Time-division multiplexing (TDM) sequence removes bias in T2 estimation and relaxation-diffusion measurements.

Purpose: To compare the performance of multi-echo (ME) and time-division multiplexing (TDM) sequences for accelerated relaxation-diffusion MRI (rdMRI) acquisition and to examine their reliability in estimating accurate rdMRI microstructure measures. Method: The ME, TDM, and the reference single-echo (SE) sequences with six echo times (TE) were implemented using Pulseq with single-band (SB-) and multi-band 2 (MB2-) acceleration factors. On a diffusion phantom, the image intensities of the three sequences were compared, and the differences were quantified using the normalized root mean squared error (NRMSE). For the in-vivo brain scan, besides the image intensity comparison and T2-estimates, different methods were used to assess sequence-related effects on microstructure estimation, including the relaxation diffusion imaging moment (REDIM) and the maximum-entropy relaxation diffusion distribution (MaxEnt-RDD). Results: TDM performance was similar to the gold standard SE acquisition, whereas ME showed greater biases (3-4× larger NRMSEs for phantom, 2× for in-vivo). T2 values obtained from TDM closely matched SE, whereas ME sequences underestimated the T2 relaxation time. TDM provided similar diffusion and relaxation parameters as SE using REDIM, whereas SB-ME exhibited a 60% larger bias in the map and on average 3.5× larger bias in the covariance between relaxation-diffusion coefficients. Conclusion: Our analysis demonstrates that TDM provides a more accurate estimation of relaxation-diffusion measurements while accelerating the acquisitions by a factor of 2 to 3.

The distinction between long-term knowledge and short-term control processes is valid and useful.

The binary distinction De Neys questions has been put forward many times since the beginnings of psychology, in slightly different forms and under different names. It has proved enormously useful and has received detailed empirical support and careful modeling. At heart the distinction is that between knowledge in long-term memory and control processes in short-term memory.

Evaluating Targeted Therapeutic Response With Predictive Blood-Based Biomarkers in Patients With Chronic Mild Traumatic Brain Injury.

Chronic consequences of mild traumatic brain injury (mTBI) are heterogeneous, but may be treatable with targeted medical and rehabilitation interventions. A biological signature for the likelihood of response to therapy (i.e., "predictive" biomarkers) would empower personalized medicine post-mTBI. The purpose of this study was to correlate pre-intervention blood biomarker levels and the likelihood of response to targeted interventions for patients with chronic issues attributable to mTBI. Patients with chronic symptoms and/or disorders secondary to mTBI >3 months previous (104 days to 15 years; n = 74) were enrolled. Participants completed pre-intervention assessments of symptom burden, comprehensive clinical evaluation, and blood-based biomarker measurements. Multi-domain targeted interventions for specific symptoms and impairments across a 6-month treatment period were prescribed. Participants completed a follow-up testing after the treatment period. An all-possible model's backward logistic regression was built to identify predictors of improvement in relation to blood biomarker levels before intervention. The minimum clinically important difference (MCID) of the change score (post-intervention subtracted from pre-intervention) for the Post-Concussion Symptom Scale (PCSS) to identify treatment responders from non-responders was the primary outcome. The MCID for total PCSS score was 10. The model to predict change in PCSS score over the 6-month intervention was significant (R2 = 0.09; p = 0.01) and identified ubiquitin C-terminal hydrolase L1 (odds ratio [OR] = 2.53; 95% confidence interval [CI], 1.18-5.46; p = 0.02) and hyperphosphorylated tau (p-tau; OR = 0.70; 95% CI, 0.51-0.96; p = 0.03) as significant predictors of symptom improvement beyond the PCSS MCID. In this cohort of chronic TBI subjects, blood biomarkers before rehabilitation intervention predicted the likelihood of response to targeted therapy for chronic disorders post-TBI.

Association of Plasma Biomarkers with Sleep Outcomes and Treatment Response After Mild Traumatic Brain Injury.

Sleep disturbances occur in up to 70% of patients with mild traumatic brain injury (mTBI). Modern mTBI management recommends targeted treatment for the patient's unique clinical manifestations (i.e., obstructive sleep apnea, insomnia). The purpose of this study was to evaluate the association of plasma biomarkers with symptom reports, overnight sleep evaluations, and response to treatment for sleep disturbances secondary to mTBI. This study is a secondary analysis of a prospective multiple interventional trial of patients with chronic issues related to mTBI. Pre- and post-intervention assessments were conducted, including overnight sleep apnea evaluation, the Pittsburgh Sleep Quality Index (PSQI), and blinded analysis of blood biomarkers. Bivariate Spearman correlations were conducted for pre-intervention plasma biomarker concentrations and 1) PSQI change scores and 2) pre-intervention sleep apnea outcomes (i.e., oxygen saturation measures). A backward logistic regression model was built to evaluate the association of pre-intervention plasma biomarkers with improvement in PSQI over the treatment period (p < 0.05). Participants were 36.3 ± 8.6 years old and 6.1 ± 3.8 years from their index mTBI. Participants reported subjective improvements (PSQI = -3.7 ± 3.8), whereas 39.3% (n = 11) had improved PSQI scores beyond the minimum clinically important difference (MCID). PSQI change scores correlated with von Willebrand factor (vWF; ρ = -0.50; p = 0.02) and tau (ρ = -0.53; p = 0.01). Hyperphosphorylated tau correlated with average saturation (ρ = -0.29; p = 0.03), lowest desaturation (ρ = -0.27; p = 0.048), and baseline saturation (ρ = -0.31; p = 0.02). The multi-variate model (R 2 = 0.33; p = 0.001) retained only pre-intervention vWF as a predictor (odds ratio = 3.41; 95% confidence interval, 1.44-8.08; p = 0.005) of improving PSQI scores beyond the MCID. vWF had good discrimination (area under the curve = 0.83; p = 0.01), with an overall accuracy of 77%, sensitivity of 46.2%, and specificity of 90.0%. Validation of vWF as a potential predictive biomarker of sleep improvement post-mTBI could optimize personalized management and healthcare utilization.

Undersampled single-shell to MSMT fODF reconstruction using CNN-based ODE solver.

BACKGROUND AND OBJECTIVE: Diffusion MRI (dMRI) has been considered one of the most popular non-invasive techniques for studying the human brain's white matter (WM). dMRI is used to delineate the brain's microstructure by approximating the WM region's fiber tracts. The achieved fiber tracts can be utilized to assess mental diseases like Multiple sclerosis, ADHD, Seizures, Intellectual disability, and others. New techniques such as high angular resolution diffusion-weighted imaging (HARDI) have been developed, providing precise fiber directions, and overcoming the limitation of traditional DTI. Unlike Single-shell, Multi-shell HARDI provides tissue fractions for white matter, gray matter, and cerebrospinal fluid, resulting in a Multi-shell Multi-tissue fiber orientation distribution function (MSMT fODF). This MSMT fODF comes up with more precise fiber directions than a Single-shell, which helps to get correct fiber tracts. In addition, various multi-compartment diffusion models, including as CHARMED and NODDI, have been developed to describe the brain tissue microstructural information. This type of model requires multi-shell data to obtain more specific tissue microstructural information. However, a major concern with multi-shell is that it takes a longer scanning time restricting its use in clinical applications. In addition, most of the existing dMRI scanners with low gradient strengths commonly acquire a single b-value (shell) upto b=1000s/mm2 due to SNR (Signal-to-noise ratio) reasons and severe imaging artifacts. METHODS: To address this issue, we propose a CNN-based ordinary differential equations solver for the reconstruction of MSMT fODF from under-sampled and fully sampled Single-shell (b=1000s/mm2) dMRI. The proposed architecture consists of CNN-based Adams-Bash-forth and Runge-Kutta modules along with two loss functions, including L1 and total variation. RESULTS: We have shown quantitative results and visualization of fODF, fiber tracts, and structural connectivity for several brain regions on the publicly available HCP dataset. In addition, the obtained angular correlation coefficients for white matter and full brain are high, showing the proposed network's utility.Finally, we have also demonstrated the effect of noise by adjusting SNR from 5 to 50 and observed the network robustness. CONCLUSION: We can conclude that our model can accurately predict MSMT fODF from under-sampled or fully sampled Single-shell dMRI volumes.

VRfRNet: Volumetric ROI fODF reconstruction network for estimation of multi-tissue constrained spherical deconvolution with only single shell dMRI.

Diffusion MRI (dMRI) is one of the most popular techniques for studying the brain structure, mainly the white matter region. Among several sampling methods in dMRI, the high angular resolution diffusion imaging (HARDI) technique has attracted researchers due to its more accurate fiber orientation estimation. However, the current single-shell HARDI makes the intravoxel structure challenging to estimate accurately. While multi-shell acquisition can address this problem, it takes a longer scanning time, restricting its use in clinical applications. In addition, most existing dMRI scanners with low gradient-strengths often acquire single-shell up to b=1000s/mm2 because of signal-to-noise ratio issues and severe image artefacts. Hence, we propose a novel generative adversarial network, VRfRNet, for the reconstruction of multi-shell multi-tissue fiber orientation distribution function from single-shell HARDI volumes. Such a transformation learning is performed in the spherical harmonics (SH) space, as raw input HARDI volume is transformed to SH coefficients to soften gradient directions. The proposed VRfRNet consists of several modules, such as multi-context feature enrichment module, feature level attention, and softmax level attention. In addition, three loss functions have been used to optimize network learning, including L1, adversarial, and total variation. The network is trained and tested using standard qualitative and quantitative performance metrics on the publicly available HCP data-set.

Stepwise Stochastic Dictionary Adaptation Improves Microstructure Reconstruction with Orientation Distribution Function Fingerprinting.

Fitting of the multicompartment biophysical model of white matter is an ill-posed optimization problem. One approach to make it computationally tractable is through Orientation Distribution Function (ODF) Fingerprinting. However, the accuracy of this method relies solely on ODF dictionary generation mechanisms which either sample the microstructure parameters on a multidimensional grid or draw them randomly with a uniform distribution. In this paper, we propose a stepwise stochastic adaptation mechanism to generate ODF dictionaries tailored specifically to the diffusion-weighted images in hand. The results we obtained on a diffusion phantom and in vivo human brain images show that our reconstructed diffusivities are less noisy and the separation of a free water fraction is more pronounced than for the prior (uniform) distribution of ODF dictionaries.

Connectome 2.0: Developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome.

The first phase of the Human Connectome Project pioneered advances in MRI technology for mapping the macroscopic structural connections of the living human brain through the engineering of a whole-body human MRI scanner equipped with maximum gradient strength of 300 mT/m, the highest ever achieved for human imaging. While this instrument has made important contributions to the understanding of macroscale connectional topology, it has also demonstrated the potential of dedicated high-gradient performance scanners to provide unparalleled in vivo assessment of neural tissue microstructure. Building on the initial groundwork laid by the original Connectome scanner, we have now embarked on an international, multi-site effort to build the next-generation human 3T Connectome scanner (Connectome 2.0) optimized for the study of neural tissue microstructure and connectional anatomy across multiple length scales. In order to maximize the resolution of this in vivo microscope for studies of the living human brain, we will push the diffusion resolution limit to unprecedented levels by (1) nearly doubling the current maximum gradient strength from 300 mT/m to 500 mT/m and tripling the maximum slew rate from 200 T/m/s to 600 T/m/s through the design of a one-of-a-kind head gradient coil optimized to minimize peripheral nerve stimulation; (2) developing high-sensitivity multi-channel radiofrequency receive coils for in vivo and ex vivo human brain imaging; (3) incorporating dynamic field monitoring to minimize image distortions and artifacts; (4) developing new pulse sequences to integrate the strongest diffusion encoding and highest spatial resolution ever achieved in the living human brain; and (5) calibrating the measurements obtained from this next-generation instrument through systematic validation of diffusion microstructural metrics in high-fidelity phantoms and ex vivo brain tissue at progressively finer scales with accompanying diffusion simulations in histology-based micro-geometries. We envision creating the ultimate diffusion MRI instrument capable of capturing the complex multi-scale organization of the living human brain - from the microscopic scale needed to probe cellular geometry, heterogeneity and plasticity, to the mesoscopic scale for quantifying the distinctions in cortical structure and connectivity that define cyto- and myeloarchitectonic boundaries, to improvements in estimates of macroscopic connectivity.

Targeted Intervention Improves Symptoms and Impairments in Patients With Mild Traumatic Brain Injury With Chronic Symptom: A Prospective, Multiple Interventional Research Trial.

BACKGROUND: Mild traumatic brain injury (mTBI) and its potential long-term consequences is a primary concern for the US military. The purpose of the study is to evaluate if participants improved in anxiety/mood symptoms, sleep quality, and vestibular/ocular symptoms following a 6-month active intervention, and to explore the effect of targeted treatment for those with specific symptoms/impairments (e.g., psychological, sleep, ocular, vestibular). MATERIALS AND METHODS: A multidisciplinary clinical team adjudicated participants (n=72, 35.8±8.6 years old, 19% female) to have one of the following primary clinical trajectories: psychological (PSYCH; n=34), sleep (SLEEP; n=25) and vestibular/ocular (VESTIB/OCULAR; n=18). Participants returned for follow-up assessment 6 months later. Assessments included the Post-Concussion Symptom Scale [PCSS], Generalized Anxiety Disorder-7 [GAD-7], Pittsburgh Sleep Quality Index [PSQI], and Dizziness Handicap Inventory [DHI]. Change in concussion symptoms and primary outcome for the given trajectory (i.e., PSYCH=GAD-7, SLEEP=PSQI, VESTIB/OCULAR=DHI) was assessed. RESULTS: Following the 6-month intervention, participants reduced PCSS Score (-14.5±2.4; p<.001; η2=0.34), GAD-7 (-3.1±0.5; p<.001; η2=0.34), PSQI (-2.7±0.5; p<.001; η2=0.34) and DHI (-9.2±2.0; p<.001; η2=0.23). PSYCH (n=34) reduced PCSS score (-17.9±3.6; p<.001; η2=0.45) and GAD-7 (-3.1±0.7; p<.001; η2=0.38). SLEEP (n=25) reduced PCSS score (-8.8±4.4; p=.06; η2=0.15) and PSQI (-3.6±0.9; p<.001; η2=0.45) scores. VESTIB/OCULAR (n=18) reduced PCSS score (-16.7±4.8; p=.03; η2=0.45), and DHI (-15.7±5.5; p=.012; η2=0.35). CONCLUSIONS: Large effects were observed for concussion, anxiety, sleep, and dizziness symptom reduction over 6-month treatment. Each primary outcome demonstrated a larger treatment effect for the given trajectory than the overall sample, indicating that targeted treatment can reduce symptom burden in patients with mTBI with chronic symptoms.

Symptom-Dependent Changes in MEG-Derived Neuroelectric Brain Activity in Traumatic Brain Injury Patients with Chronic Symptoms.

Neuroelectric measures derived from human magnetoencephalographic (MEG) recordings hold promise as aides to diagnosis and treatment monitoring and targeting for chronic sequelae of traumatic brain injury (TBI). This study tests novel MEG-derived regional brain measures of tonic neuroelectric activation for long-term test-retest reliability and sensitivity to symptoms. Resting state MEG recordings were obtained from a normative cohort, Cambridge Centre for Ageing and Neuroscience (CamCAN), baseline: n = 619; mean 16-month follow-up: n = 253) and a chronic symptomatic TBI cohort, Targeted Evaluation, Action and Monitoring of Traumatic Brain Injury (TEAM-TBI), baseline: n = 64; mean 6-month follow-up: n = 39). For the CamCAN cohort, MEG-derived neuroelectric measures showed good long-term test-retest reliability for most of the 103 automatically identified stereotypic regions. The TEAM-TBI cohort was screened for depression, somatization, and anxiety with the Brief Symptom Inventory and for insomnia with the Insomnia Severity Index. Linear classifiers constructed from the 103 regional measures from each TEAM-TBI cohort member distinguished those with and without each symptom, with p < 0.01 for each-i.e., the tonic regional neuroelectric measures of activation are sensitive to the presence/absence of these symptoms. The novel regional MEG-derived neuroelectric measures obtained and tested in this study demonstrate the necessary and sufficient properties to be clinically useful-i.e., good test-retest reliability, sensitivity to symptoms in each individual, and obtainable using automatic processing without human judgement or intervention.

MEG-Derived Symptom-Sensitive Biomarkers with Long-Term Test-Retest Reliability.

Neuroelectric measures derived from human magnetoencephalographic (MEG) recordings hold promise as aides to diagnosis and treatment monitoring and targeting for chronic sequelae of traumatic brain injury (TBI). This study tests novel MEG-derived regional brain measures of tonic neuroelectric activation for long-term test-retest reliability and sensitivity to symptoms. Resting state MEG recordings were obtained from a normative cohort (CamCAN, baseline: n = 613; mean 16-month follow-up: n = 245) and a chronic symptomatic TBI cohort (TEAM-TBI, baseline: n = 62; mean 6-month follow-up: n = 40). The MEG-derived neuroelectric measures were corrected for the empty-room contribution using a random forest classifier. The mean 16-month correlation between baseline and 16-month follow-up CamCAN measures was 0.67; test-retest reliability was markedly improved in this study compared with previous work. The TEAM-TBI cohort was screened for depression, somatization, and anxiety with the Brief Symptom Inventory and for insomnia with the Insomnia Severity Index and was assessed via adjudication for six clinical syndromes: chronic pain, psychological health, and oculomotor, vestibular, cognitive, and sleep dysfunction. Linear classifiers constructed from the 136 regional measures from each TEAM-TBI cohort member distinguished those with and without each symptom, p < 0.0003 for each, i.e., the tonic regional neuroelectric measures of activation are sensitive to the presence/absence of these symptoms and clinical syndromes. The novel regional MEG-derived neuroelectric measures obtained and tested in this study demonstrate the necessary and sufficient properties to be clinically useful, i.e., good test-retest reliability, sensitivity to symptoms in each individual, and obtainable using automatic processing without human judgement or intervention.

Multi-Shell D-MRI Reconstruction via Residual Learning utilizing Encoder-Decoder Network with Attention (MSR-Net).

Contemporary diffusion MRI based analysis with HARDI, which provides more accurate fiber orientation, can be performed using single or multiple b-values (single or multi-shell). Single shell HARDI cannot provide volume fraction for different tissue types, which can produce bias and noisier results in estimation of fiber ODF. Multi-shell acquisition can resolve this issue. However, it requires more scanning time and is therefore not very well suited in clinical setting. Considering this, we propose a novel deep learning architecture, MSR-Net, for reconstruction of diffusion MRI volumes for some b-value using acquisitions at another b-value. In this work, we demonstrate this for b = 2000 s/mm2 and b = 1000 s/mm2. We learn such a transformation in the space of spherical harmonic coefficients. The proposed network consists of encoder-decoder along-with an attention module and a feature module. We have considered L2 and Content loss for optimizing and improving the performance. We have trained and validated the network using the HCP data-set with standard qualitative and quantitative performance measures.

[18F]FDG, [11C]PiB, and [18F]AV-1451 PET Imaging of Neurodegeneration in Two Subjects With a History of Repetitive Trauma and Cognitive Decline.

Background: Trauma-related neurodegeneration can be difficult to differentiate from multifactorial neurodegenerative syndromes, both clinically and radiographically. We have initiated a protocol for in vivo imaging of patients with suspected TBI-related neurodegeneration utilizing volumetric MRI and PET studies, including [18F]FDG indexing cerebral glucose metabolism, [11C]PiB for Aß deposition, and [18F]AV-1451 for tau deposition. Objective: To present results from a neuroimaging protocol for in vivo evaluation of TBI-related neurodegeneration in patients with early-onset cognitive decline and a history of TBI. Methods: Patients were enrolled in parallel TBI studies and underwent a comprehensive neuropsychological test battery as well as an imaging protocol of volumetric MRI and PET studies. Findings from two patients were compared with two age-matched control subjects without a history of TBI. Results: Both chronic TBI patients demonstrated cognitive deficits consistent with early-onset dementia on neuropsychological testing, and one patient self-reported a diagnosis of probable early-onset AD. Imaging studies demonstrated significant [18F]AV-1451 uptake in the bilateral occipital lobes, substantial [11C]PiB uptake throughout the cortex in both TBI patients, and abnormally decreased [18F]FDG uptake in the posterior temporoparietal areas of the brain. One TBI patient also had subcortical volume loss. Control subjects demonstrated no appreciable [18F]AV-1451 or [11C]PiB uptake, had normal cortical volumes, and had normal cognition profiles on neuropsychological testing. Conclusions: In the two patients presented, the [11C]PiB and [18F]FDG PET scans demonstrate uptake patterns characteristic of AD. [11C]PiB PET scans showed widespread neocortical uptake with less abnormal uptake in the occipital lobes, whereas there was significant [18F]AV-1451 uptake in both occipital lobes.

A Preliminary High-Definition Fiber Tracking Study of the Executive Control Network in Blast-Induced Traumatic Brain Injury.

Blast-induced traumatic brain injury (bTBI) is common in veterans of the Iraq- and Afghanistan-era conflicts. However, the typical subtlety of neural alterations and absence of definitive biomarkers impede clinical detection on conventional imaging. This preliminary study examined the structure and functional correlates of executive control network (ECN) white matter in veterans to investigate the clinical utility of using high-definition fiber tracking (HDFT) to detect chronic bTBI. Demographically similar male veterans (N = 38) with and without bTBI (ages 24 to 50 years) completed standardized neuropsychological testing and magnetic resonance imaging. Quantitative HDFT metrics of subcortical-dorsolateral prefrontal cortex (DLPFC) tracts were derived. Moderate-to-large group effects were observed on HDFT metrics. Relative to comparisons, bTBI demonstrated elevated quantitative anisotropy (QA) and reduced right hemisphere volume of all examined tracts, and reduced fiber count and increased generalized fractional anisotropy in the right DLPFC-putamen tract and DLPFC-thalamus, respectively. The Group × Age interaction effect on DLPFC-caudate tract volume was large; age negatively related to volume in the bTBI group, but not comparison group. Groups performed similarly on the response inhibition measure. Performance (reaction time and commission errors) robustly correlated with HDFT tract metrics (QA and tract volume) in the comparison group, but not bTBI group. Results support anomalous density and integrity of ECN connectivity, particularly of the right DLPFC-putamen pathway, in bTBI. Results also support exacerbated aging in veterans with bTBI. Similar ECN function despite anomalous microstructure could reflect functional compensation in bTBI, although alternate interpretations are explored.

Assessment of quantitative magnetic resonance imaging metrics in the brain through the use of a novel phantom.

OBJECTIVE: Multisite and longitudinal neuroimaging studies are important in uncovering trajectories of recovery and neurodegeneration following traumatic brain injury (TBI) and concussion through the use of diffusion tensor imaging (DTI) and other imaging modalities. This study assessed differences in anisotropic diffusion measurement across four scanners using a human and a novel phantom developed in conjunction with the Chronic Effects of Neurotrauma Consortium. METHOD: Human scans provided measurement within biological tissue, and the novel physical phantom provided measures of anisotropic intra-tubular diffusion to serve as a model for intra-axonal water diffusion. Intra- and inter-scanner measurement variances were compared, and the impact on effect size was calculated. RESULTS: Intra-scanner test-retest reliability estimates for fractional anisotropy (FA) demonstrated relative stability over testing intervals. The human tissue and phantom showed similar FA ranges, high linearity and large within-device effect sizes. However, inter-scanner measures of FA indicated substantial differences, some of which exceeded typical DTI effect sizes in mild TBI. CONCLUSION: The diffusion phantom may be used to better elucidate inter-scanner variability in DTI-based measurement and provides an opportunity to better calibrate results obtained from scanners used in multisite and longitudinal studies. Novel solutions are being evaluated to understand and potentially overcome these differences.

Analysis of variability of fractional anisotropy values at 3T using a novel diffusion tensor imaging phantom.

We employed a novel diffusion tensor imaging phantom to study intra- and interscanner reproducibility on two 3T magnetic resonance (MR) scanners. Using a phantom containing thousands of hollow micron-size tubes in complex arrays, we performed two experiments using a b value of 1000 s/ms2 on two Siemens 3T Trio scanners. First, we performed 12-direction scans. Second, on one scanner, we performed two 64-direction protocols with different repetition times (TRs). We used a one-way analysis of variance to calculate differences between scanners and the Mann-Whitney U test to assess differences between 12-direction and 64-direction data. We calculated the coefficient of variation (CoV) for intrascanner and interscanner data. For 12-direction protocols, mean fractional anisotropy (FA) was 0.3003 for Scanner 1 (four scans) and 0.3094 for Scanner 2 (three scans). Lowest FA value on Scanner 1 was 2.56 standard deviations below the mean of Scanner 2. For 64-direction scans, mean FA was 0.2640 for 4000 ms TR and 0.2582 for 13,200 ms TR scans. For 12-direction scans, within-scanner CoV was 0.0326 for Scanner 1 and 0.0240 for Scanner 2; between-scanner CoV was 0.032. For 64-direction scans, CoV was 0.056 for TR 4000 ms and 0.0533 for TR 13,200 ms. The difference between median FA values of 12-direction and 64-direction scans was statistically significant ( p < 0.001). We found relatively good reproducibility on any single MR scanner. FA values from one scanner were sometimes significantly below the mean FA of another scanner, which has important implications for clinical use of DTI.

A comprehensive diffusion MRI dataset acquired on the MGH Connectome scanner in a biomimetic brain phantom.

We provide a comprehensive diffusion MRI dataset acquired with a novel biomimetic phantom mimicking human white matter. The fiber substrates in the diffusion phantom were constructed from hollow textile axons ("taxons") with an inner diameter of 11.8±1.2 µm and outer diameter of 33.5±2.3 µm. Data were acquired on the 3 T CONNECTOM MRI scanner with multiple diffusion times and multiple q-values per diffusion time, which is a dedicated acquisition for validation of microstructural imaging methods, such as compartment size and volume fraction mapping. Minimal preprocessing was performed to correct for susceptibility and eddy current distortions. Data were deposited in the XNAT Central database (project ID: dMRI_Phant_MGH).

Preliminary Evidence for Improvement in Symptoms, Cognitive, Vestibular, and Oculomotor Outcomes Following Targeted Intervention with Chronic mTBI Patients.

Introduction: To determine if targeted, active interventions would improve symptoms and impairment in previously intractable patients with chronic mild traumatic brain injury (mTBI). Materials and Methods: Twenty-six (20 males; 6 females) out of 51 (51%) former military and civilian patients with chronic (1-3 yr) mTBI enrolled in the TEAM traumatic brain injury (TBI) study completed both an initial and 6-mo post-intervention comprehensive mTBI assessment including symptoms (Post-concussion Symptom Scale [PCSS], Dizziness Handicap Inventory [DHI]), cognitive (Immediate Post-concussion Assessment and Cognitive Testing [ImPACT]), vestibular/oculomotor (Vestibular/Ocular Motor Screening [VOMS]), balance (Activities-specific Balance Confidence [ABC] scale, Balance Error Scoring System [BESS]), and cervical (Neck Disability Index [NDI]). Patients were prescribed progressive, targeted interventions and therapies (e.g., behavioral, vestibular, vision, and exertion) that matched their mTBI clinical profile. A series of paired t-tests adjusted for multiple corrections were used to compare pre- and post-intervention assessment scores. Results: Patients demonstrated significant improvement from pre- to post-intervention on total symptoms (t = 2.69, p = 0.01), verbal memory (t = -1.96, p = 0.05), ABC balance score (t = -2.05, p = 0.05), smooth pursuits (t = 2.32, p = 0.04), near-point convergence distance (t = -3.58, p = 0.01), vestibular ocular reflex (t = 2.31, p = 0.03), and visual motion sensitivity (t = 2.43, p = 0.03). Conclusions: Previously recalcitrant patients with chronic complex mTBI demonstrated significant improvement in symptoms, cognitive, vestibular, oculomotor, and balance function following targeted interventions.

Validation of diffusion MRI estimates of compartment size and volume fraction in a biomimetic brain phantom using a human MRI scanner with 300 mT/m maximum gradient strength.

Diffusion microstructural imaging techniques have attracted great interest in the last decade due to their ability to quantify axon diameter and volume fraction in healthy and diseased human white matter. The estimates of compartment size and volume fraction continue to be debated, in part due to the lack of a gold standard for validation and quality control. In this work, we validate diffusion MRI estimates of compartment size and volume fraction using a novel textile axon ("taxon") phantom constructed from hollow polypropylene yarns with distinct intra- and extra-taxonal compartments to mimic white matter in the brain. We acquired a comprehensive set of diffusion MRI measurements in the phantom using multiple gradient directions, diffusion times and gradient strengths on a human MRI scanner equipped with maximum gradient strength (Gmax) of 300 mT/m. We obtained estimates of compartment size and restricted volume fraction through a straightforward extension of the AxCaliber/ActiveAx frameworks that enables estimation of mean compartment size in fiber bundles of arbitrary orientation. The voxel-wise taxon diameter estimates of 12.2 ±â€¯0.9 µm were close to the manufactured inner diameter of 11.8 ±â€¯1.2 µm with Gmax = 300 mT/m. The estimated restricted volume fraction demonstrated an expected decrease along the length of the fiber bundles in accordance with the known construction of the phantom. When Gmax was restricted to 80 mT/m, the taxon diameter was overestimated, and the estimates for taxon diameter and packing density showed greater uncertainty compared to data with Gmax = 300 mT/m. In conclusion, the compartment size and volume fraction estimates resulting from diffusion measurements on a human scanner were validated against ground truth in a phantom mimicking human white matter, providing confidence that this method can yield accurate estimates of parameters in simplified but realistic microstructural environments. Our work also demonstrates the importance of a biologically analogous phantom that can be applied to validate a variety of diffusion microstructural imaging methods in human scanners and be used for standardization of diffusion MRI protocols for neuroimaging research.

Empirical validation of directed functional connectivity.

Mapping directions of influence in the human brain connectome represents the next phase in understanding its functional architecture. However, a host of methodological uncertainties have impeded the application of directed connectivity methods, which have primarily been validated via "ground truth" connectivity patterns embedded in simulated functional MRI (fMRI) and magneto-/electro-encephalography (MEG/EEG) datasets. Such simulations rely on many generative assumptions, and we hence utilized a different strategy involving empirical data in which a ground truth directed connectivity pattern could be anticipated with confidence. Specifically, we exploited the established "sensory reactivation" effect in episodic memory, in which retrieval of sensory information reactivates regions involved in perceiving that sensory modality. Subjects performed a paired associate task in separate fMRI and MEG sessions, in which a ground truth reversal in directed connectivity between auditory and visual sensory regions was instantiated across task conditions. This directed connectivity reversal was successfully recovered across different algorithms, including Granger causality and Bayes network (IMAGES) approaches, and across fMRI ("raw" and deconvolved) and source-modeled MEG. These results extend simulation studies of directed connectivity, and offer practical guidelines for the use of such methods in clarifying causal mechanisms of neural processing.