Neural Representations of Visual Motion Processing in the Human Brain Using Laminar Imaging at 9.4 Tesla

During natural behavior, much of the motion signal falling into our eyes is due to our own movements. Therefore, in order to correctly perceive motion in our environment, it is important to parse visual motion signals into those caused by self-motion such as eye- or head-movements and those caused by external motion. Neural mechanisms underlying this task, which are also required to allow for a stable perception of the world during pursuit eye movements, are not fully understood. Both, perceptual stability as well as perception of real-world (i.e. objective) motion are the product of integration between motion signals on the retina and efference copies of eye movements. The central aim of this thesis is to examine whether different levels of cortical depth or distinct columnar structures of visual motion regions are differentially involved in disentangling signals related to self-motion, objective, or object motion. Based on previous studies reporting segregated populations of voxels in high level visual areas such as V3A, V6, and MST responding predominantly to either retinal or extra- retinal (‘real’) motion, we speculated such voxels to reside within laminar or columnar functional units. We used ultra-high field (9.4T) fMRI along with an experimental paradigm that independently manipulated retinal and extra-retinal motion signals (smooth pursuit) while controlling for effects of eye-movements, to investigate whether processing of real world motion in human V5/MT, putative MST (pMST), and V1 is associated to differential laminar signal intensities. We also examined motion integration across cortical depths in human motion areas V3A and V6 that have strong objective motion responses. We found a unique, condition specific laminar profile in human area V6, showing reduced mid-layer responses for retinal motion only, suggestive of an inhibitory retinal contribution to motion integration in mid layers or alternatively an excitatory contribution in deep and superficial layers. We also found evidence indicating that in V5/MT and pMST, processing related to retinal, objective, and pursuit motion are either integrated or colocalized at the scale of our resolution. In contrast, in V1, independent functional processes seem to be driving the response to retinal and objective motion on the one hand, and to pursuit signals on the other. The lack of differential signals across depth in these regions suggests either that a columnar rather than laminar segregation governs these functions in these areas, or that the methods used were unable to detect differential neural laminar processing. Furthermore, the thesis provides a thorough analysis of the relevant technical modalities used for data acquisition and data analysis at ultra-high field in the context of laminar fMRI. Relying on our technical implementations we were able to conduct two high-resolution fMRI experiments that helped us to further investigate the laminar organization of self-induced and externally induced motion cues in human high-level visual areas and to form speculations about the site and the mechanisms of their integration

Detection power, temporal response, and spatial resolution of IRON fMRI in awake, behaving monkeys at 3 Tesla

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2007."September 2006."Includes bibliographical references.The main goal of this thesis was to systematically characterize the detection sensitivity, temporal response, and spatial resolution of IRON contrast for fMRI within the awake, behaving monkey. Understanding these issues provides insights into the physiology of the functional response to local changes in brain activity, enables researchers to optimize experimental designs, and delineates the advantages and limitations of neuroimaging within this important animal model. The injection of the iron oxide contrast agent (MION) provided a 9-fold increase in efficiency for block designs relatively to BOLD contrast. Because the hemodynamic response function acts as a low-pass filter on neural activation to attenuate the size of differential responses to alternate stimuli, this factor dropped to approximately 2 for rapidly presented stimuli. Detection efficiency for event-related stimulus designs for BOLD and IRON contrasts could be optimized using random or semi-random distributions for interstimulus intervals. Small increases in predictability could be traded for large gains in efficiency, particularly for the IRON method. A general linear model was successfully employed to describe IRON and BOLD impulse response functions. Both responses were accurately described by a bimodal exponential model with similar time constants, a fast (4.5 sec) and a slow (13.5 sec).(cont.) The slow response comprised 80% of IRON signal, and was responsible for the BOLD post-stimulus undershoot. It likely encompasses changes in post-arteriole blood volume. Optimized IRON activation maps do not show activation in draining veins or draining tissue, in contrast with BOLD contrast. To examine what happens at the level of small vessels and capillaries, we used point-image stimuli to measure IRON and BOLD point spread functions (PSF) in V1. We estimated an IRON PSF no larger than approximately 0.4 mm, and a BOLD PSF with twice the size. Severe image distortions arising from monkey's body motion outside of the field of view currently limit the achievable spatial resolution. Preliminary data suggests multi-shot EPI with navigators may be useful in improving image stability at higher resolution for IRON fMRI, which can employ short echo times to minimize phase variations, while achieving maximum efficiency by increasing the MION dose.by Francisca Maria Pais Horta Leite.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

Cortical Layer-Dependent Hemodynamic Regulation Investigated by Functional Magnetic Resonance Imaging

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used non-invasive neuroimaging modalities for mapping brain activation. Techniques such as blood oxygenation level dependent (BOLD) fMRI or cerebral blood volume (CBV)-weighted fMRI are based on the assumption that hemodynamic responses are tightly regulated by neural activity. However, the relationship between fMRI responses and neural activity is still unclear. To investigate this relationship, the unique properties of temporal frequency tuning of primary visual cortex neurons was used as a model since it can be used to separate the neural input and output activities of this area. During moving grating stimuli of 1, 2, 10 and 20 Hz temporal frequencies, two fMRI studies, areal and laminar studies, were conducted with different spatial resolution in a 9.4-T Varian spectrometer. In areal studies, BOLD fMRI was able to detect the difference in tuning properties between area 17 (A17), area 18 (A18) and lateral geniculate nucleus. In A17, the BOLD tuning curve seemed to reflect the local field potential (LFP) low frequency band (<12 Hz) rather than spiking activity and LFP gamma band (25-90 Hz). In laminar studies, a high spatial resolution protocol was adopted to resolve the different cortical layers in A17. In addition to BOLD fMRI, CBV-weighted fMRI was performed to eliminate the contamination from the superficial draining veins. These results showed that BOLD and CBV tuning curves do not reflect the underlying spiking activity or the LFP activity at infragranular layers (the bottom layer of three cortical layers). This implies that the hemodynamic response may not be regulated on a laminar level. Therefore, caution should be taken when interpreting BOLD responses as the sole indicator of different aspects of neural activity in areal and laminar scales

Cross-species neuroscience: closing the explanatory gap

Neuroscience has seen substantial development in non-invasive methods available for investigating the living human brain. However, these tools are limited to coarse macroscopic measures of neural activity that aggregate the diverse responses of thousands of cells. To access neural activity at the cellular and circuit level, researchers instead rely on invasive recordings in animals. Recent advances in invasive methods now permit large-scale recording and circuit level manipulations with exquisite spatiotemporal precision. Yet, there has been limited progress in relating these microcircuit measures to complex cognition and behaviour observed in humans. Contemporary neuroscience thus faces an explanatory gap between macroscopic descriptions of the human brain and microscopic descriptions in animal models. To close the explanatory gap, we propose adopting a cross-species approach. Despite dramatic differences in the size of mammalian brains this approach is broadly justified by preserved homology. Here, we outline a three-armed approach for effective cross-species investigation that highlights the need to translate different measures of neural activity into a common space. We discuss how a cross-species approach has the potential to transform basic neuroscience while also benefiting neuropsychiatric drug development where clinical translation has, to date, seen minimal success

Magnetoenkefalografian ja toiminnallisen magneettikuvauksen vertailu ja yhdistäminen tunto- ja liikejärjestelmän tutkimuksessa

MEG directly measures the neuronal events and has greater temporal resolution than fMRI, which has limited temporal resolution mainly due to the larger timescale of the hemodynamic response. On the other hand fMRI has advantages in spatial resolution, while the localization results with MEG can be ambiguous due to the non-uniqueness of the electromagnetic inverse problem. Thus, these methods could provide complementary information and could be used to create both spatially and temporally accurate models of brain function. We investigated the degree of overlap, revealed by the two imaging methods, in areas involved in sensory or motor processing in healthy subjects and neurosurgical patients. Furthermore, we used the spatial information from fMRI to construct a spatiotemporal model of the MEG data in order to investigate the sensorimotor system and to create a spatiotemporal model of its function. We compared the localization results from the MEG and fMRI with invasive electrophysiological cortical mapping. We used a recently introduced method, contextual clustering, for hypothesis testing of fMRI data and assessed the the effect of neighbourhood information use on the reproducibility of fMRI results. Using MEG, we identified the ipsilateral primary sensorimotor cortex (SMI) as a novel source area contributing to the somatosensory evoked fields (SEF) to median nerve stimulation. Using combined MEG and fMRI measurements we found that two separate areas in the lateral fissure may be the generators for the SEF responses from the secondary somatosensory cortex region. The two imaging methods indicated activation in corresponding locations. By using complementary information from MEG and fMRI we established a spatiotemporal model of somatosensory cortical processing. This spatiotemporal model of cerebral activity was in good agreement with results from several studies using invasive electrophysiological measurements and with anatomical studies in monkey and man concerning the connections between somatosensory areas. In neurosurgical patients, the MEG dipole model turned out to be more reliable than fMRI in the identification of the central sulcus. This was due to prominent activation in non-primary areas in fMRI, which in some cases led to erroneous or ambiguous localization of the central sulcus.Magnetoenkefalografia (MEG) mittaa suoraan aivojen hermosolujen sähköistä toimintaa ja sillä on parempi ajallinen erotuskyky kuin aivojen aktivaation aiheuttamia paikallisen verenkierron muutoksia kuvaava toiminnallinen magneettikuvaus (TMK). TMK:lla on toisaalta etuja paikannuksessa MEG:hen nähden ja MEG:llä saadut paikannustulokset ovat monikäsitteisiä. Nämä menetelmät voivat täydentää toisiaan ja yhdessä niillä voidaan saada tarkempi ajallinen ja paikallinen kuva aivojen toiminnasta. Käytimme näitä kahta menetelmää aivojen tunto- ja liikejärjestelmän toiminnan kuvantamisessa terveillä koehenkilöillä ja neurokirurgisilla potilailla. Tutkimme menetelmillä saatavan paikannustuloksen yhteneväisyyttä ja käytimme TMK:sta saatavaa paikannustietoa MEG:llä mitattujen aivojen magneetisten vasteiden mallinuksessa luoden mallin aivojen tuntojärjestelmän toiminnasta. Neurokirurgisilla potilailla vertasimme kuvantamismenetelmien tuloksia leikkauksenaikaiseen sähköiseen liikeaivokuoren paikannukseen. Tutkimuksessa testattiin ja sovellettin kehittämiämme uusia kuva-analyysimenetelmiä. MEG:llä ja TMK:lla havaitsimme viitteitä aktivaatiosta tuntoärsykkeen kanssa samanpuoleisella primäärillä tuntoaivokuorella. Tuloksemme viittaavat lisäksi siihen että aivojen lateraalisessa fissuurassa on ainakin kaksi erillistä lähdealuetta jotka tuottavat magneettisia tuntoherätevasteita. Mallimme aivojen toiminnasta tuntoarsykkeen käsittelyn aikana vastasi hyvin kirjallisuudessa raportoituja suoraan aivoista mitattuja eri alueiden aktivaatioaikoja. TMK-analyysimenetelmiä vertailtaessa todettiin kuva-alkion naapurustoinformaatiota käyttävien menetelmien tuottavan paremmin toistettavia tuloksia. Kehittämämme menetelmä rajasi tarkemmin aivojen aktivaatioalueen ja oli muita menetelmiä herkempi havaitsemaan heikkoja aktivaatioita. Paikannettaessa aivojen keskusuurretta leikkauksen suunnittelua ja riskien arviointia varten MEG tuotti luotettavamman tuloksen kuin TMK jossa osalla potilaista aktivaatiot muilla kuin primäärillä liikeaivokuorella olivat voimakkaimpia vaikeuttaen tulosten tulkintaa

Phase imaging for reducing macrovascular signal contributions in high-resolution fMRI

High resolution functional MRI allows for the investigation of neural activity within the cortical sheet. One consideration in high resolution fMRI is the choice of which sequence to use during imaging, as all methods come with sensitivity and specificity tradeoffs. The most used fMRI sequence is gradient-echo echo planar imaging (GE-EPI) which has the highest sensitivity but is not specific to microvasculature. GE-EPI results in a signal with pial vessel bias which increases complexity of performing studies targeted at structures within the cortex. This work seeks to explore the use of MRI phase signal as a macrovascular filter to correct this bias. First, an in-house phase combination method was designed and tested on the 7T MRI system. This method, the fitted SVD method, uses a low-resolution singular value decomposition and fitting to a polynomial basis to provide computationally efficient, phase sensitive, coil combination that is insensitive to motion. Second, a direct comparison of GE-EPI, GE-EPI with phase regression (GE-EPI-PR), and spin echo EPI (SE-EPI) was performed in humans completing a visual task. The GE-EPI-PR activation showed higher spatial similarity with SE-EPI than GE-EPI across the cortical surface. GE-EPI-PR produced a similar laminar profile to SE-EPI while maintaining a higher contrast-to-noise ratio across layers, making it a useful method in low SNR studies such as high-resolution fMRI. The final study extended this work to a resting state macaque experiment. Macaques are a common model for laminar fMRI as they allow for simultaneous imaging and electrophysiology. We hypothesized that phase regression could improve spatial specificity of the resting state data. Further analysis showed the phase data contained both system and respiratory artifacts which prevented the technique performing as expected under two physiological cleaning strategies. Future work will have to examine on-scanner physiology correction to obtain a phase timeseries without artifacts to allow for the phase regression technique to be used in macaques. This work demonstrates that phase regression reduces signal contributions from pial vessels and will improve specificity in human layer fMRI studies. This method can be completed easily with complex fMRI data which can be created using our fitted SVD method