Faculty Bibliography

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.

Mild traumatic brain injury is a growing public health problem with an estimated incidence of over 1.7 million people annually in US. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. This work aims to directly use diffusion MR images obtained within one month of trauma to detect injury, by incorporating deep learning techniques. To overcome the challenge due to limited training data, we describe each brain region using the bag of word representation, which specifies the distribution of representative patch patterns. We apply a convolutional auto-encoder to learn the patch-level features, from overlapping image patches extracted from the MR images, to learn features from diffusion MR images of brain using an unsupervised approach. Our experimental results show that the bag of word representation using patch level features learnt by the auto encoder provides similar performance as that using the raw patch patterns, both significantly outperform earlier work relying on the mean values of MR metrics in selected brain regions.

BACKGROUND AND PURPOSE/OBJECTIVE:Cerebral microhemorrhages are a known marker of mild traumatic brain injury. Blast-related mild traumatic brain injury relates to a propagating pressure wave, and there is evidence that the mechanism of injury in blast-related mild traumatic brain injury may be different from that in blunt head trauma. Two recent reports in mixed cohorts of blunt and blast-related traumatic brain injury in military personnel suggest that the prevalence of cerebral microhemorrhages is lower than in civilian head injury. In this study, we aimed to characterize the prevalence of cerebral microhemorrhages in military service members specifically with chronic blast-related mild traumatic brain injury. MATERIALS AND METHODS/METHODS:Participants were prospectively recruited and underwent 3T MR imaging. Susceptibility-weighted images were assessed by 2 neuroradiologists independently for the presence of cerebral microhemorrhages. RESULTS:Our cohort included 146 veterans (132 men) who experienced remote blast-related mild traumatic brain injury (mean, 9.4 years; median, 9 years after injury). Twenty-one (14.4%) reported loss of consciousness for <30 minutes. Seventy-seven subjects (52.7%) had 1 episode of blast-related mild traumatic brain injury; 41 (28.1%) had 2 episodes; and 28 (19.2%) had >2 episodes. No cerebral microhemorrhages were identified in any subject, as opposed to the frequency of SWI-detectable cerebral microhemorrhages following blunt-related mild traumatic brain injury in the civilian population, which has been reported to be as high as 28% in the acute and subacute stages. CONCLUSIONS:Our results may reflect differences in pathophysiology and the mechanism of injury between blast- and blunt-related mild traumatic brain injury. Additionally, the chronicity of injury may play a role in the detection of cerebral microhemorrhages.

We seek to elucidate the underlying pathophysiology of injury sustained after mild traumatic brain injury (MTBI) using multi-shell diffusion MRI, deriving compartment-specific WM tract integrity (WMTI) metrics. WMTI allows a more biophysical interpretation of WM changes by describing microstructural characteristics in both intra- and extra-axonal environments. Thirty-two patients with MTBI within 30 days of injury and twenty-one age- and sex-matched controls were imaged on a 3T MR scanner. Multi-shell diffusion acquisition was performed with 5 b-values (250 - 2500 s/mm²) along 6 - 60 diffusion encoding directions. Tract-based spatial statistics (TBSS) was used with family-wise error (FWE) correction for multiple comparisons. TBSS results demonstrate focally lower intra-axonal diffusivity (D_axon) in MTBI patients in the splenium of the corpus callosum (sCC) (p < 0.05, FWE-corrected). The Area Under the Curve (AUC)-value for was 0.76 with low sensitivity of 46.9%, but 100% specificity. These results indicate that D_axon may be a useful imaging biomarker highly specific for MTBI-related WM injury. The observed decrease in D_axon suggests restriction of the diffusion along the axons occurring shortly after injury.

Working memory is a complex cognitive process at the intersection of sensory processing, learning, and short-term memory and also has a general executive attention component. Impaired working memory is associated with a range of neurological and psychiatric disorders, but very little is known about how working memory relates to underlying white matter (WM) microstructure. In this study, we investigate the association between WM microstructure and performance on working memory tasks in healthy adults (right-handed, native English speakers). We combine compartment specific WM tract integrity (WMTI) metrics derived from multi-shell diffusion MRI as well as diffusion tensor/kurtosis imaging (DTI/DKI) metrics with Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) subtests tapping auditory working memory. WMTI is a novel tool that helps us describe the microstructural characteristics in both the intra- and extra-axonal environments of WM such as axonal water fraction (AWF), intra-axonal diffusivity, extra-axonal axial and radial diffusivities, allowing a more biophysical interpretation of WM changes. We demonstrate significant positive correlations between AWF and letter-number sequencing (LNS), suggesting that higher AWF with better performance on complex, more demanding auditory working memory tasks goes along with greater axonal volume and greater myelination in specific regions, causing efficient and faster information process.

Remarkable advances have been made in the last decade in the use of diffusion MR imaging to study mild traumatic brain injury (mTBI). Diffusion imaging shows differences between mTBI patients and healthy control groups in multiple different metrics using a variety of techniques, supporting the notion that there are microstructural injuries in mTBI patients that radiologists have been insensitive to. Future areas of discovery in diffusion MR imaging and mTBI include larger longitudinal studies to better understand the evolution of the injury and unravel the biophysical meaning that the detected changes in diffusion MR imaging represent.

Diffusion imaging of the spine has the potential to change clinical management, but is challenging due to the small size of the cord and susceptibility artifacts from adjacent structures. Reduced field-of-view (rFOV) diffusion can improve image quality by decreasing the echo train length. Over the past 2 years, we have acquired a rFOV diffusion sequence for MRI spine protocols on most inpatients and emergency room patients. We provide selected imaging diagnoses to illustrate the utility of including diffusion spine MRI in clinical practice. Our experiences support using diffusion MRI to improve diagnostic certainty and facilitate prompt treatment or clinical management.

The effect of T1 signal on FSL voxel-based morphometry modulated GM density and FreeSurfer cortical thickness is explored. The techniques rely on different analyses, but both are commonly used to detect spatial changes in GM. Standard pipelines show FSL voxel-based morphometry is sensitive to T1 signal alterations within a physiologic range, and results can appear discordant between FSL voxel-based morphometry and FreeSurfer cortical thickness. Care should be taken in extrapolating results to the effect on brain volume.