Your note, your way: how to write an inpatient progress note accurately and efficiently as an intern.

A physician's progress note is an essential piece of documentation regarding key events and the daily status of patients during their hospital stay. It serves not only as a communication tool between care team members, but also chronicles clinical status and pertinent updates to their medical care. Despite the importance of these documents, little literature exists on how to help residents to improve the quality of their daily progress notes. A narrative literature review of English language literature was performed and summated to provide recommendations on how to write an inpatient progress note more accurately and efficiently. In addition, the authors will also introduce a method to build a personal template with the goal of extracting relevant data automatically to reduce clicks for an inpatient progress note in the electronic medical record system.

Improved Prediction of Amyloid-ß and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding.

BACKGROUND: Amyloid-ß (Aß) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the "ATN framework" of AD. Current methods to detect Aß/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development). OBJECTIVE: To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements. METHODS: With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction. RESULTS: We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer's Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics. CONCLUSION: The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.

Effect of a NICU to Home Physical Therapy Intervention on White Matter Trajectories, Motor Skills, and Problem-Solving Skills of Infants Born Very Preterm: A Case Series.

Infants born very preterm (VPT; ≤29 weeks of gestation) are at high risk of developmental disabilities and abnormalities in neural white matter characteristics. Early physical therapy interventions such as Supporting Play Exploration and Early Development Intervention (SPEEDI2) are associated with improvements in developmental outcomes. Six VPT infants were enrolled in a randomised clinical trial of SPEEDI2 during the transition from the neonatal intensive care unit to home over four time points. Magnetic resonance imaging scans and fixel-based analysis were performed, and fibre density (FD), fibre cross-section (FC), and fibre density and cross-section values (FDC) were computed. Changes in white matter microstructure and macrostructure were positively correlated with cognitive, motor, and motor-based problem solving over time on developmental assessments. In all infants, the greatest increase in FD, FC, and FDC occurred between Visit 1 and 2 (mean chronological age: 2.68-6.22 months), suggesting that this is a potential window of time to optimally support adaptive development. Results warrant further studies with larger groups to formally compare the impact of intervention and disparity on neurodevelopmental outcomes in infants born VPT.

Data-driven Normative Reference of Pediatric Cranial Bone Development.

Available normative references of cranial bone development and suture fusion are incomplete or based on simplified assumptions due to the lack of large datasets. We present a fully data-driven normative model that represents the age- and sex-specific variability of bone shape, thickness, and density between birth and 10 years of age at every location of the calvaria. Methods: The model was built using a cross-sectional and multi-institutional pediatric computed tomography image dataset with 2068 subjects without cranial pathology (age 0-10 years). We combined principal component analysis and temporal regression to build a statistical model of cranial bone development at every location of the calvaria. We studied the influences of sex on cranial bone growth, and our bone density model allowed quantifying for the first time suture fusion as a continuous temporal process. We evaluated the predictive accuracy of our model using an independent longitudinal image dataset of 51 subjects. Results: Our model achieved temporal predictive errors of 2.98 ± 0.69 mm, 0.27 ± 0.29 mm, and 76.72 ± 91.50 HU in cranial bone shape, thickness, and mineral density changes, respectively. Significant sex differences were found in intracranial volume and bone surface areas (P < 0.01). No significant differences were found in cephalic index, bone thickness, mineral density, or suture fusion. Conclusions: We presented the first pediatric age- and sex-specific statistical reference for local cranial bone shape, thickness, and mineral density changes. We showed its predictive accuracy using an independent longitudinal dataset, we studied developmental differences associated with sex, and we quantified suture fusion as a continuous process.

White matter tract changes in pediatric posterior fossa brain tumor survivors after surgery and chemotherapy.

Background: Survivors of pediatric posterior fossa brain tumors are susceptible to the adverse effects of treatment as they grow into adulthood. While the exact neurobiological mechanisms of these outcomes are not yet understood, the effects of treatment on white matter (WM) tracts in the brain can be visualized using diffusion tensor (DT) imaging. We investigated these WM microstructural differences using the statistical method tract-specific analysis (TSA). We applied TSA to the DT images of 25 children with a history of posterior fossa tumor (15 treated with surgery, 10 treated with surgery and chemotherapy) along with 21 healthy controls. Between these 3 groups, we examined differences in the most used DTI metric, fractional anisotropy (FA), in 11 major brain WM tracts. Results: Lower FA was found in the splenium of the corpus callosum (CC), the bilateral corticospinal tract (CST), the right inferior frontal occipital fasciculus (IFOF) and the left uncinate fasciculus (UF) in children with brain tumors as compared to healthy controls. Lower FA, an indicator of microstructural damage to WM, was observed in 4 of the 11 WM tracts examined in both groups of children with a history of posterior fossa tumor, with an additional tract unique to children who received surgery and chemotherapy (left UF). Conclusions: Our findings indicate that a history of tumor in the posterior fossa and surgical resection may have effects on the WM in other parts of the brain.

Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation.

Lesion appearance is a crucial clue for medical providers to distinguish referable diabetic retinopathy (rDR) from non-referable DR. Most existing large-scale DR datasets contain only image-level labels rather than pixel-based annotations. This motivates us to develop algorithms to classify rDR and segment lesions via image-level labels. This paper leverages self-supervised equivariant learning and attention-based multi-instance learning (MIL) to tackle this problem. MIL is an effective strategy to differentiate positive and negative instances, helping us discard background regions (negative instances) while localizing lesion regions (positive ones). However, MIL only provides coarse lesion localization and cannot distinguish lesions located across adjacent patches. Conversely, a self-supervised equivariant attention mechanism (SEAM) generates a segmentation-level class activation map (CAM) that can guide patch extraction of lesions more accurately. Our work aims at integrating both methods to improve rDR classification accuracy. We conduct extensive validation experiments on the Eyepacs dataset, achieving an area under the receiver operating characteristic curve (AU ROC) of 0.958, outperforming current state-of-the-art algorithms.

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology.

Amyloid-ß (Aß) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer's disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of Aß/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with Aß/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts to understand hippocampal alterations that relate to Aß/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89× speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal Aß/tau. As potential biomarkers for Aß/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between Aß/tau burden and hippocampal morphometry.

Predicting Tau Accumulation in Cerebral Cortex with Multivariate MRI Morphometry Measurements, Sparse Coding, and Correntropy.

Biomarker-assisted diagnosis and intervention in Alzheimer's disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) has excellent ability to generate low-dimensional representations with strong statistical power for brain amyloid prediction. In this work, we apply this framework together with ridge regression models to predict Tau deposition in Braak12 and Braak34 brain regions separately. We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject has one pair consisting of a PET image and MRI scan which were collected at about the same times. Experimental results suggest that the representations from our MMS and PASCS-MP have stronger predictive power and their predicted Braak12 and Braak34 are closer to the real values compared to the measures derived from other approaches such as hippocampal surface area and volume, and shape morphometry features based on spherical harmonics (SPHARM).

Tetrahedral spectral feature-Based bayesian manifold learning for grey matter morphometry: Findings from the Alzheimer's disease neuroimaging initiative.

Structural and anatomical analyses of magnetic resonance imaging (MRI) data often require a reconstruction of the three-dimensional anatomy to a statistical shape model. Our prior work demonstrated the usefulness of tetrahedral spectral features for grey matter morphometry. However, most of the current methods provide a large number of descriptive shape features, but lack an unsupervised scheme to automatically extract a concise set of features with clear biological interpretations and that also carries strong statistical power. Here we introduce a new tetrahedral spectral feature-based Bayesian manifold learning framework for effective statistical analysis of grey matter morphology. We start by solving the technical issue of generating tetrahedral meshes which preserve the details of the grey matter geometry. We then derive explicit weak-form tetrahedral discretizations of the Hamiltonian operator (HO) and the Laplace-Beltrami operator (LBO). Next, the Schrödinger's equation is solved for constructing the scale-invariant wave kernel signature (SIWKS) as the shape descriptor. By solving the heat equation and utilizing the SIWKS, we design a morphometric Gaussian process (M-GP) regression framework and an active learning strategy to select landmarks as concrete shape descriptors. We evaluate the proposed system on publicly available data from the Alzheimers Disease Neuroimaging Initiative (ADNI), using subjects structural MRI covering the range from cognitively unimpaired (CU) to full blown Alzheimer's disease (AD). Our analyses suggest that the SIWKS and M-GP compare favorably with seven other baseline algorithms to obtain grey matter morphometry-based diagnoses. Our work may inspire more tetrahedral spectral feature-based Bayesian learning research in medical image analysis.

Developmental changes of the central sulcus morphology in young children.

The human brain grows rapidly in early childhood, reaching 95% of its final volume by age 6. Understanding brain growth in childhood is important both to answer neuroscience questions about anatomical changes in development, and as a comparison metric for neurological disorders. Metrics for neuroanatomical development including cortical measures pertaining to the sulci can be instrumental in early diagnosis, monitoring, and intervention for neurological diseases. In this paper, we examine the development of the central sulcus in children aged 12-60 months from structural magnetic resonance images. The central sulcus is one of the earliest sulci to develop at the fetal stage and is implicated in diseases such as Attention Deficit Hyperactive Disorder and Williams syndrome. We investigate the relationship between the changes in the depth of the central sulcus with respect to age. In our results, we observed a pattern of depth present early on, that had been previously observed in adults. Results also reveal the presence of a rightward depth asymmetry at 12 months of age at a location related to orofacial movements. That asymmetry disappears gradually, mostly between 12 and 24 months, and we suggest that it is related to the development of language skills.

Tract-specific analysis and neurocognitive functioning in sickle cell patients without history of overt stroke.

INTRODUCTION: Sickle cell disease (SCD) is a hereditary blood disorder in which the oxygen-carrying hemoglobin molecule in red blood cells is abnormal. SCD patients are at increased risks for strokes and neurocognitive deficit, even though neurovascular screening and treatments have lowered the rate of overt strokes. Tract-specific analysis (TSA) is a statistical method to evaluate microstructural WM damage in neurodegenerative disorders, using diffusion tensor imaging (DTI). METHODS: We utilized TSA and compared 11 major brain WM tracts between SCD patients with no history of overt stroke, anemic controls, and healthy controls. We additionally examined the relationship between the most commonly used DTI metric of WM tracts and neurocognitive performance in the SCD patients and healthy controls. RESULTS: Disruption of WM microstructure orientation-dependent metrics for the SCD patients was found in the genu of the corpus callosum (CC), cortico-spinal tract, inferior fronto-occipital fasciculus, right inferior longitudinal fasciculus, superior longitudinal fasciculus, and left uncinate fasciculus. Neurocognitive performance indicated slower processing speed and lower response inhibition skills in SCD patients compared to controls. TSA abnormalities in the CC were significantly associated with measures of processing speed, working memory, and executive functions. CONCLUSION: Decreased DTI-derived metrics were observed on six tracts in chronically anemic patients, regardless of anemia subtype, while two tracks with decreased measures were unique to SCD patients. Patients with WMHs had more significant FA abnormalities. Decreased FA values in the CC significantly correlated with all nine neurocognitive tests, suggesting a critical importance for CC in core neurocognitive processes.

Brain biomarkers and neuropsychological outcomes of pediatric posterior fossa brain tumor survivors treated with surgical resection with or without adjuvant chemotherapy.

PURPOSE: Children with brain tumors experience cognitive late effects, often related to cranial radiation. We sought to determine differential effects of surgery and chemotherapy on brain structure and neuropsychological outcomes in children who did not receive cranial radiation therapy (CRT). METHODS: Twenty-eight children with a history of posterior fossa tumor (17 treated with surgery, 11 treated with surgery and chemotherapy) underwent neuroimaging and neuropsychological assessment a mean of 4.5 years (surgery group) to 9 years (surgery + chemotherapy group) posttreatment, along with 18 healthy sibling controls. Psychometric measures assessed IQ, language, executive functions, processing speed, memory, and social-emotional functioning. Group differences and correlations between diffusion tensor imaging findings and psychometric scores were examined. RESULTS: The z-score mapping demonstrated fractional anisotropy (FA) values were ≥2 standard deviations lower in white matter tracts, prefrontal cortex gray matter, hippocampus, thalamus, basal ganglia, and pons between patient groups, indicating microstructural damage associated with chemotherapy. Patients scored lower than controls on visuoconstructional reasoning and memory (P ≤ .02). Lower FA in the uncinate fasciculus (R = -0.82 to -0.91) and higher FA in the thalamus (R = 0.73-0.91) associated with higher IQ scores, and higher FA in the thalamus associated with higher scores on spatial working memory (R = 0.82). CONCLUSIONS: Posterior fossa brain tumor treatment with surgery and chemotherapy affects brain microstructure and neuropsychological functioning years into survivorship, with spatial processes the most vulnerable. Biomarkers indicating cellular changes in the thalamus, hippocampus, pons, prefrontal cortex, and white matter tracts associate with lower psychometric scores.

Fetal neurodevelopmental recovery in donors after laser surgery for twin-twin transfusion syndrome.

BACKGROUND: Fetal magnetic resonance imaging (MRI) and spectroscopy (MRS) provide a unique opportunity to non-invasively measure markers of neurodevelopment in survivors of twin-twin transfusion syndrome (TTTS). OBJECTIVE: To characterize fetal brain maturation after laser surgery for TTTS by measuring brain volumes and cerebral metabolite concentrations using fetal MRI + MRS. STUDY DESIGN: Prospective study of dual surviving fetuses treated with laser surgery for TTTS. At 4-5 postoperative weeks, fetal MRI was used together with novel image analysis to automatically extract major brain tissue volumes. Fetal MRS was used to measure major metabolite concentrations in the fetal brain. RESULTS: Twenty-one twin pairs were studied. The average (±SD) gestational age at MRI was 25.89 (±2.37) weeks. Total brain volume (TBV) was lower in the donors, although cerebral volumes were not different between twin pairs. Recipients showed lower proportions of cortical and cerebellar volumes, normalized to TBV and cerebral volumes. MRS data showed that biochemical differences between twin brains were related to discrepancy in their brain volumes. CONCLUSION: Although donors have a smaller TBV compared to recipients, proportionality of brain tissue volumes are preserved in donors. MRS maturational markers of fetal brain development show that recovery in donors persists 4 weeks after surgery.

Patch-Based Surface Morphometry Feature Selection with Federated Group Lasso Regression.

Collectively, vast quantities of brain imaging data exist across hospitals and research institutions, providing valuable resources to study brain disorders such as Alzheimer's disease (AD). However, in practice, putting all these distributed datasets into a centralized platform is infeasible due to patient privacy concerns, data restrictions and legal regulations. In this study, we propose a novel federated feature selection framework that can analyze the data at each individual institution without data-sharing or accessing private patient information. In this framework, we first propose a federated group lasso optimization method based on block coordinate descent. We employ stability selection to determine statistically significant features, by solving the group lasso problem with a sequence of regularization parameters. To accelerate the stability selection, we further propose a federated screening rule, which can identify and exclude the irrelevant features before solving the group lasso. Here, we use this framework for patch based feature selection on hippocampal morphometry. Shape is characterized through two different kinds of local measures, the radial distance and the surface area determined via tensor-based morphometry (TBM). The method is tested on 1,127 T1-weighted brain magnetic resonance images (MRI) of AD, mild cognitive impairment (MCI) and elderly control subjects, randomly assigned to five independent hypothetical institutions for testing purpose. We examine the association of MRI-based anatomical measures with general cognitive assessment and amyloid burden to identify the morphometry changes related to AD deterioration and plaque accumulation. Finally, we visualize the significance of the association on the hippocampal surfaces. Our experimental results successfully demonstrate the efficiency and effectiveness of our method.

Comparison of Regularly Scheduled Ibuprofen Versus "Pro Re Nata" for Ankle Sprains in Children Treated in the Emergency Department: A Randomized Controlled Trial.

OBJECTIVE: We compared pain and degree of disability in patients with acute ankle sprains receiving regular scheduled ibuprofen versus pro re nata (PRN). METHODS: This study is a randomized single-blinded controlled trial of children aged 7 to 17 years presenting with acute ankle sprain to an emergency department. Patients were randomized to receive 10 mg/kg of ibuprofen per dose (maximum 600 mg) every 6 hours regular scheduled versus PRN. Outcome measures included a 100-mm visual analog scale pain and degree of disability at day 4. A sample size of 72 children had a power of 80% to detect a clinically meaningful difference of 20 mm between the regular and PRN group. RESULTS: We randomly assigned 99 patients to receive regular scheduled (n = 50) or PRN (n = 49) ibuprofen. Pain scores and degree of disability at day 4 showed no significant differences between groups. The rate of reported adverse effects was higher in the regular scheduled group (11.4% vs 9.5%) versus the PRN group. CONCLUSIONS: Our study suggests that there is little benefit from routinely using a regular scheduled ibuprofen regimen for acute pediatric ankle sprains.

Neurocranium thickness mapping in early childhood.

The neurocranium changes rapidly in early childhood to accommodate the growing brain. Developmental disorders and environmental factors such as sleep position may lead to abnormal neurocranial maturation. Therefore, it is important to understand how this structure develops, in order to provide a baseline for early detection of anomalies. However, its anatomy has not yet been well studied in early childhood due to the lack of available imaging databases. In hospitals, CT is typically used to image the neurocranium when a pathology is suspected, but the presence of ionizing radiation makes it harder to construct databases of healthy subjects. In this study, instead, we use a dataset of MRI data from healthy normal children in the age range of 6 months to 36 months to study the development of the neurocranium. After extracting its outline from the MRI data, we used a conformal geometry-based analysis pipeline to detect local thickness growth throughout this age span. These changes will help us understand cranial bone development with respect to the brain, as well as detect abnormal variations, which will in turn inform better treatment strategies for implicated disorders.

Down syndrome with co-occurring Marfan syndrome.

Down syndrome (DS) and Marfan syndrome (MFS) are two unique genetic disorders that share limited phenotypic overlap. There are very few reported cases in the existing literature on overlapping DS and MFS. Although these two disorders are phenotypically unique, features present in these cases are variable, resulting in mixed and dominant expressions of particular features. We present the first adolescent case of trisomy 21 associated DS and fibrillin-1 gene associated MFS in the literature who had a height at 90th percentile for an 11-year old boy and discuss the implications of this case in terms of future medical care when these two genetic syndromes are present in the same individual. Understanding of certain features of the 'non-dominating' syndrome is crucial for clinicians to recognise when DS co-occurs with MFS. Close monitoring of the cardiovascular, ophthalmologic and musculoskeletal systems is recommended if both syndromes are diagnosed given that both can be independently associated with disorders in these organ systems.

Structural neuroimaging.

Characterizing the neuroanatomical correlates of brain development is essential in understanding brain-behavior relationships and neurodevelopmental disorders. Advances in brain MRI acquisition protocols and image processing techniques have made it possible to detect and track with great precision anatomical brain development and pediatric neurologic disorders. In this chapter, we provide a brief overview of the modern neuroimaging techniques for pediatric brain development and review key normal brain development studies. Characteristic disorders affecting neurodevelopment in childhood, such as prematurity, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), epilepsy, and brain cancer, and key neuroanatomical findings are described and then reviewed. Large datasets of typically developing children and children with various neurodevelopmental conditions are now being acquired to help provide the biomarkers of such impairments. While there are still several challenges in imaging brain structures specific to the pediatric populations, such as subject cooperation and tissues contrast variability, considerable imaging research is now being devoted to solving these problems and improving pediatric data analysis.

Transient Hypoxia Model Revealed Cerebrovascular Impairment in Anemia Using BOLD MRI and Near-Infrared Spectroscopy.

BACKGROUND: Obstructive sleep apnea and nocturnal oxygen desaturations, which are prevalent in sickle cell disease (SCD) and chronic anemia disorders, have been linked to risks of stroke and silent cerebral infarcts (SCI). Cerebrovascular response to intermittent desaturations has not been well studied and may identify patients at greatest risk. PURPOSE: To investigate the cerebral dynamic response to induced desaturation in SCD patients with and without SCI, chronic anemia, and healthy subjects. STUDY TYPE: Prospective. SUBJECTS: Twenty-six SCD patients (age = 21 ± 8.2, female 46.2%), including 15 subjects without SCI and nine subjects with SCI, 15 nonsickle anemic patients (age = 22 ± 5.8, female 66.7%), and 31 controls (age = 28 ± 12.3, female 77.4%). FIELD STRENGTH/SEQUENCE: 3T, gradient-echo echo-planar imaging. ASSESSMENT: A transient hypoxia challenge of five breaths of 100% nitrogen gas was performed with blood oxygen level-dependent (BOLD) MRI and near-infrared spectroscopy (NIRS) acquisitions. Hypoxia responses were characterized by desaturation depth, time-to-peak, return-to-baseline half-life, and posthypoxia recovery in the BOLD and NIRS time courses. SCI were documented by T2 fluid-attenuation inversion recovery (FLAIR). STATISTICAL TESTS: Univariate and multivariate regressions were performed between hypoxic parameters and anemia predictors. Voxelwise two-sample t-statistic maps were used to assess the regional difference in hypoxic responses between anemic and control groups. RESULTS: Compared to controls, SCD and chronically anemic patients demonstrated significantly higher desaturation depth (P < 0.01) and shorter return-to-baseline timing response (P < 0.01). Patients having SCI had shorter time-to-peak (P < 0.01), return-to-baseline (P < 0.01), and larger desaturation depth (P < 0.01) in both white matter regions at risk and normal-appearing white matter than patients without infarcts. On multivariate analysis, desaturation depth and timing varied with age, sex, blood flow, white blood cells, and cell-free hemoglobin (r2 = 0.25 for desaturation depth; r2 = 0.18 for time-to-peak; r2 = 0.37 for return-to-baseline). DATA CONCLUSION: Transient hypoxia revealed global and regional response differences between anemic and healthy subjects. SCI was associated with extensive heterogeneity of desaturation dynamics, consistent with extensive underlying microvascular remodeling.

Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images.

BACKGROUND: Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable and high-level feature maps from image patches. OBJECTIVE: A key challenge in applying CNN to neuroimaging research is the limited labeled samples with high dimensional features. Another challenge is how to improve the prediction accuracy by joint analysis of multiple data sources (i.e., multiple time points or multiple biomarkers). To address these two challenges, we propose a novel multi-task learning framework based on CNN. METHODS: First, we pre-trained CNN on the ImageNet dataset and transferred the knowledge from the pre-trained model to neuroimaging representation. We used this deep model as feature extractor to generate high-level feature maps of different tasks. Then a novel unsupervised learning method, termed Multi-task Stochastic Coordinate Coding (MSCC), was proposed for learning sparse features of multi-task feature maps by using shared and individual dictionaries. Finally, Lasso regression was performed on these multi-task sparse features to predict AD progression measured by the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog). RESULTS: We applied this novel CNN-MSCC system on the Alzheimer's Disease Neuroimaging Initiative dataset to predict future MMSE/ADAS-Cog scales. We found our method achieved superior performances compared with seven other methods. CONCLUSION: Our work may add new insights into data augmentation and multi-task deep model research and facilitate the adoption of deep models in neuroimaging research.