Anatomo-functional correspondence in the voice-selective regions of human prefrontal cortex.

Group level analyses of functional regions involved in voice perception show evidence of 3 sets of bilateral voice-sensitive activations in the human prefrontal cortex, named the anterior, middle and posterior Frontal Voice Areas (FVAs). However, the relationship with the underlying sulcal anatomy, highly variable in this region, is still unknown. We examined the inter-individual variability of the FVAs in conjunction with the sulcal anatomy. To do so, anatomical and functional MRI scans from 74 subjects were analyzed to generate individual contrast maps of the FVAs and relate them to each subject's manually labeled prefrontal sulci. We report two major results. First, the frontal activations for the voice are significantly associated with the sulcal anatomy. Second, this correspondence with the sulcal anatomy at the individual level is a better predictor than coordinates in the MNI space. These findings offer new perspectives for the understanding of anatomical-functional correspondences in this complex cortical region. They also shed light on the importance of considering individual-specific variations in subject's anatomy.

Is Migraine Associated to Brain Anatomical Alterations? New Data and Coordinate-Based Meta-analysis.

A growing number of studies investigate brain anatomy in migraine using voxel- (VBM) and surface-based morphometry (SBM), as well as diffusion tensor imaging (DTI). The purpose of this article is to identify consistent patterns of anatomical alterations associated with migraine. First, 19 migraineurs without aura and 19 healthy participants were included in a brain imaging study. T1-weighted MRIs and DTI sequences were acquired and analyzed using VBM, SBM and tract-based spatial statistics. No significant alterations of gray matter (GM) volume, cortical thickness, cortical gyrification, sulcus depth and white-matter tract integrity could be observed. However, migraineurs displayed decreased white matter (WM) volume in the left superior longitudinal fasciculus. Second, a systematic review of the literature employing VBM, SBM and DTI was conducted to investigate brain anatomy in migraine. Meta-analysis was performed using Seed-based d Mapping via permutation of subject images (SDM-PSI) on GM volume, WM volume and cortical thickness data. Alterations of GM volume, WM volume, cortical thickness or white-matter tract integrity were reported in 72%, 50%, 56% and 33% of published studies respectively. Spatial distribution and direction of the disclosed effects were highly inconsistent across studies. The SDM-PSI analysis revealed neither significant decrease nor significant increase of GM volume, WM volume or cortical thickness in migraine. Overall there is to this day no strong evidence of specific brain anatomical alterations reliably associated to migraine. Possible explanations of this conflicting literature are discussed. Trial registration number: NCT02791997, registrated February 6th, 2015.

NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines.

Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses. Importantly, it provides shareable parameter files to facilitate replication of all analysis steps. NeuroPycon is based on the NiPype framework which facilitates data analyses by wrapping many commonly-used neuroimaging software tools into a common Python environment. In other words, rather than being a brain imaging software with is own implementation of standard algorithms for brain signal processing, NeuroPycon seamlessly integrates existing packages (coded in python, Matlab or other languages) into a unified python framework. Importantly, thanks to the multi-threaded processing and computational efficiency afforded by NiPype, NeuroPycon provides an easy option for fast parallel processing, which critical when handling large sets of multi-dimensional brain data. Moreover, its flexible design allows users to easily configure analysis pipelines by connecting distinct nodes to each other. Each node can be a Python-wrapped module, a user-defined function or a well-established tool (e.g. MNE-Python for MEG analysis, Radatools for graph theoretical metrics, etc.). Last but not least, the ability to use NeuroPycon parameter files to fully describe any pipeline is an important feature for reproducibility, as they can be shared and used for easy replication by others. The current implementation of NeuroPycon contains two complementary packages: The first, called ephypype, includes pipelines for electrophysiology analysis and a command-line interface for on the fly pipeline creation. Current implementations allow for MEG/EEG data import, pre-processing and cleaning by automatic removal of ocular and cardiac artefacts, in addition to sensor or source-level connectivity analyses. The second package, called graphpype, is designed to investigate functional connectivity via a wide range of graph-theoretical metrics, including modular partitions. The present article describes the philosophy, architecture, and functionalities of the toolkit and provides illustrative examples through interactive notebooks. NeuroPycon is available for download via github (https://github.com/neuropycon) and the two principal packages are documented online (https://neuropycon.github.io/ephypype/index.html, and https://neuropycon.github.io/graphpype/index.html). Future developments include fusion of multi-modal data (eg. MEG and fMRI or intracranial EEG and fMRI). We hope that the release of NeuroPycon will attract many users and new contributors, and facilitate the efforts of our community towards open source tool sharing and development, as well as scientific reproducibility.

A linear mass concentration detector for solvent gradient polymer separations.

Characterization of copolymers requires the measurement of two distributions-molecular weight (MW) and chemical composition (CC). Molecular weight distributions (MWD) are traditionally determined using size exclusion chromatography (SEC) run under isocratic solvent conditions. Chemical composition distributions (CCD) are often determined using liquid adsorption chromatography (LC) with solvent gradients. The use of solvent gradients, however, often limits options of compatible detectors. A gradient compatible, universal linear mass concentration detector is a longstanding unmet need. Many industrially-relevant polymers lack chromophores or other discriminating moieties requiring detectors with a universal response. Differential refractive index (dRI) is incompatible with gradient elution due to its small dynamic range. Charged aerosol detectors (CAD) and evaporative light scattering detectors (ELSD) are probably the most promising options for gradient elution detection, but both suffer from a nonlinear mass concentration response. Silicon photonic microring resonators are optical sensors that are responsive to changes in the local refractive index (RI). The substantial dynamic range of this technology makes it attractive for refractive index-based detection during solvent gradient elution. Previously, the microring resonator platform was used as a SEC detector to characterize the MWD of broadly dispersed polystyrene (PS) standards. In this study, we demonstrate the gradient compatibility of the microring resonator platform for polymer detection by quantifying the CCD of polymer blend components. Control experiments were run with UV and ELSD detection, highlighting the uniqueness of the platform as a linear mass concentration detector with a universal detector response.

Hard to wake up? The cerebral correlates of sleep inertia assessed using combined behavioral, EEG and fMRI measures.

The first minutes following awakening from sleep are typically marked by reduced vigilance, increased sleepiness and impaired performance, a state referred to as sleep inertia. Although the behavioral aspects of sleep inertia are well documented, its cerebral correlates remain poorly understood. The present study aimed at filling this gap by measuring in 34 participants the changes in behavioral performance (descending subtraction task, DST), EEG spectral power, and resting-state fMRI functional connectivity across three time points: before an early-afternoon 45-min nap, 5 min after awakening from the nap and 25 min after awakening. Our results showed impaired performance at the DST at awakening and an intrusion of sleep-specific features (spectral power and functional connectivity) into wakefulness brain activity, the intensity of which was dependent on the prior sleep duration and depth for the functional connectivity (14 participants awakened from N2 sleep, 20 from N3 sleep). Awakening in N3 (deep) sleep induced the most robust changes and was characterized by a global loss of brain functional segregation between task-positive (dorsal attention, salience, sensorimotor) and task-negative (default mode) networks. Significant correlations were observed notably between the EEG delta power and the functional connectivity between the default and dorsal attention networks, as well as between the percentage of mistake at the DST and the default network functional connectivity. These results highlight (1) significant correlations between EEG and fMRI functional connectivity measures, (2) significant correlations between the behavioral aspect of sleep inertia and measures of the cerebral functioning at awakening (both EEG and fMRI), and (3) the important difference in the cerebral underpinnings of sleep inertia at awakening from N2 and N3 sleep.

Silicon Photonic Microring Resonator Arrays for Mass Concentration Detection of Polymers in Isocratic Separations.

Molecular weight distribution (MWD) is often the most informative analytical parameter in polymer analysis, with gel permeation chromatography (GPC) being the most common approach for determining the MWD for polymer samples. Many industrially relevant polymers lack chromogenic or fluorogenic signatures, precluding use of spectroscopy-based detection. Universal detectors, such as evaporative light scattering and charged aerosol detectors, are nonlinear, limiting quantitative polymer analysis. Differential refractive index (dRI) detectors show linear mass concentration sensitivity but are limited for some analyses given that they are incompatible with gradient-based separations, have a limited dynamic range, and require extended thermal equilibration times. In this study, we investigated the utility of silicon photonic microring resonator arrays as a quantitative mass concentration detector for industrial polymer analysis. Microring resonators have optical properties that are sensitive to changes in refractive index, offer an extended dynamic range, have a broad solvent compatibility, and have a linear mass concentration detection for a range of molecular weights. Linear mass concentration detection for microrings was demonstrated through a series of isocratic GPC separations using narrow MWD polystyrene (PS) standards. This detection technology was then utilized in conjunction with conventional GPC detectors to analyze a series of broad MWD PS standards, with results in good agreement with dRI and UV/visible. These results demonstrate the potential of the microring resonator platform as a detector for industrial polymer analysis.

Could LC-NE-Dependent Adjustment of Neural Gain Drive Functional Brain Network Reorganization?

The locus coeruleus-norepinephrine (LC-NE) system is thought to act at synaptic, cellular, microcircuit, and network levels to facilitate cognitive functions through at least two different processes, not mutually exclusive. Accordingly, as a reset signal, the LC-NE system could trigger brain network reorganizations in response to salient information in the environment and/or adjust the neural gain within its target regions to optimize behavioral responses. Here, we provide evidence of the co-occurrence of these two mechanisms at the whole-brain level, in resting-state conditions following a pharmacological stimulation of the LC-NE system. We propose that these two mechanisms are interdependent such that the LC-NE-dependent adjustment of the neural gain inferred from the clustering coefficient could drive functional brain network reorganizations through coherence in the gamma rhythm. Via the temporal dynamic of gamma-range band-limited power, the release of NE could adjust the neural gain, promoting interactions only within the neuronal populations whose amplitude envelopes are correlated, thus making it possible to reorganize neuronal ensembles, functional networks, and ultimately, behavioral responses. Thus, our proposal offers a unified framework integrating the putative influence of the LC-NE system on both local- and long-range adjustments of brain dynamics underlying behavioral flexibility.

Modular structure of functional networks in olfactory memory.

Graph theory enables the study of systems by describing those systems as a set of nodes and edges. Graph theory has been widely applied to characterize the overall structure of data sets in the social, technological, and biological sciences, including neuroscience. Modular structure decomposition enables the definition of sub-networks whose components are gathered in the same module and work together closely, while working weakly with components from other modules. This processing is of interest for studying memory, a cognitive process that is widely distributed. We propose a new method to identify modular structure in task-related functional magnetic resonance imaging (fMRI) networks. The modular structure was obtained directly from correlation coefficients and thus retained information about both signs and weights. The method was applied to functional data acquired during a yes-no odor recognition memory task performed by young and elderly adults. Four response categories were explored: correct (Hit) and incorrect (False alarm, FA) recognition and correct and incorrect rejection. We extracted time series data for 36 areas as a function of response categories and age groups and calculated condition-based weighted correlation matrices. Overall, condition-based modular partitions were more homogeneous in young than elderly subjects. Using partition similarity-based statistics and a posteriori statistical analyses, we demonstrated that several areas, including the hippocampus, caudate nucleus, and anterior cingulate gyrus, belonged to the same module more frequently during Hit than during all other conditions. Modularity values were negatively correlated with memory scores in the Hit condition and positively correlated with bias scores (liberal/conservative attitude) in the Hit and FA conditions. We further demonstrated that the proportion of positive and negative links between areas of different modules (i.e., the proportion of correlated and anti-correlated areas) accounted for most of the observed differences in signed modularity. Taken together, our results provided some evidence that the neural networks involved in odor recognition memory are organized into modules and that these modular partitions are linked to behavioral performance and individual strategies.

Toward absolute chemical composition distribution measurement of polyolefins by high-temperature liquid chromatography hyphenated with infrared absorbance and light scattering detectors.

Chemical composition distribution (CCD) is a fundamental metric for representing molecular structures of copolymers in addition to molecular weight distribution (MWD). Solvent gradient interaction chromatography (SGIC) is commonly used to separate copolymers by chemical composition in order to obtain CCD. The separation of polymer in SGIC is, however, not only affected by chemical composition but also by molecular weight and architecture. The ability to measure composition and MW simultaneously after separation would be beneficial for understanding the impact of different factors and deriving true CCD. In this study, comprehensive two-dimensional chromatography (2D) was coupled with infrared absorbance (IR5) and light scattering (LS) detectors for characterization of ethylene-propylene copolymers. Polymers were first separated by SGIC as the first dimension chromatography (D1). The separated fractions were then characterized by the second dimension (D2) size exclusion chromatography (SEC) with IR5 and LS detectors. The concentrations and compositions of the separated fractions were measured online using the IR5 detector. The MWs of the fractions were measured by the ratio of LS to IR5 signals. A metric was derived from online concentration and composition data to represent CCD breadth. The metric was shown to be independent of separation gradients for an "absolute" measurement of CCD breadth. By combining online composition and MW data, the relationship of MW as a function of chemical composition was obtained. This relationship was qualitatively consistent with the results by SEC coupled to IR5, which measures chemical composition as a function of logMW. The simultaneous measurements of composition and MW give the opportunity to study the SGIC separation mechanism and derive chain architectural characteristics of polymer chains.

Distinct brain networks for remote episodic memory depending on content and emotional experience.

Memories of life episodes are the heart of individual stories. However, modelling episodic memory is a major challenge in both humans and animals when considering all its characteristics. As a consequence, the mechanisms that underlie the storage of old nontraumatic episodic memories remain enigmatic. Here, using a new task in rodents that models human episodic memory including odour/place/context components and applying advances behavioural and computational analyses, we show that rats form and recollect integrated remote episodic memories of two occasionally encountered complex episodes occurring in their daily life. Similar to humans, the information content and accuracy of memories vary across individuals and depend on the emotional relationship with odours experienced during the very first episode. We used cellular brain imaging and functional connectivity analyses, to find out the engrams of remote episodic memories for the first time. Activated brain networks completely reflect the nature and content of episodic memories, with a larger cortico-hippocampal network when the recollection is complete and with an emotional brain network related to odours that is critical in maintaining accurate and vivid memories. The engrams of remote episodic memories remain highly dynamic since synaptic plasticity processes occur during recall related to memory updates and reinforcement.

Genetic influences on cost-efficient organization of human cortical functional networks.

The human cerebral cortex is a complex network of functionally specialized regions interconnected by axonal fibers, but the organizational principles underlying cortical connectivity remain unknown. Here, we report evidence that one such principle for functional cortical networks involves finding a balance between maximizing communication efficiency and minimizing connection cost, referred to as optimization of network cost-efficiency. We measured spontaneous fluctuations of the blood oxygenation level-dependent signal using functional magnetic resonance imaging in healthy monozygotic (16 pairs) and dizygotic (13 pairs) twins and characterized cost-efficient properties of brain network functional connectivity between 1041 distinct cortical regions. At the global network level, 60% of the interindividual variance in cost-efficiency of cortical functional networks was attributable to additive genetic effects. Regionally, significant genetic effects were observed throughout the cortex in a largely bilateral pattern, including bilateral posterior cingulate and medial prefrontal cortices, dorsolateral prefrontal and superior parietal cortices, and lateral temporal and inferomedial occipital regions. Genetic effects were stronger for cost-efficiency than for other metrics considered, and were more clearly significant in functional networks operating in the 0.09-0.18 Hz frequency interval than at higher or lower frequencies. These findings are consistent with the hypothesis that brain networks evolved to satisfy competitive selection criteria of maximizing efficiency and minimizing cost, and that optimization of network cost-efficiency represents an important principle for the brain's functional organization.

Brain functional connectivity in stimulant drug dependence and obsessive-compulsive disorder.

There are reasons for thinking that obsessive-compulsive disorder (OCD) and drug dependence, although conventionally distinct diagnostic categories, might share important cognitive and neurobiological substrates. We tested this hypothesis directly by comparing brain functional connectivity measures between patients with OCD, stimulant dependent individuals (SDIs; many of whom were non-dependent users of other recreational drugs) and healthy volunteers. We measured functional connectivity between each possible pair of 506 brain regional functional MRI time series representing low frequency (0.03-0.06 Hz) spontaneous brain hemodynamics in healthy volunteers (N=18), patients with OCD (N=18) and SDIs (N=18). We used permutation tests to identify i) brain regions where strength of connectivity was significantly different in both patient groups compared to healthy volunteers; and ii) brain regions and connections which had significantly different functional connectivity between patient groups. We found that functional connectivity of right inferior and superior orbitofrontal cortex (OFC) was abnormally reduced in both disorders. Whether diagnosed as OCD or SDI, patients with higher scores on measures of compulsive symptom severity showed greater reductions of right orbitofrontal connectivity. Functional connections specifically between OFC and dorsal medial pre-motor and cingulate cortex were attenuated in both patient groups. However, patients with OCD demonstrated more severe and extensive reductions of functional connectivity compared to SDIs. OCD and stimulant dependence are not identical at the level of brain functional systems but they have some important abnormalities in common compared with healthy volunteers. Orbitofrontal connectivity may serve as a human brain systems biomarker for compulsivity across diagnostic categories.

Sex differences in brain modular organization in chronic pain.

ABSTRACT: Men and women can exhibit different pain sensitivities, and many chronic pain conditions are more prevalent in one sex. Although there is evidence of sex differences in the brain, it is not known whether there are sex differences in the organization of large-scale functional brain networks in chronic pain. Here, we used graph theory with modular analysis and machine-learning of resting-state-functional magnetic resonance imaging data from 220 participants: 155 healthy controls and 65 individuals with chronic low back pain due to ankylosing spondylitis, a form of arthritis. We found an extensive overlap in the graph partitions with the major brain intrinsic systems (ie, default mode, central, visual, and sensorimotor modules), but also sex-specific network topological characteristics in healthy people and those with chronic pain. People with chronic pain exhibited higher cross-network connectivity, and sex-specific nodal graph properties changes (ie, hub disruption), some of which were associated with the severity of the chronic pain condition. Females exhibited atypically higher functional segregation in the mid cingulate cortex and subgenual anterior cingulate cortex and lower connectivity in the network with the default mode and frontoparietal modules, whereas males exhibited stronger connectivity with the sensorimotor module. Classification models on nodal graph metrics could classify an individual's sex and whether they have chronic pain with high accuracies (77%-92%). These findings highlight the organizational abnormalities of resting-state-brain networks in people with chronic pain and provide a framework to consider sex-specific pain therapeutics.

The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG.

Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via "connector hubs" in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

Age-related changes in modular organization of human brain functional networks.

Graph theory allows us to quantify any complex system, e.g., in social sciences, biology or technology, that can be abstractly described as a set of nodes and links. Here we derived human brain functional networks from fMRI measurements of endogenous, low frequency, correlated oscillations in 90 cortical and subcortical regions for two groups of healthy (young and older) participants. We investigated the modular structure of these networks and tested the hypothesis that normal brain aging might be associated with changes in modularity of sparse networks. Newman's modularity metric was maximised and topological roles were assigned to brain regions depending on their specific contributions to intra- and inter-modular connectivity. Both young and older brain networks demonstrated significantly non-random modularity. The young brain network was decomposed into 3 major modules: central and posterior modules, which comprised mainly nodes with few inter-modular connections, and a dorsal fronto-cingulo-parietal module, which comprised mainly nodes with extensive inter-modular connections. The mean network in the older group also included posterior, superior central and dorsal fronto-striato-thalamic modules but the number of intermodular connections to frontal modular regions was significantly reduced, whereas the number of connector nodes in posterior and central modules was increased.

Generic aspects of complexity in brain imaging data and other biological systems.

A key challenge for systems neuroscience is the question of how to understand the complex network organization of the brain on the basis of neuroimaging data. Similar challenges exist in other specialist areas of systems biology because complex networks emerging from the interactions between multiple non-trivially interacting agents are found quite ubiquitously in nature, from protein interactomes to ecosystems. We suggest that one way forward for analysis of brain networks will be to quantify aspects of their organization which are likely to be generic properties of a broader class of biological systems. In this introductory review article we will highlight four important aspects of complex systems in general: fractality or scale-invariance; criticality; small-world and related topological attributes; and modularity. For each concept we will provide an accessible introduction, an illustrative data-based example of how it can be used to investigate aspects of brain organization in neuroimaging experiments, and a brief review of how this concept has been applied and developed in other fields of biomedical and physical science. The aim is to provide a didactic, focussed and user-friendly introduction to the concepts of complexity science for neuroscientists and neuroimagers.

Molecular weight distribution characterization of reactive higher ethyleneamines using size-exclusion chromatography with conventional calibration.

Ethyleneamines have been produced and commercialized for decades in the chemical industry for a diverse range of applications. The presence of amine functional groups provides them opportunity to adsorb onto surfaces which can make them a very challenging sample matrix to analyze using separation techniques. In the present report, a new aqueous SEC-RI method, which enables MWD characterization of higher ethyleneamines, is described. The sample preparation was based on the dilute-and-shoot methodology. A surface-modified SEC column with positively charged groups attached to the stationary phase was used. The mobile phase composition (salt concentration, pH) was optimized to suppress interaction between the ethyleneamines and the packing material. Very symmetrical peak shapes were achieved for low MW monodisperse ethyleneamines despite their high primary amine content. MWD calculations were conducted using conventional narrow standard calibration with partial linear extrapolation of the calibration curve. The narrow standards were of the same chemistry as the samples of interest. Consequently, the standard components display a consistent behaviour towards the column packing as the sample components which makes the present method more robust and the interpretation of the quantitative results more convenient. Effect on the measured MW averages and MW distribution due to various experimental parameters (e.g., system variability, mobile phase preparation, sample concentration) were investigated showing good repeatability (RSD < 2%) for Mn, Mw, and Mz.

Visbrain: A Multi-Purpose GPU-Accelerated Open-Source Suite for Multimodal Brain Data Visualization.

We present Visbrain, a Python open-source package that offers a comprehensive visualization suite for neuroimaging and electrophysiological brain data. Visbrain consists of two levels of abstraction: (1) objects which represent highly configurable neuro-oriented visual primitives (3D brain, sources connectivity, etc.) and (2) graphical user interfaces for higher level interactions. The object level offers flexible and modular tools to produce and automate the production of figures using an approach similar to that of Matplotlib with subplots. The second level visually connects these objects by controlling properties and interactions through graphical interfaces. The current release of Visbrain (version 0.4.2) contains 14 different objects and three responsive graphical user interfaces, built with PyQt: Signal, for the inspection of time-series and spectral properties, Brain for any type of visualization involving a 3D brain and Sleep for polysomnographic data visualization and sleep analysis. Each module has been developed in tight collaboration with end-users, i.e., primarily neuroscientists and domain experts, who bring their experience to make Visbrain as transparent as possible to the recording modalities (e.g., intracranial EEG, scalp-EEG, MEG, anatomical and functional MRI). Visbrain is developed on top of VisPy, a Python package providing high-performance 2D and 3D visualization by leveraging the computational power of the graphics card. Visbrain is available on Github and comes with a documentation, examples, and datasets (http://visbrain.org).

Conversion of calibration curves for accurate estimation of molecular weight averages and distributions of polyether polyols by conventional size exclusion chromatography.

Accurate measurement of molecular weight averages (M¯n,M¯w,M¯z) and molecular weight distributions (MWD) of polyether polyols by conventional SEC (size exclusion chromatography) is not as straightforward as it would appear. Conventional calibration with polystyrene (PS) standards can only provide PS apparent molecular weights which do not provide accurate estimates of polyol molecular weights. Using polyethylene oxide/polyethylene glycol (PEO/PEG) for molecular weight calibration could improve the accuracy, but the retention behavior of PEO/PEG is not stable in THF-based (tetrahydrofuran) SEC systems. In this work, two approaches for calibration curve conversion with narrow PS and polyol molecular weight standards were developed. Equations to convert PS-apparent molecular weight to polyol-apparent molecular weight were developed using both a rigorous mathematical analysis and graphical plot regression method. The conversion equations obtained by the two approaches were in good agreement. Factors influencing the conversion equation were investigated. It was concluded that the separation conditions such as column batch and operating temperature did not have significant impact on the conversion coefficients and a universal conversion equation could be obtained. With this conversion equation, more accurate estimates of molecular weight averages and MWDs for polyether polyols can be achieved from conventional PS-THF SEC calibration. Moreover, no additional experimentation is required to convert historical PS equivalent data to reasonably accurate molecular weight results.