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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 1,117 records · Page 62Linked to original sources

bioRxiv2017

Conditioning by Subthreshold Synaptic Input Changes the Characteristic Firing Pattern of CA3 Hippocampal Neurons

Unlike synaptic strength, intrinsic excitability is assumed to be a stable property of neurons. For example, learning of somatic conductances is generally not incorporated into computational models, and the discharge pattern of neurons in response to test stimuli is frequently used as a basis for phenotypic classification. However, it is increasingly evident that signal processing properties of neurons are more generally plastic on the timescale of minutes. Here we demonstrate that the intrinsic firing patterns of CA3 neurons of the rat hippocampus in vitro undergo rapid long-term plasticity in response to a few minutes of only subthreshold synaptic conditioning. This plasticity on the spike-timing could also be induced by intrasomatic injection of subthreshold depolarizing pulses and was blocked by kinase inhibitors, indicating that discharge dynamics are modulated locally. Cluster analysis of firing patterns before and after conditioning revealed systematic transitions towards adapting and intrinsic burst behaviours, irrespective of the patterns initially exhibited by the cells. We used a conductance-based model to decide appropriate pharmacological blockade, and found that the observed transitions are likely due to recruitment of calcium and M-type potassium conductances. We conclude that CA3 neurons adapt their conductance profile to the subthreshold activity of their input, so that their intrinsic firing pattern is not a static signature, but rather a reflection of their history of subthreshold activity. In this way, recurrent output from CA3 neurons may collectively shape the temporal dynamics of their embedding circuits.\n\nNew & NoteworthyDespite being widely conserved across the animal phyla, it is still a mystery why nerve cells present diverse discharge dynamics upon somatic step currents. Adding a new timing dimension to the intrinsic plasticity literature, here we show that CA3 neurons rapidly adapt through the space of known firing patterns in response to the subthreshold signals that they receive from their embedding circuit. This result implies that CA3 neurons collectively adjust their network processing to the temporal statistics of their circuit.

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bioRxiv2016

Distinctive BOLD Connectivity Patterns in the Schizophrenia Brain: Machine-learning based comparison between various connectivity measures

Recent functional magnetic resonance imaging (fMRI) studies have found distinctive functional connectivity in the schizophrenic brain. However, most of the studies focused on the correlation analysis to define the functional connectivity for BOLD fluctuations between brain regions, which resulted in the limited understanding to the network properties of altered wirings in the schizophrenic brain. Here I characterized the distinctiveness of BOLD connectivity pattern in the schizophrenic brain relative to healthy brain with various similarity measures in the time-frequency domain, while participants are performing the working memory task in the MRI scanner. To assess the distinctiveness of the connectivity pattern, discrimination performances of the pattern classifier machines trained by each similarity measurement were compared. As a result, the classifier machine trained by cross-spectral coherence pattern in low frequency fluctuation (LFF, 0.01-0.08 Hz band) made better performance than the machine trained by correlation-based connectivity pattern. Interestingly, the classifier machine trained by time-lagging patterns in cross-correlation function of LFF produced higher classifying sensitivity than the machines trained by other measures. These results indicate that there are unobserved but considerable features in the functional connectivity pattern of schizophrenic brain which traditional emphasis on correlation analysis does not capture.

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bioRxiv2016

Dual origins of measured phase-amplitude coupling reveal distinct neural mechanisms underlying episodic memory in the human cortex

Phase-amplitude coupling (PAC) is hypothesized to coordinate neural activity, but its role in successful memory formation in the human cortex is unknown. Measures of PAC are difficult to interpret, however. Both increases and decreases in PAC have been linked to memory encoding, and PAC may arise due to different neural mechanisms. Here, we use a waveform analysis to examine PAC in the human cortex as participants with intracranial electrodes performed a paired associates memory task. We found that successful memory formation exhibited significant decreases in left temporal lobe and prefrontal cortical PAC, and these two regions exhibited changes in PAC within different frequency bands. Two underlying neural mechanisms, nested oscillations and sharp waveforms, were responsible for the changes in these regions. Our data therefore suggest that decreases in measured cortical PAC during episodic memory reflect two distinct underlying mechanisms that are anatomically segregated in the human brain.

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bioRxiv2016

Bayesian mapping reveals that attention boosts neural responses to predicted and unpredicted stimuli

Predictive coding posits that the human brain continually monitors the environment for regularities and detects inconsistencies. It is unclear, however, what effect attention has on expectation processes, as there have been relatively few studies and the results of these have yielded contradictory findings. Here, we employed Bayesian model comparison to adjudicate between two alternative computational models. The Opposition model states that attention boosts neural responses equally to predicted and unpredicted stimuli, whereas the Interaction model assumes that attentional boosting of neural signals depends on the level of predictability. We designed a novel, audiospatial attention task that orthogonally manipulated attention and prediction by playing oddball sequences in either the attended or unattended ear. We observed sensory prediction error responses, with electroencephalography, across all attentional manipulations. Crucially, posterior probability maps revealed that, overall, the Opposition model better explained scalp and source data, suggesting that attention boosts responses to predicted and unpredicted stimuli equally. Furthermore, Dynamic Causal Modelling (DCM) showed that these Opposition effects were expressed in plastic changes within the mismatch negativity network. Our findings provide empirical evidence for a computational model of the opposing interplay of attention and expectation in the brain.

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bioRxiv2016

TrkB signaling underlies the rapid antidepressant effects of isoflurane

A brief burst-suppressing isoflurane anesthesia has been shown to rapidly alleviate symptoms of depression in a subset of patients, but the neurobiological basis of these observations remains obscure. We show that a single isoflurane anesthesia produces antidepressant-like behavioural effects in the learned helplessness paradigm and regulates molecular events implicated in the mechanism of action of rapid-acting antidepressant ketamine: activation of brain-derived neurotrophic factor (BDNF) receptor TrkB, facilitation of mammalian target of rapamycin (mTOR) signaling pathway and inhibition of glycogen synthase kinase 3{beta} (GSK3{beta}). Moreover, isoflurane affected neuronal plasticity by facilitating long-term potentiation in the hippocampus. We also found that isoflurane increased activity of the parvalbumin interneurons, and facilitated GABAergic transmission in wild type mice but not in transgenic mice with reduced TrkB expression in parvalbumin interneurons. Our findings strengthen the role of TrkB signaling in the antidepressant responses and encourage further evaluation of isoflurane as a rapid-acting antidepressant devoid of the psychotomimetic effects and abuse potential of ketamine.

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bioRxiv2016

The effect of subthreshold transcranial magnetic stimulation on the excitation of corticospinal volleys with different conduction times

Previous studies in humans interested in inhibitory synaptic activity at the level of the primary motor cortex (M1) have frequently used an electrophysiological technique call short intracortical inhibition (SICI). This technique consists of two subsequent pulses with transcranial magnetic stimulation (TMS), of which the first pulse (S1) has been argued to target local inhibitory connections, reducing corticospinal output generated by the second pulse (S2). However, the reduction of corticospinal output is not seen in every tested subject, sometimes S1 even increased corticospinal output. Thus there seems to be more occurring than just the targeting of local inhibitory connections by S1, indicating that the mechanisms of SICI are not fully understood. In the present study, in 18 healthy young subjects we applied a method allowing to segregate corticospinal volleys with different conduction times and investigated the effect of S1 on the excitation of these volleys. Our results revealed three major findings, which can be summarized as follows: i) S1 acted not only locally at the level of M1, but produced corticospinal activity which may influence corticospinal output by S2 at the spinal level; ii) there was not only reduced excitation of corticospinal volleys by S1, but also an increased excitation of corticospinal volleys with longer conduction times; iii) at the level of M1, S1 indeed targeted preferentially inhibitory synaptic connections, and these reduced corticospinal excitation of the fastest conduction corticospinal volleys.\n\nOur results indicate that underlying mechanisms in studies applying SICI should be interpreted with caution.

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bioRxiv2016

Visual processing mode switching regulated by VIP cells

The responses of neurons in mouse primary visual cortex (V1) to visual stimuli depend on behavioral states. Specifically, surround suppression is reduced during locomotion. Although locomotion-induced vasoactive intestinal polypeptide positive (VIP) interneuron depolarization can account for the reduction of surround suppression, the functions of VIP cell depolarization are not fully understood. Here we utilize a firing rate model and a computational model to elucidate the potential functions of VIP cell depolarization during locomotion. Our analyses suggest 1) that surround suppression sharpens the visual responses in V1 to a stationary scene, 2) that depolarized VIP cells enhance V1 responses to moving objects by reducing self-induced surround suppression and 3) that during locomotion V1 neuron responses to some features of the moving objects can be selectively enhanced. Thus, VIP cells regulate surround suppression to allow pyramidal neurons to optimally encode visual information independent of behavioral state.

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bioRxiv2016

Individual differences in functional connectivity during naturalistic viewing conditions

Naturalistic viewing paradigms such as movies have been shown to reduce participant head motion and improve arousal during fMRI scanning relative to task-free rest, and have been used to study both functional connectivity and task-evoked BOLD-signal changes. These task-evoked changes result in cortical activity that is synchronized across subjects and involves large areas of the cortex, and it is unclear whether individual differences in functional connectivity are enhanced or diminished under such naturalistic conditions. This work first aims to characterize variability in functional connectivity (FC) across two distinct movie conditions and eyes-open rest (n=34 healthy adults, 2 scan sessions each). At the whole-brain level, we found that movies have higher intra- and inter-subject correlations in cluster-wise FC relative to rest. The anatomical distribution of inter-subject variability was similar across conditions, with higher variability occurring at the lateral prefrontal lobes and temporoparietal junctions. Second, we used an unsupervised test-retest matching (or \"finger-printin\") algorithm that identifies individual subjects from within a group based on functional connectivity patterns, quantifying the accuracy of the algorithm across the three conditions. We also evaluated the impact of parcellation resolution, scan duration, and number of edges on observed inter-individual differences. The movies and resting state all enabled identification of individual subjects based on FC matrices, with accuracies between 62 and 100%. Overall, pairings involving movies outperformed rest, and the more social and faster-paced movie attained 100% accuracy. When the parcellation resolution, scan duration and number of edges used were increased, accuracies improved across conditions, and the pattern of movies>rest was preserved. These results suggest that using dynamic stimuli such as movies enhances the detection of FC patterns that are distinct at the individual level.\n\nHighlightsO_LIIntra- and inter-subject FC correlations are compared across rest and movies.\nC_LIO_LIMovies outperformed rest in an unsupervised identification algorithm based on FC.\nC_LIO_LIMovies outperform rest regardless of parcellation, scan length, or number of edges.\nC_LIO_LIWatching movies enhances the detection of individual differences in FC.\nC_LI

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bioRxiv2016

Sleep regulation of the distribution of cortical firing rates

Sleep is thought to mediate mnemonic and homeostatic functions. However, the mechanism by which this brain state can implement both the \"selective\" plasticity needed to consolidate novel memory traces as well as the \"general\" plasticity necessary to maintain a well-functioning neuronal system is unclear. Recent findings show that both of these functions differentially affect neurons based on their intrinsic firing rate, a ubiquitous neuronal heterogeneity. Furthermore, they are both implemented by the NREM slow oscillation, which also distinguishes neurons based on firing rate during sequential activity at the DOWN->UP transition. These findings suggest a mechanism by which spiking activity during the slow oscillation acts to maintain network statistics that promote a skewed distribution of neuronal firing rates, and \"perturbation\" of that activity by hippocampal replay acts to integrate new memory traces into the existing cortical network.

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bioRxiv2016

A modification-specific peptide-based immunization approach using CRM197 carrier protein: Development of a selective vaccine against pyroglutamate Aβ peptides

Strategies aimed at reducing cerebral accumulation of the amyloid-{beta} (A{beta}) peptides have therapeutic potential in Alzheimers disease (AD). A{beta} immunization has proven to be effective at promoting A{beta} clearance in animal models but adverse effects have hampered its clinical evaluation. The first anti-A{beta} immunization clinical trial, which assessed a full-length A{beta}1-42 vaccine, increased the risk of encephalitis most likely because of autoimmune pro-inflammatory T helper 1 (Th1) response against all forms of A{beta}. Immunization against less abundant but potentially more pathologically relevant A{beta} products, such as N-terminally-truncated pyroglutamate-3 A{beta} (A{beta}pE3), could provide efficacy and improve tolerability in A{beta} immunotherapy. Here, we describe a selective vaccine against A{beta}pE3 using the diphtheria toxin mutant CRM197 as carrier protein for epitope presentation. CRM197 is currently used in licensed vaccines and has demonstrated excellent immunogenicity and safety in humans. In mice, our A{beta}pE3:CRM197 vaccine triggered the production of specific anti-A{beta}pE3 antibodies that did not cross-react with A{beta}1-42, non-cyclized A{beta}E3, or N-terminally-truncated pyroglutamate-11 A{beta} (A{beta}pE11). A{beta}pE3:CRM197 antiserum strongly labeled A{beta}pE3 in insoluble protein extracts and decorated cortical amyloid plaques in human AD brains. Anti-A{beta}pE3 antibodies were almost exclusively of the IgG1 isotype, suggesting an anti-inflammatory Th2 response bias to the A{beta}pE3:CRM197 vaccine. To the best of our knowledge, this study shows for the first time that CRM197 has potential as a safe and suitable vaccine carrier for active and selective immunization against specific protein sequence modifications or conformations, such as A{beta}pE3.

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bioRxiv2016

Ventral tegmental area neurons use an ensemble code for representing information about ongoing actions

Multiple and unpredictable numbers of actions are often required to achieve a goal. In order to organize behavior and allocate effort so that optimal behavioral policies can be selected, it is necessary to continually monitor ongoing actions. Real-time processing of information related to actions and outcomes is typically assigned to the prefrontal cortex and basal ganglia, but also depends on midbrain regions, especially the ventral tegmental area (VTA). We were interested in how individual VTA neurons, as well as networks within the VTA, encode salient events when an unpredictable number of serial actions are required to obtain a reward. We recorded from ensembles of putative dopamine and non-dopamine neurons in the VTA as animals performed multiple cued trials in a recording session where, in each trial, serial actions were randomly rewarded. While averaging population activity did not reveal a response pattern, we observed that different neurons selectively tuned to low, medium, or high numbered actions in a trial. This preferential tuning of putative dopamine and non-dopamine VTA neurons to different subsets of actions in a trial allowed information about binned action number to be decoded from the ensemble activity. At the network level, tuning curve similarity was positively associated with action-evoked noise correlations, suggesting that action number selectivity reflects functional connectivity within these networks. Analysis of phasic responses to cue and reward revealed that the requirement to execute multiple and uncertain numbers of actions weakens both cue-evoked responses and cue-reward response correlation. The functional connectivity and ensemble coding scheme that we observe here may allow VTA neurons to cooperatively provide a real-time account of ongoing behavior. These computations may be critical to cognitive and motivational functions that have long been associated with VTA dopamine neurons.

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bioRxiv2016

The neural basis of precise visual short-term memory for complex recognisable objects

Recent evidence suggests that visual short-term memory (VSTM) capacity estimated using simple objects, such as colours and oriented bars, may not generalise well to more naturalistic stimuli. More visual detail can be stored in VSTM when complex, recognisable objects are maintained compared to simple objects. It is not yet known if it is recognisability that enhances memory precision, nor whether maintenance of recognisable objects is achieved with the same network of brain regions supporting maintenance of simple objects.\n\nWe used a novel stimulus generation method to parametrically warp photographic images along a continuum, allowing separate estimation of the precision of memory representations and the number of items retained. The stimulus generation method was also designed to create unrecognisable, though perceptually matched, stimuli, to investigate the impact of recognisability on VSTM. We adapted the widely-used change detection and continuous report paradigms for use with complex, photographic images.\n\nAcross three functional magnetic resonance imaging (fMRI) experiments, we demonstrated greater precision for recognisable objects in VSTM compared to unrecognisable objects. This clear behavioural advantage was not the result of recruitment of additional brain regions, or of stronger mean activity within the core network. Representational similarity analysis revealed greater variability across item repetitions in the representations of recognisable, compared to unrecognisable complex objects. We therefore propose that a richer range of neural representations support VSTM for complex recognisable objects.

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bioRxiv2016

Exploring Anti-correlated Resting State BOLD Signals Through Dynamic Functional Connectivity and Whole-brain Computational Modeling

Resting-state functional magnetic resonance imaging and diffusion weight imaging became a conventional tool to study brain connectivity in healthy and diseased individuals. However, both techniques provide indirect measures of brain connectivity leading to controversies on their interpretation. Among these controversies, interpretation of anti-correlated functional connections and global average signal is a major challenge for the field. In this paper, we used dynamic functional connectivity to calculate the probability of anti-correlations between brain regions. The brain regions forming task-positive and task-negative networks showed high anti-correlation probabilities. The fluctuations in anti-correlation probabilities were significantly correlated with those in global average signal and functional connectivity. We investigated the mechanisms behind these fluctuations using whole-brain computational modeling approach. We found that the underlying effective connectivity and intrinsic noise reflect the static spatiotemporal patterns, whereas the hemodynamic response function is the key factor defining the fluctuations in functional connectivity and anti-correlations. Furthermore, we illustrated the clinical implications of these findings on a group of bipolar disorder patients suffering a depressive relapse (BPD).

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bioRxiv2016

The lexical categorization model: A computational model of left ventral occipito-temporal cortex activation in visual word recognition

To characterize the left-ventral occipito-temporal cortex (lvOT) role during reading in a quantitatively explicit and testable manner, we propose the lexical categorization model (LCM). The LCM assumes that lvOT optimizes linguistic processing by allowing fast meaning access when words are familiar and filter out orthographic strings without meaning. The LCM successfully simulates benchmark results from functional brain imaging. Empirically, using functional magnetic resonance imaging, we demonstrate that quantitative LCM simulations predict lvOT activation across three studies better than alternative models. Besides, we found that word-likeness, which is assumed as input to LCM, is represented posterior to lvOT. In contrast, a dichotomous word/non-word contrast, which is assumed as the LCMs output, could be localized to upstream frontal brain regions. Finally, we found that training lexical categorization results in more efficient reading. Thus, we propose a ventral-visual-stream processing framework for reading involving word-likeness extraction followed by lexical categorization, before meaning extraction.

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bioRxiv2016

Transcriptomics reveals patterns of sexually dimorphic gene expression in an avian hypothalamic-pituitary-gonadal (HPG) axis

The hypothalamic-pituitary-gonadal (HPG) axis is a key biological system required for reproduction and associated sexual behaviors to occur. Each of these tissues, the hypothalamus in the brain, the pituitary gland, and the gonads (testes and ovaries), has specialized and sometimes sex-specific functions that promote, amongst other things, reproductive processes. To fully characterize the transcript community of each aspect of the HPG axis, as well as to uncover potentially important sex-specific differences, we characterized patterns of gene expression in sexually mature male and female rock dove (Columba livia) hypothalamus, pituitary, and gonads. We describe patterns of gene expression amongst tissues as well as patterns of sex-biased gene expression which may underlie some of the fundamental differences between male and female reproductive behavior. We report greater sex-biased differential expression in the pituitary (233 genes out of 15,102 genes) as compared to the hypothalamus (1 gene out of 15,102 genes), with multiple genes overexpressed in the male pituitary gland being related to locomotion, and multiple genes over-expressed in the female pituitary gland being related to reproduction, growth and development. These genes may be associated with facilitating the different roles the HPG system plays in sex-specific reproductive behavior, including life-history strategies characterized by short-term payoffs in males (i.e. locomotion) and longer-term payoffs in females (i.e. development and reproduction). In addition, we report novel patterns of sex-biased expression in genes involved in reproduction-associated processes. Gonadotropin-releasing hormone, Progesterone, and Androgen receptor were upregulated in the female pituitary as compared to the male pituitary. We also found greater expression of Prolactin in the female pituitary as compared to the male pituitary, but it was more highly expressed in the male hypothalamus as compared to the female hypothalamus. The Prolactin receptor was also more highly expressed in the male hypothalamus and pituitary gland as compared to corresponding female tissues. Finally, we discovered greater expression of Arginine Vasopressin Receptor 1A in the male pituitary as compared to the female pituitary. Using a more global analysis, we discovered other interesting sex-biased patterns in genes not originally targeted for investigation. These genes include like Betacellulin (BTC), which was upregulated in the female pituitary and compared to males, and Ecto-NOX Disulfide-Thiol Exchanger 1 (ENOX1), which was upregulated in the male pituitary as compared to females. These genes may play important, though currently unknown, roles in reproductive physiology and behavior. In conclusion, we reveal patterns of tissue specific and sexually dimorphic gene expression in the HPG axis, highlighting the need for sex parity in transcriptomic studies and providing new lines of investigation of the mechanisms of reproductive function.

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bioRxiv2016

Feature-coding transitions to conjunction-coding with progression through human visual cortex

Identifying an object and distinguishing it from similar items depends upon the ability to perceive its component parts as conjoined into a cohesive whole, but the brain mechanisms underlying this ability remain elusive. The ventral visual processing pathway in primates is organized hierarchically: Neuronal responses in its early stages are sensitive to the manipulation of simple visual features whereas neuronal responses in subsequent stages are tuned to increasingly complex stimulus attributes. It is widely assumed that feature-coding dominates in early visual cortex whereas later visual regions employ conjunction-coding in which object representations are different from the sum of their simple-feature parts. However, no study has demonstrated that putative object-level codes in higher visual cortex cannot be accounted for by feature-coding and that putative feature-codes in regions prior to ventral temporal cortex are not equally well characterized as object-level codes. Thus the existence of a transition from feature- to conjunction-coding in visual cortex remains unconfirmed, and, if a transition does occur, its location remains unknown. By employing multivariate analysis of human functional imaging data, we measure both feature-coding and conjunction-coding directly, using the same set of visual stimuli, and pit them against each other to reveal the relative dominance of one versus the other throughout cortex. We provide the first demonstration of a transition from feature-coding in early visual cortex to conjunction-coding in both inferior temporal and posterior parietal cortices. This novel method enables the use of experimentally controlled stimulus features to investigate population-level feature- and conjunction-codes throughout human cortex.

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bioRxiv2016

Predicting brain-age from multimodal imaging data captures cognitive impairment

The disparity between the chronological age of an individual and their brain-age measured based on biological information has the potential to offer clinically-relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brainimaging data improves age prediction. Using cortical anatomy and whole-brain functional connectivity on a large adult lifespan sample (N = 2354, age 19-82), we found that multimodal data improves brain-based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain-age measure was robust to confounding effects: head motion did not drive brain-based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N = 475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain-age prediction to confounds, generalizability across sites, and sensitivity to clinically-relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders.\n\nHighlightsO_LIBrain-based age prediction is improved with multimodal neuroimaging data.\nC_LIO_LIParticipants with cognitive impairment show increased brain aging.\nC_LIO_LIAge prediction models are robust to motion and generalize to independent datasets from other sites.\nC_LI

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bioRxiv2016

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

Brain dynamics are thought to unfold on a network determined by the pattern of axonal connections linking pairs of neuronal populations; the so-called connectome. Prior work has indicated that structural brain connectivity constrains pairwise correlations in brain dynamics (also called functional connectivity), but it is not known whether inter-regional axonal connectivity is related to the intrinsic dynamics of individual brain areas. Here we investigate this relationship using a weighted, directed mesoscale mouse connectome from the Allen Mouse Brain Connectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184 brain regions in eighteen anesthetized mice. For each brain region, we measured degree, betweenness, and clustering coefficient from weighted and unweighted, and directed and undirected versions of the connectome. We then characterized the univariate rs-fMRI dynamics at each brain region by computing 6 930 time-series properties using the time-series analysis toolbox, hctsa. After correcting for regional volume variations, strong and robust correlations between structural connectivity properties and rs-fMRI dynamics were found only when edge weights were accounted for, and were associated with variations in the autocorrelation properties of the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which was positively correlated to the autocorrelation of fMRI time series at time lag{tau} = 34s (partial Spearman correlation{rho} = 0.58), as well as a range of related measures such as relative high frequency power (f > 0.4 Hz:{rho} = -0.43). Our results indicate that the topology of inter-regional axonal connections of the mouse brain is closely related to intrinsic, spontaneous dynamics such that regions with a greater aggregate strength of incoming projections display longer timescales of activity fluctuations.

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