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Time series analysis with delay differential equations (DDEs) reveals nonlinear properties of the underlying dynamical system and can serve as a non-linear time-domain classification tool. Here global DDE models were used to analyze short segments of simulated time series from a known dynamical system, the Rössler system, in high noise regimes. In a companion paper, we apply the DDE model developed here to classify short segments of encephalographic (EEG) data recorded from patients with Parkinson's disease and healthy subjects. Nine simulated subjects in each of two distinct classes were generated by varying the bifurcation parameter b and keeping the other two parameters (a and c) of the Rössler system fixed. All choices of b were in the chaotic parameter range. We diluted the simulated data using white noise ranging from 10dB to -30dB signal-to-noise ratios (SNR). Structure selection was supervised by selecting the number of terms, delays, and order of nonlinearity of the model DDE model that best linearly separated the two classes of data. The distances d from the linear dividing hyperplane was then used to assess the classification performance by computing the area A' under the ROC curve. The selected model was tested on untrained data using repeated random sub-sampling validation. DDEs were able to accurately distinguish the two dynamical conditions, and moreover, to quantify the changes in the dynamics. There was a significant correlation between the dynamical bifurcation parameter b of the simulated data and the classification parameter d from our analysis. This correlation still held for new simulated subjects with new dynamical parameters selected from each of the two dynamical regimes. Furthermore, the correlation was robust to added noise, being significant even when the noise was greater than the signal. We conclude that DDE models may be used as a generalizable and reliable classification tool for even small segments of noisy data.

In recent years there has been a significant increase in the use of electroencephalography (EEG) and magnetoencephalography (MEG) to investigate changes in oscillatory brain activity associated with tinnitus with many conflicting results. Current view of the underlying mechanism of tinnitus is that it results from changes in brain activity in various structures of the brain as a consequence of sensory deprivation. This in turn gives rise to increased spontaneous activity and/or synchrony in the auditory centres but also involves modulation from non-auditory processes from structures of the limbic and paralimbic system. Some of the neural changes associated with tinnitus may be assessed non-invasively in humans with MEG and EEG (M/EEG) in ways which are superior to animal studies and other non-invasive imaging techniques. However, both MEG and EEG have their limitations and research results can be misinterpreted in the absence of sufficient understanding of these limitations. In this article, I intend to provide a brief review of these techniques, describe what the recorded signals reflect in terms of the underlying neural activity, and their strengths and limitations. I also discuss some pertinent methodological issues involved in tinnitus related studies and concludes with suggestions to minimise possible discrepancies between results. The overall message is that while MEG and EEG are extremely useful techniques, the interpretation of results from tinnitus studies requires much caution given the individual variability in oscillatory activity and the limits of these techniques.

Objective: 1) To determine the brain connectivity pattern associated with clinical rigidity scores in Parkinson's disease (PD) and 2) to determine the relation between clinically-assessed rigidity and quantitative metrics of motor performance.Background: Rigidity, the resistance to passive movement, is exacerbated in PD by asking the subject to move the contralateral limb, implying that rigidity involves a distributed brain network. Rigidity mainly affects subjects when they attempt to move; yet the relation between clinical rigidity scores and quantitative aspects of motor performance are unknown.Methods: Ten clinically diagnosed PD patients (off medication) and ten controls were recruited to perform an fMRI squeeze-bulb tracking task that included both visually guided and internally guided features. The direct functional connectivity between anatomically defined regions of interest was assessed with Dynamic Bayesian Networks (DBNs). Tracking performance was assessed by fitting Linear Dynamical System (LDS) models to the motor performance, and was compared to the clinical rigidity scores. A cross-validated Least Absolute Shrinkage and Selection Operator (LASSO) regression method was used to determine the brain connectivity network that best predicted clinical rigidity scores.Results: The damping ratio of the LDS models significantly correlated with clinical rigidity scores (p < 10-4). An fMRI connectivity network in subcortical and primary and premotor cortical regions accurately predicted clinical rigidity scores (p < 10-5). Conclusions: A widely distributed cortical/subcortical network is associated with rigidity observed in PD patients, which reinforces the importance of altered functional connectivity in the pathophysiology of PD. PD subjects with higher rigidity scores tend to have less overshoot in their tracking performance, and damping ratio may represent a robust, quantitative marker of the motoric effects of increasing rigidity.

Cognitive impairment in Parkinson’s disease (PD) has received little attention and as such, there are currently very few treatment options available. The aim of the present study was to evaluate the attention impairment in PD patients and to determine whether cognitive training might alleviate any such symptoms. The study, which included a pilot and experimental phase, involved assessing performance in a modified version of the Stroop task during functional magnetic resonance imaging (fMRI). The study was carried out in ten PD patients and ten paired healthy controls, half of the PD patients undergoing 6 months of cognitive training based on Sudoku exercises. The brain training programme improved the cognitive performance of the Parkinson’s patients in attention tasks and it provoked comparable patterns of cortical activation to those observed in controls. Based on these findings, we propose that cognitive training can contribute significantly to save brain resources in PD patients, maybe by readdressing the imbalance caused by the alterations to inhibitory circuitry.

Radiologists are among the first physicians to be directly affected by advances in computer technology. Computers are already capable of analyzing medical imaging data, and with decades worth of digital information available for training, will an artificial intelligence (AI) one day signal the end of the human radiologist? With the ever increasing work load combined with the looming doctor shortage, radiologists will be pushed far beyond their current estimated 3 s allotted time-of-analysis per image; an AI with super-human capabilities might seem like a logical replacement. We feel, however, that AI will lead to an augmentation rather than a replacement of the radiologist. The AI will be relied upon to handle the tedious, time-consuming tasks of detecting and segmenting outliers while possibly generating new, unanticipated results that can then be used as sources of medical discovery. This will affect not only radiologists but all physicians and also researchers dealing with medical imaging. Therefore, we must embrace future technology and collaborate interdisciplinary to spearhead the next revolution in medicine.

The study of large-scale functional interactions in the human brain with fMRI extends almost to the first applications of this technology. Due to historical reasons and preconceptions about the limitations of this brain imaging method, most studies have focused on assessing connectivity over extended periods of time. It is now clear that fMRI can resolve the temporal dynamics of functional connectivity, like other faster imaging techniques such as EEG and MEG (albeit on a different temporal scale). However, the indirect nature of fMRI measurements can hinder the interpretability of the results. After briefly summarizing recent advances in the field, we discuss how the simultaneous combination of fMRI with electrophysiological activity measurements can contribute to a better understanding of dynamic functional connectivity in humans both during rest and task, wakefulness and other brain states.

A 59-year-old man was admitted with respiratory tract infection, compromised conscience and generalized tonic–clonic seizures. His medical history included schizophrenia diagnosis, for which he had been being treated since he was 27 years old. EEG disclosed non-convulsive status epilepticus. A magnetic resonance image (MRI) acquired 3 days later showed increased left hippocampal volume with hyperintensity on T2-weighted and FLAIR sequences. After being treated with antibiotics and antiepileptic medications, the patient’s condition improved. A follow-up MRI showed reduction of the left hippocampus. The relationship between epilepsy and schizophrenia is not yet clear. This case illustrates this interaction. Hippocampal atrophy may have been caused by environmental aggression in the present patient with schizophrenia, perhaps in association with a predisposing genotype.