Identification of <scp>l</scp>‐Tryptophan by down‐field ¹H MRS: A precursor for brain NAD⁺ and serotonin syntheses

Magnetic Resonance in Medicine · 2022 · 14 citations

• Chemistry
• Physics
• Biology

Purpose To explore the presence of new resonances beyond 9.4 ppm from the human brain, down‐field proton MRS was performed in vivo in the human brain on 6 healthy volunteers at 7 T. Methods To maximize the SNR, a large voxel was placed within the brain to cover the maximal area in such a way that sinus cavities were avoided. A spectrally selective 90° E‐BURP pulse with an excitation bandwidth of 2 ppm was used to probe the spectral chemical shift range between 9.1 and 10.5 ppm. The E‐BURP pulse was integrated with PRESS spatial localization to obtain non‐water‐suppressed proton MR spectra from the desired spectral region. Results In the down‐field proton MRS obtained from all of the volunteers scanned, we identified a new peak consistently resonating at 10.1 ppm. Protons associated with this resonance are in cross‐relaxation with the bulk water, as demonstrated by the water saturation and deuterium exchange experiments. Conclusion Based on the chemical shift, this new peak was identified as the indole (–NH) proton of l ‐tryptophan ( l ‐TRP) and was further confirmed from phantom experiments on l ‐TRP. These promising preliminary results potentially pave the way to investigate the role of cerebral metabolism of l ‐TRP in healthy and disease conditions.

QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data

Nature Methods · 2021 · 315 citations

• Computer Science
• Computer Science
• Data Mining

QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI

bioRxiv (Cold Spring Harbor Laboratory) · 2020 · 25 citations

• Computer Science
• Computer Science
• Artificial Intelligence

ABSTRACT Diffusion-weighted magnetic resonance imaging (dMRI) has become the primary method for non-invasively studying the organization of white matter in the human brain. While many dMRI acquisition sequences have been developed, they all sample q-space in order to characterize water diffusion. Numerous software platforms have been developed for processing dMRI data, but most work on only a subset of sampling schemes or implement only parts of the processing workflow. Reproducible research and comparisons across dMRI methods are hindered by incompatible software, diverse file formats, and inconsistent naming conventions. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing upon a diverse set of software suites to capitalize upon their complementary strengths, QSIPrep automatically applies best practices for dMRI preprocessing, including denoising, distortion correction, head motion correction, coregistration, and spatial normalization. Throughout, QSIPrep provides both visual and quantitative measures of data quality as well as “glass-box” methods reporting. Taken together, these features facilitate easy implementation of best practices for processing of diffusion images while simultaneously ensuring reproducibility.

Structural and Functional Brain Parameters Related to Cognitive Performance Across Development: Replication and Extension of the Parieto-Frontal Integration Theory in a Single Sample

Cerebral Cortex · 2020 · 34 citations

• Psychology
• Audiology
• Neuroscience

The parieto-frontal integration theory (PFIT) identified a fronto-parietal network of regions where individual differences in brain parameters most strongly relate to cognitive performance. PFIT was supported and extended in adult samples, but not in youths or within single-scanner well-powered multimodal studies. We performed multimodal neuroimaging in 1601 youths age 8-22 on the same 3-Tesla scanner with contemporaneous neurocognitive assessment, measuring volume, gray matter density (GMD), mean diffusivity (MD), cerebral blood flow (CBF), resting-state functional magnetic resonance imaging measures of the amplitude of low frequency fluctuations (ALFFs) and regional homogeneity (ReHo), and activation to a working memory and a social cognition task. Across age and sex groups, better performance was associated with higher volumes, greater GMD, lower MD, lower CBF, higher ALFF and ReHo, and greater activation for the working memory task in PFIT regions. However, additional cortical, striatal, limbic, and cerebellar regions showed comparable effects, hence PFIT needs expansion into an extended PFIT (ExtPFIT) network incorporating nodes that support motivation and affect. Associations of brain parameters became stronger with advancing age group from childhood to adolescence to young adulthood, effects occurring earlier in females. This ExtPFIT network is developmentally fine-tuned, optimizing abundance and integrity of neural tissue while maintaining a low resting energy state.

Leveraging multi-shell diffusion for studies of brain development in youth and young adulthood

Developmental Cognitive Neuroscience · 2020 · 93 citations

• Artificial Intelligence
• Computer Science
• Psychology

Diffusion weighted imaging (DWI) has advanced our understanding of brain microstructure evolution over development. Recently, the use of multi-shell diffusion imaging sequences has coincided with advances in modeling the diffusion signal, such as Neurite Orientation Dispersion and Density Imaging (NODDI) and Laplacian-regularized Mean Apparent Propagator MRI (MAPL). However, the relative utility of recently-developed diffusion models for understanding brain maturation remains sparsely investigated. Additionally, despite evidence that motion artifact is a major confound for studies of development, the vulnerability of metrics derived from contemporary models to in-scanner motion has not been described. Accordingly, in a sample of 120 youth and young adults (ages 12-30) we evaluated metrics derived from diffusion tensor imaging (DTI), NODDI, and MAPL for associations with age and in-scanner head motion at multiple scales. Specifically, we examined mean white matter values, white matter tracts, white matter voxels, and connections in structural brain networks. Our results revealed that multi-shell diffusion imaging data can be leveraged to robustly characterize neurodevelopment, and demonstrate stronger age effects than equivalent single-shell data. Additionally, MAPL-derived metrics were less sensitive to the confounding effects of head motion. Our findings suggest that multi-shell imaging data and contemporary modeling techniques confer important advantages for studies of neurodevelopment.