétinotopique des fréquences spatiales de scènes et plasticité cérébrale au cours du vieillissement normal et pathologique

Visual analysis begins with the parallel extraction of different attributes at different spatial frequencies. The aim of this thesis was to investigatethe mechanisms and the cerebral basis of spatial frequencies processing during scene categorization and their evolution during normal and pathological aging. As a first step, we performed two functional Magnetic Resonance Imaging (fMRI) studies on young adults with normal vision in order to design a retinotopic mapping tool that allows to localize cerebral activations, which is both fast and accurate (studies 1 and 2). As a second step, we studied via fMRI (study 3) the cerebral basis involved in spatial frequencies processing during scenes categorization in young adults with normal vision (study 3). We also assessedthe influence of RMS luminance contrast (“root mean square”) normalization of filtered scenes. Within the occipital cortex, we showed a retinotopic organization of spatial frequencies processing for large visual scenes. Within the occipito-temporal cortex, we showed that scenes-selective regions (the parahippocampal place area, retrosplenial cortex and occipital place area) are specifically involved in spatial frequencies processing. Also, we highlighted the factthat luminance contrast normalization changesboth the intensity and the size of cerebral activations. As a last step, we studiedspatial frequencies processing in normal and pathological aging. We first highlighted in normal aging (study 4) a specific deficit in the ability to categorize scenes with high spatial frequencies (HSF); this deficit was associated with a decrease of activation within the occipital cortex and scenes selective regions. In patients suffering from a loss in central vision due to Age-Related Macular Degeneration (AMD patients, studies 5 and 6), we showed an even more pronounced deficit of HSF processing than observed in normal aging. Interestingly, with respect to the assistance of AMD patients, we observed that increasing the contrast luminance of HSF scenes significantly improved their ability to categorize such scenes. In the end, these results allow us to better understand the neurofunctional mechanisms involved in the visual perception of scenes and to distinguish the cortical changes related to normal aging from those resulting from a visual pathology.Keywords: Visual scenes, Spatial frequencies, fMRI, Visual cortex, Retinotopy, Scene-selective regions, Normal aging, AMD.L'analyse visuelle de scènes débute par l'extraction en parallèle de différentes caractéristiques visuelles élémentaires à différentes fréquences spatiales. L'objectif de cette thèse a été de préciser les mécanismes et les bases cérébrales du traitement des fréquences spatiales lors de la catégorisation de scènes et leur évolution au cours du vieillissement normal et pathologique. Nous avons tout d'abord mené deux études en Imagerie par Résonance Magnétique fonctionnelle (IRMf) sur des adultes jeunes avec une vision normale afin de proposer un outil de cartographie rétinotopique des aires visuelles permettant une localisation fine des activations cérébrales qui soit à la fois rapide et précis (Expériences 1 et 2). Dans un second temps, nous avons étudié via IRMf les bases cérébrales du traitement des fréquences spatiales lors de la catégorisation de scènes chez de jeunes adultes avec vision normale(Expérience 3). Nous avons également étudié l'influence de la normalisation RMS (« root mean square ») du contraste de luminance des scènes filtrées. Au sein du cortex occipital, nous avons montré une organisation rétinotopique du traitement des fréquences spatiales contenues dans de larges scènes visuelles. Au sein du cortex occipito-temporal, nous avons montré que les régions sélectives aux scènes (la « parahippocampal place area », le cortex retrosplenial et l'« occipital place area ») participent de façon distincte au traitement des fréquences spatiales. Enfin, nous avons montré que la normalisation du contraste de luminance modifiait l'intensité et l'étendue des activations cérébrales. Dans un dernier temps, nous avons ensuite étudié le traitement des fréquences spatiales au cours du vieillissement normal et pathologique. Nous avons tout d'abord montré, dans le cas du vieillissement normal (Expérience 4), un déficit spécifique de la catégorisation de scènes en hautes fréquences spatiales (HFS), associé à une hypo activation du cortex occipital et des régions sélectives aux scènes. Dans le cas de la perte de la vision centrale consécutive à une dégénérescence maculaire liée à l'âge (patients DMLA, Expériences 5 et 6), nous avons mis en évidence un déficit du traitement des HFS encore plus marqué que celui observé au cours du vieillissement normal. De façon intéressante pour l'aide aux patients DMLA, l'augmentation du contraste de luminance des scènes en HFS améliorait significativement leur catégorisation des scènes en HFS. Les résultats de ces travaux nous permettent de mieux comprendre les mécanismes neuro-fonctionnels impliqués dans la perception visuelle de scènes et de différencier les changements au niveau cortical liés au vieillissement normal de ceux résultant d'une pathologie visuelle.Mots clés : Scènes visuelles, Fréquences spatiales, IRMf, Cortex visuel, Rétinotopie, Régions sélectives aux scènes, Vieillissement normal, DMLA