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Autochthonous neurocysticercosis is exceptionally rare in Western Europe. We describe multiple brain lesions, initially mimicking metastases, in a 60-year-old man in Spain without travel history. We confirmed diagnosis by neuroimaging and positive serology. Our study highlights cryptic local Taenia solium nematode transmission risks and diagnostic challenges in nonendemic regions.

Brain atlases and templates are at the heart of neuroimaging analyses, for which they facilitate multimodal registration, enable group comparisons and provide anatomical reference. However, as atlas-based approaches rely on correspondence mapping between images they perform poorly in the presence of structural pathology. Whilst several strategies exist to overcome this problem, their performance is often dependent on the type, size and homogeneity of any lesions present. We therefore propose a new solution, referred to as Virtual Brain Grafting (VBG), which is a fully-automated, open-source workflow to reliably parcellate magnetic resonance imaging (MRI) datasets in the presence of a broad spectrum of focal brain pathologies, including large, bilateral, intra- and extra-axial, heterogeneous lesions with and without mass effect. The core of the VBG approach is the generation of a lesion-free T1-weighted image, which enables further image processing operations that would otherwise fail. Here we validated our solution based on Freesurfer recon-all parcellation in a group of 10 patients with heterogeneous gliomatous lesions, and a realistic synthetic cohort of glioma patients (n = 100) derived from healthy control data and patient data. We demonstrate that VBG outperforms a non-VBG approach assessed qualitatively by expert neuroradiologists and Mann-Whitney U tests to compare corresponding parcellations (real patients U(6,6) = 33, z = 2.738, P < .010, synthetic-patients U(48,48) = 2076, z = 7.336, P < .001). Results were also quantitatively evaluated by comparing mean dice scores from the synthetic-patients using one-way ANOVA (unilateral VBG = 0.894, bilateral VBG = 0.903, and non-VBG = 0.617, P < .001). Additionally, we used linear regression to show the influence of lesion volume, lesion overlap with, and distance from the Freesurfer volumes of interest, on labeling accuracy. VBG may benefit the neuroimaging community by enabling automated state-of-the-art MRI analyses in clinical populations using methods such as FreeSurfer, CAT12, SPM, Connectome Workbench, as well as structural and functional connectomics. To fully maximize its availability, VBG is provided as open software under a Mozilla 2.0 license (https://github.com/KUL-Radneuron/KUL_VBG). [Display omitted] (A) shows T1 images from two patients with gliomatous lesions. VBG is a lesion replacement/filling workflow with one approach for unilateral lesions (uVBG) and one for bilateral lesion (bVBG). (B) shows the lesion filling and recon-all combination selected, (C) & (D) show the output, tissue segmentations (C) and whole brain parcellations (D). If VBG is not used (non-VBG) recon-all may quit without generating a parcellation (hard failure) shown on the lower left, or finish with some errors (soft failures) in the parcellations shown on the lower right. However, using either VBG method allows recon-all to complete where it had previously failed and also improves parcellation quality. (PAT = patient, VBG = virtual brain grafting, uVBG = unilateral VBG, bVBG = bilateral VBG)

Damage to specific brain circuits can cause specific neuropsychiatric symptoms. Therapeutic stimulation to these same circuits may modulate these symptoms. To determine whether these circuits converge, we studied depression severity after brain lesions ( n  = 461, five datasets), transcranial magnetic stimulation ( n  = 151, four datasets) and deep brain stimulation ( n  = 101, five datasets). Lesions and stimulation sites most associated with depression severity were connected to a similar brain circuit across all 14 datasets ( P  < 0.001). Circuits derived from lesions, deep brain stimulation and transcranial magnetic stimulation were similar ( P  < 0.0005), as were circuits derived from patients with major depression versus other diagnoses ( P  < 0.001). Connectivity to this circuit predicted out-of-sample antidepressant efficacy of transcranial magnetic stimulation and deep brain stimulation sites ( P  < 0.0001). In an independent analysis, 29 lesions and 95 stimulation sites converged on a distinct circuit for motor symptoms of Parkinson’s disease ( P  < 0.05). We conclude that lesions, transcranial magnetic stimulation and DBS converge on common brain circuitry that may represent improved neurostimulation targets for depression and other disorders. Which brain circuits are causally involved in depression? Using the human connectome as a wiring diagram, Siddiqi et al. combine data from lesions, deep brain stimulation and transcranial magnetic stimulation studies to show that these three methods converge in identifying a single depression circuit.

Accurate segmentation of chronic stroke lesions from mono‐spectral magnetic resonance imaging scans (e.g., T1‐weighted images) is a difficult task due to the arbitrary shape, complex texture, variable size and intensities, and varied locations of the lesions. Due to this inherent spatial heterogeneity, existing machine learning methods have shown moderate performance for chronic lesion delineation. In this study, we introduced: (1) a method that integrates transformers' deformable feature attention mechanism with convolutional deep learning architecture to improve the accuracy and generalizability of stroke lesion segmentation, and (2) an ecological data augmentation technique based on inserting real lesions into intact brain regions. Our combination of these two approaches resulted in a significant increase in segmentation performance, with a Dice index of 0.82 (±0.39), outperforming the existing methods trained and tested on the same Anatomical Tracings of Lesions After Stroke (ATLAS) 2022 dataset. Our method performed relatively well even for cases with small stroke lesions. We validated the robustness of our method through an ablation study and by testing it on new unseen brain scans from the Ischemic Stroke Lesion Segmentation (ISLES) 2015 dataset. Overall, our proposed approach of transformers with ecological data augmentation offers a robust way to delineate chronic stroke lesions with clinically relevant accuracy. Our method can be extended to other challenging tasks that require automated detection and segmentation of diverse brain abnormalities from clinical scans. This work presents an innovative stroke lesion segmentation model that leverages a transformer‐based architecture, utilizing deformable feature attention mechanism for precise segmentation. It also uses an ecological data augmentation technique where real lesions are inserted into healthy brain scans, which enhances model training and improves generalization. The model demonstrates superior accuracy in identifying and delineating stroke lesions, even on new unseen data, highlighting its potential for clinical application in stroke analysis.

•We proposed a data augmentation approach CarveMix for brain lesion segmentation.•CarveMix mixes pairs of annotated images to generate synthetic training images.•The image mixing is performed according to the location and shape of the lesions.•CarveMix was validated on multiple public and private datasets.•The results show that CarveMix improves the quality of brain lesion segmentation. Brain lesion segmentation provides a valuable tool for clinical diagnosis and research, and convolutional neural networks (CNNs) have achieved unprecedented success in the segmentation task. Data augmentation is a widely used strategy to improve the training of CNNs. In particular, data augmentation approaches that mix pairs of annotated training images have been developed. These methods are easy to implement and have achieved promising results in various image processing tasks. However, existing data augmentation approaches based on image mixing are not designed for brain lesions and may not perform well for brain lesion segmentation. Thus, the design of this type of simple data augmentation method for brain lesion segmentation is still an open problem. In this work, we propose a simple yet effective data augmentation approach, dubbed as CarveMix, for CNN-based brain lesion segmentation. Like other mixing-based methods, CarveMix stochastically combines two existing annotated images (annotated for brain lesions only) to obtain new labeled samples. To make our method more suitable for brain lesion segmentation, CarveMix is lesion-aware, where the image combination is performed with a focus on the lesions and preserves the lesion information. Specifically, from one annotated image we carve a region of interest (ROI) according to the lesion location and geometry with a variable ROI size. The carved ROI then replaces the corresponding voxels in a second annotated image to synthesize new labeled images for network training, and additional harmonization steps are applied for heterogeneous data where the two annotated images can originate from different sources. Besides, we further propose to model the mass effect that is unique to whole brain tumor segmentation during image mixing. To evaluate the proposed method, experiments were performed on multiple publicly available or private datasets, and the results show that our method improves the accuracy of brain lesion segmentation. The code of the proposed method is available at https://github.com/ZhangxinruBIT/CarveMix.git.

The functional roles of the precuneus are unclear. Focal precuneus lesions are rare, making it difficult to identify robust brain–behavior relationships. Distinct functional subdivisions of the precuneus have been proposed based on unique connectivity profiles. This includes an association of the anterior division with bodily awareness, the central region with complex cognition, and the posterior division with visual processing. Our goal was to test the hypothesis that the central precuneus is preferentially involved (compared to the other sectors of the precuneus) in executive function, as estimated from performance on the trail-making test (TMT). 35 patients with focal brain lesions involving the precuneus were included from the University of Iowa and Montpellier University. Multivariate lesion symptom mapping of TMT performance was performed to evaluate whether lesion location was associated with impaired task performance. Lesion symptom mapping revealed a statistically significant association of central precuneus lesions with impaired TMT performance (r = 0.43, p < 0.01). Further, a functional network derived from this precuneus region showed connectivity to other cortical areas implicated in executive function, including the dorsolateral prefrontal cortex and inferior parietal lobe. This analysis provides support for the role of the central precuneus in executive function, consistent with the unique connectivity pattern of the central precuneus with a broader network implicated in cognitive control and executive function.

Neuropsychiatric symptoms (NPSs) are common in patients with Alzheimer’s disease (AD) and are associated with accelerated cognitive impairment and earlier deaths. This review aims to explore the neural pathogenesis of NPSs in AD and its association with the progression of AD. We first provide a literature overview on the onset times of NPSs. Different NPSs occur in different disease stages of AD, but most symptoms appear in the preclinical AD or mild cognitive impairment stage and develop progressively. Next, we describe symptom-general and -specific patterns of brain lesions. Generally, the anterior cingulate cortex is a commonly damaged region across all symptoms, and the prefrontal cortex, especially the orbitofrontal cortex, is also a critical region associated with most NPSs. In contrast, the anterior cingulate-subcortical circuit is specifically related to apathy in AD, the frontal-limbic circuit is related to depression, and the amygdala circuit is related to anxiety. Finally, we elucidate the associations between the NPSs and AD by combining the onset time with the neural basis of NPSs.

The annotation of brain lesion images is a key step in clinical diagnosis and treatment of a wide spectrum of brain diseases. In recent years, segmentation methods based on deep learning have gained unprecedented popularity, leveraging a large amount of data with high-quality voxel-level annotations. However, due to the limited time clinicians can provide for the cumbersome task of manual image segmentation, semi-supervised medical image segmentation methods present an alternative solution as they require only a few labeled samples for training. In this paper, we propose a novel semi-supervised segmentation framework that combines improved mean teacher and adversarial network. Specifically, our framework consists of (i) a student model and a teacher model for segmenting the target and generating the signed distance maps of object surfaces, and (ii) a discriminator network for extracting hierarchical features and distinguishing the signed distance maps of labeled and unlabeled data. Besides, based on two different adversarial learning processes, a multi-scale feature consistency loss derived from the student and teacher models is proposed, and a shape-aware embedding scheme is integrated into our framework. We evaluated the proposed method on the public brain lesion datasets from ISBI 2015, ISLES 2015, and BRATS 2018 for the multiple sclerosis lesion, ischemic stroke lesion, and brain tumor segmentation respectively. Experiments demonstrate that our method can effectively leverage unlabeled data while outperforming the supervised baseline and other state-of-the-art semi-supervised methods trained with the same labeled data. The proposed framework is suitable for joint training of limited labeled data and additional unlabeled data, which is expected to reduce the effort of obtaining annotated images.

Background Due to their shared embryological origin, retinal and brain tissues are affected by neurodegenerative diseases in similar ways. Optical coherence tomography (OCT) and OCT‐angiography, two non‐invasive retinal imaging modalities, have been increasingly studied as potential biomarkers for Alzheimer's disease (AD) in recent years. However, correlations between OCT/OCT‐A and neuroimaging remain understudied. We thus performed a systematic review of OCT/OCT‐A – MRI correlations in different neurodegenerative disorders associated with cognitive decline. Method Medline, Embase, and other databases were searched from January to June 2023, using keywords related to neurodegenerative conditions and OCT/OCT‐A parameters. Result We screened 2962 citations and 93 full‐text articles. We included 28 studies in the final review. For non‐vascular neurodegenerative diseases, layer‐specific retinal metrics, especially retinal nerve fiber layer (RNFL) thinning, and region‐specific retinal parameters (e.g. decreased foveal thickness) best correlated with changes on brain MRI. Vascular retinal biomarkers, especially reduced vessel and perfusion densities, have the unique capacity to reflect cerebrovascular lesions in vascular cognitive conditions. Both layer‐ or region‐specific retinal biomarkers and vascular retinal metrics can reflect global brain atrophy patterns. Microstructural alterations of the brain parenchyma best correlated with layer‐specific thinning of retina. Conclusion Layer‐ or region‐specific retinal markers are better suited for non‐vascular dementias, while vascular markers more closely reflect vascular neurodegeneration. Future research must overcome several challenges including methodological heterogeneity and the complex interactions between different degenerative mechanisms. A better understanding of the associations between retinal and brain lesions could ultimately lead to the clinical use of retinal biomarkers for the early diagnosis of neurodegenerative diseases.