Donald L. Rowe

Estimation of multiscale neurophysiologic parameters by electroencephalographic means.

It is shown that new model‐based electroencephalographic (EEG) methods can quantify neurophysiologic parameters that underlie EEG generation in ways that are complementary to and consistent with standard physiologic techniques. This is done by isolating parameter ranges that give good matches between model predictions and a variety of experimental EEG‐related phenomena simultaneously. Resulting constraints range from the submicrometer synaptic level to length scales of tens of centimeters, and from timescales of around 1 ms to 1 s or more, and are found to be consistent with independent physiologic and anatomic measures. In the process, a new method of obtaining model parameters from the data is developed, including a Monte Carlo implementation for use when not all input data are available. Overall, the approaches used are complementary to other methods, constraining allowable parameter ranges in different ways and leading to much tighter constraints overall. EEG methods often provide the most restrictive individual constraints. This approach opens a new, noninvasive window on quantitative brain analysis, with the ability to monitor temporal changes, and the potential to map spatial variations. Unlike traditional phenomenologic quantitative EEG measures, the methods proposed here are based explicitly on physiology and anatomy. Hum. Brain Mapping 23:53–72, 2004.

Multiscale brain modelling

A central difficulty of brain modelling is to span the range of spatio-temporal scales from synapses to the whole brain. This paper overviews results from a recent model of the generation of brain electrical activity that incorporates both basic microscopic neurophysiology and large-scale brain anatomy to predict brain electrical activity at scales from a few tenths of a millimetre to the whole brain. This model incorporates synaptic and dendritic dynamics, nonlinearity of the firing response, axonal propagation and corticocortical and corticothalamic pathways. Its relatively few parameters measure quantities such as synaptic strengths, corticothalamic delays, synaptic and dendritic time constants, and axonal ranges, and are all constrained by independent physiological measurements. It reproduces quantitative forms of electroencephalograms seen in various states of arousal, evoked response potentials, coherence functions, seizure dynamics and other phenomena. Fitting model predictions to experimental data enables underlying physiological parameters to be inferred, giving a new non-invasive window into brain function that complements slower, but finer-resolution, techniques such as fMRI. Because the parameters measure physiological quantities relating to multiple scales, and probe deep structures such as the thalamus, this will permit the testing of a range of hypotheses about vigilance, cognition, drug action and brain function. In addition, referencing to a standardized database of subjects adds strength and specificity to characterizations obtained.

Neurophysical Modeling of Brain Dynamics

A recent neurophysical model of brain electrical activity is outlined and applied to EEG phenomena. It incorporates single-neuron physiology and the large-scale anatomy of corticocortical and corticothalamic pathways, including synaptic strengths, dendritic propagation, nonlinear firing responses, and axonal conduction. Small perturbations from steady states account for observed EEGs as functions of arousal. Evoked response potentials (ERPs), correlation, and coherence functions are also reproduced. Feedback via thalamic nuclei is critical in determining the forms of these quantities, the transition between sleep and waking, and stability against seizures. Many disorders correspond to significant changes in EEGs, which can potentially be quantified in terms of the underlying physiology using this theory. In the nonlinear regime, limit cycles are often seen, including a regime in which they have the characteristic petit mal 3 Hz spike-and-wave form.

Simulated electrocortical activity at microscopic, mesoscopic, and global scales

Simulation of electrocortical activity requires (a) determination of the most crucial features to be modelled, (b) specification of state equations with parameters that can be determined against independent measurements, and (c) explanation of electrical events in the brain at several scales. We report our attempts to address these problems, and show that mutually consistent explanations, and simulation of experimental data can be achieved for cortical gamma activity, synchronous oscillation, and the main features of the EEG power spectrum including the cerebral rhythms and evoked potentials. These simulations include consideration of dendritic and synaptic dynamics, AMPA, NMDA, and GABA receptors, and intracortical and cortical/subcortical interactions. We speculate on the way in which Hebbian learning and intrinsic reinforcement processes might complement the brain dynamics thus explained, to produce elementary cognitive operations.

Fronto-temporal alterations within the first 200 ms during an attentional task distinguish major depression, non-clinical participants with depressed mood and healthy controls: A potential biomarker?

Attentional impairment in depression is a cardinal feature of depression and has been proposed as a candidate endophenotype for major depressive disorder. Event‐related potentials (ERPs) elicited by oddball signal detection tasks provide objective markers of selective stimulus processing, and are pertinent endophenotypic markers for depression. While previous studies have sought to determine objective markers for attentional impairment in depression, evidence is inconsistent and may involve heterogeneity in relatively small samples. Here, we brought together oddball ERP recording with source localization of neural correlates of selective attention in outpatients with major depressive disorder (MDD; n = 78) and participants with depressed mood (PDM; n = 127) relative to healthy controls (CTL; n = 116). The key finding was a dimensional exaggeration of the P200 (140–270 ms) to both target (signal) and non‐target (noise) stimuli, most pronounced in MDD, followed by PDM, relative to CTL. This exaggeration was coupled with slower and more variable response times, suggesting that neural systems are attempting to compensate for a difficulty in discriminating signal from noise. P200 alterations were localised to limbic (hippocampal), temporal and ventral prefrontal regions, key components of the signal detection network. A subsequent reduction and delay in the P300 was also revealed for MDD indicating that the pronounced lack of discrimination in clinical depression may also lead to impaired stimulus evaluation. This P200 increase in depression could provide a potential mechanism for the attentional impairment frequently observed in depression and consequent alterations in the P300 may differentiate clinically significant depression. Hum Brain Mapp, 2009.

EEG MARKERS FOR COGNITIVE DECLINE IN ELDERLY SUBJECTS WITH SUBJECTIVE MEMORY COMPLAINTS

New treatments for Alzheimers disease require early detection of cognitive decline. Most studies seeking to identify markers of early cognitive decline have focused on a limited number of measures. We sought to establish the profile of brain function measures which best define early neuropsychological decline. We compared subjects with subjective memory complaints to normative controls on a wide range of EEG derived measures, including a new measure of event-related spatio-temporal waves and biophysical modeling, which derives anatomical and physiological parameters based on subjects EEG measurements. Measures that distinguished the groups were then related to cognitive performance on a variety of learning and executive function tasks. The EEG measures include standard power measures, peak alpha frequency, EEG desynchronization to eyes-opening, and global phase synchrony. The most prominent differences in subjective memory complaint subjects were elevated alpha power and an increased number of spatio-temporal wave events. Higher alpha power and changes in wave activity related most strongly to a decline in verbal memory performance in subjects with subjective memory complaints, and also declines in maze performance and working memory reaction time. Interestingly, higher alpha power and wave activity were correlated with improved performance in reverse digit span in the subjective memory complaint group. The modeling results suggest that differences in the subjective memory complaint subjects were due to a decrease in cortical and thalamic inhibitory gains and slowed dendritic time-constants. The complementary profile that emerges from the variety of measures and analyses points to a nonlinear progression in electrophysiological changes from early neuropsychological decline to late-stage dementia, and electrophysiological changes in subjective memory complaint that vary in their relationships to a range of memory-related tasks.

Stimulant drug action in attention deficit hyperactivity disorder (ADHD): inference of neurophysiological mechanisms via quantitative modelling.

OBJECTIVE To infer the neural mechanisms underlying tonic transitions in the electroencephalogram (EEG) in 11 adolescents diagnosed with attention deficit hyperactivity disorder (ADHD) before and after treatment with stimulant medication. METHODS A biophysical model was used to analyse electroencephalographic (EEG) measures of tonic brain activity at multiple scalp sites before and after treatment with medication. RESULTS It was observed that stimulants had the affect of significantly reducing the parameter controlling activation in the intrathalamic pathway involving the thalamic reticular nucleus (TRN) and the parameter controlling excitatory cortical activity. The effect of stimulant medication was also found to be preferentially localized within subcortical nuclei projecting towards frontal and central scalp sites. CONCLUSIONS It is suggested that the action of stimulant medication occurs via suppression of the locus coeruleus, which in turn reduces stimulation of the TRN, and improves cortical arousal. The effects localized to frontal and central sites are consistent with the occurrence of frontal delta-theta EEG abnormalities in ADHD, and existing theories of hypoarousal. SIGNIFICANCE To our knowledge, this is the first study where a detailed biophysical model of the brain has been used to estimate changes in neurophysiological parameters underlying the effects of stimulant medication in ADHD.

Brain structure and function correlates of general and social cognition

AIMS To examine how general (e.g., memory, attention) and social (emotional and interpersonal processes) cognition relate to measures of brain function and structure. METHODS PCA was used to identify general and social cognitive factors from Brain Resource International Database in 1,316 subjects. The identified factors were correlated with each subjects corresponding brain structure (MRI) and function (EEG/ERP) data. RESULTS Seven core cognitive factors were identified for general and three for social. General cognition was correlated with global grey matter, while social cognition was negatively correlated with grey matter in fronto-temporal-somatosensory regions. Executive function, information processing speed and verbal memory performance were correlated with delta-theta qEEG, while most general cognitive factors negatively correlated with beta qEEG. Faster information processing speed was correlated with alpha qEEG. Executive function and information processing speed was correlated with negative-going ERP amplitude and slower ERP latency at frontal sites, but at posterior sites negative correlations were found. DISCUSSION In contrast to general cognition, social cognition is identified by different functional (automated) activity and more localized neural structures. Only general cognition, requiring more effortful, controlled processing is related to brain function measures, particularly in frontal cortices. INTEGRATIVE SIGNIFICANCE Recording measures from multiple modalities including MRI, EEG/ERP, social and general cognition within the same subject provides a method of brain profiling for use in cognitive-neurotherapy and pharmacological studies.

Off-label prescription of quetiapine in psychiatric disorders

This article reviews the off-label prescription of quetiapine in the treatment of a broad range of psychiatric disorders including obsessive–compulsive disorder, post-traumatic stress disorder, personality disorder, substance abuse, bipolar disorder (now US FDA approved), anxiety and depression. The article highlights the primary reliance on selective serotonin reuptake inhibitors (SSRIs) in the treatment of these disorders (cf bipolar disorder) and the high percentage of patients (30–60%) that do not respond to SSRIs. The studies suggest that low-dose quetiapine shows good tolerability and efficacy in patients diagnosed with these disorders, particularly in the case of treatment-resistant patients that do not respond to primary treatments including SSRIs and cognitive–behavioral therapy. Quetiapine generally appears to be very effective in trauma-related conditions by improving autonomic stability, and decreasing the stress and anxiety response that arises due to specific fears or triggers. Quetiapine also appears to be particularly useful for normalizing obsessions and compulsions, and improving low mood, irritability and aggressiveness. A greater understanding of the pharmacology of drug alternatives and the neurobiology of psychiatric disorders is required to permit a more personalized medicine approach.

Integrative neuroscience approach to predict ADHD stimulant response

Despite high rates of prescription, little is known about the long-term consequences of stimulant medication therapy for attention-deficit hyperactivity disorder (ADHD) sufferers. Historically, the clinical use of stimulants for ADHD has been based on trial and error before optimal therapy is reached. Concurrently, scientific research on the mechanism of action of stimulants has influenced neurobiological models of ADHD, but has not always informed their prescription. Whilst the two main stimulant types (methylphenidate and dexamphetamine) have numerous similarities, they also differ (slightly) in mechanism and possibly individual response. A further issue relates to differences in cost and availability compounded by the expectation for stimulants to be effective in ameliorating a broad spectrum of ADHD-related symptoms. Thus, there is an increasing need for treating clinicians to prescribe not only the most effective drug, but also the most appropriate dose with the associated release mechanism and schedule for each ADHD patient presented. In this regard, the field is witnessing an emergence of the personalized medicine approach to ADHD, in which treatment decisions are tailored to each individual. This shift requires a new approach to research into treatment response prediction. Given the heterogeneity of ADHD, a profile of information may be required to capture the most sensitive predictors of treatment response in individuals. These profiles will also benefit from the integration of data from clinical rating scales with more direct measures of cognition and brain function. In conclusion, there is a need to establish a more robust normative framework as the baseline for treatment, as well as diagnostic decisions, and as discussed, the growth of integrated neuroscience databases will be important in this regard.