Gagan S. Wig

Prediction of Individual Brain Maturity Using fMRI

Connectivity Map of the Brain The growing appreciation that clinically abnormal behaviors in children and adolescents may be influenced or perhaps even initiated by developmental miscues has stoked an interest in mapping normal human brain maturation. Several groups have documented changes in gray and white matter using structural and functional magnetic resonance imaging (fMRI) in cross-sectional and longitudinal studies. Dosenbach et al. (p. 1358) developed an index of resting-state functional connectivity (that is, how tightly neuronal activities in distinct brain regions are correlated while the subject is at rest or even asleep) from analyses of three independent data sets (each based on fMRI scans of 150 to 200 individuals from ages 6 to 35 years old). Long-range connections increased with age and short-range connections decreased, indicating that networks become sparser and sharper with brain maturation. Multivariate pattern analysis of 5-minute brain scans provides a measure of brain maturity. Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals’ brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain’s major functional networks.

Concepts and principles in the analysis of brain networks

The brain is a large‐scale network, operating at multiple levels of information processing ranging from neurons, to local circuits, to systems of brain areas. Recent advances in the mathematics of graph theory have provided tools with which to study networks. These tools can be employed to understand how the brains behavioral repertoire is mediated by the interactions of objects of information processing. Within the graph‐theoretic framework, networks are defined by independent objects (nodes) and the relationships shared between them (edges). Importantly, the accurate incorporation of graph theory into the study of brain networks mandates careful consideration of the assumptions, constraints, and principles of both the mathematics and the underlying neurobiology. This review focuses on understanding these principles and how they guide what constitutes a brain network and its elements, specifically focusing on resting‐state correlations in humans. We argue that approaches that fail to take the principles of graph theory into consideration and do not reflect the underlying neurobiological properties of the brain will likely mischaracterize brain network structure and function.

Reductions in neural activity underlie behavioral components of repetition priming

Repetition priming is a nonconscious form of memory that is accompanied by reductions in neural activity when an experience is repeated. To date, however, there is no direct evidence that these neural reductions underlie the behavioral advantage afforded to repeated material. Here we demonstrate a causal linkage between neural and behavioral priming in humans. fMRI (functional magnetic resonance imaging) was used in combination with transcranial magnetic stimulation (TMS) to target and disrupt activity in the left frontal cortex during repeated classification of objects. Left-frontal TMS disrupted both the neural and behavioral markers of priming. Neural priming in early sensory regions was unaffected by left-frontal TMS—a finding that provides evidence for separable conceptual and perceptual components of priming.

Decreased segregation of brain systems across the healthy adult lifespan.

Significance The brain is a large-scale network, not unlike many social or technological networks. Just like social networks, brain networks contain subnetworks or systems of highly related or interacting nodes (in the case of brains, nodes may represent neurons or brain areas). Using functional MRI to measure functional correlations between brain areas during periods of rest, we describe differences in brain network organization in a large group of individuals sampled across the healthy adult lifespan (20–89 y). We characterize a measure of system segregation, reflecting the degree to which the systems share connections among one another. Increasing age is accompanied by decreasing segregation of brain systems. Importantly, system segregation is predictive of measures of long-term memory function, independent of age. Healthy aging has been associated with decreased specialization in brain function. This characterization has focused largely on describing age-accompanied differences in specialization at the level of neurons and brain areas. We expand this work to describe systems-level differences in specialization in a healthy adult lifespan sample (n = 210; 20–89 y). A graph-theoretic framework is used to guide analysis of functional MRI resting-state data and describe systems-level differences in connectivity of individual brain networks. Young adults’ brain systems exhibit a balance of within- and between-system correlations that is characteristic of segregated and specialized organization. Increasing age is accompanied by decreasing segregation of brain systems. Compared with systems involved in the processing of sensory input and motor output, systems mediating “associative” operations exhibit a distinct pattern of reductions in segregation across the adult lifespan. Of particular importance, the magnitude of association system segregation is predictive of long-term memory function, independent of an individual’s age.

Interactions Between Transient and Sustained Neural Signals Support the Generation and Regulation of Anxious Emotion

Anxious emotion can manifest on brief (threat response) and/or persistent (chronic apprehension and arousal) timescales, and prior work has suggested that these signals are supported by separable neural circuitries. This fMRI study utilized a mixed block-event-related emotional provocation paradigm in 55 healthy participants to simultaneously measure brief and persistent anxious emotional responses, testing the specificity of, and interactions between, these potentially distinct systems. Results indicated that components of emotional processing networks were uniquely sensitive to transient and sustained anxious emotion. Whereas the amygdala and midbrain showed only transient responses, the ventral basal forebrain and anterior insula showed sustained activity during extended emotional contexts that tracked positively with task-evoked anxiety. States of lesser anxiety were associated with greater sustained activity in the ventromedial prefrontal cortex. Furthermore, ventromedial prefrontal recruitment was lower in individuals with higher scores on intolerance of uncertainty measures, and this hyporecruitment predicted greater transient amygdala responding to potential threat cues. This work demonstrates how brain circuitries interact across temporal scales to support brief and persistent anxious emotion and suggests potentially divergent mechanisms of dysregulation in clinical syndromes marked by brief versus persistent symptoms of anxiety.

An approach for parcellating human cortical areas using resting-state correlations.

Resting State Functional Connectivity (RSFC) reveals properties related to the brains underlying organization and function. Features related to RSFC signals, such as the locations where the patterns of RSFC exhibit abrupt transitions, can be used to identify putative boundaries between cortical areas (RSFC-Boundary Mapping). The locations of RSFC-based area boundaries are consistent across independent groups of subjects. RSFC-based parcellation converges with parcellation information from other modalities in many locations, including task-evoked activity and probabilistic estimates of cellular architecture, providing evidence for the ability of RSFC to parcellate brain structures into functionally meaningful units. We not only highlight a collection of these observations, but also point out several limitations and observations that mandate careful consideration in using and interpreting RSFC for the purposes of parcellating the brains cortical and subcortical structures.

Structural Organization of the Corpus Callosum Predicts the Extent and Impact of Cortical Activity in the Nondominant Hemisphere

Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) were combined to examine the relationship between callosal organization and cortical activity across hemispheres. Healthy young adults performed an incidental verbal encoding task (semantic judgments on words) while undergoing fMRI. Consistent with previous studies, the verbal encoding task was associated with left-lateralized activity in the inferior prefrontal cortex (LIPFC). When subjects were divided into two groups based on fractional anisotropy (FA) values in the anterior corpus callosum (DTI), individuals with low anterior callosal FA were found to exhibit greater activity in a homologous region within the right inferior prefrontal cortex (RIPFC) relative to those with high anterior callosal FA. Interestingly, whereas the magnitude of RIPFC activity did not negatively impact subsequent verbal memory performance for individuals with low anterior callosal FA, greater RIPFC activity during verbal encoding was associated with poorer subsequent memory performance for individuals with high anterior callosal FA. Together, these findings provide novel evidence that individual differences in callosal organization are related to the extent of nondominant cortical activity during performance during a lateralized task, and further, that this relationship has consequences on behavior.

Parcellating an Individual Subject's Cortical and Subcortical Brain Structures Using Snowball Sampling of Resting-State Correlations

We describe methods for parcellating an individual subjects cortical and subcortical brain structures using resting-state functional correlations (RSFCs). Inspired by approaches from social network analysis, we first describe the application of snowball sampling on RSFC data (RSFC-Snowballing) to identify the centers of cortical areas, subdivisions of subcortical nuclei, and the cerebellum. RSFC-Snowballing parcellation is then compared with parcellation derived from identifying locations where RSFC maps exhibit abrupt transitions (RSFC-Boundary Mapping). RSFC-Snowballing and RSFC-Boundary Mapping largely complement one another, but also provide unique parcellation information; together, the methods identify independent entities with distinct functional correlations across many cortical and subcortical locations in the brain. RSFC parcellation is relatively reliable within a subject scanned across multiple days, and while the locations of many area centers and boundaries appear to exhibit considerable overlap across subjects, there is also cross-subject variability—reinforcing the motivation to parcellate brains at the level of individuals. Finally, examination of a large meta-analysis of task-evoked functional magnetic resonance imaging data reveals that area centers defined by task-evoked activity exhibit correspondence with area centers defined by RSFC-Snowballing. This observation provides important evidence for the ability of RSFC to parcellate broad expanses of an individuals brain into functionally meaningful units.

Repetition Priming Influences Distinct Brain Systems: Evidence From Task-Evoked Data and Resting-State Correlations

Behavioral dissociations suggest that a single experience can separately influence multiple processing components. Here we used a repetition priming functional magnetic resonance imaging paradigm that directly contrasted the effects of stimulus and decision changes to identify the underlying brain systems. Direct repetition of stimulus features caused marked reductions in posterior regions of the inferior temporal lobe that were insensitive to whether the decision was held constant or changed between study and test. By contrast, prefrontal cortex showed repetition effects that were sensitive to the exact stimulus-to-decision mapping. Analysis of resting-state functional connectivity revealed that the dissociated repetition effects are embedded within distinct brain systems. Regions that were sensitive to changes in the stimulus correlated with perceptual cortices, whereas the decision changes attenuated activity in regions correlated with middle-temporal regions and a frontoparietal control system. These results thus explain the long-known dissociation between perceptual and conceptual components of priming by revealing how a single experience can separately influence distinct, concurrently active brain systems.

Repetition-related reductions in neural activity reveal component processes of mental simulation

In everyday life, people adaptively prepare for the future by simulating dynamic events about impending interactions with people, objects and locations. Previous research has consistently demonstrated that a distributed network of frontal-parietal-temporal brain regions supports this ubiquitous mental activity. Nonetheless, little is known about the manner in which specific regions of this network contribute to component features of future simulation. In two experiments, we used a functional magnetic resonance (fMR)-repetition suppression paradigm to demonstrate that distinct frontal-parietal-temporal regions are sensitive to processing the scenarios or what participants imagined was happening in an event (e.g., medial prefrontal, posterior cingulate, temporal-parietal and middle temporal cortices are sensitive to the scenarios associated with future social events), people (medial prefrontal cortex), objects (inferior frontal and premotor cortices) and locations (posterior cingulate/retrosplenial, parahippocampal and posterior parietal cortices) that typically constitute simulations of personal future events. This pattern of results demonstrates that the neural substrates of these component features of event simulations can be reliably identified in the context of a task that requires participants to simulate complex, everyday future experiences.