Maxime Guye

Functional connectivity changes differ in early and late-onset alzheimer's disease

At a similar stage, patients with early onset Alzheimers disease (EOAD) have greater neocortical but less medial temporal lobe dysfunction and atrophy than the late‐onset form of the disease (LOAD). Whether the organization of neural networks also differs has never been investigated. This study aims at characterizing basal functional connectivity (FC) patterns of EOAD and LOAD in two groups of 14 patients matched for disease duration and severity, relative to age‐matched controls. All subjects underwent an extensive neuropsychological assessment. Magnetic resonance imaging was used to quantify atrophy and resting‐state FC focusing on : the default mode network (DMN), found impaired in earlier studies on AD, and the anterior temporal network (ATN) and dorso‐lateral prefrontal network (DLPFN), respectively involved in declarative memory and executive functions. Patterns of atrophy and cognitive impairment in EOAD and LOAD were in accordance with previous reports. FC within the DMN was similarly decreased in both EOAD and LOAD relative to controls. However, a double‐dissociated pattern of FC changes in ATN and DLPFN was found. EOAD exhibited decreased FC in the DLPFN and increased FC in the ATN relative to controls, while the reverse pattern was found in LOAD. In addition, ATN and DLPFN connectivity correlated respectively with memory and executive performances, suggesting that increased FC is here likely to reflect compensatory mechanisms. Thus, large‐scale neural network changes in EOAD and LOAD endorse both common features and differences, probably related to a distinct distribution of pathological changes. Hum Brain Mapp 35:2978–2994, 2014.

The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread

ABSTRACT Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results. We propose a novel approach to brain interventions based on personalized brain network models derived from non‐invasive structural data of individual patients. Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient‐specific information such as brain connectivity, epileptogenic zone and MRI lesions. Using high‐performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patients empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention. HighlightsA novel approach to brain interventions is proposed based on personalized large‐scale brain network models.The approach relies on the fusion of structural data of individual patients and mathematical modeling of brain activations.Personalization is achieved by integrating patient specific brain connectivity, epileptogenic zone and MRI lesions.High‐performance computing enables systematic parameter space explorations, fitting and validation of the brain model.Large‐scale brain models foster the development of personalized strategies towards therapy and intervention.

Individual brain structure and modelling predict seizure propagation

See Lytton (doi:10.1093/awx018) for a scientific commentary on this article. Patients with drug-resistant epilepsy show different seizure propagation patterns and postsurgical outcomes. Proix et al. merge structural information from brain imaging with mathematical modelling to generate personalized brain network models. Validation of the models against presurgical stereotactic EEGs and clinical data shows that they can account for the variability observed.

Tract-specific and age-related variations of the spinal cord microstructure: a multi-parametric MRI study using diffusion tensor imaging (DTI) and inhomogeneous magnetization transfer (ihMT).

Being able to finely characterize the spinal cord (SC) microstructure and its alterations is a key point when investigating neural damage mechanisms encountered in different central nervous system (CNS) pathologies, such as multiple sclerosis, amyotrophic lateral sclerosis or myelopathy.

Defining epileptogenic networks: Contribution of SEEG and signal analysis

Epileptogenic networks are defined by the brain regions involved in the production and propagation of epileptic activities. In this review we describe the historical, methodologic, and conceptual bases of this model in the analysis of electrophysiologic intracerebral recordings. In the context of epilepsy surgery, the determination of cerebral regions producing seizures (i.e., the “epileptogenic zone”) is a crucial objective. In contrast with a traditional focal vision of focal drug‐resistant epilepsies, the concept of epileptogenic networks has been progressively introduced as a model better able to describe the complexity of seizure dynamics and realistically describe the distribution of epileptogenic anomalies in the brain. The concept of epileptogenic networks is historically linked to the development of the stereoelectroencephalography (SEEG) method and subsequent introduction of means of quantifying the recorded signals. Seizures, and preictal and interictal discharges produce clear patterns on SEEG. These patterns can be analyzed utilizing signal analysis methods that quantify high‐frequency oscillations or changes in functional connectivity. Dramatic changes in SEEG brain connectivity can be described during seizure genesis and propagation within cortical and subcortical regions, associated with the production of different patterns of seizure semiology. The interictal state is characterized by networks generating abnormal activities (interictal spikes) and also by modified functional properties. The introduction of novel approaches to large‐scale modeling of these networks offers new methods in the goal of better predicting the effects of epilepsy surgery. The epileptogenic network concept is a key factor in identifying the anatomic distribution of the epileptogenic process, which is particularly important in the context of epilepsy surgery.

Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy.

The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.

Early-onset and late-onset Alzheimer’s disease are associated with distinct patterns of memory impairment

The goal of this study was to investigate the specific patterns of memory breakdown in patients suffering from early-onset Alzheimers disease (EOAD) and late-onset Alzheimers disease (LOAD). Twenty EOAD patients, twenty LOAD patients, twenty matched younger controls, and twenty matched older controls participated in this study. All participants underwent a detailed neuropsychological assessment, an MRI scan, an FDG-PET scan, and AD patients had biomarkers as supporting evidence of both amyloïdopathy and neuronal injury. Results of the neuropsychological assessment showed that both EOAD and LOAD groups were impaired in the domains of memory, executive functions, language, praxis, and visuoconstructional abilities, when compared to their respective control groups. EOAD and LOAD groups, however, showed distinct patterns of memory impairment. Even though both groups were similarly affected on measures of episodic, short term and working memory, in contrast semantic memory was significantly more impaired in LOAD than in EOAD patients. The EOAD group was not more affected than the LOAD group in any memory domain. EOAD patients, however, showed significantly poorer performance in other cognitive domains including executive functions and visuoconstructional abilities. A more detailed analysis of the pattern of semantic memory performance among patient groups revealed that the LOAD was more profoundly impaired, in tasks of both spontaneous recall and semantic recognition. Voxel-Based Morphometry (VBM) analyses showed that impaired semantic performance in patients was associated with reduced gray matter volume in the anterior temporal lobe (ATL) region, while PET-FDG analyses revealed that poorer semantic performance was associated with greater hypometabolism in the left temporoparietal region, both areas reflecting key regions of the semantic network. Results of this study indicate that EOAD and LOAD patients present with distinct patterns of memory impairment, and that a genuine semantic impairment may represent one of the clinical hallmarks of LOAD.

Magnetic resonance spectroscopy of paragangliomas: new insights into in vivo metabolomics

Paragangliomas (PGLs) can be associated with mutations in genes of the tricarboxylic acid (TCA) cycle. Succinate dehydrogenase (SDHx) mutations are the prime examples of genetically determined TCA cycle defects with accumulation of succinate. Succinate, which acts as an oncometabolite, can be detected by ex vivo metabolomics approaches. The aim of this study was to evaluate the potential role of proton magnetic resonance (MR) spectroscopy ((1)H-MRS) for identifying SDHx-related PGLs in vivo and noninvasively. Eight patients were prospectively evaluated with single voxel (1)H-MRS. MR spectra from eight tumors (four SDHx-related PGLs, two sporadic PGLs, one cervical schwannoma, and one cervical neurofibroma) were acquired and interpreted qualitatively. Compared to other tumors, a succinate resonance peak was detected only in SDHx-related tumor patients. Spectra quality was considered good in three cases, medium in two cases, poor in two cases, and uninterpretable in the latter case. Smaller lesions had lower spectra quality compared to larger lesions. Jugular PGLs also exhibited a poorer spectra quality compared to other locations. (1)H-MRS has always been challenging in terms of its technical requisites. This is even more true for the evaluation of head and neck tumors. However, (1)H-MRS might be added to the classical MR sequences for metabolomic characterization of PGLs. In vivo detection of succinate might guide genetic testing, characterize SDHx variants of unknown significance (in the absence of available tumor sample), and even optimize a selection of appropriate therapies.

Whole-brain quantitative mapping of metabolites using short echo three-dimensional proton MRSI.

To improve the extent over which whole brain quantitative three‐dimensional (3D) magnetic resonance spectroscopic imaging (MRSI) maps can be obtained and be used to explore brain metabolism in a population of healthy volunteers.

Single-trial EEG-informed fMRI reveals spatial dependency of BOLD signal on early and late IC-ERP amplitudes during face recognition.

Simultaneous EEG-fMRI has opened up new avenues for improving the spatio-temporal resolution of functional brain studies. However, this method usually suffers from poor EEG quality, especially for evoked potentials (ERPs), due to specific artifacts. As such, the use of EEG-informed fMRI analysis in the context of cognitive studies has particularly focused on optimizing narrow ERP time windows of interest, which ignores the rich diverse temporal information of the EEG signal. Here, we propose to use simultaneous EEG-fMRI to investigate the neural cascade occurring during face recognition in 14 healthy volunteers by using the successive ERP peaks recorded during the cognitive part of this process. N170, N400 and P600 peaks, commonly associated with face recognition, were successfully and reproducibly identified for each trial and each subject by using a group independent component analysis (ICA). For the first time we use this group ICA to extract several independent components (IC) corresponding to the sequence of activation and used single-trial peaks as modulation parameters in a general linear model (GLM) of fMRI data. We obtained an occipital-temporal-frontal stream of BOLD signal modulation, in accordance with the three successive IC-ERPs providing an unprecedented spatio-temporal characterization of the whole cognitive process as defined by BOLD signal modulation. By using this approach, the pattern of EEG-informed BOLD modulation provided improved characterization of the network involved than the fMRI-only analysis or the source reconstruction of the three ERPs; the latter techniques showing only two regions in common localized in the occipital lobe.