Network neuroscience approaches in psychological science: a connectionist perspective on the biological bases of attention, cognitive ability, and on clinical disease
|Zusammenfassung der Sitzung|
Network Neuroscience, a scientific discipline positioned on the border between brain sciences and physical network theory, has recently been introduced as promising approach into psychological research. This symposium presents four studies that apply network neuroscience methods to different brain imaging modalities to gain insights into various aspects of the human mind. After a brief introduction into basics of network theory, Sebastian Markett reports results from a combined task- and resting-state fMRI study (N = 78) that challenges key assumptions of attention network theory. The second talk transitions from specific cognitive processes to individual differences in cognitive ability. Kirsten Hilger presents results from two fMRI studies (N = 281) suggesting brain network dynamics, especially in the dorsal attention network, to be associated with individual variations in general intelligence. Moreover, specific features of network dynamics are derived that allow to predict individual intelligence scores in independent subjects (N = 831) from only 5% of fMRI resting-state data. Erhan Genc demonstrates the predictive power of structural network architecture derived from DTI (N = 324) for individual variations in knowledge. Finally, Urs Braun introduces network approaches to the investigation of clinical populations. Different concepts of network dysfunction are presented with an exemplary focus on schizophrenia and dopamine function. Finally, opportunities and limits of network neuroscience approaches are discussed within an open panel.
Attention networks and the intrinsic network structure of the human brain
1Humboldt Universität zu Berlin, Deutschland; 2Universität Bonn, Deutschland
Attention network theory states that attention is not a unified construct but consists of at least three independent and distributed networks: an alerting network to deploy attentional resources in anticipation of upcoming events, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. While ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains, it remains elusive whether the domains are actually realized by separable networks. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. It is well established that the brain is intrinsically organized into several large scale networks whose modular structure persist across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks with highest spatial precision at the level of cifti-grayordinates in N = 78. Resulting group maps were compared to the group-level topology of 23 intrinsic networks which we reconstructed from the same participants’ resting-state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic brain networks. At the same time, we also observed a preference of each attentional domain for its own set of intrinsic networks. These networks, however, did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.
Intrinsic brain network dynamics and general intelligence
1Department of Psychology, Julius Maximilian University, Würzburg, Germany; 2Department of Psychology, Goethe University, Frankfurt am Main, Germany; 3Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany; 4Department of Psychological and Brain Sciences, Indiana University, Bloomington, USA; 5Brain Imaging Center, Goethe University, Frankfurt am Main, Germany
Intelligence predicts important life outcomes such as educational and occupational success. Recent advances in network neuroscience suggest variations in the modular organization of resting-state brain networks as promising neural marker of general intelligence. However, while intelligent behavior implies flexibility in adaption to changing demands, it has so far been an open question whether and how flexible changes in the configuration of brain networks relate to variations in general intelligence. In a first study we modeled subject-specific time-varying intrinsic connectivity networks from fMRI time series (N = 281) with a sliding-window approach. Higher intelligence (WASI) was associated with higher stability of brain network modularity – reflecting the balance between segregated and integrated information processing. Subjects with higher intelligence scores engaged in fewer periods of very high modularity characterized by disconnection of task-positive from task-negative networks. Regions of the dorsal attention network contributed most to the observed effect. In the second study we temporally resolved functional connectivity and developed a new machine learning-based prediction framework to show that individual intelligence scores can be predicted from only 5% of resting-state fMRI data. These time-points correspond to the highest minima and the lowest maxima of whole-brain connectivity strength. We replicate these prediction results in an independent sample (N = 831) demonstrating the generalizability of or approach to different preprocessing and other measures of cognitive ability (g-factor). In summary, our studies demonstrate that the investigation of intrinsic temporal dynamics of brain networks can enhance our understanding of the biological bases of individual differences in general intelligence.
The neural architecture of general knowledge
1Leibniz Research Centre for Working Environment and Human Factors (IfADo), Deutschland; 2Biopsychology, Ruhr University Bochum, Deutschland; 3Forschungszentrum Jülich; 4Psychological Research Methods, Humboldt University Berlin, Deutschland; 5Team Test Development, Ruhr University Bochum, Deutschland
Cognitive performance varies widely between individuals and is highly influenced by structural and functional properties of the brain. In the past, neuroscientific research was principally concerned with fluid intelligence, while neglecting its equally important counterpart crystallized intelligence. Crystallized intelligence is defined as the depth and breadth of knowledge and skills that are valued by one's culture. The accumulation of crystallized intelligence is guided by information storage capacities and is likely to be reflected in an individual's level of general knowledge. In spite of the significant role general knowledge plays for everyday life, its neural foundation largely remains unknown. In a large sample of 324 healthy individuals we used standard MRI along with diffusion-weighted imaging (DWI) to examine different estimates of brain volume and brain structural network connectivity and assessed their predictive power with regard to general knowledge. Our results show that test scores obtained by general knowledge inventories are reflected in the efficiency of structural brain networks and not brain volume. These effects were robust and not confounded by the effects of age, sex or fluid intelligence. Our findings indicate that structural brain network efficiency might be regarded as a valuable predictor of the amount of general knowledge held by an individual.
Network neuroscience perspective on schizophrenia - from brain maps to network mechanisms
Zentralinstitut für Seelische Gesundheit
In the past decade, network neuroscience has greatly contributed to establishing the view of mental disorders as dysfunctions of distributed circuits and networks, providing a biologically meaningful way of mapping large-scale abnormalities in the architecture of brain networks. However, traditional network approaches are static and therefore rather descriptive, often failing to identify (patho-)physiological mechanisms that link system-level phenomena to the multiple hierarchies of brain function. Recently, a novel set of methodological tools stemming from advances in complex systems and network science has been introduced to the field of brain imaging. These methods explicitly model dynamical aspects of brain networks and thereby have the potential to overcome said shortcomings.
In my talk, I will shortly review the main insights gained by traditional network approaches for clinical populations. In particular, I will concentrate on emerging concepts of network dysfunction such as altered rich club connectivity or altered integration/segregation balance in schizophrenia. I will highlight the limitation of these traditional approaches and provide an example how dynamical approaches can address some of these challenges. Specifically, building on the framework of network control theory, I will show how the brain controls dynamic transitions between brain-wide activity patterns during working memory and how these network control properties are related to the underlying structural network and how they influenced by multiple levels of dopamine function. In closing, I will demonstrate how network control properties are altered in schizophrenia and discuss potential future therapeutic applications/directions.