Schizophrenia is widely known to manifest in language disturbance. Namely, speech incoherence, tangentiality, derailment are indicative of thought disorder characteristic of schizophrenia. Recent advances in distributional semantics have made it possible to measure coherence in text in a unified and objective manner. It has been shown that semantic coherence measures based on distributional semantic models in English speech significantly contribute to schizophrenia diagnosis prediction and correlate with thought disorder measures. However, information on other languages and modes is either contradictory or unavailable. The goal of the current paper is to analyze semantic coherence in schizophrenia in Russian written texts. We present a dataset of short texts written by patients diagnosed with schizophrenia and matched healthy control subjects. We have developed a number of semantic coherence measures, both replicating findings in other languages and novel ones. Our results show that in Russian written texts by patients diagnosed with schizophrenia semantic coherence values are contradictory to the findings obtained for spoken English texts. However, semantic coherence in our dataset provides an effective diagnosis predictor. We discuss our results in terms of possible theoretic interpretation and outline further steps to semantic coherence measurement in schizophrenia.
Clinical Neurophysiology is dedicated to publishing scholarly reports on the pathophysiology underlying diseases of the peripheral and central nervous system of humans. Reports on clinical trials that use neurophysiological measures as endpoints are encouraged, as are manuscripts on integrated neuroimaging of peripheral and central nervous function including, but not limited to, functional MRI, brain mapping, MEG, EEG, PET, ultrasound, and other neuroimaging modalities. Studies on normal human neurophysiology are welcome, if they are relevant to disease or clinical applications. Studies on animals and technical reports must have clear applicability to human disease. Case reports may be considered (exclusively as Letters-to-the-Editor), if implying substantial advancement of knowledge. Clinical Neurophysiology covers epilepsy, developmental clinical neurophysiology, psychophysiology and psychopathology, motor control and movement disorders, somatosensory disorders including pain, motor neuron diseases, neuromuscular diseases, neuropathies, sleep and disorders of consciousness, auditory and vestibular disorders, aging, Alzheimer's disease, other dementias, other psychiatric disorders, autonomic disorders, neural plasticity and recovery, intraoperative and ICU monitoring, and therapeutic clinical neurophysiology including non-invasive and invasive brain stimulation. All studies published in Clinical Neurophysiology must stand on their own and make a substantial contribution to the literature. The journal does not afford a high priority to 'pilot' or 'preliminary' studies or to negative studies that do not advance knowledge. Reports with a focus on education or clinical practice, case reports, methodological and technical reports and studies reporting normative data on healthy subjects are preferentially being considered in Clinical Neurophysiology Practice, a companion journal of Clinical Neurophysiology. AUDIENCE: Neurologists, Clinical Neurophysiologists, Neuroscientists, Neuroimagers, Psychiatrists, Neuropsychologists, Neurosurgeons
This is a review study. The physiological responses of simple and complex cells in the primary visual cortex (V1) have been studied extensively and modeled at different levels. At the functional level, the divisive normalization model (DNM; Heeger DJ. Vis Neurosci 9: 181–197, 1992) has accounted for a wide range of single-cell recordings in terms of a combination of linear filtering, nonlinear rectification, and divisive normalization. We propose standardizing the formulation of the DNM and implementing it in software that takes static grayscale images as inputs and produces firing rate responses as outputs. We also review a comprehensive suite of 30 empirical phenomena and report a series of simulation experiments that qualitatively replicate dozens of key experiments with a standard parameter set consistent with physiological measurements. This systematic approach identifies novel falsifiable predictions of the DNM. We show how the model simultaneously satisfies the conflicting desiderata of flexibility and falsifiability. Our key idea is that, while adjustable parameters are needed to accommodate the diversity across neurons, they must be fixed for a given individual neuron. This requirement introduces falsifiable constraints when this single neuron is probed with multiple stimuli. We also present mathematical analyses and simulation experiments that explicate some of these constraints.