Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.

Summary Real-world complex systems may be mathematically modeled as graphs, revealing properties of the system. Here we study graphs of functional brain organization in healthy adults using resting state functional connectivity MRI. We propose two novel brain-wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. These graphs contain many subgraphs in good agreement with known functional brain systems. Other subgraphs lack established functional identities; we suggest possible functional characteristics for these subgraphs. Further, graph measures of the areal network indicate that the default mode subgraph shares network properties with sensory and motor subgraphs: it is internally integrated but isolated from other subgraphs, much like a “processing” system. The modified voxelwise graph also reveals spatial motifs in the patterning of systems across the cortex.

Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals’ brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain’s major functional networks.

Summary Gamma-secretase inhibitors (GSIs) block the activation of oncogenic NOTCH1 in T-cell acute lymphoblastic leukemia (T-ALL). However, limited antileukemic cytotoxicity and severe gastrointestinal toxicity have restricted the clinical application of these targeted drugs. Here we show that combination therapy with GSIs plus glucocorticoids can improve the antileukemic effects of GSIs and reduce their gut toxicity in vivo. Inhibition of NOTCH1 signaling in glucocorticoid-resistant T-ALL restored glucocorticoid receptor auto-up-regulation and induced apoptotic cell death through induction of BIM expression. GSI treatment resulted in cell cycle arrest and accumulation of goblet cells in the gut mediated by upregulation of Klf4, a negative regulator of cell cycle required for goblet cell differentiation. In contrast, glucocorticoid treatment induced transcriptional upregulation of Ccnd2 and protected mice from developing intestinal goblet cell metaplasia typically induced by inhibition of NOTCH signaling with GSIs. These results support a role for glucocorticoids plus GSIs in the treatment of glucocorticoid-resistant T-ALL.

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Waterproof epidermal microfluidics enable collection and analysis of sweat during aquatic exercise.

ObjectivesAutism spectrum disorders (ASD) are diagnosed based on early-manifesting clinical symptoms, including markedly impaired social communication. We assessed the viability of resting-state functional MRI (rs-fMRI) connectivity measures as diagnostic biomarkers for ASD and investigated which connectivity features are predictive of a diagnosis.MethodsRs-fMRI scans from 59 high functioning males with ASD and 59 age- and IQ-matched typically developing (TD) males were used to build a series of machine learning classifiers. Classification features were obtained using 3 sets of brain regions. Another set of classifiers was built from participants' scores on behavioral metrics. An additional age and IQ-matched cohort of 178 individuals (89 ASD; 89 TD) from the Autism Brain Imaging Data Exchange (ABIDE) open-access dataset (http://fcon_1000.projects.nitrc.org/indi/abide/) were included for replication.ResultsHigh classification accuracy was achieved through several rs-fMRI methods (peak accuracy 76.67%). However, classification via behavioral measures consistently surpassed rs-fMRI classifiers (peak accuracy 95.19%). The class probability estimates, P(ASD|fMRI data), from brain-based classifiers significantly correlated with scores on a measure of social functioning, the Social Responsiveness Scale (SRS), as did the most informative features from 2 of the 3 sets of brain-based features. The most informative connections predominantly originated from regions strongly associated with social functioning.ConclusionsWhile individuals can be classified as having ASD with statistically significant accuracy from their rs-fMRI scans alone, this method falls short of biomarker standards. Classification methods provided further evidence that ASD functional connectivity is characterized by dysfunction of large-scale functional networks, particularly those involved in social information processing.

The TLX1 transcription factor oncogene plays an important role in the pathogenesis of T-cell acute lymphoblastic leukemia (T-ALL). However, the specific mechanisms of T-cell transformation downstream of TLX1 remain to be elucidated. Here we show that forced expression of TLX1 in transgenic mice induces T-ALL tumors with frequent deletions and mutations in Bcl11b, and identify the presence of recurrent mutations and deletions in BCL11B in 16% of human T-ALLs. Most notably, mouse TLX1 tumors were typically aneuploid and showed a marked defect in the activation of the mitotic checkpoint. Mechanistically, TLX1 directly downregulates the expression of CHEK1 and additional mitotic control genes and induces loss of the mitotic checkpoint in non transformed preleukemic thymocytes. These results identify a novel mechanism contributing to chromosomal missegregation and aneuploidy active at the earliest stages of tumor development in the pathogenesis of cancer.