Biology subjects

Landheer, K.

Publications and source records attributed to Landheer, K..

4 recordsLinked to original sources

bioRxiv2025

Resolving Cellular Morphology in the Human Brain with Multiparametric Diffusion MR Spectroscopy

Diffusion-weighted magnetic resonance spectroscopy (dMRS) noninvasively probes the diffusion of mostly intracellular metabolites and can therefore report on brain microstructure with a cell-type specificity that water-based diffusion MRI cannot achieve. However, the morphological information accessible to conventional dMRS is limited: estimating both cell-body (soma) and neurite dimensions from a single diffusion-encoding scheme is an ill-posed problem, and neither ultra-high b-value nor diffusion-time-dependent measurements alone distinguish soma size from neurite radius. Here we introduce multiparametric dMRS in the human brain, combining diffusion-time- dependent encoding (apparent diffusion coefficient, ADC, and diffusion kurtosis, K, sampled over diffusion times of 6 ms to 250 ms and b-values up to 8 ms {micro}m-2) with double-diffusion-encoded spectroscopy (DDES). Using Monte-Carlo analysis, we show that fitting a two-compartment (soma + neurite) tissue model to diffusion-time data alone is degenerate, admitting two near-indistinguishable solutions. Adding the orthogonal angular information from DDES breaks this degeneracy: jointly fitting both experiments converges to a single, biophysically plausible solution irrespective of initialization, yielding cell-type- specific estimates of intrinsic diffusivity, soma radius, neurite radius, and neurite signal fraction for neuronal (NAA, glutamate) and glial (choline, myo-inositol) metabolites. Metabolite microscopic anisotropy approaches unity, consistent with predominantly intra-neurite diffusion, while simultaneously acquired water data reveal short-range structural disorder and intercompartmental exchange ({approx}15 ms). Multiparametric dMRS thus extends the standard model of metabolite diffusion by a soma compartment and offers a route toward in vivo, cell-type- specific morphometry of neurons and glia in humans--a foundation for biomarkers in conditions where soma and neurite morphology are altered.

neuroscience↗

bioRxiv2025

The Consequences of Statistical Tests on Using Proxy Measurements in Place of Gold Standard Measurements: An Application to Magnetic Resonance Spectroscopy

The use of proxy measurements in biomedical science is ubiquitous, due to the infeasibility or unavailability of gold-standard (i.e., most precise, accurate, and/or validated) measurements. For example, in magnetic resonance spectroscopy (MRS), short-echo time (TE) sequences are frequently employed to estimate difficult-to-measure metabolites such as GABA, despite J-difference editing being the recommended gold-standard for improved metabolic specificity. This work investigates the critical relationship between the correlation of proxy and gold-standard measurements and the associated false positive (FPR) and false negative (FNR) rates of statistical tests performed on proxy measurements. Through statistical simulations, we demonstrate that even moderately high correlations (0.6-0.7), reported in the literature for short-TE vs. J-edited estimated GABA, can lead to drastically inflated FPRs and FNRs. We show that these rates are highly sensitive to the magnitude of any introduced bias in the proxy measurement ({delta}) and the underlying true effect size ({Delta}). For instance, a small, unmeasured bias in short-TE estimated GABA, potentially arising from macromolecule contamination, can substantially inflate FPRs. Conversely, imperfect correlation can significantly reduce statistical power, leading to high FNRs, which may explain some discrepancies within the literature. Although this work focuses specifically on the relationship between short-TE and MEGA-edited GABA, the arguments presented here apply more broadly to other difficult-to-measure metabolites in MRS (e.g., glutathione, 2-hydroxyglutarate), or generally to any circumstance where statistical tests are performed on the readily available proxy measurements in place of gold-standard measurements.

bioengineering↗

bioRxiv2025

Metabolomics Ex/GWAS in the Amish reveals novel insights into cardiometabolic disease pathways

We conducted an exome and genome-wide association study (Ex/GWAS) of 1,015 metabolites in serum samples from 5,981 Amish adults. We identified 149 functional or likely functional genetic variants, based on CADD scores significantly associated (P < 5.8 x 10-8) with 519 metabolite levels, 69 of which were >10-fold enriched in the Amish versus Europeans in gnomAD. We discovered novel associations involving a PCK2 splice-donor variant (rs138881435) and metabolites important for energy metabolism and mitochondrial function. In UK Biobank participants, this variant was associated with increased longitudinal relaxation time (T1) values on liver MRI suggesting increased inflammation and fibrosis. Furthermore, we found novel associations involving ENPEP stop-gain variant (rs33966350) and peptide and amino acid metabolite levels, implicating ENPEPs roles in blood pressure regulation. Additional known and novel genetic variant-metabolite associations were identified including genes involved in Amish-enriched Mendelian diseases and cardiometabolic traits, providing insights into underlying mechanisms of these disorders.

genomics↗

bioRxiv2025

Benchmarking the Impact of Anatomical Segmentation on In Vivo Magnetic Resonance Spectroscopy

PurposeEstimation of metabolite concentrations in brain magnetic resonance spectroscopy (MRS) requires correction for differences in tissue water content, relaxation properties, and the proportions of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Accurate knowledge of the relative proportions of these tissue classes within the volume of interest is therefore essential for reliable quantification. Commonly used brain segmentation tools differ in their algorithms, priors, and implementation, potentially introducing variability in MRS-derived concentration estimates. This study investigates the impact of segmentation software on estimated absolute concentrations. MethodsThree segmentation software tools, ANTs, FSL, and SPM, were evaluated. Segmentations were applied to an in vivo test-retest MR dataset to assess (1) differences in estimated tissue fractions, and (2) how these differences propagate into tissue-corrected metabolite concentrations. As an additional validity check and biological benchmark of segmentation performance, age-related associations with GM and total creatine (tCr) were examined. ResultsSignificant differences (p < 0.0001) were observed in tissue fraction estimates between segmentation tools, leading to differences in metabolite concentration estimates of up to 9% under identical acquisition and modeling conditions. Although the strength of the correlation varied between segmentation methods, no statistically significant differences were found. ConclusionThe choice of segmentation methodology contributed substantially to variability in MRS "absolute" metabolite concentration estimates. These results underscore the need for transparent segmentation reporting to ensure reproducibility and cross-study comparability in MRS research. Quantifying the segmentation-driven variability allows researchers to contextualize cross-study differences, helping determine whether observed effects are methodological or biologically meaningful.

neuroscience↗