Exploring anterograde memory: a volumetric MRI study in patients with mild cognitive impairment.

BACKGROUND: The aim of this volumetric study was to explore the neuroanatomical correlates of the Free and Cued Selective Reminding Test (FCSRT) and the Delayed Matching-to-Sample-48 items (DMS-48), two tests widely used in France to assess verbal and visual anterograde memory. We wanted to determine to what extent the two tests rely on the medial temporal lobe, and could therefore be predictive of Alzheimer's disease, in which pathological changes typically start in this region. METHODS: We analysed data from a cohort of 138 patients with mild cognitive impairment participating in a longitudinal multicentre clinical research study. Verbal memory was assessed using the FCSRT and visual recognition memory was evaluated using the DMS-48. Performances on these two tests were correlated to local grey matter atrophy via structural MRI using voxel-based morphometry. RESULTS: Our results confirm the existence of a positive correlation between the volume of the medial temporal lobe and the performance on the FCSRT, prominently on the left, and the performance on the DMS-48, on the right, for the whole group of patients (family-wise error, P < 0.05). Interestingly, this region remained implicated only in the subgroup of patients who had deficient scores on the cued recall of the FCSRT, whereas the free recall was associated with prefrontal aspects. For the DMS-48, it was only implicated for the group of patients whose performances declined between the immediate and delayed trial. Conversely, temporo-parietal cortices were implicated when no decline was observed. Within the medial temporal lobe, the parahippocampal gyrus was prominently involved for the FCSRT and the immediate trial of the DMS-48, whereas the hippocampus was solely involved for the delayed trial of the DMS-48. CONCLUSIONS: The two tests are able to detect an amnestic profile of the medial temporal type, under the condition that the scores remain deficient after the cued recall of the FCSRT or decline on the delayed recognition trial of the DMS-48. Strategic retrieval as well as perceptual/attentional processes, supported by prefrontal and temporo-parietal cortices, were also found to have an impact on the performances. Finally, the implication of the hippocampus appears time dependent, triggered by a longer delay than the parahippocampus, rather than determined by the sense of recollection or the encoding strength associated with the memory trace.

[Imaging methods used in the differential diagnosis between brain tumour relapse and radiation necrosis after stereotactic radiosurgery of brain metastases: Literature review]. / Techniques d'imagerie utilisées dans le diagnostic différentiel entre radionécrose cérébrale et rechute tumorale après irradiation en conditions stéréotaxiques de métastases cérébrales : revue de la littérature.

After stereotactic radiosurgery for a cerebral metastasis, one of the dreaded toxicities is radionecrosis. In the follow-up of these patients, it is impossible to distinguish radiation necrosis from tumour relapse either clinically or with MRI. In current practice, many imaging methods are designed such as special sequences of MRI (dynamic susceptibility contrast perfusion and susceptibility-weighted imaging, diffusion), proton magnetic resonance spectroscopy, positron emission tomography, or more seldom 201-thallium single-photon emission computerized tomography. This article is a required literature analysis in order to establish a decision tree with the analysis of retrospective and prospective data.

A custom robot for Transcranial Magnetic Stimulation: first assessment on healthy subjects.

In this paper, a custom robotic system for Transcranial Magnetic Stimulation is assessed in clinical conditions on healthy subjects. A motor cortex mapping is performed using the robotic system with comparison to a manual approach using a neuronavigation system. Stimulation accuracy, repeatability are evaluated as well as the feeling of the system operator and the subject in terms of comfort, tiredness, stress level, ease-of-use. Very encouraging results are obtained on all these aspects, which strengthens the idea of developing robotic assistance for TMS.

White matter volume is decreased in the brain of patients with neuromyelitis optica.

BACKGROUND: Neuromyelitis optica (NMO) is an inflammatory disease involving predominantly the spinal cord and optic nerves. Whether patients with NMO have a loss in white or grey matter (GM) volumes remains to be determined. METHODS: Thirty patients with NMO, 30 healthy subjects matched for age and gender, 21 patients with multiple sclerosis (MS) and 20 patients with a clinically isolated syndrome (CIS) were studied. We applied a SIENAX post-treatment software. We compared white matter (WM) and GM volumes between groups and explored correlations of changes in NMO patients with age, gender, duration, disease severity, visual acuity and T2 hyperintensities. We also performed a voxel-based morphometry (VBM) analysis to identify the regions affected by loss of volume. RESULTS: White matter volume was significantly reduced in patients with NMO (764.4 ± 58.3 cm(3) ) compared to healthy subjects (843.1 ± 49.3 cm(3) ) (P < 0.001), whereas no difference was observed for the GM. Patients with CIS also presented an elective atrophy of WM and MS an atrophy of both WM and GM. We did not find any predictive factors of brain atrophy. The decrease in WM volume in NMO was noted even in the absence of visible MRI hypersignals. The VBM analysis found a few regions of WM atrophy (corpus callosum and optic radiations, P < 0.005, uncorrected) and a few regions of GM atrophy (thalamus and prefrontal cortex, P < 0.001, uncorrected). CONCLUSION: These results suggest a significant brain involvement in NMO, especially an involvement of WM which appears not to be limited to secondary degeneration after spinal cord and optic nerve damage.

Reliability of longitudinal brain volume loss measurements between 2 sites in patients with multiple sclerosis: comparison of 7 quantification techniques.

BACKGROUND AND PURPOSE: Brain volume loss is currently a MR imaging marker of neurodegeneration in MS. Available quantification algorithms perform either direct (segmentation-based techniques) or indirect (registration-based techniques) measurements. Because there is no reference standard technique, the assessment of their accuracy and reliability remains a difficult goal. Therefore, the purpose of this work was to assess the robustness of 7 different postprocessing algorithms applied to images acquired from different MR imaging systems. MATERIALS AND METHODS: Nine patients with MS were followed longitudinally over 1 year (3 time points) on two 1.5T MR imaging systems. Brain volume change measures were assessed using 7 segmentation algorithms: a segmentation-classification algorithm, FreeSurfer, BBSI, KN-BSI, SIENA, SIENAX, and JI algorithm. RESULTS: Intersite variability showed that segmentation-based techniques and SIENAX provided large and heterogeneous values of brain volume changes. A Bland-Altman analysis showed a mean difference of 1.8%, 0.07%, and 0.79% between the 2 sites, and a wide length agreement interval of 11.66%, 7.92%, and 11.94% for the segmentation-classification algorithm, FreeSurfer, and SIENAX, respectively. In contrast, registration-based algorithms showed better reproducibility, with a low mean difference of 0.45% for BBSI, KN-BSI and JI, and a mean length agreement interval of 1.55%. If SIENA obtained a lower mean difference of 0.12%, its agreement interval of 3.29% was wider. CONCLUSIONS: If brain atrophy estimation remains an open issue, future investigations of the accuracy and reliability of the brain volume quantification algorithms are needed to measure the slow and small brain volume changes occurring in MS.

Can injection CT scan assess the residual femoral head vascularity after acute neck fracture?

INTRODUCTION: Femoral neck fracture jeopardizes the vital prognosis of the elderly subject and the functional prognosis of the young subject. The vascular consequence is important, with the risk of osteonecrosis of the femoral head. In young patients, predicting the risk of necrosis at the acute stage seems warranted so as to optimize the choice of therapy. CT with injection could be useful to study the residual bone vascularity after an acute fracture of the femoral neck. HYPOTHESIS: The CT scan with injection can diagnose ischemia of the femoral head after neck fracture by demonstrating hypoperfusion and thus estimating the risk for osteonecrosis. PATIENTS AND METHOD: A CT scan with injection was performed prospectively in 20 adult patients who had given informed consent after verification of the inclusion and exclusion criteria. Ten presented femoral neck fracture and 10 a pertrochanteric fracture, the latter making up the control group. The second control group was the healthy side of patients presenting a femoral neck fracture. The images were analyzed after delineating a region of interest as a volume at the center of the femoral head. The results were analyzed after modeling based on the physical principle of diffusion. RESULTS: No differences were found between the "healthy hip," "fractured hip," "femoral neck fracture," and "trochanteric region fracture" groups. The only statistically significant correlation was found between the "fractured hip" and "healthy hip" of the same patient independently of the type of fracture. DISCUSSION: The results do not confirm the working hypothesis. This study was mainly limited by the small number of patients included, but this did not substantially effect the study's conclusions. According to the results, it seems that this study provided a CT evaluation of bone mineral density. At the end of the study, it seems that CT with injection is not well adapted in assessing residual femoral head vascularity or estimating the risk of progression towards avascular necrosis. According to the literature, only dynamic MRI with injection seems to be effective in this assessment and estimation. LEVEL OF EVIDENCE: Level III prospective comparative diagnostic.

Evaluation of brain atrophy estimation algorithms using simulated ground-truth data.

A number of analysis tools have been developed for the estimation of brain atrophy using MRI. Since brain atrophy is being increasingly used as a marker of disease progression in many neuro-degenerative diseases such as Multiple Sclerosis and Alzheimer's disease, the validation of these tools is an important task. However, this is complex, in the real scenario, due to the absence of gold standards for comparison. In order to create gold standards, we first propose an approach for the realistic simulation of brain tissue loss that relies on the estimation of a topology preserving B-spline based deformation fields. Using these gold standards, an evaluation of the performance of three standard brain atrophy estimation methods (SIENA, SIENAX and BSI-UCD), on the basis of their robustness to various sources of error (bias-field inhomogeneity, noise, geometrical distortions, interpolation artefacts and presence of lesions), is presented. Our evaluation shows that, in general, bias-field inhomogeneity and noise lead to larger errors in the estimated atrophy than geometrical distortions and interpolation artefacts. Experiments on 18 different anatomical models of the brain after simulating whole brain atrophies in the range of 0.2-1.5% indicate that, in the presence of bias-field inhomogeneity and noise, a mean error of 0.64+/-0.53%,4.00+/-2.41% and 1.79+/-0.97% may be expected in the atrophy estimated by SIENA, SIENAX and BSI-UCD, respectively.

SPM analysis of ictal-interictal SPECT in mesial temporal lobe epilepsy: relationships between ictal semiology and perfusion changes.

A combination of temporo-limbic hyperperfusion and extratemporal hypoperfusion was observed during complex partial seizures (CPS) in temporal lobe epilepsy (TLE). To investigate the clinical correlate of perfusion changes in TLE, we analyzed focal seizures of increasing severity using voxel-based analysis of ictal SPECT. We selected 26 pre-operative pairs of ictal-interictal SPECTs from adult mesial TLE patients, seizure-free after surgery. Ictal SPECTs were classified in three groups: motionless seizures (group ML, n=8), seizures with motor automatisms (MA) without dystonic posturing (DP) (group MA, n=8), and seizures with DP with or without MA (DP, n=10). Patients of group ML had simple partial seizures (SPS), while others had CPS. Groups of ictal-interictal SPECT were compared to a control group using statistical parametric mapping (SPM). In ML group, SPM analysis failed to show significant changes. Hyperperfusion involved the anteromesial temporal region in MA group, and also the insula, posterior putamen and thalamus in DP group. Hypoperfusion was restricted to the posterior cingulate and prefrontal regions in MA group, and involved more widespread associative anterior and posterior regions in DP group. Temporal lobe seizures with DP show the most complex pattern of combined hyper-hypoperfusion, possibly related both to a larger spread and the recruitment of more powerful inhibitory processes.

Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains.

In the frame of 3D medical imaging, accurate segmentation of multimodal brain MR images is of interest for many brain disorders. However, due to several factors such as noise, imaging artifacts, intrinsic tissue variation and partial volume effects, tissue classification remains a challenging task. In this paper, we present a unifying framework for unsupervised segmentation of multimodal brain MR images including partial volume effect, bias field correction, and information given by a probabilistic atlas. Here-proposed method takes into account neighborhood information using a Hidden Markov Chain (HMC) model. Due to the limited resolution of imaging devices, voxels may be composed of a mixture of different tissue types, this partial volume effect is included to achieve an accurate segmentation of brain tissues. Instead of assigning each voxel to a single tissue class (i.e., hard classification), we compute the relative amount of each pure tissue class in each voxel (mixture estimation). Further, a bias field estimation step is added to the proposed algorithm to correct intensity inhomogeneities. Furthermore, atlas priors were incorporated using probabilistic brain atlas containing prior expectations about the spatial localization of different tissue classes. This atlas is considered as a complementary sensor and the proposed method is extended to multimodal brain MRI without any user-tunable parameter (unsupervised algorithm). To validate this new unifying framework, we present experimental results on both synthetic and real brain images, for which the ground truth is available. Comparison with other often used techniques demonstrates the accuracy and the robustness of this new Markovian segmentation scheme.

An a contrario approach for change detection in 3D multimodal images: application to multiple sclerosis in MRI.

Estimating significant changes between two images remains a challenging problem in medical image processing. This paper proposes a non-parametric region based method to detect significant changes in 3D multimodal Magnetic Resonance (MR) sequences. The proposed approach relies on an a contrario model which defines significant changes as events with very low probability. We adapt the a contrario framework to deal with multimodal images from which are extracted measures related to intensity and volume changes. Two fusion rules are carefully designed to handle a set of decision thresholds and a set of image measures. The final decision is taken using multiple testing procedures. The efficiency of the algorithm is demonstrated in the context of multiple sclerosis (MS) lesion analysis over time in multimodal MR sequences. We evaluate the proposed method on synthetic images using the Brainweb simulator. Finally, promising results on multimodal sequences on clinical data are presented.

Hidden Markov multiple event sequence models: A paradigm for the spatio-temporal analysis of fMRI data.

This paper presents a novel, completely unsupervised fMRI brain mapping method that addresses the three problems of hemodynamic response function (HRF) variability, hemodynamic event timing, and fMRI response non-linearity. Spatial and temporal information are directly taken into account into the core of the activation detection process. In practice, activation detection at voxel v is formulated in terms of temporal alignment between sequences of hemodynamic response onsets (HROs) detected in the fMRI signal at v and in the spatial neighborhood of v, and the input sequence of stimuli or stimulus onsets. Event-related and epoch paradigms are considered. The multiple event sequence alignment problem is solved within the probabilistic framework of hidden Markov multiple event sequence models (HMMESMs), a new class of hidden Markov models. Results obtained on real and synthetic data significantly outperform those obtained with the popular statistical parametric mapping (SPM2) method without requiring any prior definition of the expected activation patterns, the HMMESM mapping approach being completely unsupervised.

[Epileptogenic and non-epileptogenic zones: blood flow studies of temporo-limbic seizures]. / Zones épileptogènes et non-épileptogènes: imagerie de perfusion des crises temporo-limbiques.

To assess the contribution of ictal SPECT to the definition of the epileptogenic zone (EZ) prior to surgery in focal drug-resistant epilepsies, we investigated the effect of the timing of injection and seizure semiology on patterns of perfusion and cerebral blood flow changes (CBF) beyond the EZ. In the rat model of amygdala-kindled seizures, we measured CBF changes with the quantitative [(14)C]-iodoantipyrine autoradiographic method during secondary generalized (SGS, n=26 fully-kindled rats) and focal seizures (FS, n=19 partially kindled rats), according to sequential timing of injection with respect to seizure onset. During SGS, the correct lateralization and rough localization of the focus within limbic structures was only possible at the early ictal and post-ictal times, in between we observed widespread rCBF increases. The switch from hyper to hypoperfusion occurred at the time of late ictal injection. The accurate localization of the EZ was obtained in the study of the more subtle FS (stage 0). At stage 1 of the kindling, there was already a remote widespread spreading of hyperperfusion. In patients surgically cured from a mesio-temporal lobe epilepsy (mean post-operative follow-up: 66 months), we retrospectively studied 26 pairs of ictal and interictal pre-operative SPECTs, classified in 3 groups according to the progression of ictal semiology. Using visual analysis of subtracted SPECTs (SISCOM) and group comparisons with a control group (using SPM), we observed more widespread combined hyper and hypoperfusion with the increasing complexity of seizures. In simple partial seizures, the SISCOM analysis allowed a correct localization of the focus in 4/8 patients, whereas the SPM analysis failed to detect significant changes, due to individual variation, spatial normalization and small magnitude of CBF changes. In complex partial seizures with automatisms, SISCOM and SPM analysis showed antero-mesial temporal hyperperfusion (overlapping the EZ), extending to the insula, basal ganglia, and thalamus in the group of patients having dystonic posturing (DP group) in addition to automatisms. Ictal hypoperfusion involved pre-frontal and parietal regions, the anterior and posterior cingulate gyri, to a greater extent in the DP group. In both human and animals studies, we observed a correlation between the extent of composite patterns of hyper/hypoperfusion and the severity of seizures, and the recruitment of remote sub-cortical structures. Hypoperfused areas belong to neural networks involved in perceptual decision making and motor planning, whose transient disruption could support purposeless actions, i.e. motor automatisms.

Retrospective evaluation of a topology preserving non-rigid registration method.

This paper proposes a comprehensive evaluation of a monomodal B-spline-based non-rigid registration algorithm allowing topology preservation in 3-D. This article is to be considered as the companion of [Noblet, V., Heinrich, C., Heitz, F., Armspach, J.-P., 2005. 3-D deformable image registration: a topology preservation scheme based on hierarchical deformation models and interval analysis optimization. IEEE Transactions on Image Processing, 14 (5), 553-566] where this algorithm, based on the minimization of an objective function, was introduced and detailed. Overall assessment is based on the estimation of synthetic deformation fields, on average brain construction, on atlas-based segmentation and on landmark mapping. The influence of the model parameters is characterized. Comparison between several objective functions is carried out and impact of their symmetrization is pointed out. An original intensity normalization scheme is also introduced, leading to significant improvements of the registration quality. The comparison benchmark is the popular demons algorithm [Thirion, J.-P., 1998. Image matching as a diffusion process: an analogy with Maxwell's demons. Medical Image Analysis, 2 (3), 243-260], that exhibited best results in a recent comparison between several non-rigid 3-D registration methods [Hellier, P., Barillot, C., Corouge, I., Gibaud, B., Le Goualher, G., Collins, D.L., Evans, A., Malandain, G., Ayache, N., Christensen, G.E., Johnson, H.J., 2003. Retrospective evaluation of intersubject brain registration. IEEE Transactions on Medical Imaging, 22 (9), 1120-1130]. The topology preserving B-spline-based method proved to outperform the commonly available ITK implementation of the demons algorithms on many points. Some limits of intensity-based registration methods are also highlighted through this work.

Magnetic resonance angiography: from anatomical knowledge modeling to vessel segmentation.

Magnetic resonance angiography (MRA) has become a common way to study cerebral vascular structures. Indeed, it enables to obtain information on flowing blood in a totally non-invasive and non-irradiant fashion. MRA exams are generally performed for three main applications: detection of vascular pathologies, neurosurgery planning, and vascular landmark detection for brain functional analysis. This large field of applications justifies the necessity to provide efficient vessel segmentation tools. Several methods have been proposed during the last fifteen years. However, the obtained results are still not fully satisfying. A solution to improve brain vessel segmentation from MRA data could consist in integrating high-level a priori knowledge in the segmentation process. A preliminary attempt to integrate such knowledge is proposed here. It is composed of two methods devoted to phase contrast MRA (PC MRA) data. The first method is a cerebral vascular atlas creation process, composed of three steps: knowledge extraction, registration, and data fusion. Knowledge extraction is performed using a vessel size determination algorithm based on skeletonization, while a topology preserving non-rigid registration method is used to fuse the information into the atlas. The second method is a segmentation process involving adaptive sets of gray-level hit-or-miss operators. It uses anatomical knowledge modeled by the cerebral vascular atlas to adapt the parameters of these operators (number, size, and orientation) to the searched vascular structures. These two methods have been tested by creating an atlas from a 18 MRA database, and by using it to segment 30 MRA images, comparing the results to those obtained from a region-growing segmentation method.

Contribution of SISCOM imaging in the presurgical evaluation of temporal lobe epilepsy related to dysembryoplastic neuroepithelial tumors.

PURPOSE: Dysembryoplastic neuroepithelial tumors (DNTs) are a group of glioneuronal supratentorial and intracortical lesions often associated with the early onset of intractable and crippling partial seizures. They are characterized by their location, multinodular architecture, and heterogeneous cell composition, with a specific glioneuronal element in the specific form. Foci of cortical dysplasia may be associated with the tumoral lesion, and identifying the presence and the extent of cortical dysplasia is not always easy on magnetic resonance images (MRIs). The purpose of this article is to evaluate, retrospectively, the usefulness of ictal single-photon emission computed tomography (SPECT) imaging to assess the presence and the extent of cortical dysplasia associated with DNTs in nine patients with intractable temporal lobe epilepsy related to histopathologically confirmed DNTs. METHODS: The results of the subtraction of ictal and interictal SPECT coregistered to MRI (SISCOM) were compared with the results of the examinations of pathological material after surgery. RESULTS: SISCOM showed a strongly hyperperfused area corresponding anatomically to electroclinical abnormalities and to the location of DNTs on MRI. A circumscribed hyperperfusion was present in DNTs without cortical dysplasia, limited to the location of the tumor on MRI. In cases of associated cortical dysplasia, a widespread hyperperfusion including areas corresponding to normal perilesional regions on MRI was found. CONCLUSIONS: SISCOM, used among presurgical investigations, contributes to detecting cortical dysplasia associated with DNTs. Concordance between the symptomatogenic zone (defined from the medical history and electroclinical data), MRI scans, SISCOM pattern, and complete resection of the epileptic zone was predictive of a good postsurgical outcome.

A joint physics-based statistical deformable model for multimodal brain image analysis.

A probabilistic deformable model for the representation of multiple brain structures is described. The statistically learned deformable model represents the relative location of different anatomical surfaces in brain magnetic resonance images (MRIs) and accommodates their significant variability across different individuals. The surfaces of each anatomical structure are parameterized by the amplitudes of the vibration modes of a deformable spherical mesh. For a given MRI in the training set, a vector containing the largest vibration modes describing the different deformable surfaces is created. This random vector is statistically constrained by retaining the most significant variation modes of its Karhunen-Loève expansion on the training population. By these means, the conjunction of surfaces are deformed according to the anatomical variability observed in the training set. Two applications of the joint probabilistic deformable model are presented: isolation of the brain from MRI using the probabilistic constraints embedded in the model and deformable model-based registration of three-dimensional multimodal (magnetic resonance/single photon emission computed tomography) brain images without removing nonbrain structures. The multi-object deformable model may be considered as a first step toward the development of a general purpose probabilistic anatomical atlas of the brain.

Determination of vessel cross section for flow rate quantification.

This study was motivated by the interest of measuring different cardiac parameters for which changes in the flow rate during a cardiac cycle needs to be determined at different positions along a vessel segment. These measurements result in a great number of images for which automatic contour detection is very helpful. A model-based algorithm for intraluminal contour detection has been developed in order to allow an accurate quantitative image analysis. The algorithm permits to select contours automatically on all the frames and slices of an imaging study. Images obtained on a flow phantom simulating the effects of blood circulation in large arteries have been used to validate the method. They were acquired with a specially designed interleaved multi slice and phase sequence, using a standard whole-body 2 Tesla NMR scanner. A potential in vivo application of the algorithm has been demonstrated on abdominal aorta images.

Three-dimensional segmentation of anatomical structures in MR images on large data bases.

In this paper an image-based method founded on mathematical morphology is presented in order to facilitate the segmentation of cerebral structures over large data bases of 3D magnetic resonance images (MRIs). The segmentation is described as an immersion simulation, applied to the modified gradient image, modeled by a generated 3D-region adjacency graph (RAG). The segmentation relies on two main processes: homotopy modification and contour decision. The first one is achieved by a marker extraction stage where homogeneous 3D-regions are identified. This stage uses contrasted regions from morphological reconstruction and labeled flat regions constrained by the RAG. Then, the decision stage intends to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a 3D extension of the watershed transform. The method has been applied on a data base of 3D brain MRIs composed of fifty patients. Results are illustrated by segmenting the ventricles, corpus callosum, cerebellum, hippocampus, pons, medulla and midbrain on our data base and the approach is validated on two phantom 3D MRIs.

Topology preserving deformable image matching using constrained hierarchical parametric models.

In this paper, we address the issue of topology preservation in deformable image matching. A novel constrained hierarchical parametric approach is presented, that ensures that the mapping is globally one-to one and thus preserves topology in the deformed image. The transformation between the source and target images is parameterized at different scales, using a decomposition of the deformation vector field over a sequence of nested (multiresolution) subspaces. The Jacobian of the mapping is controlled over the continuous domain of the transformation, ensuring actual topology preservation on the whole image support. The resulting fast nonlinear constrained optimization algorithm enables to track large nonlinear deformations while preserving the topology. Experimental results are presented both on simulated data and on real medical images.

Language functional neuro-imaging changes following focal left thalamic infarction.

The involvement of the left thalamus in language function has been largely demonstrated through the effects of thalamic lesion and/or stimulation upon language. However, the pathophysiological mechanisms underlying thalamic aphasias remain a matter of debate. We report here on changes in the pattern of brain activity in auditory word processing, verb generation and visual lexical decision in a patient who sustained left thalamic infarct following the surgery of intractable mesio-temporal epilepsy with left hippocampal sclerosis. To some extent, our findings exemplify the way the left thalamus brings on line the cortical networks involved in language processing, suggested in the term 'selective engagement'.