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. �鶹APP, 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.

�鶹APP, 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.

In humans and other primates, the adolescent years are a time when thinking and problem-solving abilities improve, and the brain continues to grow and reorganize. However, scientists still don’t fully understand how these structural brain changes influence the actual neural activity that supports cognitive performance. In this study, researchers followed monkeys throughout adolescence and measured their behavior, their neurons’ activity, and their brain structure over time to better understand this process.

The team focused on the prefrontal cortex, a brain region important for working memory—the ability to hold and use information for short periods. They found that changes in prefrontal neural activity closely matched the animals’ improvements in working memory skills. More complex patterns of neural activity evolved gradually through the teenage years, but even simple features—like the average firing rate of neurons and how much that activity varied—helped predict how well the animals performed.

The researchers also examined how changes in the brain’s wiring related to these improvements. They discovered that the development of long-distance white matter tracts—pathways that connect the frontal lobe to other brain regions—strongly predicted both the progression of neural activity and gains in cognitive performance. Surprisingly, changes in brain volume and cortical thickness, which are known to shift during human adolescence, did��not��predict these neural or behavioral changes in monkeys.

Overall, the study shows that the maturation of white matter connections plays a key role in shaping how neural activity develops during adolescence, helping to support the rise in cognitive abilities during this critical period.

Fig. 1: Saccade precision and latency improve during adolescence.

a, Sequence of events in the ODR task. The monkey is required to maintain fixation while a cue stimulus is presented and after a delay period, when the fixation point turns off, saccade to the remembered location of the cue.��b, Sequence of events in the ODR with distractor task. After the delay period, a distractor stimulus appears, which needs to be ignored. The monkey is still required to saccade to the remembered location of the cue.��c, Possible locations of the stimulus presentation on the screen.��d, Schematic illustration of variability of two groups of saccades. The gray dots represent the endpoints of individual saccades for two stimulus locations. DI, defined as the area within one s.d. from the average landing position of each target is shown.��e, DI in the ODR task, during the neural recording sessions. Each dot is one session; data from different monkeys are shown in different colors. The blue line shows the GAMM-fitted trajectory. The gray shaded regions denote the 95% confidence intervals (CIs). The dashed vertical line denotes a mid-adolescence age of 0. The horizontal dashed bar denotes significant developmental effect intervals. The horizontal solid bar denotes intervals with significant monotonic developmental effect.��f, As in��e, for the RT of saccade in the ODR task.��g, As in��e, for the DI in the ODR with distractor task.��h, As in��f, for the RT in the ODR with distractor task.��i, Schematic diagram of the three cohorts of monkeys (groups A–C) used to evaluate behavioral improvement. The image of the monkeys was created with��.��j, DI in the ODR task of groups A and B at the TP1 and TP2 time points. The violin plot shows the distribution of DI values for both groups at two distinct time points, with the width of the plot indicating the density of the data points. Statistical comparisons were performed with a two-sided, two-sample��t-test (no adjustment for multiple comparisons). TP1:��P = 0.21; TP2:��P = 2.83 ×�ĉ10⁻⁴.��k, DI in the ODR task of groups A and C at the first time point. Two-sided, two-sample��t-test (no adjustment for multiple comparisons):��P = 9.35 ×�ĉ10⁻⁵. ***P &����; 0.0001.

Magnetic resonance images of healthy brains were analyzed to better understand how resting-state BOLD signals in white matter are related to neural activity in the cortex (the outer layer of the brain). We measured how much spontaneous activity in the cortex—seen as low-frequency fluctuations in BOLD signals from gray matter—affects the resting-state BOLD signals in white matter. We found that the similarity between BOLD signals from cortical regions and white matter areas was directly linked to the strength of the cortical BOLD signal.

From these measurements, we observed that cortical networks involved in more basic functions tend to contribute more to the fluctuations in white matter than those involved in higher-level functions. We also discovered that each cortical network has its own unique spatial pattern of influence on white matter BOLD signals, and the strength of these effects is closely related to how much myelin (the protective coating around nerve fibers) the cortical network has.

Overall, our findings show that resting-state BOLD signals in white matter reflect the spontaneous activity of specific cortical networks and are shaped by the structure and myelination of the cortex.

Fig 1

(a) Relationship between subject-averaged fALFF of cortical BOLD signals and their subject-averaged mean white matter projection. Each data point represents subject-averaged measures for an ROI in the cortex. (b) Mean white matter projection of BOLD signals in the cortical functional networks analyzed. The vertical line at the top of each bar represents standard error across the 120 subjects studied. Abbreviations: prim = primary, DMN = default mode network. LECN = left executive control network. RECN = right executive control network.

The perivascular space (PVS) plays an important role in helping the brain clear out waste by allowing fluid to flow around blood vessels. A brain imaging method called DTI-ALPS was suggested as a way to measure how fluid moves in these spaces without surgery. However, it’s not clear how accurate or specific this method is. The DTI-ALPS method assumes certain patterns in brain tissue called “radial symmetrby” and interprets when these patterns are uneven (called “radial asymmetry”) as a sign of fluid movement in the PVS. But other factors in the brain’s structure might affect these measurements.

In this study, we carefully examined these possible influences using detailed brain scans from the Human Connectome Project and high-resolution imaging. We looked at how common radial asymmetry is in brain white matter, how crossing nerve fibers affect the measurements, how nerve fibers’ twisting and spreading impact results, and how blood vessels are oriented in these brain areas. We found that radial asymmetry happens widely in white matter and is mostly caused by the shape and arrangement of nerve fibers—not just fluid in the PVS. Crossing fibers made the measurements seem larger, and twisting or spreading of fibers also caused asymmetry, regardless of fluid flow. Additionally, blood vessels were not always aligned in the way the method assumes.

Overall, the DTI-ALPS measurements are strongly influenced by the brain’s nerve fiber structure rather than just fluid movement in the perivascular space. This means that using DTI-ALPS as a direct marker of the brain’s waste clearance system might be misleading unless these structural factors are considered. Future research should use more advanced methods to separate the effects of fluid flow from the complex structure of brain tissue.

Fig 1

Radial asymmetry is widespread throughout white matter. Sagittal, coronal, and axial slices of an example HCP subject show radial asymmetry at all diffusion weightings, and throughout white matter, with most regions exhibiting average asymmetry values ~1.3–1.8, with many voxels > 2.

Understanding how the human brain changes throughout life—from infancy to old age—is essential for learning about childhood development, aging, and brain disorders. In this study, we aimed to provide detailed information about the brain’s white matter pathways by examining their tiny structures (microstructure), larger organization (macrostructure), and the shape of the brain’s outer layer (cortex) connected to these pathways. We analyzed four large, high-quality datasets that included 2,789 brain scans from people aged 0 to 100 years, using advanced imaging techniques. We found that different features of white matter pathways develop and decline at different times and rates, depending on the brain area and pathway type. We also discovered connections between various features that could help explain biological changes at different life stages. Additionally, the patterns of change with age were unique for each feature, and the way white matter changes during development is strongly linked to how it changes during aging. Overall, this study provides important baseline data about white matter pathways in the human brain, which can help future research on normal brain development as well as brain diseases.

Fig 1

Microstructural, macrostructural, and cortical features associated with each of 63 white matter bundles.