Trends in Cognitive Sciences
ReviewCharacterizing Attention with Predictive Network Models
Section snippets
What Is Attention and How Do We Measure It?
Perhaps no cognitive capacity is more crucial to navigating daily life than the ability to pay attention. Although we all know what it feels like to pay attention, the concept is notoriously difficult to define. More than a century ago in what has perhaps become one of the most oft-quoted lines in psychology, William James explained attention as ‘the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought’ [1].
Attention: From Brain Areas to Brain Networks
It is hard to imagine meeting the demands of daily life without the ability to focus. Impairments in attention, which are common to clinical populations as diverse as ADHD [4], depression [5], schizophrenia [6], bipolar disorder [7], post-traumatic stress disorder [8], and traumatic brain injury [9], predict a wide range of negative outcomes, from poorer educational achievement to worse employment and job performance, peer acceptance, and family relationships [10].
Although the ability to attend
New Insights from Network Neuroscience and Predictive Modeling
A central question of cognitive neuroscience is how the brain gives rise to the mind and behavior. Some of the earliest evidence in humans came from neuropsychological studies of patients with brain damage, which found that certain lesions were associated with stereotyped deficits such as spatial neglect [53]. Neuroimaging and electrophysiological studies have also identified process-specific brain regions such as the frontal eye fields, which are responsible for covert and overt shifts of
Concluding Remarks
Models that predict attention from patterns of functional brain connectivity, some of the first generalizable neuromarkers of cognitive function, represent a significant contribution to the fast-growing field of individual differences in fMRI 86, 87. In doing so they provide empirical support for a central prediction of network neuroscience: that attention and cognition arise from the dynamic interactions of many distinct regions of the brain 60, 61, 62. Further, successful predictions from
Glossary
- Correlational versus predictive studies
- fMRI studies of individual differences often claim that a brain-based measure ‘predicts’ a behavioral measure when the two are simply correlated across individuals. Following Gabrieli et al., we reserve the term ‘prediction’ for cross-validated models; that is, models that generalize to novel individuals [86]. Although it is beyond the scope of this article, another sense in which models can be predictive is that they use baseline data from an individual
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Connectome-based predictive modeling: A new approach of predicting individual critical thinking ability
2023, Thinking Skills and CreativityTRACking health behaviors in people with Multiple Sclerosis (TRAC-MS): Study protocol and description of the study sample
2022, Contemporary Clinical Trials CommunicationsCitation Excerpt :Our prior work has established the validity of a working memory neuromarker in predicting working memory performance in PwMS [61]. Specifically, using connectome-based predictive modeling [62,63], we recently validated a neuromarker of working memory derived from 502 participants from the Human Connectome Project [64] to predict working memory in two independent samples of PwMS [61]. Fig. 3 shows the anatomical distribution of the neuromarker across the canonical networks.