Brain Connectivity Mapping Research Articles

Mapping stroke outcomes: A review of brain connectivity atlases.

The brain connectivity-based atlas is a promising tool for understanding neural communication pathways in the brain, gaining relevance in predicting personalized outcomes for various brain pathologies. This critical review examines the robustness of the brain connectivity-based atlas for predicting post-stroke outcomes. A comprehensive literature search was conducted from 2012 to May 2023 across PubMed, Scopus, EMBASE, EBSCOhost, and Medline databases. Twenty-one studies were screened, and through analysis of these studies, we identified 18 brain connectivity atlases employed by the studies for lesion analysis in their predictions. The brain atlases were assessed for study cohorts, connectivity measures, identified brain regions, atlas applications, and limitations. Based on the analysis of these studies, most atlases were based on diffusion tensor imaging and resting-state functional magnetic resonance imaging (MRI). Studies predicting post-stroke functional outcomes relied on the atlases for multivariate lesion analysis and region of interest identification, often employing atlases derived from young, healthy populations. Current brain connectivity-based atlases for stroke applications lack standardized methods to define and map brain connectivity across atlases and cover sensorimotor functional connectivity to a limited extent. In conclusion, this review highlights the need to develop more comprehensive, robust, and adaptable brain connectivity-based atlases specifically tailored to post-stroke populations.

Mapping brain metabolism, connectivity and neurotransmitters topography in early and late onset dementia with lewy bodies

IntroductionEarly-onset dementia with Lewy bodies (EO-DLB) is associated with rapid cognitive decline and severe neuropsychiatric symptoms at onset. MethodsUsing FDG-PET imaging for 62 patients (21 EO-DLB, 41 LO (late-onset)-DLB), we explored brain hypometabolism, and metabolic connectivity in the whole-brain network and resting-state networks (RSNs). We also evaluated the spatial association between brain hypometabolism and neurotransmitter pathways topography. ResultsDirect comparisons between the two clinical subgroups showed that EO-DLB was characterized by a lower metabolism in posterior cingulate/precuneus and occipital cortex. Metabolic connectivity analysis revealed significant alterations in posterior regions in both EO-DLB and LO-DLB. The EO-DLB, however, showed more severe loss of connectivity between occipital and parietal nodes and hyperconnectivity between frontal and cerebellar nodes. Spatial topography association analysis indicated significant correlations between neurotransmitter maps (i.e. acetylcholine, GABA, serotonin, dopamine) and brain hypometabolism in both EO and LO-DLB, with significantly higher metabolic correlation in the presynaptic serotonergic system for EO-DLB, supporting its major dysfunction. ConclusionsOur study revealed greater brain hypometabolism and loss of connectivity in posterior brain region in EO- than LO-DLB. Serotonergic mapping emerges as a relevant factor for further investigation addressing clinical differences between DLB subtypes.

Connectome-based predictive modeling: A new approach of predicting individual critical thinking ability

Critical thinking (i.e., CT), which is a metacognitive process, encompasses seven dimensions: truth-seeking, open-mindedness, analyticity, systematicity, self-confidence, inquisitiveness, and maturity. It enables individuals to arrive at reasonable conclusions and make well-informed decisions. Despite being regarded as a fundamental survival skill and a crucial element of higher education, the neural mechanism responsible for a highly critical brain remains unclear.To address this, we recruited 80 Chinese native speakers, half of whom majored in liberal arts and half in science. They underwent a resting-state fMRI scan and completed a classic CT scale. Using connectome-based predictive modeling, we identified brain connectivity patterns associated with high and low CT dispositions. Then a support vector machine model was trained to classify individuals’ gender and major from the top 20 edges respectively for high- and low CT networks.Results showed that the high CT network included limbic, temporal, parietal, and cerebellum lobes. The limbic and parietal lobes of high CT networks positively predicted overall and each dimensions of CT scores. Specifically, the connectivity between the left parahippocampal gyrus and right precuneus cortex significantly predicted the open-mindedness score, the connectivity between the bilateral hippocampal gyrus significantly predicted the systematicity score, and the connectivities between the right precuneus cortex and left superior parietal lobule, right middle temporal gyrus and right superior parietal lobule were found to significantly predict individual cognitive maturity. And the top 20 edges of high CT networks could distinguish participants' major but not gender, while the top 20 edges of low CT networks could not distinguish either.These above results suggest that CT dispositions can be forecasted from a person's distinctive mapping of brain connections, with the limbic lobe of the high CT network predicting open-mindedness and systematicity scores, which share common neural mechanisms with creative thinking, and the parietal lobe positively predicting self-reported cognitive maturity.

Comparative survey of multigraph integration methods for holistic brain connectivity mapping.

One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional brain template (CBT), also named network atlas, presents a powerful tool for capturing the most representative and discriminative traits of a given population while preserving its topological patterns. The idea of a CBT is to integrate a population of heterogeneous brain connectivity networks, derived from different neuroimaging modalities or brain views (e.g., structural and functional), into a unified holistic representation. Here we review current state-of-the-art methods designed to estimate well-centered and representative CBT for populations of single-view and multi-view brain networks. We start by reviewing each CBT learning method, then we introduce the evaluation measures to compare CBT representativeness of populations generated by single-view and multigraph integration methods, separately, based on the following criteria: Centeredness, biomarker-reproducibility, node-level similarity, global-level similarity, and distance-based similarity. We demonstrate that the deep graph normalizer (DGN) method significantly outperforms other multi-graph and all single-view integration methods for estimating CBTs using a variety of healthy and disordered datasets in terms of centeredness, reproducibility (i.e., graph-derived biomarkers reproducibility that disentangle the typical from the atypical connectivity variability), and preserving the topological traits at both local and global graph-levels.

RS-FetMRI: a MATLAB-SPM Based Tool for Pre-processing Fetal Resting-State fMRI Data.

Resting-state functional magnetic resonance imaging (rs-fMRI) most recently has proved to open a measureless window on functional neurodevelopment in utero. Fetal brain activation and connectivity maps can be heavily influenced by 1) fetal-specific motion effects on the time-series and 2) the accuracy of time-series spatial normalization to a standardized gestational-week (GW) specific fetal template space.Due to the absence of a standardized and generalizable image processing protocol, the objective of the present work was to implement a validated fetal rs-fMRI preprocessing pipeline (RS-FetMRI) divided into 6 inter-dependent preprocessing modules (i.e., M1 to M6) and designed to work entirely as an extension for Statistical Parametric Mapping (SPM).RS-FetMRI pipeline output analyses on rs-fMRI time-series sampled from a cohort of fetuses acquired on both 1.5T and 3T MRI scanning systems showed increased efficacy of estimation of the degree of movement coupled with an efficient motion censoring procedure, resulting in increased number of motion-uncorrupted volumes and temporal continuity in fetal rs-fMRI time-series data. Moreover, a "structural-free" SPM-based spatial normalization procedure granted a high degree of spatial overlap with high reproducibility and a significant improvement in whole-brain and parcellation-specific Temporal Signal-to-Noise Ratio (TSNR) mirrored by functional connectivity analysis.To our knowledge, the RS-FetMRI pipeline is the first semi-automatic and easy-to-use standardized fetal rs-fMRI preprocessing pipeline completely integrated in MATLAB-SPM able to remove entry barriers for new research groups into the field of fetal rs-fMRI, for both research or clinical purposes, and ultimately to make future fetal brain connectivity investigations more suitable for comparison and cross-validation.

Predicting age and clinical risk from the neonatal connectome

The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-individual differences in structural brain connectivity. Individual brain connectivity maps (connectomes) are by nature high in dimensionality and complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible and can give us clues as to how and why individual developmental trajectories differ.In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large sample of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of post menstrual age (PMA) at scan in term-born infants (mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p < 0.001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p < 0.001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to predominantly involve frontal and temporal regions, thalami, and basal ganglia. From our models of PMA at scan for infants born at term, we computed a brain maturation index (predicted age minus actual age) of individual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome and suggest that a neural substrate of brain maturation with implications for future neurodevelopment is detectable at term equivalent age from the neonatal connectome.

Magnetic resonance imaging and diffusion tensor imaging reconstruction of connectomes in a macropod, the quokka (Setonix brachyurus).

The diversity of the diprotodontids provides an excellent opportunity to study how a basic marsupial cortical plan has been modified for the needs of the mammals living in different habitats. Very little is known about the connections of the cerebral cortex with the deep brain structures (basal ganglia and thalamus) in this evolutionarily significant group of mammals. In this study, we performed mapping of brain regions and connections in a diprotodontid marsupial from data obtained from an excised brain scanned in high-field (9.4T) microstructural magnetic resonance imaging (MRI) instrument. The analysis was based on two MRI methodologies. First, high-resolution structural scans were used to map MRI visible brain regions from T1w and T2w images. Second, extensive diffusion tensor imaging (DTI) data were obtained to elucidate connectivity between brain areas using deterministic diffusion tracking of neuronal brain fibers. From the data, we were able to identify corticostriate connections between the frontal association and dorsomedial isocortex and the head of the caudate, and between the lateral somatosensory cortex and the putamen. We were also able to follow the olfactory and limbic connections by tracing fibers in the fornix, cingulum, intrabulbar part of the anterior commissure, and lateral olfactory tract. There was segregation of fibers in the anterior commissure such that olfactory connections passed through the rostroventral part and successively more dorsal cortical areas connected through more dorsal parts of the commissure. Our findings confirm a common pattern of cortical connectivity in therian mammals, even where brain expansion has occurred independently in diverse groups.

Real-time mapping of intrinsic brain connectivity to target brain stimulation

Real-time mapping of intrinsic brain connectivity to target brain stimulation

Structural and resting state functional connectivity beyond the cortex

Mapping the structural and functional connectivity of the central nervous system has become a key area within neuroimaging research. While detailed network structures across the entire brain have been probed using animal models, non-invasive neuroimaging in humans has thus far been dominated by cortical investigations. Beyond the cortex, subcortical nuclei have traditionally been less accessible due to their smaller size and greater distance from radio frequency coils. However, major neuroimaging developments now provide improved signal and the resolution required to study these structures. Here, we present an overview of the connectivity between the amygdala, brainstem, cerebellum, spinal cord and the rest of the brain. While limitations to their imaging and analyses remain, we also provide some recommendations and considerations for mapping brain connectivity beyond the cortex.

Novel tool to quantify with single-cell resolution the number of incoming AAV genomes co-expressed in the mouse nervous system

Adeno-associated viral (AAV) vectors are an established and safe gene delivery tool to target the nervous system. However, the payload capacity of <4.9 kb limits the transfer of large or multiple genes. Oversized payloads could be delivered by fragmenting the transgenes into separate AAV capsids that are then mixed. This strategy could increase the AAV cargo capacity to treat monogenic, polygenic diseases and comorbidities only if controlled co-expression of multiple AAV capsids is achieved on each transduced cell. We developed a tool to quantify the number of incoming AAV genomes that are co-expressed in the nervous system with single-cell resolution. By using an isogenic mix of three AAVs each expressing single fluorescent reporters, we determined that expression of much greater than 31 AAV genomes per neuron in vitro and 20 genomes per neuron in vivo is obtained across different brain regions including anterior cingulate, prefrontal, somatomotor and somatosensory cortex areas, and cerebellar lobule VI. Our results demonstrate that multiple AAV vectors containing different transgenes or transgene fragments, can efficiently co-express in the same neuron. This tool can be used to design and improve AAV-based interrogation of neuronal circuits, map brain connectivity, and treat genetic diseases affecting the nervous system.

FusionNet: A Deep Fully Residual Convolutional Neural Network for Image Segmentation in Connectomics

Cellular-resolution connectomics is an ambitious research direction with the goal of generating comprehensive brain connectivity maps using high-throughput, nano-scale electron microscopy. One of the main challenges in connectomics research is developing scalable image analysis algorithms that require minimal user intervention. Deep learning has provided exceptional performance in image classification tasks in computer vision, leading to a recent explosion in popularity. Similarly, its application to connectomic analyses holds great promise. Here, we introduce a deep neural network architecture, FusionNet, with a focus on its application to accomplish automatic segmentation of neuronal structures in connectomics data. FusionNet combines recent advances in machine learning, such as semantic segmentation and residual neural networks, with summation-based skip connections. This results in a much deeper network architecture and improves segmentation accuracy. We demonstrate the performance of the proposed method by comparing it with several other popular electron microscopy segmentation methods. We further illustrate its flexibility through segmentation results for two different tasks: cell membrane segmentation and cell nucleus segmentation.

Brain connectomics applied to oncological neuroscience: from a traditional surgical strategy focusing on glioma topography to a meta-network approach.

The classical way for surgical selection and planning in cerebral glioma mainly focused on tumor topography. The emerging science of connectomics, which aims of mapping brain connectivity, resulted in a paradigmatic shift from a modular account of cerebral organization to a meta-network perspective. Adaptive behavior is actually mediated by constant changes in interactions within and across large-scale delocalized neural systems underlying conation, cognition, and emotion. Here, to optimize the onco-functional balance of glioma surgery, the purpose is to switch toward a connectome-based resection taking account of both relationships between the tumor and critical distributed circuits (especially subcortical pathways) as well as the perpetual instability of the meta-network. Such dynamic in the neural spatiotemporal integration permits functional reallocation leading to neurological recovery after massive resection in structures traditionally thought as "inoperable." This better understanding of connectome increases benefit/risk ratio of surgery (i) by selecting resection in areas deemed "eloquent" according to a localizationist dogma; (ii), conversely, by refining intraoperative awake cognitive mapping and monitoring in so-called non-eloquent areas; (iii) by improving preoperative information, enabling an optimal selection of intrasurgical tasks tailored to the patient's wishes; (iv) by developing an "oncological disconnection surgery"; (v) by defining a personalized multistep surgical strategy adapted to individual brain reshaping potential; and (vi) ultimately by preserving environmentally and socially appropriate behavior, including return to work, while increasing the extent of (possibly repeated) resection(s). Such a holistic vision of neural processing can enhance reliability of connectomal surgery in oncological neuroscience and may also be applied to restorative neurosurgery.

Multivariate Brain Functional Connectivity Through Regularized Estimators.

Functional connectivity analyses are typically based on matrices containing bivariate measures of covariability, such as correlations. Although this has been a fruitful approach, it may not be the optimal strategy to fully explore the complex associations underlying brain activity. Here, we propose extending connectivity to multivariate functions relating to the temporal dynamics of a region with the rest of the brain. The main technical challenges of such an approach are multidimensionality and its associated risk of overfitting or even the non-uniqueness of model solutions. To minimize these risks, and as an alternative to the more common dimensionality reduction methods, we propose using two regularized multivariate connectivity models. On the one hand, simple linear functions of all brain nodes were fitted with ridge regression. On the other hand, a more flexible approach to avoid linearity and additivity assumptions was implemented through random forest regression. Similarities and differences between both methods and with simple averages of bivariate correlations (i.e., weighted global brain connectivity) were evaluated on a resting state sample of N = 173 healthy subjects. Results revealed distinct connectivity patterns from the two proposed methods, which were especially relevant in the age-related analyses where both ridge and random forest regressions showed significant patterns of age-related disconnection, almost completely absent from the much less sensitive global brain connectivity maps. On the other hand, the greater flexibility provided by the random forest algorithm allowed detecting sex-specific differences. The generic framework of multivariate connectivity implemented here may be easily extended to other types of regularized models.

A new method for accurate in vivo mapping of human brain connections using microstructural and anatomical information.

Diffusion magnetic resonance imaging is a noninvasive imaging modality that has been extensively used in the literature to study the neuronal architecture of the brain in a wide range of neurological conditions using tractography. However, recent studies highlighted that the anatomical accuracy of the reconstructions is inherently limited and challenged its appropriateness. Several solutions have been proposed to tackle this issue, but none of them proved effective to overcome this fundamental limitation. In this work, we present a novel processing framework to inject into the reconstruction problem basic prior knowledge about brain anatomy and its organization and evaluate its effectiveness using both simulated and real human brain data. Our results indicate that our proposed method dramatically increases the accuracy of the estimated brain networks and, thus, represents a major step forward for the study of connectivity.

TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity

Diffusion MRI tractography processing pipeline requires a large number of steps (typically 20+ steps). If parameters of these steps, number of threads, and random seed generators are not carefully controlled, the resulting tractography can easily be non-reproducible and non-replicable, even in test-test experiments. To handle these issues, we developed TractoFlow. TractoFlow is fully automatic from raw diffusion weighted images to tractography. The pipeline also outputs classical diffusion tensor imaging measures and several fiber orientation distribution function measures. TractoFlow supports the recent Brain Imaging Data Structure (BIDS) format as input and is based on two engines: Nextflow and Singularity. In this work, the TractoFlow pipeline is evaluated on three databases and shown to be efficient and reproducible from 98% to 100%, depending on parameter choices. Moreover, it is easy to use for non-technical users, with little to no installation requirements. TractoFlow is publicly available for academic research and is an important step forward for better structural brain connectivity mapping.

Could EEG Help Predict Antidepressant Response?

Could EEG Help Predict Antidepressant Response?

Interactions between stimuli-evoked cortical activity and spontaneous low frequency oscillations measured with neuronal calcium

Spontaneous brain activity has been widely used to map brain connectivity. The interactions between task-evoked brain responses and the spontaneous cortical oscillations, especially within the low frequency range of ~0.1 ​Hz, are not fully understood. Trial-to-trial variabilities in brain’s response to sensory stimuli and the ability for brain to detect under noisy conditions suggest an appreciable impact of the brain state. Using a multimodality imaging platform, we simultaneously imaged neuronal Ca2+ and cerebral hemodynamics at baseline and in response to single-pulse forepaw stimuli in rat’s somatosensory cortex. The high sensitivity of this system enables detection of responses to very weak and strong stimuli and real time determination of low frequency oscillations without averaging. Results show that the ongoing neuronal oscillations inversely modulate Ca2+ transients evoked by sensory stimuli. High intensity stimuli reset the spontaneous neuronal oscillations to an unpreferable excitability following the stimulus. Cerebral hemodynamic responses also inversely interact with the spontaneous hemodynamic oscillations, correlating with the neuronal Ca2+ transient changes. The results reveal competing interactions between spontaneous oscillations and stimulation-evoked brain activities in somatosensory cortex and the resultant hemodynamics.

Wireless Reconfigurable RF Detector Array for Focal and Multiregional Signal Enhancement.

Wirelessly Amplified NMR Detectors (WAND) can utilize wireless pumping power to amplify MRI signals in situ for sensitivity enhancement of deep-lying tissues that are difficult to access by conventional surface coils. To reconfigure between selective and simultaneous activation in a multielement array, each WAND has a dipole resonance mode for MR signal acquisition and two butterfly modes that support counter-rotating current circulation. Because detectors in the same row share the same lower butterfly frequency but different higher butterfly frequency, a pumping signal at the sum frequency of the dipole mode and the higher butterfly mode can selectively activate individual resonators, leading to 4-fold sensitivity gain over passive coupling. Meanwhile, a pumping signal at the sum frequency of the dipole mode and the lower butterfly mode can simultaneously activate multiple resonators in the same row, leading to 3-fold sensitivity gain over passive coupling. When multiple rows of detectors are parallelly aligned, each row has a unique lower butterfly frequency for consecutive activation during the acquisition interval of the others. This wireless detector array can be embedded beneath a headpost that is normally required for multi-modal brain imaging, enabling easy reconfiguration between focal imaging of individual vessels and multiregional mapping of brain connectivity.

Network building and analysis in connectomics studies: a review of algorithms, databases and technologies

The study of connectome, the complex system of connections in the human brain, has gained a central role in neurosciences. It has turned out to be popular in the field of neuroimaging to model and analyze the brain represented by a network. The graph model of the brain is a framework used to represent neuroanatomical regions or region of interest (ROI) as nodes and their connections. These connections, or edges, can either be functional, by representing physiological activity, or can be structural, representing anatomical connections formed by axonal fiber tracts. The development of new technologies for mapping of structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks, named connectome, can be examined and analyzed with a large variety of graph theory tools. The purpose of this survey is to present a review of brain network by dealing (i) how to construct a brain graph, (ii) which measures may be applied to capture the organization of a brain network, (iii) how to compare the brain networks, (iv) the tools for the network analysis of brains, and (v) finally the databases storing the brain networks.

Applications of 3D printing in small animal magnetic resonance imaging.

Three-dimensional (3D) printing has significantly impacted the quality, efficiency, and reproducibility of preclinical magnetic resonance imaging. It has vastly expanded the ability to produce MR-compatible parts that readily permit customization of animal handling, achieve consistent positioning of anatomy and RF coils promptly, and accelerate throughput. It permits the rapid and cost-effective creation of parts customized to a specific imaging study, animal species, animal weight, or even one unique animal, not routinely used in preclinical research. We illustrate the power of this technology by describing five preclinical studies and specific solutions enabled by different 3D printing processes and materials. We describe fixtures, assemblies, and devices that were created to ensure the safety of anesthetized lemurs during an MR examination of their brain or to facilitate localized, contrast-enhanced measurements of white blood cell concentration in a mouse model of pancreatitis. We illustrate expansive use of 3D printing to build a customized birdcage coil and components of a ventilator to enable imaging of pulmonary gas exchange in rats using hyperpolarized . Finally, we present applications of 3D printing to create high-quality, dual RF coils to accelerate brain connectivity mapping in mouse brain specimens and to increase the throughput of brain tumor examinations in a mouse model of pituitary adenoma.