Susan Whitfield-Gabrieli

Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia

We examined the status of the neural network mediating the default mode of brain function, which typically exhibits greater activation during rest than during task, in patients in the early phase of schizophrenia and in young first-degree relatives of persons with schizophrenia. During functional MRI, patients, relatives, and controls alternated between rest and performance of working memory (WM) tasks. As expected, controls exhibited task-related suppression of activation in the default network, including medial prefrontal cortex (MPFC) and posterior cingulate cortex/precuneus. Patients and relatives exhibited significantly reduced task-related suppression in MPFC, and these reductions remained after controlling for performance. Increased task-related MPFC suppression correlated with better WM performance in patients and relatives and with less psychopathology in all 3 groups. For WM task performance, patients and relatives had greater activation in right dorsolateral prefrontal cortex (DLPFC) than controls. During rest and task, patients and relatives exhibited abnormally high functional connectivity within the default network. The magnitudes of default network connectivity during rest and task correlated with psychopathology in the patients. Further, during both rest and task, patients exhibited reduced anticorrelations between MPFC and DLPFC, a region that was hyperactivated by patients and relatives during WM performance. Among patients, the magnitude of MPFC task suppression negatively correlated with default connectivity, suggesting an association between the hyperactivation and hyperconnectivity in schizophrenia. Hyperactivation (reduced task-related suppression) of default regions and hyperconnectivity of the default network may contribute to disturbances of thought in schizophrenia and risk for the illness.

Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks

Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.

Default Mode Network Activity and Connectivity in Psychopathology

Neuropsychiatric disorders are associated with abnormal function of the default mode network (DMN), a distributed network of brain regions more active during rest than during performance of many attention-demanding tasks and characterized by a high degree of functional connectivity (i.e., temporal correlations between brain regions). Functional magnetic resonance imaging studies have revealed that the DMN in the healthy brain is associated with stimulus-independent thought and self-reflection and that greater suppression of the DMN is associated with better performance on attention-demanding tasks. In schizophrenia and depression, the DMN is often found to be hyperactivated and hyperconnected. In schizophrenia this may relate to overly intensive self-reference and impairments in attention and working memory. In depression, DMN hyperactivity may be related to negative rumination. These findings are considered in terms of what is known about psychological functions supported by the DMN, and alteration of the DMN in other neuropsychiatric disorders.

Reward-Motivated Learning: Mesolimbic Activation Precedes Memory Formation

We examined anticipatory mechanisms of reward-motivated memory formation using event-related FMRI. In a monetary incentive encoding task, cues signaled high- or low-value reward for memorizing an upcoming scene. When tested 24 hr postscan, subjects were significantly more likely to remember scenes that followed cues for high-value rather than low-value reward. A monetary incentive delay task independently localized regions responsive to reward anticipation. In the encoding task, high-reward cues preceding remembered but not forgotten scenes activated the ventral tegmental area, nucleus accumbens, and hippocampus. Across subjects, greater activation in these regions predicted superior memory performance. Within subject, increased correlation between the hippocampus and ventral tegmental area was associated with enhanced long-term memory for the subsequent scene. These findings demonstrate that brain activation preceding stimulus encoding can predict declarative memory formation. The findings are consistent with the hypothesis that reward motivation promotes memory formation via dopamine release in the hippocampus prior to learning.

Anticorrelations in resting state networks without global signal regression

Anticorrelated relationships in spontaneous signal fluctuation have been previously observed in resting-state functional magnetic resonance imaging (fMRI). In particular, it was proposed that there exists two systems in the brain that are intrinsically organized into anticorrelated networks, the default mode network, which usually exhibits task-related deactivations, and the task-positive network, which usually exhibits task-related activations during tasks that demands external attention. However, it is currently under debate whether the anticorrelations observed in resting state fMRI were valid or were instead artificially introduced by global signal regression, a common preprocessing technique to remove physiological and other noise in resting-state fMRI signal. We examined positive and negative correlations in resting-state connectivity using two different preprocessing methods: a component base noise reduction method (CompCor, Behzadi et al., 2007), in which principal components from noise regions-of-interest were removed, and the global signal regression method. Robust anticorrelations between a default mode network seed region in the medial prefrontal cortex and regions of the task-positive network were observed under both methods. Specificity of the anticorrelations was similar between the two methods. Specificity and sensitivity for positive correlations were higher under CompCor compared to the global regression method. Our results suggest that anticorrelations observed in resting-state connectivity are not an artifact introduced by global signal regression and might have biological origins, and that the CompCor method can be used to examine valid anticorrelations during rest.

COGNITIVE PROCESSING SPEED AND THE STRUCTURE OF WHITE MATTER PATHWAYS: CONVERGENT EVIDENCE FROM NORMAL VARIATION AND LESION STUDIES

We investigated the relation between cognitive processing speed and structural properties of white matter pathways via convergent imaging studies in healthy and brain-injured groups. Voxel-based morphometry (VBM) was applied to diffusion tensor imaging data from thirty-nine young healthy subjects in order to investigate the relation between processing speed, as assessed with the Digit-Symbol subtest from WAIS-III, and fractional anisotropy, an index of microstructural organization of white matter. Digit-Symbol performance was positively correlated with fractional anisotropy of white matter in the parietal and temporal lobes bilaterally and in the left middle frontal gyrus. Fiber tractography indicated that these regions are consistent with the trajectories of the superior and inferior longitudinal fasciculi. In a second investigation, we assessed the effect of white matter damage on processing speed using voxel-based lesion-symptom mapping (VLSM) analysis of data from seventy-two patients with left-hemisphere strokes. Lesions in left parietal white matter, together with cortical lesions in supramarginal and angular gyri were associated with impaired performance. These findings suggest that cognitive processing speed, as assessed by the Digit-Symbol test, is closely related to the structural integrity of white matter tracts associated with parietal and temporal cortices and left middle frontal gyrus. Further, fiber tractography applied to VBM results and the patient findings suggest that the superior longitudinal fasciculus, a major tract subserving fronto-parietal integration, makes a prominent contribution to processing speed.

Functional and morphometric brain dissociation between dyslexia and reading ability

In functional neuroimaging studies, individuals with dyslexia frequently exhibit both hypoactivation, often in the left parietotemporal cortex, and hyperactivation, often in the left inferior frontal cortex, but there has been no evidence to suggest how to interpret the differential relations of hypoactivation and hyperactivation to dyslexia. To address this question, we measured brain activation by functional MRI during visual word rhyme judgment compared with visual cross-hair fixation rest, and we measured gray matter morphology by voxel-based morphometry in dyslexic adolescents in comparison with (i) an age-matched group, and (ii) a reading-matched group younger than the dyslexic group but equal to the dyslexic group in reading performance. Relative to the age-matched group (n = 19; mean 14.4 years), the dyslexic group (n = 19; mean 14.4 years) exhibited hypoactivation in left parietal and bilateral fusiform cortices and hyperactivation in left inferior and middle frontal gyri, caudate, and thalamus. Relative to the reading-matched group (n = 12; mean 9.8 years), the dyslexic group (n = 12; mean 14.5 years) also exhibited hypoactivation in left parietal and fusiform regions but equal activation in all four areas that had exhibited hyperactivation relative to age-matched controls as well. In regions that exhibited atypical activation in the dyslexic group, only the left parietal region exhibited reduced gray matter volume relative to both control groups. Thus, areas of hyperactivation in dyslexia reflected processes related to the level of current reading ability independent of dyslexia. In contrast, areas of hypoactivation in dyslexia reflected functional atypicalities related to dyslexia itself, independent of current reading ability, and related to atypical brain morphology in dyslexia.

Neural systems predicting long-term outcome in dyslexia

Individuals with developmental dyslexia vary in their ability to improve reading skills, but the brain basis for improvement remains largely unknown. We performed a prospective, longitudinal study over 2.5 y in children with dyslexia (n = 25) or without dyslexia (n = 20) to discover whether initial behavioral or brain measures, including functional MRI (fMRI) and diffusion tensor imaging (DTI), can predict future long-term reading gains in dyslexia. No behavioral measure, including widely used and standardized reading and language tests, reliably predicted future reading gains in dyslexia. Greater right prefrontal activation during a reading task that demanded phonological awareness and right superior longitudinal fasciculus (including arcuate fasciculus) white-matter organization significantly predicted future reading gains in dyslexia. Multivariate pattern analysis (MVPA) of these two brain measures, using linear support vector machine (SVM) and cross-validation, predicted significantly above chance (72% accuracy) which particular child would or would not improve reading skills (behavioral measures were at chance). MVPA of whole-brain activation pattern during phonological processing predicted which children with dyslexia would improve reading skills 2.5 y later with >90% accuracy. These findings identify right prefrontal brain mechanisms that may be critical for reading improvement in dyslexia and that may differ from typical reading development. Brain measures that predict future behavioral outcomes (neuroprognosis) may be more accurate, in some cases, than available behavioral measures.

Associations and dissociations between default and self-reference networks in the human brain.

Neuroimaging has revealed consistent activations in medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC) extending to precuneus both during explicit self-reference tasks and during rest, a period during which some form of self-reference is assumed to occur in the default mode of brain function. The similarity between these two patterns of midline cortical activation may reflect a common neural system for explicit and default-mode self-reference, but there is little direct evidence about the similarities and differences between the neural systems that mediate explicit self-reference versus default-mode self-reference during rest. In two experiments, we compared directly the brain regions activated by explicit self-reference during judgments about trait adjectives and by rest conditions relative to a semantic task without self-reference. Explicit self-reference preferentially engaged dorsal MPFC, rest preferentially engaged precuneus, and both self-reference and rest commonly engaged ventral MPFC and PCC. These findings indicate that there are both associations (shared components) and dissociations between the neural systems underlying explicit self-reference and the default mode of brain function.

Development of the declarative memory system in the human brain

Brain regions that are involved in memory formation, particularly medial temporal lobe (MTL) structures and lateral prefrontal cortex (PFC), have been identified in adults, but not in children. We investigated the development of brain regions involved in memory formation in 49 children and adults (ages 8–24), who studied scenes during functional magnetic resonance imaging. Recognition memory for vividly recollected scenes improved with age. There was greater activation for subsequently remembered scenes than there was for forgotten scenes in MTL and PFC regions. These activations increased with age in specific PFC, but not in MTL, regions. PFC, but not MTL, activations correlated with developmental gains in memory for details of experiences. Voxel-based morphometry indicated that gray matter volume in PFC, but not in MTL, regions reduced with age. These results suggest that PFC regions that are important for the formation of detailed memories for experiences have a prolonged maturational trajectory.