scholarly journals Long-term prognosis and educational determinants of brain network decline in older adult individuals

Nature Aging ◽  
2021 ◽  
Author(s):  
Micaela Y. Chan ◽  
Liang Han ◽  
Claudia A. Carreno ◽  
Ziwei Zhang ◽  
Rebekah M. Rodriguez ◽  
...  
Keyword(s):  
Older Adults ◽  
Large Scale ◽  
Brain Network ◽  
Network Organization ◽  
College Degree ◽  
Functional Brain Network ◽  
Brain Structure And Function ◽  
Longitudinal Imaging ◽  
Long Term Prognosis ◽  
And Function

AbstractOlder adults with lower education are at greater risk for dementia. It is unclear which brain changes lead to these outcomes. Longitudinal imaging-based measures of brain structure and function were examined in adult individuals (baseline age, 45–86 years; two to five visits per participant over 1–9 years). College degree completion differentiates individual-based and neighborhood-based measures of socioeconomic status and disadvantage. Older adults (~65 years and over) without a college degree exhibit a pattern of declining large-scale functional brain network organization (resting-state system segregation) that is less evident in their college-educated peers. Declining brain system segregation predicts impending changes in dementia severity, measured up to 10 years past the last scan date. The prognostic value of brain network change is independent of Alzheimer’s disease (AD)-related genetic risk (APOE status), the presence of AD-associated pathology (cerebrospinal fluid phosphorylated tau, cortical amyloid) and cortical thinning. These results demonstrate that the trajectory of an individual’s brain network organization varies in relation to their educational attainment and, more broadly, is a unique indicator of individual brain health during older age.

scholarly journals Towards a more inclusive and equitable developmental cognitive neuroscience

2021 ◽  
Author(s):  
Jazlyn Nketia ◽  
Dima Amso ◽  
Natalie Hiromi Brito
Keyword(s):  
Cognitive Development ◽  
Cognitive Neuroscience ◽  
Cultural Context ◽  
Large Scale ◽  
Marginalized Communities ◽  
Developmental Science ◽  
Science Theory ◽  
Brain Structure And Function ◽  
Scientific Potential ◽  
And Function

Brain and cognitive development is a burgeoning area of scientific inquiry, with tremendous potential to better the lives of children. Large scale longitudinal neuroimaging studies offer opportunities for significant scientific advances in our understanding of developing brain structure and function. The proposed manuscript will focus on the scientific potential of the HEALthy Brain and Cognitive Development (HBCD) Study, highlighting what questions these data can and what they cannot answer about child development. Specifically, we caution against the misuse of these data for advancing de-contextualized and scientifically questionable narratives about the development of children from marginalized communities. We will focus on building and organizing a framework for interpreting HBCD data through the lens of sampling, cultural context, measurement, and developmental science theory. Our goal is to thoughtfully offer the scientific community opportunities to use the large scale and collaborative nature of HBCD to collectively revise practices in developmental science that to-date have not carefully considered their own role in perpetuating narratives that support systemic injustice.

scholarly journals A whole-brain and cross-diagnostic perspective on functional brain network dysfunction

2018 ◽  
Author(s):  
Marjolein Spronk ◽  
Kaustubh Kulkarni ◽  
Jie Lisa Ji ◽  
Brian P. Keane ◽  
Alan Anticevic ◽  
...  
Keyword(s):  
Mental Illness ◽  
Resting State ◽  
Brain Network ◽  
Functional Network ◽  
Graph Distance ◽  
Network Organization ◽  
Whole Brain ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Mental Diseases

AbstractA wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations have seemed to support various theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in broad, whole-brain perspective. Using a graph distance measure – connectome-wide correlation – we found that whole-brain resting-state functional network organization in humans is highly similar across a variety of mental diseases and healthy controls. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease those differences are informative. Such small network alterations may reflect the fact that most psychiatric patients maintain overall cognitive abilities similar to those of healthy individuals (relative to, e.g., the most severe schizophrenia cases), such that whole-brain functional network organization is expected to differ only subtly even for mental diseases with devastating effects on everyday life. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases.

scholarly journals Large-Scale Functional Brain Network Architecture Changes Associated With Trauma-Related Dissociation

2020 ◽  
pp. appi.ajp.2020.1
Author(s):  
Lauren A.M. Lebois ◽  
Meiling Li ◽  
Justin T. Baker ◽  
Jonathan D. Wolff ◽  
Danhong Wang ◽  
...  
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network

Dynamic Reorganization of the Cortical Functional Brain Network in Affective Processing and Cognitive Reappraisal

2020 ◽  
Vol 30 (10) ◽  
pp. 2050051
Author(s):  
Feng Fang ◽  
Thomas Potter ◽  
Thinh Nguyen ◽  
Yingchun Zhang
Keyword(s):  
Emotion Regulation ◽  
Large Scale ◽  
Brain Networks ◽  
Cognitive Reappraisal ◽  
Brain Network ◽  
Affective Processing ◽  
Functional Brain ◽  
Affective Stimuli ◽  
Functional Brain Network ◽  
Functional Brain Networks

Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula: see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a ‘top-down’ pattern that occurs across four brain network states that arise at different time instants (0–170[Formula: see text]ms, 170–370[Formula: see text]ms, 380–620[Formula: see text]ms, and 620–1000[Formula: see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.

scholarly journals Development of a Large-Scale Functional Brain Network during Human Non-Rapid Eye Movement Sleep

2010 ◽  
Vol 30 (34) ◽  
pp. 11379-11387 ◽  
Author(s):  
V. I. Spoormaker ◽  
M. S. Schroter ◽  
P. M. Gleiser ◽  
K. C. Andrade ◽  
M. Dresler ◽  
...  
Keyword(s):  
Eye Movement ◽  
Large Scale ◽  
Brain Network ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep ◽  
Functional Brain ◽  
Functional Brain Network

P3-308: Functional brain network organization in Alzheimer's disease

2013 ◽  
Vol 9 ◽  
pp. P670-P670 ◽  
Author(s):  
Hanneke de Waal ◽  
Cornelis Stam ◽  
Marieke Lansbergen ◽  
F. Maestú ◽  
Philip Scheltens ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Network ◽  
Network Organization ◽  
Functional Brain ◽  
Functional Brain Network

scholarly journals Graph-Based Method for Anomaly Detection in Functional Brain Network using Variational Autoencoder

2019 ◽  
Author(s):  
Jalal Mirakhorli ◽  
Mojgan Mirakhorli
Keyword(s):  
Large Scale ◽  
Functional Neuroimaging ◽  
Evaluation Criteria ◽  
Region Of Interest ◽  
Brain Network ◽  
Convolutional Network ◽  
Large Scale Data ◽  
Functional Brain Network ◽  
Variational Autoencoder ◽  
Brain Connections

AbstractFunctional neuroimaging techniques using resting-state functional MRI (rs-fMRI) have accelerated progress in brain disorders and dysfunction studies. Since, there are the slight differences between healthy and disorder brains, investigation in the complex topology of human brain functional networks is difficult and complicated task with the growth of evaluation criteria. Recently, graph theory and deep learning applications have spread widely to understanding human cognitive functions that are linked to gene expression and related distributed spatial patterns. Irregular graph analysis has been widely applied in many brain recognition domains, these applications might involve both node-centric and graph-centric tasks. In this paper, we discuss about individual Variational Autoencoder and Graph Convolutional Network (GCN) for the region of interest identification areas of brain which do not have normal connection when apply certain tasks. Here, we identified a framework of Graph Auto-Encoder (GAE) with hyper sphere distributer for functional data analysis in brain imaging studies that is underlying non-Euclidean structure, in learning of strong rigid graphs among large scale data. In addition, we distinguish the possible mode correlations in abnormal brain connections.

scholarly journals Functional brain network reconfiguration during learning in a dynamic environment

2019 ◽  
Author(s):  
Chang-Hao Kao ◽  
Ankit N. Khambhati ◽  
Danielle S. Bassett ◽  
Matthew R. Nassar ◽  
Joseph T. McGuire ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Functional Integration ◽  
Dynamic Environment ◽  
Brain Network ◽  
Uncertain Environments ◽  
Whole Brain ◽  
Functional Brain Network ◽  
Brain Pattern ◽  
Brain Functional Connectivity

AbstractWhen learning about dynamic and uncertain environments, people should update their beliefs most strongly when new evidence is most informative, such as when the environment undergoes a surprising change or existing beliefs are highly uncertain. Here we show that modulations of surprise and uncertainty are encoded in a particular, temporally dynamic pattern of whole-brain functional connectivity, and this encoding is enhanced in individuals that adapt their learning dynamics more appropriately in response to these factors. The key feature of this whole-brain pattern of functional connectivity is stronger connectivity, or functional integration, between the fronto-parietal and other functional systems. Our results provide new insights regarding the association between dynamic adjustments in learning and dynamic, large-scale changes in functional connectivity across the brain.

scholarly journals Adolescent development of multiscale cortical wiring and functional connectivity in the human connectome

2021 ◽  
Author(s):  
Bo-yong Park ◽  
Casey Paquola ◽  
Richard A.I. Bethlehem ◽  
Oualid Benkarim ◽  
Bratislav Misic ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Brain Network ◽  
Network Development ◽  
Network Function ◽  
Age Related ◽  
Mri Features ◽  
Functional Brain Network ◽  
Structural Networks ◽  
Advanced Model

Adolescence is a time of profound changes in the structural wiring of the brain and maturation of large-scale functional interactions. Here, we analyzed structural and functional brain network development in an accelerated longitudinal cohort spanning 14-25 years (n = 199). Core to our work was an advanced model of cortical wiring that incorporates multimodal MRI features of (i) cortico-cortical proximity, (ii) microstructural similarity, and (iii) diffusion tractography. Longitudinal analyses assessing age-related changes in cortical wiring during adolescence identified increases in cortical wiring within attention and default-mode networks, as well as between transmodal and attention, and sensory and limbic networks, indicative of a continued differentiation of cortico-cortical structural networks. Cortical wiring changes were statistically independent from age-related cortical thinning seen in the same subjects. Conversely, resting-state functional MRI analysis in the same subjects indicated an increasing segregation of sensory and transmodal systems during adolescence, with age-related reductions in their functional connectivity alongside with an increase in structural wiring distance. Our findings provide new insights into adolescent brain network development, illustrating how the maturation of structural wiring interacts with the development of macroscale network function.

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