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Structural and functional neuroimaging techniques have recently been used to investigate the mechanisms of sexual attraction to children, a hallmark of pedophilic disorder, and have reported many contradictory or non-replicated findings. Here, our purpose was to identify through functional magnetic resonance imaging the brain responses of 25 male outpatients with pedophilic disorder to visual stimuli depicting children (VSc) and to compare them with 24 male healthy controls matched on sexual orientation (to female or male adults), age, and handedness. No region was differentially activated across the two groups in response to VSc. However, as shown by a random-effects statistical analysis (cluster-level pFWE-corrected < 0.05), in patients with pedophilia, but not in controls, the presentation of VSc induced a bilateral activation in the lateral occipital and temporal cortices, in particular in the right inferior temporal gyrus, as well as an activation in the declive of the cerebellar vermis. In addition, in patients the level of bilateral activation in the above-mentioned regions was positively correlated with ratings of perceived sexual arousal elicited by VSc. These results implicate these regions as possible candidate areas mediating sexual arousal in patients with pedophilic disorder. [ABSTRACT FROM AUTHOR]

We present and evaluate a new automated method based on support vector machine (SVM) classification of whole-brain anatomical magnetic resonance imaging to discriminate between patients with Alzheimer’s disease (AD) and elderly control subjects. We studied 16 patients with AD [mean age ± standard deviation (SD) = 74.1 ± 5.2 years, mini-mental score examination (MMSE) = 23.1 ± 2.9] and 22 elderly controls (72.3 ± 5.0 years, MMSE = 28.5 ± 1.3). Three-dimensional T1-weighted MR images of each subject were automatically parcellated into regions of interest (ROIs). Based upon the characteristics of gray matter extracted from each ROI, we used an SVM algorithm to classify the subjects and statistical procedures based on bootstrap resampling to ensure the robustness of the results. We obtained 94.5% mean correct classification for AD and control subjects (mean specificity, 96.6%; mean sensitivity, 91.5%). Our method has the potential in distinguishing patients with AD from elderly controls and therefore may help in the early diagnosis of AD. [ABSTRACT FROM AUTHOR]

Many measures have been proposed so far to extract brain functional interactivity from functional magnetic resonance imaging (fMRI) and magnetoencephalography/electroencephalography (MEG/EEG) data sets. Unfortunately, none has been able to provide a relevant, self-contained, and common definition of brain interaction. In this paper, we propose a first step in this direction. We first introduce a common terminology together with a cross- modal definition of interaction. In this setting, we investigate the commonalities shared by some measures of interaction proposed in the literature. We show that temporal correlation, nonlinear correlation, mutual information, generalized synchronization, phase synchronization, coherence, and phase locking value (PLV) actually measure the same quantity (namely correlation) when one is investigating linear interactions between independently and identically distributed Gaussian variables. We also demonstrate that these data-driven measures can only partly account for the interaction patterns that can be expressed by the effective connectivity of structural equation modeling (SEM) . To bridge this gap, we suggest the use of conditional correlation, which is shown to be related to mediated interaction. [ABSTRACT FROM AUTHOR]

A convenient way to analyze blood-oxygen-level-dependent functional magnetic resonance imaging data consists of modeling the whole brain as a stationary, linear system characterized by its transfer function: the hemodynamic response function (HRF). HRF estimation, though of the greatest interest, is still under investigation, for the problem is ill-conditioned. In this paper, we recall the most general Bayesian model for HRF estimation and show how It can beneficially be translated in terms of Bayesian graphical models, leading to 1) a clear and efficient representation of all structural and functional relationships entailed by the model, and 2) a straightforward numerical scheme to approximate the Joint posterior distribution, allowing for estimation of the HRF, as well as all other model parameters. We finally apply this novel technique on both simulations and real data. [ABSTRACT FROM AUTHOR]