scholarly journals Multi‐timepoint pattern analysis: Influence of personality and behavior on decoding context‐dependent brain connectivity dynamics

2021 ◽  
Author(s):  
Saampras Ganesan ◽  
Jinglei Lv ◽  
Andrew Zalesky
2020 ◽  
Vol 87 (9) ◽  
pp. S359-S360
Author(s):  
Rebecca Younk ◽  
Meng-chen Lo ◽  
Ethan Blackwood ◽  
Adriano Reimer ◽  
Sonia Olson ◽  
...  

2017 ◽  
Vol 22 (8) ◽  
pp. 1079-1079 ◽  
Author(s):  
E Shokri-Kojori ◽  
D Tomasi ◽  
C E Wiers ◽  
G-J Wang ◽  
N D Volkow

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 925
Author(s):  
Shuo Chen ◽  
Zhen Zhang ◽  
Chen Mo ◽  
Qiong Wu ◽  
Peter Kochunov ◽  
...  

We propose a new metric to characterize the complexity of weighted complex networks. Weighted complex networks represent a highly organized interactive process, for example, co-varying returns between stocks (financial networks) and coordination between brain regions (brain connectivity networks). Although network entropy methods have been developed for binary networks, the measurement of non-randomness and complexity for large weighted networks remains challenging. We develop a new analytical framework to measure the complexity of a weighted network via graph embedding and point pattern analysis techniques in order to address this unmet need. We first perform graph embedding to project all nodes of the weighted adjacency matrix to a low dimensional vector space. Next, we analyze the point distribution pattern in the projected space, and measure its deviation from the complete spatial randomness. We evaluate our method via extensive simulation studies and find that our method can sensitively detect the difference of complexity and is robust to noise. Last, we apply the approach to a functional magnetic resonance imaging study and compare the complexity metrics of functional brain connectivity networks from 124 patients with schizophrenia and 103 healthy controls. The results show that the brain circuitry is more organized in healthy controls than schizophrenic patients for male subjects while the difference is minimal in female subjects. These findings are well aligned with the established sex difference in schizophrenia.


2013 ◽  
Vol 20 (4) ◽  
pp. 391-401 ◽  
Author(s):  
S.M. Hadi Hosseini ◽  
Shelli R. Kesler

AbstractAdvances in breast cancer (BC) treatments have resulted in significantly improved survival rates. However, BC chemotherapy is often associated with several side effects including cognitive dysfunction. We applied multivariate pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) to find a brain connectivity pattern that accurately and automatically distinguishes chemotherapy-treated (C+) from non-chemotherapy treated (C−) BC females and healthy female controls (HC). Twenty-seven C+, 29 C−, and 30 HC underwent fMRI during an executive-prefrontal task (Go/Nogo). The pattern of functional connectivity associated with this task discriminated with significant accuracy between C+ and HC groups (72%, p = .006) and between C+ and C− groups (71%, p = .012). However, the accuracy of discrimination between C− and HC was not significant (51%, p = .46). Compared with HC, behavioral performance of the C+ and C− groups during the task was intact. However, the C+ group demonstrated altered functional connectivity in the right frontoparietal and left supplementary motor area networks compared to HC, and in the right middle frontal and left superior frontal gyri networks, compared to C−. Our results provide further evidence that executive function performance may be preserved in some chemotherapy-treated BC survivors through recruitment of additional neural connections. (JINS, 2013, 19, 1–11)


Author(s):  
Itir Onal ◽  
Emre Aksan ◽  
Burak Velioglu ◽  
Orhan Firat ◽  
Mete Ozay ◽  
...  

Author(s):  
Benjamin Gardner ◽  
Amanda L. Rebar

Many of the most pressing societal issues—e.g., health, illness, and associated costs; climate change—are rooted in behavior. Even small changes to everyday behaviors can bring considerable benefits. Many people successfully adopt new behaviors but fail to maintain them over time. This problem has inspired interest in habit. Within psychology, habitual behaviors are defined as actions triggered automatically when people encounter situations in which they have consistently done them in the past. Repeating behavior in the same context reinforces mental associations between the context and behavior. Habit is said to have formed when exposure to the context non-consciously activates the association, which in turn elicits an urge to act, influencing behavior with minimal conscious forethought. As an initially goal-directed behavior becomes habitual, control over behavior is transferred from a reasoned, reflective processing system, which elicits behavior relatively slowly based on conscious motivation, to an impulsive system, which elicits behavior rapidly and efficiently, based on learned context-behavior associations. Habitual behaviors thus become detached from conscious motivational processes. Spurred by development of self-report habit measures, studies have modeled the relationship between behavioral repetition and the strengthening of habit, showing that habit is characterized by initially rapid growth, which decelerates until a plateau is reached. Theories propose that habit has two effects on behavior in the associated context: habit will prompt frequent performance, and will override motivational tendencies in doing so, unless self-control is particularly strong in that moment. People may therefore continue to perform a habitual action even when they lack motivation. These characteristics have generated interest in the potential for habit to support long-term adoption of new behaviors. People often fail to maintain behavior changes because they lose motivation, but if people were to form habits for new behaviors, they should in theory continue to perform them despite losing motivation. This has prompted calls for interventions to move beyond merely promoting new behaviors, toward advocating context-dependent habitual performances. Some have also argued that habit formation may be fruitful for stopping unwanted behaviors, because new, “good” habits can be directly substituted for existing “bad” habits. Realistically, habit formation is not a viable standalone behavior change technique, as it requires that people first adopt a new behavior, which through repetition will become habitual. The promotion of context-dependent repetition should complement techniques that reinforce the motivation and action control required for behavioral initiation and maintenance prior to habit forming. Real-world behavior change interventions based on these principles have been found to be acceptable and appealing, and show promise for changing behavior, though few have used long-term follow-up periods. This entry highlights leading work in the application of habit formation to behavior change interventions, drawing on the most methodologically and conceptually rigorous empirical research available. Most of the development and application of habit theory to real-world social contexts has been undertaken in health and pro-environmental domains. This entry thus focuses most heavily on these domains, but the principles outlined are thought to be applicable across behaviors and settings.


2019 ◽  
Author(s):  
Abigail Dickinson ◽  
Manjari Daniel ◽  
Andrew Marin ◽  
Bilwaj Goanker ◽  
Mirella Dapretto ◽  
...  

AbstractFunctional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Mapping pre-symptomatic functional disruptions in ASD could identify infants based on neural risk, providing a crucial opportunity to mediate outcomes before behavioral symptoms emerge.Here we quantify functional connectivity using scalable EEG measures of oscillatory phase coherence (6-12Hz). Infants at high and low familial risk for ASD (N=65) underwent an EEG recording at 3 months of age and were assessed for ASD symptoms at 18 months using the Autism Diagnostic Observation Schedule-Toddler Module. Multivariate pattern analysis was used to examine early functional patterns that are associated with later ASD symptoms.Support vector regression (SVR) algorithms accurately predicted observed ASD symptoms at 18 months from EEG data at 3 months (r=0.76, p=0.02). Specifically, lower frontal connectivity and higher right temporo-parietal connectivity predicted higher ASD symptoms. The SVR model did not predict non-verbal cognitive abilities at 18 months (r=0.15, p=0.36), suggesting specificity of these brain alterations to ASD.These data suggest that frontal and temporo-parietal dysconnectivity play important roles in the early pathophysiology of ASD. Early functional differences in ASD can be captured using EEG during infancy and may inform much-needed advancements in the early detection of ASD.


2021 ◽  
Author(s):  
Nuttida Rungratsameetaweemana ◽  
Claudia Lainscsek ◽  
Sydney S Cash ◽  
Javier O Garcia ◽  
Terrence J Sejnowski ◽  
...  

Dynamic functional brain connectivity facilitates adaptive cognition and behavior. Abnormal alterations within such connectivity could result in disrupted functions observed across various neurological conditions. As one of the most common neurological disorders, epilepsy is defined by the seemingly random occurrence of spontaneous seizures. A central but unresolved question concerns the mechanisms by which extraordinarily diverse dynamics of seizures emerge. Here, we apply a graph-theoretical approach to assess dynamic reconfigurations in the functional brain connectivity before, during, and after seizures that display heterogeneous propagation patterns despite sharing similar origins. We demonstrate unique reconfigurations in globally-defined network properties preceding seizure onset that predict propagation patterns of impending seizures, and in locally-defined network properties that differentiate post-onset dynamics. These results characterize quantitative network features underlying the heterogeneity of seizure dynamics and the accompanying clinical manifestations. Decoding these network properties could improve personalized preventative treatment strategies for epilepsy as well as other neurological disorders.


2021 ◽  
pp. 1-5
Author(s):  
Gerald C Hsu ◽  

In this article, the author used 10-years’ worth of data of glucoses and prominent lifestyle details such as diet and exercise to address his glucose trend pattern analysis and progressive lifestyle behavior modifications. This progressive lifestyle behavior modification is closely related to behavior psychology. The research methodology used is the GH-Method: math-physical medicine (MPM) approach which has been developed by the author over the past decade. This “Progressive Behavior Modification concept is also a part of his Mentality-Personality Modeling (MPM). He addresses the quantitative linkage between diabetes physiological phenomena and behavior psychological influences of a type 2 diabetes (T2D) patient


2015 ◽  
Vol 18 (11) ◽  
pp. 1565-1567 ◽  
Author(s):  
Stephen M Smith ◽  
Thomas E Nichols ◽  
Diego Vidaurre ◽  
Anderson M Winkler ◽  
Timothy E J Behrens ◽  
...  

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