Objectives Research suggests that the majority of mild traumatic brain injury (mTBI) patients exhibit both cognitive and emotional dysfunction within the first weeks of injury, followed by symptom resolution 3–6 months post-injury. The neuronal correlates of said dysfunction are difficult to detect with standard clinical neuroimaging, complicating differential diagnosis and early identification of patients who may not recover. The current study examined whether resting state functional magnetic resonance imaging (FMRI) provides objective markers of injury and predicts cognitive, emotional and somatic complaints in mTBI patients semi-acutely (< 3 weeks post-injury) and in late recovery (3–5 month) phases. Methods Twenty seven semi-acute mTBI patients and 26 gender, age and education matched controls were studied. Fifteen out of 27 patients returned for a follow-up visit 3–5 months post-injury. The main dependent variables were spontaneous fluctuations (temporal correlation) in the default-mode (DMN) and fronto-parietal task-related (TRN) networks as measured by FMRI. Results Significant differences in self-reported cognitive, emotional and somatic complaints were observed (all p < .05), despite normal clinical (T1 and T2) imaging and neuropsychological testing results. Mild TBI patients demonstrated decreased functional connectivity within the DMN and hyper-connectivity between the DMN and lateral prefrontal cortex. Measures of functional connectivity exhibited high levels of sensitivity and specificity for patient classification and predicted cognitive complaints in the semi-acute injury stage. However, no changes in functional connectivity were observed across a four month recovery period. Conclusions Abnormal connectivity between the DMN and frontal cortex may provide objective biomarkers of mTBI and underlie cognitive impairment.

A strategy for using tissue water as a concentration standard in 1 H magnetic resonance spectroscopic imaging studies on the brain is presented, and the potential errors that may arise when the method is used are examined. The sensitivity of the method to errors in estimates of the different water compartment relaxation times is shown to be small at short echo times (TEs). Using data from healthy human subjects, it is shown that different image segmentation approaches that are commonly used to account for partial volume effects (SPM2, FSL's FAST, and K-means) lead to different estimates of metabolite levels, particularly in gray matter (GM), owing primarily to variability in the estimates of the cerebrospinal fluid (CSF) fraction. While consistency does not necessarily validate a method, a multispectral segmentation approach using FAST yielded the lowest intersubject variability in the estimates of GM metabolites. The mean GM and white matter (WM) levels of N-acetyl groups (NAc, primarily N-acetylaspartate), choline (Ch), and creatine (Cr) obtained in these subjects using the described method with The unsuppressed "internal" water signal was introduced as a concentration reference for single-voxel proton magnetic resonance spectroscopy ( 1 H-MRS) of the brain over a decade ago (1-4). However, to our knowledge, a detailed description of how this method could be applied to spectroscopic imaging (SI), or an examination of its potential sources of error has yet to be reported. In the majority of SI studies that reported "absolute" metabolite concentrations, the metabolite signals were converted to moles per liter or kilograms of tissue using either external metabolite solutions (5-7) or ventricle water (8,9), and relatively few groups have reported using internal water (10,11). The principal advantage of using internal water in SI studies is that certain factors and potential sources of error that need to be considered when using external concentration references (e.g., RF homogeneity, coil loading, or the SI point spread function (PSF)) are obviated, since the water and metabolite signals come from the same voxel and are acquired in essentially the same way.The major assumptions when using internal water, on the other hand, are that the water densities and signal relaxation times of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) in the region of interest (ROI) can be reliably estimated and, furthermore, do not change significantly among the studied groups. Moreover, it is essential that the volume fractions of these tissues and CSF in each SI voxel are accurately measured. Measuring partial volume effects is also a requirement when using external referencing methods, but the demand on accuracy is greater when using internal water. This is because only the signal from the combined GM-WM fraction of the total water, in which the detectable metabolites are exclusively located, is used as the concentration reference. The observed water signal, however, arises from a combination of the GM, WM, and CS...

Once an MRS dataset has been acquired, several important steps must be taken to obtain the desired metabolite concentration measures. First, the data must be preprocessed to prepare them for analysis. Next, the intensity of the metabolite signal(s) of interest must be estimated. Finally, the measured metabolite signal intensities must be converted into scaled concentration units employing a quantitative reference signal to allow meaningful interpretation. In this paper, we review these three main steps in the post-acquisition workflow of a single-voxel MRS experiment (preprocessing, analysis and quantification) and provide recommendations for best practices at each step. Abbreviations: 1 H, proton; 13 C, carbon-13; B 0 , main magnetic field; B 1 , RF field; Cr, creatine; CRMVB, Cramér-Rao minimum variance bound; CSF, cerebrospinal fluid; d GM , water density of grey matter; d WM , water density of white matter; ERETIC, Electric Reference to Access in vivo Concentrations; f CSF , volume fraction of cerebrospinal fluid inside the MRS voxel; fCSF H2O , water mole fraction in cerebrospinal fluid; fGM, volume fraction of gray matter inside the MRS voxel; fGM H2O , water mole fraction in gray matter; FFT, fast Fourier transform; FID, free induction decay; FQN, fit quality number; FWHM, full width at half maximum; f WM , volume fraction of white matter inside the MRS voxel; fWM H2O , water mole fraction in white matter; GM, grey matter; GPC, glycerophosphocholine; [H 2 O] molal , water concentration in moles of water per kilogram of tissue water = 55.49 moles/kg; [H 2 O] molar , water concentration in moles of water per liter of tissue water; HERMES, Hadamard encoding and reconstruction of MEGA-edited spectroscopy; MEGA-PRESS, Mescher-Garwood point resolved spectroscopy; [M] GM /[M] WM , assumed ratio of grey matter to white matter metabolite concentrations; MM, macromolecules; [M]molal, metabolite concentration in moles of metabolite per kilogram of tissue water; [M]molar, metabolite concentration in moles of metabolite per liter of tissue water; MRSI, magnetic resonance spectroscopic imaging; NAA, N-acetylaspartate; NAAG, N-acetylaspartylglutamate; N M , number of protons contributing to metabolite signal; N P , number of points in FID/spectrum; N pc , number of phase encoding steps in one phase cycle; N RF , number of RF channels; N tra , number of transients;PCh, phosphocholine; PCr, phosphocreatine; RH2O CSF , relaxation scaling factor for water in cerebrospinal fluid; RH2O GM , relaxation scaling factor for water in grey matter; RH2O WM , relaxation scaling factor for water in white matter; RM, relaxation scaling factor for tissue metabolite signal; RM GM , relaxation scaling factor for metabolite in grey matter; RM WM , relaxation scaling factor for metabolite in white matter; S H2O , water signal intensity; SH2O obs , observed water signal intensity in the presence of relaxation; S M , metabolite signal intensity; SM obs , observed metabolite signal intensity in the presence of relaxation; SNR, signal-to-noise r...