Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.

Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies-amyloid plaques and cerebral amyloid angiopathy-in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability suggests a route to neuropathologic deep phenotyping

Structural Stability of Lexical Semantic Spaces: Nouns in Chinese and French

Many studies in the neurosciences have dealt with the semantic processing of words or categories, but few have looked into the semantic organization of the lexicon thought as a system. The present study was designed to try to move towards this goal, using both electrophysiological and corpus-based data, and to compare two languages from different families: French and Mandarin Chinese. We conducted an EEG-based semantic-decision experiment using 240 words from eight categories (clothing, parts of a house, tools, vehicles, fruits/vegetables, animals, body parts, and people) as the material. A data-analysis method (correspondence analysis) commonly used in computational linguistics was applied to the electrophysiological signals. The present cross-language comparison indicated stability for the following aspects of the languages' lexical semantic organizations: (1) the living/nonliving distinction, which showed up as a main factor for both languages; (2) greater dispersion of the living categories as compared to the nonliving ones; (3) prototypicality of the \emph{animals} category within the living categories, and with respect to the living/nonliving distinction; and (4) the existence of a person-centered reference gradient. Our electrophysiological analysis indicated stability of the networks at play in each of these processes. Stability was also observed in the data taken from word usage in the languages (synonyms and associated words obtained from textual corpora).Comment: 17 pages, 4 figure

'SO STONED' : common sense approach of the dizzy patient

The history taking of a dizzy patient is of utmost importance in order to differentiate the possible etiologies of vertigo. The key factors that allow a first approximation of diagnosis identification are based on the time profile, symptom profile, and trigger profile of the disease. Here, the proposed mnemonic "SO STONED" comprises eight different dimensions that characterize the vertigo-related complaints of the patient and guide the clinician in his or her decision scheme. All the letters "SO STONED" have a specific meaning: Symptoms, Often (Frequency), Since, Trigger, Otology, Neurology, Evolution, and Duration. Since the most common vestibular diseases have different fingerprints when all dimensions are considered, this tool can facilitate the identification of the appropriate vestibular diagnosis

Thoughts about disordered thinking: measuring and quantifying the laws of order and disorder

Peer ReviewedPostprint (author's final draft

Validity ty of spectral analysis of evoked potentials in brain research

The averaged electronencephologram (EEG) response of the brain to an external stimulus (evoked potential, EP) is usually subjected to spectral analysis using the fast Fourier transform (FFT), especially to discover the relation of cognitive ability to so-called brain dynamics. There is indeed a discrepancy between these two systems, because the brain is a highly complex nonlinear system, analyzed by a linear system (FFT). We present in this work some inaccuracies that occurred when EPs are subjected to spectral analysis, using a model signal. First of all, the EP power spectra depended upon the number of samples used for averaging; the input EP (model signal) and the output EP (from the system) seemed to be similar in forms, but they exhibited completely different spectral power curves. It was concluded that the spectral analysis of evoked responses by using FFT (linear system analysis) in relation to brain (highly complex nonlinear system) may mislead neuroscientists

Breathing Signature as Vitality Score Index Created by Exercises of Qigong: Implications of Artificial Intelligence Tools Used in Traditional Chinese Medicine.

Rising concerns about the short- and long-term detrimental consequences of administration of conventional pharmacopeia are fueling the search for alternative, complementary, personalized, and comprehensive approaches to human healthcare. Qigong, a form of Traditional Chinese Medicine, represents a viable alternative approach. Here, we started with the practical, philosophical, and psychological background of Ki (in Japanese) or Qi (in Chinese) and their relationship to Qigong theory and clinical application. Noting the drawbacks of the current state of Qigong clinic, herein we propose that to manage the unique aspects of the Eastern 'non-linearity' and 'holistic' approach, it needs to be integrated with the Western "linearity" "one-direction" approach. This is done through developing the concepts of "Qigong breathing signatures," which can define our life breathing patterns associated with diseases using machine learning technology. We predict that this can be achieved by establishing an artificial intelligence (AI)-Medicine training camp of databases, which will integrate Qigong-like breathing patterns with different pathologies unique to individuals. Such an integrated connection will allow the AI-Medicine algorithm to identify breathing patterns and guide medical intervention. This unique view of potentially connecting Eastern Medicine and Western Technology can further add a novel insight to our current understanding of both Western and Eastern medicine, thereby establishing a vitality score index (VSI) that can predict the outcomes of lifestyle behaviors and medical conditions

Automatic colorization of non-enhanced brain CT images for clinical diagnosis

Background: The frequent use of brain computed tomography (CT) scans in emergency settings necessitates accurate reporting of CT results as quickly as possible. Conventional CT scans produce grayscale images, requiring window width and center level changes, resulting in a need for time-consuming interpretation by experienced radiologists. This study aimed to design a novel software application for automatic smart colorization of conventional brain CT images and to evaluate the diagnostic accuracy, visual quality, ease of diagnosis, and reporting time for color CT images compared to conventional grayscale CT images. Materials and Methods: First, we designed an application that converted non-enhanced grayscale brain CT images into color images according to the Hounsfield unit value of different tissues (e.g., brain, fat, bone, fluid, air) with minimal noise so that all brain tissues could be evaluated using one window level. This process took less than one second, without the need for high-end systems. Next, 75 printed images (25 unprocessed grayscale CT, 25 processed color CT, and 25 magnetic resonance imaging [MRI]) from 25 patients with hemorrhagic or ischemic stroke were read by two experienced radiologists. The radiologists scored the CT images from each patient (unprocessed grayscale and processed color) on a ten-point scale for visual quality and ease of diagnosis compared to the MRI image. Results: The mean visual quality score was 18% higher and the mean ease of diagnosis score was 23% higher for colorized images than for grayscale images (both P < 0.001). Statistically, there were no significant differences in the diagnostic accuracy or reporting time between color and grayscale images. Conclusion: This is the first study to report automatic smart colorization of non-enhanced brain CT images, producing high-quality colorized images with better visual quality and ease of diagnosis compared to grayscale CT. This low-cost solution can be widely applied in clinical settings, regardless of minimal facility or resource availability. &nbsp

Neonatal White Matter Maturation Is Associated With Infant Language Development

Background: While neonates have no sophisticated language skills, the neural basis for acquiring this function is assumed to already be present at birth. Receptive language is measurable by 6 months of age and meaningful speech production by 10-18 months of age. Fiber tracts supporting language processing include the corpus callosum (CC), which plays a key role in the hemispheric lateralization of language; the left arcuate fasciculus (AF), which is associated with syntactic processing; and the right AF, which plays a role in prosody and semantics. We examined if neonatal maturation of these fiber tracts is associated with receptive language development at 12 months of age. Methods: Diffusion-weighted imaging (DWI) was performed in 86 infants at 26.6 ± 12.2 days post-birth. Receptive language was assessed via the MacArthur-Bates Communicative Development Inventory at 12 months of age. Tract-based fractional anisotropy (FA) was determined using the NA-MIC atlas-based fiber analysis toolkit. Associations between neonatal regional FA, adjusted for gestational age at birth and age at scan, and language development at 12 months of age were tested using ANOVA models. Results: After multiple comparisons correction, higher neonatal FA was positively associated with receptive language at 12 months of age within the genu (p < 0.001), rostrum (p < 0.001), and tapetum (p < 0.001) of the CC and the left fronto-parietal AF (p = 0.008). No significant clusters were found in the right AF. Conclusion: Microstructural development of the CC and the AF in the newborn is associated with receptive language at 12 months of age, demonstrating that interindividual variation in white matter microstructure is relevant for later language development, and indicating that the neural foundation for language processing is laid well ahead of the majority of language acquisition. This suggests that some origins of impaired language development may lie in the intrauterine and potentially neonatal period of life. Understanding how interindividual differences in neonatal brain maturity relate to the acquisition of function, particularly during early development when the brain is in an unparalleled window of plasticity, is key to identifying opportunities for harnessing neuroplasticity in health and disease