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Malformations of cortical development are a group of rare disorders commonly manifesting with developmental delay, cerebral palsy or seizures. The neurological outcome is extremely variable depending on the type, extent and severity of the malformation and the involved genetic pathways of brain development. Neuroimaging plays an essential role in the diagnosis of these malformations, but several issues regarding malformations of cortical development definitions and classification remain unclear. The purpose of this consensus statement is to provide standardized malformations of cortical development terminology and classification for neuroradiological pattern interpretation. A committee of international experts in paediatric neuroradiology prepared systematic literature reviews and formulated neuroimaging recommendations in collaboration with geneticists, paediatric neurologists and pathologists during consensus meetings in the context of the European Network Neuro-MIG initiative on Brain Malformations (https://www.neuro-mig.org/). Malformations of cortical development neuroimaging features and practical recommendations are provided to aid both expert and non-expert radiologists and neurologists who may encounter patients with malformations of cortical development in their practice, with the aim of improving malformations of cortical development diagnosis and imaging interpretation worldwide.

Diffuse large B-cell lymphoma (DLBCL) is a fast-growing and aggressive neoplasm originating from B lymphocytes. Evaluation of proliferation index (PI) based on Ki67 immunohistochemical nuclear staining is used to distinguish proliferating (immunopositive) from nonproliferating (immunonegative) lymphoma cells. Human interpretation of PI varies and is time-consuming, therefore automatic computer-assisted approach may facilitate the performance. Herein we propose a new fully automatic proliferation index estimation (FLAPIE) algorithm, dedicated to detection of immunopositive and immunonegative nuclei, and evaluation of PI in digital microscopy images of DAB&H-stained samples from patients with high-grade DLBCL. FLAPIE performs nuclei detection in original RGB colour space and is independent of image brightness due to its textural-statistical approach. Validation of FLAPIE was performed in 61 non-overlapping whole-slide image fragments and compared to the results of PI estimation by QuPath open-source software, MetPiKi algorithm and manual evaluation by two independent observers. Interobserver agreement was calculated between the nuclei count and PIs by two observers. High concordance was found between both DAB and H-stained nuclei count, and PIs by two observers. Compared to MetPiKi, FLAPIE presented improved results of DAB and H-stained nuclei detection. In contrary to MetPiKi and QuPath, FLAPIE performed nuclei detection in all images and its results closely matched the number of DAB-stained nuclei evaluated by two observers. No significant difference was found between PIs by all computational methods and observers. FLAPIE achieved good results in PI estimation and prospectively aims to serve as a tool for clinical application in support of patients selection and decision to treatment.

Background: Despite the introduction of a full range of genetic diagnostic tests and sophisticated techniques in modern pathology, interpretation of histopathological images obtained from muscle biopsies remains important in the daily practice of neuropathology since it can give indications of the severity and the rate of progression of neuromuscular disease. In this paper, we propose a simple and time saving method for quantitative assessment of severity of Duchenne Muscular Dystrophy (DMD) based on computer-aided analysis of histopathological images obtained from biopsies of dystrophic muscles.

We have upgraded our own original color filtration pixel-by-pixel (CFPP) method (Klonowski et al 2018a Physiol. Meas. 39 034002) to enable not only automatic and rapid assessment of the proliferation index of a tumor or neoplasm but also quick automatic location of hot-spots (regions of interest, ROIs) in immunohistochemically stained microscopic images of neoplasms and tumors.Neoplastic cells stain differently from normal cells. By counting in a given window the number of pixels belonging to the given subspaces of (R,G,B) color space which correspond, respectively, to proliferating cells (which are mostly neoplastic) and non-proliferating cells (which are mostly normal) we calculate the local proliferation index in this window. The window is moved all around the whole histopathological virtual slide (WSI) or around a chosen part of the WSI. By adding the respective numbers calculated for all the windows covering the WSI or the chosen part of it one can easily calculate the global proliferation index.The method is rapid and does not require the time-consuming step of selecting ROIs manually nor does it need computationally complicated detection of hot-spots, both of which attempt to emulate a pathologist's way of thinking. We apply our method to a set of slide images of diffuse large B-cell lymphoma.By appropriate changes in the (R,G,B) color filtration thresholds, our method may be adapted to the analysis of other types of tumors. It may also be adapted for analysis of microscopic images in neuropathology. Because of its rapidity and simplicity it may also used for analysis of series of images to assess local dynamics of image complexity in network physiology applications.

Based on consensus meetings as part of the European Network Neuro-MIG initiative, Severino et al. provide standardized terminology and classification for malformations of cortical development, as well as practical recommendations to aid radiologists and neurologists in interpreting brain MRI in these patients., Malformations of cortical development are a group of rare disorders commonly manifesting with developmental delay, cerebral palsy or seizures. The neurological outcome is extremely variable depending on the type, extent and severity of the malformation and the involved genetic pathways of brain development. Neuroimaging plays an essential role in the diagnosis of these malformations, but several issues regarding malformations of cortical development definitions and classification remain unclear. The purpose of this consensus statement is to provide standardized malformations of cortical development terminology and classification for neuroradiological pattern interpretation. A committee of international experts in paediatric neuroradiology prepared systematic literature reviews and formulated neuroimaging recommendations in collaboration with geneticists, paediatric neurologists and pathologists during consensus meetings in the context of the European Network Neuro-MIG initiative on Brain Malformations (https://www.neuro-mig.org/). Malformations of cortical development neuroimaging features and practical recommendations are provided to aid both expert and non-expert radiologists and neurologists who may encounter patients with malformations of cortical development in their practice, with the aim of improving malformations of cortical development diagnosis and imaging interpretation worldwide.

Despite the introduction of a full range of genetic diagnostic tests and sophisticated techniques in modern pathology, interpretation of histopathological images obtained from muscle biopsies remains important in the daily practice of neuropathology since it can give indications of the severity and the rate of progression of neuromuscular disease. In this paper, we propose a simple and time saving method for quantitative assessment of severity of Duchenne Muscular Dystrophy (DMD) based on computer-aided analysis of histopathological images obtained from biopsies of dystrophic muscles. Using this method, colour filtration pixel-by-pixel of the whole virtual slides (CFPP method) is adopted to semi-quantitative evaluation of morphological structure of the muscular tissue. Results demonstrate usefulness of the proposed method in neuropathological assessement of DMD severity.

Objective We have upgraded our own original color filtration pixel-by-pixel (CFPP) method (Klonowski et al 2018a Physiol. Meas. 39 034002) to enable not only automatic and rapid assessment of the proliferation index of a tumor or neoplasm but also quick automatic location of hot-spots (regions of interest, ROIs) in immunohistochemically stained microscopic images of neoplasms and tumors. Approach Neoplastic cells stain differently from normal cells. By counting in a given window the number of pixels belonging to the given subspaces of (R,G,B) color space which correspond, respectively, to proliferating cells (which are mostly neoplastic) and non-proliferating cells (which are mostly normal) we calculate the local proliferation index in this window. The window is moved all around the whole histopathological virtual slide (WSI) or around a chosen part of the WSI. By adding the respective numbers calculated for all the windows covering the WSI or the chosen part of it one can easily calculate the global proliferation index. Main results The method is rapid and does not require the time-consuming step of selecting ROIs manually nor does it need computationally complicated detection of hot-spots, both of which attempt to emulate a pathologist's way of thinking. We apply our method to a set of slide images of diffuse large B-cell lymphoma. Significance By appropriate changes in the (R,G,B) color filtration thresholds, our method may be adapted to the analysis of other types of tumors. It may also be adapted for analysis of microscopic images in neuropathology. Because of its rapidity and simplicity it may also used for analysis of series of images to assess local dynamics of image complexity in network physiology applications.

I discuss several aspects of non-thermal effects of electromagnetic fields while showing results of experiments demonstrating how EMFs influence growth of corn seedlings and growing of cress seeds. Not only EMFs of selected frequencies but also broadband electromagnetic noise affects living organisms Hypersensitivity to EMFs should be a matter of special concern.

To reduce computational complexity, 2-D images of tumors are firstly preprocessed following our method to form two 1-D sequences, called horizontal and vertical landscapes, and then their fractal dimension is estimated using Higuchi’s fractal dimension. We validate application of this method for grading Anal Intraepithelial Neoplasia (AIN) tumors using 120 images of histological slices graded by an experienced pathologist. The method is easy and quick and we demonstrate that it may be successfully applied for differentiation between AIN stages. It can easily be adapted to other histological specimen. Of course, like any other semiautomatic method it may support the pathologist’s decision.

Nonlinear methods are better suited for analysis of EEG signals than so-called linear methods like fast Fourier transform (FFT). In this chapter, we illustrate the use of the Higuchi’s fractal dimension method. We present several examples of the usefulness of this method in application to sleep-EEG analysis, revealing influence of electromagnetic fields, monitoring anesthesia, and assessing bright light therapy (BLT) and electroconvulsive therapy (ECT). We conclude that Higuchi’s fractal dimension method is very useful in the analysis of EEG signals.

A goal of the multicenter European Cooperation in Science and Technology (COST) action MYO-MRI is to optimize Magnetic Resonance Imaging Texture Analysis (MRI-TA) methods for application in the study of muscle disease. This paper deals with recommendations on the optimal methodology to collect the MRI data, to analyse it via texture analysis and to make conclusions from the resultant texture parameter data. A full and detailed description is provided with respect to MR image quality control, sequence choice, image pre-processing, region of interest selection, texture analysis methods and data analysis. A series of conclusions are presented.

Genius is defined as a person of outstanding mental skills. These skills result from genius’ brain structure and physiology that are determined by genetics. It seems that environment and education may only inhibit development of a genius. Based on a simple model of brain phase space we formulate two hypotheses about dynamics of mental processes in genius’ brain and the difference between genius thinking and a mental illness or insanity.

Background The chess game is a good example of cognitive task which needs a lot of training and experience. The aim of this work is to compare applicability of two nonlinear methods - Higuchi Fractal Dimension and Empirical Mode Decomposition - in analysis of EEG data recorded during chess match. We analyzed data of three master chess players registered during their matches with computer program. Methods We used two nonlinear methods: Higuchi Fractal Dimension that is a good and fast tool for analyzing signal complexity and modification of Empirical Mode Decomposition, called Sliding Window Empirical Mode Decomposition, that breaks down a signal into its monocomponents. Obtained results are compared with the resting state i.e. EEG during relax witch closed eyes. Results The analysis shows higher values of Higuchi Fractal Dimension during the thinking over chess moves than in the players’ rest state. There are no statistically significant differences in contribution of EEG bands to total power of EEG calculated with Sliding Window Empirical Mode Decomposition. Conclusions Our results show beter applicability of Higuchi Fractal Dimension method for analysis of EEG signals related to chess tasks than that of Sliding Window Empirical Mode Decomposition.

This study addresses application of Higuchi's fractal dimension (FD) as a measure to evaluate the effect of external periodic stressor on electrical oscillations in the brain. Modulated microwave radiation was applied as a weak periodic stressor with strongly inhomogeneous distribution inside the brain. Experiments were performed on a group of 14 volunteers. Ten cycles (1 min on, 1 min off) of 450-MHz microwave radiation modulated at 40 Hz were applied. Higuchi's FD was calculated in eight symmetric electroencephalographic (EEG) channels located in frontal, temporal, parietal, and occipital areas. FD values averaged over a group detected a small (1-2%) but statistically significant increase with exposure in all EEG channels. FD increased for 12, decreased for one, and was constant for one subject. FD showed the most remarkable effect in temporal and parietal regions of the left hemisphere where the microwave field was maximal. Changes of FD in these regions of the right hemisphere were much higher than expected in accordance with the field distribution. Correlation of FD between different EEG channels was high and retained its value in exposed conditions. Spreading of disturbance between different brain areas is supposed to be crucial for the effect of exposure on the electrical oscillations in the brain.

The ILF (Image Landscapes’ Fractal Dimension) method and [Formula: see text] method obtained by a 2D generalization of Higuchi’s algorithm were applied to a set of 120 digital histological images of Anal Intraepithelial Neoplasia (AIN). The main goal of this research was to examine accuracy that means sensitivity and specificity of these methods and compare the applicability of both methods in the quantitative characterization and differentiation of clinical cases of AIN. Histological examination by an experienced pathologist revealed three grades of AIN tumors in the 120 histological slices: 36 of AIN1, 56 of AIN2 and 28 of AIN3. Statistical tests showed significant differences between calculated fractal dimension values in three datasets (AIN1, AIN2 and AIN3) using ILF and [Formula: see text] methods at the level of significance of 0.05. Application of the ILF and [Formula: see text] methods has an advantage when it comes to speed, accuracy, simplicity and time necessary for analysis. Both methods can be successfully applied for differentiation between AIN stages giving practically the same results. They can easily be adapted to other histological specimen.

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

This paper is based on a discussion that was held during a special session on models of mental disorders, at the NeuroMath meeting in Stockholm, Sweden, in September 2008. At this occasion, scientists from different countries and different fields of research presented their research and discussed open questions with regard to analyses and models of mental disorders, in particular depression. The content of this paper emerged from these discussions and in the presentation we briefly link biomarkers (hormones), bio-signals (EEG) and biomaps (brain-maps via EEG) to depression and its treatments, via linear statistical models as well as nonlinear dynamic models. Some examples involving EEG-data are presented.

We present two biologically inspired models, demonstrating how people make decisions – one, called Game of Choosing (GoC), demonstrates how promotion and autopromotion influence decision making, and second, called Chaosensology Model (ChS), demonstrates how emotions may influence logical decision making. Our results confirm what has been known in psycho-sociology since William James works.

We demonstrate that the methods of signal (time series) analysis developed in nonlinear and symbolic dynamics when applied to financial data in Econophysics give new possibilities of interpretation of those data. Analogies with interpretation of biosignals in Medical Physics may be observed. Hence those methods may be helpful in the diagnosis of economic illnesses like they already help in medical diagnosis.

We demonstrate that the methods of time series analysis supplied by Nonlinear Dynamics and by Symbolic Dynamics are applicable to sleep EEG-data analysis and may in future be used for computerized construction of hypnograms that are important tools in sleep disorder diagnosis.

In the past few years fractal analysis techniques have gained increasing attention in signal and image processing in Medicine. We concentrate on using fractal techniques for analysis of encephalographic data (EEG). Better understanding of general principles that govern discrete dynamics of these signals can help to reveal `the signatures' of different physiological and pathological states. Fractal complexity of the signal in time domain, calculated using Higuchi's algorithm, seems to be the simplest method and may also be used in other biomedical applications.

It has been ascertained that the human brain is a complex system studied at multiple scales, from neurons and microcircuits to macronetworks. The brain is characterized by a hierarchical organization that gives rise to its highly topological and functional complexity. Over the last decades, fractal geometry has been shown as a universal tool for the analysis and quantification of the geometric complexity of natural objects, including the brain. The fractal dimension has been identified as a quantitative parameter for the evaluation of the roughness of neural structures, the estimation of time series, and the description of patterns, thus able to discriminate different states of the brain in its entire physiopathological spectrum. Fractal-based computational analyses have been applied to the neurosciences, particularly in the field of clinical neurosciences including neuroimaging and neuroradiology, neurology and neurosurgery, psychiatry and psychology, and neuro-oncology and neuropathology. After a review of the basic concepts of fractal analysis and its main applications to the basic neurosciences in part I of this series, here, we review the main applications of fractals to the clinical neurosciences for a holistic approach towards a fractal geometry model of the brain.

We have proposed two new fractal methods that may be helpful in diagnosis of cancer. To reduce computational complexity, 2-D images of tumors are firstly preprocessed following our methods to form 1-D sequences that are subsequently analyzed using Higuchi fractal dimension. The first method, Image Signature’s Fractal Dimension, is based on analysis of 1-D sequence called the signature of the tumour mass contour. The second method, Image Landscapes’ Fractal Dimension, is based on analysis of two 1-D sequences called the horizontal and vertical landscapes. Both methods are easy and quick and we demonstrated that they may be successfully applied in oncology to support pathologist’s diagnosis.

We propose new method of assessment of histological images for medical diagnostics. 2-D image is preprocessed to form 1-D landscapes or 1-D signature of the image contour and then their complexity is analyzed using Higuchi's fractal dimension method. The method may have broad medical application, from choosing implant materials to differentiation between benign masses and malignant breast tumors.

The main goals of COST Action NeuroMath are the same as those of the open access journal Nonlinear Biomedical Physics to show how new methods that are being developed in physical disciplines can shed new light on biological phenomena and their medical applications and to bridge the gaps between specialists in physics and biomedical specialists who use these methods in practice. Medical doctors and biologists rather avoid reading physical journals because the articles published there contain ‘heavy’ mathematics; on the other hand, physicists and engineers rarely read biological and medical journals because articles there are mostly descriptive. Both COST NeuroMath Action with its workshops and the journal Nonlinear Biomedical Physics were created to enable these groups to meet together. In this Supplement to Nonlinear Biomedical Physics we publish the best papers based on the presentations shown during the joint workshop for COST Actions NeuroMath (BM0601) and Consciousness (BM0605) ‘Consciousness and its Measures’ that took place in Limassol, Cyprus, 29 November 01 December, 2009. The papers present the newest interdisciplinary achievements in both applied and theoretical research on brain and consciousness. Transient process and synchrony of cortical activity [1], different patterns of cortical activity [2] and assessment of different conscious states [3] are presented. Neurodynamics is studied based on fMRI [4] and on high-resolution EEG signals [5]. Mutual Information is used to study yoking of eyes during saccadic movements [6] and MEG around saccades is analyzed for non-invasive characterization of the human eye fields [7]. Parameter selection for cortical potential imaging [8], and classification of ADHD patients based on independent ERP components [9] are discussed. Language processing by human brain using fMRI [10] and the influence of noise due to electromagnetic interference on processing of visual information [11] are studied. We thank the Authors and the Reviewers for the great job they all have done. We also thank Dr. Kalliopi Kostelidou, Science Officer, BMBS Domain, COST Office, Brussels, and Isobel Peters, Senior Project Manager as well as The Independent Editorial Production Team of BioMed Central, London, for their invaluable assistance in publishing this Supplement to Nonlinear Biomedical Physics.

We answer several important questions concerning EEG. We also shortly discuss importance of nonlinear methods of contemporary physics in EEG analysis. Basic definitions and explanation of fundamental concepts may be found in my previous publications in NBP. It is a magnificent feeling to recognize the unity of complex phenomena which appear to be things quite apart from the direct visible truth. Albert Einstein

We discuss the BCI based on inner tones and inner music. We had some success in the detection of inner tones, the imagined tones which are not sung aloud. Rather easily imagined and controlled, they offer a set of states usable for BCI, with high information capacity and high transfer rates. Imagination of sounds or musical tunes could provide a multicommand language for BCI, as if using the natural language. Moreover, this approach could be used to test musical abilities. Such BCI interface could be superior when there is a need for a broader command language. Some computational estimates and unresolved difficulties are presented.

Since Heart Rate Variability (HRV) is an important indicator of patient’s health it is important to introduce HRV into the hybrid models of cardiorespiratory system. We decided to use Elman’s neural networks to generate off-line HRV signals of physiological and pathological patients, and we are working on application of this method on-line in our hybrid model. For analysis of the generated HRV and their comparison with input data we apply Higuchi’s Fractal Dimension method.

We propose a new very simple method to determine roughness of a surface of an implant material from its scanning electron microscopy (SEM) image. For this purpose we have combined a preprocessing method that has been used in histopathology with fractal method used in nonlinear time series analysis. In the pre-processing step the image is transformed into 1-D signals (‘landscapes’) that are subsequently analyzed. Our method draws from multiple disciplines and may find multidisciplinary applications.

Many biomedical researchers are ‘infected with HLV — “Human Linearity Virus”. They ‘think linearly’, and ignore the facts that human body, and, particularly, human brain are complex nonlinear systems. These complex nonlinear systems generate non-stationary nonlinear signals, and appropriate analysis of such signals does need new nonlinear methods.

We propose two methods of analysis of chaotic processes to be applied in sensory analysis. These methods may be used off-line in clinics e.g. for analysis of biosignals registered during sleep, or implemented into new sensor systems e.g. for drivers’ vigilance monitoring in real time; they may also be applied in new type of hybrid models of circulatory and respiratory systems.

The two goals of Nonlinear Biomedical Physics are: firstly to show how nonlinear methods can shed new light on biological phenomena and medical applications and secondly to bridge the technical, mathematical, and cultural divides between the physical disciplines where these methods are being developed and the audience for their use in the biological and medical sciences.

Methods of contemporary physics are increasingly important for biomedical research but, for a multitude of diverse reasons, most practitioners of biomedicine lack access to a comprehensive knowledge of these modern methodologies. This paper is an attempt to describe nonlinear dynamics and its methods in a way that could be read and understood by biomedical professionals who usually are not trained in advanced mathematics. After an overview of basic concepts and vocabulary of nonlinear dynamics, deterministic chaos, and fractals, application of nonlinear methods of biosignal analysis is discussed. In particular, five case studies are presented: 1. Monitoring the depth of anaesthesia and of sedation; 2. Bright Light Therapy and Seasonal Affective Disorder; 3. Analysis of posturographic signals; 4. Evoked EEG and photo-stimulation; 5. Influence of electromagnetic fields generated by cellular phones.

Using Nonlinear Dynamics methods, in particular Higuchi’s fractal dimension that is a quantitative measure of deterministic chaos, one can assess which person may be hyper-sensitive to electromagnetic fields used in mobile communication, pointing out those persons who might belong to high-risk group. The same method may be used for testing of different cellular phones models for their certification.

We present FP6. Integrated Project SENSATION and works done by our Group on application of nonlinear dynamics for analysis of biosignals measured by new nanosensors for vigilance monitoring and for biomedical applications.