Intrinsic functional connectivity predicts individual differences in distractibility

Individuality, the uniqueness that distinguishes one person from another, may manifest as diverse rearrangements of functional connectivity during heterogeneous cognitive demands; yet, the neurobiological substrates of individuality, reflected in inter-individual variations of large-scale functional connectivity, have not been fully evidenced. Accordingly, we explored inter-individual variations of functional connectivity dynamics, subnetwork patterns and modular architecture while subjects watched identical video clips designed to induce different arousal levels. How inter-individual variations are manifested in the functional brain networks was examined with respect to four contrasting divisions: edges within the anterior versus posterior part of the brain, edges with versus without corresponding anatomically-defined structural pathways, inter- versus intra-module connections, and rich club edge types. Inter-subject variation in dynamic functional connectivity occurred to a greater degree within edges localized to anterior rather than posterior brain regions, without adhering to structural connectivity, between modules as opposed to within modules, and in weak-tie local edges rather than strong-tie rich-club edges. Arousal level significantly modulates inter-subject variability in functional connectivity, edge patterns, and modularity, and particularly enhances the synchrony of rich-club edges. These results imply that individuality resides in the dynamic reconfiguration of large-scale brain networks in response to a stream of cognitive demands.

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“…Individual differences with regard to connectivity19, cognitive function34, cognition35 and clinical symptoms36 have also been explored.…”

Section: Discussionmentioning

confidence: 99%

Individuality manifests in the dynamic reconfiguration of large-scale brain networks during movie viewing

Jang

Knight

Pae

et al. 2017

Sci Rep

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“…Greater connectivity between default mode regions predicted better distractor suppression, supporting evidence of compromised default network function in a variety of clinical disorders [47,78]. Greater connectivity between the default mode and the dorsal and ventral attention networks, on the other hand, predicted worse distractor suppression, reinforcing previous findings that diminished anticorrelation between the task-positive and task-negative networks is related to impaired attention [40,78]. These support vector regression models were internally validated with a leave-one-subject-out approach; however, they were not tested for external validity beyond the study sample.…”

Section: New Insights From Network Neuroscience and Predictive Modelingmentioning

confidence: 92%

Section: New Insights From Network Neuroscience and Predictive Modelingmentioning

confidence: 99%

“…One study, for example, found that resting-state connectivity between the dorsal and ventral attention and default networks predicted the ability to suppress irrelevant visual distractors measured several months later [78]. Greater connectivity between default mode regions predicted better distractor suppression, supporting evidence of compromised default network function in a variety of clinical disorders [47,78]. Greater connectivity between the default mode and the dorsal and ventral attention networks, on the other hand, predicted worse distractor suppression, reinforcing previous findings that diminished anticorrelation between the task-positive and task-negative networks is related to impaired attention [40,78].…”

Section: New Insights From Network Neuroscience and Predictive Modelingmentioning

confidence: 99%

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Characterizing Attention with Predictive Network Models

Rosenberg

Finn

Mayes

et al. 2017

Trends in Cognitive Sciences

130

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Recent work shows that models based on functional connectivity in large-scale brain networks can predict individuals’ attentional abilities. Some of the first generalizable neuromarkers of cognitive function, these models also inform our basic understanding of attention, providing empirical evidence that (1) attention is a network property of brain computation, (2) the functional architecture that underlies attention can be measured while people are not engaged in any explicit task, and (3) this architecture supports a general attentional ability common to several lab-based tasks and impaired in attention deficit hyperactivity disorder. Looking ahead, connectivity-based predictive models of attention and other cognitive abilities and behaviors may potentially improve the assessment, diagnosis, and treatment of clinical dysfunction.

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“…Contrasted to activation analyses, functional connectivity takes into account the entire task time series, and are model‐free, capturing cofluctuations in activity not captured by extrinsic events in model‐based activation analyses (Gonzalez‐Castillo et al., 2012). Recent studies using functional connectivity analyses suggest that this approach is sensitive to I‐ELT (Philip et al., 2013; Philip et al., 2014), as well as individual differences in anxiety, intelligence, cognitive ability, and ADHD symptoms (Finn et al., 2015; Fortenbaugh, DeGutis, & Esterman, in press; Kim et al., 2011; Poole et al., 2016; Rosenberg et al., 2016). This approach may thus reveal previously unobserved brain–behavior associations that are relevant to our understanding of cognition and psychopathology.…”

Section: Introductionmentioning

confidence: 99%

Interpersonal early‐life trauma alters amygdala connectivity and sustained attention performance

et al. 2017

Self Cite

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IntroductionInterpersonal early life trauma (I‐ELT) is associated with a myriad of functional impairments in adulthood, increased risk of drug addiction, and neuropsychiatric disorders. While deficits in emotional regulation and amygdala functioning are well characterized, deficits in general cognitive functioning have also been documented. However, the neural underpinnings of cognitive dysfunction in adults with a history of I‐ELT and the potential relationship between amygdala‐based functional connectivity and behavioral performance are currently poorly understood. This study examined how I‐ELT affects the cognitive and neural mechanisms supporting sustained attention.MethodsA total of 66 Veterans (18 with and 48 without a history of I‐ELT) completed a nonemotional sustained attention task during functional MRI.ResultsThe individuals with I‐ELT showed significant impairments in sustained attention (i.e., higher error rates, greater response variability). This cohort exhibited increased amygdala functional connectivity with the prefrontal cortex and decreased functional connectivity with the parahippocampal gyrus when compared to those without I‐ELT. These connections were significantly correlated with individual differences in sustained attention performance. Notably, classification analyses revealed that the pattern of amygdala connectivity across the whole brain was able to classify I‐ELT status with 70% accuracy.ConclusionThese results provide evidence of a lasting negative impact for those with a history of I‐ELT on sustained attention ability. They also highlight a critical role for amygdala functioning in cognitive control and sustained attention for those with a history of I‐ELT, which may underlie the observed attention deficits in clinical assessments and cognitive tests involving both emotional and nonemotional stimuli.

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Intrinsic functional connectivity predicts individual differences in distractibility

Cited by 67 publications

References 70 publications

Individuality manifests in the dynamic reconfiguration of large-scale brain networks during movie viewing

Individuality manifests in the dynamic reconfiguration of large-scale brain networks during movie viewing

Characterizing Attention with Predictive Network Models

Interpersonal early‐life trauma alters amygdala connectivity and sustained attention performance

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