The two halves of the brain are not perfectly identical, and these differences in��white 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 than��35,000 healthy individuals��ranging 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 Yeh��.��(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.

Swelling around��brain tumors��can make it difficult to identify nearby��white matter—the bundles of nerve fibers that carry signals between different parts of the brain—on standard��magnetic resonance imaging (MRI). This can complicate surgical planning by making it harder to determine the safest path for removing a tumor while preserving important brain connections. In this proof-of-concept study, the researchers evaluated whether advanced��diffusion MRI��techniques, which model how water moves through different microscopic tissue compartments, could better identify white matter in areas affected by swelling (edema). Using MRI data from five patients with��meningiomas��(typically benign brain tumors), they compared conventional��diffusion tensor imaging (DTI)��with two advanced methods:��Neurite Orientation Dispersion and Density Imaging (NODDI)��and the��Standard Model (SM). The advanced techniques preserved measures of white matter organization in swollen tissue much better than standard DTI and more successfully traced white matter pathways through these regions. These findings suggest that biophysical diffusion MRI models may improve the mapping of critical white matter tracts before brain surgery, helping surgeons better plan procedures in patients with tumors surrounded by edema.

Figure 1.��Representative (A) post-contrast T₁-weighted images, (B) T2-weighted FLAIR images, (C) FA maps, (D) single-shell FW-FA maps, (E) multi-shell FW-FA maps, (F) ODI maps, and (G) P₂��maps are shown. Post-contrast T₁-weighted images best reflect tumor location, and T₂-weighted FLAIR images best reflect tumor plus edema location. DTI’s FA (where WM is typically represented by a brighter signal intensity) fails to identify WM tracts through regions of edema (seen as hyperintense signal traced in T₂-FLAIR images), whereas NODDI’s ODI (where WM is represented by a darker signal intensity) retains WM structure irrespective of edema presence. Similarly, the SM’s P₂map (where WM is typically represented by a brighter signal intensity) succeeds. Representative image planes were chosen on a per-patient basis to best reflect the lesion.

Changes in the brain’s��limbic white matter—the nerve fiber pathways involved in memory, learning, and emotion—are common in aging and��Alzheimer’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 involved��CDH19, a gene linked to��oligodendrocytes, the cells responsible for producing myelin, the protective coating that surrounds nerve fibers. Several other genes, including��RORA,��FAM107B, and��KC6, 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 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).��.��Magnetic Resonance Imaging, 131, 110694.��

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.��Methodology. The highly oversampled acquisition (top) enabled creation of subset combinations of nine time-equivalent 10��min acquisitions (bottom). In the schematic, short blocks denote b��=��0 volumes and long blocks denote diffusion-weighted volumes (DWIs). Color encodes the phase-encoding axis (blue��=��AP/PA; red��=��LR/RL), and the shading direction within each block indicates phase-encoding polarity (e.g., AP vs PA; LR vs RL). A full 10��min acquisition includes 64 uniformly distributed diffusion weighted directions (with b��=��0 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 b��=��0 (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/tooth-loss-is-associated-with-subsequent-brain-white-matter-degradation-up-to-over-a-decade-tooth-loss-and-brain-white-matter-degradation/ Tue, 26 May 2026 20:34:14 +0000 /valiant/?p=6767 Tian, Qu.; Qi, Xiang.; Greig, Erin E.; Landman, Bennett A.; Davatzikos, Christos.; Resnick, Susan M.; Wu, Bei.; Ferrucci, Luigi. (2026).��.��Journal of Dentistry, 171, 106732.��

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.��Study design.��Legend: Created in BioRender. Greig, E. (2026)��.

]]> /valiant/2026/04/29/longitudinal-changes-in-white-matter-hypointensities-in-recurrent-late-life-depression/ Wed, 29 Apr 2026 02:36:51 +0000 /valiant/?p=6521 Pearcy, Leigh B.; Costa, Ana Paula; Butters, Meryl A.; Krafty, Robert; Boyd, Brian D.; Banihashemi, Layla; Szymkowicz, Sarah M.; Landman, Bennett A.; Ajilore, Olusola; Taylor, Warren D.; Andreescu, Carmen; Karim, Helmet T. (2026).��.��American Journal of Geriatric Psychiatry, 34(6), 844–856.��

This study looks at how changes in brain structure are linked to the return of depression in older adults. Specifically, it focuses on��white matter hyperintensities (WMH)�����Ի���hypointensities (WMh)—areas in brain scans that appear unusually bright or dark and are thought to reflect small blood vessel damage and increased vascular (blood flow–related) risk. These markers are commonly seen in older individuals and have been associated with late-life depression (LLD), but it has been unclear whether changes in these brain features over time contribute to depression coming back after recovery.

To investigate this, researchers followed 223 older adults (average age about 67), including people whose depression had improved (remitted LLD) and a comparison group without depression. Brain scans were taken every 8 months over two years to track changes in WMh. During this period, about half of the participants who had recovered from depression experienced a relapse. The researchers found that people who relapsed already had higher levels of WMh at the start of the study compared to those without depression. However, the��rate at which these brain changes increased over time was not significantly different between groups. When looking more closely, individuals who started with high WMh levels and also showed faster accumulation over time had nearly three times the risk of relapse compared to those with low levels and slow changes.

Overall, the findings suggest that having a higher burden of these brain changes at baseline is an important risk factor for depression returning in older adults, while the speed of progression alone may be less informative. However, people with both high initial levels and rapid increases may be at especially high risk and could benefit from closer monitoring and care to help prevent relapse.

Figure. 1��Mixed effects model predictions. (A), (B): Results of the model comparing HC vs. remLLD. (C), (D): Results comparing HC vs. REM vs. EarlyREL vs. LateREL. EarlyREL and LateREL represent individuals that relapse within 250 days of baseline and after 250 days of baseline, respectively. Both models adjusted for time (days since baseline), vascular disease burden using CIRS-G, age at baseline, ICV at baseline, sex, education, race, study site, group, and time * group effects.

This study followed cognitively normal adults over time to determine which magnetic resonance imaging (MRI) biomarkers best predict future cognitive impairment. Researchers examined 154 different MRI-based measurements in 509 participants from the Baltimore Longitudinal Study of Aging who were age 50 or older and cognitively normal at the start of the study. Participants underwent repeated cognitive testing and 3 Tesla (3T) MRI scans, including T1- and T2-weighted imaging to assess brain structure and diffusion tensor imaging (DTI) to measure white matter microstructural integrity. The analyses accounted for factors such as age and other confounders and also examined differences by sex and amyloid beta (Aβ) status, a biological marker associated with Alzheimer’s disease.

Over an average follow-up of 4.6 years, individuals who later developed cognitive impairment showed greater declines in white matter integrity compared to those who remained cognitively stable. These changes were especially pronounced in major white matter tracts, including the corpus callosum, cingulum bundle, and inferior fronto-occipital fasciculus, which are pathways that connect different brain regions. To a lesser extent, thinning and atrophy in the temporal lobe were also linked to later impairment. The associations between brain changes and future cognitive decline were stronger in men and in individuals who were amyloid-positive.

Overall, the findings suggest that early changes in white matter microstructure, as measured by DTI, are particularly sensitive indicators of future mild cognitive impairment (MCI) and dementia. Certain MRI metrics may therefore be especially useful for identifying risk in people who are still cognitively normal.

��

FIGURE 1

Study overview. Participants were selected from the BLSA neuroimaging substudy based on cognitively normal (CN) status and age 50 or older at baseline. The study data included longitudinal cognitive assessments, clinical diagnoses (Dx), 3T magnetic resonance imaging scans, and baseline plasma biomarkers related to Alzheimer’s disease and related dementias, specifically amyloid beta 42/40, collected between 2008 and 2019. The subsequently impaired (SI) group (also CN at baseline) included individuals who later developed mild cognitive impairment (MCI) or dementia or were “Impaired, not MCI/dementia.” Impairment onset dates ranged from 2012 to 2019 (≈1- to 9-year interval).

This review looks at recent research showing that blood oxygenation level–dependent (BOLD) signals in white matter (WM) contain meaningful information about brain activity. These signals are influenced by the structure of white matter, its blood supply, and its metabolism, and they are closely connected to functional MRI (fMRI) signals in gray matter (GM). BOLD signals in WM can be detected both during tasks and at rest, where their natural fluctuations reveal coordinated activity between white and gray matter. Even so, many fMRI studies have traditionally ignored WM signals or treated them as noise.

New evidence shows that WM BOLD signals reflect how different brain regions communicate. Studies have found that the strength and behavior of these signals depend on features such as myelination, neurite density, mitochondrial content, and blood vessels within white matter tracts. Different types of fibers, such as association and projection fibers, show different BOLD patterns, and some heavily myelinated fibers may show little or no detectable signal. Research has also clarified how WM BOLD signals relate to GM networks, including during resting-state activity. Together, these findings suggest that WM BOLD signals provide valuable insight into brain function and should be included in fMRI analyses to better understand how the brain is organized and operates.

Fig. 1.��Population maps of HRF features show qualitative differences between GM and WM. Shown are the MNI T1, WM and GM masks for anatomical reference. Population-averaged features of the HRF are shown for FWHM, Height, PSC, time to Peak, Time to Dip, and Dip Height.

As people age, having both thinking problems and movement problems at the same time—a pattern called a dual cognitive–motor deficit—is known to strongly increase the risk of developing dementia. However, it has not been clear how this combined deficit affects the brain’s structure, especially in vulnerable gray matter regions that are important for memory and movement. This study set out to better understand these brain changes.

The researchers studied 582 adults between the ages of 36 and 90 and grouped them into four categories: those with both cognitive and motor deficits, those with only cognitive deficits, those with only motor deficits, and a control group with neither. They examined brain tissue using advanced MRI techniques, including diffusion tensor imaging and mean apparent propagator imaging, which are well suited for detecting subtle microstructural changes in gray matter and white matter. In total, they analyzed 27 brain regions related to temporal (memory-related) and motor functions, as well as key white matter pathways.

The results showed that people with a dual cognitive–motor deficit had widespread microstructural changes in the brain. These alterations were not seen in individuals who had only cognitive deficits or only motor deficits once rigorous statistical corrections were applied. The observed changes are thought to reflect lower cellular density in temporal gray matter, reduced organization of nerve fibers, and possible loss of myelin in white matter tracts.

Together, these findings suggest that having combined cognitive and motor difficulties is linked to distinct and measurable changes in brain microstructure. Understanding these changes may help explain why this group is at particularly high risk for dementia and could support the development of earlier interventions aimed at slowing brain aging and delaying neurodegeneration.

FIGURE 1

Investigated regions of interest. Three-dimensional rendering of (A) temporal meta-ROIs and motor-related GM regions, and (B) associated WM tracts. A total of 27 ROIs were investigated in the current study. GM, gray matter; ROIs, regions of interest; WM, white matter.