Resolution enhancement, noise suppression, and joint T2* decay estimation in dual-echo sodium-23 MR imaging using anatomically guided reconstruction.

PURPOSE: Sodium MRI is challenging because of the low tissue concentration of the 23 Na nucleus and its extremely fast biexponential transverse relaxation rate. In this article, we present an iterative reconstruction framework using dual-echo 23 Na data and exploiting anatomical prior information (AGR) from high-resolution, low-noise, 1 H MR images. This framework enables the estimation and modeling of the spatially varying signal decay due to transverse relaxation during readout (AGRdm), which leads to images of better resolution and reduced noise resulting in improved quantification of the reconstructed 23 Na images. METHODS: The proposed framework was evaluated using reconstructions of 30 noise realizations of realistic simulations of dual echo twisted projection imaging (TPI) 23 Na data. Moreover, three dual echo 23 Na TPI brain datasets of healthy controls acquired on a 3T Siemens Prisma system were reconstructed using conventional reconstruction, AGR and AGRdm. RESULTS: Our simulations show that compared to conventional reconstructions, AGR and AGRdm show improved bias-noise characteristics in several regions of the brain. Moreover, AGR and AGRdm images show more anatomical detail and less noise in the reconstructions of the experimental data sets. Compared to AGR and the conventional reconstruction, AGRdm shows higher contrast in the sodium concentration ratio between gray and white matter and between gray matter and the brain stem. CONCLUSION: AGR and AGRdm generate 23 Na images with high resolution, high levels of anatomical detail, and low levels of noise, potentially enabling high-quality 23 Na MR imaging at 3T.

Standardized reporting for Head CT Scans in patients suspected of traumatic brain injury (TBI): An international expert endeavor.

BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) is a major source of health loss and disability worldwide. Accurate and timely diagnosis of TBI is critical for appropriate treatment and management of the condition. Neuroimaging plays a crucial role in the diagnosis and characterization of TBI. Computed tomography (CT) is the first-line diagnostic imaging modality typically utilized in patients with suspected acute mild, moderate and severe TBI. Radiology reports play a crucial role in the diagnostic process, providing critical information about the location and extent of brain injury, as well as factors that could prevent secondary injury. However, the complexity and variability of radiology reports can make it challenging for healthcare providers to extract the necessary information for diagnosis and treatment planning. METHODS/RESULTS/CONCLUSION: In this article, we report the efforts of an international group of TBI imaging experts to develop a clinical radiology report template for CT scans obtained in patients suspected of TBI and consisting of fourteen different subdivisions (CT technique, mechanism of injury or clinical history, presence of scalp injuries, fractures, potential vascular injuries, potential injuries involving the extra-axial spaces, brain parenchymal injuries, potential injuries involving the cerebrospinal fluid spaces and the ventricular system, mass effect, secondary injuries, prior or coexisting pathology).

Deep multi-task learning and random forest for series classification by pulse sequence type and orientation.

PURPOSE: Increasingly complex MRI studies and variable series naming conventions reveal limitations of rule-based image routing, especially in health systems with multiple scanners and sites. Accurate methods to identify series based on image content would aid post-processing and PACS viewing. Recent deep/machine learning efforts classify 5-8 basic brain MR sequences. We present an ensemble model combining a convolutional neural network and a random forest classifier to differentiate 25 brain sequences and image orientation. METHODS: Series were grouped by descriptions into 25 sequences and 4 orientations. Dataset A, obtained from our institution, was divided into training (16,828 studies; 48,512 series; 112,028 images), validation (4746 studies; 16,612 series; 26,222 images) and test sets (6348 studies; 58,705 series; 3,314,018 images). Dataset B, obtained from a separate hospital, was used for out-of-domain external validation (1252 studies; 2150 series; 234,944 images). We developed an ensemble model combining a 2D convolutional neural network with a custom multi-task learning architecture and random forest classifier trained on DICOM metadata to classify sequence and orientation by series. RESULTS: The neural network, random forest, and ensemble achieved 95%, 97%, and 98% overall sequence accuracy on dataset A, and 98%, 99%, and 99% accuracy on dataset B, respectively. All models achieved > 99% orientation accuracy on both datasets. CONCLUSION: The ensemble model for series identification accommodates the complexity of brain MRI studies in state-of-the-art clinical practice. Expanding on previous work demonstrating proof-of-concept, our approach is more comprehensive with greater sequence diversity and orientation classification.

Training a neural network for Gibbs and noise removal in diffusion MRI.

PURPOSE: To develop and evaluate a neural network-based method for Gibbs artifact and noise removal. METHODS: A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images. RESULTS: Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions. CONCLUSIONS: The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.

Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.

Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep-learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an eye towards clinical applications, we summarize the active field of deep-learning-based MR image reconstruction. We review the basic concepts of how deep-learning algorithms aid in the transformation of raw k-space data to image data, and specifically examine accelerated imaging and artifact suppression. Recent efforts in these areas show that deep-learning-based algorithms can match and, in some cases, eclipse conventional reconstruction methods in terms of image quality and computational efficiency across a host of clinical imaging applications, including musculoskeletal, abdominal, cardiac, and brain imaging. This article is an introductory overview aimed at clinical radiologists with no experience in deep-learning-based MR image reconstruction and should enable them to understand the basic concepts and current clinical applications of this rapidly growing area of research across multiple organ systems.

Effect of intravoxel incoherent motion on diffusion parameters in normal brain.

At very low diffusion weighting the diffusion MRI signal is affected by intravoxel incoherent motion (IVIM) caused by dephasing of magnetization due to incoherent blood flow in capillaries or other sources of microcirculation. While IVIM measurements at low diffusion weightings have been frequently used to investigate perfusion in the body as well as in malignant tissue, the effect and origin of IVIM in normal brain tissue is not completely established. We investigated the IVIM effect on the brain diffusion MRI signal in a cohort of 137 radiologically-normal patients (62 male; mean age = 50.2 ±â€¯17.8, range = 18 to 94). We compared the diffusion tensor parameters estimated from a mono-exponential fit at b = 0 and 1000 s/mm2 versus at b = 250 and 1000 s/mm2. The asymptotic fitting method allowed for quantitative assessment of the IVIM signal fraction f* in specific brain tissue and regions. Our results show a mean (median) percent difference in the mean diffusivity of about 4.5 (4.9)% in white matter (WM), about 7.8 (8.7)% in cortical gray matter (GM), and 4.3 (4.2)% in thalamus. Corresponding perfusion fraction f* was estimated to be 0.033 (0.032) in WM, 0.066 (0.065) in cortical GM, and 0.033 (0.030) in the thalamus. The effect of f* with respect to age was found to be significant in cortical GM (Pearson correlation ρ â€‹= â€‹0.35, p â€‹= â€‹3*10-5) and the thalamus (Pearson correlation ρ = 0.20, p = 0.022) with an average increase in f* of 5.17*10-4/year and 3.61*10-4/year, respectively. Significant correlations between f* and age were not observed for WM, and corollary analysis revealed no effect of gender on f*. Possible origins of the IVIM effect in normal brain tissue are discussed.

COVID-19-associated Diffuse Leukoencephalopathy and Microhemorrhages.

Diffuse leukoencephalopathy and juxtacortical and/or callosal microhemorrhages were brain imaging features in critically ill patients with coronavirus disease 2019. Coronavirus disease 2019 (COVID-19) has been reported in association with a variety of brain imaging findings such as ischemic infarct, hemorrhage, and acute hemorrhagic necrotizing encephalopathy. Herein, the authors report brain imaging features in 11 critically ill patients with COVID-19 with persistently diminished mental status who underwent MRI between April 5 and April 25, 2020. These imaging features include (a) confluent T2 hyperintensity and mild restricted diffusion in bilateral supratentorial deep and subcortical white matter (in 10 of 11 patients) and (b) multiple punctate microhemorrhages in juxtacortical and callosal white matter (in seven of 11 patients). The authors also discuss potential pathogeneses.

Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study.

OBJECTIVE. Deep learning (DL) image reconstruction has the potential to disrupt the current state of MRI by significantly decreasing the time required for MRI examinations. Our goal was to use DL to accelerate MRI to allow a 5-minute comprehensive examination of the knee without compromising image quality or diagnostic accuracy. MATERIALS AND METHODS. A DL model for image reconstruction using a variational network was optimized. The model was trained using dedicated multisequence training, in which a single reconstruction model was trained with data from multiple sequences with different contrast and orientations. After training, data from 108 patients were retrospectively undersampled in a manner that would correspond with a net 3.49-fold acceleration of fully sampled data acquisition and a 1.88-fold acceleration compared with our standard twofold accelerated parallel acquisition. An interchangeability study was performed, in which the ability of six readers to detect internal derangement of the knee was compared for clinical and DL-accelerated images. RESULTS. We found a high degree of interchangeability between standard and DL-accelerated images. In particular, results showed that interchanging the sequences would produce discordant clinical opinions no more than 4% of the time for any feature evaluated. Moreover, the accelerated sequence was judged by all six readers to have better quality than the clinical sequence. CONCLUSION. An optimized DL model allowed acceleration of knee images that performed interchangeably with standard images for detection of internal derangement of the knee. Importantly, readers preferred the quality of accelerated images to that of standard clinical images.

Quantitative multivoxel proton MR spectroscopy for the identification of white matter abnormalities in mild traumatic brain injury: Comparison between regional and global analysis.

BACKGROUND: 3D brain proton MR spectroscopic imaging (1 H MRSI) facilitates simultaneous metabolic profiling of multiple loci, at higher, sub-1 cm3 , spatial resolution than single-voxel 1 H MRS with the ability to separate tissue-type partial volume contribution(s). PURPOSE: To determine if: 1) white matter (WM) damage in mild traumatic brain injury (mTBI) is homogeneously diffuse, or if specific regions are more affected; 2) partial-volume-corrected, structure-specific 1 H MRSI voxel averaging is sensitive to regional WM metabolic abnormalities. STUDY TYPE: Retrospective cross-sectional cohort study. POPULATION: Twenty-seven subjects: 15 symptomatic mTBI patients, 12 matched controls. FIELD STRENGTH/SEQUENCE: 3T using 3D 1 H MRSI over a 360-cm3 volume of interest (VOI) centered over the corpus callosum, partitioned into 480 voxels, each 0.75 cm3 . ASSESSMENT: N-acetyl-aspartate (NAA), creatine, choline, and myo-inositol concentrations estimated in predominantly WM regions: body, genu, and splenium of the corpus callosum, corona radiata, frontal, and occipital WM. STATISTICAL TESTS: Analysis of covariance (ANCOVA) to compare patients with controls in terms of regional concentrations. The effect sizes (Cohen's d) of the mean differences were compared across regions and with previously published global data obtained with linear regression of the WM over the entire VOI in the same dataset. RESULTS: Despite patients' global VOI WM NAA being significantly lower than the controls', no regional differences were observed for any metabolite. Regional NAA comparisons, however, were all unidirectional (patients' NAA concentrations < controls') within a narrow range: 0.3 ≤ Cohen's d ≤ 0.6. DATA CONCLUSION: Since the patient group was symptomatic and exhibiting global WM NAA deficits, these findings suggest: 1) diffuse axonal mTBI damage; that is 2) below the 1 H MRSI detection threshold in small regions. Therefore, larger, ie, more sensitive, single-voxel 1 H MRS, placed anywhere in WM regions, may be well suited for mTBI 1 H MRS studies, given that these results are confirmed in other cohorts. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1424-1432.

State of the Art: Machine Learning Applications in Glioma Imaging.

OBJECTIVE: Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MRI radiomics of gliomas. CONCLUSION: We discuss available resources, state-of-the-art segmentation methods, and machine learning radiomics for glioma. We highlight the challenges of these techniques as well as the future potential in clinical diagnostics, prognostics, and decision making.

Preoperative Imaging for Facial Transplant: A Guide for Radiologists.

Facial transplant (FT) is a viable option for patients with severe craniomaxillofacial deformities. Transplant imaging requires coordination between radiologists and surgeons and an understanding of the merits and limitations of imaging modalities. Digital subtraction angiography and CT angiography are critical to mapping vascular anatomy, while volume-rendered CT allows evaluation of osseous defects and landmarks used for surgical cutting guides. This article highlights the components of successful FT imaging at two institutions and in two index cases. A deliberate stepwise approach to performance and interpretation of preoperative FT imaging, which consists of the modalities and protocols described here, is essential to seamless integration of the multidisciplinary FT team. ©RSNA, 2019 See discussion on this article by Lincoln .

Quantitative magnetic resonance evaluation of the trigeminal nerve in familial dysautonomia.

PURPOSE: Familial dysautonomia (FD) is a rare autosomal recessive disease that affects the development of sensory and autonomic neurons, including those in the cranial nerves. We aimed to determine whether conventional brain magnetic resonance imaging (MRI) could detect morphologic changes in the trigeminal nerves of these patients. METHODS: Cross-sectional analysis of brain MRI of patients with genetically confirmed FD and age- and sex-matched controls. High-resolution 3D gradient-echo T1-weighted sequences were used to obtain measurements of the cisternal segment of the trigeminal nerves. Measurements were obtained using a two-reader consensus. RESULTS: Twenty pairs of trigeminal nerves were assessed in ten patients with FD and ten matched controls. The median (interquartile range) cross-sectional area of the trigeminal nerves in patients with FD was 3.5 (2.1) mm2, compared to 5.9 (2.0) mm2 in controls (P < 0.001). No association between trigeminal nerve area and age was found in patients or controls. CONCLUSIONS: Using conventional MRI, the caliber of the trigeminal nerves was significantly reduced bilaterally in patients with FD compared to controls, a finding that appears to be highly characteristic of this disorder. The lack of correlation between age and trigeminal nerve size supports arrested neuronal development rather than progressive atrophy.

Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis.

Dynamic Causal Modeling (DCM) is an advanced biophysical model which explicitly describes the entire process from experimental stimuli to functional magnetic resonance imaging (fMRI) signals via neural activity and cerebral hemodynamics. To conduct a DCM study, one needs to represent the experimental stimuli as a compact vector-valued function of time, which is hard in complex tasks such as book reading and natural movie watching. Deep learning provides the state-of-the-art signal representation solution, encoding complex signals into compact dense vectors while preserving the essence of the original signals. There is growing interest in using Recurrent Neural Networks (RNNs), a major family of deep learning techniques, in fMRI modeling. However, the generic RNNs used in existing studies work as black boxes, making the interpretation of results in a neuroscience context difficult and obscure. In this paper, we propose a new biophysically interpretable RNN built on DCM, DCM-RNN. We generalize the vanilla RNN and show that DCM can be cast faithfully as a special form of the generalized RNN. DCM-RNN uses back propagation for parameter estimation. We believe DCM-RNN is a promising tool for neuroscience. It can fit seamlessly into classical DCM studies. We demonstrate face validity of DCM-RNN in two principal applications of DCM: causal brain architecture hypotheses testing and effective connectivity estimation. We also demonstrate construct validity of DCM-RNN in an attention-visual experiment. Moreover, DCM-RNN enables end-to-end training of DCM and representation learning deep neural networks, extending DCM studies to complex tasks.

Tumoral Presentation of Homonymous Hemianopia and Prosopagnosia in Cerebral Amyloid Angiopathy-Related Inflammation.

While cerebral amyloid angiopathy is a common cause of lobar hemorrhage, rarely it may be associated with an inflammatory response, thought to be incited by amyloid deposits. We report a 73-year-old woman with an extensive cancer history who presented with tumor-like lesions and symptoms of homonymous hemianopia and prosopagnosia. Found to have cerebral amyloid angiopathy-related inflammation proven by brain biopsy, she was treated successfully with immunosuppression.

MR Imaging Applications in Mild Traumatic Brain Injury: An Imaging Update.

Mild traumatic brain injury (mTBI), also commonly referred to as concussion, affects millions of Americans annually. Although computed tomography is the first-line imaging technique for all traumatic brain injury, it is incapable of providing long-term prognostic information in mTBI. In the past decade, the amount of research related to magnetic resonance (MR) imaging of mTBI has grown exponentially, partly due to development of novel analytical methods, which are applied to a variety of MR techniques. Here, evidence of subtle brain changes in mTBI as revealed by these techniques, which are not demonstrable by conventional imaging, will be reviewed. These changes can be considered in three main categories of brain structure, function, and metabolism. Macrostructural and microstructural changes have been revealed with three-dimensional MR imaging, susceptibility-weighted imaging, diffusion-weighted imaging, and higher order diffusion imaging. Functional abnormalities have been described with both task-mediated and resting-state blood oxygen level-dependent functional MR imaging. Metabolic changes suggesting neuronal injury have been demonstrated with MR spectroscopy. These findings improve understanding of the true impact of mTBI and its pathogenesis. Further investigation may eventually lead to improved diagnosis, prognosis, and management of this common and costly condition. (©) RSNA, 2016.

N-acetyl-aspartate levels correlate with intra-axonal compartment parameters from diffusion MRI.

Diffusion MRI combined with biophysical modeling allows for the description of a white matter (WM) fiber bundle in terms of compartment specific white matter tract integrity (WMTI) metrics, which include intra-axonal diffusivity (Daxon), extra-axonal axial diffusivity (De||), extra-axonal radial diffusivity (De┴), axonal water fraction (AWF), and tortuosity (α) of extra-axonal space. Here we derive these parameters from diffusion kurtosis imaging to examine their relationship to concentrations of global WM N-acetyl-aspartate (NAA), creatine (Cr), choline (Cho) and myo-Inositol (mI), as measured with proton MR spectroscopy ((1)H-MRS), in a cohort of 25 patients with mild traumatic brain injury (MTBI). We found statistically significant (p<0.05) positive correlations between NAA and Daxon, AWF, α, and fractional anisotropy; negative correlations between NAA and De,┴ and the overall radial diffusivity (D┴). These correlations were supported by similar findings in regional analysis of the genu and splenium of the corpus callosum. Furthermore, a positive correlation in global WM was noted between Daxon and Cr, as well as a positive correlation between De|| and Cho, and a positive trend between De|| and mI. The specific correlations between NAA, an endogenous probe of the neuronal intracellular space, and WMTI metrics related to the intra-axonal space, combined with the specific correlations of De|| with mI and Cho, both predominantly present extra-axonally, corroborate the overarching assumption of many advanced modeling approaches that diffusion imaging can disentangle between the intra- and extra-axonal compartments in WM fiber bundles. Our findings are also generally consistent with what is known about the pathophysiology of MTBI, which appears to involve both intra-axonal injury (as reflected by a positive trend between NAA and Daxon) as well as axonal shrinkage, demyelination, degeneration, and/or loss (as reflected by correlations between NAA and De┴, AWF, and α).

Use of computed tomography to assess volume change after endoscopic orbital decompression for Graves' ophthalmopathy.

BACKGROUND: Orbital decompression is frequently performed in the management of patients with sight-threatening and disfiguring Graves' ophthalmopathy. The quantitative measurements of the change in orbital volume after orbital decompression procedures are not definitively known. Furthermore, the quantitative effect of septal deviation on volume change has not been previously analyzed. OBJECTIVES: To provide quantitative measurement of orbital volume change after medial and inferior endoscopic decompression and describe a straightforward method of measuring this change using open-source technologies. A secondary objective was to assess the effect of septal deviation on orbital volume change. METHODS: A retrospective review was performed on all patients undergoing medial and inferior endoscopic orbital decompression for Graves' ophthalmopathy at a tertiary care academic medical center. Pre-operative and post-operative orbital volumes were calculated from computed tomography (CT) data using a semi-automated segmenting technique and Osirix™, an open-source DICOM reader. Data were collected for pre-operative and post-operative orbital volumes, degree of septal deviation, time to follow-up scan, and individual patient Hertel scores. RESULTS: Nine patients (12 orbits) were imaged before and after decompression. Mean pre-operative orbital volume was 26.99 cm(3) (SD=2.86 cm(3)). Mean post-operative volume was 33.07 cm(3) (SD=3.96 cm(3)). The mean change in volume was 6.08 cm(3) (SD=2.31 cm(3)). The mean change in Hertel score was 4.83 (SD=0.75). Regression analysis of change in volume versus follow-up time to imaging indicates that follow-up time to imaging has little effect on change in volume (R=-0.2), and overall mean maximal septal deviation toward the operative side was -0.5mm. Negative values were attributed to deviation away form the operative site. A significant correlation was demonstrated between change in orbital volume and septal deviation distance site (R=0.66), as well as between change in orbital volume and septal deviation angle (R=0.67). Greater volume changes were associated with greater degree of septal deviation away from the surgical site, whereas smaller volume changes were associated with greater degree of septal deviation toward the surgical site. CONCLUSION: A straightforward, semi-automated segmenting technique for measuring change in volume following endoscopic orbital decompression is described. This method proved useful in determining that a mean increase of approximately 6 cm in volume was achieved in this group of patients undergoing medial and inferior orbital decompression. Septal deviation appears to have an effect on the surgical outcome and should be considered during operative planning.

Automated whole-brain N-acetylaspartate proton MRS quantification.

Concentration of the neuronal marker, N-acetylaspartate (NAA), a quantitative metric for the health and density of neurons, is currently obtained by integration of the manually defined peak in whole-head proton ((1) H)-MRS. Our goal was to develop a full spectral modeling approach for the automatic estimation of the whole-brain NAA concentration (WBNAA) and to compare the performance of this approach with a manual frequency-range peak integration approach previously employed. MRI and whole-head (1) H-MRS from 18 healthy young adults were examined. Non-localized, whole-head (1) H-MRS obtained at 3 T yielded the NAA peak area through both manually defined frequency-range integration and the new, full spectral simulation. The NAA peak area was converted into an absolute amount with phantom replacement and normalized for brain volume (segmented from T1 -weighted MRI) to yield WBNAA. A paired-sample t test was used to compare the means of the WBNAA paradigms and a likelihood ratio test used to compare their coefficients of variation. While the between-subject WBNAA means were nearly identical (12.8 ± 2.5 mm for integration, 12.8 ± 1.4 mm for spectral modeling), the latter's standard deviation was significantly smaller (by ~50%, p = 0.026). The within-subject variability was 11.7% (±1.3 mm) for integration versus 7.0% (±0.8 mm) for spectral modeling, i.e., a 40% improvement. The (quantifiable) quality of the modeling approach was high, as reflected by Cramer-Rao lower bounds below 0.1% and vanishingly small (experimental - fitted) residuals. Modeling of the whole-head (1) H-MRS increases WBNAA quantification reliability by reducing its variability, its susceptibility to operator bias and baseline roll, and by providing quality-control feedback. Together, these enhance the usefulness of the technique for monitoring the diffuse progression and treatment response of neurological disorders.

Characterization of thalamo-cortical association using amplitude and connectivity of functional MRI in mild traumatic brain injury.

PURPOSE: To examine thalamic and cortical injuries using fractional amplitude of low-frequency fluctuations (fALFFs) and functional connectivity MRI (fcMRI) based on resting state (RS) and task-related fMRI in patients with mild traumatic brain injury (MTBI). MATERIALS AND METHODS: Twenty-seven patients and 27 age-matched controls were recruited. The 3 Tesla fMRI at RS and finger tapping task were used to assess fALFF and fcMRI patterns. fALFFs were computed with filtering (0.01-0.08 Hz) and scaling after preprocessing. fcMRI was performed using a standard seed-based correlation method, and delayed fcMRI (coherence) in frequency domain were also performed between thalamus and cortex. RESULTS: In comparison with controls, MTBI patients exhibited significantly decreased fALFFs in the thalamus (and frontal/temporal subsegments) and cortical frontal and temporal lobes; as well as decreased thalamo-thalamo and thalamo-frontal/ thalamo-temporal fcMRI at rest based on RS-fMRI (corrected P < 0.05). This thalamic and cortical disruption also existed at task-related condition in patients. CONCLUSION: The decreased fALFFs (i.e., lower neuronal activity) in the thalamus and its segments provide additional evidence of thalamic injury in patients with MTBI. Our findings of fALFFs and fcMRI changes during motor task and resting state may offer insights into the underlying cause and primary location of disrupted thalamo-cortical networks after MTBI.