scholarly journals A positive-negative mode of population covariation links brain connectivity, demographics and behavior

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

Alteration of Brain Connectivity and Behavior Using a Precisely Timed Electrical Stimulation Paradigm in a Fear Regulation Circuit

2020 ◽  
Vol 87 (9) ◽  
pp. S359-S360
Author(s):  
Rebecca Younk ◽  
Meng-chen Lo ◽  
Ethan Blackwood ◽  
Adriano Reimer ◽  
Sonia Olson ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Brain Connectivity ◽  
And Behavior ◽  
Stimulation Paradigm

scholarly journals Linking brain connectivity and behavior after acute and chronic alcohol exposure

2017 ◽  
Vol 22 (8) ◽  
pp. 1079-1079 ◽  
Author(s):  
E Shokri-Kojori ◽  
D Tomasi ◽  
C E Wiers ◽  
G-J Wang ◽  
N D Volkow
Keyword(s):  
Brain Connectivity ◽  
Alcohol Exposure ◽  
Chronic Alcohol ◽  
Chronic Alcohol Exposure ◽  
And Behavior

scholarly journals Brain network dynamics codify heterogeneity in seizure propagation

2021 ◽  
Author(s):  
Nuttida Rungratsameetaweemana ◽  
Claudia Lainscsek ◽  
Sydney S Cash ◽  
Javier O Garcia ◽  
Terrence J Sejnowski ◽  
...  
Keyword(s):  
Neurological Disorders ◽  
Brain Connectivity ◽  
Clinical Manifestations ◽  
Treatment Strategies ◽  
Brain Network ◽  
Functional Brain ◽  
Graph Theoretical Approach ◽  
Network Properties ◽  
Adaptive Cognition ◽  
And Behavior

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.

scholarly journals Applications of MRI Connectomics

2021 ◽  
Author(s):  
Jessica S. Damoiseaux ◽  
Andre Altmann ◽  
Jonas Richiardi ◽  
Sepideh Sadaghiani
Keyword(s):  
Clinical Research ◽  
Brain Connectivity ◽  
Current Status ◽  
Disease Biomarkers ◽  
Brain Structure And Function ◽  
And Function ◽  
And Behavior ◽  
The Impact ◽  
Behavior And Cognition ◽  
Brain Connectomics

Structural and functional brain connectomics are considered a basis for an individual's behavior and cognition. Therefore, deviations from typical connectivity patterns may indicate disease processes, and can potentially serve as disease biomarkers. To date, the direct clinical application of brain connectivity measures for diagnostics or treatment is limited. Nonetheless, the extant literature on fundamental and clinical research applications reveals important advances in our understanding of typical and atypical brain structure and function. In this chapter we discuss the current status of the field regarding: (1) the impact of the connectome on cognitive processes and behavior, (2) the connectome across the lifespan, and (3) clinical research applications of connectomics. In addition, we highlight some limitations of connectomics for research and clinical translation.

scholarly journals Associative Learning and Motivation Differentially Requires Neuroligin-1 at Excitatory Synapses

2020 ◽  
Author(s):  
Jiaqi Luo ◽  
Jessica M Tan ◽  
Jess Nithianantharajah
Keyword(s):  
Decision Making ◽  
Brain Connectivity ◽  
Excitatory Synapses ◽  
Response Effort ◽  
Trade Off ◽  
Signaling Complex ◽  
Neural Computations ◽  
Decision Making Processes ◽  
And Behavior ◽  
The Brain

ABSTRACTIn a changing environment, a challenge for the brain is to flexibly guide adaptive behavior towards survival. Understanding how these decision-making processes and underlying neural computations are orchestrated by the structural components of the brain, from circuits to cells, and ultimately the signaling complex of proteins at synapses, is central to elucidating the mechanisms that shape normal and abnormal brain connectivity, plasticity and behavior. At excitatory synapses, neuroligin-1 (Nlgn1) a postsynaptic cell-adhesion molecule required for the formation of trans-synaptic complexes with presynaptic partners is critical for regulating synapse specification, function and plasticity. Extensive evidence shows Nlgn1 is essential for synaptic transmission and long-term plasticity, but how these signaling processes ultimately regulate components of cognitive behavior is much less understood. Here, employing a comprehensive battery of touchscreen-based cognitive assays, we measured two key decision problems: i) the ability to learn and exploit the associative structure of the environment and ii) the trade-off between potential rewards and costs, or positive and negative utilities associated with available actions. We found that mice lacking Nlgn1 have an intact capacity to acquire complex associative structures and adjust learned associations. However, loss of Nlgn1 alters motivation leading to a reduced willingness to overcome response effort for reward and an increased willingness to exert effort to escape an aversive situation. We suggest Nlgn1 may be important for balancing the weighting on positive and negative utilities in reward-cost trade-off. Our findings identify Nlgn1 is essential for regulating distinct cognitive processes underlying decision-making, providing evidence of a new model for dissociating the computations underlying learning and motivational processing.

scholarly journals Topoisomerase IIIβ Deficiency Induces Neuro-Behavioral Changes and Brain Connectivity Alterations in Mice

2021 ◽  
Vol 22 (23) ◽  
pp. 12806
Author(s):  
Faiz Ur Rahman ◽  
You-Rim Kim ◽  
Eun-Kyeung Kim ◽  
Hae-rim Kim ◽  
Sang-Mi Cho ◽  
...  
Keyword(s):  
Olfactory Bulb ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Normal Brain ◽  
Dna And Rna ◽  
Positron Emission ◽  
Normal Brain Function ◽  
Ct Analysis ◽  
The Central Nervous System ◽  
And Behavior

Topoisomerase IIIβ (Top3β), the only dual-activity topoisomerase in mammals that can change topology of both DNA and RNA, is known to be associated with neurodevelopment and mental dysfunction in humans. However, there is no report showing clear associations of Top3β with neuropsychiatric phenotypes in mice. Here, we investigated the effect of Top3β on neuro-behavior using newly generated Top3β deficient (Top3β−/−) mice. We found that Top3β−/− mice showed decreased anxiety and depression-like behaviors. The lack of Top3β was also associated with changes in circadian rhythm. In addition, a clear expression of Top3β was demonstrated in the central nervous system of mice. Positron emission tomography/computed tomography (PET/CT) analysis revealed significantly altered connectivity between many brain regions in Top3β−/− mice, including the connectivity between the olfactory bulb and the cerebellum, the connectivity between the amygdala and the olfactory bulb, and the connectivity between the globus pallidus and the optic nerve. These connectivity alterations in brain regions are known to be linked to neurodevelopmental as well as psychiatric and behavioral disorders in humans. Therefore, we conclude that Top3β is essential for normal brain function and behavior in mice and that Top3β could be an interesting target to study neuropsychiatric disorders in humans.

scholarly journals Deep Neural Networks Carve the Brain at its Joints

2020 ◽  
Author(s):  
Maxwell A. Bertolero ◽  
Danielle S. Bassett
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Linear Models ◽  
Brain Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Linear Functions ◽  
Complex Relationships ◽  
And Behavior ◽  
The Brain

AbstractHow an individual’s unique brain connectivity determines that individual’s cognition, behavior, and risk for pathology is a fundamental question in basic and clinical neuroscience. In seeking answers, many have turned to machine learning, with some noting the particular promise of deep neural networks in modelling complex non-linear functions. However, it is not clear that complex functions actually exist between brain connectivity and behavior, and thus if deep neural networks necessarily outperform simpler linear models, or if their results would be interpretable. Here we show that, across 52 subject measures of cognition and behavior, deep neural networks fit to each brain region’s connectivity outperform linear regression, particularly for the brain’s connector hubs—regions with diverse brain connectivity—whereas the two approaches perform similarly when fit to brain systems. Critically, averaging deep neural network predictions across brain regions results in the most accurate predictions, demonstrating the ability of deep neural networks to easily model the various functions that exists between regional brain connectivity and behavior, carving the brain at its joints. Finally, we shine light into the black box of deep neural networks using multislice network models. We determined that the relationship between connector hubs and behavior is best captured by modular deep neural networks. Our results demonstrate that both simple and complex relationships exist between brain connectivity and behavior, and that deep neural networks can fit both. Moreover, deep neural networks are particularly powerful when they are first fit to the various functions of a system independently and then combined. Finally, deep neural networks are interpretable when their architectures are structurally characterized using multislice network models.

Effects of a spaceflight analog environment on brain connectivity and behavior

NeuroImage ◽  
2016 ◽  
Vol 141 ◽  
pp. 18-30 ◽  
Author(s):  
Kaitlin Cassady ◽  
Vincent Koppelmans ◽  
Patricia Reuter-Lorenz ◽  
Yiri De Dios ◽  
Nichole Gadd ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
And Behavior

scholarly journals When makes you unique: temporality of the human brain fingerprint

2021 ◽  
Author(s):  
Dimitri Van De Ville ◽  
Younes Farouj ◽  
Maria Giulia Preti ◽  
Raphaël Liégeois ◽  
Enrico Amico
Keyword(s):  
Human Brain ◽  
Time Scales ◽  
Brain Connectivity ◽  
Affective Processing ◽  
Optimal Time ◽  
Sufficient Information ◽  
Temporal Features ◽  
Higher Cognitive Functions ◽  
And Behavior ◽  
Temporal Windows

AbstractThe extraction of “fingerprints” from human brain connectivity data has become a new frontier in neuroscience. However, the time scales of human brain identifiability have not been addressed yet. In other words, what temporal features make our brains more “identifiable”? We here explore the dynamics of brain fingerprints (or brainprints) along two complementary axes: 1) what is the optimal time scale at which brainprints integrate sufficient information, 2) when best identification happens. Using dynamic identifiability, we show that the best identification emerges at longer time scales (~300s); however, short transient “bursts of identifiability” persist even when looking at shorter functional interactions. We find that these bursts of identifiability might be strongly associated with neuronal activity. Furthermore, we report evidence that different parts of connectome fingerprints relate to different time scales: i.e., more visual-somatomotor at short temporal windows, more frontoparietal-DMN driven by increasing temporal windows. Finally, using a meta-analytic approach, we show that there is a broad spectrum of associations between brainprints and behavior. At faster time scales, human brain fingerprints are linked to multisensory stimulation, eye movements, affective processing, visuospatial attention. At slower time scales instead, we find higher-cognitive functions, such as language and verbal semantics, awareness, declarative and working memory, social cognition. We hope that this first investigation of the temporality of the human brain fingerprint will pave the way towards a better understanding of what and when makes our brains unique.

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