Effects of unilateral cortical resection of the visual cortex on bilateral human white matter

Three-branch feature enhancement and fusion network for focal cortical dysplasia lesions segmentation using multimodal imaging

OBJECTIVE

Conventional multimodal imaging, including MRI and fluorodeoxyglucose positron emission tomography (FDG-PET), has difficulty in accurately detecting subtle or blurred focal cortical dysplasia (FCD) lesions. Morphometric maps assist localization by highlighting abnormal regions, whereas wavelet-filtered images emphasize texture and edge details. Therefore, we propose a three-branch feature enhancement and fusion network (TBFEF-Net) that integrates conventional multimodal imaging, morphometric maps, and wavelet-filtered images to enhance the accuracy of FCD localization.

METHODS

The proposed TBFEF-Net comprises a semantic segmentation backbone, a cross-branch feature enhancement (CFE) module, and a multi-feature fusion (MFF) module. In the semantic segmentation backbone, three UNet-based branches separately extract semantic features from conventional multimodal imaging, morphometric maps, and wavelet-filtered images. In the encoding stage, the CFE incorporates a residual-based convolutional block attention module (CBAM) to aggregate features from all branches, enhancing the feature representation of FCD lesions. While in the decoding stage, the MFF integrates edge detail features from the wavelet-filtered imaging branch into the conventional multimodal imaging branch, enhancing the ability to capture lesion edges. As a result, this approach enables more precise segmentation.

RESULTS

Experimental results show that TBFEF-Net surpasses several state-of-the-art methods in FCD segmentation. In the primary cohort, the Dice and sensitivity reached 59.73 % and 67.13 %, respectively, while in the open cohort, the Dice and sensitivity were 54.67 % and 54.81 %, respectively.

SIGNIFICANCE

We introduced wavelet-filtered images for the first time in FCD segmentation, offering a novel approach and perspective for FCD lesions localization.

Delineating abnormal individual structural covariance brain network organization in pediatric epilepsy with unilateral resection of visual cortex

Although several previous studies have used resting-state functional magnetic resonance imaging and diffusion tensor imaging to report topological changes in the brain in epilepsy, it remains unclear whether the individual structural covariance network (SCN) changes in epilepsy, especially in pediatric epilepsy with visual cortex resection but with normal functions. Herein, individual SCNs were mapped and analyzed for seven pediatric patients with epilepsy after surgery and 15 age-matched healthy controls. A whole-brain individual SCN was constructed based on an automated anatomical labeling template, and global and nodal network metrics were calculated for statistical analyses. Small-world properties were exhibited by pediatric patients after brain surgery and by healthy controls. After brain surgery, pediatric patients with epilepsy exhibited a higher shortest path length, lower global efficiency, and higher nodal efficiency in the cuneus than those in healthy controls. These results revealed that pediatric epilepsy after brain surgery, even with normal functions, showed altered topological organization of the individual SCNs, which revealed residual network topological abnormalities and may provide initial evidence for the underlying functional impairments in the brain of pediatric patients with epilepsy after surgery that can occur in the future.

EEG Source Imaging of Infarct Core and Penumbra for Ischemic Stroke Patients

A quantitative method of analyzing EEG signals after stroke onset can help monitor disease progression and tailor treatments. In this work, we present an EEG-based imaging algorithm to estimate the location and size of the stroke infarct core and penumbra tissues. Building on recent advancements in localizing neural silences, we develop an algorithm that utilizes known spectral properties of the infarct core and penumbra to separately localize them. Our algorithm uses these properties to estimate source contributions to the scalp EEG recordings in different frequency bands. Subsequently, it utilizes optimization techniques to search for the affected brain sources iteratively. We test our algorithm on simulated datasets using a realistic MRI head model, achieving center-of-mass error of 12.80mm and 17.24mm, and size estimation error of 21.78% and 36.62% for infarct core and penumbra respectively.

On Hallucinations in Tomographic Image Reconstruction

Tomographic image reconstruction is generally an ill-posed linear inverse problem. Such ill-posed inverse problems are typically regularized using prior knowledge of the sought-after object property. Recently, deep neural networks have been actively investigated for regularizing image reconstruction problems by learning a prior for the object properties from training images. However, an analysis of the prior information learned by these deep networks and their ability to generalize to data that may lie outside the training distribution is still being explored. An inaccurate prior might lead to false structures being hallucinated in the reconstructed image and that is a cause for serious concern in medical imaging. In this work, we propose to illustrate the effect of the prior imposed by a reconstruction method by decomposing the image estimate into generalized measurement and null components. The concept of a hallucination map is introduced for the general purpose of understanding the effect of the prior in regularized reconstruction methods. Numerical studies are conducted corresponding to a stylized tomographic imaging modality. The behavior of different reconstruction methods under the proposed formalism is discussed with the help of the numerical studies.

Neural silences can be localized rapidly using noninvasive scalp EEG

A rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

Compressible Latent-Space Invertible Networks for Generative Model-Constrained Image Reconstruction

There remains an important need for the development of image reconstruction methods that can produce diagnostically useful images from undersampled measurements. In magnetic resonance imaging (MRI), for example, such methods can facilitate reductions in data-acquisition times. Deep learning-based methods hold potential for learning object priors or constraints that can serve to mitigate the effects of data-incompleteness on image reconstruction. One line of emerging research involves formulating an optimization-based reconstruction method in the latent space of a generative deep neural network. However, when generative adversarial networks (GANs) are employed, such methods can result in image reconstruction errors if the sought-after solution does not reside within the range of the GAN. To circumvent this problem, in this work, a framework for reconstructing images from incomplete measurements is proposed that is formulated in the latent space of invertible neural network-based generative models. A novel regularization strategy is introduced that takes advantage of the multiscale architecture of certain invertible neural networks, which can result in improved reconstruction performance over classical methods in terms of traditional metrics. The proposed method is investigated for reconstructing images from undersampled MRI data. The method is shown to achieve comparable performance to a state-of-the-art generative model-based reconstruction method while benefiting from a deterministic reconstruction procedure and easier control over regularization parameters.

Large-scale resculpting of cortical circuits in children after surgical resection

Despite the relative successes in the surgical treatment of pharmacoresistant epilepsy, there is rather little research on the neural (re)organization that potentially subserves behavioral compensation. Here, we examined the post-surgical functional connectivity (FC) in children and adolescents who have undergone unilateral cortical resection and, yet, display remarkably normal behavior. Conventionally, FC has been investigated in terms of the mean correlation of the BOLD time courses extracted from different brain regions. Here, we demonstrated the value of segregating the voxel-wise relationships into mutually exclusive populations that were either positively or negatively correlated. While, relative to controls, the positive correlations were largely normal, negative correlations among networks were increased. Together, our results point to reorganization in the contralesional hemisphere, possibly suggesting competition for cortical territory due to the demand for representation of function. Conceivably, the ubiquitous negative correlations enable the differentiation of function in the reduced cortical volume following a unilateral resection.