scholarly journals Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals

2020 ◽  
Vol 32 (2) ◽  
pp. 241-255 ◽  
Author(s):  
Emily W. Avery ◽  
Kwangsun Yoo ◽  
Monica D. Rosenberg ◽  
Abigail S. Greene ◽  
Siyuan Gao ◽  
...  
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Functional Connectivity ◽  
Sustained Attention ◽  
Fluid Intelligence ◽  
Memory Performance ◽  
Data Set ◽  
Human Connectome Project ◽  
Connectivity Patterns ◽  
Intelligence Models

Individual differences in working memory relate to performance differences in general cognitive ability. The neural bases of such individual differences, however, remain poorly understood. Here, using a data-driven technique known as connectome-based predictive modeling, we built models to predict individual working memory performance from whole-brain functional connectivity patterns. Using n-back or rest data from the Human Connectome Project, connectome-based predictive models significantly predicted novel individuals' 2-back accuracy. Model predictions also correlated with measures of fluid intelligence and, with less strength, sustained attention. Separate fluid intelligence models predicted working memory score, as did sustained attention models, again with less strength. Anatomical feature analysis revealed significant overlap between working memory and fluid intelligence models, particularly in utilization of prefrontal and parietal regions, and less overlap in predictive features between working memory and sustained attention models. Furthermore, showing the generality of these models, the working memory model developed from Human Connectome Project data generalized to predict memory in an independent data set of 157 older adults (mean age = 69 years; 48 healthy, 54 amnestic mild cognitive impairment, 55 Alzheimer disease). The present results demonstrate that distributed functional connectivity patterns predict individual variation in working memory capability across the adult life span, correlating with constructs including fluid intelligence and sustained attention.

scholarly journals Behavioral and neural signatures of working memory in childhood

2019 ◽  
Author(s):  
Monica D. Rosenberg ◽  
Steven A. Martinez ◽  
Kristina M. Rapuano ◽  
May I. Conley ◽  
Alexandra O. Cohen ◽  
...  
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Fluid Intelligence ◽  
Short Term Memory ◽  
Memory Performance ◽  
Memory Function ◽  
The United States ◽  
Reward Processing ◽  
Stop Signal Task ◽  
Back Task

AbstractWorking memory function changes across development and varies across individuals. The patterns of behavior and brain function that track individual differences in working memory during development, however, are not well understood. Here we establish associations between working memory, cognitive abilities, and functional MRI activation in data from over 4,000 9–10-year-olds enrolled in the Adolescent Brain Cognitive Development study, an ongoing longitudinal study in the United States. Behavioral analyses reveal robust relationships between working memory, short-term memory, language skills, and fluid intelligence. Analyses relating out-of-scanner working memory performance to memory-related fMRI activation in an emotional n-back task demonstrate that frontoparietal activity in response to an explicit memory challenge indexes working memory ability. Furthermore, this relationship is domain-specific, such that fMRI activation related to emotion processing during the emotional n-back task, inhibitory control during a stop-signal task, and reward processing during a monetary incentive delay task does not track memory abilities. Together these results inform our understanding of the emergence of individual differences in working memory and lay the groundwork for characterizing the ways in which they change across adolescence.

scholarly journals Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders

2020 ◽  
Author(s):  
Daniel S Barron ◽  
Siyuan Gao ◽  
Javid Dadashkarimi ◽  
Abigail S Greene ◽  
Marisa N Spann ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Psychiatric Disorders ◽  
Memory Performance ◽  
Brain Networks ◽  
Memory Deficits ◽  
Computational Method ◽  
Long Term Memory ◽  
Whole Brain ◽  
Human Connectome Project

Abstract Memory deficits are observed in a range of psychiatric disorders, but it is unclear whether memory deficits arise from a shared brain correlate across disorders or from various dysfunctions unique to each disorder. Connectome-based predictive modeling is a computational method that captures individual differences in functional connectomes associated with behavioral phenotypes such as memory. We used publicly available task-based functional MRI data from patients with schizophrenia (n = 33), bipolar disorder (n = 34), attention deficit hyper-activity disorder (n = 32), and healthy controls (n = 73) to model the macroscale brain networks associated with working, short- and long-term memory. First, we use 10-fold and leave-group-out analyses to demonstrate that the same macroscale brain networks subserve memory across diagnostic groups and that individual differences in memory performance are related to individual differences within networks distributed throughout the brain, including the subcortex, default mode network, limbic network, and cerebellum. Next, we show that diagnostic groups are associated with significant differences in whole-brain functional connectivity that are distinct from the predictive models of memory. Finally, we show that models trained on the transdiagnostic sample generalize to novel, healthy participants (n = 515) from the Human Connectome Project. These results suggest that despite significant differences in whole-brain patterns of functional connectivity between diagnostic groups, the core macroscale brain networks that subserve memory are shared.

scholarly journals Adult neuromarkers of sustained attention and working memory predict inter- and intra-individual differences in these processes in youth

2021 ◽  
Author(s):  
Omid Kardan ◽  
Andrew J Stier ◽  
Carlos Cardenas-Inigues ◽  
Julia C Pruin ◽  
Kathryn E Schertz ◽  
...  
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Cognitive Processes ◽  
Sustained Attention ◽  
Memory Load ◽  
Memory Performance ◽  
Working Memory Load ◽  
Using Data ◽  
Working Memory Model ◽  
Back Task

Sustained attention and working memory are central cognitive processes that vary between individuals, fluctuate over time, and have consequences for life and health outcomes. Here we characterize the functional brain architecture of these abilities in 9-11-year-old children using models based on functional magnetic resonance imaging functional connectivity. Using data from the Adolescent Brain Cognitive Development (ABCD) Study, we asked whether connectome-based models built to predict sustained attention and working memory in adults generalize to capture inter- and intra-individual differences in sustained attention and working memory performance in youth. Results revealed that a predefined connectome-based model of sustained attention predicted children's performance on the 0-back task, an attentionally taxing low-working-memory-load task. A predefined connectome-based model of working memory, on the other hand, also predicted performance on the 2-back task, an attentionally taxing high-working-memory-load task. The sustained attention model's predictive power was comparable to that achieved when predicting adults' 0-back performance and by a connectome-based model of cognition defined in the ABCD sample itself. Finally, the working memory model predicted children's recognition memory for n-back task stimuli. Together these results demonstrate that connectome-based models of sustained attention and working memory generalize to youth, reflecting the functional architecture of these processes in the developing brain.

scholarly journals Functional connectivity predicts changes in attention observed across minutes, days, and months

2020 ◽  
Vol 117 (7) ◽  
pp. 3797-3807 ◽  
Author(s):  
Monica D. Rosenberg ◽  
Dustin Scheinost ◽  
Abigail S. Greene ◽  
Emily W. Avery ◽  
Young Hye Kwon ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Sustained Attention ◽  
Deep Sedation ◽  
Brain Connectivity ◽  
Attentional State ◽  
Single Person ◽  
Connectivity Patterns ◽  
State Changes ◽  
Attention Function

The ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same patterns capture fluctuations in attention within individuals remains unclear. Here, across five independent studies, we demonstrate that the sustained attention connectome-based predictive model (CPM), a validated model of sustained attention function, generalizes to predict attentional state from data collected across minutes, days, weeks, and months. Furthermore, the sustained attention CPM is sensitive to within-subject state changes induced by propofol as well as sevoflurane, such that individuals show functional connectivity signatures of stronger attentional states when awake than when under deep sedation and light anesthesia. Together, these results demonstrate that fluctuations in attentional state reflect variability in the same functional connectivity patterns that predict individual differences in sustained attention.

scholarly journals Functional connectivity predicts changes in attention over minutes, days, and months

2019 ◽  
Author(s):  
Monica D. Rosenberg ◽  
Dustin Scheinost ◽  
Abigail S. Greene ◽  
Emily W. Avery ◽  
Young Hye Kwon ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Sustained Attention ◽  
Deep Sedation ◽  
Brain Connectivity ◽  
Attentional State ◽  
Single Person ◽  
Connectivity Patterns ◽  
State Changes ◽  
Attention Function

AbstractThe ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same patterns capture fluctuations in attention in single individuals remains unclear. Here, across five independent studies, we demonstrate that the sustained attention connectome-based predictive model (CPM), a validated model of sustained attention function, generalizes to predict attention changes across minutes, days, weeks, and months. Furthermore, the sustained attention CPM is sensitive to within-subject state changes induced by propofol as well as sevoflurane, such that individuals show functional connectivity signatures of stronger attentional states when awake than when under deep sedation and light anesthesia. Together these results demonstrate that fluctuations in attentional state reflect variability in the same functional connectivity patterns that predict individual differences in sustained attention.

scholarly journals Propofol modulates functional connectivity signatures of sustained attention

2021 ◽  
Author(s):  
Taylor A Chamberlain ◽  
Monica D. Rosenberg
Keyword(s):  
Individual Differences ◽  
Cognitive Function ◽  
Functional Connectivity ◽  
Sustained Attention ◽  
Fluid Intelligence ◽  
Pharmacological Intervention ◽  
Attentional State ◽  
Attention Networks ◽  
Functional Connections ◽  
Functional Brain Networks

Sustained attention is a critical cognitive function reflected in an individuals whole-brain pattern of fMRI functional connectivity. However sustained attention is not a purely static trait. Rather, attention waxes and wanes over time. Do functional brain networks that underlie individual differences in sustained attention also underlie changes in attentional state? To investigate, we replicate the finding that a validated connectome-based model of individual differences in sustained attention tracks pharmacologically induced changes in attentional state. Specifically, preregistered analyses revealed that participants exhibited functional connectivity signatures of stronger attention when awake than when under deep sedation with the anesthetic agent propofol. Furthermore, this effect was relatively specific to the predefined sustained attention networks: propofol administration modulated strength of the sustained attention networks more than it modulated strength of canonical resting-state networks and a network defined to predict fluid intelligence, and the functional connections most affected by propofol sedation overlapped with the sustained attention networks. Thus, propofol modulates functional connectivity signatures of sustained attention within individuals. More broadly these findings underscore the utility of pharmacological intervention in testing both the generalizability and specificity of network-based models of cognitive function.

scholarly journals Education did not interact with major depression on performance of memory tests in acute southern Brazilian in patients

2007 ◽  
Vol 1 (1) ◽  
pp. 24-31 ◽  
Author(s):  
Analuiza Camozzato ◽  
Marcelo Pio de Almeida Fleck ◽  
Vera Delgado ◽  
Marcia Lorena Fagundes Chaves
Keyword(s):  
Working Memory ◽  
Major Depression ◽  
Sustained Attention ◽  
Rating Scale ◽  
Memory Performance ◽  
Remote Memory ◽  
Independent Effect ◽  
Psychoactive Drugs ◽  
Memory Tests ◽  
Cognitive Memory

Abstract The relationship of cognitive function to depression in older adults has become a topic of extensive clinical interest and research. Objective: To analyze association between cognitive/memory performance, Major Depression, and education in 206 inpatients from the Psychiatry and Internal Medicine Departments. Methods: Patients were evaluated by the Mini Mental State Examination, a battery of memory tests, and the MontgomeryÅsberg Depression Rating Scale. Depression patients comprised 45 severe and 42 mild/moderate, according to the Montgomery-Asberg scale. The effect of psychoactive drugs was recorded (30% drug-free). Education was measured in years. Cognitive/memory tests assessed five domains: general mental functioning, attention, sustained attention/working memory, learning memory (verbal), and remote memory. An index for memory impairment was created (positivity: 50% of tests below cutoff). Results: The chief effect on worse performance was Major Depression for the domains (age and education adjusted) of attention, learning, remote memory, and general functioning. For the domain "sustained attention and working memory", only severely depressed patients differed from the medical controls (p=.008). Education showed an independent effect on test performances. No interaction between depression and educational status was observed. We also observed an independent effect of psychoactive drugs on some cognitive/ memory domains. Logistic Regression showed Major Depression as the main risk for cognitive impairment. Conclusions: These data demonstrated association of Major Depression with impaired cognitive performance independent of educational attainment or psychiatric medications.

scholarly journals A sub+ cortical fMRI-based surface parcellation

2019 ◽  
Author(s):  
John D. Lewis ◽  
Gleb Bezgin ◽  
Vladimir S. Fonov ◽  
D. Louis Collins ◽  
Alan C. Evans
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Life Span ◽  
Brain Volume ◽  
Subcortical Structures ◽  
Common Space ◽  
Cortical Parcellation ◽  
Connectivity Patterns ◽  
Deep Gray Matter ◽  
Volumetric Registration

AbstractBoth the cortex and the subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping these areas is of fundamental importance to neuroscience. A central obstacle to this task is the inaccuracy associated with mapping results from individuals into a common space. The vast individual differences in morphology pose a serious problem for volumetric registration. Surface-based approaches fare substantially better, but have thus far been used only for cortical parcellation. We extend this surface-based approach to include also the subcortical deep gray-matter structures. Using the life-span data from the Enhanced Nathan Klein Institute - Rockland Sample, comprised of data from 590 individuals from 6 to 85 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that our extended functional parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels. We also show that our subcortical parcels align with known subnuclei. Further, we show that this parcellation is appropriate for use with data from other modalities; we generate cortical and subcortical white/gray contrast measures for this same dataset, and draw on the fact that areal differences are evident in the relation of white/gray contrast to age, to sex, to brain volume, and to interactions of these terms; we show that our extended functional parcellation provides an improved fit to the complexity of the life-span changes in the white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels. We provide our extended functional parcellation for the use of the neuroimaging community.

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