PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data

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238

Concepts organize the relationship among individual stimuli or events by highlighting shared features. Often, new goals require updating conceptual knowledge to reflect relationships based on different goal-relevant features. Here, our aim is to determine how hippocampal (HPC) object representations are organized and updated to reflect changing conceptual knowledge. Participants learned two classification tasks in which successful learning required attention to different stimulus features, thus providing a means to index how representations of individual stimuli are reorganized according to changing task goals. We used a computational learning model to capture how people attended to goal-relevant features and organized object representations based on those features during learning. Using representational similarity analyses of functional magnetic resonance imaging data, we demonstrate that neural representations in left anterior HPC correspond with model predictions of concept organization. Moreover, we show that during early learning, when concept updating is most consequential, HPC is functionally coupled with prefrontal regions. Based on these findings, we propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal.category learning | attention | computational modeling | hippocampus | fMRI C oncepts are organizing principles that define how items or events are similar to one another. Goals are critical to shaping concepts, by emphasizing some shared features over others. When goals change, previously experienced events may be organized in new ways, resulting in an updated concept that highlights the features most critical to the new goal. For instance, consider purchasing a home. One must learn which features make for the most desirable home. A young couple seeking a cosmopolitan lifestyle may organize potential houses based on trendy features like exposed brick walls, a wet bar, and room for vintage record collections. However, with the news of a baby on the way, the couple's goals are likely to shift. After pouring through parenting books and web forums to learn what makes for a child-friendly home, they may look at those previously seen potential homes in a different light. Instead, family-oriented features such as whether or not a home has a bathtub, is within walking distance to a park, and is in a well-respected school district may matter more resulting in a reorganization of which homes are a good buy. At the core of this example are the fundamental challenges we face in flexible goaldirected learning. When learning new concepts (e.g., child-friendly instead of a trendy house), attention changes focus to different information and items that were conceptually dissimilar (e.g., two houses with and without a wet bar) may become more similar (e.g., they both are close to a park) and vice versa (1). Understanding how conceptual knowledge is created and updated during learning is a cen...

Section: Methodsmentioning

confidence: 99%

Dynamic updating of hippocampal object representations reflects new conceptual knowledge

Mack

Love

Preston

2016

197

238

“…Multivoxel pattern analysis (MVPA) (30,31) was performed using sparse multinomial logistic regression (SMLR) implemented in PyMVPA (32). For each participant, a classifier was trained to differentiate viewing of face, object, scrambled object, and passive fixation stimuli on the basis of activation patterns in face-sensitive regions (i.e., FFA and posterior fusiform gyrus).…”

Section: Methodsmentioning

confidence: 99%

Memory reactivation during rest supports upcoming learning of related content

Schlichting

Preston

2014

104

126

Although a number of studies have highlighted the importance of offline processes for memory, how these mechanisms influence future learning remains unknown. Participants with established memories for a set of initial face-object associations were scanned during passive rest and during encoding of new related and unrelated pairs of objects. Spontaneous reactivation of established memories and enhanced hippocampal-neocortical functional connectivity during rest was related to better subsequent learning, specifically of related content. Moreover, the degree of functional coupling during rest was predictive of neural engagement during the new learning experience itself. These results suggest that through rest-phase reactivation and hippocampal-neocortical interactions, existing memories may come to facilitate encoding during subsequent related episodes.N umerous empirical studies (1-4) and theoretical accounts (5, 6) highlight the importance of offline processes-such as reinstatement of recent experience and enhanced interregional communication-for episodic memory. It has been proposed that through hippocampal (HPC)-neocortical interactions (6, 7), memories are reactivated during periods of sleep and awake rest. Such reactivation (or "replay") is thought to support the strengthening and transfer of memory traces from the HPC to neocortical regions for long-term storage, a process termed "consolidation." The functional significance of reactivation of recent experience for memory has been demonstrated during awake rest using neurophysiological techniques in rodents (2) and, more recently, in humans using pattern information analysis of functional magnetic resonance imaging (fMRI) data (1, 3). For instance, more delay period reactivation has been observed for stimuli that were remembered, relative to those that were forgotten in a subsequent test (3). Moreover, studies have shown that the degree of HPCneocortical functional coupling during rest periods following learning relates to later memory for the learned content (4).This existing body of work demonstrates that rest-phase neural signatures relate to memory for prior experiences. However, one important quality of memory is that it is inherently prospective (8); that is, memories are formed for maximal utility in future situations. Whereas research shows that rest-phase reactivation impacts memory for the reactivated content itself (1, 3), how this mechanism might be prospectively advantageous remains unknown. In the present study, we turn our attention to this question: How does spontaneous reactivation of established memories and enhanced HPC-neocortical connectivity during rest affect learning during subsequent related episodes?

“…Multivariate pattern analysis (MVPA) was carried out using PyMVPA (35). Spatiotemporal patterns were constructed for each correct-response trial and ROI using the z-scored BOLD response from TRs 4-6 (the period during which the operation was performed, after shifting by a 4-s estimate of the HRF delay).…”

Section: Methodsmentioning

confidence: 99%

Network structure and dynamics of the mental workspace

Schlegel

Kohler

Fogelson

et al. 2013

Significance We do not know how the human brain mediates complex and creative behaviors such as artistic, scientific, and mathematical thought. Scholars theorize that these abilities require conscious experience as realized in a widespread neural network, or “mental workspace,” that represents and manipulates images, symbols, and other mental constructs across a variety of domains. Evidence for such a complex, interconnected network has been difficult to produce with current techniques that mainly study brain activity in isolation and are insensitive to distributed informational processes. The present work takes advantage of emerging techniques in network and information analysis to provide empirical support for such a widespread and interconnected information processing network in the brain that supports the manipulation of visual imagery.