Blind signal separation from optical imaging recordings with extended spatial decorrelation

Primate V2 Receptive Fields Derived from Anatomically Identified Large-Scale V1 Inputs

In the primate visual system, visual object recognition involves a series of cortical areas arranged hierarchically along the ventral visual pathway. As information flows through this hierarchy, neurons become progressively tuned to more complex image features. The circuit mechanisms and computations underlying the increasing complexity of these receptive fields (RFs) remain unidentified. To understand how this complexity emerges in the secondary visual area (V2), we investigated the functional organization of inputs from the primary visual cortex (V1) to V2 by combining retrograde anatomical tracing of these inputs with functional imaging of feature maps in macaque monkey V1 and V2. We found that V1 neurons sending inputs to single V2 orientation columns have a broad range of preferred orientations, but are strongly biased towards the orientation represented at the injected V2 site. For each V2 site, we then constructed a feedforward model based on the linear combination of its anatomically-identified large-scale V1 inputs, and studied the response proprieties of the generated V2 RFs. We found that V2 RFs derived from the linear feedforward model were either elongated versions of V1 filters or had spatially complex structures. These modeled RFs predicted V2 neuron responses to oriented grating stimuli with high accuracy. Remarkably, this simple model also explained the greater selectivity to naturalistic textures of V2 cells compared to their V1 input cells. Our results demonstrate that simple linear combinations of feedforward inputs can account for the orientation selectivity and texture sensitivity of V2 RFs.

Orientation pinwheels in primary visual cortex of a highly visual marsupial

Primary visual cortices in many mammalian species exhibit modular and periodic orientation preference maps arranged in pinwheel-like layouts. The role of inherited traits as opposed to environmental influences in determining this organization remains unclear. Here, we characterize the cortical organization of an Australian marsupial, revealing pinwheel organization resembling that of eutherian carnivores and primates but distinctly different from the simpler salt-and-pepper arrangement of eutherian rodents and rabbits. The divergence of marsupials from eutherians 160 million years ago and the later emergence of rodents and rabbits suggest that the salt-and-pepper structure is not the primitive ancestral form. Rather, the genetic code that enables complex pinwheel formation is likely widespread, perhaps extending back to the common therian ancestors of modern mammals.

Monitoring cerebral hemodynamic change during transcranial ultrasound stimulation using optical intrinsic signal imaging

Transcranial ultrasound stimulation (tUS) is a promising non-invasive approach to modulate brain circuits. The application is gaining popularity, however the full effect of ultrasound stimulation is still unclear and further investigation is needed. This study aims to apply optical intrinsic signal imaging (OISI) for the first time, to simultaneously monitor the wide-field cerebral hemodynamic change during tUS on awake animal with high spatial and temporal resolution. Three stimulation paradigms were delivered using a single-element focused transducer operating at 425 kHz in pulsed mode having the same intensity (I_SPPA = 1.84 W/cm², I_SPTA = 129 mW/cm²) but varying pulse repetition frequencies (PRF). The results indicate a concurrent hemodynamic change occurring with all actual tUS but not under a sham stimulation. The stimulation initiated the increase of oxygenated hemoglobin (HbO) and decrease of deoxygenated hemoglobin (RHb). A statistically significant difference (p < 0.05) was found in the amplitude change of hemodynamics evoked by varying PRF. Moreover, the acoustic stimulation was able to trigger a global as well as local cerebral hemodynamic alteration in the mouse cortex. Thus, the implementation of OISI offers the possibility of directly investigating brain response in an awake animal during tUS through cerebral hemodynamic change.

Evaluation of intraoperative optical imaging analysis methods by phantom and patient measurements

Intraoperative optical imaging (IOI) is a localization method for functional areas of the human brain cortex during neurosurgical procedures. The aim of the current work was to develop of a new analysis technique for the computation of two-dimensional IOI activity maps that is suited especially for use in clinical routine. The new analysis technique includes a stimulation scheme that comprises 30-s rest and 30-s stimulation conditions, in connection with pixelwise spectral power analysis for activity map calculation. A software phantom was used for verification of the implemented algorithms as well as for the comparison with the commonly used relative difference imaging method. Furthermore, the analysis technique was tested using intraoperative measurements on eight patients. The comparison with the relative difference algorithm revealed an averaged improvement of the signal-to-noise ratio between 95% and 130% for activity maps computed from intraoperatively acquired patient datasets. The results show that the new imaging technique improves the activity map quality of IOI especially under difficult intraoperative imaging conditions and is therefore especially suited for use in clinical routine.

Dynamic linear model analysis of optical imaging data acquired from the human neocortex

The amount of light absorbed and scattered by neocortical tissue is altered by neuronal activity. Imaging of intrinsic optical signals (ImIOS), a technique for mapping these activity-evoked optical changes with an imaging detector, has the potential to be useful for both clinical and experimental investigations of the human neocortex. However, its usefulness for human studies is currently limited because intraoperatively acquired ImIOS data is noisy. To improve the reliability and usefulness of ImIOS for human studies, it is desirable to find appropriate methods for the removal of noise artifacts and its statistical analysis. Here we develop a Bayesian, dynamic linear modeling approach that appears to address these problems. A dynamic linear model (DLM) was constructed that included cyclic components to model the heartbeat and respiration artifacts, and a local linear component to model the activity-evoked response. The robustness of the model was tested on a set of ImIOS data acquired from the exposed cortices of six human subjects illuminated with either 535nm or 660nm light. The DLM adequately reduced noise artifacts in these data while reliably preserving their activity-evoked optical responses. To demonstrate how these methods might be used for intraoperative neurosurgical mapping, optical data acquired from a single human subject during direct electrical stimulation of the cortex were quantitatively analyzed. This example showed that the DLM can be used to provide quantitative information about human ImIOS data that is not available through qualitative analysis alone.

Functional optical topography analysis using statistical parametric mapping (SPM) methodology with and without physiological confounds

Functional optical topography (OT) measures the changes in oxygenated and deoxygenated hemoglobin (HbO(2), HHb) across multiple brain sites which occur in response to neuronal activation of the cerebral cortex. However, identification of areas of cortical activation is a complex task due to intrinsic physiological noise and systemic interference and careful statistical analysis is therefore required. A total of 10 young healthy adults were studied. The activation paradigm comprised of anagrams followed by finger tapping. 12 channels of the OT system were positioned over the frontal cortex and 12 channels over the motor cortex while the systemic physiology (mean blood pressure (MBP), heart rate (HR), scalp flux) was simultaneously monitored. Analysis was done using the functional Optical Signal Analysis (fOSA) software and Statistical Parametric Mapping (SPM), where we utilized two approaches: (i) using only HbO(2) as a regressor in the general linear model (GLM) and (ii) using all of the explanatory variables (HbO(2), MBP, HR and scalp flux) as regressors. Group analysis using SPM showed significant correlation in a large number of OT channels between HbO(2) and systemic regressors; however no differences in activation areas were seen between the two approaches.

Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density

Local field potentials have good temporal resolution but are blurred due to the slow spatial decay of the electric field. For simultaneous recordings on regular grids one can reconstruct efficiently the current sources (CSD) using the inverse Current Source Density method (iCSD). It is possible to decompose the resultant spatiotemporal information about the current dynamics into functional components using Independent Component Analysis (ICA). We show on test data modeling recordings of evoked potentials on a grid of 4 × 5 × 7 points that meaningful results are obtained with spatial ICA decomposition of reconstructed CSD. The components obtained through decomposition of CSD are better defined and allow easier physiological interpretation than the results of similar analysis of corresponding evoked potentials in the thalamus. We show that spatiotemporal ICA decompositions can perform better for certain types of sources but it does not seem to be the case for the experimental data studied. Having found the appropriate approach to decomposing neural dynamics into functional components we use the technique to study the somatosensory evoked potentials recorded on a grid spanning a large part of the forebrain. We discuss two example components associated with the first waves of activation of the somatosensory thalamus. We show that the proposed method brings up new, more detailed information on the time and spatial location of specific activity conveyed through various parts of the somatosensory thalamus in the rat.

Blind identification of evoked human brain activity with independent component analysis of optical data

Diffuse optical tomography (DOT) methods observe hemodynamics in the brain by measuring light transmission through the scalp, skull, and brain. Thus, separating signals due to heart pulsations, breathing movements, and systemic blood flow fluctuations from the desired brain functional responses is critical to the fidelity of the derived maps. Herein, we applied independent component analysis (ICA) to temporal signals obtained from a high-density DOT system used for functional mapping of the visual cortex. DOT measurements were taken over the occipital cortex of human adult subjects while they viewed stimuli designed to activate two spatially distinct areas of the visual cortex. ICA was able to extract clean functional hemodynamic signals and separate brain activity sources from hemodynamic fluctuations related to heart and breathing without knowledge of the stimulus paradigm. Furthermore, independent components were found defining distinct functional responses to each stimulus type. Images generated from single ICA components were comparable, with regard to spatial extent and resolution, to images from block averaging (with knowledge of the block stimulus paradigm). Both images and estimated time-series signals demonstrated that ICA was superior to principal component analysis in extracting the true event-evoked response signals. Our results suggest that ICA can extract the time courses and the corresponding spatial extent of functional responses in DOT imaging.

Removal of spatial biological artifacts in functional maps by local similarity minimization

Functional maps obtained by various technologies, including optical imaging techniques, f-MRI, PET, and others, may be contaminated with biological artifacts such as vascular patterns or large patches of parenchyma. These artifacts originate mostly from changes in the microcirculation that result from either activity-dependent changes in volume or from oximetric changes that do not co-localize with neuronal activity per se. Standard methods do not always suffice to reduce such artifacts, in which case conspicuous spatial artifacts mask details of the underlying activity patterns. Here we propose a simple algorithm that efficiently removes spatial biological artifacts contaminating high-resolution functional maps. We validated this procedure by applying it to cortical maps resulting from optical imaging, based either on voltage-sensitive dye signals or on intrinsic signals. To remove vascular spatial patterns we first constructed a template of typical artifacts (vascular/cardiac pulsation/vasomotion), using principle components derived from baseline information obtained in the absence of stimulation. Next, we modified this template by means of local similarity minimization (LSM), achieved by measuring neighborhood similarity between contaminated data and the artifact template and then abolishing the similarity. LSM thus removed spatial patterns originating from the cortical vasculature components, including large fields of capillary parenchyma, helping to unveil details of neuronal activity patterns that were otherwise masked by these vascular artifacts. Examples obtained from our imaging experiments with anaesthetized cats and behaving monkeys showed that the LSM method is both general and reproducible, and is often superior to other available procedures.

Functional optical signal analysis: a software tool for near-infrared spectroscopy data processing incorporating statistical parametric mapping

Optical topography (OT) relies on the near infrared spectroscopy (NIRS) technique to provide noninvasively a spatial map of functional brain activity. OT has advantages over conventional fMRI in terms of its simple approach to measuring the hemodynamic response, its ability to distinguish between changes in oxy- and deoxy-hemoglobin and the range of human participants that can be readily investigated. We offer a new software tool, functional optical signal analysis (fOSA), for analyzing the spatially resolved optical signals that provides statistical inference capabilities about the distribution of brain activity in space and time and by experimental condition. It does this by mapping the signal into a standard functional neuroimaging analysis software, statistical parametric mapping (SPM), and forms, in effect, a new SPM toolbox specifically designed for NIRS in an OT configuration. The validity of the program has been tested using synthetic data, and its applicability is demonstrated with experimental data.

Spatiotemporal independent component analysis for the detection of functional responses in cat retinal images

In the early stages of some retinal diseases, such as glaucoma, loss of retinal activity may be difficult to detect with current clinical instruments. Because current instruments require unattainable levels of patient cooperation, high sensitivity and specificity are difficult to attain. We have devised a new retinal imaging system that detects intrinsic optical signals which reflect functional changes in the retina and that do not require patient cooperation. Measured changes in reflectance in response to the visual stimulus are on the order of 0.1%-1% of the total reflected intensity level, which makes the functional signal difficult to detect by standard methods. The desired functional signal is masked by other physiological signals and by imaging system noise. In this paper, we quantify the limits of independent component analysis (ICA) for detecting the low intensity functional signal and apply ICA to 60 video sequences from experiments using an anesthetized cat whose retina is presented with different patterned stimuli. The results of the analysis show that using ICA, in principle, signal levels of 0.1% can be detected. The study found that in 86% of the animal experiments the patterned stimuli effects on the retina can be detected and extracted.

Independent component analysis of high-resolution imaging data identifies distinct functional domains

In the vertebrate brain external stimuli are often represented in distinct functional domains distributed across the cortical surface. Fast imaging techniques used to measure patterns of population activity record movies with many pixels and many frames, i.e., data sets with high dimensionality. Here we demonstrate that principal component analysis (PCA) followed by spatial independent component analysis (sICA), can be exploited to reduce the dimensionality of data sets recorded in the olfactory bulb and the somatosensory cortex of mice as well as the visual cortex of monkeys, without loosing the stimulus-specific responses. Different neuronal populations are separated based on their stimulus-specific spatiotemporal activation. Both, spatial and temporal response characteristics can be objectively obtained, simultaneously. In the olfactory bulb, groups of glomeruli with different response latencies can be identified. This is shown for recordings of olfactory receptor neuron input measured with a calcium-sensitive axon tracer and for network dynamics measured with the voltage-sensitive dye RH 1838. In the somatosensory cortex, barrels responding to the stimulation of single whiskers can be automatically detected. In the visual cortex orientation columns can be extracted. In all cases artifacts due to movement, heartbeat or respiration were separated from the functional signal by sICA and could be removed from the data set. sICA following PCA is therefore a powerful technique for data compression, unbiased analysis and dissection of imaging data of population activity, collected with high spatial and temporal resolution.

Spatiotemporal dynamics of the functional architecture for gain fields in inferior parietal lobule of behaving monkey

Intrinsic optical imaging has revealed a representation of eye position smoothly mapped across the surface of the inferior parietal lobule in behaving monkeys. We demonstrate here that blood vessels imaged along with the cortex have large signals tuned sometimes, but not always, to match the surrounding tissue. The relationship between the vessels and surrounding tissue in both space and time was explored using independent component analysis (ICA). Working only with single-trial data, ICA discovered a sequence of regions corresponding to the vascular propagation of activated signals from remote loci into the blood vessels. The vascular signals form a novel map of cortical function--the functional angioarchitecture--superimposed upon the cortical functional architecture. Furthermore, the incorporation of temporal aspects in optical data permitted the tuning of the inferior parietal lobule to be tracked in time through the task, demonstrating the expression of unusual tuning properties that might be exploited for higher cognitive functions.

Spatio-temporal autoregressive models defined over brain manifolds

Multivariate Autoregressive time series models (MAR) are an increasingly used tool for exploring functional connectivity in Neuroimaging. They provide the framework for analyzing the Granger Causality of a given brain region on others. In this article, we shall limit our attention to linear MAR models, in which a set of matrices of autoregressive coefficients Ak (k = 1,...,p) describe the dependence of present values of the image on lagged values of its past. Methods for estimating the Ak and determining which elements that are zero are well-known and are the basis for directed measures of influence. However, to date, MAR models are limited in the number of time series they can handle, forcing the a priori selection of a (small) number of voxels or regions of interest for analysis. This ignores the full spatio-temporal nature of functional brain data which are, in fact, collections of time series sampled over an underlying continuous spatial manifold the brain. A fully spatio-temporal MAR model (ST-MAR) is developed within the framework of functional data analysis. For spatial data, each row of a matrix Ak is the influence field of a given voxel. A Bayesian ST-MAR model is specified in which the influence fields for all voxels are required to vary smoothly over space. This requirement is enforced by penalizing the spatial roughness of the influence fields. This roughness is calculated with a discrete version of the spatial Laplacian operator. A massive reduction in dimensionality of computations is achieved via the singular value decomposition, making an interactive exploration of the model feasible. Use of the model is illustrated with an fMRI time series that was gathered concurrently with EEG in order to analyze the origin of resting brain rhythms.

Optical imaging of the retinotopic organization of V1 in the common marmoset

We examined the retinotopic mapping of the visual world in the primary visual cortex of the marmoset monkey using differential optical imaging. Two sets of complementary stripe-like locations were visually stimulated in turn. Their difference depicts the cortical representations of continuous bands of visual space. By rotating the sets of stripe-like locations it is possible to map different spatial axes. Analogous to the macaque we found that the V1/V2 border represented the vertical meridian, while horizontal, 45-, and 135-degree angled stripes of space were also represented in a continuous manner. We developed a new automatic method of calculating local measures of cortical magnification from our optical retinotopic maps. Using this method we found no evidence of any local anisotropies in cortical representation. Overall our results indicate that space is mapped isotropically in the primary visual cortex of the common marmoset.

Shedding light on brain mapping: advances in human optical imaging

Several functional brain imaging techniques have been used to study human cortical organization. Optical imaging of intrinsic signals (OIS) offers perhaps the best combination of spatial coverage, resolution and speed for mapping the functional topography of human cortex. In this review, we discuss recent advances in optical imaging technology and methodology that have made human OIS easier to implement and more accessible, including improvements in detector characteristics and the development of sophisticated algorithms for reducing motion artifact. Moreover, we discuss how these advances have helped enhance our understanding of the functional organization of the human brain. We also review newly developed analyses for interpreting and validating optical signals, including refined signal analysis techniques and multimodality comparisons. Combined, these advances have enabled the study of not only primary sensory and motor cortices, but also higher cognitive processes such as language production and comprehension. Continued improvement and implementation of this technique promises to shed new light on the functional organization of human cortex.

An optimization approach to signal extraction from noisy multivariate data

We consider a problem of blind signal extraction from noisy multivariate data, in which each datum represents a system's response, observed under a particular experimental condition. Our prototype example is multipixel functional images of brain activity in response to a set of prescribed experimental stimuli. We present a novel multivariate analysis technique, which identifies the different activity patterns (signals) that are attributable to specific experimental conditions, without a priori knowledge about the signal or the noise characteristics. The extracted signals, which we term the generalized indicator functions, are optimal in the sense that they maximize a weighted difference between the signal variance and the noise variance. With an appropriate choice of the weighting parameter, the method returns a set of images whose signal-to-noise ratios satisfy some user-defined level of significance. We demonstrate the performance of our method in optical intrinsic signal imaging of cat cortical area 17. We find that the method performs effectively and robustly in all tested data, which include both real experimental data and numerically simulated data. The method of generalized indicator functions is related to canonical variate analysis, a multivariate analysis technique that directly solves for the maxima of the signal-to-noise ratio, but important theoretical and practical differences exist, which can make our method more appropriate in certain situations.

A sensor array processing approach to object region detection

This paper presents a new approach for medical image analysis. It translates the object region-detection problem into a sensor array processing framework and detects the number of object regions based on the signal eigenstructure of the converted array system. The theoretical and experimental results obtained by using this approach on various medical images were in good agreement.

Imaging Brain Dynamics Using Independent Component Analysis

The analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging (fMRI) data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain.

Blind separation of slow waves and spikes from gastrointestinal myoelectrical recordings

Myoelectrical recordings of the gut contains slow waves (slow rhythmicity) and spikes (fast rhythmicity). While the slow wave determines the frequency and propagation of gastrointestinal contractions, spike activities are directly associated with the contractions. Traditionally, spikes are extracted from the myoelectrical recording using high-pass/bandpass filters. Due to sharp rising edge (high-frequency component) of the slow wave, the conventional method is not accurate in the separation of the slow wave and spikes, although it has been used for years. In this paper, a novel and fast blind source separation algorithm was developed. Experimental results showed that the proposed method was able to accurately extract spike activities from the myoelectrical recordings obtained in dogs and that its performance was not affected by the high-frequency components of the slow wave.

Separation of signal and noise from in vivo optical recording in Guinea pigs using independent component analysis

Optical recording in vivo severely suffers from the interference of heartbeat noise. So far, heartbeat noise has been minimized by subtracting from each experimental trial an average of interlaced control recordings. This method, however, is time-consuming and increases tissue damage due to phototoxicity. Here we applied independent component analysis (ICA) to in vivo optical recordings, for separation of auditory signals and noises. Our results show that ICA can be successfully used to separate sound-evoked signals and heartbeat noises. Compared with the previous method, ICA has a comparable power of separation and does not require background recordings.

Novelty indices: identifiers of potentially interesting time-courses in functional MRI data

In a systematic study of hybrid MR time-series with simulated "activation" (contrast-to-noise range 1-10) we investigated the power and compared the ability of the "novelty indices" (NI) kurtosis, negentropy, and 1-lag autocorrelation, to discriminate between potentially interesting ("structured") and uninteresting ("noisy") time-courses (TCs). NIs may be employed as preprocessing tools to focus only on the interesting TCs prior to any further exploratory or confirmatory approach.