Connectome Mapping Research Articles

Human distal lung maps and lineage hierarchies reveal a bipotent progenitor.

Mapping the spatial distribution and molecular identity of constituent cells is essential for understanding tissue dynamics in health and disease. We lack a comprehensive map of human distal airways, including the terminal and respiratory bronchioles (TRBs), which are implicated in respiratory diseases1-4. Here, using spatial transcriptomics and single-cell profiling of microdissected distal airways, we identify molecularly distinct TRB cell types that have not-to our knowledge-been previously characterized. These include airway-associated LGR5+ fibroblasts and TRB-specific alveolar type-0 (AT0) cells and TRB secretory cells (TRB-SCs). Connectome maps and organoid-based co-cultures reveal that LGR5+ fibroblasts form a signalling hub in the airway niche. AT0 cells and TRB-SCs are conserved in primates and emerge dynamically during human lung development. Using a non-human primate model of lung injury, together with human organoids and tissue specimens, we show that alveolar type-2 cells in regenerating lungs transiently acquire an AT0 state from which they can differentiate into either alveolar type-1 cells or TRB-SCs. This differentiation programme is distinct from that identified in the mouse lung5-7. Our study also reveals mechanisms that drive the differentiation of the bipotent AT0 cell state into normal or pathological states. In sum, our findings revise human lung cell maps and lineage trajectories, and implicate an epithelial transitional state in primate lung regeneration and disease.

Optogenetic and thermogenetic manipulation of defined neural circuits and behaviors in Drosophila.

Neural network dynamics underlying flexible animal behaviors remain elusive. The fruit fly Drosophila melanogaster is considered an excellent model in behavioral neuroscience because of its simple neuroanatomical architecture and the availability of various genetic methods. Moreover, Drosophila larvae's transparent body allows investigators to use optical methods on freely moving animals, broadening research directions. Activating or inhibiting well-defined events in excitable cells with a fine temporal resolution using optogenetics and thermogenetics led to the association of functions of defined neural populations with specific behavioral outputs such as the induction of associative memory. Furthermore, combining optogenetics and thermogenetics with state-of-the-art approaches, including connectome mapping and machine learning-based behavioral quantification, might provide a complete view of the experience- and time-dependent variations of behavioral responses. These methodologies allow further understanding of the functional connections between neural circuits and behaviors such as chemosensory, motivational, courtship, and feeding behaviors and sleep, learning, and memory.

Translational Structural and Functional Signatures of Chronic Alcohol Effects in Mice

BackgroundAlcohol acts as an addictive substance that may lead to alcohol use disorder. In humans, magnetic resonance imaging showed diverse structural and functional brain alterations associated with this complex pathology. Single magnetic resonance imaging modalities are used mostly but are insufficient to portray and understand the broad neuroadaptations to alcohol. Here, we combined structural and functional magnetic resonance imaging and connectome mapping in mice to establish brain-wide fingerprints of alcohol effects with translatable potential. MethodsMice underwent a chronic intermittent alcohol drinking protocol for 6 weeks before being imaged under medetomidine anesthesia. We performed open-ended multivariate analysis of structural data and functional connectivity mapping on the same subjects. ResultsStructural analysis showed alcohol effects for the prefrontal cortex/anterior insula, hippocampus, and somatosensory cortex. Integration with microglia histology revealed distinct alcohol signatures, suggestive of advanced (prefrontal cortex/anterior insula, somatosensory cortex) and early (hippocampus) inflammation. Functional analysis showed major alterations of insula, ventral tegmental area, and retrosplenial cortex connectivity, impacting communication patterns for salience (insula), reward (ventral tegmental area), and default mode (retrosplenial cortex) networks. The insula appeared as a most sensitive brain center across structural and functional analyses. ConclusionsThis study demonstrates alcohol effects in mice, which possibly underlie lower top-down control and impaired hedonic balance documented at the behavioral level, and aligns with neuroimaging findings in humans despite the potential limitation induced by medetomidine sedation. This study paves the way to identify further biomarkers and to probe neurobiological mechanisms of alcohol effects using genetic and pharmacological manipulations in mouse models of alcohol drinking and dependence.

Connectome spatial smoothing (CSS): Concepts, methods, and evaluation

Structural connectomes are increasingly mapped at high spatial resolutions comprising many hundreds—if not thousands—of network nodes. However, high-resolution connectomes are particularly susceptible to image registration misalignment, tractography artifacts, and noise, all of which can lead to reductions in connectome accuracy and test-retest reliability. We investigate a network analogue of image smoothing to address these key challenges. Connectome Spatial Smoothing (CSS) involves jointly applying a carefully chosen smoothing kernel to the two endpoints of each tractography streamline, yielding a spatially smoothed connectivity matrix. We develop computationally efficient methods to perform CSS using a matrix congruence transformation and evaluate a range of different smoothing kernel choices on CSS performance. We find that smoothing substantially improves the identifiability, sensitivity, and test-retest reliability of high-resolution connectivity maps, though at a cost of increasing storage burden. For atlas-based connectomes (i.e. low-resolution connectivity maps), we show that CSS marginally improves the statistical power to detect associations between connectivity and cognitive performance, particularly for connectomes mapped using probabilistic tractography. CSS was also found to enable more reliable statistical inference compared to connectomes without any smoothing. We provide recommendations for optimal smoothing kernel parameters for connectomes mapped using both deterministic and probabilistic tractography. We conclude that spatial smoothing is particularly important for the reliability of high-resolution connectomes, but can also provide benefits at lower parcellation resolutions. We hope that our work enables computationally efficient integration of spatial smoothing into established structural connectome mapping pipelines.

Development of functional connectome gradients during childhood and adolescence

Connectome mapping studies have documented a principal primary-to-transmodal gradient in the adult brain network, capturing a functional spectrum that ranges from perception and action to abstract cognition. However, how this gradient pattern develops and whether its development is linked to cognitive growth, topological reorganization, and gene expression profiles remain largely unknown. Using longitudinal resting-state functional magnetic resonance imaging data from 305 children (aged 6–14 years), we describe substantial changes in the primary-to-transmodal gradient between childhood and adolescence, including emergence as the principal gradient, expansion of global topography, and focal tuning in primary and default-mode regions. These gradient changes are mediated by developmental changes in network integration and segregation, and are associated with abstract processing functions such as working memory and expression levels of calcium ion regulated exocytosis and synaptic transmission-related genes. Our findings have implications for understanding connectome maturation principles in normal development and developmental disorders.

The application of the Human Connectome Project in large scale brain network imaging: A potential biomarker for Alzheimer's disease and related dementias

AbstractBackgroundAlzheimer’s disease and related dementias (ADRD) result in progressive dysfunction of large scale brain networks (LSBNs) including the default mode network (DMN). Resting state functional MRI (rs‐fMRI) is an imaging tool offering the potential for tracking LSBN degeneration, but been underutilized as an imaging biomarker. Potential reasons include complexity of imaging protocols and analyzing imaging data. If these steps were simplified and operationalized, further integration of rs‐fMRI into clinical trials as a surrogate endpoint may be feasible. We describe an automated connectome mapping program (Infinitome) that leverages rs‐fMRI scans with standardized protocols, Human Connectome Project (HCP) data, and machine learning to understand longitudinal LSBN degeneration in dementia (figure 1).MethodThe Infinitome program creates a subject specific version of the Human Connectome Project Multimodal Parcellation (HCP‐MMP1) atlas using diffusion tractography (figure 2). Analytics are performed on both diffusion tensor imaging and rs‐fMRI. Outlier detection using a tangent space connectivity matrix is performed by comparing results with a subset of 300 normal HCP subject fMRI samples to determine the range of normal correlations for each regions of interest in a LSBN. Abnormal connectivity is determined as a 3‐sigma outlier for that correlation (figure 3). The imaging protocol for rs‐fMRI is limited to 8 minutes, enabling each study to be readily added on to structural MRI sequences. The program also provides automated image processing and analysis, thus simplifying the process of accessing imaging data and incorporating rs‐fMRI into routine practice.ResultPreliminary work has shown that connectomic analysis using this software in ADRD is feasible and can detect functional anomalies involving regions of interest described by the HCP.ConclusionBy providing support for fMRI processing through a cloud‐based server and incorporating normal control data from the HCP, the Infinitome program addresses the challenges associated with the analysis and interpretation of rs‐fMRI data. Thus, it has the potential to simplify the incorporation of rs‐fMRI into clinical trials as a biomarker for progressive neurodegeneration of vulnerable networks in ADRD. Further studies are planned evaluating LSBNs in AD and dementia with Lewy bodies.

Neuromodulation for Pain: A Comprehensive Survey and Systematic Review of Clinical Trials and Connectomic Analysis of Brain Targets

Background: Chronic pain is a debilitating condition that imposes a tremendous burden on health-care systems around the world. While frontline treatments for chronic pain involve pharmacological and psychological approaches, neuromodulation can be considered for treatment-resistant cases. Neuromodulatory approaches for pain are diverse in both modality and target and their mechanism of action is incompletely understood. Objectives: The objectives of this study were to (i) understand the current landscape of pain neuromodulation research through a comprehensive survey of past and current registered clinical trials (ii) investigate the network underpinnings of these neuromodulatory treatments by performing a connectomic mapping analysis of cortical and subcortical brain targets that have been stimulated for pain relief. Methods: A search for clinical trials involving pain neuromodulation was conducted using 2 major trial databases (ClinicalTrials.gov and the International Clinical Trials Registry Platform). Trials were categorized by variables and analyzed to gain an overview of the contemporary research landscape. Additionally, a connectomic mapping analysis was performed to investigate the network connectivity patterns of analgesic brain stimulation targets using a normative connectome based on a functional magnetic resonance imaging dataset. Results: In total, 487 relevant clinical trials were identified. Noninvasive cortical stimulation and spinal cord stimulation trials represented 49.3 and 43.7% of this count, respectively, while deep brain stimulation trials accounted for <3%. The mapping analysis revealed that superficial target connectomics overlapped with deep target connectomics, suggesting a common pain network across the targets. Conclusions: Research for pain neuromodulation is a rapidly growing field. Our connectomic network analysis reinforced existing knowledge of the pain matrix, identifying both well-described hubs and more obscure structures. Further studies are needed to decode the circuits underlying pain relief and determine the most effective targets for neuromodulatory treatment.

Dopamine Neurons That Cotransmit Glutamate, From Synapses to Circuits to Behavior.

Discovered just over 20 years ago, dopamine neurons have the ability to cotransmit both dopamine and glutamate. Yet, the functional roles of dopamine neuron glutamate cotransmission and their implications for therapeutic use are just emerging. This review article encompasses the current body of evidence investigating the functions of dopamine neurons of the ventral midbrain that cotransmit glutamate. Since its discovery in dopamine neuron cultures, further work in vivo confirmed dopamine neuron glutamate cotransmission across species. From there, growing interest has led to research related to neural functioning including roles in synaptic signaling, development, and behavior. Functional connectome mapping reveals robust connections in multiple forebrain regions to various cell types, most notably to cholinergic interneurons in both the medial shell of the nucleus accumbens and the lateral dorsal striatum. Glutamate markers in dopamine neurons reach peak levels during embryonic development and increase in response to various toxins, suggesting dopamine neuron glutamate cotransmission may serve neuroprotective roles. Findings from behavioral analyses reveal prominent roles for dopamine neuron glutamate cotransmission in responses to psychostimulants, in positive valence and cognitive systems and for subtle roles in negative valence systems. Insight into dopamine neuron glutamate cotransmission informs the pathophysiology of neuropsychiatric disorders such as addiction, schizophrenia and Parkinson Disease, with therapeutic implications.

Macroscopic Structural and Connectome Mapping of the Mouse Brain Using Diffusion Magnetic Resonance Imaging.

Translational work in rodents elucidates basic mechanisms that drive complex behaviors relevant to psychiatric and neurological conditions. Nonetheless, numerous promising studies in rodents later fail in clinical trials, highlighting the need for improving the translational utility of preclinical studies in rodents. Imaging of small rodents provides an important strategy to address this challenge, as it enables a whole-brain unbiased search for structural and dynamic changes that can be directly compared to human imaging. The functional significance of structural changes identified using imaging can then be further investigated using molecular and genetic tools available for the mouse. Here, we describe a pipeline for unbiased search and characterization of structural changes and network properties, based on diffusion MRI data covering the entire mouse brain at an isotropic resolution of 100 µm. We first used unbiased whole-brain voxel-based analyses to identify volumetric and microstructural alterations in the brain of adult mice exposed to unpredictable postnatal stress (UPS), which is a mouse model of complex early life stress (ELS). Brain regions showing structural abnormalities were used as nodes to generate a grid for assessing structural connectivity and network properties based on graph theory. The technique described here can be broadly applied to understand brain connectivity in other mouse models of human disorders, as well as in genetically modified mouse strains. Graphic abstract: Pipeline for characterizing structural connectome in the mouse brain using diffusion magnetic resonance imaging. Scale bar = 1 mm.

Uncovering the genetic blueprint of the C. elegans nervous system

Despite rapid advances in connectome mapping and neuronal genetics, we lack theoretical and computational tools to unveil, in an experimentally testable fashion, the genetic mechanisms that govern neuronal wiring. Here we introduce a computational framework to link the adjacency matrix of a connectome to the expression patterns of its neurons, helping us uncover a set of genetic rules that govern the interactions between neurons in contact. The method incorporates the biological realities of the system, accounting for noise from data collection limitations, as well as spatial restrictions. The resulting methodology allows us to infer a network of 19 innexin interactions that govern the formation of gap junctions in Caenorhabditis elegans, five of which are already supported by experimental data. As advances in single-cell gene expression profiling increase the accuracy and the coverage of the data, the developed framework will allow researchers to systematically infer experimentally testable connection rules, offering mechanistic predictions for synapse and gap junction formation.

Topographic organization of the human subcortex unveiled with functional connectivity gradients.

Brain atlases are fundamental to understanding the topographic organization of the human brain, yet many contemporary human atlases cover only the cerebral cortex, leaving the subcortex a terra incognita. We use functional MRI (fMRI) to map the complex topographic organization of the human subcortex, revealing large-scale connectivity gradients and new areal boundaries. We unveil four scales of subcortical organization that recapitulate well-known anatomical nuclei at the coarsest scale and delineate 27 new bilateral regions at the finest. Ultrahigh field strength fMRI corroborates and extends this organizational structure, enabling the delineation of finer subdivisions of the hippocampus and the amygdala, while task-evoked fMRI reveals a subtle subcortical reorganization in response to changing cognitive demands. A new subcortical atlas is delineated, personalized to represent individual differences and used to uncover reproducible brain-behavior relationships. Linking cortical networks to subcortical regions recapitulates a task-positive to task-negative axis. This new atlas enables holistic connectome mapping and characterization of cortico-subcortical connectivity.

Personalized Connectome Mapping to Guide Targeted Therapy and Promote Recovery of Consciousness in the Intensive Care Unit

There are currently no therapies proven to promote early recovery of consciousness in patients with severe brain injuries in the intensive care unit (ICU). For patients whose families face time-sensitive, life-or-death decisions, treatments that promote recovery of consciousness are needed to reduce the likelihood of premature withdrawal of life-sustaining therapy, facilitate autonomous self-expression, and increase access to rehabilitative care. Here, we present the Connectome-based Clinical Trial Platform (CCTP), a new paradigm for developing and testing targeted therapies that promote early recovery of consciousness in the ICU. We report the protocol for STIMPACT (Stimulant Therapy Targeted to Individualized Connectivity Maps to Promote ReACTivation of Consciousness), a CCTP-based trial in which intravenous methylphenidate will be used for targeted stimulation of dopaminergic circuits within the subcortical ascending arousal network (ClinicalTrials.gov NCT03814356). The scientific premise of the CCTP and the STIMPACT trial is that personalized brain network mapping in the ICU can identify patients whose connectomes are amenable to neuromodulation. Phase 1 of the STIMPACT trial is an open-label, safety and dose-finding study in 22 patients with disorders of consciousness caused by acute severe traumatic brain injury. Patients in Phase 1 will receive escalating daily doses (0.5-2.0mg/kg) of intravenous methylphenidate over a 4-day period and will undergo resting-state functional magnetic resonance imaging and electroencephalography to evaluate the drug's pharmacodynamic properties. The primary outcome measure for Phase 1 relates to safety: the number of drug-related adverse events at each dose. Secondary outcome measures pertain to pharmacokinetics and pharmacodynamics: (1) time to maximal serum concentration; (2) serum half-life; (3) effect of the highest tolerated dose on resting-state functional MRI biomarkers of connectivity; and (4) effect of each dose on EEG biomarkers of cerebral cortical function. Predetermined safety and pharmacodynamic criteria must be fulfilled in Phase 1 to proceed to Phase 2A. Pharmacokinetic data from Phase 1 will also inform the study design of Phase 2A, where we will test the hypothesis that personalized connectome maps predict therapeutic responses to intravenous methylphenidate. Likewise, findings from Phase 2A will inform the design of Phase 2B, where we plan to enroll patients based on their personalized connectome maps. By selecting patients for clinical trials based on a principled, mechanistic assessment of their neuroanatomic potential for a therapeutic response, the CCTP paradigm and the STIMPACT trial have the potential to transform the therapeutic landscape in the ICU and improve outcomes for patients with severe brain injuries.

Neuronal Network Topology Indicates Distinct Recovery Processes after Stroke.

Despite substantial recent progress in network neuroscience, the impact of stroke on the distinct features of reorganizing neuronal networks during recovery has not been defined. Using a functional connections-based approach through 2-photon in vivo calcium imaging at the level of single neurons, we demonstrate for the first time the functional connectivity maps during motion and nonmotion states, connection length distribution in functional connectome maps and a pattern of high clustering in motor and premotor cortical networks that is disturbed in stroke and reconstitutes partially in recovery. Stroke disrupts the network topology of connected inhibitory and excitatory neurons with distinct patterns in these 2 cell types and in different cortical areas. These data indicate that premotor cortex displays a distinguished neuron-specific recovery profile after stroke.

Connectome mapping with edge density imaging differentiates pediatric mild traumatic brain injury from typically developing controls: proof of concept.

Although acute neurologic impairment might be transient, other long-term effects can be observed with mild traumatic brain injury. However, when pediatric patients with mild traumatic brain injury present for medical care, conventional imaging with CT and MR imaging often does not reveal abnormalities. To determine whether edge density imaging can separate pediatric mild traumatic brain injury from typically developing controls. Subjects were recruited as part of the "Therapeutic Resources for Attention Improvement using Neuroimaging in Traumatic Brain Injury" (TRAIN-TBI) study. We included 24 adolescents (χ=14.1years of age, σ=1.6years, range 10-16years), 14 with mild traumatic brain injury (TBI) and 10 typically developing controls. Neurocognitive assessments included the pediatric version of the California Verbal Learning Test (CVLT) and the Attention Network Task (ANT). Diffusion MR imaging was acquired on a 3-tesla (T) scanner. Edge density images were computed utilizing fiber tractography. Principal component analysis (PCA) and support vector machines (SVM) were used in an exploratory analysis to separate mild TBI and control groups. The diagnostic accuracy of edge density imaging, neurocognitive tests, and fractional anisotropy (FA) from diffusion tensor imaging (DTI) was computed with two-sample t-tests and receiver operating characteristic (ROC) metrics. Support vector machine-principal component analysis of edge density imaging maps identified three white matter regions distinguishing pediatric mild TBI from controls. The bilateral tapetum, sagittal stratum, and callosal splenium identified mild TBI subjects with sensitivity of 79% and specificity of 100%. Accuracy from the area under the ROC curve (AUC) was 94%. Neurocognitive testing provided an AUC of 61% (CVLT) and 71% (ANT). Fractional anisotropy yielded an AUC of 48%. In this proof-of-concept study, we show that edge density imaging is a new form of connectome mapping that provides better diagnostic delineation between pediatric mild TBI and healthy controls than DTI or neurocognitive assessments of memory or attention.

Deep brain stimulation: Imaging on a group level

Deep Brain Stimulation (DBS) is an established treatment option for movement disorders and is under investigation for treatment in a growing number of other brain diseases. It has been shown that exact electrode placement crucially affects the efficacy of DBS and this should be considered when investigating novel indications or DBS targets. To measure clinical improvement as a function of electrode placement, neuroscientific methodology and specialized software tools are needed. Such tools should have the goal to make electrode placement comparable across patients and DBS centers, and include statistical analysis options to validate and define optimal targets. Moreover, to allow for comparability across different centers, these need to be performed within an algorithmically and anatomically standardized and openly available group space. With the publication of Lead-DBS software in 2014, an open-source tool was introduced that allowed for precise electrode reconstructions based on pre- and postoperative neuroimaging data. Here, we introduce Lead Group, implemented within the Lead-DBS environment and specifically designed to meet aforementioned demands. In the present article, we showcase the various processing streams of Lead Group in a retrospective cohort of 51 patients suffering from Parkinson’s disease, who were implanted with DBS electrodes to the subthalamic nucleus (STN). Specifically, we demonstrate various ways to visualize placement of all electrodes in the group and map clinical improvement values to subcortical space. We do so by using active coordinates and volumes of tissue activated, showing converging evidence of an optimal DBS target in the dorsolateral STN. Second, we relate DBS outcome to the impact of each electrode on local structures by measuring overlap of stimulation volumes with the STN. Finally, we explore the software functions for connectomic mapping, which may be used to relate DBS outcomes to connectivity estimates with remote brain areas. The manuscript is accompanied by a walkthrough tutorial which allows users to reproduce all main results presented here. All data and code needed to reproduce results are openly available.

Measuring Cognitive Flexibility with the Flexible Item Selection Task: From fMRI Adaptation to Individual Connectome Mapping.

Cognitive flexibility, the ability to appropriately adjust behavior in a changing environment, has been challenging to operationalize and validate in cognitive neuroscience studies. Here, we investigate neural activation and directed functional connectivity underlying cognitive flexibility using an fMRI-adapted version of the Flexible Item Selection Task (FIST) in adults (n = 32, ages 19-46 years). The fMRI-adapted FIST was reliable, showed comparable performance to the computer-based version of the task, and produced robust activation in frontoparietal, anterior cingulate, insular, and subcortical regions. During flexibility trials, participants directly engaged the left inferior frontal junction, which influenced activity in other cortical and subcortical regions. The strength of intrinsic functional connectivity between select brain regions was related to individual differences in performance on the FIST, but there was also significant individual variability in functional network topography supporting cognitive flexibility. Taken together, these results suggest that the FIST is a valid measure of cognitive flexibility, which relies on computations within a broad corticosubcortical network driven by inferior frontal junction engagement.

Towards deep learning for connectome mapping: A block decomposition framework

We propose a new framework to map structural connectomes using deep learning and diffusion MRI. We show that our framework not only enables connectome mapping with a convolutional neural network (CNN), but can also be straightforwardly incorporated into conventional connectome mapping pipelines to enhance accuracy. Our framework involves decomposing the entire brain volume into overlapping blocks. Blocks are sufficiently small to ensure that a CNN can be efficiently trained to predict each block’s internal connectivity architecture. We develop a block stitching algorithm to rebuild the full brain volume from these blocks and thereby map end-to-end connectivity matrices. To evaluate our block decomposition and stitching (BDS) framework independent of CNN performance, we first map each block’s internal connectivity using conventional streamline tractography. Performance is evaluated using simulated diffusion MRI data generated from numerical connectome phantoms with known ground truth connectivity. Due to the redundancy achieved by allowing blocks to overlap, we find that our block decomposition and stitching steps per se can enhance the accuracy of probabilistic and deterministic tractography algorithms by up to 20–30%. Moreover, we demonstrate that our framework can improve the strength of structure-function coupling between in vivo diffusion and functional MRI data. We find that structural brain networks mapped with deep learning correlate more strongly with functional brain networks (r ​= ​0.45) than those mapped with conventional tractography (r ​= ​0.36). In conclusion, our BDS framework not only enables connectome mapping with deep learning, but its two constituent steps can be straightforwardly incorporated as part of conventional connectome mapping pipelines to enhance accuracy.

Extracellular recording of direct synaptic signals with a CMOS-nanoelectrode array.

The synaptic connections between neurons are traditionally determined by correlating the action potentials (APs) of a pre-synaptic neuron and small-amplitude subthreshold potentials of a post-synaptic neuron using invasive intracellular techniques, such as patch clamping. Extracellular recording by a microelectrode array can non-invasively monitor network activities of a large number of neurons, but its reduced sensitivity usually prevents direct measurements of synaptic signals. Here, we demonstrate that a newly developed complementary metal-oxide-semiconductor (CMOS) nanoelectrode array (CNEA) is capable of extracellularly determining direct synaptic connections in dense, multi-layer cultures of dissociated rat neurons. We spatiotemporally correlate action potential signals of hundreds of active neurons, detect small (∼1 pA after averaging) extracellular synaptic signals at the region where pre-synaptic axons and post-synaptic dendrites/somas overlap, and use those signals to map synaptic connections. We use controlled stimulation to assess stimulation-dependent synaptic strengths and to titrate a synaptic blocker (CNQX: IC50 ∼ 1 μM). The new capabilities demonstrated here significantly enhance the utilities of CNEAs in connectome mapping and drug screening applications.

Toward an MRI-Based Mesoscale Connectome of the Squid Brain.

SummaryUsing high-resolution diffusion magnetic resonance imaging (dMRI) and a suite of old and new staining techniques, the beginnings of a multi-scale connectome map of the squid brain is erected. The first of its kind for a cephalopod, this includes the confirmation of 281 known connections with the addition of 145 previously undescribed pathways. These and other features suggest a suite of functional attributes, including (1) retinotopic organization through the optic lobes and into other brain areas well beyond that previously recognized, (2) a level of complexity and sub-division in the basal lobe supporting ideas of convergence with the vertebrate basal ganglia, and (3) differential lobe-dependent growth rates that mirror complexity and transitions in ontogeny.

Network communication models improve the behavioral and functional predictive utility of the human structural connectome.

The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35–65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome.