Search

Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of the magnetic fields and electric potentials. An enduring challenge in this imaging modality is estimating the number, location, and time course of sources, especially for the reconstruction of distributed brain sources with complex spatial extent. Here, we introduce a novel robust empirical Bayesian algorithm that enables better reconstruction of distributed brain source activity with two key ideas: kernel smoothing and hyperparameter tiling. Since the proposed algorithm builds upon many of the performance features of the sparse source reconstruction algorithm - Champagne and we refer to this algorithm as Smooth Champagne. Smooth Champagne is robust to the effects of high levels of noise, interference, and highly correlated brain source activity. Simulations demonstrate excellent performance of Smooth Champagne when compared to benchmark algorithms in accurately determining the spatial extent of distributed source activity. Smooth Champagne also accurately reconstructs real MEG and EEG data.

How spontaneous brain neural activities emerge from the underlying anatomical architecture, characterized by structural connectivity (SC), has puzzled researchers for a long time. Over the past decades, much effort has been directed toward the graph modeling of SC, in which the brain SC is generally considered as relatively invariant. However, the graph representation of SC is unable to directly describe the connections between anatomically unconnected brain regions and fail to model the negative functional correlations. Here, we extend the static graph model to a spatiotemporal varying hypergraph Laplacian diffusion (STV-HGLD) model to describe the propagation of the spontaneous neural activity in human brain by incorporating the Laplacian of the hypergraph representation of the structural connectome (${h}$ SC) into the regular wave equation. Theoretical solution shows that the dynamic functional couplings between brain regions fluctuate in the form of an exponential wave regulated by the spatiotemporal varying Laplacian of ${h}$ SC. Empirical study suggests that the cortical wave might give rise to resonance with SC during the self-organizing interplay between excitation and inhibition among brain regions, which orchestrates the cortical waves propagating with harmonics emanating from the ${h}$ SC while being bound by the natural frequencies of SC. Besides, the average statistical dependencies between brain regions, normally defined as the functional connectivity (FC), arises just at the moment before the cortical wave reaches the steady state after the wave spreads across all the brain regions. Comprehensive tests on four extensively studied empirical brain connectome datasets with different resolutions confirm our theory and findings. [ABSTRACT FROM AUTHOR]

Infancy is a critical period for the human brain development, and brain age is one of the indices for the brain development status associated with neuroimaging data. The difference between the predicted age based on neuroimaging and the chronological age can provide an important early indicator of deviation from the normal developmental trajectory. In this study, we utilize the Graph Convolutional Network (GCN) to predict the infant brain age based on resting-state fMRI data. The brain connectivity obtained from rs-fMRI can be represented as a graph with brain regions as nodes and functional connections as edges. However, since the brain connectivity is a fully connected graph with features on edges, current GCN cannot be directly used for it is a node-based method for sparse graphs. Hence, we propose an edge-based Graph Path Convolution (GPC) method, which aggregates the information from different paths and can be naturally applied on dense graphs. We refer the whole model as Brain Connectivity Graph Convolutional Networks (BC-GCN). Further, two upgraded network structures are proposed by including the residual and attention modules, referred as BC-GCN-Res and BC-GCN-SE to emphasize the information of the original data and enhance influential channels. Moreover, we design a two-stage coarse-to-fine framework, which determines the age group first and then predicts the age using group-specific BC-GCN-SE models. To avoid accumulated errors from the first stage, a cross-group training strategy is adopted for the second stage regression models. We conduct experiments on infant fMRI scans from 6 to 811 days of age. The coarse-to-fine framework shows significant improvements when being applied to several models (reducing error over 10 days). Comparing with state-of-the-art methods, our proposed model BC-GCN-SE with coarse-to-fine framework reduces the mean absolute error of the prediction from >70 days to 49.9 days. The code is now available at https://github.com/SCUT-Xinlab/BC-GCN. [ABSTRACT FROM AUTHOR]

Multi-modal Magnetic Resonance Imaging (MRI) can provide complementary information for automatic brain tumor segmentation, which is crucial for diagnosis and prognosis. While missing modality data is common in clinical practice and it can result in the collapse of most previous methods relying on complete modality data. Current state-of-the-art approaches cope with the situations of missing modalities by fusing multi-modal images and features to learn shared representations of tumor regions, which often ignore explicitly capturing the correlations among modalities and tumor regions. Inspired by the fact that modality information plays distinct roles to segment different tumor regions, we aim to explicitly exploit the correlations among various modality-specific information and tumor-specific knowledge for segmentation. To this end, we propose a Dual Disentanglement Network (D2-Net) for brain tumor segmentation with missing modalities, which consists of a modality disentanglement stage (MD-Stage) and a tumor-region disentanglement stage (TD-Stage). In the MD-Stage, a spatial-frequency joint modality contrastive learning scheme is designed to directly decouple the modality-specific information from MRI data. To decompose tumor-specific representations and extract discriminative holistic features, we propose an affinity-guided dense tumor-region knowledge distillation mechanism in the TD-Stage through aligning the features of a disentangled binary teacher network with a holistic student network. By explicitly discovering relations among modalities and tumor regions, our model can learn sufficient information for segmentation even if some modalities are missing. Extensive experiments on the public BraTS-2018 database demonstrate the superiority of our framework over state-of-the-art methods in missing modalities situations. Codes are available at https://github.com/CityU-AIM-Group/D2Net. [ABSTRACT FROM AUTHOR]

Brain network classification using resting-state functional magnetic resonance imaging (rs-fMRI) is an effective analytical method for diagnosing brain diseases. In recent years, brain network classification methods based on deep learning have attracted increasing attention. However, these methods only consider the spatial topological characteristics of the brain network but ignore its proximity relationships in semantic space. To overcome this problem, we propose a novel brain network classification method based on deep graph hashing learning named BNC-DGHL. Specifically, we first extract the deep features of the brain network and then learn a graph hash function based on clinical phenotype labels and the similarity of diagnostic labels. Secondly, we use the learned graph hash function to convert deep features into hash codes, which can maintain the original semantic spatial relationships. Finally, we calculate the distance between hash codes to obtain the predicted category of the brain network. Experimental results on ABIDE I, ABIDE II, and ADHD-200 datasets demonstrate that our method achieves better classification performance of brain diseases compared with some state-of-the-art methods, and the abnormal functional connectivities between brain regions identified may serve as biomarkers associated with related brain diseases. [ABSTRACT FROM AUTHOR]

Constructing and analyzing functional brain networks (FBN) has become a promising approach to brain disorder classification. However, the conventional successive construct-and-analyze process would limit the performance due to the lack of interactions and adaptivity among the subtasks in the process. Recently, Transformer has demonstrated remarkable performance in various tasks, attributing to its effective attention mechanism in modeling complex feature relationships. In this paper, for the first time, we develop Transformer for integrated FBN modeling, analysis and brain disorder classification with rs-fMRI data by proposing a Diffusion Kernel Attention Network to address the specific challenges. Specifically, directly applying Transformer does not necessarily admit optimal performance in this task due to its extensive parameters in the attention module against the limited training samples usually available. Looking into this issue, we propose to use kernel attention to replace the original dot-product attention module in Transformer. This significantly reduces the number of parameters to train and thus alleviates the issue of small sample while introducing a non-linear attention mechanism to model complex functional connections. Another limit of Transformer for FBN applications is that it only considers pair-wise interactions between directly connected brain regions but ignores the important indirect connections. Therefore, we further explore diffusion process over the kernel attention to incorporate wider interactions among indirectly connected brain regions. Extensive experimental study is conducted on ADHD-200 data set for ADHD classification and on ADNI data set for Alzheimer’s disease classification, and the results demonstrate the superior performance of the proposed method over the competing methods. [ABSTRACT FROM AUTHOR]

Many machine learning tasks in neuroimaging aim at modeling complex relationships between a brain’s morphology as seen in structural MR images and clinical scores and variables of interest. A frequently modeled process is healthy brain aging for which many image-based brain age estimation or age-conditioned brain morphology template generation approaches exist. While age estimation is a regression task, template generation is related to generative modeling. Both tasks can be seen as inverse directions of the same relationship between brain morphology and age. However, this view is rarely exploited and most existing approaches train separate models for each direction. In this paper, we propose a novel bidirectional approach that unifies score regression and generative morphology modeling and we use it to build a bidirectional brain aging model. We achieve this by defining an invertible normalizing flow architecture that learns a probability distribution of 3D brain morphology conditioned on age. The use of full 3D brain data is achieved by deriving a manifold-constrained formulation that models morphology variations within a low-dimensional subspace of diffeomorphic transformations. This modeling idea is evaluated on a database of MR scans of more than 5000 subjects. The evaluation results show that our bidirectional brain aging model (1) accurately estimates brain age, (2) is able to visually explain its decisions through attribution maps and counterfactuals, (3) generates realistic age-specific brain morphology templates, (4) supports the analysis of morphological variations, and (5) can be utilized for subject-specific brain aging simulation. [ABSTRACT FROM AUTHOR]

Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images. Four non-linear relations are considered: “cumulative relation,” “relative relation,” “maximal relation” and “minimal relation.” These four relations are learned simultaneously from one deep neural network which has two parts: feature extraction and relation regression. We use an efficient convolutional neural network to extract deep features from the pair of input images and apply a Transformer for relation learning. The proposed method is evaluated on a merged dataset with 6,049 subjects with ages of 0–97 years using 5-fold cross-validation for the task of brain age estimation. The experimental results have shown that the proposed method achieved a mean absolute error (MAE) of 2.38 years, which is lower than the MAEs of 8 other state-of-the-art algorithms with statistical significance (p<0.05) in paired T-test (two-side). [ABSTRACT FROM AUTHOR]

Determining brain hemodynamics plays a critical role in the diagnosis and treatment of various cerebrovascular diseases. In this work, we put forth a physics-informed deep learning framework that augments sparse clinical measurements with one-dimensional (1D) reduced-order model (ROM) simulations to generate physically consistent brain hemodynamic parameters with high spatiotemporal resolution. Transcranial Doppler (TCD) ultrasound is one of the most common techniques in the current clinical workflow that enables noninvasive and instantaneous evaluation of blood flow velocity within the cerebral arteries. However, it is spatially limited to only a handful of locations across the cerebrovasculature due to the constrained accessibility through the skull’s acoustic windows. Our deep learning framework uses in vivo real-time TCD velocity measurements at several locations in the brain combined with baseline vessel cross-sectional areas acquired from 3D angiography images and provides high-resolution maps of velocity, area, and pressure in the entire brain vasculature. We validate the predictions of our model against in vivo velocity measurements obtained via four-dimensional (4D) flow magnetic resonance imaging (MRI) scans. We then showcase the clinical significance of this technique in diagnosing cerebral vasospasm (CVS) by successfully predicting the changes in vasospastic local vessel diameters based on corresponding sparse velocity measurements. We show this capability by generating synthetic blood flow data after cerebral vasospasm at various levels of stenosis. Here, we demonstrate that the physics-based deep learning approach can estimate and quantify the subject-specific cerebral hemodynamic variables with high accuracy despite lacking knowledge of inlet and outlet boundary conditions, which is a significant limitation for the accuracy of the conventional purely physics-based computational models. [ABSTRACT FROM AUTHOR]

High angular resolution diffusion imaging (HARDI) is a type of diffusion magnetic resonance imaging (dMRI) that measures diffusion signals on a sphere in q-space. It has been widely used in data acquisition for human brain structural connectome analysis. To more accurately estimate the structural connectome, dense samples in q-space are often acquired, potentially resulting in long scanning times and logistical challenges. This paper proposes a statistical method to select q-space directions optimally and estimate the local diffusion function from sparse observations. The proposed approach leverages relevant historical dMRI data to calculate a prior distribution to characterize local diffusion variability in each voxel in a template space. For a new subject to be scanned, the priors are mapped into the subject-specific coordinate and used to help select the best q-space samples. Simulation studies demonstrate big advantages over the existing HARDI sampling and analysis framework. We also applied the proposed method to the Human Connectome Project data and a dataset of aging adults with mild cognitive impairment. The results indicate that with very few q-space samples (e.g., 15 or 20), we can recover structural brain networks comparable to the ones estimated from 60 or more diffusion directions with the existing methods. [ABSTRACT FROM AUTHOR]

Fast and accurate MRI image reconstruction from undersampled data is crucial in clinical practice. Deep learning based reconstruction methods have shown promising advances in recent years. However, recovering fine details from undersampled data is still challenging. In this paper, we introduce a novel deep learning based method, Pyramid Convolutional RNN (PC-RNN), to reconstruct images from multiple scales. Based on the formulation of MRI reconstruction as an inverse problem, we design the PC-RNN model with three convolutional RNN (ConvRNN) modules to iteratively learn the features in multiple scales. Each ConvRNN module reconstructs images at different scales and the reconstructed images are combined by a final CNN module in a pyramid fashion. The multi-scale ConvRNN modules learn a coarse-to-fine image reconstruction. Unlike other common reconstruction methods for parallel imaging, PC-RNN does not employ coil sensitive maps for multi-coil data and directly model the multiple coils as multi-channel inputs. The coil compression technique is applied to standardize data with various coil numbers, leading to more efficient training. We evaluate our model on the fastMRI knee and brain datasets and the results show that the proposed model outperforms other methods and can recover more details. The proposed method is one of the winner solutions in the 2019 fastMRI competition. [ABSTRACT FROM AUTHOR]

Studies have shown that there is a tight connection between cognition skills and brain morphology during infancy. Nonetheless, it is still a great challenge to predict individual cognitive scores using their brain morphological features, considering issues like the excessive feature dimension, small sample size and missing data. Due to the limited data, a compact but expressive feature set is desirable as it can reduce the dimension and avoid the potential overfitting issue. Therefore, we pioneer the path signature method to further explore the essential hidden dynamic patterns of longitudinal cortical features. To form a hierarchical and more informative temporal representation, in this work, a novel cortical feature based path signature neural network (CF-PSNet) is proposed with stacked differentiable temporal path signature layers for prediction of individual cognitive scores. By introducing the existence embedding in path generation, we can improve the robustness against the missing data. Benefiting from the global temporal receptive field of CF-PSNet, characteristics consisted in the existing data can be fully leveraged. Further, as there is no need for the whole brain to work for a certain cognitive ability, a top ${K}$ selection module is used to select the most influential brain regions, decreasing the model size and the risk of overfitting. Extensive experiments are conducted on an in-house longitudinal infant dataset within 9 time points. By comparing with several recent algorithms, we illustrate the state-of-the-art performance of our CF-PSNet (i.e., root mean square error of 0.027 with the time latency of 518 milliseconds for each sample). [ABSTRACT FROM AUTHOR]

Brain networks allow a topological understanding into the pathophysiology of stroke-induced motor deficits, and have been an influential tool for investigating brain functions. Unfortunately, currently applied methods generally lack in the recognition of the dynamic changes in the cortical networks related to muscle activity, which is crucial to clarify the alterations of the cooperative working patterns in the motor control system after stroke. In this study, we integrate corticomuscular and intermuscular interactions to cortico-cortical network and propose a novel closed-loop construction of cortico-muscular-cortical functional network, named closed-loop network (CLN). Directional characteristic in terms of differentiating causal interactions is endowed on basis of the CLN framework, further expanding the definition of functional connectivity (FC) and effective connectivity (EC) dedicated to CLN. Next, CLN is applied to stroke patients to reveal the underlying after-effects mechanism of low frequency repetitive transcranial magnetic stimulation (rTMS) induced alterations of cortical physiologic functions during movement. Results show that the short-term modulation of rTMS is reflected in the enhancement of information interaction within the interhemispheric primary motor regions and inhibition of the coupling between motor cortex and effector muscles. CLN provides a new perspective for the study of motor-related cortical networks with muscle activities involvement instead of being restricted to brain network analysis of behaviors. [ABSTRACT FROM AUTHOR]

Elastic degree of anisotropy (DoA) is a diagnostically relevant biomarker in muscle, kidney, breast, and other organs. Previously, elastic DoA was qualitatively assessed as the ratio of peak displacements (PD) achieved with the long-axis of a spatially asymmetric Acoustic Radiation Force Impulse (ARFI) excitation point spread function (PSF) aligned along versus across the axis of symmetry (AoS) in transversely isotropic materials. However, to better enable longitudinal and cross-sectional analyses, a quantitative measure of elastic DoA is desirable. In this study, qualitative ARFI PD ratios are converted to quantitative DoA, measured as the ratio of longitudinal over transverse shear elastic moduli, using a model empirically derived from Field II and finite element method (FEM) simulations. In silico, the median absolute percent error (MAPE) in ARFI-derived shear moduli ratio (SMR) was 1.75%, and predicted SMRs were robust to variations in transverse shear modulus, Young’s moduli ratio, speed of sound, attenuation, density, and ARFI excitation PSF dimension. Further, ARFI-derived SMRs distinguished two materials when the true SMRs of the compared materials differed by as little as 10%. Experimentally, ARFI-derived SMRs linearly correlated with the corresponding ratios measured by Shear Wave Elasticity Imaging (SWEI) in excised pig skeletal muscle ($\text{R}^{{2}} ={0.91}$ , MAPE = 13%) and in pig kidney, in vivo ($\text{R}^{{2}}= {0.99}$ , MAPE = 5.3%). These results demonstrate the feasibility of using the ARFI PD to quantify elastic DoA in biological tissues. [ABSTRACT FROM AUTHOR]

In this paper, we present a deep learning method, DDMReg, for accurate registration between diffusion MRI (dMRI) datasets. In dMRI registration, the goal is to spatially align brain anatomical structures while ensuring that local fiber orientations remain consistent with the underlying white matter fiber tract anatomy. DDMReg is a novel method that uses joint whole-brain and tract-specific information for dMRI registration. Based on the successful VoxelMorph framework for image registration, we propose a novel registration architecture that leverages not only whole brain information but also tract-specific fiber orientation information. DDMReg is an unsupervised method for deformable registration between pairs of dMRI datasets: it does not require nonlinearly pre-registered training data or the corresponding deformation fields as ground truth. We perform comparisons with four state-of-the-art registration methods on multiple independently acquired datasets from different populations (including teenagers, young and elderly adults) and different imaging protocols and scanners. We evaluate the registration performance by assessing the ability to align anatomically corresponding brain structures and ensure fiber spatial agreement between different subjects after registration. Experimental results show that DDMReg obtains significantly improved registration performance compared to the state-of-the-art methods. Importantly, we demonstrate successful generalization of DDMReg to dMRI data from different populations with varying ages and acquired using different acquisition protocols and different scanners. [ABSTRACT FROM AUTHOR]

Modeling of brain tumor dynamics has the potential to advance therapeutic planning. Current modeling approaches resort to numerical solvers that simulate the tumor progression according to a given differential equation. Using highly-efficient numerical solvers, a single forward simulation takes up to a few minutes of compute. At the same time, clinical applications of tumor modeling often imply solving an inverse problem, requiring up to tens of thousands of forward model evaluations when used for a Bayesian model personalization via sampling. This results in a total inference time prohibitively expensive for clinical translation. While recent data-driven approaches become capable of emulating physics simulation, they tend to fail in generalizing over the variability of the boundary conditions imposed by the patient-specific anatomy. In this paper, we propose a learnable surrogate for simulating tumor growth which maps the biophysical model parameters directly to simulation outputs, i.e. the local tumor cell densities, whilst respecting patient geometry. We test the neural solver in a Bayesian model personalization task for a cohort of glioma patients. Bayesian inference using the proposed surrogate yields estimates analogous to those obtained by solving the forward model with a regular numerical solver. The near real-time computation cost renders the proposed method suitable for clinical settings. The code is available at https://github.com/IvanEz/tumor-surrogate. [ABSTRACT FROM AUTHOR]

There is increasing interest in identifying changes in the underlying states of brain networks. The availability of large scale neuroimaging data creates a strong need to develop fast, scalable methods for detecting and localizing in time such changes and also identify their drivers, thus enabling neuroscientists to hypothesize about potential mechanisms. This paper presents a fast method for detecting break points in exceedingly long time series neurogimaging data, based on vector autoregressive (Granger causal) models. It uses a multi-step strategy based on a regularized objective function that leads to fast identification of candidate break points, followed by clustering steps to select the final set of break points and subsequent estimation with false positives control of the underlying Granger causal networks. The latter provide insights into key changes in network connectivity that led to the presence of break points. The proposed methodology is illustrated on synthetic data varying in their length, dimensionality, number of break points, strength of signal and also applied to EEG data related to visual tasks. [ABSTRACT FROM AUTHOR]

Tucker decomposition can provide an intuitive summary to understand brain function by decomposing multi-subject fMRI data into a core tensor and multiple factor matrices, and was mostly used to extract functional connectivity patterns across time/subjects using orthogonality constraints. However, these algorithms are unsuitable for extracting common spatial and temporal patterns across subjects due to distinct characteristics such as high-level noise. Motivated by a successful application of Tucker decomposition to image denoising and the intrinsic sparsity of spatial activations in fMRI, we propose a low-rank Tucker-2 model with spatial sparsity constraint to analyze multi-subject fMRI data. More precisely, we propose to impose a sparsity constraint on spatial maps by using an $ \ell _{p} $ norm (${0}< {p}\le {1}$), in addition to adding low-rank constraints on factor matrices via the Frobenius norm. We solve the constrained Tucker-2 model using alternating direction method of multipliers, and propose to update both sparsity and low-rank constrained spatial maps using half quadratic splitting. Moreover, we extract new spatial and temporal features in addition to subject-specific intensities from the core tensor, and use these features to classify multiple subjects. The results from both simulated and experimental fMRI data verify the improvement of the proposed method, compared with four related algorithms including robust Kronecker component analysis, Tucker decomposition with orthogonality constraints, canonical polyadic decomposition, and block term decomposition in extracting common spatial and temporal components across subjects. The spatial and temporal features extracted from the core tensor show promise for characterizing subjects within the same group of patients or healthy controls as well. [ABSTRACT FROM AUTHOR]

For interpretation of electroencephalography (EEG) and magnetoencephalography (MEG) data, multiple solutions of the respective forward problems are needed. In this paper, we assess performance of the mixed-hybrid finite element method (MHFEM) applied to EEG and MEG modeling. The method provides an approximate potential and induced currents and results in a system with a positive semi-definite matrix. The system thus can be solved with a variety of standard methods (e.g. the preconditioned conjugate gradient method). The induced currents satisfy discrete charge conservation law making the method conservative. We studied its performance on unstructured tetrahedral grids for a layered spherical head model as well as a realistic head model. We also compared its accuracy versus the conventional nodal finite element method (${P}_{1}$ FEM). To avoid modeling singular sources, we completed our computations with a subtraction approach; the derived expression for the MEG response different from earlier published and involves integration of finite quantities only. We conclude that although the MHFEM is more computationally demanding than the ${P}_{1}$ FEM, its use is justified for EEG and MEG modeling on low-resolution head models where ${P}_{1}$ FEM loses accuracy. [ABSTRACT FROM AUTHOR]

Dynamic functional connectivity (dFC) network inferred from resting-state fMRI reveals macroscopic dynamic neural activity patterns for brain disease identification. However, dFC methods ignore the causal influence between the brain regions. Furthermore, due to the complex non-Euclidean structure of brain networks, advanced deep neural networks are difficult to be applied for learning high-dimensional representations from brain networks. In this paper, a group constrained Kalman filter (gKF) algorithm is proposed to construct dynamic effective connectivity (dEC), where the gKF provides a more comprehensive understanding of the directional interaction within the dynamic brain networks than the dFC methods. Then, a novel virtual adversarial training convolutional neural network (VAT-CNN) is employed to extract the local features of dEC. The VAT strategy improves the robustness of the model to adversarial perturbations, and therefore avoids the overfitting problem effectively. Finally, we propose the high-order connectivity weight-guided graph attention networks (cwGAT) to aggregate features of dEC. By injecting the weight information of high-order connectivity into the attention mechanism, the cwGAT provides more effective high-level feature representations than the conventional GAT. The high-level features generated from the cwGAT are applied for binary classification and multiclass classification tasks of mild cognitive impairment (MCI). Experimental results indicate that the proposed framework achieves the classification accuracy of 90.9%, 89.8%, and 82.7% for normal control (NC) vs. early MCI (EMCI), EMCI vs. late MCI (LMCI), and NC vs. EMCI vs. LMCI classification respectively, outperforming the state-of-the-art methods significantly. [ABSTRACT FROM AUTHOR]

Perfusion imaging is of great clinical importance and is used to assess a wide range of diseases including strokes and brain tumors. Commonly used approaches for the quantitative analysis of perfusion images are based on measuring the effect of a contrast agent moving through blood vessels and into tissue. Contrast-agent free approaches, for example, based on intravoxel incoherent motion and arterial spin labeling, also exist, but are so far not routinely used clinically. Existing contrast-agent-dependent methods typically rely on the estimation of the arterial input function (AIF) to approximately model tissue perfusion. These approaches neglect spatial dependencies. Further, as reliably estimating the AIF is non-trivial, different AIF estimates may lead to different perfusion measures. In this work we therefore propose PIANO, an approach that provides additional insights into the perfusion process. PIANO estimates the velocity and diffusion fields of an advection-diffusion model best explaining the contrast dynamics without using an AIF. PIANO accounts for spatial dependencies and neither requires estimating the AIF nor relies on a particular contrast agent bolus shape. Specifically, we propose a convenient parameterization of the estimation problem, a numerical estimation approach, and extensively evaluate PIANO. Simulation experiments show the robustness and effectiveness of PIANO, along with its ability to distinguish between advection and diffusion. We further apply PIANO on a public brain magnetic resonance (MR) perfusion dataset of acute stroke patients, and demonstrate that PIANO can successfully resolve velocity and diffusion field ambiguities and results in sensitive measures for the assessment of stroke, comparing favorably to conventional measures of perfusion. [ABSTRACT FROM AUTHOR]

Chronological age of healthy people is able to be predicted accurately using deep neural networks from neuroimaging data, and the predicted brain age could serve as a biomarker for detecting aging-related diseases. In this paper, a novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data. Compared with existing methods, TSAN has the following improvements. First, TSAN uses a two-stage cascade network architecture, where the first-stage network estimates a rough brain age, then the second-stage network estimates the brain age more accurately from the discretized brain age by the first-stage network. Second, to our knowledge, TSAN is the first work to apply novel ranking losses in brain age estimation, together with the traditional mean square error (MSE) loss. Third, densely connected paths are used to combine feature maps with different scales. The experiments with 6586 MRIs showed that TSAN could provide accurate brain age estimation, yielding mean absolute error (MAE) of 2.428 and Pearson’s correlation coefficient (PCC) of 0.985, between the estimated and chronological ages. Furthermore, using the brain age gap between brain age and chronological age as a biomarker, Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI) can be distinguished from healthy control (HC) subjects by support vector machine (SVM). Classification AUC in AD/HC and MCI/HC was 0.904 and 0.823, respectively. It showed that brain age gap is an effective biomarker associated with risk of dementia, and has potential for early-stage dementia risk screening. The codes and trained models have been released on GitHub: https://github.com/Milan-BUAA/TSAN-brain-age-estimation. [ABSTRACT FROM AUTHOR]

Estimating effective connectivity from functional magnetic resonance imaging (fMRI) time series data has become a very hot topic in neuroinformatics and brain informatics. However, it is hard for the current methods to accurately estimate the effective connectivity due to the high noise and small sample size of fMRI data. In this paper, we propose a novel framework for estimating effective connectivity based on recurrent generative adversarial networks, called EC-RGAN. The proposed framework employs the generator that consists of a set of effective connectivity generators based on recurrent neural networks to generate the fMRI time series of each brain region, and uses the discriminator to distinguish between the joint distributions of the real and generated fMRI time series. When the model is well-trained and generated fMRI data is similar to real fMRI data, EC-RGAN outputs the effective connectivity by means of the causal parameters of the effective connectivity generators. Experimental results on both simulated and real-world fMRI time series data demonstrate the efficacy of our proposed framework. [ABSTRACT FROM AUTHOR]

The kinetic analysis of $^{{18}}\text{F}$ -FET time-activity curves (TAC) can provide valuable diagnostic information in glioma patients. The analysis is most often limited to the average TAC over a large tissue volume and is normally assessed by visual inspection or by evaluating the time-to-peak and linear slope during the late uptake phase. Here, we derived and validated a linearized model for TACs of $^{{18}}\text{F}$ -FET in dynamic PET scans. Emphasis was put on the robustness of the numerical parameters and how reliably automatic voxel-wise analysis of TAC kinetics was possible. The diagnostic performance of the extracted shape parameters for the discrimination between isocitrate dehydrogenase (IDH) wildtype (wt) and IDH-mutant (mut) glioma was assessed by receiver-operating characteristic in a group of 33 adult glioma patients. A high agreement between the adjusted model and measured TACs could be obtained and relative, estimated parameter uncertainties were small. The best differentiation between IDH-wt and IDH-mut gliomas was achieved with the linearized model fitted to the averaged TAC values from dynamic FET PET data in the time interval 4–50 min p.i.. When limiting the acquisition time to 20–40 min p.i., classification accuracy was only slightly lower (-3%) and was comparable to classification based on linear fits in this time interval. Voxel-wise fitting was possible within a computation time $\approx ~{1}$ min per image slice. Parameter uncertainties smaller than 80% for all fits with the linearized model were achieved. The agreement of best-fit parameters when comparing voxel-wise fits and fits of averaged TACs was very high (p < 0.001). [ABSTRACT FROM AUTHOR]

Imaging genetics is an effective tool used to detect potential biomarkers of Alzheimer’s disease (AD) in imaging and genetic data. Most existing imaging genetics methods analyze the association between brain imaging quantitative traits (QTs) and genetic data [e.g., single nucleotide polymorphism (SNP)] by using a linear model, ignoring correlations between a set of QTs and SNP groups, and disregarding the varied associations between longitudinal imaging QTs and SNPs. To solve these problems, we propose a novel temporal group sparsity regression and additive model (T-GSRAM) to identify associations between longitudinal imaging QTs and SNPs for detection of potential AD biomarkers. We first construct a nonparametric regression model to analyze the nonlinear association between QTs and SNPs, which can accurately model the complex influence of SNPs on QTs. We then use longitudinal QTs to identify the trajectory of imaging genetic patterns over time. Moreover, the SNP information of group and individual levels are incorporated into the proposed method to boost the power of biomarker detection. Finally, we propose an efficient algorithm to solve the whole T-GSRAM model. We evaluated our method using simulation data and real data obtained from AD neuroimaging initiative. Experimental results show that our proposed method outperforms several state-of-the-art methods in terms of the receiver operating characteristic curves and area under the curve. Moreover, the detection of AD-related genes and QTs has been confirmed in previous studies, thereby further verifying the effectiveness of our approach and helping understand the genetic basis over time during disease progression. [ABSTRACT FROM AUTHOR]

The combination of multimodal imaging and genomics provides a more comprehensive way for the study of mental illnesses and brain functions. Deep network-based data fusion models have been developed to capture their complex associations, resulting in improved diagnosis of diseases. However, deep learning models are often difficult to interpret, bringing about challenges for uncovering biological mechanisms using these models. In this work, we develop an interpretable multimodal fusion model to perform automated diagnosis and result interpretation simultaneously. We name it Grad-CAM guided convolutional collaborative learning (gCAM-CCL), which is achieved by combining intermediate feature maps with gradient-based weights. The gCAM-CCL model can generate interpretable activation maps to quantify pixel-level contributions of the input features. Moreover, the estimated activation maps are class-specific, which can therefore facilitate the identification of biomarkers underlying different groups. We validate the gCAM-CCL model on a brain imaging-genetic study, and demonstrate its applications to both the classification of cognitive function groups and the discovery of underlying biological mechanisms. Specifically, our analysis results suggest that during task-fMRI scans, several object recognition related regions of interests (ROIs) are activated followed by several downstream encoding ROIs. In addition, the high cognitive group may have stronger neurotransmission signaling while the low cognitive group may have problems in brain/neuron development due to genetic variations. [ABSTRACT FROM AUTHOR]

Brain connectivity alterations associated with mental disorders have been widely reported in both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information from the vast amount of information afforded by brain networks remains a great challenge. Capturing network topology, graph convolutional networks (GCNs) have demonstrated to be superior in learning network representations tailored for identifying specific brain disorders. Existing graph construction techniques generally rely on a specific brain parcellation to define regions-of-interest (ROIs) to construct networks, often limiting the analysis into a single spatial scale. In addition, most methods focus on the pairwise relationships between the ROIs and ignore high-order associations between subjects. In this letter, we propose a mutual multi-scale triplet graph convolutional network (MMTGCN) to analyze functional and structural connectivity for brain disorder diagnosis. We first employ several templates with different scales of ROI parcellation to construct coarse-to-fine brain connectivity networks for each subject. Then, a triplet GCN (TGCN) module is developed to learn functional/structural representations of brain connectivity networks at each scale, with the triplet relationship among subjects explicitly incorporated into the learning process. Finally, we propose a template mutual learning strategy to train different scale TGCNs collaboratively for disease classification. Experimental results on 1,160 subjects from three datasets with fMRI or dMRI data demonstrate that our MMTGCN outperforms several state-of-the-art methods in identifying three types of brain disorders. [ABSTRACT FROM AUTHOR]

Multi-compartment models (MCM) are increasingly used to characterize the brain white matter microstructure from diffusion-weighted imaging (DWI). Their use in clinical studies is however limited by the inability to resample an MCM image towards a common reference frame, or to construct atlases from such brain microstructure models. We propose to solve this problem by first identifying that these two tasks amount to the same problem. We propose to tackle it by viewing it as a simplification problem, solved thanks to spectral clustering and the definition of semi-metrics between several usual compartments encountered in the MCM literature. This generic framework is evaluated for two models: the multi-tensor model where individual fibers are modeled as individual tensors and the diffusion direction imaging (DDI) model that differentiates intra- and extra-axonal components of each fiber. Results on simulated data, simulated transformations and real data show the ability of our method to well interpolate MCM images of these types. We finally present as an application an MCM template of normal controls constructed using our approach. [ABSTRACT FROM AUTHOR]

Prolonged seizures in children with focal epilepsy (FE) may impair language functions and often reoccur after surgical intervention. This study is aimed at developing a novel deep relational reasoning network to investigate whether conventional diffusion-weighted imaging connectome analysis can be improved when predicting expressive and receptive scores of preoperative language impairments and classifying postoperative seizure outcomes (seizure freedom or recurrence) in individual FE children. To deeply reason the dependencies of axonal connections that are sparsely distributed in the whole brain, this study proposes the “dilated CNN + RN”, a dilated convolutional neural network (CNN) combined with a relation network (RN). The performance of the dilated CNN + RN was evaluated using whole brain connectome data from 51 FE children. It was found that when compared with other state-of-the-art algorithms, the dilated CNN + RN led to an average improvement of 90.2% and 97.3% in predicting expressive and receptive language scores, and 2.2% and 4% improvement in classifying seizure freedom and seizure recurrence, respectively. These improvements were independent of the prefixed connectome densities. Also, the dilated CNN + RN could provide an explainable artificial intelligence (AI) model by computing gradient-based regression/classification activation maps. This mapping analysis revealed left superior-medial frontal cortex, bilateral hippocampi, and cerebellum as crucial hubs, facilitating important connections that were most predictive of language function and seizure refractoriness after surgery. [ABSTRACT FROM AUTHOR]

Tractography is an important technique that allows the in vivo reconstruction of structural connections in the brain using diffusion MRI. Although tracking algorithms have improved during the last two decades, results of validation studies and international challenges warn about the reliability of tractography and point out the need for improved algorithms. In propagation-based tracking, connections have traditionally been modeled as piece-wise linear segments. In this work, we propose a novel propagation-based tracker that is capable of generating geometrically smooth (${C}^{{1}}$) curves using parallel transport frames. Notably, our approach does not increase the complexity of the propagation problem that remains two-dimensional. Moreover, our tracker has a novel mechanism to reduce noise related propagation errors by incorporating topographic regularity of connections, a neuroanatomic property of many brain pathways. We ran extensive experiments and compared our approach against deterministic and other probabilistic algorithms. Our experiments on FiberCup and ISMRM 2015 challenge datasets as well as on 56 subjects of the Human Connectome Project show highly promising results both visually and quantitatively. Open-source implementations of the algorithm are shared publicly. [ABSTRACT FROM AUTHOR]

This paper proposes a mixture of linear dynamical systems model for quantifying the heterogeneous progress of Parkinson’s disease from DaTscan Images. The model is fitted to longitudinal DaTscans from the Parkinson’s Progression Marker Initiative. Fitting is accomplished using robust Bayesian inference with collapsed Gibbs sampling. Bayesian inference reveals three image-based progression subtypes which differ in progression speeds as well as progression trajectories. The model reveals characteristic spatial progression patterns in the brain, each pattern associated with a time constant. These patterns can serve as disease progression markers. The subtypes also have different progression rates of clinical symptoms measured by MDS-UPDRS Part III scores. [ABSTRACT FROM AUTHOR]

Objective: We present a unified statistical framework for characterizing community structure of brain functional networks that captures variation across individuals and evolution over time. Existing methods for community detection focus only on single-subject analysis of dynamic networks; while recent extensions to multiple-subjects analysis are limited to static networks. Method: To overcome these limitations, we propose a multi-subject, Markov-switching stochastic block model (MSS-SBM) to identify state-related changes in brain community organization over a group of individuals. We first formulate a multilayer extension of SBM to describe the time-dependent, multi-subject brain networks. We develop a novel procedure for fitting the multilayer SBM that builds on multislice modularity maximization which can uncover a common community partition of all layers (subjects) simultaneously. By augmenting with a dynamic Markov switching process, our proposed method is able to capture a set of distinct, recurring temporal states with respect to inter-community interactions over subjects and the change points between them. Results: Simulation shows accurate community recovery and tracking of dynamic community regimes over multilayer networks by the MSS-SBM. Application to task fMRI reveals meaningful non-assortative brain community motifs, e.g., core-periphery structure at the group level, that are associated with language comprehension and motor functions suggesting their putative role in complex information integration. Our approach detected dynamic reconfiguration of modular connectivity elicited by varying task demands and identified unique profiles of intra and inter-community connectivity across different task conditions. Conclusion: The proposed multilayer network representation provides a principled way of detecting synchronous, dynamic modularity in brain networks across subjects. [ABSTRACT FROM AUTHOR]

Brain source imaging is an important method for noninvasively characterizing brain activity using Electroencephalogram (EEG) or Magnetoencephalography (MEG) recordings. Traditional EEG/MEG Source Imaging (ESI) methods usually assume the source activities at different time points are unrelated, and do not utilize the temporal structure in the source activation, making the ESI analysis sensitive to noise. Some methods may encourage very similar activation patterns across the entire time course and may be incapable of accounting the variation along the time course. To effectively deal with noise while maintaining flexibility and continuity among brain activation patterns, we propose a novel probabilistic ESI model based on a hierarchical graph prior. Under our method, a spanning tree constraint ensures that activity patterns have spatiotemporal continuity. An efficient algorithm based on an alternating convex search is presented to solve the resulting problem of the proposed model with guaranteed convergence. Comprehensive numerical studies using synthetic data on a realistic brain model are conducted under different levels of signal-to-noise ratio (SNR) from both sensor and source spaces. We also examine the EEG/MEG datasets in two real applications, in which our ESI reconstructions are neurologically plausible. All the results demonstrate significant improvements of the proposed method over benchmark methods in terms of source localization performance, especially at high noise levels. [ABSTRACT FROM AUTHOR]

Effective fusion of structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) data has the potential to boost the accuracy of infant age prediction thanks to the complementary information provided by different imaging modalities. However, functional connectivity measured by fMRI during infancy is largely immature and noisy compared to the morphological features from sMRI, thus making the sMRI and fMRI fusion for infant brain analysis extremely challenging. With the conventional multimodal fusion strategies, adding fMRI data for age prediction has a high risk of introducing more noises than useful features, which would lead to reduced accuracy than that merely using sMRI data. To address this issue, we develop a novel model termed as disentangled-multimodal adversarial autoencoder (DMM-AAE) for infant age prediction based on multimodal brain MRI. Specifically, we disentangle the latent variables of autoencoder into common and specific codes to represent the shared and complementary information among modalities, respectively. Then, cross-reconstruction requirement and common-specific distance ratio loss are designed as regularizations to ensure the effectiveness and thoroughness of the disentanglement. By arranging relatively independent autoencoders to separate the modalities and employing disentanglement under cross-reconstruction requirement to integrate them, our DMM-AAE method effectively restrains the possible interference cross modalities, while realizing effective information fusion. Taking advantage of the latent variable disentanglement, a new strategy is further proposed and embedded into DMM-AAE to address the issue of incompleteness of the multimodal neuroimages, which can also be used as an independent algorithm for missing modality imputation. By taking six types of cortical morphometric features from sMRI and brain functional connectivity from fMRI as predictors, the superiority of the proposed DMM-AAE is validated on infant age (35 to 848 days after birth) prediction using incomplete multimodal neuroimages. The mean absolute error of the prediction based on DMM-AAE reaches 37.6 days, outperforming state-of-the-art methods. Generally, our proposed DMM-AAE can serve as a promising model for prediction with multimodal data. [ABSTRACT FROM AUTHOR]

This paper describes a novel approach of detecting different stages of Alzheimer’s disease (AD) and imaging beta-amyloid plaques and tau tangles in the brain using RF sensors. Dielectric measurements were obtained from grey matter and white matter regions of brain tissues with severe AD pathology at a frequency range of 200 MHz to 3 GHz using a vector network analyzer and dielectric probe. Computational models were created on CST Microwave Suite using a realistic head model and the measured dielectric properties to represent affected brain regions at different stages of AD. Simulations were carried out to test the performance of the RF sensors. Experiments were performed using textile-based RF sensors on fabricated phantoms, representing a human brain with different volumes of AD-affected brain tissues. Experimental data was collected from the sensors and processed in an imaging algorithm to reconstruct images of the affected areas in the brain. Measured dielectric properties in brain tissues with AD pathology were found to be different from healthy human brain tissues. Simulation and experimental results indicated a correlated shift in the captured reflection coefficient data from RF sensors as the amount of affected brain regions increased. Finally, images reconstructed from the imaging algorithm successfully highlighted areas of the brain affected by plaques and tangles as a result of AD. The results from this study show that RF sensing can be used to identify areas of the brain affected by AD pathology. This provides a promising new non-invasive technique for monitoring the progression of AD. [ABSTRACT FROM AUTHOR]

Human brain development is a complex and dynamic process caused by several factors such as genetics, sex hormones, and environmental changes. A number of recent studies on brain development have examined functional connectivity (FC) defined by the temporal correlation between time series of different brain regions. We propose to add the directional flow of information during brain maturation. To do so, we extract effective connectivity (EC) through Granger causality (GC) for two different groups of subjects, i.e., children and young adults. The motivation is that the inclusion of causal interaction may further discriminate brain connections between two age groups and help to discover new connections between brain regions. The contributions of this study are threefold. First, there has been a lack of attention to EC-based feature extraction in the context of brain development. To this end, we propose a new kernel-based GC (KGC) method to learn nonlinearity of complex brain network, where a reduced Sine hyperbolic polynomial (RSP) neural network was used as our proposed learner. Second, we used causality values as the weight for the directional connectivity between brain regions. Our findings indicated that the strength of connections was significantly higher in young adults relative to children. In addition, our new EC-based feature outperformed FC-based analysis from Philadelphia neurocohort (PNC) study with better discrimination of different age groups. Moreover, the fusion of these two sets of features (FC + EC) improved brain age prediction accuracy by more than 4%, indicating that they should be used together for brain development studies. [ABSTRACT FROM AUTHOR]

Multi-modal neuroimages, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), can provide complementary structural and functional information of the brain, thus facilitating automated brain disease identification. Incomplete data problem is unavoidable in multi-modal neuroimage studies due to patient dropouts and/or poor data quality. Conventional methods usually discard data-missing subjects, thus significantly reducing the number of training samples. Even though several deep learning methods have been proposed, they usually rely on pre-defined regions-of-interest in neuroimages, requiring disease-specific expert knowledge. To this end, we propose a spatially-constrained Fisher representation framework for brain disease diagnosis with incomplete multi-modal neuroimages. We first impute missing PET images based on their corresponding MRI scans using a hybrid generative adversarial network. With the complete (after imputation) MRI and PET data, we then develop a spatially-constrained Fisher representation network to extract statistical descriptors of neuroimages for disease diagnosis, assuming that these descriptors follow a Gaussian mixture model with a strong spatial constraint (i.e., images from different subjects have similar anatomical structures). Experimental results on three databases suggest that our method can synthesize reasonable neuroimages and achieve promising results in brain disease identification, compared with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Imaging fluorescence through millimeters or centimeters of tissue has important in vivo applications, such as guiding surgery and studying the brain. Often, the important information is the location of one of more optical reporters, rather than the specifics of the local geometry, motivating the need for a localization method that provides this information. We present an optimization approach based on a diffusion model for the fast localization of fluorescent inhomogeneities in deep tissue with expanded beam illumination that simplifies the experiment and the reconstruction. We show that the position of a fluorescent inhomogeneity can be estimated while assuming homogeneous tissue parameters and without having to model the excitation profile, reducing the computational burden and improving the utility of the method. We perform two experiments as a demonstration. First, a tumor in a mouse is localized using a near infrared folate-targeted fluorescent agent (OTL38). This result shows that localization can quickly provide tumor depth information, which could reduce damage to healthy tissue during fluorescence-guided surgery. Second, another near infrared fluorescent agent (ATTO647N) is injected into the brain of a rat, and localized through the intact skull and surface tissue. This result will enable studies of protein aggregation and neuron signaling. [ABSTRACT FROM AUTHOR]

Inferring brain connectivity networks from fMRI data can take place at the Region of Interest (ROI) or voxel level. With most ROI–based approaches, the signals from same-ROI voxels are simply averaged, neglecting any inhomogeneity in each ROI and assuming that the same voxels will interact with different ROIs in a similar manner. In this paper, we propose a novel method of representing ROI activity and estimating brain connectivity that takes into account the regionally-specific nature of brain activity, the spatial location of concentrated activity, and activity in other ROIs. The proposed method is able to integrate intrinsic regional structures into a network modelling framework, which we call local activity constrained canonical correlation analysis (LA-cCCA). We evaluated LA-cCCA on both simulated and real fMRI data. The simulation results demonstrated that LA-cCCA had improved accuracy of the estimated brain connectivity networks compared to the average–signal or Principal Component Analysis (PCA)–based correlation methods and the Canonical Correlation Analysis (CCA) method. We further examined the performance of LA-cCCA on real fMRI data set from the Human Connectome Project. LA-cCCA outperformed the other three approaches in terms of connectivity reproducibility. The proposed method explores the potentials of regional activity representation and is a reliable model for connectivity network estimation. It may serve as a promising tool for studying both the healthy and diseased brain. [ABSTRACT FROM AUTHOR]

Electromagnetic stimulation of the human brain is a key tool for neurophysiological characterization and the diagnosis of several neurological disorders. Transcranial magnetic stimulation (TMS) is a commonly used clinical procedure. However, personalized TMS requires a pipeline for individual head model generation to provide target-specific stimulation. This process includes intensive segmentation of several head tissues based on magnetic resonance imaging (MRI), which has significant potential for segmentation error, especially for low-contrast tissues. Additionally, a uniform electrical conductivity is assigned to each tissue in the model, which is an unrealistic assumption based on conventional volume conductor modeling. This study proposes a novel approach for fast and automatic estimation of the electric conductivity in the human head for volume conductor models without anatomical segmentation. A convolutional neural network is designed to estimate personalized electrical conductivity values based on anatomical information obtained from T1- and T2-weighted MRI scans. This approach can avoid the time-consuming process of tissue segmentation and maximize the advantages of position-dependent conductivity assignment based on the water content values estimated from MRI intensity values. The computational results of the proposed approach provide similar but smoother electric field distributions of the brain than that provided by conventional approaches. [ABSTRACT FROM AUTHOR]

Multi-label probabilistic maps, a.k.a. probabilistic segmentations, parameterize a population of intimately co-existing anatomical shapes and are useful for various medical imaging applications, such as segmentation, anatomical atlases, shape analysis, and consensus generation. Existing methods to estimate probabilistic segmentations rely on ad hoc intermediate representations (e.g., average of Gaussian-smoothed label maps and smoothed signed distance maps) that do not necessarily conform to the underlying generative process. Generative modeling of such maps could help discover as well as aide in the statistical analysis of sub-groups in a population via clustering and mixture modeling techniques. In this paper, we propose an estimation of multi-label probabilistic maps and showcase their favorable performance for modeling anatomical shapes such as the left atrium of the human heart and brain structures. The proposed formulation relies on a constrained optimization in the natural parameter space of the exponential family form of categorical distributions. A smoothness prior provides generalizability in the model and helps achieve greater performance in modeling tasks for unseen samples. We demonstrate and compare the effectiveness of the proposed method for Bayesian image segmentation, multi-atlas segmentation, and shape-based clustering. [ABSTRACT FROM AUTHOR]

We propose a novel Coupled Hidden Markov Model (CHMM) to detect and localize epileptic seizures in clinical multichannel scalp electroencephalography (EEG) recordings. Our model captures the spatio-temporal spread of a seizure by assigning a sequence of latent states (i.e. baseline or seizure) to each EEG channel. The state evolution is coupled between neighboring and contralateral channels to mimic clinically observed spreading patterns. Since the latent state space is exponential, a structured variational algorithm is developed for approximate inference. The model is evaluated on simulated and clinical EEG from two different hospitals. One dataset contains seizure recordings of adult focal epilepsy patients at the Johns Hopkins Hospital; the other contains publicly available non-specified seizure recordings from pediatric patients at Boston Children’s Hospital. Our CHMM model outperforms standard machine learning techniques in the focal dataset and achieves comparable performance to the best baseline method in the pediatric dataset. We also demonstrate the ability to track seizures, which is valuable information to localize focal onset zones. [ABSTRACT FROM AUTHOR]

The segmentation of brain tissue in MRI is valuable for extracting brain structure to aid diagnosis, treatment and tracking the progression of different neurologic diseases. Medical image data are volumetric and some neural network models for medical image segmentation have addressed this using a 3D convolutional architecture. However, this volumetric spatial information has not been fully exploited to enhance the representative ability of deep networks, and these networks have not fully addressed the practical issues facing the analysis of multimodal MRI data. In this paper, we propose a spatially-weighted 3D network (SW-3D-UNet) for brain tissue segmentation of single-modality MRI, and extend it using multimodality MRI data. We validate our model on the MRBrainS13 and MALC12 datasets. This unpublished model ranked first on the leaderboard of the MRBrainS13 Challenge. [ABSTRACT FROM AUTHOR]

Bioelectric source analysis in the human brain from scalp electroencephalography (EEG) signals is sensitive to the conductivities of different head tissues. The conductivity of tissues is subject dependent, so non-invasive methods for conductivity estimation are necessary to fine tune EEG models. To do so, the EEG forward problem solution (so-called lead field matrix) must be computed for a large number of conductivity configurations. Computing a lead field requires a matrix inversion which is computationally intensive for realistic head models. Thus, the required time for computing a large number of lead fields can become impractical. In this work, we propose to approximate the lead field matrix for a set of conductivity configurations, using the exact solution only for a small set of support points in the conductivity space. Our approach accelerates the computation time, while controlling the approximation error. Our method is tested on simulated and measured EEG data for brain and skull conductivity estimation. This test demonstrates that the approximation does not introduce any bias and runs significantly faster than if exact lead field were to be computed. [ABSTRACT FROM AUTHOR]

Accurate scatter correction is essential for qualitative and quantitative PET imaging. Until now, scatter correction based on Monte Carlo simulation (MCS) has been recognized as the most accurate method of scatter correction for PET. However, the major disadvantage of MCS is its long computational time, which makes it unfeasible for clinical usage. Meanwhile, single scatter simulation (SSS) is the most widely used method for scatter correction. Nevertheless, SSS has the disadvantage of limited robustness for dynamic measurements and for the measurement of large objects. In this work, a newly developed implementation of MCS using graphics processing unit (GPU) acceleration is employed, allowing full MCS-based scatter correction in clinical 3D brain PET imaging. Starting from the generation of annihilation photons to their detection in the simulated PET scanner, all relevant physical interactions and transport phenomena of the photons were simulated on GPUs. This resulted in an expected distribution of scattered events, which was subsequently used to correct the measured emission data. The accuracy of the approach was validated with simulations using GATE (Geant4 Application for Tomography Emission), and its performance was compared to SSS. The comparison of the computation time between a GPU and a single-threaded CPU showed an acceleration factor of 776 for a voxelized brain phantom study. The speedup of the MCS implemented on the GPU represents a major step toward the application of the more accurate MCS-based scatter correction for PET imaging in clinical routine. [ABSTRACT FROM AUTHOR]

Quantifying causal (effective) interactions between different brain regions are very important in neuroscience research. Many conventional methods estimate effective connectivity based on linear models. However, using linear connectivity models may oversimplify the functions and dynamics of the brain. In this paper, we propose a causal relationship estimator called nonlinear Causal Relationship Estimation by Artificial Neural Network (nCREANN) that identifies both linear and nonlinear components of effective connectivity in the brain. Furthermore, it can distinguish between these two types of connectivity components by calculating the linear and nonlinear parts of the network input-output mapping. The nCREANN performance has been verified using synthesized data and then it has been applied on EEG data collected during rest in children with autism spectrum disorder (ASD) and typically developing (TD) children. The results show that overall linear connectivity in TD subjects is higher, while the nonlinear connectivity component is more dominant in ASDs. We suggest that our findings may represent different underlying neural activation dynamics in ASD and TD subjects. The results of nCREANN may provide new insight into the connectivity between the interactive brain regions. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (CNN) have recently achieved superior performance at the task of medical image segmentation compared to classic models. However, training a generalizable CNN requires a large amount of training data, which is difficult, expensive, and time-consuming to obtain in medical settings. Active Learning (AL) algorithms can facilitate training CNN models by proposing a small number of the most informative data samples to be annotated to achieve a rapid increase in performance. We proposed a new active learning method based on Fisher information (FI) for CNNs for the first time. Using efficient backpropagation methods for computing gradients together with a novel low-dimensional approximation of FI enabled us to compute FI for CNNs with a large number of parameters. We evaluated the proposed method for brain extraction with a patch-wise segmentation CNN model in two different learning scenarios: universal active learning and active semi-automatic segmentation. In both scenarios, an initial model was obtained using labeled training subjects of a source data set and the goal was to annotate a small subset of new samples to build a model that performs well on the target subject(s). The target data sets included images that differed from the source data by either age group (e.g. newborns with different image contrast) or underlying pathology that was not available in the source data. In comparison to several recently proposed AL methods and brain extraction baselines, the results showed that FI-based AL outperformed the competing methods in improving the performance of the model after labeling a very small portion of target data set (<0.25%). [ABSTRACT FROM AUTHOR]

The ability to perceive and regulate emotion is a key component of cognition that is often disrupted by disease. Current neuroimaging studies regarding emotion regulation have implicated a number of cortical regions and identified several EEG features of interest, including the late positive potential and frontal asymmetry. Unfortunately, currently applied methods generally lack in the resolution necessary to capture focal cortical activity and explore the causal interactions between brain regions. In this paper, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data were simultaneously recorded from 20 subjects undergoing emotion processing and regulation tasks. Cortical activity with high-spatiotemporal resolution and accuracy was reconstructed using a novel multimodal EEG/fMRI integration method. A detailed causal brain network associated with emotion processing and regulation was then identified, and the network changes that facilitate different emotion conditions were investigated. The cortical activity of the ventrolateral prefrontal (VLPFC) and posterior parietal cortices depicted conditionally-sensitive spike and wave patterns evidenced in inter-regional communication. The VLPFC was found to behave as a main network source, with conditionally-specific interactions supporting emotional shifts. The results provide unique insight into the cortical activity that supports emotional perception and regulation, the origins of known EEG phenomena, and the manner in which brain regions coordinate to affect behavior. [ABSTRACT FROM AUTHOR]

In recent years, we have witnessed the explosion of large-scale data in various fields. Classical statistical methodologies, such as linear regression or generalized linear regression, often show inadequate performance on heterogeneous data because the key homogeneity assumption fails. In this paper, we present a flexible framework to handle heterogeneous populations that can be naturally grouped into several ordered subtypes. A local model technique utilizing ordinal class labels during the training stage is proposed. We define a new “progression score” that captures the progression of ordinal classes, and use a truncated Gaussian kernel to construct the weight function in a local regression framework. Furthermore, given the weights, we apply sparse shrinkage on the local fitting to handle high dimensionality. In this way, our local model is able to conduct variable selection on each query point. Numerical studies show the superiority of our proposed method over several existing ones. Our method is also applied to the Alzheimer’s Disease Neuroimaging Initiative data to make predictions on the longitudinal clinical scores based on different modalities of baseline brain image features. [ABSTRACT FROM AUTHOR]

Computational methods, such as the linear parametric neurotransmitter PET (lp-ntPET) method, have been developed to characterize the transient changes in radiotracer kinetics in the target tissue during endogenous neurotransmitter release. In this paper, we describe and evaluate a parametric reconstruction algorithm that uses an expectation maximization framework, along with the lp-ntPET model, to estimate the endogenous neurotransmitter response to stimuli directly from the measured PET data. Computer simulations showed that the proposed direct reconstruction method offers improved accuracy and precision for the estimated timing parameters of the neurotransmitter response at the voxel level (${t}_{d}=1\pm 2 $ min, for activation onset bias and standard deviation) compared with conventional post reconstruction modeling (${t}_{d}=4\pm 7 $ min). In addition, we applied the proposed direct parameter estimation methodology to a [11C]raclopride displacement study of an awake rat and generated parametric maps illustrating the magnitude of ligand displacement from striatum. Although the estimated parametric maps of activation magnitude obtained from both direct and post reconstruction methodologies suffered from false positive activations, the proposed direct reconstruction framework offered more reliable parametric maps when the activation onset parameter was constrained. [ABSTRACT FROM AUTHOR]