Co-activation Patterns Research Articles

Task-based co-activation patterns reliably predict resting state canonical network engagement during development

Neurodevelopmental research has traditionally focused on development of individual structures, yet multiple lines of evidence indicate parallel development of large-scale systems, including canonical neural networks (i.e., default mode, frontoparietal). However, the relationship between region- vs. network-level development remains poorly understood. The current study tests the ability of a recently developed multi-task coactivation matrix approach to predict canonical resting state network engagement at baseline and at two-year follow-up in a large and cohort of young adolescents. Pre-processed tabulated neuroimaging data were obtained from the Adolescent Brain and Cognitive Development (ABCD) study, assessing youth at baseline (N = 6073, age = 10.0 ± 0.6 years, 3056 female) and at two-year follow-up (N = 3539, age = 11.9 ± 0.6 years, 1726 female). Individual multi-task co-activation matrices were constructed from the beta weights of task contrasts from the stop signal task, the monetary incentive delay task, and emotional N-back task. Activation-based predictive modeling, a cross-validated machine learning approach, was adopted to predict resting-state canonical network engagement from multi-task co-activation matrices at baseline. Note that the tabulated data used different parcellations of the task fMRI data (“ASEG” and Desikan) and the resting-state fMRI data (Gordon). Despite this, the model successfully predicted connectivity within the default mode network (DMN, rho = 0.179 ± 0.002, p < 0.001) across participants and identified a subset of co-activations within parietal and occipital macroscale brain regions as key contributors to model performance, suggesting an underlying common brain functional architecture across cognitive domains. Notably, predictive features for resting-state connectivity within the DMN identified at baseline also predicted DMN connectivity at two-year follow-up (rho = 0.258). These results indicate that multi-task co-activation matrices are functionally meaningful and can be used to predict resting-state connectivity. Interestingly, given that predictive features within the co-activation matrices identified at baseline can be extended to predictions at a future time point, our results suggest that task-based neural features and models are valid predictors of resting state network level connectivity across the course of development. Future work is encouraged to verify these findings with more consistent parcellations between task-based and resting-state fMRI, and with longer developmental trajectories.

Benchmarking functional connectivity by the structure and geometry of the human brain.

The brain's structural connectivity supports the propagation of electrical impulses, manifesting as patterns of coactivation, termed functional connectivity. Functional connectivity emerges from the underlying sparse structural connections, particularly through polysynaptic communication. As a result, functional connections between brain regions without direct structural links are numerous, but their organization is not completely understood. Here we investigate the organization of functional connections without direct structural links. We develop a simple, data-driven method to benchmark functional connections with respect to their underlying structural and geometric embedding. We then use this method to reweigh and reexpress functional connectivity. We find evidence of unexpectedly strong functional connectivity among distal brain regions and within the default mode network. We also find unexpectedly strong functional connectivity at the apex of the unimodal-transmodal hierarchy. Our results suggest that both phenomena-functional modules and functional hierarchies-emerge from functional interactions that transcend the underlying structure and geometry. These findings also potentially explain recent reports that structural and functional connectivity gradually diverge in transmodal cortex. Collectively, we show how structural connectivity and geometry can be used as a natural frame of reference with which to study functional connectivity patterns in the brain.

Functional gradients in the human lateral prefrontal cortex revealed by a comprehensive coordinate-based meta-analysis.

The lateral prefrontal cortex (LPFC) of humans enables flexible goal-directed behavior. However, its functional organization remains actively debated after decades of research. Moreover, recent efforts aiming to map the LPFC through meta-analysis are limited, either in scope or in the inferred specificity of structure-function associations. These limitations are in part due to the limited expressiveness of commonly-used data analysis tools, which restricts the breadth and complexity of questions that can be expressed in a meta-analysis. Here, we adopt NeuroLang, a novel approach to more expressive meta-analysis based on probabilistic first-order logic programming, to infer the organizing principles of the LPFC from 14,371 neuroimaging studies. Our findings reveal a rostrocaudal and a dorsoventral gradient, respectively explaining the most and second most variance in meta-analytic connectivity across the LPFC. Moreover, we identify a unimodal-to-transmodal spectrum of coactivation patterns along with a concrete-to-abstract axis of structure-function associations extending from caudal to rostral regions of the LPFC. Finally, we infer inter-hemispheric asymmetries along the principal rostrocaudal gradient, identifying hemisphere-specific associations with topics of language, memory, response inhibition, and sensory processing. Overall, this study provides a comprehensive meta-analytic mapping of the LPFC, grounding future hypothesis generation on a quantitative overview of past findings.

Brain networks under uncertainty: A coordinate-based meta-analysis of brain imaging studies

In recent years, uncertainty has been extensively studied as a core factor in anxiety models. However, it remains unclear whether there is a stable brain circuitry to cope with uncertainty. Addressing this yet open question, we first distinguish uncertainty into three different states: risky, ambiguity, and threat anticipation. Then, we performed three meta-analyses of fMRI studies to identify those regions that are commonly activated by the three domains using activation likelihood estimation (ALE). The overlapping analyses of the three ALE maps revealed major conjunctions of the risk decision making, ambiguity decision making, and the threat anticipation in specifically the right insula. Contrast analysis further confirmed this finding. In addition, different uncertainty states also have different brain networks involved. Specifically, a large number of brain regions in the frontal-parietal cortex were recruited under ambiguity state, while subcortical gray matter regions were recruited under risk decision making, and the bilateral insula were closely associated with threat anticipation. Additionally, we assessed the co-activation pattern of identified regions using meta-analytic connectivity modeling (MACM) to investigate the potential network underlying the relationship of three domains. The MACM analysis further confirmed that different uncertain states have specific brain network basis. We concluded that the right insula serves as a convergent brain region for brain regions recruited for different uncertain states, and its co-activation pattern also corresponds to the brain network of the three uncertain states. This study is a preliminary attempt to further uncover the brain circuitry of anxiety models with uncertainty at their core.

A locus coeruleus-dorsal CA1 dopaminergic circuit modulates memory linking

Individual memories are often linked so that the recall of one triggers the recall of another. For example, contextual memories acquired close in time can be linked, and this is known to depend on a temporary increase in excitability that drives the overlap between dorsal CA1 (dCA1) hippocampal ensembles that encode the linked memories. Here, we show that locus coeruleus (LC) cells projecting to dCA1 have a key permissive role in contextual memory linking, without affecting contextual memory formation, and that this effect is mediated by dopamine. Additionally, we found that LC-to-dCA1-projecting neurons modulate the excitability of dCA1 neurons and the extent of overlap between dCA1 memory ensembles as well as the stability of coactivity patterns within these ensembles. This discovery of a neuromodulatory system that specifically affects memory linking without affecting memory formation reveals a fundamental separation between the brain mechanisms modulating these two distinct processes.

Cross-modal and cross-language activation in bilinguals reveals lexical competition even when words or signs are unheard or unseen

We exploit the phenomenon of cross-modal, cross-language activation to examine the dynamics of language processing. Previous within-language work showed that seeing a sign coactivates phonologically related signs, just as hearing a spoken word coactivates phonologically related words. In this study, we conducted a series of eye-tracking experiments using the visual world paradigm to investigate the time course of cross-language coactivation in hearing bimodal bilinguals (Spanish-Spanish Sign Language) and unimodal bilinguals (Spanish/Basque). The aim was to gauge whether (and how) seeing a sign could coactivate words and, conversely, how hearing a word could coactivate signs and how such cross-language coactivation patterns differ from within-language coactivation. The results revealed cross-language, cross-modal activation in both directions. Furthermore, comparison with previous findings of within-language lexical coactivation for spoken and signed language showed how the impact of temporal structure changes in different modalities. Spoken word activation follows the temporal structure of that word only when the word itself is heard; for signs, the temporal structure of the sign does not govern the time course of lexical access (location coactivation precedes handshape coactivation)-even when the sign is seen. We provide evidence that, instead, this pattern of activation is motivated by how common in the lexicon the sublexical units of the signs are. These results reveal the interaction between the perceptual properties of the explicit signal and structural linguistic properties. Examining languages across modalities illustrates how this interaction impacts language processing.

Meta-analytic connectivity modeling of the left and right inferior frontal gyri

BackgroundNeurocognitive models of language processing highlight the role of the left inferior frontal gyrus (IFG) in the functional network underlying language. Furthermore, neuroscience research has shown that IFG is not a uniform region anatomically, cytoarchitectonically or functionally. However, no previous study explored the language-related functional connectivity patterns of IFG subdivisions using a meta-analytic connectivity modeling (MACM) approach. PurposeThe present MACM study aimed to identify language-related coactivation patterns of the left and right IFG subdivisions. MethodSix regions of interest (ROIs) were defined using a probabilistic brain atlas corresponding to pars opercularis, pars triangularis and pars orbitalis of IFG in both hemispheres. The ROIs were used to search the BrainMap functional database to identify neuroimaging experiments with healthy, right-handed participants reporting language-related activations in each ROI. Activation likelihood estimation analyses were then performed on the foci extracted from the identified studies to compute functional convergence for each ROI, which was also contrasted with the other ROIs within the same hemisphere. ResultsA primarily left-lateralized functional network was revealed for the left and right IFG subdivisions. The left-hemispheric ROIs exhibited more robust coactivation than the right-hemispheric ROIs. Particularly, the left pars opercularis was associated with the most extensive coactivation pattern involving bilateral frontal, bilateral parietal, left temporal, left subcortical, and right cerebellar regions, while the left pars triangularis and orbitalis revealed a predominantly left-lateralized involvement of frontotemporal regions. ConclusionThe findings align with the neurocognitive models of language processing that propose a division of labor among the left IFG subdivisions and their respective functional networks. Also, the opercular part of left IFG stands out as a major hub in the language network with connections to diverse cortical, subcortical and cerebellar structures.

Local dynamic stability of the trunk after prolonged seating with axial load

Fatigue from prolonged seating with an axial load on the trunk may impair neuromuscular control and spine stability which may elevate risk of low back pain (LBP) for dynamic tasks following seating. The objective of this study was to assess local dynamic trunk stability using the maximum Lyapunov exponent (λMAX) with corresponding coactivation patterns to understand possible effects from prolonged seating. An increase in λMAX would indicate decreased stability. Twenty participants (10 male, 10 female) performed a controlled, cyclic sagittal flexion task at 40 cycles per minute before and after three hours of seating in a simulated helicopter-seating environment with a weighted vest. A statistically significant decrease was seen in λMAX (bits/s) (Pre-Test = 0.654 ± 0.172; Post-Test = 0.829 ± 0.268, p = 0.002), trunk cumulative coactivation index (unitless/s) (Pre-Test = 1.71 ± 0.97; Post-Test = 1.59 ± 0.96, p = 0.0095), and abdominal activation (normalized) (Pre-Test = 0.46 ± 0.17, Post-Test = 0.41 ± 0.18, p = 0.0146) post-seating exposure. Trunk extension was reduced (∼4°, p = 0.0004) during the post-seating cyclic test with slight corresponding increases in flexion. This study provides evidence of potential effects of fatigue from prolonged seating to neuromuscular control, which may have implications for occupations requiring highly dynamic tasks after prolonged seated postures. Future studies would repeat the tests with dynamic environments (i.e., vibration), test the cyclic flexion protocols with different seating interventions, and continue to test the approach to develop a tool to assess back impairment or intervention effectiveness.

Spatial-topographic nestedness of interoceptive regions within the networks of decision making and emotion regulation: Combining ALE meta-analysis and MACM analysis

Prominent theories propose that interoception modulates our behavioral and emotional responses involving decision-making and emotion regulation. Are the regions implicated in interoception also spatially related to and possibly nested within the networks of decision making and emotion regulation? Addressing this question, we performed three meta-analyses of functional magnetic resonance imaging studies to identify the regions that are commonly activated by the three domains using activation likelihood estimation (ALE). Additionally, we assessed the coactivation pattern of identified common regions using meta-analytic connectivity modeling (MACM). The results showed major overlaps of interoception with both decision making and emotion regulation in specifically the right dorsal anterior insula. The pairwise contrast analyses confirmed this finding and revealed conjunction-based activities in decision making and emotion regulation in the dorsal anterior cingulate cortex (dACC). MACM based on the identified insula revealed a widespread convergent coactivation pattern with the left anterior insula, dACC, and bilateral thalamus which, together, constitute the salience network. Among these co-activated regions, bilateral insula and the dACC were shared among all three domains. These results suggest that the regions mediating interoception including intero-exteroceptive integration and salience attribution are contained and thus spatially nested within the more extensive networks recruited during decision making and emotion regulation.

Transient brain-wide coactivations and structured transitions revealed in hemodynamic imaging data

Brain-wide patterns in resting human brains, as either structured functional connectivity (FC) or recurring brain states, have been widely studied in the neuroimaging literature. In particular, resting-state FCs estimated over windowed timeframe neuroimaging data from sub-minutes to minutes using correlation or blind source separation techniques have reported many brain-wide patterns of significant behavioral and disease correlates. The present pilot study utilized a novel whole-head cap-based high-density diffuse optical tomography (DOT) technology, together with data-driven analysis methods, to investigate recurring transient brain-wide patterns in spontaneous fluctuations of hemodynamic signals at the resolution of single timeframes from thirteen healthy adults in resting conditions. Our results report that a small number, i.e., six, of brain-wide coactivation patterns (CAPs) describe major spatiotemporal dynamics of spontaneous hemodynamic signals recorded by DOT. These CAPs represent recurring brain states, showing spatial topographies of hemispheric symmetry, and exhibit highly anticorrelated pairs. Moreover, a structured transition pattern among the six brain states is identified, where two CAPs with anterior-posterior spatial patterns are significantly involved in transitions among all brain states. Our results further elucidate two brain states of global positive and negative patterns, indicating transient neuronal coactivations and co-deactivations, respectively, over the entire cortex. We demonstrate that these two brain states are responsible for the generation of a subset of peaks and troughs in global signals (GS), supporting the recent reports on neuronal relevance of hemodynamic GS. Collectively, our results suggest that transient neuronal events (i.e., CAPs), global brain activity, and brain-wide structured transitions co-exist in humans and these phenomena are closely related, which extend the observations of similar neuronal events recently reported in animal hemodynamic data. Future studies on the quantitative relationship among these transient events and their relationships to windowed FCs along with larger sample size are needed to understand their changes with behaviors and diseased conditions.

Effects of Computerized Biofeedback-based Balance Intervention on the Muscle Coactivation Patterns during Dynamic Postural Control in Traumatic Brain Injury.

Balance Dysfunction (BDF) is a severe conse-quence of Traumatic Brain Injury (TBI) that significantly increases the falls risk. However, the neuromuscular mecha-nisms of the BDF are not adequately researched. Therefore, in this study, our objective was to investigate the effects of a Computerized Biofeedback-based Balance Intervention (CBBI) on the muscle coactivation patterns in a group of TBI participants. This study presents the findings from 13 TBI individuals randomized into the Intervention group (TBI - INT, N=6) and Control group (TBI-CTL, N=7). Using a computerized posturography platform (Neurocom Balance Master) during baseline and follow-up assessment visits, the participant's pos-tural response to anterior-posterior balance perturbations were recorded in a multimodal setup including electroencephalogra-phy (EEG), electromyography (EMG), and the platform sway in terms of center of pressure (COP). The muscle responses were recorded from lower-limb muscles, including tibialis an-terior (TA) and gastrocnemius (GAST), whose coactivation was computed using a metric called Co-Contraction Index (CCI). Clinical outcome measures such as Berg Balance Scale (BBS), 10 Meter Walk Test (10MWT), and Timed Up-and-Go (TUG) tests were used to evaluate functional balance and mobility. The comparison of CCI values across time points (baseline and follow-up) revealed a significant decrease (p<0.01) in the TBI-INT group but not TBI-CTL. The intervention-related changes in CCI correlated with the changes in BBS score (from baseline to follow-up). These preliminary findings demonstrate that the CBBI training may help postural stability by facilitating the coactivation between muscles involved in postural control. Clinical relevance- The current knowledge of changes in the neuromuscular response to balance perturbation in TBI is limited. Our study opens the possibility of using the muscle CCI metric to evaluate the muscle response in individuals with impaired balance.

Brain-wide neural co-activations in resting human

Spontaneous neural activity in human as assessed with resting-state functional magnetic resonance imaging (fMRI) exhibits brain-wide coordinated patterns in the frequency of < 0.1 Hz. However, understanding of fast brain-wide networks at the timescales of neuronal events (milliseconds to sub-seconds) and their spatial, spectral, and transitional characteristics remain limited due to the temporal constraints of hemodynamic signals. With milli-second resolution and whole-head coverage, scalp-based electroencephalography (EEG) provides a unique window into brain-wide networks with neuronal-timescale dynamics, shedding light on the organizing principles of brain functions. Using the state-of-the-art signal processing techniques, we reconstructed cortical neural tomography from resting-state EEG and extracted component-based co-activation patterns (cCAPs). These cCAPs revealed brain-wide intrinsic networks and their dynamics, indicating the configuration/reconfiguration of resting human brains into recurring and transitional functional states, which are featured with the prominent spatial phenomena of global patterns and anti-state pairs of co-(de)activations. Rich oscillational structures across a wide frequency band (i.e., 0.6 Hz, 5 Hz, and 10 Hz) were embedded in the nonstationary dynamics of these functional states. We further identified a superstructure that regulated between-state immediate and long-range transitions involving the entire set of identified cCAPs and governed a significant aspect of brain-wide network dynamics. These findings demonstrated how resting-state EEG data can be functionally decomposed using cCAPs to reveal rich dynamic structures of brain-wide human neural activations.

The co-activation patterns of multiple brain regions in Juvenile Myoclonic Epilepsy.

Juvenile myoclonic epilepsy (JME) as an idiopathic generalized epilepsy has been studied by many advanced neuroimaging techniques to elucidate its neuroanatomical basis and pathophysiological mechanisms. In this paper, we used co-activation patterns (CAPs) to explore the differences of dynamic brain activity changes in resting state between JME patients and healthy controls. 27 cases JME patients and 27 cases healthy of fMRI data were collected. The structural image data of the subjects were analyzed by voxel-based morphological analysis, and the regions with gray matter volume atrophy and high voxel were selected as the regions of interest. Further, the mean disease duration was used as boundary to divide the patients' data into the below-average time and the above-average time groups, which were defined as patient disease duration groups. And these data were used to construct CAPs and to compare changes in brain dynamics. It was found that the number of patterns occurrences and the possibility of switching between patterns were smaller than those in the healthy control, which indicated patients with damage to brain regions. For the patient time control group, the number of patterns occurrences and the possibility of switching between patterns were similar, while there was linear regression between the three values and disease duration. Collectively, this study provides important evidence for revealing the key brain regions of JME by studying the transformation between CAPs. Future studies could investigate the effects of receiving treatment on patient dynamic brain activity.

Pattern decorrelation in the mouse medial prefrontal cortex enables social preference and requires MeCP2

Sociability is crucial for survival, whereas social avoidance is a feature of disorders such as Rett syndrome, which is caused by loss-of-function mutations in MECP2. To understand how a preference for social interactions is encoded, we used in vivo calcium imaging to compare medial prefrontal cortex (mPFC) activity in female wild-type and Mecp2-heterozygous mice during three-chamber tests. We found that mPFC pyramidal neurons in Mecp2-deficient mice are hypo-responsive to both social and nonsocial stimuli. Hypothesizing that this limited dynamic range restricts the circuit’s ability to disambiguate coactivity patterns for different stimuli, we suppressed the mPFC in wild-type mice and found that this eliminated both pattern decorrelation and social preference. Conversely, stimulating the mPFC in MeCP2-deficient mice restored social preference, but only if it was sufficient to restore pattern decorrelation. A loss of social preference could thus indicate impaired pattern decorrelation rather than true social avoidance.

Myoelectric interface training enables targeted reduction in abnormal muscle co-activation

BackgroundAbnormal patterns of muscle co-activation contribute to impaired movement after stroke. Previously, we developed a myoelectric computer interface (MyoCI) training paradigm to improve stroke-induced arm motor impairment by reducing the abnormal co-activation of arm muscle pairs. However, it is unclear to what extent the paradigm induced changes in the overall intermuscular coordination in the arm, as opposed to changing just the muscles trained with the MyoCI. This study examined the intermuscular coordination patterns of thirty-two stroke survivors who participated in 6 weeks of MyoCI training.MethodsWe used non-negative matrix factorization to identify the arm muscle synergies (coordinated patterns of muscle activity) during a reaching task before and after the training. We examined the extent to which synergies changed as the training reduced motor impairment. In addition, we introduced a new synergy analysis metric, disparity index (DI), to capture the changes in the individual muscle weights within a synergy.ResultsThere was no consistent pattern of change in the number of synergies across the subjects after the training. The composition of muscle synergies, calculated using a traditional synergy similarity metric, also did not change after the training. However, the disparity of muscle weights within synergies increased after the training in the participants who responded to MyoCI training—that is, the specific muscles that the MyoCI was targeting became less correlated within a synergy. This trend was not observed in participants who did not respond to the training.ConclusionsThese findings suggest that MyoCI training reduced arm impairment by decoupling only the muscles trained while leaving other muscles relatively unaffected. This suggests that, even after injury, the nervous system is capable of motor learning on a highly fractionated level. It also suggests that MyoCI training can do what it was designed to do—enable stroke survivors to reduce abnormal co-activation in targeted muscles.Trial registration This study was registered at ClinicalTrials.gov (NCT03579992, Registered 09 July 2018—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03579992?term=NCT03579992&draw=2&rank=1)

Central Commands to the Elbow and Shoulder Muscles During Circular Planar Movements of Hand With Simultaneous Generation of Tangential Forces.

This study examines some of the non-linear effects of signal transduction in the human motor system, with particular emphasis on muscle hysteresis. The movement tests were analyzed in a group of eight subjects, which were asked to develop tangential force using visual biofeedback while performing slow, externally imposed, circular movements of right hand holding a moving handle operated by a computerized mechatronic system. The positional changes in the averaged EMGs of the elbow and shoulder muscles were compared for all combinations of direction of movement and generated force. Additionally, for one of the subjects, there was carried out MRI identification and 3D printing of the bones of the forelimb, shoulder, scapula and collarbone, which made it possible to reconstruct for him the length and force traces of all the muscles under study. The averaged EMG traces in muscles of both joints show their close correspondence to the related force traces, however, the co-activation patterns of activity in agonists and antagonists were also often encountered. The EMG waves related to the respective force waves were strongly dependent on the predominant direction of the muscle length changes within the correspondent force wave locations: the EMG intensities were higher for the shortening muscle movements (concentric contractions) and lower during muscle lengthening (eccentric contractions). The data obtained allows to suggest that for two-joint movements of the forelimbs, it is sufficient to consider the force and activation synergies (patterns of simultaneous activity in different muscles), ignoring at the first stage the effects associated with kinematic synergy. On the other hand, the data obtained indicate that the movement kinematics has a strong modulating effect on the activation synergy, dividing it into concentric and eccentric subtypes, in accordance with the known non-linear features of the muscle dynamics. It has been shown that the concentric and eccentric differences in the responses of the shoulder muscles are more clearly distinguishable than those in the elbow muscles. The shoulder muscles also have a more pronounced symmetry of the averaged EMG responses with respect to the ascending and descending phases of force waves, while demonstrating a lower degree of antagonist cocontraction. The data obtained suggest that the central commands in two-joint movements are determined mainly by the interdependence of force and activation synergies including both intra- and inter-joint components, while kinematic synergy can be interpreted as a potent modulator of activation synergy.

The subfornical organ regulates acidosis-evoked fear by engaging microglial acid-sensor TDAG8 and forebrain neurocircuits in male mice.

An important role of pH homeostasis has been suggested in the physiology of panic disorder, with acidosis as an interoceptive trigger leading to fear and panic. Identification of novel mechanisms that can translate acidosis into fear will promote a better understanding of panic physiology. The current study explores a role of the subfornical organ (SFO), a blood-brain barrier compromised brain area, in translating acidosis to fear-relevant behaviors. We performed SFO-targeted acidification in male, wild-type mice and mice lacking microglial acid-sensing G protein-coupled receptor-T-cell death-associated gene 8 (TDAG8). Localized SFO acidification evoked significant freezing and reduced exploration that was dependent on the presence of acid-sensor TDAG8. Acidosis promoted the activation of SFO microglia and neurons that were absent in TDAG8-deficient mice. The assessment of regional neuronal activation in wild-type and TDAG8-deficient mice following SFO acidification revealed significant acidosis and genotype-dependent alterations in the hypothalamus, amygdala, prefrontal cortex, and periaqueductal gray nuclei. Furthermore, mapping of interregional co-activation patterns revealed that SFO acidosis promoted positive hypothalamic-cortex associations and desynchronized SFO-cortex and amygdala-cortex associations, suggesting an interplay of homeostatic and fear regulatory areas. Importantly, these alterations were not evident in TDAG8-deficient mice. Overall, our data support a regulatory role of subfornical organ microglial acid sensing in acidosis-evoked fear, highlighting a centralized role of blood-brain barrier compromised nodes in interoceptive sensing and behavioral regulation. Identification of pathways by which humoral information can modulate fear behavior is relevant to panic disorder, where aberrant interoceptive signaling has been reported.

Resting-State Functional MRI of Healthy Adults: Temporal Dynamic Brain Coactivation Patterns.

Background The aging brain is typically associated with aberrant interactions of large-scale intrinsic networks. However, the dynamic variation of these networks' coactivation or deactivation across the adult lifespan remains unclear. Purpose To promote the interpretation of dynamic brain network variations underlying the complex aging process by quantifying activation levels and obtaining a clear definition of coactivation patterns (CAPs) with resting-state functional MRI (rsfMRI). Materials and Methods In a retrospective study (October 2010 to September 2013), rsfMRI data from healthy participants in the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository were used to generate CAPs by applying single-volume temporal clustering analysis. Spatial clustering analysis was then performed to capture dynamic coactivation and deactivation within or between primary sensory networks and high-order cognitive networks (including the default mode network [DMN], attentional network [AN], and frontoparietal network [FPN]). Linear relationships between dynamic metrics and age were revealed with Spearman partial correlations. Results A total of 614 participants (mean age, 54 years ± 18 [SD]; 311 women) ranging in age from 18 to 88 years were evaluated. There was a negative correlation of the CAPs (Spearman correlations: r = -0.98, P < .001) with loss of coactivation (partial correlations: r = -0.17, P < .001) and deactivation (partial correlations: r = 0.216, P < .001) with aging. The CAPs, characterized by negative correlation patterns between the DMN and AN, occurred (partial correlations: r = 0.14, P = .003) and dwelled (partial correlations: r = 0.10, P = .04) more with aging. Moreover, the AN and DMN CAP transitioned more to the AN and FPN CAP with aging (partial correlations: r = 0.17, P < .001). Conclusion The dynamics of the healthy aging brain are characterized mainly by more flexibility of the high-order cognitive networks while maintaining primary sensory functions (networks). Online supplemental material is available for this article. © RSNA, 2022 See also the editorial by Holodny in this issue.

Distributed latent dynamics underlie breathing control in the brainstem during normal breathing, OIRD, and gasping

The neural control of breathing involves the coordinated activity of diverse populations of medullary neurons distributed along the rostrocaudal aspect of the ventrolateral medulla. Here, we use high‐density electrophysiological recordings (Neuropixels) to record spiking activity simultaneously from hundreds of neurons across the ventral respiratory column (VRC, including the Bötzinger and pre‐Bötzinger complexes) in anesthetized, freely breathing mice. Across sequential recordings, we have recorded from over 13,000 medullary neurons. We couple this with an optogenetic tagging approach to identify ChAT, Dbx1, Vgat, and Vglut2 positive neurons; and reconstruct the three dimensional location of each recorded neuron. We find diverse patterns of respiratory‐related neural activity throughout the medulla that tile the breathing phase such that discrete functional cell classes cannot be quantitatively defined. Moreover, while certain genotypes are biased towards particular anatomical locations or functional activity patterns, cells active during all parts of the respiratory phase are found across the VRC, and across all genotypes. We use dimensionality reduction techniques to show that the neural populations governing breathing follow rotational trajectories on low dimensional neural manifolds. Analyses of these trajectories indicate that the termination of inspiration (“inspiratory‐off switch”) may represent an attractor in the low‐dimensional neural space. Further, during opioid induced respiratory depression (OIRD), the network reconfigures, but the low‐dimensional trajectories of the neural population are preserved. Thus, patterns of neural coactivity remain the same, while breathing is slowed. Finally, we expose animals to hypoxia which reconfigures the respiratory network into gasping. The low‐dimensional neural activity no longer follows rotational trajectories. Instead, gasping is a ballistic, non‐rotational dynamic mode strikingly different than eupnea or OIRD. The transition to gasping is represented in the low dimensional trajectories by a gradual sequestering of the neural activity away from normal eupneic rotations. Together these data suggest that the VRC functions as a distributed and well‐coordinated population. The eupneic cycle begins with the inspiratory off‐switch and is maintained by the reciprocal interactions between inspiratory and expiratory neurons. This reciprocity is lost as the network transitions into gasping, a rhythm governed by fundamentally different mechanisms.

Task-Based Co-Activation Patterns Reliably Predict Resting State Canonical Network Engagement During Development

Neurodevelopmental research has traditionally focused on development of individual structures, yet multiple lines of evidence indicate parallel development of large-scale systems, including canonical neural networks. However, the relationship between region- vs. network-level development remains poorly understood.