Fronto-thalamic networks and the left ventral thalamic nuclei play a key role in aphasia after thalamic stroke.

Thalamic aphasia results from focal thalamic lesions that cause dysfunction of remote but functionally connected cortical areas due to language network perturbation. However, specific local and network-level neural substrates of thalamic aphasia remain incompletely understood. Using lesion symptom mapping, we demonstrate that lesions in the left ventrolateral and ventral anterior thalamic nucleus are most strongly associated with aphasia in general and with impaired semantic and phonemic fluency and complex comprehension in particular. Lesion network mapping (using a normative connectome based on fMRI data from 1000 healthy individuals) reveals a Thalamic aphasia network encompassing widespread left-hemispheric cerebral connections, with Broca's area showing the strongest associations, followed by the superior and middle frontal gyri, precentral and paracingulate gyri, and globus pallidus. Our results imply the critical involvement of the left ventrolateral and left ventral anterior thalamic nuclei in engaging left frontal cortical areas, especially Broca's area, during language processing.

WarpDrive: Improving spatial normalization using manual refinements.

Spatial normalization-the process of mapping subject brain images to an average template brain-has evolved over the last 20+ years into a reliable method that facilitates the comparison of brain imaging results across patients, centers & modalities. While overall successful, sometimes, this automatic process yields suboptimal results, especially when dealing with brains with extensive neurodegeneration and atrophy patterns, or when high accuracy in specific regions is needed. Here we introduce WarpDrive, a novel tool for manual refinements of image alignment after automated registration. We show that the tool applied in a cohort of patients with Alzheimer's disease who underwent deep brain stimulation surgery helps create more accurate representations of the data as well as meaningful models to explain patient outcomes. The tool is built to handle any type of 3D imaging data, also allowing refinements in high-resolution imaging, including histology and multiple modalities to precisely aggregate multiple data sources together.

Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks.

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

Optimal deep brain stimulation sites and networks for stimulation of the fornix in Alzheimer's disease.

Deep brain stimulation (DBS) to the fornix is an investigational treatment for patients with mild Alzheimer's Disease. Outcomes from randomized clinical trials have shown that cognitive function improved in some patients but deteriorated in others. This could be explained by variance in electrode placement leading to differential engagement of neural circuits. To investigate this, we performed a post-hoc analysis on a multi-center cohort of 46 patients with DBS to the fornix (NCT00658125, NCT01608061). Using normative structural and functional connectivity data, we found that stimulation of the circuit of Papez and stria terminalis robustly associated with cognitive improvement (R = 0.53, p < 0.001). On a local level, the optimal stimulation site resided at the direct interface between these structures (R = 0.48, p < 0.001). Finally, modulating specific distributed brain networks related to memory accounted for optimal outcomes (R = 0.48, p < 0.001). Findings were robust to multiple cross-validation designs and may define an optimal network target that could refine DBS surgery and programming.

Ocular Manifestations Related to Antibodies Positivity and Inflammatory Biomarkers in a Rheumatological Cohort.

Purpose: Ocular involvement is frequent in autoimmune diseases and even can be the first manifestation. There are multiple descriptions in the literature around the world regarding this topic. However, we evidenced a lack of studies analyzing the relationship between the ocular manifestations and systemic biomarkers, especially in Latinamerica. Therefore, this study aimed to examine the relationship between the positivity of inflammatory biomarkers and the ocular manifestations in a Colombian cohort of rheumatological patients. Patients and Methods: We conducted an observational, descriptive, non-comparative cross-sectional study in a rheumatology center, in Bogotá, Colombia, from 2013 to 2019. We calculated a sample size of 797 patients to assess the prevalence of ocular manifestations and inflammatory biomarkers. We performed univariate analyses for categorical and continuous variables and bivariate analyses using the Chi-square and Fisher's exact test for categorical variables. Results: Women represented 84% of the population, and the mean age was 54.61± 15.64 years. Of 797 patients, 21.45% reported one or more ophthalmological diagnoses, being keratoconjunctivitis sicca (KCS) the most common (15.93%), followed by uveitis, and cataract (1.38%, each one). Regarding ophthalmological symptoms, 35% presented at least one, being dry eye sensation (DE) the most common (30.86%), followed by ocular pain (2.76%), red eye, and decreased visual acuity (2.63%, each one). The antibodies or inflammatory biomarkers most frequently found were antinuclear antibodies (ANAs) (35.3%), C-reactive protein (28.7%), and rheumatoid factor (27.9%). We found statistical associations between consumption of complement 3, anti-CCP, anti-RO, and anti-LA antibodies with ocular manifestations such as photophobia, DE, conjunctivitis, KCS, uveitis, retinal vasculitis, and maculopathy. Conclusion: Ocular manifestations are frequently found in patients with positive antibodies and inflammatory biomarkers. Our results suggest antibodies and inflammatory molecules could be biomarkers for ocular manifestations in patients with rheumatological diseases. This study provides the basis for future longitudinal studies.