Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches. We hope these ten rules will increase the use of predictive models with neuroimaging data.

To achieve a comprehensive understanding of brain function requires multiple imaging modalities with complementary strengths. We present an approach for concurrent wide-field optical and functional MRI. By merging these modalities, we can simultaneously acquire whole-brain blood oxygen level-dependent (BOLD) and whole-cortex calcium-sensitive fluorescent measures of brain activity. In a transgenic murine model, we show that calcium predicts the BOLD signal, using a model that optimizes a Gamma-variant transfer function. We find consistent predictions across the cortex, which are best at low frequency (0.009–0.08Hz). Furthermore, we show that the relationship between modality connectivity strengths varies by region. Our approach links cell type- specific optical measurements of activity to the most widely used method for assessing human brain function.

Proteorhodopsins are typical retinal-binding light-driven proton pumps of heptahelical architecture widely distributed in marine and freshwater bacteria. Recently, we have shown that green proteorhodopsin (GPR) can be prepared in a lipid-bound state that gives well-resolved magic angle spinning (MAS) NMR spectra in samples with different patterns of reverse labelling. Here, we present 3D and 4D sequential chemical shift assignments identified through experiments conducted on a uniformly (13)C,(15)N-labelled sample. These experiments provided the assignments for 153 residues, with a particularly high density in the transmembrane regions ( approximately 74% of residues). The extent of assignments permitted a detailed examination of the secondary structure and dynamics in GPR. In particular, we present experimental evidence of mobility of the protein's termini and of the A-B, C-D, and F-G loops, the latter being possibly coupled to the GPR ion-transporting function.

BACKGROUND: Autism spectrum disorder and attention-deficit/hyperactivity disorder (ADHD) are associated with complex changes as revealed by functional magnetic resonance imaging. To date, neuroimaging-based models are not able to characterize individuals with sufficient sensitivity and specificity. Further, although evidence shows that ADHD traits occur in individuals with autism spectrum disorder, and autism spectrum disorder traits in individuals with ADHD, the neurofunctional basis of the overlap is undefined. METHODS: Using individuals from the Autism Brain Imaging Data Exchange and ADHD-200, we apply a data-driven, subject-level approach, connectome-based predictive modeling, to resting-state functional magnetic resonance imaging data to identify brain-behavior associations that are predictive of symptom severity. We examine crossdiagnostic commonalities and differences. RESULTS: Using leave-one-subject-out and split-half analyses, we define networks that predict Social Responsiveness Scale, Autism Diagnostic Observation Schedule, and ADHD Rating Scale scores and confirm that these networks generalize to novel subjects. Networks share minimal overlap of edges (,2%) but some common regions of high hubness (Brodmann areas 10, 11, and 21, cerebellum, and thalamus). Further, predicted Social Responsiveness Scale scores for individuals with ADHD are linked to ADHD symptoms, supporting the hypothesis that brain organization relevant to autism spectrum disorder severity shares a component associated with attention in ADHD. Predictive connections and high-hubness regions are found within a wide range of brain areas and across conventional networks. CONCLUSIONS: An individual's functional connectivity profile contains information that supports dimensional, nonbinary classification in autism spectrum disorder and ADHD. Furthermore, we can determine disorder-specific and shared neurofunctional pathology using our method.

We used high-resolution proton-detected multidimensional NMR to study the solvent-exposed parts of a seven-helical integral membrane proton pump, proteorhodopsin (PR). PR samples were prepared by growing the apoprotein on fully deuterated medium and reintroducing protons to solvent-accessible sites through exchange with protonated buffer. This preparation leads to NMR spectra with proton resolution down to ca. 0.2 ppm at fast spinning (28 kHz) in a protein back-exchanged at a level of 40%. Novel three-dimensional proton-detected chemical shift correlation spectroscopy allowed for the identification and resonance assignment of the solvent-exposed parts of the protein. Most of the observed residues are located at the membrane interface, but there are notable exceptions, particularly in helix G, where most of the residues are susceptible to H/D exchange. This helix contains Schiff base-forming Lys231, and many conserved polar residues in the extracellular half, such as Asn220, Tyr223, Asn224, Asp227, and Asn230. We proposed earlier that high mobility of the F-G loop may transiently expose a hydrophilic cavity in the extracellular half of the protein, similar to the one found in xanthorhodopsin. Solvent accessibility of helix G is in line with this hypothesis, implying that such a cavity may be a part of the proton-conducting pathway lined by this helix.