Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an ill-posed inverse problem. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of QSM for measuring the susceptibility of tissues near the brain edges, e.g., quantifying cortical layers and generating superficial venography. To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM. The neural network has a modified U-net structure and is trained using QSM maps computed by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82 years were employed for patch-wise network training. The network was validated on data dissimilar to the training data, e.g. in vivo mouse brain data and brains with lesions, which suggests that the network has generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. AutoQSM was able to recover magnetic susceptibility of anatomical structures near the edges of the brain including the veins covering the cortical surface, spinal cord and nerve tracts near the mouse brain boundaries. The advantages of high-quality maps, no need for brain volume extraction and high reconstruction speed demonstrate its potential for future applications.Comment: 26 page

Removing Learning Barriers in Self-paced Online STEM Education

Self-paced online learning provides great flexibility for learning, yet it brings some inherent learning barriers because of the nature of this educational paradigm. This review paper suggests some corresponding strategies to address these barriers in order to create a more supportive self-paced online learning environment. These strategies include a) increasing students’ self-awareness of learning, b) identifying struggling students, and c) facilitating mastery learning.Focusing on Science, Technology, Engineering, and Mathematics (STEM) disciplines’ delivery of self-paced online learning, this paper reviewed the role of formative assessment for learning. It is proposed that systematically designing and embedding adaptive practicing in STEM courses would be an effective learning design solution to implement these strategies. By examining the goals and context of adaptive practicing requested in this study, the feature requirements are depicted for such an adaptive practicing model. The models and techniques that can be used for adaptive assessment were then reviewed. Based on the review results, this paper argues that a reinforcement learning-based adaptive practicing model would be the best option to meet those feature requirements. Finally, we point out a research gap in this field and suggest a future research direction for ourselves and other researchers

Investigation of anterior cingulate cortex gamma-aminobutyric acid and glutamate-glutamine levels in obsessive-compulsive disorder using magnetic resonance spectroscopy.

BACKGROUND: Obsessive-compulsive disorder (OCD) is a relatively common and disabling psychiatric disorder whose pathophysiology is incompletely understood. In this study, we utilized magnetic resonance spectroscopy (MRS) in an effort to provide a better understanding of the role of brain gamma-aminobutyric acid (GABA) and glutamate in the pathophysiology of OCD. We hypothesized that beyond the separate effects of these neurotransmitter systems, a disruption in the balance between GABA and glutamate could be particularly relevant to OCD. METHODS: We obtained MRS measures of GABA and glutamate concentrations in the anterior cingulate cortex from 23 adult patients with OCD and 20 sex- and age-matched healthy community volunteers. Established clinical rating scales were used to assess the severities of OCD, anxiety, and depression symptoms. Statistical analysis involved the assessment of patient-control group differences in the individual measures of GABA and glutamate, as well as in the ratio of the GABA to glutamate measures. Additionally, we explored whether differences in the MRS measures existed between two subgroups of patients formed according to the severity of their OCD symptoms. Finally, we assessed the relations of demographic and clinical variables to the MRS measures. RESULTS: Patients with OCD displayed a higher estimated GABA level and a higher GABA to glutamate ratio than healthy participants, but no significant group differences were observed in the measure of glutamate. The MRS measures did not vary by subgroup and showed no correlations with demographic and clinical variables. CONCLUSIONS: These results indicate that GABA abnormalities within the anterior cingulate cortex contribute to the pathophysiology of OCD. The results fail to provide evidence that glutamate abnormalities alone are involved in adult OCD. Yet, it seems that a disruption in the balance between glutamate and GABA neurotransmission may have a particularly important role to play in OCD pathophysiology

Abnormal Voxel-Wise Degree Centrality in Patients With Late-Life Depression: A Resting-State Functional Magnetic Resonance Imaging Study.

Objectives:Late-life depression (LLD) has negative impacts on somatic, emotional and cognitive domains of the lives of patients. Elucidating the abnormality in the brain networks of LLD patients could help to strengthen the understanding of LLD pathophysiology, however, the studies exploring the spontaneous brain activity in LLD during the resting state remain limited. This study aimed at identifying the voxel-level whole-brain functional connectivity changes in LLD patients. Methods:Fifty patients with late-life depression (LLD) and 33 healthy controls were recruited. All participants underwent a resting-state functional magnetic resonance imaging scan to assess the voxel-wise degree centrality (DC) changes in the patients. Furthermore, DC was compared between two patient subgroups, the late-onset depression (LOD) and the early-onset depression (EOD). Results:Compared with the healthy controls, LLD patients showed increased DC in the inferior parietal lobule, parahippocampal gyrus, brainstem and cerebellum (p < 0.05, AlphaSim-corrected). LLD patients also showed decreased DC in the somatosensory and motor cortices and cerebellum (p < 0.05, AlphaSim-corrected). Compared with EOD patients, LOD patients showed increased centrality in the superior and middle temporal gyrus and decreased centrality in the occipital region (p < 0.05, AlphaSim-corrected). No significant correlation was found between the DC value and the symptom severity or disease duration in the patients after the correction for multiple comparisons. Conclusions:These findings indicate that the intrinsic abnormality of network centrality exists in a wide range of brain areas in LLD patients. LOD patients differ with EOD patients in cortical network centrality. Our study might help to strengthen the understanding of the pathophysiology of LLD and the potential neural substrates underlie related emotional and cognitive impairments observed in the patients