Resting-state brain networks represent the intrinsic state of the brain during the majority of cognitive and sensorimotor tasks. However, no study has yet presented concise predictors of task-induced vigilance variability from spectrospatial features of the pre-task, resting-state electroencephalograms (EEG). We asked ten healthy volunteers (6 females, 4 males) aged from 22 to 45.5 to participate in 105-minute fixed-sequence-varying-duration sessions of sustained attention to response task (SART). A novel and adaptive vigilance scoring scheme was designed based on the performance and response time in consecutive trials, and demonstrated large inter-participant variability in terms of maintaining consistent tonic performance. Multiple linear regression using feature relevance analysis obtained significant predictors of the mean cumulative vigilance score (CVS), mean response time, and variabilities of these scores from the resting-state, bandpower ratios of EEG signals, p<0. Brodmanns areas 35 and 36). Higher ratios of parietal alpha (8-12 Hz) from the Brodmann's areas 18, 19, and 37 during the eyes-open states predicted slower responses but more consistent CVS and reactions associated with the superior ability in vigilance maintenance. The proposed framework and these first findings on the most stable and significant attention predictors from the intrinsic EEG power ratios can be used to model attention variations during the calibration sessions of BCI applications and vigilance monitoring systems. Single-layer neural networks trained with cross-validation also captured different associations for the beta sub-bands. Increase in the gamma (28-48 Hz) and upper beta (24-28 Hz) ratios from the left central and temporal regions predicted slower reactions and more inconsistent vigilance as explained by the increased activation of default mode network (DMN) and differences between the high-and low-attention networks at temporal regions (

Functional properties of a neuron are coupled with its morphology, particularly the morphology of dendritic spines. Spine volume has been used as the primary morphological parameter in order the characterize the structure and function coupling. However, this reductionist approach neglects the rich shape repertoire of dendritic spines. First step to incorporate spine shape information into functional coupling is classifying main spine shapes that were proposed in the literature. Due to the lack of reliable and fully automatic tools to analyze the morphology of the spines, such analysis is often performed manually, which is a laborious and time intensive task and prone to subjectivity. In this paper we present an automated approach to extract features using basic image processing techniques, and classify spines into mushroom or stubby by applying machine learning algorithms. Out of 50 manually segmented mushroom and stubby spines, Support Vector Machine was able to classify 98% of the spines correctly.Özetçe -Sinir hücresinin işlevsel özellikleri dendrit dikenlerinin morfolojisiyle yakından ilişkilidir. Dendrit diken hacmi, yapı ve fonksiyon arasındaki ilişkiyi anlamak için kullanılan temel morfolojik parametredir. Fakat bu indirgemeci yaklaşım dikenlerin zenginşekil repertuvarını ihmal etmektedir. Dikeņ sekil bilgisini fonksiyonu ile ilişkilendirmenin ilk adımı dikenleri literatürde önerilen temelşekil gruplarına göre sınıflandırmaktır. Diken morfolojisini inceleyen güvenilir ve tamamen otomatik bir aracın bulunmaması analizlerin insanlar tarafından el ile yapılmasına yol açmaktadır. Bu da yorucu, zaman alan bir ugraştır ve subjektif sonuçlar ortaya çıkarmaktadır. Bu çalışmada temel görüntü işleme tekniklerini kullanarak dikenlerden öznitelik çıkarmayı ve makine ögrenme algoritmaları ile dikenleri mantar ya da güdük olarak sınıflandırmayı öneriyoruz. El ile bölütlenmiş mantar ve güdük gruplarından oluşan toplam 50 diken, Destek Vektör Makineleri kullanılarak %98 dogruluk payıyla sınıflandırılmıştır.Anahtar Kelimeler-Dendritik dikenler, sınıflandırma , kümeleme, sinirbilim.

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