Estimation of field inhomogeneity map following magnitude-based ambiguity-resolved water-fat separation.

Magnitude-based PDFF (Proton Density Fat Fraction) and R2∗ mapping with resolved water-fat ambiguity is extended to calculate field inhomogeneity (field map) using the phase images. The estimation is formulated in matrix form, resolving the field map in a least-squares sense. PDFF and R2∗ from magnitude fitting may be updated using the estimated field maps. The limits of quantification of our voxel-independent implementation were assessed. Bland-Altman was used to compare PDFF and field maps from our method against a reference complex-based method on 152 UK Biobank subjects (1.5 T Siemens). A separate acquisition (3 T Siemens) presenting field inhomogeneities was also used. The proposed field mapping was accurate beyond double the complex-based limit range. High agreement was obtained between the proposed method and the reference in UK. Robust field mapping was observed at 3 T, for inhomogeneities over 400 Hz including rapid variation across edges. Field mapping following unambiguous magnitude-based water-fat separation was demonstrated in-vivo and showed potential at 3 T.

HiHi fMRI: a data-reordering method for measuring the hemodynamic response of the brain with high temporal resolution and high SNR.

There is emerging evidence that sampling the blood-oxygen-level-dependent (BOLD) response with high temporal resolution opens up new avenues to study the in vivo functioning of the human brain with functional magnetic resonance imaging. Because the speed of sampling and the signal level are intrinsically connected in magnetic resonance imaging via the T1 relaxation time, optimization efforts usually must make a trade-off to increase the temporal sampling rate at the cost of the signal level. We present a method, which combines a sparse event-related stimulus paradigm with subsequent data reshuffling to achieve high temporal resolution while maintaining high signal levels (HiHi). The proof-of-principle is presented by separately measuring the single-voxel time course of the BOLD response in both the primary visual and primary motor cortices with 100-ms temporal resolution.

Comparison between magnetic resonance and ultrasound-derived indicators of hepatic steatosis in a pooled NAFLD cohort.

BACKGROUND & AIMS: MRI-based proton density fat fraction (PDFF) and the ultrasound-derived controlled attenuation parameter (CAP) are non-invasive techniques for quantifying liver fat, which can be used to assess steatosis in patients with non-alcoholic fatty liver disease (NAFLD). This study compared both of these techniques to histopathological graded steatosis for the assessment of fat levels in a large pooled NAFLD cohort. METHODS: This retrospective study pooled N = 581 participants from two suspected NAFLD cohorts (mean age (SD) 56 (12.7), 60% females). Steatosis was graded according to NASH-CRN criteria. Liver fat was measured non-invasively using PDFF (with Liver MultiScan's Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation method, LMS-IDEAL, Perspectum, Oxford) and CAP (FibroScan, Echosens, France), and their diagnostic performances were compared. RESULTS: LMS-IDEAL and CAP detected steatosis grade ≥ 1 with AUROCs of 1.00 (95% CI, 0.99-1.0) and 0.95 (95% CI, 0.91-0.99), respectively. LMS-IDEAL was superior to CAP for detecting steatosis grade ≥ 2 with AUROCs of 0.77 (95% CI, 0.73-0.82] and 0.60 (95% CI, 0.55-0.65), respectively. Similarly, LMS-IDEAL outperformed CAP for detecting steatosis grade ≥ 3 with AUROCs of 0.81 (95% CI, 0.76-0.87) and 0.63 (95% CI, 0.56-0.70), respectively. CONCLUSION: LMS-IDEAL was able to diagnose individuals accurately across the spectrum of histological steatosis grades. CAP performed well in identifying individuals with lower levels of fat (steatosis grade ≥1); however, its diagnostic performance was inferior to LMS-IDEAL for higher levels of fat (steatosis grades ≥2 and ≥3). TRIAL REGISTRATION: ClinicalTrials.gov (NCT03551522); https://clinicaltrials.gov/ct2/show/NCT03551522. UMIN Clinical Trials Registry (UMIN000026145); https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000026145.

Magnitude-intrinsic water-fat ambiguity can be resolved with multipeak fat modeling and a multipoint search method.

PURPOSE: To develop a postprocessing algorithm for multiecho chemical-shift encoded water-fat separation that estimates proton density fat fraction (PDFF) maps over the full dynamic range (0-100%) using multipeak fat modeling and multipoint search optimization. To assess its accuracy, reproducibility, and agreement with state-of-the-art complex-based methods, and to evaluate its robustness to artefacts in abdominal PDFF maps. METHODS: We introduce MAGO (MAGnitude-Only), a magnitude-based reconstruction that embodies multipeak liver fat spectral modeling and multipoint optimization, and which is compatible with asymmetric echo acquisitions. MAGO is assessed first for accuracy and reproducibility on publicly available phantom data. Then, MAGO is applied to N = 178 UK Biobank cases, in which its liver PDFF measures are compared using Bland-Altman analysis with those from a version of the hybrid iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL) algorithm, LiverMultiScan IDEAL (LMS IDEAL, Perspectum Diagnostics Ltd, Oxford, UK). Finally, MAGO is tested on a succession of high field challenging cases for which LMS IDEAL generated artefacts in the PDFF maps. RESULTS: Phantom data showed accurate, reproducible MAGO PDFF values across manufacturers, field strengths, and acquisition protocols. Moreover, we report excellent agreement between MAGO and LMS IDEAL for 6-echo, 1.5 tesla human acquisitions (bias = -0.02% PDFF, 95% confidence interval = ±0.13% PDFF). When tested on 12-echo, 3 tesla cases from different manufacturers, MAGO was shown to be more robust to artefacts compared to LMS IDEAL. CONCLUSION: MAGO resolves the water-fat ambiguity over the entire fat fraction dynamic range without compromising accuracy, therefore enabling robust PDFF estimation where phase data is inaccessible or unreliable and complex-based and hybrid methods fail.

Validation of a standardized MRI method for liver fat and T2* quantification.

PURPOSE: Several studies have demonstrated the accuracy, precision, and reproducibility of proton density fat fraction (PDFF) quantification using vendor-specific image acquisition protocols and PDFF estimation methods. The purpose of this work is to validate a confounder-corrected, cross-vendor, cross field-strength, in-house variant LMS IDEAL of the IDEAL method licensed from the University of Wisconsin, which has been developed for routine clinical use. METHODS: LMS IDEAL is implemented using a combination of patented and/or published acquisition and some novel model fitting methods required to correct confounds which result from the imaging and estimation processes, including: water-fat ambiguity; T2* relaxation; multi-peak fat modelling; main field inhomogeneity; T1 and noise bias; bipolar readout gradients; and eddy currents. LMS IDEAL has been designed to use image acquisition protocols that can be installed on most MRI scanners and cloud-based image processing to provide fast, standardized clinical results. Publicly available phantom data were used to validate LMS IDEAL PDFF calculations against results from originally published IDEAL methodology. LMS PDFF and T2* measurements were also compared with an independent technique in human volunteer data (n = 179) acquired as part of the UK Biobank study. RESULTS: We demonstrate excellent agreement of LMS IDEAL across vendors, field strengths, and over a wide range of PDFF and T2* values in the phantom study. The performance of LMS IDEAL was then assessed in vivo against widely accepted PDFF and T2* estimation methods (LMS Dixon and LMS T2*, respectively), demonstrating the robustness of LMS IDEAL to potential sources of error. CONCLUSION: The development and clinical validation of the LMS IDEAL algorithm as a chemical shift-encoded MRI method for PDFF and T2* estimation contributes towards robust, unbiased applications for quantification of hepatic steatosis and iron overload, which are key features of chronic liver disease.

Real-time decoding of covert attention in higher-order visual areas.

Brain-computer-interfaces (BCI) provide a means of using human brain activations to control devices for communication. Until now this has only been demonstrated in primary motor and sensory brain regions, using surgical implants or non-invasive neuroimaging techniques. Here, we provide proof-of-principle for the use of higher-order brain regions involved in complex cognitive processes such as attention. Using realtime fMRI, we implemented an online 'winner-takes-all approach' with quadrant-specific parameter estimates, to achieve single-block classification of brain activations. These were linked to the covert allocation of attention to real-world images presented at 4-quadrant locations. Accuracies in three target regions were significantly above chance, with individual decoding accuracies reaching upto 70%. By utilising higher order mental processes, 'cognitive BCIs' access varied and therefore more versatile information, potentially providing a platform for communication in patients who are unable to speak or move due to brain injury.

Evaluation of MLACF based calculated attenuation brain PET imaging for FDG patient studies.

Calculating attenuation correction for brain PET imaging rather than using CT presents opportunities for low radiation dose applications such as pediatric imaging and serial scans to monitor disease progression. Our goal is to evaluate the iterative time-of-flight based maximum-likelihood activity and attenuation correction factors estimation (MLACF) method for clinical FDG brain PET imaging. FDG PET/CT brain studies were performed in 57 patients using the Biograph mCT (Siemens) four-ring scanner. The time-of-flight PET sinograms were acquired using the standard clinical protocol consisting of a CT scan followed by 10 min of single-bed PET acquisition. Images were reconstructed using CT-based attenuation correction (CTAC) and used as a gold standard for comparison. Two methods were compared with respect to CTAC: a calculated brain attenuation correction (CBAC) and MLACF based PET reconstruction. Plane-by-plane scaling was performed for MLACF images in order to fix the variable axial scaling observed. The noise structure of the MLACF images was different compared to those obtained using CTAC and the reconstruction required a higher number of iterations to obtain comparable image quality. To analyze the pooled data, each dataset was registered to a standard template and standard regions of interest were extracted. An SUVr analysis of the brain regions of interest showed that CBAC and MLACF were each well correlated with CTAC SUVrs. A plane-by-plane error analysis indicated that there were local differences for both CBAC and MLACF images with respect to CTAC. Mean relative error in the standard regions of interest was less than 5% for both methods and the mean absolute relative errors for both methods were similar (3.4% ± 3.1% for CBAC and 3.5% ± 3.1% for MLACF). However, the MLACF method recovered activity adjoining the frontal sinus regions more accurately than CBAC method. The use of plane-by-plane scaling of MLACF images was found to be a crucial step in order to obtain improved activity estimates. Presence of local errors in both MLACF and CBAC based reconstructions would require the use of a normal database for clinical assessment. However, further work is required in order to assess the clinical advantage of MLACF over CBAC based method.

The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data.

BACKGROUND: Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies. NEW METHODS: We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps - from flexible read-in of data formats to GLM regressor/contrast creation - without any manual intervention. RESULTS: We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N=35). COMPARISON WITH EXISTING METHODS: The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data. CONCLUSIONS: Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.

Classification of amyloid status using machine learning with histograms of oriented 3D gradients.

Brain amyloid burden may be quantitatively assessed from positron emission tomography imaging using standardised uptake value ratios. Using these ratios as an adjunct to visual image assessment has been shown to improve inter-reader reliability, however, the amyloid positivity threshold is dependent on the tracer and specific image regions used to calculate the uptake ratio. To address this problem, we propose a machine learning approach to amyloid status classification, which is independent of tracer and does not require a specific set of regions of interest. Our method extracts feature vectors from amyloid images, which are based on histograms of oriented three-dimensional gradients. We optimised our method on 133 18F-florbetapir brain volumes, and applied it to a separate test set of 131 volumes. Using the same parameter settings, we then applied our method to 209 11C-PiB images and 128 18F-florbetaben images. We compared our method to classification results achieved using two other methods: standardised uptake value ratios and a machine learning method based on voxel intensities. Our method resulted in the largest mean distances between the subjects and the classification boundary, suggesting that it is less likely to make low-confidence classification decisions. Moreover, our method obtained the highest classification accuracy for all three tracers, and consistently achieved above 96% accuracy.

Semiquantitative slab view display for visual evaluation of 123I-FP-CIT SPECT.

OBJECTIVE: Dopamine transporter single-photon emission computed tomography (SPECT) with I-FP-CIT is used widely in the diagnosis of clinically uncertain parkinsonian syndromes. In terms of the evaluation of FP-CIT SPECT, some practice guidelines state that visual interpretation alone is generally sufficient in clinical patient care, whereas other guidelines consider semiquantitative analysis of striatal dopamine transporter availability mandatory. This discrepancy might be because of a relative lack of widely available display tools for FP-CIT SPECT. In this study, we evaluate a semiquantitative slab view display optimized for visual evaluation of FP-CIT SPECT that might resolve the discrepancy. PATIENTS AND METHODS: The reconstructed FP-CIT SPECT image was stereotactically normalized and scaled voxel by voxel to the mean uptake in the entire brain without striata. From the resulting distribution volume ratio image, a 12-mm-thick transversal slice (slab) through the striata was displayed with a standard colour table with predefined fixed thresholds on the distribution volume ratio. Visual scoring of the semiquantitative slab view was performed twice by four independent readers in 235 unselected patients. The specific binding ratio in the caudate and putamen was computed by fully automated semiquantitative analysis with predefined standard regions of interest in template space. RESULTS: Intrarater and inter-rater agreement of binary visual categorization as 'normal' or 'reduced' was excellent (mean Cohen's κ=0.88 and 0.83, respectively). The area under the receiver-operator characteristic curve of the specific putamen-binding ratio for differentiation between visually normal and visually reduced (majority read) was 0.96. CONCLUSION: Visual interpretation of FP-CIT SPECT on the basis of the semiquantitative slab view display provides excellent stability within and between readers as well as very high agreement with semiquantitative analysis. This suggests that the slab view display enables reliable visual interpretation of FP-CIT SPECT in clinical routine patient care.

Quantification of 18F-florbetapir PET: comparison of two analysis methods.

PURPOSE: (18)F-Florbetapir positron emission tomography (PET) can be used to image amyloid burden in the human brain. A previously developed research method has been shown to have a high test-retest reliability and good correlation between standardized uptake value ratio (SUVR) and amyloid burden at autopsy. The goal of this study was to determine how well SUVRs computed using the research method could be reproduced using an automatic quantification method, developed for clinical use. METHODS: Two methods for the quantitative analysis of (18)F-florbetapir PET were compared in a diverse clinical population of 604 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and in a group of 74 younger healthy controls (YHC). Cortex to cerebellum SUVRs were calculated using the research method, which is based on SPM, yielding 'research SUVRs', and using syngo.PET Amyloid Plaque, yielding 'sPAP SUVRs'. RESULTS: Mean cortical SUVRs calculated using the two methods for the 678 subjects were correlated (r = 0.99). Linear regression of sPAP SUVRs on research SUVRs was used to convert the research method SUVR threshold for florbetapir positivity of 1.10 to a corresponding threshold of 1.12 for sPAP. Using the corresponding thresholds, categorization of SUVR values were in agreement between research and sPAP SUVRs for 96.3 % of the ADNI images. SUVRs for all YHC were below the corresponding thresholds. CONCLUSION: Automatic florbetapir PET quantification using sPAP yielded cortex to cerebellum SUVRs which were correlated and in good agreement with the well-established research method. The research SUVR threshold for florbetapir positivity was reliably converted to a corresponding threshold for sPAP SUVRs.

Connectivity changes underlying neurofeedback training of visual cortex activity.

Neurofeedback based on real-time functional magnetic resonance imaging (fMRI) is a new approach that allows training of voluntary control over regionally specific brain activity. However, the neural basis of successful neurofeedback learning remains poorly understood. Here, we assessed changes in effective brain connectivity associated with neurofeedback training of visual cortex activity. Using dynamic causal modeling (DCM), we found that training participants to increase visual cortex activity was associated with increased effective connectivity between the visual cortex and the superior parietal lobe. Specifically, participants who learned to control activity in their visual cortex showed increased top-down control of the superior parietal lobe over the visual cortex, and at the same time reduced bottom-up processing. These results are consistent with efficient employment of top-down visual attention and imagery, which were the cognitive strategies used by participants to increase their visual cortex activity.

Elucidating neuroanatomical alterations in the at risk mental state and first episode psychosis: a combined voxel-based morphometry and voxel-based cortical thickness study.

Previous studies have reported alterations in grey matter volume and cortical thickness in individuals at high risk of developing psychosis and patients in the early stages of the disorder. Because these studies have typically focused on either grey matter volume or cortical thickness separately, the relationship between these two types of alterations is currently unclear. In the present investigation we used both voxel-based cortical thickness (VBCT) and voxel-based morphometry (VBM) to examine neuroanatomical differences in 21 individuals with an At Risk Mental State (ARMS) for psychosis, 26 patients with a First Episode of Psychosis (FEP) and 24 healthy controls. Statistical inferences were made at P<0.05 after correction for multiple comparisons. Cortical thinning in the right superior temporal gyrus was observed in both individuals at high risk of developing psychosis and patients with a first episode of the disorder, and therefore is likely to represent a marker of vulnerability. In contrast, the right posterior cingulate cortex showed cortical thinning in FEP patients relative to individuals at high risk, and therefore appears to be implicated in the onset of the disease. These neuroanatomical differences were expressed in terms of cortical thickness but not in terms of grey matter volume, and therefore may reflect specific cortical atrophy as opposed to variations in sulcal and gyral morphology.

Characterizing aging in the human brainstem using quantitative multimodal MRI analysis.

Aging is ubiquitous to the human condition. The MRI correlates of healthy aging have been extensively investigated using a range of modalities, including volumetric MRI, quantitative MRI (qMRI), and diffusion tensor imaging. Despite this, the reported brainstem related changes remain sparse. This is, in part, due to the technical and methodological limitations in quantitatively assessing and statistically analyzing this region. By utilizing a new method of brainstem segmentation, a large cohort of 100 healthy adults were assessed in this study for the effects of aging within the human brainstem in vivo. Using qMRI, tensor-based morphometry (TBM), and voxel-based quantification (VBQ), the volumetric and quantitative changes across healthy adults between 19 and 75 years were characterized. In addition to the increased R2* in substantia nigra corresponding to increasing iron deposition with age, several novel findings were reported in the current study. These include selective volumetric loss of the brachium conjunctivum, with a corresponding decrease in magnetization transfer and increase in proton density (PD), accounting for the previously described "midbrain shrinkage." Additionally, we found increases in R1 and PD in several pontine and medullary structures. We consider these changes in the context of well-characterized, functional age-related changes, and propose potential biophysical mechanisms. This study provides detailed quantitative analysis of the internal architecture of the brainstem and provides a baseline for further studies of neurodegenerative diseases that are characterized by early, pre-clinical involvement of the brainstem, such as Parkinson's and Alzheimer's diseases.

Using structural neuroimaging to make quantitative predictions of symptom progression in individuals at ultra-high risk for psychosis.

Neuroimaging holds the promise that it may one day aid the clinical assessment of individual psychiatric patients. However, the vast majority of studies published so far have been based on average differences between groups, which do not permit accurate inferences at the level of the individual. We examined the potential of structural Magnetic Resonance Imaging (MRI) data for making accurate quantitative predictions about symptom progression in individuals at ultra-high risk for developing psychosis. Forty people at ultra-high risk for psychosis were scanned using structural MRI at first clinical presentation and assessed over a period of 2 years using the Positive and Negative Syndrome Scale. Using a multivariate machine learning method known as relevance vector regression (RVR), we examined the relationship between brain structure at first clinical presentation, characterized in terms of gray matter (GM) volume and cortical thickness (CT), and symptom progression at 2-year follow-up. The application of RVR to whole-brain CT MRI data allowed quantitative prediction of clinical scores with statistically significant accuracy (correlation = 0.34, p = 0.026; Mean Squared-Error = 249.63, p = 0.024). This prediction was informed by regions traditionally associated with schizophrenia, namely the right lateral and medial temporal cortex and the left insular cortex. In contrast, the application of RVR to GM volume did not allow prediction of symptom progression with statistically significant accuracy. These results provide proof-of-concept that it could be possible to use structural MRI to inform quantitative prediction of symptom progression in individuals at ultra-high risk of developing psychosis. This would enable clinicians to target those individuals at greatest need of preventative interventions thereby resulting in a more efficient use of health care resources.

High-resolution functional MRI at 3 T: 3D/2D echo-planar imaging with optimized physiological noise correction.

High-resolution functional MRI (fMRI) offers unique possibilities for studying human functional neuroanatomy. Although high-resolution fMRI has proven its potential at 7 T, most fMRI studies are still performed at rather low spatial resolution at 3 T. We optimized and compared single-shot two-dimensional echo-planar imaging (EPI) and multishot three-dimensional EPI high-resolution fMRI protocols. We extended image-based physiological noise correction from two-dimensional EPI to multishot three-dimensional EPI. The functional sensitivity of both acquisition schemes was assessed in a visual fMRI experiment. The physiological noise correction increased the sensitivity significantly, can be easily applied, and requires simple recordings of pulse and respiration only. The combination of three-dimensional EPI with physiological noise correction provides exceptional sensitivity for 1.5 mm high-resolution fMRI at 3 T, increasing the temporal signal-to-noise ratio by more than 25% compared to two-dimensional EPI.

Retrospective correction of physiological noise in DTI using an extended tensor model and peripheral measurements.

Diffusion tensor imaging is widely used in research and clinical applications, but this modality is highly sensitive to artefacts. We developed an easy-to-implement extension of the original diffusion tensor model to account for physiological noise in diffusion tensor imaging using measures of peripheral physiology (pulse and respiration), the so-called extended tensor model. Within the framework of the extended tensor model two types of regressors, which respectively modeled small (linear) and strong (nonlinear) variations in the diffusion signal, were derived from peripheral measures. We tested the performance of four extended tensor models with different physiological noise regressors on nongated and gated diffusion tensor imaging data, and compared it to an established data-driven robust fitting method. In the brainstem and cerebellum the extended tensor models reduced the noise in the tensor-fit by up to 23% in accordance with previous studies on physiological noise. The extended tensor model addresses both large-amplitude outliers and small-amplitude signal-changes. The framework of the extended tensor model also facilitates further investigation into physiological noise in diffusion tensor imaging. The proposed extended tensor model can be readily combined with other artefact correction methods such as robust fitting and eddy current correction.

Phase informed model for motion and susceptibility.

Field inhomogeneities caused by variations in magnetic susceptibility throughout the head lead to geometric distortions, mainly in the phase-encode direction of echo-planar images (EPI). The magnitude and spatial characteristics of the distortions depend on the orientation of the head in the magnetic field and will therefore vary with head movement. A new method is presented, based on a phase informed model for motion and susceptibility (PIMMS), which estimates the change in geometric distortion associated with head motion. This method fits a model of the head motion parameters and scanner hardware characteristics to EPI phase time series. The resulting maps of the model fit parameters are used to correct for susceptibility artifacts in the magnitude images. Results are shown for EPI-based fMRI time-series acquired at 3T, demonstrating that compared with conventional rigid body realignment, PIMMS removes residual variance associated with motion-related distortion effects. Furthermore, PIMMS can lead to a reduction in false negatives compared with the widely accepted approach which uses standard rigid body realignment and includes the head motion parameters in the statistical model. The PIMMS method can be used with any standard EPI sequence for which accurate phase information is available.

Degeneration of the injured cervical cord is associated with remote changes in corticospinal tract integrity and upper limb impairment.

BACKGROUND: Traumatic spinal cord injury (SCI) leads to disruption of axons and macroscopic tissue loss. Using diffusion tensor imaging (DTI), we assessed degeneration of the corticospinal tract (CST) in the cervical cord above a traumatic lesion and explored its relationship with cervical atrophy, remote axonal changes within the cranial CST and upper limb function. METHODS: Nine cervical injured volunteers with bilateral motor and sensory impairment and ten controls were studied. DTI of the cervical cord and brain provided measurements of fractional anisotropy (FA), while anatomical MRI assessed cross-sectional spinal cord area (i.e. cord atrophy). Spinal and central regions of interest (ROI) included the bilateral CST in the cervical cord and brain. Regression analysis identified correlations between spinal FA and cranial FA in the CST and disability. RESULTS: In individuals with SCI, FA was significantly lower in both CSTs throughout the cervical cord and brain when compared with controls (p≤0.05). Reduced FA of the cervical cord in patients with SCI was associated with smaller cord area (p = 0.002) and a lower FA of the cranial CST at the internal capsule level (p = 0.001). Lower FA in the cervical CST also correlated with impaired upper limb function, independent of cord area (p = 0.03). CONCLUSION: Axonal degeneration of the CST in the atrophic cervical cord, proximal to the site of injury, parallels cranial CST degeneration and is associated with disability. This DTI protocol can be used in longitudinal assessment of microstructural changes immediately following injury and may be utilised to predict progression and monitor interventions aimed at promoting spinal cord repair.

Improving visual perception through neurofeedback.

Perception depends on the interplay of ongoing spontaneous activity and stimulus-evoked activity in sensory cortices. This raises the possibility that training ongoing spontaneous activity alone might be sufficient for enhancing perceptual sensitivity. To test this, we trained human participants to control ongoing spontaneous activity in circumscribed regions of retinotopic visual cortex using real-time functional MRI-based neurofeedback. After training, we tested participants using a new and previously untrained visual detection task that was presented at the visual field location corresponding to the trained region of visual cortex. Perceptual sensitivity was significantly enhanced only when participants who had previously learned control over ongoing activity were now exercising control and only for that region of visual cortex. Our new approach allows us to non-invasively and non-pharmacologically manipulate regionally specific brain activity and thus provide "brain training" to deliver particular perceptual enhancements.