scholarly journals Functional neuroimaging and schizophrenia: a view towards effective connectivity modeling and polygenic risk

2013 ◽  
Vol 15 (3) ◽  
pp. 279-289 ◽  
Keyword(s):  
Functional Neuroimaging ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Imaging Genetics ◽  
Causal Modeling ◽  
Intermediate Phenotype ◽  
Polygenic Risk ◽  
Intermediate Phenotypes ◽  
Psychiatric Syndrome ◽  
The Impact

We review critical trends in imaging genetics as applied to schizophrenia research, and then discuss some future directions of the field. A plethora of imaging genetics studies have investigated the impact of genetic variation on brain function, since the paradigm of a neuroimaging intermediate phenotype for schizophrenia first emerged. It was initially posited that the effects of schizophrenia susceptibility genes would be more penetrant at the level of biologically based neuroimaging intermediate phenotypes than at the level of a complex and phenotypically heterogeneous psychiatric syndrome. The results of many studies support this assumption, most of which show single genetic variants to be associated with changes in activity of localized brain regions, as determined by select cognitive controlled tasks. From these basic studies, functional neuroimaging analysis of intermediate phenotypes has progressed to more complex and realistic models of brain dysfunction, incorporating models of functional and effective connectivity, including the modalities of psycho-physiological interaction, dynamic causal modeling, and graph theory metrics. The genetic association approaches applied to imaging genetics have also progressed to more sophisticated multivariate effects, including incorporation of two-way and three-way epistatic interactions, and most recently polygenic risk models. Imaging genetics is a unique and powerful strategy for understanding the neural mechanisms of genetic risk for complex CNS disorders at the human brain level.

scholarly journals Dynamic Causal Modeling Self-Connectivity Findings in the Functional Magnetic Resonance Imaging Neuropsychiatric Literature

2021 ◽  
Vol 15 ◽  
Author(s):  
Andrew D. Snyder ◽  
Liangsuo Ma ◽  
Joel L. Steinberg ◽  
Kyle Woisard ◽  
Frederick G. Moeller
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Functional Neuroimaging ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Causal Modeling ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Dynamic Causal Modeling

Dynamic causal modeling (DCM) is a method for analyzing functional magnetic resonance imaging (fMRI) and other functional neuroimaging data that provides information about directionality of connectivity between brain regions. A review of the neuropsychiatric fMRI DCM literature suggests that there may be a historical trend to under-report self-connectivity (within brain regions) compared to between brain region connectivity findings. These findings are an integral part of the neurologic model represented by DCM and serve an important neurobiological function in regulating excitatory and inhibitory activity between regions. We reviewed the literature on the topic as well as the past 13 years of available neuropsychiatric DCM literature to find an increasing (but still, perhaps, and inadequate) trend in reporting these results. The focus of this review is fMRI as the majority of published DCM studies utilized fMRI and the interpretation of the self-connectivity findings may vary across imaging methodologies. About 25% of articles published between 2007 and 2019 made any mention of self-connectivity findings. We recommend increased attention toward the inclusion and interpretation of self-connectivity findings in DCM analyses in the neuropsychiatric literature, particularly in forthcoming effective connectivity studies of substance use disorders.

scholarly journals Altered Prefrontal–Basal Ganglia Effective Connectivity in Patients With Poststroke Cognitive Impairment

2020 ◽  
Vol 11 ◽  
Author(s):  
Jing Zhang ◽  
Zixiao Li ◽  
Xingxing Cao ◽  
Lijun Zuo ◽  
Wei Wen ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Basal Ganglia ◽  
Cognitive Decline ◽  
Effective Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Neuronal Model ◽  
Causal Modeling ◽  
Healthy Controls

We investigated the association between poststroke cognitive impairment and a specific effective network connectivity in the prefrontal–basal ganglia circuit. The resting-state effective connectivity of this circuit was modeled by employing spectral dynamic causal modeling in 11 poststroke patients with cognitive impairment (PSCI), 8 poststroke patients without cognitive impairment (non-PSCI) at baseline and 3-month follow-up, and 28 healthy controls. Our results showed that different neuronal models of effective connectivity in the prefrontal–basal ganglia circuit were observed among healthy controls, non-PSCI, and PSCI patients. Additional connected paths (extra paths) appeared in the neuronal models of stroke patients compared with healthy controls. Moreover, changes were detected in the extra paths of non-PSCI between baseline and 3-month follow-up poststroke, indicating reorganization in the ipsilesional hemisphere and suggesting potential compensatory changes in the contralesional hemisphere. Furthermore, the connectivity strengths of the extra paths from the contralesional ventral anterior nucleus of thalamus to caudate correlated significantly with cognitive scores in non-PSCI and PSCI patients. These suggest that the neuronal model of effective connectivity of the prefrontal–basal ganglia circuit may be sensitive to stroke-induced cognitive decline, and it could be a biomarker for poststroke cognitive impairment 3 months poststroke. Importantly, contralesional brain regions may play an important role in functional compensation of cognitive decline.

scholarly journals A neurocognitive investigation of the impact of socializing with a robot on empathy for pain

2019 ◽  
Vol 374 (1771) ◽  
pp. 20180034 ◽  
Author(s):  
Emily S. Cross ◽  
Katie A. Riddoch ◽  
Jaydan Pratts ◽  
Simon Titone ◽  
Bishakha Chaudhury ◽  
...  
Keyword(s):  
Social Relationships ◽  
Functional Neuroimaging ◽  
Region Of Interest ◽  
Brain Regions ◽  
Social Robot ◽  
Human Robot Interaction ◽  
Functional Region ◽  
Repetition Suppression ◽  
Empathy For Pain ◽  
The Impact

To what extent can humans form social relationships with robots? In the present study, we combined functional neuroimaging with a robot socializing intervention to probe the flexibility of empathy, a core component of social relationships, towards robots. Twenty-six individuals underwent identical fMRI sessions before and after being issued a social robot to take home and interact with over the course of a week. While undergoing fMRI, participants observed videos of a human actor or a robot experiencing pain or pleasure in response to electrical stimulation. Repetition suppression of activity in the pain network, a collection of brain regions associated with empathy and emotional responding, was measured to test whether socializing with a social robot leads to greater overlap in neural mechanisms when observing human and robotic agents experiencing pain or pleasure. In contrast to our hypothesis, functional region-of-interest analyses revealed no change in neural overlap for agents after the socializing intervention. Similarly, no increase in activation when observing a robot experiencing pain emerged post-socializing. Whole-brain analysis showed that, before the socializing intervention, superior parietal and early visual regions are sensitive to novel agents, while after socializing, medial temporal regions show agent sensitivity. A region of the inferior parietal lobule was sensitive to novel emotions, but only during the pre-socializing scan session. Together, these findings suggest that a short socialization intervention with a social robot does not lead to discernible differences in empathy towards the robot, as measured by behavioural or brain responses. We discuss the extent to which long-term socialization with robots might shape social cognitive processes and ultimately our relationships with these machines. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

scholarly journals Functional neuroimaging of psychiatric disorders: exploring hidden behaviour

2004 ◽  
Vol 34 (4) ◽  
pp. 577-581 ◽  
Author(s):  
P. C. FLETCHER
Keyword(s):  
Functional Neuroimaging ◽  
Brain Activity ◽  
Cognitive Strategies ◽  
Healthy Person ◽  
Brain Regions ◽  
Task Demands ◽  
Unrealistic Expectation ◽  
Positron Emission ◽  
The Impact ◽  
Function Mapping

From the outset, people have had high expectations of functional neuroimaging. Many will have been disappointed. After roughly a decade of widespread use, even an enthusiastic advocate must be diffident about the impact of the two most frequently used techniques – positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) – upon clinical psychiatry. Perhaps this disappointment arises from an unrealistic expectation of what these techniques are able to tell us about the workings of the normal and the disordered brain. Anyone who hoped for intricate and unambiguous region-to-function mapping was always going to be disappointed. This expectation presupposes, among other things, a thorough understanding of the cognitive functions that are to be mapped onto the brain regions. This understanding, however, while developing, is still rudimentary. Mapping disorder along comparable lines is even more complex since it demands two levels of understanding. The first is of the healthy region-to-function mapping, the second of the disordered region-to-function mapping, which immediately demands a consideration of the nature of the function in the disordered state. After all, someone with schizophrenia, when confronted with a psychological task, might tackle it in a very different way, in terms of the cognitive strategies used, from a healthy person confronted with the same task. The observation that brain activity differs across the two individuals would only be interpretable insofar as one thoroughly understood the processes that each individual invoked in response to the task demands.

scholarly journals Altered Effective Connectivity in Sensorimotor Cortices: a Novel Signature of Severity and Clinical Course in Depression

2021 ◽  
Author(s):  
Dipanjan Ray ◽  
Dmitry Bezmaternykh ◽  
Mikhail Mel’nikov ◽  
Karl J Friston ◽  
Moumita Das
Keyword(s):  
Empirical Bayes ◽  
Hierarchical Modeling ◽  
Depressive Symptomatology ◽  
Functional Neuroimaging ◽  
Effective Connectivity ◽  
Causal Modeling ◽  
Depression Severity ◽  
Sensory Cortices ◽  
Leave One Out

AbstractFunctional neuroimaging research on depression has traditionally targeted neural networks associated with the psychological aspects of depression. In this study, in stead, we focus on alterations of sensorimotor function in depression. We used resting-state functional MRI data and Dynamic Causal Modeling (DCM) to assess the hypothesis that depression is associated with aberrant effective connectivity within and between key regions in the sensorimotor hierarchy. Using hierarchical modeling of between-subject effects in DCM with Parametric Empirical Bayes we first established the architecture of effective connectivity in sensorimotor cortices. We found that in (interoceptive and exteroceptive) sensory cortices across participants, the backward connections are predominantly inhibitory whereas the forward connections are mainly excitatory in nature. In the motor cortices these parities were reversed. With increasing depression severity, these patterns are depreciated in exteroceptive and motor cortices and augmented in interoceptive cortex: an observation that speaks to depressive symptomatology. We established the robustness of these results in a leave-one-out cross validation analysis and by reproducing the main results in a follow-up dataset. Interestingly, with (non-pharmacological) treatment, depression associated changes in backward and forward effective connectivity partially reverted to group mean levels. Overall, altered effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of depression severity and treatment response.

scholarly journals Altered effective connectivity in sensorimotor cortices is a signature of severity and clinical course in depression

2021 ◽  
Vol 118 (40) ◽  
pp. e2105730118
Author(s):  
Dipanjan Ray ◽  
Dmitry Bezmaternykh ◽  
Mikhail Mel’nikov ◽  
Karl J. Friston ◽  
Moumita Das
Keyword(s):  
Empirical Bayes ◽  
Hierarchical Modeling ◽  
Depressive Symptomatology ◽  
Functional Neuroimaging ◽  
Effective Connectivity ◽  
Causal Modeling ◽  
Depression Severity ◽  
Sensory Cortices ◽  
Leave One Out

Functional neuroimaging research on depression has traditionally targeted neural networks associated with the psychological aspects of depression. In this study, instead, we focus on alterations of sensorimotor function in depression. We used resting-state functional MRI data and dynamic causal modeling (DCM) to assess the hypothesis that depression is associated with aberrant effective connectivity within and between key regions in the sensorimotor hierarchy. Using hierarchical modeling of between-subject effects in DCM with parametric empirical Bayes we first established the architecture of effective connectivity in sensorimotor cortices. We found that in (interoceptive and exteroceptive) sensory cortices across participants, the backward connections are predominantly inhibitory, whereas the forward connections are mainly excitatory in nature. In motor cortices these parities were reversed. With increasing depression severity, these patterns are depreciated in exteroceptive and motor cortices and augmented in the interoceptive cortex, an observation that speaks to depressive symptomatology. We established the robustness of these results in a leave-one-out cross-validation analysis and by reproducing the main results in a follow-up dataset. Interestingly, with (nonpharmacological) treatment, depression-associated changes in backward and forward effective connectivity partially reverted to group mean levels. Overall, altered effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of depression severity and treatment response.

scholarly journals A neurocognitive investigation of the impact of socialising with a robot on empathy for pain

2018 ◽  
Author(s):  
Emily S. Cross ◽  
Katie A. Riddoch ◽  
Jaydan Pratts ◽  
Simon Titone ◽  
Bishakha Chaudhury ◽  
...  
Keyword(s):  
Social Relationships ◽  
Functional Neuroimaging ◽  
Region Of Interest ◽  
Brain Regions ◽  
Social Robot ◽  
Functional Region ◽  
Repetition Suppression ◽  
Before And After ◽  
Empathy For Pain ◽  
The Impact

To what extent can humans form social relationships with robots? In the present study, we combined functional neuroimaging with a robot socialising intervention to probe the flexibility of empathy, a core component of social relationships, toward robots. Twenty-six individuals underwent identical fMRI sessions before and after being issued a social robot to take home and interact with over the course of a week. While undergoing fMRI, participants observed videos of a human actor or a robot experiencing pain or pleasure in response to electrical stimulation. Repetition suppression of activity in the pain network, a collection of brain regions associated with empathy and emotional responding, was measured to test whether socialising with a social robot leads to greater overlap in neural mechanisms when observing human and robotic agents experiencing pain or pleasure. In contrast to our hypothesis, functional region-of-interest analyses revealed no change in neural overlap for agents after the socialising intervention. Similarly, no increase in activation when observing a robot experiencing pain emerged post-socialising. Whole-brain analysis showed that, before the socialising intervention, superior parietal and early visual regions are sensitive to novel agents, while after socialising, medial temporal regions show agent sensitivity. A region of the inferior parietal lobule was sensitive to novel emotions, but only during the pre-socialising scan session. Together, these findings suggest that a short socialisation intervention with a social robot does not lead to discernible differences in empathy toward the robot, as measured by behavioural or brain responses. We discuss the extent to which longer term socialisation with robots might shape social cognitive processes and ultimately our relationships with these machines.

The role of neuroimaging in understanding the impact of neuroplasticity after CNS damage

2015 ◽  
pp. 161-173
Author(s):  
Nick Ward
Keyword(s):  
Functional Neuroimaging ◽  
Recovery Of Function ◽  
Complete Recovery ◽  
Brain Regions ◽  
Functional Brain Imaging ◽  
Brain Organization ◽  
Brain Reorganization ◽  
Invasive Techniques ◽  
The Impact

After stroke, there is little restitution of neural tissue, but reorganization of surviving neural networks appears to be important for recovery of function. Non-invasive techniques such as functional magnetic resonance imaging and magnetoencephalography allow some aspects of this brain reorganization to be studied. For example, early after stroke there appears to be an upregulation in task-related activity, which diminishes with time and recovery. Those with the most complete recovery tend to have the most ‘normal’ activation pattern, and those with less complete recovery tend to rely on additional brain regions. This reorganization is functionally relevant. Advances in functional neuroimaging allow the study of alterations in connections between brain regions. Understanding how brain organization is related to anatomical damage, as well as impairment and recovery that can take place over weeks and months following stroke opens the way for functional brain imaging to become a clinically useful tool in rehabilitation.

scholarly journals Neuroimaging studies of autobiographical event memory

2001 ◽  
Vol 356 (1413) ◽  
pp. 1441-1451 ◽  
Author(s):  
Eleanor A. Maguire
Keyword(s):  
Dorsolateral Prefrontal Cortex ◽  
Functional Neuroimaging ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Event Memory ◽  
General Terms ◽  
Left Hippocampus ◽  
Dorsolateral Prefrontal ◽  
Neuroimaging Data ◽  
Autobiographical Event

Commonalities and differences in findings across neuroimaging studies of autobiographical event memory are reviewed. In general terms, the overall pattern across studies is of medial and left–lateralized activations associated with retrieval of autobiographical event memories. It seems that the medial frontal cortex and left hippocampus in particular are responsive to such memories. However, there are also inconsistencies across studies, for example in the activation of the hippocampus and dorsolateral prefrontal cortex. It is likely that methodological differences between studies contribute to the disparate findings. Quantifying and assessing autobiographical event memories presents a challenge in many domains, including neuroimaging. Methodological factors that may be pertinent to the interpretation of the neuroimaging data and the design of future experiments are discussed. Consideration is also given to aspects of memory that functional neuroimaging might be uniquely disposed to examine. These include assessing the functionality of damaged tissue in patients and the estimation of inter–regional communication (effective connectivity) between relevant brain regions.

scholarly journals Alternation in Effective Connectivity With Cognitive Aging: A Longitudinal Study of Elderly Populations

2021 ◽  
Vol 13 ◽  
Author(s):  
Xingxing Cao ◽  
Tao Liu ◽  
Jiyang Jiang ◽  
Hao Liu ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Elderly People ◽  
Cognitive Aging ◽  
Aging Process ◽  
Effective Connectivity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Causal Modeling ◽  
Angular Gyrus ◽  
Time Points

In this research, we investigated the alterations in the directionality and strength of regional interactions within functionally changed brain networks and their relationship to cognitive decline during the aging process in normal elderly individuals. Thirty-seven cognitively normal elderly people received resting-state fMRI scans and cognitive assessments at baseline (age = 78.65 ± 3.56 years) and at 4-year follow-up. Functional connectivity analyses were used to identify networks containing brain regions whose functions changed with age as regions of interest. The spectral dynamic causal modeling (spDCM) method was used to estimate the causal interactions within networks in subjects at different time points and in subjects with different cognitive levels to explore the alterations with cognitive aging. The results showed that, at both time points, all the networks, except the frontal-parietal network (FPN) at baseline, had mutual interactions between each pair of nodes. Furthermore, when the subjects were divided with global cognition level, lost connections were only found in the subgroup with better performance. These indicated that elderly people appeared to need more interaction pathways between brain areas with cognitive decline. We also observed that the strength of the flow of information from the left angular gyrus to the precuneus, which is associated with activation of memory retrieval and the functional hub involved in various cognitive domains, was predictive of declines in executive function with the aging process, making it a potential predictor of such situation.

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