Accurately identifying theÌýepileptogenic zone (EZ)—the area of the brain where seizures begin—is essential for planning surgery in people withÌýmedication-resistant epilepsy. While combiningÌýelectroencephalography (EEG)Ìý·É¾±³Ù³óÌýfunctional magnetic resonance imaging (fMRI)Ìýcan help locate this region, the technique requires specialized equipment and is not widely available. In this study, the researchers developed a new fMRI-based measure called theÌýdynamic functional connectivity (dFC) peak index, which aims to identify seizure-related brain activity without the need for simultaneous EEG. They evaluated the method in 62 patients with focal epilepsy, most of whom hadÌýtemporal lobe epilepsy (TLE), and compared the results with those from 109 healthy volunteers. The dFC peak index was elevated in brain regions known to be involved in temporal lobe epilepsy, particularly the medial temporal lobe. Patients whose surgeries removed areas with higher dFC peak index values were more likely to have better seizure outcomes, including those whose standard MRI scans did not show visible abnormalities. These findings suggest that the dFC peak index may provide valuable additional information for identifying the epileptogenic zone and could help guide surgical planning for people with medication-resistant epilepsy.

FIGURE 1

Negative dFCÌýpeaks. (A) Description of negative dFC peaks. Temporally Z-scored timeseries for the DMNÌýand a region are shown. The two timeseries are multiplied together at each timepoint to calculate the dFC timeseries between the two regions. Negative dFC peaks are highlighted with black circles, with solid circles denoting peaks of interest (DMN deactivation) and dotted circles denoting peaks of no interest (DMN activation). (B) Examples of negative dFC peaks following interictal epileptic discharges in a patient with left medial temporal lobe epilepsy (top) and right lateral temporal lobe epilepsy (bottom). The EEG-based interictal epileptic discharge activation maps are shown on the left along with a map of the DMN. The timeseries for the DMN, the activated region from the interictal discharges, and the dFC between the two regions are shown in red, yellow, and blue, respectively. DMN, default mode network; dFC, dynamic functional connectivity; EEG: electroencephalography; fMRI, functional magnetic resonance imaging.

The two halves of the brain are not perfectly identical, and these differences inÌýwhite matter—the bundles of nerve fibers that connect different brain regions—are thought to support specialized functions such as language and spatial reasoning. Although previous studies have examined white matter asymmetry, most have been limited by small sample sizes, narrow age ranges, or a focus on only a few brain pathways. In this study, the researchers analyzed brain imaging data from more thanÌý35,000 healthy individualsÌýranging in age from birth to 100 years, creating the most comprehensive maps to date of white matter asymmetry across the lifespan. They examined 30 major white matter pathways and measured multiple features related to both their microscopic tissue structure and overall anatomy. The results showed that asymmetry is present in every pathway studied, but its direction and degree vary depending on the specific pathway and the structural feature being measured. The patterns of asymmetry also changed throughout life, with distinct developmental changes in childhood and adolescence and a general trend toward greater asymmetry with advancing age, particularly in later adulthood. These findings provide a valuable reference for understanding how white matter develops and changes over the lifespan and may help researchers better identify brain changes associated with healthy aging and neurological disorders.

FIGURE 1

Overview of the study datasets, white matter features, and analytical framework. (A) Age distributions for each of the 50 contributing datasets (violin plots), illustrating broad coverage from 0 to 100 years. Color encodes the number of participants per dataset (log scale). (B) Features extracted for each of the 30 bilateral pathways. Microstructural indices (e.g., Fractional Anisotropy, and Mean, Axial and Radial diffusivities; FA, MD, AD, and RD) summarize tissue organization and axonal/myelin density; macrostructural indices (e.g., tract volume and length) capture pathway size and geometry. Macrostructural cartoon reproduced under CC-BY from YehÌý.Ìý(C) Analysis pipeline. For each participant, white matter pathways were segmented, and features were extracted. A Lateralization Index (LI) was calculated for each tract-feature pair. These LIs were used as input for a normative modeling framework (GAMLSS) to generate age-specific population centile curves.

Changes in the brain’sÌýlimbic white matter—the nerve fiber pathways involved in memory, learning, and emotion—are common in aging andÌýAlzheimer’s disease (AD), but the genetic factors that influence these changes are not well understood. This study analyzed brain imaging and genetic data from 2,614 older adults across seven research cohorts, including many participants with cognitive impairment. The researchers found that differences in limbic white matter structure are strongly influenced by genetics and identified six regions of the genome associated with these brain changes. One of the strongest signals involvedÌýCDH19, a gene linked toÌýoligodendrocytes, the cells responsible for producing myelin, the protective coating that surrounds nerve fibers. Several other genes, includingÌýRORA,ÌýFAM107B, andÌýKC6, were also associated with cognitive performance and the brain changes seen in Alzheimer’s disease. The findings further suggest that biological pathways related to insulin signaling, immune function, and cardiovascular health may contribute to white matter changes during aging. Overall, the study provides new evidence that the structure of limbic white matter is influenced by genetics and identifies several genes and biological pathways that may play a role in Alzheimer’s disease and age-related cognitive decline.

FIGURE 1

SNP heritability estimates for limbic white matter (WM) microstructure. SNP heritability of 35 FW-corrected dMRI metrics from seven WM tracts in the limbic system. Abbreviations: AxD, axial diffusivity; dMRI, diffusion magnetic resonance imaging; FA, fractional anisotropy; FW_corr, free-water corrected; MD, mean diffusivity; RD, radial diffusivity; WM, white matter.

The relationship between dietary fat andÌýAlzheimer’s disease (AD)Ìýhas remained unclear, with previous studies reporting mixed results. This study examined whether fat intake was associated with changes in brain structure over time and whether those associations differed based on factors such as cognitive status, sex, andÌýAPOE ε4Ìýstatus—a genetic variant that increases the risk of developing Alzheimer’s disease. The researchers followed 758 participants for an average of 4.6 years, including individuals with normal cognition and those withÌýmild cognitive impairment (MCI), an early stage of cognitive decline. Among cognitively unimpaired participants, a higher percentage of calories from fat was associated with faster shrinkage of theÌýtemporal lobe, a brain region important for memory. In contrast, among participants with MCI, higher fat intake was associated with slower enlargement of theÌýinferior lateral ventricle, a change that is often linked to brain atrophy. This association appeared to be driven primarily by women and individuals carrying the APOE ε4 genetic variant. The findings suggest that the relationship between dietary fat and brain health may differ depending on a person’s stage of cognitive decline and underlying risk factors. The authors note that, in higher-risk groups, the slower progression of some brain changes may reflect a compensatory response rather than a protective effect of a high-fat diet.

Ìý

FIGURE 1

Pfat × Cognitive status and Tfat × Cognitive status on longitudinal structural MRI variables. Lines reflect structural MRI variables corresponding to Pfat and Tfat. The gray shaded area reflects 95% confidence interval. (A) Associations between Tfat and hippocampal volume, stratified by cognitive status; CU participantsÌýβÌý=Ìý−18.7,ÌýpÌý=Ìý0.008; MCI participantsÌýβÌý=Ìý11.2, pÌý=Ìý0.43. (B) Associations between Pfat and temporal lobe volume, stratified by cognitive status; CU participantsÌýβÌý=Ìý−47.2,ÌýpÌý=Ìý0.007; MCI participantsÌýβÌý=Ìý50.9,ÌýpÌý=Ìý0.21. (C) Associations between Pfat and inferior lateral ventricle volume, stratified by cognitive status; CU participantsÌýβÌý=Ìý−1.89,ÌýpÌý=Ìý0.27; MCI participantsÌýβÌý=Ìý−22.5,ÌýpÌý=Ìý0.006. CU, cognitively unimpaired; MCI, mild cognitive impairment; MRI, magnetic resonance imaging; Pfat, percentage of energy from fat; Tfat, total fat intake.

The human brain depends on a network of connections to work properly, and white matter is the part that carries signals between different brain regions, much like a communication highway. When these pathways are disrupted, they are linked to many neurological, psychiatric, and developmental disorders. Doctors already use growth charts to track how children grow, and researchers have also created reference charts for whole-brain size and gray matter, but until now there has not been a similar standard for white matter. This study fills that gap by creating lifespan reference charts for human brain white matter. The researchers analyzed and standardized 35,120 brain scans from studies around the world to show how white matter pathways normally develop from birth to age 100, including growth, maturation, and later decline. These charts provide a baseline for healthy brain development and aging, so researchers and clinicians can compare an individual’s brain with typical patterns and identify unusual changes linked to disease. Because the charts are open access, they can also be used broadly in future clinical and neuroscience research.

Fig. 1: Global WM brain charts across the human lifespan.

]]> /valiant/2026/05/27/advancing-high-resolution-7-t-diffusion-mri-evaluating-phase-encoding-correction-strategies-for-distortion-correction-from-basic-to-four-way-acquisitions/ Wed, 27 May 2026 01:55:11 +0000 /valiant/?p=6797 Schilling, Kurt G.; Beckett, Alexander J. S.; Amandola, Matthew.; Walker, Erica B.; Feinberg, David A.; Bunge, Silvia A.; Vu, An T. (2026).Ìý.ÌýMagnetic Resonance Imaging, 131, 110694.Ìý

This study looked at how to make very high-resolution 7T diffusion MRI scans more accurate and reliable. Diffusion MRI is used to study the brain’s white matter pathways, but at such high field strength the images can be distorted, which can reduce anatomical accuracy and make results less reproducible. The researchers tested different ways of collecting and correcting the scans by using five healthy adults who each had two MRI sessions. They compared several methods, ranging from uncorrected scans to more advanced approaches that used multiple phase-encoding directions, which are different ways of collecting the image data to help correct distortion. They then checked how well each method lined up with standard anatomical MRI images and how consistent the measurements were when the scan was repeated. All of the correction methods improved image accuracy compared with uncorrected scans, but using a full set of reversed phase-encoding images worked better than the common approach of using only one reversed reference image. The best results came from using a four-direction phase-encoding scheme, which produced the most accurate images and the most reproducible measurements. This approach also allowed the researchers to reconstruct both large and very fine white matter pathways with high quality. Overall, the study shows that collecting diffusion MRI data in multiple directions is important for getting dependable results from high-resolution 7T brain scans.

Fig. 1.ÌýMethodology. The highly oversampled acquisition (top) enabled creation of subset combinations of nine time-equivalent 10Ìýmin acquisitions (bottom). In the schematic, short blocks denote bÌý=Ìý0 volumes and long blocks denote diffusion-weighted volumes (DWIs). Color encodes the phase-encoding axis (blueÌý=ÌýAP/PA; redÌý=ÌýLR/RL), and the shading direction within each block indicates phase-encoding polarity (e.g., AP vs PA; LR vs RL). A full 10Ìýmin acquisition includes 64 uniformly distributed diffusion weighted directions (with bÌý=Ìý0 images interspersed every 16 volumes). This is repeated once for each of the four PE directions (AP, PA, RL, and LR). The nine corrected acquisitions depicted here fall into four categories: (1–4) single reverse PE bÌý=Ìý0 (PA, AP, RL, LR); (5–6) full blip-up/blip-down with unique DWIs (AP-PA, RL-LR); (7–8) full blip-up/blip-down with repeated DWIs (AP-PAr, RL-LRr); (9) -way PE (AP-PA-RL-LR). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

]]> /valiant/2026/05/26/association-of-mri-visible-perivascular-spaces-with-longitudinal-cognitive-decline-over-a-decade/ Tue, 26 May 2026 21:10:06 +0000 /valiant/?p=6783 Kohno, Kyoko.; Sun, Yunyi.; LeFevre, James D.; Robb, W. Hudson.; Jackson, T. Bryan.; Liu, Dandan.; Vyas, Yukti.; Sweely, Benjamin.; Pechman, Kimberly R.; Shashikumar, Niranjana.; Peterson, Amalia.; Landman, Bennett.; Davis, Larry Taylor.; Hohman, Timothy J.; Jefferson, Angela L. (2026).Ìý.ÌýNeurology, 106(9), e214803.Ìý

Cerebral small vessel disease is a common cause of dementia-related brain damage, and it affects the brain’s tiny blood vessels. Several MRI signs of this disease often appear together, which makes it hard to tell which ones are most important for thinking skills. One of these signs is enlarged perivascular spaces, which are small fluid-filled spaces around blood vessels that can be seen on MRI. Earlier work from the same group showed that enlarged perivascular spaces in the basal ganglia, a deep brain region, were linked to poorer thinking ability at a single point in time, even after accounting for other signs of small vessel disease. In this study, the researchers followed 750 adults in the Vanderbilt Memory and Aging Project, a long-term study of aging, for about five years on average, with some participants followed for as long as 11 years. At the beginning of the study, everyone had a brain MRI to measure several markers of small vessel disease, including perivascular spaces, white matter hyperintensities, lacunes, and microbleeds. The participants also completed repeated thinking and memory tests over time. The results showed that people with more enlarged perivascular spaces in the basal ganglia at the start tended to decline more over time in several abilities, including naming, processing speed, executive function, visual organization, and memory. When the researchers compared the different MRI markers directly, basal ganglia perivascular spaces still predicted worse long-term executive function and visual-spatial skills on their own. Overall, the findings suggest that enlarged perivascular spaces in the basal ganglia may be an early warning sign of later decline in specific thinking abilities as people age.

Ìý

Figure 2ÌýBasal Ganglia PVS Volume Fraction and Longitudinal Neuropsychological Outcomes

Solid blue line reflects unadjusted values of neuropsychological outcomes (y-axis) corresponding to basal ganglia PVS volume fraction (x-axis). Shading reflects a 95% CI. PVS = MRI-visible perivascular spaces. Significant differences were seen among Animal Naming, Boston Naming Test, Coding, Executive Function, Visual Organization, and Episodic Memory annual changes (pÌý= 0.047, 0.03, 0.009, 0.0001, 0.04, 0.004).

Multiple system atrophy, or MSA, is a fast-moving brain disease that can be hard to diagnose early and monitor over time. This study looked at whether a special kind of MRI scan called quantitative susceptibility mapping, or QSM, can detect abnormal iron buildup in the brain, since iron imbalance may play a role in MSA and could be useful for tracking the disease. The researchers scanned 38 people with MSA, including 10 with early-stage disease who were followed again after 12 months, along with 43 people with Parkinson’s disease and 23 healthy adults of similar age. They measured iron-related changes in several brain regions, including the substantia nigra, globus pallidus, putamen, and dentate nucleus. Compared with both Parkinson’s disease and healthy controls, people with MSA had higher iron-related signal changes in the globus pallidus and substantia nigra, with smaller changes in the putamen. A more sensitive analysis that focused on higher values within each region detected these differences better than simple median measurements, suggesting it was better at picking up small, localized areas of iron buildup. Higher iron levels in the globus pallidus were also linked to worse clinical symptoms. In the 12-month follow-up, iron-related changes increased in the substantia nigra and globus pallidus, showing that these abnormalities can worsen over time. Overall, the findings suggest that QSM MRI may be a useful way to help diagnose MSA earlier, follow disease progression, and evaluate treatments aimed at reducing iron-related damage.

Fig. 1.ÌýRepresentative QSM images from each diagnostic group.Quantitative susceptibility maps (QSM) for representative female participants matched for age across cohorts: a healthy control (HC, 67 years), Parkinson’s disease (PD, 62 years), PD from the bioMUSE study (69 years), multiple system atrophy (MSA) from bioMUSE (65 years), and MSA from the cross-sectional cohort (72 years). The PD (bioMUSE) participant was initially enrolled as MSA but later reclassified as PD based on longitudinal clinical evaluation. The MSA (bioMUSE) participant represents an early-stage case, whereas the MSA (cross-sectional) participant had more advanced disease. Each row displays a different subcortical region: the putamen (PT) and globus pallidus (GP) (top), the substantia nigra (SN) (middle), and the dentate nucleus (DN) (bottom). The leftmost column shows the QSM overlaid on the T1-weighted image to provide anatomical context. The second column shows the zoomed QSM for the HC participant with atlas-derived ROI outlines in red and anatomical labels. The remaining columns show the zoomed QSM for each diagnostic group. White arrows indicate the structures of interest in each row. Increases in magnetic susceptibility (brighter signal) are visible in the PT, GP, and SN in MSA participants compared with HC and PD. Susceptibility values are displayed in parts per million (ppm) using a grayscale colormap windowed from −0.1 to 0.2 ppm.

Tooth loss has been linked to memory problems and faster cognitive decline in older adults, but it is not known whether losing teeth is also associated with changes in brain structure, especially in white matter, the brain tissue that carries signals between regions and can be affected by inflammation and blood vessel problems. In this study, researchers followed 375 participants from the Baltimore Longitudinal Study of Aging for an average of 4.8 years and compared clinically measured tooth loss with changes seen on MRI brain scans and diffusion tensor imaging (DTI), a type of scan that shows the health of white matter. The participants, who had an average age of 65.5 years, were tracked over as long as 12 years. The results showed that people with more tooth loss were more likely to already have signs of brain changes, including a larger fourth ventricle, which is a fluid-filled space in the brain, smaller brain volumes in temporal regions, more abnormalities in deep white matter, and lower white matter integrity in the corpus callosum, the major fiber tract connecting the two sides of the brain. Over time, each lost tooth was linked to a faster decline in white matter health in the corpus callosum and corona radiata, suggesting ongoing damage to these pathways. Tooth loss was also associated with higher levels of blood markers related to inflammation, such as white blood cells and neutrophils, and with lower albumin, a protein that can reflect overall health. However, these inflammation markers did not explain the brain imaging findings. The study suggests that tooth loss may be a warning sign of worsening white matter health in aging, even apart from the inflammation measures examined here.

Fig. 1.ÌýStudy design.ÌýLegend: Created in BioRender. Greig, E. (2026)Ìý.

]]> /valiant/2026/05/26/what-needs-to-be-standardized-for-reliable-reproducible-and-robust-tractography/ Tue, 26 May 2026 20:24:44 +0000 /valiant/?p=6764 Legarreta, Jon Haitz.; Schiavi, Simona.; Tang, Wei.; Banks, Garrett.; Cieslak, Matthew.; Schilling, Kurt.; De Luca, Alberto.; Tournier, Jacques-Donald.; Kruper, John.; Rheault, Francois.; Sotiropoulos, Stamatios N.; Pestilli, Franco.; Veraart, Jelle.; Yang, Joseph Yuan-Mou.; Descoteaux, Maxime.; Heilbronner, Sarah.; Rokem, Ariel. (2026).Ìý.ÌýGigaScience, 15.Ìý

Tractography is an important tool for mapping how different parts of the brain are connected. It uses brain imaging data to trace white matter pathways, but because the field is changing quickly, different research groups often use different methods and software. This lack of standardization can lead to inconsistent results, making studies harder to reproduce and limiting use in clinical settings. Differences in how data are collected, how brain images are aligned and processed, and natural anatomical variation between people, age groups, and even species all add to the problem. Another challenge is that there is no broad agreement on the best way to perform tractography, which makes it harder to build reliable automated quality checks and slows clinical translation. This article reviews the main challenges in standardizing tractography and highlights the parts of the process that most need consistent methods so the results can be more reliable, reproducible, and useful across studies and applications.

Figure 1

Summary of main challenges and suggested standardization solutions toward reliable, reproducible, and robust tractography.

]]>