Network structure of the mouse brain connectome with voxel resolution

A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity

The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features-e.g. diffusion parameters-or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. We find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples, and the networks maintain a number of desirable features. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.

Rethinking dopamine-guided action sequence learning

As opposed to those requiring a single action for reward acquisition, tasks necessitating action sequences demand that animals learn action elements and their sequential order and sustain the behaviour until the sequence is completed. With repeated learning, animals not only exhibit precise execution of these sequences but also demonstrate enhanced smoothness and efficiency. Previous research has demonstrated that midbrain dopamine and its major projection target, the striatum, play crucial roles in these processes. Recent studies have shown that dopamine from the substantia nigra pars compacta (SNc) and the ventral tegmental area (VTA) serve distinct functions in action sequence learning. The distinct contributions of dopamine also depend on the striatal subregions, namely the ventral, dorsomedial and dorsolateral striatum. Here, we have reviewed recent findings on the role of striatal dopamine in action sequence learning, with a focus on recent rodent studies.

Spatial-Temporal Analysis of Neural Desynchronization in Sleeplike States Reveals Critical Dynamics

Sleep is characterized by nonrapid eye movement sleep, originating from widespread neuronal synchrony, and rapid eye movement sleep, with neuronal desynchronization akin to waking behavior. While these were thought to be global brain states, recent research suggests otherwise. Using time-frequency analysis of mesoscopic voltage-sensitive dye recordings of mice in a urethane-anesthetized model of sleep, we find transient neural desynchronization occurring heterogeneously across the cortex within a background of synchronized neural activity, in a manner reminiscent of a critical spreading process and indicative of an "edge-of-synchronization" phase transition.

Altered asymmetry of functional connectome gradients in major depressive disorder

Introduction

Major depressive disorder (MDD) is a debilitating disease involving sensory and higher-order cognitive dysfunction. Previous work has shown altered asymmetry in MDD, including abnormal lateralized activation and disrupted hemispheric connectivity. However, it remains unclear whether and how MDD affects functional asymmetries in the context of intrinsic hierarchical organization.

Methods

Here, we evaluate intra- and inter-hemispheric asymmetries of the first three functional gradients, characterizing unimodal-transmodal, visual-somatosensory, and somatomotor/default mode-multiple demand hierarchies, to study MDD-related alterations in overarching system-level architecture.

Results

We find that, relative to the healthy controls, MDD patients exhibit alterations in both primary sensory regions (e.g., visual areas) and transmodal association regions (e.g., default mode areas). We further find these abnormalities are woven in heterogeneous alterations along multiple functional gradients, associated with cognitive terms involving mind, memory, and visual processing. Moreover, through an elastic net model, we observe that both intra- and inter-asymmetric features are predictive of depressive traits measured by BDI-II scores.

Discussion

Altogether, these findings highlight a broad and mixed effect of MDD on functional gradient asymmetry, contributing to a richer understanding of the neurobiological underpinnings in MDD.

Cell-type-specific optogenetic fMRI on basal forebrain reveals functional network basis of behavioral preference

The basal forebrain (BF) is a complex structure that plays key roles in regulating various brain functions. However, it remains unclear how cholinergic and non-cholinergic BF neurons modulate large-scale functional networks and their relevance in intrinsic and extrinsic behaviors. With an optimized awake mouse optogenetic fMRI approach, we revealed that optogenetic stimulation of four BF neuron types evoked distinct cell-type-specific whole-brain BOLD activations, which could be attributed to BF-originated low-dimensional structural networks. Additionally, optogenetic activation of VGLUT2, ChAT, and PV neurons in the BF modulated the preference for locomotion, exploration, and grooming, respectively. Furthermore, we uncovered the functional network basis of the above BF-modulated behavioral preference through a decoding model linking the BF-modulated BOLD activation, low-dimensional structural networks, and behavioral preference. To summarize, we decoded the functional network basis of differential behavioral preferences with cell-type-specific optogenetic fMRI on the BF and provided an avenue for investigating mouse behaviors from a whole-brain view.

Space wandering in the rodent default mode network

The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here, we use multisite GCaMP (a genetically encoded calcium indicator) fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats. We examined neural dynamics in three core DMN nodes-the retrosplenial cortex, cingulate cortex, and prelimbic cortex-as well as the anterior insula node of the salience network, and their association with the rats' spatial exploration behaviors. We found that DMN nodes displayed a hierarchical functional organization during spatial exploration, characterized by stronger coupling with each other than with the anterior insula. Crucially, these DMN nodes encoded the kinematics of spatial exploration, including linear and angular velocity. Additionally, we identified latent brain states that encoded distinct patterns of time-varying exploration behaviors and found that higher linear velocity was associated with enhanced DMN activity, heightened synchronization among DMN nodes, and increased anticorrelation between the DMN and anterior insula. Our findings highlight the involvement of the DMN in collectively and dynamically encoding spatial exploration in a real-world setting. Our findings challenge the notion that the DMN is primarily a "task-negative" network disengaged from the external world. By illuminating the DMN's role in naturalistic behaviors, our study underscores the importance of investigating brain network function in ecologically valid contexts.

Non-homogenous axonal bouton distribution in whole-brain single-cell neuronal networks

We examined the distribution of pre-synaptic contacts in axons of mouse neurons and constructed whole-brain single-cell neuronal networks using an extensive dataset of 1,891 fully reconstructed neurons. We found that bouton locations were not homogeneous throughout the axon and among brain regions. As our algorithm was able to generate whole-brain single-cell connectivity matrices from full morphology reconstruction datasets, we further found that non-homogeneous bouton locations have a significant impact on network wiring, including degree distribution, triad census, and community structure. By perturbing neuronal morphology, we further explored the link between anatomical details and network topology. In our in silico exploration, we found that dendritic and axonal tree span would have the greatest impact on network wiring, followed by synaptic contact deletion. Our results suggest that neuroanatomical details must be carefully addressed in studies of whole-brain networks at the single-cell level.

An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control

Mean-field models have been developed to replicate key features of epileptic seizure dynamics. However, the precise mechanisms and the role of the brain area responsible for seizure onset and propagation remain incompletely understood. In this study, we employ computational methods within The Virtual Brain framework and the Epileptor model to explore how the location and connectivity of an Epileptogenic Zone (EZ) in a mouse brain are related to focal seizures (seizures that start in one brain area and may or may not remain localized), with a specific focus on the hippocampal region known for its association with epileptic seizures. We then devise computational strategies to confine seizures (prevent widespread propagation), simulating medical-like treatments such as tissue resection and the application of an anti-seizure drugs or neurostimulation to suppress hyperexcitability. Through selectively removing (blocking) specific connections informed by the structural connectome and graph network measurements or by locally reducing outgoing connection weights of EZ areas, we demonstrate that seizures can be kept constrained around the EZ region. We successfully identified the minimal connections necessary to prevent widespread seizures, with a particular focus on minimizing surgical or medical intervention while simultaneously preserving the original structural connectivity and maximizing brain functionality.

Intrinsic Functional Connectivity between the Anterior Insular and Retrosplenial Cortex as a Moderator and Consequence of Cocaine Self-Administration in Rats

While functional brain imaging studies in humans suggest that chronic cocaine use alters functional connectivity (FC) within and between key large-scale brain networks, including the default mode network (DMN), the salience network (SN), and the central executive network (CEN), cross-sectional studies in humans are challenging to obtain brain FC prior to cocaine use. Such information is critical to reveal the relationship between individual's brain FC and the subsequent development of cocaine dependence and brain changes during abstinence. Here, we performed a longitudinal study examining functional magnetic resonance imaging (fMRI) data in male rats (n = 7), acquired before cocaine self-administration (baseline), on 1 d of abstinence following 10 d of cocaine self-administration, and again after 30 d of experimenter-imposed abstinence. Using repeated-measures analysis of variance (ANOVA) with network-based statistics (NBS), significant connectivity changes were found between anterior insular cortex (AI) of the SN, retrosplenial cortex (RSC) of the DMN, somatosensory cortex, and caudate-putamen (CPu), with AI-RSC FC showing the most robust changes between baseline and 1 d of abstinence. Additionally, the level of escalated cocaine intake is associated with AI-RSC and AI-CPu FC changes between 1 d and 30 d of abstinence; further, the subjects' AI-RSC FC prior to cocaine intake is a significant moderator for the AI-RSC changes during abstinence. These results provide novel insights into the roles of AI-RSC FC before and after cocaine intake and suggest this circuit to be a potential target to modulate large-scale network and associated behavioral changes in cocaine use disorders.

Macroscale coupling between structural and effective connectivity in the mouse brain

Exploring how the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the major goals of modern neuroscience. At the macroscale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be considered: the directionality of the structural connectome and limitations in explaining networks functions through an undirected measure such as FC. Here, we employed an accurate directed SC of the mouse brain acquired through viral tracers and compared it with single-subject effective connectivity (EC) matrices derived from a dynamic causal model (DCM) applied to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their respective couplings by conditioning on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks; only within sensory motor networks did we observe connections that align in terms of both effective and structural strength.

Fragmentation of functional resting state brain networks in a transgenic mouse model of tau pathology: A metabolic connectivity study using [18F]FDG-PET

In a previous study, regional reductions in cerebral glucose metabolism have been demonstrated in the tauopathy mouse model rTg4510 (Endepols et al., 2022). Notably, glucose hypometabolism was present in some brain regions without co-localized synaptic degeneration measured with [18F]UCB-H. We hypothesized that in those regions hypometabolism may reflect reduced functional connectivity rather than synaptic damage. To test this hypothesis, we performed seed-based metabolic connectivity analyses using [18F]FDG-PET data in this mouse model. Eight rTg4510 mice at the age of seven months and 8 non-transgenic littermates were injected intraperitoneally with 11.1 ± 0.8 MBq [18F]FDG and spent a 35-min uptake period awake in single cages. Subsequently, they were anesthetized and measured in a small animal PET scanner for 30 min. Three seed-based connectivity analyses were performed per group. Seeds were selected for apparent mismatch between [18F]FDG and [18F]UCB-H. A seed was placed either in the medial orbitofrontal cortex, dorsal hippocampus or dorsal thalamus, and correlated with all other voxels of the brain across animals. In the control group, the emerging correlative pattern was strongly overlapping for all three seed locations, indicating a uniform fronto-thalamo-hippocampal resting state network. In contrast, rTg4510 mice showed three distinct networks with minimal overlap. Frontal and thalamic networks were greatly diminished. The hippocampus, however, formed a new network with the whole parietal cortex. We conclude that resting-state functional networks are fragmented in the brain of rTg4510 mice. Thus, hypometabolism can be explained by reduced functional connectivity of brain areas devoid of tau-related pathology, such as the thalamus.

Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains

Previous studies have adopted an edge-centric framework to study fine-scale network dynamics in human fMRI. To date, however, no studies have applied this framework to data collected from model organisms. Here, we analyze structural and functional imaging data from lightly anesthetized mice through an edge-centric lens. We find evidence of "bursty" dynamics and events - brief periods of high-amplitude network connectivity. Further, we show that on a per-frame basis events best explain static FC and can be divided into a series of hierarchically-related clusters. The co-fluctuation patterns associated with each cluster centroid link distinct anatomical areas and largely adhere to the boundaries of algorithmically detected functional brain systems. We then investigate the anatomical connectivity undergirding high-amplitude co-fluctuation patterns. We find that events induce modular bipartitions of the anatomical network of inter-areal axonal projections. Finally, we replicate these same findings in a human imaging dataset. In summary, this report recapitulates in a model organism many of the same phenomena observed in previously edge-centric analyses of human imaging data. However, unlike human subjects, the murine nervous system is amenable to invasive experimental perturbations. Thus, this study sets the stage for future investigation into the causal origins of fine-scale brain dynamics and high-amplitude co-fluctuations. Moreover, the cross-species consistency of the reported findings enhances the likelihood of future translation.

Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization

Large-scale functional networks have been characterized in both rodent and human brains, typically by analyzing fMRI-BOLD signals. However, the relationship between fMRI-BOLD and underlying neural activity is complex and incompletely understood, which poses challenges to interpreting network organization obtained using this technique. Additionally, most work has assumed a disjoint functional network organization (i.e., brain regions belong to one and only one network). Here, we employ wide-field Ca2+ imaging simultaneously with fMRI-BOLD in mice expressing GCaMP6f in excitatory neurons. We determine cortical networks discovered by each modality using a mixed-membership algorithm to test the hypothesis that functional networks exhibit overlapping organization. We find that there is considerable network overlap (both modalities) in addition to disjoint organization. Our results show that multiple BOLD networks are detected via Ca2+ signals, and networks determined by low-frequency Ca2+ signals are only modestly more similar to BOLD networks. In addition, the principal gradient of functional connectivity is nearly identical for BOLD and Ca2+ signals. Despite similarities, important differences are also detected across modalities, such as in measures of functional connectivity strength and diversity. In conclusion, Ca2+ imaging uncovers overlapping functional cortical organization in the mouse that reflects several, but not all, properties observed with fMRI-BOLD signals.

Mapping and comparing fMRI connectivity networks across species

Technical advances in neuroimaging, notably in fMRI, have allowed distributed patterns of functional connectivity to be mapped in the human brain with increasing spatiotemporal resolution. Recent years have seen a growing interest in extending this approach to rodents and non-human primates to understand the mechanism of fMRI connectivity and complement human investigations of the functional connectome. Here, we discuss current challenges and opportunities of fMRI connectivity mapping across species. We underscore the critical importance of physiologically decoding neuroimaging measures of brain (dys)connectivity via multiscale mechanistic investigations in animals. We next highlight a set of general principles governing the organization of mammalian connectivity networks across species. These include the presence of evolutionarily conserved network systems, a dominant cortical axis of functional connectivity, and a common repertoire of topographically conserved fMRI spatiotemporal modes. We finally describe emerging approaches allowing comparisons and extrapolations of fMRI connectivity findings across species. As neuroscientists gain access to increasingly sophisticated perturbational, computational and recording tools, cross-species fMRI offers novel opportunities to investigate the large-scale organization of the mammalian brain in health and disease.

Exercise alters cortico-basal ganglia network metabolic connectivity: a mesoscopic level analysis informed by anatomic parcellation defined in the mouse brain connectome

The basal ganglia are important modulators of the cognitive and motor benefits of exercise. However, the neural networks underlying these benefits remain poorly understood. Our study systematically analyzed exercise-associated changes in metabolic connectivity in the cortico-basal ganglia-thalamic network during the performance of a new motor task, with regions-of-interest defined based on mesoscopic domains recently defined in the mouse brain structural connectome. Mice were trained on a motorized treadmill for six weeks or remained sedentary (control), thereafter undergoing [14C]-2-deoxyglucose metabolic brain mapping during wheel walking. Regional cerebral glucose uptake (rCGU) was analyzed in 3-dimensional brains reconstructed from autoradiographic brain sections using statistical parametric mapping. Metabolic connectivity was assessed by calculating inter-regional correlation of rCGU cross-sectionally across subjects within a group. Compared to controls, exercised animals showed broad decreases in rCGU in motor areas, but increases in limbic areas, as well as the visual and association cortices. In addition, exercised animals showed (i) increased positive metabolic connectivity within and between the motor cortex and caudoputamen (CP), (ii) newly emerged negative connectivity of the substantia nigra pars reticulata with the globus pallidus externus, and CP, and (iii) reduced connectivity of the prefrontal cortex (PFC). Increased metabolic connectivity in the motor circuit in the absence of increases in rCGU strongly suggests greater network efficiency, which is also supported by the reduced involvement of PFC-mediated cognitive control during the performance of a new motor task. Our study delineates exercise-associated changes in functional circuitry at the subregional level and provides a framework for understanding the effects of exercise on functions of the cortico-basal ganglia-thalamic network.

Microglia complement signaling promotes neuronal elimination and normal brain functional connectivity

Complement signaling is thought to serve as an opsonization signal to promote the phagocytosis of synapses by microglia. However, while its role in synaptic remodeling has been demonstrated in the retino-thalamic system, it remains unclear whether complement signaling mediates synaptic pruning in the brain more generally. Here we found that mice lacking the Complement receptor 3, the major microglia complement receptor, failed to show a deficit in either synaptic pruning or axon elimination in the developing mouse cortex. Instead, mice lacking Complement receptor 3 exhibited a deficit in the perinatal elimination of neurons in the cortex, a deficit that is associated with increased cortical thickness and enhanced functional connectivity in these regions in adulthood. These data demonstrate a role for complement in promoting neuronal elimination in the developing cortex.

Whole-Brain Evaluation of Cortical Microconnectomes

The brain is an organ that functions as a network of many elements connected in a nonuniform manner. In the brain, the neocortex is evolutionarily newest and is thought to be primarily responsible for the high intelligence of mammals. In the mature mammalian brain, all cortical regions are expected to have some degree of homology, but have some variations of local circuits to achieve specific functions performed by individual regions. However, few cellular-level studies have examined how the networks within different cortical regions differ. This study aimed to find rules for systematic changes of connectivity (microconnectomes) across 16 different cortical region groups. We also observed unknown trends in basic parameters in vitro such as firing rate and layer thickness across brain regions. Results revealed that the frontal group shows unique characteristics such as dense active neurons, thick cortex, and strong connections with deeper layers. This suggests the frontal side of the cortex is inherently capable of driving, even in isolation and that frontal nodes provide the driving force generating a global pattern of spontaneous synchronous activity, such as the default mode network. This finding provides a new hypothesis explaining why disruption in the frontal region causes a large impact on mental health.

A parcellation scheme of mouse isocortex based on reversals in connectivity gradients

The brain is composed of several anatomically clearly separated structures. This parcellation is often extended into the isocortex, based on anatomical, physiological, or functional differences. Here, we derive a parcellation scheme based purely on the spatial structure of long-range synaptic connections within the cortex. To that end, we analyzed a publicly available dataset of average mouse brain connectivity, and split the isocortex into disjunct regions. Instead of clustering connectivity based on modularity, our scheme is inspired by methods that split sensory cortices into subregions where gradients of neuronal response properties, such as the location of the receptive field, reverse. We calculated comparable gradients from voxelized brain connectivity data and automatically detected reversals in them. This approach better respects the known presence of functional gradients within brain regions than clustering-based approaches. Placing borders at the reversals resulted in a parcellation into 41 subregions that differs significantly from an established scheme in nonrandom ways, but is comparable in terms of the modularity of connectivity between regions. It reveals unexpected trends of connectivity, such as a tripartite split of somatomotor regions along an anterior to posterior gradient. The method can be readily adapted to other organisms and data sources, such as human functional connectivity.

A low dimensional embedding of brain dynamics enhances diagnostic accuracy and behavioral prediction in stroke

Large-scale brain networks reveal structural connections as well as functional synchronization between distinct regions of the brain. The latter, referred to as functional connectivity (FC), can be derived from neuroimaging techniques such as functional magnetic resonance imaging (fMRI). FC studies have shown that brain networks are severely disrupted by stroke. However, since FC data are usually large and high-dimensional, extracting clinically useful information from this vast amount of data is still a great challenge, and our understanding of the functional consequences of stroke remains limited. Here, we propose a dimensionality reduction approach to simplify the analysis of this complex neural data. By using autoencoders, we find a low-dimensional representation encoding the fMRI data which preserves the typical FC anomalies known to be present in stroke patients. By employing the latent representations emerging from the autoencoders, we enhanced patients' diagnostics and severity classification. Furthermore, we showed how low-dimensional representation increased the accuracy of recovery prediction.

Space wandering in the rodent default mode network

The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here we use multisite GCaMP fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats. We examined neural dynamics in three core DMN nodes- the retrosplenial cortex, cingulate cortex, and prelimbic cortex- as well as the anterior insula node of the salience network, and their association with the rats' spatial exploration behaviors. We found that DMN nodes displayed a hierarchical functional organization during spatial exploration, characterized by stronger coupling with each other than with the anterior insula. Crucially, these DMN nodes encoded the kinematics of spatial exploration, including linear and angular velocity. Additionally, we identified latent brain states that encoded distinct patterns of time-varying exploration behaviors and discovered that higher linear velocity was associated with enhanced DMN activity, heightened synchronization among DMN nodes, and increased anticorrelation between the DMN and anterior insula. Our findings highlight the involvement of the DMN in collectively and dynamically encoding spatial exploration in a real-world setting. Our findings challenge the notion that the DMN is primarily a "task-negative" network disengaged from the external world. By illuminating the DMN's role in naturalistic behaviors, our study underscores the importance of investigating brain network function in ecologically valid contexts.

Gradients of neurotransmitter receptor expression in the macaque cortex

Dynamics and functions of neural circuits depend on interactions mediated by receptors. Therefore, a comprehensive map of receptor organization across cortical regions is needed. In this study, we used in vitro receptor autoradiography to measure the density of 14 neurotransmitter receptor types in 109 areas of macaque cortex. We integrated the receptor data with anatomical, genetic and functional connectivity data into a common cortical space. We uncovered a principal gradient of receptor expression per neuron. This aligns with the cortical hierarchy from sensory cortex to higher cognitive areas. A second gradient, driven by serotonin 5-HT1A receptors, peaks in the anterior cingulate, default mode and salience networks. We found a similar pattern of 5-HT1A expression in the human brain. Thus, the macaque may be a promising translational model of serotonergic processing and disorders. The receptor gradients may enable rapid, reliable information processing in sensory cortical areas and slow, flexible integration in higher cognitive areas.

Longitudinal investigation of changes in resting-state co-activation patterns and their predictive ability in the zQ175 DN mouse model of Huntington's disease

Huntington's disease (HD) is a neurodegenerative disorder caused by expanded (≥ 40) glutamine-encoding CAG repeats in the huntingtin gene, which leads to dysfunction and death of predominantly striatal and cortical neurons. While the genetic profile and clinical signs and symptoms of the disease are better known, changes in the functional architecture of the brain, especially before the clinical expression becomes apparent, are not fully and consistently characterized. In this study, we sought to uncover functional changes in the brain in the heterozygous (HET) zQ175 delta-neo (DN) mouse model at 3, 6, and 10 months of age, using resting-state functional magnetic resonance imaging (RS-fMRI). This mouse model shows molecular, cellular and circuitry alterations that worsen through age. Motor function disturbances are manifested in this model at 6 and 10 months of age. Specifically, we investigated, longitudinally, changes in co-activation patterns (CAPs) that are the transient states of brain activity constituting the resting-state networks (RSNs). Most robust changes in the temporal properties of CAPs occurred at the 10-months time point; the durations of two anti-correlated CAPs, characterized by simultaneous co-activation of default-mode like network (DMLN) and co-deactivation of lateral-cortical network (LCN) and vice-versa, were reduced in the zQ175 DN HET animals compared to the wild-type mice. Changes in the spatial properties, measured in terms of activation levels of different brain regions, during CAPs were found at all three ages and became progressively more pronounced at 6-, and 10 months of age. We then assessed the cross-validated predictive power of CAP metrics to distinguish HET animals from controls. Spatial properties of CAPs performed significantly better than the chance level at all three ages with 80% classification accuracy at 6 and 10 months of age.

Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI

Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.

Identifying reproducible resting state networks and functional connectivity alterations following chronic restraint stress in anaesthetized rats

Background

Resting-state functional MRI (rs-fMRI) in rodent models have the potential to bridge invasive experiments and observational human studies, increasing our understanding of functional alterations in the brains of patients with depression. A major limitation in current rodent rs-fMRI studies is that there has been no consensus on healthy baseline resting-state networks (RSNs) that are reproducible in rodents. Therefore, the present study aimed to construct reproducible RSNs in a large dataset of healthy rats and then evaluate functional connectivity changes within and between these RSNs following a chronic restraint stress (CRS) model within the same animals.

Methods

A combined MRI dataset of 109 Sprague Dawley rats at baseline and after two weeks of CRS, collected during four separate experiments conducted by our lab in 2019 and 2020, was re-analysed. The mICA and gRAICAR toolbox were first applied to detect optimal and reproducible ICA components and then a hierarchical clustering algorithm (FSLNets) was applied to construct reproducible RSNs. Ridge-regularized partial correlation (FSLNets) was used to evaluate the changes in the direct connection between and within identified networks in the same animals following CRS.

Results

Four large-scale networks in anesthetised rats were identified: the DMN-like, spatial attention-limbic, corpus striatum, and autonomic network, which are homologous across species. CRS decreased the anticorrelation between DMN-like and autonomic network. CRS decreased the correlation between amygdala and a functional complex (nucleus accumbens and ventral pallidum) in the right hemisphere within the corpus striatum network. However, a high individual variability in the functional connectivity before and after CRS within RSNs was observed.

Conclusion

The functional connectivity changes detected in rodents following CRS differ from reported functional connectivity alterations in patients with depression. A simple interpretation of this difference is that the rodent response to CRS does not reflect the complexity of depression as it is experienced by humans. Nonetheless, the high inter-subject variability of functional connectivity within networks suggests that rats demonstrate different neural phenotypes, like humans. Therefore, future efforts in classifying neural phenotypes in rodents might improve the sensitivity and translational impact of models used to address aetiology and treatment of psychiatric conditions including depression.

Integration of reconfigurable microchannels into aligned three-dimensional neural networks for spatially controllable neuromodulation

Anisotropically organized neural networks are indispensable routes for functional connectivity in the brain, which remains largely unknown. While prevailing animal models require additional preparation and stimulation-applying devices and have exhibited limited capabilities regarding localized stimulation, no in vitro platform exists that permits spatiotemporal control of chemo-stimulation in anisotropic three-dimensional (3D) neural networks. We present the integration of microchannels seamlessly into a fibril-aligned 3D scaffold by adapting a single fabrication principle. We investigated the underlying physics of elastic microchannels' ridges and interfacial sol-gel transition of collagen under compression to determine a critical window of geometry and strain. We demonstrated the spatiotemporally resolved neuromodulation in an aligned 3D neural network by local deliveries of KCl and Ca²⁺ signal inhibitors, such as tetrodotoxin, nifedipine, and mibefradil, and also visualized Ca²⁺ signal propagation with a speed of ~3.7 μm/s. We anticipate that our technology will pave the way to elucidate functional connectivity and neurological diseases associated with transsynaptic propagation.

Modeling Brain Dysconnectivity in Rodents

Altered or atypical functional connectivity as measured with functional magnetic resonance imaging (fMRI) is a hallmark feature of brain connectopathy in psychiatric, developmental, and neurological disorders. However, the biological underpinnings and etiopathological significance of this phenomenon remain unclear. The recent development of MRI-based techniques for mapping brain function in rodents provides a powerful platform to uncover the determinants of functional (dys)connectivity, whether they are genetic mutations, environmental risk factors, or specific cellular and circuit dysfunctions. Here, we summarize the recent contribution of rodent fMRI toward a deeper understanding of network dysconnectivity in developmental and psychiatric disorders. We highlight substantial correspondences in the spatiotemporal organization of rodent and human fMRI networks, supporting the translational relevance of this approach. We then show how this research platform might help us comprehend the importance of connectional heterogeneity in complex brain disorders and causally relate multiscale pathogenic contributors to functional dysconnectivity patterns. Finally, we explore how perturbational techniques can be used to dissect the fundamental aspects of fMRI coupling and reveal the causal contribution of neuromodulatory systems to macroscale network activity, as well as its altered dynamics in brain diseases. These examples outline how rodent functional imaging is poised to advance our understanding of the bases and determinants of human functional dysconnectivity.

Macroscale coupling between structural and effective connectivity in the mouse brain

How the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the biggest questions of modern neuroscience. At the macro-scale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be taken into account: the directionality of the structural connectome and the limitations of describing network functions in terms of FC. Here, we employed an accurate directed SC of the mouse brain obtained by means of viral tracers, and related it with single-subject effective connectivity (EC) matrices computed by applying a recently developed DCM to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their couplings by conditioning both on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks. Only the connections within sensory motor networks align both in terms of effective and structural strength.

Optogenetic stimulation of anterior insular cortex neurons in male rats reveals causal mechanisms underlying suppression of the default mode network by the salience network

The salience network (SN) and default mode network (DMN) play a crucial role in cognitive function. The SN, anchored in the anterior insular cortex (AI), has been hypothesized to modulate DMN activity during stimulus-driven cognition. However, the causal neural mechanisms underlying changes in DMN activity and its functional connectivity with the SN are poorly understood. Here we combine feedforward optogenetic stimulation with fMRI and computational modeling to dissect the causal role of AI neurons in dynamic functional interactions between SN and DMN nodes in the male rat brain. Optogenetic stimulation of Chronos-expressing AI neurons suppressed DMN activity, and decreased AI-DMN and intra-DMN functional connectivity. Our findings demonstrate that feedforward optogenetic stimulation of AI neurons induces dynamic suppression and decoupling of the DMN and elucidates previously unknown features of rodent brain network organization. Our study advances foundational knowledge of causal mechanisms underlying dynamic cross-network interactions and brain network switching.

Neuronal dynamics of the default mode network and anterior insular cortex: Intrinsic properties and modulation by salient stimuli

The default mode network (DMN) is critical for self-referential mental processes, and its dysfunction is implicated in many neuropsychiatric disorders. However, the neurophysiological properties and task-based functional organization of the rodent DMN are poorly understood, limiting its translational utility. Here, we combine fiber photometry with functional magnetic resonance imaging (fMRI) and computational modeling to characterize dynamics of putative rat DMN nodes and their interactions with the anterior insular cortex (AI) of the salience network. Our analysis revealed neuronal activity changes in AI and DMN nodes preceding fMRI-derived DMN activations and cyclical transitions between brain network states. Furthermore, we demonstrate that salient oddball stimuli suppress the DMN and enhance AI neuronal activity and that the AI causally inhibits the retrosplenial cortex, a prominent DMN node. These findings elucidate the neurophysiological foundations of the rodent DMN, its spatiotemporal dynamical properties, and modulation by salient stimuli, paving the way for future translational studies.

Intrinsic macroscale oscillatory modes driving long range functional connectivity in female rat brains detected by ultrafast fMRI

Spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals correlate across distant brain areas, shaping functionally relevant intrinsic networks. However, the generative mechanism of fMRI signal correlations, and in particular the link with locally-detected ultra-slow oscillations, are not fully understood. To investigate this link, we record ultrafast ultrahigh field fMRI signals (9.4 Tesla, temporal resolution = 38 milliseconds) from female rats across three anesthesia conditions. Power at frequencies extending up to 0.3 Hz is detected consistently across rat brains and is modulated by anesthesia level. Principal component analysis reveals a repertoire of modes, in which transient oscillations organize with fixed phase relationships across distinct cortical and subcortical structures. Oscillatory modes are found to vary between conditions, resonating at faster frequencies under medetomidine sedation and reducing both in number, frequency, and duration with the addition of isoflurane. Peaking in power within clear anatomical boundaries, these oscillatory modes point to an emergent systemic property. This work provides additional insight into the origin of oscillations detected in fMRI and the organizing principles underpinning spontaneous long-range functional connectivity.

Connectome topology of mammalian brains and its relationship to taxonomy and phylogeny

Network models of anatomical connections allow for the extraction of quantitative features describing brain organization, and their comparison across brains from different species. Such comparisons can inform our understanding of between-species differences in brain architecture and can be compared to existing taxonomies and phylogenies. Here we performed a quantitative comparative analysis using the MaMI database (Tel Aviv University), a collection of brain networks reconstructed from ex vivo diffusion MRI spanning 125 species and 12 taxonomic orders or superorders. We used a broad range of metrics to measure between-mammal distances and compare these estimates to the separation of species as derived from taxonomy and phylogeny. We found that within-taxonomy order network distances are significantly closer than between-taxonomy network distances, and this relation holds for several measures of network distance. Furthermore, to estimate the evolutionary divergence between species, we obtained phylogenetic distances across 10,000 plausible phylogenetic trees. The anatomical network distances were rank-correlated with phylogenetic distances 10,000 times, creating a distribution of coefficients that demonstrate significantly positive correlations between network and phylogenetic distances. Collectively, these analyses demonstrate species-level organization across scales and informational sources: we relate brain networks distances, derived from MRI, with evolutionary distances, derived from genotyping data.

Multiscale imaging informs translational mouse modeling of neurological disease

Multiscale neurophysiology reveals that simple motor actions are associated with changes in neuronal firing in virtually every brain region studied. Accordingly, the assessment of focal pathology such as stroke or progressive neurodegenerative diseases must also extend widely across brain areas. To derive mechanistic information through imaging, multiple resolution scales and multimodal factors must be included, such as the structure and function of specific neurons and glial cells and the dynamics of specific neurotransmitters. Emerging multiscale methods in preclinical animal studies that span micro- to macroscale examinations fill this gap, allowing a circuit-based understanding of pathophysiological mechanisms. Combined with high-performance computation and open-source data repositories, these emerging multiscale and large field-of-view techniques include live functional ultrasound, multi- and single-photon wide-scale light microscopy, video-based miniscopes, and tissue-penetrating fiber photometry, as well as variants of post-mortem expansion microscopy. We present these technologies and outline use cases and data pipelines to uncover new knowledge within animal models of stroke, Alzheimer's disease, and movement disorders.

Heritability and cross-species comparisons of human cortical functional organization asymmetry

The human cerebral cortex is symmetrically organized along large-scale axes but also presents inter-hemispheric differences in structure and function. The quantified contralateral homologous difference, that is asymmetry, is a key feature of the human brain left-right axis supporting functional processes, such as language. Here, we assessed whether the asymmetry of cortical functional organization is heritable and phylogenetically conserved between humans and macaques. Our findings indicate asymmetric organization along an axis describing a functional trajectory from perceptual/action to abstract cognition. Whereas language network showed leftward asymmetric organization, frontoparietal network showed rightward asymmetric organization in humans. These asymmetries were heritable in humans and showed a similar spatial distribution with macaques, in the case of intra-hemispheric asymmetry of functional hierarchy. This suggests (phylo)genetic conservation. However, both language and frontoparietal networks showed a qualitatively larger asymmetry in humans relative to macaques. Overall, our findings suggest a genetic basis for asymmetry in intrinsic functional organization, linked to higher order cognitive functions uniquely developed in humans.

Chemogenetic stimulation of tonic locus coeruleus activity strengthens the default mode network

The default mode network (DMN) of the brain is functionally associated with a wide range of behaviors. In this study, we used functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and spectral fiber photometry to investigate the selective neuromodulatory effect of norepinephrine (NE)-releasing noradrenergic neurons in the locus coeruleus (LC) on the mouse DMN. Chemogenetic-induced tonic LC activity decreased cerebral blood volume (CBV) and glucose uptake and increased synchronous low-frequency fMRI activity within the frontal cortices of the DMN. Fiber photometry results corroborated these findings, showing that LC-NE activation induced NE release, enhanced calcium-weighted neuronal spiking, and reduced CBV in the anterior cingulate cortex. These data suggest that LC-NE alters conventional coupling between neuronal activity and CBV in the frontal DMN. We also demonstrated that chemogenetic activation of LC-NE neurons strengthened functional connectivity within the frontal DMN, and this effect was causally mediated by reduced modulatory inputs from retrosplenial and hippocampal regions to the association cortices of the DMN.

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.

Functional Connectivity of the Brain Across Rodents and Humans

Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.

Increased fMRI connectivity upon chemogenetic inhibition of the mouse prefrontal cortex

While shaped and constrained by axonal connections, fMRI-based functional connectivity reorganizes in response to varying interareal input or pathological perturbations. However, the causal contribution of regional brain activity to whole-brain fMRI network organization remains unclear. Here we combine neural manipulations, resting-state fMRI and in vivo electrophysiology to probe how inactivation of a cortical node causally affects brain-wide fMRI coupling in the mouse. We find that chronic inhibition of the medial prefrontal cortex (PFC) via overexpression of a potassium channel increases fMRI connectivity between the inhibited area and its direct thalamo-cortical targets. Acute chemogenetic inhibition of the PFC produces analogous patterns of fMRI overconnectivity. Using in vivo electrophysiology, we find that chemogenetic inhibition of the PFC enhances low frequency (0.1–4 Hz) oscillatory power via suppression of neural firing not phase-locked to slow rhythms, resulting in increased slow and δ band coherence between areas that exhibit fMRI overconnectivity. These results provide causal evidence that cortical inactivation can counterintuitively increase fMRI connectivity via enhanced, less-localized slow oscillatory processes.

Pathological perturbation affects whole brain network activity. Here the authors show in mice that cortical inactivation unexpectedly results in increased fMRI connectivity between the manipulated regions and its direct axonal targets.

Unique spatiotemporal fMRI dynamics in the awake mouse brain

Human imaging studies have shown that spontaneous brain activity exhibits stereotypic spatiotemporal reorganization in awake, conscious conditions with respect to minimally conscious states. However, whether and how this phenomenon can be generalized to lower mammalian species remains unclear. Leveraging a robust protocol for resting-state fMRI (rsfMRI) mapping in non-anesthetized, head-fixed mice, we investigated functional network topography and dynamic structure of spontaneous brain activity in wakeful animals. We found that rsfMRI networks in the awake state, while anatomically comparable to those observed under anesthesia, are topologically configured to maximize interregional communication, departing from the underlying community structure of the mouse axonal connectome. We further report that rsfMRI activity in wakeful animals exhibits unique spatiotemporal dynamics characterized by a state-dependent, dominant occurrence of coactivation patterns encompassing a prominent participation of arousal-related forebrain nuclei and functional anti-coordination between visual-auditory and polymodal cortical areas. We finally show that rsfMRI dynamics in awake mice exhibits a stereotypical temporal structure, in which state-dominant coactivation patterns are configured as network attractors. These findings suggest that spontaneous brain activity in awake mice is critically shaped by state-specific involvement of basal forebrain arousal systems and document that its dynamic structure recapitulates distinctive, evolutionarily relevant principles that are predictive of conscious states in higher mammalian species.

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fMRI networks in awake mice depart from underlying anatomical structure

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fMRI dynamics in wakeful mice is critically shaped by arousal-related nuclei

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Occurrence and topography of rsfMRI coactivation patterns define conscious states

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fMRI coactivation dynamics defines a signature of consciousness in the mouse brain

Functional ultrasound imaging: A useful tool for functional connectomics?

Functional ultrasound (fUS) is a hemodynamic-based functional neuroimaging technique, primarily used in animal models, that combines a high spatiotemporal resolution, a large field of view, and compatibility with behavior. These assets make fUS especially suited to interrogating brain activity at the systems level. In this review, we describe the technical capabilities offered by fUS and discuss how this technique can contribute to the field of functional connectomics. First, fUS can be used to study intrinsic functional connectivity, namely patterns of correlated activity between brain regions. In this area, fUS has made the most impact by following connectivity changes in disease models, across behavioral states, or dynamically. Second, fUS can also be used to map brain-wide pathways associated with an external event. For example, fUS has helped obtain finer descriptions of several sensory systems, and uncover new pathways implicated in specific behaviors. Additionally, combining fUS with direct circuit manipulations such as optogenetics is an attractive way to map the brain-wide connections of defined neuronal populations. Finally, technological improvements and the application of new analytical tools promise to boost fUS capabilities. As brain coverage and the range of behavioral contexts that can be addressed with fUS keep on increasing, we believe that fUS-guided connectomics will only expand in the future. In this regard, we consider the incorporation of fUS into multimodal studies combining diverse techniques and behavioral tasks to be the most promising research avenue.

The conserved core of the nereid brain: Circular CNS, apical nervous system and lhx6-arx-dlx neurons

When bilaterian animals first emerged, an enhanced perception of the Precambrian environment was key to their stunning success. This occurred through the acquisition of an anterior brain, as found in most extant bilaterians. What were the core circuits of the first brain, and how do they relate to today's diversity? With two landmark resources - the full connectome and a multimodal cellular atlas combining gene expression and ultrastructure - the young worm of the marine annelid Platynereis dumerilii takes center stage in comparative bilaterian neuroanatomy. The new data suggest a composite structure of the ancestral bilaterian brain, with the anterior end of a circular CNS fused to a sensory-neurosecretory apical system, and with lhx6-arx-dlx chemosensory circuits giving rise to associative centers in the descending bilaterian lineages.

The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections

Diffusion MRI tractography is the only noninvasive method to measure the structural connectome in humans. However, recent validation studies have revealed limitations of modern tractography approaches, which lead to significant mistracking caused in part by local uncertainties in fiber orientations that accumulate to produce larger errors for longer streamlines. Characterizing the role of this length bias in tractography is complicated by the true underlying contribution of spatial embedding to brain topology. In this work, we compare graphs constructed with ex vivo tractography data in mice and neural tracer data from the Allen Mouse Brain Connectivity Atlas to random geometric surrogate graphs which preserve the low-order distance effects from each modality in order to quantify the role of geometry in various network properties. We find that geometry plays a substantially larger role in determining the topology of graphs produced by tractography than graphs produced by tracers. Tractography underestimates weights at long distances compared to neural tracers, which leads tractography to place network hubs close to the geometric center of the brain, as do corresponding tractography-derived random geometric surrogates, while tracer graphs place hubs further into peripheral areas of the cortex. We also explore the role of spatial embedding in modular structure, network efficiency and other topological measures in both modalities. Throughout, we compare the use of two different tractography streamline node assignment strategies and find that the overall differences between tractography approaches are small relative to the differences between tractography- and tracer-derived graphs. These analyses help quantify geometric biases inherent to tractography and promote the use of geometric benchmarking in future tractography validation efforts.

Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes

Autism Spectrum Disorder (ASD) is characterized by substantial, yet highly heterogeneous abnormalities in functional brain connectivity. However, the origin and significance of this phenomenon remain unclear. To unravel ASD connectopathy and relate it to underlying etiological heterogeneity, we carried out a bi-center cross-etiological investigation of fMRI-based connectivity in the mouse, in which specific ASD-relevant mutations can be isolated and modeled minimizing environmental contributions. By performing brain-wide connectivity mapping across 16 mouse mutants, we show that different ASD-associated etiologies cause a broad spectrum of connectional abnormalities in which diverse, often diverging, connectivity signatures are recognizable. Despite this heterogeneity, the identified connectivity alterations could be classified into four subtypes characterized by discrete signatures of network dysfunction. Our findings show that etiological variability is a key determinant of connectivity heterogeneity in ASD, hence reconciling conflicting findings in clinical populations. The identification of etiologically-relevant connectivity subtypes could improve diagnostic label accuracy in the non-syndromic ASD population and paves the way for personalized treatment approaches.

Interpreting the Entire Connectivity of Individual Neurons in Micropatterned Neural Culture With an Integrated Connectome Analyzer of a Neuronal Network (iCANN)

The function of a neural circuit can be determined by the following: (1) characteristics of individual neurons composing the circuit, (2) their distinct connection structure, and (3) their neural circuit activity. However, prior research on correlations between these three factors revealed many limitations. In particular, profiling and modeling of the connectivity of complex neural circuits at the cellular level are highly challenging. To reduce the burden of the analysis, we suggest a new approach with simplification of the neural connection in an array of honeycomb patterns on 2D, using a microcontact printing technique. Through a series of guided neuronal growths in defined honeycomb patterns, a simplified neuronal circuit was achieved. Our approach allowed us to obtain the whole network connectivity at cellular resolution using a combination of stochastic multicolor labeling via viral transfection. Therefore, we were able to identify several types of hub neurons with distinct connectivity features. We also compared the structural differences between different circuits using three-node motif analysis. This new model system, iCANN, is the first experimental model of neural computation at the cellular level, providing neuronal circuit structures for the study of the relationship between anatomical structure and function of the neuronal network.

An isotropic EPI database and analytical pipelines for rat brain resting-state fMRI

Resting-state functional magnetic resonance imaging (fMRI) has drastically expanded the scope of brain research by advancing our knowledge about the topologies, dynamics, and interspecies translatability of functional brain networks. Several databases have been developed and shared in accordance with recent key initiatives in the rodent fMRI community to enhance the transparency, reproducibility, and interpretability of data acquired at various sites. Despite these pioneering efforts, one notable challenge preventing efficient standardization in the field is the customary choice of anisotropic echo planar imaging (EPI) schemes with limited spatial coverage. Imaging with anisotropic resolution and/or reduced brain coverage has significant shortcomings including reduced registration accuracy and increased deviation in brain feature detection. Here we proposed a high-spatial-resolution (0.4 mm), isotropic, whole-brain EPI protocol for the rat brain using a horizontal slicing scheme that can maintain a functionally relevant repetition time (TR), avoid high gradient duty cycles, and offer unequivocal whole-brain coverage. Using this protocol, we acquired resting-state EPI fMRI data from 87 healthy rats under the widely used dexmedetomidine sedation supplemented with low-dose isoflurane on a 9.4 T MRI system. We developed an EPI template that closely approximates the Paxinos and Watson's rat brain coordinate system and demonstrated its ability to improve the accuracy of group-level approaches and streamline fMRI data pre-processing. Using this database, we employed a multi-scale dictionary-learning approach to identify reliable spatiotemporal features representing rat brain intrinsic activity. Subsequently, we performed k-means clustering on those features to obtain spatially discrete, functional regions of interest (ROIs). Using Euclidean-based hierarchical clustering and modularity-based partitioning, we identified the topological organizations of the rat brain. Additionally, the identified group-level FC network appeared robust across strains and sexes. The "triple-network" commonly adapted in human fMRI were resembled in the rat brain. Through this work, we disseminate raw and pre-processed isotropic EPI data, a rat brain EPI template, as well as identified functional ROIs and networks in standardized rat brain coordinates. We also make our analytical pipelines and scripts publicly available, with the hope of facilitating rat brain resting-state fMRI study standardization.

Dynamical Mechanism Underlying Scale-Free Network Reorganization in Low Acetylcholine States Corresponding to Slow Wave Sleep

Sleep is indispensable for most animals' cognitive functions, and is hypothesized to be a major factor in memory consolidation. Although we do not fully understand the mechanisms of network reorganisation driving memory consolidation, available data suggests that sleep-associated neurochemical changes may be important for such processes. In particular, global acetylcholine levels change across the sleep/wake cycle, with high cholinergic tone during wake and REM sleep and low cholinergic tone during slow wave sleep. Furthermore, experimental perturbation of cholinergic tone has been shown to impact memory storage. Through in silico modeling of neuronal networks, we show how spiking dynamics change in highly heterogenous networks under varying levels of cholinergic tone, with neuronal networks under high cholinergic modulation firing asynchronously and at high frequencies, while those under low cholinergic modulation exhibit synchronous patterns of activity. We further examined the network's dynamics and its reorganization mediated via changing levels of acetylcholine within the context of different scale-free topologies, comparing network activity within the hub cells, a small group of neurons having high degree connectivity, and with the rest of the network. We show a dramatic, state-dependent change in information flow throughout the network, with highly active hub cells integrating information in a high-acetylcholine state, and transferring it to rest of the network in a low-acetylcholine state. This result is experimentally corroborated by frequency-dependent frequency changes observed in vivo experiments. Together, these findings provide insight into how new neurons are recruited into memory traces during sleep, a mechanism which may underlie system memory consolidation.

mTOR-related synaptic pathology causes autism spectrum disorder-associated functional hyperconnectivity

Postmortem studies have revealed increased density of excitatory synapses in the brains of individuals with autism spectrum disorder (ASD), with a putative link to aberrant mTOR-dependent synaptic pruning. ASD is also characterized by atypical macroscale functional connectivity as measured with resting-state fMRI (rsfMRI). These observations raise the question of whether excess of synapses causes aberrant functional connectivity in ASD. Using rsfMRI, electrophysiology and in silico modelling in Tsc2 haploinsufficient mice, we show that mTOR-dependent increased spine density is associated with ASD -like stereotypies and cortico-striatal hyperconnectivity. These deficits are completely rescued by pharmacological inhibition of mTOR. Notably, we further demonstrate that children with idiopathic ASD exhibit analogous cortical-striatal hyperconnectivity, and document that this connectivity fingerprint is enriched for ASD-dysregulated genes interacting with mTOR or Tsc2. Finally, we show that the identified transcriptomic signature is predominantly expressed in a subset of children with autism, thereby defining a segregable autism subtype. Our findings causally link mTOR-related synaptic pathology to large-scale network aberrations, revealing a unifying multi-scale framework that mechanistically reconciles developmental synaptopathy and functional hyperconnectivity in autism.

Contribution of animal models toward understanding resting state functional connectivity

Functional connectivity, which reflects the spatial and temporal organization of intrinsic activity throughout the brain, is one of the most studied measures in human neuroimaging research. The noninvasive acquisition of resting state functional magnetic resonance imaging (rs-fMRI) allows the characterization of features designated as functional networks, functional connectivity gradients, and time-varying activity patterns that provide insight into the intrinsic functional organization of the brain and potential alterations related to brain dysfunction. Functional connectivity, hence, captures dimensions of the brain's activity that have enormous potential for both clinical and preclinical research. However, the mechanisms underlying functional connectivity have yet to be fully characterized, hindering interpretation of rs-fMRI studies. As in other branches of neuroscience, the identification of the neurophysiological processes that contribute to functional connectivity largely depends on research conducted on laboratory animals, which provide a platform where specific, multi-dimensional investigations that involve invasive measurements can be carried out. These highly controlled experiments facilitate the interpretation of the temporal correlations observed across the brain. Indeed, information obtained from animal experimentation to date is the basis for our current understanding of the underlying basis for functional brain connectivity. This review presents a compendium of some of the most critical advances in the field based on the efforts made by the animal neuroimaging community.

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