Publications by Year: 2021

The B-SNIP consortium identified three brain-based Biotypes across the psychosis spectrum, independent of clinical phenomenology. To externally validate the Biotype model, we used free-water fractional volume (FW) and free-water corrected fractional anisotropy (FA) to compare white matter differences across Biotypes and clinical diagnoses. Diffusion tensor imaging data from 167 individuals were included: 41 healthy controls, 55 schizophrenia probands, 47 schizoaffective disorder probands, and 24 probands with psychotic bipolar disorder. Compared to healthy controls, FAt reductions were observed in the body of corpus callosum (BCC) for schizoaffective disorder (d = 0.91) and schizophrenia (d = 0.64). Grouping by Biotype, Biotype 1 showed FAt reductions in the CC and fornix, with largest effect in the BCC (d = 0.87). Biotype 2 showed significant FAt reductions in the BCC (d = 0.90). Schizoaffective disorder individuals had elevated FW in the CC, fornix and anterior corona radiata (ACR), with largest effect in the BCC (d = 0.79). Biotype 2 showed elevated FW in the CC, fornix and ACR, with largest effect in the BCC (d = 0.94). While significant diagnosis comparisons were observed, overall greater discrimination from healthy controls was observed for lower FAt in Biotype 1 and elevated FW in Biotype 2. However, between-group differences were modest, with one region (cerebral peduncle) showing a between-Biotype effect. No between-group effects were observed for diagnosis groupings.

In the present work, an amphiphilic and magnetically recyclable graphene oxide (MR-GO) demulsifier was devised and synthesized by graft of magnetic nanoparticles (FeO@SiO-APTES) and ethylenediamine on the GO surface. The wettability and surface charges of MR-GO under various pH conditions can be regulated via adjusting the contents and species of surface functional groups (such as amino, carboxyl, and hydroxyl). In the demulsificaition test, MR-GO displayed favorable demulsification performance for crude oil-in-water (O/W) emulsion under pH of 2.0-10.0, thusly greatly improving the application scope of common demulsifier. The optimal dosage of MR-GO was 200 mg/L and the demulsification efficiency attained a maximum value of 99.7% for crude O/W emulsion with pH of 6.0. What’s more, owing to its magnetic response performance, the MR-GO can be reused and the demulsification efficiency remained above 91.0% after six cycles. Based on the strong interfacial activity, MR-GO can arrive to the crude oil-water interface. With the synergy effects of interfacial adsorption (π-π/n-π) interactions and electrostatic attraction of demulsifier and interfacial films, and the aid of external mechanical forces, the interfacial films stabilized the emulsion were disrupted. Therefore, the oil droplets coated on the water droplets were gathered rapidly to form oily flocs and then migrated to the water surface to accomplish the demulsification of crude O/W emulsion.

Probing the cellular structure of in vivo biological tissue is a fundamental problem in biomedical imaging and medical science. This work introduces an approach for analyzing diffusion magnetic resonance imaging data acquired by the novel tensor-valued encoding technique for characterizing tissue microstructure. Our approach first uses a signal model to estimate the variance and skewness of the distribution of apparent diffusion tensors modeling the underlying tissue. Then several novel imaging indices, such as weighted microscopic anisotropy and microscopic skewness, are derived to characterize different ensembles of diffusion processes that are indistinguishable by existing techniques. The contributions of this work also include a theoretical proof that shows that, to estimate the skewness of a diffusion tensor distribution, the encoding protocol needs to include full-rank tensor diffusion encoding. This proof provides a guideline for the application of this technique. The properties of the proposed indices are illustrated using both synthetic data and in vivo data acquired from a human brain.

Diffusion kurtosis imaging (DKI) is a diffusion MRI approach that enables the measurement of brain microstructural properties, reflecting molecular restrictions and tissue heterogeneity. DKI parameters such as mean kurtosis (MK) provide additional subtle information to that provided by popular diffusion tensor imaging (DTI) parameters, and thus have been considered useful to detect white matter abnormalities, especially in populations that are not expected to show severe brain pathologies. However, DKI parameters often yield artifactual output values that are outside of the biologically plausible range, which diminish sensitivity to identify true microstructural changes. Recently we have proposed the mean-kurtosis-curve (MK-Curve) method to correct voxels with implausible DKI parameters, and demonstrated its improved performance against other approaches that correct artifacts in DKI. In this work, we aimed to evaluate the utility of the MK-Curve method to improve the identification of white matter abnormalities in group comparisons. To do so, we compared group differences, with and without the MK-Curve correction, between 115 individuals at clinical high risk for psychosis (CHR) and 93 healthy controls (HCs). We also compared the correlation of the corrected and uncorrected DKI parameters with clinical characteristics. Following the MK-curve correction, the group differences had larger effect sizes and higher statistical significance (i.e., lower p-values), demonstrating increased sensitivity to detect group differences, in particular in MK. Furthermore, the MK-curve-corrected DKI parameters displayed stronger correlations with clinical variables in CHR individuals, demonstrating the clinical relevance of the corrected parameters. Overall, following the MK-curve correction our analyses found widespread lower MK in CHR that overlapped with lower fractional anisotropy (FA), and both measures were significantly correlated with a decline in functioning and with more severe symptoms. These observations further characterize white matter alterations in the CHR stage, demonstrating that MK and FA abnormalities are widespread, and mostly overlap. The improvement in group differences and stronger correlation with clinical variables suggest that applying MK-curve would be beneficial for the detection and characterization of subtle group differences in other experiments as well.

PURPOSE: To estimate for each distinct fiber population within voxels containing multiple brain tissue types. METHODS: A diffusion- correlation experiment was carried out in an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions of diffusion tensors and longitudinal relaxation rates . Orientation distribution functions (ODFs) of the highly anisotropic components of were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white matter fiber bundles. RESULTS: Parameter maps corresponding to ’s statistical descriptors were obtained, exhibiting the expected contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in between major crossing fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup. CONCLUSIONS: Our Monte Carlo framework enables the nonparametric estimation of fiber-specific diffusion- features, thereby showing potential for characterizing developmental or pathological changes in within a given fiber bundle, and for investigating interbundle differences.

Free-water imaging is a diffusion MRI technique that separately models water diffusion hindered by fiber tissue and water that disperses freely in the extracellular space. Studies using this technique have shown that schizophrenia is characterized by a lower level of fractional anisotropy of the tissue compartment (FA) and higher free-water fractional volume (FW). It is unknown, however, whether such abnormalities are an expression of pre-existing (genetic) risk for schizophrenia or a manifestation of the illness. To investigate the contribution of familial risk factors to white matter abnormalities, we used the free-water imaging technique to assess FA and FW in a large cohort of 471 participants including 161 patients with schizophrenia, 182 non-psychotic siblings, and 128 healthy controls. In this sample, patients did not show significant differences in FA as compared to controls, but did exhibit a higher level of FW relative to both controls and siblings in the left uncinate fasciculus, superior corona radiata and fornix / stria terminalis. This increase in FW was found to be related to, though not solely explained by, ventricular enlargement. Siblings did not show significant FW abnormalities. However, siblings did show a higher level of FA as compared to controls and patients, in line with results of a previous study on the same data using conventional DTI. Taken together, our findings suggest that extracellular free-water accumulation in patients is likely a manifestation of established disease rather than an expression of familial risk for schizophrenia and that super-normal levels of FA in unaffected siblings may reflect a compensatory process.

White matter (WM) abnormalities are repeatedly demonstrated across the schizophrenia time-course. However, our understanding of how demographic and clinical variables interact, influence, or are dependent on WM pathologies is limited. The most well-known barriers to progress are heterogeneous findings due to small sample sizes and the confounding influence of age on WM. The present study leverages access to the harmonized diffusion magnetic-resonance-imaging data and standardized clinical data from 13 international sites (597 schizophrenia patients (SCZ)). Fractional anisotropy (FA) values for all major WM structures in patients were predicted based on FA models estimated from a healthy population (n = 492). We utilized the deviations between predicted and real FA values to answer three essential questions. (1) "Which clinical variables explain WM abnormalities?". (2) "Does the degree of WM abnormalities predict symptom severity?". (3) "Does sex influence any of those relationships?". Regression and mediator analyses revealed that a longer duration-of-illness is associated with more severe WM abnormalities in several tracts. In addition, they demonstrated that a higher antipsychotic medication dose is related to more severe corpus callosum abnormalities. A structural equation model revealed that patients with more WM abnormalities display higher symptom severity. Last, the results exhibited sex-specificity. Males showed a stronger association between duration-of-illness and WM abnormalities. Females presented a stronger association between WM abnormalities and symptom severity, with IQ impacting this relationship. Our findings provide clear evidence for the interaction of demographic, clinical, and behavioral variables with WM pathology in SCZ. Our results also point to the need for longitudinal studies, directly investigating the casualty and sex-specificity of these relationships, as well as the impact of cognitive resiliency on structure-function relationships.

We examined the impact of treatment with fish oil (FO), a rich source of omega-3 polyunsaturated fatty acids (n-3 PUFA), on white matter in 37 recent-onset psychosis patients receiving risperidone in a double-blind placebo-controlled randomized clinical trial. Patients were scanned at baseline and randomly assigned to receive 16-weeks of treatment with risperidone + FO or risperidone + placebo. Eighteen patients received follow-up MRIs (FO, n = 10/Placebo, n = 8). Erythrocyte levels of n-3 PUFAs eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and docosapentaenoic acid (DPA) were obtained at both time points. We employed Free Water Imaging metrics representing the extracellular free water fraction (FW) and fractional anisotropy of the tissue (FA-t). Analyses were conducted using Tract-Based-Spatial-Statistics and nonparametric permutation-based tests with family-wise error correction. There were significant positive correlations of FA-t with DHA and DPA among all patients at baseline. Patients treated with risperidone + placebo demonstrated reductions in FA-t and increases in FW, whereas patients treated with risperidone + FO exhibited no significant changes in FW and FA-t reductions were largely attenuated. The correlations of DPA and DHA with baseline FA-t support the hypothesis that n-3 PUFA intake or biosynthesis are associated with white matter abnormalities in psychosis. Adjuvant FO treatment may partially mitigate against white matter alterations observed in recent-onset psychosis patients following risperidone treatment.

BACKGROUND AND OBJECTIVE: This paper proposes a new and highly efficient implementation of 3D+t groupwise registration based on the free-form deformation paradigm. METHODS: Deformation is posed as a cascade of 1D convolutions, achieving great reduction in execution time for evaluation of transformations and gradients. RESULTS: The proposed method has been applied to 4D cardiac MRI and 4D thoracic CT monomodal datasets. Results show an average runtime reduction above 90%, both in CPU and GPU executions, compared with the classical tensor product formulation. CONCLUSIONS: Our implementation, although fully developed for the metric sum of squared differences, can be extended to other metrics and its adaptation to multiresolution strategies is straightforward. Therefore, it can be extremely useful to speed up image registration procedures in different applications where high dimensional data are involved.

Diffusion encoding along multiple spatial directions per signal acquisition can be described in terms of a b-tensor. The benefit of tensor-valued diffusion encoding is that it unlocks the ’shape of the b-tensor’ as a new encoding dimension. By modulating the b-tensor shape, we can control the sensitivity to microscopic diffusion anisotropy which can be used as a contrast mechanism; a feature that is inaccessible by conventional diffusion encoding. Since imaging methods based on tensor-valued diffusion encoding are finding an increasing number of applications we are prompted to highlight the challenge of designing the optimal gradient waveforms for any given application. In this review, we first establish the basic design objectives in creating field gradient waveforms for tensor-valued diffusion MRI. We also survey additional design considerations related to limitations imposed by hardware and physiology, potential confounding effects that cannot be captured by the b-tensor, and artifacts related to the diffusion encoding waveform. Throughout, we discuss the expected compromises and tradeoffs with an aim to establish a more complete understanding of gradient waveform design and its impact on accurate measurements and interpretations of data.