Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that free-streaming subject-driven cognitive states can be decoded using a novel whole-brain functional connectivity analysis. Ninety functional regions of interest (ROIs) were defined across 14 large-scale resting-state brain networks to generate a 3960 cell matrix reflecting whole-brain connectivity. We trained a classifier to identify specific patterns of whole-brain connectivity as subjects rested quietly, remembered the events of their day, subtracted numbers, or (silently) sang lyrics. In a leave-one-out cross-validation, the classifier identified these 4 cognitive states with 84% accuracy. More critically, the classifier achieved 85% accuracy when identifying these states in a second, independent cohort of subjects. Classification accuracy remained high with imaging runs as short as 30-60 s. At all temporal intervals assessed, the 90 functionally defined ROIs outperformed a set of 112 commonly used structural ROIs in classifying cognitive states. This approach should enable decoding a myriad of subject-driven cognitive states from brief imaging data samples.

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Brain structural and functional development, throughout childhood and into adulthood, underlies the maturation of increasingly sophisticated cognitive abilities. High-level attentional and cognitive control processes rely on the integrity of, and dynamic interactions between, core neurocognitive networks. The right fronto-insular cortex (rFIC) is a critical component of a salience network (SN) that mediates interactions between large-scale brain networks involved in externally-oriented attention (central executive network, CEN) and internally-oriented cognition (default mode network, DMN). How these systems reconfigure and mature with development is a critical question for cognitive neuroscience, with implications for neurodevelopmental pathologies affecting brain connectivity. Using functional and effective connectivity measures applied to fMRI data, we examine interactions within and between the SN, CEN, and DMN. We find that functional coupling between key network nodes is stronger in adults than in children, as are causal links emanating from the rFIC. Specifically, the causal influence of the rFIC on nodes of the SN and CEN was significantly greater in adults compared to children. Notably, these results were entirely replicated on an independent dataset of matched children and adults. Developmental changes in functional and effective connectivity were related to structural connectivity along these links. DTI tractography revealed increased structural integrity in adults compared to children along both within- and between-network pathways associated with the rFIC. These results suggest that structural and functional maturation of rFIC pathways is a critical component of the process by which human brain networks mature during development to support complex, flexible cognitive processes in adulthood.

The right inferior frontal cortex (rIFC) and the right anterior insula (rAI) have been implicated consistently in inhibitory control, but their differential roles are poorly understood. Here we use multiple quantitative techniques to dissociate the functional organization and roles of the rAI and rIFC. We first conducted a meta-analysis of 70 published inhibitory control studies to generate a commonly activated right fronto-opercular cortex volume of interest (VOI). We then segmented this VOI using two types of features: (1) intrinsic brain activity; and (2) stop-signal task-evoked hemodynamic response profiles. In both cases, segmentation algorithms identified two stable and distinct clusters encompassing the rAI and rIFC. The rAI and rIFC clusters exhibited several distinct functional characteristics. First, the rAI showed stronger intrinsic and task-evoked functional connectivity with the anterior cingulate cortex, whereas the rIFC had stronger intrinsic and task-evoked functional connectivity with dorsomedial prefrontal and lateral fronto-parietal cortices. Second, the rAI showed greater activation than the rIFC during Unsuccessful, but not Successful, Stop trials, and multivoxel response profiles in the rAI, but not the rIFC, accurately differentiated between Successful and Unsuccessful Stop trials. Third, activation in the rIFC, but not rAI, predicted individual differences in inhibitory control abilities. Crucially, these findings were replicated in two independent cohorts of human participants. Together, our findings provide novel quantitative evidence for the dissociable roles of the rAI and rIFC in inhibitory control. We suggest that the rAI is particularly important for detecting behaviorally salient events, whereas the rIFC is more involved in implementing inhibitory control.

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.