Connectivity-enhanced diffusion analysis reveals white matter density disruptions in first episode and chronic schizophrenia.

Reduced fractional anisotropy (FA) is a well-established correlate of schizophrenia, but it remains unclear whether these tensor-based differences are the result of axon damage and/or organizational changes and whether the changes are progressive in the adult course of illness. Diffusion MRI data were collected in 81 schizophrenia patients (54 first episode and 27 chronic) and 64 controls. Analysis of FA was combined with "fixel-based" analysis, the latter of which leverages connectivity and crossing-fiber information to assess both fiber bundle density and organizational complexity (i.e., presence and magnitude of off-axis diffusion signal). Compared with controls, patients with schizophrenia displayed clusters of significantly lower FA in the bilateral frontal lobes, right dorsal centrum semiovale, and the left anterior limb of the internal capsule. All FA-based group differences overlapped substantially with regions containing complex fiber architecture. FA within these clusters was positively correlated with principal axis fiber density, but inversely correlated with both secondary/tertiary axis fiber density and voxel-wise fiber complexity. Crossing fiber complexity had the strongest (inverse) association with FA (r = -0.82). When crossing fiber structure was modeled in the MRtrix fixel-based analysis pipeline, patients exhibited significantly lower fiber density compared to controls in the dorsal and posterior corpus callosum (central, postcentral, and forceps major). Findings of lower FA in patients with schizophrenia likely reflect two inversely related signals: reduced density of principal axis fiber tracts and increased off-axis diffusion sources. Whereas the former confirms at least some regions where myelin and or/axon count are lower in schizophrenia, the latter indicates that the FA signal from principal axis fiber coherence is broadly contaminated by macrostructural complexity, and therefore does not necessarily reflect microstructural group differences. These results underline the need to move beyond tensor-based models in favor of acquisition and analysis techniques that can help disambiguate different sources of white matter disruptions associated with schizophrenia

Inference in supervised spectral classifiers for on-board hyperspectral imaging: An overview

Machine learning techniques are widely used for pixel-wise classification of hyperspectral images. These methods can achieve high accuracy, but most of them are computationally intensive models. This poses a problem for their implementation in low-power and embedded systems intended for on-board processing, in which energy consumption and model size are as important as accuracy. With a focus on embedded anci on-board systems (in which only the inference step is performed after an off-line training process), in this paper we provide a comprehensive overview of the inference properties of the most relevant techniques for hyperspectral image classification. For this purpose, we compare the size of the trained models and the operations required during the inference step (which are directly related to the hardware and energy requirements). Our goal is to search for appropriate trade-offs between on-board implementation (such as model size anci energy consumption) anci classification accuracy

GPU-friendly neural networks for remote sensing scene classification

Convolutional neural networks (CNNs) have proven to be very efficient for the analysis of remote sensing (RS) images. Due to the inherent complexity of extracting features from these images, along with the increasing amount of data to be processed (and the diversity of applications), there is a clear tendency to develop and employ increasingly deep and complex CNNs. In this regard, graphics processing units (GPUs) are frequently used to optimize their execution, both for the training and inference stages, optimizing the performance of neural models through their many-core architecture. Hence, the efficient use of the GPU resources should be at the core of optimizations. This letter analyzes the possibilities of using a new family of CNNs, denoted as TResNets, to provide an efficient solution to the RS scene classification problem. Moreover, the considered models have been combined with mixed precision to enhance their training performance. Our experimental results, conducted over three publicly available RS data sets, show that the proposed networks achieve better accuracy and more efficient use of GPU resources than other state-of-the-art networks. Source code is available at https://github.com/mhaut/GPUfriendlyRS

Amnesia Associated with Bilateral Hippocampal and Bilateral Basal Ganglia Lesions in Anoxia with Stimulant Use

We report a case of a 55-year-old man with ischemic lesions of the bilateral hippocampus and bilateral basal ganglia following a myocardial infarction during an episode of multiple drug use with subsequent anoxia requiring resuscitation. He presented for a neuropsychological evaluation with an anterograde amnesia for both explicit and procedural memory. There are two main points to this case, the unique aspects of the bilateral multifocal lesions and the functional, cognitive impact of these lesions. We hypothesize that his rare focal bilateral lesions of both the hippocampus and basal ganglia are a result of anoxia acting in synergy with his stimulant drug use (cocaine and/or 3,4-methylenedioxy-methamphetamine). Second, his unique lesions produced an explicit and implicit/procedural anterograde amnesia

Breastfeeding Duration Is Associated with Regional, but Not Global, Differences in White Matter Tracts

Extended breastfeeding through infancy confers benefits on neurocognitive performance and intelligence tests, though few have examined the biological basis of these effects. To investigate correlations with breastfeeding, we examined the major white matter tracts in 4–8 year-old children using diffusion tensor imaging and volumetric measurements of the corpus callosum. We found a significant correlation between the duration of infant breastfeeding and fractional anisotropy scores in left-lateralized white matter tracts, including the left superior longitudinal fasciculus and left angular bundle, which is indicative of greater intrahemispheric connectivity. However, in contrast to expectations from earlier studies, no correlations were observed with corpus callosum size, and thus no correlations were observed when using such measures of global interhemispheric white matter connectivity development. These findings suggest a complex but significant positive association between breastfeeding duration and white matter connectivity, including in pathways known to be functionally relevant for reading and language development

AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks

Convolutional neural networks (CNNs) are trained using stochastic gradient descent (SGD)-based optimizers. Recently, the adaptive moment estimation (Adam) optimizer has become very popular due to its adaptive momentum, which tackles the dying gradient problem of SGD. Nevertheless, existing optimizers are still unable to exploit the optimization curvature information efficiently. This paper proposes a new AngularGrad optimizer that considers the behavior of the direction/angle of consecutive gradients. This is the first attempt in the literature to exploit the gradient angular information apart from its magnitude. The proposed AngularGrad generates a score to control the step size based on the gradient angular information of previous iterations. Thus, the optimization steps become smoother as a more accurate step size of immediate past gradients is captured through the angular information. Two variants of AngularGrad are developed based on the use of Tangent or Cosine functions for computing the gradient angular information. Theoretically, AngularGrad exhibits the same regret bound as Adam for convergence purposes. Nevertheless, extensive experiments conducted on benchmark data sets against state-of-the-art methods reveal a superior performance of AngularGrad. The source code will be made publicly available at: https://github.com/mhaut/AngularGrad

Infection and venous thromboembolism in patients undergoing colorectal surgery: what is the relationship?

BACKGROUND: There is evidence demonstrating an association between infection and venous thromboembolism. We recently identified this association in the postoperative setting; however, the temporal relationship between infection and venous thromboembolism is not well defined OBJECTIVE: We sought to determine the temporal relationship between venous thromboembolism and postoperative infectious complications in patients undergoing colorectal surgery. DESIGN, SETTING, AND PATIENTS: A retrospective cohort analysis was performed using data for patients undergoing colorectal surgery in the National Surgical Quality Improvement Project 2010 database. MAIN OUTCOME MEASURES: The primary outcome measures were the rate and timing of venous thromboembolism and postoperative infection among patients undergoing colorectal surgery during 30 postoperative days. RESULTS: Of 39,831 patients who underwent colorectal surgery, the overall rate of venous thromboembolism was 2.4% (n = 948); 729 (1.8%) patients were diagnosed with deep vein thrombosis, and 307 (0.77%) patients were diagnosed with pulmonary embolism. Eighty-eight (0.22%) patients were reported as developing both deep vein thrombosis and pulmonary embolism. Following colorectal surgery, the development of a urinary tract infection, pneumonia, organ space surgical site infection, or deep surgical site infection was associated with a significantly increased risk for venous thromboembolism. The majority (52%-85%) of venous thromboembolisms in this population occurred the same day or a median of 3.5 to 8 days following the diagnosis of infection. The approximate relative risk for developing any venous thromboembolism increased each day following the development of each type of infection (range, 0.40%-1.0%) in comparison with patients not developing an infection. LIMITATIONS: We are unable to account for differences in data collection, prophylaxis, and venous thromboembolism surveillance between hospitals in the database. Additionally, there is limited patient follow-up. CONCLUSIONS: These findings of a temporal association between infection and venous thromboembolism suggest a potential early indicator for using certain postoperative infectious complications as clinical warning signs that a patient is more likely to develop venous thromboembolism. Further studies into best practices for prevention are warranted

Pilot Randomized Trial of Active Music Engagement Intervention Parent Delivery for Young Children With Cancer

Objectives: To examine the feasibility/acceptability of a parent-delivered Active Music Engagement (AME + P) intervention for young children with cancer and their parents. Secondary aim to explore changes in AME + P child emotional distress (facial affect) and parent emotional distress (mood; traumatic stress symptoms) relative to controls. Methods: A pilot two-group randomized trial was conducted with parents/children (ages 3-8 years) receiving AME + P ( n  =  9) or attention control ( n  =  7). Feasibility of parent delivery was assessed using a delivery checklist and child engagement; acceptability through parent interviews; preliminary outcomes at baseline, postintervention, 30 days postintervention. Results: Parent delivery was feasible, as they successfully delivered AME activities, but interviews indicated parent delivery was not acceptable to parents. Emotional distress was lower for AME + P children, but parents derived no benefit. Conclusions: Despite child benefit, findings do not support parent delivery of AME + P

Memory systems in schizophrenia: Modularity is preserved but deficits are generalized

OBJECTIVE: Schizophrenia patients exhibit impaired working and episodic memory, but this may represent generalized impairment across memory modalities or performance deficits restricted to particular memory systems in subgroups of patients. Furthermore, it is unclear whether deficits are unique from those associated with other disorders. METHOD: Healthy controls (n=1101) and patients with schizophrenia (n=58), bipolar disorder (n=49) and attention-deficit-hyperactivity-disorder (n=46) performed 18 tasks addressing primarily verbal and spatial episodic and working memory. Effect sizes for group contrasts were compared across tasks and the consistency of subjects\u27 distributional positions across memory domains was measured. RESULTS: Schizophrenia patients performed poorly relative to the other groups on every test. While low to moderate correlation was found between memory domains (r=.320), supporting modularity of these systems, there was limited agreement between measures regarding each individual\u27s task performance (ICC=.292) and in identifying those individuals falling into the lowest quintile (kappa=0.259). A general ability factor accounted for nearly all of the group differences in performance and agreement across measures in classifying low performers. CONCLUSIONS: Pathophysiological processes involved in schizophrenia appear to act primarily on general abilities required in all tasks rather than on specific abilities within different memory domains and modalities. These effects represent a general shift in the overall distribution of general ability (i.e., each case functioning at a lower level than they would have if not for the illness), rather than presence of a generally low-performing subgroup of patients. There is little evidence that memory impairments in schizophrenia are shared with bipolar disorder and ADHD