Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

Structural connectivity changes in unilateral hearing loss

Network connectivity, as mapped by the whole brain connectome, plays a crucial role in regulating auditory function. Auditory deprivation such as unilateral hearing loss might alter structural network connectivity; however, these potential alterations are poorly understood. Thirty-seven acoustic neuroma patients with unilateral hearing loss (19 left-sided and 18 right-sided) and 19 healthy controls underwent diffusion-weighted and T1-weighted imaging to assess edge strength, node strength, and global efficiency of the structural connectome. Edge strength was estimated by pair-wise normalized streamline density from tractography and connectomics. Node strength and global efficiency were calculated through graph theory analysis of the connectome. Pure-tone audiometry and word recognition scores were used to correlate the degree and duration of unilateral hearing loss with node strength and global efficiency. We demonstrate significantly stronger edge strength and node strength through the visual network, weaker edge strength and node strength in the somatomotor network, and stronger global efficiency in the unilateral hearing loss patients. No discernible correlations were observed between the degree and duration of unilateral hearing loss and the measures of node strength or global efficiency. These findings contribute to our understanding of the role of structural connectivity in hearing by facilitating visual network upregulation and somatomotor network downregulation after unilateral hearing loss.

CDKL5 sculpts functional callosal connectivity to promote cognitive flexibility

Functional and structural connectivity alterations in short- and long-range projections have been reported across neurodevelopmental disorders (NDD). Interhemispheric callosal projection neurons (CPN) represent one of the major long-range projections in the brain, which are particularly important for higher-order cognitive function and flexibility. However, whether a causal relationship exists between interhemispheric connectivity alterations and cognitive deficits in NDD remains elusive. Here, we focused on CDKL5 Deficiency Disorder (CDD), a severe neurodevelopmental disorder caused by mutations in the X-linked Cyclin-dependent kinase-like 5 (CDKL5) gene. We found an increase in homotopic interhemispheric connectivity and functional hyperconnectivity across higher cognitive areas in adult male and female CDKL5-deficient mice by resting-state functional MRI (rs-fMRI) analysis. This was accompanied by an increase in the number of callosal synaptic inputs but decrease in local synaptic connectivity in the cingulate cortex of juvenile CDKL5-deficient mice, suggesting an impairment in excitatory synapse development and a differential role of CDKL5 across excitatory neuron subtypes. These deficits were associated with significant cognitive impairments in CDKL5 KO mice. Selective deletion of CDKL5 in the largest subtype of CPN likewise resulted in an increase of functional callosal inputs, without however significantly altering intracortical cingulate networks. Notably, such callosal-specific changes were sufficient to cause cognitive deficits. Finally, when CDKL5 was selectively re-expressed only in this CPN subtype, in otherwise CDKL5-deficient mice, it was sufficient to prevent the cognitive impairments of CDKL5 mutants. Together, these results reveal a novel role of CDKL5 by demonstrating that it is both necessary and sufficient for proper CPN connectivity and cognitive function and flexibility, and further validates a causal relationship between CPN dysfunction and cognitive impairment in a model of NDD.

The significance of structural rich club hubs for the processing of hierarchical stimuli

The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.

Cortical timescales and the modular organization of structural and functional brain networks

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

Connectome-based reservoir computing with the conn2res toolbox

The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses high-dimensional, nonlinear dynamical systems to perform computations and approximate cognitive functions. Here we present conn2res: an open-source Python toolbox for implementing biological neural networks as artificial neural networks. conn2res is modular, allowing arbitrary network architecture and dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from spiking neurons to memristive dynamics. The versatility of the conn2res toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, conn2res sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.

Towards a biologically annotated brain connectome

The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.

Neuromodulation of striatal D1 cells shapes BOLD fluctuations in anatomically connected thalamic and cortical regions

Understanding how the brain's macroscale dynamics are shaped by underlying microscale mechanisms is a key problem in neuroscience. In animal models, we can now investigate this relationship in unprecedented detail by directly manipulating cellular-level properties while measuring the whole-brain response using resting-state fMRI. Here, we focused on understanding how blood-oxygen-level-dependent (BOLD) dynamics, measured within a structurally well-defined striato-thalamo-cortical circuit in mice, are shaped by chemogenetically exciting or inhibiting D1 medium spiny neurons (MSNs) of the right dorsomedial caudate putamen (CPdm). We characterize changes in both the BOLD dynamics of individual cortical and subcortical brain areas, and patterns of inter-regional coupling (functional connectivity) between pairs of areas. Using a classification approach based on a large and diverse set of time-series properties, we found that CPdm neuromodulation alters BOLD dynamics within thalamic subregions that project back to dorsomedial striatum. In the cortex, changes in local dynamics were strongest in unimodal regions (which process information from a single sensory modality) and weakened along a hierarchical gradient towards transmodal regions. In contrast, a decrease in functional connectivity was observed only for cortico-striatal connections after D1 excitation. Our results show that targeted cellular-level manipulations affect local BOLD dynamics at the macroscale, such as by making BOLD dynamics more predictable over time by increasing its self-correlation structure. This contributes to ongoing attempts to understand the influence of structure-function relationships in shaping inter-regional communication at subcortical and cortical levels.

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6800 time-series features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is co-localized with multiple micro-architectural features, including gene expression gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

A systematic review of the relationship between magnetic resonance imaging based resting-state and structural networks in the rodent brain

Recent developments in rodent brain imaging have enabled translational characterization of functional and structural connectivity at the whole brain level in vivo. Nevertheless, fundamental questions about the link between structural and functional networks remain unsolved. In this review, we systematically searched for experimental studies in rodents investigating both structural and functional network measures, including studies correlating functional connectivity using resting-state functional MRI with diffusion tensor imaging or viral tracing data. We aimed to answer whether functional networks reflect the architecture of the structural connectome, how this reciprocal relationship changes throughout a disease, how structural and functional changes relate to each other, and whether changes follow the same timeline. We present the knowledge derived exclusively from studies that included in vivo imaging of functional and structural networks. The limited number of available reports makes it difficult to draw general conclusions besides finding a spatial and temporal decoupling between structural and functional networks during brain disease. Data suggest that when overcoming the currently limited evidence through future studies with combined imaging in various disease models, it will be possible to explore the interaction between both network systems as a disease or recovery biomarker.

Cortical timescales and the modular organization of structural and functional brain networks

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

Connectome-based prediction of functional impairment in experimental stroke models

Experimental rat models of stroke and hemorrhage are important tools to investigate cerebrovascular disease pathophysiology mechanisms, yet how significant patterns of functional impairment induced in various models of stroke are related to changes in connectivity at the level of neuronal populations and mesoscopic parcellations of rat brains remain unresolved. To address this gap in knowledge, we employed two middle cerebral artery occlusion models and one intracerebral hemorrhage model with variant extent and location of neuronal dysfunction. Motor and spatial memory function was assessed and the level of hippocampal activation via Fos immunohistochemistry. Contribution of connectivity change to functional impairment was analyzed for connection similarities, graph distances and spatial distances as well as the importance of regions in terms of network architecture based on the neuroVIISAS rat connectome. We found that functional impairment correlated with not only the extent but also the locations of the injury among the models. In addition, via coactivation analysis in dynamic rat brain models, we found that lesioned regions led to stronger coactivations with motor function and spatial learning regions than with other unaffected regions of the connectome. Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. Our study provides a comprehensive analytical framework in predictive identification of remote regions not directly altered by stroke events and their functional implication.

Functional brain networks reflect spatial and temporal autocorrelation

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler, low-dimensional statistics is largely unknown. To explore this question, we examined resting-state functional magnetic resonance imaging (rs-fMRI) data using complex topology measures from network neuroscience. Here we show that spatial and temporal autocorrelation are reliable statistics that explain numerous measures of network topology. Surrogate time series with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely used complexity measures may help link them to neurobiology.

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6 800 timeseries features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is colocalized with multiple micro-architectural features, including genomic gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

Bidirectionally connected cores in a mouse connectome: towards extracting the brain subnetworks essential for consciousness

Where in the brain consciousness resides remains unclear. It has been suggested that the subnetworks supporting consciousness should be bidirectionally (recurrently) connected because both feed-forward and feedback processing are necessary for conscious experience. Accordingly, evaluating which subnetworks are bidirectionally connected and the strength of these connections would likely aid the identification of regions essential to consciousness. Here, we propose a method for hierarchically decomposing a network into cores with different strengths of bidirectional connection, as a means of revealing the structure of the complex brain network. We applied the method to a whole-brain mouse connectome. We found that cores with strong bidirectional connections consisted of regions presumably essential to consciousness (e.g. the isocortical and thalamic regions, and claustrum) and did not include regions presumably irrelevant to consciousness (e.g. cerebellum). Contrarily, we could not find such correspondence between cores and consciousness when we applied other simple methods that ignored bidirectionality. These findings suggest that our method provides a novel insight into the relation between bidirectional brain network structures and consciousness.

Extracting Dynamical Understanding From Neural-Mass Models of Mouse Cortex

New brain atlases with high spatial resolution and whole-brain coverage have rapidly advanced our knowledge of the brain's neural architecture, including the systematic variation of excitatory and inhibitory cell densities across the mammalian cortex. But understanding how the brain's microscale physiology shapes brain dynamics at the macroscale has remained a challenge. While physiologically based mathematical models of brain dynamics are well placed to bridge this explanatory gap, their complexity can form a barrier to providing clear mechanistic interpretation of the dynamics they generate. In this work, we develop a neural-mass model of the mouse cortex and show how bifurcation diagrams, which capture local dynamical responses to inputs and their variation across brain regions, can be used to understand the resulting whole-brain dynamics. We show that strong fits to resting-state functional magnetic resonance imaging (fMRI) data can be found in surprisingly simple dynamical regimes-including where all brain regions are confined to a stable fixed point-in which regions are able to respond strongly to variations in their inputs, consistent with direct structural connections providing a strong constraint on functional connectivity in the anesthetized mouse. We also use bifurcation diagrams to show how perturbations to local excitatory and inhibitory coupling strengths across the cortex, constrained by cell-density data, provide spatially dependent constraints on resulting cortical activity, and support a greater diversity of coincident dynamical regimes. Our work illustrates methods for visualizing and interpreting model performance in terms of underlying dynamical mechanisms, an approach that is crucial for building explanatory and physiologically grounded models of the dynamical principles that underpin large-scale brain activity.

Network analysis of neuroimaging in mice

Graph theory allows assessing changes of neuronal connectivity and interactions of brain regions in response to local lesions, e.g., after stroke, and global perturbations, e.g., due to psychiatric dysfunctions or neurodegenerative disorders. Consequently, network analysis based on constructing graphs from structural and functional MRI connectivity matrices is increasingly used in clinical studies. In contrast, in mouse neuroimaging, the focus is mainly on basic connectivity parameters, i.e., the correlation coefficient or fiber counts, whereas more advanced network analyses remain rarely used. This review summarizes graph theoretical measures and their interpretation to describe networks derived from recent in vivo mouse brain studies. To facilitate the entry into the topic, we explain the related mathematical definitions, provide a dedicated software toolkit, and discuss practical considerations for the application to rs-fMRI and DTI. This way, we aim to foster cross-species comparisons and the application of standardized measures to classify and interpret network changes in translational brain disease studies.

Emerging imaging methods to study whole-brain function in rodent models

In the past decade, the idea that single populations of neurons support cognition and behavior has gradually given way to the realization that connectivity matters and that complex behavior results from interactions between remote yet anatomically connected areas that form specialized networks. In parallel, innovation in brain imaging techniques has led to the availability of a broad set of imaging tools to characterize the functional organization of complex networks. However, each of these tools poses significant technical challenges and faces limitations, which require careful consideration of their underlying anatomical, physiological, and physical specificity. In this review, we focus on emerging methods for measuring spontaneous or evoked activity in the brain. We discuss methods that can measure large-scale brain activity (directly or indirectly) with a relatively high temporal resolution, from milliseconds to seconds. We further focus on methods designed for studying the mammalian brain in preclinical models, specifically in mice and rats. This field has seen a great deal of innovation in recent years, facilitated by concomitant innovation in gene-editing techniques and the possibility of more invasive recordings. This review aims to give an overview of currently available preclinical imaging methods and an outlook on future developments. This information is suitable for educational purposes and for assisting scientists in choosing the appropriate method for their own research question.

Dynamical consequences of regional heterogeneity in the brain's transcriptional landscape

Brain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles or MRI-derived estimates of myeloarchitecture. We further show that regional transcriptional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional-activity time scales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptomic data to constrain models of large-scale brain function.

Topographic gradients of intrinsic dynamics across neocortex

The intrinsic dynamics of neuronal populations are shaped by both microscale attributes and macroscale connectome architecture. Here we comprehensively characterize the rich temporal patterns of neural activity throughout the human brain. Applying massive temporal feature extraction to regional haemodynamic activity, we systematically estimate over 6000 statistical properties of individual brain regions' time-series across the neocortex. We identify two robust spatial gradients of intrinsic dynamics, one spanning a ventromedial-dorsolateral axis and dominated by measures of signal autocorrelation, and the other spanning a unimodal-transmodal axis and dominated by measures of dynamic range. These gradients reflect spatial patterns of gene expression, intracortical myelin and cortical thickness, as well as structural and functional network embedding. Importantly, these gradients are correlated with patterns of meta-analytic functional activation, differentiating cognitive versus affective processing and sensory versus higher-order cognitive processing. Altogether, these findings demonstrate a link between microscale and macroscale architecture, intrinsic dynamics, and cognition.

Regional, Layer, and Cell-Type-Specific Connectivity of the Mouse Default Mode Network

The evolutionarily conserved default mode network (DMN) is a distributed set of brain regions coactivated during resting states that is vulnerable to brain disorders. How disease affects the DMN is unknown, but detailed anatomical descriptions could provide clues. Mice offer an opportunity to investigate structural connectivity of the DMN across spatial scales with cell-type resolution. We co-registered maps from functional magnetic resonance imaging and axonal tracing experiments into the 3D Allen mouse brain reference atlas. We find that the mouse DMN consists of preferentially interconnected cortical regions. As a population, DMN layer 2/3 (L2/3) neurons project almost exclusively to other DMN regions, whereas L5 neurons project in and out of the DMN. In the retrosplenial cortex, a core DMN region, we identify two L5 projection types differentiated by in- or out-DMN targets, laminar position, and gene expression. These results provide a multi-scale description of the anatomical correlates of the mouse DMN.

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Mouse resting-state default mode network anatomy described at high resolution in 3D

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Systematic axon tracing shows cortical DMN regions are preferentially interconnected

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Layer 2/3 DMN neurons project mostly in the DMN; layer 5 neurons project in and out

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Retrosplenial cortex contains distinct types of in- and out-DMN projection neurons

Reconsidering the Border between the Visual and Posterior Parietal Cortex of Mice

The posterior parietal cortex (PPC) contributes to multisensory and sensory-motor integration, as well as spatial navigation. Based on primate studies, the PPC is composed of several subdivisions with differing connection patterns, including areas that exhibit retinotopy. In mice the composition of the PPC is still under debate. We propose a revised anatomical delineation in which we classify the higher order visual areas rostrolateral area (RL), anteromedial area (AM), and Medio-Medial-Anterior cortex (MMA) as subregions of the mouse PPC. Retrograde and anterograde tracing revealed connectivity, characteristic for primate PPC, with sensory, retrosplenial, orbitofrontal, cingulate and motor cortex, as well as with several thalamic nuclei and the superior colliculus in the mouse. Regarding cortical input, RL receives major input from the somatosensory barrel field, while AM receives more input from the trunk, whereas MMA receives strong inputs from retrosplenial, cingulate, and orbitofrontal cortices. These input differences suggest that each posterior PPC subregion may have a distinct function. Summarized, we put forward a refined cortical map, including a mouse PPC that contains at least 6 subregions, RL, AM, MMA and PtP, MPta, LPta/A. These anatomical results set the stage for a more detailed understanding about the role that the PPC and its subdivisions play in multisensory integration-based behavior in mice.

Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

Intrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain's underlying structural connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale- related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6,000 time series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region's spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

Cortical Excitation:Inhibition Imbalance Causes Abnormal Brain Network Dynamics as Observed in Neurodevelopmental Disorders

Abnormal brain development manifests itself at different spatial scales. However, whether abnormalities at the cellular level can be diagnosed from network activity measured with functional magnetic resonance imaging (fMRI) is largely unknown, yet of high clinical relevance. Here a putative mechanism reported in neurodevelopmental disorders, that is, excitation-to-inhibition ratio (E:I), was chemogenetically increased within cortical microcircuits of the mouse brain and measured via fMRI. Increased E:I caused a significant "reduction" of long-range connectivity, irrespective of whether excitatory neurons were facilitated or inhibitory Parvalbumin (PV) interneurons were suppressed. Training a classifier on fMRI signals, we were able to accurately classify cortical areas exhibiting increased E:I. This classifier was validated in an independent cohort of Fmr1y/- knockout mice, a model for autism with well-documented loss of parvalbumin neurons and chronic alterations of E:I. Our findings demonstrate a promising novel approach towards inferring microcircuit abnormalities from macroscopic fMRI measurements.

Power spectrum of resting-state blood-oxygen-level-dependent signal

Hemodynamic modeling is used to explore the origin, predict, and analyze the power spectrum of the resting-state blood-oxygen-level-dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI), which has been reported to have a power-law form, i.e., P(f)∝f^{-s}, where P(f) is the power, f is the frequency, and s>0 is the power-law exponent. However, current fMRI experimental paradigms have limited acquisition durations, affecting the spectral resolution of fMRI data at the low-frequency regime. Here, the claimed power-law spectrum is investigated by using a recent hemodynamic model to analytically derive the BOLD power spectrum, with parameters that are related to neurophysiology. The theoretical results show that, for all realistic parameter combinations, the BOLD power spectrum is flat at f≲0.01Hz, has a weak resonance originating from intrinsic oscillations of vasodilatory response, and becomes a power law for high frequencies, all of which is in agreement with an empirical data set that describes the spectrum of one subject and brain region. However, the results are contrary to studies reporting a pure power-law spectrum at f≲0.2Hz. The discrepancy is attributed largely to data averaging employed by current approaches that averages together important properties of the BOLD power spectrum, such as its resonance, that biases the spectrum to only show a power law. Data averaging also reduces the high-frequency power-law exponent relative to individual cases. Overall, this work demonstrates how the model can reproduce BOLD dynamics and further analyze its low-frequency behavior. Moreover, it also uses the model to explain the impact of procedures, such as data averaging, on the reported features of the BOLD power spectrum.

Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis

Preclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories worldwide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a mouse default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. RSN spatial specificity was enhanced in datasets acquired at higher field strength, with cryoprobes, in ventilated animals, and under medetomidine-isoflurane combination sedation. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.

Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation

The dependence between pairs of time series is commonly quantified by Pearson's correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher's transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors - before or after Fisher's transformation - becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardised Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical "xDF" method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.

Cross-Species Evidence of Interplay Between Neural Connectivity at the Micro- and Macroscale of Connectome Organization in Human, Mouse, and Rat Brain

The mammalian brain describes a multiscale system. At the microscale, axonal, dendritic, and synaptic elements ensure neuron-to-neuron communication, and at the macroscale, large-scale projections form the anatomical wiring for communication between cortical areas. Although it is clear that both levels of neural organization play a crucial role in brain functioning, their interaction is not extensively studied. Connectome studies of the mammalian brain in cat, macaque, and human have recently shown that regions with larger and more complex pyramidal cells to have more macroscale corticocortical connections. In this study, we aimed to further validate these cross-scale findings in the human, mouse, and rat brain. We combined neuron reconstructions from the NeuroMorpho.org neuroarchitecture database with macroscale connectivity data derived from connectome mapping by means of tract-tracing (rat, mouse) and in vivo diffusion magnetic resonance imaging (human). Across these three mammalian species, we show cortical variation in neural organization to be associated with features of macroscale connectivity, with cortical variation in neuronal complexity explaining significant proportions of cortical variation in the number of white matter projections of cortical areas. Our findings converge on the notion of a relationship between features of micro- and macroscale neural connectivity to form a central aspect of mammalian neural architecture.

Multimodal gradients across mouse cortex

The primate cerebral cortex displays a hierarchy that extends from primary sensorimotor to association areas, supporting increasingly integrated function underpinned by a gradient of heterogeneity in the brain's microcircuits. The extent to which these hierarchical gradients are unique to primate or may reflect a conserved mammalian principle of brain organization remains unknown. Here we report the topographic similarity of large-scale gradients in cytoarchitecture, gene expression, interneuron cell densities, and long-range axonal connectivity, which vary from primary sensory to prefrontal areas of mouse cortex, highlighting an underappreciated spatial dimension of mouse cortical specialization. Using the T1-weighted:T2-weighted (T1w:T2w) magnetic resonance imaging map as a common spatial reference for comparison across species, we report interspecies agreement in a range of large-scale cortical gradients, including a significant correspondence between gene transcriptional maps in mouse cortex with their human orthologs in human cortex, as well as notable interspecies differences. Our results support the view of systematic structural variation across cortical areas as a core organizational principle that may underlie hierarchical specialization in mammalian brains.

Rich-club neurocircuitry: function, evolution, and vulnerability

Over the past decades, network neuroscience has played a fundamental role in the understanding of large-scale brain connectivity architecture. Brains, and more generally nervous systems, can be modeled as sets of elements (neurons, assemblies, or cortical chunks) that dynamically interact through a highly structured and adaptive neurocircuitry. An interesting property of neural networks is that elements rich in connections are central to the network organization and tend to interconnect strongly with each other, forming so-called rich clubs. The ubiquity of rich-club organization across different species and scales of investigation suggests that this topology could be a distinctive feature of biological systems with information processing capabilities. This review surveys recent neuroimaging, computational, and cross-species comparative literature to offer an insight into the function and origin of rich-club architecture in nervous systems, discussing its relevance to human cognition and behavior, and vulnerability to brain disorders.

High-resolution data-driven model of the mouse connectome

Knowledge of mesoscopic brain connectivity is important for understanding inter- and intraregion information processing. Models of structural connectivity are typically constructed and analyzed with the assumption that regions are homogeneous. We instead use the Allen Mouse Brain Connectivity Atlas to construct a model of whole-brain connectivity at the scale of 100 μm voxels. The data consist of 428 anterograde tracing experiments in wild type C57BL/6J mice, mapping fluorescently labeled neuronal projections brain-wide. Inferring spatial connectivity with this dataset is underdetermined, since the approximately 2 × 105 source voxels outnumber the number of experiments. To address this issue, we assume that connection patterns and strengths vary smoothly across major brain divisions. We model the connectivity at each voxel as a radial basis kernel-weighted average of the projection patterns of nearby injections. The voxel model outperforms a previous regional model in predicting held-out experiments and compared with a human-curated dataset. This voxel-scale model of the mouse connectome permits researchers to extend their previous analyses of structural connectivity to much higher levels of resolution, and it allows for comparison with functional imaging and other datasets.

Directed functional connectivity using dynamic graphical models

There are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity. DGMs are a multivariate graphical model with time-varying coefficients that describe instantaneous directed relationships between nodes. A further benefit of DGMs is that networks may contain loops and that large networks can be estimated. We use network simulations and human resting-state fMRI (N = 500) to investigate the validity and reliability of the estimated networks. We simulate systematic lags of the hemodynamic response at different brain regions to investigate how these lags potentially bias directionality estimates. In the presence of such lag confounds (0.4-0.8 s offset between connected nodes), our method has a sensitivity of 72%-77% to detect the true direction. Stronger lag confounds have reduced sensitivity, but do not increase false positives (i.e., directionality estimates of the opposite direction). In human resting-state fMRI, the default mode network has consistent influence on the cerebellar, the limbic and the auditory/temporal networks. We also show a consistent reciprocal relationship between the visual medial and visual lateral network. Finally, we apply the method in a small mouse fMRI sample and discover a highly plausible relationship between areas in the hippocampus feeding into the cingulate cortex. We provide a computationally efficient implementation of DGM as a free software package for R.

Dysfunctional Autism Risk Genes Cause Circuit-Specific Connectivity Deficits With Distinct Developmental Trajectories

Autism spectrum disorders (ASD) are a set of complex neurodevelopmental disorders for which there is currently no targeted therapeutic approach. It is thought that alterations of genes regulating migration and synapse formation during development affect neural circuit formation and result in aberrant connectivity within distinct circuits that underlie abnormal behaviors. However, it is unknown whether deviant developmental trajectories are circuit-specific for a given autism risk-gene. We used MRI to probe changes in functional and structural connectivity from childhood to adulthood in Fragile-X (Fmr1-/y) and contactin-associated (CNTNAP2-/-) knockout mice. Young Fmr1-/y mice (30 days postnatal) presented with a robust hypoconnectivity phenotype in corticocortico and corticostriatal circuits in areas associated with sensory information processing, which was maintained until adulthood. Conversely, only small differences in hippocampal and striatal areas were present during early postnatal development in CNTNAP2-/- mice, while major connectivity deficits in prefrontal and limbic pathways developed between adolescence and adulthood. These findings are supported by viral tracing and electron micrograph approaches and define 2 clearly distinct connectivity endophenotypes within the autism spectrum. We conclude that the genetic background of ASD strongly influences which circuits are most affected, the nature of the phenotype, and the developmental time course of the associated changes.

Estimating the impact of structural directionality: How reliable are undirected connectomes?

Directionality is a fundamental feature of network connections. Most structural brain networks are intrinsically directed because of the nature of chemical synapses, which comprise most neuronal connections. Because of the limitations of noninvasive imaging techniques, the directionality of connections between structurally connected regions of the human brain cannot be confirmed. Hence, connections are represented as undirected, and it is still unknown how this lack of directionality affects brain network topology. Using six directed brain networks from different species and parcellations (cat, mouse, C. elegans, and three macaque networks), we estimate the inaccuracies in network measures (degree, betweenness, clustering coefficient, path length, global efficiency, participation index, and small-worldness) associated with the removal of the directionality of connections. We employ three different methods to render directed brain networks undirected: (a) remove unidirectional connections, (b) add reciprocal connections, and (c) combine equal numbers of removed and added unidirectional connections. We quantify the extent of inaccuracy in network measures introduced through neglecting connection directionality for individual nodes and across the network. We find that the coarse division between core and peripheral nodes remains accurate for undirected networks. However, hub nodes differ considerably when directionality is neglected. Comparing the different methods to generate undirected networks from directed ones, we generally find that the addition of reciprocal connections (false positives) causes larger errors in graph-theoretic measures than the removal of the same number of directed connections (false negatives). These findings suggest that directionality plays an essential role in shaping brain networks and highlight some limitations of undirected connectomes.

The Microglial Innate Immune Receptor TREM2 Is Required for Synapse Elimination and Normal Brain Connectivity

The triggering receptor expressed on myeloid cells 2 (TREM2) is a microglial innate immune receptor associated with a lethal form of early, progressive dementia, Nasu-Hakola disease, and with an increased risk of Alzheimer's disease. Microglial defects in phagocytosis of toxic aggregates or apoptotic membranes were proposed to be at the origin of the pathological processes in the presence of Trem2 inactivating mutations. Here, we show that TREM2 is essential for microglia-mediated synaptic refinement during the early stages of brain development. The absence of Trem2 resulted in impaired synapse elimination, accompanied by enhanced excitatory neurotransmission and reduced long-range functional connectivity. Trem2^-/- mice displayed repetitive behavior and altered sociability. TREM2 protein levels were also negatively correlated with the severity of symptoms in humans affected by autism. These data unveil the role of TREM2 in neuronal circuit sculpting and provide the evidence for the receptor's involvement in neurodevelopmental diseases.

What We Know About the Brain Structure-Function Relationship

How the human brain works is still a question, as is its implication with brain architecture: the non-trivial structure–function relationship. The main hypothesis is that the anatomic architecture conditions, but does not determine, the neural network dynamic. The functional connectivity cannot be explained only considering the anatomical substrate. This involves complex and controversial aspects of the neuroscience field and that the methods and methodologies to obtain structural and functional connectivity are not always rigorously applied. The goal of the present article is to discuss about the progress made to elucidate the structure–function relationship of the Central Nervous System, particularly at the brain level, based on results from human and animal studies. The current novel systems and neuroimaging techniques with high resolutive physio-structural capacity have brought about the development of an integral framework of different structural and morphometric tools such as image processing, computational modeling and graph theory. Different laboratories have contributed with in vivo, in vitro and computational/mathematical models to study the intrinsic neural activity patterns based on anatomical connections. We conclude that multi-modal techniques of neuroimaging are required such as an improvement on methodologies for obtaining structural and functional connectivity. Even though simulations of the intrinsic neural activity based on anatomical connectivity can reproduce much of the observed patterns of empirical functional connectivity, future models should be multifactorial to elucidate multi-scale relationships and to infer disorder mechanisms.

Timescales of Intrinsic BOLD Signal Dynamics and Functional Connectivity in Pharmacologic and Neuropathologic States of Unconsciousness

Environmental events are processed on multiple timescales via hierarchical organization of temporal receptive windows (TRWs) in the brain. The dependence of neural timescales and TRWs on altered states of consciousness is unclear. States of reduced consciousness are marked by a shift toward slowing of neural dynamics (<1 Hz) in EEG/ECoG signals. We hypothesize that such prolongation of intrinsic timescales are also seen in blood-oxygen-level-dependent (BOLD) signals. To test this hypothesis, we measured the timescales of intrinsic BOLD signals using mean frequency (MF) and temporal autocorrelation (AC) in healthy volunteers (n = 23; male/female 14/9) during graded sedation with propofol. We further examined the relationship between the intrinsic timescales (local/voxel level) and its regional connectivity (across neighboring voxels; regional homogeneity, ReHo), global (whole-brain level) functional connectivity (GFC), and topographical similarity (Topo). Additional results were obtained from patients undergoing deep general anesthesia (n = 12; male/female: 5/7) and in patients with disorders of consciousness (DOC) (n = 21; male/female: 14/7). We found that MF, AC, and ReHo increased, whereas GFC and Topo decreased, during propofol sedation. The local alterations occur before changes of distant connectivity. Conversely, all of these parameters decreased in deep anesthesia and in patients with DOC. We conclude that propofol synchronizes local neuronal interactions and prolongs the timescales of intrinsic BOLD signals. These effects may impede communication among distant brain regions. Furthermore, the intrinsic timescales exhibit distinct dynamic signatures in sedation, deep anesthesia, and DOC. These results improve our understanding of the neural mechanisms of unconsciousness in pharmacologic and neuropathologic states.

SIGNIFICANCE STATEMENT Information processing in the brain occurs through a hierarchy of temporal receptive windows (TRWs) in multiple timescales. Anesthetic drugs induce a reversible suppression of consciousness and thus offer a unique opportunity to investigate the state dependence of neural timescales. Here, we demonstrate for the first time that sedation with propofol is accompanied by the prolongation of the timescales of intrinsic BOLD signals presumably reflecting enlarged TRWs. We show that this is accomplished by an increase of local and regional signal synchronization, effects that may disrupt information exchange among distant brain regions. Furthermore, we show that the timescales of intrinsic BOLD signals exhibit distinct dynamic signatures in sedation, deep anesthesia, and disorders of consciousness.

Visibility graphs for fMRI data: Multiplex temporal graphs and their modulations across resting-state networks

Visibility algorithms are a family of methods that map time series into graphs, such that the tools of graph theory and network science can be used for the characterization of time series. This approach has proved a convenient tool, and visibility graphs have found applications across several disciplines. Recently, an approach has been proposed to extend this framework to multivariate time series, allowing a novel way to describe collective dynamics. Here we test their application to fMRI time series, following two main motivations, namely that (a) this approach allows vs to simultaneously capture and process relevant aspects of both local and global dynamics in an easy and intuitive way, and (b) this provides a suggestive bridge between time series and network theory that nicely fits the consolidating field of network neuroscience. Our application to a large open dataset reveals differences in the similarities of temporal networks (and thus in correlated dynamics) across resting-state networks, and gives indications that some differences in brain activity connected to psychiatric disorders could be picked up by this approach.

Structural and functional, empirical and modeled connectivity in the cerebral cortex of the rat

Connectomics data from animal models provide an invaluable opportunity to reveal the complex interplay between structure and function in the mammalian brain. In this work, we investigate the relationship between structural and functional connectivity in the rat brain cortex using a directed anatomical network generated from a carefully curated meta-analysis of published tracing data, along with resting-state functional MRI data obtained from a group of 14 anesthetized Wistar rats. We found a high correspondence between the strength of functional connections, measured as blood oxygen level dependent (BOLD) signal correlations between cortical regions, and the weight of the corresponding anatomical links in the connectome graph (maximum Spearman rank-order correlation ρ=0.48). At the network-level, regions belonging to the same functionally defined community tend to form more mutual weighted connections between each other compared to regions located in different communities. We further found that functional communities in resting-state networks are enriched in densely connected anatomical motifs. Importantly, these higher-order structural subgraphs cannot be explained by lower-order topological properties, suggesting that dense structural patterns support functional associations in the resting brain. Simulations of brain-wide resting-state activity based on neural mass models implemented on the empirical rat anatomical connectome demonstrated high correlation between the simulated and the measured functional connectivity (maximum Pearson correlation ρ=0.53), further suggesting that the topology of structural connections plays an important role in shaping functional cortical networks.

Structural Basis of Large-Scale Functional Connectivity in the Mouse

Translational neuroimaging requires approaches and techniques that can bridge between multiple different species and disease states. One candidate method that offers insights into the brain's functional connectivity (FC) is resting-state fMRI (rs-fMRI). In both humans and nonhuman primates, patterns of FC (often referred to as the functional connectome) have been related to the underlying structural connectivity (SC; also called the structural connectome). Given the recent rise in preclinical neuroimaging of mouse models, it is an important question whether the mouse functional connectome conforms to the underlying SC. Here, we compared FC derived from rs-fMRI in female mice with the underlying monosynaptic structural connectome as provided by the Allen Brain Connectivity Atlas. We show that FC between interhemispheric homotopic cortical and hippocampal areas, as well as in cortico-striatal pathways, emerges primarily via monosynaptic structural connections. In particular, we demonstrate that the striatum (STR) can be segregated according to differential rs-fMRI connectivity patterns that mirror monosynaptic connectivity with isocortex. In contrast, for certain subcortical networks, FC emerges along polysynaptic pathways as shown for left and right STR, which do not share direct anatomical connections, but high FC is putatively driven by a top-down cortical control. Finally, we show that FC involving cortico-thalamic pathways is limited, possibly confounded by the effect of anesthesia, small regional size, and tracer injection volume. These findings provide a critical foundation for using rs-fMRI connectivity as a translational tool to study complex brain circuitry interactions and their pathology due to neurological or psychiatric diseases across species.SIGNIFICANCE STATEMENT A comprehensive understanding of how the anatomical architecture of the brain, often referred to as the "connectome," corresponds to its function is arguably one of the biggest challenges for understanding the brain and its pathologies. Here, we use the mouse as a model for comparing functional connectivity (FC) derived from resting-state fMRI with gold standard structural connectivity measures based on tracer injections. In particular, we demonstrate high correspondence between FC measurements of cortico-cortical and cortico-striatal regions and their anatomical underpinnings. This work provides a critical foundation for studying the pathology of these circuits across mouse models and human patients.