Teacher-Child Interaction Training: A Pilot Study With Random Assignment.

Teacher-Child Interaction Training (TCIT), adapted from Parent-Child Interaction Therapy (PCIT), is a classroom-based program designed to provide teachers with behavior management skills that foster positive teacher-student relationships and to improve student behavior by creating a more constructive classroom environment. The purpose of this pilot study was to evaluate TCIT in more classrooms than previously reported in the literature, with older children than previously reported, using random assignment of classrooms to TCIT or to a no-TCIT control condition and conducting all but two sessions within the classroom to enhance feasibility. Participants included 11 kindergarten and first grade classroom teachers and their 118 students from three urban, public schools in Manhattan, with five classrooms randomly assigned to receive TCIT and six to the no-TCIT control condition. Observations of teacher skill acquisition were conducted before, during, and after TCIT for all 11 teachers, and teacher reports of student behavior were obtained at these same time points. Teacher satisfaction with TCIT was assessed following training. Results suggested that after receiving TCIT, teachers increased rates of positive attention to students' appropriate behavior, decreased rates of negative attention to misbehavior, reported significantly less distress related to student disruptive behavior, and reported high satisfaction with the training program. Our study supports the growing evidence-base suggesting that TCIT is a promising approach for training teachers in positive behavior management strategies and for improving student disruptive behavior in the classroom.

Personality is reflected in the brain's intrinsic functional architecture.

Personality describes persistent human behavioral responses to broad classes of environmental stimuli. Investigating how personality traits are reflected in the brain's functional architecture is challenging, in part due to the difficulty of designing appropriate task probes. Resting-state functional connectivity (RSFC) can detect intrinsic activation patterns without relying on any specific task. Here we use RSFC to investigate the neural correlates of the five-factor personality domains. Based on seed regions placed within two cognitive and affective 'hubs' in the brain--the anterior cingulate and precuneus--each domain of personality predicted RSFC with a unique pattern of brain regions. These patterns corresponded with functional subdivisions responsible for cognitive and affective processing such as motivation, empathy and future-oriented thinking. Neuroticism and Extraversion, the two most widely studied of the five constructs, predicted connectivity between seed regions and the dorsomedial prefrontal cortex and lateral paralimbic regions, respectively. These areas are associated with emotional regulation, self-evaluation and reward, consistent with the trait qualities. Personality traits were mostly associated with functional connections that were inconsistently present across participants. This suggests that although a fundamental, core functional architecture is preserved across individuals, variable connections outside of that core encompass the inter-individual differences in personality that motivate diverse responses.

PDE-based spatial smoothing: a practical demonstration of impacts on MRI brain extraction, tissue segmentation and registration.

Spatial smoothing is typically used to denoise magnetic resonance imaging (MRI) data. Gaussian smoothing kernels, associated with heat equations or isotropic diffusion (ISD), are widely adopted for this purpose because of their easy implementation and efficient computation, but despite these advantages, Gaussian smoothing kernels blur the edges, curvature and texture of images. To overcome these issues, researchers have proposed anisotropic diffusion (ASD) and non-local means [i.e., diffusion (NLD)] kernels. However, these new filtering paradigms are rarely applied to MRI analyses. In the current study, using real degraded MRI data, we demonstrated the effect of denoising using ISD, ASD and NLD kernels. Furthermore, we evaluated their impact on three common preprocessing steps of MRI data analysis: brain extraction, segmentation and registration. Results suggest that NLD-based spatial smoothing is most effective at improving the quality of MRI data preprocessing and thus should become the new standard method of smoothing in MRI data processing.

Toward discovery science of human brain function.

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.

Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach.

Functional connectivity analyses of resting-state fMRI data are rapidly emerging as highly efficient and powerful tools for in vivo mapping of functional networks in the brain, referred to as intrinsic connectivity networks (ICNs). Despite a burgeoning literature, researchers continue to struggle with the challenge of defining computationally efficient and reliable approaches for identifying and characterizing ICNs. Independent component analysis (ICA) has emerged as a powerful tool for exploring ICNs in both healthy and clinical populations. In particular, temporal concatenation group ICA (TC-GICA) coupled with a back-reconstruction step produces participant-level resting state functional connectivity maps for each group-level component. The present work systematically evaluated the test-retest reliability of TC-GICA derived RSFC measures over the short-term (<45 min) and long-term (5-16 months). Additionally, to investigate the degree to which the components revealed by TC-GICA are detectable via single-session ICA, we investigated the reproducibility of TC-GICA findings. First, we found moderate-to-high short- and long-term test-retest reliability for ICNs derived by combining TC-GICA and dual regression. Exceptions to this finding were limited to physiological- and imaging-related artifacts. Second, our reproducibility analyses revealed notable limitations for template matching procedures to accurately detect TC-GICA based components at the individual scan level. Third, we found that TC-GICA component's reliability and reproducibility ranks are highly consistent. In summary, TC-GICA combined with dual regression is an effective and reliable approach to exploratory analyses of resting state fMRI data.

Residual functional connectivity in the split-brain revealed with resting-state functional MRI.

Split-brain patients present a unique opportunity to address controversies regarding subcortical contributions to interhemispheric coordination. We characterized residual functional connectivity in a complete commissurotomy patient by examining patterns of low-frequency BOLD functional MRI signal. Using independent components analysis and region-of-interest-based functional connectivity analyses, we demonstrate bilateral resting state networks in a patient lacking all major cerebral commissures. Compared with a control group, the patient's interhemispheric correlation scores fell within the normal range for two out of three regions examined. Thus, we provide evidence for bilateral resting state networks in a patient with complete commissurotomy. Such continued interhemispheric interaction suggests that, at least in part, cortical networks in the brain can be coordinated by subcortical mechanisms.

Choice of radiotherapy planning modality influences toxicity in the treatment of locally advanced esophageal cancer.

PURPOSE: Three-dimensional computed tomography-based radiotherapy planning (3DCTP) is increasingly employed in the treatment of esophageal cancer. It is unknown whether a 3DCTP approach influences outcomes compared to two-dimensional planning (2DP). This study compares clinical outcomes for homogeneously treated patient cohorts stratified by planning modality. METHODS AND MATERIALS: A retrospective chart review was conducted on patients with T3/4 and/or node-positive esophageal carcinoma treated at the Cleveland Clinic between July 1, 2003 and May 31, 2006 who were managed with an institutional regimen consisting of preoperative radiotherapy, whether 3DCTP or 2DP [30 Gy/20 fractions/1.5 Gy twice daily over 2 weeks], with concurrent cisplatin and 5-fluorouracil the first week. Following definitive resection, an identical postoperative course of concurrent chemoradiotherapy (CRT) was delivered. RESULTS: One hundred and forty-one patients completed preoperative CRT and were available for review. The median follow-up of living patients is 21.7 months. Fifty-five percent underwent 3DCTP and 45% had 2DP. The treatment groups were similar, with the exception of clinical stage group, with 2DP having more stage II and fewer stage III patients than 3DCTP (p = 0.02). 3DCTP plans had significantly smaller field sizes by area (p < 0.0001). Pathologic response, locoregional control, distant control, and overall survival were equivalent between the two planning modalities. Esophagitis was significantly less common with a 3D approach compared to 2D planning (49% vs. 71%, p = 0.0096), with other toxicities equivalent between the groups. CONCLUSIONS: 3DCTP reduces acute esophagitis in patients receiving multimodality therapy for esophageal cancer without compromising clinical outcomes.