Voxel Pairs Research Articles

Real-Time Path Guiding Using Bounding Voxel Sampling

We propose a real-time path guiding method, Voxel Path Guiding (VXPG), that significantly improves fitting efficiency under limited sampling budget. Our key idea is to use a spatial irradiance voxel data structure across all shading points to guide the location of path vertices. For each frame, we first populate the voxel data structure with irradiance and geometry information. To sample from the data structure for a shading point, we need to select a voxel with high contribution to that point. To importance sample the voxels while taking visibility into consideration, we adapt techniques from offline many-lights rendering by clustering pairs of shading points and voxels. Finally, we unbiasedly sample within the selected voxel while taking the geometry inside into consideration. Our experiments show that VXPG achieves significantly lower perceptual error compared to other real-time path guiding and virtual point light methods under equal-time comparison. Furthermore, our method does not rely on temporal information, but can be used together with other temporal reuse sampling techniques such as ReSTIR to further improve sampling efficiency.

A multivariate to multivariate approach for voxel-wise genome-wide association analysis.

The joint analysis of imaging-genetics data facilitates the systematic investigation of genetic effects on brain structures and functions with spatial specificity. We focus on voxel-wise genome-wide association analysis, which may involve trillions of single nucleotide polymorphism (SNP)-voxel pairs. We attempt to identify underlying organized association patterns of SNP-voxel pairs and understand the polygenic and pleiotropic networks on brain imaging traits. We propose a bi-clique graph structure (ie, a set of SNPs highly correlated with a cluster of voxels) for the systematic association pattern. Next, we develop computational strategies to detect latent SNP-voxel bi-cliques and an inference model for statistical testing. We further provide theoretical results to guarantee the accuracy of our computational algorithms and statistical inference. We validate our method by extensive simulation studies, and then apply it to the whole genome genetic and voxel-level white matter integrity data collected from 1052 participants of the human connectome project. The results demonstrate multiple genetic loci influencing white matter integrity measures on splenium and genu of the corpus callosum.

Improving image segmentation with contextual and structural similarity

Deep learning models for medical image segmentation are usually trained with voxel-wise losses, e.g., cross-entropy loss, focusing on unary supervision without considering inter-voxel relationships. This oversight potentially leads to semantically inconsistent predictions. Here, we propose a contextual similarity loss (CSL) and a structural similarity loss (SSL) to explicitly and efficiently incorporate inter-voxel relationships for improved performance. The CSL promotes consistency in predicted object categories for each image sub-region compared to ground truth. The SSL enforces compatibility between the predictions of voxel pairs by computing pair-wise distances between them, ensuring that voxels of the same class are close together whereas those from different classes are separated by a wide margin in the distribution space. The effectiveness of the CSL and SSL is evaluated using a clinical cone-beam computed tomography (CBCT) dataset of patients with various craniomaxillofacial (CMF) deformities and a public pancreas dataset. Experimental results show that the CSL and SSL outperform state-of-the-art regional loss functions in preserving segmentation semantics.

A modified U-net with graph representation for dose prediction in esophageal cancer radiotherapy plans

The manual design of esophageal cancer radiotherapy plan is time-consuming and labor-intensive. Automatic planning (AP) is prevalent nowadays to increase physicists’ work efficiency. Because of the intuitiveness of dose distribution in AP evaluation, obtaining reasonable dose prediction provides effective guarantees to generate a satisfactory AP. Existing fully convolutional network-based methods for predicting dose distribution in esophageal cancer radiotherapy plans often capture features in a limited receptive field. Additionally, the correlations between voxel pairs are often ignored. This work modifies the U-net architecture and exploits graph convolution to capture long-range information for dose prediction in esophageal cancer plans. Meanwhile, attention mechanism gets correlations between planning target volume (PTV) and organs at risk, and adaptively learns their feature weights. Finally, a novel loss function that considers features between voxel pairs is used to highlight the predictions. 152 subjects with prescription doses of 50 Gy or 60 Gy are collected in this study. The mean absolute error and standard deviation of conformity index, homogeneity index, and max dose for PTV achieved by the proposed method are 0.036 ± 0.030, 0.036 ± 0.027, and 0.930 ± 1.162, respectively, which outperform other state-of-the-art models. The superior performance demonstrates that our proposed method has great potential for AP generation.

A stopping criterion for iterative proton CT image reconstruction based on correlated noise properties.

Whereas filtered back projection algorithms for voxel-based CT image reconstruction have noise properties defined by the filter, iterative algorithms must stop at some point in their convergence and do not necessarily produce consistent noise properties for images with different degrees of heterogeneity. A least-squares iterative algorithm for proton CT (pCT) image reconstruction converges toward a unique solution for relative stopping power (RSP) that optimally fits the protons. We present a stopping criterion that delivers solutions with the property that correlations of RSP noise between voxels are relatively low. This provides a method to produce pCT images with consistent noise properties useful for proton therapy treatment planning, which relies on summing RSP along lines of voxels. Consistent noise properties will also be useful for future studies of image quality using metrics such as contrast to noise ratio, and to compare RSP noise and dose of pCT with other modalities such as dual-energyCT. With simulated and real images with varying heterogeneity from a prototype clinical proton imaging system, we calculate average RSP correlations between voxel pairs in uniform regions-of-interest versus distance between voxels. We define a parameter r, the remaining distance to the unique solution relative to estimated RSP noise, and our stopping criterion is based on r falling below a chosenvalue. We find large correlations between voxels for larger values of r, and anticorrelations for smaller values. For r in the range of 0.5-1, voxels are relatively uncorrelated, and compared to smaller values of r have lower noise with only slight loss of spatialresolution. Iterative algorithms not using a specific metric or rationale for stopping iterations may produce images with an unknown and arbitrary level of convergence or smoothing. We resolve this issue by stopping iterations of a least-squares iterative algorithm when r reaches the range of 0.5-1. This defines a pCT image reconstruction method with consistent statistical properties optimal for clinical use, including for treatment planning with pCTimages.

Task-specific subnetworks extend from prefrontal cortex to striatum.

Functional magnetic resonance imaging (fMRI) studies on the dynamic representation of task content focus preferentially on the cerebral cortex. However, neurophysiological studies report coding of task-relevant features also by neurons in the striatum, suggesting basal ganglia involvement in cognitive decision-making. Here we use fMRI data to show that also in humans the striatum is an integrated part of the cognitive brain network. Twelve participants performed 3 cognitive tasks in the scanner, i.e., the Eriksen flanker task, a 2-back matching spatial working memory task, and a response scheme switching task. First, we use region of interest-based multivariate pattern classification to demonstrate that each task reliably induces a unique activity pattern in the striatum and in the lateral prefrontal cortex. We show that the three tasks can also be distinguished in putamen, caudate nucleus and ventral striatum alone. We additionally establish that the contribution of striatum to cognition is not sensitive to habituation or learning. Secondly, we use voxel-to-voxel functional connectivity to establish that voxels in the lateral prefrontal cortex and in the striatum that prefer the same task show significantly stronger functional coupling than voxel pairs in these remote structures that prefer different tasks. These results suggest that striatal neurons form subnetworks with cognition-related regions of the prefrontal cortex. These remote neuron populations are interconnected via functional couplings that exceed the time of execution of the specific tasks.

Improved Quantification of Cell Density in the Arterial Wall-A Novel Nucleus Splitting Approach Applied to 3D Two-Photon Laser-Scanning Microscopy.

Accurate information on vascular smooth muscle cell (VSMC) content, orientation, and distribution in blood vessels is indispensable to increase understanding of arterial remodeling and to improve modeling of vascular biomechanics. We have previously proposed an analysis method to automatically characterize VSMC orientation and transmural distribution in murine carotid arteries under well-controlled biomechanical conditions. However, coincident nuclei, erroneously detected as one large nucleus, were excluded from the analysis, hampering accurate VSMC content characterization and distorting transmural distributions. In the present study, therefore, we aim to (1) improve the previous method by adding a “nucleus splitting” procedure to split coinciding nuclei, (2) evaluate the accuracy of this novel method, and (3) test this method in a mouse model of VSMC apoptosis. After euthanasia, carotid arteries from SM22α-hDTR Apoe–/– and control Apoe–/– mice were bluntly dissected, excised, mounted in a biaxial biomechanical tester and brought to in vivo axial stretch and a pressure of 100 mmHg. Nuclei and elastin fibers were then stained using Syto-41 and Eosin-Y, respectively, and imaged using 3D two-photon laser scanning microscopy. Nuclei were segmented from images and coincident nuclei were split. The nucleus splitting procedure determines the likelihood that voxel pairs within coincident nuclei belong to the same nucleus and utilizes these likelihoods to identify individual nuclei using spectral clustering. Manual nucleus counts were used as a reference to assess the performance of our splitting procedure. Before and after splitting, automatic nucleus counts differed −26.6 ± 9.90% (p < 0.001) and −1.44 ± 7.05% (p = 0.467) from the manual reference, respectively. Whereas the slope of the relative difference between the manual and automated counts as a function of the manual count was significantly negative before splitting (p = 0.008), this slope became insignificant after splitting (p = 0.653). Smooth muscle apoptosis led to a 33.7% decrease in VSMC density (p = 0.008). Nucleus splitting improves the accuracy of automated cell content quantification in murine carotid arteries and overcomes the progressively worsening problem of coincident nuclei with increasing cell content in vessels. The presented image analysis framework provides a robust tool to quantify cell content, orientation, shape, and distribution in vessels to inform experimental and advanced computational studies on vascular structure and function.

Segmentation of multicorrelated images with copula models and conditionally random fields.

Purpose: Multisource images are interesting in medical imaging. Indeed, multisource images enable the use of complementary information of different sources such as for T1 and T2 modalities in MRI imaging. However, such multisource data can also be subject to redundancy and correlation. The question is how to efficiently fuse the multisource information without reinforcing the redundancy. We propose a method for segmenting multisource images that are statistically correlated. Approach: The method that we propose is the continuation of a prior work in which we introduce the copula model in hidden Markov fields (HMF). To achieve the multisource segmentations, we use a functional measure of dependency called "copula." This copula is incorporated in the conditionally random fields (CRF). Contrary to HMF, where we consider a prior knowledge on the hidden states modeled by an HMF, in CRF, there is no prior information and only the distribution of the hidden states conditionally to the observations can be known. This conditional distribution depends on the data and can be modeled by an energy function composed of two terms. The first one groups the voxels having similar intensities in the same class. As for the second term, it encourages a pair of voxels to be in the same class if the difference between their intensities is not too big. Results: A comparison between HMF and CRF is performed via theory and experimentations using both simulated and real data from BRATS 2013. Moreover, our method is compared with different state-of-the-art methods, which include supervised (convolutional neural networks) and unsupervised (hierarchical MRF). Our unsupervised method gives similar results as decision trees for synthetic images and as convolutional neural networks for real images; both methods are supervised. Conclusions: We compare two statistical methods using the copula: HMF and CRF to deal with multicorrelated images. We demonstrate the interest of using copula. In both models, the copula considerably improves the results compared with individual segmentations.

Latency structure of BOLD signals within white matter in resting-state fMRI

PurposePrevious studies have demonstrated that BOLD signals in gray matter in resting-state functional MRI (RSfMRI) have variable time lags, representing apparent propagations of fMRI BOLD signals in gray matter. We complemented existing findings and explored the corresponding variations of signal latencies in white matter. MethodsWe used data from the Brain Genomics Superstruct Project, consisting of 1412 subjects (both sexes included) and divided the dataset into ten equal groups to study both the patterns and reproducibility of latency estimates within white matter. We constructed latency matrices by computing cross-covariances between voxel pairs. We also applied a clustering analysis to identify functional networks within white matter, based on which latency analysis was also performed to investigate lead/lag relationship at network level. A dataset consisting of various sensory states (eyes closed, eyes open and eyes open with fixation) was also included to examine the relationship between latency structure and different states. ResultsProjections of voxel latencies from the latency matrices were highly correlated (average Pearson correlation coefficient = 0.89) across the subgroups, confirming the reproducibility and structure of signal lags in white matter. Analysis of latencies within and between networks revealed a similar pattern of inter- and intra-network communication to that reported for gray matter. Moreover, a dominant direction, from inferior to superior regions, of BOLD signal propagation was revealed by higher resolution clustering. The variations of lag structure within white matter are associated with different sensory states. ConclusionsThese findings provide additional insight into the character and roles of white matter BOLD signals in brain functions.

Generation of homogeneous 3D Gaussian noise with spherically symmetric covariance.

In this paper, an approach for 3D noise generation is presented. The proposed algorithm might be a useful tool for the generation of correlated phase screens. These phase screens can be used for the simulation and modeling of optical wave propagation through atmospheric turbulence. Arbitrary user-defined covariance functions between voxel pairs can be achieved. Correlated 3D noise is formed by superposition of multiple uncorrelated 3D Gaussian noise patterns. These uncorrelated input noise patterns are of different dimensions. They are upsampled to the same target dimensions by linear interpolation. Each input pattern then contributes to total covariance on different spatial scales. The covariances between different voxels are expressed analytically by propagation of error. For a subset of randomly chosen voxels in the entire voxel space, relative deviations between the analytical and user-defined covariances are calculated. A sum of squares of these relative deviations is then minimized by machine learning methods. The optimized parameters are the weighting factors of individual uncorrelated 3D noise patterns. Corresponding covariance functions are numerically evaluated for two current atmospheric turbulence spectra. The first one is the generalized modified atmospheric spectrum. The second one is the generalized modified von Karman spectrum. Based on these covariance functions, optimal superpositions are calculated. Finally, statistical properties of these patterns are validated by ensemble sample covariance analysis.

Functional connectivity of the right inferior frontal gyrus and orbitofrontal cortex in depression.

The orbitofrontal cortex extends into the laterally adjacent inferior frontal gyrus. We analyzed how voxel-level functional connectivity of the inferior frontal gyrus and orbitofrontal cortex is related to depression in 282 people with major depressive disorder (125 were unmedicated) and 254 controls, using FDR correction P < 0.05 for pairs of voxels. In the unmedicated group, higher functional connectivity was found of the right inferior frontal gyrus with voxels in the lateral and medial orbitofrontal cortex, cingulate cortex, temporal lobe, angular gyrus, precuneus, hippocampus and frontal gyri. In medicated patients, these functional connectivities were lower and toward those in controls. Functional connectivities between the lateral orbitofrontal cortex and the precuneus, posterior cingulate cortex, inferior frontal gyrus, ventromedial prefrontal cortex and the angular and middle frontal gyri were higher in unmedicated patients, and closer to controls in medicated patients. Medial orbitofrontal cortex voxels had lower functional connectivity with temporal cortex areas, the parahippocampal gyrus and fusiform gyrus, and medication did not result in these being closer to controls. These findings are consistent with the hypothesis that the orbitofrontal cortex is involved in depression, and can influence mood and behavior via the right inferior frontal gyrus, which projects to premotor cortical areas.

Damage to the structural connectome reflected in resting-state fMRI functional connectivity.

The relationship between structural and functional connectivity has been mostly examined in intact brains. Fewer studies have examined how differences in structure as a result of injury alters function. In this study we analyzed the relationship of structure to function across patients with stroke among whom infarcts caused heterogenous structural damage. We estimated relationships between distinct brain regions of interest (ROIs) from functional MRI in two pipelines. In one analysis pipeline, we measured functional connectivity by using correlation and partial correlation between 114 cortical ROIs. We found fMRI-BOLD partial correlation was altered at more edges as a function of the structural connectome (SC) damage, relative to the correlation. In a second analysis pipeline, we limited our analysis to fMRI correlations between pairs of voxels for which we possess SC information. We found that voxel-level functional connectivity showed the effect of structural damage that we could not see when examining correlations between ROIs. Further, the effects of structural damage on functional connectivity are consistent with a model of functional connectivity, diffusion, which expects functional connectivity to result from activity spreading over multiple edge anatomical paths.

Construction of brain structural connectivity network using a novel integrated algorithm based on ensemble average propagator

An important task for neuroscience is to accurately construct structural connectivity network of human brain. Tractography constructed based on high angular resolution diffusion imaging (HARDI) provides valuable information of human brain structural connections. Existing algorithms, mainly categorized as deterministic or probabilistic, come with inherent limitations (e.g., fiber direction uncertainty induced by noise, or anatomically unreasonable connections and heavy computational cost). In this study, a novel integrated algorithm was proposed to construct brain structural connectivity network by incorporating the deterministic path planning and probabilistic connection strength estimation, based on ensemble average propagator (EAP). We first estimated EAPs from multi-shell samples using the spherical polar Fourier imaging (SPFI), and then extracted diffusion orientations coinciding with neural fiber tracts. Only under angular constraints, the deterministic path planning algorithm was subsequently used to find all reasonable pathways between pairwise white matter (WM) voxels in different regions of interest (ROIs). Consequently, a train of consecutive WM voxels along each of the identified pathways was determined, and the connection strength of these pathways was computed by integrating their EAP alignment over a solid angle. The connection strength of a pair of WM voxels was assigned as the connection strength with the largest connection possibility. Finally, the connection strength between two ROIs was calculated as the sum of all the connection probabilities of each pair of WM voxels in the ROIs. A comparison against voxel-graph based probabilistic tractography method was performed on Fibercup phantom dataset, and the results demonstrated that the proposed method can produce better structural connection and is more computationally economical. Lastly, three datasets from Human Connectome Project (HCP) S1200 group were tested and their structural connectivity networks were constructed for topological analysis. The results showed great consistency in network metrics with previous WM network studies in healthy adults.

Statistical analysis of fMRI using wavelets: Big Data, denoising, large‐p‐small‐n matrices

AbstractOver the past decade functional Magnetic Resonance Imaging (fMRI) has been intensively used to study the complex functional network organization of the human brain and how it changes in time. An fMRI machine produces 3D time‐course cerebral images that contain hundreds of thousands of voxels and each voxel is scanned for hundreds of times. This potentially allows the researchers to explore functional connectivity on a voxel‐to‐voxel level, and also yields a number of serious statistical complications. First of all, the high‐dimension property of fMRI data turns it into Big Data. Furthermore, the study of functional brain network for so many voxels involves the problem of estimation of and simultaneous inference for large‐p‐small‐n cross‐covariance matrices. Furthermore, all problems should be solved in the presence of notoriously large fMRI noise which often forces statisticians to average signals over large areas instead of considering a network between individual voxels. An attractive alternative to the averaging, discussed in the paper, is a multiresolution wavelet analysis complemented by special procedures of estimating noise and estimation and simultaneous inference for cross‐covariance and cross‐correlation matrices for hundreds of thousands pairs of voxels, and it is fair to say that if wavelets have not been already known, fMRI applications would necessitates their creation. Both task and resting‐state fMRI are considered, and lessons from the wavelet analysis of ultra‐fast and conventional neuroplasticity fMRI experiments are presented. The article is self‐contained and does not require familiarity with wavelets or fMRI.This article is categorized under: Algorithms and Computational Methods &gt; Numerical Methods Applications of Computational Statistics &gt; Signal and Image Processing and Coding

A Pore-Skeleton-Based Method for Calculating Permeability and Capillary Pressure

We have developed a new method for calculating permeability and capillary pressure from the pore skeleton that is extracted from a fractured rock model, which might comprises medial axes of matrix pores and/or medial surfaces of fracture voids. Such a skeleton, therefore, is able to encapsulate the total connected fluid flow paths in the pore-void space. To do pore-network flow simulations, the pore skeleton needs to be further “discretised” into a network of interconnected nodes and bonds to capture local pore morphology. Jiang et al. (Adv Water Resour 107:280–289, 2017) developed a method to extract pore skeletons of this type and a discretisation to construct a pore-network model that is optimal in many aspects. In this work, we develop a new in-place discretisation method, by simply inserting a virtual link, a bond, between every pair of skeleton voxels, nodes, which are either face or only edge adjacent under certain conditions. This new method results in a simpler pore-network model, i.e. a virtual network, in which each node or bond is assumed as either a cylinder or a tiny fracture, as well as prescribed with length and inscribed radius/aperture only. As a result, a simpler pore-network simulator is also developed using improved formulae of conductance and capillary pressure according to where each virtual link falls, appropriately distinguishing every local configuration within matrixes or fractures. We verify our methods by comparing the simulation results against with those of lattice Boltzmann methods and a laboratory flooding experiment and demonstrate the accuracy and efficiency of our methods with sensitivity analysis.

P242 Dynamic time warping distance based connectivity: A new method for resting-state FMRI functional connectivity analysis

Objectives Traditionally resting-state networks are analysed with methods implying zero-lag linear dependence between brain regions, i.e. functional connectivity strength between voxel pairs is characterized by the correlation-coefficient of the two measured signal. It is known that the shape and timing of hemodynamic response function differs between brain regions and this introduces artefacts in linear measures. Methods We proposed Dynamic Time Warping (DTW) distance to be used as an alternative similarity-measure between BOLD signals. Our method was validated in a longitudinal single-subject study where seed-based and whole-brain functional connectivity was calculated based on both DTW similarity and correlation. DTW connectivity’s sensitivity was assessed in a classification task, where a preprocessed public dataset was used: 126 subjects’ resting-state data and phenotypic information, including diagnosis for ADHD. Results The results of the single-subject measurements revealed that DTW similarity-based connectivity map calculation is more stable in multiple measurements than a correlation-based paradigm, as DTW-based connectivity strengths are similarly stable within and between sessions, while correlation yields larger variations between sessions. Furthermore, the stability of the DTW-based connectivity patterns result in significantly higher classification performance than the same classifiers trained on correlation-based features of connectivity. Discussion As DTW handles non-stationery processes, it results in stable connectivity patterns in multiple measurements, while its sensitivity for group differences is higher than correlation’s as the classification study shows. Conclusion The results demonstrate that DTW similarity is indeed an applicable and advantageous tool of resting-state functional connectivity analysis. Significance DTW-based connectivity can be efficiently used in longitudinal studies and in connectome-based classification tasks.

Resting state networks in empirical and simulated dynamic functional connectivity

It is well-established that patterns of functional connectivity (FC) - measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging - are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio-temporal dynamics. Over the last years, a multitude of methods have been proposed to reveal these dynamics on the level of the whole brain. One finding is that the brain transitions through different FC configurations over time, and substantial effort has been put into characterizing these configurations. However, the dynamics governing these transitions are more elusive, specifically, the contribution of stationary vs. non-stationary dynamics is an active field of inquiry. In this study, we use a whole-brain approach, considering FC dynamics between 66 ROIs covering the entire cortex. We combine an innovative dimensionality reduction technique, tensor decomposition, with a mean field model which possesses stationary dynamics. It has been shown to explain resting state FC averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. First, we apply tensor decomposition to resting state scans from 24 healthy controls in order to characterize spatio-temporal dynamics present in the data. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions (“communities”) that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features pooled from all subjects, thereby ensuring that they generalize. We find that, on the group level, the data give rise to four distinct communities that resemble known resting state networks (RSNs): default mode network, visual network, control networks, and somatomotor network. Second, we simulate data with our stationary mean field model whose nodes are connected according to results from DTI and fiber tracking. In this model, all spatio-temporal structure is due to noisy fluctuations around the average FC. We analyze the simulated data in the same way as the empirical data in order to determine whether stationary dynamics can explain the emergence of distinct FC patterns (RSNs) which have their own time courses. We find that this is the case for all four networks using the spatio-temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model-based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings show that stationary dynamics can account for the emergence of RSNs. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations.

Apparent brain temperature imaging with multi-voxel proton magnetic resonance spectroscopy compared with cerebral blood flow and metabolism imaging on positron emission tomography in patients with unilateral chronic major cerebral artery steno-occlusive disease.

The purpose of the present study was to determine whether apparent brain temperature imaging using multi-voxel proton magnetic resonance (MR) spectroscopy correlates with cerebral blood flow (CBF) and metabolism imaging in the deep white matter of patients with unilateral chronic major cerebral artery steno-occlusive disease. Apparent brain temperature and CBF and metabolism imaging were measured using proton MR spectroscopy and 15O-positron emission tomography (PET), respectively, in 35 patients. A set of regions of interest (ROIs) of 5×5 voxels was placed on an MR image so that the voxel row at each edge was located in the deep white matter of the centrum semiovale in each cerebral hemisphere. PET images were co-registered with MR images with these ROIs and were re-sliced automatically using image analysis software. In 175 voxel pairs located in the deep white matter, the brain temperature difference (affected hemisphere-contralateral hemisphere: ΔBT) was correlated with cerebral blood volume (CBV) (r=0.570) and oxygen extraction fraction (OEF) ratios (affected hemisphere/contralateral hemisphere) (r=0.641). We excluded voxels that contained ischemic lesions or cerebrospinal fluid and calculated the mean values of voxel pairs in each patient. The mean ΔBT was correlated with the mean CBF (r=-0.376), mean CBV (r=0.702), and mean OEF ratio (r=0.774). Apparent brain temperature imaging using multi-voxel proton MR spectroscopy was correlated with CBF and metabolism imaging in the deep white matter of patients with unilateral major cerebral artery steno-occlusive disease.

Examination of the validity of the atlas-informed approach to functional parcellation: a resting functional MRI study.

With the advancement in MRI, functional parcellation (FP) of brain structure(s) has become an important topic. However, the large number of voxels is a major obstacle. A-priori partitioning of the brain into several regions of interest (ROIs) is the main data-reduction strategy to simplify brain informatics. This study aims to examine the validity of ROI-based approach to FP by exploring the concordance of the relative distance structures between voxel-wise (raw data) and atlas-informed analyses. Structural and resting state functional MRI (rfMRI) scans of 26 right-handed healthy individuals were selected from the Rockland dataset. Four target regions were included in the analyses, that is, left and right thalamus and amygdala. For each voxel in the target region, four classes of correlation maps (sampling strategies) were constructed from the rfMRI: whole brain, cortex, 150 ROIs, and 70 ROIs (ROIs are informed by anatomical atlases). The relative distance metric between two different voxels was defined as the mean absolute difference of their associated correlation maps. Considering all the possible pairs of voxels in a target region, the relative distance structure was derived and stored in a matrix (distance map). For every target region, the distance maps were very similar across the four classes of sampling strategies, with the grand mean correlation coefficient reaching 0.95. The results confirm the validity of previous ROI-based analyses of rfMRI data in FP. The rationale and limitation are discussed and an analytic strategy of whole-brain FP is proposed.

Distributed representation of context by intrinsic subnetworks in prefrontal cortex

Human prefrontal cortex supports goal-directed behavior by representing abstract information about task context. The organizational basis of these context representations, and of representations underlying other higher-order processes, is unknown. Here, we use multivariate decoding and analyses of spontaneous correlations to show that context representations are distributed across subnetworks within prefrontal cortex. Examining targeted prefrontal regions, we found that pairs of voxels with similar context preferences exhibited spontaneous correlations that were approximately twice as large as those between pairs with opposite context preferences. This subnetwork organization was stable across task-engaged and resting states, suggesting that abstract context representations are constrained by an intrinsic functional architecture. These results reveal a principle of fine-scaled functional organization in association cortex.