DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images.

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4 .

Full-dose challenge of moderate, severe, and unknown beta-lactam allergies in the emergency department.

OBJECTIVE: This study aims to assess the outcome of challenging documented moderate, severe, or unknown beta-lactam allergies with full dose administration of a beta-lactam antibiotic in emergency department (ED) patients admitted for acute bacterial infection. METHODS: A single-center, retrospective, descriptive study of adult patients challenged with a full dose of beta-lactam in the ED from January 2021 to December 2022 was conducted. Included patients had at least one documented moderate, severe, or unknown beta-lactam allergy in the electronic medical record (EMR) without documentation of prior tolerance. Patient demographics, prior beta-lactam antibiotic reaction, beta-lactam administered in the ED, inpatient beta-lactam continuation, adverse drug reactions, and updates to allergy profiles were collected. Descriptive statistics for data analysis were performed using SPSS Version 22. RESULTS: Of the 184 ED encounters with full-dose beta-lactam challenges, five (2.7%) patients with documented moderate, severe, or unknown beta-lactam allergies experienced an allergic reaction after the challenge; one (0.5%) patient had an allergic reaction in the ED, and the remaining four (2.2%) occurred after admission. No anaphylactic reactions occurred. All allergic reactions were limited to mild rash or itching. Most patients (98.9%) were challenged with a cephalosporin. A beta-lactam was continued in 86.4% of cases, and the allergy profile was updated for future utilization in 73.4% of patients. CONCLUSIONS: This study suggests that full-dose challenge of moderate, severe, or unknown beta-lactam allergies can be safely accomplished in the ED. This approach avoids unnecessary penicillin allergy skin testing and reduces utilization of suboptimal alternative antibiotic regimens.

Quantification of mediation effects of white matter functional characteristics on cognitive decline in aging.

Cognitive decline with aging involves multifactorial processes, including changes in brain structure and function. This study focuses on the role of white matter functional characteristics, as reflected in blood oxygenation level-dependent signals, in age-related cognitive deterioration. Building on previous research confirming the reproducibility and age-dependence of blood oxygenation level-dependent signals acquired via functional magnetic resonance imaging, we here employ mediation analysis to test if aging affects cognition through white matter blood oxygenation level-dependent signal changes, impacting various cognitive domains and specific white matter regions. We used independent component analysis of resting-state blood oxygenation level-dependent signals to segment white matter into coherent hubs, offering a data-driven view of white matter's functional architecture. Through correlation analysis, we constructed a graph network and derived metrics to quantitatively assess regional functional properties based on resting-state blood oxygenation level-dependent fluctuations. Our analysis identified significant mediators in the age-cognition relationship, indicating that aging differentially influences cognitive functions by altering the functional characteristics of distinct white matter regions. These findings enhance our understanding of the neurobiological basis of cognitive aging, highlighting the critical role of white matter in maintaining cognitive integrity and proposing new approaches to assess interventions targeting cognitive decline in older populations.

Individualized spectral filters alleviate persistent photophobia, headaches and migraines in active duty military and Veterans following brain trauma.

PURPOSE: Consistent with association between photophobia and headache, growing evidence suggests an underlying causal relationship between light sensitivity and central pain. We investigated whether an intervention to regulate light sensitivity by filtering only wavelengths causing difficulties for the specific individual could alleviate headaches/migraines resulting from traumatic brain injury (TBI). METHODS: Secondary data analysis of a clinical database including N = 392 military personnel (97% men, 3% women), ranging in age from 20 to 51 years, diagnosed with TBI, persistent headaches/migraines, and light sensitivity. The average elapsed time from TBI diagnosis to intervention was 3 years. Headache/migraine severity, frequency, medication use, and difficulties related to daily functioning were assessed pre and 4-12 weeks post-intervention with individualized spectral filters. RESULTS: Monthly migraine frequency decreased significantly from an average of 14.8 to 1.9, with 74% reporting no migraines post-intervention. Prescription and over-the-counter medication use decreased by more than 70%. Individuals also reported significant improvement in light sensitivity, headaches/migraine severity, and physical and perceptual symptoms. CONCLUSIONS: Wearing individualized spectral filters was associated with symptom relief, increased subjective quality of reported health and well-being, and decreased objective medication use for TBI-related persistent headaches/migraines. These results support a suggested relationship between dysregulated light sensitivity and central regulation of pain.

Optimization of the flip angles of narrow-band editing pulses in J-difference edited MRS of lactate at 3T.

PURPOSE: Application of highly selective editing RF pulses provides a means of minimizing co-editing of contaminants in J-difference MRS (MEGA), but it causes reduction in editing yield. We examined the flip angles (FAs) of narrow-band editing pulses to maximize the lactate edited signal with minimal co-editing of threonine. METHODS: The effect of editing-pulse FA on the editing performance was examined, with numerical and phantom analyses, for bandwidths of 17.6-300 Hz in MEGA-PRESS editing of lactate at 3T. The FA and envelope of 46 ms Gaussian editing pulses were tailored to maximize the lactate edited signal at 1.3 ppm and minimize co-editing of threonine. The optimized editing-pulse FA MEGA scheme was tested in brain tumor patients. RESULTS: Simulation and phantom data indicated that the optimum FA of MEGA editing pulses is progressively larger than 180° as the editing-pulse bandwidth decreases. For 46 ms long 17.6 Hz bandwidth Gaussian pulses and other given sequence parameters, the lactate edited signal was maximum at the first and second editing-pulse FAs of 241° and 249°, respectively. The edit-on and difference-edited lactate peak areas of the optimized FA MEGA were greater by 43% and 25% compared to the 180°-FA MEGA, respectively. In-vivo data confirmed the simulation and phantom results. The lesions of the brain tumor patients showed elevated lactate and physiological levels of threonine. CONCLUSION: The lactate MEGA editing yield is significantly increased with editing-pulse FA much larger than 180° when the editing-pulse bandwidth is comparable to the lactate quartet frequency width.

Comprehensive behavioral and physiologic assessment of peripheral and central auditory function in individuals with mild traumatic brain injury.

Auditory complaints are frequently reported by individuals with mild traumatic brain injury (mTBI) yet remain difficult to detect in the absence of clinically significant hearing loss. This highlights a growing need to identify sensitive indices of auditory-related mTBI pathophysiology beyond pure-tone thresholds for improved hearing healthcare diagnosis and treatment. Given the heterogeneity of mTBI etiology and the diverse peripheral and central processes required for normal auditory function, the present study sought to determine the audiologic assessments sensitive to mTBI pathophysiology at the group level using a well-rounded test battery of both peripheral and central auditory system function. This test battery included pure-tone detection thresholds, word understanding in quiet, sentence understanding in noise, distortion product otoacoustic emissions (DPOAEs), middle-ear muscle reflexes (MEMRs), and auditory evoked potentials (AEPs), including auditory brainstem responses (ABRs), middle latency responses (MLRs), and late latency responses (LLRs). Each participant also received magnetic resonance imaging (MRI). Compared to the control group, we found that individuals with mTBI had reduced DPOAE amplitudes that revealed a compound effect of age, elevated MEMR thresholds for an ipsilateral broadband noise elicitor, longer ABR Wave I latencies for click and 4 kHz tone burst elicitors, longer ABR Wave III latencies for 4 kHz tone bursts, larger MLR Na and Nb amplitudes, smaller MLR Pb amplitudes, longer MLR Pa latencies, and smaller LLR N1 amplitudes for older individuals with mTBI. Further, mTBI individuals with combined hearing difficulty and noise sensitivity had a greater number of deficits on thalamic and cortical AEP measures compared to those with only one/no self-reported auditory symptoms. This finding was corroborated with MRI, which revealed significant structural differences in the auditory cortical areas of mTBI participants who reported combined hearing difficulty and noise sensitivity, including an enlargement of left transverse temporal gyrus (TTG) and bilateral planum polare (PP). These findings highlight the need for continued investigations toward identifying individualized audiologic assessments and treatments that are sensitive to mTBI pathophysiology.

Nonlinear changes in pupillary attentional orienting responses across the lifespan.

The cognitive aging process is not necessarily linear. Central task-evoked pupillary responses, representing a brainstem-pupil relationship, may vary across the lifespan. Thus we examined, in 75 adults ranging in age from 19 to 86, whether task-evoked pupillary responses to an attention task may serve in as an index of cognitive aging. This is because the locus coeruleus (LC), located in the brainstem, is not only among the earliest sites of degeneration in pathological aging, but also supports both attentional and pupillary behaviors. We assessed brief, task-evoked phasic attentional orienting to behaviorally relevant and irrelevant auditory tones, stimuli known specifically to recruit the LC in the brainstem and evoke pupillary responses. Due to potential nonlinear changes across the lifespan, we used a novel data-driven analysis on 6 dynamic pupillary behaviors on 10% of the data to reveal cut off points that best characterized the three age bands: young (19-41 years old), middle aged (42-68 years old), and older adults (69 + years old). Follow-up analyses on independent data, the remaining 90%, revealed age-related changes such as monotonic decreases in tonic pupillary diameter and dynamic range, along with curvilinear phasic pupillary responses to the behaviorally relevant target events, increasing in the middle-aged group and then decreasing in the older group. Additionally, the older group showed decreased differentiation of pupillary responses between target and distractor events. This pattern is consistent with potential compensatory LC activity in midlife that is diminished in old age, resulting in decreased adaptive gain. Beyond regulating responses to light, pupillary dynamics reveal a nonlinear capacity for neurally mediated gain across the lifespan, thus providing evidence in support of the LC adaptive gain hypothesis.

Effect of Meditation on Brain Activity during an Attention Task: A Comparison Study of ASL and BOLD Task fMRI.

Focused attention meditation (FAM) training has been shown to improve attention, but the neural basis of FAM on attention has not been thoroughly understood. Here, we aim to investigate the neural effect of a 2-month FAM training on novice meditators in a visual oddball task (a frequently adopted task to evaluate attention), evaluated with both ASL and BOLD fMRI. Using ASL, activation was increased in the middle cingulate (part of the salience network, SN) and temporoparietal (part of the frontoparietal network, FPN) regions; the FAM practice time was negatively associated with the longitudinal changes in activation in the medial prefrontal (part of the default mode network, DMN) and middle frontal (part of the FPN) regions. Using BOLD, the FAM practice time was positively associated with the longitudinal changes of activation in the inferior parietal (part of the dorsal attention network, DAN), dorsolateral prefrontal (part of the FPN), and precentral (part of the DAN) regions. The effect sizes for the activation changes and their association with practice time using ASL are significantly larger than those using BOLD. Our study suggests that FAM training may improve attention via modulation of the DMN, DAN, SN, and FPN, and ASL may be a sensitive tool to study the FAM effect on attention.

DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images.

T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4.

Seasonal variations of functional connectivity of human brains.

Seasonal variations have long been observed in various aspects of human life. While there is an abundance of research that has characterized seasonality effects in, for example, cognition, mood, and behavior, including studies of underlying biophysical mechanisms, direct measurements of seasonal variations of brain functional activities have not gained wide attention. We have quantified seasonal effects on functional connectivity as derived from MRI scans. A cohort of healthy human subjects was divided into four groups based on the seasons of their scanning dates as documented in the image database of the Human Connectome Project. Sinusoidal functions were used as regressors to determine whether there were significant seasonal variations in measures of brain activities. We began with the analysis of seasonal variations of the fractional amplitudes of low frequency fluctuations of regional functional signals, followed by the seasonal variations of functional connectivity in both global- and network-level. Furthermore, relevant environmental factors, including average temperature and daylength, were found to be significantly associated with brain functional activities, which may explain how the observed seasonal fluctuations arise. Finally, topological properties of the brain functional network also showed significant variations across seasons. All the observations accumulated revealed seasonality effects of human brain activities in a resting-state, which may have important practical implications for neuroimaging research.

Locus coeruleus neuromelanin accumulation and dissipation across the lifespan.

The pigment neuromelanin, produced in the locus coeruleus (LC) as a byproduct of catecholamine synthesis, gives the "blue spot" its name, and both identifies LC neurons and is thought to play an important yet complex role in normal and pathological aging. Using neuromelanin-sensitive T1-weighted turbo spin echo MRI scans we characterized volume and neuromelanin signal intensity in the LC of 96 participants between the ages of 19 and 86. Although LC volume did not change significantly throughout the lifespan, LC neuromelanin signal intensity increased from early adulthood, peaked around age 60 and precipitously declined thereafter. Neuromelanin intensity was greater in the caudal relative to rostral extent and in women relative to men. With regard to function, rostral LC neuromelanin intensity was associated with fluid cognition in older adults (60+) only in those above the 50th percentile of cognitive ability for age. The gradual accumulation of LC neuromelanin across the lifespan, its sudden dissipation in later life, and relation to preserved cognitive function, is consistent with its complex role in normal and pathological aging.

Attention-dependent coupling with forebrain and brainstem neuromodulatory nuclei changes across the lifespan.

Attentional states continuously reflect the predictability and uncertainty in one's environment having important consequences for learning and memory. Beyond well known cortical contributions, rapid shifts in attention are hypothesized to also originate from deep nuclei, such as the basal forebrain (BF) and locus coeruleus (LC) neuromodulatory systems. These systems are also the first to change with aging. Here we characterized the interplay between these systems and their regulation of afferent targets - the hippocampus (HPC) and posterior cingulate cortex (PCC) - across the lifespan. To examine the role of attentional salience on task-dependent functional connectivity, we used a target-distractor go/no go task presented during functional MRI. In younger adults, BF coupling with the HPC, and LC coupling with the PCC, increased with behavioral relevance (targets vs distractors). Although the strength and presence of significant regional coupling changed in middle age, the most striking change in network connectivity was in old age, such that in older adults BF and LC coupling with their cortical afferents was largely absent and replaced by stronger interconnectivity between LC-BF nuclei. Overall rapid changes in attention related to behavioral relevance revealed distinct roles of subcortical neuromodulatory systems. The pronounced changes in functional network architecture across the lifespan suggest a decrease in these distinct roles, with deafferentation of cholinergic and noradrenergic systems associated with a shift towards mutual support during attention guided to external stimuli.

Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis.

Recent studies have revealed the production of time-locked blood oxygenation level-dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to tasks, challenging the existence of sparse and localized brain functions and highlighting the pervasiveness of potential false negative fMRI findings. "Whole-brain" actually refers to gray matter, the only tissue traditionally studied with fMRI. However, several reports have demonstrated reliable detection of BOLD signals in white matter, which have previously been largely ignored. Using simple tasks and analyses, we demonstrate BOLD signal changes across the whole brain, in both white and gray matters, in similar manner to previous reports of whole brain studies. We investigated whether white matter displays time-locked BOLD signals across multiple structural pathways in response to a stimulus in a similar manner to the cortex. We find that both white and gray matter show time-locked activations across the whole brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that, just like all gray matter, essentially all white matter in the brain shows time-locked BOLD signal changes in response to multiple stimuli, challenging the idea of sparse functional localization and the prevailing wisdom of treating white matter BOLD signals as artifacts to be removed.

Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain.

Magnetic resonance spectroscopy (MRS) is one of the few non-invasive imaging modalities capable of making neurochemical and metabolic measurements in vivo. Traditionally, the clinical utility of MRS has been narrow. The most common use has been the "single-voxel spectroscopy" variant to discern the presence of a lactate peak in the spectra in one location in the brain, typically to evaluate for ischemia in neonates. Thus, the reduction of rich spectral data to a binary variable has not classically necessitated much signal processing. However, scanners have become more powerful and MRS sequences more advanced, increasing data complexity and adding 2 to 3 spatial dimensions in addition to the spectral one. The result is a spatially- and spectrally-variant MRS image ripe for image processing innovation. Despite this potential, the logistics for robustly accessing and manipulating MRS data across different scanners, data formats, and software standards remain unclear. Thus, as research into MRS advances, there is a clear need to better characterize its image processing considerations to facilitate innovation from scientists and engineers. Building on established neuroimaging standards, we describe a framework for manipulating these images that generalizes to the voxel, spectral, and metabolite level across space and multiple imaging sites while integrating with LCModel, a widely used quantitative MRS peak-fitting platform. In doing so, we provide examples to demonstrate the advantages of such a workflow in relation to recent publications and with new data. Overall, we hope our characterizations will lower the barrier of entry to MRS processing for neuroimaging researchers.

Automatic Preprocessing Pipeline for White Matter Functional Analyses of Large-Scale Databases.

Recently, increasing evidence suggests that fMRI signals in white matter (WM), conventionally ignored as nuisance, are robustly detectable using appropriate processing methods and are related to neural activity, while changes in WM with aging and degeneration are also well documented. These findings suggest variations in patterns of BOLD signals in WM should be investigated. However, existing fMRI analysis tools, which were designed for processing gray matter signals, are not well suited for large-scale processing of WM signals in fMRI data. We developed an automatic pipeline for high-performance preprocessing of fMRI images with emphasis on quantifying changes in BOLD signals in WM in an aging population. At the image processing level, the pipeline integrated existing software modules with fine parameter tunings and modifications to better extract weaker WM signals. The preprocessing results primarily included whole-brain time-courses, functional connectivity, maps and tissue masks in a common space. At the job execution level, this pipeline exploited a local XNAT to store datasets and results, while using DAX tool to automatic distribute batch jobs that run on high-performance computing clusters. Through the pipeline, 5,034 fMRI/T1 scans were preprocessed. The intraclass correlation coefficient (ICC) of test-retest experiment based on the preprocessed data is 0.52 - 0.86 (N=1000), indicating a high reliability of our pipeline, comparable to previously reported ICC in gray matter experiments. This preprocessing pipeline highly facilitates our future analyses on WM functional alterations in aging and may be of benefit to a larger community interested in WM fMRI studies.

Affine image registration of arterial spin labeling MRI using deep learning networks.

Convolutional neural networks (CNN) have demonstrated good accuracy and speed in spatially registering high signal-to-noise ratio (SNR) structural magnetic resonance imaging (sMRI) images. However, some functional magnetic resonance imaging (fMRI) images, e.g., those acquired from arterial spin labeling (ASL) perfusion fMRI, are of intrinsically low SNR and therefore the quality of registering ASL images using CNN is not clear. In this work, we aimed to explore the feasibility of a CNN-based affine registration network (ARN) for registration of low-SNR three-dimensional ASL perfusion image time series and compare its performance with that from the state-of-the-art statistical parametric mapping (SPM) algorithm. The six affine parameters were learned from the ARN using both simulated motion and real acquisitions from ASL perfusion fMRI data and the registered images were generated by applying the transformation derived from the affine parameters. The speed and registration accuracy were compared between ARN and SPM. Several independent datasets, including meditation study (10 subjects × 2), bipolar disorder study (26 controls, 19 bipolar disorder subjects), and aging study (27 young subjects, 33 older subjects), were used to validate the generality of the trained ARN model. The ARN method achieves superior image affine registration accuracy (total translation/total rotation errors of ARN vs. SPM: 1.17 mm/1.23° vs. 6.09 mm/12.90° for simulated images and reduced MSE/L1/DSSIM/Total errors of 18.07% / 19.02% / 0.04% / 29.59% for real ASL test images) and 4.4 times (ARN vs. SPM: 0.50 s vs. 2.21 s) faster speed compared to SPM. The trained ARN can be generalized to align ASL perfusion image time series acquired with different scanners, and from different image resolutions, and from healthy or diseased populations. The results demonstrated that our ARN markedly outperforms the iteration-based SPM both for simulated motion and real acquisitions in terms of registration accuracy, speed, and generalization.

Functional alterations in bipartite network of white and grey matters during aging.

The effects of normal aging on functional connectivity (FC) within various brain networks of gray matter (GM) have been well-documented. However, the age effects on the networks of FC between white matter (WM) and GM, namely WM-GM FC, remains unclear. Evaluating crucial properties, such as global efficiency (GE), for a WM-GM FC network poses a challenge due to the absence of closed triangle paths which are essential for assessing network properties in traditional graph models. In this study, we propose a bipartite graph model to characterize the WM-GM FC network and quantify these challenging network properties. Leveraging this model, we assessed the WM-GM FC network properties at multiple scales across 1,462 cognitively normal subjects aged 22-96 years from three repositories (ADNI, BLSA and OASIS-3) and investigated the age effects on these properties throughout adulthood and during late adulthood (age ≥70 years). Our findings reveal that (1) heterogeneous alterations occurred in region-specific WM-GM FC over the adulthood and decline predominated during late adulthood; (2) the FC density of WM bundles engaged in memory, executive function and processing speed declined with age over adulthood, particularly in later years; and (3) the GE of attention, default, somatomotor, frontoparietal and limbic networks reduced with age over adulthood, and GE of visual network declined during late adulthood. These findings provide unpresented insights into multi-scale alterations in networks of WM-GM functional synchronizations during normal aging. Furthermore, our bipartite graph model offers an extendable framework for quantifying WM-engaged networks, which may contribute to a wide range of neuroscience research.

Changes in white matter functional networks across late adulthood.

Introduction: The aging brain is characterized by decreases in not only neuronal density but also reductions in myelinated white matter (WM) fibers that provide the essential foundation for communication between cortical regions. Age-related degeneration of WM has been previously characterized by histopathology as well as T2 FLAIR and diffusion MRI. Recent studies have consistently shown that BOLD (blood oxygenation level dependent) effects in WM are robustly detectable, are modulated by neural activities, and thus represent a complementary window into the functional organization of the brain. However, there have been no previous systematic studies of whether or how WM BOLD signals vary with normal aging. We therefore performed a comprehensive quantification of WM BOLD signals across scales to evaluate their potential as indicators of functional changes that arise with aging. Methods: By using spatial independent component analysis (ICA) of BOLD signals acquired in a resting state, WM voxels were grouped into spatially distinct functional units. The functional connectivities (FCs) within and among those units were measured and their relationships with aging were assessed. On a larger spatial scale, a graph was reconstructed based on the pair-wise connectivities among units, modeling the WM as a complex network and producing a set of graph-theoretical metrics. Results: The spectral powers that reflect the intensities of BOLD signals were found to be significantly affected by aging across more than half of the WM units. The functional connectivities (FCs) within and among those units were found to decrease significantly with aging. We observed a widespread reduction of graph-theoretical metrics, suggesting a decrease in the ability to exchange information between remote WM regions with aging. Discussion: Our findings converge to support the notion that WM BOLD signals in specific regions, and their interactions with other regions, have the potential to serve as imaging markers of aging.

Experimental demonstration of diffusion limitations on resolution and SNR in MR microscopy.

PURPOSE: MR microscopy is in principle capable of producing images at cellular resolution (<10 µm), but various factors limit the quality achieved in practice. A recognized limit on the signal to noise ratio and spatial resolution is the dephasing of transverse magnetization caused by diffusion of spins in strong gradients. Such effects may be reduced by using phase encoding instead of frequency encoding read-out gradients. However, experimental demonstration of the quantitative benefits of phase encoding are lacking, and the exact conditions in which it is preferred are not clearly established. We quantify the conditions where phase encoding outperforms a readout gradient with emphasis on the detrimental effects of diffusion on SNR and resolution. METHODS: A 15.2 T Bruker MRI scanner, with 1 T/m gradients, and micro solenoid RF coils < 1 mm in diameter, were used to quantify diffusion effects on resolution and the signal to noise ratio of frequency and phase encoded acquisitions. Frequency and phase encoding's spatial resolution and SNR per square root time were calculated and measured for images at the diffusion limited resolution. The point spread function was calculated and measured for phase and frequency encoding using additional constant time phase gradients with voxels 3-15 µm in dimension. RESULTS: The effect of diffusion during the readout gradient on SNR was experimentally demonstrated. The achieved resolutions of frequency and phase encoded acquisitions were measured via the point-spread-function and shown to be lower than the nominal resolution. SNR per square root time and actual resolution were calculated for a wide range of maximum gradient amplitudes, diffusion coefficients, and relaxation properties. The results provide a practical guide on how to choose between phase encoding and a conventional readout. Images of excised rat spinal cord at 10 µm × 10 µm in-plane resolution demonstrate phase encoding's benefits in the form of higher measured resolution and higher SNR than the same image acquired with a conventional readout. CONCLUSION: We provide guidelines to determine the extent to which phase encoding outperforms frequency encoding in SNR and resolution given a wide range of voxel sizes, sample, and hardware properties.