Alexandre Andrade

A primal sketch of the cortex mean curvature: a morphogenesis based approach to study the variability of the folding patterns

In this paper, we propose a new representation of the cortical surface that may be used to study the cortex folding process and to recover some putative stable anatomical landmarks called sulcal roots usually buried in the depth of adult brains. This representation is a primal sketch derived from a scale space computed for the mean curvature of the cortical surface. This scale-space stems from a diffusion equation geodesic to the cortical surface. The primal sketch is made up of objects defined from mean curvature minima and saddle points. The resulting sketch aims first at highlighting significant elementary cortical folds, second at representing the fold merging process during brain growth. The relevance of the framework is illustrated by the study of central sulcus sulcal roots from antenatal to adult age. Some results are proposed for ten different brains. Some preliminary results are also provided for superior temporal sulcus.

Detection of fMRI activation using Cortical Surface Mapping

A methodology for fMRI data analysis confined to the cortex, Cortical Surface Mapping (CSM), is presented. CSM retains the flexibility of the General Linear Model based estimation, but the procedures involved are adapted to operate on the cortical surface, while avoiding to resort to explicit flattening. The methodology is tested by means of simulations and application to a real fMRI protocol. The results are compared with those obtained with a standard, volume‐oriented approach (SPM), and it is shown that CSM leads to local differences in sensitivity, with generally higher sensitivity for CSM in two of the three subjects studied. The discussion provided is focused on the benefits of the introduction of anatomical information in fMRI data analysis, and the relevance of CSM as a step toward this goal. Hum. Brain Mapping 12:79–93, 2001.

Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis.

OBJECTIVE Intrinsic complexity subserves adaptability in biological systems. One recently developed measure of intrinsic complexity of biological systems is multiscale entropy (MSE). Autism spectrum conditions (ASC) have been described in terms of reduced adaptability at a behavioural level and by patterns of atypical connectivity at a neural level. Based on these observations we aimed to test the hypothesis that adults with ASC would show atypical intrinsic complexity of brain activity as indexed by MSE analysis of electroencephalographic (EEG) activity. METHODS We used MSE to assess the complexity of EEG data recorded from 15 participants with ASC and 15 typical controls, during a face and chair matching task. RESULTS Results demonstrate a reduction of EEG signal complexity in the ASC group, compared to typical controls, over temporo-parietal and occipital regions. No significant differences in EEG power spectra were observed between groups, indicating that changes in complexity values are not a reflection of changes in EEG power spectra. CONCLUSIONS The results are consistent with a model of atypical neural integrative capacity in people with ASC. SIGNIFICANCE Results suggest that EEG complexity, as indexed by MSE measures, may also be a marker for disturbances in task-specific processing of information in people with autism.

Ambiguous results in functional neuroimaging data analysis due to covariate correlation.

In this note we draw attention to a source of potential ambiguity in functional neuroimaging results when data analysis is based on the resolution of a linear model. This ambiguity arises whenever there exists correlation between the model covariates. A single-subject PET activation experiment helps to illustrate to what extent correlation can affect statistical results interpretation, possibly leading to misinterpretation of part of the activation pattern. This note is intended to clarify this point and to suggest the use of a simple and well-known procedure to deal with these situations. In the Appendix, we suggest a convenient mathematical formulation for statistical tests particularly useful in such cases.

Task-related functional connectivity in autism spectrum conditions: an EEG study using wavelet transform coherence

BackgroundAutism Spectrum Conditions (ASC) are a set of pervasive neurodevelopmental conditions characterized by a wide range of lifelong signs and symptoms. Recent explanatory models of autism propose abnormal neural connectivity and are supported by studies showing decreased interhemispheric coherence in individuals with ASC. The first aim of this study was to test the hypothesis of reduced interhemispheric coherence in ASC, and secondly to investigate specific effects of task performance on interhemispheric coherence in ASC.MethodsWe analyzed electroencephalography (EEG) data from 15 participants with ASC and 15 typical controls, using Wavelet Transform Coherence (WTC) to calculate interhemispheric coherence during face and chair matching tasks, for EEG frequencies from 5 to 40 Hz and during the first 400 ms post-stimulus onset.ResultsResults demonstrate a reduction of interhemispheric coherence in the ASC group, relative to the control group, in both tasks and for all electrode pairs studied. For both tasks, group differences were generally observed after around 150 ms and at frequencies lower than 13 Hz. Regarding within-group task comparisons, while the control group presented differences in interhemispheric coherence between faces and chairs tasks at various electrode pairs (FT7-FT8, TP7-TP8, P7-P8), such differences were only seen for one electrode pair in the ASC group (T7-T8). No significant differences in EEG power spectra were observed between groups.ConclusionsInterhemispheric coherence is reduced in people with ASC, in a time and frequency specific manner, during visual perception and categorization of both social and inanimate stimuli and this reduction in coherence is widely dispersed across the brain.Results of within-group task comparisons may reflect an impairment in task differentiation in people with ASC relative to typically developing individuals.Overall, the results of this research support the value of WTC in examining the time-frequency microstructure of task-related interhemispheric EEG coherence in people with ASC.

Learning to Like: A Role for Human Orbitofrontal Cortex in Conditioned Reward

A great deal of human behavior and motivation is based on the intrinsic emotional significance of rewarding or aversive events, as well as on the associations formed between such emotional events and concurrent environmental stimuli. Recent functional neuroimaging studies have implicated the ventral striatum, orbitofrontal cortex (OFC), and amygdala in the representation of reward values and/or in the anticipation of rewarding events. Here, we use functional magnetic resonance imaging to compare brain activation during the presentation of reward with that during presentation of (conditioned) stimuli that have been paired previously with reward. Specifically, we aimed to investigate conditioned reward in the absence of explicit reward anticipation. Twenty-two healthy volunteers were scanned while monochrome visual patterns were incidentally associated with reward or negative feedback in the context of a simple card game. In the subsequent session, visual patterns, including the conditioned stimuli, were presented without reward or negative feedback, and the affective valence of these stimuli was assessed behaviorally. The presentation of reward compared with negative feedback activated the ventral striatum and OFC. Activation in the same OFC region was observed when, in the subsequent session, subjects passively viewed the stimuli that had been paired with reward, without the administration of reward and with subjects being essentially unaware of the conditioning manipulation. These findings suggest that the OFC in humans plays an important role in the representation of both rewarding stimuli and conditioned stimuli that have acquired reward value.

Wavelet analysis of autonomic outflow of normal subjects on head‐up tilt, cold pressor test, Valsalva manoeuvre and deep breathing

Non‐invasive autonomic evaluation has used fast Fourier transform (FFT) to assign a range of low (LF) and high frequencies (HF) as markers of sympathetic and parasympathetic influences, respectively. However, FFT cannot be applied to brief transient phenomena, such as those observed on performing autonomic tests where the acute changes of cardiovascular signals (blood pressure and heart rate) that represent the first and most important stage of the autonomic performance towards a new state of equilibrium occur. Wavelet analysis has been proposed as a method to overcome and complement information taken exclusively in the frequency domain. With discrete wavelet transform (DWT), a time–frequency analysis can be done, allowing the visualization in time of the contribution of LF and HF to the observed changes of a particular signal. In this study, we evaluate with wavelets the acute changes in R–R intervals and systolic blood pressure that are observed in normal subjects during four classical autonomic tests: head‐up tilt (HUT), cold pressor test (CPT), deep breathing (DB) and Valsalva manoeuvre (VM). Continuous monitoring of ECG and blood presure was performed. Also LF, HF and LF/HF were calculated. Consistent with previous interpretations, data showed an increase of sympathetic activity in HUT, CPT and VM. On DB, results reflected an increase in parasympathetic activity and frequencies. In conclusion, when compared with FFT, wavelet analysis allows the evaluation of autonomic variability during short and non‐stationary periods of time and may constitute a useful advance in the assessment of autonomic function in both physiological and pathological conditions.

Correlation Dimension Maps of EEG from Epileptic Absences

The understanding of brain activity, and in particular events such as epileptic seizures, lies on the characterisation of the dynamics of the neural networks. The theory of non-linear dynamics provides signal analysis techniques which may give new information on the behaviour of such networks. Methods: We calculated correlation dimension maps for 19-channel EEG data from 3 patients with a total of 7 absence seizures. The signals were analysed before, during and after the seizures. Phase randomised surrogate data was used to test chaos. Results: In the seizures of two patients we could distinguish two dynamical regions on the cerebral cortex, one that seemed to exhibit chaos whereas the other seemed to exhibit noise. The pattern shown is essentially the same for seizures triggered by hyperventilation, but differ for seizures triggered by light flashes. The chaotic dynamics that one seems to observe is determined by a small number of variables and has low complexity. On the other hand, in the seizures of another patient no chaotic region was found. Before and during the seizures no chaos was found either, in all cases. Conclusions: The application of non-linear signal analysis revealed the existence of differences in the spatial dynamics associated to absence seizures. This may contribute to the understanding of those seizures and be of assistance in clinical diagnosis.

Screening of obstructive sleep apnea using Hilbert-Huang decomposition of oronasal airway pressure recordings

Polysomnographic signals are usually recorded from patients exhibiting symptoms related to sleep disorders such as obstructive sleep apnea (OSA). Analysis of polysomnographic data allows for the determination of the type and severity of sleep apnea or other sleep-related disorders by a specialist or technician. The usual procedure entails an overnight recording several hours long. This paper presents a methodology to help with the screening of OSA using a 5-min oronasal airway pressure signal emanating from a polysomnographic recording during the awake period, eschewing the need for an overnight recording. The clinical sample consisted of a total of 41 subjects, 20 non-OSA individuals and 21 individuals with OSA. A signal analysis technique based on the Hilbert-Huang transform was used to extract intrinsic oscillatory modes from the signals. The frequency distribution of both the first mode and second mode and their sum were shown to differ significantly between non-OSA subjects and OSA patients. An index measure based on the distribution frequencies of the oscillatory modes yielded a sensitivity of 81.0% (for 95% specificity) for the detection of OSA. Two other index measures based on the relation between the area and the maximum of the 1st and 2nd halves of the frequency histogram both yielded a sensitivity of 76.2% (for 95% specificity). Although further tests will be needed to test the reproducibility of these results, the proposed measures seem to provide a fast method to screen OSA patients, thus reducing the costs and the waiting time for diagnosis.

Lag-based effective connectivity applied to fMRI: a simulation study highlighting dependence on experimental parameters and formulation.

A vast repertoire of methods is currently available to study effective brain connectivity based on neuroimaging data, among which lag-based measures can be distinguished. Although several studies have previously assessed the performance of such measures, their validity in different conditions remains unclear. In the current study, several lag-based effective connectivity measures are tested and benchmarked using simulated fMRI data, conceived to reflect a broad range of different situations with practical interest. The main goal is two-fold: 1) to provide a thorough overview of lag-based effective connectivity measures, and 2) to assess their performance in specific experimental conditions, thereby providing guidance for future effective connectivity studies involving fMRI. We focus on well-known lag-based measures, cover existing improvements and alternative formulations in some cases: Granger causality (GC), Gewekes Granger causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), phase slope index (PSI), and transfer entropy (TE). Benchmarking consists in identifying causal relations in local field potential (LFP) networks that have their output convolved with a canonical hemodynamic response function (HRF) with varying node number, topology, coupling strength, neuronal delay, repetition time (TR), signal-to-noise ratio (SNR) and HRF variability. In a first set of simulations, we cover all possible combinations of discretized values of the previous variables, for networks with 2 and 3 nodes, and find that the measure with best performance (time-domain Granger Causality) is able to detect neuronal delays of a few hundreds of milliseconds with TRs between 0.25 and 2s and neuronal delays below 100ms for TRs that are also below 100ms, with more than 80% accuracy in realistic conditions. For networks with more than 3 nodes, we find that the number of nodes and the density of causal links degrade sensitivity, especially if the number of observations does not compensate for the increase in nodes, and that clustered networks can be more easily identified. In conclusion, this study argues in favor of the applicability of lag-based measures in the context of fMRI, provided that a stringent set of experimental specifications is met and that the chosen measure is applied with full knowledge of its limitations and specific constraints.