Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer's disease.

INTRODUCTION: The purpose of this study was to study the effect of donepezil on the rate of hippocampal atrophy in prodromal Alzheimer's disease (AD). METHODS: A double-blind, randomized, placebo-controlled parallel group design using donepezil (10 mg/day) in subjects with suspected prodromal AD. Subjects underwent two brain magnetic resonance imaging scans (baseline and final visit). The primary efficacy outcome was the annualized percentage change (APC) of total hippocampal volume (left + right) measured by an automated segmentation method. RESULTS: Two-hundred and sixteen only subjects were randomized across 28 French expert clinical sites. In the per protocol population (placebo = 92 and donepezil = 82), the donepezil group exhibited a significant reduced rate of hippocampal atrophy (APC = -1.89%) compared with the placebo group (APC = -3.47%), P < .001. There was no significant difference in neuropsychological performance between treatment groups. DISCUSSION: A 45% reduction of rate of hippocampal atrophy was observed in prodromal AD following 1 year of treatment with donepezil compared with placebo.

Behavioural and magnetoencephalographic evidence for the interaction between semantic and episodic memory in healthy elderly subjects.

The relationship between episodic and semantic memory systems has long been debated. Some authors argue that episodic memory is contingent on semantic memory (Tulving 1984), while others postulate that both systems are independent since they can be selectively damaged (Squire 1987). The interaction between these memory systems is particularly important in the elderly, since the dissociation of episodic and semantic memory defects characterize different aging-related pathologies. Here, we investigated the interaction between semantic knowledge and episodic memory processes associated with faces in elderly subjects using an experimental paradigm where the semantic encoding of famous and unknown faces was compared to their episodic recognition. Results showed that the level of semantic awareness of items affected the recognition of those items in the episodic memory task. Event-related magnetic fields confirmed this interaction between episodic and semantic memory: ERFs related to the old/new effect during the episodic task were markedly different for famous and unknown faces. The old/new effect for famous faces involved sustained activities maximal over right temporal sensors, showing a spatio-temporal pattern partly similar to that found for famous versus unknown faces during the semantic task. By contrast, an old/new effect for unknown faces was observed on left parieto-occipital sensors. These findings suggest that the episodic memory for famous faces activated the retrieval of stored semantic information, whereas it was based on items' perceptual features for unknown faces. Overall, our results show that semantic information interfered markedly with episodic memory processes and suggested that the neural substrates of these two memory systems overlap.

Improving quantification of functional networks with EEG inverse problem: evidence from a decoding point of view.

Decoding experimental conditions from single trial Electroencephalographic (EEG) signals is becoming a major challenge for the study of brain function and real-time applications such as Brain Computer Interface. EEG source reconstruction offers principled ways to estimate the cortical activities from EEG signals. But to what extent it can enhance informative brain signals in single trial has not been addressed in a general setting. We tested this using the minimum norm estimate solution (MNE) to estimate spectral power and coherence features at the cortical level. With a fast implementation, we computed a support vector machine (SVM) classifier output from these quantities in real-time, without prior on the relevant functional networks. We applied this approach to single trial decoding of ongoing mental imagery tasks using EEG data recorded in 5 subjects. Our results show that reconstructing the underlying cortical network dynamics significantly outperforms a usual electrode level approach in terms of information transfer and also reduces redundancy between coherence and power features, supporting a decrease of volume conduction effects. Additionally, the classifier coefficients reflect the most informative features of network activity, showing an important contribution of localized motor and sensory brain areas, and of coherence between areas up to 6cm distance. This study provides a computationally efficient and interpretable strategy to extract information from functional networks at the cortical level in single trial. Moreover, this sets a general framework to evaluate the performance of EEG source reconstruction methods by their decoding abilities.

Somatosensory cortical remodelling after rehabilitation and clinical benefit of in writer's cramp.

In order to explore the pathophysiological basis of a new rehabilitation therapy in writer's cramp (WC), healthy controls, untreated WC patients and WC patients who recovered a legible handwriting after rehabilitation were explored using magnetoencephalography, and the somatosensory evoked fields of fingers I, II, III and V in the sensory cortex were studied. In the cortex controlling the dystonic limb, the size of the hand representation in the trained patients was similar to that of healthy controls, and significantly different from that of untrained patients. Trained patients exhibited 'super-normal' reorganisation of the finger maps. In the cortex controlling the non-dystonic limb, there was little difference between trained and untrained patients, and the hand representation was enlarged and disorganised. The authors hypothesise that prolonged tailored rehabilitation in WC may induce long-term plasticity phenomena, lateralised to the cortex controlling the dystonic hand.

Can voxel based morphometry, manual segmentation and automated segmentation equally detect hippocampal volume differences in acute depression?

CONTEXT: According to meta-analyses, depression is associated with a smaller hippocampus. Most magnetic resonance imaging (MRI) studies among middle aged acute depressed patients are based on manual segmentation of the hippocampus. Few studies used automated methods such as voxel-based morphometry (VBM) or automated segmentation that can overcome certain drawbacks of manual segmentation (essentially intra- and inter-rater variability and operator time consumption). OBJECTIVE: The aim of our study was to compare the sensitivity of manual segmentation, automated segmentation and VBM to detect hippocampal structural changes in middle aged acute depressed population. METHOD: Twenty-one middle aged depressed inpatients and 21 matched controls were compared regarding their hippocampal structure using VBM with SPM5, manual segmentation and an automated segmentation algorithm. The VBM-ROI analysis was performed using two different normalization methods: the standard approach implemented in SPM5 and the most recent DARTEL algorithm. RESULTS: Using VBM-DARTEL, when corrected for multiple comparisons, significant volume differences were detected between groups in different regions and more specifically in hippocampus with ROI analyses. Whereas using standard VBM (without DARTEL), ROI analyses did not show bilateral volume between group differences. Significant hippocampal volume reductions between patients and controls were also detected using manual segmentation (-11.6% volume reduction, p<0.05) and automated segmentation (-9.7% volume reduction, p<0.05). VBM-DARTEL and automated segmentation show equal sensitivity in detecting hippocampal differences in depressed patients, while standard VBM was unable to detect hippocampal changes. Both VBM-DARTEL and automated segmentation could be used to perform large scale volumetric studies in humans. The new automated segmentation technique could further explore and detect hippocampal subpart differences that could be very useful for clarifying physiopathology of psychiatric disorders.

Simultaneous MEG and intracranial EEG recordings during attentive reading.

The relationship between neural oscillations recorded at various spatial scales remains poorly understood partly due to an overall dearth of studies utilizing simultaneous measurements. In an effort to study quantitative markers of attention during reading, we performed simultaneous magnetoencephalography (MEG) and intracranial electroencephalography (iEEG) recordings in four epileptic patients. Patients were asked to attend to a specific color when presented with an intermixed series of red words and green words, with words of a given color forming a cohesive story. We analyzed alpha, beta, and gamma band oscillatory responses to the word presentation and compared the strength and spatial organization of those responses in both electrophysiological recordings. Time-frequency analysis of iEEG revealed a network of clear attention-modulated high gamma band (50-150 Hz) power increases and alpha/beta (9-25 Hz) suppressions in response to the words. In addition to analyses at the sensor level, MEG time-frequency analysis was performed at the source level using a sliding window beamformer technique. Strong alpha/beta suppressions were observed in MEG reconstructions, in tandem with iEEG effects. While the MEG counterpart of high gamma band enhancement was difficult to interpret at the sensor level in two patients, MEG time-frequency source reconstruction revealed additional activation patterns in accordance with iEEG results. Importantly, iEEG allowed us to confirm that several sources of gamma band modulation observed with MEG were indeed of cortical origin rather than EMG muscular or ocular artifact.

Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging.

We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hippocampi, which are segmented from magnetic resonance images (MRI) using a fully automatic method that we previously developed. SPHARM coefficients are used as features in a classification procedure based on support vector machines (SVM). The most relevant features for classification are selected using a bagging strategy. We evaluate the accuracy of our method in a group of 23 patients with AD (10 males, 13 females, age+/-standard-deviation (SD)=73+/-6 years, mini-mental score (MMS)=24.4+/-2.8), 23 patients with amnestic MCI (10 males, 13 females, age+/-SD=74+/-8 years, MMS=27.3+/-1.4) and 25 elderly healthy controls (13 males, 12 females, age+/-SD=64+/-8 years), using leave-one-out cross-validation. For AD vs controls, we obtain a correct classification rate of 94%, a sensitivity of 96%, and a specificity of 92%. For MCI vs controls, we obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of 84%. This accuracy is superior to that of hippocampal volumetry and is comparable to recently published SVM-based whole-brain classification methods, which relied on a different strategy. This new method may become a useful tool to assist in the diagnosis of Alzheimer's disease.

Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI.

The hippocampus is among the first structures affected in Alzheimer's disease (AD). Hippocampal magnetic resonance imaging volumetry is a potential biomarker for AD but is hindered by the limitations of manual segmentation. We proposed a fully automatic method using probabilistic and anatomical priors for hippocampus segmentation. Probabilistic information is derived from 16 young controls and anatomical knowledge is modeled with automatically detected landmarks. The results were previously evaluated by comparison with manual segmentation on data from the 16 young healthy controls, with a leave-one-out strategy, and eight patients with AD. High accuracy was found for both groups (volume error 6 and 7%, overlap 87 and 86%, respectively). In this article, the method was used to segment 145 patients with AD, 294 patients with mild cognitive impairment (MCI), and 166 elderly normal subjects from the Alzheimer's Disease Neuroimaging Initiative database. On the basis of a qualitative rating protocol, the segmentation proved acceptable in 94% of the cases. We used the obtained hippocampal volumes to automatically discriminate between AD patients, MCI patients, and elderly controls. The classification proved accurate: 76% of the patients with AD and 71% of the MCI converting to AD before 18 months were correctly classified with respect to the elderly controls, using only hippocampal volume.

Detection of event-related modulations of oscillatory brain activity with multivariate statistical analysis of MEG data.

We describe a method to detect brain activation in cortically constrained maps of current density computed from magnetoencephalography (MEG) data using multivariate statistical inference. We apply time-frequency (wavelet) analysis to individual epochs to produce dynamic images of brain signal power on the cerebral cortex in multiple time-frequency bands. We form vector observations by concatenating the power in each frequency band, and fit them into separate multivariate linear models for each time band and cortical location with experimental conditions as predictor variables. The resulting Roy's maximum root statistic maps are thresholded for significance using permutation tests and the maximum statistic approach. A source is considered significant if it exceeds a statistical threshold, which is chosen to control the familywise error rate, or the probability of at least one false positive, across the cortical surface. We compare and evaluate the multivariate approach with existing univariate approaches to time-frequency MEG signal analysis, both on simulated data and experimental data from an MEG visuomotor task study. Our results indicate that the multivariate method is more powerful than the univariate approach in detecting experimental effects when correlations exist between power across frequency bands. We further describe protected F-tests and linear discriminant analysis to identify individual frequencies that contribute significantly to experimental effects.

Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus.

PURPOSE: To prospectively evaluate the accuracy of automated hippocampal volumetry to help distinguish between patients with Alzheimer disease (AD), patients with mild cognitive impairment (MCI), and elderly controls, by using established criteria for patients with AD and MCI as the reference standard. MATERIALS AND METHODS: The regional ethics committee approved the study and written informed consent was obtained from all participants. The study included 25 patients with AD (11 men, 14 women; mean age +/- standard deviation [SD], 73 years +/- 6; Mini-Mental State Examination (MMSE) score, 24.4 +/- 2.7), 24 patients with amnestic MCI (10 men, 14 women; mean age +/- SD, 74 years +/- 8; MMSE score, 27.2 +/- 1.4) and 25 elderly healthy controls (13 men, 12 women; mean age +/- SD, 64 years +/- 8). For each participant, the hippocampi were automatically segmented on three-dimensional T1-weighted magnetic resonance (MR) images with high spatial resolution. Segmentation was performed by using recently developed software that allows fast segmentation with minimal user input. Group differences in hippocampal volume were assessed by using Student t tests. To obtain robust estimates of P values, the correct classification rate, sensitivity, and specificity, bootstrap methods were used. RESULTS: Significant hippocampal volume reductions were detected in all groups of patients (-32% in AD patients vs controls, P < .001; -19% in MCI patients vs controls, P < .001; and -15% in AD patients vs MCI patients, P < .01). Individual classification on the basis of hippocampal volume resulted in 84% correct classification (sensitivity, 84%; specificity, 84%) between AD patients and controls and 73% correct classification (sensitivity, 75%; specificity, 70%) between MCI patients and controls. CONCLUSION: This automated method can serve as an alternative to manual tracing and may thus prove useful in assisting with the diagnosis of AD.

Prediction of performance level during a cognitive task from ongoing EEG oscillatory activities.

OBJECTIVE: Tracking the level of performance in cognitive tasks may be useful in environments, such as aircraft, in which the awareness of the pilots is critical for security. In this paper, the usefulness of EEG for the prediction of performance is investigated. METHODS: We present a new methodology that combines various ongoing EEG measurements to predict performance level during a cognitive task. We propose a voting approach that combines the outputs of elementary support vector machine (SVM) classifiers derived from various sets of EEG parameters in different frequency bands. The spectral power and phase synchrony of the oscillatory activities are used to classify the periods of rapid reaction time (RT) versus the slow RT responses of each subject. RESULTS: The voting algorithm significantly outperforms classical SVM and gives a good average classification accuracy across 12 subjects (71%) and an average information transfer rate (ITR) of 0.49bit/min. The main discriminating activities are laterally distributed theta power and anterio-posterior alpha synchronies, possibly reflecting the role of a visual-attentional network in performance. CONCLUSIONS: Power and synchrony measurements enable the discrimination between periods of high average reaction time versus periods of low average reaction time in a same subject. Moreover, the proposed approach is easy to interpret as it combines various types of measurements for classification, emphasizing the most informative. SIGNIFICANCE: Ongoing EEG recordings can predict the level of performance during a cognitive task. This can lead to real-time EEG monitoring devices for the anticipation of human mistakes.

Time-frequency analysis reveals decreased high-frequency oscillations in writer's cramp.

High-frequency oscillations (HFO) have been suggested to reflect the activity of thalamocortical and/or intracortical neurons bursting at high frequencies. These circuits seem to be involved in pathophysiological mechanisms of focal dystonia. In healthy subjects, we characterized the spectrotemporal properties of HFO patterns evoked by dominant-hand median-nerve stimulation, using magnetoencephalography coupled with time-frequency analysis. Then, we investigated HFO in patients with writer's cramp and found that HFO patterns are strongly decreased in power and disorganized in time. This supports the assumption that abnormal HFOs reflect pathophysiological mechanisms occurring in focal dystonia, possibly resulting from a dysfunction of somatosensory processing.

Characterization and correction of distortions in stereotactic magnetic resonance imaging for bilateral subthalamic stimulation in Parkinson disease.

OBJECT: High-frequency stimulation of the subthalamic nucleus (STN) is effective for treating refractory idiopathic Parkinson disease (PD). In stereotactic conditions magnetic resonance (MR) imaging is used by many teams to perform preoperative targeting of the STN. The goal of this study was to analyze and correct the geometrically observed MR imaging acquisitions used for targeting of the STN. METHODS: A dedicated phantom of known geometry was used. The authors calculated existing shifts between measured points and theoretically defined points on the same T1- and T2-weighted sequences used to target the STN. A shifting volume was built to correct the phantom images and images acquired preoperatively in 13 patients with PD. A quantitative study of the correction was conducted using the phantom images and acquisitions acquired in these patients. To quantify the distortion corrections, the authors segmented the lateral ventricles and calculated the overlap of the corrected and uncorrected values between T1 and T2 segmentation. The authors found that the distortions were greater in the direction of slice selection and frequency encoding and weaker on three-dimensional T1-weighted acquisitions. On T2-weighted acquisitions, the maximum shifts were 2.19 mm in the frequency-encoding direction and 3.81 mm in slice selection. The geometrical distortion was significantly reduced and smaller than pixel size after distortion correction. Assessment of the patients' scans showed that the mean ventricular overlap was 76% before and 94% after correction. CONCLUSIONS: The authors found that significant distortions can be observed on T2-weighted images used to demonstrate the STN. These distortions can be corrected using appropriate software.

Dystonia: lessons from brain mapping.

Functional neuroimaging with positron emission tomography, single photon emission computer tomography, magnetic resonance imaging, and magnetoencephalography have provided powerful tools to elucidate anatomo-functional impairment underlying movement disorders such as dystonic movements. They have revealed that presymptomatic cerebral abnormalities may be a common feature in dystonia whatever the clinical status. Techniques using specific markers have recently focused on the type of receptors that may be dysactivated and on the kind of neurotransmitter that may be dysregulated in dystonia.

What is common to brain activity evoked by the perception of visual and auditory filled durations? A study with MEG and EEG co-recordings.

EEG and MEG scalp data were simultaneously recorded while human participants were performing a duration discrimination task in visual and auditory modality, separately. Short durations were used ranging from 500 to 900 ms, among which participants had to discriminate a previously memorized 700-ms "standard" duration. Behavioral results show accurate but variable performance within and between participants with expected modality effects: the percentage of responses was greater and the mean response time was shorter for auditory than for visual signals. Sustained electric and magnetic activities were obtained correlatively to duration estimation, but with distinct spatiotemporal properties. Electric CNV-like potentials showed fronto-central negativity in both modalities, whereas magnetic sustained fields were distributed with respect to the modality of the interval to be timed. Time courses of these slow brain activities were found to be dependent on stimulus duration but not on its modality nor on the recording signal (EEG or MEG). Source reconstruction demonstrated that these sustained potentials/fields were generated by superimposed contributions from visual and auditory cortices (sustained sensory responses, SSR) and from prefrontal and parietal regions. By using these two complementary techniques, we thus demonstrated the involvement of frontal and parietal cerebral cortex in human timing.

Neuromagnetic imaging of cortical oscillations accompanying tactile stimulation.

We applied a new method of imaging frequency-specific changes in brain activity in humans during a finger brushing task in order to measure changes in cortical rhythms during tactile stimulation. Neuromagnetic recordings were conducted in five subjects using a whole-head MEG system during tactile stimulation of the right index finger, with or without visual feedback, and while viewing another individual's index finger being stimulated. Volumetric images of changes in source power relative to pre-stimulus baseline levels were computed with 2 mm resolution over the entire brain using a minimum-variance beamforming algorithm (synthetic aperture magnetometry). Onset of tactile stimulation produced a brief (200-300 ms) suppression of mu band (8-15 Hz) and beta band (15-30 Hz) cortical activity in the primary somatosensory and primary motor cortex, respectively, followed by a bilateral increase in beta band activity ('beta rebound') in motor cortex. This pattern of suppression/rebound was absent when subjects observed finger brushing or brushing motions without receiving stimulation. In contrast, these conditions resulted in bilateral increases in beta band activity in sensorimotor areas and decreased power in the alpha (8-12 Hz) band in primary visual areas. These results show that spatially filtered MEG provides a useful method for directly imaging the temporal sequence of changes in cortical rhythms during transient tactile stimulation, and provide evidence that observation of tactile input to another individual's hand, or object motion itself, can influence independent rhythmic activity in visual and sensorimotor cortex.

Estimation of neural dynamics from MEG/EEG cortical current density maps: application to the reconstruction of large-scale cortical synchrony.

There is a growing interest in elucidating the role of specific patterns of neural dynamics--such as transient synchronization between distant cell assemblies--in brain functions. Magnetoencephalography (MEG)/electroencephalography (EEG) recordings consist in the spatial integration of the activity from large and multiple remotely located populations of neurons. Massive diffusive effects and poor signal-to-noise ratio (SNR) preclude the proper estimation of indices related to cortical dynamics from nonaveraged MEG/EEG surface recordings. Source localization from MEG/EEG surface recordings with its excellent time resolution could contribute to a better understanding of the working brain. We propose a robust and original approach to the MEG/EEG distributed inverse problem to better estimate neural dynamics of cortical sources. For this, the surrogate data method is introduced in the MEG/EEG inverse problem framework. We apply this approach on nonaveraged data with poor SNR using the minimum norm estimator and find source localization results weakly sensitive to noise. Surrogates allow the reduction of the source space in order to reconstruct MEG/EEG data with reduced biases in both source localization and time-series dynamics. Monte Carlo simulations and results obtained from real MEG data indicate it is possible to estimate non invasively an important part of cortical source locations and dynamic and, therefore, to reveal brain functional networks.

Inter-brain synchronization during social interaction.

During social interaction, both participants are continuously active, each modifying their own actions in response to the continuously changing actions of the partner. This continuous mutual adaptation results in interactional synchrony to which both members contribute. Freely exchanging the role of imitator and model is a well-framed example of interactional synchrony resulting from a mutual behavioral negotiation. How the participants' brain activity underlies this process is currently a question that hyperscanning recordings allow us to explore. In particular, it remains largely unknown to what extent oscillatory synchronization could emerge between two brains during social interaction. To explore this issue, 18 participants paired as 9 dyads were recorded with dual-video and dual-EEG setups while they were engaged in spontaneous imitation of hand movements. We measured interactional synchrony and the turn-taking between model and imitator. We discovered by the use of nonlinear techniques that states of interactional synchrony correlate with the emergence of an interbrain synchronizing network in the alpha-mu band between the right centroparietal regions. These regions have been suggested to play a pivotal role in social interaction. Here, they acted symmetrically as key functional hubs in the interindividual brainweb. Additionally, neural synchronization became asymmetrical in the higher frequency bands possibly reflecting a top-down modulation of the roles of model and imitator in the ongoing interaction.

The amnestic syndrome of hippocampal type in Alzheimer's disease: an MRI study.

The Free and Cued Selective Reminding Test (FCSRT) is a verbal episodic memory test used to identify patients with mild Alzheimer's disease (AD). The present study investigates the relationships between performance on FCSRT and grey matter atrophy assessed with structural MRI in patients with AD. Three complementary MRI-based analyses (VBM analysis, ROI-based analysis, and three-dimensional hippocampal surface-based shape analysis) were performed in 35 patients with AD to analyze correlations between regional atrophy and their scores for episodic memory using the FCSRT. With VBM analysis, the total score on the FCSRT was correlated with left medial temporal lobe atrophy including the left hippocampus but also the thalami. In addition, using ROI-based analysis, the total recall score on the FCSRT was correlated with the left hippocampal volume. With three-dimensional hippocampal surface-based shape analysis, both free recall and total recall scores were correlated with regions corresponding approximately to the CA1 field. No correlation was found with short term memory scores using any of these methods of analysis. In AD, the FCSRT may be considered as a useful clinical marker of memory disorders due to medial temporal damage, specially the CA1 field of the hippocampus.

Brain volumes and Val66Met polymorphism of the BDNF gene: local or global effects?

A common Single-Nucleotide Polymorphism in the Brain-Derived Neurotrophic Factor (BDNF) gene coding the Val66Met substitution in the pro-BDNF protein has been associated with a number of behavioural and neuroanatomical phenotypes; the latter include, for example, regional differences in volumes of the hippocampus and prefrontal grey matter. Here, we show that the observed regional differences may not stem from a localised effect of this gene. Our analysis of regional brain volume in a cohort of 331 adolescents indicates that the Val66Met substitution has a global effect on brain volume, and that the observed local differences are to be expected if brain allometry-the covariance pattern of regional brain volumes-is taken into account.