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 (AÎČ) 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.

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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.ÌęPopulation 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.

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, didÌęnotÌępredict 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.Ìęb, 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.Ìęc, Possible locations of the stimulus presentation on the screen.Ìęd, 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.Ìęe, 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.Ìęf, As inÌęe, for the RT of saccade in the ODR task.Ìęg, As inÌęe, for the DI in the ODR with distractor task.Ìęh, As inÌęf, for the RT in the ODR with distractor task.Ìęi, Schematic diagram of the three cohorts of monkeys (groups A–C) used to evaluate behavioral improvement. The image of the monkeys was created withÌę.Ìęj, 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-sampleÌęt-test (no adjustment for multiple comparisons). TP1:ÌęP = 0.21; TP2:ÌęP = 2.83 ×â¶Ä‰10^−4.Ìęk, DI in the ODR task of groups A and C at the first time point. Two-sided, two-sampleÌęt-test (no adjustment for multiple comparisons):ÌęP = 9.35 ×â¶Ä‰10^−5. ***P &±ôłÙ; 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.

Previous research has explored how the structure of white matter in the brain relates to academic skills like reading and math. Some studies have suggested that white matter—specifically, how it allows signals to travel through the brain—can predict a child’s academic abilities, while others have found no connection. However, studies that follow children over time (called longitudinal studies) have found that changes in white matter within the same child may be linked to learning progress.

This study aimed to replicate and expand on earlier findings by looking at how changes in a specific white matter pathway in the brain, called the left arcuate fasciculus, relate to reading development. The researchers followed 340 students from first through fourth grade, using diffusion MRI scans to measure white matter and tracking their reading and math scores over time. The results showed that year-to-year improvements in reading—but not math—were connected to changes in the left arcuate fasciculus. These findings offer more evidence that the brain’s white matter can change along with learning, and they underscore the value of long-term studies in understanding how children develop academic skills.

Fig 1

(A) Average estimated tract profiles for MD in the left arcuate fasciculus generated by the GAMM for four different quartiles of reading score change (reading state). Each color represents the magnitude of change relative to the average individual reading score. Shaded areas represent the standard errors of the predictions. (B) The estimated smoothing effect of time elapsed since the first study observation on average MD in the left arcuate. (C) Relationship between overall mean Woodcock–Johnson reading scores and MD in the left arcuate at each time point in the study.

Understanding how the brain changes as we age—especially in conditions like Alzheimer’s disease—is an important area of research. One part of the brain that hasn’t been studied as much is the white matter, which acts like a network of “wires” connecting different brain areas. This large study looked at how the structure of white matter relates to thinking and memory skills in older adults, including those with Alzheimer’s.

Researchers analyzed data from 9 separate studies, including nearly 4,500 adults aged 50 and older. Participants had brain scans using a technique called diffusion MRI (dMRI), along with memory and thinking tests over time. Most participants were cognitively healthy, while some had mild memory problems or Alzheimer’s dementia. The study focused on a specific feature of white matter called “free water” (FW), which can indicate damage or degeneration.

They found that higher levels of FW in white matter—especially in regions connected to memory like the cingulum and fornix—were strongly linked to worse memory and faster cognitive decline. These changes were even more noticeable in people who had other signs of Alzheimer’s, such as brain shrinkage, a genetic risk factor called APOE Δ4, or positive tests for amyloid buildup (a hallmark of Alzheimer’s).

Overall, the study shows that changes in white matter—particularly FW—are an important piece of the puzzle in understanding memory loss and aging. These findings suggest that future brain studies should pay close attention to FW and highlight the importance of brain regions like the cingulum and fornix in Alzheimer’s-related decline.

Figure 1. ÌęCohort Characteristics and Data Harmonization

A, Participants were drawn from 9 well-established cohorts, including 3213 cognitively unimpaired (CU) individuals, 972 with mild cognitive impairment (MCI), and 282 with Alzheimer disease (AD) at baseline. B, The study also incorporated longitudinal data across 9208 cognitive sessions, spanning up to 13 years of follow-up. Longitudinal ComBat harmonization was applied to all imaging features to account for variability across imaging batches. C and D, Associations are shown between cingulum free-water (FW) and memory performance, using both raw (C) and harmonized (D) FW data, with points and lines color coded by imaging batch. Harmonized data were used across all analyses. ADNI indicates Alzheimer’s Disease Neuroimaging Initiative; BIOCARD, Biomarkers of Cognitive Decline Among Normal Adults; BLSA, Baltimore Longitudinal Study of Aging; MAP, Rush Memory and Aging Project; MARS, Minority Aging Research Study; NACC, National Alzheimer’s Coordinating Center; ROS, Religious Orders Study; VMAP, Vanderbilt Memory and Aging Project; WRAP, Wisconsin Registry for Alzheimer’s Prevention.

Most brain imaging studies have focused on gray matter—the part of the brain that processes information—while paying less attention to white matter, which connects different brain regions. This study looks at how gray and white matter work together by introducing two new measurements. The first, called gray-white matter functional connectivity, tracks how closely the activity in gray and white matter is timed together. The second, called the gray-white blood oxygenation-level dependent (BOLD) power ratio, compares how strong the brain signals are in gray matter versus white matter.

The study found that gray-white matter functional connectivity follows patterns related to how the brain is wired and organized, especially in areas that control movement and basic senses—suggesting that signals move efficiently between gray and white matter. On the other hand, the power ratio showed the opposite pattern, with higher values in brain regions involved in more complex thinking, which may mean that those areas require more energy. The power ratio also increased with age, from 8 to 21 years old, suggesting that as the brain develops, its energy needs shift.

Together, these two measurements show how gray and white matter work together differently—one focused on signal clarity, the other on energy use—helping us better understand brain development and communication.

Fig. 1: Overview of functional contrast across the gray-white matter boundary.

aÌęThe white matter surfaces are shown from a lateral view, highlighting the primary motor cortex in blue.ÌębÌęMesh surface of mid-thickness cortex (red) and gray-white matter boundary (blue).ÌęcÌęSchematic of functional contrast measures normal to the surface. For a given vertex v_ion the boundary surface, its corresponding GM vertex (G) and WM point (W) are identified along the line perpendicular to the surface.ÌędÌęSchematic of functional connectivity measures tangential to the surface. ReHo is calculated for each vertex v_iÌęon the mid-thickness surface by measuring the correlation of BOLD signals among neighboring vertices, which are connected along the tangential direction to the surface. ReHo regional homogeneity.