Biology subjects

Sadaghiani, S.

Publications and source records attributed to Sadaghiani, S..

17 recordsLinked to original sources

bioRxiv2026

Representations of spatial saliency in auditory cortex are selectively organized through temporal coordination

Selective processing of behaviorally relevant sounds in complex environments requires dynamic modulation of auditory representations. Here, we show that behaviorally relevant sound locations selectively organize auditory cortical activity through temporal coordination rather than changes in firing rate. Using laminar electrophysiology in mice, we recorded activity in primary auditory cortex (A1) activity during a task in which reward was associated with a specific sound location while neutral, non-rewarded sounds were presented across multiple spatial positions. Neurons exhibited increased response reliability, temporal precision, and spike-field coupling to gamma oscillations selectively for sounds presented from behaviorally relevant locations, even when those sounds were unrewarded. These effects were absent during passive listening. Neurons with stronger gamma coupling also had sharper spatial tuning and greater trial-to-trial reliability, linking temporal coordination to representational fidelity. These findings identify gamma-mediated synchronization as a mechanism through which spatial relevance dynamically reshapes auditory encoding.

neuroscience↗

bioRxiv2025

Disrupted white matter microstructure in chronic Zika virus-infected adults

Systemic viral infections with neurotropic potential pose significant global health challenges. The Zika virus (ZIKV) is known for its pronounced neurotropism, with recent infectious clusters raising renewed public health concerns. While research has predominantly focused on congenital populations, growing evidence suggests that the mature central nervous system (CNS) is also vulnerable. However, no study has examined the long-term impact of ZIKV infection on the adult human brain. To address this gap, we studied a rare group of adult ZIKV patients presenting with both peripheral (Guillain-Barre Syndrome; GBS) and CNS-related neurological symptoms. We compared these patients at the chronic stage (5 to 12 months post-infection) to healthy controls and to patients with GBS of non-ZIKV etiology (total N=43). Structural and functional measures included cortical thickness, white matter hyperintensities, diffusion metrics, and resting-state functional connectivity. Despite the rarity of both patient populations, power analyses indicated that our sample size could detect large group differences--effect sizes deemed reasonable given the severity of neurological symptoms in the ZIKV group. Nonetheless, our multimodal analyses yielded null results, with Bayesian statistics (where applicable) providing evidence for a lack of effects. The null findings suggest that chronic ZIKV infection in adults is not associated with brain changes of large magnitude. Importantly, this study offers detailed clinical characterization of a heavily understudied group. In light of recent ZIKV outbreaks, this characterization underscores the need to monitor, study, and provide longitudinal care to adult survivors of severe ZIKV infections.

neuroscience↗

bioRxiv2025

The impact of intrinsic connectome dynamics on perception is context-dependent

1.The behavioral relevance of the static functional connectome is well-established, but the real-time relationship between ongoing connectome dynamics and behavior remains unclear. Because behavior shifts from moment to moment, an important question arises: Are changes in behavioral outcomes linked to fluctuations in task-specific or task-general processes like arousal? Task-specific versus task-general processes should manifest as distinct versus shared connectome patterns that predict moment-to-moment behavioral outcomes across tasks. We analyzed fMRI data from three groups of healthy participants (total N=35), each performing one of three ambiguous perception tasks maximizing behavioral fluctuations: (1) Reporting faces or vase percepts on Rubins figure, (2) detecting near-random motion, or (3) identifying a near-threshold tone. Leveraging long inter-stimulus intervals (>20s), we examined how pre-stimulus connectome states influenced perception on a trial-by-trial basis. Using SVM models, pre-stimulus connectome reliably predicted upcoming post-stimulus perception. Distinct, non-overlapping sets of task-specific connections supported prediction in each task, spanning task-relevant sensory networks as well as higher-order cognitive control networks. Only when predictive connections were aggregated over the well-known intrinsic connectivity networks (ICNs), did we observe partial overlap across tasks. Our findings underscore the functional impact of ongoing connectome dynamics in shaping moment-to-moment behavior, and show that this impact is predominantly context-dependent.

neuroscience↗

bioRxiv2025

Developing an anatomically valid segmentation protocol for anterior regions of the medial temporal lobe for neurodegenerative diseases

BackgroundThe anterior portion of the medial temporal lobe (MTL) is one of the first regions targeted by pathology in sporadic Alzheimers disease (AD) and Limbic-predominant Age-related TDP-43 Encephalopathy (LATE) indicating a potential for metrics from this region to serve as imaging biomarkers. Leveraging a unique post-mortem dataset of histology and magnetic resonance imaging (MRI) scans we aimed to 1) develop an anatomically valid segmentation protocol for anterior entorhinal cortex (ERC), Brodmann Area (BA) 35, and BA36 for in vivo 3 tesla (T) MRI and 2) incorporate this protocol in an automated approach. MethodsWe included 20 cases (61-97 years old, 50% females) with and without neurodegenerative diseases (11 vs. 9 cases) to ensure generalizability of the developed protocol. Digitized MTL Nissl-stained coronal histology sections from these cases were annotated and registered to same-subject post-mortem MRI. The protocol was developed by determining the location of histological borders of the MTL cortices in relation to anatomical landmarks. Subsequently the protocol was applied to 15 cases twice, with a 2-week interval, to assess intra-rater reliability with the Dice Similarity Index (DSI). Thereafter it was implemented in our in- house Automatic Segmentation of Hippocampal Subfields (ASHS)-T1 approach and evaluated with DSIs. ResultsThe anterior histological border distances of ERC, BA35 and BA36 were evaluated with respect to various anatomical landmarks and the distance relative to the beginning of the hippocampus was chosen. To formulate segmentation rules, we examined the histological sections for the location of borders in relationship to anatomical landmarks in the coronal sections. The DSI for the anterior MTL cortices for the intra-rater reliability was 0.85-0.88 and for the ASHS-T1 against the manual segmentation was 0.62-0.65. DiscussionWe developed a reliable segmentation protocol and incorporated it in an automated approach. Given the vulnerability of the anterior MTL cortices to tau deposition in AD and LATE, the updated approach is expected to improve imaging biomarkers for these diseases.

neuroscience↗

bioRxiv2024

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

Decades of neuroscience research has shown that macroscale brain dynamics can be reliably decomposed into a subset of large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them can vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. To address this problem, we have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and sixteen widely used functional brain atlases, consistent with recommended reporting standards developed by the Organization for Human Brain Mapping. The atlases included in the toolbox show some topographical convergence for specific networks, such as those labeled as "default" or "visual". Network naming varies across atlases, particularly for networks spanning frontoparietal association cortices. For this reason, quantitative comparison with multiple atlases is recommended to benchmark novel neuroimaging findings. We provide several exemplar demonstrations using the Human Connectome Project task fMRI results and UK Biobank independent component analysis maps to illustrate how researchers can use the NCT to report their own findings through quantitative evaluation against multiple published atlases. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The NCT also includes functionality to incorporate additional atlases in the future. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

neuroscience↗

bioRxiv2024

Consistency of resting-state correlations between fMRI networks and EEG band-power

Several simultaneous EEG-fMRI studies have aimed to identify the relationship between EEG band power and fMRI resting state networks (RSNs) to elucidate their neurobiological significance. Although common patterns have emerged, inconsistent results have also been reported. This study examines the consistency of these correlations across subjects and to understand how factors such as the hemodynamic response delay and the use of different EEG data spaces (source/scalp) influence them. Using three distinct EEG-fMRI datasets, acquired independently on 1.5T, 3T and 7T MRI scanners (comprising 42 subjects in total), we evaluate the generalizability of our findings across different acquisition conditions. We found consistent correlations between fMRI RSN and EEG band-power time-series across subjects in the three datasets studied, with systematic variations with RSN, EEG frequency-band, and HRF delay, but not with EEG space. Qualitatively, the majority of these correlations were similar across the three datasets, despite important differences in field strength, number of subjects and resting-state conditions. Our findings support consistent correlations across specific fMRI RSNs and EEG bands and highlight the importance of methodological considerations in interpreting them that may explain conflicting reports in existing literature.

neuroscience↗

bioRxiv2024

Rapid dynamics of electrophysiological connectome states are heritable

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, are a prominent feature of brain activity with broad functional implications. While infra-slow (<0.1Hz) connectome dynamics have been extensively studied with fMRI, rapid dynamics highly relevant for cognition are poorly understood. Here, we asked whether rapid electrophysiological connectome dynamics constitute subject-specific brain traits and to what extent they are under genetic influence. Using source-localized EEG connectomes during resting-state (N=928, 473 females), we quantified heritability of multivariate (multi-state) features describing temporal or spatial characteristics of connectome dynamics. States switched rapidly every [~]60-500ms. Temporal features were heritable, particularly, Fractional Occupancy (in theta, alpha, beta, and gamma bands) and Transition Probability (in theta, alpha, and gamma bands), representing the duration spent in each state and the frequency of state switches, respectively. Genetic effects explained a substantial proportion of phenotypic variance of these features: Fractional Occupancy in beta (44.3%) and gamma (39.8%) bands and Transition Probability in theta (38.4%), alpha (63.3%), beta (22.6%), and gamma (40%) bands. However, we found no evidence for heritability of spatial features, specifically states Modularity and connectivity pattern. We conclude that genetic effects strongly shape individuals connectome dynamics at rapid timescales, specifically states overall occurrence and sequencing.

neuroscience↗

bioRxiv2024

Cognitive abilities are associated with rapid dynamics of electrophysiological connectome states

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, hold significant implications for cognition. However, connectome dynamics at fast (> 1Hz) timescales highly relevant to cognition are poorly understood due to the dominance of inherently slow fMRI in connectome studies. Here, we investigated the behavioral significance of rapid electrophysiological connectome dynamics using source-localized EEG connectomes during resting-state (N=926, 473 females). We focused on dynamic connectome features pertinent to individual differences, specifically those with established heritability: Fractional Occupancy (i.e., the overall duration spent in each recurrent connectome state) in beta and gamma bands, and Transition Probability (i.e., the frequency of state switches) in theta, alpha, beta, and gamma bands. Canonical correlation analysis found a significant relationship between the heritable phenotypes of sub-second connectome dynamics and cognition. Specifically, principal components of Transition Probabilities in alpha (followed by theta and gamma bands) and a cognitive factor representing visuospatial processing (followed by verbal and auditory working memory) most notably contributed to the relationship. We conclude that the specific order in which rapid connectome states are sequenced shapes individuals cognitive abilities and traits. Such sub-second connectome dynamics may inform about behavioral function and dysfunction and serve as endophenotypes for cognitive abilities.

neuroscience↗

bioRxiv2022

Human concurrent intracranial EEG and fMRI reveals multiple temporally independent but spatially similar connectome trajectories across timescales

Complex brain function comprises a multitude of neural operations in parallel and often at different speeds. Each of these operations is carried out across a network of distributed brain regions. How multiple distributed processes are facilitated in parallel is largely unknown. We postulate that such processing relies on a multiplex of dynamic network patterns emerging in parallel but from different functional connectivity (FC) timescales. Given the dominance of inherently slow fMRI in network science, it is unknown whether the brain leverages such multi-timescale network dynamics. We studied FC dynamics concurrently across a breadth of timescales (from infraslow to {gamma}-range) in rare, simultaneously recorded intracranial EEG and fMRI in humans, and source-localized scalp EEG-fMRI data. We examined spatial and temporal convergence of connectome trajectories across timescales. Spatial convergence refers to spatially similar EEG and fMRI connectome patterns, while temporal convergence signifies the more specific case of spatial convergence at corresponding timepoints in EEG and fMRI. We observed spatial convergence but temporal divergence across FC timescales; connectome states (recurrent FC patterns) with partial spatial similarity were found in fMRI and all EEG frequency bands, but these occurred asynchronously across FC timescales. Our findings suggest that hemodynamic and frequency-specific electrophysiological signals, while involving similar large-scale networks, represent functionally distinct connectome trajectories that operate at different FC speeds and in parallel. This multiplex is poised to enable concurrent connectivity across multiple sets of brain regions independently.

neuroscience↗

bioRxiv2021

Temporal characteristics of functional connectome reconfigurations are heritable and related to cognition.

The brains functional connectome is dynamic, constantly reconfiguring in an individual-specific manner. However, which characteristics of such reconfigurations are subject to genetic effects, and to what extent, is largely unknown. Here, we identified heritable dynamic features, quantified their heritability, and determined their association with cognitive phenotypes. In resting-state fMRI, we obtained multivariate features, each describing a temporal or spatial characteristic of connectome dynamics jointly over a set of connectome states. We found strong evidence for heritability of temporal features, particularly fractional occupancy (FO) and transition probability (TP), describing the trajectory of state transitions. Genetic effects explained a substantial proportion of phenotypic variance of these features (h2=.39, 95% CI= [.24.54] for FO; h2=.43, 95% CI=[.29.57] for TP). Moreover, these temporal phenotypes were associated with cognitive performance. Contrarily, we found no robust evidence for heritability of spatial features of the dynamic states (i.e., states Modularity and connectivity pattern). Genetic effects may therefore primarily contribute to how the connectome transitions across states, rather than the precise spatial instantiation of the states in individuals. In sum, genetic effects impact the duration spent in each connectivity configuration and the frequency of shifting between configurations, and such temporal features may act as endophenotypes for cognitive abilities.

neuroscience↗

bioRxiv2020

Genetic variation in CSMD1 affects amygdala connectivity and prosocial behavior

The amygdala is one of the most widely connected structures in the primate brain and plays a key role in social and emotional behavior. Here, we present the first genome-wide association study (GWAS) of whole-brain resting-state amygdala networks to discern whether connectivity in these networks could serve as an endophenotype for social behavior. Leveraging published resting-state amygdala networks as a priori endophenotypes in a GWAS meta-analysis of two adolescent cohorts, we identified a common polymorphism on chr.8p23.2 (rs10105357 A/G, MAF (G)=0.35) associated with stronger connectivity in the medial amygdala network (beta=0.20, p=2.97x10-8). This network contains regions that support reward processes and affiliative behavior. People carrying two copies of the minor allele for rs10105357 participate in more prosocial behaviors (t=2.644, p=0.008) and have higher CSMD1 expression in the temporal cortex (t=3.281, p=0.002) than people with one or no copy of the allele. In post-mortem brains across the lifespan, we found that CSMD1 expression is relatively high in the amygdala (2.79 fold higher than white matter, p=1.80x10-29), particularly so for nuclei in the medial amygdala, reaching a maximum in later stages of development. Amygdala network endophenotyping has the potential to accelerate genetic discovery in disorders of social function, such as autism, in which CSMD1 may serve as a diagnostic and therapeutic target.

neuroscience↗

bioRxiv2020

Safety and data quality of simultaneous EEG-fMRI using multi-band fMRI imaging

PurposeSimultaneously recorded electroencephalography and functional magnetic resonance imaging (EEG-fMRI) is highly informative yet technically challenging. Until recently, there has been little information about EEG data quality and safety when used with newer multi-band (MB) fMRI sequences. Here, we measure the relative heating of a MB protocol compared with a standard single-band (SB) protocol considered to be safe. We also evaluated EEG quality recorded concurrently with the MB protocol on humans. Materials and MethodsWe compared radiofrequency (RF)-related heating at multiple electrodes and magnetic field magnitude, B1+RMS, of a MB fMRI sequence with whole-brain coverage (TR = 440 ms, MB factor = 4) against a previously recommended, safe SB sequence using a phantom outfitted with a 64-channel EEG cap. Next, 9 human subjects underwent eyes-closed resting state EEG-fMRI using the MB sequence. Additionally, in three of the subjects resting state EEG was recorded also during the SB sequence and in an fMRI-free condition to directly compare EEG data quality across scanning conditions. EEG data quality was assessed by the ability to remove gradient and cardioballistic artifacts along with a clean spectrogram. ResultsThe heating induced by the MB sequence was lower than that of the SB sequence by a factor of 0.73 {+/-} 0.38. This is consistent with an expected heating ratio of 0.64, calculated from the square of the ratio of B1+RMS values of the sequences. In the resting state EEG data, gradient and cardioballistic artifacts were successfully removed using traditional template subtraction. All subjects showed an individual alpha peak in the spectrogram with a posterior topography characteristic of eyes-closed EEG. The success of artifact rejection for the MB sequence was comparable to that in traditional SB sequences. ConclusionsOur study shows that B1+RMS is a useful indication of the relative heating of fMRI protocols. This observation indicates that simultaneous EEG-fMRI recordings using this MB sequence can be safe in terms of RF-related heating, and that EEG data recorded using this sequence is of acceptable quality after traditional artifact removal techniques.

neuroscience↗

bioRxiv2020

Oscillation-based connectivity architecture is dominated by an intrinsic spatial organization, not cognitive state or frequency

Functional connectivity (FC) of neural oscillations (~1-150Hz) is thought to facilitate neural information exchange across brain areas by forming malleable neural ensembles in the service of cognitive processes. However, neural oscillations and their FC are not restricted to certain cognitive demands and continuously unfold in all cognitive states. To what degree is the spatial organization of oscillation-based FC affected by cognitive state or governed by an intrinsic architecture? And what is the impact of oscillation frequency and FC mode (phase-versus amplitude coupling)? Using ECoG recordings of 18 presurgical patients, we quantified the state-dependency of oscillation-based FC in five canonical frequency bands and across an array of 6 task states. For both phase- and amplitude coupling, static FC analysis revealed a spatially largely state-invariant (i.e. intrinsic) component in all frequency bands. Further, the observed intrinsic FC pattern was spatially similar across all frequency bands. However, temporally independent FC dynamics in each frequency band allow for frequency-specific malleability in information exchange. In conclusion, the spatial organization of oscillation-based FC is largely stable over cognitive states, i.e. primarily intrinsic in nature, and shared across frequency bands. The state-invariance is in line with prior findings at the other temporal extreme of brain activity, the infraslow range (~<0.1Hz) observed in fMRI. Our observations have implications for conceptual frameworks of oscillation-based FC and the analysis of task-related FC changes.

neuroscience↗

bioRxiv2020

EEG and fMRI connectomes are reliably related: a simultaneous EEG-fMRI study from 1.5T to 7T

Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation (fMRI) and neurophysiological recordings (EEG) are indirectly coupled. The electrophysiological and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage). Here, we investigated the reproducibility of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reproducibly correlates across different datasets and that a moderate crossmodal correlation between EEG and fMRI connectivity of r{approx}0.3 can be reproducibly extracted in low- and high-field scanners. The crossmodal correlation was strongest in the EEG-{beta} frequency band but exists across all frequency bands. Both homotopic and within intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship. This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects that are dominantly linked through a functional core of ICNs across spanning across the different timescales measured by EEG and fMRI. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. This observed level of reproducibility also defines a baseline for the study of alterations of this coupling in pathological conditions and their role as potential clinical markers.

neuroscience↗

bioRxiv2019

Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG-frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG-frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG. The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond-range in all canonical frequencies of FCEEG to second-range of FCfMRI. Conversely, the second component additionally exposes linked but spatially divergent neuronal processing at the two timescales. This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals.

neuroscience↗

bioRxiv2019

Identifying Severe Stroke Patients Likely to Benefit From Intervention Despite a Delay of a Day

Background and PurposeSevere ischemic strokes caused by large vessel occlusion (LVO) are treatable up to 24 hours after onset. But not all patients will benefit either because the infarct enlarges too rapidly or because the patient is not at center that is capable of treatment. The purpose was to find biomarkers that reliably identify patients who are likely to benefit from intervention despite long delays. MethodsThirty-eight acute ischemic stroke patients with LVO who did not undergo thrombolysis or thrombectomy had serial imaging with MRI at presentation and then at ~12 hours, ~1 day, & ~2 days after onset. Presenting clinical and neuroimaging predictors of a therapeutically auspicious ischemic core size (<50 ml) one day after stroke onset were identified. ResultsIschemic core initial growth rate (IGR) was the only independent predictor of functional outcome at 3 months (P=0.002), and a receiver operator characteristics (ROC) analysis revealed that an IGR of <5.4 ml/hr had a 95% sensitivity with a low (5.6%) false positive rate for identifying patients who would have an ischemic core of <50 ml at one day post ictus. Selection using an initial DWI lesion volume <37 ml was similarly powerful with a 90% sensitivity and a low false positive rate. Other imaging markers derived from CT scans were less effective, but may be more practical. Published reports of over 600 LVO ischemic stroke patients disclosed that about half met the highly favorable IGR criteria of <5.4 ml/hr at presentation. ConclusionsIschemic core initial growth rates of <5.4ml/hr and initial ischemic cores of <37 ml are excellent biomarkers for selecting LVO patients that are likely to benefit from intervention despite long delays due to transfer to thrombectomy-capable stroke centers. Published accounts suggest that about half of all LVO stroke patients meet these criteria.

clinical trials↗

bioRxiv2018

Concurrent EEG- and fMRI-derived functional connectomes exhibit linked dynamics

Connectivity across distributed brain regions commonly measured with functional Magnetic Resonance Imaging (fMRI) exhibits infraslow (<0.1Hz) spatial reconfigurations of potentially critical importance to cognition. Cognitively relevant neural communication, however, employs synchrony at fast speeds. It is unclear how fast oscillation-coupling across the whole-brain connectome relates to connectivity changes in fMRI, an indirect measure of neural activity. In two datasets, electroencephalography (EEG) revealed that synchronization in all canonical oscillation-bands reconfigures at infraslow speeds, coinciding with connectivity changes in concurrently recorded fMRI in corresponding region-pairs. The cross-modal tie of connectivity dynamics was widely distributed across the connectome irrespective of EEG frequency-band. However, the cross-modal tie was strongest in visual to somatomotor connections for slower EEG-bands, and in connections involving the Default Mode Network for faster EEG-bands. The findings provide evidence that functionally relevant neural synchrony in all oscillation-bands slowly reconfigures across the whole-brain connectome, and that fMRI can reliably measure such dynamics.

neuroscience↗