Prenatal alcohol exposure (PAE) can affect how the brain develops, but its effects on the shape and structure of the brain’s outer layer, or cortex, are not fully understood. Using MRI scans from 121 people aged 7–21 years, including 56 with PAE, we examined several features of cortical structure and how they changed with age. Youth with PAE showed differences in brain folding, groove depth, and structural complexity in specific brain regions compared with unexposed youth. Some of these features also developed differently with age in the PAE group. Overall, the findings suggest that measures of brain folding, particularly gyrification, may be more sensitive to the effects of prenatal alcohol exposure than commonly studied measures such as cortical thickness.

Fig. 1. A: Distribution of ages for participants with PAE (blue) or unexposed (salmon). MRI scans are represented by dots with horizontal lines connecting scans of the same participant. There are 163 scans (73 PAE) from 123 participants (58 PAE). 84 participants had 1 scan, 38 participants had 2, and 1 participant had 3 scans. Figure was created using ggplot2 in R (). B: Flowchart depicting the number of scans (and participants) at each stage of data acquisition and processing. Note that there were no significant group differences in the quality of T1-weighted data or MaCRUISE segmentations.

Learning through language requires the brain to rapidly transform words into meaningful concepts, but how this process unfolds over time remains unclear. Using a combination of functional MRI and electroencephalography (EEG), we tracked brain activity in healthy adults as they processed language. We identified a sequence of brain networks that became active within about 700 milliseconds, progressing from visual word recognition to retrieving meaning, interpreting context, integrating sentence structure and meaning, and ultimately forming a coherent concept. Transitions between these stages involved shared brain regions, particularly the left temporoparietal junction. People with stronger language comprehension showed greater involvement of later processes related to interpreting and integrating information. These findings reveal how different brain networks work together over fractions of a second to transform language into conceptual knowledge.

Figure 1

Spatial and temporal perspectives on language processing. Current neuroimaging approaches examine language processing through separate methodological silos, limiting our understanding of how distributed brain networks coordinate dynamically in real time. a) Spatial perspective: brain systems supporting language comprehension, after Fedorenko et al. (2024), showing the distributed architecture of perception of linguistic input (green), core language processing (red), motor planning (navy), and higher-order cognition (purple). Regions are rendered on an inflated left-hemisphere cortical surface from probabilistic functional atlases for the language, multiple demand (light purple), and default mode (dark purple) networks (Lipkin et al. 2022; Yeo et al. 2011), and Harvard-Oxford for the perception and motor areas. While fMRI reveals the spatial organization of these networks, it lacks the temporal resolution to capture their millisecond-scale coordination. b) Temporal perspective: a schematic ERP waveform illustrating the N400 (semantic processing, ~400 ms) and P600 (syntactic/integrative processing, ~600 ms) components that reflect the rapid timecourse of language comprehension. c) Topographical maps illustrating the spatial distribution of ERP effects, reproduced from Schacht et al. (2014, Fig 1B and 1C): the main effect of semantics in the N400 window (top) and the grammaticality-violation effect in the P600 window (bottom). Licensed under CC BY; cropped from the original. While EEG provides precise temporal information about language processing stages, it offers limited spatial resolution for localizing these processes within the distributed brain networks shown in (a). This methodological divide has hindered our understanding of how spatially distributed language networks achieve their rapid, coordinated function during real-world comprehension.

]]> /valiant/2026/08/24/a-comparative-evaluation-of-multiple-enlarged-perivascular-space-segmentation-tools/ Mon, 24 Aug 2026 19:28:42 +0000 /valiant/?p=7380 LeFevre, James D.; Robb, W. Hudson; Liu, Dandan; Jackson, T. Bryan; Pechman, Kimberly R.; Shashikumar, Niranjana; Vyas, Yukti; Landman, Bennett A.; Davis, L. Taylor; Hohman, Timothy J.; Jefferson, Angela L. (2026). . Magnetic Resonance Imaging, 134, 110749.

Enlarged perivascular spaces (ePVS) are fluid-filled spaces around small blood vessels in the brain that can become more visible with aging and small vessel disease and may reflect reduced clearance of waste from the brain. Measuring ePVS manually on MRI scans is time-consuming and impractical for large studies. To address this, researchers developed DORES, a deep learning tool that automatically identifies and measures ePVS using two types of brain MRI images. The model was developed using data from the 91�Թ��� Memory and Aging Project and evaluated against expert manual measurements and three other automated tools. DORES showed good performance in identifying ePVS in both white matter and the basal ganglia, a group of structures deep within the brain, and its estimates of ePVS number and volume agreed well with expert measurements. Testing on an independent Alzheimer’s disease imaging dataset showed somewhat lower performance, as was also observed with the other automated methods. Results also varied depending on the type of MRI scanner used, suggesting that scanner differences can affect measurement consistency. Overall, DORES provides a promising automated approach for measuring ePVS in older adults, although scanner-related differences should be considered when applying the method across multiple research sites.

Fig. 1.��Representative White Matter ePVS Segmentations of DORES Performance in VMAP.

T₁-weighted axial scans were selected to illustrate DORES performance at the 25th (top row; Dice = 0.55), 50th (middle row; Dice = 0.63), and 75th (bottom row; Dice = 0.73) percentiles of white matter regional Dice scores. The first column displays the raw, skull-stripped images in the white matter. The second column displays the corresponding segmentation overlays. Manual segmentations are shown in blue, whereas DORES predictions are shown in red. Voxels where manual and DORES segmentations overlap appear purple. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

]]> /valiant/2026/08/24/diffusion-mri-with-a-multi-shot-rosette-readout/ Mon, 24 Aug 2026 19:23:05 +0000 /valiant/?p=7361 Harkins, Kevin D.; Lee, Tzu-Wei; Martin, Jonathan B.; Alderson, Hannah E.; Gore, John C.; Does, Mark D. (2026). . Journal of Magnetic Resonance, 391, 108132.

Diffusion-weighted imaging (DWI) is an MRI technique that measures the movement of water in tissue, but its image quality can be affected by magnetic field variations, electrical currents generated during scanning, and differences between repeated measurements. This study tested whether a spiral-like scanning pattern called a rosette readout trajectory could reduce these common artifacts. Multi-shot rosette DWI, which collects an image over several measurements, was evaluated in test objects and an isolated ferret brain at 7 Tesla, as well as in the spinal cords of living mice at 15.2 Tesla. The rosette approach produced consistently high-quality images at both magnetic field strengths. Correcting differences between repeated measurements also prevented unwanted loss of the diffusion-weighted signal. Overall, multi-shot rosette DWI effectively measured and corrected several important sources of MRI artifacts, demonstrating its potential for producing more reliable, high-quality diffusion images.

Fig. 1.��A rosette readout can be divided into multiple shots, here comprised of all 5 leafs of a single rosette acquisition window, or multiple segments. Each segment consists of portions from all shots that cross the center of k-space at the same time in the acquisition window. Images reconstructed from individual segments can be used to estimate the background static  field, while images reconstructed from individual shots can be used to estimate shot-to-shot phase variations. This example 5 leaf rosette contains 6 segments, as the first inout segment and the last outin segment are considered individually.

MRI scanning is often limited for patients with deep brain stimulation (DBS) implants because radiofrequency (RF) energy used during MRI can heat the implanted leads and potentially cause tissue injury. This study developed and tested a six-channel head coil designed to control the distribution of the MRI electric field and reduce heating near DBS implants. Using computer-based electromagnetic simulations, the researchers adjusted individual coil elements to reshape the transmitted RF field. The approach was tested first with a simple conductive wire and then with four realistic DBS lead models. The coil could be optimized either to reduce the electric field at a specific location, such as the tip of a DBS lead, or to reduce the maximum specific absorption rate (SAR), a measure of RF energy absorbed by tissue, across the entire head. Depending on the optimization method, the electric field at a targeted location was reduced by 45.9% to 68.3%, while whole-head SAR was reduced by an average of 72.09% across the four DBS models. These findings demonstrate that the proposed coil can flexibly reshape the MRI transmit field and substantially reduce simulated RF-related heating around DBS implants, providing a promising approach for improving MRI safety in patients with these devices.

FIGURE 1.��

[A] Simulation model of a 3 T body transmit (Tx) coil combined with a decoupled six-channel wireless head coil array (blue). [B] Zoomed-in view of the 6-channel wireless resonator array with the birdcage coil model hidden. The image combines the human head and the simplified DBS lead model. The wireless resonator conductors are shown in blue, and the DBS electrode is shown in red. [C] Combined human head and DBS lead model. All DBS lead positions are shown together for illustration purposes; however, in the simulations, each DBS configuration was evaluated independently.

]]> /valiant/2026/08/24/is-correction-for-gradient-nonlinearity-necessary-in-a-brain-diffusion-tensor-mri-clinical-study/ Mon, 24 Aug 2026 18:54:20 +0000 /valiant/?p=7352 Kanakaraj, Praitayini; Yao, Tianyuan; Li, Zhiyuan; Newlin, Nancy R.; Kim, Michael E.; Gao, Chenyu; Yu, Tian; Krishnan, Aravind; Rogers, Baxter P.; Hohman, Tim; Jefferson, Angela L.; Shashikumar, Niranjana; Pechman, Kimberly R.; Davis, L. Taylor; Moyer, Daniel; Schilling, Kurt G.; Archer, Derek; Anderson, Adam; Landman, Bennett A. (2026). . PLOS ONE, 21(7), e0350808.

Diffusion tensor imaging (DTI) is an MRI technique used to study the structure of brain tissue, particularly white matter. However, imperfections in the MRI system’s magnetic field gradients can slightly alter diffusion measurements across different parts of the brain and may bias results if not corrected. This study examined the effects of correcting these gradient nonlinearities (GNL) and whether they could influence conclusions about aging and neurological conditions. We analyzed 948 imaging sessions from the 91�Թ��� Memory & Aging Project, including measurements of white and gray matter. GNL correction produced changes of about 1% in fractional anisotropy and 3.3% in mean diffusivity, two measures of brain tissue microstructure, as well as changes of about 5 degrees in the estimated direction of nerve fibers, affecting at least 20% of the brain. Some brain regions, particularly superior, occipital, and parietal areas, were more affected, while larger-scale structural measurements changed by up to 12%. Although the effects were generally small at the individual level, they could become statistically important in large studies, particularly those involving multiple MRI scanners or sites. These findings suggest that GNL effects should be considered and, when possible, corrected or measured in large brain imaging studies to improve the reliability of comparisons across individuals, scanners, and clinical groups.

Fig 1.��(a) The MCI clinical cohort comprises 327 participants, each undergoing up to four diffusion MRI sessions acquired on Scanner A (blue) or Scanner B (green).

(b) Maps of the diagonal elements of the gradient nonlinearity tensor, L_xx, L_yy, and L_zz, estimated from empirical phantom field maps for each scanner (FOV 384 × 384 × 384 mm). These entries describe the local scaling of nominal gradients applied along the x, y, and z axes and illustrate scanner-dependent spatial variation in effective diffusion weighting. (c) At a representative brain location (marked in panel b), the sphere plot shows the angular deviation between nominal and GNL-corrected b-vectors (example shown for Scanner B), and the line plot shows the corresponding effective b-values across diffusion volumes for Scanner A (blue) and Scanner B (green) relative to the nominal b-value (orange). Together, these panels summarize the cohort, the underlying gradient nonlinearity fields, and their impact on the effective diffusion encoding used in our analyses. Our study analyzes the effects of GNL on this clinical cohort.

Multi-echo gradient echo (ME-GRE) is an MRI technique that can improve visualization of gray and white matter in the spinal cord and help detect lesions in people with multiple sclerosis (MS). However, breathing can interfere with these scans, causing image artifacts and signal loss. We developed a correction method that uses a one-dimensional (1D) phase navigator, a brief MRI measurement that tracks magnetic field changes, before image data are collected. This approach reduces measurement errors and does not require separate monitoring of breathing. We tested the method at 3T MRI in the thoracic spinal cord of 20 healthy volunteers and three people with MS. Compared with standard image reconstruction, navigator correction improved signal quality and gray-to-white matter contrast, reduced breathing-related ghosting artifacts, and provided clearer visualization of spinal cord structures. Preliminary results in the three participants with MS also showed fewer artifacts, clearer anatomy, and improved visibility of lesions. These findings suggest that the proposed navigator correction can improve the quality and reliability of thoracic spinal cord ME-GRE imaging and may increase its usefulness for evaluating MS.

FIGURE 1

(A) Pulse sequence diagram illustrating the ME-GRE sequence with the navigator incorporated before the first echo of each echo series. (B) ME-GRE pipeline of navigator-based correction in the spinal cord. Raw data was converted to image space using a 1D fast Fourier transform. Static phase contributions were removed, and dynamic B₀ fluctuations were isolated by phase normalization. Within each slice, an SNR-weighted averaging across coils was computed. Finally, using the center phase value of the combined navigator signal, the expected phase accrual induced by the estimated phase field-estimates was removed by demodulation, producing the navigator-reconstructed image.

Magnetic resonance imaging (MRI) often uses inductively coupled coils—small receiver coils placed near the area being imaged—to improve image quality. However, when these coils are positioned close to the scanner’s primary coil, they can interfere with each other, causing effects that have traditionally been viewed as reducing image quality. This study investigated why inductively coupled coils can still perform well despite this strong interaction and examined the role of modern MRI preamplifiers (electronic components that amplify weak signals from the coils). The researchers tested different coil configurations and preamplifier settings in laboratory experiments and validated their findings with MRI scans at 7 tesla, a high-field MRI system. They found that modern low-input-impedance preamplifiers largely prevented the signal losses typically associated with strong coil coupling, allowing the secondary coils to function effectively even when placed very close to the primary coil. Although the interaction between the coils altered the electrical properties of the primary coil, it had little effect on the overall signal-to-noise ratio (SNR), a key measure of image quality. In contrast, reducing the effectiveness of the preamplifiers led to a 21%–23% decrease in SNR. These findings suggest that modern preamplifiers play a critical role in maintaining MRI performance and could simplify the design of inductively coupled coils for future imaging systems.

FIGURE 1

(A) Setup and results of measuring the impedance of a 10-cm-diameter circular 7 T RF coil on a bottle phantom. (B) Setup and results of the same coil (primary coil) when a smaller 5-cm-diameter inductively coupled coil was placed underneath the primary coil but above the phantom. The primary coil was not retuned or rematched after introducing the inductively coupled coil. (C) Simplified equivalent circuit model of the coupled inductively coupled and primary coils illustrating resonance splitting due to strong mutual coupling.

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.

Multiple sclerosis (MS) affects both the brain and spinal cord, but these areas are often studied separately using magnetic resonance imaging (MRI). This study examined whether tissue damage develops in similar ways across both regions and whether an advanced MRI technique could provide more useful information than conventional imaging methods. The researchers used the same MRI session to image the brain and cervical (neck) spinal cord of 34 people with relapsing-remitting MS and 36 healthy volunteers. They compared traditional diffusion tensor imaging (DTI) with a newer technique called standard model imaging with free water (SMIfw), which provides more detailed information about the brain’s and spinal cord’s microscopic structure. Both methods detected MS-related changes, but their performance differed depending on the region being studied. A measure derived from SMIfw, called neurite density fraction, consistently identified MS-related tissue damage in both the brain and spinal cord, while commonly used DTI measures performed as well only in the brain. The findings also suggest that although damage to nerve fibers is a common feature of MS throughout the central nervous system, the inflammatory environment surrounding lesions differs between the brain and spinal cord. Overall, the results indicate that SMIfw could improve the assessment of MS across the entire central nervous system and may be valuable for future clinical trials and disease monitoring.

FIGURE 1

All diffusion models can be roughly summarized similar concepts, which can be captured either by “sticks” or cylinders, spheres with isometric diffusion, or diffusion tensors with directional diffusion. In this depiction, SMI represents both SMI and SMIfw models, with SMIfw including a f_w measure not otherwise included in the SMI model.