Paul J. Laurienti

Precentral gyrus discrepancy in electronic versions of the Talairach atlas.

Electronic versions of the atlas of Talairach and Tournoux, including the Talairach Daemon and the official versions published by Thieme, contain a discrepant region of the precentral gyrus on axial slice +35 mm that extends far forward into the frontal lobe. This area is anatomically incorrect and internally inconsistent within the digital atlas software applications using their multiplanar cross-referencing tools. By cross-referencing the axial, sagittal, and coronal plates from the original printed atlas, we demonstrate that the discrepant area should be labeled middle frontal gyrus. The mislabeled portion encompasses a 3 x 1.5-cm region in the axial plane and has significant implications for sensorimotor studies that rely on the digital atlases for anatomic labeling.

Deactivation of Sensory-Specific Cortex by Cross-Modal Stimuli

Visual and auditory cortices traditionally have been considered to be modality-specific. Thus, their activity has been thought to be unchanged by information in other sensory modalities. However, using functional magnetic resonance imaging (fMRI), the present experiments revealed that ongoing activity in the visual cortex could be modulated by auditory information and ongoing activity in the auditory cortex could be modulated by visual information. In both cases, this cross-modal modulation of activity took the form of deactivation. Yet, the deactivation response was not evident in either cortical area during the paired presentation of visual and auditory stimuli. These data suggest that cross-modal inhibitory processes operate within traditional modality-specific cortices and that these processes can be switched on or off in different circumstances.

Comparison of characteristics between region-and voxel-based network analyses in resting-state fMRI data.

Small-world networks are a class of networks that exhibit efficient long-distance communication and tightly interconnected local neighborhoods. In recent years, functional and structural brain networks have been examined using network theory-based methods, and consistently shown to have small-world properties. Moreover, some voxel-based brain networks exhibited properties of scale-free networks, a class of networks with mega-hubs. However, there are considerable inconsistencies across studies in the methods used and the results observed, particularly between region-based and voxel-based brain networks. We constructed functional brain networks at multiple resolutions using the same resting-state fMRI data, and compared various network metrics, degree distribution, and localization of nodes of interest. It was found that the networks with higher resolutions exhibited the properties of small-world networks more prominently. It was also found that voxel-based networks were more robust against network fragmentation compared to region-based networks. Although the degree distributions of all networks followed an exponentially truncated power law rather than true power law, the higher the resolution, the closer the distribution was to a power law. The voxel-based analyses also enhanced visualization of the results in the 3D brain space. It was found that nodes with high connectivity tended have high efficiency, a co-localization of properties that was not as consistently observed in the region-based networks. Our results demonstrate benefits of constructing the brain network at the finest scale the experiment will permit.

Biological parametric mapping: A statistical toolbox for multimodality brain image analysis

In recent years, multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality-specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric mapping (BPM), based on a voxel-wise use of the general linear model. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in Matlab with a user-friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely used T-field, has been implemented in the correlation analysis for more accurate results. An example with in vivo data is presented, demonstrating the potential of the BPM methodology as a tool for multimodal image analysis.

Enhanced multisensory integration in older adults

Information from the different senses is seamlessly integrated by the brain in order to modify our behaviors and enrich our perceptions. It is only through the appropriate binding and integration of information from the different senses that a meaningful and accurate perceptual gestalt can be generated. Although a great deal is known about how such cross-modal interactions influence behavior and perception in the adult, there is little knowledge as to the impact of aging on these multisensory processes. In the current study, we examined the speed of discrimination responses of aged and young individuals to the presentation of visual, auditory or combined visual-auditory stimuli. Although the presentation of multisensory stimuli speeded response times in both groups, the performance gain was significantly greater in the aged. Most strikingly, multisensory stimuli restored response times in the aged to those seen in young subjects to the faster of the two unisensory stimuli (i.e., visual). The current results suggest that despite the decline in sensory processing that accompanies aging, the use of multiple sensory channels may represent an effective compensatory strategy to overcome these unisensory deficits.

Semantic congruence is a critical factor in multisensory behavioral performance

It has repeatedly been demonstrated that the presence of multiple cues in different sensory modalities can enhance behavioral performance by speeding responses, increasing accuracy, and/or improving stimulus detection. Despite an extensive knowledge base as to how the spatial, temporal, and physical (e.g., intensity) characteristics of multisensory stimuli influence such enhancements, little is known about the role of semantic or contextual congruence. Our hypothesis was that semantically congruent multisensory stimuli would result in enhanced behavioral performance, and that semantically incongruent multisensory stimuli would result in either no enhancement or a decrement in behavioral performance. The results from a redundant cue feature discrimination task clearly demonstrate that congruent cross-modal stimulation improves behavioral performance. This effect is specific to the multisensory stimuli, as no improvements are seen in the presence of redundant unimodal stimulus pairs. In contrast, incongruent stimulus pairs result in behavioral decrements for both multisensory and paired unimodal stimuli. These results highlight that in addition to such simple stimulus features as space, time and relative effectiveness, the semantic content of a multisensory stimulus plays a critical role in determining how it is processed by the nervous system.

On the use of superadditivity as a metric for characterizing multisensory integration in functional neuroimaging studies

A growing number of brain imaging studies are being undertaken in order to better understand the contributions of multisensory processes to human behavior and perception. Many of these studies are designed on the basis of the physiological findings from single neurons in animal models, which have shown that multisensory neurons have the capacity for integrating their different sensory inputs and give rise to a product that differs significantly from either of the unisensory responses. At certain points these multisensory interactions can be superadditive, resulting in a neural response that exceeds the sum of the unisensory responses. Because of the difficulties inherent in interpreting the results of imaging large neuronal populations, superadditivity has been put forth as a stringent criterion for identifying potential sites of multisensory integration. In the present manuscript we discuss issues related to using the superadditive model in human brain imaging studies, focusing on population responses to multisensory stimuli and the relationship between single neuron measures and functional brain imaging measures. We suggest that the results of brain imaging studies be interpreted with caution in regards to multisensory integration. Future directions for imaging multisensory integration are discussed in light of the ideas presented.

Using Network Science to Evaluate Exercise-Associated Brain Changes in Older Adults

Literature has shown that exercise is beneficial for cognitive function in older adults and that aerobic fitness is associated with increased hippocampal tissue and blood volumes. The current study used novel network science methods to shed light on the neurophysiological implications of exercise-induced changes in the hippocampus of older adults. Participants represented a volunteer subgroup of older adults that were part of either the exercise training (ET) or healthy aging educational control (HAC) treatment arms from the Seniors Health and Activity Research Program Pilot (SHARP-P) trial. Following the 4-month interventions, MRI measures of resting brain blood flow and connectivity were performed. The ET groups hippocampal cerebral blood flow (CBF) exhibited statistically significant increases compared to the HAC group. Novel whole-brain network connectivity analyses showed greater connectivity in the hippocampi of the ET participants compared to HAC. Furthermore, the hippocampus was consistently shown to be within the same network neighborhood (module) as the anterior cingulate cortex only within the ET group. Thus, within the ET group, the hippocampus and anterior cingulate were highly interconnected and localized to the same network neighborhood. This project shows the power of network science to investigate potential mechanisms for exercise-induced benefits to the brain in older adults. We show a link between neurological network features and CBF, and it is possible that this alteration of functional brain networks may lead to the known improvement in cognitive function among older adults following exercise.

A New Measure of Centrality for Brain Networks

Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central nodes may be identified via centrality metrics, with degree, betweenness, and eigenvector centrality being three of the most popular measures. Degree identifies the most connected nodes, whereas betweenness centrality identifies those located on the most traveled paths. Eigenvector centrality considers nodes connected to other high degree nodes as highly central. In the work presented here, we propose a new centrality metric called leverage centrality that considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its immediate neighbors rely on that node for information. Although similar in concept, there are essential differences between eigenvector and leverage centrality that are discussed in this manuscript. Degree, betweenness, eigenvector, and leverage centrality were compared using functional brain networks generated from healthy volunteers. Functional cartography was also used to identify neighborhood hubs (nodes with high degree within a network neighborhood). Provincial hubs provide structure within the local community, and connector hubs mediate connections between multiple communities. Leverage proved to yield information that was not captured by degree, betweenness, or eigenvector centrality and was more accurate at identifying neighborhood hubs. We propose that this metric may be able to identify critical nodes that are highly influential within the network.

Dietary caffeine consumption modulates fMRI measures.

Caffeine is the most widely used stimulant in the world. The stimulant effects of caffeine are mediated through its antagonistic properties on neuronal adenosine receptors. In addition, caffeine blocks neurovascular adenosine receptors and decreases cerebral perfusion. Although the effects of caffeine on blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging measures are extremely important, there are few studies addressing this issue in the literature. Because chronic caffeine use causes an upregulation of adenosine receptors, the differential effects of caffeine in low and high users is of particular interest. The present study was designed to test the hypothesis that caffeine has differential effects on the BOLD signal in high and low caffeine users. We demonstrated that the BOLD signal change in visual cortex was significantly greater in high users than in low users in the presence of caffeine. In addition, the magnitude of the BOLD signal was significantly correlated with caffeine consumption. We propose that the outcome observed here was due to an upregulation of adenosine receptors in high users, resulting in differential contributions of the neural and vascular effects of adenosine in the two study populations.