Specific Connectivity Patterns Research Articles

T175. The Effects of Lorazepam on Resting-State Functional Connectivity

Benzodiazepines, such as lorazepam, are commonly used to treat anxiety. The pharmacological mechanism of action of benzodiazepines is well understood; however, it remains unclear which neural networks and systems are involved during resting-state functional connectivity. The objective of this study is to investigate the effects of lorazepam administration (12 hours or less prior to rsMRI) compared to non-lorazepam during the resting state in patients with psychosis. We focused our analyses on the effects of lorazepam on corticostriatal connectivity, as we have previously demonstrated that a specific pattern of striatal connectivity, the striatal connectivity index (SCI; Sarpal et al., 2016), was related to antipsychotic drug response.

Connectivity between the visual word form area and the parietal lobe improves after the first year of reading instruction: a longitudinal MRI study in children.

Shortly after reading instruction, a region in the ventral occipital temporal cortex (vOTC) of the left hemisphere, the Visual Word Form Area (VWFA), becomes specialized for written words. Its reproducible location across scripts suggests important anatomical constraints, such as specific patterns of connectivity, notably to spoken language areas. Here, we explored the structural connectivity of the emerging VWFA in terms of its specificity relative to other ventral visual regions and its stability throughout the process of reading instruction in ten children studied longitudinally over 2years. Category-specific regions for words, houses, faces, and tools were identified in the left vOTC of each subject with functional MRI. With diffusion MRI and tractography, we reconstructed the connections of these regions at two time points (mean age ± standard deviation: 6.2 ± 0.3, 7.2 ± 0.4years). We first showed that the regions for each visual category harbor their own specific connectivity, all of which precede reading instruction and remain stable throughout development. The most specific connections of the VWFA were to the dorsal posterior parietal cortex. We then showed that microstructural changes in these connections correlated with improvements in reading scores over the first year of instruction but not 1 year later in a subsample of eight children (age: 8.4 ± 0.3years). These results suggest that the VWFA location depends on its connectivity to distant regions, in particular, the left inferior parietal region which may play a crucial role in visual field maps and eye movement dynamics in addition to attentional control in letter-by-letter reading and disambiguation of mirror-letters during the first stages of learning to read.

Contaminant source localization via Bayesian global optimization

Abstract. Contaminant source localization problems require efficient and robust methods that can account for geological heterogeneities and accommodate relatively small data sets of noisy observations. As realism commands hi-fidelity simulations, computation costs call for global optimization algorithms under parsimonious evaluation budgets. Bayesian optimization approaches are well adapted to such settings as they allow the exploration of parameter spaces in a principled way so as to iteratively locate the point(s) of global optimum while maintaining an approximation of the objective function with an instrumental quantification of prediction uncertainty. Here, we adapt a Bayesian optimization approach to localize a contaminant source in a discretized spatial domain. We thus demonstrate the potential of such a method for hydrogeological applications and also provide test cases for the optimization community. The localization problem is illustrated for cases where the geology is assumed to be perfectly known. Two 2-D synthetic cases that display sharp hydraulic conductivity contrasts and specific connectivity patterns are investigated. These cases generate highly nonlinear objective functions that present multiple local minima. A derivative-free global optimization algorithm relying on a Gaussian process model and on the expected improvement criterion is used to efficiently localize the point of minimum of the objective functions, which corresponds to the contaminant source location. Even though concentration measurements contain a significant level of proportional noise, the algorithm efficiently localizes the contaminant source location. The variations of the objective function are essentially driven by the geology, followed by the design of the monitoring well network. The data and scripts used to generate objective functions are shared to favor reproducible research. This contribution is important because the functions present multiple local minima and are inspired from a practical field application. Sharing these complex objective functions provides a source of test cases for global optimization benchmarks and should help with designing new and efficient methods to solve this type of problem.

How larval traits of six flatfish species impact connectivity

AbstractConnectivity and dispersal are important factors for ecosystem dynamics, conservation, and resource management. Dispersal and recruitment success are determined in early life for many marine species. For those larvae that are pelagic, transport from spawning to nursery grounds is driven by hydrodynamic processes. Other environmental factors such as temperature and biological factors such as ecophysiology, behavior, and reproductive strategy (spawning period and spawning grounds) influence the final dispersal pattern and larval survival. We utilized a Lagrangian particle tracking model coupled with a three‐dimensional hydrodynamic model (Larvae&Co) to assess the connectivity patterns between spawning and nursery grounds of six commercially exploited flatfish species in the North Sea over a 10‐yr period (1997–2006). Standardized analyses have highlighted how spawning and nursery grounds are connected under the combined pressure of environment and life‐history traits. Results showed that the six flatfishes can be divided in two groups, each with their specific connectivity patterns. Turbot, common sole, and brill live in two subpopulations in the North Sea; common dab, European flounder, and European plaice represent a single mixed population. In general, the modeled and genetic patterns match, hence showing the strong impact of larval connectivity. The large overlap in connectivity for species that spawn during the same period and the seasonal change in hydrodynamics highlight the strong impact of a summer front in larval dispersal. Our results prove that individual‐based modeling is a powerful tool to guide resource management, even in cases of limited biological information.

Expression and Roles of the Immunoglobulin Superfamily Recognition Molecule Sidekick1 in Mouse Retina.

Processes of >100 types of interneurons (bipolar and amacrine cells) and projection neurons (retinal ganglion cells, RGCs) form specific and stereotyped patterns of connections in the inner plexiform layer (IPL) of the mouse retina. Four closely related homophilic immunoglobulin superfamily recognition molecules (Sidekick [Sdk] 1, Sdk 2, Dscam, and DscamL1) have been shown to play roles in patterning neuronal arbors and connections in chick retina, and all but Sdk1 have been shown to play related roles in mice. Here, we compare patterns of Sdk1 and Sdk2 expression in mouse retina and use genetic methods to assess roles of Sdk1. In adult retina, 3 neuronal types express sdk1 but not sdk2 at detectable levels, 5 express sdk2 but not sdk1 and 3 express both. Patterns of gene expression and protein localization at or near synapses are established during the first postnatal week. Dendrites of amacrine cells and RGCs that express sdk1 but not sdk2 arborize in the same narrow stratum in the center of the IPL. In the absence of Sdk1, this laminar restriction is degraded. Overexpression of sdk1 in developing cells that normally express sdk2 reorients their dendrites to resemble those of endogenously Sdk1-positive cells, indicating that Sdk1 plays an instructive role in patterning the IPL. Sdk1 fails to affect arbors when introduced after they are mature, suggesting that it is required to form but not maintain laminar restrictions. The effect of ectopically expressed sdk1 requires the presence of endogenous Sdk1, suggesting that the effect requires homophilic interactions among Sdk1-positive neurites. Together with previous results on Sdk2, Dscam, DscamL1, as well as the related Contactins, our results support the idea that an elaborate immunoglobulin superfamily code plays a prominent role in establishing neural circuits in the retina by means of tightly regulated cell type-specific expression and homophilically restricted intercellular interactions.

Connectivity patterns in contrasting types of tableland sandstone relief revealed by Topographic Wetness Index

Recognition of structural connectivity is particularly challenging in terrains lacking a hierarchical fluvial system, but typified by strong bedrock control, extreme ruggedness of relief, the presence of sinks (closed depressions) as in karst, or considerably modified by anthropogenic interventions. In this paper, the issue of connectivity mapping in such a very rugged terrain – a sedimentary tableland underlain by sandstones, mudstones and marls – is addressed. Three specific geomorphic contexts were selected for detailed study. These are steep escarpments, canyon-riddled cuesta backslopes, and residual tabular hills (mesas), with relative relief of 100–300 m. This work is primarily based on geomorphometric approach, with the Topographic Wetness Index used as a tool to recognize pathways of water and possible sediment transfer across the sandstone tableland. In addition, maps of distribution of closed depressions were generated. High resolution (1 m) digital terrain models provided input topographical data. The results of desk research were verified by the results of field work aimed at recognition of visible signatures of geomorphological connectivity in the physical landscape. Specific connectivity patterns were identified for each setting, with two common features being strong structural control due to regular joint pattern and the presence of numerous sinks, resulting in widespread surface disconnectivity. Furthermore, differences between structural and functional connectivity emerge. The latter occurs very rarely, during rather infrequent extreme precipitation events, and there are only a few evident, permanent sediment transfer pathways in the areas subject to analysis. The presence of well-jointed and porous sandstones accounts for drainage diversion underground and restricted surface runoff from the tableland and hence, for an underdeveloped network of perennial streams and clearly identifiable valley morphology.

How do spatially distinct frequency specific MEG networks emerge from one underlying structural connectome? The role of the structural eigenmodes

Functional networks obtained from magnetoencephalography (MEG) from different frequency bands show distinct spatial patterns. It remains to be elucidated how distinct spatial patterns in MEG networks emerge given a single underlying structural network. Recent work has suggested that the eigenmodes of the structural network might serve as a basis set for functional network patterns in the case of functional MRI. Here, we take this notion further in the context of frequency band specific MEG networks. We show that a selected set of eigenmodes of the structural network can predict different frequency band specific networks in the resting state, ranging from delta (1–4 Hz) to the high gamma band (40–70 Hz). These predictions outperform predictions based from surrogate data, suggesting a genuine relationship between eigenmodes of the structural network and frequency specific MEG networks. We then show that the relevant set of eigenmodes can be excited in a network of neural mass models using linear stability analysis only by including delays. Excitation of an eigenmode in this context refers to a dynamic instability of a network steady state to a spatial pattern with a corresponding coherent temporal oscillation. Simulations verify the results from linear stability analysis and suggest that theta, alpha and beta band networks emerge very near to the bifurcation. The delta and gamma bands in the resting state emerges further away from the bifurcation. These results show for the first time how delayed interactions can excite the relevant set of eigenmodes that give rise to frequency specific functional connectivity patterns.

Shared and specific functional connectivity alterations in unmedicated bipolar and major depressive disorders based on the triple-network model.

Bipolar disorder (BD) is frequently misdiagnosed as major depressive disorder (MDD) in clinical practice, especially during depressive episodes. A unifying triple-network model, involving the default mode network (DMN), central executive network (CEN) and salience network (SN), has been proposed to explain the neural physiopathology of psychiatric and neurological disorders. Although several studies revealed shared and specific alterations between BD and MDD in key regions of DMN, CEN, and SN, and a few studies used different measures to detect detailed alterations in the triple networks in BD and MDD, their shared and specific patterns of altered functional connectivity (FC) in the triple networks has remained unclear. In this study, we acquired resting-state fMRI (R-fMRI) data from 38 unmedicated BD and 35 unmedicated MDD patients during depressive episodes along with 47 healthy controls. We first determined the spatially independent components of the DMN, SN, and CEN by using independent component analysis (ICA); then we estimated the inter-ROI and inter-network FC for each group. By comparing the differences between the three groups, we obtained the following results: (1) both the BD and MDD patients showed shared weaker intra-network FC in the left mPFC and right precuneus within the DMN as well as weaker inter-ROI FC between the left AI and right AI compared with the healthy controls; (2) the BD had weaker while the MDD had stronger intra-network FC in the right dlPFC within the rCEN as well as stronger inter-ROI FC between the right dlPFC and right ANG compared with the healthy controls; (3) the BD showed specific, stronger inter-ROI FC between the left PPC and right AI as well as stronger inter-network FC between the lCEN and SN compared with either the MDD or the control group. Our findings provide new information for understanding the neural physiopathology and clinical symptoms of depressed BD and MDD patients.

Distinct Patterns of Atypical Functional Connectivity in Lower-Functioning Autism

BackgroundFunctional magnetic resonance imaging research on autism spectrum disorders (ASDs) has been largely limited to individuals with near-average intelligence. Although cognitive impairment is common in ASDs, functional network connectivity in this population remains poorly understood. Specifically, it remains unknown whether lower-functioning individuals exhibit exacerbated connectivity abnormalities similar to those previously detected in higher-functioning samples or specific divergent patterns of connectivity. MethodsResting-state functional magnetic resonance imaging data from 88 children (44 ASD, 44 typically developing; average age: 11 years) were included. Based on IQ, individuals with ASDs were assigned to either a lower-functioning group (mean IQ = 77 ± 6) or a higher-functioning group (mean IQ = 123 ± 8). Two typically developing comparison groups were matched to these groups on head motion, handedness, and age. Seeds in the medial prefrontal cortex, posterior cingulate cortex, posterior superior temporal sulcus, insula, and amygdala were used to contrast whole-brain functional connectivity across groups. ResultsLower-functioning ASD participants (compared with higher-functioning ASD participants) showed significant underconnectivity within the default mode network and the ventral visual stream. Higher-functioning ASD participants (compared with matched typically developing participants) showed significantly decreased anticorrelations among default mode, salience, and task-positive regions. Effect sizes of detected differences were large (Cohen’s d > 1.46). ConclusionsLower- and higher-functioning individuals with ASDs demonstrated distinct patterns of atypical connectivity. Findings suggest a gross pattern of predominantly reduced network integration in lower-functioning ASDs (affecting default mode and visual networks) and predominantly reduced network segregation in higher-functioning ASDs. Results indicate the need for stratification by general functional level in studies of functional connectivity in ASDs.

Functional and anatomical connectivity-based parcellation of human cingulate cortex.

IntroductionHuman cingulate cortex (CC) has been implicated in many functions, which is highly suggestive of the existence of functional subregions.MethodsIn this study, we used resting‐state functional magnetic resonance imaging (rs‐fMRI) and diffusion tensor imaging (DTI) to parcellate the human cingulate cortex (CC) based on resting‐state functional connectivity (rsFC) patterns and anatomical connectivity (AC) patterns, to analyze the rsFC patterns and the AC patterns of different subregions, and to recognize whether the parcellation results obtained by the two different methods were consistent.ResultsThe CC was divided into six functional subregions, including the anterior cingulate cortex, dorsal anterior midcingulate cortex, ventral anterior midcingulate cortex, posterior midcingulate cortex, dorsal posterior cingulate cortex, and ventral posterior cingulate cortex. The CC was also divided into ten anatomical subregions, termed Subregion 1 (S1) to Subregion 10 (S10). Each subregion showed specific connectivity patterns, although the functional subregions and the anatomical subregions were internally consistent.ConclusionsUsing different model MRI images, we established a parcellation scheme, which is internally consistent for the human CC, which may provide an in vivo guide for subregion‐level studies and improve our understanding of this brain area at subregional levels.

Brain responses during strategic online gaming of varying proficiencies: Implications for better gaming.

BackgroundOnline gaming is a complex and competitive activity. However, little attention has been paid to brain activities relating to gaming proficiency.MethodsIn the current study, fMRI data were obtained from 70 subjects while they were playing online games. Based on their playing, we selected 24 clips from each subject for three levels of gaming proficiency (good, poor, and average), with each clip lasting for 8 seconds.ResultsWhen comparing the brain responses during the three conditions, good‐play trials, relative to poor‐ or average‐play trials, were associated with greater activation of the declive, postcentral gyrus, and striatum. In post‐hoc analyses taking the identified clusters as regions of interest to calculate their functional connectivity, activation of the declive during good‐play conditions was associated with that in the precentral gyrus and thalamus, and activation in the striatum was associated with that in the inferior frontal gyrus and middle frontal cortex.ConclusionsTaken together, findings suggest specific regional brain activations and functional connectivity patterns involving brain regions and circuits involved in sensory, motor, automatic and executive functioning and their coordination are associated with better gaming. Specifically, for basic functions, such as simple reaction, motor control, and motor coordination, people need to perform them automatically; for highly cognitive functions, such as plan and strategic playing, people need to engage more executive functions in finishing these works. The automatically processed basic functions spare cognitive resources for the highly cognitive functions, which facilitates their gaming behaviors.

Sparse bursts optimize information transmission in a multiplexed neural code

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets.

Multivariate pattern classification of brain white matter connectivity predicts classic trigeminal neuralgia.

Trigeminal neuralgia (TN) is a severe form of chronic facial neuropathic pain. Increasing interest in the neuroimaging of pain has highlighted changes in the root entry zone in TN, but also group-level central nervous system gray and white matter (WM) abnormalities. Group differences in neuroimaging data are frequently evaluated with univariate statistics; however, this approach is limited because it is based on single, or clusters of, voxels. By contrast, multivariate pattern analyses consider all the model's neuroanatomical features to capture a specific distributed spatial pattern. This approach has potential use as a prediction tool at the individual level. We hypothesized that a multivariate pattern classification method can distinguish specific patterns of abnormal WM connectivity of classic TN from healthy controls (HCs). Diffusion-weighted scans in 23 right-sided TN and matched controls were processed to extract whole-brain interregional streamlines. We used a linear support vector machine algorithm to differentiate interregional normalized streamline count between TN and HC. This algorithm successfully differentiated between TN and HC with an accuracy of 88%. The structural pattern emphasized WM connectivity of regions that subserve sensory, affective, and cognitive dimensions of pain, including the insula, precuneus, inferior and superior parietal lobules, and inferior and medial orbital frontal gyri. Normalized streamline counts were associated with longer pain duration and WM metric abnormality between the connections. This study demonstrates that machine-learning algorithms can detect characteristic patterns of structural alterations in TN and highlights the role of structural brain imaging for identification of neuroanatomical features associated with neuropathic pain disorders.

Multiple-Brain Connectivity During Third Party Punishment: an EEG Hyperscanning Study

Compassion is a particular form of empathic reaction to harm that befalls others and is accompanied by a desire to alleviate their suffering. This altruistic behavior is often manifested through altruistic punishment, wherein individuals penalize a deprecated human’s actions, even if they are directed toward strangers. By adopting a dual approach, we provide empirical evidence that compassion is a multifaceted prosocial behavior and can predict altruistic punishment. In particular, in this multiple-brain connectivity study in an EEG hyperscanning setting, compassion was examined during real-time social interactions in a third-party punishment (TPP) experiment. We observed that specific connectivity patterns were linked to behavioral and psychological intra- and interpersonal factors. Thus, our results suggest that an ecological approach based on simultaneous dual-scanning and multiple-brain connectivity is suitable for analyzing complex social phenomena.

Thalamocortical Projection Neuron and Interneuron Numbers in the Visual Thalamic Nuclei of the Adult C57BL/6 Mouse.

A key parameter to constrain predictive, bottom-up circuit models of a given brain domain is the number and position of the neuronal populations involved. These include not only the neurons whose bodies reside within the domain, but also the neurons in distant regions that innervate the domain. The mouse visual cortex receives its main subcortical input from the dorsal lateral geniculate nucleus (dLGN) and the lateral posterior (LP) complex of the thalamus. The latter consists of three different nuclei: lateral posterior lateral (LPL), lateral posterior medial rostral (LPMR), and lateral posterior medial caudal (LPMC), each exhibiting specific patterns of connections with the various visual cortical areas. Here, we have determined the number of thalamocortical projection neurons and interneurons in the LP complex and dLGN of the adult C57BL/6 male mouse. We combined Nissl staining and histochemical and immunolabeling methods for consistently delineating nuclei borders, and applied unbiased stereological cell counting methods. Thalamic interneurons were identified using GABA immunolabeling. The C57BL/6 dLGN contains ∼21,200 neurons, while LP complex contains ∼31,000 total neurons. The dLGN and LP are the only nuclei of the mouse dorsal thalamus containing substantial numbers GABA-immunoreactive interneurons. These interneurons, however, are scarcer than previously estimated; they are 5.6% of dLGN neurons and just 1.9% of the LP neurons. It can be thus inferred that the dLGN contains ∼20,000 and the LP complex ∼30,400 thalamocortical projection neurons (∼12,000 in LPL, 15,200 in LPMR, and 4,200 in LPMC). The present dataset is relevant for constraining models of mouse visual thalamocortical circuits, as well as for quantitative comparisons between genetically modified mouse strains, or across species.

Regional homogeneity and functional connectivity patterns in major depressive disorder, cognitive vulnerability to depression and healthy subjects

BackgroundCognitive vulnerability to depression (CVD) is a high risk for depressive disorder. Recent studies focus on individuals with CVD to determine the neural basis of major depressive disorder (MDD) neuropathology. However, whether CVD showed specific or similar brain functional activity and connectivity patterns, compared to MDD, remain largely unknown. MethodsHere, using resting-state functional magnetic resonance imaging in subjects with CVD, healthy controls (HC) and MDD, regional homogeneity (ReHo) and resting-state functional connectivity (R-FC) analyses were conducted to assess local synchronization and changes in functional connectivity patterns. ResultsSignificant ReHo differences were found in right posterior lobe of cerebellum (PLC), left lingual gyrus (LG) and precuneus. Compared to HC, CVD subjects showed increased ReHo in the PLC, which was similar to the difference found between MDD and HC. Compared to MDD patients, CVD subjects showed decreased ReHo in PLC, LG, and precuneus. R-FC analyses found increased functional connections between LG and left inferior parietal lobule, posterior cingulate cortex, and dorsolateral prefrontal cortex in CVD compared to both HC and MDD. Moreover, Regional mean ReHo values were positively correlated with Center for Epidemiologic Studies Depression Scale scores. ConclusionThese analyses revealed that PLC and functional connections between LG and left inferior parietal lobule, posterior cingulate cortex, and dorsolateral prefrontal cortex may be a potential marker for CVD.

Teneurin-3 controls topographic circuit assembly in the hippocampus.

Brain functions rely on specific patterns of connectivity. Teneurins are evolutionarily conserved transmembrane proteins that instruct synaptic partner matching in Drosophila and are required for vertebrate visual system development. The roles of vertebrate teneurins in connectivity beyond the visual system remain largely unknown and their mechanisms of action have not been demonstrated. Here we show that mouse teneurin-3 is expressed in multiple topographically interconnected areas of the hippocampal region, including proximal CA1, distal subiculum, and medial entorhinal cortex. Viral-genetic analyses reveal that teneurin-3 is required in both CA1 and subicular neurons for the precise targeting of proximal CA1 axons to distal subiculum. Furthermore, teneurin-3 promotes homophilic adhesion in vitro in a splicing isoform-dependent manner. These findings demonstrate striking genetic heterogeneity across multiple hippocampal areas and suggest that teneurin-3 may orchestrate the assembly of a complex distributed circuit in the mammalian brain via matching expression and homophilic attraction.

Characterizing synchrony patterns across cognitive task stages of associative recognition memory.

Numerous studies seek to understand the role of oscillatory synchronization in cognition. This problem is particularly challenging in the context of complex cognitive behavior, which consists of a sequence of processing steps with uncertain duration. In this study, we analyzed oscillatory connectivity measures in time windows that previous computational models had associated with a specific sequence of processing steps in an associative memory recognition task (visual encoding, familiarity, memory retrieval, decision making, and motor response). The timing of these processing steps was estimated on a single‐trial basis with a novel hidden semi‐Markov model multivariate pattern analysis (HSMM‐MVPA) method. We show that different processing stages are associated with specific patterns of oscillatory connectivity. Visual encoding is characterized by a dense network connecting frontal, posterior, and temporal areas as well as frontal and occipital phase locking in the 4–9 Hz theta band. Familiarity is associated with frontal phase locking in the 9–14 Hz alpha band. Decision making is associated with frontal and temporo‐central interhemispheric connections in the alpha band. During decision making, a second network in the theta band that connects left‐temporal, central, and occipital areas bears similarity to the neural signature for preparing a motor response. A similar theta band network is also present during the motor response, with additionally alpha band connectivity between right‐temporal and posterior areas. This demonstrates that the processing stages discovered with the HSMM‐MVPA method are indeed linked to distinct synchronization patterns, leading to a closer understanding of the functional role of oscillations in cognition.

Heparan Sulfate Proteoglycans as Emerging Players in Synaptic Specificity.

Neural circuits consist of distinct neuronal cell types connected in specific patterns. The specificity of these connections is achieved in a series of sequential developmental steps that involve the targeting of neurites, the identification of synaptic partners, and the formation of specific types of synapses. Cell-surface proteins play a critical role in each of these steps. The heparan sulfate proteoglycan (HSPG) family of cell-surface proteins is emerging as a key regulator of connectivity. HSPGs are expressed throughout brain development and play important roles in axon guidance, synapse development and synapse function. New insights indicate that neuronal cell types express unique combinations of HSPGs and HS-modifying enzymes. Furthermore, HSPGs interact with cell type-specific binding partners to mediate synapse development. This suggests that cell type-specific repertoires of HSPGs and specific patterns of HS modifications on the cell surface are required for the development of specific synaptic connections. Genome-wide association studies have linked these proteins to neurodevelopmental and neuropsychiatric diseases. Thus, HSPGs play an important role in the development of specific synaptic connectivity patterns important for neural circuit function, and their dysfunction may be involved in the development of brain disorders.

Efficient encoding of motion is mediated by gap junctions in the fly visual system.

Understanding the computational implications of specific synaptic connectivity patterns is a fundamental goal in neuroscience. In particular, the computational role of ubiquitous electrical synapses operating via gap junctions remains elusive. In the fly visual system, the cells in the vertical-system network, which play a key role in visual processing, primarily connect to each other via axonal gap junctions. This network therefore provides a unique opportunity to explore the functional role of gap junctions in sensory information processing. Our information theoretical analysis of a realistic VS network model shows that within 10 ms following the onset of the visual input, the presence of axonal gap junctions enables the VS system to efficiently encode the axis of rotation, θ, of the fly’s ego motion. This encoding efficiency, measured in bits, is near-optimal with respect to the physical limits of performance determined by the statistical structure of the visual input itself. The VS network is known to be connected to downstream pathways via a subset of triplets of the vertical system cells; we found that because of the axonal gap junctions, the efficiency of this subpopulation in encoding θ is superior to that of the whole vertical system network and is robust to a wide range of signal to noise ratios. We further demonstrate that this efficient encoding of motion by this subpopulation is necessary for the fly's visually guided behavior, such as banked turns in evasive maneuvers. Because gap junctions are formed among the axons of the vertical system cells, they only impact the system’s readout, while maintaining the dendritic input intact, suggesting that the computational principles implemented by neural circuitries may be much richer than previously appreciated based on point neuron models. Our study provides new insights as to how specific network connectivity leads to efficient encoding of sensory stimuli.