We used fMRI-informed EEG source-imaging in humans to characterize the dynamics of cortical responses during a disparity-discrimination task. After the onset of a disparity-defined target, decision-related activity was found within an extended cortical network that included several occipital regions of interest (ROIs): V4, V3A, hMT+ and the Lateral Occipital Complex (LOC). By using a response-locked analysis, we were able to determine the timing relationships in this network of ROIs relative to the subject's behavioral response. Choice-related activity appeared first in the V4 ROI almost 200 ms before the button press and then subsequently in the V3A ROI. Modeling of the responses in the V4 ROI suggests that this area provides an early contribution to disparity discrimination. Choice-related responses were also found after the button-press in ROIs V4, V3A, LOC and hMT+. Outside the visual cortex, choice-related activity was found in the frontal and temporal pole before the button-press. By combining the spatial resolution of fMRI-informed EEG source imaging with the ability to sort out neural activity occurring before, during and after the behavioral manifestation of the decision, our study is the first to assign distinct functional roles to the extra-striate ROIs involved in perceptual decisions based on disparity, the primary cue for depth.
Decision making; binocular vision; EEG; neural imaging; disparity processing
In BOLD fMRI, stimulus related phase changes have been repeatedly observed in humans. However, virtually all fMRI processing utilizes the magnitude information only, while ignoring the phase. This results in an unnecessary loss of physiological information and signal-to-noise efficiency. A widely held view is that the BOLD phase change is zero for a voxel containing randomly orientated blood vessels and that phase changes are only due to the presence of large vessels. Based on a previously developed theoretical model, we show through simulations and experimental human BOLD fMRI data that a non-zero phase change can be present in a region with randomly oriented vessels. Using simulations of the model, we first demonstrate that a spatially distributed susceptibility results in a non-zero phase distribution. Next, experimental data in a finger-tapping experiment show consistent bipolar phase distribution across multiple subjects. This model is then used to show that in theory a bipolar phase distribution can also be produced by the model. Finally, we show that the model can produce a bipolar phase pattern consistent with that observed in the experimental data. Understanding of the mechanisms behind the experimentally observed phase changes in BOLD fMRI would be an important step forward and will enable biophysical model based methods for integrating the phase and magnitude information in BOLD fMRI experiments.
fMRI; BOLD; Lorentz Sphere; Phase Changes
Transcranial magnetic stimulation (TMS) has shown promise as a treatment tool, with one FDA approved use. While TMS alone is able to up- (or down-) regulate a targeted neural system, we argue that TMS applied as an adjuvant is more effective for repetitive physical, behavioral and cognitive therapies, that is, therapies which are designed to alter the network properties of neural systems through Hebbian learning. We tested this hypothesis in the context of a slow motor learning paradigm. Healthy right-handed individuals were assigned to receive 5 Hz TMS (TMS group) or sham TMS (sham group) to the right primary motor cortex (M1) as they performed daily motor practice of a digit sequence task with their non-dominant hand for 4 weeks. Resting cerebral blood flow (CBF) was measured by H215O PET at baseline and after 4 weeks of practice. Sequence performance was measured daily as the number of correct sequences performed, and modeled using a hyperbolic function. Sequence performance increased significantly at 4 weeks relative to baseline in both groups. The TMS group had a significant additional improvement in performance, specifically, in the rate of skill acquisition. In both groups, an improvement in sequence timing and transfer of skills to non-trained motor domains was also found. Compared to the sham group, the TMS group demonstrated increases in resting CBF specifically in regions known to mediate skill learning namely, the M1, cingulate cortex, putamen, hippocampus, and cerebellum. These results indicate that TMS applied concomitantly augments behavioral effects of motor practice, with corresponding neural plasticity in motor sequence learning network. These findings are the first demonstration of the behavioral and neural enhancing effects of TMS on slow motor practice and have direct application in neurorehabilitation where TMS could be applied in conjunction with physical therapy.
TMS; Primary motor cortex; Motor learning; Digit sequence practice; Hebbian learning; Hyperbolic function; Motor system; Skill transfer; Motor learning network
[11C]NOP-1A is a novel high-affinity PET ligand for imaging nociceptin/orphanin FQ peptide (NOP) receptors. Here, we report reproducibility and reliability measures of binding parameter estimates for [11C]NOP-1A binding in brain of healthy humans.
After intravenous injection of [11C]NOP-1A, PET scans were conducted twice on eleven healthy volunteers on the same (10/11 subjects) or different (1/11 subjects) days. Subjects underwent serial sampling of radial arterial blood to measure parent radioligand concentrations. Distribution volume (VT; a measure of receptor density) was determined by compartmental (one- and two-tissue) modeling in large regions and by simpler regression methods (graphical Logan and bilinear MA1) in both large regions and voxel data. Retest variability and intraclass correlation coefficient (ICC) of VT were determined as measures of reproducibility and reliability, respectively.
Regional [11C]NOP-1A uptake in brain was high, with a peak radioactivity concentration of 4 – 7 SUV (standardized uptake value) and a rank order of putamen > cingulate cortex > cerebellum. Brain time-activity curves fitted well in 10 of 11 subjects by unconstrained two-tissue compartmental model. The retest variability of VT was moderately good across brain regions except cerebellum, and was similar across different modeling methods, averaging 12% for large regions and 14% for voxel-based methods. The retest reliability of VT was also moderately good in most brain regions, except thalamus and cerebellum, and was similar across different modeling methods averaging 0.46 for large regions and 0.48 for voxels having gray matter probability > 20%. The lowest retest variability and highest retest reliability of VT was achieved by compartmental modeling for large regions, and by the parametric Logan method for voxel-based methods.
Moderately good reproducibility and reliability measures of VT for [11C]NOP-1A make it a useful PET ligand for comparing NOP receptor binding between different subject groups or under different conditions in the same subject.
NOP receptors; nociceptin; test-retest imaging; PET; retest variability; intraclass correlation coefficient
FMRI data are acquired as complex-valued spatiotemporal images. Despite the fact that several studies have identified the presence of novel information in the phase images, they are usually discarded due to their noisy nature. Several approaches have been devised to incorporate magnitude and phase data, but none of them has performed between-group inference or classification. Multiple kernel learning (MKL) is a powerful field of machine learning that finds an automatic combination of kernel functions that can be applied to multiple data sources. By analyzing this combination of kernels, the most informative data sources can be found, hence providing a better understanding of the analyzed learning task. This paper presents a methodology based on a new MKL algorithm (ν-MKL) capable of achieving a tunable sparse selection of features’ sets (brain regions’ patterns) that improves the classification accuracy rate of healthy controls and schizophrenia patients by 5% when phase data is included. In addition, the proposed method achieves accuracy rates that are equivalent to those obtained by the state of the art lp-norm MKL algorithm on the schizophrenia dataset and we argue that it better identifies the brain regions that show discriminative activation between groups. This claim is supported by the more accurate detection achieved by ν-MKL of the degree of information present on regions of spatial maps extracted from a simulated fMRI dataset. In summary, we present an MKL-based methodology that improves schizophrenia characterization by using both magnitude and phase fMRI data and is also capable of detecting the brain regions that convey most of the discriminative information between patients and controls.
complex-valued fMRI data; multiple kernel learning; feature selection; independent component analysis; support vector machines; schizophrenia
Pre-stimulus α power has been shown to correlate with the behavioral accuracy of perceptual decisions. In most cases, these correlations have been observed by comparing α power for different behavioral outcomes (e.g. correct vs incorrect trials). In this paper we investigate such covariation within the context of behaviorally-latent fluctuations in task-relevant post-stimulus neural activity. Specially we consider variations of pre-stimulus α power with post-stimulus EEG components in a two alternative forced choice visual discrimination task. EEG components, discriminative of stimulus class, are identified using a linear multivariate classifier and only the variability of the components for correct trials (regardless of stimulus class, and for nominally identical stimuli) are correlated with the corresponding pre-stimulus α power. We find a significant relationship between the mean and variance of the pre-stimulus α power and the variation of the trial-to-trial magnitude of an early post-stimulus EEG component. This relationship is not seen for a later EEG component that is also discriminative of stimulus class and which has been previously linked to the quality of evidence driving the decision process. Our results suggest that early perceptual representations, rather than temporally later neural correlates of the perceptual decision, are modulated by pre-stimulus state.
decision-making; sensory encoding; single-trial; alpha power; electroencephalogram (EEG)
Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for the study are nearly two times the Alzheimer’s disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and under sampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1). a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2). sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results.
Alzheimer’s disease; classification; imbalanced data; undersampling; oversampling; feature selection
Magnetoencephalography (MEG) is an important non-invasive method for studying activity within the human brain. Source localization methods can be used to estimate spatiotemporal activity from MEG measurements with high temporal resolution, but the spatial resolution of these estimates is poor due to the ill-posed nature of the MEG inverse problem. Recent developments in source localization methodology have emphasized temporal as well as spatial constraints to improve source localization accuracy, but these methods can be computationally intense. Solutions emphasizing spatial sparsity hold tremendous promise, since the underlying neurophysiological processes generating MEG signals are often sparse in nature, whether in the form of focal sources, or distributed sources representing large-scale functional networks. Recent developments in the theory of compressed sensing (CS) provide a rigorous framework to estimate signals with sparse structure. In particular, a class of CS algorithms referred to as greedy pursuit algorithms can provide both high recovery accuracy and low computational complexity. Greedy pursuit algorithms are difficult to apply directly to the MEG inverse problem because of the high-dimensional structure of the MEG source space and the high spatial correlation in MEG measurements. In this paper, we develop a novel greedy pursuit algorithm for sparse MEG source localization that overcomes these fundamental problems. This algorithm, which we refer to as the Subspace Pursuit-based Iterative Greedy Hierarchical (SPIGH) inverse solution, exhibits very low computational complexity while achieving very high localization accuracy. We evaluate the performance of the proposed algorithm using comprehensive simulations, as well as the analysis of human MEG data during spontaneous brain activity and somatosensory stimuli. These studies reveal substantial performance gains provided by the SPIGH algorithm in terms of computational complexity, localization accuracy, and robustness.
MEG; EEG; source localization; sparse representations; compressive sampling; greedy algorithms; evoked fields analysis
An almost sinusoidal, large amplitude ∼0.1 Hz oscillation in cortical hemodynamics has been repeatedly observed in species ranging from mice to humans. However, the occurrence of ‘slow sinusoidal hemodynamic oscillations’ (SSHOs) in human functional magnetic resonance imaging (fMRI) studies is rarely noted or considered. As a result, little investigation into the cause of SSHOs has been undertaken, and their potential to confound fMRI analysis, as well as their possible value as a functional biomarker has been largely overlooked.
Here, we report direct observation of large-amplitude, sinusoidal ∼0.1 Hz hemodynamic oscillations in the cortex of an awake human undergoing surgical resection of a brain tumor. Intraoperative multispectral optical intrinsic signal imaging (MS-OISI) revealed that SSHOs were spatially localized to distinct regions of the cortex, exhibited wave-like propagation, and involved oscillations in the diameter of specific pial arterioles, confirming that the effect was not the result of systemic blood pressure oscillations. fMRI data collected from the same subject 4 days prior to surgery demonstrates that ∼0.1 Hz oscillations in the BOLD signal can be detected around the same region. Intraoperative optical imaging data from a patient undergoing epilepsy surgery, in whom sinusoidal oscillations were not observed, is shown for comparison.
This direct observation of the ‘0.1 Hz wave’ in the awake human brain, using both intraoperative imaging and pre-operative fMRI, confirms that SSHOs occur in the human brain, and can be detected by fMRI. We discuss the possible physiological basis of this oscillation and its potential link to brain pathologies, highlighting its relevance to resting-state fMRI and its potential as a novel target for functional diagnosis and delineation of neurological disease.
Negative BOLD signals that are synchronous with resting state fluctuations have been observed in large vessels in the cortical sulci and surrounding the ventricles. In this study, we investigated the origin of these negative BOLD signals by applying a Cued Deep Breathing (CDB) task to create transient hypocapnia and a resultant global fMRI signal decrease. We hypothesized that a global stimulus would amplify the effect in large vessels and that using a global negative (vasoconstrictive) stimulus would test whether these voxels exhibit either inherently negative or simply anti-correlated BOLD responses. Significantly anti-correlated, but positive, BOLD signal changes during respiratory challenges were identified in voxels primarily located near edges of brain spaces containing CSF. These positive BOLD responses occurred earlier than the negative CDB response across most of gray matter voxels. These findings confirm earlier suggestions that in some brain regions, local, fractional changes in CSF volume may overwhelm BOLD-related signal changes, leading to signal anti-correlation. We show that regions with CDB anti-correlated signals coincide with most, but not all, of the regions with negative BOLD signal changes observed during a visual and motor stimulus task. Thus, the addition of a physiological challenge to fMRI experiments can help identify which negative BOLD signals are passive physiological anti-correlations and which may have a putative neuronal origin.
fMRI; Negative BOLD; Anti-correlated BOLD; Physiology; Respiratory challenge; Deactivation
White matter of the brain contains a majority of long T2 components as well as a minority of short T2 components. These are not detectable using clinical magnetic resonance imaging (MRI) sequences with conventional echo times (TEs). In this study we used ultrashort echo time (UTE) sequences to investigate the ultrashort T2 components in white matter of the brain and quantify their T2*s and relative proton densities (RPDs) (relative to water with a proton density of 100%) using a clinical whole body 3T scanner. An adiabatic inversion recovery prepared dual echo UTE (IR-dUTE) sequence was used for morphological imaging of the ultrashort T2 components in white matter. IR-dUTE acquisitions at a constant TR of 1000 ms and a series of TIs were performed to determine the optimal TI which corresponded to the minimum signal to noise ratio (SNR) in white matter of the brain on the second echo image. T2*s of the ultrashort T2 components were quantified using mono-exponential decay fitting of the IR-dUTE signal at a series of TEs. RPD was quantified by comparing IR-dUTE signal of the ultrashort T2 components with that of a rubber phantom. Nine healthy volunteers were studied. The IR-dUTE sequence provided excellent image contrast for the ultrashort T2 components in white matter of the brain with a mean signal to noise ratio of 18.7 ± 3.7 and a contrast to noise ratio of 14.6 ± 2.4 between the ultrashort T2 white matter and gray matter in a 4.4 min scan time with a nominal voxel size of 1.25×1.25×5.0 mm3. On average a T2* value of 0.42 ± 0.08 ms and a RPD of 4.05 ± 0.88% were demonstrated for the ultrashort T2 components in white matter of the brain of healthy volunteers at 3T.
Ultrashort echo time; adiabatic IR; ultrashort T2; white matter; T2*; proton density
The neural mechanisms underlying conscious visual perception have been extensively investigated using bistable perception paradigms. Previous functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) studies suggest that the right anterior superior parietal (r-aSPL) and the right posterior superior parietal lobule (r-pSPL) have opposite roles in triggering perceptual reversals. It has been proposed that these two areas are part of a hierarchical network whose dynamics determine perceptual switches. However, how these two parietal regions interact with each other and with the rest of the brain during bistable perception is not known. Here, we investigated such a model by recording brain activity using fMRI while participants viewed a bistable structure-from-motion stimulus. Using dynamic causal modeling (DCM), we found that resolving such perceptual ambiguity was specifically associated with reciprocal interactions between these parietal regions and V5/MT. Strikingly, the strength of bottom-up coupling between V5/MT to r-pSPL and from r-pSPL to r-aSPL predicted individual mean dominance duration. Our findings are consistent with a hierarchical predictive coding model of parietal involvement in bistable perception and suggest that visual information processing underlying spontaneous perceptual switches can be described as changes in connectivity strength between parietal and visual cortical regions.
•Two parietal regions involve spontaneous perceptual switches.•The two parietal regions and V5/MT form hierarchical model.•Strength of DCM parameters predicts individual switch frequency.
Primate studies show slow ramping activity in posterior parietal cortex (PPC) neurons during perceptual decision-making. These findings have inspired a rich theoretical literature to account for this activity. These accounts are largely unrelated to Bayesian theories of perception and predictive coding, a related formulation of perceptual inference in the cortical hierarchy. Here, we tested a key prediction of such hierarchical inference, namely that the estimated precision (reliability) of information ascending the cortical hierarchy plays a key role in determining both the speed of decision-making and the rate of increase of PPC activity. Using dynamic causal modelling of magnetoencephalographic (MEG) evoked responses, recorded during a simple perceptual decision-making task, we recover ramping-activity from an anatomically and functionally plausible network of regions, including early visual cortex, the middle temporal area (MT) and PPC. Precision, as reflected by the gain on pyramidal cell activity, was strongly correlated with both the speed of decision making and the slope of PPC ramping activity. Our findings indicate that the dynamics of neuronal activity in the human PPC during perceptual decision-making recapitulate those observed in the macaque, and in so doing we link observations from primate electrophysiology and human choice behaviour. Moreover, the synaptic gain control modulating these dynamics is consistent with predictive coding formulations of evidence accumulation.
•MEG and DCM used to characterise neuronal dynamics during decision making.•DCM suggested plausible hierarchical network architecture.•Rate of accumulation best explained by pyramidal cell gain.•Results support predictive coding models of evidence accumulation.
In this work we propose a proof of principle that dynamic causal modelling can identify plausible mechanisms at the synaptic level underlying brain state changes over a timescale of seconds. As a benchmark example for validation we used intracranial electroencephalographic signals in a human subject. These data were used to infer the (effective connectivity) architecture of synaptic connections among neural populations assumed to generate seizure activity. Dynamic causal modelling allowed us to quantify empirical changes in spectral activity in terms of a trajectory in parameter space — identifying key synaptic parameters or connections that cause observed signals. Using recordings from three seizures in one patient, we considered a network of two sources (within and just outside the putative ictal zone). Bayesian model selection was used to identify the intrinsic (within-source) and extrinsic (between-source) connectivity. Having established the underlying architecture, we were able to track the evolution of key connectivity parameters (e.g., inhibitory connections to superficial pyramidal cells) and test specific hypotheses about the synaptic mechanisms involved in ictogenesis. Our key finding was that intrinsic synaptic changes were sufficient to explain seizure onset, where these changes showed dissociable time courses over several seconds. Crucially, these changes spoke to an increase in the sensitivity of principal cells to intrinsic inhibitory afferents and a transient loss of excitatory–inhibitory balance.
•We propose a framework to characterise slow dynamical changes in the brain.•Dynamical causal modelling finds the most likely connectivity among two brain areas.•The synaptic weights defining these connections are tracked in time.•We analyse brain activity of an epileptic subject, at the focus and just outside it.•We point to modulations of synaptic connections as responsible of the seizure.
DCM, dynamical causal modelling; SOZ, seizure onset zone; EEG, electroencephalography; CSD, cross spectral density; Dynamical causal modelling; Neural mass models; Seizure onset; Dynamical connectivity; Electroencephalography; Epilepsy
Large variability between individual response times, even in identical conditions, is a ubiquitous property of animal behavior. However, the origins of this stochasticity and its relation to action decisions remain unclear. Here we focus on the state of the perception–action network in the pre-stimulus period and its influence on subsequent saccadic response time and choice in humans. We employ magnetoencephalography (MEG) and a correlational source reconstruction approach to identify the brain areas where pre-stimulus oscillatory activity predicted saccadic response time to visual targets. We find a relationship between future response time and pre-stimulus power, but not phase, in occipital (including V1), parietal, posterior cingulate and superior frontal cortices, consistently across alpha, beta and low gamma frequencies, each accounting for between 1 and 4% of the RT variance. Importantly, these correlations were not explained by deterministic sources of variance, such as experimental factors and trial history. Our results further suggest that occipital areas mainly reflect short-term (trial to trial) stochastic fluctuations, while the frontal contribution largely reflects longer-term effects such as fatigue or practice. Parietal areas reflect fluctuations at both time scales. We found no evidence of lateralization: these effects were indistinguishable in both hemispheres and for both saccade directions, and non-predictive of choice — a finding with fundamental consequences for models of action decision, where independent, not coupled, noise is normally assumed.
•We searched for oscillatory predictors of saccadic reaction times in humans.•Pre-stimulus power at 5–45 Hz throughout dorsal pathway correlates with response time.•These correlations mainly reflect spontaneous variance rather than deterministic factors.•These correlations are dominated by different time scales across the brain.•We found no evidence for a relationship between latency and phase of oscillations.
Saccades; MEG; Phase; Amplitude; Decision; Free choice
Statistical regularities exist at different timescales in temporally unfolding event sequences. Recent studies have identified brain regions that are sensitive to the levels of regularity in sensory inputs, enabling the brain to construct a representation of environmental structure and adaptively generate actions or predictions. However, the temporal specificity of the statistical regularity to which the brain responds remains largely unknown. This uncertainty applies to the regularities of sensory inputs as well as instrumental actions. Here, we used fMRI to investigate the neural correlates of regularity in sequences of task events and action selections in a visuomotor choice task. We quantified timescale-dependent regularity measures by calculating Shannon's entropy and surprise from a sliding-window of consecutive task events and actions. Activity in the frontopolar cortex negatively correlated with the entropy in action selection, while activity in the temporoparietal junction, the striatum, and the cerebellum negatively correlated with the entropy in stimulus events at longer timescales. In contrast, activity in the supplementary motor area, the superior frontal gyrus, and the superior parietal lobule was positively correlated with the surprise of each stimulus across different timescales. The results suggest a spatial distribution of regions sensitive to various information regularities according to a temporal hierarchy, which may play a central role in concurrently monitoring the regularity in previous and current events over different timescales to optimize behavioral control in a dynamic environment.
•A sliding-window approach for quantifying trial and selection entropies•Selection entropy correlates with frontopolar activity at a short timescale.•A temporal and striatal network is sensitive to task entropy at long timescales.•Sensorimotor and parietal cortex are sensitive to repetition or surprise in events.
fMRI; Information theory; Selection entropy; Trial entropy; Voluntary selection
Functional magnetic resonance imaging (fMRI) in the resting state, particularly fMRI based on the blood-oxygenation level-dependent (BOLD) signal, has been extensively used to measure functional connectivity in the brain. However, the mechanisms of vascular regulation that underlie the BOLD fluctuations during rest are still poorly understood. In this work, using dual-echo pseudo-continuous arterial spin labeling and MR angiography (MRA), we assess the spatio-temporal contribution of cerebral blood flow (CBF) to the resting-state BOLD signals and explore how the coupling of these signals is associated with regional vasculature. Using a general linear model analysis, we found that statistically significant coupling between resting-state BOLD and CBF fluctuations is highly variable across the brain, but the coupling is strongest within the major nodes of established resting-state networks, including the default-mode, visual, and task-positive networks. Moreover, by exploiting MRA-derived large vessel (macrovascular) volume fraction, we found that the degree of BOLD–CBF coupling significantly decreased as the ratio of large vessels to tissue volume increased. These findings suggest that the portion of resting-state BOLD fluctuations at the sites of medium-to-small vessels (more proximal to local neuronal activity) is more closely regulated by dynamic regulations in CBF, and that this CBF regulation decreases closer to large veins, which are more distal to neuronal activity.
Cerebral blood flow (CBF); Resting-state BOLD; Arterial-spin labeling (ASL); MR angiography; Blood volume fraction
That physiological oscillations of various frequencies are present in fMRI
signals is the rule, not the exception. Herein, we propose a novel theoretical framework,
spatio-temporal Granger causality, which allows us to more reliably and precisely estimate
the Granger causality from experimental datasets possessing time-varying properties caused
by physiological oscillations. Within this framework, Granger causality is redefined as a
global index measuring the directed information flow between two time series with
time-varying properties. Both theoretical analyses and numerical examples demonstrate that
Granger causality is a monotonically increasing function of the temporal resolution used
in the estimation. This is consistent with the general principle of coarse graining, which
causes information loss by smoothing out very fine-scale details in time and space. Our
results confirm that the Granger causality at the finer spatio-temporal scales
considerably outperforms the traditional approach in terms of an improved consistency
between two resting-state scans of the same subject. To optimally estimate the Granger
causality, the proposed theoretical framework is implemented through a combination of
several approaches, such as dividing the optimal time window and estimating the parameters
at the fine temporal and spatial scales. Taken together, our approach provides a novel and
robust framework for estimating the Granger causality from fMRI, EEG, and other related
This paper presents feature-based morphometry (FBM), a new, fully data-driven technique for discovering patterns of group-related anatomical structure in volumetric imagery. In contrast to most morphometry methods which assume one-to-one correspondence between subjects, FBM explicitly aims to identify distinctive anatomical patterns that may only be present in subsets of subjects, due to disease or anatomical variability. The image is modeled as a collage of generic, localized image features that need not be present in all subjects. Scale-space theory is applied to analyze image features at the characteristic scale of underlying anatomical structures, instead of at arbitrary scales such as global or voxel-level. A probabilistic model describes features in terms of their appearance, geometry, and relationship to subject groups, and is automatically learned from a set of subject images and group labels. Features resulting from learning correspond to group-related anatomical structures that can potentially be used as image biomarkers of disease or as a basis for computer-aided diagnosis. The relationship between features and groups is quantified by the likelihood of feature occurrence within a specific group vs. the rest of the population, and feature significance is quantified in terms of the false discovery rate. Experiments validate FBM clinically in the analysis of normal (NC) and Alzheimer's (AD) brain images using the freely available OASIS database. FBM automatically identifies known structural differences between NC and AD subjects in a fully data-driven fashion, and an equal error classification rate of 0.80 is achieved for subjects aged 60-80 years exhibiting mild AD (CDR=1).
morphometry; brain image analysis; scale-invariant features; group differences; machine learning; image biomarkers; computer-aided diagnosis; Alzheimer's disease
Dystrophin, the main component of the dystrophin–glycoprotein complex, plays an important role in maintaining the structural integrity of cells. It is also involved in the formation of the blood–brain barrier (BBB). To elucidate the impact of dystrophin disruption in vivo, we characterized changes in cerebral perfusion and diffusion in dystrophin-deficient mice (mdx) by magnetic resonance imaging (MRI). Arterial spin labeling (ASL) and diffusion-weighted MRI (DWI) studies were performed on 2-month-old and 10-month-old mdx mice and their age-matched wild-type controls (WT). The imaging results were correlated with Evan's blue extravasation and vascular density studies. The results show that dystrophin disruption significantly decreased the mean cerebral diffusivity in both 2-month-old (7.38± 0.30 × 10−4mm2/s) and 10-month-old (6.93 ± 0.53 × 10−4 mm2/s) mdx mice as compared to WT (8.49±0.24×10−4, 8.24±0.25× 10−4mm2/s, respectively). There was also an 18% decrease in cerebral perfusion in 10-month-old mdx mice as compared to WT, which was associated with enhanced arteriogenesis. The reduction in water diffusivity in mdx mice is likely due to an increase in cerebral edema or the existence of large molecules in the extracellular space from a leaky BBB. The observation of decreased perfusion in the setting of enhanced arteriogenesis may be caused by an increase of intracranial pressure from cerebral edema. This study demonstrates the defects in water handling at the BBB and consequently, abnormal perfusion associated with the absence of dystrophin.
dystrophin; perfusion; diffusion; cryoimaging
Diseases involving the medial temporal lobes (MTL) such as Alzheimer’s disease and mesial temporal sclerosis pose an ongoing diagnostic challenge because of the difficulty in identifying conclusive imaging features, particularly in pre-clinical states. Abnormal neuronal connectivity may be present in the circuitry of the MTL, but current techniques cannot reliably detect those abnormalities. Diffusion tensor imaging (DTI) has shown promise in defining putative abnormalities in connectivity, but DTI studies of the MTL performed to date have shown neither dramatic nor consistent differences across patient populations. Conventional DTI methodology provides an inadequate depiction of the complex microanatomy present in the medial temporal lobe because of a typically employed low isotropic resolution of 2.0–2.5mm, a low signal-to-noise ratio (SNR), and echo-planar imaging (EPI) geometric distortions that are exacerbated by the inhomogeneous magnetic environment at the skull base. In this study, we pushed the resolving power of DTI to near-mm isotropic voxel size to achieve a detailed depiction of mesial temporal microstructure at 3T. High image fidelity and SNR at this resolution are achieved through several mechanisms: (1) acquiring multiple repetitions of the minimum field of view required for hippocampal coverage to boost SNR; (2) utilizing a single-refocused diffusion preparation to enhance SNR further; (3) performing a phase correction to reduce Rician noise; (4) minimizing distortion and maintaining left-right distortion symmetry with axial-plane parallel imaging; and (5) retaining anatomical and quantitative accuracy through the use of motion correction coupled with a higher-order eddy-current correction scheme. We combined this high-resolution methodology with a detailed segmentation of the MTL to identify tracks in all subjects that may represent the major pathways of the MTL, including the perforant pathway. Tractography performed on a subset of the data identified similar tracks, although they were lesser in number. This detailed analysis of MTL substructure may have applications to clinical populations.
DTI; hippocampus; perforant pathway; tractography; medial temporal lobe; MRI
Spectral domain optical coherence tomography (SD-OCT) is a high resolution imaging technique that generates excellent contrast based on intrinsic optical properties of the tissue, such as neurons and fibers. The SD-OCT data acquisition is performed directly on the tissue block, diminishing the need for cutting, mounting and staining. We utilized SD-OCT to visualize the laminar structure of the isocortex and compared cortical cytoarchitecture with the gold standard Nissl staining, both qualitatively and quantitatively. In histological processing, distortions routinely affect registration to the blockface image and prevent accurate 3D reconstruction of regions of tissue. We compared blockface registration to SD-OCT and Nissl, respectively, and found that SD-OCT-blockface registration was significantly more accurate than Nissl-blockface registration. Two independent observers manually labeled cortical laminae (e.g. III, IV and V) in SD-OCT images and Nissl stained sections. Our results show that OCT images exhibit sufficient contrast in the cortex to reliably differentiate the cortical layers. Furthermore, the modalities were compared with regard to cortical laminar organization and showed good agreement. Taken together, these SD-OCT results suggest that SD-OCT contains information comparable to standard histological stains such as Nissl in terms of distinguishing cortical layers and architectonic areas. Given these data, we propose that SD-OCT can be used to reliably generate 3D reconstructions of multiple cubic centimeters of cortex that can be used to accurately and semi-automatically perform standard histological analyses.
Perception of facial expressions is typically investigated by presenting isolated face stimuli. In everyday life, however, faces are rarely seen without a surrounding visual context that affects perception and interpretation of the facial expression. Conversely, fearful faces may act as a cue, heightening the sensitivity of the visual system to effectively detect potential threat in the environment. In the present study, we used steady-state visually evoked potentials (ssVEPs) to examine the mutual effects of facial expressions (fearful, neutral, happy) and affective visual context (pleasant, neutral, threat). By assigning two different flicker frequencies (12 vs. 15 Hz) to the face and the visual context scene, cortical activity to the concurrent stimuli was separated, which represents a novel approach to independently tracking the cortical processes associated with the face and the context. Twenty healthy students viewed flickering faces overlaid on flickering visual scenes, while performing a simple change-detection task at fixation, and high-density EEG was recorded. Arousing background scenes generally drove larger ssVEP amplitudes than neutral scenes. Importantly, background and expression interacted: When viewing fearful facial expressions, the ssVEP in response to threat context was amplified compared to other backgrounds. Together, these findings suggest that fearful faces elicit vigilance for potential threat in the visual periphery.
fear; vigilance; facial expression; visual context; visual cortex