Insular gliomas and tractographic visualization of the connectome

In this video, the authors present a connectome-guided surgical resection of an insular glioma in a 39-year-old woman. Preoperative study with constrained spherical deconvolution (CSD)–based tractography revealed the surrounding brain connectome architecture around the tumor relevant for safe surgical resection. Connectomic information provided detailed maps of the surrounding language and salience networks, including eloquent white matter fibers and cortical regions, which were visualized intraoperatively with image guidance and artificial intelligence (AI)–based brain mapping software. Microsurgical dissection is presented with detailed discussion of the safe boundaries and angles of resection when entering the insular operculum defined by connectomic information. The video can be found here: https://stream.cadmore.media/r10.3171/2021.10.FOCVID21194

Identifying a Whole-brain Connectome-based Model in Drug-naïve Parkinson’s Disease for Predicting Motor Impairment

Identifying a whole-brain connectome-based predictive model in drug-naïve patients with Parkinson’s disease and verifying its predictions on drug-managed patients would be useful in determining the intrinsic functional underpinnings of motor impairment and establishing general brain-behavior associations. In this study, we constructed a predictive model from the resting-state functional data of 47 drug-naïve patients by using a connectome-based approach. This model was subsequently validated in 115 drug-managed patients. The severity of motor impairment was assessed by calculating Unified Parkinson’s Disease Rating Scale Part III scores. The predictive performance of model was evaluated using the correlation coefficient (rtrue) between predicted and observed scores. As a result, a connectome-based model for predicting individual motor impairment in drug-naïve patients was identified with significant performance (rtrue = 0.845, p < 0.001, ppermu = 0.002). Two patterns of connection were identified according to correlations between connection strength and the severity of motor impairment. The negative motor-impairment-related network contained more within-network connections in the motor, visual-related, and default mode networks, whereas the positive motor-impairment-related network was constructed mostly with between-network connections coupling the motor-visual, motor-limbic, and motor-basal ganglia networks. Finally, this predictive model constructed around drug-naïve patients was confirmed with significant predictive efficacy on drug-managed patients (r = 0.209, p = 0.025), suggesting a generalizability in Parkinson’s disease patients under long-term drug influence. In conclusion, this study identified a whole-brain connectome-based model that could predict the severity of motor impairment in Parkinson’s patients and furthers our understanding of the functional underpinnings of the disease.

Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities

Objective: Graph theory applications are commonly used in connectomics research to better understand connectivity architecture and characterize its role in cognition, behavior and disease conditions. One of the numerous open questions in the field is how to represent inter-individual differences with graph theoretical methods to make inferences for the population. Here, we proposed and tested a simple intuitive method that is based on finding the correlation between the rank-ordering of nodes within each connectome with respect to a given metric to quantify the differences/similarities between different connectomes.Methods: We used the diffusion imaging data of the entire HCP-1065 dataset of the Human Connectome Project (HCP) (n = 1,065 subjects). A customized cortical subparcellation of HCP-MMP atlas (360 parcels) (yielding a total of 1,598 ROIs) was used to generate connectivity matrices. Six graph measures including degree, strength, coreness, betweenness, closeness, and an overall “hubness” measure combining all five were studied. Group-level ranking-based aggregation method (“measure-then-aggregate”) was used to investigate network properties on population level.Results: Measure-then-aggregate technique was shown to represent population better than commonly used aggregate-then-measure technique (overall rs: 0.7 vs 0.5). Hubness measure was shown to highly correlate with all five graph measures (rs: 0.88–0.99). Minimum sample size required for optimal representation of population was found to be 50 to 100 subjects. Network analysis revealed a widely distributed set of cortical hubs on both hemispheres. Although highly-connected hub clusters had similar distribution between two hemispheres, average ranking values of homologous parcels of two hemispheres were significantly different in 71% of all cortical parcels on group-level.Conclusion: In this study, we provided experimental evidence for the robustness, limits and applicability of a novel group-level ranking-based hubness analysis technique. Graph-based analysis of large HCP dataset using this new technique revealed striking hemispheric asymmetry and intraparcel heterogeneities in the structural connectivity of the human brain.

Predicting cognitive scores with graph neural networks through sample selection learning

Analyzing the relation between intelligence and neural activity is of the utmost importance in understanding the working principles of the human brain in health and disease. In existing literature, functional brain connectomes have been used successfully to predict cognitive measures such as intelligence quotient (IQ) scores in both healthy and disordered cohorts using machine learning models. However, existing methods resort to flattening the brain connectome (i.e., graph) through vectorization which overlooks its topological properties. To address this limitation and inspired from the emerging graph neural networks (GNNs), we design a novel regression GNN model (namely RegGNN) for predicting IQ scores from brain connectivity. On top of that, we introduce a novel, fully modular sample selection method to select the best samples to learn from for our target prediction task. However, since such deep learning architectures are computationally expensive to train, we further propose a learning-based sample selection method that learns how to choose the training samples with the highest expected predictive power on unseen samples. For this, we capitalize on the fact that connectomes (i.e., their adjacency matrices) lie in the symmetric positive definite (SPD) matrix cone. Our results on full-scale and verbal IQ prediction outperforms comparison methods in autism spectrum disorder cohorts and achieves a competitive performance for neurotypical subjects using 3-fold cross-validation. Furthermore, we show that our sample selection approach generalizes to other learning-based methods, which shows its usefulness beyond our GNN architecture.

New approach of using cortico-cortical evoked potential for functional brain evaluation

Cortico-cortical evoked potential (CCEP) mapping is a rapidly developing method for visualizing the brain network and estimating cortical excitability. The CCEP comprises the early N1 component the occurs at 10-30 ms poststimulation, indicating anatomic connectivity, and the late N2 component that appears at < 200 ms poststimulation, suggesting long-lasting effective connectivity. A later component at 200-1,000 ms poststimulation can also appear as a delayed response in some studied areas. Such delayed responses occur in areas with changed excitability, such as an epileptogenic zone. CCEP mapping has been used to examine the brain connections causally in functional systems such as the language, auditory, and visual systems as well as in anatomic regions including the frontoparietal neocortices and hippocampal limbic areas. Task-based CCEPs can be used to measure behavior. In addition to evaluations of the brain connectome, single-pulse electrical stimulation (SPES) can reflect cortical excitability, and so it could be used to predict a seizure onset zone. CCEP brain mapping and SPES investigations could be applied both extraoperatively and intraoperatively. These underused electrophysiologic tools in basic and clinical neuroscience might be powerful methods for providing insight into measures of brain connectivity and dynamics. Analyses of CCEPs might enable us to identify causal relationships between brain areas during cortical processing, and to develop a new paradigm of effective therapeutic neuromodulation in the future.

Dyslexia as the most prevalent form of specific learning disabilities

Dyslexia is the most common form of specific learning disabilities. Dyslexia is observed in 5-17.5 % of schoolchildren, and among children with specific learning disabilities, it accounts for about 70-80 %. Usually, dyslexia manifests itself as the inability to achieve an appropriate level of reading skills development that would be proportional to their intellectual abilities and writing and spelling skills. Secondary consequences of dyslexia may include problems in reading comprehension and reduced reading experience that can impede the growth of vocabulary and background skills. The review discusses neurological management of reading and writing as complex higher mental functions, including many components that are provided by various brain areas. The principles of dyslexia classification, the main characteristics of its traditionally defined forms are given: phonemic, optical, mnestic, semantic, agrammatic. The article analyzes the cerebral mechanisms of dyslexia development, the results of studies using neuropsychological methods, functional neuroimaging, and the study of the brain connectome. The contribution to dyslexia development of disturbances in phonological awareness, rapid automated naming (RAN), the volume of visual attention (VAS), components of the brain executive functions is discussed. The origin of emotional disorders in children with dyslexia, risk factors for dyslexia development (including genetic predisposition) are considered. Dyslexia manifestations in children are listed, about which their parents seek the advice of a specialist for the first time. In the process of diagnosing dyslexia, attention should be paid to the delay in the child’s speech development, cases of speech and language development disorders and specific learning disabilities among family members. It is necessary to consider possible comorbidity of dyslexia in a child with attention deficit hyperactivity disorder, dyscalculia, developmental dyspraxia, disorders of emotional control and brain executive functions. Timely diagnosis determines the effectiveness of early intervention programs based on an integrated multimodal approach.

Ramsey’s Theory Meets The Human Brain Connectome

Ramsey&rsquo;s theory (RAM) from combinatorics and network theory goes looking for regularities and repeated patterns inside structures equipped with nodes and edges. RAM represents the outcome of a dual methodological commitment: by one side a top-down approach evaluates the possible arrangement of specific subgraphs when the number of graph&rsquo;s vertices is already known, by another side a bottom-up approach calculates the possible number of graph&rsquo;s vertices when the arrangement of specific subgraphs is already known. Since natural neural networks are often represented in terms of graphs, we suggest to utilize RAM for the analytical and computational assessment of a peculiar structure supplied with neuronal vertices and axonal edges, i.e., the human brain connectome. We discuss how a RAM approach in neuroscientific issues might be able to locate and trace unexplored motifs shared between different cortical and subcortical subareas. Furthermore, we will describe how notable RAM outcomes, such as the Ramsey&rsquo;s theorem and the Ramsey&rsquo;s number, could contribute to uncover still unknown anatomical connexions endowed in neuronal networks and unexpected functional interactions among grey zones of the human brain.

Combining CRISPR/Cas9 and brain imaging: from genes to molecules to networks

Receptors, transporters and ion channels are important targets for therapy development in neurological diseases including Alzheimeŕs disease, Parkinsońs disease, epilepsy, schizophrenia and major depression. Several receptors and ion channels identified by next generation sequencing may be involved in disease initiation and progression but their mechanistic role in pathogenesis is often poorly understood. Gene editing and in vivo imaging approaches will help to identify the molecular and functional role of these targets and the consequence of their regional dysfunction on whole brain level. Here, we combine CRISPR/Cas9 gene-editing with in vivo positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) to investigate the direct link between genes, molecules, and the brain connectome. The extensive knowledge of the Slc18a2 gene encoding the vesicular monoamine transporter (VMAT2), involved in the storage and release of dopamine, makes it an excellent target for studying the gene networks relationships while structurally preserving neuronal integrity and function. We edited the Slc18a2 in the substantia nigra pars compacta of adult rats and used in vivo molecular imaging besides behavioral, histological, and biochemical assessments to characterize the CRISPR/Cas9-mediated VMAT2 knockdown. Simultaneous PET/fMRI was performed to investigate molecular and functional brain alterations. We found that stage-specific adaptations of brain functional connectivity follow the selective impairment of presynaptic dopamine storage and release. Our study reveals that recruiting different brain networks is an early response to the dopaminergic dysfunction preceding neuronal cell loss. Our combinatorial approach is a novel tool to investigate the impact of specific genes on brain molecular and functional dynamics which will help to develop tailored therapies for normalizing brain function. The method can easily be transferred to higher-order species allowing for a direct comparison of the molecular imaging findings.