Chester F. Carlson Center for Imaging Science

Major depressive disorder (MDD) is associated with increased functional connectivity in specific neural networks. Electroconvulsive therapy (ECT), the gold-standard treatment for acute, treatment-resistant MDD, but temporal dependencies between networks associated with ECT response have yet to be investigated. In the present longitudinal, case-control investigation, we used independent component analysis to identify distinct networks of brain regions with temporally coherent hemodynamic signal change and functional network connectivity (FNC) to assess component time course correlations across these networks. MDD subjects completed imaging and clinical assessments immediately prior to the ECT series and a minimum of 5 days after the last ECT treatment. We focused our analysis on four networks affected in MDD: the subcallosal cingulate gyrus, default mode, dorsal lateral prefrontal cortex, and dorsal medial prefrontal cortex (DMPFC). In an older sample of ECT subjects (n = 12) with MDD, remission associated with the ECT series reverses the relationship from negative to positive between the posterior default mode (p_DM) and two other networks: the DMPFC and left dorsal lateral prefrontal cortex (l_DLPFC). Relative to demographically healthy subjects (n = 12), the FNC between the p_DM areas and the DMPFC normalizes with ECT response. The FNC changes following treatment did not correlate with symptom improvement; however, a direct comparison between ECT remitters and non-remitters showed the pattern of increased FNC between the p_DM and l_DLPFC following ECT to be specific to those who responded to the treatment.The differences between ECT remitters and non-remitters suggest that this increased FNC between p_DM areas and the left dorsolateral prefrontal cortex is a neural correlate and potential biomarker of recovery from a depressed episode.

Spatial variability in intrinsic brain networks has not been well studied in fMRI. Independent vector analysis (IVA), is a blind source separation approach that can be used for segregating fMRI data into temporally coherent, maximally spatially independent networks enabling comparison among subjects similar to group independent component analysis (GICA). Using simulated and small sample real data, it has been shown that spatial independence in IVA is achieved while jointly maximizing the dependence across subjects. This study was motivated by the fact that IVA has not yet been applied to a large sample size or to analyze multi-group data for spatial differences. We introduce several new ways to quantify differences in variability of IVA-derived connectivity networks between schizophrenia patients (SZ = 82) from healthy controls (HC = 89) in a large (N=171) data set. Results show that IVA identified significant group differences in the auditory cortex, the basal ganglia, the sensorimotor network and medial visual cortex. Variance maps of the spatial networks showed that there is greater variability in the patients primarily in sensory networks whereas the default mode network showed more variability in the controls. In summary, IVA enables the study of spatial variation in intrinsic brain networks, an area that has not been in focus.

Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and vis...

Abstract Behavioral studies suggest that humans intercept moving targets by maintaining a constant bearing angle (CBA). The purely feedback-driven CBA strategy has been contrasted with the strategy of predicting the eventual time and location of the future interception point. This study considers an intermediate anticipatory strategy of moving so as to produce a CBA a short duration into the future.

People can often anticipate the outcome of another person's actions based on visual information available in the movements of the other person's body. We investigated this problem by studying how goalkeepers anticipate the direction of a penalty kick in soccer. The specific aim was to determine whether the information used to anticipate kick direction is best characterized as local to a particular body segment or distributed across multiple segments. In Experiment 1, we recorded the movements of soccer players as they kicked balls into a net. Using a novel method for analyzing motion capture data, we identified sources of local and distributed information that were reliable indicators of kick direction. In Experiments 2 and 3, subjects were presented with animations of kickers' movements prior to foot-to-ball contact and instructed to judge kick direction. Judgments were consistent with the use of distributed information, with a possible small contribution of local information.

Behavioral studies suggest that humans intercept moving targets by maintaining a constant bearing angle (CBA). The purely feedback-driven CBA strategy has been contrasted with the strategy of predicting the eventual time and location of the future interception point. This study considers an intermediate anticipatory strategy of moving so as to produce a CBA a short duration into the future. Subjects controlled their speed of self-motion along a linear path through a simulated environment to intercept a moving target. When targets changed speed midway through the trial in Experiment 1, subjects abandoned an ineffective CBA strategy in favor of a strategy of anticipating the most likely change in target speed. In Experiment 2, targets followed paths of varying curvature. Behavior was inconsistent with both the CBA and the purely predictive strategy. To investigate the intermediate anticipatory strategy, human performance was compared with a model of interceptive behavior that, at each timestep t, produced the velocity adjustment that would minimize the change in bearing angle at time t ? Dt, taking into account the target's behavior during that interval. Values of Dt at which the model best fit the human data for practiced subjects varied between 0.5 and 3.5 s, suggesting that actors adopt an anticipatory strategy to keep the bearing angle constant a short time into the future.