Within the Alzheimer disease (AD) spectrum, metabolic alterations occur in addition to proteinopathies. While hypometabolism is frequently observed in the late symptomatic stages, characterizing the metabolic changes during early AD stages might aid in both understanding its pathophysiology and in patient selection for future treatments. In this study, we conduct an exploratory, data-driven analysis aimed at better understanding the timing and specificity of the metabolic changes in early AD. Using kinetic analysis of [18F]FDG PET brain imaging data coming from 223 individuals, including 33 preclinical individuals and 19 symptomatic individuals, we find new evidence of a more complex spatiotemporal trajectory of glucose metabolism in early AD, which includes a paradoxical regional increase in glucose phosphorylation during preclinical AD. These findings suggest that the pathologic phases of AD parallel changes in brain glucose metabolism, which is readily assessable with [18F]FDG PET imaging. Moreover, they may indicate that metabolic interventions may work differently during preclinical AD as compared to the early symptomatic phase.

Intrinsic brain activity is characterized by pervasive long-range temporal correlations. While these scale-invariant dynamics are a fundamental hallmark of brain function, their implications for individual-level metabolic regulation remain poorly understood. Here, we address this gap by integrating resting-state functional Magnetic Resonance Imaging (fMRI) and dynamic [18F]FDG Positron Emission Tomography (PET) data acquired from the same cohort of participants. We uncover a systematic relationship between long-range temporal correlations, quantified via the Hurst exponent, and glucose metabolism. Our findings reveal that persistent temporal dependencies impose a measurable metabolic cost, with brains exhibiting higher long-range temporal correlations incurring greater energetic demands. Beyond glucose metabolism, we also show that these dynamics are likely supported by continuous biosynthetic processes, such as protein synthesis, which are critical for neural circuit maintenance and remodeling. Overall, our results suggest that a significant fraction of the brain's so-called "Dark Energy" is actively spent to power spontaneous long-range temporal correlations.

Cerebral glucose metabolism and cortical morphology are known to undergo significant changes across the lifespan, yet their network-level coordination remains poorly understood. This study aimed to investigate whether individual-level metabolic connectivity (MC) reflects underlying inter-areal morphometric similarity, and to determine how this metabolic–morphometric coupling evolves across the adult lifespan. Dynamic [18F]FDG-PET and structural MRI data were acquired from 67 healthy adults (age range: 38–86 years). Individual MC networks were estimated based on the similarity between regional time–activity curves. Corresponding structural similarity networks were generated using the morphometric inverse divergence (MIND) framework, which integrates multiple vertex-wise features of cortical morphology. The correspondence between metabolic and structural networks was quantified at both global and local scales using Spearman correlations. General linear models were employed to assess age-related effects on MC–MIND similarity. MC demonstrated a robust positive association with cortical morphometric similarity (ρ = 0.32, p < 0.0001), an association that persisted after distance correction and was replicated at the individual level. Regional coupling followed a topographic gradient, peaking in heteromodal association cortices and reaching its minimum in paralimbic areas. Crucially, morphology–metabolism alignment systematically strengthened with age at the global level (β = 0.59, p < 0.001). Local age-related increases were spatially heterogeneous, predominantly affecting visual, dorsal parietal, and premotor cortices alongside adjacent multimodal regions. Individual-level MC captures the morphometric organisation of the brain. The age-related increase in morphology–metabolism coupling indicates that metabolic coordination becomes progressively more aligned with cortical architecture, consistent with reduced neuroenergetic flexibility in the ageing brain.

There is increasing evidence for an association between white matter hyperintensities (WMH) and brain beta-amyloid deposition. WMH are not exclusive correlates of a single etiology, and the spatial topography can associate with different pathologic markers of vascular or neurodegenerative disease. How WMH are longitudinally associated with brain beta-amyloid burden requires further investigation, particularly with respect to co-existent vascular risk factors and differences across brain regions. We retrospectively measured WMH on MRI and vascular risk factors in a combined neuroimaging data set comprised of the ADNI, AIBL and OASIS3 studies, which included harmonized centiloid estimates of beta-amyloid burden from PET imaging. WMH were measured using the TrUE-Net algorithm. Vascular risk factors were extracted from provided clinical data and used to calculate individual revised Framingham Stroke Risk Profile (FSRP) scores. Five established data-driven WM regions (juxtacortical, deep frontal, periventricular, parietal, posterior) were used for regional relationships with WMH volume and growth. Linear mixed effects modelling was used to determine the relationship between the growth rate of normalized regional WMH volumes and baseline beta-amyloid burden, controlling for age, sex, APOE4 status, and vascular risk factors. 1245 participants [48.7% female, mean age 71.7 y (SD 7.6 y)] had at least 3 brain MRIs suitable for WMH volume measurement. Linear mixed models demonstrate robust independent cross-sectional relationships between WMH and baseline beta-amyloid burden ( p  <0.001), age ( p  <0.001) and FSRP ( p  <0.001). Growth rates of WMH increased with baseline beta-amyloid burden ( p  <0.05) and decreased with vascular risk ( p  <0.001), above and beyond age, sex, and APOE4 status. Regional analyses revealed both baseline beta-amyloid burden and FSRP associated with the juxtacortical deep frontal and periventricular regions, but the parietal region was unique to beta-amyloid ( p  <0.05). Longitudinally, the association for beta-amyloid burden ( p  <0.005) persisted in only the parietal WMH and no normalized regional values associated longitudinally with FSRP. Our study suggests that in Alzheimer disease research cohorts, WMH progression is associated with beta-amyloid burden, particularly in parietal white matter. Vascular risk associated WMH were influential for WMH volume but did not associate with WMH progression in a regionally specific manner.

Structural brain aging trajectories are increasingly well characterized in very large datasets, but metabolic trajectories remain less clear. It has been proposed that CMRglc may fall faster than CMRO2 with age, though this view has been questioned. In this context, Duffy et al. now provide a rare contribution by assembling brain arteriovenous data from >200 adults aged 19-45 years. Their results offer an opportunity to refine our understanding of brain metabolism trajectories and highlight the challenges in studying this question.

Task-evoked decreases in blood-oxygenation-level-dependent (BOLD) signals are a well-recognized phenomenon in functional magnetic resonance imaging (fMRI) studies. These deactivations are most prominent in the default mode network (DMN), a set of regions most active at rest. The metabolic basis of task-induced BOLD fMRI deactivations remains unclear. To address this question, we used PET/MRI to simultaneously measure BOLD fMRI and cerebral glucose consumption (CMRglc) during visuomotor and language tasks in 22 cognitively unimpaired older adults (15 female, 7 male). Task performance increased BOLD signals in task-relevant regions and decreased BOLD signals in the DMN. Positive BOLD responses generally coincided with increases in CMRglc. In contrast, CMRglc did not decrease in regions showing negative BOLD responses; instead, it typically increased. In particular, the posterior cingulate cortex showed significant CMRglc elevations in conjunction with negative BOLD responses. Whole-brain intensity normalization partially restored task-induced decreases in CMRglc, indicating that relative reductions appear in regions in which CMRglc increases are smaller than the global average. Overall, our results imply that BOLD fMRI deactivations can occur in conjunction with stable or even increased glucose consumption.

Oxygen utilization is important for studies of brain metabolism, alongside other measurements such as for glucose metabolism. Oxygen and other measurements with [ 15 O] tracers and PET, however, are significantly more challenging than measurements of [ 18 F]fluorodeoxyglucose, the standard for probing tissue glucose metabolism in vivo, in part due to the much shorter radioactive half-life of [ 15 O]. This work examines details of precision measurement of [ 15 O] tracers and their kinetics. Investigations of arterial input functions (AIFs) and image-derived input functions (IDIFs) have figured prominently for PET, but [ 15 O] tracers are rarely studied given the small numbers of PET facilities equipped to work with these tracers, particularly in their inhaled form. Estimates of IDIFs and AIFs for [ 15 O] tracers have demonstrably distinct characteristics arising from instrumentation as well as circulatory physiology. To reconcile IDIFs and AIFs, we developed a generalizable model for bolus tracer transport, corrected for known effects of instrumentation for measuring IDIFs and AIFs, and found intravascular[ 15 O]CO to be especially suited for constructing a robust recovery coefficient for IDIFs compared against AIFs. Within a Bayesian framework for posterior estimation and estimating data evidence, IDIFs provide parameter estimates compatible with AIFs in the setting of biological variability. IDIFs also provide data evidence that exceeds that of results from AIFs. These suggest that scalar recovery coefficients may be adequate to estimate partial volume effects, and that the circulatory consistency of internal carotid IDIFs with brain tissue perfusion provides greater precision than what can be estimated using radial artery AIFs, which exhibit greater variability of recirculation wave forms.

Magnetic resonance imaging (MRI) of hyperpolarized (HP) [1-13C]pyruvate is a promising method for measuring cerebral energy metabolism in vivo. The substantial increase in signal provided by HP makes it possible to dynamically monitor the conversion of [1-13C]pyruvate to [1-13C]lactate and [13C]bicarbonate. The HP [1-13C]lactate signal is commonly associated with glycolic activity, whereas [13C]bicarbonate, a by-product of the reaction that forms acetyl-CoA, is linked to oxidative metabolism. However, there is compelling evidence that other factors, such as the concentration of monocarboxylate transporters, influence the production of HP [1-13C]lactate. To clarify the processes responsible for producing the topography of HP [1-13C]pyruvate and its metabolites, we spatially correlated group-average HP 13C MRI images with [18F]FDG, [15O]H2O, [15O]O2, and [15O]CO positron emission topography (PET) images from a separate group of 35 age- and sex-matched adults. We found that [1-13C]pyruvate correlated best with cerebral blood volume (CBV), whereas [1-13C]lactate and [13C]bicarbonate were most strongly associated with cerebral blood flow (CBF), glucose consumption (CMRglc), and oxygen metabolism (CMRO2). Neither [1-13C]lactate nor [13C]bicarbonate was correlated with non-oxidative glucose consumption, also known as aerobic glycolysis. These results are consistent with the view that in the healthy brain, the production of [1-13C]lactate reflects overall energy metabolism rather than being specific to glycolysis.

The brain’s resting-state energy consumption is expected to be driven by spontaneous activity. We previously used 50 resting-state fMRI (rs-fMRI) features to predict [ 18 F]FDG SUVR as a proxy of glucose metabolism. Here, we expanded on our effort by estimating [ 18 F]FDG kinetic parameters K i (irreversible uptake), K 1 (delivery), k 3 (phosphorylation) in a large healthy control group (n = 47). Describing the parameters’ spatial distribution at high resolution (216 regions), we showed that K 1 is the least redundant (strong posteromedial pattern), and K i and k 3 have relevant differences (occipital cortices, cerebellum, thalamus). Using multilevel modeling, we investigated how much spatial variance of [ 18 F]FDG parameters could be explained by a combination of a) rs-fMRI variables, b) cerebral blood flow (CBF) and metabolic rate of oxygen (CMRO 2 ) from 15 O PET. Rs-fMRI-only models explained part of the individual variance in K i (35%), K 1 (14%), k 3 (21%), while combining rs-fMRI and CMRO 2 led to satisfactory description of K i (46%) especially. K i was sensitive to both local rs-fMRI variables ( ReHo ) and CMRO 2 , k 3 to ReHo , K 1 to CMRO 2 . This work represents a comprehensive assessment of the complex underpinnings of brain glucose consumption, and highlights links between 1) glucose phosphorylation and local brain activity, 2) glucose delivery and oxygen consumption.

Alterations in metabolism, stress response, sleep, circadian rhythms, and neuroendocrine processes are key features of aging and neurodegeneration. These fundamental processes are regulated by the hypothalamus, yet how its functionally distinct subregions and cell types change during human aging and Alzheimer's Disease (AD) remains largely unexplored. Here, we present HypoAD , a comprehensive atlas of the human hypothalamus in aging and AD, integrating high-resolution MRI from 202 individuals with single-nucleus RNA-seq (snRNA-seq) of 614,403 nuclei from young, AD, and age-matched non-dementia controls. Our analysis reveals that hypothalamic subregions governing metabolism, stress, and circadian rhythms are particularly vulnerable, exhibiting significant changes in both volumes and gene expression during aging and AD. At the molecular level, machine learning models identified the inflammatory response and regulators of circadian rhythms as key cellular predictors of AD. These signatures were reflected in specific cell types: microglia transitioned to a pro-inflammatory state, while inhibitory neurons within sleep-and circadian-regulating hypothalamic subregions showed the most profound transcriptional alterations, including disruptions in ligand-receptor interactions and G-protein-coupled receptor signaling. Together, HypoAD provides a high-resolution volumetric map and a comprehensive transcriptomic atlas of the human hypothalamus in aging and AD, linking lifestyle and behavioral changes to their underlying volumetric and molecular pathways. Additionally, HypoAD provides a framework to investigate hypothalamic dysfunction and establishes a roadmap for targeted interventions aimed at mitigating physiological disruptions to potentially slow disease progression.

The interplay between brain metabolism and function supports the brain’s adaptive capacity in cognitively demanding processes. Prior work has linked glucose metabolism to resting-state fMRI activity, but often overlooks both hemodynamic confounders in the BOLD signal and the brain’s dynamic nature. To address this, we employed a novel effective connectivity decomposition, separating symmetric partial covariance, capturing “true” statistical dependencies between regions, from antisymmetric differential covariance, reflecting directional brain flow. In 42 healthy subjects, we show that partial covariance corresponds to metabolic connectivity across regions, while node directionality relates to standardized uptake value ratio, a proxy for local glucose consumption. We subsequently tested the sensitivity of detected couplings in 43 glioma patients, identifying disruptions in both local and network-level effective–metabolic interactions that varied with tumor anatomical location. Our findings provide novel insights into the coupling between brain metabolism and functional dynamics at rest, advancing understanding of healthy and pathological brain states. This study reveals dual coupling between brain metabolism and function: symmetric dependencies align with metabolic connectivity, while directional flow reflects local glucose use. Its disruption differentiates glioma anatomical locations.

Introduction:Despite accounting for only 2% of body weight, the human brain requires significant amounts of glucose, even at rest, underscoring the importance of functional-metabolic relationships. Previous studies revealed moderate associations between resting-state fMRI functional connectivity (FC) and local metabolism via [18F]FDG-PET, yet much remains to be understood, particularly regarding their coupling between functional and metabolic networks. Methods:To this end, we employed multivariate Partial Least Squares Correlation (PLSC) to investigate the functional-metabolic relationship at both nodal and network level. From dynamic [18F]FDG-PET data we estimated parameters describing glucose metabolism -delivery rate ( K 1 ), phosphorylation rate ( k 3 ), and fractional uptake ( K i )- and generated within-individual metabolic connectivity (MC) networks. FC was derived from fMRI data filtered into two frequency bands and summarized as region-wise strength to capture nodal characteristics. Results:Our findings revealed that glucose delivery is linked with FC strength, particularly when fMRI signal frequencies include greater hemodynamic contributions. Even stronger functional-metabolic coupling occurs at the network level in the low-frequency fMRI band, with higher MC between sensory/attention and transmodal networks supporting stronger FC within sensory/attention areas. Conclusions:By leveraging PLSC, this work deepens our understanding of the functional-metabolic synergy in the healthy brain, providing new insights into its organization.

There is increasing evidence for an association between white matter hyperintensities (WMH) and brain beta-amyloid deposition. How WMH are longitudinally associated with brain beta-amyloid burden requires further investigation, particularly with respect to co-existent vascular risk factors and differences across white matter regions. We measured WMH on MRI and vascular risk factors in a combined neuroimaging data set of cognitively normal and individuals with dementia comprised of the ADNI, AIBL and OASIS3 studies, which includes harmonized centiloid estimates of beta-amyloid burden from PET imaging. WMH were measured using the TrUE-Net algorithm. Vascular risk factors were extracted from provided clinical data and used to calculate individual revised Framingham Stroke Risk Profile (FSRP) scores. Linear mixed effects modelling was used to determine the relationship between the growth rate of WMH and baseline beta-amyloid burden, controlling for age, sex, APOE4 status, and vascular risk factors. 1243 participants [49% female, mean age 71.7 y (SD 7.6 y)] had at least 3 brain MRIs. Linear mixed models demonstrate robust independent cross-sectional relationships between WMH and baseline beta-amyloid burden (beta coefficient=0.27, p<0.001), age (beta coefficient=0.04, p<0.001) and vascular risk factors (beta coefficient=0.25, p<0.001). Growth rates of WMH increased with baseline beta-amyloid burden (slope=0.021, p<0.001) and decreased with anti-hypertensive medications (slope=-0.019, p=0.002), above and beyond age, APOE4 status, and other vascular risk factors. The longitudinal association for beta-amyloid burden persisted in a similar analysis for parietal WM. Our study suggests that in Alzheimer disease research cohorts, WMH progression is associated with age and beta-amyloid burden, particularly in parietal white matter, and slowed by anti-hypertensive treatment.

Cerebral glucose metabolism (CMRGlc) systematically decreases with advancing age. We sought to identify correlates of decreased CMRGlc in the spectral properties of fMRI signals imaged in the task-free state. We analyzed lifespan resting-state fMRI data acquired in 455 healthy adults (ages 18-87 years) and cerebral metabolic data acquired in a separate cohort of 94 healthy adults (ages 25-45 years, 65-85 years). We characterized the spectral properties of the fMRI data in terms of the relative predominance of slow vs. fast activity using the spectral slope (SS) measure. We found that the relative proportion of fast activity increases with advancing age (SS flattening) across most cortical regions. The regional distribution of spectral slope was topographically correlated with CMRGlc in young adults. Notably, whereas most older adults maintained a youthful pattern of SS topography, a distinct subset of older adults significantly diverged from the youthful pattern. This subset of older adults also diverged from the youthful pattern of CMRGlc metabolism. This divergent pattern was associated with T2-weighted signal changes in frontal lobe white matter, an independent marker of small vessel disease. These findings suggest that BOLD signal spectral slope flattening may represent a biomarker of age-associated neurometabolic pathology.

Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain’s spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain’s metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain’s ‘dark energy’.

The authors have developed a paradigm using positron emission tomography (PET) with multiple radiopharmaceutical tracers that combines measurements of cerebral metabolic rate of glucose (CMRGlc), cerebral metabolic rate of oxygen (CMRO 2 ), cerebral blood flow (CBF), and cerebral blood volume (CBV), culminating in estimates of brain aerobic glycolysis (AG). These in vivo estimates of oxidative and non-oxidative glucose metabolism are pertinent to the study of the human brain in health and disease. The latest positron emission tomography-computed tomography (PET-CT) scanners provide time-of-flight (TOF) imaging and critical improvements in spatial resolution and reduction of artifacts. This has led to significantly improved imaging with lower radiotracer doses. Optimized methods for the latest PET-CT scanners involve administering a sequence of inhaled 15 O-labeled carbon monoxide (CO) and oxygen (O 2 ), intravenous 15 O- labeled water (H 2 O), and 18 F-deoxyglucose (FDG)-all within 2-h or 3-h scan sessions that yield high-resolution, quantitative measurements of CMRGlc, CMRO 2 , CBF, CBV, and AG. This methods paper describes practical aspects of scanning designed for quantifying brain metabolism with tracer kinetic models and arterial blood samples and provides examples of imaging measurements of human brain metabolism.

This study evaluates the potential of within-individual Metabolic Connectivity (wi-MC), from dynamic [18F]FDG PET data, based on the Euclidean Similarity method. This approach leverages the biological information of the tracer’s full temporal dynamics, enabling the direct extraction of individual metabolic connectomes. Specifically, the proposed framework, applied to glioma pathology, seeks to assess sensitivity to metabolic dysfunctions in the whole brain, while simultaneously providing further insights into the pathophysiological mechanisms regulating glioma progression. We designed an index (Distance from Healthy Group, DfHG) based on the alteration of wi-MC in each patient (n = 44) compared to a healthy reference (from 57 healthy controls), to individually quantify metabolic connectivity abnormalities, resulting in an Impairment Map highlighting significantly compromised areas. We then assessed whether our measure of metabolic network alteration is associated with well-established markers of disease severity (tumor grade and volume, with and without edema). Subsequently, we investigated disruptions in wi-MC homotopic connectivity, assessing both affected and seemingly healthy tissue to deepen the pathology’s impact on neural communication. Finally, we compared network impairments with local metabolic alterations determined from SUVR, a validated diagnostic tool in clinical practice. Our framework revealed how gliomas cause extensive alterations in the topography of brain networks, even in structurally unaffected regions outside the lesion area, with a significant reduction in connectivity between contralateral homologous regions. High-grade gliomas have a stronger impact on brain networks, and edema plays a mediating role in global metabolic alterations. As compared to the conventional SUVR-based analysis, our approach offers a more holistic view of the disease burden in individual patients, providing interesting additional insights into glioma-related alterations. Considering our results, individual PET connectivity estimates could hold significant clinical value, potentially allowing the identification of new prognostic factors and personalized treatment in gliomas or other focal pathologies.

PET imaging is a pivotal tool for biomarker research aimed at personalized medicine. Leveraging the quantitative nature of PET requires knowledge of plasma radiotracer concentration. Typically, the arterial input function (AIF) is obtained through arterial cannulation, an invasive and technically demanding procedure. A less invasive alternative, especially for [18F]FDG, is the image-derived input function (IDIF), which, however, often requires correction for partial volume effect (PVE), usually performed via venous blood samples. The aim of this paper is to present EMATA: Extraction and Modeling of Arterial inputs for Tracer kinetic Analysis, an open-source MATLAB toolbox. EMATA automates IDIF extraction from [18F]FDG brain PET images and additionally includes a PVE correction procedure that does not require any blood sampling. To assess the toolbox generalizability and present example outputs, EMATA was applied to brain [18F]FDG dynamic data of 80 subjects, extracted from two distinct datasets (40 healthy controls, 40 glioma patients). Additionally, to compare with the reference standard, quantification using both IDIF and AIF was carried out on a third open-access dataset of 18 healthy individuals. EMATA consistently performs IDIF extraction across all datasets, despite differences in scanners and acquisition protocols. Remarkably high agreement is observed when comparing Patlak’s Ki between IDIF and AIF (R2: 0.98 ± 0.02). EMATA proved adaptability to different datasets characteristics and the ability to provide arterial input functions that can be used for reliable PET quantitative analysis.

White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation. Here, we compare TrUE-Net to six established automated WMH segmentation tools, including a semi-manual method. We evaluated the techniques at both global and regional level to compare their ability to detect the established relationship between WMH burden and age. We found that TrUE-Net was highly reliable at identifying WMH regions with low false positive rates, when compared to semi-manual segmentation as the reference standard. TrUE-Net performed similarly or favorably when compared to the other automated techniques. Moreover, TrUE-Net was able to detect relationships between WMH and age to a similar degree as the reference standard semi-manual segmentation at both the global and regional level. These results support the use of TrUE-Net for identifying WMH at the global or regional level, including in large, combined datasets.

This systematic review examines the prevalence, underlying mechanisms, cohort characteristics, evaluation criteria, and cohort types in white matter hyperintensity (WMH) pipeline and implementation literature spanning the last two decades. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we categorized WMH segmentation tools based on their methodologies from January 1, 2000, to November 18, 2022. Inclusion criteria involved articles using openly available techniques with detailed descriptions, focusing on WMH as a primary outcome. Our analysis identified 1007 visual rating scales, 118 pipeline development articles, and 509 implementation articles. These studies predominantly explored aging, dementia, psychiatric disorders, and small vessel disease, with aging and dementia being the most prevalent cohorts. Deep learning emerged as the most frequently developed segmentation technique, indicative of a heightened scrutiny in new technique development over the past two decades. We illustrate observed patterns and discrepancies between published and implemented WMH techniques. Despite increasingly sophisticated quantitative segmentation options, visual rating scales persist, with the SPM technique being the most utilized among quantitative methods and potentially serving as a reference standard for newer techniques. Our findings highlight the need for future standards in WMH segmentation, and we provide recommendations based on these observations.