Effects of packetization on communication dynamics in brain networks.

Computational studies in network neuroscience build models of communication dynamics in the connectome that help us understand the structure-function relationships of the brain. In these models, the dynamics of cortical signal transmission in brain networks are approximated with simple propagation strategies such as random walks and shortest path routing. Furthermore, the signal transmission dynamics in brain networks can be associated with the switching architectures of engineered communication systems (e.g., message switching and packet switching). However, it has been unclear how propagation strategies and switching architectures are related in models of brain network communication. Here, we investigate the effects of the difference between packet switching and message switching (i.e., whether signals are packetized or not) on the transmission completion time of propagation strategies when simulating signal propagation in mammalian brain networks. The results show that packetization in the connectome with hubs increases the time of the random walk strategy and does not change that of the shortest path strategy, but decreases that of more plausible strategies for brain networks that balance between communication speed and information requirements. This finding suggests an advantage of packet-switched communication in the connectome and provides new insights into modeling the communication dynamics in brain networks.

Communication dynamics in brain networks have been modeled with various approximations to signaling in the connectome. These approximations differ in their assumptions about propagation strategies (random walks, shortest path routing) and switching architectures (message switching, packet switching); however, their relationships in brain network communication models have been unclear so far. Here, we link them by investigating how the difference between packet and message switching (whether signals are packetized or not) affects the transmission completion time of propagation strategies in communication simulations in the connectome. We find that packetization selectively reduces the time of physiologically plausible strategies for the connectome that balance communication speed and information requirements. This study sheds light on the utility of packet switching for modeling efficient communication in brain networks.

Genetic diversity among the present Japanese population: evidence from genotyping of human cell lines established in Japan.

Japan is often assumed to have a highly homogeneous ethnic population, because it is an island country. This is evident in human cell lines collected from cell banks; however, these genotypes have not been thoroughly characterized. To examine the population genotypes of human cell lines established in Japan, we conducted SNP genotyping on 57 noncancerous cell lines and 43 lung cancer cell lines. Analysis of biogeographic ancestry revealed that 58 cell lines had non-admixed Japanese genotypes, 21 cell lines had an admixture of Japanese and East Asian genotypes, and the remaining 21 cell lines had East Asian genotypes. The proportion of non-admixed Japanese genotypes was similar between lung cancer and noncancerous cell lines, suggesting that patients in Japan may not exclusively have Japanese genotypes. This could influence the incidence of inherited diseases and should be taken into account in personalized medicine tailored to genetic background. The genetic makeup of the present-day Japanese population cannot be fully explained by the ancestral Jomon and Yayoi lineages. Instead, it is necessary to consider a certain level of genetic admixture between Japanese and neighboring Asian populations. Our study revealed genetic variation among human cell lines derived from Japanese individuals, reflecting the diversity present within the Japanese population.

Three-dimensional topography of rat trigeminal ganglion neurons using a combination of retrograde labeling and tissue-clearing techniques.

The trigeminal nerve is the sensory afferent of the orofacial regions and divided into three major branches. Cell bodies of the trigeminal nerve lie in the trigeminal ganglion and are surrounded by satellite cells. There is a close interaction between ganglion cells via satellite cells, but the function is not fully understood. In the present study, we clarified the ganglion cells' three-dimensional (3D) localization, which is essential to understand the functions of cell-cell interactions in the trigeminal ganglion. Fast blue was injected into 12 sites of the rat orofacial regions, and ganglion cells were retrogradely labeled. The labeled trigeminal ganglia were cleared by modified 3DISCO, imaged with confocal laser-scanning microscopy, and reconstructed in 3D. Histograms of the major axes of the fast blue-positive somata revealed that the peak major axes of the cells innervating the skin/mucosa were smaller than those of cells innervating the deep structures. Ganglion cells innervating the ophthalmic, maxillary, and mandibular divisions were distributed in the anterodorsal, central, and posterolateral portions of the trigeminal ganglion, respectively, with considerable overlap in the border region. The intermingling in the distribution of ganglion cells within each division was also high, in particular, within the mandibular division. Specifically, intermingling was observed in combinations of tongue and masseter/temporal muscles, maxillary/mandibular molars and masseter/temporal muscles, and tongue and mandibular molars. Double retrograde labeling confirmed that some ganglion cells innervating these combinations were closely apposed. Our data provide essential information for understanding the function of ganglion cell-cell interactions via satellite cells.

THP-1 reference data: Proposal of an in vitro branched evolution model for cancer cell lines.

THP-1 is a representative leukemia cell line and is registered with four different numbers in JCRB and RIKEN BRC cell banks. However, differences between these four lines remain unclear. In our study, these four THP-1 cell lines, JCRB0112, JCRB0112.1 (corresponding to ATCC TIB-202), RCB1189 (DSMZ ACC-16) and RCB3686, have been compared at chromosome and DNA sequence levels. Our results reveal that ploidy has been changed in JCRB0112 and RCB1189, which are triploid and tetraploid, respectively. Patterns of variant frequencies from target sequencing are unique to each ploidy, estimating whole genomic status based on partial sequence data. SNP microarrays showed four distinct profiles with a large-scale loss of heterozygosity, reflected in subtle differences in STR genotypes. Transcriptome patterns suggest that JCRB0112.1 has diverged highly from the other three lines. RCB1189 and JCRB0112.1 responded to PMA faster than RCB3686 and JCRB0112. We have identified RCB3686 as the closest to the original THP-1, which can be an optimal model of AML-M5. These four THP-1 genomes and transcriptomes exhibit significant differences, indicating four independent sublines and demonstrating the influence of genetic drift on gene expression. As these cells share the same name, THP-1 must be accompanied by their registration number of each cell repository. Our data provide genomic features of four THP-1 sublines and serve as a reference profile to classify widely spread THP-1 progenies, which could be distinguished by a comparison of 24 STR markers. Multiple sublines can be generated by separate cell cultures, which would be explained by in vitro branched evolution.

Modular origins of high-amplitude cofluctuations in fine-scale functional connectivity dynamics.

The topology of structural brain networks shapes brain dynamics, including the correlation structure of brain activity (functional connectivity) as estimated from functional neuroimaging data. Empirical studies have shown that functional connectivity fluctuates over time, exhibiting patterns that vary in the spatial arrangement of correlations among segregated functional systems. Recently, an exact decomposition of functional connectivity into frame-wise contributions has revealed fine-scale dynamics that are punctuated by brief and intermittent episodes (events) of high-amplitude cofluctuations involving large sets of brain regions. Their origin is currently unclear. Here, we demonstrate that similar episodes readily appear in silico using computational simulations of whole-brain dynamics. As in empirical data, simulated events contribute disproportionately to long-time functional connectivity, involve recurrence of patterned cofluctuations, and can be clustered into distinct families. Importantly, comparison of event-related patterns of cofluctuations to underlying patterns of structural connectivity reveals that modular organization present in the coupling matrix shapes patterns of event-related cofluctuations. Our work suggests that brief, intermittent events in functional dynamics are partly shaped by modular organization of structural connectivity.

The Periodontium Damage Induces Neuronal Cell Death in the Trigeminal Mesencephalic Nucleus and Neurodegeneration in the Trigeminal Motor Nucleus in C57BL/6J Mice.

Proprioception from masticatory apparatus and periodontal ligaments comes through the trigeminal mesencephalic nucleus (Vmes). We evaluated the effects of tooth loss on neurodegeneration of the Vmes and trigeminal motor nucleus (Vmo). Bilateral maxillary molars of 2-month-old C57BL/6J mice were extracted under anesthesia. Neural projections of the Vmes to the periodontium were confirmed by injecting Fluoro-Gold (FG) retrogradely into the extraction sockets, and for the anterograde labeling adeno-associated virus encoding green fluorescent protein (AAV-GFP) was applied. For immunohistochemistry, Piezo2, ATF3, Caspase 3, ChAT and TDP-43 antibodies were used. At 1 month after tooth extraction, the number of Piezo2-immunoreactive (IR) Vmes neurons were decreased significantly. ATF3-IR neurons were detected on day 5 after tooth extraction. Dead cleaved caspase-3-IR neurons were found among Vmes neurons on days 7 and 12. In the Vmo, neuronal cytoplasmic inclusions (NCIs) formation type of TDP-43 increased at 1 and 2 months after extraction. These indicate the existence of neural projections from the Vmes to the periodontium in mice and that tooth loss induces the death of Vmes neurons followed by TDP-43 pathology in the Vmo. Therefore, tooth loss induces Vmes neuronal cell death, causing Vmo neurodegeneration and presumably affecting masticatory function.

Structural determinants of dynamic fluctuations between segregation and integration on the human connectome.

While segregation and integration of neural information in the neocortex are thought to be important for human behavior and cognition, the neural substrates enabling their dynamic fluctuations remain elusive. To tackle this problem, we aim to identify specific network features of the connectome that are responsible for the emergence of dynamic fluctuations between segregated and integrated patterns in human resting-state functional connectivity. Here we examine the contributions of network features to dynamic fluctuations by constructing rewired surrogate connectome in which network features of interest are selectively preserved, and then by assessing the magnitude of fluctuations simulated with these surrogates. Our analysis demonstrates significant contributions from global geometry and topology of the connectome, as well as from localized structural connections involving visual areas. By providing structural accounts of dynamic fluctuations in functional connectivity, this study offers new insights into generative mechanisms driving temporal changes in segregation and integration in the brain.

Temporal stability of functional brain modules associated with human intelligence.

Individual differences in general cognitive ability (i.e., intelligence) have been linked to individual variations in the modular organization of functional brain networks. However, these analyses have been limited to static (time-averaged) connectivity, and have not yet addressed whether dynamic changes in the configuration of brain networks relate to general intelligence. Here, we used multiband functional MRI resting-state data (N = 281) and estimated subject-specific time-varying functional connectivity networks. Modularity optimization was applied to determine individual time-variant module partitions and to assess fluctuations in modularity across time. We show that higher intelligence, indexed by an established composite measure, the Wechsler Abbreviated Scale of Intelligence (WASI), is associated with higher temporal stability (lower temporal variability) of brain network modularity. Post-hoc analyses reveal that subjects with higher intelligence scores engage in fewer periods of extremely high modularity - which are characterized by greater disconnection of task-positive from task-negative networks. Further, we show that brain regions of the dorsal attention network contribute most to the observed effect. In sum, our study suggests that investigating the temporal dynamics of functional brain network topology contributes to our understanding of the neural bases of general cognitive abilities.

Signature Patterns for Top-Down and Bottom-Up Information Processing via Cross-Frequency Coupling in Macaque Auditory Cortex.

Predictive coding is a theoretical framework that provides a functional interpretation of top-down and bottom-up interactions in sensory processing. The theory suggests there are differences in message passing up versus down the cortical hierarchy. These differences result from the linear feedforward of prediction errors, and the nonlinear feedback of predictions. This implies that cross-frequency interactions should predominate top-down. But it remains unknown whether these differences are expressed in cross-frequency interactions in the brain. Here we examined bidirectional cross-frequency coupling across four sectors of the auditory hierarchy in the macaque. We computed two measures of cross-frequency coupling, phase-amplitude coupling (PAC) and amplitude-amplitude coupling (AAC). Our findings revealed distinct patterns for bottom-up and top-down information processing among cross-frequency interactions. Both top-down and bottom-up interactions made prominent use of low frequencies: low-to-low-frequency (theta, alpha, beta) and low-frequency-to-high- gamma couplings were predominant top-down, while low-frequency-to-low-gamma couplings were predominant bottom-up. These patterns were largely preserved across coupling types (PAC and AAC) and across stimulus types (natural and synthetic auditory stimuli), suggesting that they are a general feature of information processing in auditory cortex. Our findings suggest the modulatory effect of low frequencies on gamma-rhythms in distant regions is important for bidirectional information transfer. The finding of low-frequency-to-low-gamma interactions in the bottom-up direction suggest that nonlinearities may also play a role in feedforward message passing. Altogether, the patterns of cross-frequency interaction we observed across the auditory hierarchy are largely consistent with the predictive coding framework.

TDP-43 accelerates deadenylation of target mRNAs by recruiting Caf1 deadenylase.

TAR DNA-binding protein 43 (TDP-43) is an RNA-binding protein, whose loss-of-function mutation causes amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration. Recent studies demonstrated that TDP-43 binds to the 3' untranslated region (UTR) of target mRNAs to promote mRNA instability. Here, we show that TDP-43 recruits Caf1 deadenylase to mRNA targets and accelerates their deadenylation. Tethering TDP-43 to the mRNA 3'UTR recapitulates destabilization of the mRNA, and TDP-43 accelerates their deadenylation. This accelerated deadenylation is inhibited by a dominant negative mutant of Caf1. We find that TDP-43 physically interacts with Caf1. In addition, we provide evidence that TDP-43 regulates poly(A) tail length of endogenous Progranulin (GRN) mRNA. These results may shed light on the link between dysregulation of TDP-43-mediated mRNA deadenylation and pathogenesis of neurodegenerative diseases.

Comparison of fluctuations in global network topology of modeled and empirical brain functional connectivity.

Dynamic models of large-scale brain activity have been used for reproducing many empirical findings on human brain functional connectivity. Features that have been shown to be reproducible by comparing modeled to empirical data include functional connectivity measured over several minutes of resting-state functional magnetic resonance imaging, as well as its time-resolved fluctuations on a time scale of tens of seconds. However, comparison of modeled and empirical data has not been conducted yet for fluctuations in global network topology of functional connectivity, such as fluctuations between segregated and integrated topology or between high and low modularity topology. Since these global network-level fluctuations have been shown to be related to human cognition and behavior, there is an emerging need for clarifying their reproducibility with computational models. To address this problem, we directly compared fluctuations in global network topology of functional connectivity between modeled and empirical data, and clarified the degree to which a stationary model of spontaneous brain dynamics can reproduce the empirically observed fluctuations. Modeled fluctuations were simulated using a system of coupled phase oscillators wired according to brain structural connectivity. By performing model parameter search, we found that modeled fluctuations in global metrics quantifying network integration and modularity had more than 80% of magnitudes of those observed in the empirical data. Temporal properties of network states determined based on fluctuations in these metrics were also found to be reproducible, although their spatial patterns in functional connectivity did not perfectly matched. These results suggest that stationary models simulating resting-state activity can reproduce the magnitude of empirical fluctuations in segregation and integration, whereas additional factors, such as active mechanisms controlling non-stationary dynamics and/or greater accuracy of mapping brain structural connectivity, would be necessary for fully reproducing the spatial patterning associated with these fluctuations.

Vertebral fracture after removing pedicle screws used for posterior lumbar interbody fusion: A case report.

It is uncommon to remove pedicle screws after posterolateral lumbar interbody fusion (PLIF), and there are a few case reports of vertebral fracture involving holes remaining after screw removal. We report a case of the vertebral fracture after removing pedicle screws instrumented for PLIF. A 66-year-old woman with osteopenia, who underwent PLIF at L4-S1 10 years earlier, underwent PLIF at L3-L4 to correct adjacent-segment degeneration. We removed L5 and S1 pedicle screws, and inserted L3 pedicle screws. After surgery, she presented with severe progressive low back pain. Computed tomography (CT) and magnetic resonance imaging (MRI) revealed an L5 body fracture involving the pedicle screw holes. Revision surgery, including posterolateral fusion from L3 to S2 alar iliac and L5 vertebroplasty, was performed. One year after the revision surgery, the patient remained pain-free and returned to normal activity. Possible reasons for instrumented vertebral body fracture after pedicle screw removal are decrease of vertebral mass, increased mechanical stress caused by PLIF at the adjacent segment, stress-shielding-related osteopenia, and spinal imbalance. Vertebral body fractures associated with spinal implant removal are rare, but possible, especially in elderly patients with osteopenia and osteoporosis.

Fluctuations between high- and low-modularity topology in time-resolved functional connectivity.

Modularity is an important topological attribute for functional brain networks. Recent human fMRI studies have reported that modularity of functional networks varies not only across individuals being related to demographics and cognitive performance, but also within individuals co-occurring with fluctuations in network properties of functional connectivity, estimated over short time intervals. However, characteristics of these time-resolved functional networks during periods of high and low modularity have remained largely unexplored. In this study we investigate basic spatiotemporal properties of time-resolved networks in the high and low modularity periods during rest, with a particular focus on their spatial connectivity patterns, temporal homogeneity and test-retest reliability. We show that spatial connectivity patterns of time-resolved networks in the high and low modularity periods are represented by increased and decreased dissociation of the default mode network module from task-positive network modules, respectively. We also find that the instances of time-resolved functional connectivity sampled from within the high (respectively, low) modularity period are relatively homogeneous (respectively, heterogeneous) over time, indicating that during the low modularity period the default mode network interacts with other networks in a variable manner. We confirmed that the occurrence of the high and low modularity periods varies across individuals with moderate inter-session test-retest reliability and that it is correlated with previously-reported individual differences in the modularity of functional connectivity estimated over longer timescales. Our findings illustrate how time-resolved functional networks are spatiotemporally organized during periods of high and low modularity, allowing one to trace individual differences in long-timescale modularity to the variable occurrence of network configurations at shorter timescales.

Structure-function relationships during segregated and integrated network states of human brain functional connectivity.

Structural white matter connections are thought to facilitate integration of neural information across functionally segregated systems. Recent studies have demonstrated that changes in the balance between segregation and integration in brain networks can be tracked by time-resolved functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and that fluctuations between segregated and integrated network states are related to human behavior. However, how these network states relate to structural connectivity is largely unknown. To obtain a better understanding of structural substrates for these network states, we investigated how the relationship between structural connectivity, derived from diffusion tractography, and functional connectivity, as measured by rs-fMRI, changes with fluctuations between segregated and integrated states in the human brain. We found that the similarity of edge weights between structural and functional connectivity was greater in the integrated state, especially at edges connecting the default mode and the dorsal attention networks. We also demonstrated that the similarity of network partitions, evaluated between structural and functional connectivity, increased and the density of direct structural connections within modules in functional networks was elevated during the integrated state. These results suggest that, when functional connectivity exhibited an integrated network topology, structural connectivity and functional connectivity were more closely linked to each other and direct structural connections mediated a larger proportion of neural communication within functional modules. Our findings point out the possibility of significant contributions of structural connections to integrative neural processes underlying human behavior.

Thalamic connections of the core auditory cortex and rostral supratemporal plane in the macaque monkey.

In the primate auditory cortex, information flows serially in the mediolateral dimension from core, to belt, to parabelt. In the caudorostral dimension, stepwise serial projections convey information through the primary, rostral, and rostrotemporal (AI, R, and RT) core areas on the supratemporal plane, continuing to the rostrotemporal polar area (RTp) and adjacent auditory-related areas of the rostral superior temporal gyrus (STGr) and temporal pole. In addition to this cascade of corticocortical connections, the auditory cortex receives parallel thalamocortical projections from the medial geniculate nucleus (MGN). Previous studies have examined the projections from MGN to auditory cortex, but most have focused on the caudal core areas AI and R. In this study, we investigated the full extent of connections between MGN and AI, R, RT, RTp, and STGr using retrograde and anterograde anatomical tracers. Both AI and R received nearly 90% of their thalamic inputs from the ventral subdivision of the MGN (MGv; the primary/lemniscal auditory pathway). By contrast, RT received only ∼45% from MGv, and an equal share from the dorsal subdivision (MGd). Area RTp received ∼25% of its inputs from MGv, but received additional inputs from multisensory areas outside the MGN (30% in RTp vs. 1-5% in core areas). The MGN input to RTp distinguished this rostral extension of auditory cortex from the adjacent auditory-related cortex of the STGr, which received 80% of its thalamic input from multisensory nuclei (primarily medial pulvinar). Anterograde tracers identified complementary descending connections by which highly processed auditory information may modulate thalamocortical inputs.

Intrinsic Connections of the Core Auditory Cortical Regions and Rostral Supratemporal Plane in the Macaque Monkey.

In the ventral stream of the primate auditory cortex, cortico-cortical projections emanate from the primary auditory cortex (AI) along 2 principal axes: one mediolateral, the other caudorostral. Connections in the mediolateral direction from core, to belt, to parabelt, have been well described, but less is known about the flow of information along the supratemporal plane (STP) in the caudorostral dimension. Neuroanatomical tracers were injected throughout the caudorostral extent of the auditory core and rostral STP by direct visualization of the cortical surface. Auditory cortical areas were distinguished by SMI-32 immunostaining for neurofilament, in addition to established cytoarchitectonic criteria. The results describe a pathway comprising step-wise projections from AI through the rostral and rostrotemporal fields of the core (R and RT), continuing to the recently identified rostrotemporal polar field (RTp) and the dorsal temporal pole. Each area was strongly and reciprocally connected with the areas immediately caudal and rostral to it, though deviations from strictly serial connectivity were observed. In RTp, inputs converged from core, belt, parabelt, and the auditory thalamus, as well as higher order cortical regions. The results support a rostrally directed flow of auditory information with complex and recurrent connections, similar to the ventral stream of macaque visual cortex.

[Information Processing in the Auditory Ventral Stream].

The auditory cortex in humans comprises multiple auditory fields organized hierarchically, similar to that in non-human primates. The ventral auditory stream of the macaque consists of several subdivisions on the supratemporal plane (STP) and the superior temporal gyrus (STG). There are two main axes (caudorostral and mediolateral) for processing auditory information in the STP and STG. Here, we review the neural basis of the integration of spectral and temporal auditory information along the two axes of the ventral auditory stream in the macaque.

Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks.

We investigate the relationship of resting-state fMRI functional connectivity estimated over long periods of time with time-varying functional connectivity estimated over shorter time intervals. We show that using Pearson's correlation to estimate functional connectivity implies that the range of fluctuations of functional connections over short time-scales is subject to statistical constraints imposed by their connectivity strength over longer scales. We present a method for estimating time-varying functional connectivity that is designed to mitigate this issue and allows us to identify episodes where functional connections are unexpectedly strong or weak. We apply this method to data recorded from N=80 participants, and show that the number of unexpectedly strong/weak connections fluctuates over time, and that these variations coincide with intermittent periods of high and low modularity in time-varying functional connectivity. We also find that during periods of relative quiescence regions associated with default mode network tend to join communities with attentional, control, and primary sensory systems. In contrast, during periods where many connections are unexpectedly strong/weak, default mode regions dissociate and form distinct modules. Finally, we go on to show that, while all functional connections can at times manifest stronger (more positively correlated) or weaker (more negatively correlated) than expected, a small number of connections, mostly within the visual and somatomotor networks, do so a disproportional number of times. Our statistical approach allows the detection of functional connections that fluctuate more or less than expected based on their long-time averages and may be of use in future studies characterizing the spatio-temporal patterns of time-varying functional connectivity.

Temporal and rate code analysis of responses to low-frequency components in the bird's own song by song system neurons.

Auditory feedback (AF) plays a critical role in vocal learning. Previous studies in songbirds suggest that low-frequency (<~1 kHz) components may be salient cues in AF. We explored this with auditory stimuli including the bird's own song (BOS) and BOS variants with increased relative power at low frequencies (LBOS). We recorded single units from BOS-selective neurons in two forebrain nuclei (HVC and Area X) in anesthetized zebra finches. Song-evoked responses were analyzed based on both rate (spike counts) and temporal coding of spike trains. The BOS and LBOS tended to evoke similar spike-count responses in substantially overlapping populations of neurons in both HVC and Area X. Analysis of spike patterns demonstrated temporal coding information that discriminated among the BOS and LBOS stimuli significantly better than spike counts in the majority of HVC (94 %) and Area X (85 %) neurons. HVC neurons contained more and a broader range of temporal coding information to discriminate among the stimuli than Area X neurons. These results are consistent with a role of spike timing in coding differences in the spectral components of BOS in HVC and Area X neurons.

Studying brain functions with mesoscopic measurements: Advances in electrocorticography for non-human primates.

Our brain is organized in a modular structure. Information in different modalities is processed within distinct cortical areas. However, individual cortical areas cannot enable complex cognitive functions without interacting with other cortical areas. Electrocorticography (ECoG) has recently become an important tool for studying global network activity across cortical areas in animal models. With stable recordings of electrical field potentials from multiple cortical areas, ECoG provides an opportunity to systematically study large-scale cortical activity at a mesoscopic spatiotemporal resolution under various experimental conditions. Recent developments in thin, flexible ECoG electrodes permit recording field potentials from not only gyral but intrasulcal cortical surfaces. Our review here focuses on the recent advances of ECoG applications to non-human primates.