scholarly journals Beyond the evoked/intrinsic neural process dichotomy

2018 ◽  
Vol 2 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Taylor Bolt ◽  
Michael L. Anderson ◽  
Lucina Q. Uddin
Keyword(s):  
Brain Function ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Activity Patterns ◽  
Intrinsic Activity ◽  
Spontaneous Brain Activity ◽  
Evoked Activity ◽  
Functional Brain Activity ◽  
Temporal Trajectory ◽  
And Task

Contemporary functional neuroimaging research has increasingly focused on characterization of intrinsic or “spontaneous” brain activity. Analysis of intrinsic activity is often contrasted with analysis of task-evoked activity that has traditionally been the focus of cognitive neuroscience. But does this evoked/intrinsic dichotomy adequately characterize human brain function? Based on empirical data demonstrating a close functional interdependence between intrinsic and task-evoked activity, we argue that the dichotomy between intrinsic and task-evoked activity as unobserved contributions to brain activity is artificial. We present an alternative picture of brain function in which the brain’s spatiotemporal dynamics do not consist of separable intrinsic and task-evoked components, but reflect the enaction of a system of mutual constraints to move the brain into and out of task-appropriate functional configurations. According to this alternative picture, cognitive neuroscientists are tasked with describing both the temporal trajectory of brain activity patterns across time, and the modulation of this trajectory by task states, without separating this process into intrinsic and task-evoked components. We argue that this alternative picture of brain function is best captured in a novel explanatory framework called enabling constraint. Overall, these insights call for a reconceptualization of functional brain activity, and should drive future methodological and empirical efforts.

scholarly journals A dynamical systems framework to uncover the drivers of large-scale cortical activity

2019 ◽  
Author(s):  
Arian Ashourvan ◽  
Sérgio Pequito ◽  
Maxwell Bertolero ◽  
Jason Z. Kim ◽  
Danielle S. Bassett ◽  
...  
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Intrinsic Activity ◽  
Model Parameters ◽  
Sensory Stimuli ◽  
Human Connectome Project ◽  
External Stimuli ◽  
And Task

ABSTRACTA fundamental challenge in neuroscience is to uncover the principles governing complex interactions between the brain and its external environment. Over the past few decades, the development of functional neuroimaging techniques and tools from graph theory, network science, and computational neuroscience have markedly expanded opportunities to study the intrinsic organization of brain activity. However, many current computational models are fundamentally limited by little to no explicit assessment of the brain’s interactions with external stimuli. To address this limitation, we propose a simple scheme that jointly estimates the intrinsic organization of brain activity and extrinsic stimuli. Specifically, we adopt a linear dynamical model (intrinsic activity) under unknown exogenous inputs (e.g., sensory stimuli), and jointly estimate the model parameters and exogenous inputs. First, we demonstrate the utility of this scheme by accurately estimating unknown external stimuli in a synthetic example. Next, we examine brain activity at rest and task for 99 subjects from the Human Connectome Project, and find significant task-related changes in the identified system, and task-related increases in the estimated external inputs showing high similarity to known task regressors. Finally, through detailed examination of fluctuations in the spatial distribution of the oscillatory modes of the estimated system during the resting state, we find an apparent non-stationarity in the profile of modes that span several brain regions including the visual and the dorsal attention systems. The results suggest that these brain structures display a time-varying relationship, or alternatively, receive non-stationary exogenous inputs that can lead to apparent system non-stationarities. Together, our embodied model of brain activity provides an avenue to gain deeper insight into the relationship between cortical functional dynamics and their drivers.

scholarly journals Peripheral BDNF: a candidate biomarker of healthy neural activity during learning is disrupted in schizophrenia

2014 ◽  
Vol 45 (4) ◽  
pp. 841-854 ◽  
Author(s):  
A. J. Skilleter ◽  
C. S. Weickert ◽  
A. Vercammen ◽  
R. Lenroot ◽  
T. W. Weickert
Keyword(s):  
Functional Neuroimaging ◽  
Brain Activity ◽  
Healthy Adults ◽  
Enzyme Linked Immunosorbent Assay ◽  
Association Learning ◽  
Healthy Humans ◽  
Important Regulator ◽  
Learning Test ◽  
Parietal Cortices ◽  
And Task

Background.Brain-derived neurotrophic factor (BDNF) is an important regulator of synaptogenesis and synaptic plasticity underlying learning. However, a relationship between circulating BDNF levels and brain activity during learning has not been demonstrated in humans. Reduced brain BDNF levels are found in schizophrenia and functional neuroimaging studies of probabilistic association learning in schizophrenia have demonstrated reduced activity in a neural network that includes the prefrontal and parietal cortices and the caudate nucleus. We predicted that brain activity would correlate positively with peripheral BDNF levels during probabilistic association learning in healthy adults and that this relationship would be altered in schizophrenia.Method.Twenty-five healthy adults and 17 people with schizophrenia or schizo-affective disorder performed a probabilistic association learning test during functional magnetic resonance imaging (fMRI). Plasma BDNF levels were measured by enzyme-linked immunosorbent assay (ELISA).Results.We found a positive correlation between circulating plasma BDNF levels and brain activity in the parietal cortex in healthy adults. There was no relationship between plasma BDNF levels and task-related activity in the prefrontal, parietal or caudate regions in schizophrenia. A direct comparison of these relationships between groups revealed a significant diagnostic difference.Conclusions.This is the first study to show a relationship between peripheral BDNF levels and cortical activity during learning, suggesting that plasma BDNF levels may reflect learning-related brain activity in healthy humans. The lack of relationship between plasma BDNF and task-related brain activity in patients suggests that circulating blood BDNF may not be indicative of learning-dependent brain activity in schizophrenia.

Assessment of spontaneous brain activity patterns in patients with iridocyclitis

Neuroreport ◽  
2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Yan Tong ◽  
Xin Huang ◽  
Chen-Xing Qi ◽  
Yin Shen
Keyword(s):  
Brain Activity ◽  
Activity Patterns ◽  
Spontaneous Brain Activity

scholarly journals Enhancing Task fMRI Preprocessing via Individualized Model-Based Filtering of Intrinsic Activity Dynamics

2020 ◽  
Author(s):  
Matthew F. Singh ◽  
Anxu Wang ◽  
Michael Cole ◽  
ShiNung Ching ◽  
Todd S. Braver
Keyword(s):  
Statistical Power ◽  
Brain Activity ◽  
Intrinsic Activity ◽  
Bold Signal ◽  
Group Level ◽  
Temporal Precision ◽  
Brain Responses ◽  
Evoked Activity ◽  
Activity Dynamics ◽  
Do So

AbstractBrain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work we describe a method to improve the estimation of task-evoked brain activity by first “filtering-out” the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; [1]) models built from individualized resting-state data (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, estimates based upon our technique better generalize between tasks measuring the same construct (cognitive control) and better predict individual differences in behavior. Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural activity.

scholarly journals Functional neuroimaging studies of the effects of psychotherapy

2014 ◽  
Vol 16 (1) ◽  
pp. 75-81 ◽  
Keyword(s):  
Functional Neuroimaging ◽  
Brain Activity ◽  
Behavioral Changes ◽  
Neural Mechanisms ◽  
Psychological Interventions ◽  
Emotional States ◽  
Major Depressive ◽  
Psychotherapeutic Interventions ◽  
Functional Brain Activity ◽  
Neuroimaging Techniques

It has been long established that psychological interventions can markedly alter patients' thinking patterns, beliefs, attitudes, emotional states, and behaviors. Little was known about the neural mechanisms mediating such alterations before the advent of functional neuroimaging techniques. Since the turn of the new millenium, several functional neuroimaging studies have been conducted to tackle this important issue. Some of these studies have explored the neural impact of various forms of psychotherapy in individuals with major depressive disorder. Other neuroimaging studies have investigated the effects of psychological interventions for anxiety disorders. I review these studies in the present article, and discuss the putative neural mechanisms of change in psychotherapy. The findings of these studies suggest that mental and behavioral changes occurring during psychotherapeutic interventions can lead to a normalization of functional brain activity at a global level.

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