Orla M. Doyle

CNVs conferring risk of autism or schizophrenia affect cognition in controls

In a small fraction of patients with schizophrenia or autism, alleles of copy-number variants (CNVs) in their genomes are probably the strongest factors contributing to the pathogenesis of the disease. These CNVs may provide an entry point for investigations into the mechanisms of brain function and dysfunction alike. They are not fully penetrant and offer an opportunity to study their effects separate from that of manifest disease. Here we show in an Icelandic sample that a few of the CNVs clearly alter fecundity (measured as the number of children by age 45). Furthermore, we use various tests of cognitive function to demonstrate that control subjects carrying the CNVs perform at a level that is between that of schizophrenia patients and population controls. The CNVs do not all affect the same cognitive domains, hence the cognitive deficits that drive or accompany the pathogenesis vary from one CNV to another. Controls carrying the chromosome 15q11.2 deletion between breakpoints 1 and 2 (15q11.2(BP1-BP2) deletion) have a history of dyslexia and dyscalculia, even after adjusting for IQ in the analysis, and the CNV only confers modest effects on other cognitive traits. The 15q11.2(BP1-BP2) deletion affects brain structure in a pattern consistent with both that observed during first-episode psychosis in schizophrenia and that of structural correlates in dyslexia.

A Spatiotemporal Profile of In Vivo Cerebral Blood Flow Changes Following Intranasal Oxytocin in Humans

BACKGROUND Animal and human studies highlight the role of oxytocin in social cognition and behavior and the potential of intranasal oxytocin (IN-OT) to treat social impairment in individuals with neuropsychiatric disorders such as autism. However, extensive efforts to evaluate the central actions and therapeutic efficacy of IN-OT may be marred by the absence of data regarding its temporal dynamics and sites of action in the living human brain. METHODS In a placebo-controlled study, we used arterial spin labeling to measure IN-OT-induced changes in resting regional cerebral blood flow (rCBF) in 32 healthy men. Volunteers were blinded regarding the nature of the compound they received. The rCBF data were acquired 15 min before and up to 78 min after onset of treatment onset (40 IU of IN-OT or placebo). The data were analyzed using mass univariate and multivariate pattern recognition techniques. RESULTS We obtained robust evidence delineating an oxytocinergic network comprising regions expected to express oxytocin receptors, based on histologic evidence, and including core regions of the brain circuitry underpinning social cognition and emotion processing. Pattern recognition on rCBF maps indicated that IN-OT-induced changes were sustained over the entire posttreatment observation interval (25-78 min) and consistent with a pharmacodynamic profile showing a peak response at 39-51 min. CONCLUSIONS Our study provides the first visualization and quantification of IN-OT-induced changes in rCBF in the living human brain unaffected by cognitive, affective, or social manipulations. Our findings can inform theoretical and mechanistic models regarding IN-OT effects on typical and atypical social behavior and guide future experiments (e.g., regarding the timing of experimental manipulations).

Quantifying the Attenuation of the Ketamine Pharmacological Magnetic Resonance Imaging Response in Humans: A Validation Using Antipsychotic and Glutamatergic Agents

Ketamine acts as an N-methyl-D-aspartate receptor antagonist and evokes psychotomimetic symptoms resembling schizophrenia in healthy humans. Imaging markers of acute ketamine challenge have the potential to provide a powerful assay of novel therapies for psychiatric illness, although to date this assay has not been fully validated in humans. Pharmacological magnetic resonance imaging (phMRI) was conducted in a randomized, placebo-controlled crossover design in healthy volunteers. The study comprised a control and three ketamine infusion sessions, two of which included pretreatment with lamotrigine or risperidone, compounds hypothesized to reduce ketamine-induced glutamate release. The modulation of the ketamine phMRI response was investigated using univariate analysis of prespecified regions and a novel application of multivariate analysis across the whole-brain response. Lamotrigine and risperidone resulted in widespread attenuation of the ketamine-induced increases in signal, including the frontal and thalamic regions. A contrasting effect across both pretreatments was observed only in the subgenual prefrontal cortex, in which ketamine produced a reduction in signal. Multivariate techniques proved successful in both classifying ketamine from placebo (100%) and identifying the probability of scans belonging to the ketamine class (ketamine pretreated with placebo: 0.89). Following pretreatment, these predictive probabilities were reduced to 0.58 and 0.49 for lamotrigine and risperidone, respectively. We have provided clear demonstration of a ketamine phMRI response and its attenuation with both lamotrigine and risperidone. The analytical methodology used could be readily applied to investigate the mechanistic action of novel compounds relevant for psychiatric disorders such as schizophrenia and depression.

Heart rate variability during sleep in healthy term newborns in the early postnatal period

Normative time- and frequency-domain heart rate variability (HRV) measures were extracted during quiet sleep (QS) and active sleep (AS) periods in 30 healthy babies. All newborn infants studied were less than 12 h old and the sleep state was classified using multi-channel video EEG. Three bands were extracted from the heart rate (HR) spectrum: very low frequency (VLF), 0.01-0.04 Hz; low frequency (LF), 0.04-0.2 Hz, and high frequency (HF), >0.2 Hz. All metrics were averaged across all patients and per sleep state to produce a table of normative values. A noticeable peak corresponding to activity in the RSA band was found in 80% patients during QS and 0% of patients during AS, although some broadband activity was observed. The majority of HRV metrics showed a statistically significant separation between QS and AS. It can be concluded that (i) activity in the RSA band is present during QS in the healthy newborn, in the first 12 h of life, (ii) HRV measures are affected by sleep state and (iii) the averaged HRV metrics reported here could assist the interpretation of HRV data from newborns with neonatal illnesses.

Bayesian multi-task learning for decoding multi-subject neuroimaging data

Decoding models based on pattern recognition (PR) are becoming increasingly important tools for neuroimaging data analysis. In contrast to alternative (mass-univariate) encoding approaches that use hierarchical models to capture inter-subject variability, inter-subject differences are not typically handled efficiently in PR. In this work, we propose to overcome this problem by recasting the decoding problem in a multi-task learning (MTL) framework. In MTL, a single PR model is used to learn different but related “tasks” simultaneously. The primary advantage of MTL is that it makes more efficient use of the data available and leads to more accurate models by making use of the relationships between tasks. In this work, we construct MTL models where each subject is modelled by a separate task. We use a flexible covariance structure to model the relationships between tasks and induce coupling between them using Gaussian process priors. We present an MTL method for classification problems and demonstrate a novel mapping method suitable for PR models. We apply these MTL approaches to classifying many different contrasts in a publicly available fMRI dataset and show that the proposed MTL methods produce higher decoding accuracy and more consistent discriminative activity patterns than currently used techniques. Our results demonstrate that MTL provides a promising method for multi-subject decoding studies by focusing on the commonalities between a group of subjects rather than the idiosyncratic properties of different subjects.

Multivariate decoding of brain images using ordinal regression.

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations — whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds — lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection.

Heart rate based automatic seizure detection in the newborn

This work investigates the efficacy of heart rate (HR) based measures for patient-independent, automatic detection of seizures in newborns. Sixty-two time-domain and frequency-domain features were extracted from the neonatal heart rate signal. These features were classified using a sophisticated support vector machine (SVM) scheme. The performance was evaluated on a large dataset of 208 h from 14 newborn infants. It was shown that the HR can be useful for the detection of neonatal seizures for certain patients yielding an area under the receiver operating characteristic (ROC) curve of up to 82%. On evaluating the system using multiple patients an average ROC area of 0.59 with sensitivity of 60% and specificity of 60%, were obtained. Feature selection was performed and in the majority of patients the performance was degraded. Further analysis of the feature weights found significant variability in feature ranking across all patients. Overall, the patient-independent system presented here was seen to perform well in some patients (2 out of 14) but performed poorly when tested on the entire group.

Predicting Progression of Alzheimer's Disease Using Ordinal Regression

We propose a novel approach to predicting disease progression in Alzheimer’s disease (AD) – multivariate ordinal regression – which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression – the ORCHID (Ordinal Regression Characteristic Index of Dementia) score. We applied ordinal regression to 1023 baseline structural MRI scans from two studies: the US-based Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the European based AddNeuroMed program. Here, the acquired AddNeuroMed dataset was used as a completely independent test set for the ordinal regression model trained on the ADNI cohort providing an optimal assessment of model generalizability. Distinguishing CTL-like (CTL and stable MCI) from AD-like (MCI converters and AD) resulted in balanced accuracies of 82% (cross-validation) for ADNI and 79% (independent test set) for AddNeuroMed. For prediction of conversion from MCI to AD, balanced accuracies of 70% (AUC of 0.75) and 75% (AUC of 0.81) were achieved. The ORCHID score was computed for all subjects. We showed that this measure significantly correlated with MMSE at 12 months (ρ = –0.64, ADNI and ρ = –0.59, AddNeuroMed). Additionally, the ORCHID score can help fractionate subjects with unstable diagnoses (e.g. reverters and healthy controls who later progressed to MCI), moderately late converters (12–24 months) and late converters (24–36 months). A comparison with results in the literature and direct comparison with a binary classifier suggests that the performance of this framework is highly competitive.

The effect of frequency band on quantitative EEG measures in neonates with Hypoxic-ischaemic encephalopathy

The effect of frequency ranges on three quantitative EEG measures as related to neurodevelopmental outcome at 12-24 months is reported here. Thirteen EEG records from term neonates with moderate hypoxic-ischaemic encephalopathy (HIE) were analyzed. The spectral entropy, spectral edge frequency and relative power were calculated for each EEG channel. 4 separate frequency ranges were employed and their respective variations examined. Graphical and statistical analysis was carried out on the results. Statistical separation between the mean distributions of SEF, Hs and RP was not observed. The optimal frequency band is dependent on the qEEG measure in question.

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of this and other clinical conditions. However, the considerable variability in reported neuroimaging results mirrors the heterogeneity of the disorder. Machine learning methods capable of representing invariant features could circumvent this problem. In this structural MRI study, we trained a deep learning model known as deep belief network (DBN) to extract features from brain morphometry data and investigated its performance in discriminating between healthy controls (N = 83) and patients with schizophrenia (N = 143). We further analysed performance in classifying patients with a first-episode psychosis (N = 32). The DBN highlighted differences between classes, especially in the frontal, temporal, parietal, and insular cortices, and in some subcortical regions, including the corpus callosum, putamen, and cerebellum. The DBN was slightly more accurate as a classifier (accuracy = 73.6%) than the support vector machine (accuracy = 68.1%). Finally, the error rate of the DBN in classifying first-episode patients was 56.3%, indicating that the representations learned from patients with schizophrenia and healthy controls were not suitable to define these patients. Our data suggest that deep learning could improve our understanding of psychiatric disorders such as schizophrenia by improving neuromorphometric analyses.