Neural oscillations track natural but not artificial fast speech: Novel insights from speech-brain coupling using MEG.

Neural oscillations contribute to speech parsing via cortical tracking of hierarchical linguistic structures, including syllable rate. While the properties of neural entrainment have been largely probed with speech stimuli at either normal or artificially accelerated rates, the important case of natural fast speech has been largely overlooked. Using magnetoencephalography, we found that listening to naturally-produced speech was associated with cortico-acoustic coupling, both at normal (∼6 syllables/s) and fast (∼9 syllables/s) rates, with a corresponding shift in peak entrainment frequency. Interestingly, time-compressed sentences did not yield such coupling, despite being generated at the same rate as the natural fast sentences. Additionally, neural activity in right motor cortex exhibited stronger tuning to natural fast rather than to artificially accelerated speech, and showed evidence for stronger phase-coupling with left temporo-parietal and motor areas. These findings are highly relevant for our understanding of the role played by auditory and motor cortex oscillations in the perception of naturally produced speech.

Generalization vs. Specificity: In Which Cases Should a Clinic Train its Own Segmentation Models?

As artificial intelligence for image segmentation becomes increasingly available, the question whether these solutions generalize between different hospitals and geographies arises. The present study addresses this question by comparing multi-institutional models to site-specific models. Using CT data sets from four clinics for organs-at-risk of the female breast, female pelvis and male pelvis, we differentiate between the effect from population differences and differences in clinical practice. Our study, thus, provides guidelines to hospitals, in which case the training of a custom, hospital-specific deep neural network is to be advised and when a network provided by a third-party can be used. The results show that for the organs of the female pelvis and the heart the segmentation quality is influenced solely on bases of the training set size, while the patient population variability affects the female breast segmentation quality above the effect of the training set size. In the comparison of site-specific contours on the male pelvis, we see that for a sufficiently large data set size, a custom, hospital-specific model outperforms a multi-institutional one on some of the organs. However, for small hospital-specific data sets a multi-institutional model provides the better segmentation quality.

Task-Modulated Corticocortical Synchrony in the Cognitive-Motor Network Supporting Handwriting.

Both motor and cognitive aspects of behavior depend on dynamic, accurately timed neural processes in large-scale brain networks. Here, we studied synchronous interplay between cortical regions during production of cognitive-motor sequences in humans. Specifically, variants of handwriting that differed in motor variability, linguistic content, and memorization of movement cues were contrasted to unveil functional sensitivity of corticocortical connections. Data-driven magnetoencephalography mapping (n = 10) uncovered modulation of mostly left-hemispheric corticocortical interactions, as quantified by relative changes in phase synchronization. At low frequencies (~2-13 Hz), enhanced frontoparietal synchrony was related to regular handwriting, whereas premotor cortical regions synchronized for simple loop production and temporo-occipital areas for a writing task substituting normal script with loop patterns. At the beta-to-gamma band (~13-45 Hz), enhanced synchrony was observed for regular handwriting in the central and frontoparietal regions, including connections between the sensorimotor and supplementary motor cortices and between the parietal and dorsal premotor/precentral cortices. Interpreted within a modular framework, these modulations of synchrony mainly highlighted interactions of the putative pericentral subsystem of hand coordination and the frontoparietal subsystem mediating working memory operations. As part of cortical dynamics, interregional phase synchrony varies depending on task demands in production of cognitive-motor sequences.

Clinical evaluation of a full-image deep segmentation algorithm for the male pelvis on cone-beam CT and CT.

AIM: The segmentation of organs from a CT scan is a time-consuming task, which is one hindrance for adaptive radiation therapy. Through deep learning, it is possible to automatically delineate organs. Metrics like dice score do not necessarily represent the impact for clinical practice. Therefore, a clinical evaluation of the deep neural network is needed to verify the segmentation quality. METHODS: In this work, a novel deep neural network is trained on 300 CT and 300 artificially generated pseudo CBCTs to segment bladder, prostate, rectum and seminal vesicles from CT and cone beam CT scans. The model is evaluated on 45 CBCT and 5 CT scans through a clinical review performed by three different clinics located in Europe, North America and Australia. RESULTS: The deep learning model is scored either equally good (prostate and seminal vesicles) or better (bladder and rectum) than the structures from routine clinical practice. No or minor corrections are required for 97.5% of the segmentations of the bladder, 91.5% of the prostate, 94% of the rectum and seminal vesicles. Overall, for 82.5% of the patients none of the organs need major corrections or a redraw. CONCLUSION: This study shows that modern deep neural networks are capable of producing clinically applicable organ segmentation for the male pelvis. The model is able to produce acceptable structures as frequently as current clinical routine. Therefore, deep neural networks can simplify the clinical workflow by offering initial segmentations. The study further shows that to retain the clinicians' personal preferences a structure review and correction is necessary for structures both created by other clinicians and deep neural networks.

A Full-Image Deep Segmenter for CT Images in Breast Cancer Radiotherapy Treatment.

Radiation therapy is one of the key cancer treatment options. To avoid adverse effects in the healthy tissue, the treatment plan needs to be based on accurate anatomical models of the patient. In this work, an automatic segmentation solution for both female breasts and the heart is constructed using deep learning. Our newly developed deep neural networks perform better than the current state-of-the-art neural networks while improving inference speed by an order of magnitude. While manual segmentation by clinicians takes around 20 min, our automatic segmentation takes less than a second with an average of 3 min manual correction time. Thus, our proposed solution can have a huge impact on the workload of clinical staff and on the standardization of care.

[Lung cancer in a young female patient]. / Nuoren naisen mikrobi- ja astmalääkitykseen reagoimaton yskä ja hengenahdistus.

Lung cancer is the most significant cause of cancer-related deaths in Finland. The diagnosis is still a challenge in outpatient settings. With early diagnosis, patients may receive curable treatment. We describe a young female lung cancer patient with prolonged upper airway infection as a symptom of the disease.

Neural interactions at the core of phonological and semantic priming of written words.

Word processing is often probed with experiments where a target word is primed by preceding semantically or phonologically related words. Behaviorally, priming results in faster reaction times, interpreted as increased efficiency of cognitive processing. At the neural level, priming reduces the level of neural activation, but the actual neural mechanisms that could account for the increased efficiency have remained unclear. We examined whether enhanced information transfer among functionally relevant brain areas could provide such a mechanism. Neural activity was tracked with magnetoencephalography while subjects read lists of semantically or phonologically related words. Increased priming resulted in reduced cortical activation. In contrast, coherence between brain regions was simultaneously enhanced. Furthermore, while the reduced level of activation was detected in the same area and time window (superior temporal cortex [STC] at 250-650 ms) for both phonological and semantic priming, the spatiospectral connectivity patterns appeared distinct for the 2 processes. Causal interactions further indicated a driving role for the left STC in phonological processing. Our results highlight coherence as a neural mechanism of priming and dissociate semantic and phonological processing via their distinct connectivity profiles.

MEG evoked responses and rhythmic activity provide spatiotemporally complementary measures of neural activity in language production.

Phase-locked evoked responses and event-related modulations of spontaneous rhythmic activity are the two main approaches used to quantify stimulus- or task-related changes in electrophysiological measures. The relationship between the two has been widely theorized upon but empirical research has been limited to the primary visual and sensorimotor cortex. However, both evoked responses and rhythms have been used as markers of neural activity in paradigms ranging from simple sensory to complex cognitive tasks. While some spatial agreement between the two phenomena has been observed, typically only one of the measures has been used in any given study, thus disallowing a direct evaluation of their exact spatiotemporal relationship. In this study, we sought to systematically clarify the connection between evoked responses and rhythmic activity. Using both measures, we identified the spatiotemporal patterns of task effects in three magnetoencephalography (MEG) data sets, all variants of a picture naming task. Evoked responses and rhythmic modulation yielded largely separate networks, with spatial overlap mainly in the sensorimotor and primary visual areas. Moreover, in the cortical regions that were identified with both measures the experimental effects they conveyed differed in terms of timing and function. Our results suggest that the two phenomena are largely detached and that both measures are needed for an accurate portrayal of brain activity.

Modulation of brain activity after learning predicts long-term memory for words.

The acquisition and maintenance of new language information, such as picking up new words, is a critical human ability that is needed throughout the life span. Most likely you learned the word "blog" quite recently as an adult, whereas the word "kipe," which in the 1970s denoted stealing, now seems unfamiliar. Brain mechanisms underlying the long-term maintenance of new words have remained unknown, albeit they could provide important clues to the considerable individual differences in the ability to remember words. After successful training of a set of novel object names we tracked, over a period of 10 months, the maintenance of this new vocabulary in 10 human participants by repeated behavioral tests and magnetoencephalography measurements of overt picture naming. When naming-related activation in the left frontal and temporal cortex was enhanced 1 week after training, compared with the level at the end of training, the individual retained a good command of the new vocabulary at 10 months; vice versa, individuals with reduced activation at 1 week posttraining were less successful in recalling the names at 10 months. This finding suggests an individual neural marker for memory, in the context of language. Learning is not over when the acquisition phase has been successfully completed: neural events during the access to recently established word representations appear to be important for the long-term outcome of learning.

Cellular automata model for swelling-controlled drug release.

A cellular automata approach for modeling swelling-controlled drug release is presented. In the model, a drug release device is divided into a square grid space. Each cell in the grid contains information about the material, drug, polymer or solvent in that domain. Cells are allowed to change their state according to statistical rules designed to mimic physical phenomena. Diffusion and swelling are modeled by a random walk of mobile cells, and kinetics of chemical or physical processes by probabilities of conversion from one state to another. The model is applied to drug release from a swelling binary polymer/drug device. The effect of simulation parameters on the drug release profiles and the locations of erosion and diffusion fronts are considered. The model was able to produce realistic simulations and is proposed as a new tool for the design of controlled release devices.

Cellular automata model for drug release from binary matrix and reservoir polymeric devices.

Kinetics of drug release from polymeric tablets, inserts and implants is an important and widely studied area. Here we present a new and widely applicable cellular automata model for diffusion and erosion processes occurring during drug release from polymeric drug release devices. The model divides a 2D representation of the release device into an array of cells. Each cell contains information about the material, drug, polymer or solvent that the domain contains. Cells are then allowed to rearrange according to statistical rules designed to match realistic drug release. Diffusion is modeled by a random walk of mobile cells and kinetics of chemical or physical processes by probabilities of conversion from one state to another. This is according to the basis of diffusion coefficients and kinetic rate constants, which are on fundamental level just probabilities for certain occurrences. The model is applied to three kinds of devices with different release mechanisms: erodable matrices, diffusion through channels or pores and membrane controlled release. The dissolution curves obtained are compared to analytical models from literature and the validity of the model is considered. The model is shown to be compatible with all three release devices, highlighting easy adaptability of the model to virtually any release system and geometry. Further extension and applications of the model are envisioned.

Motor cortex dynamics in visuomotor production of speech and non-speech mouth movements.

We investigated timing and hemispheric balance of motor cortex activation when kinetically similar speech and non-speech mouth movements and sequences of such movements were triggered by visually presented letter- and symbol-strings. As an index of motor cortex activation, we used magnetoencephalographic recording of task-related change of precentral 20 Hz (16-24 Hz) activity. Suppression of the 20 Hz rhythm revealed pre-movement activation in the face representation areas that was tied to visual instruction, not movement onset. The 20 Hz rhythm remained suppressed throughout the preparation and execution of mouth movements and was followed by post-movement rebound. Left hemisphere preceded the right at the onset and offset of the suppression, similarly for isolated and sequential speech and non-speech movements. Pattern of task-related change in 20 Hz activity was otherwise symmetrical. In the face areas, the overall modulation of 20 Hz activity increased with sequence length and motor demands. Hand representation areas showed also weak reactivity, with systematically larger modulation of 20 Hz activity for non-speech than speech movements. Our results suggest an active role for the motor cortex in cognitive control of visually triggered mouth movements, not limited to movement execution.