A group model for stable multi-subject ICA on fMRI datasets

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study

Cryogenic Solid Solubility Measurements for HFC-32 + CO2 Binary Mixtures at Temperatures Between (132 and 217) K

Accurate phase equilibrium data for mixtures of eco-friendly but mildly-flammable refrigerants with inert components like CO2 will help the refrigeration industry safely employ working fluids with 80 % less global warming potential than those of many widely-used refrigerants. In this work, a visual high-pressure measurement setup was used to measure solid–fluid equilibrium (SFE) of HFC-32 + CO2 binary systems at temperatures between (132 and 217) K. The experimental data show a eutectic composition of around 11 mol % CO2 with a eutectic temperature of 131.9 K at solid–liquid–vapour (SLVE) condition. Measured SLVE and solid–liquid equilibrium data were used to tune a thermodynamic model implemented in the ThermoFAST software package by adjusting the binary interaction parameter (BIP) in the Peng–Robinson equation of state. The tuned model represents the measured melting points for binary mixtures with a root mean square deviation (RMSD) of 3.2 K, which is 60 % less than achieved with the default BIP. An RMSD of 0.5 K was obtained using the tuned model for the mixtures with CO2 fractions over 28 mol % relative to an RMSD of 3.4 K obtained with the default model. The new property data and improved model presented in this work will help avoid solid deposition risk in cryogenic applications of the HFC-32 + CO2 binary system and promote wider applications of more environmentally-friendly refrigerant mixtures

Simplified, Enhanced Protein Purification Using an Inducible, Autoprocessing Enzyme Tag

We introduce a new method for purifying recombinant proteins expressed in bacteria using a highly specific, inducible, self-cleaving protease tag. This tag is comprised of the Vibrio cholerae MARTX toxin cysteine protease domain (CPD), an autoprocessing enzyme that cleaves exclusively after a leucine residue within the target protein-CPD junction. Importantly, V. cholerae CPD is specifically activated by inositol hexakisphosphate (InsP6), a eukaryotic-specific small molecule that is absent from the bacterial cytosol. As a result, when His6-tagged CPD is fused to the C-terminus of target proteins and expressed in Escherichia coli, the full-length fusion protein can be purified from bacterial lysates using metal ion affinity chromatography. Subsequent addition of InsP6 to the immobilized fusion protein induces CPD-mediated cleavage at the target protein-CPD junction, releasing untagged target protein into the supernatant. This method condenses affinity chromatography and fusion tag cleavage into a single step, obviating the need for exogenous protease addition to remove the fusion tag(s) and increasing the efficiency of tag separation. Furthermore, in addition to being timesaving, versatile, and inexpensive, our results indicate that the CPD purification system can enhance the expression, integrity, and solubility of intractable proteins from diverse organisms

Markov models for fMRI correlation structure: is brain functional connectivity small world, or decomposable into networks?

Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems

Conforming to accreditation in Iranian hospitals

This paper examines the operation of an accreditation programme for hospitals in Iran. It explores the process of accreditation as a regulatory control system and analyses hospitals’ responses to this type of control. We draw on the notion of steering and argue that the accreditation system is transactional in nature. Our findings show that hospitals conform to the scheme, although they also resist some of its requirements. On a wider policy level, we suggest that accreditations offer the accreditor the opportunity to impact on how activities are undertaken, but hospitals require incentives in order to make the necessary organisational changes

Investigating human audio-visual object perception with a combination of hypothesis-generating and hypothesis-testing fMRI analysis tools

Primate multisensory object perception involves distributed brain regions. To investigate the network character of these regions of the human brain, we applied data-driven group spatial independent component analysis (ICA) to a functional magnetic resonance imaging (fMRI) data set acquired during a passive audio-visual (AV) experiment with common object stimuli. We labeled three group-level independent component (IC) maps as auditory (A), visual (V), and AV, based on their spatial layouts and activation time courses. The overlap between these IC maps served as definition of a distributed network of multisensory candidate regions including superior temporal, ventral occipito-temporal, posterior parietal and prefrontal regions. During an independent second fMRI experiment, we explicitly tested their involvement in AV integration. Activations in nine out of these twelve regions met the max-criterion (A < AV > V) for multisensory integration. Comparison of this approach with a general linear model-based region-of-interest definition revealed its complementary value for multisensory neuroimaging. In conclusion, we estimated functional networks of uni- and multisensory functional connectivity from one dataset and validated their functional roles in an independent dataset. These findings demonstrate the particular value of ICA for multisensory neuroimaging research and using independent datasets to test hypotheses generated from a data-driven analysis

Covert Waking Brain Activity Reveals Instantaneous Sleep Depth

The neural correlates of the wake-sleep continuum remain incompletely understood, limiting the development of adaptive drug delivery systems for promoting sleep maintenance. The most useful measure for resolving early positions along this continuum is the alpha oscillation, an 8–13 Hz electroencephalographic rhythm prominent over posterior scalp locations. The brain activation signature of wakefulness, alpha expression discloses immediate levels of alertness and dissipates in concert with fading awareness as sleep begins. This brain activity pattern, however, is largely ignored once sleep begins. Here we show that the intensity of spectral power in the alpha band actually continues to disclose instantaneous responsiveness to noise—a measure of sleep depth—throughout a night of sleep. By systematically challenging sleep with realistic and varied acoustic disruption, we found that sleepers exhibited markedly greater sensitivity to sounds during moments of elevated alpha expression. This result demonstrates that alpha power is not a binary marker of the transition between sleep and wakefulness, but carries rich information about immediate sleep stability. Further, it shows that an empirical and ecologically relevant form of sleep depth is revealed in real-time by EEG spectral content in the alpha band, a measure that affords prediction on the order of minutes. This signal, which transcends the boundaries of classical sleep stages, could potentially be used for real-time feedback to novel, adaptive drug delivery systems for inducing sleep

Overdominant effect of a CHRNA4 polymorphism on cingulo-opercular network activity and cognitive control

The nicotinic system plays an important role in cognitive control and is implicated in several neuropsychiatric conditions. However, the contributions of genetic variability in this system to individuals’ cognitive control abilities are poorly understood and the brain processes that mediate such genetic contributions remain largely unidentified. In this first large-scale neuroimaging genetics study of the human nicotinic receptor system (two cohorts, males and females, fMRI total N = 1586, behavioral total N = 3650), we investigated a common polymorphism of the high-affinity nicotinic receptor α4β2 (rs1044396 on the CHRNA4 gene) previously implicated in behavioral and nicotine-related studies (albeit with inconsistent major/minor allele impacts). Based on our prior neuroimaging findings, we expected this polymorphism to affect neural activity in the cingulo-opercular (CO) network involved in core cognitive control processes including maintenance of alertness. Consistent across the cohorts, all cortical areas of the CO network showed higher activity in heterozygotes compared with both types of homozygotes during cognitive engagement. This inverted U-shaped relation reflects an overdominant effect; that is, allelic interaction (cumulative evidence p = 1.33 * 10−5). Furthermore, heterozygotes performed more accurately in behavioral tasks that primarily depend on sustained alertness. No effects were observed for haplotypes of the surrounding CHRNA4 region, supporting a true overdominant effect at rs1044396. As a possible mechanism, we observed that this polymorphism is an expression quantitative trait locus modulating CHRNA4 expression levels. This is the first report of overdominance in the nicotinic system. These findings connect CHRNA4 genotype, CO network activation, and sustained alertness, providing insights into how genetics shapes individuals’ cognitive control abilities

Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases

Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high resolution of 135 postmortem human brain tissue specimens imaged at 0.3 mm$^{3}$ isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We then segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter. We show generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm^3 and 0.16 mm^3 isotropic T2*w FLASH sequence at 7T. We then compute localized cortical thickness and volumetric measurements across key regions, and link them with semi-quantitative neuropathological ratings. Our code, Jupyter notebooks, and the containerized executables are publicly available at: https://pulkit-khandelwal.github.io/exvivo-brain-upennComment: Preprint submitted to NeuroImage Project website: https://pulkit-khandelwal.github.io/exvivo-brain-upen