What is the neural basis of individual differences in the ability to hold information in long-term memory (LTM)? Here, we first characterize two whole-brain functional connectivity networks based on fMRI data acquired during an n -back task that robustly predict individual differences in two important forms of LTM, recognition and recollection. We then focus on the recognition memory model and contrast it with a working memory model. Although functional connectivity during the n -back task also predicts working memory performance and the two networks have some shared components, they are also largely distinct from each other: The recognition memory model performance remains robust when we control for working memory, and vice versa. Functional connectivity only within regions traditionally associated with LTM formation, such as the medial temporal lobe and those that show univariate subsequent memory effect, have little predictive power for both forms of LTM. Interestingly, the interactions between these regions and other brain regions play a more substantial role in predicting recollection memory than recognition memory. These results demonstrate that individual differences in LTM are dependent on the configuration of a whole-brain functional network including but not limited to regions associated with LTM during encoding and that such a network is separable from what supports the retention of information in working memory.

Individual differences in working memory relate to performance differences in general cognitive ability. The neural bases of such individual differences, however, remain poorly understood. Here, using a data-driven technique known as connectome-based predictive modeling, we built models to predict individual working memory performance from whole-brain functional connectivity patterns. Using n -back or rest data from the Human Connectome Project, connectome-based predictive models significantly predicted novel individuals' 2-back accuracy. Model predictions also correlated with measures of fluid intelligence and, with less strength, sustained attention. Separate fluid intelligence models predicted working memory score, as did sustained attention models, again with less strength. Anatomical feature analysis revealed significant overlap between working memory and fluid intelligence models, particularly in utilization of prefrontal and parietal regions, and less overlap in predictive features between working memory and sustained attention models. Furthermore, showing the generality of these models, the working memory model developed from Human Connectome Project data generalized to predict memory in an independent data set of 157 older adults (mean age = 69 years; 48 healthy, 54 amnestic mild cognitive impairment, 55 Alzheimer disease). The present results demonstrate that distributed functional connectivity patterns predict individual variation in working memory capability across the adult life span, correlating with constructs including fluid intelligence and sustained attention.

Although we typically talk about attention as a single process, it comprises multiple independent components. But what are these components, and how are they represented in the functional organization of the brain? To investigate whether long-studied components of attention are reflected in the brain's intrinsic functional organization, here we apply connectome-based predictive modeling (CPM) to predict the components of Posner and Petersen's influential model of attention: alerting (preparing and maintaining alertness and vigilance), orienting (directing attention to a stimulus), and executive control (detecting and resolving cognitive conflict) [Posner, M. I., & Petersen, S. E. The attention system of the human brain. Annual Review of Neuroscience , 13 , 25–42, 1990]. Participants performed the Attention Network Task (ANT), which measures these three factors, and rested during fMRI scanning. CPMs tested with leave-one-subject-out cross-validation successfully predicted novel individual's overall ANT accuracy, RT variability, and executive control scores from functional connectivity observed during ANT performance. CPMs also generalized to predict participants' alerting scores from their resting-state functional connectivity alone, demonstrating that connectivity patterns observed in the absence of an explicit task contain a signature of the ability to prepare for an upcoming stimulus. Suggesting that significant variance in ANT performance is also explained by an overall sustained attention factor, the sustained attention CPM, a model defined in prior work to predict sustained attentional abilities, predicted accuracy, RT variability, and executive control from task-based data and predicted RT variability from resting-state data. Our results suggest that, whereas executive control may be closely related to sustained attention, the infrastructure that supports alerting is distinct and can be measured at rest. In the future, CPM may be applied to elucidate additional independent components of attention and relationships between the functional brain networks that predict them.

Constructing a rich and coherent visual experience involves maintaining visual information that is not perceptually available in the current view. Recent studies suggest that briefly thinking about a stimulus ( refreshing ) can modulate activity in category-specific visual areas. Here, we tested the nature of such perceptually refreshed representations in the parahippocampal place area (PPA) and retrosplenial cortex (RSC) using fMRI. We asked whether a refreshed representation is specific to a restricted view of a scene, or more view-invariant. Participants saw a panoramic scene and were asked to think back to (refresh) a part of the scene after it disappeared. In some trials, the refresh cue appeared twice on the same side (e.g., refresh left–refresh left), and other trials, the refresh cue appeared on different sides (e.g., refresh left–refresh right). A control condition presented halves of the scene twice on same sides (e.g., perceive left–perceive left) or different sides (e.g., perceive left–perceive right). When scenes were physically repeated, both the PPA and RSC showed greater activation for the different-side repetition than the same-side repetition, suggesting view-specific representations. When participants refreshed scenes, the PPA showed view-specific activity just as in the physical repeat conditions, whereas RSC showed an equal amount of activation for different- and same-side conditions. This finding suggests that in RSC, refreshed representations were not restricted to a specific view of a scene, but extended beyond the target half into the entire scene. Thus, RSC activity associated with refreshing may provide a mechanism for integrating multiple views in the mind.

Our environment contains regularities distributed in space and time that can be detected by way of statistical learning. This unsupervised learning occurs without intent or awareness, but little is known about how it relates to other types of learning, how it affects perceptual processing, and how quickly it can occur. Here we use fMRI during statistical learning to explore these questions. Participants viewed statistically structured versus unstructured sequences of shapes while performing a task unrelated to the structure. Robust neural responses to statistical structure were observed, and these responses were notable in four ways: First, responses to structure were observed in the striatum and medial temporal lobe, suggesting that statistical learning may be related to other forms of associative learning and relational memory. Second, statistical regularities yielded greater activation in category-specific visual regions (object-selective lateral occipital cortex and word-selective ventral occipito-temporal cortex), demonstrating that these regions are sensitive to information distributed in time. Third, evidence of learning emerged early during familiarization, showing that statistical learning can operate very quickly and with little exposure. Finally, neural signatures of learning were dissociable from subsequent explicit familiarity, suggesting that learning can occur in the absence of awareness. Overall, our findings help elucidate the underlying nature of statistical learning.

Cognition constantly involves retrieving and maintaining information that is not perceptually available in the current environment. Studies on visual imagery and working memory suggest that such high-level cognition might, in part, be mediated by the revival of perceptual representations in the inferior temporal cortex. Here, we provide new support for this hypothesis, showing that reflectively accessed information can have similar consequences for subsequent perception as actual perceptual input. Participants were presented with pairs of frames in which a scene could appear, and were required to make a category judgment on the second frame. In the critical condition, a scene was presented in the first frame, but the second frame was blank. Thus, it was necessary to refresh the scene from the first frame in order to make the category judgment. Scenes were then repeated in subsequent trials to measure the effect of refreshing on functional magnetic resonance imaging repetition attenuation—a neural index of memory—in a scene-selective region of the visual cortex. Surprisingly, the refreshed scenes produced equal attenuation as scenes that had been presented twice during encoding, and more attenuation than scenes that had been presented once during encoding, but that were not refreshed. Thus, the top-down revival of a percept had a similar effect on memory as actually seeing the stimulus again. These findings indicate that high-level cognition can activate stimulus-specific representations in the ventral visual cortex, and that such top-down activation, like that from sensory stimulation, produces memorial changes that affect perceptual processing during a later encounter with the stimulus.