Time-varying Functional Connectivity Research Articles

Time-varying functional connectivity as Wishart processes

Abstract We investigate the utility of Wishart processes (WPs) for estimating time-varying functional connectivity (TVFC), which is a measure of changes in functional coupling as the correlation between brain region activity in functional magnetic resonance imaging (fMRI). The WP is a stochastic process on covariance matrices that can model dynamic covariances between time series, which makes it a natural fit to this task. Recent advances in scalable approximate inference techniques and the availability of robust open-source libraries have rendered the WP practically viable for fMRI applications. We introduce a comprehensive benchmarking framework to assess WP performance compared with a selection of established TVFC estimation methods. The framework comprises simulations with specified ground-truth covariance structures, a subject phenotype prediction task, a test-retest study, a brain state analysis, an external stimulus prediction task, and a novel data-driven imputation benchmark. The WP performed competitively across all the benchmarks. It outperformed a sliding window (SW) approach with adaptive cross-validated window lengths and a dynamic conditional correlation (DCC)-multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) baseline on the external stimulus prediction task, while being less prone to false positives in the TVFC null models.

Dynamic Functional Hyperconnectivity After Psilocybin Intake Is Primarily Associated With Oceanic Boundlessness

BackgroundPsilocybin is a widely studied psychedelic substance that leads to the psychedelic state, a specific altered state of consciousness. To date, the relationship between the psychedelic state’s neurobiological and experiential patterns remains undercharacterized because they are often analyzed separately. We investigated the relationship between neurobiological and experiential patterns after psilocybin by focusing on the link between dynamic cerebral connectivity and retrospective questionnaire assessment. MethodsHealthy participants were randomized to receive either psilocybin (n = 22) or placebo (n = 27) and scanned for 6 minutes in an eyes-open resting state during the peak subjective drug effect (102 minutes posttreatment) in ultrahigh field 7T magnetic resonance imaging. The 5-Dimensional Altered States of Consciousness Rating Scale was administered 360 minutes after drug intake. ResultsUnder psilocybin, there were alterations across all dimensions of the 5-Dimensional Altered States of Consciousness Rating Scale and widespread increases in averaged brain functional connectivity. Time-varying functional connectivity analysis unveiled a recurrent hyperconnected pattern characterized by low blood oxygen level–dependent signal amplitude, suggesting heightened cortical arousal. In terms of neuroexperiential links, canonical correlation analysis showed higher transition probabilities to the hyperconnected pattern with feelings of oceanic boundlessness and secondly with visionary restructuralization. ConclusionsPsilocybin generates profound alterations at both the brain and the experiential levels. We suggest that the brain’s tendency to enter a hyperconnected-hyperarousal pattern under psilocybin represents the potential to entertain variant mental associations. These findings illuminate the intricate interplay between brain dynamics and subjective experience under psilocybin, thereby providing insights into the neurophysiology and neuroexperiential qualities of the psychedelic state.

The brain’s “dark energy” puzzle: How strongly is glucose metabolism linked to resting-state brain activity?

Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain’s spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain’s metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain’s ‘dark energy’.

2.5-Year changes of connectivity dynamism are relevant for physical and cognitive deterioration in multiple sclerosis

Background: In MS, functional connectivity (FC) dynamism may influence disease evolution. Objectives: The objective is to assess time-varying functional connectivity (TVFC) changes over time at 2.5-year follow-up in MS patients according to physical and cognitive worsening. Methods: We collected 3T magnetic resonance imaging (MRI) for TVFC assessment (performed using sliding-window analysis of centrality) and clinical evaluations at baseline and 2.5-year follow-up from 28 healthy controls and 129 MS patients. Of these, 79 underwent baseline and follow-up neuropsychological assessment. At 2.5 years, physical/cognitive worsening was defined according to disability/neuropsychological score changes. Results: At follow-up, 25/129 (19.3%) MS patients worsened physically and 14/79 (17.7%) worsened cognitively. At baseline, MS patients showed reduced TVFC versus controls. At 2.5-year follow-up, no TVFC changes were detected in controls. Conversely, TVFC decreased over time in parieto-temporal regions in stable MS patients and in default-mode network in worsened MS. In physically worsened MS, basal ganglia TVFC reductions were also found. Reduced TVFC over time in the putamen in physically worsened and reduced TVFC in the precuneus in cognitively worsened were significant versus stable MS. Discussion: At 2.5-year follow-up, default-mode network TVFC reductions were found in worsening MS. Moreover, reduced deep gray matter TVFC characterized physically worsened patients, whereas precuneus involvement characterized cognitively worsened MS patients.

Time-varying functional connectivity predicts fluctuations in sustained attention in a serial tapping task.

The mechanisms for how large-scale brain networks contribute to sustained attention are unknown. Attention fluctuates from moment to moment, and this continuous change is consistent with dynamic changes in functional connectivity between brain networks involved in the internal and external allocation of attention. In this study, we investigated how brain network activity varied across different levels of attentional focus (i.e., "zones"). Participants performed a finger-tapping task, and guided by previous research, in-the-zone performance or state was identified by low reaction time variability and out-of-the-zone as the inverse. In-the-zone sessions tended to occur earlier in the session than out-of-the-zone blocks. This is unsurprising given the way attention fluctuates over time. Employing a novel method of time-varying functional connectivity, called the quasi-periodic pattern analysis (i.e., reliable, network-level low-frequency fluctuations), we found that the activity between the default mode network (DMN) and task positive network (TPN) is significantly more anti-correlated during in-the-zone states versus out-of-the-zone states. Furthermore, it is the frontoparietal control network (FPCN) switch that differentiates the two zone states. Activity in the dorsal attention network (DAN) and DMN were desynchronized across both zone states. During out-of-the-zone periods, FPCN synchronized with DMN, while during in-the-zone periods, FPCN switched to synchronized with DAN. In contrast, the ventral attention network (VAN) synchronized more closely with DMN during in-the-zone periods compared with out-of-the-zone periods. These findings demonstrate that time-varying functional connectivity of low frequency fluctuations across different brain networks varies with fluctuations in sustained attention or other processes that change over time.

Time-varying phase synchronization of resting-state functional magnetic resonance imaging reveals a shift toward self-referential processes during sustained pain.

Growing evidence has suggested that time-varying functional connectivity between different brain regions might underlie the dynamic experience of pain. This study used a novel, data-driven framework to characterize the dynamic interactions of large-scale brain networks during sustained pain by estimating recurrent patterns of phase-synchronization. Resting-state functional magnetic resonance imaging signals were collected from 50 healthy participants before (once) and after (twice) the onset of sustained pain that was induced by topical application of capsaicin cream. We first decoded the instantaneous phase of neural activity and then applied leading eigenvector dynamic analysis on the time-varying phase-synchronization. We identified 3 recurrent brain states that show distinctive phase-synchronization. The presence of state 1, characterized by phase-synchronization between the default mode network and auditory, visual, and sensorimotor networks, together with transitions towards this brain state, increased during sustained pain. These changes can account for the perceived pain intensity and reported unpleasantness induced by capsaicin application. In contrast, state 3, characterized by phase-synchronization between the cognitive control network and sensory networks, decreased after the onset of sustained pain. These results are indicative of a shift toward internally directed self-referential processes (state 1) and away from externally directed cognitive control processes (state 3) during sustained pain.

Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis

BackgroundPsychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state functional magnetic resonance imaging (rs-fMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains. Methodsrs-fMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron emission tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients. ResultsCompared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta-power. Exploratory analyses revealed a close statistical relationship between LEN and positive symptom severity in patients. ConclusionOur entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.

Abnormal Spatial and Temporal Overlap of Time-Varying Brain Functional Networks in Patients with Schizophrenia.

Schizophrenia (SZ) is a complex psychiatric disorder with unclear etiology and pathological features. Neuroscientists are increasingly proposing that schizophrenia is an abnormality in the dynamic organization of brain networks. Previous studies have found that the dynamic brain networks of people with SZ are abnormal in both space and time. However, little is known about the interactions and overlaps between hubs of the brain underlying spatiotemporal dynamics. In this study, we aimed to investigate different patterns of spatial and temporal overlap of hubs between SZ patients and healthy individuals. Specifically, we obtained resting-state functional magnetic resonance imaging data from the public dataset for 43 SZ patients and 49 healthy individuals. We derived a representation of time-varying functional connectivity using the Jackknife Correlation (JC) method. We employed the Betweenness Centrality (BC) method to identify the hubs of the brain's functional connectivity network. We then applied measures of temporal overlap, spatial overlap, and hierarchical clustering to investigate differences in the organization of brain hubs between SZ patients and healthy controls. Our findings suggest significant differences between SZ patients and healthy controls at the whole-brain and subnetwork levels. Furthermore, spatial overlap and hierarchical clustering analysis showed that quasi-periodic patterns were disrupted in SZ patients. Analyses of temporal overlap revealed abnormal pairwise engagement preferences in the hubs of SZ patients. These results provide new insights into the dynamic characteristics of the network organization of the SZ brain.

Toward stability of dynamic FC estimates in neuroimaging and electrophysiology: Solutions and limits.

Time-varying functional connectivity (FC) methods are used to map the spatiotemporal organization of brain activity. However, their estimation can be unstable, in the sense that different runs of the inference may yield different solutions. But to draw meaningful relations to behavior, estimates must be robust and reproducible. Here, we propose two solutions using the hidden Markov model (HMM) as a descriptive model of time-varying FC. The first, best ranked HMM, involves running the inference multiple times and selecting the best model based on a quantitative measure combining fitness and model complexity. The second, hierarchical-clustered HMM, generates stable cluster state time series by applying hierarchical clustering to the state time series obtained from multiple runs. Experimental results on fMRI and magnetoencephalography data demonstrate that these approaches substantially improve the stability of time-varying FC estimations. Overall, hierarchical-clustered HMM is preferred when the inference variability is high, while the best ranked HMM performs better otherwise.

Discovering individual fingerprints in resting-state functional connectivity using deep neural networks.

Non-negligible idiosyncrasy due to interindividual differences is an ongoing issue in resting-state functional MRI (rfMRI) analysis. We show that a deep neural network (DNN) can be employed for individual identification by learning important features from the time-varying functional connectivity (FC) of rfMRI in the Human Connectome Project. We employed the trained DNN to identify individuals from an independent dataset acquired at our institution. The results revealed that the DNN could successfully identify 300 individuals with an error rate of 2.9% using 15 s time-window and 870 individuals with an error rate of 6.7%. A trained DNN with nonlinear hidden layers led to the proposal of the "fingerprint of FC" (fpFC) as representative edges of individual FC. The fpFCs for individuals exhibited commonly important and individual-specific edges across time-window lengths (from 5 min to 15 s). Furthermore, the utility of our model for another group of subjects was validated, supporting the feasibility of our technique in the context of transfer learning. In conclusion, our study offers an insight into the discovery of the intrinsic mode of the human brain using whole-brain resting-state FC and DNNs.

Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior.

Experimental work across species has demonstrated that spontaneously generated behaviors are robustly coupled to variations in neural activity within the cerebral cortex. Functional magnetic resonance imaging data suggest that temporal correlations in cortical networks vary across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these data generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior observed in awake animals. Here, we used wide-field mesoscopic calcium imaging to monitor cortical dynamics in awake mice and developed an approach to quantify rapidly time-varying functional connectivity. We show that spontaneous behaviors are represented by fast changes in both the magnitude and correlational structure of cortical network activity. Combining mesoscopic imaging with simultaneous cellular-resolution two-photon microscopy demonstrated that correlations among neighboring neurons and between local and large-scale networks also encode behavior. Finally, the dynamic functional connectivity of mesoscale signals revealed subnetworks not predicted by traditional anatomical atlas-based parcellation of the cortex. These results provide new insights into how behavioral information is represented across the neocortex and demonstrate an analytical framework for investigating time-varying functional connectivity in neural networks.

Heterogeneity of dynamic synergetic configurations of salience network in children with autism spectrum disorder.

Atypical functional connectivity (FC) patterns have been identified in autism spectrum disorders (ASD), especially within salience network (SN) and between SN and default mode network (DMN) and central executive network (CEN). But whether the dynamic configuration of intra-SN and inter-SN (SN with DMN and CEN) FC in ASD is also heterogeneous remains unknown. Based on the resting-state functional magnetic resonance imaging data from 105 ASD and 102 typically-developing controls (TC), we calculated the time-varying FC of intra-SN and inter-SN (SN with DMN and CEN). Then, the joint recurrence features for the time-varying FC were calculated to assess how the SN dynamically recruits different configurations of network segregation and integration in ASD, that is, synergies, from the dynamical systems perspective. We analyzed the differences in synergetic patterns between ASD subtypes obtained by k-means clustering algorithm based on the synergy of SN and TC, and investigated the relationships between synergy of SN and severity of clinical symptoms of ASD for ASD subtypes. Two ASD subtypes were revealed, where the synergy of SN in ASD subtype 1 has lower stability and periodicity compared to the TC, and ASD subtype 2 exhibits the opposite alteration. Synergy of SN for ASD subtype 1 and 2 was found to predict the severity of communication impairments and restricted and repetitive behaviors in ASD, respectively. These results suggest the existence of subtypes with distinct patterns of the synergy of SN in ASD, and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.

Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states.

Assessment of functional connectivity (FC) has revealed a great deal of knowledge about the macroscale spatiotemporal organization of the brain network. Recent studies found task-versus-rest network reconfigurations were crucial for cognitive functioning. However, brain network reconfiguration remains unclear among different cognitive states, considering both aggregate and time-resolved FC profiles. The current study utilized static FC (sFC, i.e., long timescale aggregate FC) and sliding window-based dynamic FC (dFC, i.e., short timescale time-varying FC) approaches to investigate the similarity and alterations of edge weights and network topology at different cognitive loads, particularly their relationships with specific cognitive process. Both dFC/sFC networks showed subtle but significant reconfigurations that correlated with task performance. At higher cognitive load, brain network reconfiguration displayed increased functional integration in the sFC-based aggregate network, but faster and larger variability of modular reorganization in the dFC-based time-varying network, suggesting difficult tasks require more integrated and flexible network reconfigurations. Moreover, sFC-based network reconfigurations mainly linked with the sensorimotor and low-order cognitive processes, but dFC-based network reconfigurations mainly linked with the high-order cognitive process. Our findings suggest that reconfiguration profiles of sFC/dFC networks provide specific information about cognitive functioning, which could potentially be used to study brain function and disorders.

Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG.

Early discrimination and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency. Our research is based on resting-state electroencephalography (EEG) and the current dataset includes 137 consensus-diagnosed, community-dwelling Black Americans (ages 60-90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time-varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity-based score. The leave-one-out cross-validation accuracy is 91.97% and 3-fold accuracy is 91.17%. The 9 to 18 months' progression trend prediction accuracy over an availability-limited subset sample is 84.61%. The EEG-based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.

Individual differences in time-varying and stationary brain connectivity during movie watching from childhood to early adulthood: Age, sex, and behavioral associations

Spatially remote brain regions exhibit dynamic functional interactions across various task conditions. While time-varying functional connectivity during movie watching shows sensitivity to movie content, stationary functional connectivity remains relatively stable across videos. These findings suggest that dynamic and stationary functional interactions may represent different aspects of brain function. However, the relationship between individual differences in time-varying and stationary connectivity and behavioral phenotypes remains elusive. To address this gap, we analyzed an open-access functional MRI dataset comprising participants aged 5–22 years, who watched two cartoon movie clips. We calculated regional brain activity, time-varying connectivity, and stationary connectivity, examining associations with age, sex, and behavioral assessments. Model comparison revealed that time-varying connectivity was more sensitive to age and sex effects compared with stationary connectivity. The preferred age models exhibited quadratic log age or quadratic age effects, indicative of inverted-U shaped developmental patterns. In addition, females showed higher consistency in regional brain activity and time-varying connectivity than males. However, in terms of behavioral predictions, only stationary connectivity demonstrated the ability to predict full-scale intelligence quotient. These findings suggest that individual differences in time-varying and stationary connectivity may capture distinct aspects of behavioral phenotypes.

Manifold learning for fMRI time-varying functional connectivity.

Whole-brain functional connectivity (FC) measured with functional MRI (fMRI) evolves over time in meaningful ways at temporal scales going from years (e.g., development) to seconds [e.g., within-scan time-varying FC (tvFC)]. Yet, our ability to explore tvFC is severely constrained by its large dimensionality (several thousands). To overcome this difficulty, researchers often seek to generate low dimensional representations (e.g., 2D and 3D scatter plots) hoping those will retain important aspects of the data (e.g., relationships to behavior and disease progression). Limited prior empirical work suggests that manifold learning techniques (MLTs)-namely those seeking to infer a low dimensional non-linear surface (i.e., the manifold) where most of the data lies-are good candidates for accomplishing this task. Here we explore this possibility in detail. First, we discuss why one should expect tvFC data to lie on a low dimensional manifold. Second, we estimate what is the intrinsic dimension (ID; i.e., minimum number of latent dimensions) of tvFC data manifolds. Third, we describe the inner workings of three state-of-the-art MLTs: Laplacian Eigenmaps (LEs), T-distributed Stochastic Neighbor Embedding (T-SNE), and Uniform Manifold Approximation and Projection (UMAP). For each method, we empirically evaluate its ability to generate neuro-biologically meaningful representations of tvFC data, as well as their robustness against hyper-parameter selection. Our results show that tvFC data has an ID that ranges between 4 and 26, and that ID varies significantly between rest and task states. We also show how all three methods can effectively capture subject identity and task being performed: UMAP and T-SNE can capture these two levels of detail concurrently, but LE could only capture one at a time. We observed substantial variability in embedding quality across MLTs, and within-MLT as a function of hyper-parameter selection. To help alleviate this issue, we provide heuristics that can inform future studies. Finally, we also demonstrate the importance of feature normalization when combining data across subjects and the role that temporal autocorrelation plays in the application of MLTs to tvFC data. Overall, we conclude that while MLTs can be useful to generate summary views of labeled tvFC data, their application to unlabeled data such as resting-state remains challenging.

Parallel processing relies on a distributed, low-dimensional cortico-cerebellar architecture.

A characteristic feature of human cognition is our ability to 'multi-task'-performing two or more tasks in parallel-particularly when one task is well learned. How the brain supports this capacity remains poorly understood. Most past studies have focussed on identifying the areas of the brain-typically the dorsolateral prefrontal cortex-that are required to navigate information-processing bottlenecks. In contrast, we take a systems neuroscience approach to test the hypothesis that the capacity to conduct effective parallel processing relies on a distributed architecture that interconnects the cerebral cortex with the cerebellum. The latter structure contains over half of the neurons in the adult human brain and is well suited to support the fast, effective, dynamic sequences required to perform tasks relatively automatically. By delegating stereotyped within-task computations to the cerebellum, the cerebral cortex can be freed up to focus on the more challenging aspects of performing the tasks in parallel. To test this hypothesis, we analysed task-based fMRI data from 50 participants who performed a task in which they either balanced an avatar on a screen (balance), performed serial-7 subtractions (calculation) or performed both in parallel (dual task). Using a set of approaches that include dimensionality reduction, structure-function coupling, and time-varying functional connectivity, we provide robust evidence in support of our hypothesis. We conclude that distributed interactions between the cerebral cortex and cerebellum are crucially involved in parallel processing in the human brain.

Longitudinal Changes of Functional Connectivity Dynamism Are Relevant for Disability Worsening in Multiple Sclerosis: A 2.5-Year Study (P10-3.018)

<h3>Objective:</h3> To investigate changes in time-varying functional connectivity (TVFC) over 2.5 years of follow-up in multiple sclerosis (MS) patients and their association with disability progression. <h3>Background:</h3> In MS patients, TVFC analysis showed abnormalities of the main functional networks, correlated with more severe clinical and cognitive disability. Preliminary evidence showed that MS-related cognitive decline was associated with progressive TVFC destabilization. However, the clinical relevance of longitudinal TVFC changes has not been investigated yet. <h3>Design/Methods:</h3> 3T-MRI scans and clinical evaluations were obtained at baseline and at median follow-up of 2.5 years from 129 right-handed MS patients (103 relapsing-remitting [RR] and 26 progressive [P]) and 28 matched healthy controls (HC). At 2.5-year follow-up, patients were classified as clinically stable/worsened according to their disability change. TVFC was quantified at voxel-wise level as the coefficient of variation (CoV) across sliding-windows of degree centrality. <h3>Results:</h3> At follow-up, 25/129 (19.3%) MS patients worsened clinically. At baseline, MS patients showed reduced TVFC <i>vs</i> HC in the bilateral orbitofrontal cortex (p&lt;0.05, family-wise error corrected), left cerebellum, right precuneus and left thalamus. At 2.5-year follow-up, a widespread reduction of TVFC over time (p&lt;0.05, family-wise error corrected) was found in MS patients in temporal, parietal, occipital and frontal lobes, as well as in the cerebellum. Such a pattern of TVFC reduction was also found when looking at clinically stable MS patients. Clinically worsened MS presented peculiar TVFC reductions in areas of the default-mode network and basal ganglia. Reduced TVFC in the left putamen in clinically worsened <i>vs</i> stable MS patients was significant at time <i>x</i> group interaction analysis. <h3>Conclusions:</h3> After 2.5 years of follow-up, MS patients showed widespread TVFC reduction in cortical lobes and cerebellum. A peculiar involvement of deep grey matter was found in clinically worsened MS patients. Accrual of TVFC abnormalities in deep grey matter regions may represent a biological substrate of disability worsening. <b>Disclosure:</b> Dr. D’Amore has nothing to disclose. Paola Valsasina has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for ACCMED. Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Bristol Myers Squibb . Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Sanofi Genzyme. Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Novartis. Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Roche. Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Merck. Monica Margoni has received research support from MAGNIMS. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Bayer, Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi Genzyme, Takeda, and Teva Pharmaceutical Industries. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Bayer, Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi Genzyme, Takeda, and Teva Pharmaceutical Industries. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Springer Nature. The institution of Dr. Filippi has received research support from Biogen Idec, Merck-Serono, Novartis, Roche, Teva Pharmaceutical Industries, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, and ARiSLA (Fondazione Italiana di Ricerca per la SLA). Maria Assunta Rocca has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Bayer, Biogen, Bristol Myers Squibb, Celgene, Genzyme, Merck Serono, Novartis, Roche, and Teva. The institution of Maria Assunta Rocca has received research support from Italian Ministry of Health, MS Society of Canada and Fondazione Italiana Sclerosi Multipla.

Time-Varying Functional Connectivity of the Hippocampus Is Associated With Cognitive Performance in Multiple Sclerosis Patients (P10-3.014)

<h3>Objective:</h3> To explore hippocampal static (sFC) and time-varying functional connectivity (TVFC) and assess their association with cognition in multiple sclerosis (MS). <h3>Background:</h3> The hippocampus has a key role in cognition. Hippocampal TVFC in MS needs to be further explored. <h3>Design/Methods:</h3> 3T structural and resting-state (RS) functional MRI scans were acquired from 108 MS patients and 63 healthy controls (HC). Subjects underwent neuropsychological evaluation. Sliding-window correlation analysis using the left (L) and right (R) hippocampus as seed regions assessed TVFC, quantified by the standard deviation of connectivity across windows. Mean connectivity indicated sFC. <h3>Results:</h3> MS patients had decreased sFC <i>vs</i> HC between L hippocampus and temporo-parietal regions, and increased sFC between L and R hippocampus and thalamus, precuneus and superior frontal regions. TVFC was decreased in MS patients between L hippocampus and temporo-parietal regions. Conversely, TVFC was increased in MS patients between L and R hippocampus and L pre- and postcentral gyri, cuneus, orbitofrontal cortex and inferior temporal gyrus (ITG). In MS patients, better global cognition correlated with higher TVFC between L hippocampus and L pre- and postcentral gyri (r=0.21–0.28; p=0.04–0.006). Better verbal memory correlated with higher TVFC between L hippocampus and L precentral gyrus (r=0.21, p=0.03), and better visuospatial memory correlated with higher TVFC between L and R hippocampus and L cuneus, pre- and postcentral gyri and ITG (r=0.19–0.23, p=0.02–0.04). Better information processing speed correlated with higher TVFC between L hippocampus and L postcentral gyrus (r=0.21, p=0.03) and with higher sFC between R hippocampus and L superior frontal cortex (SFC) (r=0.21, p=0.03). Finally, better attention scores correlated with higher TVFC between L hippocampus and L temporal cortex (r=0.24, p=0.01) and with higher sFC between R hippocampus and L SFC (r=0.20, p=0.05). <h3>Conclusions:</h3> Increased hippocampal connectivity contributed to explain better cognitive performances in MS, with a peculiar association between higher hippocampal TVFC and better memory scores. <b>Disclosure:</b> Monica Margoni has received research support from MAGNIMS. Paola Valsasina has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for ACCMED. Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Bristol Myers Squibb . Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Sanofi Genzyme. Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Novartis. Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Roche. Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Merck. Olga Marchesi has nothing to disclose. Carmen Vizzino has nothing to disclose. Damiano Mistri has nothing to disclose. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Bayer, Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi Genzyme, Takeda, and Teva Pharmaceutical Industries. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Bayer, Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi Genzyme, Takeda, and Teva Pharmaceutical Industries. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Springer Nature. The institution of Dr. Filippi has received research support from Biogen Idec, Merck-Serono, Novartis, Roche, Teva Pharmaceutical Industries, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, and ARiSLA (Fondazione Italiana di Ricerca per la SLA). Maria Assunta Rocca has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Bayer, Biogen, Bristol Myers Squibb, Celgene, Genzyme, Merck Serono, Novartis, Roche, and Teva. The institution of Maria Assunta Rocca has received research support from Italian Ministry of Health, MS Society of Canada and Fondazione Italiana Sclerosi Multipla.

AGE-RELATED DIFFERENCES IN THEORY OF MIND: A TIME-VARYING FUNCTIONAL CONNECTIVITY APPROACH

Abstract Theory of Mind (ToM) refers to the act of inferring someone’s inner state, such as their emotions and thoughts. Previous work also demonstrated that ToM performance declines across age, but the mechanisms underlying this are not well-understood. The brain regions underlying ToM are generally in the default mode network (DMN) – a group of brain regions particularly vulnerable to pathological aging. Recent work suggests that declines in functional connectivity (FC; correlations between brain regions) during resting state (a period of undirected thought) predict age deficits in ToM. However, these findings come from aggregated FC, ignoring potentially informative details of dynamic changes during resting state. Researching these details may provide more specificity related to social cognitive aging. To test this, 35 older adults (OA; M = 75.61; 22 female) and 40 young adults (YA; M = 21.58; 25 female) underwent resting state and task-based fMRI. During the task, they completed a standard ToM task. We conducted time-varying functional connectivity analyses in the DMN to identify dynamic changes over time. Overall, OA had more variability than YA, but variability benefitted YAs, and not OAs. This occurred specifically in a DMN subnetwork associated with contextual information in memory. These results suggest that variability is useful, but only to an extent. With respect to ToM, variability may promote how YA integrate contextual information to memory, but is ineffective for OA.