Edge time series components of functional connectivity and cognitive function in Alzheimer's disease.

Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer's disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer's Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer's disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.

Edge Time Series Components of Functional Connectivity and Cognitive Function in Alzheimer's Disease.

Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer's disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer's Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer's disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.

Hierarchical organization of spontaneous co-fluctuations in densely sampled individuals using fMRI.

Edge time series decompose functional connectivity into its framewise contributions. Previous studies have focused on characterizing the properties of high-amplitude frames (time points when the global co-fluctuation amplitude takes on its largest value), including their cluster structure. Less is known about middle- and low-amplitude co-fluctuations (peaks in co-fluctuation time series but of lower amplitude). Here, we directly address those questions, using data from two dense-sampling studies: the MyConnectome project and Midnight Scan Club. We develop a hierarchical clustering algorithm to group peak co-fluctuations of all magnitudes into nested and multiscale clusters based on their pairwise concordance. At a coarse scale, we find evidence of three large clusters that, collectively, engage virtually all canonical brain systems. At finer scales, however, each cluster is dissolved, giving way to increasingly refined patterns of co-fluctuations involving specific sets of brain systems. We also find an increase in global co-fluctuation magnitude with hierarchical scale. Finally, we comment on the amount of data needed to estimate co-fluctuation pattern clusters and implications for brain-behavior studies. Collectively, the findings reported here fill several gaps in current knowledge concerning the heterogeneity and richness of co-fluctuation patterns as estimated with edge time series while providing some practical guidance for future studies.

Uncovering individual differences in fine-scale dynamics of functional connectivity.

Functional connectivity (FC) profiles contain subject-specific features that are conserved across time and have potential to capture brain-behavior relationships. Most prior work has focused on spatial features (nodes and systems) of these FC fingerprints, computed over entire imaging sessions. We propose a method for temporally filtering FC, which allows selecting specific moments in time while also maintaining the spatial pattern of node-based activity. To this end, we leverage a recently proposed decomposition of FC into edge time series (eTS). We systematically analyze functional magnetic resonance imaging frames to define features that enhance identifiability across multiple fingerprinting metrics, similarity metrics, and data sets. Results show that these metrics characteristically vary with eTS cofluctuation amplitude, similarity of frames within a run, transition velocity, and expression of functional systems. We further show that data-driven optimization of features that maximize fingerprinting metrics isolates multiple spatial patterns of system expression at specific moments in time. Selecting just 10% of the data can yield stronger fingerprints than are obtained from the full data set. Our findings support the idea that FC fingerprints are differentially expressed across time and suggest that multiple distinct fingerprints can be identified when spatial and temporal characteristics are considered simultaneously.

Cortico-subcortical interactions in overlapping communities of edge functional connectivity.

Both cortical and subcortical regions can be functionally organized into networks. Regions of the basal ganglia are extensively interconnected with the cortex via reciprocal connections that relay and modulate cortical function. Here we employ an edge-centric approach, which computes co-fluctuations among region pairs in a network to investigate the role and interaction of subcortical regions with cortical systems. By clustering edges into communities, we show that cortical systems and subcortical regions couple via multiple edge communities, with hippocampus and amygdala having a distinct pattern from striatum and thalamus. We show that the edge community structure of cortical networks is highly similar to one obtained from cortical nodes when the subcortex is present in the network. Additionally, we show that the edge community profile of both cortical and subcortical nodes can be estimates solely from cortico-subcortical interactions. Finally, we used a motif analysis focusing on edge community triads where a subcortical region coupled to two cortical regions and found that two community triads where one community couples the subcortex to the cortex were overrepresented. In summary, our results show organized coupling of the subcortex to the cortex that may play a role in cortical organization of primary sensorimotor/attention and heteromodal systems and puts forth the motif analysis of edge community triads as a promising method for investigation of communication patterns in networks.

Individualized event structure drives individual differences in whole-brain functional connectivity.

Resting-state functional connectivity is typically modeled as the correlation structure of whole-brain regional activity. It is studied widely, both to gain insight into the brain's intrinsic organization but also to develop markers sensitive to changes in an individual's cognitive, clinical, and developmental state. Despite this, the origins and drivers of functional connectivity, especially at the level of densely sampled individuals, remain elusive. Here, we leverage novel methodology to decompose functional connectivity into its precise framewise contributions. Using two dense sampling datasets, we investigate the origins of individualized functional connectivity, focusing specifically on the role of brain network "events" - short-lived and peaked patterns of high-amplitude cofluctuations. Here, we develop a statistical test to identify events in empirical recordings. We show that the patterns of cofluctuation expressed during events are repeated across multiple scans of the same individual and represent idiosyncratic variants of template patterns that are expressed at the group level. Lastly, we propose a simple model of functional connectivity based on event cofluctuations, demonstrating that group-averaged cofluctuations are suboptimal for explaining participant-specific connectivity. Our work complements recent studies implicating brief instants of high-amplitude cofluctuations as the primary drivers of static, whole-brain functional connectivity. Our work also extends those studies, demonstrating that cofluctuations during events are individualized, positing a dynamic basis for functional connectivity.

Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series.

Functional connectivity (FC) describes the statistical dependence between neuronal populations or brain regions in resting-state fMRI studies and is commonly estimated as the Pearson correlation of time courses. Clustering or community detection reveals densely coupled sets of regions constituting resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs but appear to fluctuate on shorter timescales. Here, we propose a new approach to reveal temporal fluctuations in neuronal time series. Unwrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair or edge, and allows tracking of fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging runs, capture individual differences, and disclose fine-scale temporal expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.

Perceptual decisions about object shape bias visuomotor coordination during rapid interception movements.

Visual processing in parietal areas of the dorsal stream facilitates sensorimotor transformations for rapid movement. This action-related visual processing is hypothesized to play a distinct functional role from perception-related processing in the ventral stream. However, it is unclear how the two streams interact when perceptual identification is a prerequisite to executing an accurate movement. In the current study, we investigated how perceptual decision-making involving the ventral stream influences arm and eye movement strategies. Participants (n = 26) moved a robotic manipulandum using right whole arm movements to rapidly reach a stationary object or intercept a moving object on an augmented-reality display. On some blocks of trials, participants needed to identify the shape of the object (circle or ellipse) as a cue to either hit the object (circle) or move to a predefined location away from the object (ellipse). We found that during perceptual decision-making, there was an increased urgency to act during interception movements relative to reaching, which was associated with more decision errors. Faster hand reaction times were associated with a strategy to adjust the movement postinitiation, and this strategy was more prominent during interception. Saccadic reaction times were faster and initial saccadic peak velocity, initial gaze lags, and gains greater during decisions, suggesting that eye movements adapt to perceptual decision-making requirements. Together, our findings suggest that the integration of ventral stream information with visuomotor planning depends on imposed (or perceived) task demands.NEW & NOTEWORTHY Visual processing for perception and for action is thought to be mediated by two specialized neural pathways. Using a visuomotor decision-making task, we show that participants differentially utilized online perceptual decision-making in reaching and interception and that eye movements necessary for perception influenced motor decision strategies. These results provide evidence that task complexity modulates how pathways processing perception versus action information interact during the visual control of movement.

Consistent inter-individual differences in susceptibility to bodily illusions.

Illusory senses of ownership and agency (that the hand or effector that we see belongs to us and moves at our will, respectively) support the embodiment of prosthetic limbs, tele-operated surgical devices, and human-machine interfaces. We exposed forty-eight individuals to four different procedures known to elicit illusory ownership or agency over a fake visible rubber hand or finger. The illusory ownership or agency arising from the hand correlated with that of the finger. For both body parts, sensory stimulation across different modalities (visual with tactile or visual with kinesthetic) produced illusions of similar strength. However, the strengths of the illusions of ownership and agency were unrelated within individuals, supporting the proposal that distinct neuropsychological processes underlie these two senses. Developing training programs to enhance susceptibility to illusions of agency or ownership for people with lower natural susceptibility could broaden the usefulness of the above technologies.

Sense of ownership and not the sense of agency is spatially bounded within the space reachable with the unaugmented hand.

While reaching for a coffee cup, we are aware that the hand we see belongs to us and it moves at our will (reflecting our senses of ownership and agency, respectively), and that the cup is within our hand's reach rather than beyond it (i.e., in reachable space, RS, rather than in non-reachable space, NRS). Accepted psychological explanations of our sense of ownership, sense of agency, and our perception of space surrounding the body as RS or NRS propose a unitary dependence on Euclidean distance from the body. Here, we propose an alternate, affordance-based explanation of experienced ownership, agency, and perception of space surrounding the body as RS and NRS. Adult participants experienced the static rubber hand illusion (RHI) and its dynamic variant, while the rubber hand was either within their arm's reach (i.e., in self-identified RS) or beyond it (i.e., in self-identified NRS). We found that when the participants experienced synchronous visual and tactile signals in the static RHI, and synchronous visual and kinesthetic signals in the dynamic RHI, they felt illusory ownership when the rubber hand was in RS but not when it was in NRS. Conversely, when the participants experienced synchronous visual and kinesthetic signals in the dynamic RHI, they felt agency, regardless of the rubber hand's location. In addition, illusory ownership was accompanied by proprioceptive drift, a feeling that their hand was closer to the rubber hand than it actually was, but agency was not accompanied by proprioceptive drift. Together, these results indicate that our sense of ownership, while malleable enough to incorporate visible non-corporeal objects resembling a body part, is spatially constrained by proprioceptive signals specifying that body part's actual location. In contrast, our sense of agency can incorporate a visible non-corporeal object, independent of its location with respect to the body. We propose that the psychological processes mediating our sense of ownership are closely linked with our perception of space surrounding the body, and that the spatial independence of our sense of agency reflects the coupling between our actions and perception of the environment, such as while using handheld tools as extensions of our body.