15q11.2 CNV affects cognitive, structural and functional correlates of dyslexia and dyscalculia.

Several copy number variants have been associated with neuropsychiatric disorders and these variants have been shown to also influence cognitive abilities in carriers unaffected by psychiatric disorders. Previously, we associated the 15q11.2(BP1-BP2) deletion with specific learning disabilities and a larger corpus callosum. Here we investigate, in a much larger sample, the effect of the 15q11.2(BP1-BP2) deletion on cognitive, structural and functional correlates of dyslexia and dyscalculia. We report that the deletion confers greatest risk of the combined phenotype of dyslexia and dyscalculia. We also show that the deletion associates with a smaller left fusiform gyrus. Moreover, tailored functional magnetic resonance imaging experiments using phonological lexical decision and multiplication verification tasks demonstrate altered activation in the left fusiform and the left angular gyri in carriers. Thus, by using convergent evidence from neuropsychological testing, and structural and functional neuroimaging, we show that the 15q11.2(BP1-BP2) deletion affects cognitive, structural and functional correlates of both dyslexia and dyscalculia.

Ketamine induces a robust whole-brain connectivity pattern that can be differentially modulated by drugs of different mechanism and clinical profile.

Ketamine, an N-methyl-D-aspartate receptor (NMDAR) antagonist, has been studied in relation to the glutamate hypothesis of schizophrenia and increases dissociation, positive and negative symptom ratings. Ketamine effects brain function through changes in brain activity; these activity patterns can be modulated by pre-treatment of compounds known to attenuate the effects of ketamine on glutamate release. Ketamine also has marked effects on brain connectivity; we predicted that these changes would also be modulated by compounds known to attenuate glutamate release. Here, we perform task-free pharmacological magnetic resonance imaging (phMRI) to investigate the functional connectivity effects of ketamine in the brain and the potential modulation of these effects by pre-treatment of the compounds lamotrigine and risperidone, compounds hypothesised to differentially modulate glutamate release. Connectivity patterns were assessed by combining windowing, graph theory and multivariate Gaussian process classification. We demonstrate that ketamine has a robust effect on the functional connectivity of the human brain compared to saline (87.5 % accuracy). Ketamine produced a shift from a cortically centred, to a subcortically centred pattern of connections. This effect is strongly modulated by pre-treatment with risperidone (81.25 %) but not lamotrigine (43.75 %). Based on the differential effect of these compounds on ketamine response, we suggest the observed connectivity effects are primarily due to NMDAR blockade rather than downstream glutamatergic effects. The connectivity changes contrast with amplitude of response for which no differential effect between pre-treatments was detected, highlighting the necessity of these techniques in forming an informed view of the mechanistic effects of pharmacological compounds in the human brain.

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.

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.

Predicting the neurodevelopmental outcome in newborns with hypoxic-ischaemic injury.

The prediction of outcome in newborns with hypoxic ischemic encephalopathy (HIE) is a problematic task. Here, the ability of a combination of clinical, heart rate and EEG measures to predict outcome at 2 years is investigated. One hour of EEG and ECG recordings were obtained from newborns 24 hours after birth. Each newborn was reassessed at 24 months to investigate their neurodevelopmental outcome. From the EEG and ECG recordings, a set of 12 features was extracted. To classify each baby's outcome this data, along with clinical information was fed to a support vector machine. On a per patient basis an ROC area of 0.768 was achieved with 73.68% of newborns being assigned the correct outcome. Overall, this system presents a promising step towards the use of multimodal data for the prediction of neurodevelopmental outcome in newborns with HIE.

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.

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.

Characterisation of heart rate changes and their correlation with EEG during neonatal seizures.

The effect of seizures on instantaneous HR (iHR) in 12 neonates is investigated here. HR can be readily extracted from the ECG and can be employed as an additional signal in seizure detection algorithms. The change in instantaneous HR and its correlation with the change in RMS EEG amplitude were examined. Two methods were employed to classify significant iHR changes. Significant correlation (p 0.05) during seizure was observed in 100% of patients (83.33% of seizures). Overall, significant iHR changes (classified by either method) were found in 83% of patients (50% of seizures). It was found that a markedly higher iHR was observed in patients whose seizures were not classified as having significant iHR changes.

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, H(s) and RP was not observed. The optimal frequency band is dependent on the qEEG measure in question.