While fMRI studies typically collapse data from many subjects, brain functional organization varies between individuals. Here, we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a “fingerprint” that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual’s connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but notably, the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence; the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects based on functional connectivity fMRI.

Using visual information to guide behaviour requires storage in a temporary buffer, known as visual short-term memory (VSTM), that sustains attended information across saccades and other visual interruptions. There is growing debate on whether VSTM capacity is limited to a fixed number of objects or whether it is variable. Here we report four experiments using functional magnetic resonance imaging that resolve this controversy by dissociating the representation capacities of the parietal and occipital cortices. Whereas representations in the inferior intra-parietal sulcus (IPS) are fixed to about four objects at different spatial locations regardless of object complexity, those in the superior IPS and the lateral occipital complex are variable, tracking the number of objects held in VSTM, and representing fewer than four objects as their complexity increases. These neural response patterns were observed during both VSTM encoding and maintenance. Thus, multiple systems act together to support VSTM: whereas the inferior IPS maintains spatial attention over a fixed number of objects at different spatial locations, the superior IPS and the lateral occipital complex encode and maintain a variable subset of the attended objects, depending on their complexity. VSTM capacity is therefore determined both by a fixed number of objects and by object complexity.

Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person’s overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention—symptoms of attention deficit hyperactivity disorder—from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

Neuroimaging is a fast developing research area where anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale datasets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: 1) feature selection, 2) feature summarization, 3) model building, and 4) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a significant amount of the variance in these measures. It has been demonstrated that the CPM protocol performs equivalently or better than most of the existing approaches in brain-behavior prediction. However, because CPM focuses on linear modeling and a purely data-driven driven approach, neuroscientists with limited or no experience in machine learning or optimization would find it easy to implement the protocols. Depending on the volume of data to be processed, the protocol can take 10-100 minutes

Attention is a core property of all perceptual and cognitive operations. Given limited capacity to process competing options, attentional mechanisms select, modulate, and sustain focus on information most relevant for behavior. A significant problem, however, is that attention is so ubiquitous that it is unwieldy to study. We propose a taxonomy based on the types of information that attention operates over--the targets of attention. At the broadest level, the taxonomy distinguishes between external attention and internal attention. External attention refers to the selection and modulation of sensory information. External attention selects locations in space, points in time, or modality-specific input. Such perceptual attention can also select features defined across any of these dimensions, or object representations that integrate over space, time, and modality. Internal attention refers to the selection, modulation, and maintenance of internally generated information, such as task rules, responses, long-term memory, or working memory. Working memory, in particular, lies closest to the intersection between external and internal attention. The taxonomy provides an organizing framework that recasts classic debates, raises new issues, and frames understanding of neural mechanisms.

The role of the hippocampus and adjacent medial temporal lobe structures in memory systems has long been debated. Here we show in humans that these neural structures are important for encoding implicit contextual information from the environment. We used a contextual cuing task in which repeated visual context facilitates visual search for embedded target objects. An important feature of our task is that memory traces for contextual information were not accessible to conscious awareness, and hence could be classified as implicit. Amnesic patients with medial temporal system damage showed normal implicit perceptual/skill learning but were impaired on implicit contextual learning. Our results demonstrate that the human medial temporal memory system is important for learning contextual information, which requires the binding of multiple cues.

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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 categoryspecific visual regions (object-selective lateral occipital cortex and word-selective ventral occipitotemporal 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.